Reviewerly/exHarmony huggingface dataset:
A Conceptual Learning Design in Virtual Reality–The Cognitive VR Classroom for Education After the Pandemic Era

ABSTRACT The COVID-19 pandemic has created extraordinary global crises and unprecedented disruption to education. The current situation has become changeable and unpredictable, which may lead to chronic cognitive issues for students who have no access to education. At the same time, the use of virtual reality (VR) technology has emerged as a powerful instrument for re-thinking and enhancing the current educational patterns during the post- pandemic era. However, although ample reviews have summarized developments in VR and education, few studies have synthesized these findings into an integrated learning design. In response to this insufficiency, this study discusses potential solutions for current educational challenges and presents a novel design – the cognitive VR classroom. It is a conceptual design that could enable students to access rich learning resources within an immersive VR system. The design of the cognitive VR Classroom is based on the amalgamation of brain- based learning theory (BBL) and the cognitive-affective model of immersive learning (CAMIL). Further, six extended dimensions of classroom learning were developed to structure the cognitive VR classroom. To examine their validity, a correlation analysis was conducted to present the correlation coefficient and the strength of the association. The result shows that the cognitive VR classroom is a promising theoretical framework to facilitate educational diversity and a powerful model to develop smart classroom learning. KEYWORDS: The Covid-19 Pandemic, Virtual Reality, Cognitive Education, Classroom Learning
Quality Enhancement for Drone Based Video using FPGA

Nowadays Drones are being widely used for surveillance and various other activities. The video stream produced by the drone can be disturbing or can contain noise data which might reduce the quality of the video stream. The video stream can be enhanced so that there is no disturbance in the video stream. The video enhancement can be done in real-time with the help of field programmable gate array (FPGA) which reduces the processing time with low energy consumption. Our project mainly focuses on enhancing the quality of the video stream using enhanced super-resolution generative adversarial networks (ESRGAN), contrast-limited Adaptive histogram equalization (CLAHE), Gamma Correction and Saturation Adjustment by integrating the image source in the drone with the FPGA.
Artificial intelligence (AI)-based mobile learning in ELT for EFL learners: The implementation and learners' attitudes

Mobile learning powered by artificial intelligence (AI) is a promising vision for transforming education by introducing tutoring systems that can personalize learning. It is being developed to ensure that all learners have the opportunity to be autonomous and to advance collaborative learning. NovoLearning was used in this study to implement mobile learning powered by artificial intelligence (AI). NovoLearning's mobile learning platform powered by artificial intelligence (AI) provides a pleasurable experience and practical language learning for English as a Foreign Language students. Each NovoLearning level includes meaningful learning objectives in the areas of vocabulary, grammar syntax, listening, pronunciation, and role-playing. The purpose of this study was to examine the improvement of English as a Foreign Language (EFL) University students at Universitas Lambung Mangkurat in Indonesia who studied the subject of English using artificial intelligence-based mobile learning. This study used a pretest-posttest non-equivalent control group design, recruiting two groups: one as the control group, which did not use artificial intelligence-based mobile learning, and another as the experimental group, which did. The result indicates that the t critical value (6.373) is greater than the t table, as indicated by the positive values of the lower score (4.4928) and upper score (8.5195). It is less than 0.05 in the Sig (2-tailed) row. Then it can be concluded that the experimental group's English competence was significantly greater than the control group's. Students' positive attitudes toward AI-based mobile learning encompassed two outcomes: (1) increased opportunities for learning for students and (2) NovoLearning areas for improvement during implementation.
Towards Agile Cybersecurity Risk Management for Autonomous Software Engineering Teams

In this study, a framework was developed, based on a literature review, to help managers incorporate cybersecurity risk management in agile development projects. The literature review used predefined codes that were developed by extending previously defined challenges in the literature—for developing secure software in agile projects—to include aspects of agile cybersecurity risk management. Five steps were identified based on the insights gained from how the reviewed literature has addressed each of the challenges: (1) risk collection; (2) risk refinement; (3) risk mitigation; (4) knowledge transfer; and (5) escalation. To assess the appropriateness of the identified steps, and to determine their inclusion or exclusion in the framework, a survey was submitted to 145 software developers using a four-point Likert scale to measure the attitudes towards each step. The resulting framework presented herein serves as a starting point to help managers and developers structure their agile projects in terms of cybersecurity risk management, supporting less overloaded agile processes, stakeholder insights on relevant risks, and increased security assurance.
Attention regularized semi-supervised learning with class-ambiguous data for image classification

Data augmentation via randomly combining training instances and interpolating the corresponding labels has shown impressive gains in image classification. However, model attention regions are not necessarily meaningful in class semantics, especially for the case of limited supervision. In this paper, we present a semi-supervised classification model based on Class-Ambiguous Data with Attention Regularization, which is referred to as CADAR. Specifically, we adopt a Random Regional Interpolation (RRI) module to construct complex and effective class-ambiguous data, such that the model behavior can be regularized around decision boundaries. By aggregating the parameters of a classification network over training epochs to produce more reliable predictions on unlabeled data, RRI can also be applied to them as well as labeled data. Further, the classifier is enforced to apply consistent attention on the original and constructed data. This is important for inducing the model to learn discriminative features from the class-related regions. The experiment results demonstrate that CADAR significantly benefits from the constructed data and attention regularization, and thus achieves superior performance across multiple standard benchmarks and different amounts of labeled data.
Third-Person Perspective Avatar Embodiment in Augmented Reality: Examining the Proteus Effect on Physical Performance

Embodiment in augmented reality (AR) is applicable to various fields such as exercise and education. However, full-body embodiment in AR is still challenging to implement due to technical problems such as low body tracking accuracy. Therefore, the study on the impact of an avatar in AR on user performance is limited. We implemented an AR embodiment system and investigated its impact on user physical performance. The system allows users to see their avatar instead of their real body from a third-person perspective. The results show that a muscular avatar improves user physical performance during and after controlling the avatar.
Logit Averaging: Capturing Global Relation for Session-Based Recommendation

Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, those need more computation cost and time. We propose a novel algorithm called Logit Averaging (LA), utilizing both (i) local-level logits, which come from intra-sessions item transitions and (ii) global-level logits, which come from gathered logits of related sessions. The proposed method shows an improvement in recommendation performance in respect of accuracy and diversity through extensive experiments.
Unifying Cosine and PLDA Back-ends for Speaker Verification

State-of-art speaker verification (SV) systems use a back-end model to score the similarity of speaker embeddings extracted from a neural network model. The commonly used back-end models are the cosine scoring and the probabilistic linear discriminant analysis (PLDA) scoring. With the recently developed neural embeddings, the theoretically more appealing PLDA approach is found to have no advantage against or even be inferior the simple cosine scoring in terms of SV system performance. This paper presents an investigation on the relation between the two scoring approaches, aiming to explain the above counter-intuitive observation. It is shown that the cosine scoring is essentially a special case of PLDA scoring. In other words, by properly setting the parameters of PLDA, the two back-ends become equivalent. As a consequence, the cosine scoring not only inherits the basic assumptions for the PLDA but also introduces additional assumptions on the properties of input embeddings. Experiments show that the dimensional independence assumption required by the cosine scoring contributes most to the performance gap between the two methods under the domain-matched condition. When there is severe domain mismatch and the dimensional independence assumption does not hold, the PLDA would perform better than the cosine for domain adaptation.
Hybrid Working Method: An Integrative Review

Hybrid working is the combination of working both from an office and from home wherever the roles will allow and wherever they work best, Greater control on working styles and flexibility at work are the core reasons to choose hybrid working model in organizations. The intention of the paper is to investigate hybrid working, different types of teleworks and flexible work arrangements that are been followed in the organizations by reviewing the papers that are published from last one decade which resulted in improved employee wellbeing, productivity, work life balance, work and non-work balance, individual and team performance, positive and negative work home interaction and employee turnover. After examining the results of the earlier research on hybrid working, telework and flexible work arrangements it was found that employees are highly recommending the organizations to encourage work from home policies and make adjustment in the working culture of the organization in order to survive in this competitive world by retaining their top talented work force
Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization

This paper investigates a method for simulating natural conversation in the model training of end-to-end neural diarization (EEND). Due to the lack of any annotated real conversational dataset, EEND is usually pretrained on a large-scale simulated conversational dataset first and then adapted to the target real dataset. Simulated datasets play an essential role in the training of EEND, but as yet there has been insufficient investigation into an optimal simulation method. We thus propose a method to simulate natural conversational speech. In contrast to conventional methods, which simply combine the speech of multiple speakers, our method takes turn-taking into account. We define four types of speaker transition and sequentially arrange them to simulate natural conversations. The dataset simulated using our method was found to be statistically similar to the real dataset in terms of the silence and overlap ratios. The experimental results on two-speaker diarization using the CALLHOME and CSJ datasets showed that the simulated dataset contributes to improving the performance of EEND.
Student Public Opinion Management in Campus Commentary Based on Deep Learning

Aiming at the needs of network public opinion analysis and crisis public opinion early warning in colleges and universities, the semantic sentiment analysis method is studied in this paper. Most of the public opinion information comes from short text comment information, and its text is separated from the written language, the structure becomes simpler, and it lacks normativity, which brings certain difficulties to the extraction of text features. Traditional sentiment analysis methods often rely on emotional dictionaries and feature extraction, and with the continuous change of Internet culture, a technical help is needed to make even the dictionary updated. Based on the analysis and study of attention mechanism and deep learning related technologies, an LSTM model is proposed to mine the deep semantic characteristics of text, which can accurately determine its emotional tendency. The main tasks are as follows: according to the CNN and LSTM text processing, CNN can better extract the local features of the text, and LSTM can retain the text history information and effectively extract the global features of the sequence. The CBOW model is optimized to pay more attention to the feature vectors that affect the classification results during the calculation process. Finally, the improved model in this paper compares the accuracy, recall rate, loss rate, and F1 value of the traditional model to indicate the performance evaluation index of the model.
A Comprehensive Survey on Lightweight Asymmetric Key Cryptographic Algorithm for Resource Constrained Devices

Elliptic Curve Cryptography, being a popular lightweight asymmetric key cryptographic algorithm that is widely adapted to meet high security requirement of resource constrained devices, were surveyed in this work. Further, ECC-based ElGamal cryptosystem, Elliptic Curve Digital Signature Algorithm, and Elliptic Curve Diffie Hellman Key Exchange Algorithm have been comprehensively reviewed with its characteristics and preferred applications. In addition, few related work are analyzed and suggestions for suitable target applications were provided. Moreover, ECC being a popular asymmetric key cryptographic technique is reported to be modeled using Vivado tool for various target implementation on FPGA devices. Techniques that enhances throughput, area, and computation time that caters for IoT applications were also reviewed. Design implementations on the advanced FPGA boards for IoT device/similar applications were also analyzed and compared.
Self-Guided Smartphone Excursions in University Teaching—Experiences From Exploring “Water in the City”

Like many other university teachers, we were faced with an unprecedented situation in spring 2020, when we had to cancel on-site teaching and excursions due to the Covid-19 pandemic. However, we were in the fortunate position that we had already started to develop a smartphone-based self-guided excursion on the topic of “Water in the City”. We accelerated this development and used it to replace the traditional group excursion in our Bachelor level introductory course in Hydrology and Climatology. The excursion of this course is visited by around 150 students each year. Because the student feedback was overall very positive, we used the self-guided excursion again in 2021 and plan to continue to use it in the coming years. In this paper, we describe the excursion, discuss the experiences of the students and ourselves, and present recommendations and ideas that could be useful for similar excursions at other universities.
Implementation of a robotic-transformed five-phase inquiry learning to foster students' computational thinking and engagement: a mobile learning perspective

Many studies have used a robot to engage students in higher-order thinking tasks. With such a learning task, it remains challenging to design instruction in deriving basic knowledge through technological tools to support students in developing their higher-order thinking skills. However, a computer-controlled robot maybe not a challenge yet, and it is not convenient to get the information or supplementary materials in a learning activity. Many scholars have recognised mobile technologies as a potential tool enabling students to learn across in and out-of-classes. This study developed a mobile application to control a robot and manipulate data in authentic situations. Afterward, this study applied a five-phase inquiry-based learning approach to implementing the mobile application in a Thai high school robotic learning class. A repeated measures research design was employed to examine the effectiveness of implementing a robot-transformed five-phase inquiry learning regarding students' improvement of computational thinking concepts, practices and perspectives. Besides, the students could be engaged to learn, leading to improvement of computational thinking.
Smart Home Activity Recognition for Ambient Assisted Living (AAL)

With the increasing age of an individual, the chances of being prone to chronic diseases like diabetes or non-curable diseases like Alzheimer’s Syndrome or Parkinson's Syndrome increases. Due to the health issues, elderly must be accompanied by caretakers to monitor their well-being at all times. With growing responsibilities and work pressure, the family members may find it challenging to find a trustworthy caretaker. In such scenarios, an assisted living environment acts as a boon. A normal home embedded with different sensors to monitor an individual’s well-being is called as Ambient Assisted Living(AAL). This living environment detects anomalous behaviour and recognizes human activities. In this research paper, a smart home activity recognition model is proposed and implemented using four machine learning algorithms using six different publicly available datasets. It has been observed that Random Forest machine learning algorithm shows the best accuracy on most of the dataset.
<i>Regulating the Crypto Economy</i>. By Iris H.-Y. Chiu

The crypto economy refers to economic activities that take place on the blockchain, which is a consensus protocol that creates an append-only log recorded in a ledger constructed by participants and secured with cryptography.1 The transactions recorded on the ledger are verifiable in a permanent manner. Blockchains can be permissioned or permissionless. Unlike permissioned blockchains, permissionless blockchains do not have a central authority to control or amend the entries to the ledger. Currently, the main uses or applications of the permissionless blockchain are bitcoins and Ethereum, the latter being a decentralized platform that enables the creation of smart contracts and decentralized applications (called Dapps).2 However, despite the rise of enterprise activities on the blockchains in the last decade, the regulators have largely focused on the financialization of the crypto economy, as evidenced by the raft of regulations dealing with crypto-currency or initial coin offerings (ICOs) globally. For instance, the USA, Singapore, and Switzerland regulate ICOs by requiring that certain types of offering of tokens will have to comply with the country’s securities regulation, while other countries, such as South Korea, have outright banned ICOs.3
Model-Based Actor-Critic with Chance Constraint for Stochastic System

Safety is essential for reinforcement learning (RL) applied in real-world situations. Chance constraints are suitable to represent the safety requirements in stochastic systems. Previous chance constrained RL methods usually learn an either conservative or unsafe policy, and some of them also suffer from a low convergence rate. In this paper, we propose a model-based chance constrained actor-critic (CCAC) algorithm which can efficiently learn a safe and non-conservative policy. Different from existing methods that optimize a conservative lower bound, CCAC directly solves the original chance constrained problems, where the objective function and safe probability are simultaneously optimized with adaptive weights. In order to improve the convergence rate, CCAC utilizes the gradient of dynamic model to accelerate policy optimization. The effectiveness of CCAC is demonstrated by a stochastic car-following task. Experiments indicate that CCAC achieves good performance while guaranteeing safety, with a five times faster convergence rate compared with model-free RL methods. It also has 100 times higher online computation efficiency than traditional safety techniques such as stochastic model predictive control.
Pro-PULSE: Learning Progressive Encoders of Latent Semantics in GANs for Photo Upsampling

The state-of-the-art photo upsampling method, PULSE, demonstrates that a sharp, high-resolution (HR) version of a given low-resolution (LR) input can be obtained by exploring the latent space of generative models. However, mapping an extreme LR input (162) directly to an HR image (10242) is too ambiguous to preserve faithful local facial semantics. In this paper, we propose an enhanced upsampling approach, Pro-PULSE, that addresses the issues of semantic inconsistency and optimization complexity. Our idea is to learn an encoder that progressively constructs the HR latent codes in the extended W+ latent space of StyleGAN. This design divides the complex 64× upsampling problem into several steps, and therefore small-scale facial semantics can be inherited from one end to the other. In particular, we train two encoders, the base encoder maps latent vectors in W space and serves as a foundation of the HR latent vector, while the second scale-specific encoder performed in W+ space gradually replaces the previous vector produced by the base encoder at each scale. This process produces intermediate side-outputs, which injects deep supervision into the training of encoder. Extensive experiments demonstrate superiorities over the latest latent space exploration methods, in terms of efficiency, quantitative quality metrics, and qualitative visual results.