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2024 IEEE International Conference on Big Data, BigData 2024

作者： Berger, Armin Berghaus, David Bashir, Ali Hamza Grigull, Lorenz Fendrich, Lara Lagones, Tom Anglim Högl, Henriette Ernst, Gundula Schmidt, Ralf Bascom, David Deußer, Tobias Bell, Thiago Lübbering, Max Sifa, Rafet Fraunhofer Iais Department of Media Engineering Germany University of Bonn Department of Computer Science Germany Federal Ministry of Education and Research West-AI Germany Lamarr Institute Germany University Hospital Bonn Center for Rare Diseases Germany Department of Health Queensland Australia Griffith University School of Medicine Australia Children's Network for Chronic Illnesses and Disabilities Germany Medical School Hannover Department of Medical Psychology Germany

ISBN: (纸本)9798350362480

This study explores efficient algorithms to enhance user matching in ***, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse SPLADE embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as TopicBERT for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform. © 2024 IEEE.

关键词： Personalized medicine

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Monitoring and detecting abnormal behavior in mobile cloud infrastructure

Monitoring and detecting abnormal behavior in mobile cloud i...

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IEEE Symposium on network Operations and Management

作者： Taehyun Kim Yeongrak Choi Seunghee Han Jae Yoon Chung Jonghwan Hyun Jian Li James Won-Ki Hong Division of IT Convergence Engineering POSTECH Pohang South Korea Network Technology Laboratories Daejeon South Korea Department of Computer Science and Engineering POSTECH Pohang South Korea

Recently, several mobile services are changing to cloud-based mobile services with richer communications and higher flexibility. We present a new mobile cloud infrastructure that combines mobile devices and cloud services. This new infrastructure provides virtual mobile instances through cloud computing. To commercialize new services with this infrastructure, service providers should be aware of security issues. In this paper, we first define new mobile cloud services through mobile cloud infrastructure and discuss possible security threats through the use of several service scenarios. Then, we propose a methodology and architecture for detecting abnormal behavior through the monitoring of both host and network data. To validate our methodology, we injected malicious programs into our mobile cloud test bed and used a machine learning algorithm to detect the abnormal behavior that arose from these programs.

关键词： Mobile communication Mobile handsets Monitoring Cloud computing Mobile computing Malware IEEE 802.11 Standards

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A facial feature discovery framework for race classification using deep learning

arXiv

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arXiv 2021年

作者： Khan, Khalil Ali, Jehad Uddin, Irfan Khan, Sahib Roh, Byeong-Hee Department of Information Technology and Computer Science Pak-Austria Institute of Applied Science and Technology Haripur Pakistan Dept. of Computer Engineering Dept. of AI Convergence Network Suwon Korea Republic of Departmet of Computer Science Superior College Lahore Pakistan Department of Electronics and Telecommunications Politecnico di Torino Torino Italy

Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a race-classification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used to create segmentation results. The probabilistic classification method is used, and probability maps (PMs) are created for each semantic class. We investigated five salient facial features from among seven that help in race classification. Features are extracted from the PMs of five classes, and a new model is trained based on the DCNN. We assessed the performance of the proposed race classification method on four standard face datasets, reporting superior results compared with previous studies. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Authentication of Multi-agent System with Verifiable Computation and Distributed Aggregation

Authentication of Multi-agent System with Verifiable Computa...

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IEEE Conference on Decision and Control

作者： Seungbeom Lee Dongwoo Kim Heewon Chung Junsoo Kim Hyungbo Shim Department of Electrical and Computer Engineering ASRI Seoul National University Korea Department of AI · SW Convergence Dongguk University Korea DESILO Inc Korea Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose an authentication scheme for a multiagent system over integers, based on verifiable computation primitives. The naive approach, employing Freivalds’ algorithm in centralized way, faces several challenges. Specifically, unreliability of the network introduces the risk of information tampering by other agents. To this end, it requires locally updating and merging the proofs of the individual states in a distributed manner. Our proposed method addresses these issues with assuming presence of a leader agent who is responsible for validating the correctness of all the states of the agents. This can be achieved by a distributed protocol that aggregates proofs from the individual agents, relying on the well-known knowledge-of-exponent assumption. By using this distributed protocol, computational complexity and communication cost are reduced compared to centralized verification. Furthermore, we incorporate a clustering technique to distribute storage requirements.

关键词： Protocols Costs Data integrity Aggregates Merging Authentication Clustering algorithms Computational complexity Faces Multi-agent systems

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Performance Study of Relay Coordination Schemes for Two-Hop networks: Two-Cell Case

Performance Study of Relay Coordination Schemes for Two-Hop ...

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IEEE International Conference on Communications

作者： Kasun T. Hemachandra Norman C. Beaulieu Department of Electrical and Computer Engineering University of Alberta School of Information and Communication Engineering and the Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications

ISBN: (纸本)9781467364300

The outage probability of a two-hop coordinated decode-and-forward relay network is investigated. Relay coordination is performed only with the dominant interferer. Different levels of network coordination are studied and their performances are compared. Exact expressions are derived for the outage probability of a particular mobile user for each coordination scheme. Asymptotic outage probability is derived to identify the diversity and coding gains for each coordination scheme.

关键词： Cochannel interference Decode-and-forward Outage probability Relay coordination Zero-forcing

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A QoE-Driven FEC Rate Adaptation Scheme for Scalable Video Transmissions over MIMO Systems

A QoE-Driven FEC Rate Adaptation Scheme for Scalable Video T...

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IEEE International Conference on Communications

作者： Xiang Chen Haiqing Du Jenq-Neng Hwang James A. Ritcey Chung-Nan Lee Department of Electrical Engineering University of Washington Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Department of Computer Science and Engineering National Sun Yat-Sen University

ISBN: (纸本)9781467364300

We propose a forward error correction (FEC) coding rate adaptation scheme which maximizes the quality of experience (QoE), for scalable video coding (SVC) based video transmissions over multi-input multi-output (MIMO) systems. The proposed scheme adaptively selects the best set of spatial channels, number of video layers and their corresponding FEC coding rate according to channel state information (CSI) from the receiver. Unlike previous work, our proposed scheme distributes the FEC encoded bit streams to multiple spatial channels so that additional diversity gains can be obtained. Due to the complexity of the optimization, we decompose the original problem into several sub-problems, which can then be solved by a heuristic algorithm. The optimal solution can be found by choosing the best among all the candidate solutions obtained from the sub-problems. The effectiveness and superb performance of our proposed scheme can be demonstrated by many simulations with different videos and channel conditions.

关键词： FEC QoE SVC MIMO Rate adaptation

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Multiple-Error Interceptive Voter Designs for Safety-Critical Applications

Multiple-Error Interceptive Voter Designs for Safety-Critica...

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International Test Conference in Asia (ITC-Asia)

作者： Xuehua Li Jie Song Chunjiong Zhang Yuting He Xiaoqing Wen Zhengfeng Huang School of Computer Science and Technology Anhui University Hefei China Department of AI Convergence Network Ajou University Suwon Korea Department of Computer Science and Networks Kyushu Institute of Technology Fukuoka Japan School of Microelectronics Hefei University of Technology Hefei China

ISBN: (数字)9798331540333

ISBN: (纸本)9798331540340

This paper proposes multiple-error interceptive voter designs for safety-critical applications. The proposed baseline voters comprise two-stage error-filters, in which the first stage includes two parallel C-elements (CEs) and the second stage includes one CE to filter soft errors. The voters have high-speed versions, any of which is embedded with a high-speed path from its original input to its output to reduce delay. Simulation results demonstrate the soft error tolerance of the proposed voters. Moreover, compared with the triple-modular-redundancy (TMR) voter that can only tolerate single soft errors, the proposed 3-input baseline and 4-input high-speed voters can tolerate double soft errors and can reduce the area-power-delay product by 77.03% and 95.05%, respectively, due to the use of a few transistors and a high-speed path. The voters are extended to intercept N-1 soft errors, N being the number of inputs of each voter. Note that the voters are also extended to make so that they can tolerate hard/permanent errors in addition to soft errors.

关键词： Simulation Asia Delays Transistors

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Large language models are fixated by red herrings: exploring creative problem solving and einstellung effect using the only connect wall dataset 23

Large language models are fixated by red herrings: exploring...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Saeid Alavi Naeini Raeid Saqur Mozhgan Saeidi John Giorgi Babak Taati Kite Research Institute Toronto Rehabilitation Institute University Health Network and Institute of Biomedical Engineering University of Toronto and Vector Institute for AI Department of Computer Science University of Toronto and Vector Institute for AI Department of Computer Science University of Toronto and Vector Institute for AI and Department of Biomedical Data Science Stanford University Department of Computer Science University of Toronto and Vector Institute for AI and Donnelly Centre for Cellular & Biomolecular Research University of Toronto Kite Research Institute Toronto Rehabilitation Institute University Health Network and Department of Computer Science University of Toronto and Institute of Biomedical Engineering University of Toronto and Vector Institute for AI

The quest for human imitative ai has been an enduring topic in ai research since its inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to the cultural zeitgeist. While recent NLP evaluation benchmark tasks test some aspects of human-imitative behavior (e.g., BIG-bench's 'human-like behavior' tasks), few, if not none, examine creative problem. solving abilities. Creative problem solving in humans is a well-studied topic in cognitive neuroscience with standardized tests that predominantly use the ability to associate (heterogeneous) connections among clue words as a metric for creativity. Exposure to misleading stimuli — distractors dubbed red herrings — impede human performance in such tasks via the fixation effect and Einstellung paradigm. In cognitive neuroscience studies, such fixations are experimentally induced by pre-exposing participants to orthographically similar incorrect words to subsequent word-fragments or clues. The popular British quiz show Only Connect's Connecting Wall segment essentially mimics Mednick's Remote Associates Test (RAT) formulation with built-in, deliberate red herrings, which makes it an ideal proxy task to explore and study the fixation effect and Einstellung paradigm from cognitive neuroscience in LLMs. In this paper, we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs on creative problem solving tasks like grouping clue words by heterogeneous connections and identifying correct open knowledge domain connections in respective groups. We synthetically generate two additional datasets: OCW-Randomized, OCW-WordNet to further analyze our red-herrings hypothesis in language models. The code and link to the dataset are available at https://***/TaatiTeam/OCW.

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SRBML: A Single-Event-Upset Recoverable and BTI-Mitigated Latch Design for Long-Term Reliability Enhancement

SRBML: A Single-Event-Upset Recoverable and BTI-Mitigated La...

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International Test Conference in Asia (ITC-Asia)

作者： Fan Xia Jing Zhang Jehad Ali Chunjiong Zhang Xiaoqing Wen aibin Yan School of Computer Science and Technology Anhui University Hefei China Department of AI Convergence Network Ajou University Suwon Korea Department of Computer Science and Networks Kyushu Institute of Technology Fukuoka Japan School of Microelectronic Hefei University of Technology Hefei China

ISBN: (数字)9798331540333

ISBN: (纸本)9798331540340

Soft-errors and aging are considered as two primary factors affecting the long-term reliability of aerospace integrated circuits (ICs). As one of the key components in aerospace ICs, latches play a pivotal role to ensure desirable circuit functionality. This paper presents a single-event-upset recovery latch, namely SRBML, with bias-temperature-instability (BTI)-mitigation. By optimizing its internal structure, the latch can recover from single-event-upsets (SEUs) and reduce the stress time of transistors in feedback loops to simultaneously mitigate the impact of BTI on the latch. Simulation results demonstrate that the soft error rate increase due to BTI is reduced by roughly 34% for SRBML after BTI-mitigation. In addition, the delay of SRBML is not affected, and the area and power increase are limited compared to BTI-unmitigated latches.

关键词： Integrated circuits Feedback loop Latches Simulation Aging Reliability engineering Delays

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Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking 38

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc. China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024 Neural information processing systems foundation. All rights reserved.

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