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For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidates abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidates data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system. [ABSTRACT FROM AUTHOR]

In vehicular cloud computing (VCC) systems, the vehicle cloud (VC) consists of computing resources for multiple vehicles to assist the remote cloud (RC) in making real-time decisions on driving vehicles, to improve road safety and driving comfort. The acquisition of additional cloud facilities to increase the capacity of VCC systems when processing intensive requests for cloud resources is costly. A recent approach uses multiple remote microclouds (RMCs) to assist RCs in processing requests because they are smaller and less costly than RCs. Therefore, this paper presents a VCC system with multiple RCs and RMCs, in which an algorithm for allocating request resources among VCs, RMCs, and RCs. Subsequently, this paper proposes an improved simulated annealing algorithm (ImSA) based on density of vehicles in a geographical area to deploy RCs and RMCs with the minimal deployment cost and response cost. For real applications, the accuracy and effectiveness of the proposed algorithm are evaluated on the basis of the real traffic data from three different areas (i.e., urban area, outskirts, and suburban area) in one week. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including breast cancer image classification, intelligent engineering management with data mining, and identifying optimal methods for power grid error parameters.

Edge computing is one of the technological areas currently considered among the most promising for the implementation of many types of applications. In particular, IoT-type applications can benefit from reduced latency and better data protection. However, the price typically to be paid in order to benefit from the offered opportunities includes the need to use a reduced amount of resources compared to the traditional cloud environment. Indeed, it may happen that only one computing node can be used. In these situations, it is essential to introduce computing and memory resource management techniques that allow resources to be optimized while still guaranteeing acceptable performance, in terms of latency and probability of rejection. For this reason, the use of serverless technologies, managed by reinforcement learning algorithms, is an active area of research. In this paper, we explore and compare the performance of some machine learning algorithms for managing horizontal function autoscaling in a serverless edge computing system. In particular, we make use of open serverless technologies, deployed in a Kubernetes cluster, to experimentally fine-tune the performance of the algorithms. The results obtained allow both the understanding of some basic mechanisms typical of edge computing systems and related technologies that determine system performance and the guiding of configuration choices for systems in operation. [ABSTRACT FROM AUTHOR]

Computer vision in sports analytics is gaining in popularity. Monitoring players' performance using cameras is more flexible and does not interfere with player equipment compared to systems using sensors. This provides a wide set of opportunities for computer vision systems that help coaches, reporters, and audiences. This paper provides an introduction to the problem of measuring boxers' performance, with a comprehensive survey of approaches in current science. The main goal of the paper is to provide a system to automatically detect punches in Olympic boxing using a single static camera. The authors use Euclidean distance to measure the distance between boxers and convolutional neural networks to classify footage frames. In order to improve classification performance, we provide and test three approaches to manipulating the images prior to fitting the classifier. The proposed solution achieves 95% balanced accuracy, 49% F1 score for frames with punches, and 97% for frames without punches. Finally, we present a working system for analyses of a boxing scene that marks boxers and labelled frames with detected clashes and punches. [ABSTRACT FROM AUTHOR]

Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux's completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper. [ABSTRACT FROM AUTHOR]

With the growth of the Internet, more and more business is being done online, for example, online offices, online education and so on. While this makes people's lives more convenient, it also increases the risk of the network being attacked by malicious code. Therefore, it is important to identify malicious codes on computer systems efficiently. However, most of the existing malicious code detection methods have two problems: (1) The ability of the model to extract features is weak, resulting in poor model performance. (2) The large scale of model data leads to difficulties deploying on devices with limited resources. Therefore, this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet (LCMISNet). In this paper, the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module. The feature slicing module reduces the number of parameters by grouping features. The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes. In addition, this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet. The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%, respectively. It proves that LCMISNet has a powerful malicious code recognition performance. In addition, compared with other network models, LCMISNet has better performance, and a lower number of parameters and computations. [ABSTRACT FROM AUTHOR]

File sharing, streaming media, collaborative computing, and other P2P systems are all unicast to establish the corresponding overlapping network. The superimposed network is generally carried out based on the existing primary network. In this way, the access of each node is random. At the same time, this will cause the topological structure of the upper and lower layers to be inconsistent. This will increase the communication delay between nodes and cause an excellent bandwidth burden to the underlying network. The existing topology matching methods still face problems, such as poor scalability and long node aggregation time. This paper aims to design a topological distributed node aggregation method based on network coordination and distributed hash table (DHT) algorithm. This paper established a two-dimensional mesh model of nodes based on equal-distance concentric circles and divided into two equal areas. The parts of multiple namespaces correspond one by one according to their location. Because nodes are kept close, neighbours can be aggregated through DHT's primary "publish" and "search" primitives. Experimental results show that the TANRA method can match the network's topology under a slight delay and a large number of nodes. The TANRA method can effectively reduce the path delay in structured networks. [ABSTRACT FROM AUTHOR]

Approximate computing is an emerging paradigm that can tolerate some loss of accuracy to improve the energy consumption and design complexity. Multiplication is one of the important computational components in various digital signal processing (DSP) applications. Improvement of multipliers, as the building blocks of an approximate computing systems, has received significant attention. Compressors are key elements in an approximate multiplier to have an optimal design with high performance. This paper proposes an approximate/exact 4–2 compressor and an exact 5–2 compressor in carbon nanotube field-effect transistors (CNTFET) technology for utilisation in an approximate multiplier. Four efficient designs for utilising the proposed compressors are introduced and analysed for a Wallace multiplier. All of the proposed circuits are analysed in 32 nm CNTFET Stanford technology and are simulated by HSPICE. In addition, as an image processing application of approximate multipliers, this paper compares the quality metrics of the image multiplication for the proposed designs and previously presented approximated multipliers. For example, the proposed schemes provide excellent results in structural similarity index up to 99%, and peak signal-to-noise ratio more than 45 dB. Also, it uses figure of metric (FOM) that includes the accuracy and quality metrics of the approximate multipliers. The simulation results demonstrate the efficiency of proposed designs. [ABSTRACT FROM AUTHOR]

Railway interlocking systems are essential safety components in rail transportation, designed to prevent train collisions. They regulate the transitions between sections of a railway station using rail traffic control elements. An interlocking system can assess whether the configuration of these control elements poses a collision risk. Over the years, researchers have developed various algebraic models to tackle this issue, highlighting the potential use of computer algebra systems in implementing interlocking systems. In this work, we aim to enhance these systems' capabilities. Not only will they indicate whether a situation is dangerous, but if it is, they will also provide guidance on how to configure certain rail traffic control elements to ensure safety. In this paper, we introduce an algebraic model that represents the railway station through polynomials. This approach transforms the task of identifying dangerous situations into calculating the residue of a polynomial over a set of polynomials. The monomials contained in this residue polynomial encode all possible configurations that would render the situation safe. [ABSTRACT FROM AUTHOR]

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious actors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evaluation, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multi-state honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or non-lethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, reliability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment. [ABSTRACT FROM AUTHOR]

Cloud computing technology delivers services, resources, and computer systems over the internet, enabling the easy modification of resources. Each field has its challenges, and the challenges of data transfer in the cloud pose unique obstacles for forensic analysts, making it necessary for them to investigate and adjust the evolving landscape of cloud computing. This is where cloud forensics emerges as a critical component. Cloud forensics, a specialized field within digital forensics, focuses on uncovering evidence of exploitation, conducting thorough investigations, and presenting findings to law enforcement for legal action against perpetrators. This paper examines the primary challenges encountered in cloud forensics, reviews the relevant literature, and analyzes the strategies implemented to address these obstacles. [ABSTRACT FROM AUTHOR]

The proliferation of mobile devices and the increasing use of networked applications have generated enormous data that require real-time processing and low-latency responses. For real-time applications, Edge Computing (EC) gained a lot of attention which helps to overcome the challenges with traditional computing systems. There is a massive growth of data generated at the web edge, but its limited computing resources and boundary dynamics pose significant challenges. For proficient resource utilization and network management, Software-Defined Networks (SDN) integration with EC can provide control and programmability in edge networking management. However, the optimization of resources is a fundamental need and challenge of EC based on SDN. Besides, the utilization of SDN in EC deals with a centralized and distributed infrastructure further enables the processing of data in closer proximity to its origin and helps in the growing demand for efficient and high-performance computing systems. This study presents a systematic review of the current state-of-the-art pertaining to resource optimization in EC and its integration with SDN. The review of challenges, benefits, and various proposed methodologies for managing resources in Edge and SDN-based Edge environments are investigated. This paper offers valuable insights into the current state and future directions of Edge, SDN-based EC systems, which can assist organizations in designing and deploying more efficient resource consumption solutions [ABSTRACT FROM AUTHOR]

Edge computing provides a real-time guarantee for the industrial internet. Server deployment is the first step to building an edge computing system, and the deployment strategy has a huge impact on system delay and reliability. Industrial environments place higher demands on reliability than other common environments. However, existing research on edge server deployment assumes that the server will not fail and thus rarely considers edge server reliability while reducing delay. This paper focuses on edge server deployment approach for delay reduction and reliability enhancement in the industrial internet. Firstly, a basic edge server deployment model is established to optimize server load balancing and delay. On this basis, an edge server deployment model with enhanced reliability is established. This model improves system reliability by adding fault-tolerant servers when deploying edge servers. When some edge servers in the system fail, the fault-tolerant servers will intervene in time and start working to replace the failed edge servers. To solve the optimal deployment strategy, an Improved Grey Wolf-Genetic Algorithm-based deployment algorithm is proposed. The deployment algorithm improves the traditional genetic algorithm's performance to find the global optimal solution by improving the initialization and mutation part of the genetic algorithm. The deployment algorithm selects appropriate wireless access points (WAPs) to deploy fault-tolerant edge servers among a large number of WAPs. Simulation results show that the established fault-tolerant model can significantly improve system reliability while reducing system delay. The proposed algorithm performs better compared with existing algorithms in terms of convergence speed and finding the optimal deployment location. [ABSTRACT FROM AUTHOR]

SD-WANs are an innovative software-defined network (SDN) technology used to reinvent networks, services, and applications in wide area network (WANs). The development of SD-WANs ranges from network optimization in the past to service provision platforms at present and distributed computing systems in the future. The existing surveys on SD-WANs are fragmented, covering specific problems only, and are not comprehensive with detailed research directions. This paper seeks to provide a systematic survey on SD-WANs by introducing major research directions and stating specific problems. Therefore, four major research directions related to traffic engineering, network optimization and systems, service orchestration, and the security issues of SD-WANs are sequentially introduced, along with detailed statements relating to specific problems and the classification of state-of-the-art research. Finally, the trends and challenges regarding SD-WANs are summarized and our future work is described. [ABSTRACT FROM AUTHOR]

Debugging (or troubleshooting) provides a rich context to foster problem-solving. Yet, while we know much about some problems and strategies that novices face in programming on-screen, we know far less about debugging and troubleshooting in the context of physical computing, where coding issues may overlap with materially embedded problems. In this paper, we study the thought processes novice students employed and the challenges they faced in debugging an electronic textile project with multiple overlapping problems that crossed physical, electronic, and computational domains. We employed a think-aloud protocol to develop an instrumental case study by video-recording 45 minutes of one pair of 9th-grade students debugging and fixing a buggy e-textile project. The problem space included the computational system's programmatic, electronic, and physical spatial aspects, which are more generally reflective of physical computing systems. We found that (1) students' troubleshooting was more recursive and less linear than traditional approaches that usually propose linear, procedural, step-wise activities, and (2) students coordinated their approach across multiple modalities, taking advantage of a distributed set of tools and people in order to tackle a complex set of problems. In the discussion, we address various pedagogical implications for improving teaching about troubleshooting. [ABSTRACT FROM AUTHOR]

The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional "high–resolution" simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real‐world climate via a set of four 40 day simulations covering all 4 seasons of the year. Plain Language Summary: This paper describes the design and development of a 3 km version of the Energy Exascale Earth System Model (E3SM) atmosphere model, which has been fully rewritten in C++ using the Kokkos library for performance portability. This newly rewritten model is able to take advantage of the state–of–the–science high performance computing systems which use graphical processor units (GPUs) to mitigate much of the computational expense which typically plagues high–resolution global modeling. Taking advantage of this high–performance we are able to run four seasons of simulations at 3 km global resolution. We discuss the biases, including the diurnal cycle, by comparing model results with satellite and Atmospheric Radiation Measurement ground‐based site data. Key Points: Describes the C++/Kokkos implementation of the Simple Cloud–Resolving E3SM Atmosphere Model (SCREAMv1)SCREAMv1 leverages GPUs to surpass one simulated year per compute day at global 3 km resolutionHigh resolution improves some meso‐scale features and the diurnal cycle but large‐scale biases require improvement across all four seasons [ABSTRACT FROM AUTHOR]

Particle systems can achieve many scenarios that are difficult to achieve in the field or expensive in reality. In this paper, the requirements of 3D film special effects and the design process of particle systems are studied. Unity3D engine was used to simulate 3D movie special effects. Then, the motion trajectory planning of 3D video group animation characters based on particle swarm optimization is proposed. Then, the system models the animated characters' moving track to achieve the realism's dynamic effect. This project intends to use the gravity optimization method for particle swarm optimization. The aim is to overcome the optimization difficulty caused by particle swarm optimization, which is easy to fall into local extreme values. Finally, the generated trajectory information is input into the 3D simulation system for conflict detection and clustering tests. Experiments show that the proposed algorithm can effectively render memorable scenes such as movies and TV. The picture has a high real-time frame rate and is realistic. [ABSTRACT FROM AUTHOR]

With the maturity of artificial intelligence (AI) technology, applications of AI in edge computing will greatly promote the development of industrial technology. However, the existing studies on the edge computing framework for the Industrial Internet of Things (IIoT) still face several challenges, such as deep hardware and software coupling, diverse protocols, difficult deployment of AI models, insufficient computing capabilities of edge devices, and sensitivity to delay and energy consumption. To solve the above problems, this paper proposes a software-defined AI-oriented three-layer IIoT edge computing framework and presents the design and implementation of an AI-oriented edge computing system, aiming to support device access, enable the acceptance and deployment of AI models from the cloud, and allow the whole process from data acquisition to model training to be completed at the edge. In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments carried out to verify the feasibility and effectiveness of the proposed method are reported. The model training time with the proposed method is generally 30% to 50% less than that with the random device selection method, and the training energy consumption under the proposed method is generally 35% to 55% less. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence is currently being observed in many areas of life. In addition to assisting in intellectual work, solving complex computational problems, or analyzing various types of data, the aforementioned techniques can also be applied in the process of providing security to people. The paper proposes an emergency identification system based on Artificial Intelligence that aims to provide timely detection and notification of dangerous situations. The proposed solution consider the position of a person "hands up" as an emergency situation that will indicate a potential danger for a person. Because people in the face of potential danger are mostly forced to raise their hands up and this pose attracts attention, emphasizes the emotional reaction to certain events and is usually used as a sign of risk or as a means of subjugation. The system should recognize the pose of a person, detect it, and consequently inform about the threat. In this paper, an AI based emergency identification system was proposed to detect the human pose "hands up" for emergency identification using the PoseNet Machine Learning Model. The assumption consists that the utilization only of 6 key points made allows reducing the computing resources of the system since the conclusion is made taking into account a smaller amount of data. For the study, a dataset of 1510 images was created for training an Artificial Intelligence model, and the decisions were verified. Supervised Machine Learning methods are used to classify the definition of an emergency. Alternative methods: Support Vector Machine, Logistic Regression, Naïve Bayes Classifier, Discriminant Analysis Classifier, and K-nearest Neighbours Classifier based on the accuracy were evaluated. Overall, the paper presents a comprehensive and innovative approach to emergency identification for quick response to them using the proposed system. [ABSTRACT FROM AUTHOR]

The development of new processor capabilities which enable hardware-based memory encryption, capable of isolating and encrypting application code and data in memory, have led to the rise of confidential computing techniques that protect data when processed on untrusted computing resources (e.g., cloud). Before confidential computing technologies, applications that needed data-in-use protection, like outsourced or secure multiparty computation, used purely cryptographic techniques, which had a large negative impact on the processing performance. Processing data in trusted enclaves protected by confidential computing technologies promises to protect data-in-use while possessing a negligible performance penalty. In this paper, we have analyzed the state-of-the-art in the field of confidential computing and present a Confidential Computing System for Artificial Intelligence (CoCoS.ai), a system for secure multiparty computation, which uses virtual machine-based trusted execution environments (in this case, AMD Secure Encrypted Virtualization (SEV)). The security of the proposed solution, as well as its performance, have been formally analyzed and measured. The paper reveals many gaps not reported previously that still exist in the current confidential computing solutions for the secure multiparty computation use case, especially in the processes of creating new secure virtual machines and their attestation, which are tailored for single-user use cases. [ABSTRACT FROM AUTHOR]

The digital transfer of the mind to a computer system (i.e., mind uploading) requires representing the mind as a finite sequence of bits (1s and 0s). The classic "stored-program computer" paradigm, in turn, implies the equivalence between program and data, so that the sequence of bits themselves can be interpreted as a program, which will be algorithmically executed in the receiving device. Now, according to a previous proof, on which this paper is based, a computational or algorithmic machine, however complex, cannot be free (in the sense of 'self-determined'). Consequently, a finite sequence of bits cannot adequately represent a free mind and, therefore, a free mind cannot be digitally transferred, quod erat demonstrandum. The impossibility of making this transfer, as demonstrated here, should be a concern especially for those who wish to achieve it. Since we intend this to be a rigorous demonstration, we must give precise definitions and conditions of validity. The most important part of the paper is devoted to explaining the meaning and reasonableness of these definitions and conditions (for example that being truly free means being self-determined). Special attention is paid, also, to the philosophical implications of the demonstration. Finally, this thesis is distinguished from other closely related issues (such as other possible technological difficulties to "discretize" the mind; or, whether it is possible to transfer the mind from one material support to another one in a non-digital way). [ABSTRACT FROM AUTHOR]

The richness and complexity of consent present challenges to those aiming to make related contributions to computer information systems (CIS). This paper aims to support consent-related research in CIS by simplifying the understanding of existing literature and facilitating the framing of future consent management research. Firstly, it outlines existing consent management research and shows how it relates to the literature in law and ethics. Secondly, it presents some fundamental explanations and definitions that must be considered for further contributions to the consent management literature. Thirdly, it identifies five types of consent-related stances often taken in the consent management literature and explains each in some detail. Fourth, it explains one of the identified types of stances (i.e., the disciplinary stance) by expanding on the links between consent as a legal construct and its ethical counterpart. Fifth, considering another of the identified types of stances (i.e., the theoretical stances normally adopted in the consent management literature), the paper presents the key requirements for legally and ethically effective consent management based on three prominent theories. Sixth, it presents the identified types of stances in a conceptual model, contending that the model is novel, relevant, understandable, and useful. [ABSTRACT FROM AUTHOR]

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Two curves are affinely equivalent if there exists an affine mapping transforming one of them onto the other. Thus, detecting affine equivalence comprises, as important particular cases, similarity, congruence and symmetry detection. In this paper we generalized previous results by the authors to provide an algorithm for computing the affine equivalences between two parametric curves of certain types, in any dimension. In more detail, the algorithm is valid for rational curves, and for parametric curves with nonrational but meromorphic components, it admits an also meromorphic, and in fact rational, inverse. Unlike other algorithms already known for rational curves, the algorithm completely avoids polynomial system solving, and instead uses bivariate factoring as a fundamental tool. The algorithm has been implemented in the computer algebra system Maple and can be freely downloaded and used. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, distributed systems have become an indispensable part of the field of information storage and management. In the process of large-scale data processing, it is an important issue to compress or replay files to ensure their integrity. In order to solve the problem of large-scale computing resources and data storage, the distributed file system emerged as a new system structure. The purpose of this paper to is study the key technologies of the distributed memory file system of high-performance computers is to improve the capability and efficiency of the distributed system. This paper mainly uses the experimental method and the comparative method to analyze the key technology of the distributed memory file system of the high-performance computer. Experimental results show that the maximum bandwidth value of DFMS in file memory processing can reach more than 2000, and the value becomes more stable as the file increases. [ABSTRACT FROM AUTHOR]

In Victoria, Australia, senior secondary mathematics students are expected to use technology and thus need to make decisions about using pen-and-paper (P&P) or technology when solving mathematics problems. The predominant technology is a Computer Algebra System (CAS). This study investigated the beliefs about CAS held by twelve Year 11 students as they learnt to use CAS and whether these beliefs were stable over time. These students held a range of beliefs related to the usefulness of CAS, speed of CAS compared to P&P, whether CAS is proper mathematics, choice of CAS or P&P, ease of use, the correctness of answers and solving problems in Mathematical Methods (i.e. the mathematics subject studied). Beliefs are often described as being stable (e.g. McLeod, 1992), but some researchers stress stability needs to be determined empirically rather than being seen as a characteristic of beliefs (e.g. Liljedahl et al., 2012). For this sample of students, stability (rather than instability) is a feature of students' beliefs about CAS. [ABSTRACT FROM AUTHOR]

In this paper we review selected significant developments in the use of digital technology in the teaching and learning of mathematics over the last five years. We focus on a number of important topics in this field, including the evolvement of STEAM and critical making as well as the process of redefining learning spaces in the transformation of the mathematics classroom. We also address the increasing use of computer algebra systems and dynamic geometry packages; and the issue of student collaboration online, especially using learning environments and social media. We briefly touch on artificial intelligence systems, including hyper-personalisation of learning, multimodality and videos. We include a brief discussion on the impact of COVID-19 on mathematics education, and lastly on the more theoretical perspective of the epistemology of digital technology and the construct of humans-with-media. We conclude the discussion with some possible concerns and mentioning some possible new topics for research in the field. [ABSTRACT FROM AUTHOR]

Today, reinforcement learning is one of the most effective machine learning approaches in the tasks of automatically adapting computer systems to user needs. However, implementing this technology into a digital product requires addressing a key challenge: determining the reward model in the digital environment. This paper proposes a usability reward model in multi-agent reinforcement learning. Well-known mathematical formulas used for measuring usability metrics were analyzed in detail and incorporated into the usability reward model. In the usability reward model, any neural network-based multi-agent reinforcement learning algorithm can be used as the underlying learning algorithm. This paper presents a study using independent and actor-critic reinforcement learning algorithms to investigate their impact on the usability metrics of a mobile user interface. Computational experiments and usability tests were conducted in a specially designed multi-agent environment for mobile user interfaces, enabling the implementation of various usage scenarios and real-time adaptations. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning method in which client nodes train deep neural network models locally using their own training data and then send that trained model to a server, which then aggregates all of the trained models into a globally trained model. This protects personal information while enabling machine learning with vast amounts of data through parallel learning. Nodes that train local models are typically mobile or edge devices from which data can be easily obtained. These devices typically run on batteries and use wireless communication, which limits their power, making their computing performance and reliability significantly lower than that of high-performance computing servers. Therefore, training takes a long time, and if something goes wrong, the client may have to start training again from the beginning. If this happens frequently, the training of the global model may slow down and the final performance may deteriorate. In a general computing system, a checkpointing method can be used to solve this problem, but applying an existing checkpointing method to FL may result in excessive overheads. This paper proposes a new FL method for situations with many fault-prone nodes that efficiently utilizes checkpoints. [ABSTRACT FROM AUTHOR]

This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts' knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper's results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the ".NET" framework. The computing system allows one to represent decision-makers' preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper's results have a general character and were applied to analyze power engineering problems. [ABSTRACT FROM AUTHOR]

Structured mixture item response models (StrMixIRMs) are a special type of constrained confirmatory mixture item response theory (IRT) model for detecting latent performance differences in a measurement instrument by characteristic item groups, and classifying respondents according to these differences. In light of limited software options for estimating StrMixIRMs under existing frameworks, this paper proposes reparameterizing it as a confirmatory mixture IRT model using interaction effects between latent classes and item groups. The reparameterization allows for easier implementation of StrMixIRMs with multiple software programs that have mixture modeling capabilities, including open-source ones. This widens the accessibility to these models to a broad range of users and thus can facilitate research and applications of StrMixIRMs. This paper serves two main goals: First, we introduce StrMixIRMs, focusing on the proposed reparameterization based on interaction effects and its various extensions. Second, we illustrate use cases of this novel reparameterization within the mirt 1.41 package in R by employing two empirical datasets. Detailed R code with notes are provided for the applications along with an interpretation of the outputs. [ABSTRACT FROM AUTHOR]