Reviewerly/exHarmony huggingface dataset:
Cybersecurity awareness in Zimbabwean universities: Perspectives from the students

Abstract The study investigated the cybersecurity awareness (CSA) in Zimbabwean universities from the perspectives of the students. The study hypothesized a model to proffer theoretical and practical solutions to higher education students on cybersecurity training programs. The paper used a positivist approach to test three hypotheses on CSA among Zimbabwe universities. To test the hypothesized model, a total of 322 questionnaires were distributed among three universities in Zimbabwe. Descriptive and inferential statistics were used to test the data. ANOVA tests and regressions were used to test three hypotheses from the hypothesized model. Chi square was used for cross‐case analysis to test relationships between age, gender, education level, and institution on recoded variable CSA. The findings reveal that the social engineering attacks, malware attacks, and the internet of things attacks are all positively related to CSA findings from cross‐case analysis indicated that there were no differences on gender and age on CSA while there were differences on education level and institution on CSA. The findings from this study have implications for decision‐makers to come up with policies concerning CSA in the higher education sector.
Indian Regional Spoken Language Identification Using Deep Learning Approach

In thePaul, Bachchu lastPhadikar, Santanu decadeBera, Somnath speech is a thirsty area of research to the researchers. Man–machine interaction through voice is now making us an efficient and effortless mechanism. In our proposed work of language identification, we have taken the International Institute of Information Technology, Hyderabad (IIIT-H) Indic speech corpus where seven languages have been used and each language has 1000 uttered sentence. Thus, a total of 7000 audio samples have been used in our model of language identification. We have done a pre-processing phase, followed by a pitch and Mel Frequency Cepstral Coefficients (MFCC) feature extraction method and finally a Long Short-Term Memory (LSTM) sequence classification has been used for correct identification of the spoken language and obtained a highest training accuracy of 99.8% for the different hyper-parameters discussed in Sect. 5.
Challenges and Future Work Directions in Healthcare Data Management Using Blockchain Technology

In recent times, blockchain becomes a hot research topic and several research works have been made to investigate the applications of the blockchain to non-financial use cases. Healthcare is an important industry in which blockchain is anticipated to have substantial impacts. Exploration of this domain is certainly new and increasing rapidly in present times. Therefore, healthcare informatics research communities and physicians are struggling to retain pace with research progress in this area. This survey offers a detailed review of the ongoing study in the application of blockchain technologies in the healthcare sector. This survey elaborates on several research works involving the applicability of blockchain in healthcare, but there is an absence of enough prototype implementation and studies to describe the efficiency of these presented use cases. The survey additionally highlights the existing developments of blockchain applications in the medical domain, benefits, challenges, and future scope.
Distributed finite time cubature information filtering with unknown correlated measurement noises

This paper addresses the distributed state estimation problem for a class of discrete nonlinear system over sensor networks subject to unknown correlated measurement noises. Firstly, under the condition of network connectivity, a novel communication protocol is developed to ensure every sensor node can gather the information distributed throughout the network within finite communication time. Then a fully distributed estimator is designed by periodically fusing the local information and neighbor’s information according to the covariance intersection fusion strategy. Theoretically, it is proved that the distributed estimator in each sensor node is stable with the exponentially bounded estimation error in mean square. Finally, some numerous simulations are performed to illustrate the practical effectiveness and superiority of the proposed state estimator.
A Differential Evolution Algorithm With Adaptive Niching and <i>K</i>-Means Operation for Data Clustering

Clustering, as an important part of data mining, is inherently a challenging problem. This article proposes a differential evolution algorithm with adaptive niching and k -means operation (denoted as DE_ANS_AKO) for partitional data clustering. Within the proposed algorithm, an adaptive niching scheme, which can dynamically adjust the size of each niche in the population, is devised and integrated to prevent premature convergence of evolutionary search, thus appropriately searching the space to identify the optimal or near-optimal solution. Furthermore, to improve the search efficiency, an adaptive k -means operation has been designed and employed at the niche level of population. The performance of the proposed algorithm has been evaluated on synthetic as well as real datasets and compared with related methods. The experimental results reveal that the proposed algorithm is able to reliably and efficiently deliver high quality clustering solutions and generally outperforms related methods implemented for comparisons.
The effect of an android-based application on T-Mobile learning model to improve students’ listening competence

This paper proposes is to improve the competence of students' listening by applying T-Mobile learning model that on android-based learning application. An approach to designing and developing the interactive mobile application T-Mobile Learning for learner with special needs, suitable for the Android based mobile operating system, is presented. In the literature, these students are referred to as learner with special educational needs. This study uses a quantitative experimental research with pretest group design and posttest control. It consisted of seventy-two students with random sampling technique. The result of the analysis shows that there is a significant difference between the competence of the students' listening which is taught using T-Mobile Learning application with listening based assisted picture.
Extracting Velocity-Based User-Tracking Features to Predict Learning Gains in a Virtual Reality Training Application

Virtual Reality (VR) for training and education of real-world tasks has been researched extensively and has growing use in industry. The data generated by trainees in VR could be leveraged to improve the ability to evaluate learning beyond that which is possible in traditional training scenarios. In this paper, we present a machine learning approach that is able to classify users into participants with low-learning (LL) and high-learning (HL) gains, based on a knowledge test, using only the linear and angular velocities of the head-mounted display (HMD) and handheld controllers. To collect this data, we conduct a VR training user study. We demonstrate that even with a limited data set, it is possible to train a machine learning classifier to predict a trainee's learning performance for a given task with high degrees of accuracy and confidence. We investigate three different sets of velocity-based input features and two feature representations in a machine learning experiment. Our results indicate that all feature combinations resulted in high degrees of accuracy and confidence for predicting learning gains in our testing data. By employing a novel visualization technique, we were able to determine that participants with HL gains moved with greater velocities and fewer changes in direction than those with LL gains. These results indicate that it may be feasible to create VR training applications that can predict a user's learning gains and dynamically adapt the training to better support the user's learning, based on commonly available tracking data.
Beyond Ones and Zeros: Conceptualizing Cyber Crises

The parallel universe of cyberspace, created by computer networks, ones, and zeros, has changed the rules of the game. It would be inevitable for crisis management research to remain unaffected by the new challenges arising. The present article delves into the underexplored concept of cyber crises, by initiating an academic problematization of crisis conceptualization. Drawing from transboundary crisis management theory, it examines two major cyberattacks, the 2017 WannaCry ransomware attack and the 2016 hacking of the DNC, and explores an empirical puzzle: why are these cases not characterized as crises even though they fit already existing definitions? The article contributes to the theory of transboundary crises by shedding light on previously overlooked cyber crisis characteristics and attempting to propose a definition of cyber crises. The way we perceive crises has a direct impact on how we manage them. Broadening the research agenda and understanding the concept of a cyber crisis could pave the way for a more effective and proactive response to new threats, which is what the following pages attempt to demonstrate.
Power System Disturbance Classification With Online Event-Driven Neuromorphic Computing

Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring. Although many conventional machine learning techniques are very successful in classifying events, they rely on extracting information from PMU data at control centers and processing them through CPU/GPUs, which are highly inefficient in terms of energy consumption. To solve this challenge without compromising accuracy, this article presents a novel methodology based on event-driven neuromorphic computing architecture for classification of power system disturbances. A Spiking Neural Network (SNN)-based computing framework is proposed, which exploits sparsity in disturbances and promotes local event-driven operation for unsupervised learning and inference from incoming data. Spatio-temporal information of PMU signals is first extracted and encoded into spike trains and classification is achieved with SNN-based supervised and unsupervised learning framework. In addition, benefits of deep spiking networks for complex multi-class event identification problem are presented by leveraging increasing dynamic neural sparse spiking events with network depth. Moreover, a QR decomposition-based selection technique is proposed to identify signals participating in the low rank subspace of multiple disturbance events. Performance of the proposed method is validated on data collected from a 16-machine, 5-area New England-New York system.
Multimodal Deep Fusion Network for Visibility Assessment With a Small Training Dataset

Visibility is a measure of the transparency of the atmosphere, which is an important factor for road, air, and water transportation safety. Recently, features extracted from convolutional neural networks (CNNs) have obtained state-of-the-art results for the estimation of the visibility range for images of foggy weather. However, existing CNN-based approaches have only adopted visible images as observational data. Unlike these previous studies, in this paper, visible-infrared image pairs are used to estimate the visibility range. A novel multimodal deep fusion architecture based on a CNN is then proposed to learn the robust joint features of the two sensor modalities. Our network architecture is composed of two integrated residual network processing streams and one CNN stream, which are connected in parallel. In addition, we construct a visible-infrared multimodal dataset for various fog densities and label the visibility range. We then compare our proposed method with conventional deep-learning-based approaches and analyze the contributions of various observational and classical deep fusion models to the classification of the visibility range. The experimental results demonstrate that both accuracy and robustness can be strongly enhanced using the proposed method, especially for small training datasets.
Radar Fusion for Multipath Mitigation in Indoor Environments

One of the main challenges of radar-based localization applications in indoor environments is the presence of strong multipath.When the radar bandwidth is large enough, multipath components can be resolved in range but they result in unwanted ghost targets.We propose a novel multipath mitigation approach that exploits the fact that multipaths are highly dependent on the scene geometry.The multipath mitigation approach discards the ghost targets based on the fused information of multiple radars located at different positions in the scene.For such radar fusion, the output of the radar signal processing chain is translated into the world coordinate system that is common for all the radars.We propose a radar alignment approach to estimate the translation and rotation parameters from radar to world coordinate system and vice versa.Our multipath mitigation method is combined with an unscented Kalman filter to improve the localization accuracy.We demonstrate the effectiveness of our complete approach with a real experiment using two radars to detect and track a target in a room with severe multipath.
Commutative Encryption and Watermarking Algorithm Based on Feature Invariants for Secure Vector Map

The existing commutative encryption and watermarking (CEW) methods based on feature invariants can achieve both the robustness of the watermarking algorithm and the security of the encryption algorithm. However, they are only applicable to the raster data such as images, videos, etc. In particular, the organization structure and storage structure of vector map have not been considered in these methods. Therefore, they cannot be used for vector map. This paper derives two feature invariants to solve this problem, which are the sum of inner angles and the storage direction of two adjacent objects according to the inherent characteristics of vector map. Based on these two feature invariants, a new CEW method is proposed in this paper, which includes the feature invariants based watermarking algorithm and the perceptual stream cipher based encryption algorithm on coordinates. Since the coordinate values used in encryption and the feature invariants used in watermarking are independent of each other, the commutativity is achieved for the proposed CEW method. The experiments are given to verify that the proposed CEW method can achieve the commutativity between encryption and watermarking without deteriorating accuracy of data. Besides, it has been verified that the proposed method is more robust to rotate, scaling, translation, and projection transformation compared with the existing CEW methods and has high security. The proposed algorithm has good scalability of encryption, and arbitrary encryption methods based on encrypting the coordinate values can be applied without affecting the extracted feature invariants.
Understanding the Continuous-Time Dynamics of Phase-Transition Nano-Oscillator-Based Ising Hamiltonian Solver

Many combinatorial optimization problems can be mapped onto the ground-state search problem of an Ising model. Exploiting the continuous-time dynamics of a network of coupled phase-transition nano-oscillators (PTNOs) allows building an Ising Hamiltonian solver for obtaining optimum or near-optimum solution with a large speed-up over discrete-time iterative digital hardware. Here, we provide insights into the continuous-time dynamics of such a PTNO-based Ising Hamiltonian solver. We highlight the formation of stable attractor states in the phase space of the coupled PTNO network using second-harmonic injection locking (SHIL) that corresponds to the minima of the Ising Hamiltonian. We show that the emergent synchronized dynamics of the PTNO network is maximized near the critical point of oscillator phase bistability beyond which the dynamics is limited by freeze-out effects. Such dynamical freeze-out severely limits the performance of the PTNO-based Ising solver from obtaining the global optimum. We highlight an improvement in the success probability of reaching the ground state by introducing an annealing scheme with linearly increasing SHIL amplitude compared with a constant SHIL. Finally, we estimate the “effective temperature” of the PTNO-based Ising solver by comparing it with the Markov chain Monte Carlo simulations. The PTNO-based Ising solver behaves like a low-temperature Ising spin system, indicating its effectiveness for optimization tasks.
Adversarially learned one-class novelty detection with confidence estimation

Given samples from a particular normal class, image novelty detection is aimed at determining whether a query sample is from the normal class. Images from this particular class are termed inliers or normal images, whereas images not belonging to the class are termed novelties. Most novelty detection approaches use deep neural networks. However, few of them are end-to-end, and state-of-the-art neural networks tend to be overconfident in their predictions. This may result in incorrect predictions and may negatively affect novelty detection. In this paper, we propose a novel model termed adversarially learned one-class novelty detection with confidence estimation for image novelty detection. The proposed model consists of a representation and a detection module, which are adversarially trained to collaboratively learn the inlier distribution. Moreover, the model uses confidence estimation so that the detection module can more effective. The proposed model is end-to-end and does not require additional calculations such as novelty scores after training. We conduct comprehensive experiments on four publicly available datasets that are commonly used for novelty detection, and the model is compared with state-of-the-art methods to demonstrate its performance.
Automatic short answer grading by encoding student responses via a graph convolutional network

Automatic short answer grading (ASAG) is a challenging task that aims to predict a score for a given student response. Previous works on ASAG mainly use nonneural or neural methods. However, the former depends on handcrafted features and is limited by its inflexibility and high cost, and the latter ignores global word cooccurrence in a corpus and global interaction among the samples in datasets. However, ASAG requires this global information to learn the different expressions conveying the same meaning and the relations between the expressions and the grading labels. To address these limitations, we explore the use of a two-layer graph convolutional network (GCN) to encode the undirected heterogeneous graph of all student responses. The graph has sentence-level and word/bigram-level nodes. An edge is constructed between two nodes according to their inclusion or cooccurrence relationship. The sentence-level TF-IDF value or the PMI value is calculated as the edge weights to reflect the correlation degree between two nodes. Experimental results on the SemEval-2013 benchmark dataset and a two-subject dataset show that our model performs better.
Automated Detection of Acute Lymphocytic Leukemia Using Blast Cell Morphological Features

Diagnosing leukemia accurately is a vital issue in the medical field so that it is effectively treated. Abnormalities in the lymphatic system causes ‘Leukemia’. Compared to adults, children are more affected by this disease. White Blood cells which are known as potent infection fighters are involved in causing leukemia. Leukemia can affect both lymphocytes and myelocytes in the bone marrow. The major types of leukemia are lymphocytic leukemia and myelocytic leukemia. If the malignant cells are capable of performing their normal biological functions, it is known as Chronic Leukemia or else it is known as Acute Leukemia. In this work, detection of Acute Lymphocytic Leukemia (ALL) is focused as it is more decisive when compared to other types of leukemia. Based on blast cell morphological features, the given samples are classified into benign and malignant classes. The performance of SVM-L, SVM-R and KNN classifiers are analyzed in detection and classification of cells. For the application of noisy training samples, the measured accuracy, sensitivity and specificity are 90.33%, 90%, and 90.9% respectively for KNN classifier whereas for SVM-R classifier the accuracy, sensitivity and specificity are obtained as 91.5%, 90%, and 92% respectively. Based on the results obtained through the experiments and analysis of the results, it is concluded that KNN classifier produces better results for noise free data.
DISTRIBUTED LEDGERS, ARTIFICIAL INTELLIGENCE AND THE PURPOSE OF THE CORPORATION

Abstract Distributed ledgers and blockchain technology are widely expected to promote more direct shareholder involvement in corporate governance by reducing costs of voting and trade clearance. Meanwhile, artificial intelligence may shrink the decision-making terrain where corporations rely on human management. This article analyses these technologies and concludes that, while such outcomes are plausible, their potential corporate governance impacts are likely more complex and contingent. Despite the implicit libertarianism that characterises much of the discourse, we in fact have choices to make about how such technologies are developed and deployed – and these policy decisions will have to be grounded in a normative conception of corporate purpose external to the technology itself.
Quality in Use Measurement of Google Classroom in Online Learning

Lecturers have various ways of connecting thinking, communication, and creativity to their students. There are some instructors who choose to use the latest methods in teaching, especially those that support the use of technological aids for the delivery of material so that it affects a range of attitudes to the habits of their students. Our research uses a survey to determine the intensity, methods, and constraints of educators in learning to use media and the internet in the UIN Jakarta campus environment. This paper is aimed at defining a quality model to assess the level of quality in the use of Google Classroom used by most lecturers. The model is founded on the ISO/IEC 25010 standard. The result shows that only the Effectiveness factor is in accordance with ISO/IEC 25010 Standard based on Quality in Use research in the Google Classroom application, and almost all respondents stated that using media and the internet made it easy for them to deliver learning material, 75% of respondents stated that the material they delivered could be found on the internet and there were 18.5% of respondents who always reuploaded the learning material they delivered in class. Ironically, almost all respondents stated that the main obstacle in the implementation of teaching by utilizing media and the internet was the lack of access and reliability of the internet infrastructure that they used.