Development Of Computer Systems Research Articles

Leveraging multiplexed metasurfaces for multi-task learning with all-optical diffractive processors

Abstract Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80 % for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems.

Analysis of etched drain based Cylindrical agate‐all‐around tunnel field effect transistor based static random access memory cell design

AbstractThis paper aims to propose a novel method for designing an static random access memory (SRAM) cell using an etched drain based Cyl GAA TFET with a hetero‐substrate material and an elevated density strip. The aim is to reduce power dissipation and improve stability, as demonstrated through analysis utilizing static noise margin (SNM) as well as N‐curve methods. With respect to the 16 nm MOSFET based SRAM cell, the proposed device‐based SRAM cell shows significant improvements with a 68.305% reduction in leakage power, a 15.58% increase in static voltage noise margin (SVNM), an 8.623% increase in static current noise margin (SINM), an 8.152% increase in write trip voltage (WTV), a 12.86% increase in write trip current (WTI), a 27.62% increase in static power noise margin (SPNM), and a 19.95% increase in write trip power (WTP). The design is implemented and analyzed using Cadence Virtuoso software, and a novel approach of look up tables and Verilog A is utilized for the device to circuit application. These results indicate promising advancements in the design of SRAM cells, which could have significant implications for the development of advanced computer systems.

The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres.

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole-proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole-proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole-proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems.

Prospects of Computational Intelligence in Society: Human-Centric Solutions, Challenges, and Research Areas

The future of society could be shaped by artificial intelligence (AI), which is a part of soft computing that deals with probabilistic reasoning, neural networks, fuzzy logic, and evolutionary algorithms. This paper focuses on issues that are faced by soft computing as it moves towards human-centric solutions, outlines the challenges for soft computing, and provides possible research directions for development. Currently, there is an increasing emphasis on human-centered solutions in various fields, such as the healthcare industry, transportation sector, financial institutions, and education systems in the world today. Using soft computing techniques in this way comes with its own advantages, especially when managing unclearness or ambiguity or unsureness existing within humans' decision-making processes. Soft computing based on using neural networks may help to develop computer systems capable of faking human behavior and preferences as they evolve over time by using fuzzy logic and evolutionary algorithms. This human-centric approach helps improve the user experience while creating more symbiotic ties between technology and society, leading to examples such as personalized medicine treatment plans, among others. Soft computing’s future in society will be, to a large extent, determined by the provision of human-friendly AI while addressing the ethical, legal, and social challenges of its applications. Rights on AI represent humanity and shall be accountable. Collaborating with interdisciplinary scientists and carrying out frontier studies on developing human-friendly AI systems from ethical, legal, and social perspectives and welfare has the potential to have an influence on community and policy-maker decisions, facilitate a suitable climate for soft computing and AI in society, leverage it for empowering individuals, communities, and societies, and to improve the quality of human life while respecting human values and dignity. It also paves the way for society to be more inclusive and equitable.

Knowledge transfer, not just true beliefs: virtue epistemology as a philosophical basis for education

Introduction. Modern pedagogical practice is being reformed due to systemic changes in the ways of social interaction. Knowledge transfer is being significantly modified under the influence of technical innovations, although it remains traditionally one of the main tasks of the educational process. The development of artificial intelligence, neural networks and other analytical computer systems shows that propositional knowledge is not the privilege of the human mind. This state of affairs changes the idea of knowledge as such. In this regard, the participants of the educational process face the task of revising the philosophical basis of modern learning in terms of finding a new definition of knowledge. It is also necessary to develop new methodological recommendations that would correspond to modern epistemological theories of knowledge, belief and understanding. Materials and research methods. The article uses the methods of conceptual analysis and comparative study characteristic of philosophy. The research materials are sources on theory of knowledge, education and also on applied epistemology. Results. Virtue epistemology is one of the most actively developing theories of knowledge in contemporary philosophy and humanities. It is based on the idea that knowledge cannot be an accidental belief, but must be the merit of a cognitive agent. This approach solves many problems of the philosophical theory of knowledge and creates a subjectoriented view of the nature of the cognitive process. This attitude allows us to use virtue epistemology in debates about applied issues, including those related to the explanation of the tasks and content of education. Discussion. A feature of the analysis within virtue epistemology is attention to cognitive skills (reliabilism) and sustained intellectual dispositions (responsibilism) in their relationship to the process of formation of true beliefs. Virtue epistemology defines knowledge through the notion of merit in the manifestation of subjective qualities, so the student's motivation and the context of belief formation should be taken into account when developing specific methodological recommendations. Such an approach explains why standardized testing cannot serve as the sole and primary means of assessing learners. At the same time, the general semantic peculiarity of the notions of virtue allows us to appeal to examples of individuals as ideal types of certain qualities (this is called exemplarism). At the same time, it is necessary to explain why these traits are the excellences that make their bearer better in epistemic terms. Conclusion. In explaining the knowledge transfer, pedagogy can be based on the philosophical principles of virtue epistemology. Such integration enriches both disciplines: pedagogy gets a conceptual scheme adequate to the modern social and technological situation, and epistemology gets a practical application.

Building a fuzzy model for determining the level of social well-being of the population

This paper considers objects that affect the social security of the country. The complexity of such objects makes the development of computer systems in sociological research a difficult algorithmic task because of information uncertainty. Human thinking is based on inaccurate, approximate data, the analysis of which makes it possible to formulate clear decisions. In practice, there are usually no precise mathematical models that describe social objects. In such cases, it is advisable to use fuzzy mathematics as a tool for solving this problem. The main advantage of this approach compared to other artificial intelligence methods is the ability to interpret the results obtained. To assess the level of social well-being of the population, we used the mathematical apparatus of fuzzy set theory and fuzzy inference. The study is based on the OECD Better Life Index, which was developed by the Organization for Economic Cooperation and Development (OECD) to help countries assess and improve the quality of life of their citizens. In the course of the study, a fuzzy inference system was built to measure the social well-being of the population based on the indicators of the OECD Better Life Index. Since determining the level of social well-being is a complex task, a hierarchical structure with two main groups of social well-being indicators was constructed to simplify it. The resultant system evaluates each social indicator included in the OECD’s Better Life Index. Using the fuzzy inference model built, it was possible to assess the social well-being of the country’s population in a simple and transparent way in comparison with the OECD member countries. The results of the study make it possible to understand which indicators of social well-being of the country’s population are desirable or need to be improved in the future

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.

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