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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Wu, Zhijing Xu, Hua Fang, Jingliang Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China China School of Information Science and Engineering Hebei University of Science and Technology China

ISBN: (纸本)9781955917766

Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge. In this paper, MA-MRC, a continual MRC model with uncertainty-aware fixed Memory and Adversarial domain adaptation, is proposed. In MA-MRC, a fixed size memory stores a small number of samples in previous domain data along with an uncertainty-aware updating strategy when new domain data arrives. For incremental learning, MA-MRC not only keeps a stable understanding by learning both memory and new domain data, but also makes full use of the domain adaptation relationship between them by adversarial learning strategy. The experimental results show that MA-MRC is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Continuous time systems

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A survey of script learning

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Frontiers of information technology & Electronic engineering 2021年第3期22卷 341-373页

作者： Yi HAN Linbo QIAO Jianming ZHENG Hefeng WU Dongsheng LI Xiangke LIAO Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410000China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410000China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China

Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible *** learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw ***,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script *** research field contains three main topics:event representations,script learning models,and evaluation *** each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative *** also discuss the current state of the research and possible future directions.

关键词： Script learning Natural language processing Commonsense knowledge modeling Event reasoning

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Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

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computer systems Science & engineering 2023年第5期45卷 1759-1773页

作者： Manar Ahmed Hamza Haya Mesfer Alshahrani Sami Dhahbi Mohamed K Nour Mesfer Al Duhayyim ElSayed M.Tag El Din Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of *** spite of this,it is challenging to design energy-efficient *** routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of *** order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network *** this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for *** aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in *** accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution ***,the inclusion of DE in traditional AOA helps in overcoming local optima *** addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and *** order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.

关键词： Wireless sensor network routing multihop communication arithmetic optimization algorithm fitness function

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Empowering Cybersecurity: Unveiling the Art of Identifying and Thwarting Malicious Tactics Within the Sandbox Environment

Empowering Cybersecurity: Unveiling the Art of Identifying a...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON

作者： Tharaniyawarma Kumaralingam Sanika K. Wijayasekara Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka CIET - SLIIT Discovery Lab Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In order to combat typical cyber-security concerns, malware analysis is essential. As highly sophisticated malware develops, it calls for a technique to install and study behavioral software properties. In order to lessen the impact on current infrastructure, environments that are isolated from production systems have historically been created using virtual machines. Malware developers usually create evasion tactics to avoid detection in sandbox environments since they are aware of this. This research delved into examining anti-evasion mechanisms to assess their effectiveness in detecting malware attempting to hide within a conventional sandbox setting. In the same way as ticking time bombs count down the seconds until they delete data or get new commands from internet, for example, worms, viruses, and other malware may do the same. Here a novel virtual-machine-based analytic technique for automatically establishing a malware piece is described, or when events take place, so that rest of the analysis may identify the events risk. When responding to quickly spreading malware, this information may be very helpful, and self-driven identification can give more correct data with shorter wait time considered to painstaking manual investigation. A challenging research issue is developing an automated system that constructs a malware's chronology. In this study, implementation of the detection of timers without assuming the integrity of the kernel of the compromised system, which is a crucial part of such a system is provided.

关键词： Production systems computer viruses Weapons Manuals Malware Telecommunications Kernel

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Image Classification Analysis to Identify the Tourism Potential Sukabumi on Instagram Using A Deep Learning Approach

Image Classification Analysis to Identify the Tourism Potent...

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International Conference on Informatics and Computing (ICIC)

作者： Falentino Sembiring Rieska Rahayu Ayuningsih Muhamad Muslih Muhammad Andy Maulana Gina Wahdayani Futri Mohd Zainuri Saringat Rachma Siva Ainunnisa Anggun Fergina Information Systems Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This research focuses on image classification, grouping images based on shared characteristics. The study aims to identify the highest-potential category within various fields, from high to low potential. By understanding existing potential, researchers can inform targeted strategies for future development. The methodology combines Logistic Regression in Orange Data Mining to enhance classification accuracy. Hierarchical Clustering is used to analyze 7,777 image results. Findings reveal that Tourism (C3) has the most potential, followed by Culinary (C2) and Culture (C4), while Handcrafts (C1) exhibit the least potential. In conclusion, logistic regression successfully identifies influential aspects of Sukabumi through Instagram images, aiding potential development

关键词： Deep learning Logistic regression Accuracy Web sites Multimedia communication Data mining Cultural differences Informatics Image classification

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Enhancing Traffic engineering Policy in SDN Addressing TCAM limitations using Bloom Filters 4

Enhancing Traffic Engineering Policy in SDN Addressing TCAM ...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Ghosh, Akash Barik, Lalbihari Rout, Suchismita Rawat, Bhupender Singh Jena, Prakash Chandra Patra, Sudhansu Shekhar School of Computer Applications KIIT Deemed To be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia College of Smart Computing COER University Roorkee India Department of Computer Sc and Engg. Hi-Tech College of Engineering Bhubaneswar India

ISBN: (纸本)9798350305258

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches. © 2023 IEEE.

关键词： Bloom Filter False positive IP addressing multi-hashing Prefix match SDN TCAM Traffic engineering

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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

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computers, Materials & Continua 2022年第10期73卷 1637-1648页

作者： Rajneesh Pareek Karthikeyan Rajagopal Himanshu Sharma Nidhi Gour Arun Kumar Sami Althahabi Haya Mesfer Alshahrani Mohamed Mousa Manar Ahmed Hamza Department of Computer Science&Engineering JECRC UniversityJaipur303905India Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia Department of Electronics and Communication JECRC UniversityJaipur303905India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multi and single carrier waveforms are utilized in cellular systems for high-speed data *** The Fifth Generation(5G)system,several waveform techniques based on multi carrier waveforms are ***,the Peak to Average Power Ratio(PAPR)is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the *** proposed article documents the study,progress,and implementation of PAPR reduction algorithms for the 5G radio *** compare the PAPR algorithm performance for advanced and conventional *** simulation results reveal that the advanced Partial Transmission Sequence(PTS)and Selective Mapping(SLM)methods enhanced the throughput and gain of the 5G ***,we have also analyzed the performance of Orthogonal Time Frequency Space Modulation(OTFSM)based on a single carrier system and found that PAPR is significantly low and is best suited to fading *** is seen that the conventional algorithms lower the PAPR but increase the *** proposed PTS and SLM have shown good performance with low computational complexity.

关键词： PAPR 5G OFDM NOMA OTFSM PTS SLM

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Object Identification and Localization of Visual Explanation for Weapon Detection

Object Identification and Localization of Visual Explanation...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Yuenyong, Sumeth Wongpatikaseree, Konlakorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Mahidol University Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Weapon detection is a difficult task that requires accurate identification of weapon objects in images. The object localization approach is mostly used because it combines a gradient with a convolutional layer to create a map of key locations on the image. This paper presents an Eigen-CAM method to localize and detect objects in an image for a Faster Region-based Convolutional Neural Network (Faster R-CNN) residual neural network (ResNet 50) model, giving a visual explanation. The Internet Movie Firearms Database (IMFDB) was used to train a deep learning model with the Faster R-CNN ResNet 50 model of the pre-trained PyTorch framework. Experimental results indicated that the Faster R-CNN ResNet 50 model achieved the highest mAP of 0.497 with 0.5 IoU. The Eigen-CAM method performed effectively for visual image representation. © 2022 IEEE.

关键词： Convolutional neural networks

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An Analysis of Machine Learning Algorithms for Smart Healthcare systems 1

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Alduailij, Mai Mishra, Anupama Zamzami, Ikhlas Fuad Psannis, Konstantinos Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Faculty of Business King Abdulaziz University Rabigh Saudi Arabia University of Macedonia Thessaloniki Greece

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

The epidemic taught us to keep digital health records. It also showed us how wearable observing gear, video conferences, and AI-powered chatbots may give good treatment remotely. Real-time data from health care devices across the globe helped attack and track the infection. Biomedical imaging, sensors, and machine learning have improved health in recent years. Medical care and biomedical sciences are now information science sectors that demand enhanced data mining approaches. Biomedical data have high dimensionality, class irregularity, and few tests. AI uses information and computations to imitate how people learn, continually improving its accuracy. ML is an important part of information science. Calculations utilising measurable methods reveal essential information about information mining operations. This paper explains and compares ML algorithms that can detect diseases sooner. We summarise ML’s algorithms and processes to extract information for a data-driven society. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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