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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rui Zhou Wenqiang Pu Licheng Zhao Ming-Yi You Qingjiang Shi Sergios Theodoridis Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China School of Software Engineering Tongji University Shanghai China Department of Electronics Systems Aalborg University Denmark Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece

A fundamental problem in cognitive radio is spectrum sensing, which detects the presence of the primary users in a licensed spectrum. To boost the detection performance and robustness, the multiantenna detector has been investigated and various related methods have been developed, e.g., the energy detector, the eigenvalue arithmetic-to-geometric mean detector, and the generalized likelihood ratio test detector. Cooperative sensing, which makes use of multiple receivers distributed in different locations, has the advantage of being able to make full use of the distributed antennas and enjoy a high spatial diversity gain. However, the successful employment of cooperative sensing depends on the reliable information exchange among the cooperating receivers over a long range, which may be impractical for real-world scenarios. In this paper, we consider the scenario where each receiving node can only broadcast its received raw data in a short-range communication fashion. We propose a novel cooperative sensing scheme by allowing each node to send to the fusion center only local correlation coefficients, computed within a neighborhood. A detection algorithm, based on matrix factorization of the partially received sample covariance matrix, i.e., with missing entries, is proposed. The performance of our proposed cooperative scheme is verified via numerical experiments.

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Don’t Forget the Exception! : Considering Robustness Changes to Identify Design Problems

Don’t Forget the Exception! : Considering Robustness Change...

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International Mining software Repositories, MSR

作者： Anderson Oliveira João Correia Leonardo Sousa Wesley K. G. Assunção Daniel Coutinho Alessandro Garcia Willian Oizumi Caio Barbosa Anderson Uchôa Juliana Alves Pereira Informatics Department – Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro Brazil Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States Institute of Software Systems Engineering Johannes Kepler University (JKU) Linz Austria Federal University of Ceará (UFC) Itapajé Brazil

Modern programming languages, such as Java, use exception-handling mechanisms to guarantee the robustness of software systems. Although important, the quality of exception code is usually poor and neglected by developers. Indiscriminate robustness changes (e.g., the addition of empty catch blocks) can indicate design decisions that negatively impact the internal quality of software systems. As it is known in the literature, multiple occurrences of poor code structures, namely code smells, are strong indicators of design problems. Still, existing studies focus mainly on the correlation of maintainability smells with design problems. However, using only these smells may not be enough since developers need more context (e.g., system domain) to identify the problems in certain scenarios. Moreover, these studies do not explore how changes in the exceptional code of the methods combined with maintainability smells can give complementary evidence of design problems. By covering both regular and exception codes, the developer can have more context about the system and find complementary code smells that reinforce the presence of design problems. This work aims to leverage the identification of design problems by tracking poor robustness changes combined with maintainability smells. We investigated the correlation between robustness changes and maintainability smells on the commit history of more than 160k methods from different releases of 10 open-source software systems. We observed that maintainability smells can be worsened or even introduced when robustness changes are performed. This scenario mainly happened for the smells Feature Envy, Long Method, and Dispersed Coupling. We also analyzed the co-occurrence between robustness and maintainability smells. We identified that the empty catch block and catch throwable robustness smells were the ones that co-occurred the most with maintainability smells related to the Concern Overload and Misplaced Concern design problems. T

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State-based Sandbox Tool for Distributed Malware Detection with Avoid Techniques

State-based Sandbox Tool for Distributed Malware Detection w...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Pavlo Rehida George Markowsky Anatoliy Sachenko Oleg Savenko Department of Computer Engineering and Information Systems Khmelnytskyi National University Khmelnytskyi Ukraine Department of Software Engineering and Game Development Kennesaw State University Marietta USA Department of Informatics Kazimierz Pulaski University of Technology and Humanities Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technologies are described. Various anti-emulation techniques that are used in modern malware considered. Obfuscation as one primary approach to hide malware malicious actions described and discussed. State of emulator is presented, and the advantages of its usage are covered. Distributed model for malware detection is considered. Basic emulator and its current capabilities presented. Prepared files that represent malware are described. Experimental results for developed files that differs with included avoid techniques are presented. Disadvantages of proposed approach is described. Future research and sandbox improvement are described.

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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The Significance of Artificial Intelligence in Contemporary Cinema

The Significance of Artificial Intelligence in Contemporary ...

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Trends in Electrical, Electronics, Computer engineering Conference (TEECCON)

作者： Aneesh Pradeep Atabek Satmuratov Iqbol Yeshbayev Oripov Khasan Melikuziev Iqboljon Agzamov Daniyor Software Engineering New Uzbekistan University Tashkent Uzbekistan Mechanical Engineering New Uzbekistan University Tashkent Uzbekistan Department of Philosophy Samarkand State University Samarkand Uzbekistan Sound Directing and Cinematography State Institute of Art and Culture of Uzbekistan Tashkent Uzbekistan Music Sound Direction and Informatics State Conservatory of Uzbekistan Tashkent Uzbekistan

Artificial intelligence (AI) has emerged as a potent force that is transforming numerous industries. AI technologies are revolutionizing the creation, production, and consumption of motion pictures. This study examines the impact of AI on narrative, production techniques, visual effects, and audience experiences in contemporary cinema. Filmmaking techniques have been revolutionized by AI technologies. It investigates AI applications in fields such as cinematography, editing, visual effects, and post-production, highlighting AI-powered tools and techniques that have improved efficiency, creativity, and visual aesthetics in contemporary cinema. The paper also discusses the broader implications of AI in the film industry, such as changes in production processes, economic implications, and its impact on film distribution, marketing, and audience targeting. Ethical and societal issues pertaining to AI-generated content are discussed, as is its impact on employment and job positions in the film industry. This study seeks to shed light on the opportunities, challenges, and implications of AI’s presence in the contemporary cinematic landscape by analyzing the integration of AI in these areas.

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HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses

arXiv

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arXiv 2023年

作者： Ma, Caoyuan Liu, Yu-Lun Wang, Zhixiang Liu, Wu Liu, Xinchen Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China National Yang Ming Chiao Tung University Taiwan The University of Tokyo Japan National Institute of Informatics Japan School of Information Science and Technology University of Science and Technology of China China JD Explore Academy

We present HumanNeRF-SE, a simple yet effective method that synthesizes diverse novel pose images with simple input. Previous HumanNeRF works require a large number of optimizable parameters to fit the human images. Instead, we reload these approaches by combining explicit and implicit human representations to design both generalized rigid deformation and specific non-rigid deformation. Our key insight is that explicit shape can reduce the sampling points used to fit implicit representation, and frozen blending weights from SMPL constructing a generalized rigid deformation can effectively avoid overfitting and improve pose generalization performance. Our architecture involving both explicit and implicit representation is simple yet effective. Experiments demonstrate our model can synthesize images under arbitrary poses with few-shot input and increase the speed of synthesizing images by 15 times through a reduction in computational complexity without using any existing acceleration modules. Compared to the state-of-the-art HumanNeRF studies, HumanNeRF-SE achieves better performance with fewer learnable parameters and less training time. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Review of techniques in faceted search applications

Review of techniques in faceted search applications

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2020 International Symposium on Networks, Computers and Communications, ISNCC 2020

作者： Mahdi, Mohammed Najah Ahmad, Abdul Rahim Ismail, Roslan Subhi, Mohammed Universiti Tenaga Nasional Institute of Informatics and Computing in Energy Selangor Malaysia Universiti Tenaga Nasional College of Computing Informatics Department of Computing Selangor Malaysia Universiti Tenaga Nasional Institute of Informatics and Computing in Energy Department of Software Engineering College of Computing and Informatics Selangor Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electric and Electronic Engineering Selangor Malaysia

ISBN: (纸本)9781728156286

In this article, we present a faceted search with new strategies that reward users with multiple dimensions or filters to scan. This is to allow users to identify the categories that they want and access the required data quicker, but they also contribute to the overall data fee. The cycle will continue but that does not mean that we should (or might) stop trying to improve our knowledge collection, distribution, and management methods. That is why we believe that filtering information overload is an extremely important issue for better decisions. Although there are numerous search engines, most of them cannot always make the search engines accessible details. To obtain information about a certain subject, searchers must perform inquiries. This information overload problem can be solved by exploratory search technologies. It is transparent and has many apps that can enable users to learn important information and increase their knowledge of the mission. The main contribution of this paper is to examine previous exploratory-based research, the technology and existing implementations used, such as faceted search as a method in which filters are built to restrict the search results. © 2020 IEEE.

关键词： Search engines

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Multi-model Fusion Solution for IEEE UV 2022 'Vision Meets Algae' Object Detection Challenge 6

Multi-model Fusion Solution for IEEE UV 2022 'Vision Meets A...

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6th IEEE International Conference on Universal Village, UV 2022

作者： Peng, Xiaoxiao Wang, Yueyi Chen, Dayu Tian, Yuchen Huang, Keyu Zheng, Jianfeng Robot Technology Co. Ltd Department of Automatic Driving Software Engineering Shanghai China King's College London Department of Informatics London United Kingdom Didi Chuxing Department of Algorithm Strategy Hangzhou China Institute of Advanced Technology University of Science and Technology of China Anhui China Koto Communication Technology Co. Ltd Department of Algorithm Strategy Xiamen China

ISBN: (纸本)9781665474771

This report summarizes the fourth-place solution of the 'Vision Meets Algae' object detection challenge held on IEEE UV'2022 focuses on object detection in marine biology images obtained through the microscope. First, we experimented with a large number of backbones and necks to improve mAP by enhancing the model structure. Then, we designed and tested a variety of data augmentation schemes based on algal characteristics from a data perspective. Finally, with multiple models ensembled adopted, our methods achieve 57.579% mAP on the test set. © 2022 IEEE.

关键词： Algae

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Towards Generating Structurally Realistic Models by Generative Adversarial Networks

Towards Generating Structurally Realistic Models by Generati...

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Conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Abbas Rahimi Massimo Tisi Shekoufeh Kolahdouz Rahimi Luca Berardinelli Institute of Business Informatics - Software Engineering Johannes Kepler University Linz Austria MDSE Research Group University of Isfahan Isfahan Iran IMT Atlantique LS2N (UMR CNRS 6004) Nantes France School of Arts University of Roehampton London United Kingdom

Context. Several activities in model-driven engineering (MDE), like model transformation testing, would require the availability of big sets of realistic models. However, the commu-nity has failed so far in producing large model repositories, and the lack of freely available industrial models has been raised as one of the most important problems in MDE. Consequently, MDE researchers have developed various tools and methods to generate models using different approaches, such as graph grammar, partitioning, and random generation. However, these tools rarely focus on producing new models, considering their realism. Contribution. In this work, we utilize generative deep learning, in particular, Generative Adversarial Networks (GANs), to present an approach for generating new structurally realistic models. Built atop the Eclipse Modeling Framework, the proposed tool can produce new artificial models from a metamodel and one big instance model as inputs. Graph-based metrics have been used to evaluate the approach. The preliminary statistical results illustrate that using GAN s can be promising for creating new realistic models.

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Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Naeem, J. Gul, O.M. Parlak, I.B. Karpouzis, K. Salman, Y.B. Kadry, S.N. Department of Computer Engineering Bahcesehir University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Engineering Galatasaray University Istanbul Turkey Panteion University of Social and Political Sciences Greece Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform in depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms. © 2024 J. Naeem et al., licensed to EAI.

关键词： COVID-19

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