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LightSword: A Customized Virtual Reality Exergame for Long-Term Cognitive Inhibition Training in Older Adults 光剑：用于老年人长期认知抑制训练的定制虚拟现实游戏
ArXiv Pub Date : 2024-03-08 DOI: 10.1145/3613904.3642187
Qiuxin Du, Zhen Song, Haiyan Jiang, Xiaoying Wei, Dongdong Weng, Mingming Fan
{"title":"LightSword: A Customized Virtual Reality Exergame for Long-Term Cognitive Inhibition Training in Older Adults","authors":"Qiuxin Du, Zhen Song, Haiyan Jiang, Xiaoying Wei, Dongdong Weng, Mingming Fan","doi":"10.1145/3613904.3642187","DOIUrl":"https://doi.org/10.1145/3613904.3642187","url":null,"abstract":"The decline of cognitive inhibition significantly impacts older adults' quality of life and well-being, making it a vital public health problem in today's aging society. Previous research has demonstrated that Virtual reality (VR) exergames have great potential to enhance cognitive inhibition among older adults. However, existing commercial VR exergames were unsuitable for older adults' long-term cognitive training due to the inappropriate cognitive activation paradigm, unnecessary complexity, and unbefitting difficulty levels. To bridge these gaps, we developed a customized VR cognitive training exergame (LightSword) based on Dual-task and Stroop paradigms for long-term cognitive inhibition training among healthy older adults. Subsequently, we conducted an eight-month longitudinal user study with 12 older adults aged 60 years and above to demonstrate the effectiveness of LightSword in improving cognitive inhibition. After the training, the cognitive inhibition abilities of older adults were significantly enhanced, with benefits persisting for 6 months. This result indicated that LightSword has both short-term and long-term effects in enhancing cognitive inhibition. Furthermore, qualitative feedback revealed that older adults exhibited a positive attitude toward long-term training with LightSword, which enhanced their motivation and compliance.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"31 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modeling Dynamic (De)Allocations of Local Memory for Translation Validation 为翻译验证建立本地内存动态（去）分配模型
ArXiv Pub Date : 2024-03-08 DOI: 10.1145/3649863
Abhishek Rose, Sorav Bansal
{"title":"Modeling Dynamic (De)Allocations of Local Memory for Translation Validation","authors":"Abhishek Rose, Sorav Bansal","doi":"10.1145/3649863","DOIUrl":"https://doi.org/10.1145/3649863","url":null,"abstract":"End-to-End Translation Validation is the problem of verifying the executable code generated by a compiler against the corresponding input source code for a single compilation. This becomes particularly hard in the presence of dynamically-allocated local memory where addresses of local memory may be observed by the program. In the context of validating the translation of a C procedure to executable code, a validator needs to tackle constant-length local arrays, address-taken local variables, address-taken formal parameters, variable-length local arrays, procedure-call arguments (including variadic arguments), and the alloca() operator. We provide an execution model, a definition of refinement, and an algorithm to soundly convert a refinement check into first-order logic queries that an off-the-shelf SMT solver can handle efficiently. In our experiments, we perform blackbox translation validation of C procedures (with up to 100+ SLOC), involving these local memory allocation constructs, against their corresponding assembly implementations (with up to 200+ instructions) generated by an optimizing compiler with complex loop and vectorizing transformations.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"31 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AQuA: Automated Question-Answering in Software Tutorial Videos with Visual Anchors AQuA：利用视觉锚点在软件教程视频中自动答题
ArXiv Pub Date : 2024-03-08 DOI: 10.1145/3613904.3642752
Saelyne Yang, Jo Vermeulen, G. Fitzmaurice, Justin Matejka
{"title":"AQuA: Automated Question-Answering in Software Tutorial Videos with Visual Anchors","authors":"Saelyne Yang, Jo Vermeulen, G. Fitzmaurice, Justin Matejka","doi":"10.1145/3613904.3642752","DOIUrl":"https://doi.org/10.1145/3613904.3642752","url":null,"abstract":"Tutorial videos are a popular help source for learning feature-rich software. However, getting quick answers to questions about tutorial videos is difficult. We present an automated approach for responding to tutorial questions. By analyzing 633 questions found in 5,944 video comments, we identified different question types and observed that users frequently described parts of the video in questions. We then asked participants (N=24) to watch tutorial videos and ask questions while annotating the video with relevant visual anchors. Most visual anchors referred to UI elements and the application workspace. Based on these insights, we built AQuA, a pipeline that generates useful answers to questions with visual anchors. We demonstrate this for Fusion 360, showing that we can recognize UI elements in visual anchors and generate answers using GPT-4 augmented with that visual information and software documentation. An evaluation study (N=16) demonstrates that our approach provides better answers than baseline methods.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ShuffleBench: A Benchmark for Large-Scale Data Shuffling Operations with Distributed Stream Processing Frameworks ShuffleBench：使用分布式流处理框架的大规模数据洗牌操作基准
ArXiv Pub Date : 2024-03-07 DOI: 10.1145/3629526.3645036
Sören Henning, Adriano Vogel, Michael Leichtfried, Otmar Ertl, Rick Rabiser
{"title":"ShuffleBench: A Benchmark for Large-Scale Data Shuffling Operations with Distributed Stream Processing Frameworks","authors":"Sören Henning, Adriano Vogel, Michael Leichtfried, Otmar Ertl, Rick Rabiser","doi":"10.1145/3629526.3645036","DOIUrl":"https://doi.org/10.1145/3629526.3645036","url":null,"abstract":"Distributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. This paper introduces ShuffleBench, a novel benchmark to evaluate the performance of modern stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where stream processing frameworks are mainly employed for shuffling (i.e., re-distributing) data records to perform state-local aggregations, while the actual aggregation logic is considered as black-box software components. ShuffleBench is inspired by requirements for near real-time analytics of a large cloud observability platform and takes up benchmarking metrics and methods for latency, throughput, and scalability established in the performance engineering research community. Although inspired by a real-world observability use case, it is highly configurable to allow domain-independent evaluations. ShuffleBench comes as a ready-to-use open-source software utilizing existing Kubernetes tooling and providing implementations for four state-of-the-art frameworks. Therefore, we expect ShuffleBench to be a valuable contribution to both industrial practitioners building stream processing applications and researchers working on new stream processing approaches. We complement this paper with an experimental performance evaluation that employs ShuffleBench with various configurations on Flink, Hazelcast, Kafka Streams, and Spark in a cloud-native environment. Our results show that Flink achieves the highest throughput while Hazelcast processes data streams with the lowest latency.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"22 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation 利用重要性采样和原型-实例关系提炼进行对比式持续学习
ArXiv Pub Date : 2024-03-07 DOI: 10.1609/aaai.v38i12.29259
Jiyong Li, Dilshod Azizov, Yang Li, Shangsong Liang
{"title":"Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation","authors":"Jiyong Li, Dilshod Azizov, Yang Li, Shangsong Liang","doi":"10.1609/aaai.v38i12.29259","DOIUrl":"https://doi.org/10.1609/aaai.v38i12.29259","url":null,"abstract":"Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://github.com/lijy373/CCLIS.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"25 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Video-Driven Animation of Neural Head Avatars 神经头像的视频驱动动画
ArXiv Pub Date : 2024-03-07 DOI: 10.2312/vmv.20231237
Wolfgang Paier, Paul Hinzer, A. Hilsmann, P. Eisert
{"title":"Video-Driven Animation of Neural Head Avatars","authors":"Wolfgang Paier, Paul Hinzer, A. Hilsmann, P. Eisert","doi":"10.2312/vmv.20231237","DOIUrl":"https://doi.org/10.2312/vmv.20231237","url":null,"abstract":"We present a new approach for video-driven animation of high-quality neural 3D head models, addressing the challenge of person-independent animation from video input. Typically, high-quality generative models are learned for specific individuals from multi-view video footage, resulting in person-specific latent representations that drive the generation process. In order to achieve person-independent animation from video input, we introduce an LSTM-based animation network capable of translating person-independent expression features into personalized animation parameters of person-specific 3D head models. Our approach combines the advantages of personalized head models (high quality and realism) with the convenience of video-driven animation employing multi-person facial performance capture. We demonstrate the effectiveness of our approach on synthesized animations with high quality based on different source videos as well as an ablation study.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"21 25‐26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Automating the Information Extraction from Semi-Structured Interview Transcripts 从半结构化访谈记录中自动提取信息
ArXiv Pub Date : 2024-03-07 DOI: 10.1145/3589335.3651230
Angelina Parfenova
{"title":"Automating the Information Extraction from Semi-Structured Interview Transcripts","authors":"Angelina Parfenova","doi":"10.1145/3589335.3651230","DOIUrl":"https://doi.org/10.1145/3589335.3651230","url":null,"abstract":"This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysis process. Our research investigates various topic modeling techniques and concludes that the best model for analyzing interview texts is a combination of BERT embeddings and HDBSCAN clustering. We present a user-friendly software prototype that enables researchers, including those without programming skills, to efficiently process and visualize the thematic structure of interview data. This tool not only facilitates the initial stages of qualitative analysis but also offers insights into the interconnectedness of topics revealed, thereby enhancing the depth of qualitative analysis.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"17 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation 揭开深度滤镜的泡沫：短视频推荐中的窄曝光率
ArXiv Pub Date : 2024-03-07 DOI: 10.1145/3589334.3648159
Nicholas Sukiennik, Chen Gao, Nian Li
{"title":"Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation","authors":"Nicholas Sukiennik, Chen Gao, Nian Li","doi":"10.1145/3589334.3648159","DOIUrl":"https://doi.org/10.1145/3589334.3648159","url":null,"abstract":"Filter bubbles have been studied extensively within the context of online content platforms due to their potential to cause undesirable outcomes such as user dissatisfaction or polarization. With the rise of short-video platforms, the filter bubble has been given extra attention because these platforms rely on an unprecedented use of the recommender system to provide relevant content. In our work, we investigate the deep filter bubble, which refers to the user being exposed to narrow content within their broad interests. We accomplish this using one-year interaction data from a top short-video platform in China, which includes hierarchical data with three levels of categories for each video. We formalize our definition of a\"deep\"filter bubble within this context, and then explore various correlations within the data: first understanding the evolution of the deep filter bubble over time, and later revealing some of the factors that give rise to this phenomenon, such as specific categories, user demographics, and feedback type. We observe that while the overall proportion of users in a filter bubble remains largely constant over time, the depth composition of their filter bubble changes. In addition, we find that some demographic groups that have a higher likelihood of seeing narrower content and implicit feedback signals can lead to less bubble formation. Finally, we propose some ways in which recommender systems can be designed to reduce the risk of a user getting caught in a bubble.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"14 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Persona Extraction Through Semantic Similarity for Emotional Support Conversation Generation 通过语义相似性提取角色，生成情感支持对话
ArXiv Pub Date : 2024-03-07 DOI: 10.1109/icassp48485.2024.10445957
Seunghee Han, Se Jin Park, Chae Won Kim, Y. Ro
{"title":"Persona Extraction Through Semantic Similarity for Emotional Support Conversation Generation","authors":"Seunghee Han, Se Jin Park, Chae Won Kim, Y. Ro","doi":"10.1109/icassp48485.2024.10445957","DOIUrl":"https://doi.org/10.1109/icassp48485.2024.10445957","url":null,"abstract":"Providing emotional support through dialogue systems is becoming increasingly important in today's world, as it can support both mental health and social interactions in many conversation scenarios. Previous works have shown that using persona is effective for generating empathetic and supportive responses. They have often relied on pre-provided persona rather than inferring them during conversations. However, it is not always possible to obtain a user persona before the conversation begins. To address this challenge, we propose PESS (Persona Extraction through Semantic Similarity), a novel framework that can automatically infer informative and consistent persona from dialogues. We devise completeness loss and consistency loss based on semantic similarity scores. The completeness loss encourages the model to generate missing persona information, and the consistency loss guides the model to distinguish between consistent and inconsistent persona. Our experimental results demonstrate that high-quality persona information inferred by PESS is effective in generating emotionally supportive responses.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"15 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Message-Observing Sessions 信息服务会话
ArXiv Pub Date : 2024-03-07 DOI: 10.1145/3649859
Ryan Kavanagh, B. Pientka
{"title":"Message-Observing Sessions","authors":"Ryan Kavanagh, B. Pientka","doi":"10.1145/3649859","DOIUrl":"https://doi.org/10.1145/3649859","url":null,"abstract":"We present Most, a process language with message-observing session types. Message-observing session types extend binary session types with type-level computation to specify communication protocols that vary based on messages observed on other channels. Hence, Most allows us to express global invariants about processes, rather than just local invariants, in a bottom-up, compositional way. We give Most a semantic foundation using traces with binding, a semantic approach for compositionally reasoning about traces in the presence of name generation. We use this semantics to prove type soundness and compositionality for Most processes. We see this as a significant step towards capturing message-dependencies and providing more precise guarantees about processes.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"24 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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