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Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-12-01
Abstract Local differential privacy (LDP) approaches to collecting sensitive information for frequent itemset mining (FIM) can reliably guarantee privacy. Most current approaches to FIM under LDP add “padding and sampling” steps to obtain frequent itemsets and their frequencies because each user transaction represents a set of items. The current state-of-the-art approach, namely set-value itemset mining
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Motion-Inspired Real-Time Garment Synthesis with Temporal-Consistency J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-12-01
Abstract Synthesizing garment dynamics according to body motions is a vital technique in computer graphics. Physics-based simulation depends on an accurate model of the law of kinetics of cloth, which is time-consuming, hard to implement, and complex to control. Existing data-driven approaches either lack temporal consistency, or fail to handle garments that are different from body topology. In this
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Automatic Target Description File Generation J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-12-01
Abstract Agile hardware design is gaining increasing momentum and bringing new chips in larger quantities to the market faster. However, it also takes new challenges for compiler developers to retarget existing compilers to these new chips in shorter time than ever before. Currently, retargeting a compiler backend, e.g., an LLVM backend to a new target, requires compiler developers to write manually
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Community-Preserving Social Graph Release with Node Differential Privacy J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Sen Zhang, Wei-Wei Ni, Nan Fu
The goal of privacy-preserving social graph release is to protect individual privacy while preserving data utility. Community structure, which is an important global pattern of nodes, is a crucial data utility as it is fundamental to many graph analysis tasks. Yet, most existing methods with differential privacy (DP) commonly fall into edge-DP to sacrifice security in exchange for utility. Moreover
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A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Jia-Yu Liu, Fei Wang, Hai-Ping Ma, Zhen-Ya Huang, Qi Liu, En-Hong Chen, Yu Su
Cognitive diagnosis is an important issue of intelligent education systems, which aims to estimate students’ proficiency on specific knowledge concepts. Most existing studies rely on the assumption of static student states and ignore the dynamics of proficiency in the learning process, which makes them unsuitable for online learning scenarios. In this paper, we propose a unified temporal item response
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Shapelet Based Two-Step Time Series Positive and Unlabeled Learning J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Han-Bo Zhang, Peng Wang, Ming-Ming Zhang, Wei Wang
In the last decade, there has been significant progress in time series classification. However, in real-world industrial settings, it is expensive and difficult to obtain high-quality labeled data. Therefore, the positive and unlabeled learning (PU-learning) problem has become more and more popular recently. The current PU-learning approaches of the time series data suffer from low accuracy due to
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Visual Topic Semantic Enhanced Machine Translation for Multi-Modal Data Efficiency J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Chao Wang, Si-Jia Cai, Bei-Xiang Shi, Zhi-Hong Chong
The scarcity of bilingual parallel corpus imposes limitations on exploiting the state-of-the-art supervised translation technology. One of the research directions is employing relations among multi-modal data to enhance performance. However, the reliance on manually annotated multi-modal datasets results in a high cost of data labeling. In this paper, the topic semantics of images is proposed to alleviate
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2k-Vertex Kernels for Cluster Deletion and Strong Triadic Closure J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Wen-Yu Gao, Hang Gao
Cluster deletion and strong triadic closure are two important NP-complete problems that have received significant attention due to their applications in various areas, including social networks and data analysis. Although cluster deletion and strong triadic closure are closely linked by induced paths on three vertices, there are subtle differences between them. In some cases, the solutions of strong
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Characterization of Exact One-Query Quantum Algorithms for Partial Boolean Functions J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Ze-Kun Ye, Lv-Zhou Li
The query model (or black-box model) has attracted much attention from the communities of both classical and quantum computing. Usually, quantum advantages are revealed by presenting a quantum algorithm that has a better query complexity than its classical counterpart. In the history of quantum algorithms, the Deutsch algorithm and the Deutsch-Jozsa algorithm play a fundamental role and both are exact
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Hardware Acceleration for SLAM in Mobile Systems J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Zhe Fan, Yi-Fan Hao, Tian Zhi, Qi Guo, Zi-Dong Du
The emerging mobile robot industry has spurred a flurry of interest in solving the simultaneous localization and mapping (SLAM) problem. However, existing SLAM platforms have difficulty in meeting the real-time and low-power requirements imposed by mobile systems. Though specialized hardware is promising with regard to achieving high performance and lowering the power, designing an efficient accelerator
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wrBench: Comparing Cache Architectures and Coherency Protocols on ARMv8 Many-Core Systems J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Wan-Rong Gao, Jian-Bin Fang, Chun Huang, Chuan-Fu Xu, Zheng Wang
Cache performance is a critical design constraint for modern many-core systems. Since the cache often works in a “black-box” manner, it is difficult for the software to reason about the cache behavior to match the running software to the underlying hardware. To better support code optimization, we need to understand and characterize the cache behavior. While cache performance characterization is heavily
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A Novel Three-Staged Generative Model for Skeletonizing Chinese Characters with Versatile Styles J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Ye-Chuan Tian, Song-Hua Xu, Cheickna Sylla
Skeletons of characters provide vital information to support a variety of tasks, e.g., optical character recognition, image restoration, stroke segmentation and extraction, and style learning and transfer. However, automatically skeletonizing Chinese characters poses a steep computational challenge due to the large volume of Chinese characters and their versatile styles, for which traditional image
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Composing Like an Ancient Chinese Poet: Learn to Generate Rhythmic Chinese Poetry J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Ming He, Yan Chen, Hong-Ke Zhao, Qi Liu, Le Wu, Yu Cui, Gui-Hua Zeng, Gui-Quan Liu
Automatic generation of Chinese classical poetry is still a challenging problem in artificial intelligence. Recently, Encoder-Decoder models have provided a few viable methods for poetry generation. However, by reviewing the prior methods, two major issues still need to be settled: 1) most of them are one-stage generation methods without further polishing; 2) they rarely take into consideration the
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Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Gui-Rong Bai, Qing-Bin Liu, Shi-Zhu He, Kang Liu, Jun Zhao
Although neural approaches have yielded state-of-the-art results in the sentence matching task, their performance inevitably drops dramatically when applied to unseen domains. To tackle this cross-domain challenge, we address unsupervised domain adaptation on sentence matching, in which the goal is to have good performance on a target domain with only unlabeled target domain data as well as labeled
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M-LSM: An Improved Multi-Liquid State Machine for Event-Based Vision Recognition J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-11-30 Lei Wang, Sha-Sha Guo, Lian-Hua Qu, Shuo Tian, Wei-Xia Xu
Event-based computation has recently gained increasing research interest for applications of vision recognition due to its intrinsic advantages on efficiency and speed. However, the existing event-based models for vision recognition are faced with several issues, such as large network complexity and expensive training cost. In this paper, we propose an improved multi-liquid state machine (M-LSM) method
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FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Bi-Ying Yan, Chao Yang, Feng Chen, Kohei Takeda, Changjun Wang
With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge with great social and economic impact. The radar echo extrapolation approaches for precipitation nowcasting take radar echo images as input, aiming to generate future radar echo images by learning from the historical images
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Top-down Text-Level Discourse Rhetorical Structure Parsing with Bidirectional Representation Learning J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Long-Yin Zhang, Xin Tan, Fang Kong, Pei-Feng Li, Guo-Dong Zhou
Early studies on discourse rhetorical structure parsing mainly adopt bottom-up approaches, limiting the parsing process to local information. Although current top-down parsers can better capture global information and have achieved particular success, the importance of local and global information at various levels of discourse parsing is different. This paper argues that combining local and global
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Reinvent Cloud Software Stacks for Resource Disaggregation J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Chen-Xi Wang, Yi-Zhou Shan, Peng-Fei Zuo, Hui-Min Cui
Due to the unprecedented development of low-latency interconnect technology, building large-scale disaggregated architecture is drawing more and more attention from both industry and academia. Resource disaggregation is a new way to organize the hardware resources of datacenters, and has the potential to overcome the limitations, e.g., low resource utilization and low reliability, of conventional datacenters
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Cognition: Accurate and Consistent Linear Log Parsing Using Template Correction J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Ran Tian, Zu-Long Diao, Hai-Yang Jiang, Gao-Gang Xie
Logs contain runtime information for both systems and users. As many of them use natural language, a typical log-based analysis needs to parse logs into the structured format first. Existing parsing approaches often take two steps. The first step is to find similar words (tokens) or sentences. Second, parsers extract log templates by replacing different tokens with variable placeholders. However, we
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Side-Channel Analysis for the Re-Keying Protocol of Bluetooth Low Energy J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Pei Cao, Chi Zhang, Xiang-Jun Lu, Hai-Ning Lu, Da-Wu Gu
In the era of the Internet of Things, Bluetooth low energy (BLE/BTLE) plays an important role as a well-known wireless communication technology. While the security and privacy of BLE have been analyzed and fixed several times, the threat of side-channel attacks to BLE devices is still not well understood. In this work, we highlight a side-channel threat to the re-keying protocol of BLE. This protocol
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VTensor: Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Feng Yu, Jia-Cheng Zhao, Hui-Min Cui, Xiao-Bing Feng, Jingling Xue
Tensors are a popular programming interface for developing artificial intelligence (AI) algorithms. Layout refers to the order of placing tensor data in the memory and will affect performance by affecting data locality; therefore the deep neural network library has a convention on the layout. Since AI applications can use arbitrary layouts, and existing AI systems do not provide programming abstractions
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Parallel Bounded Search for the Maximum Clique Problem J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Hua Jiang, Ke Bai, Hai-Jiao Liu, Chu-Min Li, Felip Manyà, Zhang-Hua Fu
Given an undirected graph, the Maximum Clique Problem (MCP) is to find a largest complete subgraph of the graph. MCP is NP-hard and has found many practical applications. In this paper, we propose a parallel Branch-and- Bound (BnB) algorithm to tackle this NP-hard problem, which carries out multiple bounded searches in parallel. Each search has its upper bound and shares a lower bound with the rest
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Model Checking for Probabilistic Multiagent Systems J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Chen Fu, Andrea Turrini, Xiaowei Huang, Lei Song, Yuan Feng, Li-Jun Zhang
In multiagent systems, agents usually do not have complete information of the whole system, which makes the analysis of such systems hard. The incompleteness of information is normally modelled by means of accessibility relations, and the schedulers consistent with such relations are called uniform. In this paper, we consider probabilistic multiagent systems with accessibility relations and focus on
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Dalea: A Persistent Multi-Level Extendible Hashing with Improved Tail Performance J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Zi-Wei Xiong, De-Jun Jiang, Jin Xiong, Ren Ren
Persistent memory (PM) promises byte-addressability, large capacity, and durability. Main memory systems, such as key-value stores and in-memory databases, benefit from such features of PM. Due to the great popularity of hashing index in main memory systems, a number of research efforts are made to provide high average performance persistent hashing. However, suboptimal tail performance in terms of
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Path-Based Multicast Routing for Network-on-Chip of the Neuromorphic Processor J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Zi-Yang Kang, Shi-Ming Li, Shi-Ying Wang, Lian-Hua Qu, Rui Gong, Wei Shi, Wei-Xia Xu, Lei Wang
Network-on-Chip (NoC) is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks (SNNs). However, SNNs generate enormous spiking packets due to the one-to-many traffic pattern. The spiking packets may cause communication pressure on NoC. We propose a path-based multicast routing method to alleviate the pressure. Firstly, all destination nodes of
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Chinese Named Entity Recognition Augmented with Lexicon Memory J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Yi Zhou, Xiao-Qing Zheng, Xuan-Jing Huang
Inspired by the concept of content-addressable retrieval from cognitive science, we propose a novel fragmentbased Chinese named entity recognition (NER) model augmented with a lexicon-based memory in which both characterlevel and word-level features are combined to generate better feature representations for possible entity names. Observing that the boundary information of entity names is particularly
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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Chun-Yu Hu, Li-Sha Hu, Lin Yuan, Dian-Jie Lu, Lei Lyu, Yi-Qiang Chen
Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption. However, most wearable health data is distributed across different organizations, such as hospitals, research institutes, and companies, and can only be accessed by the owners of the data in compliance with data privacy regulations. The first
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Accurate Robotic Grasp Detection with Angular Label Smoothing J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-09-30 Min Shi, Hao Lu, Zhao-Xin Li, Deng-Ming Zhu, Zhao-Qi Wang
Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment. Despite the steady progress in robotic grasping, it is still difficult to achieve both real-time and high accuracy grasping detection. In this paper, we propose a real-time robotic grasp detection method, which can accurately predict potential grasp for parallel-plate
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Unsupervised Dialogue State Tracking for End-to-End Task-Oriented Dialogue with a Multi-Span Prediction Network J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Qing-Bin Liu, Shi-Zhu He, Cao Liu, Kang Liu, Jun Zhao
This paper focuses on end-to-end task-oriented dialogue systems, which jointly handle dialogue state tracking (DST) and response generation. Traditional methods usually adopt a supervised paradigm to learn DST from a manually labeled corpus. However, the annotation of the corpus is costly, time-consuming, and cannot cover a wide range of domains in the real world. To solve this problem, we propose
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Probabilistic Fault Diagnosis of Clustered Faults for Multiprocessor Systems J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Xue-Li Sun, Jian-Xi Fan, Bao-Lei Cheng, Yan Wang, Li Zhang
With the development of high-performance computing and the expansion of large-scale multiprocessor systems, it is significant to study the reliability of systems. Probabilistic fault diagnosis is of practical value to the reliability analysis of multiprocessor systems. In this paper, we design a linear time diagnosis algorithm with the multiprocessor system whose threshold is set to 3, where the probability
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Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Yi-Ge Xu, Xi-Peng Qiu, Li-Gao Zhou, Xuan-Jing Huang
Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing (NLP) and yield state-of-the-art results on many downstream tasks. Recent studies on adapting BERT to new tasks mainly focus on modifying the model structure, re-designing the pre-training tasks, and leveraging external data and knowledge. The fine-tuning strategy itself has yet to be fully
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Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Xinliang Wei, Jiyao Liu, Yu Wang
To overcome the limitations of long latency and privacy concerns from cloud computing, edge computing along with distributed machine learning such as federated learning (FL), has gained much attention and popularity in academia and industry. Most existing work on FL over the edge mainly focuses on optimizing the training of one shared global model in edge systems. However, with the increasing applications
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Functional Verification for Agile Processor Development: A Case for Workflow Integration J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Yi-Nan Xu, Zi-Hao Yu, Kai-Fan Wang, Hua-Qiang Wang, Jia-Wei Lin, Yue Jin, Lin-Juan Zhang, Zi-Fei Zhang, Dan Tang, Sa Wang, Kan Shi, Ning-Hui Sun, Yun-Gang Bao
Agile hardware development methodology has been widely adopted over the past decade. Despite the research progress, the industry still doubts its applicability, especially for the functional verification of complicated processor chips. Functional verification commonly employs a simulation-based method of co-simulating the design under test with a reference model and checking the consistency of their
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DyPipe: A Holistic Approach to Accelerating Dynamic Neural Networks with Dynamic Pipelining J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Yi-Min Zhuang, Xing Hu, Xiao-Bing Chen, Tian Zhi
Dynamic neural network (NN) techniques are increasingly important because they facilitate deep learning techniques with more complex network architectures. However, existing studies, which predominantly optimize the static computational graphs by static scheduling methods, usually focus on optimizing static neural networks in deep neural network (DNN) accelerators. We analyze the execution process
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Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Ruo-Shi Li, Ping Peng, Zhi-Yuan Shao, Hai Jin, Ran Zheng
Computer vision (CV) algorithms have been extensively used for a myriad of applications nowadays. As the multimedia data are generally well-formatted and regular, it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV algorithms. Single Instruction Multiple Data (SIMD) instructions, capable of conducting the same operation on
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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Wen-Hong Tian, Min-Xian Xu, Guang-Yao Zhou, Kui Wu, Cheng-Zhong Xu, Rajkumar Buyya
Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines (VMs) after allocation is the traditional way for load balancing and consolidation. However, it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring instability. Therefore, we provide
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Real-Time Underwater Image Enhancement Using Adaptive Full-Scale Retinex J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Xing-Gui Xu, Xiang-Suo Fan, Yong-Li Liu
Current Retinex-based image enhancement methods with fixed scale filters cannot adapt to situations involving various depths of field and illuminations. In this paper, a simple but effective method based on adaptive full-scale Retinex (AFSR) is proposed to clarify underwater images or videos. First, we design an adaptive full-scale filter that is guided by the optical transmission rate to estimate
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Multimodal Interactive Network for Sequential Recommendation J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Teng-Yue Han, Peng-Fei Wang, Shao-Zhang Niu
Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items. Recent work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse problem. The data sparse problem now is attracting a lot of attention in both industry and academic community. However,
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PESTA: An Elastic Motion Capture Data Retrieval Method J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Zi-Fei Jiang, Wei Li, Yan Huang, Yi-Long Yin, C.-C. Jay Kuo, Jing-Liang Peng
Prevalent use of motion capture (MoCap) produces large volumes of data and MoCap data retrieval becomes crucial for efficient data reuse. MoCap clips may not be neatly segmented and labeled, increasing the difficulty of retrieval. In order to effectively retrieve such data, we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment (PESTA)
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Improving Performance of Virtual Machine Covert Timing Channel Through Optimized Run-Length Encoding J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-07-31 Chong Wang, Rong-Liang Chen, Liang Gu
With its wider acceptability, cloud can host a diverse set of data and applications ranging from entertainment to personal to industry. The foundation of cloud computing is based on virtual machines where boundaries among the application data are very thin, and the potential of data leakage exists all the time. For instance, a virtual machine covert timing channel is an aggressive mechanism to leak
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Augmenting Trigger Semantics to Improve Event Coreference Resolution J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Min Huan, Sheng Xu, Pei-Feng Li
Due to the small size of the annotated corpora and the sparsity of the event trigger words, the event coreference resolver cannot capture enough event semantics, especially the trigger semantics, to identify coreferential event mentions. To address the above issues, this paper proposes a trigger semantics augmentation mechanism to boost event coreference resolution. First, this mechanism performs a
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Self-Supervised Monocular Depth Estimation by Digging into Uncertainty Quantification J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Yuan-Zhen Li, Sheng-Jie Zheng, Zi-Xin Tan, Tuo Cao, Fei Luo, Chun-Xia Xiao
Based on well-designed network architectures and objective functions, self-supervised monocular depth estimation has made great progress. However, lacking a specific mechanism to make the network learn more about the regions containing moving objects or occlusion scenarios, existing depth estimation methods likely produce poor results for them. Therefore, we propose an uncertainty quantification method
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A Quantitative Evaluation of Vector Transcendental Functions on ARMv8-Based Processors J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Jie Shen, Biao Long, Chun Huang
Transcendental functions are important functions in various high performance computing applications. Because these functions are time-consuming and the vector units on modern processors become wider and more scalable, there is an increasing demand for developing and using vector transcendental functions in such performance-hungry applications. However, the performance of vector transcendental functions
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Learning Local Contrast for Crisp Edge Detection J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Xiao-Nan Fang, Song-Hai Zhang
In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further post-processing including non-maximum suppression and morphological thinning. In this paper, we demonstrate that the blurry effect arises from the binary cross-entropy loss, and crisp
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Multi-Feature Fusion Based Structural Deep Neural Network for Predicting Answer Time on Stack Overflow J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Shi-Kai Guo, Si-Wen Wang, Hui Li, Yu-Long Fan, Ya-Qing Liu, Bin Zhang
Stack Overflow provides a platform for developers to seek suitable solutions by asking questions and receiving answers on various topics. However, many questions are usually not answered quickly enough. Since the questioners are eager to know the specific time interval at which a question can be answered, it becomes an important task for Stack Overflow to feedback the answer time to the question. To
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Bi-GAE: A Bidirectional Generative Auto-Encoder J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Qin Hua, Han-Wen Hu, Shi-You Qian, Ding-Yu Yang, Jian Cao
Improving the generative and representational capabilities of auto-encoders is a hot research topic. However, it is a challenge to jointly and simultaneously optimize the bidirectional mapping between the encoder and the decoder/generator while ensuing convergence. Most existing auto-encoders cannot automatically trade off bidirectional mapping. In this work, we propose Bi-GAE, an unsupervised bidirectional
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Graph Enhanced Transformer for Aspect Category Detection J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Chen Chen, Hou-Feng Wang, Qing-Qing Zhu, Jun-Fei Liu
Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure
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A Survey on 360° Images and Videos in Mixed Reality: Algorithms and Applications J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Fanglue Zhang, Junhong Zhao, Yun Zhang, Stefanie Zollmann
Mixed reality technologies provide real-time and immersive experiences, which bring tremendous opportunities in entertainment, education, and enriched experiences that are not directly accessible owing to safety or cost. The research in this field has been in the spotlight in the last few years as the metaverse went viral. The recently emerging omnidirectional video streams, i.e., 360° videos, provide
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Query Authentication Using Intel SGX for Blockchain Light Clients J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Qi-Feng Shao, Zhao Zhang, Che-Qing Jin, Ao-Ying Zhou
Due to limited computing and storage resources, light clients and full nodes coexist in a typical blockchain system. Any query from light clients must be forwarded to full nodes for execution, and light clients verify the integrity of query results returned. Since existing verifiable queries based on an authenticated data structure (ADS) suffer from significant network, storage and computing overheads
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A Semi-Tensor Product Based All Solutions Boolean Satisfiability Solver J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Hong-Yang Pan, Zhu-Fei Chu
Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula. All solutions SAT (AllSAT) is a variant of the SAT problem. In the fields of formal verification and pattern generation, AllSAT is particularly useful because it efficiently enumerates
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Natural Image Matting with Attended Global Context J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Yi-Yi Zhang, Li Niu, Yasushi Makihara, Jian-Fu Zhang, Wei-Jie Zhao, Yasushi Yagi, Li-Qing Zhang
Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels
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PuzzleNet: Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Hao-Yu Liu, Jian-Wei Guo, Hai-Yong Jiang, Yan-Chao Liu, Xiao-Peng Zhang, Dong-Ming Yan
We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. Despite raising attention to 3D registration with complex or low overlapping patterns, few methods consider shape
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PCRTAM-Net: A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Hua-Deng Wang, Zi-Zheng Li, Idowu Paul Okuwobi, Bing-Bing Li, Xi-Peng Pan, Zhen-Bing Liu, Ru-Shi Lan, Xiao-Nan Luo
Retinal images play an essential role in the early diagnosis of ophthalmic diseases. Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background. At the same time, automated models struggle to capture representative and discriminative retinal vascular features. To fully utilize the structural
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Semi-Supervised Intracranial Aneurysm Segmentation from CTA Images via Weight-Perceptual Self-Ensembling Model J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Cai-Zi Li, Rui-Qiang Liu, Huan-Xin Zhong, Jun-Ming Fan, Wei-Xin Si, Meng Zhang, Pheng-Ann Heng
Segmentation of intracranial aneurysm (IA) from computed tomography angiography (CTA) images is of significant importance for quantitative assessment of IA and further surgical treatment. Manual segmentation of IA is a labor-intensive, time-consuming job and suffers from inter- and intra-observer variabilities. Training deep neural networks usually requires a large amount of labeled data, while annotating
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RC-Net: Row and Column Network with Text Feature for Parsing Floor Plan Images J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Teng Wang, Wei-Liang Meng, Zheng-Da Lu, Jian-Wei Guo, Jun Xiao, Xiao-Peng Zhang
The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a challenging task due to the vast amount of historical data available. By leveraging neural networks to identify and parse floor plans, the process of converting these images into electronic
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Improving Open Set Domain Adaptation Using Image-to-Image Translation and Instance-Weighted Adversarial Learning J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Hong-Jie Zhang, Ang Li, Jie Guo, Yan-Wen Guo
We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature space. Our approach, called Open Set Translation and Adaptation Network (OSTAN), consists of two main components: translation and adaptation. The translation is a cycle-consistent generative adversarial network, which translates any source image to the “style” of a target domain to
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Emotion-Aware Music Driven Movie Montage J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-05-30 Wu-Qin Liu, Min-Xuan Lin, Hai-Bin Huang, Chong-Yang Ma, Yu Song, Wei-Ming Dong, Chang-Sheng Xu
In this paper, we present Emotion-Aware Music Driven Movie Montage, a novel paradigm for the challenging task of generating movie montages. Specifically, given a movie and a piece of music as the guidance, our method aims to generate a montage out of the movie that is emotionally consistent with the music. Unlike previous work such as video summarization, this task requires not only video content understanding
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xCCL: A Survey of Industry-Led Collective Communication Libraries for Deep Learning J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-03-31 Adam Weingram, Yuke Li, Hao Qi, Darren Ng, Liuyao Dai, Xiaoyi Lu
Machine learning techniques have become ubiquitous both in industry and academic applications. Increasing model sizes and training data volumes necessitate fast and efficient distributed training approaches. Collective communications greatly simplify inter- and intra-node data transfer and are an essential part of the distributed training process as information such as gradients must be shared between
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LMM: A Fixed-Point Linear Mapping Based Approximate Multiplier for IoT J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-03-30 Ying Wu, Chen-Yi Wen, Xun-Zhao Yin, Cheng Zhuo
The development of IoT (Internet of Things) calls for circuit designs with energy and area efficiency for edge devices. Approximate computing which trades unnecessary computation precision for hardware cost savings is a promising direction for error-tolerant applications. Multipliers, as frequently invoked basic modules which consume non-trivial hardware costs, have been introduced approximation to
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On the Security of Smart Home Systems: A Survey J. Comput. Sci. Tech. (IF 1.9) Pub Date : 2023-03-30 Bin Yuan, Jun Wan, Yu-Han Wu, De-Qing Zou, Hai Jin
Among the plethora of IoT (Internet of Things) applications, the smart home is one of the fastest-growing. However, the rapid development of the smart home has also made smart home systems a target for attackers. Recently, researchers have made many efforts to investigate and enhance the security of smart home systems. Toward a more secure smart home ecosystem, we present a detailed literature review