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Proposed intelligence systems based on digital Forensics: Review paper

Materials Today: Proceedings 2023年 80卷 2647-2651页

作者： Ismael Sahib, Hussein Qahatan AlSudani, Mustafa Hasan Ali, Mohammed Qassim Abbas, Haydar Moorthy, Kohbalan Mundher Adnan, Myasar Faculty of Computer Science and Information Technology UniversitiTun Hussein Onn Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The field of information security, in general, has seen shifts a traditional approach to an intelligence system. Moreover, an increasing of researchers to focus on propose intelligence systems and framework based on the forensic case studies because of the limitations of traditional methods such as analysis intensive data manually, intelligence visualization to make the evidence more understandable and intelligence system for store data. However, most of these intelligence systems are still facing different limitations. Furthermore, the primary goal of this work analysis popular intelligence system that was used based on forensic. Moreover, propose new algorithms and hybrid model which it's achieved good results in dif-ferent other fields to develop the forensic systems in the future. © 2021

关键词： Digital forensics

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Understanding Perceived Academic Stress among Filipino Students during COVID-19 using Machine Learning 23

Understanding Perceived Academic Stress among Filipino Stude...

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24th Annual Conference on information technology Education, SIGITE 2023

作者： Geronimo, Sheila M Hernandez, Alexander A Abisado, Mideth B Rodriguez, Ramon L Nova, Avonn C Caluya, Susan S Blancaflor, Eric B College of Computing and Information Technologies National University Philippines College of Computer Studies and Systems University of the East Manila Philippines School of Information Technology Mapua University Makati Philippines

ISBN: (纸本)9798400701306

Stress is everyone's concern in the academic life of students. However, stress elevates due to several factors, including academic demands, family, and social realities. More recently, due to the COVID-19 pandemic, several universities shifted to online modality, which may contribute to students' academic stress considering its complex requirements. To date, no study has been conducted to examine the perceived academic stress of students in Philippine higher education institutions. Thus, this paper aims to develop stress prediction models using machine learning through a classification approach based on student's perceived academic stress (PAS) scale survey responses. Results show that most students have high to very high perceived academic stress in both male and female respondents. Also, results show that decision tree (91%), naïve bayes (94%), support vector machine (95%), logistics regression (90%), and k-nearest neighbors (91%) have comparable classification performance. Furthermore, this study found that the difficulty of examination, unrealistic expectations from the teacher, examination time, excessive workload, and weakness of character contribute to the perceived academic stress of students. This study confirms the potential of machine learning to classify students' academic stress. This work presents some contributions and practical recommendations on academic stress experienced by students. © 2023 ACM.

关键词： Students

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Refining Large Language Models for Tabular Data Analysis in Business Domain by Laymen Text

Refining Large Language Models for Tabular Data Analysis in ...

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2024 International Research Conference on Smart computing and systems Engineering, SCSE 2024

作者： Nirusanan, Thavachelvam Prasanth, Senthan Banujan, Kuhaneswaran Kumara, Samantha Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Belihuloya Sri Lanka Australia

ISBN: (纸本)9798350375688

Organizations face various challenges when analyzing tabular data. One of the key challenges is the complexity of business data analytics tasks. These tasks involve handling large volumes of data, organizing and structuring it, and extracting valuable insights. Additionally, employees who work in financial analysis often encounter a significant workload on tools such as Excel, SAP, PowerBI, and Tabula. This workload can result in increased effort and time required to analyze and make sense of the data. Organizations must address these challenges to ensure efficient and effective analysis of tabular data. Organizations spend more money to perform business tasks. Nowadays, there are many efficient models in artificial intelligence to perform text, audio, video, and image-based tasks, but no efficient models are available to perform tabular-based tasks specifically. Pandas Python library provides various functionalities and APIs that are useful for business data analysis. This research solved the problem with tabular data using Python Pandas code generation. Here, the researchers used two datasets, each with 50 records. The Large Language Model (LLM) is a supervised Learning Pre-Trained Foundation Model (SSL PFM) category based on text generation. The SSL PFM helps the language models learn the context of language and world knowledge. During this research, models such as LLaMA-2, Falcon, CodeLlama, and Mistral were considered for analysis. Each of these models consists of a Billion parameters. Moreover, Quantization techniques were incorporated to reduce model size, enabling models to load with minimal hardware. After quantizing the model, Parameter Efficient Fine Tuning (PEFT) trains the dataset using only a few model layers;other layers are frozen. Well-known experts with Pandas evaluate the fine-tuned models of chosen language models. Finally, mistral-7B produced prominent results in analyzing business tasks and producing summarized results. © 2024 IEEE.

关键词： Data Analytics

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Experimental Evaluation of Distributed k-Core Decomposition

arXiv

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arXiv 2024年

作者： Guo, Bin Zhao, Runze Department of Computing & Information Systems Trent University Peterborough Canada Department of Computing & Software McMaster University Hamilton Canada

Given an undirected graph, the k-core is a subgraph in which each node has at least k connections, which is widely used in graph analytics to identify core subgraphs within a larger graph. The sequential k-core decomposition algorithm faces limitations due to memory constraints and data graphs can be inherently distributed. A distributed approach is proposed to overcome limitations by allowing each vertex to independently do calculation by only using local information. This paper explores the experimental evaluation of a distributed k-core decomposition algorithm. By assuming that each vertex is a client as a single computing unit, we simulate the process using Golang, leveraging its Goroutine and message passing. Due to the fact that the real-world data graphs can be large with millions of vertices, it is expensive to build such a distributed environment with millions of clients if the experiments run in a real-life scenario. Therefore, our experimental simulation can effectively evaluate the running time and message passing for the distributed k-core decomposition. © 2024, CC BY.

关键词： Message passing

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Performance of VM in SDN-Assisted Cloud Data Center with Working Vacations 7

Performance of VM in SDN-Assisted Cloud Data Center with Wor...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Barik, Lalbihari Dash, Bibhuti Bhusan Ranjan Mishra, Manoj Rout, Suchismita De, Utpal Chandra Shekhar Patra, Sudhansu King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia KIIT Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350341485

The Internet of Things (IoT)'s exponentially growing network traffic has a significant impact on energy utilization and network performance. A recommended power-saving traffic control approach for cloud data centers uses virtual machines (VM) vacation. The essential idea is that the (VM) in the cloud system may go on vacation through the utilization of software-defined network (SDN) paradigm. The reduction of active VMs while upholding QoS standards is the aim for the QoS optimization. The M/M/1 queue model with working vacations (WV) and customer requests impatience behavior is examined in this research study. This study considers the possibility that the customer's impatience is caused by a WV. The requests to be served at the cloud data center by the VMs are at slow rate than normal during WVs and are prone to get impatient. An "impatience timer"that follows exponential distribution is activated whenever customer's request reached to the cloud system and discovers that the VM is on WV. A customer leaves the queue and does not come back if their service is not finished before their timer runs out. The system's numerous performance parameters such as average system size, the sojourn time, the percentage of customer's request being served, and the abandonment frequency are calculated. Finally, some numerical findings are obtained to show how some specific parameters affect the system performance indicators. © 2023 IEEE.

关键词： Energy utilization

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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Computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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How Do HCI Researchers Study Cognitive Biases? A Scoping Review 25

How Do HCI Researchers Study Cognitive Biases? A Scoping Rev...

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2025 CHI Conference on Human Factors in computing systems, CHI 2025

作者： Boonprakong, Nattapat Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia School of Computer Science and Engineering University of New South Wales SydneyNSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

computing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour. © 2025 Copyright held by the owner/author(s).

关键词： Real time systems

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Intelligent System to Maximize Customer Experience in Restaurants 5

Intelligent System to Maximize Customer Experience in Restau...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Jayalal, R.U.A. Nimeshika, Y.E. Samarasekara, G.N.N. Wijerathna, G.R.C.I. Gamage, Narmada Gunathilake, Poojani Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

With technological improvements in the restaurant industry, competition is fierce, and a restaurant's success depends on providing excellent and luxury customer satisfaction. The food industry has had to continuously advance with technology in order to adequately satisfy the wants of people leading increasingly luxurious lives. While some restaurants still rely on manpower, the majority are eager to advance with technology in order to improve the standard of their offerings and enhance their customers' experiences. In order to optimize customer satisfaction and experience, this research project intends to investigate the potential of intelligence in the restaurant business. This research has explored intelligent systems such as digital self-service menus for ordering food, augmented reality (AR) visualization systems for customers, the observation of chefs in the kitchen, and the usage of robots to serve meals. This research paper analyzes the advantages of automating restaurant operations and how to do so to enhance customer satisfaction. Overall, this study has the potential to transform the restaurant industry and increase customer satisfaction. © 2023 IEEE.

关键词： Augmented reality

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Supporting Artefact Awareness in Partially-Replicated Workspaces

Supporting Artefact Awareness in Partially-Replicated Worksp...

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2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Poh, Emran Tang, Anthony Lee, Jeannie S. Shengdong, Zhao National University of Singapore NUS-HCI Lab School of Computing Singapore Singapore Management University School of Computing and Information Systems Singapore Singapore Institute of Technology Centre for Immersification Infocomm Technology Cluster Singapore

ISBN: (纸本)9798350328912

Using Cross Reality (CR) approaches for remote collaboration will often result in partially-replicated workspaces. Here, workspace artefacts are not equally accessible - i.e. a physical artefact may only be manipulated by one collaborator - and in general, the artefacts become desynchronised over time. In this paper, we introduce a framework for artefact awareness that can help collaborators maintain an understanding of each others' manipulations with workspace artefacts. We illustrate our design explorations through sketches, and outline how we aim to study the effectiveness and utility of artefact awareness in cross reality remote collaboration. In our work, we expect to show that effectively supporting artefact awareness will help make cross-reality remote collaboration smoother. © 2023 IEEE.

关键词： Computation theory

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Code Based Selected Object-Oriented Mechanisms Identification 10

Code Based Selected Object-Oriented Mechanisms Identificatio...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Jablonický, Kristián Lang, Ján Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia

This article deals with select mechanisms that are provided by the object-oriented programming language Java. Using these mechanisms is crucial for a proper approach to object-oriented programming. However, this is often difficult for many programmers that are new to writing code in the said language. This paper looks at these mechanisms, defines them by quoting various sources, and proposes a solution that spots these described mechanisms in code. It does so by reading source code from .java files inputted by its user, gathering information about the defined and used classes, interfaces, methods, etc. With this information gained it outputs data which would often require a larger context of the code and good knowledge of the files’ contents. Thanks to the strict definitions and rules, that will be stated later in this paper, the application can consistently spot these mechanisms in code and inform its user about the fact. As a result, the application can help programmers that are new to object-oriented programming languages by ensuring them, that they implemented these mechanisms correctly, or by informing them about their absence. The core mechanisms described in this paper are inheritance, encapsulation, class association, and polymorphism. © 2023 Copyright for this paper by its authors.

关键词： class association encapsulation inheritance Java object-oriented polymorphism programming refactoring

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