Computer Research Articles

U.S. vs. Russia: Cyber «Drang nach Osten»

In June 2022, the commander of U.S. Cyber Command, Paul Nakasone, disclosed that his soldiers had conducted offensive cyberattacks against Russia before the Special military operation in Ukraine began. These so-called “hunt forward” operations are part of USCYBERCOM’s doctrine of persistent engagement. This doctrine involves the personnel of the Cyber Nation Mission Forces deploying to partner countries to work with their cyber defenders in their most critical networks and computer systems. Their mission is to search for and degrade enemy activities, while also researching their tools and tactics in cyberspace. This doctrine is one of Nakasone’s key innovations in Cyber Command’s course of action. Previously, U.S. cyber operations required authorization from the U.S. President. This restriction made actions like persistent engagement and hunt forward operations nearly impossible, as they require extensive activities within other countries’ networks even before a cyberattack is prepared. It is impractical to expect the President to approve every such request. Nakasone changed this by removing the requirement for presidential authorization. Today, this allows Cyber Command to conduct such operations - both defensive and offensive - much more broadly, and they are taking full advantage of this opportunity. The problem lies in the sphere of international law - both in the field of the law of war (jus in bello) and the right to war (jus ad bellum). U.S. politicians often claim that cyber operations can achieve strategic goals without crossing the threshold of armed conflict. In any other domain actions with similar effects could provoke war but not in cyberspace. This feature is its main advantage and the reason the U.S. sabotages other countr ies’ initiatives (particularly Russian) to establish a legal regime for state use of cyberspace. However, there is no consensus among U.S. specialists about the legal basis that legitimizes such operations against another country, both internationally and domestically. Yet, this ambiguity seems to work in the U.S. favor. In fact, U.S. soldiers have attacked another country’s (Russian) territory and this has not led to a direct war between Russia and the U.S. U.S. officials consider the hunt forward operation experience in Ukraine as both successful and useful for refining U.S. strategies in cyberspace, including the National Cybersecurity Strategy and the DoD Cyber Strategy. They plan to use such tools more broadly against Russia. Currently, the U.S. is actively working in the Post-Soviet area against Russia to disrupt Russia’s national interests. Among other things, Washington is attempting to coerce Central Asian states into alignment with U.S. interests. Hunt forward operations are an obvious tool of the United States, which Russia will most likely have to counteract there.

Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction.

Computational systems biology employs computational algorithms and integrates diverse data sources, such as gene expression profiles, molecular interactions, and network modeling, to identify promising drug candidates through repurposing existing compounds in response to urgent healthcare needs. This study tackles the urgent need for rapid therapeutic development against emerging infectious diseases. We introduce a novel analytic expression for sensitivity analysis based on the Kronecker product and enhance model prediction performance using Graph Convolutional Networks (GCNs) with sensitivity-based graph reduction. Our algorithm refines prediction performance by leveraging sensitivity-based graph reduction. By integrating RNA-seq data, molecular interactions, and GCNs, we identify disease-related genes and pathways, construct heterogeneous graph models, and predict potential drugs. This approach involves novel analytical expressions that assess sensitivity to model loss, employing the Kronecker product approach. Subgraph analysis identifies nodes for removal, leading to a refined graph used for model retraining. This cost-effective pipeline focuses on computational methods for drug repurposing, targeting infectious diseases such as Zika virus and COVID-19 infection. Applied to these infections, our methodology integrates 659 proteins and 703 drugs for Zika virus, and 495 proteins and 468 drugs for COVID-19, along with their interactions derived from gene expression profiles. Top candidate drugs, such as Betamethasone phosphate and Bizelesin for Zika virus, and Chloroquine, Heparin Disaccharide, and Resveratrol for COVID-19, were validated through literature review or docking analysis. This scalable approach demonstrates promise in repurposing drugs for urgent healthcare challenges.

Exploring Zig-Zag Triangles: H-Coverings and Practical Applications

Graph theory is widely used to analyze the structure models in chemistry, biology, computer science, operations research and sociology. Molecular bonds, species movement between regions, development of computer algorithms, shortest spanning tree in a weighted graphs, aircraft scheduling and exploration of diffusion mechanism are some of these structure models. A computing system C can execute an algorithm A if A is graph-isomorphic to a subgraph of CG. This is equivalent of defining a graph isomorphism between A and subgraph of CG. This research article gives a small description of graph theory applications and one of graph model (zig-zag triangle) to define a suitable computer algorithm. Let Γ be a connected, simple graph with finite vertices v and edges e. A family {Γ1, Γ2, . . . , Γp} ⊂ Γ of subgraphs such that for all e ∈ E, e ∈ Γl, for some l, l = 1, 2, . . . , p is an edge-covering of Γ. If Γl ∼= Γ′, ∀l, then Γ has an Γ′-covering. Graph Γ with Γ′-covering is an (ad, d)-Γ′-antimagic if f : V ∪ E → {1, 2, . . . , |V | + |E|} a bijection exists and the sum over all vertex-weights and edge-weights of Γ′ form a set {ad, ad + d, . . . , ad + (p − 1)d}. The labeling ξ is super for ξ(VΓ) = {1, 2, 3, . . . , |VΓ|} and graph Γ is Γ′-supermagic for d = 0. This manuscript investigates super (ad, d)-Γ′-antimagic labelings of zig-zag triangles for differences d = 1, 2, . . . , 8.

Robotics at the University of Pennsylvania from Birth to Maturity: A Review of 30 Years of Research

This article is an attempt to reexamine the evolution of robotics research at the University of Pennsylvania and all that it entailed, covering the successes and struggles of PhD students, postdoctoral researchers, and a few dedicated faculty between 1972 and 2000. In 1945, Penn's Moore School of Electrical Engineering was famous for developing the first electronic digital computer, ENIAC, but after a few years, the research had diminished. In 1972, a new Department of Computer and Information Science was formed. I came to Penn from Stanford University's Artificial Intelligence Laboratory full of energy, enthusiasm, and the goal of establishing a similar lab at Penn. This article demonstrates the creativity and ingenuity of young professionals at the General Robotics, Automation, Sensing, and Perception (GRASP) Laboratory. We built hardware. We built software. We collaborated with psychologists and electrical and mechanical engineers and tried to build a community of roboticists. Our curiosity led us to build new vision and tactile systems and to investigate cooperative robotics systems on the ground and in the air. We used computational models supported and verified by experiments. Today, I am proud to say that almost all of the students who cycled through the GRASP Lab are successful in academia or industry.

Barycentric and pairwise Rényi quantum leakage with application to privacy-utility trade-off

Barycentric and pairwise quantum Rényi leakages are proposed as two measures of information leakage for privacy and security analysis in quantum computing and communication systems. These quantities both require minimal assumptions on the eavesdropper, i.e. they do not make any assumptions on the eavesdropper’s attack strategy or the statistical prior on the secret or private classical data encoded in the quantum system. They also satisfy important properties of positivity, independence, post-processing inequality and unitary invariance. The barycentric quantum Rényi leakage can be computed by solving a semi-definite program; the pairwise quantum Rényi leakage possesses an explicit formula. The barycentric and pairwise quantum Rényi leakages form upper bounds on the maximal quantum leakage, the sandwiched quantum α -mutual information, the accessible information and the Holevo’s information. Furthermore, differentially private quantum channels are shown to bound these measures of information leakage. Global and local depolarizing channels, that are common models of noise in quantum computing and communication, restrict private or secure information leakage. Finally, a privacy-utility trade-off formula in quantum machine learning using variational circuits is developed. The privacy guarantees can only be strengthened, i.e. information leakage can only be reduced, if the performance degradation grows larger and vice versa .

Multi-Agent Cooperation for Computing Power Scheduling in UAVs Empowered Aerial Computing Systems

Multi-Agent Cooperation for Computing Power Scheduling in UAVs Empowered Aerial Computing Systems

Artificial intelligence technologies and ethics in educational processes: solution suggestions and results

Artificial intelligence is a technology used to imitate the human-like thinking and decision-making abilities of computer systems. This technology enables computers to perform complex tasks such as data analysis, learning, problem solving and decision making. It is used in the field of education as well as in every field. While the use of artificial intelligence in the field of education provides advantages such as providing personalized learning experiences to students, providing teachers with intuition about student performance and developing educational materials, the ethical dimension should not be ignored. Therefore, the aim of this study is to produce solutions to ethical problems in the teaching and evaluation processes of artificial intelligence technologies in education. Qualitative research method was used in this study. It has adopted the phenomenological research approach among qualitative research methods. The concept of phenomenon is also the ethics of artificial intelligence. The working group consists of teachers, educational technologists and academicians. When selecting the working group, it was taken into consideration that there were teachers who use artificial intelligence applications in education and academics and technologists working in this field. Document analysis and focus group interviews were used as data collection tools. Content analysis was performed on the data obtained. According to the results of the study, ethical problems encountered with the use of artificial intelligence in education were identified and solution suggestions were offered.

A new optimal space vector modulation with DTC switching strategy for induction motor control

Efforts to achieve swift and precise dynamic torque control have been central in AC drive research. Recent advancements in embedded computer systems have highlighted direct torque control (DTC) and field-oriented control (FOC) as key methods for enhancing torque dynamics, both utilizing space vector modulation (SVM) to optimize voltage source inverter positioning. This study introduces a novel synthesis by integrating DTC with SVM to address limitations in conventional DTC, which suffers from limited voltage vector availability, leading to undesirable torque behavior and significant current fluctuations. The primary goal is to develop an optimal switching modulator for the fastest torque response through the combined application of DTC and SVM. The proposed strategy optimizes DC bus usage, reduces torque fluctuations, minimizes total harmonic distortion in AC motor current, decreases switching losses, and ensures seamless digital system integration. Simulations using MATLAB/SIMULINK demonstrate significant torque, current, and flux linkage ripple reductions, validating the approach's effectiveness. This integration overcomes established limitations, extending the capabilities of motor control methodologies and offering enhanced performance and operational integrity in induction motor drive systems.

Statistical Long-Range Dependencies and Statistical Self-Similarity in Computer Systems Processing – The Case of Cache Bytes Counter

Statistical Long-Range Dependencies and Statistical Self-Similarity in Computer Systems Processing – The Case of Cache Bytes Counter

Are They Doing Artificial Intelligence? (Re)Constructing the Primary Activity in Data Science

Data science (DS) is concerned with building so-called artificial intelligence, i.e., computer systems that automate tasks based on historical data. This article is the first attempt to examine DS using Adele E. Clarke’s framework of social worlds. The main goal of this paper is to show the (re)construction of primary activity based on the example of the social world of DS in Poland. Methodological reflection on this (re)construction is an underdeveloped element in the study of social worlds; therefore, this paper strives to make this process explicit. The empirical background is a three-year ethnographic study, following Clarke’s situational analysis approach. The methodological results demonstrate the indispensability of collaborative ethnography in (re)constructing primary activity and the importance of finding palpable elements as those being crucial to understanding primary activity. The substantive results focus on the idea that data scientists do not refer to their activity as doing artificial intelligence.

Design and Implementation of a High-Performance VLSI Architecture for Canonical Huffman Encoding

A key component of contemporary computer systems, data compression makes it possible for information to be stored and transmitted efficiently. A popular technique for lossless data compression, Huffman coding can drastically cut down on data size without sacrificing intensity. But conventional Huffman encoding techniques can have scalability and speed issues, especially when used in hardware. A high-throughput Very Large-Scale Integration (VLSI) design for a Canonical Huffman Encoder is shown in this study. To accomplish quick and effective encoding, the suggested approach makes use of parallel processing and improved algorithms. The system's use of the canonical Huffman algorithm lowers computational complexity and streamlines hardware implementation without sacrificing compression performance. The architecture incorporates real-time operation-optimized modules for frequency analysis, code generation, and symbol encoding. Results from simulations show that the suggested design achieves significantly higher throughput compared to conventional approaches, making it suitable for applications in data storage, multimedia, and communication systems. This study contributes to advancing VLSI designs for high-performance hardware Key Words: Data Compression, Huffman Coding, Lossless Compression, Very Large-Scale Integration (VLSI), Canonical Huffman Encoder, High-Throughput Encoding, Parallel Processing, Efficient Encoding, Hardware Implementation, Computational Complexity, Code Generation, Symbol Encoding, Frequency Analysis, Real-Time Operation, Throughput Optimization, Multimedia Systems, Communication Systems, Data Storage, Hardware Architecture, VLSI Design, Performance Optimization, Simulations, Compression Performance, Hardware Design, Throughput Enhancement, System-Level Optimization, Power Efficiency, Resource Management, Data Transfer Efficiency, Low-Cost Implementation.

Fault correcting adder design for low power applications

Field Programmable Gate Arrays are extensively used in space, military, and commercial sectors due to their reprogrammable nature. In high-safety environments, ensuring fault tolerance is crucial to improving the performance of electronic and computational systems. Common fault-tolerant methods include time redundancy, double modular redundancy, triple modular redundancy, hardware redundancy, self-checking, self-repairing, and Operand Width Aware Hardware Reuse. This paper introduces a novel approach based on error correction and detection techniques, aimed at reducing hardware complexity while optimizing for low power consumption, high speed, and minimal transistor count. The proposed technique is applicable to various arithmetic circuits. Fault-correcting and fault-detecting adders were designed using a combination of components, including a full adder (comprising one XOR gate, one NOT gate, and two 2:1 multiplexers), three XOR gates, three XNOR gates, one functional unit, two inverters, and two multiplexers (2:1 MUX). To assess the fault tolerance of the design, the technique was applied to an adder circuit, with its performance in terms of power consumption, hardware usage, energy efficiency, and delay simulated using Cadence Virtuoso @90 nm technology. Pre-layout and post-layout simulation results showed a 98% reduction in power consumption, 82% energy savings, 36.5 and 55.95% transistor savings, and a 215% reduction in area overhead compared to existing fault-tolerant adders. Additionally, multiplier designs were tested to validate the fault-correcting adder design.

Perceived challenges and improvement strategies for computer-based test in the nursing and midwifery council of nigeria final examination at a university in south eastern Nigeria

IntroductionComputer-based tests (CBT) have been noted as a stellar alternative for examinations, having been expected to address the deficiencies of traditional examination model. This study assessed the perceptions, challenges and strategies for improving the Nursing and Midwifery Council of Nigeria computer-based final qualifying examination.MethodsThe study was a cross-sectional descriptive survey using all the 214 year 2023 graduates of the Department of Nursing Sciences, University of Nigeria, Enugu Campus. Data was collected using a researcher-developed questionnaire.ResultsMajority had a positive perception towards CBT (74.3%). Their was a preference for CBT compared to traditional pen and paper examination (2.96 ± 0.96), Respondents experienced challenges answering long story-like question amidst limited time (74.3%) among others. It was suggested that use of good CBT environment to prevent time wastage, (3.32 ± 0.83), and fixing of malfunctioning computer systems prior to examination (3.32 ± 0.85) will help ameliorate challenges. There were no significant association between perception and sex (p = .308) as well as between the challenges experienced and age (p = .322).ConclusionThe perception of candidates on the use of CBT for final qualifying nursing examinations was positive. Addressing the challenges experienced will ensure a sustained positive and seamless experience for candidates.

Enhanced photonic reservoir computing using an optically injected VCSEL with random polarized optical feedback.

We propose and numerically demonstrate a photonic time-delay reservoir computing (TDRC) system exhibiting enhanced parallel task processing performance, where an optically injected vertical-cavity surface-emitting laser (VCSEL) under random distributed optical feedback acts as the reservoir computer. To assess its effectiveness, we perform two benchmark tasks including chaotic time-series prediction and waveform recognition task, where the TDRC is associated with two different random feedback structures, i.e., orthogonally polarized optical feedback (OPOF) and parallelly polarized optical feedback (PPOF). Benefiting from the enhanced nonlinearity offered by the random distributed optical feedback, the proposed TDRC excels at parallel task processing with the PPOF structure, whereas the performance of the OPOF structure may be deteriorated. Additionally, we reveal the effect of the injection strength, feedback strength, pump current, and number of virtual nodes on the proposed TDRC. Our work paves the way for the performance enhancement of parallel task processing based on polarization multiplexing in a VCSEL-based TDRC.

UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness

The rising popularity of UAVs and other autonomous control systems coupled with real-time operating systems has increased the complexity of developing systems with the proper robustness, performance, and reactivity. The growing demand for more sophisticated computational tasks, proportionally larger payloads, battery limitations, and smaller take-off mass requires higher energy efficiency for all avionics and mission computers. This paper aims to develop a technique for experimentally studying the indicators of reactivity and energy consumption in a computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the ‘Boryviter 0.1’ computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. This paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the ‘Boryviter 0.1’ computing platform can be used as a UAV mission computer for typical flight control tasks requiring real-time computing under the influence of external factors. As a further work direction, we plan to investigate the proposed energy-saving algorithms within the planned NASA F’Prime software flight framework. Such an investigation, which should use the mission computer’s actual flight computation load, will help to qualify the obtained energy-saving methods and their implementation results.

Physicians’ ethical concerns about artificial intelligence in medicine: a qualitative study: “The final decision should rest with a human”

Background/aimArtificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance.MethodsThis qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare.ResultsContent analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional.ConclusionThe authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity.

Promoting the Resilience of Health Care Information Systems—The Day Hospitals Stood Still

This Viewpoint describes health care disruption owing to a global computer system outage and advocates for preventive methods to avoid future IT events disrupting the health care sector.

Computer Prediction and Systems Analysis of Deformation Processes of Polymer Textile Materials

Computer Prediction and Systems Analysis of Deformation Processes of Polymer Textile Materials

Drivers of Advanced Digital Technologies

Advancements in digital technologies, especially computer and information systems, force firms to adopt them in their production processes. Artificial intelligence, cloud computing, big data analytics, robotics, smart devices, and blockchain are the leading advanced technologies. This study explores the drivers of firms’ adoption of these technologies by estimating a multivariate probit model utilizing a Eurobarometer dataset. A statistically significant and positive correlation between the error terms of all models indicates that investigating the adoption of all advanced digital technologies together is more appropriate than independent analyses. Drivers of advanced digital technologies appear similar with decisive factors in using new technologies, and implementation of any type of innovation significantly increases the probability of adoption. The other determinants are the firm size, interaction with international markets, and the network structure of the market in which firms operate. Furthermore, location positively impacts the adoption of cloud computing and big data analytics, while it exerts no significant influence on the adoption of other types of advanced digital technologies.

Multi-resource maximin share fair allocation in the cloud-edge collaborative computing system with bandwidth demand compression

Multi-resource maximin share fair allocation in the cloud-edge collaborative computing system with bandwidth demand compression