Reviewerly/exHarmony huggingface dataset:
HoloLens 2 and Virtual Reality as Methods for Managing Phobias

Almost everyone experiences some form of phobia an overwhelming fear and inappropriate response to particular stimuli - directly or indirectly at some point in their existence. Even those without phobias themselves probably know someone who is affected by them in their daily lives. This article presents a cutting-edge educational tool to control phobia-related dread. Teaching a phobia sufferer to recognize and manage his fear is a crucial part of the therapy process. Utilizing virtual reality software, the client may be exposed to a variety of terrifying events connected to his or her phobia and watch how they affect him or her. This enables a more precise assessment of the severity of the phobia and, as a result, an improvement in patient coaching to control the fear. Another method of treating phobias is augmented/mixed reality. We chose to work with the Microsoft HoloLens 2. Using these glasses, images, videos and 3D models can be displayed and rendered into the participant’s real environment. Which means that the participant can be in contact with their phobia directly in an environment they are familiar with (e.g. their apartment, room, etc). With arachnophobia, spiders can appear all over the floor and walls of his room which would be projected directly in the glasses he would be wearing on his head.
Privacy-Preserving Object Detection with Secure Convolutional Neural Networks for Vehicular Edge Computing

With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing.
Deformable Alignment And Scale-Adaptive Feature Extraction Network For Continuous-Scale Satellite Video Super-Resolution

Video super-resolution (VSR), especially continuous-scale VSR, plays a crucial role in improving the quality of satellite video. Continuous-scale VSR aims to use a single model to process arbitrary (integer or non-integer) scale factors, which is conducive to meeting the needs of video images transmission with different compression ratios and arbitrarily zooming by rolling the mouse wheel. In this article, we propose a novel network to achieve continuous-scale satellite VSR (CAVSR). Specifically, first, we propose a time-series-aware dynamic routing deformable alignment module (TDAM) for feature alignment. Second, we develop a scale-adaptive feature extraction module (SFEM), which uses the proposed scale-adaptive convolution (SA-Conv) to dynamically generate different filters based on the input scale information. Finally, we design a global implicit function feature-adaptive walk continuous-scale upsampling module (GFCUM), which can perform feature-adaptive walks according to the input features with different scale information and finally complete the continuous-scale mapping from coordinates to pixel values. Experimental results have demonstrated the CAVSR has superior reconstruction performance.
Audio to Sign Language Converter

Abstract: The hearing and speech disabled people have a communication problem with other people. It is hard for such individuals to express themselves since everyone is not familiar with the sign language. The aim of this paper is to design a system which is helpful for the people with hearing / speech disabilities and convert a voice in Indian sign language (ISL). The task of learning a sign language can be cumbersome for people so this paper proposes a solution to this problem using speech recognition and image processing. The Sign languages have developed a means of easy communication primarily for the deaf and hard of hearing people. In this work we propose a real time system that recognizes the voice input through Pyaudio, SPHINX and Google speech recognition API and converts it into text, followed by sign language output of text which is displayed on the screen of the machine in the form of series of images or motioned video by the help of various python libraries.
Private universities in Indonesia: Perspective on students’ satisfaction and marketing strategy in Post Pandemic Era

This research aimed to analyse the student’s satisfaction toward e-learning implementation in private universities and how it can be used as a marketing tool in the post-pandemic era. Furthermore, the antecedents of e-learning were also studied. We selected an ICT-based private university in Bandung as a model. The data were collected using e-questionnaire, distributed to students who was subjected to PLS-SEM to be used in the analysis. The results showed that e-learning implementation positively contributed to students’ satisfaction. Additionally, from the perspective of students as consumers, e-learning implementation significantly leads to the increase of their satisfaction which contributes to a good marketing strategy. This study has novel input to enrich the literature of e-learning domain as one of the requirements in creating a digitalized university as a marketing tool for the initiation of digitalized university
Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Breast cancer seriously affects many women.If breast cancer is detected at an early stage, it may be cured.This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage.It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models.Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms.All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD).Experiments were implemented to obtain the best classification process.Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively.The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values.Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms.Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.
Virtual tourism and digital communication in the context of the post-pandemic scenario

Despite the growing interest and discussions on Virtual Reality (VR) and Augmented Reality (AR) in tourism, we do not yet know systematically the knowledge that has been built from academic papers on VR and AR in tourism; if and how VR and AR research intersect; the methodologies used to research VR and AR in tourism; and the emerging contexts in which VR and AR have surfaced in hospitality and tourism research. In tourism, where informative communication of intangible products has always been vital, the impending arrivals of VR and AR ranged from being hailed as a new horizon to virtual threats. In a post-COVID-19 landscape, there is growing interest and opportunity for cutting-edge technologies to contribute to destination recovery. From an interdisciplinary perspective, this chapter aims to present contributions to marketing, virtual tourism and pandemic management.
Efficient parallel branch-and-bound approaches for exact graph edit distance problem

Graph edit distance (GED) is a well-known measure used in the graph matching to measure the similarity/dissimilarity between two graphs by computing the minimum cost of edit operations needed to transform one graph into another. This process, Which appears to be simple, is known NP-hard and time consuming since the search space is increasing exponentially. One way to optimally solve this problem is by using branch and bound (B&B) algorithms, Which reduce the computation time required to explore the whole search space by performing an implicit enumeration of the search space instead of an exhaustive one based on a pruning technique. nevertheless, They remain inefficient when dealing with large problem instances due to the impractical running time needed to explore the whole search space. To overcome this issue, We propose in this paper three parallel B&B approaches based on shared memory to exploit the multi-core CPU processors: First, a work-stealing approach where several instances of the B&B algorithm explore a single search tree concurrently achieving speedups up to 24 × faster than the sequential version. Second, a tree-based approach where multiple parts of the search tree are explored simultaneously by independent B&B instances achieving speedups up to 28 × . Finally, Due to the irregular nature of the GED problem, two load-balancing strategies are proposed to ensure a fair workload between parallel processes achieving impressive speedups up to 300 × . all experiments have been carried out on well-known datasets • Parallel B&B approaches based on shared memory model for Graph Edit Distance problem. • Dynamic load balancing strategies that adjust the workload at runtime. • Take advantage of the diversification gain to achieve high speedup performance. • Detailed experiments using well-known datasets.
STUDENTS’ PERCEPTIONS OF USING GEOGEBRA SOFTWARE IN MATHEMATICS LEARNING

This study adopted a qualitative approach to determine the views of grade eight students concerning mathematics learning with GeoGebra software (GGS). In total, 37 grade eight students (11–14 years old) from two classes participated in the study and 14 students volunteered to participate in focus groups (7 from each class). The data from the two focus groups were analysed using thematic analysis. The results of this study that emerged from the two focus group sessions comprised three main themes: improvements in students’ understanding, the amount of time and effort saved, and student engagement. The students believed that GGS enhanced their understanding of mathematics and improved their learning and visualisation. In addition, they found that using GGS required less writing and drawing shapes using the app made learning easier and took less effort. In addition, the students reported that GGS improved their enjoyment in class, fostering a positive attitude toward mathematics and offering them exciting lessons and the ability to create geometric shapes independently.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0967/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Dialectica logical principles: not only rules

Abstract Gödel’s Dialectica interpretation was designed to obtain the consistency of Peano arithmetic via a proof of consistency of Heyting arithmetic and double negation. In recent years, proof theoretic transformations (the so-called proof interpretations) based on Gödel’s Dialectica interpretation have been used systematically to extract new content from proofs and so the interpretation has found relevant applications in several areas of mathematics and computer science. Following our previous work on ‘Gödel fibrations’, we present a (hyper)doctrine characterization of the Dialectica, which corresponds exactly to the logical description of the interpretation. To show that, we derive the soundness of the interpretation of the implication connective, as expounded on by Spector and Troelstra, in the categorical model. This requires extra logical principles, going beyond intuitionistic logic, namely Markov Principle and the Independence of Premise principle, as well as some choice. We show how these principles are satisfied in the categorical setting, establishing a tight (internal language) correspondence between the logical system and the categorical framework. We make sure that this tight correspondence extends to the use of the principles above, instead of the weaker rules we had proved earlier on. This tight correspondence should come handy not only when discussing the traditional applications of the Dialectica but also when dealing with newer uses in modelling games or concurrency theory.
A pedagogical approach in interleaving software quality concerns at an artificial intelligence course

The software engineering industry is an everchanging domain requiring professionals to have a good knowledge base and adaptability skills.Artificial Intelligence (AI) has achieved substantial success in enhancing program analysis techniques and applications, including bug prediction. It is a promising direction by applying advanced Machine Learning techniques into suitable software engineering tasks.
Lighting up supervised learning in user review-based code localization: dataset and benchmark

As User Reviews (URs) of mobile Apps are proven to provide valuable feedback for maintaining and evolving applications, how to make full use of URs more efficiently in the release cycle of mobile Apps has become a widely concerned and researched topic in the Software Engineering (SE) community. In order to speed up the completion of coding work related to URs to shorten the release cycle as much as possible, the task of User Review-based code localization is proposed and studied in depth. However, due to the lack of large-scale ground truth dataset (i.e., truly related <UR, Code> pairs), existing methods are all unsupervised learning-based. In order to light up supervised learning approaches, which are driven by large labeled datasets, for Review2Code, and to compare their performances with unsupervised learning-based methods, we first introduce a large-scale human-labeled <UR, Code> ground truth dataset, including the annotation process and statistical analysis. Then, a benchmark consisting of two SOTA unsupervised learning-based and four supervised learning-based Review2Code methods is constructed based on this dataset. We believe that this paper can provide a basis for in-depth exploration of the supervised learning-based Review2Code solutions.
A Fault Diagnosis Method of Rolling Bearing Based on Attention Entropy and Adaptive Deep Kernel Extreme Learning Machine

To address the difficulty of early fault diagnosis of rolling bearings, this paper proposes a rolling bearing diagnosis method by combining the attention entropy and adaptive deep kernel extreme learning machine (ADKELM). Firstly, the wavelet threshold denoising method is employed to eliminate the noise in the vibration signal. Then, the empirical wavelet transform (EWT) is utilized to decompose the denoised signal, and extract the attention entropy of the intrinsic mode function (IMF) as the feature vector. Next, the hyperparameters of the deep kernel extreme learning machine (DKELM) are optimized using the marine predators algorithm (MPA), so as to achieve the adaptive changes in the DKELM parameters. By analyzing the fault diagnosis performances of the ADKELM model with different kernel functions and hidden layers, the optimal ADKELM model is determined. Compared with conventional machine learning models such as extreme learning machine (ELM), back propagation neural network (BPNN) and probabilistic neural network (PNN), the high efficiency of the method proposed in this paper is verified.
Identity-Based and Leakage-Resilient Broadcast Encryption Scheme for Cloud Storage Service

Cloud storage services are an important application of cloud computing. An increasing number of data owners store their data on cloud platforms. Since cloud platforms are far away from users, data security and privacy protection are very important issues that need to be addressed. Identity-based broadcast encryption (IBBE) is an important method to provide security and privacy protection for cloud storage services. Because the side channel attacks may lead to the disclosure of the key information of the cryptographic system, which will damage the security of the system, this paper provides an identity-based broadcast encryption with leakage resilience by state partition (LR-SP-IBBE). By using a binary extractor to compensate for the loss in entropy of the symmetric key caused by side-channel attacks, the proposed scheme randomizes the encapsulated symmetric key. Furthermore, using a state partition technique, we split the private key into two parts, and the corresponding decryption was divided into two stages. Through the double-system encryption skill, the security and leakage-resilience were proved in the composite order group model.
Siamese network based on global and local feature matching for object tracking

Some tracking algorithms based on Siamese network have made great progress in similarity learning via features cross-correlation between an object branch and a search branch. However, it is significantly challenging for object tracking in video sequences in terms of target deformation with greatly varying. We propose a Siamese network based on global and local feature matching for object tracking including three phases with the aim of addressing the above issues. In the first phase, obtaining the global similarity matching and local relational mapping similarity of the template branch and the search branch by a selection mechanism of object template-aware features are to reduce the impact of background features on the local matching. In the second phase, introducing correlation matching of the local feature for establishing correspondence among partial-level pixels. Finally, combining the classification and regression results with global matching features and local matching features in a weighted fusion. Extensive experiments are conducted on datasets (OTB-100, LaSOT and GOT-10K) demonstrate that the proposed network enables to achieve superiority compared against the state-of-the-art method and provides an efficient scenario for tackling the issue.
Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures

Modern Public Key Infrastructures (PKIs) allow users to create and maintain centrally stored cryptographic certificates. These infrastructures use a so-called certificate chain. At the root of the chain, a root Certification Authority (CA) is responsible for issuing the base certificate. Every verification and certification step within the chain is based upon the security of said root CA. Thus, its operation security is of great concern. Since the root certificates are stored locally on the root CA, any Denial of Service (DoS) attack may render the whole certificate chain, which is based on of the attacked root CA, inoperable. Therefore, this article evaluates different approaches to a decentralized data storage system that is based on the Distributed Ledger Technology (DLT). To show the real-world potential of the proposed approaches, we also evaluate the different technologies using a novel PKI mechanism called Near Field Communication Key Exchange (NFC-KE). The results indicate that modern distributed data storage solutions such as Interplanetary Filesystem (IPFS) and SIA can have significant performance and decentralization benefits in comparison to purely Blockchain-based technologies like Hyperledger Fabric. However, they lack any Smart Contract functionality, which requires a software developer to implement verification mechanisms in centralized software solutions.
Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality

Machine learning models are incorporated in different fields and disciplines in which some of them require a high level of accountability and transparency, for example, the healthcare sector. With the General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. A widely used category of explanation techniques attempts to explain models’ predictions by quantifying the importance score of each input feature. However, summarizing such scores to provide human-interpretable explanations is challenging. Another category of explanation techniques focuses on learning a domain representation in terms of high-level human-understandable concepts and then utilizing them to explain predictions. These explanations are hampered by how concepts are constructed, which is not intrinsically interpretable. To this end, we propose Concept-based Local Explanations with Feedback (CLEF), a novel local model agnostic explanation framework for learning a set of high-level transparent concept definitions in high-dimensional tabular data that uses clinician-labeled concepts rather than raw features. CLEF maps the raw input features to high-level intuitive concepts and then decompose the evidence of prediction of the instance being explained into concepts. In addition, the proposed framework generates counterfactual explanations, suggesting the minimum changes in the instance’s concept based explanation that will lead to a different prediction. We demonstrate with simulated user feedback on predicting the risk of mortality. Such direct feedback is more effective than other techniques, that rely on hand-labelled or automatically extracted concepts, in learning concepts that align with ground truth concept definitions.