Computational Components Research Articles

Efficient Quantum Private Comparison with Unitary Operations

Quantum private comparison (QPC) is a crucial component of quantum multiparty computing (QMPC), allowing parties to compare their private inputs while ensuring that no sensitive information is disclosed. Many existing QPC protocols that utilize Bell states encounter efficiency challenges. In this paper, we present a novel and efficient QPC protocol that capitalizes on the distinct characteristics of Bell states to enable secure comparisons. Our method transforms private inputs into unitary operations on shared Bell states, which are then returned to a third party to obtain the comparison results. This approach enhances efficiency and decreases the reliance on complex quantum resources. A single Bell state can compare two classical bits, achieving a qubit efficiency of 100%. We illustrate the feasibility of the protocol through a simulation on the IBM Quantum Cloud Platform. The security analysis confirms that our protocol is resistant to both eavesdropping and attacks from participants.

Improving the dietaryindex R Package: A Proposal to Include Additional Components for More Accurate DII Computation in NHANES

Improving the dietaryindex R Package: A Proposal to Include Additional Components for More Accurate DII Computation in NHANES

Promote students’ computational thinking in rural areas: The moderating role of gender

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.

Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems

IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with Aquila Optimization Algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the Aquila optimization algorithm (AOA) model. At last, the XAIAOA-WPC method incorporates the XAI model LIME for superior perception and explainability of the black-box process for precise identification of intrusions. The simulation outcome of the XAIAOA-WPC method is examined on a benchmark database. The experimental validation of the XAIAOA-WPC method exhibited a superior accuracy value of 99.29 % over existing techniques.

Photonic Ising machines for combinatorial optimization problems

The demand for efficient solvers of complicated combinatorial optimization problems, especially those classified as NP-complete or NP-hard, has recently led to increased exploration of novel computing architectures. One prominent collective state computing paradigm embodied in the so-called Ising machines has recently attracted considerable research attention due to its ability to optimize complex problems with large numbers of interacting variables. Ising model-inspired solvers, thus named due to mathematical similarities to the well-known model from solid-state physics, represent a promising alternative to traditional von Neumann computer architectures due to their high degree of inherent parallelism. While there are many possible physical realizations of Ising solvers, just as there are many possible implementations of any binary computer, photonic Ising machines (PIMs) use primarily optical components for computation, taking advantage of features like lower power consumption, fast calculation speeds, the leveraging of physical optics to perform the calculations themselves, possessing decent scalability and noise tolerance. Photonic computing in the form of PIMs may offer certain computational advantages that are not easily achieved with non-photonic approaches and is nonetheless an altogether fascinating application of photonics to computing. In this review, we provide an overview of Ising machines generally, introducing why they are useful, what types of problems they can tackle, and how different Ising solvers can be compared and benchmarked. We delineate their various operational mechanisms, advantages, and limitations vis-à-vis non-photonic Ising machines. We describe their scalability, interconnectivity, performance, and physical dimensions. As research in PIMs continues to progress, there is a potential that photonic computing could well emerge as a way to handle large and challenging optimization problems across diverse domains. This review serves as a comprehensive resource for researchers and practitioners interested in understanding capabilities and potential of PIMs in addressing such complex optimization problems.

Improving mathematical proof based on computational thinking components for prospective teachers in abstract algebra courses

Understanding and constructing mathematical proofs is fundamental for students in abstract algebra courses. The computational thinking approach can aid the process of compiling mathematical proofs. This study examined the impact of integrating computational thinking components in constructing mathematical proofs. The researcher employed a sequential explanatory approach to ascertain the enhancement of algebraic proof capability based on computational thinking through the Wilcoxon test. A total of 32 prospective teachers in mathematics education programs were provided with worksheets for seven meetings, which were combined with computational thinking stages. Quantitative data were collected from initial and subsequent test instruments. Moreover, three prospective teachers were examined through case studies to investigate their mathematical proof capability using computational thinking components, including decomposition, abstraction, pattern recognition, and algorithmic thinking. The study's findings indicated that CT intervention enhanced students' logical reasoning, proof-writing abilities, and overall engagement with abstract algebra concepts. The findings illustrate that integrating computational thinking into learning strategies can provide a framework for developing higher-order thinking skills, especially in proving, which are essential for studies in mathematics education programs.

Data caching technologies in modern microprocessors

Objective. The study presented in the paper is aimed at studying the methods for improving the efficiency of software in modern computing systems with a hierarchical memory structure. Method. The study is based on data caching technologies in microprocessors. Result. The article presents the results of the analysis of various approaches to the development of efficient software taking into account the characteristics of the memory subsystem of the computing system, which made it possible to prove the importance of cache memory in improving the performance and interaction of computer components. Conclusion. Cache memory is a critical element in the architecture of microprocessors, playing a key role in determining the performance of the computing system. Optimizing the use of cache can significantly improve data access time and, as a result, overall system performance. Software developers need to pay special attention to the characteristics of the memory subsystem when designing and implementing solutions.

The Development of an Automated Approach for Designing Quantum Algorithms Using Circuits Generated By Generative Adversarial Networks (Gans)

The advent of quantum computing has inaugurated a novel epoch of computational prowess, offering the potential to tackle intricate problems with unparalleled speed (Zoufal et al., 2019). Quantum circuits, which are essential components of quantum computation, serve as representations of sequences of quantum gates designed for specific quantum processes (Zoufal et al., 2019). Nevertheless, the task of creating efficient quantum circuits continues to be a formidable and labor-intensive undertaking. This research proposal presents a unique methodology that utilizes Generative Adversarial Networks (GANs) to automate the process of generating quantum circuits that are specifically designed for particular quantum gates and operations (Zoufal et al., 2019). The main goal of this study is to create a model based on Generative Adversarial Networks (GANs) that can generate quantum circuits by leveraging the collaborative efforts of the generator and discriminator networks (Zoufal et al., 2019). The GAN model will be trained using a carefully selected dataset that includes established quantum circuits and their corresponding required quantum operations (Zoufal et al., 2019). This dataset will form the basis for the training process. Following this, the quantum circuits that are produced will be thoroughly assessed in terms of fidelity, efficiency, and resource allocation (Zoufal et al., 2019). Additionally, the objective of this work is to refine and optimize the circuits that are formed by employing reinforcement learning and gradient-based techniques (Zoufal et al., 2019). In addition to investigating circuit production, this research will delve into the practical implications and consequences of quantum circuits formed by Generative Adversarial Networks (GANs) on the development of quantum algorithms (Zoufal et al., 2019). The study's value is in its capacity to accelerate the creation of quantum algorithms through the automation of circuit design (Zoufal et al., 2019). This research makes a valuable contribution to the field of quantum computing by improving the efficiency and resource utilization of quantum circuits (Zoufal et al., 2019). These developments are crucial for the development of practical quantum computing applications and will play a significant role in the evolution of quantum algorithms and processing capabilities in the future (Zoufal et al., 2019).

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture

Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such as network topology, connection rules, and compatibility with multiple programming languages. We proposed a multiscale distributed neural computing model database (NCMD), which is a framework designed for ARM-based multi-core hardware. Various neural computing components, including ion channels, synapses, and neurons, are encompassed in NCMD. We demonstrated how NCMD constructs and deploys multi-compartmental detailed neuron models as well as spiking neural networks (SNNs) in BrainS, a distributed multi-ARM neuromorphic system. We demonstrated that the electrodiffusive Pinsky–Rinzel (edPR) model developed by NCMD is well-suited for BrainS. All dynamic properties, such as changes in membrane potential and ion concentrations, can be easily explored. In addition, SNNs constructed by NCMD can achieve an accuracy of 86.67% on the test set of the Iris dataset. The proposed NCMD offers an innovative approach to applying BrainS in neuroscience, cognitive decision-making, and artificial intelligence research.

Maximum permissible levels of laser radiation as a tool for ensuring the sanitary and epidemiological well-being of the population

Introduction. Laser radiation is the youngest production factor, but not the most harmless. The rapid development of modern laser technologies and their introduction into various fields of science and technology has allowed lasers entering widely into everyday human life, as well as into the sphere of culture, fine arts, and the show industry. Thus, the development of a unified algorithm for calculating the maximum permissible levels (MPL) of laser radiation is becoming particularly relevant. The purpose of this work is to assess the subjective feelings of persons exposed to laser radiation at levels not exceeding the maximum permissible levels and clarify the methodology for calculating acceptable levels of laser radiation. Materials and methods. Graphical modelling was used to develop an algorithm for calculating the MPL. To assess the subjective perception of laser radiation, a survey of 20–25 years five hundred eighteen people was used according to a specially designed questionnaire. Results. Exposure to laser radiation at levels not exceeding the MPL caused unpleasant sensations in the eyes of the participants of the show, temporary violation of colour perception, temporary blindness, headache. An algorithm for calculating the MPL of laser radiation has been developed, consisting of six stages, which allows optimizing the computational component of laser safety assessment in the implementation of State sanitary and Epidemiological supervision and control. Limitations. The age range of the study participants is 20–25 years old. Conclusion. It is proposed to revise the standards of MPL for the population based on the temporary effects of radiation. This will minimize the negative impact on the organ of vision in terms of reversible changes and qualitatively improve the hygienic assessment of laser radiation. The developed algorithm for calculating the permissible levels of laser radiation will help minimize errors and reduce the time spent by specialists when assessing the safety of exposure to laser radiation on the body.

Research on Electro-Optic Hybrid Multidigit Digital Multiplier Based on Surface Plasmon Polariton Technology

Digital multipliers are the core components of digital computers, and improving the speed of transistor electronic computers during computation has almost reached its limit, with high power consumption. In this paper, we proposed an electro-optic hybrid multidigit digital multiplier based on SPP technology, which has the advantages of high speed and low power consumption in optical logic, as well as flexible electrical operation and easy storage. The electro-optic hybrid digital multiplier mainly consists of an electrical AND logic gate, an electro-optic hybrid half adder, and an electro-optic hybrid full adder. The optical logic unit is controlled by activated ITO materials to achieve optical-domain operations, and then the multiplication calculation results are converted into electrical signals through photoelectric conversion. The experimental results show that when the scale is 64 × 64 bits, compared with transistor digital multiplication, the energy consumption is reduced by 48.8%; the speed is increased by a factor of 28; and the volume of the electro-optic hybrid digital multiplier device is larger than that of the transistor multiplier, saving 59.9% of the area. For optical transmission loss, a single adder outputs 0.31 dB at different device scales, while the carry output continuously increases with device scale. At scales of 8 × 8 bits, 16 × 16 bits, and 64 × 64 bits, the insertion losses at the sum output ports are 1.03 dB/μm and 1.87 dB/μm, respectively.

A complex-valued encoding golden jackal optimization for multilevel thresholding image segmentation

Multilevel thresholding image segmentation is not only an indispensable component of image computation and machine vision but also a fundamental element of image analysis and feature extraction, which has received extensive attention from many domestic and international academics in recent years. However, the threshold level rises in direct proportion to the computational complexity. Therefore, this paper presents a complex-valued encoding golden jackal optimization (CGJO) established on Kapur’s entropy to accomplish this issue, and the intention is to split the available image into a multitude of conspicuous salient portions that illustrate concepts and procedures of the pertinent object. The golden jackal optimization (GJO) employs the cooperated foraging of the jackals to imitate prey searching, enclosing and pouncing to generate the optimum solution. The complex-valued encoding employs the diploid’s notion to alter the real and imaginary of the search agent, which amplifies the information multiplicity and reinforces the inherent parallelism to motivate productive search and inhibit voracious convergence. The functionality and viability of the CGJO are confirmed by comparison with BWOA, DOA, COA, LCA, OOA, SABO, GJO and CRWOA. The experimental results reveal that the CGJO explores and exploits to acquire a superior convergence speed, greater numerical precision and stronger segmentation quality. Additionally, the CGJO offers excellent practicability and stability to address image segmentation successfully.

Nonlinear optical encoding enabled by recurrent linear scattering

AbstractOptical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity—a critical component of computation—remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design’s efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

FAIRsoft-a practical implementation of FAIR principles for research software.

Software plays a crucial and growing role in research. Unfortunately, the computational component in Life Sciences research is often challenging to reproduce and verify. It could be undocumented, opaque, contain unknown errors that affect the outcome, or be directly unavailable and impossible to use for others. These issues are detrimental to the overall quality of scientific research. One step to address this problem is the formulation of principles that research software in the domain should meet to ensure its quality and sustainability, resembling the FAIR (findable, accessible, interoperable, and reusable) data principles. We present here a comprehensive series of quantitative indicators based on a pragmatic interpretation of the FAIR Principles and their implementation on OpenEBench, ELIXIR's open platform providing both support for scientific benchmarking and an active observatory of quality-related features for Life Sciences research software. The results serve to understand the current practices around research software quality-related features and provide objective indications for improving them. Software metadata, from 11 different sources, collected, integrated, and analysed in the context of this manuscript are available at https://doi.org/10.5281/zenodo.7311067. Code used for software metadata retrieval and processing is available in the following repository: https://gitlab.bsc.es/inb/elixir/software-observatory/FAIRsoft_ETL.

Examination of social media usage habits of generation Z.

Undoubtedly, social media is one of the significant components of computer and internet technologies. It offers users the opportunity to share emotions, thoughts, and life experiences through various means such as text, images, and videos on the internet. In doing so, it not only provides a multifaceted communication and interaction opportunity for users of all ages but also creates a dynamic and entertaining parallel universe. This universe shapes and transforms individual, social, and cultural life, as well as habits. With the daily increase in user numbers, social media has become an integral part of our lives. The primary objective of this study is to determine the social media usage habits of fourth-grade students, known as Generation Z. The research was conducted using semi-structured interviews, a qualitative data collection method. The universe of the study consisted of 654 fourth-grade students attending state schools in seven different provincial centers representing the seven geographical regions of Turkey during the 2021-2022 academic year. The data were collected using an interview form developed for this research and analyzed with the content analysis technique. According to the research findings, it was determined that approximately three-quarters of primary school fourth-grade students use social media on a daily basis. Moreover, most participants stated that they use social media for educational, communicative, and entertainment purposes. The most preferred social media platforms by students were identified as YouTube, WhatsApp, TikTok, and Instagram.

Deduplication on Encrypted Data in Cloud Computing

Cloud storage is a crucial component of cloud computing, allowing users to expand their storage without upgrading their equipment and overcome resource constraints. Cloud users' data is always encrypted before being outsourced to ensure their security and privacy. However, encrypted data may result in a significant waste of cloud resources. Storage complicates data sharing among authorized users. We continue to face issues with encrypted data storage and deduplication. Traditional deduplication strategies are designed for certain application settings where data owners or cloud servers have full control over the process. They cannot meet data owners' varying requests based on data sensitivity. This study proposes a flexible data storage management method that combines deduplication and access control across various Clouds. Service Providers (CSP). We assess its performance through security analyses, comparisons, and implementations. The results demonstrate security, effectiveness, and efficiency for actual applications.

Efficient multi-party PSI and its application in port management

Private Set Intersection (PSI) technology is a cryptographic tool that allows the parties holding private data to determine the intersection of sets in a joint computation without revealing any additional privacy information. As a critical component of secure multi-party computation, this technology has been widely used in the security domain of artificial intelligence and data mining. With the emergence of the era of multi-source data sharing, protocols for private set intersection computation applicable to multiple participants have also emerged. However, the performance of existing multi-party private set intersection (MPSI) protocols is suboptimal when some participants use devices with limited communication or computational capabilities, such as mobile devices. To overcome the above issues, we design a cloud-aided multi-party private set intersection protocol (Cloud-Aided-MPSI) based on oblivious programmable pseudorandom functions (OPPRF) and oblivious key–value stores (OKVS). Due to the protocol efficiently outsourcing partial computational and communication tasks to cloud servers, our performance has been further enhanced compared to existing work. Through the Cloud-Aided-MPSI protocol, we propose a port scheduling protocol for coordinated management scenarios of ships arriving and departing from ports. The protocol effectively addresses the privacy protection concerns of port management when scheduling the arrival and departure of ships. We analyze the performance of this protocol.

The ADMORPH Approach for Adaptively Morphing Embedded Systems

Due to the increasing performance demands of missionand safety-critical Cyber-Physical Systems (of Systems), these systems exhibit a rapidly growing complexity, manifested by an increasing number of (distributed) computational cores and application components connected via complex networks. However, with these systems' growing complexity and interconnectivity, the chances of hardware failures and disruptions due to cyber-attacks will also quickly increase.

High-efficiency realization of the super-robust Rydberg Deutsch gate

Quantum logic gates are the essential components of quantum computation and have broad practical applications, especially for the multiple-qubit logic gates. Compared with appliying a series of single- and two-qubit gates, constrcuting quantum computation with multiple-qubit logic gates can be more efficient and high-fidelity. As a three-qubit logic gate, Deutsch gate enables the realization of any feasible quantum computations. Based on neutral atoms, we present a scheme of super-robust Deutsch gate via optimal control technique. Utilizing the Rydberg blockade effect of neutral atoms, we design an implementable D(β) based on the three-step program. One of the notable advantages of this function is that β can be achieved arbitrarily between 0 and π with the operation of target atom. In addition, we give an analytical solution agreed very well with numerical simulations for the residual blocking effect between next-neighbor atoms that affects the performance quality of Deutsch gate. The fidelity of our proposed scheme is demonstrated by numerical simulation of the master equation based on the full Hamiltonian, and the robustness of our scheme with control errors is also illustrated. Our proposal offers an alternative promising scheme for fault-tolerant quantum computation. Published by the American Physical Society 2024

Poligras: Policy-Based Graph Summarization

Large graphs are ubiquitous. Their sizes, rates of growth, and complexity, however, have significantly outpaced human capabilities to ingest and make sense of them. As a cost-effective graph simplification technique, graph summarization is aimed to reduce large graphs into concise, structure-preserving, and quality-enhanced summaries readily available for efficient graph storage, processing, and visualization. Concretely, given a graph G , graph summarization condenses G into a succinct representation comprising (1) a supergraph with supernodes representing disjoint sets of vertices of G and superedges depicting aggregate-level connections between supernodes, and (2) a set of correction edges that help reconstruct G losslessly from the supergraph. Existing graph summarization solutions offer non-optimal graph summaries and are time-demanding in real-world large graphs. In this paper, we propose a learning-enhanced graph summarization approach, Poligras ( Poli cy-based gra ph summarization), to model the most critical computational component in graph summarization: supernode selection and merging. Specifically, we design a probabilistic policy learned and optimized by neural networks for efficient optimal supernode pair selection. As the first learning-enhanced, scalable graph summarization method, Poligras achieves significantly improved performance over state-of-the-art graph summarization solutions in real-world large graphs.