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Information technology (ACIT)

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

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

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.

关键词： Hands Java Source coding Programming Software Libraries Generators Security Information technology Testing

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

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computer science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Samaneh Madanian Sherry Dai Priyadarshini Natarajan Hamidreza Rasoulipanah Dept. of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large.

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Sentiment Analysis on the Elevated Expressway in Bangladesh: A Machine Learning and Deep Learning Approach

Sentiment Analysis on the Elevated Expressway in Bangladesh:...

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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Samiha Maisha Jeba Rajib Khan Md Shoaib Ahmed Tanjim Taharat Aurpa Satya Ranjan Dash Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Department of Computer Science Boise State University Boise Idaho USA Dept. of Educational Technology Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh School of Computer Applications KIIT University Odisha India

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

User comments on social media or any video streaming platforms show how people feel about something, including different opinions, feelings, and problems of people interacting with it. It can assist people and organizations in making wellinformed choices by offering insightful information on how people feel about a topic or issue. The Elevated Expressway is a crucial infrastructure in Bangladesh and a controversial project that has sparked public debate and criticism. The 46.73 km road project connecting Hazrat Shahjalal International Airport to Kutubkhali on the Dhaka-Chittagong Highway has eased the commute time from the Airport to Farmgate from over an hour to 10 minutes. Regarding this project, People's comments on YouTube as a digital platform for voicing their opinions and contributing to a shared narrative that goes beyond traditional media. In this work, we examine users' sentiments about Elevated Expressway from YouTube comments, employing various Machine Learning (ML) algorithms and Deep Learning (ML) architecture. Based on these analyses, the BERT architecture arises as the most satisfying model, with an accuracy of 84.34% and a macro f1 score of 80.14% for classifying public sentiment as positive, negative, or neutral.

关键词： Deep learning Sentiment analysis Video on demand Social networking (online) Roads Organizations Airports Transformers Web sites Stakeholders

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Utilizing an Ensemble Classification Method to Assess the Severity of SQL Injection Attacks and XSS 12

Utilizing an Ensemble Classification Method to Assess the Se...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Chauhan, Vineet Kumar Kumar, Awadhesh Kumar, Suresh Singh, Trilok Dwivedi, Rakesh Kumar Galgotias College of Engg. & Tech Dept. of Information Technology Greater Noida India Kamla Nehru Institute of Technology Dept. of Computer Science & Engg. Sultanpur India School of Computing Graphic Era Hill University Haldwani India Ccsit Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9798350369861

In this paper, we present two interconnected security methodologies for enhancing database and web application protection. The first method focuses on safeguarding sensitive data, including identity information, passwords, and financial records in databases. It utilizes an ensemble classification algorithm trained on 22 features from queries containing malicious code to perform realtime analysis of SQL queries, enabling the detection and classification of SQL injection attacks with an accuracy exceeding 98%. These attacks are further categorized as simple, unified, or lateral, with tailored responses such as query blocking and source IP address banning. The second approach addresses the prevalent challenge of Cross-Site Scripting (XSS) attacks, notorious for their impact on web security. Diverging from traditional XSS detection methods, our innovative approach employs ensemble learning through Bayesian networks enriched with domain knowledge and threat intelligence. The results are presented in a user-friendly format through a node sorting technique, offering valuable insights into the nature of the attack. Experimental validation with real-world data demonstrates the method's effectiveness, particularly as attack volumes increase. Furthermore, the sorted node results empower security teams with actionable information to proactively manage the evolving XSS threat landscape. Together, these strategies reinforce cybersecurity defenses, providing robust protection against a diverse and ever-evolving range of cyber threats. © 2023 IEEE.

关键词： Bayesian networks

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The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias

arXiv

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arXiv 2024年

作者： Lakkaraju, Kausik Gupta, Aniket Srivastava, Biplav Valtorta, Marco Wu, Dezhi AI Institute University of South Carolina ColumbiaSC United States Dept. of Computer Science Netaji Subhas University of Technology Delhi India Dept. of Computer Science and Engineering University of South Carolina ColumbiaSC United States Dept. of Integrated Information Technology University of South Carolina ColumbiaSC United States

Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that output polarity and emotional intensity when given a piece of text as input. Like other AIs, SASs are also known to have unstable behavior when subjected to changes in data which can make them problematic to trust out of concerns like bias when AI works with humans and data has protected attributes like gender, race, and age. Recently, an approach was introduced to assess SASs in a blackbox setting without training data or code, and rating them for bias using synthetic English data. We augment it by introducing two human-generated chatbot datasets and also considering a round-trip setting of translating the data from one language to the same through an intermediate language. We find that these settings show SASs performance in a more realistic light. Specifically, we find that rating SASs on the chatbot data showed more bias compared to the synthetic data, and round-tripping using Spanish and Danish as intermediate languages reduces the bias (up to 68% reduction) in human-generated data while, in synthetic data, it takes a surprising turn by increasing the bias! Our findings will help researchers and practitioners refine their SAS testing strategies and foster trust as SASs are considered part of more mission-critical applications for global use. © 2024, CC BY-NC-ND.

关键词： Sentiment analysis

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Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework

arXiv

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arXiv 2023年

作者： Li, Hongru Yu, Wentao He, Hengtao Shao, Jiawei Song, Shenghui Zhang, Jun Letaief, Khaled B. Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications. Most existing deep learning (DL)-based task-oriented communication systems adopt a closed-world scenario, assuming either the same data distribution for training and testing, or the system could have access to a large out-of-distribution (OoD) dataset for retraining. However, in practical open-world scenarios, task-oriented communication systems need to handle unknown OoD data. Under such circumstances, the powerful approximation ability of learning methods may force the task-oriented communication systems to overfit the training data (i.e., in-distribution data) and provide overconfident judgments when encountering OoD data. Based on the information bottleneck (IB) framework, we propose a class conditional IB (CCIB) approach to address this problem in this paper, supported by information-theoretical insights. The idea is to extract distinguishable features from in-distribution data while keeping their compactness and informativeness. This is achieved by imposing the class conditional latent prior distribution and enforcing the latent of different classes to be far away from each other. Simulation results shall demonstrate that the proposed approach detects OoD data more efficiently than the baselines and state-of-the-art approaches, without compromising the rate-distortion tradeoff. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Open Interface System Protocols for Space Based Power Systems

Open Interface System Protocols for Space Based Power System...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Ayodele A Periola Innocent E. Davidson French-South African Institute of Technology Dept. of Electrical Electronic and Computer Engineering Cape Peninsula University of Technology South Africa

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

Renewable energy systems have been recognized to play an important role in future environmentally friendly energy systems. An important source of renewable energy is solar power derived when solar panels intercept with incident solar radiation. The degradation effect of cloud obstruction has led to the consideration of space-based solar power systems. In space-based solar power systems, satellites with large solar panel payload transmit solar energy to earth-based rectennas. The deployment of multiple space based solar power satellites is necessary to ensure that more energy is derived from space based solar power. However, the realization of such a system requires the design of communication protocols between in-orbiting space-based power satellites. The use of such a system enables multiple receiving antenna ground stations to concurrently receive power from solar power satellites. The presented research aims to present the design of system protocols that enable the realization of this goal, considering heterogeneous solar power satellites and operational conditions. This is carried out while considering communications between in-orbiting space-based solar power satellites, along with satellite rectennas. Furthermore, performance analysis and investigations show that the use of the proposed solution enhances power accessibility and reduces deficit by an average of 18%.

关键词： Renewable energy sources Satellites Protocols Rectennas Prototypes computer architecture Solar energy Power systems Solar panels Solar radiation

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Automated Management of Attribute-Based Policies for Access Control Using Tag-Matching

Automated Management of Attribute-Based Policies for Access ...

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Annual Conference of Industrial Electronics Society

作者： Alex Chiquito Ulf Bodin Olov Schelén Dept. of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

Data sharing is becoming increasingly important as organizations seek to improve their operations and gain a competitive advantage. The data sharing between organizations, stakeholders, and even internal teams requires access control policies that define who can access what data, under what circumstances, and for what purposes. Attribute-based access control (ABAC) provides a flexible and fine-grained mechanism for enforcing such policies, preventing data leakage, and improving security and compliance. A challenge is that these policies should be able to support the agility and adaptability of constantly evolving modern industrial systems, where new data sources, services, and users are frequently added and removed. As the number of data sources and associated policies grows, the manual management of ABAC policies in evolving systems becomes a bottleneck, which prevents the adoption of fine-grained access control mechanisms. This paper presents a model based on tag-matching to automate the process of connecting new data sources and users to existing access control policies. In the proposed model, tag-matching means matching metadata elements to policy attributes to reduce the administrative work of managing access policies. The model takes advantage of the identity abstraction of ABAC policies to connect existing rules between attributes and the target resource or user. We present a proof-of-concept implementation of the tag-matching model in Eclipse Arrowhead and provide an evaluation of the proposed solution.

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Forward and Backward Warping for Optical Flow-Based Frame Interpolation 4

Forward and Backward Warping for Optical Flow-Based Frame In...

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4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Shimizu, Joi Sun, Heming Katto, Jiro Waseda University Dept. of Computer Science and Communications Engineering Tokyo Japan Waseda University Waseda Research Institute for Science and Engineering Tokyo Japan JST PRESTO Saitama Kawaguchi Japan

ISBN: (纸本)9781665458184

Frame interpolation methods generate intermediate frames by taking consecutive frames as inputs. This enables the generation of high frame rate videos from low frame rate videos. Recently, many deep learning-based frame interpolation methods have been proposed. One way of frame interpolation is by using the bi-directional optical flow. In many cases, these methods use backward warping to warp the input images to the desired frame. However, forward warping can also be used to warp the input frames. In this paper, we propose a frame interpolation method that utilizes both forward warping and backward warping. Experimental results show that utilizing both warping methods can enhance the performance compared to only using backward warping. © 2022 IEEE.

关键词： Optical flows

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Towards Adoption of Green Blockchain with Emphasis on Blockchain Type, Consensus Protocols, Data Sharding and Smart Contracts

Towards Adoption of Green Blockchain with Emphasis on Blockc...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Shraddha Phansalkar Deepa Mishra Neha Chaube Rahul Sonkamble Dept. of Computer Science & Engineering MIT Art Design and Technology University Pune Maharashtra

Blockchain and related Distributed Ledger Technologies (DLT) are anticipated to transform a the world of web from a centralised document-sharing platform to a comprehensive decentralised platform that facilitates the exchange of digital currency and supports autonomous management of digital assets. The central server is susceptible to attacks, distrust and collusion. If the web can assure reliable, safe, and responsible updates among independent participants without the need for a centralized server, the perception of a decentralised web can be re-instantiated. One of the essential technologies required to restore the openness of the internet while maintaining its security is distributed ledger technology (DLT). DLTs may now totally handle business and legal transactions online, creating a more trustworthy and accountable environment. Blockchain technology marks a major breakthrough by removing the need for a centralised trusted authority in a widely distributed network. Instead, a consensus must be reached among several sources of trust, based on an algorithm, that this transaction may be believed to be legitimate. The consensus algorithms in blockchain technology offer an immutable and permanent record of a transaction that is immutable, trustworthy and secured. Consensus algorithms are however energy consuming because of their computation heavy nature. This has been a biggest inhibition towards blockchain adoption. The energy needs for committing a blockchain transaction is also governed by whether it is a public/permissioned blockchain, its consensus algorithm, onchain-offchain data and the code complexity of smart contracts. The paper presents a state-of art evaluation of the current blockchain platforms and cryptos and evaluate them with the energy consumptions. Additionally, it also proposes a framework architecture of a green blockchain application. A green blockchain refers to the implementation of environmentally sustainable algorithms, tools and platforms

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