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DG-CNN: Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images
Xiao-Zheng Xie, Jian-Wei Niu, Xue-Feng Liu, Qing-Feng Li, Yong Wang, Jie Han, and Shaojie Tang
Journal of Computer Science and Technology    2022, 37 (2): 277-294.   DOI: 10.1007/s11390-020-0192-0
Accepted: 02 June 2020

Abstract908)      PDF   

Although using convolutional neural networks (CNN) for computer-aided diagnosis (CAD) has made tremendous progress in the last few years, the small medical datasets remain to be the major bottleneck in this area. To address this problem, researchers start looking for information out of the medical datasets. Previous efforts mainly leverage information from natural images via transfer learning. More recent research work focuses on integrating knowledge from medical practitioners, either letting networks resemble how practitioners are trained, how they view images, or using extra annotations. In this paper, we propose a scheme named Domain Guided-CNN (DG-CNN) to incorporate the margin information, a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound (BUS) images. In DG-CNN, attention maps that highlight margin areas of tumors are first generated, and then incorporated via different approaches into the networks. We have tested the performance of DG-CNN on our own dataset (including 1485 ultrasound images) and on a public dataset. The results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their performance. For example, experimental results on our dataset show that with a certain integrating mode, the improvement of using DG-CNN over a baseline network structure ResNet18 is 2.17% in accuracy, 1.69% in sensitivity, 2.64% in specificity and 2.57% in AUC (Area Under Curve). To the best of our knowledge, this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

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Connecting the Dots in Self-Supervised Learning: A Brief Survey for Beginners
Peng-Fei Fang, Xian Li, Yang Yan, Shuai Zhang, Qi-Yue Kang, Xiao-Fei Li, and Zhen-Zhong Lan
Journal of Computer Science and Technology    2022, 37 (3): 507-526.   DOI: 10.1007/s11390-022-2158-x
Accepted: 18 May 2022

Abstract743)      PDF   

The artificial intelligence (AI) community has recently made tremendous progress in developing self-supervised learning (SSL) algorithms that can learn high-quality data representations from massive amounts of unlabeled data. These methods brought great results even to the fields outside of AI. Due to the joint efforts of researchers in various areas, new SSL methods come out daily. However, such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses. This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work. We see these researches as the "dots" of SSL and connect them through how they evolve. Hopefully, by viewing the connections of these dots, readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing, computer vision, graph learning, audio processing, and protein learning.

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Analyzing and Optimizing Packet Corruption in RDMA Network
Yi-Xiao Gao, Chen Tian, Wei Chen, Duo-Xing Li, Jian Yan, Yuan-Yuan Gong, Bing-Quan Wang, Tao Wu, Lei Han, Fa-Zhi Qi, Shan Zeng, Wan-Chun Dou, and Gui-Hai Chen
Journal of Computer Science and Technology    2022, 37 (4): 743-762.   DOI: 10.1007/s11390-022-2123-8
Abstract682)      PDF   
Remote direct memory access (RDMA) has become one of the state-of-the-art high-performance network technologies in datacenters. The reliable transport of RDMA is designed based on a lossless underlying network and cannot endure a high packet loss rate. However, except for switch buffer overflow, there is another kind of packet loss in the RDMA network, i.e., packet corruption, which has not been discussed in depth. The packet corruption incurs long application tail latency by causing timeout retransmissions. The challenges to solving packet corruption in the RDMA network include: 1) packet corruption is inevitable with any remedial mechanisms and 2) RDMA hardware is not programmable. This paper proposes some designs which can guarantee the expected tail latency of applications with the existence of packet corruption. The key idea is controlling the occurring probabilities of timeout events caused by packet corruption through transforming timeout retransmissions into out-of-order retransmissions. We build a probabilistic model to estimate the occurrence probabilities and real effects of the corruption patterns. We implement these two mechanisms with the help of programmable switches and the zero-byte message RDMA feature. We build an ns-3 simulation and implement optimization mechanisms on our testbed. The simulation and testbed experiments show that the optimizations can decrease the flow completion time by several orders of magnitudes with less than 3% bandwidth cost at different packet corruption rates.
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GAM: A GPU-Accelerated Algorithm for MaxRS Queries in Road Networks
Jian Chen, Kai-Qi Zhang, Tian Ren, Zhen-Qing Wu, and Hong Gao
Journal of Computer Science and Technology    2022, 37 (5): 1005-1025.   DOI: 10.1007/s11390-022-2330-3
Abstract641)      PDF   
In smart phones, vehicles and wearable devices, GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real world. Given a set of weighted points and a rectangle r in the space, a maximizing range sum (MaxRS) query is to find the position of r, so as to maximize the total weight of the points covered by r (i.e., the range sum). It has a wide spectrum of applications in spatial crowdsourcing, facility location and traffic monitoring. Most of the existing research focuses on the euclidean space; however, in real life, the user's moving route is constrained by the road network, and the existing MaxRS query algorithms in the road network are inefficient. In this paper, we propose a novel GPU-accelerated algorithm, namely, GAM, to tackle MaxRS queries in road networks in two phases efficiently. In phase 1, we partition the entire road network into many small cells by grid and theoretically prove the correctness of parallel query results by grid shifting, and then we propose an effective multi-grained pruning technique, by which the majority of cells can be pruned without further checking. In phase 2, we design a GPU-friendly storage structure, cell-based road network (CRN), and a two-level parallel framework to compute the final result in the remaining cells. Finally, we conduct extensive experiments on two real-world road networks, and experimental results demonstrate that GAM is on average one order faster than state-of-the-art competitors, and the maximum speedup can achieve about 55 times.
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A Comprehensive Review of Redirected Walking Techniques: Taxonomy, Methods, and Future Directions
Yi-Jun Li, Frank Steinicke, and Miao Wang
Journal of Computer Science and Technology    2022, 37 (3): 561-583.   DOI: 10.1007/s11390-022-2266-7
Accepted: 18 May 2022

Abstract533)      PDF   
Virtual reality (VR) allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual objects. However, the limited physical tracking space usually restricts the exploration of large virtual spaces, and VR users have to use special locomotion techniques to move from one location to another. Among these techniques, redirected walking (RDW) is one of the most natural locomotion techniques to solve the problem based on near-natural walking experiences. The core idea of the RDW technique is to imperceptibly guide users on virtual paths, which might vary from the paths they physically walk in the real world. In a similar way, some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking space. In this survey, we first present a taxonomy of existing RDW work. Based on this taxonomy, we compare and analyze both contributions and shortcomings of the existing methods in detail, and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries, while virtual environment manipulation methods can provide users with consistent movement but have limited application scenarios. Finally, we discuss possible future research directions, indicating combining artificial intelligence with this area will be effective and intriguing.
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A Comparative Study of CNN- and Transformer-Based Visual Style Transfer
Hua-Peng Wei, Ying-Ying Deng, Fan Tang, Xing-Jia Pan, and Wei-Ming Dong
Journal of Computer Science and Technology    2022, 37 (3): 601-614.   DOI: 10.1007/s11390-022-2140-7
Abstract531)      PDF   
Vision Transformer has shown impressive performance on the image classification tasks. Observing that most existing visual style transfer (VST) algorithms are based on the texture-biased convolution neural network (CNN), here raises the question of whether the shape-biased Vision Transformer can perform style transfer as CNN. In this work, we focus on comparing and analyzing the shape bias between CNN- and transformer-based models from the view of VST tasks. For comprehensive comparisons, we propose three kinds of transformer-based visual style transfer (Tr-VST) methods (Tr-NST for optimization-based VST, Tr-WCT for reconstruction-based VST and Tr-AdaIN for perceptual-based VST). By engaging three mainstream VST methods in the transformer pipeline, we show that transformer-based models pre-trained on ImageNet are not proper for style transfer methods. Due to the strong shape bias of the transformer-based models, these Tr-VST methods cannot render style patterns. We further analyze the shape bias by considering the influence of the learned parameters and the structure design. Results prove that with proper style supervision, the transformer can learn similar texture-biased features as CNN does. With the reduced shape bias in the transformer encoder, Tr-VST methods can generate higher-quality results compared with state-of-the-art VST methods.
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NfvInsight: A Framework for Automatically Deploying and Benchmarking VNF Chains
Tian-Ni Xu, Hai-Feng Sun, Di Zhang, Xiao-Ming Zhou, Xiu-Feng Sui, Sa Wang, Qun Huang, and Yun-Gang Bao
Journal of Computer Science and Technology    2022, 37 (3): 680-698.   DOI: 10.1007/s11390-020-0434-1
Abstract499)      PDF   
With the advent of virtualization techniques and software-defined networking (SDN), network function virtualization (NFV) shifts network functions (NFs) from hardware implementations to software appliances, between which exists a performance gap. How to narrow the gap is an essential issue of current NFV research. However, the cumbersomeness of deployment, the water pipe effect of virtual network function (VNF) chains, and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV system. To pinpoint the NFV system performance, we propose NfvInsight, a framework for automatic deployment and benchmarking VNF chains. Our framework tackles the challenges in NFV performance analysis. The framework components include chain graph generation, automatic deployment, and fine granularity measurement. The design and implementation of each component have their advantages. To the best of our knowledge, we make the first attempt to collect rules forming a knowledge base for generating reasonable chain graphs. NfvInsight deploys the generated chain graphs automatically, which frees the network operators from executing at least 391 lines of bash commands for a single test. To diagnose the performance bottleneck, NfvInsight collects metrics from multiple layers of the software stack. Specifically, we collect the network stack latency distribution ingeniously, introducing only less than 2.2% overhead. We showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying system. Leveraging our framework, we find several design flaws of the network stack, which are unsuitable for packet forwarding inside one single server under the NFV circumstance. Our optimization for these flaws gains at most 3x performance improvement.
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ovAFLow: Detecting Memory Corruption Bugs with Fuzzing-Based Taint Inference
Gen Zhang, Peng-Fei Wang, Tai Yue, Xiang-Dong Kong, Xu Zhou, and Kai Lu
Journal of Computer Science and Technology    2022, 37 (2): 405-422.   DOI: 10.1007/s11390-021-1600-9
Accepted: 15 November 2021

Abstract495)      PDF   

Grey-box fuzzing is an effective technology to detect software vulnerabilities, such as memory corruption. Previous fuzzers in detecting memory corruption bugs either use heavy-weight analysis, or use techniques which are not customized for memory corruption detection. In this paper, we propose a novel memory bug guided fuzzer, ovAFLow. To begin with, we broaden the memory corruption targets where we frequently identify bugs. Next, ovAFLow utilizes light-weight and effective methods to build connections between the fuzzing inputs and these corruption targets. Based on the connection results, ovAFLow uses customized techniques to direct the fuzzing process closer to memory corruption. We evaluate ovAFLow against state-of-the-art fuzzers, including AFL (american fuzzy lop), AFLFast, FairFuzz, QSYM, Angora, TIFF, and TortoiseFuzz. The evaluation results show better vulnerability detection ability of ovAFLow, and the performance overhead is acceptable. Moreover, we identify 12 new memory corruption bugs and two CVEs (common vulnerability exposures) with the help of ovAFLow.

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Tetris: A Heuristic Static Memory Management Framework for Uniform Memory Multicore Neural Network Accelerators
Xiao-Bing Chen, Hao Qi, Shao-Hui Peng, Yi-Min Zhuang, Tian Zhi, and Yun-Ji Chen
Journal of Computer Science and Technology    2022, 37 (6): 1255-1270.   DOI: 10.1007/s11390-021-1213-3
Accepted: 31 May 2021

Abstract493)      PDF   
Uniform memory multicore neural network accelerators (UNNAs) furnish huge computing power to emerging neural network applications. Meanwhile, with neural network architectures going deeper and wider, the limited memory capacity has become a constraint to deploy models on UNNA platforms. Therefore how to efficiently manage memory space and how to reduce workload footprints are urgently significant. In this paper, we propose Tetris: a heuristic static memory management framework for UNNA platforms. Tetris reconstructs execution flows and synchronization relationships among cores to analyze each tensor’s liveness interval. Then the memory management problem is converted to a sequence permutation problem. Tetris uses a genetic algorithm to explore the permutation space to optimize the memory management strategy and reduce memory footprints. We evaluate several typical neural networks and the experimental results demonstrate that Tetris outperforms the state-of-the-art memory allocation methods, and achieves an average memory reduction ratio of 91.9% and 87.9% for a quad-core and a 16-core Cambricon-X platform, respectively.
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Self-Supervised Task Augmentation for Few-Shot Intent Detection
Peng-Fei Sun, Ya-Wen Ouyang, Ding-Jie Song, and Xin-Yu Dai
Journal of Computer Science and Technology    2022, 37 (3): 527-538.   DOI: 10.1007/s11390-022-2029-5
Abstract485)      PDF   
Few-shot intent detection is a practical challenge task, because new intents are frequently emerging and collecting large-scale data for them could be costly. Meta-learning, a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks, has been a popular way to tackle this problem. However, the existing meta-learning models have been evidenced to be overfitting when the meta-training tasks are insufficient. To overcome this challenge, we present a novel self-supervised task augmentation with meta-learning framework, namely STAM. Firstly, we introduce the task augmentation, which explores two different strategies and combines them to extend meta-training tasks. Secondly, we devise two auxiliary losses for integrating self-supervised learning into meta-learning to learn more generalizable and transferable features. Experimental results show that STAM can achieve consistent and considerable performance improvement to existing state-of-the-art methods on four datasets.
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PLQ: An Efficient Approach to Processing Pattern-Based Log Queries
Jia Chen, Peng Wang, Fan Qiao, Shi-Qing Du, and Wei Wang
Journal of Computer Science and Technology    2022, 37 (5): 1239-1254.   DOI: 10.1007/s11390-020-0653-5
Accepted: 30 November 2020

Abstract464)      PDF   
As software systems grow more and more complex, extensive techniques have been proposed to analyze the log data to obtain the insight of the system status. However, during log data analysis, tedious manual efforts are paid to search interesting or informative log patterns from a huge volume of log data, named pattern-based queries. Although existing log management tools and DMBS systems can also support pattern-based queries, they suffer from a low efficiency. To deal with this problem, we propose a novel approach, named PLQ (Pattern-based Log Query). First, PLQ organizes logs into disjoint chunks and builds chunk-wise bitmap indexes for log types and attribute values. Then, based on bitmap indexes, PLQ finds candidate logs with a set of efficient bit-wise operations. Finally, PLQ fetches candidate logs and validates them according to the queried pattern. Extensive experiments are conducted on real-life datasets. According to experimental results, compared with existing log management systems, PLQ is more efficient in querying log patterns and has a higher pruning rate for filtering irrelevant logs. Moreover, in PLQ, since the ratio of the index size to the data size does not exceed 2.5% for log datasets of different sizes, PLQ has a high scalability.
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Document-Level Neural Machine Translation with Hierarchical Modeling of Global Context
Xin Tan, Long-Yin Zhang, and Guo-Dong Zhou
Journal of Computer Science and Technology    2022, 37 (2): 295-308.   DOI: 10.1007/s11390-021-0286-3
Accepted: 11 January 2021

Abstract431)      PDF   

Document-level machine translation (MT) remains challenging due to its difficulty in efficiently using document-level global context for translation. In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT). This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence. With this hierarchical architecture, we feedback the extracted document-level global context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context. Notably, we explore the effect of three popular attention functions during the information backward-distribution phase to take a deep look into the global context information distribution of our model. In addition, since large-scale in-domain document-level parallel corpora are usually unavailable, we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability. Experimental results of our model on Chinese-English and English-German corpora significantly improve the Transformer baseline by 4.5 BLEU points on average which demonstrates the effectiveness of our proposed hierarchical model in document-level NMT.

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Quality of Service Support in RPL Networks: Standing State and Future Prospects
Ibrahim S. Alsukayti
Journal of Computer Science and Technology    2022, 37 (2): 344-368.   DOI: 10.1007/s11390-022-1027-y
Accepted: 05 March 2022

Abstract431)      PDF   

The development of IP-based Internet of Things (IoT) networks would facilitate more effective end-to-end IP network architectures, but it remains a challenge. Network routing needs to be effectively addressed in the IoT environments of scarce computational and energy resources. Accordingly, the Internet Engineering Task Force (IETF) has specified the IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to provide a bespoke IPv6-based routing framework for IoT networks. However, RPL comes with no Quality of Service (QoS) support which is an essential requirement for many IoT applications. The network research community has introduced a number of research proposals enhancing RPL with different QoS solutions. This paper presents a review of these proposed solutions and aims to establish a firm understanding of recent QoS developments for RPL and possible areas for future IoT routing research. The focus is on comprehending the protocol and networking properties that can affect QoS performance in RPL networks. Consideration is also given to different objective functions developed for addressing varying QoS aspects such as throughput, delay, and packet loss. RPL is also extended in a number of QoS solutions following different approaches at the MAC, network, and application layers. However, there is still a need for further developments to address effective QoS support, particularly for dynamic RPL networks.

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Imputing DNA Methylation by Transferred Learning Based Neural Network
Xin-Feng Wang, Xiang Zhou, Jia-Hua Rao, Zhu-Jin Zhang, and Yue-Dong Yang
Journal of Computer Science and Technology    2022, 37 (2): 320-329.   DOI: 10.1007/s11390-021-1174-6
Accepted: 18 February 2022

Abstract405)      PDF   

DNA methylation is one important epigenetic type to play a vital role in many diseases including cancers. With the development of the high-throughput sequencing technology, there is much progress to disclose the relations of DNA methylation with diseases. However, the analyses of DNA methylation data are challenging due to the missing values caused by the limitations of current techniques. While many methods have been developed to impute the missing values, these methods are mostly based on the correlations between individual samples, and thus are limited for the abnormal samples in cancers. In this study, we present a novel transfer learning based neural network to impute missing DNA methylation data, namely the TDimpute-DNAmeth method. The method learns common relations between DNA methylation from pan-cancer samples, and then fine-tunes the learned relations over each specific cancer type for imputing the missing data. Tested on 16 cancer datasets, our method was shown to outperform other commonly-used methods. Further analyses indicated that DNA methylation is related to cancer survival and thus can be used as a biomarker of cancer prognosis.

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Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling
Xiao-Qing Deng, Bo-Lin Chen, Wei-Qi Luo, and Da Luo
Journal of Computer Science and Technology    2022, 37 (5): 1134-1145.   DOI: 10.1007/s11390-021-0572-0
Accepted: 29 June 2021

Abstract398)      PDF   
Recently, steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features. However, most existing methods based on deep learning are specially designed for one image domain (i.e., spatial or JPEG), and they often take long time to train. To make a balance between the detection performance and the training time, in this paper, we propose an effective and relatively fast steganalytic network called US-CovNet (Universal Steganalytic Covariance Network) for both {the} spatial and JPEG domains. To this end, we carefully design several important components of {US-CovNet} that will significantly affect the detection performance, including the high-pass filter set, the shortcut connection and the pooling {layer}. Extensive experimental results show that compared with the current best steganalytic networks (i.e., SRNet and J-YeNet), {US-CovNet} can achieve the state-of-the-art results for detecting spatial steganography and have competitive performance for detecting JPEG steganography. For example, the detection accuracy of US-CovNet is at least 0.56% higher than that of SRNet in the spatial domain. In the JPEG domain, US-CovNet performs slightly worse than J-YeNet in some cases with the degradation of less than 0.78%. However, the training time of US-CovNet is significantly reduced, which is less than 1/4 and 1/2 of SRNet and J-YeNet respectively.
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Self-Supervised Music Motion Synchronization Learning for Music-Driven Conducting Motion Generation
Fan Liu, De-Long Chen, Rui-Zhi Zhou, Sai Yang, and Feng Xu
Journal of Computer Science and Technology    2022, 37 (3): 539-558.   DOI: 10.1007/s11390-022-2030-z
Accepted: 10 March 2022

Abstract396)      PDF   
The correlation between music and human motion has attracted widespread research attention. Although recent studies have successfully generated motion for singers, dancers, and musicians, few have explored motion generation for orchestral conductors. The generation of music-driven conducting motion should consider not only the basic music beats, but also mid-level music structures, high-level music semantic expressions, and hints for different parts of orchestras (strings, woodwind, etc.). However, most existing conducting motion generation methods rely heavily on human-designed rules, which significantly limits the quality of generated motion. Therefore, we propose a novel Music Motion Synchronized Generative Adversarial Network (M2S-GAN), which generates motions according to the automatically learned music representations. More specifically, M2S-GAN is a cross-modal generative network comprising four components: 1) a music encoder that encodes the music signal; 2) a generator that generates conducting motion from the music codes; 3) a motion encoder that encodes the motion; 4) a discriminator that differentiates the real and generated motions. These four components respectively imitate four key aspects of human conductors: understanding music, interpreting music, precision and elegance. The music and motion encoders are first jointly trained by a self-supervised contrastive loss, and can thus help to facilitate the music motion synchronization during the following adversarial learning process. To verify the effectiveness of our method, we construct a large-scale dataset, named ConductorMotion100, which consists of unprecedented 100 hours of conducting motion data. Extensive experiments on ConductorMotion100 demonstrate the effectiveness of M2S-GAN. Our proposed approach outperforms various comparison methods both quantitatively and qualitatively. Through visualization, we show that our approach can generate plausible, diverse, and music-synchronized conducting motion.
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Intent-Slot Correlation Modeling for Joint Intent Prediction and Slot Filling
Jun-Feng Fan, Mei-Ling Wang, Chang-Liang Li, Zi-Qiang Zhu, and Lu Mao
Journal of Computer Science and Technology    2022, 37 (2): 309-319.   DOI: 10.1007/s11390-020-0326-4
Accepted: 20 September 2020

Abstract393)      PDF   

Slot filling and intent prediction are basic tasks in capturing semantic frame of human utterances. Slots and intent have strong correlation for semantic frame parsing. For each utterance, a specific intent type is generally determined with the indication information of words having slot tags (called as slot words), and in reverse the intent type decides that words of certain categories should be used to fill as slots. However, the Intent-Slot correlation is rarely modeled explicitly in existing studies, and hence may be not fully exploited. In this paper, we model Intent-Slot correlation explicitly and propose a new framework for joint intent prediction and slot filling. Firstly, we explore the effects of slot words on intent by differentiating them from the other words, and we recognize slot words by solving a sequence labeling task with the bi-directional long short-term memory (BiLSTM) model. Then, slot recognition information is introduced into attention-based intent prediction and slot filling to improve semantic results. In addition, we integrate the Slot-Gated mechanism into slot filling to model dependency of slots on intent. Finally, we obtain slot recognition, intent prediction and slot filling by training with joint optimization. Experimental results on the benchmark Air-line Travel Information System (ATIS) and Snips datasets show that our Intent-Slot correlation model achieves state-of-the-art semantic frame performance with a lightweight structure.

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Unified Enclave Abstraction and Secure Enclave Migration on Heterogeneous Security Architectures
Jin-Yu Gu, Hao Li, Yu-Bin Xia, Hai-Bo Chen, Cheng-Gang Qin, and Zheng-Yu He
Journal of Computer Science and Technology    2022, 37 (2): 468-486.   DOI: 10.1007/s11390-021-1083-8
Accepted: 21 February 2021

Abstract378)      PDF   

Nowadays, application migration becomes more and more attractive. For example, it can make computation closer to data sources or make service closer to end-users, which may significantly decrease latency in edge computing. Yet, migrating applications among servers that are controlled by different platform owners raises security issues. We leverage hardware-secured trusted execution environment (TEE, aka., enclave) technologies, such as Intel SGX, AMD SEV, and ARM TrustZone, for protecting critical computations on untrusted servers. However, these hardware TEEs propose non-uniform programming abstractions and are based on heterogeneous architectures, which not only forces programmers to develop secure applications targeting some specific abstraction but also hinders the migration of protected applications. Therefore, we propose UniTEE which gives a unified enclave programming abstraction across the above three hardware TEEs by using a microkernel-based design and enables the secure enclave migration by integrating heterogeneous migration techniques. We have implemented the prototype on real machines. The evaluation results show the migration support incurs nearly-zero runtime overhead and the migration procedure is also efficient.

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Byte Frequency Based Indicators for Crypto-Ransomware Detection from Empirical Analysis
Geun Yong Kim, Joon-Young Paik, Yeongcheol Kim, and Eun-Sun Cho
Journal of Computer Science and Technology    2022, 37 (2): 423-442.   DOI: 10.1007/s11390-021-0263-x
Accepted: 21 July 2021

Abstract373)      PDF   

File entropy is one of the major indicators of crypto-ransomware because the encryption by ransomware increases the randomness of file contents. However, entropy-based ransomware detection has certain limitations; for example, when distinguishing ransomware-encrypted files from normal files with inherently high-level entropy, misclassification is very possible. In addition, the entropy evaluation cost for an entire file renders entropy-based detection impractical for large files. In this paper, we propose two indicators based on byte frequency for use in ransomware detection; these are termed EntropySA and DistSA, and both consider the interesting characteristics of certain file subareas termed "sample areas'' (SAs). For an encrypted file, both the sampled area and the whole file exhibit high-level randomness, but for a plain file, the sampled area embeds informative structures such as a file header and thus exhibits relatively low-level randomness even though the entire file exhibits high-level randomness. EntropySA and DistSA use "byte frequency" and a variation of byte frequency, respectively, derived from sampled areas. Both indicators cause less overhead than other entropy-based detection methods, as experimentally proven using realistic ransomware samples. To evaluate the effectiveness and feasibility of our indicators, we also employ three expensive but elaborate classification models (neural network, support vector machine and threshold-based approaches). Using these models, our experimental indicators yielded an average F1-measure of 0.994 and an average detection rate of 99.46% for file encryption attacks by realistic ransomware samples.

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ARSlice: Head-Mounted Display Augmented with Dynamic Tracking and Projection
Yu-Ping Wang, Sen-Wei Xie, Li-Hui Wang, Hongjin Xu, Satoshi Tabata, and Masatoshi Ishikawa
Journal of Computer Science and Technology    2022, 37 (3): 666-679.   DOI: 10.1007/s11390-022-2173-y
Abstract367)      PDF   
Computed tomography (CT) generates cross-sectional images of the body. Visualizing CT images has been a challenging problem. The emergence of the augmented and virtual reality technology has provided promising solutions. However, existing solutions suffer from tethered display or wireless transmission latency. In this paper, we present ARSlice, a proof-of-concept prototype that can visualize CT images in an untethered manner without wireless transmission latency. Our ARSlice prototype consists of two parts, the user end and the projector end. By employing dynamic tracking and projection, the projector end can track the user-end equipment and project CT images onto it in real time. The user-end equipment is responsible for displaying these CT images into the 3D space. Its main feature is that the user-end equipment is a pure optical device with light weight, low cost, and no energy consumption. Our experiments demonstrate that our ARSlice prototype provides part of six degrees of freedom for the user, and a high frame rate. By interactively visualizing CT images into the 3D space, our ARSlice prototype can help untrained users better understand that CT images are slices of a body.
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FlexPDA: A Flexible Programming Framework for Deep Learning Accelerators
Lei Liu, Xiu Ma, Hua-Xiao Liu, Guang-Li Li, and Lei Liu
Journal of Computer Science and Technology    2022, 37 (5): 1200-1220.   DOI: 10.1007/s11390-021-1406-9
Accepted: 18 September 2021

Abstract366)      PDF   
There are a wide variety of intelligence accelerators with promising performance and energy efficiency, deployed in a broad range of applications such as computer vision and speech recognition. However, programming productivity hinders the deployment of deep learning accelerators. The low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model, is designed to reduce the programming burden on the intelligence accelerators. Unfortunately, it is inflexible for developers to build a network model for every deep learning application, which probably brings unnecessary repetitive implementation. In this paper, a flexible and efficient programming framework for deep learning accelerators, FlexPDA, is proposed, which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast upgrades. We evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end network. The experimental results validate the effectiveness of FlexPDA, which achieves an end-to-end performance improvement of 1.620x over the low-level library.
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Accelerating Data Transfer in Dataflow Architectures Through a Look-Ahead Acknowledgment Mechanism
Yu-Jing Feng, De-Jian Li, Xu Tan, Xiao-Chun Ye, Dong-Rui Fan, Wen-Ming Li, Da Wang, Hao Zhang, and Zhi-Min Tang
Journal of Computer Science and Technology    2022, 37 (4): 942-959.   DOI: 10.1007/s11390-020-0555-6
Accepted: 17 December 2020

Abstract353)      PDF   
The dataflow architecture, which is characterized by a lack of a redundant unified control logic, has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency, especially of applications used in high-performance computing (HPC). Importantly, the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous manner. Therefore, a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different contexts. Possible solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection, or a handshake mechanism in which the data is only sent after acknowledgments are received. However, these mechanisms are characterized by both inefficient data transfer and increased area cost. Good performance of the dataflow architecture depends on the efficiency of data transfer. In order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost, we propose a Look-Ahead Acknowledgment (LAA) mechanism. LAA accelerates the execution flow by speculatively acknowledging ahead without penalties. Our simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%, with a reduction in the average execution time by 17.4% and an increase in the average power efficiency of dataflow processors by 22.4%. Crucially, our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%. In conclusion, the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.
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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture
Xin Zhang, Siyuan Lu, Shui-Hua Wang, Xiang Yu, Su-Jing Wang, Lun Yao, Yi Pan, and Yu-Dong Zhang
Journal of Computer Science and Technology    2022, 37 (2): 330-343.   DOI: 10.1007/s11390-020-0679-8
Accepted: 30 March 2021

Abstract342)      PDF   

COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

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SMRI: A New Method for siRNA Design for COVID-19 Therapy
Meng-Xin Chen, Xiao-Dong Zhu, Hao Zhang, Zhen Liu, and Yuan-Ning Liu
Journal of Computer Science and Technology    2022, 37 (4): 991-1002.   DOI: 10.1007/s11390-021-0826-x
Accepted: 31 August 2021

Abstract339)      PDF   
First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.
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Neural Emotion Detection via Personal Attributes
Xia-Bing Zhou, Zhong-Qing Wang, Xing-Wei Liang, Min Zhang, and Guo-Dong Zhou
Journal of Computer Science and Technology    2022, 37 (5): 1146-1160.   DOI: 10.1007/s11390-021-0606-7
Accepted: 13 April 2021

Abstract321)      PDF   
There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.
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Probability-Based Channel Pruning for Depthwise Separable Convolutional Networks
Han-Li Zhao, Kai-Jie Shi, Xiao-Gang Jin, Ming-Liang Xu, Hui Huang, Wang-Long Lu, and Ying Liu
Journal of Computer Science and Technology    2022, 37 (3): 584-600.   DOI: 10.1007/s11390-022-2131-8
Abstract318)      PDF   
Channel pruning can reduce memory consumption and running time with least performance damage, and is one of the most important techniques in network compression. However, existing channel pruning methods mainly focus on the pruning of standard convolutional networks, and they rely intensively on time-consuming fine-tuning to achieve the performance improvement. To this end, we present a novel efficient probability-based channel pruning method for depthwise separable convolutional networks. Our method leverages a new simple yet effective probability-based channel pruning criterion by taking the scaling and shifting factors of batch normalization layers into consideration. A novel shifting factor fusion technique is further developed to improve the performance of the pruned networks without requiring extra time-consuming fine-tuning. We apply the proposed method to five representative deep learning networks, namely MobileNetV1, MobileNetV2, ShuffleNetV1, ShuffleNetV2, and GhostNet, to demonstrate the efficiency of our pruning method. Extensive experimental results and comparisons on publicly available CIFAR10, CIFAR100, and ImageNet datasets validate the feasibility of the proposed method.
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Accelerating DAG-Style Job Execution via Optimizing Resource Pipeline Scheduling
Yubin Duan, Ning Wang, and Jie Wu
Journal of Computer Science and Technology    2022, 37 (4): 852-868.   DOI: 10.1007/s11390-021-1488-4
Accepted: 23 November 2021

Abstract313)      PDF   
The volume of information that needs to be processed in big data clusters increases rapidly nowadays. It is critical to execute the data analysis in a time-efficient manner. However, simply adding more computation resources may not speed up the data analysis significantly. The data analysis jobs usually consist of multiple stages which are organized as a directed acyclic graph (DAG). The precedence relationships between stages cause scheduling challenges. General DAG scheduling is a well-known NP-hard problem. Moreover, we observe that in some parallel computing frameworks such as Spark, the execution of a stage in DAG contains multiple phases that use different resources. We notice that carefully arranging the execution of those resources in pipeline can reduce their idle time and improve the average resource utilization. Therefore, we propose a resource pipeline scheme with the objective of minimizing the job makespan. For perfectly parallel stages, we propose a contention-free scheduler with detailed theoretical analysis. Moreover, we extend the contention-free scheduler for three-phase stages, considering the computation phase of some stages can be partitioned. Additionally, we are aware that job stages in real-world applications are usually not perfectly parallel. We need to frequently adjust the parallelism levels during the DAG execution. Considering reinforcement learning (RL) techniques can adjust the scheduling policy on the fly, we investigate a scheduler based on RL for online arrival jobs. The RL-based scheduler can adjust the resource contention adaptively. We evaluate both contention-free and RL-based schedulers on a Spark cluster. In the evaluation, a real-world cluster trace dataset is used to simulate different DAG styles. Evaluation results show that our pipelined scheme can significantly improve CPU and network utilization.
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SOCA-DOM: A Mobile System-on-Chip Array System for Analyzing Big Data on the Move
Le-Le Li, Jiang-Yi Liu, Jian-Ping Fan, Xue-Hai Qian, Kai Hwang, Yeh-Ching Chung, and Zhi-Bin Yu
Journal of Computer Science and Technology    2022, 37 (6): 1271-1289.   DOI: 10.1007/s11390-022-1087-z
Accepted: 25 April 2022

Abstract307)      PDF   
Recently, analyzing big data on the move is booming. It requires that the hardware resource should be low volume, low power, light in weight, high-performance, and highly scalable whereas the management software should be flexible and consume little hardware resource. To meet these requirements, we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier “software-defined” resource manager named Chameleon. First, we design an Ethernet communication board to support an array of mobile system-on-chips. Second, we propose a two-tier software architecture for Chameleon to make it flexible. Third, we devise data, configuration, and control planes for Chameleon to make it “software-defined” and in turn consume hardware resources on demand. Fourth, we design an accurate synthetic metric that represents the computational power of a computing node. We employ 12 Apache Spark benchmarks to evaluate SOCA-DOM. Surprisingly, SOCA-DOM consumes up to 9.4x less CPU resources and 13.5x less memory than Mesos which is an existing resource manager. In addition, we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon servers. Based on the results, we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.
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Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing
She Sun, Shuai Ma, Jing-He Song, Wen-Hai Yue, Xue-Lian Lin, and Tiejun Ma
Journal of Computer Science and Technology    2022, 37 (5): 1026-1048.   DOI: 10.1007/s11390-022-2409-x
Abstract299)      PDF   
With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices, trajectory data is continuously growing. While large-scale trajectories provide opportunities for various applications, the locations in trajectories pose a threat to individual privacy. Recently, there has been an interesting debate on the reidentifiability of individuals in the Science magazine. The main finding of Sánchez et al. is exactly opposite to that of De Montjoye et al., which raises the first question: "what is the true situation of the privacy preservation for trajectories in terms of reidentification?'' Furthermore, it is known that anonymization typically causes a decline of data utility, and anonymization mechanisms need to consider the trade-off between privacy and utility. This raises the second question: "what is the true situation of the utility of anonymized trajectories?'' To answer these two questions, we conduct a systematic experimental study, using three real-life trajectory datasets, five existing anonymization mechanisms (i.e., identifier anonymization, grid-based anonymization, dummy trajectories, k-anonymity and ε-differential privacy), and two practical applications (i.e., travel time estimation and window range queries). Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories, and essentially close the debate between De Montjoye et al. and Sánchez et al. To the best of our knowledge, this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.
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Quasi-Developable B-Spline Surface Design with Control Rulings
Zi-Xuan Hu, Peng-Bo Bo, and Cai-Ming Zhang
Journal of Computer Science and Technology    2022, 37 (5): 1221-1238.   DOI: 10.1007/s11390-022-0680-5
Accepted: 10 February 2022

Abstract293)      PDF   
We propose a method for generating a ruled B-spline surface fitting to a sequence of pre-defined ruling lines and the generated surface is required to be as-developable-as-possible. Specifically, the terminal ruling lines are treated as hard constraints. Different from existing methods that compute a quasi-developable surface from two boundary curves and cannot achieve explicit ruling control, our method controls ruling lines in an intuitive way and serves as an effective tool for computing quasi-developable surfaces from freely-designed rulings. We treat this problem from the point of view of numerical optimization and solve for surfaces meeting the distance error tolerance allowed in applications. The performance and the efficacy of the proposed method are demonstrated by the experiments on a variety of models including an application of the method for path planning in 5-axis computer numerical control (CNC) flank milling.
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Gaze-Assisted Viewport Control for 360° Video on Smartphone
Linfeng Shen, Yuchi Chen, and Jiangchuan Liu
Journal of Computer Science and Technology    2022, 37 (4): 906-918.   DOI: 10.1007/s11390-022-2037-5
Abstract293)      PDF   
360° video has been becoming one of the major media in recent years, providing immersive experience for viewers with more interactions compared with traditional videos. Most of today's implementations rely on bulky Head-Mounted Displays (HMDs) or require touch screen operations for interactive display, which are not only expensive but also inconvenient for viewers. In this paper, we demonstrate that interactive 360° video streaming can be done with hints from gaze movement detected by the front camera of today's mobile devices (e.g., a smartphone). We design a lightweight real-time gaze point tracking method for this purpose. We integrate it with streaming module and apply a dynamic margin adaption algorithm to minimize the overall energy consumption for battery-constrained mobile devices. Our experiments on state-of-the-art smartphones show the feasibility of our solution and its energy efficiency toward cost-effective real-time 360° video streaming.
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Differential Privacy via a Truncated and Normalized Laplace Mechanism
William Croft, Jörg-Rüdiger Sack, and Wei Shi
Journal of Computer Science and Technology    2022, 37 (2): 369-388.   DOI: 10.1007/s11390-020-0193-z
Accepted: 20 August 2020

Abstract285)      PDF   

When querying databases containing sensitive information, the privacy of individuals stored in the database has to be guaranteed. Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses. However, most such mechanisms do not take into consideration the valid range of the query being posed. Thus, noisy responses that fall outside of this range may potentially be produced. To rectify this and therefore improve the utility of the mechanism, the commonly-used Laplace distribution can be truncated to the valid range of the query and then normalized. However, such a data-dependent operation of normalization leaks additional information about the true query response, thereby violating the differential privacy guarantee. Here, we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace distribution. We adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint configurations. We provide derivations of the optimal scaling parameter (i.e., the minimal value that preserves differential privacy) for each class or provide an approximation thereof. As a result of this work, one can use the Laplace distribution to answer queries in a range-adherent and differentially private manner. To demonstrate the benefits of our proposed method of normalization, we present an experimental comparison against other range-adherent mechanisms. We show that our proposed approach is able to provide improved utility over the alternative mechanisms.

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BADF: Bounding Volume Hierarchies Centric Adaptive Distance Field Computation for Deformable Objects on GPUs
Xiao-Rui Chen, Min Tang, Cheng Li, Dinesh Manocha, and Ruo-Feng Tong
Journal of Computer Science and Technology    2022, 37 (3): 731-740.   DOI: 10.1007/s11390-022-0331-x
Abstract284)      PDF   
We present a novel algorithm BADF (Bounding Volume Hierarchy Based Adaptive Distance Fields) for accelerating the construction of ADFs (adaptive distance fields) of rigid and deformable models on graphics processing units. Our approach is based on constructing a bounding volume hierarchy (BVH) and we use that hierarchy to generate an octree-based ADF. We exploit the coherence between successive frames and sort the grid points of the octree to accelerate the computation. Our approach is applicable to rigid and deformable models. Our GPU-based (graphics processing unit based) algorithm is about 20x--50x faster than current mainstream central processing unit based algorithms. Our BADF algorithm can construct the distance fields for deformable models with 60k triangles at interactive rates on an NVIDIA GTX GeForce 1060. Moreover, we observe 3x speedup over prior GPU-based ADF algorithms.
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SMART: Speedup Job Completion Time by Scheduling Reduce Tasks
Jia-Qing Dong, Ze-Hao He, Yuan-Yuan Gong, Pei-Wen Yu, Chen Tian, Wan-Chun Dou, Gui-Hai Chen, Nai Xia, and Hao-Ran Guan
Journal of Computer Science and Technology    2022, 37 (4): 763-778.   DOI: 10.1007/s11390-022-2118-5
Abstract284)      PDF   
Distributed computing systems have been widely used as the amount of data grows exponentially in the era of information explosion. Job completion time (JCT) is a major metric for assessing their effectiveness. How to reduce the JCT for these systems through reasonable scheduling has become a hot issue in both industry and academia. Data skew is a common phenomenon that can compromise the performance of such distributed computing systems. This paper proposes SMART, which can effectively reduce the JCT through handling the data skew during the reducing phase. SMART predicts the size of reduce tasks based on part of the completed map tasks and then enforces largest-first scheduling in the reducing phase according to the predicted reduce task size. SMART makes minimal modifications to the original Hadoop with only 20 additional lines of code and is readily deployable. The robustness and the effectiveness of SMART have been evaluated with a real-world cluster against a large number of datasets. Experiments show that SMART reduces JCT by up to 6.47%, 9.26%, and 13.66% for Terasort, WordCount and InvertedIndex respectively with the Purdue MapReduce benchmarks suite (PUMA) dataset.
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Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application
Rong-Yu Cao, Yi-Xuan Cao, Gan-Bin Zhou, and Ping Luo
Journal of Computer Science and Technology    2022, 37 (3): 699-718.   DOI: 10.1007/s11390-021-1076-7
Accepted: 09 May 2021

Abstract283)      PDF   
In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications (e.g., passage-based retrieval and high-quality information extraction). However, long documents, containing hundreds or even thousands of pages and a variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper position on the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. As for evaluations, we find that previous studies ignore the error that the depth of a node is correct while its path to the root is wrong. Since such mistakes may worsen the downstream applications seriously, a new measure is developed for a more careful evaluation. The empirical experiments based on thousands of long documents from Chinese financial market, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.972,6, 0.729,1 and 0.957,8 in the Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that the logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications.
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Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm
Jian-Fu Zhu, Zhong-Kai Hao, Qi Liu, Yu Yin, Cheng-Qiang Lu, Zhen-Ya Huang, and En-Hong Chen
Journal of Computer Science and Technology    2022, 37 (6): 1464-1477.   DOI: 10.1007/s11390-021-0970-3
Accepted: 20 April 2021

Abstract277)      PDF   
Generating molecules with desired properties is an important task in chemistry and pharmacy. An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19. Data mining and artificial intelligence may be good ways to find an efficient method. Recently, both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule’s properties. However, existing methods have defects in the experimental evaluation standards. These methods also need to be improved in efficiency and performance. To solve these problems, we propose a method named the Chemical Genetic Algorithm for Large Molecular Space (CALM). Specifically, CALM employs a scalable and efficient molecular representation called molecular matrix. And we design corresponding crossover, mutation, and mask operators inspired by domain knowledge and previous studies. We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization. The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods, where the z tests performed on the results of several experiments show that our method is more than 99% likely to be significant. At the same time, based on the experimental results, we point out the defects in the experimental evaluation standard which affects the fair evaluation of all previous work. Avoiding these defects helps to objectively evaluate the performance of all work.
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RV16: An Ultra-Low-Cost Embedded RISC-V Processor Core
Yuan-Hu Cheng, Li-Bo Huang, Yi-Jun Cui, Sheng Ma, Yong-Wen Wang, and Bing-Cai Sui
Journal of Computer Science and Technology    2022, 37 (6): 1307-1319.   DOI: 10.1007/s11390-022-0910-x
Accepted: 07 May 2022

Abstract275)      PDF   
Embedded and Internet of Things (IoT) devices have extremely strict requirements on the area and power consumption of the processor because of the limitation on its working environment. To reduce the overhead of the embedded processor as much as possible, this paper designs and implements a configurable 32-bit in-order RISC-V processor core based on the 16-bit data path and units, named RV16. The evaluation results show that, compared with the traditional 32-bit RISC-V processor with similar features, RV16 consumes fewer hardware resources and less power consumption. The maximum performance of RV16 running Dhrystone and CoreMark benchmarks is 0.92 DMIPS/MHz and 1.51 CoreMark/MHz, respectively, reaching 75% and 71% of traditional 32-bit processors, respectively. Moreover, a properly configured RV16 running program also consumes less energy than a traditional 32-bit processor.
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Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems
Bushra Alhijawi and Ghazi AL-Naymat
Journal of Computer Science and Technology    2022, 37 (4): 975-990.   DOI: 10.1007/s11390-021-0420-2
Accepted: 29 April 2021

Abstract273)      PDF   
Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.
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Unconditionally Secure Oblivious Polynomial Evaluation: A Survey and New Results
Louis Cianciullo and Hossein Ghodosi
Journal of Computer Science and Technology    2022, 37 (2): 443-458.   DOI: 10.1007/s11390-022-0878-6
Accepted: 20 January 2022

Abstract256)      PDF   

Oblivious polynomial evaluation (OPE) is a two-party protocol that allows a receiver, R to learn an evaluation f(α), of a sender, S's polynomial f(x), whilst keeping both α and f(x) private. This protocol has attracted a lot of attention recently, as it has wide ranging applications in the field of cryptography. In this article we review some of these applications and, additionally, take an in-depth look at the special case of information theoretic OPE. Specifically, we provide a current and critical review of the existing information theoretic OPE protocols in the literature. We divide these protocols into two distinct cases (three-party and distributed OPE) allowing for the easy distinction and classification of future information theoretic OPE protocols. In addition to this work, we also develop several modifications and extensions to existing schemes, resulting in increased security, flexibility and efficiency. Lastly, we also identify a security flaw in a previously published OPE scheme.

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Conjugate-Gradient Progressive-Iterative Approximation for Loop and Catmull-Clark Subdivision Surface Interpolation
Yusuf Fatihu Hamza and Hong-Wei Lin
Journal of Computer Science and Technology    2022, 37 (2): 487-504.   DOI: 10.1007/s11390-020-0183-1
Accepted: 20 August 2020

Abstract252)      PDF   

Loop and Catmull-Clark are the most famous approximation subdivision schemes, but their limit surfaces do not interpolate the vertices of the given mesh. Progressive-iterative approximation (PIA) is an efficient method for data interpolation and has a wide range of applications in many fields such as subdivision surface fitting, parametric curve and surface fitting among others. However, the convergence rate of classical PIA is slow. In this paper, we present a new and fast PIA format for constructing interpolation subdivision surface that interpolates the vertices of a mesh with arbitrary topology. The proposed method, named Conjugate-Gradient Progressive-Iterative Approximation (CG-PIA), is based on the Conjugate-Gradient Iterative algorithm and the Progressive Iterative Approximation (PIA) algorithm. The method is presented using Loop and Catmull-Clark subdivision surfaces. CG-PIA preserves the features of the classical PIA method, such as the advantages of both the local and global scheme and resemblance with the given mesh. Moreover, CG-PIA has the following features. 1) It has a faster convergence rate compared with the classical PIA and W-PIA. 2) CG-PIA avoids the selection of weights compared with W-PIA. 3) CG-PIA does not need to modify the subdivision schemes compared with other methods with fairness measure. Numerous examples for Loop and Catmull-Clark subdivision surfaces are provided in this paper to demonstrate the efficiency and effectiveness of CG-PIA.

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