With a military airfield and control centre to defend Vasylkiv’s mayor and populace are learning about war in real time Shaun Walker in VasylkivMon 28 Feb 2022 21.15 CETLast modified on Mon 28 Feb 2022 23.47 CETShareNatalia Balasynovych woke up at 5.13am last Thursday and thought there was a fireworks display outside She quickly realised that, in fact, Vladimir Putin had launched an assault on Ukraine quiet town of 36,000 people about 20 miles outside Kyiv and is home to one of Ukraine’s four air defence control centres This meant it was an early target for the Russians who want control of the airfield to land troops and launch an advance on Kyiv from the south Balasynovych had read all the speculation about a possible Russian invasion and as mayor had gone over drills of what to do if it really happened “I had 10 minutes of panic when I was running around the house and I had no idea what to do Then I pulled myself together and went to work,” she said speaking at an interview in a fortified building in the town where the local administration is working to keep the town running has been in local politics since her 20s and is known as a campaigner for women’s rights and the victims of domestic abuse Now she finds herself coordinating her city’s response to a Russian assault There have been surprises about who turned out to be the most resilient among her team of local administrators now cast in the role of military organisers and local defenders “My secretary is 19 years old and she is here almost 24 hours a day She isn’t scared of bombs and she’s completely fearless; I wouldn’t have guessed it,” Balasynovych said has become the commander of the city’s “territorial defence forces” Weapons have been handed out to all who want them something she concedes will probably fuel a domestic violence catastrophe in future Smoke billows over Vasylkiv after a missile strike on its oil depot Photograph: Dimitar Dilkoff/AFP/Getty ImagesOn the third night of the Russian assault The target this time was a fuel storage facility which exploded causing a huge boom to rumble through the town Smoke was still rising from the location on Monday afternoon Also hit was a technical college on the main street blowing a huge crater in the centre of the building and tossing pipes and debris over a wide area Ukrainian service personnel shelter at Vasylkiv’s military airbase Photograph: Maksim Levin/ReutersAmid the attacks work has begun in earnest to keep the town fed as food and fuel shortages threaten to make life even harder for the millions of Ukrainians sheltering from Putin’s aggression and there were long queues at pharmacies and supermarkets Amid widespread confusion and deliberate disinformation strategies it has been hard to pin down details of exactly what has happened in many places across Ukraine in the past few days Ukrainian authorities have claimed that two Ilyushin transport planes from which the Russians were trying to land troops and hardware at Vasylkiv and that Russian troops launched an assault on the airbase and were repelled and Melnyk said he had been involved in shooting battles with Russian soldiers who had been landed Balasynovych said there were “about 10” casualties on the Ukrainian side and 28 people still in hospital But no convincing public evidence has surfaced about the two downed planes or about a drop of paratroopers in Vasylkiv there has not been evidence of a Russian airborne assault on Vasylkiv though Russian forces may have sent a ground force detachment there early on from the north,” said Michael Kofman a military analyst who has been closely tracking the Russian assault Also difficult to verify were Balasynovych’s claims about a large network of Russian agents many of whom had spent months blending into local communities won over people’s trust and had been carefully identifying targets and making measurements to pass back to their bosses in Moscow When asked for evidence or further information about their arrests People against a backdrop of smoke from the oil depot in Vasylkiv Photograph: Dimitar Dilkoff/AFP/Getty ImagesThere is certainly no doubt about the missile attacks on Vasylkiv – on Monday afternoon shocked students walked through the destroyed remains of their former technical college presumably hit by a missile aimed at the military college across the street Sights like these have helped to consolidate the mood that has been gradually maturing over the last eight years in towns like Vasylkiv which were never previously known as hotbeds of Ukrainian nationalism “Every second person here has family links to Russia,” said Balasynovych Men came from all over the Soviet Union to study at Vasylkiv’s aviation academy met a local woman at a military ball and settled in Vasylkiv and her cousin is a paratrooper in the Belarusian army With Belarus edging closer to full participation in Putin’s war she now faces the prospect that her own cousin could be fighting to take over her town Ukrainian defenders take positions at the military airbase in Vasylkiv Photograph: Maksim Levin/ReutersAs she spoke of these painful splits a call came in: a Russian sabotage group had been apprehended at a checkpoint just outside the town She pulled on a flak jacket – the first time she had ever worn one she said – and summoned a large van with blacked-out windows The van was bought with an international grant and in normal times was used to transport victims of domestic violence from locations across Ukraine to a shelter she drove past a checkpoint where a man was giving lessons in how to make molotov cocktails the symbol of this new Ukrainian resistance an 83-year-old veteran of the Soviet missile forces was at the checkpoint to request a few molotov cocktails to throw from his house Kravets did his military service in the Russian far north in 1959 and used to have warm feelings for the country but said he was now disgusted with Putin’s Russia and wanted to fight his invading army “I tried to buy a gun but they told me I was too old I at least want one of these to throw at the fuckers,” he said with a cackle symbolic of just how much Putin’s actions have damaged affection for Russia even in the older generation the van was turned back before the scene of the shootout the national guard sealed off the checkpoint The story of the Russian sabotage group was another claim that could not be verified Balasynovych said that while it was important Ukraine did not capitulate she hoped negotiations with Russia might bring some kind of compromise to avoid the trauma that would come from an extended war “People used to think about new car or iPhone When old people used to wish each other peace The National Agency on Corruption Prevention (NACP) has drawn up a report on committing an administrative offence by the former head of the Vasylkiv City District Court of Kyiv Region under Article 172-7 of the Code of Administrative Offences (violation of requirements for preventing and resolving conflicts of interest) The report was sent to the court for consideration. The court decision may result in a fine and the official's name being entered into the Unified State Register of Persons Who Committed Corruption or Corruption-Related Offences The official failed to notify the Council of Judges of Ukraine of his conflict of interest due to the need to decide on his own dismissal from the position of a judge 125 of the Law of Ukraine ‘On the Judiciary and the Status of Judges’ it is the head of the court who must issue an order to terminate the judge's employment with the relevant court in connection with the entry into force of a court decision on the civil confiscation of his assets In October 2024, the Supreme Court issued a decision upholding the decision of the High Anti-Corruption Court and recovering part of the value of the apartment worth over UAH 3.6 million for the state recognising that the ex-judge's asset was unjustified. Entire content is available under licence Creative Commons Attribution 4.0 International license Please enable JS and disable any ad blocker Donate Located 30 km south of central Kyiv, Vasylkiv military airbase is adjacent to the small city of Vasylkiv and several smaller settlements. The airbase was one of Ukraine’s four air defence control centres and was attacked early in the full-scale invasion A missile attack occurred early on the 24th February in an attempt to destroy the fighter fleet based at Vasylkiv airbase; as these were airborne The inhalational exposure risks led to local evacuations, advice to residents as far away as central Kyiv to keep windows closed and the development of a seven-point plan for responding to future incidents The airbase was again struck by a missile attack, which hit fuel or lubricant storage tanks a fuel tanker and warehouses likely storing ammunition The largest fire at the oil tanks was contained and extinguished within six hours In addition to the 25 fuel and lubricant tanks destroyed, there was also severe damage to the ammunition storage warehouse, runway, control tower and to vehicles around the site. In spite of this, the airbase remained operational The destruction of ammunition storage warehouses would have added additional munition-relevant pollutants to the atmosphere and to surrounding soils These include heavy metals and energetic materials Deposits from the substantial smoke plumes are likely to be a source of long-term pollution discharges of oils and lubricants and PFAS chemicals to the ground surface can contaminate soils and be mobilised into ground and surface waters potentially impacting local water resources In 2015 a substantial fire occurred at the same fuel depot and any ground assessment of the harm will be complicated by the presence of pre-existing contamination at the airfield Some of the footage from the 2015 fire at Vasylkiv was re-used in the social media reporting of the fire at the nearby Kalynivka oil storage depot Return to the country map here © 2025 Conflict and Environment Observatory | Charity No: 1174115 | Design by Open & Honest Subscribe to BuzzFeed Daily NewsletterCaret DownThe New Mayor Of This Ukrainian Town Became An Overnight Wartime LeaderNatalia Balasynovych was elected mayor of Vasylkiv as an independent progressive Now she is helping defend her city’s critical military airport from a Russian assault by Christopher MillerBuzzFeed News Reporter Ukraine — When Natalia Balasynovych was elected mayor here as an independent at the start of the pandemic she believed the biggest challenges would be the health crisis and the modernization of this historically military town she opened a high-speed train route to Kyiv and one of Ukraine’s first shelters for survivors of domestic abuse with her country under siege by Russian forces and her city targeted with airstrikes and paratroopers parachuting in by plane and rappelling by helicopter Her town includes a strategic military airbase that President Vladimir Putin hopes to seize and use to bring in more troops in order to encircle the capital When BuzzFeed News visited the city of 37,000 on Monday it seemed that nearly every person had a Kalashnikov slung over their shoulder Windows were taped to prevent the glass from shattering from explosions Checkpoints manned by jumpy volunteers were erected at Vasylkiv’s entrances and exits Ukrainians make Molotov cocktails alongside a road on Feb A group of more than 20 men worked to fortify a newly built checkpoint They dug trenches and used the dirt to fill bags that were stacked around cement blocks filling bottles with gasoline and bits of styrofoam an ingredient that helps the burning liquid stick to targets A van marked “Gruz 200” — military code used in post-Soviet states to signify the transport of soldiers killed in battle — trundled through town That new reality arrived on Thursday at 5:13 a.m. when Putin launched a brutal full-scale war against Ukraine that included an assault on Vasylkiv Balasynovych immediately coordinated her city’s response she didn’t believe a new Russian invasion would happen until explosions rocked her city “I had 10 minutes of panic when I was running around the house and I had no idea what to do,” she said “Then I pulled myself together and went to work.” Russia attempted to drop paratroopers and armor into Vasylkiv using two Ilyushin transport planes Kyiv said the aircraft were downed by surface-to-air missiles in the area but that Russian troops managed to land in the city Balasynovych said she had been informed about the downed planes but declined to provide more specific information She said that Ukrainian forces had driven out most of the paratroopers from the city but she believed some were hiding nearby in the woods head of the Vasylkiv Territorial Defense Brigade and an adviser to Balasynovych said he had fought in the streets against the Russians before they vanished were killed and another 28 people were wounded a technical high school that was hit by rockets in Vasylkiv nobody was killed when a missile struck a technical college across from a military base early Saturday The attack left the building a hollowed-out wreck — three floors completely collapsed where the missile hit Debris from classrooms was still strewn about the ground on Monday The blast was so powerful that it snapped trees in half and shattered the windows of buildings down the block The only thing that appeared to be in its right place was a photograph of national poet Taras Shevchenko hanging on a wall They were just throwing things in their cars and going,” chemist Maksym Dekhtarenko told BuzzFeed News outside the ruined college Then war came to us on the wings of Russian cruise missiles.” Dekhtarenko had plans to fly to Paris on Tuesday to begin a new postdoctoral program with all commercial air traffic to and from Ukraine halted he remains in Russia’s crosshairs in Vasylkiv The fighting intensified on Saturday morning causing an explosion that was seen from Kyiv and spewing acrid black fumes into the air Balasynovych said the blast was terrifying and shook the walls of the bomb shelter she was huddled in with 70 other people The group called a priest and together prayed that they would make it through the night Balasynovych counted the oil depot as one of several important sites in Vasylkiv and she said the Russian military knew to hit it because of a network of covert agents present in the city She said Russian agents had been active for a “minimum [of] one year,” blending into the community and posing as contributing members of society while compiling information on residents and “preparing maps” of strategic targets whom she said Ukrainian intelligence was aware of but she declined to name him for security reasons “He would bring bread to old people and worked hard to fit in and seem normal.” When Russia launched its attack last Thursday it aligns with how Russia has operated in Ukraine for years People prepare meals for soldiers stationed nearby on Feb The strikes have unnerved everyone in the city There was a loud rumble inside the kitchen where Balasynovych sat on Monday for an interview flanked by her staff and a team of heavily armed guards concerned that it might be another missile attack The sound was just the industrial dishwasher thumping along Just as the interview was coming to a close Melnyk received a call about a group of Russian saboteurs that local volunteers had allegedly caught and killed at a checkpoint Balasynovych threw on a bulletproof vest for what she said was the first time in her life and drove to the site “I want to look him in the eye and talk to him,” she said she received a call from the National Guard forbidding a visit to the site Balasynovych said the war has changed her city “When people always wished each other peace and health there were loud explosions heard emanating from the area Surface-to-air missiles were seen shooting through the sky over the city “Right now we are being bombarded with rockets,” she said “Dear fellow townsmen, our airdrome in Vasylkiv has come under fire. It is not yet known exactly, what weaponry was used,” Balasynovych wrote Please use them,” Balasynovych noted A reminder that Ukrainian Foreign Affairs Minister Dmytro Kuleba stated earlier that Putin had launched a full-scale invasion of Ukraine While citing and using any materials on the Internet links to the website ukrinform.net not lower than the first paragraph are mandatory citing the translated materials of foreign media outlets is possible only if there is a link to the website ukrinform.net and the website of a foreign media outlet Materials marked as "Advertisement" or with a disclaimer reading "The material has been posted in accordance with Part 3 of Article 9 of the Law of Ukraine "On Advertising" No 1996 and the Law of Ukraine "On the Media" No 2023 and on the basis of an agreement/invoice Online media entity; Media identifier - R40-01421 Home page » Topics » Ambassadors » Vasylkiv as seen by Jerry Heil leaving the surrounding provinces in their shadow Less than half an hour’s drive from Kyiv is the historic town of Vasylkiv; though small and often overlooked with an important spiritual and religious heritage there are local ‘ambassadors’ who can tell it better than any guide book or history book singer and blogger Jerry Heil (real name Yana Shemayeva) introduces her native Vasylkiv the place where she spent her childhood and fell in love with music Jerry Heil shares her experience on social media One of her most popular topics is raw eating which the singer has practiced since May 2018 she became a vegetarian and began shopping for food in specialised stores Lyudmyla Degtyariova owns the vegetarian shop “Natural Boom” in Vasylkiv where Jerry Heil used to buy her groceries Lyudmyla tells us that she moved to Vasylkiv from Donetsk in 2014 when the war broke out in the east of Ukraine: “At first and not everyone here is interested in this kind of thing Ukraine is full of places named after other places: Kyivan Venice subtly points to a kind of inferiority complex suggesting that these places are not worthy of their own names you can find the “Vasylkiv Carpathians” (or “Velykobuhayivski Carpathians”) one of the area’s most significant landscape reserves Jerry Heil became popular via the YouTube channel she created in 2012 where she uploaded cover versions of well-known Ukrainian and foreign songs Her cover versions gained recognition and popularity; some of her videos have been viewed 100 000 times English designer who never uses leather or fur Involved in the preparation of the material 13 volunteers Project support: Fundacja Euromaidan-Warszawa Use of materials is only permitted upon providing the source: Ukrainer.net Дизайн — Артем Зубкевич Розробка — Deluxcode -However, questions remain about the operation’s success and whether Ukraine can bolster its defenses against such attacks The Su-57 and other airplanes reportedly just fired Kh-69, Kinzhal, and Iskander-M missiles at ground targets in Ukraine 19FortyFive cannot confirm that with our sources but that is what is being reported in various media This tactical development comprises the stealth Su-57 targeting cities and air bases where air defenses are thickest and then destroying surface-to-air missile systems Russia’s Su-57 (Image Credit: Sukhoi Design Bureau this configuration would back up my estimation of the Russian aerial tactics comprising of Su-57s flying to destroy Ukrainian air defenses – a tactic allowing bombers to attack with more munitions in a follow-on maneuver The Russian air force may have found a recipe for success if the last operation on December 31 is confirmed. The Su-57 does have stealth attributes and can deliver many different types of missiles. When they pair it with bombers after air defenses have been eroded, it is more effective than the regular glide bomb attacks that Russia often uses against cities. Russian Su-57 and Su-57 Stealth Fighters. Image Credit: Russian Government. Now serving as 1945s Defense and National Security Editor, Brent M. Eastwood, PhD, is the author of Humans, Machines, and Data: Future Trends in Warfare. He is an Emerging Threats expert and former U.S. Army Infantry officer. Volume 3 - 2021 | https://doi.org/10.3389/fcomp.2021.674333 This article is part of the Research TopicLanguage and Vision in Robotics: Emerging Neural and On-Device ApproachesView all 5 articles Storytelling plays a central role in human socializing and entertainment and research on conducting storytelling with robots is gaining interest much of this research assumes that story content is curated we introduce the task of collaborative story generation and a person collaborate to create a unique story by taking turns adding to it We present a collaborative story generation system which works with a human storyteller to create a story by generating new utterances based on the story so far Our collaborative story generation system consists of a publicly-available large scale language model that was tuned on a dataset of writing prompts and short stories and a ranker that samples from the language model and chooses the best possible output We improve storytelling quality by optimizing the ranker’s sample size to strike a balance between quality and computational cost Since latency can be detrimental to human-robot interaction we examine the performance-latency trade-offs of our approach and find the optimal ranker sample size that strikes the best balance between quality and computational cost We evaluate our system by having human participants play the collaborative story generation game and comparing the stories they create with our system to a naive baseline we conduct a detailed elicitation survey that sheds light on issues to consider when adapting our collaborative story generation system to a social robot in a first step towards allowing human players to control the genre or mood of stories generated we present preliminary work on steering story generation sentiment polarity with a sentiment analysis model We find that our proposed method achieves a good balance of steering capability and text coherence Our evaluation shows that participants have a positive view of collaborative story generation with a social robot and consider rich emotive capabilities to be key to an enjoyable experience most work works under the assumption that story content would be curated in advance recent breakthroughs in language modeling present a new opportunity: language we introduce a novel game of collaborative story generation where a human player and an artificial intelligence agent or robot construct a story together The game starts with the AI agent reciting one of a curated set of story starters–opening sentences meant to kick-start participants’ storytelling creativity–and the human player responds by adding a line which we refer to from here on out as a story continuation The AI agent and human player then take turns adding to the story until the human player concludes it The game is designed to have a few restrictions as possible and contrasts with traditional storytelling settings where the narrative is fixed in advance Collaborative story generation builds on a rich tradition of collaboration in storytelling that includes Dungeons and Dragons, improvisational comedy, and theater. It also bears some resemblance to Choose Your Own Adventure style novels1 where users make narrative choices to progress the story collaborative story generation could be a useful tool for encouraging creativity and overcoming writer’s block as well as being an entertaining game in its own right Our end goal is to make it possible for intelligent agents, such as robot companions and avatars (Park et al., 2019; Gomez, 2020; Gomez et al., 2020), to play the collaborative story generation game, as shown in Figure 1. The examples throughout this paper come from real stories that were constructed by humans collaborating with a text interface version of our storytelling system2 Collaborative storytelling with an intelligent agent We introduce the task of collaborative story generation where a human and an intelligent agent or social robot construct a story together and consider the arising technical and presentational challenges We present a sample-and-rank-based approach to collaborative story generation that combines a large-scale neural language model with a sampler and ranker to maximize story generation quality We conduct rigorous analysis of our approach to fully understand its performance and the trade-offs between quality and latency and we use these findings to determine the optimal number of story continuation candidates to generate We conduct qualitative evaluation including evaluation of stories generated by our system in isolation and two distinct populations of human judges of actual stories constructed by humans playing with our collaborative story generation system The evaluation confirmed our optimized ranker model’s contribution to story generation quality 5. We conduct a detailed elicitation survey to gain insight into potential user preferences for a collaborative story generation game with the social robot, Haru (Gomez, 2020) Survey findings were positive overall and highlighted the importance of the robot conveying the emotional contents of the stories with the goal of allowing players to specify the mood or genre of the story during collaborative story generation we present preliminary work on steering story generation using a sentiment analysis model showing we could successfully influence story generation sentiment without degrading quality The collaborative story generation game flows as follows: 1. The intelligent agent selects and recites a story starter from our curated collection: these are catchy opening lines meant to kickstart the storytelling process with an interesting premise. Examples are given in Tables 1, 2 The human player responds by adding a line to the story and the human player can have the story continue however they want The intelligent agent follows by reading the story up to the current point and adding a line that seems likely to follow the human contribution The collaborative story generation game continues by alternating between steps 2) and 3) until it concludes Example story starters from r/WritingPrompts There are many possible strategies that could be used to conclude the collaborative story generation game: the human player could simply declare The End; the intelligent agent could decide based on player engagement levels or other information that is it time to generate an ending; or after deciding to end the story the agent could cheer on the human player on to develop an ending Story ending generation is an important research topic that has grown in interest recently (Zhao et al., 2018; Guan et al., 2019; Luo et al., 2019) We plan to address this in future work but limit the scope of the collaborative story generation presented here to a fixed number of exchanges between human player and intelligent agent in order to simplify evaluation We design the collaborative story generation game to have as few restrictions on the human player as possible our storytelling system can handle input of arbitrary length and content and stories can last as long as the human player wants the mode of interaction does impose some constraints: when playing the game with a text-based interface story generation quality will degrade if spelling and punctuation are not in agreement with the system’s conventions the quality of the robot’s speech recognition can have a similar impact We plan to thoroughly explore these issues in future work but at this stage we do not constrain the players’ input Our collaborative storytelling system architecture An example of the collaborative story generation process The Generator is a unidirectional autoregressive language model which is sampled from multiple times to generate candidate story continuations. We used the publicly-available pretrained 774M parameter GPT-2-large model3 (Radford et al., 2019) One issue with using a language model for generation is the output may be ill-formed or lacking in logical coherence The main solutions for this issue are the use of larger models and the use of various methods of traversing the search space of possible sentences larger models are at greater risk of over-fitting and result in large increases in memory usage for modest gains in quality we focused on sampling and searching through ranking The most popular approaches for sampling from autoregressive models have predominantly focused on techniques for truncating the low-quality tail of the model distribution, like top-k and nucleus sampling (Holtzman et al., 2019) Sampling is used in most GPT-2 based text generation systems superseding greedy or untruncated sampling we use nucleus sampling with p = 0.9 The Ranker model scores each story continuation candidate and selects the highest scoring one It is a standard GPT-2-large model with a final classification head consisting of a linear layer outputting a single scalar for each token The input format to the model is:(context)<|endoftext|>(choice)<|endoftext|> We chose a neural network-based ranker model to select the best story completion from the Generator output because it offers us control over the trade-off between text generation quality and computational demand while avoiding the significantly increased memory footprint and inflexibility in computational cost of using a larger language model The amount of computational resources used is easily adjustable by changing the number of rollouts considered by the Ranker This serves as a middle ground between the intractable extreme of searching the entire space of all vocablength possible sentences and the computation-efficient but suboptimal solution of sampling without any branching or backtracking One popular alternative search solution making a similar trade-off is beam search, which keeps a dynamic list of generation candidates. Beam search has been applied in many language generation tasks, including machine translation (Tillmann and Ney, 2003). However, sampling from a language model using beam search can lead to degenerate text (which is typically repetitive and uninteresting), in an open-ended task such as storytelling (Holtzman et al., 2019) These issues are avoided using a neural network-based ranker model because it has richer text representations it scores full text utterances rather than incomplete text fragments and it can incorporate additional information about the storytelling domain from its training data In this section we describe our datasets: 1) a collaborative story generation dataset constructed by crowdsourcing workers interacting with our collaborative story generation system that are used to train the Ranker model and for evaluation and 2) a writing prompts dataset comprised of short stories written in response to writing prompts posted to a Web forum that are used to train the Generator model FIGURE 3. Web interface for collaborative storytelling annotation task. Participants select from amongst ten possible story continuations generated by the system before adding their own line to the story. Reproduced with permission from (Nichols et al., 2020) one of the continuations in the choice type interaction is a distractor which is made by concatenating randomly sampled words The distractors are also filtered through Mechanical Turk beforehand by asking workers whether the sentences are coherent or not and only the ones labelled incoherent by workers are used if a worker selects a distractor during a choice type interaction We collected a total of 2,200 stories, which we randomly partitioned into a training split of 2,000 stories, and validation and test splits of 100 stories each. Some example stories generated by human participants together with our system are shown in Table 4 Example stories generated by the tuned system with a human through the collaborative storytelling annotation task We constructed a dataset of stories from the r/WritingPrompts subreddit4, consisting of all posts with score greater than 3 made before 2019-11-24, amounting to 140 k stories in total. Some heuristics were used to clean the stories5. This data was used to train the Generator model. Example stories are given in Table 2 We chose to collect our own WritingPrompts dataset instead of using the FAIR WritingPrompts dataset (Fan et al., 2018) because it gave us the flexibility to filter stories by custom score thresholds as well as to perform the different preprocessing necessary for GPT-2 Our dataset also contains more than an additional year’s worth of data compared to the FAIR dataset To generate story continuations from our system sentences are generated from the Generator model and filtered using a set of cleanliness heuristics until the desired number of samples is achieved Our heuristic rejected sentences with less than 60% alphabetic characters or words like “chapter” that are not typically part of the story The Ranker model then computes a score for each story continuation and selects the highest scoring one The Generator model is trained with a maximum likelihood estimation loss function using Adafactor (Shazeer and Stern, 2018) with a learning rate of 5e − 5 on a weighted mixture of the WritingPrompts and BookCorpus (Zhu et al., 2015) datasets The addition of BookCorpus helps reduce the risk of over-fitting on the comparatively smaller WritingPrompts dataset The Ranking model is trained on the WritingPrompts dataset and eight copies of the training split of the collaborative story generation dataset Each batch for the Ranking model consists of 20 sentences taken from a single story only the sentences that fit within 400 tokens are used resulting in some batches with less than 20 sentences The majority of stories do not have to be truncated The Ranker model is also trained on synthetic collaborative story generation data that we create from the WritingPrompts dataset Stories with less than 100 characters or 35 sentences are first removed from the Ranking model’s training data Then the first sentence of the story is used as the story starter and the next 20 sentences are all used as the preferred story continuations of choice type interactions where the other nine incorrect choices are sampled from the 25th and subsequent sentences of the story The Ranking model is trained using Adam (Kingma and Ba, 2014) with a maximum learning rate of 1e − 5 The entire model is trained; no layers are frozen The checkpoint is resumed from a GPT-2 text generation model that was tuned on the BookCorpus and WritingPrompts datasets in the same way as the Generator model Krause et al. (2020) proposed GeDi a method for language model steering that combines the probability distribution over categories from a classification model with the probability distribution over the next token to be generated by an LM This has the advantage that no retraining of the LM is necessary GeDi and similar approaches often suffer from degraded text generation quality if the classification model exerts too much influence over generation Inspired by GeDi (Krause et al., 2020) we propose a sentence-level approach to language-model steering that can benefit from an external classification model while avoiding text degradation We do so by combining our Ranker model with a classification model to select sentences that are more likely to share target categories Because our goal is to allow human collaborative story generation players to specify story moods or genres as an initial trial we use a sentiment classification model to steer text generation towards either positive or negative sentiment TABLE 5. Examples from the TweetEval (Rosenthal et al., 2017; Barbieri et al., 2020) dataset To verify that our sentiment analyzer has sufficient performance on collaborative story generation text we compare it to existing sentiment analyzers on a small evaluation dataset we construct by having Mechanical Turk workers annotate 100 randomly-selected sentences from our collaborative story generationdataset described in Section 3.4.1 with sentiment polarity judgements and we use majority voting to select the correct label We then evaluated the performance of a small number of transformer-based sentiment analyzers that were trained on different datasets The results are shown in Table 6 where our approach is labeled system G (rank_sent) We can see that models trained on Twitter data (systems F G) outperform models trained on restaurant reviews (system A) or movie reviews (systems B E) and that architecture variants do not have much impact on performance Our model performs comparably to the best-performing publicly-released model: a RoBERTa model that was trained on Twitter data providing evidence that using our own GPT-2-based sentiment analyzer is unlikely to degrade performance Comparison of the sentiment analysis accuracy of several state-of-the-art transformer-based sentiment analysis systems To combine the sentiment analyzer score with the ranking model score, we use use Bayes theorem with the ranking model to provide a prior and updating with a weighted version of the sentiment model (with weight ω, indicating our degree of confidence in the sentiment model). This scoring is similar to Krause et al. (2020) but acts at the sentence rather than token level sentence level sentiment models are robust to a much wider range of sentiment weight values As will be seen in the evaluation in Section 4.6 the generated text is still generally coherent we summarize relevant research in story generation the primary assumption of these works is that story generation is conducted without human interaction While research on collaborative language generation is still sparse there are a few notable recent developments AI Dungeon6 is a text adventure game that is generated by a GPT-2 language model (Radford et al., 2019) tuned on a collection of text adventure play-throughs players assume the first person and interact with the world by inputting commands or actions The language model is used to generate the world’s reaction to the player’s actions Our collaborative storytelling task and approach are similar to AI Dungeon but our task is not constrained to the genre of first-person adventures In order for an AI agent to participate in collaborative storytelling it must be able to generate story continuations A language model (LM) is a mathematical model that assigns likelihoods to sequences of words where sequences that are more likely in a target language are given higher scores Early language models estimated token sequence likelihood based on token sequence counts taken from large collections of text together with various smoothing methods to handle novel token sequences (Ney et al., 1994). Later, RNNs and other sequential neural networks models became popular due to their ability to apply distributed word representations (Bengio et al., 2003; Mikolov et al., 2011; Sutskever et al., 2011) but RNNs have issues with vanishing gradients and modelling long-term dependencies found in text The model we use as a basis for our system, GPT-2 (Radford et al., 2019) is a large-scale neural network using the transformer architecture GPT-2 is a general purpose unidirectional LM trained on a large corpus which has been successfully applied to many downstream tasks PPLM) introduce a series of control codes that can be inserted into a transformer-based LM at the beginning of generation to steer generation toward a target class Token-level LM steering has the advantage that the target class can influence every token in the generation this can often lead to degradation in text quality we develop a sentence-level LM steering method that uses ranking to allow the target class to influence generation while preserving text generation quality Finally, (Sun et al., 2017) investigated joint storytelling with children participants where a child participant created a story by selecting from a predefined set of characters and settings a human experimenter made suggestions on story continuations following a limited number of engagement strategies and a puppeteered robot later helped child participants recall the stories they created in order to evaluate the efficacy of the engagement strategies Our task setting is different in that it focuses on having an AI agent automatically generating story continuations without restrictions in settings or content rather than on evaluating strategies for humans to engage with other human storytellers in order to gain insights into adapting interactive storytelling to a social robot we conduct an elicitation survey with the second survey group The participants in this study consisted of two groups The first group was recruited on Amazon Mechanical Turk (MTurk) and the second group were students from a local university in Winnipeg One hundred participants were recruited from MTurk (N = 100) and twenty two participants from the university (N = 22) In the group of the university students were 11 males and 11 females with age ranging from 20 to 40 yr old (M = 28.5 Participants’ backgrounds were not collected Story continuation prediction acceptability measures the accuracy of the Ranker model at predicting the continuation chosen by the Mechanical Turk worker that interacted with the model to produce the story. This metric is a proxy for how often the tuned+ranked picks the best continuation of the story, but its usefulness is diminished by variance in human annotators and the possibility of multiple equally good continuations. The results are summarized in Table 7 we find that our Ranker model outperforms chance by a factor of over two providing evidence that it is able to capture the preferences of human annotators to an extent Accuracy of the tuned+ranked model at predicting the story continuation that was selected by the Mechanical Turker who constructed the story As an additional measure of our systems’ capacity to generate story continuations that match human preferences we formulate the story continuation acceptability task each story continuation generated by a system is classified as either acceptable or unacceptable and we compare their mean acceptability precision We annotated the acceptability of candidate story continuations by asking Mechanical Turk workers to classify each continuation given the context of the story generated so far. To ensure annotation quality, we have three workers evaluate each choice interaction per story from both the validation and test sets and take the majority vote across the three labels as the final label7 These choice interactions consist of nine story continuations generated by the system and one incoherent distractor If a worker labels a distractor acceptable We use this method to evaluate how often each model produces outputs that are an acceptable continuation of the story Since the tuned and tuned+ranked systems use the same language model samples we use the test set to evaluate their performance considering the mean acceptability of all of the sampled continuations from tuned and the acceptability of the single continuation selected by tuned+ranked for each choice interaction in the datasets we gather and evaluate 100 choice interactions by having Mechanical Turkers construct stories with the untuned system The results are summarized in Table 8 the tuned system outperforms the untuned system showing that tuning the language model on storytelling data is important in improving generation quality We also find that tuned+ranked greatly outperforms the other two systems providing supporting evidence that our Ranking model is effective at helping our language model produce story continuations that are likely to be preferred by humans Mean acceptability of story continuations in the test set In order to fully understand the trade-offs between quality and latency and ensure our collaborative storytelling model is fast enough to work with a robot agent we investigate the optimal number of story continuation candidates to generate We analyze the effect of varying the number of choices presented to the Ranker on the mean story continuation acceptability metric presented in the previous section Results are shown in Figure 4 While increasing the number of choices considered by the Ranker has the greatest effect on Acceptability between 0 and 10 choices we observe a slower but continued improvement in quality throughout The continued monotonic improvement indicates that the Ranker model is robust and prefers better continuations even when given a larger number of choices We measure the latency of our system for various numbers of Ranker choices to ensure that latency is acceptable. Our results are shown in Figure 4 We measure the latency using a single 1080Ti for the Generator model and with the Ranker model run on CPU due to insufficient GPU memory We take the mean across 100 different stories we only look at the latency for the final completion to provide an approximate upper bound as latency increases towards the end of a story Conducting qualitative evaluation of collaborative storytelling is challenging for several reasons it is challenging to automatically evaluate their quality and many methods have to make due with indirect measures such as entropy that offer limited insights or ending generation where example desired text is known the highly interactive nature of the task means that the influence of human participants makes it difficult to isolate the performance of the system To gain an understanding of our storytelling system’s performance in isolation we conduct self-chat evaluation of stories produced by conversing with itself To understand participants’ subjective perfection of the storytelling task we evaluate human chat stories constructed by humans engaging in the collaborative storytelling task with our system Questions asked to human evaluators of collaborative storytelling systems Example stories generated by self-chat with the tuned+ranked system FIGURE 5. The Web interface for comparing stories for self-chat and human chat evaluations with Acute-Eval. Reproduced with permission from (Nichols et al., 2020) The results of the evaluation are summarized in Figure 6 the pairs of models are shown as stacked bar graphs where a larger portion represents a stronger preference for that system and tuned+ranked is preferred over tuned for all characteristics and overall story preferences providing evidence that tuning the language model on storytelling data and ranking the generated story continuations make complementary contributions to our collaborative storytelling system’s performance The preferred system is indicated by a larger portion of the bar students and collaborative storytelling systems: ranker30 (left) and untuned (right) Human evaluation of stories created with human collaborators comparing our selected ranker ranker30 (here labeled tuned+ranked) to an untuned baseline Our ranking model is shown to be preferred over a baseline on every characteristic by both groups of participants, with statistically-significance differences in most cases9 reinforcing findings from our self-chat evaluation that tuning and ranking a large-scale language model performs better at storytelling than an untuned model and suggesting that our optimization of the Ranking model further improved performance we present results from an elicitation survey designed to give insight into adapting our collaborative storytelling system for use in a robot As our target robot, we selected the social robot Haru (Gomez et al., 2018; Gomez, 2020), due to its rich, emotive capability. Haru is an experimental tabletop robot for multimodal communication that uses verbal and non-verbal channels for interactions. Haru’s design is centered on its potential to communicate empathy through richness in expressivity (Gomez et al., 2018) Haru has five motion degrees of freedom (namely base rotation that allows it to perform expressive motions each of the eyes includes a 3-inch TFT screen display in which the robot eyes are displayed Inside the body there is an addressable LED matrix (the mouth) Haru can communicate via text-to-speech (TTS) Haru’s range of communicative strategies positions the robot as a potent embodied communication agent that has the ability to support long-term interaction with people We conduct the elicitation survey with the university student group of participants described in Section 4.1 The crowdsourcing workers were excluded due to challenges associated with taking lengthy surveys on the MTurk platform Survey participants are shown a video simulation of a human playing the collaborative storytelling game with Haru. After completing the video, they answer a series of questions covering topics ranging from potential target demographics to emoting in robot storytelling. The questions and their responses are shown in Figure 9 Questions and responses from the elicitation survey children was overwhelmingly the most suggested age group (19 responses) with adults and elderly suggested less than half as often collaborative storytelling (“Haru creates a story with you”) was the most suggested storytelling mode for Haru (22 responses); alternatives received a maximum of 11 responses One participant suggested having Haru teach reading We also found that participants overwhelmingly prefer making short stories to long stories (21 yes responses vs suggesting long storytelling sessions could be tiring for participants positive moods were far more popular than negative: {exiting,funny,happy} received 52 cumulative votes vs These results may be reflective of children’s popularity as a suggested target demographic Genre preferences showed a similar trend: {fantasy,mystery,sci-fi} (49 cumulative votes) were more popular than horror (eight cumulative votes) though the effect was not as strong with comedy (11 cumulative votes) These results suggest that human players may enjoy having control over mood or genre during collaborative story generation participants greatly prefer an emotive robot (17 votes) to a disembodied voice (three votes) and want the robot’s voice to reflect the content of the story (22 yes votes) an overwhelming majority of participants think a robot emoting based on story content would contribute positively to the storytelling experience (19 yes votes) but there is room for Haru’s emoting to improve as few participants felt Haru’s emotive performance contributed positively (14 yes votes) These survey results suggest that collaborative storytelling with Haru is a potentially enjoyable application, but they also reinforce recent findings (Mutlu et al., 2006; Ham et al., 2015; Gomez et al., 2020) that emphasize the importance of convincing emotive delivery in robot storytelling we must carefully consider Haru’s emotive delivery during collaborative storytelling for it to be impactful In this section, we investigate the effectiveness of sentence-level sentiment steering for collaborative story generation by comparing our proposed approach (rank_sent) to the token-level steering approach of GeDi (gedi_token) (Krause et al., 2020) we use Salesforce’s publicly-released implementation of GeDi we use the same story generation model for each approach: the tuned GPT-2 language model from the self-chat evaluation in Section 4.4.2 since gedi_token requires a token-level sentiment analyzer we use the GPT-2-based sentence-level sentiment analyzer we created in Section 3.7 for rank_sent and GeDi’s default token-level sentiment analyzer for gedi_token The settings labels for each system will be used throughout the rest of this paper Both rank_sent and gedi_token apply a weight term ω to the classifier’s predictions the influence of the classifier also grows to infinity Since rank_sent combines the classifier’s scores with a ranker’s and doesn’t apply it directly to generation it is the same as using the classifier to rank candidate sentences since gedi_token applies the classifier at every token generation step it is much more sensitive to changes in ω We explored higher values of ω for gedi_token_high We conducted Acute-Eval evaluation using the MTurk population from Section 4.1, using the questions in Table 9 including addition questions designed to measure characteristics important to sentiment steering Repetitiveness measures if the classifier influence results in repetitive text generation Target sentiment evaluates the success or failure of steering sentiment in story generation target domain measures if the classifier influence pulls text generation away from the storytelling domain All comparisons are done against a tuned storytelling model baseline that does not use ranking or sentiment steering Acute-Eval results are summarized in Figures 10, 11 settings with negative sentiment steering exhibited overall higher repetitiveness indicating that steering models could be fixating on negative terms rank_sent_pos_medium and rank_sent_pos_high also had higher repetitiveness which is surprising as token-level generation wasn’t directly effected during generation sentiment steering systems scored overall lower suggesting human evaluators don’t find sentiment steered stories as story-like almost all settings achieved high scores indicating successful steering of target sentiment these results suggest that sentiment steering can successfully influence the sentiment of story generation without excessive quality degradation or the need for token-level sentiment steering rank_sent_low stands out as achieving best blend of sentiment steering and text generation quality Acute-Eval Human evaluation of self-chat stories with sentiment steering comparing various systems against a tuned baseline without steering Human evaluation of self-chat stories with sentiment steering Example stories comparing rank_sent and gedi_token at the same sentiment polarity and weight are given in Tables 12–17. All stories are generated from the same starter. To give a sense of the generated stories’ sentiment, the sentiment of tokens are automatically colorized using Stanford CoreNLP10: Very positive Since these tags are automatically generated but they are useful for providing an overall picture We provide some comments on each story below: • baseline: a negative narrative about a spacecraft taking damage but is a mysterious hero about to spring into action • rank_sent_pos_low: a coherent narrative that ends on positive note–the crew successfully lands spacecraft • gedi_token_pos_low: incoherent with foreign language text mixed in; not very positive overall • rank_sent_pos_medium: a vaguely positive alien encounters • gedi_token_pos_medium: starts coherent and positive narrative about passenger but drifts into unrelated narrative about cathedral and monks • rank_sent_pos_high: a positive narrative about captain where nothing happens • gedi_token_pos_high: an overall positive narrative but it ignores space for Middle Earth instead • rank_sent_neg_low: shares the first few lines with the baseline implying strong ranking and negative sentiment classification correlation but progressively grows more negative until the spacecraft falls apart • gedi_token_neg_low: a somewhat coherent description of an alien attack on the spacecraft • rank_sent_neg_medium: more repetition of baseline opening lines continuing into negative but coherent narrative about extensive damage to the spacecraft • gedi_token_neg_medium: a coherent narrative about a mysterious space sickness • v rank_sent_neg_high: more repetition of baseline opening lines continuing into extremely negative narrative about a damaged spacecraft and dying crew • gedi_token_neg_high: an incoherent but extremely negative narrative Example stories generated with low positive sentiment steering Example stories generated with medium positive sentiment steering Example stories generated with Very positive sentiment steering Example stories generated with Negative sentiment steering Example stories generated with medium negative sentiment steering Example stories generated with Very negative sentiment steering we find that rank_sent provides more coherent narratives than gedi_token but they still capture the target sentiment gedi_token can capture more intense sentiments in some cases but it comes with the trade-off that text generation can drift onto unrelated topics These findings provide support that rank_sent provides a balance of steering capabilities and stability that could act as a foundation for steering more fine-grained story mood or story genre we discuss the advantages and disadvantages of our approach to collaborative storytelling The advantages of our approach are that our storytelling system can produce well-formed story contributions that display creativity and react to the contributions made by human storytellers. In Collaborative Storytelling Story one from Table 4 when our system introduces the plot twist that the man and women not only know each other but have been living together for year we see our system’s ability to play along with a human storyteller when the system accepts its collaborator’s assertion that the squirrel can speak English and starts crafting dialogue for it Our preliminary evaluation of sentiment steering through sentence ranking also showed that we could successfully steer the sentiment of stories being generated without significantly degrading story generation quality The disadvantages of our approach are that our storytelling system has a very shallow model of the world, which can lead to incoherent output. This is illustrated by the untuned collaborative story in Figures 5, 7: the narrative makes jarring shifts in setting and lacks overall cohesion Such problems in cohesion are often amplified in self-chat settings as the model lacks human input to reign it in because the storytelling model lacks explicit story structure it can be hard to steer toward desired output We plan to address these issues in future work by adding more structure to the data used to train our models evaluation of this task is challenging: because interaction with human players introduces variance into the output it is difficult to directly compare generated stories evaluation limited to self-chat is not fully reflective of our desired task setting Once our system has been implemented in a suitable agent we plan to carry out detailed subjective evaluation of the collaborative storytelling experience of volunteers to gain further insights about our task and approach we introduced the task of collaborative storytelling We presented a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far Our collaborative storytelling system consists of a publicly-available large scale language model that was tuned on a dataset of writing prompts and short stories We improved storytelling quality by optimizing the ranker’s sample size to strike a balance between quality and computational cost we examined the performance-latency trade-offs of our approach and find the optimal ranker sample size that strikes the best balance between quality and computational cost We evaluated our system by having human participants play the collaborative storytelling game and comparing the stories they create with our system to a naive baseline we conducted a detailed elicitation survey that sheds light on issues to consider when adapting our collaborative storytelling system to a social robot Our evaluation shows that participants have a positive view of collaborative storytelling with a social robot and consider rich emoting capabilities to be key to an enjoyable experience we presented preliminary work on steering story generation sentiment polarity with a sentiment analysis model Evaluation shows our proposed method of sentiment steering through sentence ranking provides a balance of steering capabilities and stability that could act as a foundation for steering more fine-grained story mood or story genre The original contributions presented in the study are included in the article/Supplementary Material further inquiries can be directed to the corresponding authors Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements The patients/participants provided their written informed consent to participate in this study EN is the primary author and formulated the task and approach as well analysis of the findings LG implemented the Collaborative Story Generationsystem and contributed the idea of using a ranker to improve story generation quality and carried out the MTurk-based evaluation YV conducted the elicitation survey and contributed to analysis of findings RG supervised the research and provided invaluable guidance and feedback Authors EN and RG were employed by the company Honda Research Institute Japan Co. Author LG was employed by the company EleutherAI The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher The work presented here includes content that was previously presented at the 13th Annual ACM SIGGRAPH Conference on Motion, Interaction and Games (Nichols et al., 2020) and work that is to appear at the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (Nichols et al., 2021) and is reproduced here with permission 1 https://en.wikipedia.org/wiki/Choose_Your_Own_Adventure 2Stories were edited for brevity 3 https://github.com/openai/gpt-2 4 https://www.reddit.com/r/WritingPrompts/ 5We removed smart quotes and all HTML entities and markdown formatting 6 https://aidungeon.cc 7The workers reached unanimous agreement 41.9% of the time on the test data 8We exclude the Wizard of Wikipedia metric because knowledgeability is not directly relevant to our collaborative storytelling setting 9We test for significance using a two-sided binomial test with null hypothesis μ0 = 0.5 and Story Preference metrics achieve significance at the p < 0.005 level on the MTurk evaluation The Humanness and Story Preference metrics achieve significance at the p < 0.05 level on the university evaluation 10 https://stanfordnlp.github.io/CoreNLP/ “TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification,” in Findings of the Association for Computational Linguistics: EMNLP 2020 (Online: Association for Computational Linguistics) CrossRef Full Text | Google Scholar CrossRef Full Text | Google Scholar In International Conference on Technologies for E-Learning and Digital Entertainment CrossRef Full Text | Google Scholar Cho, H., and May, J. 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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use distribution or reproduction in other forums is permitted provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited in accordance with accepted academic practice distribution or reproduction is permitted which does not comply with these terms *Correspondence: Eric Nichols, ZS5uaWNob2xzQGpwLmhvbmRhLXJpLmNvbQ== Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. 94% of researchers rate our articles as excellent or goodLearn more about the work of our research integrity team to safeguard the quality of each article we publish. One of the first places to come under attack in the whole of Ukraine was Vasylkiv, which is 30 miles south of Kyiv. It was attacked first by rockets and later by paratroopers because of a military airfield nearby. We’re joined by Natalia Balasynovych, the Mayor of Vasylkiv. Photo: Vasyatka1Ukraine poultry companies report major losses caused by the Russian invasion and direct attacks on poultry production assets and warehouses Ukraine’s largest egg producer, Avangard, said that the Russian aggression caused significant losses in production capacity, with several key egg farms shut down and destroyed, while at some operational farms the birds were left without feed and “condemned to death”. To date, the overall financial losses the company sustained have reached 1.5 billion hryvnias (US$51 million). “In particular, Europe’s largest egg cluster, Chornobayivska in Kherson Region, has lost the ability to feed the birds, transport workers to the farm and ship finished products to customers due to military action by the Russian Federation, which threatens to kill the birds and cause an environmental disaster,” Avangard said. The egg farm has been completely cut off from the power supply and production has been suspended. Some of the finished products and laying hens were given away to the local population amid the heavy shelling, but most of the flock was slaughtered as it was impossible to continue feeding birds, the company said. “Nearly 3 million hens will perish without the ability to dispose of them in an environmentally sustainable manner. In the near future, other key enterprises of the company – namely the egg farm in the village of Makariv and the egg farm at Brovary in Kyiv region – may also find themselves in a similar situation,” Avangard said, adding that the company is likely to lose 1 million heads of hens as a result. The shutdown of key production facilities may result in a shortage of chicken eggs in Ukraine, the company warned. Ukraine’s biggest broiler meat producer, MHP, said that the company suffered losses of 230 million hryvnias (US$8 million) as a result of shelling by Russian troops of the largest frozen food warehouse in Ukraine in the village of Kvitneve in the Kyiv region. In addition to MHP, large retail chains also used this warehouse. “This is not the first food warehouse near Kyiv destroyed by the occupiers over the past few days. The enemy is attacking the food security of Ukraine,” the company said. This website is using a security service to protect itself from online attacks. The action you just performed triggered the security solution. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. You can email the site owner to let them know you were blocked. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page. Notifications can be managed in browser preferences. The Russian president’s move was condemned by the US as fighting in Ukraine continues and more sanctions put in place against Moscow I would like to be emailed about offers, events and updates from The Independent. Read our Privacy notice A day of heavy airstrikes, fierce fighting on the ground and fluctuating military fortunes ended with the Ukraine conflict on Sunday reaching new and dangerous levels of risk with Vladimir Putin ordering Russia’s nuclear force to be put on high alert The move by the Russian president, which he said was in response to “aggressive statements” by Nato which accused him of “continuing to escalate the war in a manner which is unacceptable” The prime minister suggested his actions were more to do with the fact that his forces were meeting with “more resistance than the Kremlin had bargained for” while Nato condemned the “dangerous rhetoric” and accused Mr Putin of acting “irresponsibly” But there were also flickering signs of hope that a way may be found to end the strife after the Russian and Ukrainian side agreed to meet at the Belarus border for preliminary talks had repeatedly tried to call Mr Putin on the eve of the invasion His calls were refused; but the Russians are now believed to have agreed to talks without preconditions Russian forces have, however, failed to achieve the swift successes Mr Putin would have expected. They have failed to enter Kyiv, the main prize, and failed to capture any of the main urban centres. They went into Kharkiv, the country’s second largest city on Sunday morning, but were pushed back after a few hours. “Control over Kharkiv is completely ours! The armed forces, the police, and the defence forces are working, and the city is being completely cleansed of the enemy,” the Ukrainian governor of Kharkiv, Oleh Synyehubov, wrote triumphantly on Telegram. At the same time Moscow faces unprecedented rounds of international sanctions, hitting Mr Putin himself and his foreign minister Sergei Lavrov, and hitting every sector of the economy. These continued on Sunday when one of Britain’s biggest companies, BP, decided to exit its 19.75 per cent stake in Russian oil giant Rosneft. Significantly, Norway announced that its $1.3 trillion sovereign wealth fund – the world’s largest – will divest its Russian assets because of the invasion. Meanwhile supplies of weapons to Ukraine have been hugely stepped up despite the Russian president warning the west against interfering in the conflict. The European Union announced it will purchase weapons for Ukraine. “For the first time ever, the European Union will finance the purchase and delivery of weapons and other equipment to a country that is under attack,” Ursula von der Leyen, the European Commission president, said. She also outlined three new sanctions. All Russian aircraft would be banned from EU airspace: state-backed Russian media outlets as well as their subsidiaries, “will no longer be able to spread their lies to justify Putin’s war”, and a widening of existing sanctions targeting Belarus. “Lukashenko’s regime is complicit in the vicious attack against Ukraine,” said Ms Von der Leyen. Significantly, German chancellor Olaf Scholz also announced Berlin would sharply increase its spending on defence to more than 2 per cent of its economic output, around £80bn. Liz Truss, the British foreign secretary, said she would “absolutely” support individual Britons going to fight Russians, insisting: “People can make their own decisions.” The nuclear order by Mr Putin makes it easier to launch weapons, but it is seen as signalling a warning to the west rather than an overt threat to use them. His order came at a meeting with the defence minister, Sergei Shoigu, and the chief of the general staff of the armed forces of Russia, Valery Gerasimov. “Senior officials of the leading Nato countries also allow aggressive statements against our country, therefore I order the minister of defence and the chief of the general staff to transfer the deterrence forces of the Russian army to a special mode of combat duty,” Mr Putin said in a televised statement. “Western countries are not only taking unfriendly actions against our country in the economic sphere, but top officials from leading Nato members made aggressive statements regarding our country.” The US ambassador to the United Nations, Linda Thomas-Greenfield, said: “President Putin is continuing to escalate this war in a manner that is totally unacceptable. And we have to continue to condemn his actions in the most strong, strongest possible way.” She also suggested that the Russian leader could employ chemical or biological weapons. “Certainly nothing is off the table with this guy. He’s willing to use whatever tools he can to intimidate Ukrainians and the world,” she told CBS. Talking about the nuclear order made by Mr Putin, Nato secretary general, Jens Stoltenberg, commented: “This is dangerous rhetoric. This is a behaviour which is irresponsible.” Earlier in the afternoon, the Kremlin announced that a delegation had flown to the Belarusian capital, Minsk, for talks with Ukrainian officials. The Kyiv government had objected to travelling to Minsk with troops from Belarus, part of Moscow’s invasion force. But soon after Mr Putin’s nuclear move President Zelensky’s office said Ukrainian officials will be travelling to the Belarusian border for talks. As the war continued, the growing humanitarian crisis worsened. Nearly 400,000 Ukrainian civilians, mainly women and children, have now fled into neighbouring countries. Hundreds were stranded in Kyiv on Sunday waiting for trains to take them west, away from the fighting. Vitali Klitschko, the mayor of Kyiv, said the capital was “encircled” and a civilian evacuation would not be possible. “We are at the border of a humanitarian catastrophe,” he warned. Protests took place around the world on Sunday, including in Mr Putin’s hometown of St Petersburg. More than 4,000 people have now been arrested in Russia protesting against the war. At least 198 Ukrainians, including three children, have been killed in the invasion, Ukraine’s Health Ministry said. A United Nations agency reported 64 civilian deaths and a Ukrainian presidential adviser said 3,500 Russian soldiers had been killed or wounded, though that figure was not verified. Meanwhile, the United Nations security council is due to vote on Sunday to call for a rare emergency special session of the 193-member general assembly on the invasion. The vote needs nine votes in favour and cannot be vetoed by Russia, one of the permanent five. Only 10 such emergency special sessions have been convened since 1950. Join thought-provoking conversations, follow other Independent readers and see their replies A woman stands in front of a destroyed building after a Russian missile attack in the town of Vasylkiv, near Kyiv govt and politics","score":0.915993},{"label":"/law govt and politics/armed forces","score":0.835402},{"label":"/law govt and politics/politics","score":0.724867}],"mantis":[{"label":"law_govt_politics","score":0.915993},{"label":"armed_forces","score":0.835402},{"label":"politics","score":0.724867}]},"sentiment":"veryNegative"},"article":{"title":"Putin raises stakes and puts nuclear forces ‘on alert’ as west steps up response to Russia","description":"The Russian president’s move was condemned by the US At the beginning of April after Borodianka city near Kyiv Ukraine was freed from occupation by Russians took a photo of a kitchen cabinet that miraculously remained intact after collapse of a kitchen in one of the apartment buildings this photo was taken as the basis for a series of memes using the kitchen cabinet to demonstrate people’s resilience in the war time It was only later that attentive lookers saw and recognized the rooster perching on the cabinet The rooster turned out to be one of those created at Vasylkiv majolica factory Its creation was immediately attributed to Prokip Bidasiuk who during 1940s used to work at this factory presented the inscription under a work from the exhibition “Kind animals” that took place in the National Museum of Folk Applied Arts in Kyiv in the 2019 and was presented by gallerist Pavlo Gudimov one of the Vasylkiv majolica factory artists it was revealed that it is a work of Protoryev couple – Valerii and Nadiya it was interesting to learn the opinion of Mr a person who had dedicated his entire life to Vasylkiv majolica factory on how realistic a renewal of the production was Who is the author of the ceramic rooster that became known after it had survived on a kitchen cabinet of a ruined apartment building in Borodyanka This work is 99% a work of Valerii Protoryev because earlier when we used to sign such works to submit them to exhibitions we indicated Valerii Protoryev as the author The work was mistakenly assigned the authorship of P Bidasiuk at the exhibition of Pavlo Gudimov This rooster is from the factory reference models Such works had mandatory passports because each of them was approved by the Art Council Bidasiuk in general had few references of his own in production All the decorative details of this rooster are characteristic of V Prokip Bidasiuk was a plaster model workshop master He wasn’t even listed as an artist at the factory Some of his “kumanetses” (Ukrainian style donut-shaped jugs) were taken to production but is happened back in 1950s And this rooster was created in the 1960s and its manufacture continued till 1980s several thousands of such roosters were manufactured Could you tell us about the fate of the Vasylkiv majolica factory What are its present conditions and is it possible to renew its operation Only one separate workshop of decorative painting continued to operate till 2021 making ornaments on porcelain sets and plates not more than 5 artists who worked at this factory are alive So that means that it is possible to renew production at something that will be called Vasylkiv majolica factory but this is not enough It can bear the name of the factory but a phenomenon exists only when there exists a certain tradition And when it is interrupted the name can remain but the entire thing will be different And it’s not even the matter of several artists because ceramics production is a large complex time flies; new technologies are developed and everything is moving forward But the technologies used at the factory are already outdated and obsolete It would be possible to recreate some of the models if the model stock survived If those forms and models had been saved they could have been reproduced But their absence makes this practically impossible Plaster models and forms must be stored in dry conditions because moisture or freezing temperatures impact plaster The factory finally stopped its existence in 2004 only one workshop dedicated to painting porcelain and headed by Mr It continued operation till 2020 and preserved the Vasylkiv factory spirit Some of the painter girls from the factory continued working in this workshop They were masters of Flandrivka (or Flendrivka: a specific technique of pottery ornamenting in Ukrainian folk majolica) so this one remaining factory workshop headed by Mr Furmanov gave the girls freedom to paint the way they like and they used Flandrivka to decorate standard porcelain sets Maybe the color palette on these items was different because of changes in the technologies All the compartments and rooms of the factory are now rented There is no ceramics at this place any more The factory was organized because all the preconditions were met for this: as they say “all the stars aligned at this place at the same time” Valerii and Nadiya Protoryev came to work at the factory in the 1950s; my father The technological foundation was laid by Mykhailo N from the same Oleshnia village; later Volodymyr Kovalenko and Nelly Isupova joined Vasylkiv majolica and plastic arts were created due to these artists Those were the pillars: painted pottery and decorative sculpture but the leading part in zoomorphic plastic art was played by Mr What was the main reason for the decline of the majolica factory Ukraine used to have a lot of porcelain factories At the time when we had about 450 employees both Korosten` and Baranivka factories had several thousand There were 2 factories in Polonne; 5 or 6 factories in Zhytomyr region alone Almost each region had a porcelain factory But all of them had been closed much earlier than Vasylkiv factory was In 1990s prices for natural gas and consequently for everything rose catastrophically and this situation destroyed the line from producers to consumers It was still possible to save our factory but incompetence of bureaucrats played its negative role in this My strongest personal pain are the museums that used to exist at each of the factories We also had a large room with reference models But it happened so that no official with corresponding authorities ordered to transfer all these local museums with their exhibits to the State museums all these collections eventually got to private hands just think what a rich collection of Ukrainian porcelain would for instance Mystetsky (Art) Arsenal Museum have had How strong was impact of soviet propaganda on the works The impact on decorative arts was lesser than on fine arts but with decorative arts it impacted only works that were submitted to exhibitions There existed a stylistic kind of censorship When Europe went to the direction of simplicity and geometricity of forms here traditional forms continued to be more or less observed Decorations were also based on the tradition And at the time we didn’t experience Russian craft impacts because there was no Russian pottery craft in 19th century – only mass factory production I myself and Lesya Denysenko continue the tradition; only Ms Denysenko has nothing to do with the factory Her father tried to talk her out of the artist profession because he thought there was no prestige in it Denysenko turned to this profession anyway The battle for the Ukrainian city of Vasylkiv ended in victory of the Ukrainian Armed Forces She said the attack has been repelled and the city is gradually calming down It can be said that there is already silence in Vasylkiv and the terrible night is coming to an end," the Mayor said She explained that there had been a rocket attack on the military units and airfields since yesterday Vasylkiv is located about 35 kilometers from the capital Kiev This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page Independent journalism at the University of Twente Ukrainian UT students saw Russia invade their country exactly one month ago who have to watch the war from Twente with dismay 'It's strange how quickly Ukrainians now say they are doing well.' Mykhailo Brytskyi (19) sat in his room for hours on February 24 the Chemical Science & Engineering student decided to come to campus anyway the country's blue-and-yellow flag slung over his proud back but my 'good' is different from your 'good' It's strange how quickly Ukrainians now say they are doing well,' Brytskyi says 'That's because I have friends who have it much tougher A comrade asked his parents the other day how things were going and only got an answer two weeks later but live in a relatively quiet part of the metropolis 'They haven't even been to the bomb shelter for days It is very cold there and they feel relatively safe in their homes now My parents want me to keep focusing on my studies.' because for now I can't go back to Ukraine Following the study programme well is my job I appreciate the support of fellow students and teachers who ask how I'm doing but sometimes it's hard because I have to answer the same questions over and over again.' recognizes Brytskyi's description of answers to the question of how she is doing 'It's very strange how quickly I got used to reality I texted my parents in Kyiv how they had spent the night We all accept it as a fact that it's war.' Konashchuk is relieved that the information flow in Kyiv is much better than it was during the first two weeks 'Day by day my parents get updates on the situation That way I also know daily how they are doing.' The student finds it difficult to look ahead Experts contradict each other daily about the war 'The shortest scenario appeals to me the most I notice that Ukrainian students from the UT community are also starting to worry about the collapsed economy Many of us are still supported financially by parents Brytskyi complements her: 'In Kyiv the damage is already enormous but cities like Mariupol have been completely bombed out It's going too far to say Ivanna Vasylkiv (19) is doing well but the Chemical Science and Engineering student is doing better than she was four weeks ago 'I have the relative good fortune that my family is in Kyiv It helps to think as little as possible about the war and only take news about Kyiv to me Vasylkiv was spending her Christmas vacations still in Kyiv Kyiv is the place I love most and at the moment I can't go back home I remember walking there in December and thinking about when I would be back in Kyiv It feels very crazy that that can take a long time.' What helps Vasylkiv is the tremendous engagement on campus in the lecture hall and with teachers: everywhere people show interest in her situation The focus on my studies is not one hundred percent I am a freshman and want to pass this year no matter what Russian UT employees are also affected by the Russian invasion in Ukraine but also worry about family and friends in their homeland 'I am relieved that my grandfather and grandmother are not going through this anymore.' Ook Russische UT-medewerkers zijn aangeslagen door de inval in Oekraïne maar maken zich ook zorgen om familie en vrienden in hun thuisland ‘Ik ben blij dat mijn opa en oma dit niet meer meemaken.’ Oekraïense vluchteling-wetenschappers kunnen hun onderzoek in Europese buurlanden voortzetten De Europese Commissie heeft 25 miljoen euro voor hen uitgetrokken in het onderzoeksprogramma Horizon Europe Displaced Ukrainian scientists can continue their research in neighbouring European countries The European Commission has set aside 25 million euros for them in the Horizon Europe research programme Het kabinet heeft nog eens 2,5 miljoen euro uitgetrokken voor noodsteun aan studenten die getroffen zijn door de oorlog in Oekraïne 2,3 miljoen euro wordt door universiteiten en hogescholen verdeeld en de rest door het mbo The Cabinet has allocated another 2.5 million euros in emergency funding for students hit by the war in Ukraine 2.3 million euros will be shared by institutions in higher education and the rest will go to secondary vocational education (mbo) Op verzoek van het kabinet bevriezen universiteiten hogescholen en academische ziekenhuizen hun formele samenwerking met Rusland en Belarus Tegelijkertijd steunen ze getroffen studenten en medewerkers higher education institutions and teaching hospitals are suspending all formal cooperation with Russia and Belarus they are offering support to students and staff affected With the magazine ROOTS we want to connect students and companies We do this by bringing stories of starters on the labor market They talk about living and working in the region companies come into the spotlight of students and students get an idea of the life that awaits them and what opportunities there are in the region Sign In Register ESTEVAN - Anyone who's spent more than a couple of months in the Estevan area knows that getting a job at the mines is almost like drawing a winning ticket pays well and comes with a lot of opportunities and valued benefits Every year throughout most of its almost 119-year history the Mercury has been sharing stories of people dedicating their lives to coal mining Yet hardly any of those stories were told by women Even though the industry has been changing lately and female representation has been growing over the past years coal mining still remains a male-dominated industry those women who had the skills and guts to step into this world are happy with the decision most of them made a long time ago. This year the Mercury spoke to four women working different jobs for Westmoreland Mining Holdings LLC's Estevan Mine who currently works as an operations/production supervisor II has been with the mines for a quarter century now She started in 1997 when she was just 19 years old and it just wasn't really cut out for me I was taking a year off and I ended up in the oil patch inspecting drill pipes for about four months And my dad's friend worked at the mines worst happens is they don't hire me.' So I went out there It was awesome," Eagles recalled the beginning of her times at the mine "I was the first woman actually to run equipment at that mine that were males was definitely a little intimidating." then switched to the scraper and then back to the dozer She also worked in the service bay dragline oiling and got to operate the dragline here and there She took a break in 2008 when she had a daughter Eagles moved into a training supervisor position she is currently going into operations/production supervisor II I never really got involved in the plant too much That's something I really miss now that I'm not on equipment And there is a lot of good people out there And it's never the same," Eagles said Learning the needed skills for new jobs wasn't always easy but the challenges only made her push harder She noticed that a lot has changed since she first started coming from no equipment experience whatsoever I got to the point where I was even told that I didn't belong there [by a co-worker] I can do this.' And I did it and I think I did pretty good I had to prove myself and it was difficult being a female.” Sometimes it feels like you can't make a mistake because it seems that you're looked upon a little bit quicker than if someone else maybe made a mistake And it feels like you sometimes have to over-excel just to prove yourself [to some] "But I don't feel like I need to do that anymore There's more women in the environment now and some are doing so amazing," Eagles shared She said she's never regretted her decision to apply for work at the mines as the industry and the employer have been good to her. The mining industry definitely looks after you I feel like I've always been protected there And the people are really good to work with We've been through many different companies but I feel like we're always looked after She also encouraged other women to leave their fears behind and go for it if it's something they might be interested in doing Even if it's just an opportunity to try it It's a direction I never thought I'd ever go So even if you don't go into it for the rest of your life I think the opportunity is great," Eagles said joined the mine about 15 years ago and her path to the industry ran through a farming background "I've been a farm girl all my life I always enjoyed running farm equipment and then I obtained my 1A driver's licence through hauling grain and driving semis so when I applied to work here I was definitely qualified And this was the first job I ended up with that had something like full benefits I've never had before And I've been here 15 years now," Fraser shared While running farm and mine equipment was similar to a point the mine site as a whole was new and different for her and the mine gave her many opportunities to get involved with that She's been a part of the occupational health and safety committee for years and she was a part of the mine rescue team at one point Fraser has also tried several different jobs She was on a coal haul truck for about four years and for another four she ran a scraper before she ended up with a grader she said she never felt bad because of that Especially on the part of getting involved in the safety committee there were a number of men that I worked with that were involved in the safety committee and they wanted me involved because of my previous background I've always found it very welcoming that way And there's never been more than six to eight women in our workforce of over 300," Fraser said The mine also gave her all she was looking for from a job "I've always liked operating equipment I don't mind working alone as you're alone in your equipment all day And I've always had a little bit of a mechanical sense too," Fraser said benefits and opportunities the mine provides are hard to outbid the opportunity to learn new skills and bid on different jobs was always a big advantage and there are opportunities for apprenticeships for trades as well Even though there are still not very many women in the industry Fraser sees more and more females joining the field and she hopes that this trend will continue "In terms of encouraging women to work this type of industry if you ever hear people talk about wage inequality this is an industry where women can make a much better wage so everyone's kept equal," Fraser said Dozer operator and current mine rescue team captain Jessica Klarholm has been with the mine for about 10 years now and she said that the industry gives "everything you would look for in a job" She had a background in operating machinery and the qualifications the employer was looking for "It was actually quite quick and painless," Klarholm recalled adding that she indeed had some doubts at the time since the equipment she ran before was much smaller "It's a little intimidating when you walk out and you're not even as tall as the tire on your machine And I guess I did because I'm still here." She said that working in the male-dominated industry has never been an issue for her "The question I get the most is what it's like working with all the men It's really a nonissue," Klarholm said being a female in mining has its challenges and takes more consideration in certain aspects climbing into equipment became way more difficult the employer allowed her to move to a dozer operator position that was going to be the safest for her if something should ever happen "They were super understanding that way," Klarholm said She added that meshing home life with a shift work schedule at the mine is the most challenging aspect as being a parent poses extra challenges for a woman But challenging doesn't mean that it's impossible Klarholm has been an equipment operator for 10 years working various machines before she switched to running a dozer And that flexibility is something she likes about the job "Even if you're in one bid position you generally get to cross-train on other machines There's definitely a lot of diversity in that way," Klarholm said Running big equipment and working as a part of a team makes the work rewarding you have your dragline operator and oiler and then the dozer And it's very rewarding to problem-solve when you can get the job done making those machines operate efficiently," Klarholm said She's also been with the MRT for nine years now another valued experience as the skills mine rescuers get trained for are the skills she wanted to have in her daily life "I wanted to know how to handle an emergency And then it turned into a personal favourite And it's super challenging in lots of ways," said Klarholm There have been a couple of other women on the team She added that being a woman on MRT has its aspects "It's different … Obviously it's just it feels different for me to try and be that commanding captain It's just different," Klarholm explained She added that being union-based helps a lot when it comes to unbalanced representation as the union stands with employees no matter what gender they are And while work at the mines requires quite a bit from the candidates Klarholm believes women can succeed in it as much as men "The best way to succeed is really just to do your job and do it well And it doesn't matter if your man or woman in that case," Klarholm said Ivanna Vasylkiv is one of the recent hires She joined Westmoreland Mining Holdings LLC's Estevan Mine in October 2021 and she said getting a job with the mines was her long-time goal and dream "It was very exciting for me since I always wanted to work at a place like this There is an interesting process that we do out here I believe they don't have [other similar] plants in Canada … So I'm very pleased to have this opportunity to work here," Vasylkiv said She graduated with a degree in science and technology from the Lviv Polytechnic National University in Ukraine before she moved to Canada Polytechnic in Saskatoon to receive a diploma in power engineering She also gained some relevant experience before she applied for the job at the mines she didn't have any problems getting into the industry The power engineer or plant operator position includes two types of work – a field operator and a panel operator and Vasylkiv said both are interesting experiences for her "For me working in the field is hands-on But working the panel is basically starting everything remotely and working with the Delta V Program for power engineers is a great experience," Vasylkiv said adding that the job allows for constant learning and growth She added that the processes and the products they make are also something that makes her job interesting Companies buy activated carbon to make other products out of it Activated carbon is made at the plant where I work but we also have a char plant here at Bienfait mine," Vasylkiv said She had nothing but a good experience at the mines So I've really enjoyed it," Vasylkiv said And it's not a different experience or anything it's a good experience overall because everyone has a nice personality it comes to everyone being a team and helping each other." She added that she appreciates the changes in the world of trades and the opportunities those changes give to women "There's way more women going in this kind of spheres lately And I think it's a good thing [for women] to know they have this choice and they are being accepted And there is no difference made if you're male or female and we just have to be safe at the end of the day and complete our tasks be part of a team and do our job," Vasylkiv said