A New Signature Scheme with Shared Verification

1Introduction Withthedevelopmentofcomputerandnetworkcom municationtechnology,andwiththedifferenceofuser s demands,digitalsignaturetechniquesarebeingexpand edgreatly,fromthesinglesignature,singleverification modetothemulti user sone.Forexample,adocument sy…     

Cognitive Wireless Communication Networks (Hossian, E. and Bhargava, V.; 2007) [Book Review]

?Cognitive radio? is emerging as a promising technology to cope with the spectrum scarcity as well as the spectrum underutilization problem in the next generation wireless communications systems. This book, Cognitive Wireless Communication Networks, edited by Ekram Hossain and Vijay Bhargava, puts together a rich set of research articles featuring recent advances in theory, design, and analysis of cognitive wireless communication networks. The book consists of 15 invited articles from distinguished researchers in this area, which cover a wide range of topics related to the cognitive radio technology. In particular, the topics covered in this book include fundamental challenges and issues in designing cognitive radio systems, information-theoretic analysis of cognitive radio systems, spectrum sensing and co-existence issues, adaptive physical layer protocols and link adaptation techniques for cognitive radio, orthogonal frequency division multiple access (OFDM) and ultra wide band (UWB)- based cognitive radio, different techniques for spectrum access by distributed cognitive radio, cognitive medium access control (MAC) protocols, decentralized learning-based dynamic spectrum access methods, and microeconomic models for spectrum management in cognitive radio.     

现勘图像检索综述

现勘图像检索是进行证据图像比对以获取物证信息的重要手段.本文基于目前应用广泛的现勘图像数据库,根据图像内容将图像分为鞋印、指纹、纹身等种类.并通过对现勘图像的两项关键技术即低层数字特征提取和高层语义分析的总结,从颜色特征、纹理特征、边缘提取等方面综述了现勘图像低层数字特征提取技术,从利用语义模板和数据库本体结构、机器学习算法、引入人工反馈三大类高层语义提取技术综述了现勘图像高层语义分析的研究成果.最后,结合公安行业利用现勘图像获取物证线索的实际应用需求,指出了通过引入公安行业先验知识来提高检索效率等研究方向.     

A systematic literature review of blockchain cyber security

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public-key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security.     

Residual spatiotemporal convolutional networks for face anti-spoofing

Previous deep learning studies on Face Anti-Spoofing (FAS) systems have exploited many aspects of spatial data for face anti-spoofing detection, but few have used end-to-end spatiotemporal approaches to solving FAS problems. This paper aims to provide new perspectives for end-to-end spatiotemporal systems to deal with FAS problems, using five residual spatiotemporal convolutional models. This work analyzes and detects which network is the most appropriate for identifying spoofing on video-based identification systems. These five models were adapted to specific features of the FAS problem and its performance (accuracy and computational cost) were tested with OULU-NPU and SiW datasets. In addition, a cross-dataset validation was carried out. The experimentation shows the strengths and weaknesses of each model against the dependency on the temporal dimension, data initialization and different FAS environment conditions. According to experimentation, residual networks outperform the state-of-the-art, being the model based on decomposing spatial and temporal flow the best option.     

Functional verification of instruction processing units through control flow modeling

The design verification of state-of-the-art high-performance microprocessors has become a significant challenge for test engineers. Deep pipelines, multiple execution units, out-of-order and speculative execution techniques, typically found in such microprocessors, contribute much to this complexity. Conventional methods, which treat the processor as a logic state machine or apply architectural level tests, fail to provide coverage of all possible corner cases in the design. This paper presents a functional verification method for modern microprocessors, which is based on innovative models of the microprocessor architecture, intended to cover the testing of all corner cases. In order to test the models presented in this work, an architecture independent coverage measurement system has been developed. The models were tested with both random code and real world applications in order to determine which of the two achieves higher coverage.     

Important citation identification using sentiment analysis of in-text citations

Citation represents the relationship between the cited and the citing document and vice versa. Citations are widely used to measure the different aspects of knowledge-based achievements such as institutional ranking, author ranking, the impact factor of the journal, research grants, and peer judgments. A fair evaluation of research required a quantitative and qualitative assessment of citations. To perform the qualitative analysis of citations, researchers tried to classify the citations into binary classes (i.e., important and non-important). To perform this task, researchers used metadata, content, citations count, cue words or phrases, sentiment analysis, keywords, and machine learning approaches for citation classification. However, the state-of-the-art results of binary classification are inadequate for the calculation of different aspects of the researcher and their work. Therefore, this research proposed an in-text citation sentiment analysis-based approach for binary classification which effectively enhanced the results of the state-of-the-art. In this research, different machine learning-based models are evaluated to determine the in-text citations sentiments. These sentiment results are further used for positive-negative, and neutral citation counts. Furthermore, the scores of cosine similarity between paper citation pairs are also calculated and used as a feature. This sentiment and cosine similarity scores are further used as features in binary classification. The classification is performed through SVM, KLR, and Random Forest. The proposed approach is evaluated and compared with two state-of-the-art approaches on the benchmark dataset. The proposed approach can achieve 0.83 f-measure with the improvement of 13.6% for dataset 1 and 0.67 with an improvement of 8% for dataset two with a random forest classification model.     

基于特征不变矩的签名鉴别方案

脱机手写签名鉴别的主要困难在于有效特征的提取,因此本文主要围绕提取能反映签名本质的特征进行了相关研究。在具体解决签名鉴别时,一方面要考虑签名的静态特征,另一方面寻找动态特征。重点研究了静态特征。提取静态特征时,利用伪Zernike矩的尺度及位移不变性,计算签名图像的0~10阶伪Zernike矩来组成特征向量。在此基础上,对基于上述两种不同特征的加权欧氏距离分类器进行性能比较,并找到了一个有效的数据融合方案。     

基于深度学习的垃圾邮件文本分类方法

文中旨在研究基于深度学习的垃圾邮件文本分类方法，该方法结合了卷积神经网络（CNN）和循环神经网络（RNN）的模型，通过对邮件文本进行特征提取和分类，能高效、准确地对垃圾邮件进行分类。文中以卷积神经网络和循环神经网络为实验对象，提出了一种垃圾邮件文本分类方法，并在公开数据集上进行了实验。实验结果表明，该方法在垃圾邮件文本分类任务上具有较高的准确率和召回率。     

雷达辐射源个体识别综述

雷达辐射源个体识别通过提取个体特征来辨识雷达个体,是电子对抗领域的热点研究方向。近年来随着深度学习的飞速发展及其在各领域的成功应用,基于深度学习的雷达辐射源个体识别成为焦点。虽然研究多年,成果丰富,但目前尚缺少关于该方向全面、细致的综述。基于此,该文从雷达辐射源个体特征机理分析、基于手工特征的识别方法、基于深度学习的识别方法以及数据集构建4个方面着手,对雷达辐射源个体识别开展系统的综述工作,并对当前现状和未来方向进行总结与展望,旨在推动雷达辐射源个体识别理论和方法研究的新发展。     

A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems

A breakdown of the electrical insulation system causes catastrophic failure of the electrical machine and brings large process downtime losses. To determine the conditions of the stator insulation system of motor drive systems, various testing and monitoring methods have been developed. This paper presents an in-depth literature review of testing and monitoring methods, categorizing them into online and offline methods, each of which is further grouped into specific areas according to their physical nature. The main focus of this paper is on testing and monitoring techniques that diagnose the condition of the turn-to-turn insulation of low-voltage machines, which is a rapidly expanding area for both research and product development efforts. In order to give a compact overview, the results are summarized in two tables. In addition to monitoring methods on turn-to-turn insulation, some of the most common methods to assess the stator's phase-to-ground and phase-to-phase insulation conditions are included in the tables as well.      

基于可区分边界和加权对比度优化的显著度检测算法

针对目前基于先验背景的显著度算法中,把图像的所有边界同等对待带来的误判别问题,本文提出一种基于可区分边界和加权对比度优化的显著度检测算法.为了客观评价显著度,本文首先设计了一种粗略评估显著度的指标,用来选择较好的背景图.以该指标为基础,该算法先利用Hausdorff距离对边界进行区分,再利用测地线距离变换完成可靠的背景检测;然后,构造了一种前景-背景加权的对比度来计算初始显著度;最后,使用加权的优化模型进行显著度的优化.在5个公开数据集上的实验结果表明,本文算法在保持快速、无训练等优点的同时,检测性能优于目前主流算法.     

Deep frameworks based on residual dense network and reinforcement learning for mmWave massive MIMO systems

In millimeter wave (mmWave) massive multiple-input-multiple-output (MIMO) systems, hybrid precoding plays a pivotal role in reducing complexity and cost while providing a good spectral efficiency. However, implementation of digital precoders with large number of antennas is difficult due to hardware constraints, while analog precoders offer confined performance. This leads to high computational complexity and cannot fully exploit the spatial information. Previous studies on hybrid precoding were based on exhaustive search solutions or greedy schemes, which result in higher complexity system performance. To face these challenges, this paper proposes deep hybrid precoding framework with phase quantization and residual dense network to design the matrix of analog and digital precoders. The proposed deep hybrid precoding technique consists of offline training stage and online deployment stage. In offline training stage, hybrid precoding is obtained assuming the approximate phase quantization. While in the online deployment stage, the matrix of analog precoding is calculated by exchanging approximate phase quantization with ideal phase and grouping the analog precoding vectors. In this paper, we also propose a deep reinforcement learning-based hybrid precoding. It consists of a deep reinforcement learning with employing convolutional neural network and long short-term memory (LSTM) methods. In our proposed frameworks, structures of proposed techniques are trained for maximum spectral efficiency. Our proposed techniques are compared with other precoding techniques. Results illustrate that the proposed techniques outperforms the other precoding techniques in terms of the spectral efficiency.     

实体识别技术研究进展综述

实体识别技术作为知识图谱构建的重要步骤,已广泛用于语义网络、机器翻译、问答系统等自然语言处理中,在推动自然语言处理技术落地实践的过程中起着非常关键的作用。本文根据实体识别技术的发展历程调研了现有的实体识别方法,主要为早期基于规则和词典的实体识别方法、基于机器学习的以及基于深度学习的命名实体识别方法;整理了每种实体识别方法的关键思路、优缺点和具有代表性的模型,特别对目前使用较多的基于双向长短期记忆网络(BiLSTM)模型和基于Transformer模型的实体识别方法进行了概述;介绍了目前主流的数据集以及评价标准。最后,面向未来机器类通信的语义需求,总结了实体识别技术面临的挑战,并对其未来在物联网业务数据方面的发展进行了展望。     

A brief survey on adaptive video streaming quality assessment

Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.     

What factors contribute to the acceptance of artificial intelligence? A systematic review

Artificial Intelligence (AI) agents are predicted to infiltrate most industries within the next decade, creating a personal, industrial, and social shift towards the new technology. As a result, there has been a surge of interest and research towards user acceptance of AI technology in recent years. However, the existing research appears dispersed and lacks systematic synthesis, limiting our understanding of user acceptance of AI technologies. To address this gap in the literature, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and meta-Analysis guidelines using five databases: EBSCO host, Embase, Inspec (Engineering Village host), Scopus, and Web of Science. Papers were required to focus on both user acceptance and AI technology. Acceptance was defined as the behavioural intention or willingness to use, buy, or try a good or service. A total of 7912 articles were identified in the database search. Sixty articles were included in the review. Most studies (n = 31) did not define AI in their papers, and 38 studies did not define AI for their participants. The extended Technology Acceptance Model (TAM) was the most frequently used theory to assess user acceptance of AI technologies. Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy significantly and positively predicted behavioural intention, willingness, and use behaviour of AI across multiple industries. However, in some cultural scenarios, it appears that the need for human contact cannot be replicated or replaced by AI, no matter the perceived usefulness or perceived ease of use. Given that most of the methodological approaches present in the literature have relied on self-reported data, further research using naturalistic methods is needed to validate the theoretical model/s that best predict the adoption of AI technologies.     

Applying SMT-based verification to hardware/software partitioning in embedded systems

When performing hardware/software co-design for embedded systems, the problem of which functions of the system should be implemented in hardware (HW) or in software (SW) emerges. This problem is known as HW/SW partitioning. Over the last 10 years, a significant research effort has been carried out in this area. In this paper, we present two new approaches to solve the HW/SW partitioning problem by using verification techniques based on satisfiability modulo theories (SMT). We compare the results using the traditional technique of integer linear programming, specifically binary integer programming and a modern method of optimization by genetic algorithm. The experimental results show that SMT-based verification techniques can be effective in particular cases to solve the HW/SW partition problem optimally using a state-of-the-art model checker based on SMT solvers, when compared against traditional techniques.     

Image inpainting using scene constraints

Recent deep learning-based inpainting methods have shown significant improvements and generate plausible images. However, most of these methods may either synthesis unrealistic and blurry texture details or fail to capture object semantics. Furthermore, they employ huge models with inefficient mechanisms such as attention. Motivated by these observations, we propose a new end-to-end generative-based multi-stage architecture for image inpainting. Specifically, our model exploits the segmentation labels predictions to robustly reconstruct the object boundaries and avoid blurry or semantically incorrect images. Meanwhile, it employs edges predictions to recover the image structure. Different than previous approaches, we do not predict the segmentation labels/edges from the corrupted image. Instead, we employ a coarse image that contains more valuable global structure data. We conduct a set of extensive experiments to investigate the impact of merging these auxiliary pieces of information. Experiments show that our computationally efficient model achieves competitive qualitative and quantitative results compared to the state-of-the-art methods on multiple datasets.     

Diving deeper into underwater image enhancement: A survey

The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time; hence, a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, (1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and (2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before.We first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented, including architecture, parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.