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arXiv 2023年

作者： Li, Qian Ji, Cheng Guo, Shu Liang, Zhaoji Wang, Lihong Li, Jianxin School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China China

Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges due to the presence of different types of information, including neighboring entities, multimodal attributes, and entity types. Directly incorporating the above information (e.g., concatenation or attention) can lead to an unaligned information space. To address these challenges, we propose a novel MMEA transformer, called MoAlign, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task. Taking advantage of the transformer's ability to better integrate multiple information, we design a hierarchical modifiable self-attention block in a transformer encoder to preserve the unique semantics of different information. Furthermore, we design two entity-type prefix injection methods to integrate entity-type information using type prefixes, which help to restrict the global information of entities not present in the MMKGs. Our extensive experiments on benchmark datasets demonstrate that our approach outperforms strong competitors and achieves excellent entity alignment performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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Real-time Stream Processing in IoT Environments

Real-time Stream Processing in IoT Environments

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： P. Rajasekar Rupali Shekar Bhosale Indhumathi C Sandeep K V Prasanthi Kumari N Rajendiran M Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Telecommunication Engineering Bharati Vidyapeeth Institute of Technology Navi Mumbai Maharashtra India Department of Computer Science and Business Systems RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electronics & Telecommunication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India School of Computer Sciences University of Petroleum and Energy Studies Dehradun Uttarakhand 248007 India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions.

关键词： Privacy data analysis Protocols Smart cities Scalability Decision making Real-time systems

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A large-scale study of performance and equity of commercial remote identity verification technologies across demographics

A large-scale study of performance and equity of commercial ...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Kaniz Fatima Michael Schuckers Gerardo Cruz-Ortiz Daqing Hou Sandip Purnapatra Tiffany Andrews Ambuj Neupane Brandeis Marshall Stephanie Schuckers Electrical and Computer Engineering Clarkson University Potsdam NY USA School of Data Science Univ. of North Carolina Charlotte Charlotte NC USA General Services Administration (GSA) USA DataedX Group LLC USA

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

As more types of transactions move online, there is an increasing need to verify someone’s identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver’s license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was in-equitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.

关键词： Statistical analysis Error analysis Face recognition Licenses Skin Software Smart devices Faces

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection

Machine Learning is All You Need: A Simple Token-Based Appro...

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International Conference on Software engineering (ICSE)

作者： Siyue Feng Wenqi Suo Yueming Wu Deqing Zou Yang Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab Jinyinhu Laboratory Wuhan China School of Cyber Science and Engineering HUST Wuhan China Nanyang Technological University Singapore School of Computer Science and Technology HUST Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

关键词： Codes Scalability Computational modeling Cloning Machine learning Detectors Feature extraction

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ColorByte: A real time MOT method using fast appearance feature based on ByteTrack

ColorByte: A real time MOT method using fast appearance feat...

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International Conference on Advanced Cloud and Big data (CBD)

作者： Lin Shen Mengyang Liu Caishan Weng Jinghui Zhang Fang Dong Fa Zheng Big Data Computing Center Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350309720

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. With the wide application of embedded devices, MOT methods with acceptable accuracy that performed in real-time on these weak devices is becoming more and more important. In order to realize real-time MOT on embedded IoT devices, the best trade-off between accuracy and inference latency is the key to achieve. To achieve high accuracy, popular MOT methods introduce Re-ID module to integrate with the detection-based MOT method and train two DNNs simultaneously. However, the integration causes a conflict between computing cost and training both tasks to achieve good results. To address this key issue, we design a fast appearance feature, which is a simple but relatively accurate method, to substitute cumbersome Re-ID component. Besides, ByteTrack is the new SOTA association algorithm in MOT benchmarks which introduce an extra association on objects with low score. Based on ByteTrack, we propose an improved association method to remove most of the background interference based on the results from appearance extraction and recover part of lost detection boxes after the association based on IoU. In addition, we turn down the detection threshold and release more boxes for the low sensitivity of our own feature extraction method. We evaluate our methods and achieve 70.1 MOTA, 81.9 MOTP and 69.2 IDFI with real-time running speed on NVIDIA Xavier NX.

关键词： Training Performance evaluation Sensitivity Interference Feature extraction Real-time systems Inference algorithms

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Attention Synchronous Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Prediction 3

Attention Synchronous Based Spatial-Temporal Graph Convoluti...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Cao, Zhuyou Luo, Tianwang Xiao, Jianhua Xiao, Hongbo Qing, Xinyi School of Computer Science and Engineering Huaihua University Hunan China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huai-hua Hunan418000 China

ISBN: (纸本)9798400716775

This study introduces AS-STGCN, a novel traffic flow prediction model that incorporates an attention mechanism and a synchronized spatio-temporal graph convolutional network. In the field of traffic prediction, deep learning models (e.g., STGCN) have outperformed traditional methods, especially in capturing temporal and spatial correlations. AS-STGCN introduces a spatio-temporal attention mechanism and simultaneous convolution, which improves the prediction accuracy and demonstrates a unique capability in capturing dynamic spatio-temporal patterns in urban traffic networks. This research is of great significance in advancing the field of traffic flow prediction and providing a more comprehensive and accurate method for predicting urban traffic dynamics. © 2023 ACM.

关键词： Convolution

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BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement ?

arXiv

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arXiv 2022年

作者： Wang, Xin Luo, Xinzhe Zhuang, Xiahai School of Data Science Fudan University Shanghai China Department of Electrical and Computer Engineering University of Washington Seattle United States

Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve developing similarity measures over the joint intensity profile of all images, which may be computationally prohibitive for large image groups and unstable under various conditions. To tackle these issues, we propose BInGo, a general unsupervised hierarchical Bayesian framework based on deep learning, to learn intrinsic structural representations to measure the similarity of multimodal images. Particularly, a variational auto-encoder with a novel posterior is proposed, which facilitates the disentanglement learning of structural representations and spatial transformations, and characterizes the imaging process from the common structure with shape transition and appearance variation. Notably, BInGo is scalable to learn from small groups, whereas being tested for large-scale groupwise registration, thus significantly reducing computational costs. We compared BInGo with five iterative or deep-learning methods on three public intrasubject and intersubject datasets, i.e. BraTS, MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior accuracy and computational efficiency, even for very large group sizes (e.g., over 1300 2D images from MS-CMR in each group). Copyright © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

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UniTSFace: unified threshold integrated sample-to-sample loss for face recognition 23

UniTSFace: unified threshold integrated sample-to-sample los...

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

作者： Qiufu Li Xi Jia Jiancan Zhou Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and School of Computer Science University of Birmingham UK National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and Aqara Lumi United Technology Co. Ltd China School of Computer Science University of Birmingham UK and Alan Turing Institute UK

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace.

关键词：

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An Adaptive Intrusion Detection System for IoT Networks Using Deep Learning

An Adaptive Intrusion Detection System for IoT Networks Usin...

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International Conference on Advances in Electronics, computers and Communications (ICAECC)

作者： Pon Harshavardhanan V Muneeswaran D. Saravanan P. Vijayakarthik R Natchadalingam Pundru Chandra Shaker Reddy School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Computer Science and Engineering R.L Jalappa Institute of Technology Doddaballapur Karnataka India CSE-Data Science R.L. Jalappa Institute of Technology Bengaluru Karnataka India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

The rapid expansion of the Internet-of-Things (IoT) has never before attracted the attention of cybercriminals. The increasing prevalence of cyberattacks against IoT devices and intermediary communication channels lends credence to this assertion. If an IoT attack goes unnoticed for too long, it might disrupt services severely, costing money. In addition, it poses the risk of having one's identity stolen. For IoT-enabled services to be dependable, secure and financially fruitful, real-time intrusion detection on IoT devices is a must. A new intrusion detection design for IoT devices based on Deep Learning (DL) is presented in this research. In order to identify potentially harmful traffic that could lead to assaults on Internet of Things (IoT) gadgets, this smart framework employs a 4layer deep Fully-Connected (FC) network architecture. To simplify deployment, the suggested system was designed to work with any available communication protocol. During experimental performance evaluation, the suggested system has proven to be effective against both simulated and real invasions. According to the data, the proposed model is more accurate than existing methods. This novel deep learning-based IDS has highest detection rate of 95.7 percent, making it suitable for bolstering the safety of IoT networks.

关键词： Deep learning Accuracy computer hacking Intrusion detection Training data Real-time systems Safety

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CMD: A Cache-assisted GPU Memory Deduplication Architecture

arXiv

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arXiv 2024年

作者： Zhao, Wei Feng, Dan Tong, Wei Wei, Xueliang Wu, Bing The Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology China

Massive off-chip accesses in GPUs are the main performance bottleneck, and we divided these accesses into three types: (1) Write, (2) data-Read, and (3) Read-Only. Besides, We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the inter-dup and inter-dup lines. As for the inter-dup detection, we reduce the extra read requests caused by the traditional read-verify hash process. Besides, we design several techniques to manage duplicate blocks. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 37.79%. Besides, CMD can improve the performance of memory-intensive workloads by 50.18%. © 2024, CC BY-NC-ND.

关键词： Cache memory

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