Reviewerly/exHarmony huggingface dataset:
Human Error: The Nemesis of Access Control

[enteDaily, cybersecurity specialists worldwide are presented with new vulnerabilities that could jeopardize their businesses. The scope and scale of the problem is huge, especially considering the growing attack surface of IT, operational technology (OT), and internet of things (IoT) devices. In 2020 [1], 730 officially publicized data breaches compromised over 22 billion records throughout the world. The security elements that regulate how users and systems communicate and interact with one another are known as access controls. They safeguard systems and resources against unwanted access and can be a cornerstone of identifying the level of authorization once an authentication procedure has been performed successfully. Access restrictions that are broken are a frequent and often dangerous security flaw. Access control design and management is a complicated and a dynamic challenge that involves the application of corporate, organizational, and regulatory restrictions to a technological implementation. Humans, not technology, must make judgments on access control design, and the risk of mistake is large. This paper delves into several access control topics, current cybersecurity statistics and lastly focuses on the element of human error and its potentially disastrous impact.
Development of Students’ Results Help Desk System for First Tier Tertiary Institutions

The paper attempts to develop an effective students’ results help desk system for third-tier higher institutions in Nigeria (such as Colleges of Education). This system offers help desk assistance or services, that is, technical and information assistance for students through enrolling, storing, unifying, tracking, and undertaking students’ results issues and challenges. These were previously handled and conducted manually using paper and pen approach. The target of this electronic-based results help system is to assist students to communicate common results computation and publication issues for effective service delivery, students’ satisfaction, and enhanced the reliability of formative and summative assessments records undertaken by the students. The new system was prototyped using PHP, CSS, HTML and MySQL Server Web development platform. The outcomes show that new system enables the educational needs of students to be timely catered for in more effective ways. This system enables results corrections fraud cases due to lack of audit trail and record keeping by the institutions’ staff in old system to be minimized. It encourages the use of ICT to automate and perform operations more effectively and speedily.
A Report on the Fifth Workshop on Software Engineering Education (SEED 2022)

The 5th International Workshop on Software Engineering Education (SEED 2022), co-located with the 15th Innovations in Software Engineering Conference (ISEC 2022), aims to provide a unique forum to bring together researchers, educators, students, and practitioners to report on their experiences and their ongoing efforts in meeting the recent demands of remote teaching and learning in Software Engineering. The theme of SEED 2022 is Software Engineering Education amid a global pandemic - How can software engineering teaching meet the challenge of the sudden shift to online education triggered by the COVID-19 pandemic? Strategies for project-based learning, hybrid learning, blended learning, use of tools in teaching and learning are specifically targeted in this workshop. Further, it aims to provide a unique opportunity to Software Engineering educators and practitioners to come together and build collaborations for Software Engineering education research and practice.
Sentiment Analysis

With people constantly expressing themselves on various online platforms, there are multitudes of data available that can be used in research. In most cases, these data are unstructured and can be intimidating to analyse due to the large volumes. However, techniques such as sentiment analysis can be used to analyse these data and obtain insights that are useful for researchers. Without the academic and emotional support typically found in a classroom, informal language learners control their own learning environment. This study illustrates how sentiment analysis can be utilised to obtain insights on the experiences of informal L2 learners when they engage with chatbots for L2 learning. Data were collected from various online platforms to reflect experiences with four language learning applications with chatbot features over several years. Results suggest informal L2 learners have mixed sentiments on their experiences and sentiment analysis being a viable approach to analysing informal L2 learner qualitative data.
IBM Small-Scale Superconducting Quantum Computer Not Gate Error Test

Abstract Quantum NOT gates play an important role in the process of quantum information conversion. However, when the X-gate operation is executed on a real quantum computer, there is a large deviation between the actual operation result and the theory, which will lead to inaccurate results when the quantum algorithm containing the X-gate operation is executed. In order to facilitate users to understand the error fluctuations of the X gate in time before executing the quantum algorithm containing the X gate operation on the IBM quantum cloud platform, this paper proposes a method to measure the X gate error. By measuring the X-gate error of four small-scale superconducting quantum computers on the back end of the IBM quantum cloud platform, analyze the degree of fluctuation of the actual measurement value of the quantum device; at the same time, under different execution times shots, the influence of the X-gate test is analyzed. The test results show that the measured value of each qubit of different quantum devices fluctuates to different degrees; the execution times of shots are different, and the test results of X-gate error will be affected to different degrees. This test method helps users select the optimal performance of the qubits before executing quantum algorithms with X gate on IBM's small-scale quantum computers.
Security of Cloud Computing Using Quantum Zero-Knowledge Proof System

Security and privacy play a significant role while using cloud computing. Users can use cloud services, if and only if they find it trust worthy. Users can outsource their sensitive data to cloud environment provided they fill that their data is secured at cloud environment. The popularity of cloud computing depends upon how it can provide the security and privacy of users’ valuable data. Thus, a secured cloud computing is the basic agenda prior to adoption of cloud computing. It is doubtless that the security of cloud computing can only be achieved provided by users of it is the authentic one. In this context, this endeavor has proposed the unique mechanisms to verify the authenticity of the users of the cloud. Apart from this trivial concept of verifying the authenticity, the Zero-Knowledge Proof concept has been introduced here. Furthermore, the concept of quantum mechanics has been incorporated here to provide Zero-Knowledge Proof Engine (ZKE) more effective. Thus, a quantum-based ZKE framework along with algorithm is the main concept of this manuscript. An experimental result has been shown to lay bare the effectiveness of the proposed algorithm.
Sentence Segmentation in Chinese Standard Vehicles Verification Note Texts Based on Context Rules

To improve the effective in the inspection of road transport standard vehicles, it is important to segment sentences in road transport vehicles verification note texts. However the standard vehicles verification note texts contain vehicles technical contents which have many contexts morphological rules of vehicle verification technical description. Hence the sentence segmentation based on vehicle verification technical description rules is proposed. To verify the proposed method, the compliant vehicles verification note texts dataset is constructed, which includes 3803 real compliant vehicles verification note notes texts. These note texts include 9995 sentences. In the experimental results, the mean accuracy of sentence number prediction and the sentence segmentation are 0.8322 and 0.9250 respectively. It is demonstrated that the proposed method is effective in real road transport vehicles verification note texts sentence segmentation.
Wasserstein Graph Auto-Encoder

Conventional Graph Auto-Encoder-based(GAE) minimizes the variational lower bound by Kullback-Leibler divergence in link prediction task and forces a variational posterior distribution for each node to match the prior Gaussian distribution. During the matching process, different variational posterior distributions may intersect. The nodes located at the intersection are subjected to multiple variations which is not suitable for graph reconstruction. We propose Wasserstein Graph Auto-Encoder (WGAE) to minimize the penalty in the Wasserstein distance between the model distribution and the target distribution, which encourages continuous mixed distributions to match the prior distribution. The latent codes of different nodes are far away from each other in the mixed distribution, which is conducive to reconstruction. In addition, gradients are employed to update the mean and variance of the Gaussian distribution in Variational Graph Auto-Encoder(VGAE), which may fall into a kind of local optimization. For each node, the variational posterior distribution is always equal to the prior distribution, and the latent vector is treated as a kind of noise and be ignored by the model. WGAE uses a d-1 dimensional manifold distribution on a d-dimensional hyper-sphere von Mises-Fisher distribution, controlled by mean direction and concentration parameter. For the prior distribution and posterior distribution, different concentration parameters can be selected and set as hyper-parameters to avoid the local optimum. The von Mises-Fisher distribution also learns a richer topology in non-linear dimensionality reduction. Extensive experiments show that the accuracy of our proposed WGAE in the Cora data set is 2% higher than those of the GAE and VGAE, and 3% higher in the Citeseer data set.
From the database of publications to the WEB-system for accounting of scientists’ intellectual activity results

The article describes a WEB-system developed by the authors that implements services related to the formation and provision of multifaceted information about the results of scientific activities (publications, copyright certificates and reports at scientific events) of employees of an organization or a group of organizations. The system is focused both on the end user interested in obtaining specific data, and on the administrative staff, who generates reporting materials for the parent organization. The information base of the system contains metadata on the following classes of objects: persons (authors), organizations and their subdivisions; publications at analytical, monographic and summary levels; copyright certificates; scientific events (conferences, symposia, seminars); reports. The system includes two modules – an administrative one (intended for entering and editing data) and a user one, which is a special search engine that searches for information, visualizes it, provides navigation among related resources and exports data. A distinctive feature of the system is the introduced concept of “equivalent” objects. Objects are considered equivalent if they are represented in the system by different metadata, but referring to the same physical entity. Such objects are “persons” corresponding to one author with different spellings of the surname in the bibliographic descriptions of publications; organizations with different variants of names; articles published unchanged in various languages. In accordance with modern requirements for reporting on publications, the system reflects the sources of research funding, as well as the affiliations indicated in the articles for each author.
Sensor Data Normalization Among Heterogeneous Smartphones for Implicit Authentication

Nowadays, smartphones have become an important part in people’s life. Existing traditional explicit authentication mechanisms can hardly protect the privacy of users, which makes implicit authentication mechanisms a hot topic. Considering the non-uniform sensor data, which are collected from heterogeneous smartphones, e.g., different series or versions, a sensor data normalization solution among heterogeneous smartphones is proposed in this paper. First, the sensors used to collect data can be determined by whether they can describe users’ behavior characteristics well. On this basis, we propose a sensor data normalization solution from three aspects: the proportion-based one, statistics-based one and attitude-based normalization. Particularly, we establish an adaptive complementary filter with a dynamic parameter, making it possible to adjust the proportion of accelerometer and gyroscope. Finally, based on 2,000 samples collected from 40 volunteers on three different smartphones, we perform experiments to evaluate our proposed solution. Compared with non-normalized data, the authentication efficiency through our solution can respectively increase 15% and 18% in Accuracy and AUC, and reduce FAR and FRR by 10% and 18%, respectively.
Research on Dam Deformation Prediction Model Based on Wavelet Neural Network

The BP traditional neural network dam deformation prediction model has the disadvantages of slow convergence speed, easy to fall into local extreme points and poor generalization ability, a dam deformation prediction model based on wavelet neural network is constructed by combining the time-frequency local analysis ability of wavelet and the self-learning and self-organizing characteristics of neural network; the measured value of settlement of Lixi barrage in Guangzhou is selected as the research object, From January 2016 to June 2019, a total of 557 sets of data were used as model training set to establish the model; from July 2019 to October 2019, 95 sets of data were used as model verification set to evaluate the model performance. The test set prediction results of wavelet neural network and BP traditional neural network show that the mean square error of deformation prediction model based on BP traditional neural network is 0.138 and the certainty coefficient is 0.748; based on wavelet analysis, the mean square error of the coupled neural network deformation prediction model is 0.025 and the certainty coefficient is 0.927. Wavelet neural network has fast convergence speed, high prediction accuracy and strong generalization ability in dam deformation prediction And other characteristics.
Trouble in the metaverse

Throughout history, there has been backlash to technological progress – so why should now be any different, asks James Ball
Repairmance Strategy Model of Intelligent System Information Facilities based on Repairmance Cost

On the basis of analyzing the working principle and repairmance support strategy of information facilities in intelligent system - four rotor UAV automatic control system, according to its high technical content and expensive repairmance cost, the responsibility relationship model between repairmance cost and repairmance interval is established by combining repairmance after failure and preventive repairmance. The reliability relationship model between repairmance cost and repairmance interval is established by triming the repairmance interval Time ensures the minimum repairmance cost. The serviceability and sensibility of the model are proved by examples.
A<sup>2</sup>P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks

In this work, to limit the number of required attention inference hops in memory-augmented neural networks, we propose an online adaptive approach called [Formula: see text]-memory-augmented neural network (MANN). By exploiting a small neural network classifier, an adequate number of attention inference hops for the input query are determined. The technique results in the elimination of a large number of unnecessary computations in extracting the correct answer. In addition, to further lower computations in [Formula: see text]-MANN, we suggest pruning weights of the final fully connected (FC) layers. To this end, two pruning approaches, one with negligible accuracy loss and the other with controllable loss on the final accuracy, are developed. The efficacy of the technique is assessed by applying it to two different MANN structures and two question answering (QA) datasets. The analytical assessment reveals, for the two benchmarks, on average, 50% fewer computations compared to the corresponding baseline MANNs at the cost of less than 1% accuracy loss. In addition, when used along with the previously published zero-skipping technique, a computation count reduction of approximately 70% is achieved. Finally, when the proposed approach (without zero skipping) is implemented on the CPU and GPU platforms, on average, a runtime reduction of 43% is achieved.
Network abnormal traffic detection method based on multi kernel KPCA-PSO-ELM

In order to improve the detection rate of network abnormal traffic and reduce the false alarm rate, a network abnormal traffic detection method based on multi-core kpca-pso-elm is proposed. Firstly, Gauss radial basis function and d-order polynomial kernel function are mixed into multi-core kernel function, which has the generalization ability and learning ability of global and local kernel functions at the same time, so as to improve the efficiency of anomaly detection; Then, particle swarm optimization (PSO) is used to optimize the initial parameters of extreme learning machine (ELM), obtain the optimal solution of the parameters, reduce the influence of blindness in the process of random selection of parameters on the detection performance of limit learning machine, and improve the accuracy of anomaly detection. Finally, the optimized elm model is used to detect and classify the data. In order to verify the effectiveness of this algorithm, MATLAB and KDDCUP99 data set are used for experimental verification. The experimental results show that the detection effect of multi-core kpca-pso-elm model is significantly improved compared with PSO-SVM and PCA-BiGAN, and effectively improves the detection efficiency of network abnormal traffic detection.
Comparison of Ant Colony Optimization Algorithms for Small-Sized Travelling Salesman Problems

This paper deals with Ant Colony Optimization (ACO) applied to the Travelling Salesman Problem (TSP). TSP is a well-known combinatorial problem which aim is to find the shortest path between a designated set of nodes. ACO is an algorithm inspired by the natural behavior of ants. When travelling from the nest to a food source, ants leave pheromones behind. This algorithm can be applied to TSP in order to find the shortest path. In this paper, the variants of ACO are shortly explained and a new Improved Ant Colony Optimization (IACO) algorithm is proposed. The IACO is applied to small-sized TSP. It is shown that the proposed IACO performs better in some cases, especially when there are more cities in the TSP.
Evaluation of Tools Used for 3D Reconstruction of 2D Medical Images

Three-dimensional image reconstruction from a set of sequence of two-dimensional medical images plays a key role in analysing the anatomy of a human body. These 2D medical images are obtained through magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) and other modalities of 3D imaging. This paper discusses evaluation of few tools available to reconstruct a 3D image from a set of 2D images. The tools considered for evaluation are: 3D Slicer, MITK, InVesalius, RadiAnt, Real3d VolViCon, ITK-SNAP and Volume Viewer. The evaluation parameters considered in this paper for making a comparative study are: data import facility, data export facility, handling metadata, 2D viewing facility, 3D viewing facility and technical support provided by the developers of the respective tool. These tools do not allow additional 3D image processing algorithms other than existing functionalities of the respective tools; 3D Logical Image Processing System (3DLIPS) allows people to implement their own 3D image processing algorithms. This paper also discusses various functionalities and algorithms supported by 3DLIPS.
The Adoption of Cloud Computing in South Africa’s Oil and Gas Industry

Cloud computing has become an increasingly attractive option for oil and gas organizations looking to reduce costs while increasing operational excellence. The cloud is not only a tool for faster computing power and higher performance, but also as an instrument to faster application deployment, lower service costs and a step towards digital transformation. This study aimed to determine the level of cloud computing adoption within selected organizations within the oil and gas industry in South Africa. The study also attempted to understand the factors that influence this adoption and how SA oil and gas companies are utilizing cloud technologies, as well as the factors that influence the presumed lack of adoption. This research was guided by the Technology, Organization, and Environment (TOE) framework and used a qualitatively approach, based on semi-structured interviews from seven participants, from four oil and gas companies in SA. The findings of this research show a high level of awareness but low level of adoption of cloud computing. The factors that were found to have a positive and significant influence on the intention to adopt cloud computing included security, relative advantage, compatibility, top management support, vendor support and competition. The factors that were found to be a risk or challenge towards the adoption of cloud computing included complexity, government regulations, organizational readiness, bandwidth, trust and vendor lock-in.
Using Deep Learning and Image Recognition to Realize The Electronic Management of Paper Original Records

In this paper, a large number of paper-based original records generated in the detection process, which are cumbersome in circulation and difficult in management, are proposed to realize the electronic management of original records by using image recognition. Through the learning and recognition of original record format, simulated original record and handwriting, the key data area of paper original record can be recognized.
Differentiated Learning in the Context of Immersive Technologies

This article includes a literature review about the impact of fully immersive virtual reality experiences on a students’ well-being, focusing on side effects such as cybersickness and depersonalization-derealization disorder. The purpose of this article is to emphasize the importance of some aspects of pedagogical work, in case of using virtual reality technology, such as the importance of educators being informed about the reasons why a student may not want to use virtual reality technology, significance of ability to understand student’s reactions during virtual reality experience, the possibility to offer alternatives and find the best solutions, so student can have the most positive and safe virtual reality learning experience. Therefore, this article raises the question of the need for a differentiated learning in the context of extended reality technologies in school education, identifying several possible alternatives such as desktop virtual reality and augmented reality to fully immersive virtual reality.