Development Of Computer Systems Research Articles

Identification of digital device hardware vulnerabilities based on scanning systems and semi-natural modeling

Objectives. The development of computer technology and information systems requires the consideration of issues of their security, various methods for detecting hardware vulnerabilities of digital device components, as well as protection against unauthorized access. An important aspect of this problem is to study existing methods for the possibility and ability to identify hardware errors or search for errors on the corresponding models. The aim of this work is to develop approaches, tools and technology for detecting vulnerabilities in hardware at an early design stage, and to create a methodology for their detection and risk assessment, leading to recommendations for ensuring security at all stages of the computer systems development process.Methods. Methods of semi-natural modeling, comparison and identification of hardware vulnerabilities, and stress testing to identify vulnerabilities were used.Results. Methods are proposed for detecting and protecting against hardware vulnerabilities: a critical aspect in ensuring the security of computer systems. In order to detect vulnerabilities in hardware, methods of port scanning, analysis of communication protocols and device diagnostics are used. The possible locations of hardware vulnerabilities and their variations are identified. The attributes of hardware vulnerabilities and risks are also described. In order to detect vulnerabilities in hardware at an early design stage, a special semi-natural simulation stand was developed. A scanning algorithm using the Remote Bitbang protocol is proposed to enable data to be transferred between OpenOCD and a device connected to the debug port. Based on scanning control, a verification method was developed to compare a behavioral model with a standard. Recommendations for ensuring security at all stages of the computer systems development process are provided.Conclusions. This paper proposes new technical solutions for detecting vulnerabilities in hardware, based on methods such as FPGA system scanning, semi-natural modeling, virtual model verification, communication protocol analysis and device diagnostics. The use of the algorithms and methods thus developed will allow developers to take the necessary measures to eliminate hardware vulnerabilities and prevent possible harmful effects at all stages of the design process of computer devices and information systems.

Development of Computational and Experimental Imaging Sensing System for Tokamak Plasma Optical Boundary Reconstruction

Development of Computational and Experimental Imaging Sensing System for Tokamak Plasma Optical Boundary Reconstruction

Comparative analysis of single-board computers for the development of a microarchitectural computing system for fire detection

Objectives. The purpose of the work is to select the basic computing microplatform of the onboard microarchitectural computing complex for the detection of anomalous situations in the territory of the Republic of Belarus from space on the basis of artificial intelligence methods.Methods. The method of comparative analysis is used to select a computing platform. A series of performance tests and comparative analysis (benchmarking) are performed on the selected equipment. The methods of comparative and benchmarking analysis are performed in accordance with the terms of reference to the current project.Results. A comparative analysis and performance testing of Raspberry Pi 4 Model B and Cool Pi 4 Model B single-board computers, as well as AI-accelerator Google Coral USB Accelerator with Google Edge TPU have been performed. The comparative analysis showed that Raspberry Pi 4 Model B and Cool Pi 4 Model B fully meet the terms of reference to the current project. At the same time Cool Pi 4 Model B handles neural network calculations well, but four times slower than similar calculations on Google Coral USB Accelerator. Neural network computations on the Raspberry Pi 4 Model B are 22 times slower than similar computations on the Google Coral USB Accelerator. Cool Pi 4 Model B outperforms Raspberry Pi 4 Model B by the factor of two to three for data copying and compression and almost six times faster for neural network computations.Conclusion. Despite the fact that Raspberry Pi 4 Model B meets the terms of reference to the project as a computational basis, when developing an on-board microarchitectural computing system for detecting anomalous situations, it is worth using more powerful alternatives with built-in AI-accelerators (e.g., Radxa Rock 5 Model A) or with an additional external AI-accelerator (e.g., a combination of Cool Pi 4 Model B and Google Coral USB Accelerator). Using a Raspberry Pi 4 Model B with an additional AI-accelerator is also acceptable and will speed up computations by several dozen times. AI-accelerators provide the fastest neural network computations, but there are features related to the novelty of the technology that will be explored in further development.

A Web Knowledge-Driven Multimodal Retrieval Method in Computational Social Systems: Unsupervised and Robust Graph Convolutional Hashing

Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs; 2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit; and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised and robust graph convolutional hashing</i> (URGCH). The specific implementations are as follows: first, a “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secondary semantic self-fusion</i> ” approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking; second, a “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive computing</i> ” approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features; Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\%$</tex-math> </inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.7\%$</tex-math> </inline-formula> in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS.

Bismuth-based ferroelectric memristive device induced by interface barrier for neuromorphic computing

The development of the Internet of Things (IoT) not only facilitates our lives but also dramatically grows data. Artificial synaptic devices appear to represent an emerging physical solution compared to the existing computational architectures. Memristive devices are of great interest with their high integration and low power consumption in the field of synaptic devices. In this work, we demonstrate short-term plasticity (STP) and long-term plasticity (LTP) by regulating synaptic weights with different stimulus pulses. Moreover, the application in neuromorphic computing is further exhibited by image recognition with an accuracy of 95.2 % under the modified National Institute of Standards and Technology database. Therefore, Nd-doped Bi4Ti3O12 memristors, as emerging artificial synaptic devices, are expected to achieve breakthroughs in artificial intelligence and promote the development of neuromorphic computing and intelligent systems.

Contribution of Artificial Intelligence to Learning the Arabic Language

The knowledge revolution in the middle of the twentieth century brought about a digital transformation in various aspects of human development, and in view of the knowledge data in machine language, it became necessary to computerize the Arabic language as a means of transferring knowledge, and to make it compatible with modern means of communication in artificial intelligence algorithms to help humans overcome the difficulties of communication and learning. Therefore, researchers in the field of artificial intelligence sought to understand human perception and try to simulate it through the development of computer systems that deal with different levels of languages. Researching a descriptive and analytical reading of the reality of the Arabic language in its relationship to artificial intelligence and computing, and the extent to which it benefits from technological and informational progress, as well as possible horizons that will contribute to solving its problems and challenges.

Integrating NLP Chatbot into Career Guidance Web Application

Artificial Intelligence (AI) is the development of computer systems that can perform tasks that typically require human intelligence. The development of an advanced web-based chatbot system involves the integration of various technologies and methodologies to create a seamless conversational interface. Leveraging HTML, CSS, and JavaScript for frontend development, alongside Python Flask framework for backend functionality, the project aims to deliver a robust and intuitive user experience. Key to the system's operation is the incorporation of PyTorch for machine learning, allowing for the implementation of sophisticated natural language processing (NLP) techniques. Through tokenization and stemming using NLTK (Natural Language Toolkit), the system enhances its understanding of user queries, enabling it to provide relevant and contextually appropriate responses. At the core of the system lies a supervised learning model trained on a custom dataset compiled from user interactions. This model utilizes bag-of-words representations and neural network architectures to predict responses based on input text. By continuously refining its predictions through feedback mechanisms, the system improves its accuracy and responsiveness over time, ensuring a more tailored and effective user experience. Voice input functionality, facilitated by speech recognition technology, further enhances user interaction by allowing users to communicate with the chatbot using spoken commands. This feature expands the accessibility and convenience of the system, catering to a broader range of users with varying preferences and abilities. In addition to its conversational capabilities, the system prioritizes security and privacy, implementing robust user authentication mechanisms and secure data storage practices. By adhering to industry best practices and standards, the project aims to instill trust and confidence in users regarding the confidentiality and integrity of their data.User interface design and usability are also key considerations in the development process, with careful attention given to creating an intuitive and visually appealing interface. Through thoughtful design choices and user feedback, the system seeks to optimize the user experience and streamline the interaction flow, ensuring that users can easily navigate and utilize its features. Overall, the project endeavors to create an intelligent and user-friendly chatbot system that enhances communication and interaction between users and technology. By leveraging advanced technologies and methodologies, the system aims to provide personalized assistance and support to users, ultimately improving productivity, efficiency, and satisfaction in various domains and applications

Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review

ABSTRACT Artificial intelligence (AI) is the development of computer systems that can do tasks that normally require human intelligence. A number of dental specialties, including pediatric dentistry, now use AI and its subsets, machine learning, and deep learning. The evolution of AI in healthcare has been linked to the creation of AI applications meant to support medical professionals in diagnosing patients and choosing the best course of treatment. AI is the capability of robots to learn and use that information to carry out a range of cognitive tasks, including language processing, learning, reasoning, and making decisions—basically imitating human behavior. This review gives an overview of the numerous applications of AI that are beneficial to pediatric dentistry.

Voice based E-Mail System

Abstract: The development of computer systems has made communication much simpler. Many communication features, like texting and voice calling, are available on a mobile device. We have put forth a system that will benefit people with physical disabilities. This utility allows voice to text conversion as well as text to voice conversion. With the help of this technology, we won't be able to access anything at all using keyboards—we'll only be able to utilise speech and mouse clicks. This system is also accessible to the average individual for reading purposes. It is easy to use and effective as well.

HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System

Considerable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the echo chamber effect. However, existing studies still lack the ability to effectively support the key processes of news information consumption and quantitatively identify a political stance towards the information. In this paper, we present HearHere, an AI-based web system designed to help users accommodate information and opinions from diverse perspectives. HearHere facilitates the key processes of news information consumption through two visualizations. Visualization 1 provides political news with quantitative political stance information, derived from our graph-based political classification model, and users can experience diverse perspectives (Hear). Visualization 2 allows users to express their opinions on specific political issues in a comment form and observe the position of their own opinions relative to pro-liberal and pro-conservative comments presented on a map interface (Here). Through a user study with 94 participants, we demonstrate the feasibility of HearHere in supporting the consumption of information from various perspectives. Our findings highlight the importance of providing political stance information and quantifying users' political status as a means to mitigate political polarization. In addition, we propose design implications for system development, including the consideration of demographics such as political interest and providing users with initiatives.

The Impact of Artificial Intelligence on Education

Artificial Intelligence (AI) is an increasingly progressive field that aims to develop computer systems capable of producing inventive and creative content, including text, images, music, video, and audio, that closely resemble human-generated material. AI has significantly impacted education, with both positive and negative implications. While it has gained widespread popularity, it has also raised concerns about bias, misinformation, misuse, and risk, emphasising the need for the responsible implementation and development of generative AI in education. There have been discussions about whether it should be prohibited or whether teachers and students should receive adequate training to use it effectively and ethically. The purpose of AI in education should be to embrace the opportunities it presents while maintaining high academic standards. Renowned universities have developed guidelines and manuals for the responsible use of generative AI tools. This study examines the impact of generative AI on education, analysing its advantages and disadvantages in schools, its outcomes, and how teachers and students can use it for educational purposes. The study also emphasizes the effective use of AI in education, employing qualitative and quantitative methods to evaluate its usage. The results highlight the benefits and drawbacks of generative AI in education, concluding with recommendations and future applications of generative AI in education.

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Recent advancements in deep learning and video analysis, combined with the efficiency of contemporary computational resources, have catalyzed the development of advanced real-time computational systems, significantly impacting various fields. This paper introduces a cutting-edge video analysis framework that was specifically designed to bolster security in museum environments. We elaborate on the proposed framework, which was evaluated and integrated into a real-time video analysis pipeline. Our research primarily focused on two innovative approaches: action recognition for identifying potential threats at the individual level and trajectory extraction for monitoring museum visitor movements, serving the dual purposes of security and visitor flow analysis. These approaches leverage a synergistic blend of deep learning models, particularly CNNs, and traditional computer vision techniques. Our experimental findings affirmed the high efficacy of our action recognition model in accurately distinguishing between normal and suspicious behaviors within video feeds. Moreover, our trajectory extraction method demonstrated commendable precision in tracking and analyzing visitor movements. The integration of deep learning techniques not only enhances the capability for automatic detection of malevolent actions but also establishes the trajectory extraction process as a robust and adaptable tool for various analytical endeavors beyond mere security applications.

An Empirical Study on the Role of Artificial Intelligence in Human Capital Management

The world began to change and adapt accordingly to the dynamic technological advances. As same as the application of advanced technology in the present day is known as Artificial Intelligence (AI) which is referred to as the development of computer systems that perform tasks typically involving human intelligence. AI is being observed to be applied to various fields of business, especially to Human Resources being one important wing among the list. AI interferes with learning, reasoning, problem-solving, and understanding natural language. AI is increasingly used in human resources to help drive decisions in employee hiring, retention, and development. It can also be applied to automate tasks like payroll, but it’s being also used for the rapid creation of new policies, contracts, job descriptions, interview questions, etc. This empirical study is conducted to illuminate the concepts, impact, role, and recent trends of AI in Human capital management. Hence, an overview of the scope of AI in Human capital management has been built in the study.

ARTIFICIAL INTELLIGENCE (AI) IN MARKETING

The computer industry that focuses on creating and developing computer systems that reproduce aspects of human behaviour to signify scaled primitive brainpower is known as artificial intelligence. A management process that involves providing clients with goods and services is referred to as marketing. It is based on a corporate philosophy that looks at client needs and satisfaction from a strategic standpoint. The goal of artificial intelligence is to replicate intelligence, which consists of elements that support reasoning, knowledge acquisition, and response to environmental changes. It is a nexus between computer science, cognitive science, philosophy, neuroscience, linguistics, and engineering scientists. The application of artificial intelligence is typically limited to specialized equipment or computers. KEY WORDS: AI – Artificial Intelligence, ML - Machine Learning , R&amp;D-Research and development

Cybercrimes: An Emerging Category of Offenses within the Frame of the International Criminal Court Jurisdiction

In our interconnected society, crime persists, demanding joint efforts by national and international authorities to detect, prevent, and prosecute criminal activities. Jurisprudential evolution mandates lawmakers to possess a comprehensive legal vision, adapting laws to changing social contexts and emerging criminal methods, especially in technology, including computer technology and Artificial Intelligence (AI). This article comments on the escalating vulnerability of sovereign states and their economies to cyber-attacks. The radical evolution of computer systems has led to new modes of aggression, targeting not only traditional legal assets but also individuals familiar with advanced technology. The emergence of "cyberwarfare" prompts inquiries into potential categorizations within international legal frameworks. Recent global conflicts highlight the potential classification of cyber-attacks on critical infrastructure as war crimes or acts of aggression, urging the International Criminal Court (ICC) to consider incorporating cybercrimes into its core interests. While normative references may lack in existing conventions, the Martens Clause emphasizes treating attacks using technology as equivalent to conventional means. Article 51 of the United Nations Charter implies that cyber weapons could be deemed equivalent to conventional weapons under international law. The article stresses the importance of education and advanced training for legal personnel skilled in identifying cybercrime perpetrators, challenging the ICC to recruit or train individuals with the necessary legal and technical expertise for effective cybersecurity responses. The article briefly explores challenges in conceptualizing and categorizing cybercrimes within existing legal frameworks. The intersection of law and technology necessitates harmonious collaboration between legal and technical experts, acknowledging the intricate web of cyberspace and the implications of cyber threats on global stability and security. In conclusion, the article advocates a fundamental shift in the approach to justice, recognizing the ICC's imperative evolution in addressing cybercrimes. Integrating cybercrimes into the ICC's purview aligns with international law principles, emphasizing the equivalence of cyber weapons to conventional arms. Collaboration between legal and technical experts is essential in navigating the complexities of cybercrimes, ensuring accountability, and upholding justice in the digital age. The article concludes by highlighting the proactive role of the ICC in shaping the future of global justice amid emerging cyber threats.

Athena – The NSF AI Institute for Edge Computing

AbstractThe National Science Foundation (NSF) Artificial Intelligence (AI) Institute for Edge Computing Leveraging Next Generation Networks (Athena) seeks to foment a transformation in modern edge computing by advancing AI foundations, computing paradigms, networked computing systems, and edge services and applications from a completely new computing perspective. Led by Duke University, Athena leverages revolutionary developments in computer systems, machine learning, networked computing systems, cyber‐physical systems, and sensing. Members of Athena form a multidisciplinary team from eight universities. Athena organizes its research activities under four interrelated thrusts supporting edge computing: Foundational AI, Computer Systems, Networked Computing Systems, and Services and Applications, which constitute an ambitious and comprehensive research agenda. The research tasks of Athena will focus on developing AI‐driven next‐generation technologies for edge computing and new algorithmic and practical foundations of AI and evaluating the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use‐inspired research. The researchers of Athena demonstrate a cohesive effort by synergistically integrating the research outcomes from the four thrusts into three pillars: Edge Computing AI Systems, Collaborative Extended Reality (XR), and Situational Awareness and Autonomy. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its domestic and international collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships.

Deep Learning Improves Accuracy of Laparoscopic Imaging Classification for Endometriosis Diagnosis

Purpose: The purpose of this research was the development and evaluation of an intelligent assistive classification system for the recognition of endometriosis and the improvement of the accuracy of laparoscopic imaging in diagnosis. The research was developed with the use of deep learning approaches. Methods: Data from 4448 laparoscopy images were used in a retrospective chart analysis. The data were divided into two folders, healthy and pathological including 2157 healthy and 2291 pathological images. Based on simple clinical and imaging information and criteria such as the diagnosis of endometriosis (included in an open-source dataset GLENDA of Kaggle repository), data mining algorithms were used to improve laparoscopic imaging accuracy. Results: The final developed computer system based on the ResNet50 algorithm predicted the best outcome for all participants who had laparoscopic surgical therapy. The Keras tool was used and the generated code was implemented in Python programming language providing a mean accuracy of &gt;95%. Conclusion: The intelligent approach revealed better performance than the commonly used imaging criteria in predicting endometriosis improving the time and the total accuracy of diagnostic approaches.

Counterintelligence, Artificial Intelligence and National Security: Synergy and Challenges

Counterintelligence (CI) and Artificial Intelligence (AI) represent two distinct yet interconnected domains that play pivotal roles in safeguarding National and International Security. On the first hand, CI involves activities and measures taken to identify, prevent and counter any Intelligence activities of hostile entities, such as spying, sabotage and information gathering. On the other hand, AI refers to the development and use of computer systems that can perform tasks that typically require human intelligence, such as learning, reasoning and problem-solving. Subsequently, in the ever-evolving landscape of global security, the rise of AI has ushered in a new era of CI practices. The present paper delves into the intersection of CI and AI, exploring the profound impact of AI on the CI processes and how it is transforming National Security strategies, highlighting at the same time the fields of mutually influence. Ultimately, underscores the imperative of harnessing AI's potential to strengthen CI efforts in an ever-evolving threat landscape. Plus, it investigates the ethical concerns and privacy implications associated with AI in CI emphasizing the imperative of responsible AI development and deployment. Finally, through comprehensive international case studies, offers insights into how United States, China, Russia and Israel have integrated AI into their Intelligence and CI strategies, shedding light on the diverse approaches and challenges faced by different countries. Summarizing, the paper underscores the potential synergy between AI and CI, while also acknowledging the formidable challenges it presents, such as privacy concerns and adversarial AI. Striking a balance between harnessing AI's power and safeguarding national interests remains a pivotal task for policymakers and intelligence agencies in the ever-evolving landscape of national security.

Computer Vision for Parkinson's Disease Evaluation: A Survey on Finger Tapping.

Parkinson's disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up.

Cybercrime in the context of the digital age: analysis of threats, legal challenges and strategies

The current period of social evolution is commonly referred to as the information stage of society’s development or the digital age. The introduction of the most advanced tools significantly speeds up the process of obtaining, processing and analyzing data. Wide and prompt access to information resources increases the efficiency of their use, which becomes an integral part of managing all institutions and processes. Various forms of crime are emerging globally as science and information technology evolve. The emergence and development of cybercrime is taking place, in particular, due to the globalization of the information environment and the rapid development of computer systems around the world. Currently, information technologies accompany people in various spheres of their activities, and accordingly, their use in committing criminal offences is increasing. In connection with the abovementioned, the issues of legal support for information security occupy a specific place in the public relations spectrum. The topic of cybersecurity in the era of information technology is increasingly spreading in the world community nowadays. After all, because of technological development, the number of cybercrimes that disrupt the activities of states, their institutions and a large number of private companies is rapidly increasing, with institutions and organizations incurring significant costs and disrupting their normal operation. The purpose of the academic paper is to define the concept and to review the classifications of cybercrime, taking into account the features of its commission and counteraction to this type of abuse in the context of digital society.