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Joint 30th International Conference on computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Zhipeng Zhang, Xiaoming Zhang, Litian Yu, Zelong School of Cyber Science and Technology Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (纸本)9782493814104

Due to the explosion of various dialogue scenes, summarizing the dialogue into a short message has drawn much attention recently. In the multi-modal dialogue scene, people tend to use tone and body language to illustrate their intentions. While traditional dialogue summarization has predominantly focused on textual content, this approach may overlook vital visual and audio information essential for understanding multi-modal interactions. Recognizing the established field of multi-modal dialogue summarization, we develop a new multi-modal dialogue summarization dataset (MDS), which aims to enhance the variety and scope of data available for this research area. MDS provides a demanding testbed for multi-modal dialogue summarization. Subsequently, we conducted a comparative analysis of various summarization techniques on MDS and found that the existing methods tend to produce redundant and incoherent summaries. All of the models generate unfaithful facts to some degree, suggesting future research directions. MDS is available at https://***/R00kkie/MDS. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Dialogue Summarization Language Resources Multi-Modal Learning

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Efficient and Lossless Integrity-preserving Training Scheme for High-dimensional Logistic Regression over Vertical Data

Efficient and Lossless Integrity-preserving Training Scheme ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xu, Wei Zhu, Hui Liu, Ruikang Zheng, Yandong Wang, Fengwei Feng, Dengguo Xidian University School of Cyber Engineering Xi'an710126 China Institute of Software State Key Laboratory of Computer Science Beijing100190 China

ISBN: (纸本)9798350351255

Logistic regression is a widely used and efficient machine learning algorithm for data analysis. However, data is often distributed among different participants, and building high-quality models often involves data interaction, which might easily lead to sensitive information leakage. While some schemes have been proposed to address the data security issue through privacy-preserving technologies such as multi-party computation, homomorphic encryption, and trusted execution environment (TEE), they still suffer from some limitations in terms of security, efficiency, and model integrity and are not suitable for the secure outsourcing training. Therefore, in this paper, we propose an efficient and lossless integrity-preserving training for high-dimensional logistic regression over vertically partitioned data. First, we utilize a block strategy to encrypt the data in batches and outsource them to a cloud with TEE to achieve the non-interactive training. Considering TEE's limited memory, we employ random sampling technology to achieve mini-batch training within TEE, which ensures the data security. We find that the cloud might launch the active attacks to drop the model accuracy, such as lazy attack and replacement attack. To prevent the attacks, we introduce an integrity protection mechanism to verify data integrity and reduce verification frequency using a mixed-probability checking method, including fixed and random probability checking. Detailed analysis has confirmed that our scheme is secure and integrity-preserving. Extensive experiments have demonstrated that our scheme can achieve lossless, non-interactive, and efficient model training. © 2024 IEEE.

关键词： Information leakage

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Hyperbolic Graph Neural Network for Temporal Knowledge Graph Completion 30

Hyperbolic Graph Neural Network for Temporal Knowledge Graph...

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Joint 30th International Conference on computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Yancong Zhang, Xiaoming Cui, Ying Ma, Shuai School of Cyber Science and Technology Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (纸本)9782493814104

Temporal Knowledge Graphs (TKGs) represent a crucial source of structured temporal information and exhibit significant utility in various real-world applications. However, TKGs are susceptible to incompleteness, necessitating Temporal Knowledge Graph Completion (TKGC) to predict missing facts. Existing models have encountered limitations in effectively capturing the intricate temporal dynamics and hierarchical relations within TKGs. To address these challenges, HyGNet is proposed, leveraging hyperbolic geometry to effectively model temporal knowledge graphs. The model comprises two components: the Hyperbolic Gated Graph Neural Network (HGGNN) and the Hyperbolic Convolutional Neural Network (HCNN). HGGNN aggregates neighborhood information in hyperbolic space, effectively capturing the contextual information and dependencies between entities. HCNN interacts with embeddings in hyperbolic space, effectively modeling the complex interactions between entities, relations, and timestamps. Additionally, a consistency loss is introduced to ensure smooth transitions in temporal embeddings. The extensive experimental results conducted on four benchmark datasets for TKGC highlight the effectiveness of HyGNet. It achieves state-of-the-art performance in comparison to previous models, showcasing its potential for real-world applications that involve temporal reasoning and knowledge prediction. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Geometry

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LAMB: Label-Induced Mixed-Level Blending for Multimodal Multi-label Emotion Detection 19th

LAMB: Label-Induced Mixed-Level Blending for Multimodal Mul...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Qian, Shuwei Guo, Ming Fan, Zhicheng Chen, Mingcai Wang, Chongjun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031545276

To better understand complex human emotions, there is growing interest in utilizing heterogeneous sensory data to detect multiple co-occurring emotions. However, existing studies have focused on extracting static information from each modality, while overlooking various interactions within and between modalities. Additionally, the label-to-modality and label-to-label dependencies still lack exploration. In this paper, we propose LAbel-induced Mixed-level Blending (LAMB) to address these challenges. Mixed-level blending leverages shallow but manifold self-attention and cross-attention encoders in parallel to model unimodal context dependency and cross-modal interaction simultaneously. This is in contrast to previous works either use one of them or cascade them successively, which ignores the diversity of interaction in multimodal data. LAMB also employs label-induced aggregation to allow different labels to attend to the most relevant blended tokens adaptively using a transformer-based decoder, which facilitates the exploration of label-to-modality dependency. Unlike common low-order strategies in multi-label learning, correlations among multiple labels can be learned by self-attention in label embedding space before being treated as queries. Comprehensive experiments demonstrate the effectiveness of our methods for multimodal multi-label emotion detection. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Emotion Recognition

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USee: Ultrasound-based Device-free Eye Movement Sensing 21

USee: Ultrasound-based Device-free Eye Movement Sensing

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Cheng, Wen Pang, Mingzhi Wan, Haoran Dong, Shichen Liu, Dongxu Wang, Wei State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Princeton University Princeton United States

ISBN: (纸本)9798331519186

Eye movements play a significant role in human-computer interaction and are widely recognized as an essential health indicator, making their detection both appealing and technically challenging. In this paper, we present a system named USEE that achieves high-precision capture of weak and aperiodic eye movements by utilizing fine-grained and ubiquitous ultrasound signals, capturing both blinking and more subtle saccades. We first identify signal changes associated with eye movements by capturing the unique impact of blinking. Further, we establish a pioneering relationship between the residuals from signal decomposition and subtle eye movements. Utilizing innovative signal processing architectures, we mitigate interference and effectively extract eye movement features. Subsequently, we employ one-dimensional convolutional operations in place of signal cross-correlation, designing filters for motion category identification and a lightweight convolutional neural network for saccade direction classification. This enables our system to serve as a foundational sensing layer for eye movement tracking, applicable across diverse applications. We implement USEE on both a research-purpose platform and a commodity Raspberry Pi. Extensive experimental results demonstrate the effectiveness of our system, achieving 91% accuracy in saccade recognition and 94% in blink detection. The system proves robust, even in challenging scenarios with strong interference, such as the presence of moving pedestrians. © 2024 IEEE.

关键词： Motion sensors

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Piercing Diametral Disks Induced by Edges of Maximum Spanning Trees

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Journal of Graph Algorithms and Applications 2024年第3期28卷 3-10页

作者： Karim Abu-Affash, A. Carmi, Paz Maman, Meytal Department of Software Engineering Shamoon College of Engineering Israel Department of Computer Science Ben-Gurion University Israel

Let P be a set of points in the plane and let T be a maximum-weight spanning tree of P. For an edge (p, q), let Dpq be the diametral disk induced by (p, q), i.e., the disk having the segment pq as its diameter. Let DT be the set of the diametral disks induced by the edges of T. In this paper, we show that one point is sufficient to pierce all the disks in DT. Actually, we show that the center of the smallest enclosing circle of P is contained in all the disks of DT, and thus the piercing point can be computed in linear time. © 2024, Brown University. All rights reserved.

关键词： Piercing

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

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14th IEEE International Conference on software Engineering and Service science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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The Impact of SYCL Data Management on Performance Portability 15th

The Impact of SYCL Data Management on Performance Portabil...

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15th International Conference on parallel Processing and Applied Mathematics, PPAM 2024

作者： Marowka, Ami Parallel Research Lab Tel-Aviv Israel

ISBN: (纸本)9783031856990

SYCL programming model does not guarantee performance portability across different architectures. However, the HPC community severely needs platform-independent performance portable applications more than ever. Therefore, the main challenge of SYCL implementers and application developers is to look for direct or indirect solutions in order to improve the portability, performance, and performance portability of SYCL applications. In this paper we study and analyze the impact of the two main SYCL abstractions for data management, i.e., the unified shared memory and buffer-accessor approaches, on the three pillars of performance portability: portability, productivity, and performance. Experiments were carried out on state-of-the-art CPU and GPU platforms in order to shed light on the effect of different SYCL features on performance. The conclusions that emerged from this study show that by avoiding the use of SYCL performance portability inhibitors, it is possible to develop applications with a realistic level of performance portability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information management

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String Constraints with Regex-Counting and String-Length Solved More Efficiently 9th

String Constraints with Regex-Counting and String-Length S...

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9th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Hu, Denghang Wu, Zhilin State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819986637

Regular expressions (regex for short) and string-length function are widely used in string-manipulating programs. Counting is a frequently used feature in regexes that counts the number of matchings of sub-patterns. The state-of-the-art string solvers are incapable of solving string constraints with regex-counting and string-length efficiently, especially when the counting and length bounds are large. In this work, we propose an automata-theoretic approach for solving such class of string constraints. The main idea is to symbolically model the counting operators by registers in automata instead of unfolding them explicitly, thus alleviating the state explosion problem. Moreover, the string-length function is modeled by a register as well. As a result, the satisfiability of string constraints with regex-counting and string-length is reduced to the satisfiability of linear integer arithmetic, which the off-the-shelf SMT solvers can then solve. To improve the performance further, we also propose techniques to reduce the sizes of automata. We implement the algorithms and validate our approach on 48,843 benchmark instances. The experimental results show that our approach can solve more instances than the state-of-the-art solvers, at a comparable or faster speed, especially when the counting and length bounds are large. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Jianqing Huang, Jianguo Jiang, Wenyu Zhang, Baoming Li, Shuangjie Wang, Chongjun State Key Laboratory of Novel Software Technology Nanjing University China Department of Statistics and Data Science Southern University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore

Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal prediction methods provide a theoretical guarantee for node classification tasks, ensuring that the conformal prediction set contains the ground-truth label with a desired probability (e.g., 95%). In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets while maintaining valid marginal coverage. This observation motivates us to propose a novel algorithm named Similarity-Navigated Adaptive Prediction Sets (SNAPS), which aggregates the non-conformity scores based on feature similarity and structural neighborhood. The key idea behind SNAPS is that nodes with high feature similarity or direct connections tend to have the same label. By incorporating adaptive similar nodes information, SNAPS can generate compact prediction sets and increase the singleton hit ratio (correct prediction sets of size one). Moreover, we theoretically provide a finite-sample coverage guarantee of SNAPS. Extensive experiments demonstrate the superiority of SNAPS, improving the efficiency of prediction sets and singleton hit ratio while maintaining valid coverage. © 2024 Neural information processing systems foundation. All rights reserved.

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