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Software Code De-Compilation Techniques and Approaches: A Comparative Study 25

Software Code De-Compilation Techniques and Approaches: A Co...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Fawareh, Hamed Al-Smadi, Yazan Hassan, Mohammad Fawareh, Faid AlNoor Elrashidi, Ali Zarqa University Dept. of Software Engineering Jordan Zarqa University Dept. of Computer Science Jordan Zarqa University Dept. Of Mathmitical Science Jordan University of Business and Technology Dept. of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

De-compilation is the process of executable files as input where the de-compilation process tries to get away the source code, or the majority of it, if the file's source code is lost or damaged for whatever cause. De-compilation has many uses such as code recovery, security testing, and errors corrections, however, de-compilation is cautious since it depends on certain approaches and techniques. Nowadays de-compilation done by many automated and semi-automated tools, but the accuracy and the efficiency of it depend on the proposed approaches and techniques to stand in front of reengineering challenges. This paper aims to highlight up-to-date prior contributions in the field where 8 published research papers from May 2020 until May 2023 were collected to investigate the state-of-art. As a result, some of the researchers tend to develop their own tools and others tend to use popular automated with different approaches and techniques where the majority of the researchers focused on C, C++, and JAVA programming languages where 50% were de-compilation on C language. © 2024 IEEE.

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Resume Parser and Job Description Matcher 8

Resume Parser and Job Description Matcher

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Resume Parser and Job Description Matcher is an innovative project aimed at enhancing the efficiency and accuracy of job candidate screening and matching processes. Leveraging natural language processing (NLP) techniques, particularly using SpaCy and machine learning models, the system parses resumes and job descriptions to extract relevant skills, experience, and qualifications. By analyzing textual data and applying advanced algorithms, such as Support Vector Machines (SVM), the system learns to match candidates with job postings based on keyword relevance and contextual understanding. Our paper demonstrates improved matching results compared to traditional methods, facilitating streamlined recruitment processes, and enhancing workforce management strategies. © 2024 IEEE.

关键词： Support vector machines

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Factors Affecting Mental Health of High School Students due to COVID-19: An Exploratory Study

Factors Affecting Mental Health of High School Students due ...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Reza, Md. Rokonuzzaman Islam, Muhammad Nazrul Islam, Mohian Mumu, Afnan Alauddin Faisal, Faiz Al The International University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh LTIMindtree Ltd. Washington United States Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Mental health is one of the most significant factors in the human life span. It also influences how we interact with people, manage stress, and make good decisions. From childhood and youth through maturity, mental health is crucial at every stage of life. In the era of the COVID-19 pandemic, it becomes more vulnerable and affects teenagers as others. In this study, high school students' (10-19 years old) mental health states and the most contributing factors that affect their mental health were assessed through a statistical and machine learning-based approach. The study data were collected through an online survey and received responses from 158 respondents. The data were analyzed statistically with a chi-square test and path analysis considering all the possible factors of anxiety and depression to determine the most contributing factors. Besides, a few Machine Learning (ML) algorithms have been developed such as Logistic Regression(LR), Support Vector Machine(SVM), Naive Bayes(NB), AdaBoost(AB), and Random Forest(RF) for the prediction of anxiety and depression and found that AdaBoost achieved the highest score (86%). As outcomes, the overall analysis revealed that academic concerns such as poor understanding of online classes, daily activities like sleep pattern disruption, and extended uses of electronic devices are the major causes of the deterioration of high school students' mental health. © 2023 IEEE.

关键词： COVID-19

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Emotion Classification to Augment the Emotional Intelligence of Autistic Individuals 5

Emotion Classification to Augment the Emotional Intelligence...

引用

5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Guruvammal, S. Kesavamoorthy, R. Anju, M.I. Chellatamilan, T. Jegatha Deborah, L. Jain University Dept. of Cse Kanakapura India Cmr Institute of Technology Dept. of Cse Bengaluru India Srm Institute of Science and Technology Dept. of Ece Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India University College of Engineering Computer Science and Engineering Tindivanam India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

The collaboration of machine learning (ML) algorithms and cognitive psychological frameworks supports the enhancement of emotional intelligence. In light of Plutchik's emotional wheel, this study investigates how to identify human emotions. By utilizing 24 different emotions, 8 high-intensive (secondary) emotions are identified. We employed 9 different classification algorithms K-nearest neighbors (KNN), decision trees (DT), naïve bayes (NB), support vector machine (SVM), Logistic regression, random forest (RF), artificial neural network (ANN), gradient boosting machines (GBM), and bagging classifier (BC) to identify the emotions accurately and the performance of them are compared. This research uses the labelled dataset consisting of 11 columns as similar word 1, similar word 2, typical sensation 1, typical sensation 2, opposite intensity low, opposite intensity medium, opposite intensity high, tells, helps, emotion and cluster. © 2024 IEEE.

关键词： Random forests

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Lightweight Authentication System Based on Zero Knowledge Proof for IoT Devices 2

Lightweight Authentication System Based on Zero Knowledge Pr...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Khandait, Ashish Umesh Pattanshetti, Tanuja R. Coep Technological University Pune Dept. of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350388916

In the rapidly expanding field of the Internet of Things, ensuring secure and efficient communication between IoT devices and servers is crucial. This paper henceforth designs a lightweight authentication system under the Zero Knowledge Proof (ZKP) protocol, utilizing the HMAC hashing algorithm for the improvement of security measures put in place. This system is designed to enable the authentication of IoT devices without sending their actual password, thus avoiding risks due to password interception at the point of authentication. For instance, if using the phrase "God is great"as the password, one can get it hashed into HMAC with this method and still be assured that it is secure even if proof is intercepted. Simulations in the simulator shall evaluate the system's effectiveness and performance. Performance metrics include accuracy, response time, memory consumption, latency, and throughput. The presented results intend to serve lightweight device authentication mechanisms for IoT devices such that the trustworthiness of IoT devices is achieved without hampering their performance. The presented results are also shown to outperform conventional methods. © 2024 IEEE.

关键词： Authentication Protocol

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

引用

2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Bui, Thanh Duc Panda, Brajendra University of Arkansas Dept. of Computer Science and Computer Engineering Fayetteville United States

ISBN: (纸本)9798350327595

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset. © 2023 IEEE.

关键词： Cloud Computing Cyber Security Data Mining Machine Learning

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Identification and Segmentation of Polyp Using Deep Learning Approaches 4

Identification and Segmentation of Polyp Using Deep Learning...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Yaligar, Suman Kallur, Shanta S. Patil, Vijeeta Thotad, Puneeth N. K. L. E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K. L. E. Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Colorectal cancer (CRC) is a major health problem, and accurate detection of polyps plays a major role in its prevention and treatment. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the rise in deep learning, more and more rigorous segmentation of medical images dependent on deep learning networks have emerged, making significant progress in this field. This paper aims to develop an efficient model for polyp detection and segmentation using machine learning techniques. The model will assist clinicians in accurately locating and segmenting polyp regions, which will help in the prevention and treatment of CRC. The work will first review traditional methods that are dependent manually on extracted features and deep segmentation algorithms, then detail benchmark datasets relevant for the topic. The two datasets Kvasir-SEG and CVC-ClinicDB are used for implementation. Specifically, we carried out a comprehensive evaluation of recent deep learning CNN methods. The model gave an accuracy with Pranet method of about 99% in polyp segmentation and identification respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Semi-automatic performance support robot that can attach and detach guitars 13

Semi-automatic performance support robot that can attach and...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tsurumi, Kazuki Marutsuka, Ryo Koutaki, Gou Kumamoto University Dept. of Computer Science and Electrical Engineering Kumamoto860-8555 Japan

ISBN: (纸本)9798350355079

Research has primarily focused on fully automatic instrument-playing robots. However, it is difficult to support the experience of playing musical instruments for people with physical disabilities or those who are inexperienced in playing musical instruments. To address this issue, we studied a "semi-automatic"instrument-playing robot based on human-robot collaboration. Our research specifically focused on the guitar, which are stringed instruments with complex pressed strings. The developed robot enables individuals with physical disabilities and those without musical experience to easily play musical instruments. Additionally, the instruments can be easily removed without causing damage. © 2024 IEEE.

关键词： User centered design

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Polygenic Risk Scores are Associated to Aβ-τ Protein Accumulation in Alzheimer's Disease 3

Polygenic Risk Scores are Associated to Aβ-τ Protein Accum...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Treccani, Irene Dolci, Giorgio Brusini, Lorenza Cruciani, Federica Galazzo, Ilaria Boscolo Menegaz, Gloria University of Verona Dept. of Engineering for Innovation Medicine Verona Italy University of Verona Dept. of Computer Science Verona Italy

ISBN: (纸本)9798350378009

Alzheimer's (AD) is a complex and multi-domain neurodegenerative disease characterized by amyloid-β (Aβ) and proteins aggregation holding both genotype and phenotype signatures. Increasing evidence suggests that the accumulation of -τ proteins plays a crucial role in disease onset and progression. In order to shade light on these issues, in this study we exploited the Partial Least Squares model to investigate the link between neuroimaging-derived features and genetic factors. Cortical thickness and Polygenic Risk Scores were used as pheno/geno-typing features, respectively. The model was trained on a cohort of subjects including healthy controls (CN) and AD patients, and tested on a discovery set consisting of - τ - and+τ+individuals. The working hypothesis being that these last two groups with different biological profiles would cluster closer to the CN and AD classes, respectively. In agreement with previous results, segregation was observed in the latent space between CN and AD and was also confirmed when applying the model to the - - and++discovery set. These outcomes, together with a significant association found among the respective projection weights, encourage further investigation. © 2024 IEEE.

关键词： Neuroimaging

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Adaptive Forward Error Correction for Voice over IP with Random Linear Network Coding 2

Adaptive Forward Error Correction for Voice over IP with Ran...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Su, Yi-Chiuan Wu, Quincy National Chi Nan University Dept. of Computer Science and Information Engineering Nantou Taiwan

ISBN: (纸本)9798331530099

This paper proposes the use of Random Linear Network Coding (RLNC) as an erasure code in the forward error correction (FEC) mechanism for Internet telephony. It also designs and implements a forward error correction algorithm applied to the Real-time Transport Protocol (RTP), which we call Adaptive Systematic RLNC (AS-RLNC). The algorithm learns the current packet loss rate of the network through the Real-time Transport Control Protocol (RTCP) and dynamically adjusts the number of redundant packets sent. Compared with traditional forward error correction methods based on block codes, this approach can enhance the reliability of data transmission and improve audio quality by 31%. © 2024 IEEE.

关键词： Internet telephony

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