Reviewerly/exHarmony huggingface dataset:
COVID-19: Strategies for Online Engagement of Remote Learners

The COVID-19 outbreak across the globe has forced educational institutions including medical schools to suspend campus learning in order to curb the spread of the virus. This has forced the teaching community to think of new avenues and alternate strategies for engaging our students. Many institutes of higher education have shifted to online-mode to ensure continuity of teaching-learning and assessment processes. In online mode of flexible learning, students are provided with a variety of choices for their learning and allows them to take more responsibility for their own learning. Since, many of the teachers are new to online mode of teaching their apprehension towards active online engagement of their students is inevitable. This document presents a variety of strategies for increasing and ensuring higher levels of student engagement in online teaching.
Augmented reality applications for K-12 education: A systematic review from the usability and user experience perspective

In the past two decades, we have witnessed soaring efforts in applying Augmented Reality (AR) technology in education. Several systematic literature reviews (SLRs) were conducted to study AR educational applications (AREAs) and associated methodologies, primarily from the pedagogical rather than from the human–computer interaction (HCI) perspective. These reviews vary in goal, scale, scope, technique, outcome and quality. To bridge the gaps identified in these SLRs, ours is to meet fourfold objectives: to ground the analysis deeper in the usability and user experience (UX) core concepts and methods; to study the learning effect and usability/UX of AREAs and their relations by learner age; to reflect on the prevailing SLR process and propose improvement; to draw implications for the future development of AREAs. Our searches in four databases returned 714 papers of which 42, together with 7 from three existing SLRs, were included in the final analysis. Several intriguing findings have been identified: (i) the insufficient grounding in usability/UX frameworks indicates that there seems a disconnection between the HCI and technology-enhanced learning community; (ii) a lack of innovative AR-specific usability/UX evaluation methods and the continuing reliance on questionnaire may hamper the advances of AREAs; (iii) the learner age seems not a significant factor in determining the perceived usability and UX or the learning effect of AREAs; (iv) a limited number of studies at home suggests the missed opportunity of mobilizing parents to support children to deploy AREAs in different settings; (v) the number of AREAs for children with special needs remains disappointedly low; (vi) the threat of predatory journals to the quality of bibliometric sources amplifies the need for a robust approach to the quality assessment for SLR and transparency of interim results. Implications of these issues for future research and practice on AREAs are drawn.
WhatsApp Network Forensics: Discovering the IP Addresses of Suspects

Call record analysis is the most critical task for the Law Enforcement Agencies (LEAs) in a cyber-investigation process. It provides valuable information in the investigation, such as time and date and the duration of incoming and outgoing calls. The technological advancement of smartphones and the versatility of Instant Messaging (IM) applications provide multiple communication channels to cybercriminals for communication, making it difficult for the LEAs to monitor/investigate using traditional forensics tools and techniques. The most challenging part is to retrieve specific information from the network traffic of a particular IM Application such as WhatsApp. This research article's primary purpose is to find the IP address of the cybercriminal using WhatsApp through existing sniffing techniques and tools. A method called rule-based extraction for sniffing packets is proposed for extracting the most relevant data from the network traffic. The results support LEAs to identify the cybercriminals' specific traffic and help in analyzing and comparing the mobile phone data with the network traffic.
Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity

Abstract Ising machines based on nonlinear analog systems are a promising method to accelerate computation of NP-hard optimization problems. Yet, their analog nature is also causing amplitude inhomogeneity which can deteriorate the ability to find optimal solutions. Here, we investigate how the system’s nonlinear transfer function can mitigate amplitude inhomogeneity and improve computational performance. By simulating Ising machines with polynomial, periodic, sigmoid and clipped transfer functions and benchmarking them with MaxCut optimization problems, we find the choice of transfer function to have a significant influence on the calculation time and solution quality. For periodic, sigmoid and clipped transfer functions, we report order-of-magnitude improvements in the time-to-solution compared to conventional polynomial models, which we link to the suppression of amplitude inhomogeneity induced by saturation of the transfer function. This provides insights into the suitability of nonlinear systems for building Ising machines and presents an efficient way for overcoming performance limitations.
Building the Foundation of Robot Explanation Generation Using Behavior Trees

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.
Multi-view group representation learning for location-aware group recommendation

With the development of location-based services (LBS), many location-based social sites like Foursquare and Plancast have emerged. People can organize and participate in group activities on those sites. Therefore, recommending venues for group activities is of practical value. However, the group decision making process is complicated, requiring trade-offs among group members. And the data sparsity and cold-start problems make it difficult to make effective group recommendation. In this manuscript, we propose a Multi-view Group Representation Learning (MGPL) framework for location-aware group recommendation. The proposed multi-view group representation learning framework can leverage multiple types of information for deep representation learning of group preferences and incorporate the spatial attributes of locations to further capture the group mobility preferences. Experiments on two real datasets Foursqaure and Plancast show that our method significantly outperforms the-state-of-art approaches.
KR-DBSCAN: A density-based clustering algorithm based on reverse nearest neighbor and influence space

Density-based clustering is one of the most commonly used analysis methods in data mining and machine learning, with the advantage of locating non-ball-shaped clusters without specifying the number of clusters in advance. However, it has notable shortcomings, such as an inability to distinguish adjacent clusters of different densities. We propose a density-based clustering algorithm, KR-DBSCAN , which is based on the reverse nearest neighbor and influence space. The core objects are identified according to the reverse nearest neighborhood, and their influence spaces are determined by calculating the k-nearest neighborhood and reverse nearest neighborhood for each data object under the Euclidean distance metric. In particular, a new cluster expansion condition is defined using the reverse nearest neighborhood and its influence space, and when the core objects are within their influence spaces, they are added to the cluster by breadth-first traversal. As a result, adjacent clusters with different densities are effectively distinguished, and the computational load is substantially reduced. Boundary objects and noise objects are identified, also using k-nearest neighbors. KR-DBSCAN is experimentally validated on the UCI dataset and some synthetic datasets. • We define a new cluster expanding condition for the density-based clustering. • We design a new noise removal approach in the density-based clustering analysis. • We propose a density-based clustering algorithm KR-DBSCAN. • KR-DBSCAN is evaluated through extensive experiments.
Learning Disentangled Representation for Mixed- Reality Human Activity Recognition With a Single IMU Sensor

Together with the rapid development of the sensors technology in recent years, sensor-based human activity recognition (HAR) has shown promising performance using well-known supervised deep learning methods. But it remains challenging in a realistic scenario, i.e., limit number of labeled samples and sensors. This paper proposes a novel deep learning method to achieve accurate and robust HAR with only a single IMU sensor. Our contributions are two-fold: 1) Based on the SMPL (Skinned Multi-Person Linear) model, we build a large synthetic HAR dataset containing multi-modal measurements: acceleration and angular velocity, which were generated according to the forward kinematics; 2) We propose a multiple-level domain adaptive learning model with information-theoretically stimulated constraints to simultaneously align the distributions of low- and high-level representations of virtual and real HAR data. The proposed mutual information constraints encourage the neural network to learn a disentangled representation for the multi-modal sensing data. Comprehensive experimental results on three publicly available datasets demonstrate that the proposed method compares favorably with competing ones and has robust performance with variable labeled samples.
Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network

Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) cameras to monitor crowd activity efficiently.As a result, these cameras generate an enormous amount of visual data through manual or offline monitoring, requiring numerous human resources for efficient tracking. Therefore, there is an urgent need to develop an intelligent and automatic system in order to efficiently monitor crowds and identify abnormal activity.The existing method is incapable of extracting discriminative features from surveillance videos as pre-trained weights of different architectures were used. This paper develops a lightweight approach for accurately identifying violent activity in surveillance environments. As the first step of the proposed framework, a lightweight CNN model is trained on our own pilgrim's dataset to detect pilgrims from the surveillance cameras. These preprocessed salient frames are passed to a lightweight CNN model for spatial features extraction in the second step. In the third step, a Long Short Term Memory network (LSTM) is developed to extract temporal features. Finally, in the last step, in the case of violent activity or accidents, the proposed system will generate an alarm in real time to inform law enforcement agencies to take appropriate action, thus helping to avoid accidents and stampedes.We have conducted multiple experiments on two publicly available violent activity datasets, such as Surveillance Fight and Hockey Fight datasets; our proposed model achieved accuracies of 81.05 and 98.00, respectively.
Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study (Preprint)

<sec> <title>BACKGROUND</title> The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. </sec> <sec> <title>OBJECTIVE</title> The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. </sec> <sec> <title>METHODS</title> We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. </sec> <sec> <title>RESULTS</title> We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. </sec> <sec> <title>CONCLUSIONS</title> Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19. </sec>
Detecting a Target With Quantum Entanglement

Over the last decade, research activity has focused on the use of quantum entanglement as a resource for remote target detection, i.e., on the design of a quantum radar. The literature on this subject uses tools of quantum optics and quantum information theory, and therefore, results are often obscure to radar engineers. This tutorial has been written with the purpose of removing this obscurity. As such, it contains a review of the main advances in quantum radar literature accompanied by a thorough introduction of the quantum optics background necessary for its understanding.
Deep Learning Classification of Breast Cancer Tissue from Terahertz Imaging Through Wavelet Synchro-Squeezed Transformation and Transfer Learning

Terahertz imaging and spectroscopy is an exciting technology that has the potential to provide insights in medical imaging. Prior research has leveraged statistical inference to classify tissue regions from terahertz images. To date, these approaches have shown that the segmentation problem is challenging for images of fresh tissue and for tumors that have invaded muscular regions. Artificial intelligence, particularly machine learning and deep learning, has been shown to improve performance in some medical imaging challenges. This paper builds on that literature by modifying a set of deep learning approaches to the challenge of classifying tissue regions of images captured by terahertz imaging and spectroscopy of freshly excised murine xenograft tissue. Our approach is to preprocess the images through a wavelet synchronous-squeezed transformation (WSST) to convert time-sequential terahertz data of each THz pixel to a spectrogram. Spectrograms are used as input tensors to a deep convolution neural network for pixel-wise classification. Based on the classification result of each pixel, a cancer tissue segmentation map is achieved. In experimentation, we adopt leave-one-sample-out cross-validation strategy, and evaluate our chosen networks and results using multiple metrics such as accuracy, precision, intersection, and size. The results from this experimentation demonstrate improvement in classification accuracy compared to statistical methods, an improvement to segmentation between muscle and cancerous regions in xenograft tumors, and identify areas to improve the imaging and classification methodology.
R2CI: Information theoretic-guided feature selection with multiple correlations

Information theoretic-guided feature selection approaches (ITFSs), which exploit the uncertainty of information to measure the correlation of features, aim to select the most informative features. However, most previous approaches suffer from two drawbacks. 1) Complementarity and interaction are not valued, leading to features with potential discriminatory information for learning tasks such as classification not being excavated and affecting the effectiveness of learning. 2) The various correlations that exist between features for the class have not been fully considered, and their differentiation and relationships have not been well reflected. To address the former issue, guided by information theory, the complementarity and interaction between features are studied. For the latter, firstly, some ITFSs are reviewed and analyzed in terms of feature correlation. The analysis reveals that considering feature multi-correlation is absent in the selection process. Motivated by this problem, a feature selection algorithm with class-based relevance, redundancy, complementarity, and interaction (R2CI) is designed for the first time. Moreover, the distinctions and connections among different correlations are also explored. The results of comparisons and hypothesis test against competitive algorithms show that R2CI has significant advantages in most cases.
An investigative study of links between terrorist attacks and cryptocurrency markets

Our objective in this paper is to explore the impact of 21 terrorist attacks on the risk and return of cryptocurrencies. This is motivated by the rapid increase in Bitcoin and other cryptocurrency prices in conjunction with uncertainty about cryptocurrency fundamental value and how this value is determined. Using daily cryptocurrency returns and the event study methodology, we estimate cryptocurrency abnormal returns around terrorist activities. Asset pricing models are fitted with interaction variables to identify the impact of individual attacks. ARCH models are used to determine changes in systematic risk. Our findings indicate that terrorist attacks positively contribute to the returns of cryptocurrencies whilst the attacks also result in short-term risk shifting behavior for different cryptocurrencies.
Curriculum Learning for Dense Retrieval Distillation

Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a generic curriculum learning based optimization framework called CL-DRD that controls the difficulty level of training data produced by the re-ranking (teacher) model. CL-DRD iteratively optimizes the dense retrieval (student) model by increasing the difficulty of the knowledge distillation data made available to it. In more detail, we initially provide the student model coarse-grained preference pairs between documents in the teacher's ranking, and progressively move towards finer-grained pairwise document ordering requirements. In our experiments, we apply a simple implementation of the CL-DRD framework to enhance two state-of-the-art dense retrieval models. Experiments on three public passage retrieval datasets demonstrate the effectiveness of our proposed framework.
Bitcoin's innovative aspects, return volatility and uncertainty shocks

This paper investigates how Bitcoin's (BTC) return volatility is affected by the main global financial market indicators, its own innovative aspects and uncertainty. First, the strategy consists in estimate structural break tests to find significative regime switching in BTC returns. Next, conditional volatility parameters are estimated from multivariate perspective using the DCC-MGARCH model. At this stage, the Standard & Poor 500 index, China's SSEC stock index and the price of gold were used to estimate the quasi-covariances and quasi-correlations matrices of global drivers of BTC returns. In the last stage, impulse response functions are also estimated, in order to understand how BTC returns are affected by risk and uncertainty shocks. There are many factors and uncertainty surrounding the BTC market microstructure and indicators of a speculative bubble from the end of 2017 to mid-2018. The findings seem to point to barriers for investors and development of blockchain benefits.
Fraud detection and verification system for online transactions: a brief overview

Conducting transactions online is a general user behaviour nowadays with an alarming increase rate. It is completely hassle-free and becoming a habit of the users. Its most common example can be seen on various e-commerce platforms, where an increasing number of users are multiplying the number of transactions daily. With such a large network of online transactions, it is expected to have certain loopholes rendering such platforms to frauds and glitches. Therefore, in this paper, we will be discussing a fraud detection and verification system targeting online transactions mainly e-commerce. The paper stipulates certain techniques that can be used to authenticate any online transaction and if found fraudulent, it is followed by a verification mechanism that more or less helps to validate the authenticity of the transaction. The aspects laid down in this paper can no doubt be further scrutinised to build a robust fraud detection system, but certainly, they form a basis towards building a viable fraud detection system.
Authenticity in Objects and Activities: Determinants of Satisfaction with Virtual Reality Experiences of Heritage and Non-Heritage Tourism Sites

Virtual Reality (VR) is becoming an increasingly important technology in a host of industries, including tourism. VR can provide virtual experiences before, during, or in lieu of real-world visits to tourism sites. Hence, providing authentic experiences is essential to satisfy guests with the site and technology. This study analyzes survey data using PLS to identify the determinants of satisfaction with non-immersive VR experiences of heritage and non-heritage tourism sites. Results from 193 subjects reveal the linkages between system quality, object-related authenticity, activity-related authenticity, and presence, as well their relationship with satisfaction.