Reviewerly/exHarmony huggingface dataset:
The effect of notification window length on the epidemiological impact of COVID-19 contact tracing mobile applications

Abstract Background The reduction in SARS-CoV-2 transmission facilitated by mobile contact tracing applications (apps) depends both on the proportion of relevant contacts notified and on the probability that those contacts quarantine after notification. The proportion of relevant contacts notified depends upon the number of days preceding an infector’s positive test that their contacts are notified, which we refer to as an app’s notification window . Methods We use an epidemiological model of SARS-CoV-2 transmission that captures the profile of infection to consider the trade-off between notification window length and active app use. We focus on 5-day and 2-day windows, the notification windows of the NHS COVID-19 app in England and Wales before and after 2nd August 2021, respectively. Results Our analyses show that at the same level of active app use, 5-day windows result in larger reductions in transmission than 2-day windows. However, short notification windows can be more effective at reducing transmission if they are associated with higher levels of active app use and adherence to isolation upon notification. Conclusions Our results demonstrate the importance of understanding adherence to interventions when setting notification windows for COVID-19 contact tracing apps.
VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation

The cluster evaluation process is of great importance in areas of machine learning and data mining. Evaluating the clustering quality of clusters shows how much any proposed approach or algorithm is competent. Nevertheless, evaluating the quality of any cluster is still an issue. Although many cluster validity indices have been proposed, there is a need for new approaches that can measure the clustering quality more accurately because most of the existing approaches measure the cluster quality correctly when the shape of the cluster is spherical. However, very few clusters in the real world are spherical. Therefore, a new Validity Index for Arbitrary-Shaped Clusters based on the kernel density estimation (the VIASCKDE Index) to overcome the mentioned issue was proposed in the study. In the VIASCKDE Index, we used separation and compactness of each data to support arbitrary-shaped clusters and utilized the kernel density estimation (KDE) to give more weight to the denser areas in the clusters to support cluster compactness. To evaluate the performance of our approach, we compared it to the state-of-the-art cluster validity indices. Experimental results have demonstrated that the VIASCKDE Index outperforms the compared indices.
Phishing Detection using Machine Learning Techniques

Phishing is a type of cyber-crime where spammed messages and false sites allure exploited people to give delicate data to the phishers. The obtained touchy data is along these lines used to take characters or access cash. To battle against spamming, a cloud-based framework Microsoft azure and uses prescient investigation with machine making sense of how to manufacture confidence in personalities. The goal of this paper is to construct a spam channel utilizing various machine learning techniques. Classification is a machine learning strategy uses that can be viably used to recognize spam, builds and tests models, utilizing diverse blends of settings, and compares various machine learning technique, and measure the exactness of a prepared model and figures a lot of assessment measurements. The present study compares the predictive accuracy, f1 score, precession and recall of several machine learning methods including Logistic Regression (LR), Support Vector Machines (SVM), Decision Tree (DT), and Neural Networks (NNet) for predicting phishing emails and improves logistic regression technique by using feature selection methods and improves the accuracy to detect phishing.
Batch-Wise Permutation Feature Importance Evaluation and Problem-Specific Bigraph for Learn-to-Branch

The branch-and-bound algorithm for combinatorial optimization typically relies on a plethora of handcraft expert heuristics, and a research direction, so-called learn-to-branch, proposes to replace the expert heuristics in branch-and-bound with machine learning models. Current studies in this area typically use an imitation learning (IL) approach; however, in practice, IL often suffers from limited training samples. Thus, it has been emphasized that a small-dataset fast-training scheme for IL in learn-to-branch is worth studying, so that other methods, e.g., reinforcement learning, may be used for subsequent training. Thus, this paper focuses on the IL part of a mixed training approach, where a small-dataset fast-training scheme is considered. The contributions are as follows. First, to compute feature importance metrics so that the state-of-the-art bigraph representation can be effectively reduced for each problem type, a batch-wise permutation feature importance evaluation method is proposed, which permutes features within each batch in the forward pass. Second, based on the evaluated importance of the bigraph features, a reduced bigraph representation is proposed for each of the benchmark problems. The experimental results on four MILP benchmark problems show that our method improves branching accuracy by 8% and reduces solution time by 18% on average under the small-dataset fast-training scheme compared to the state-of-the-art bigraph-based learn-to-branch method. The source code is available online at GitHub.
Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3+

In this study, an advanced semantic segmentation method and deep convolutional neural network was applied to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images, thereby facilitating image interpretation and diagnosis by providing radiologists an objective second opinion. A total of 684 images (380 benign and 308 malignant tumours) from 343 patients (190 benign and 153 malignant breast tumour patients) were analysed in this study. Six malignancy-related standardised BI-RADS features were selected after analysis. The DeepLab v3+ architecture and four decode networks were used, and their semantic segmentation performance was evaluated and compared. Subsequently, DeepLab v3+ with the ResNet-50 decoder showed the best performance in semantic segmentation, with a mean accuracy and mean intersection over union (IU) of 44.04% and 34.92%, respectively. The weighted IU was 84.36%. For the diagnostic performance, the area under the curve was 83.32%. This study aimed to automate identification of the malignant BI-RADS lexicon on breast ultrasound images to facilitate diagnosis and improve its quality. The evaluation showed that DeepLab v3+ with the ResNet-50 decoder was suitable for solving this problem, offering a better balance of performance and computational resource usage than a fully connected network and other decoders.
Program synthesis with evolutionary algorithms: Status quo

Program synthesis techniques offer the potential to allow non-programmers to create computer programs. Approaches based on evolutionary algorithms are known for their performance in program synthesis. Since 2015, the general program synthesis benchmark suite offers a common pool of benchmark problems, which makes different approaches comparable. To analyze what has been achieved so far, we identified in a recent literature review the main evolutionary program synthesis approaches, determined the difficulty of the common benchmark problems, and discussed current challenges. In this short paper, we present the difficulty ranking of the benchmark problems and summarize the current challenges in program synthesis with evolutionary algorithms.
AN OVERVIEW OF MODERN METHODOLOGICAL APPROACHES OF IT EDUCATION

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5812-5817ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1363Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Augmented reality pictorial storybook: How does it influence on elementary school mathematics anxiety?

This study aims to explore the effect of media Augmented reality pictorial storybook on the anxiety of elementary school students in mathematics learning. This study used a quantitative approach with a quasi-experimental type of research. The experimental design used was a pretest-posttest control group design. The sample of this study was 60 students of fifth grade elementary school which were divided into two groups, namely the experimental group and the control group. The sampling technique used is cluster random sampling. The data were collected by using a mathematical anxiety questionnaire which was analyzed by means of a comparison technique, score trend test, and t-test. The results showed that the use of Augmented reality pictorial storybooks can reduce students' anxiety levels in mathematics learning. This can be seen from the average value of the pretest and posttest in the experimental class which showed a more significant decrease in anxiety towards mathematics learning (47.73%) than the decrease in anxiety in the control class (4.73%). The t-test results show that the significance value is 0.00 (&amp;lt;0.05), which means that Augmented reality pictorial storybooks have a significant effect on students' anxiety in mathematics learning.
Language Model Guided Knowledge Graph Embeddings

Knowledge graph embedding models have become a popular approach for knowledge graph completion through predicting the plausibility of (potential) triples.This is performed by transforming the entities and relations of the knowledge graph into an embedding space.However, knowledge graphs often include further textual information stored in literal, which is ignored by such embedding models.As a consequence, the learning process stays limited to the structure and the connections between the entities, which has the potential to negatively influence the performance.We bridge this gap by leveraging the capabilities of pretrained language models to include textual knowledge in the learning process of embedding models.This is achieved by introducing a new loss function that guides embedding models in measuring the likelihood of triples by taking such complementary knowledge into consideration.The proposed solution is a modelindependent loss function that can be plugged into any knowledge graph embedding model.In this paper, Sentence-BERT and fastText are used as pre-trained language models from which the embeddings of the textual knowledge are obtained and injected into the loss function.The loss function contains a trainable slack variable that determines the degree to which the language models influence the plausibility of triples.Our experimental evaluation on six benchmarks, namely Nations, UMLS, WordNet, and three versions of CodEx confirms the advantage of using pre-trained language models for boosting the accuracy of knowledge graph embedding models.We showcase this by performing evaluations on top of the five wellknown knowledge graph embedding models such as TransE, RotatE, ComplEx, DistMult, and QuatE.The results show an improvement in accuracy up to 9% on UMLS dataset for the Distmult model and 4.2% on the Nations dataset for the ComplEx model when they are guided by pre-trained language models.We additionally studied the effect of multiple factors such as the structure of the knowledge graphs and training steps and presented them as ablation studies.
A Novel SVM Network Using HOG Feature for Prohibition Traffic Sign Recognition

To recognize prohibition traffic sign, this paper proposes a novel method that is trained by a small number of samples and uses the feature of histogram of oriented gradient (HOG) and support vector machine (SVM) network. The recognition method is mainly divided into three stages. The first stage is image preprocessing, which includes image interception based on ellipse detection, image resizing, and Gamma correction. In the part of image interception, a new ellipse detection method called RHT_MCN is proposed based on RHT, which uses the maximum coincidence number (MCN) of image edge points and detected ellipse edge to choose the final ellipse for image interception. The second stage is the feature extraction of HOG. The third stage is the prohibition traffic sign recognition (PTSR) based on SVM network. In the design and implementation of the PTSR model, a new single-layer SVM network is proposed. The ascending spiral training method of the recognition model is introduced in detail. Finally, the data from GTSRB is used to test and analyze the prohibition traffic sign recognition method. The method is proven to have good applicability.
ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

A trusted execution environment (TEE) is a system-on-chip and CPU system with a wide security solution available on today's Arm application (APP) processors, which dominate the smartphone market. Generally, mobile APPs create a trusted application (TA) in the TEE to process sensitive information, such as payment or message encryption, which is transparent to the APPs running in the rich execution environments (REEs). In detail, the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA. Such an operation definitely increases the overhead of mobile APPs. In this paper, we first present a comprehensive analysis of the performance of open-source TEE encrypted text. We then propose a high energy-efficient task scheduling strategy (ETS-TEE). By leveraging the deep learning algorithm, our policy considers the complexity of TA tasks, which are dynamically scheduled between modeling on the local device and offloading to an edge server. We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server. The results show that compared with the default scheduling strategy on the local device, our approach achieves an average of 38.0% energy reduction and 1.6 × speedup. This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications, so that the trusted execution environment has both security and high performance.
Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Cryptocurrency based on blockchain technology has gradually become a choice for people to invest in, and several users have participated in the accumulation of massive transaction data. Complete transaction records in blockchains and the openness of data provide researchers with opportunities to mine and analyze data in blockchains. Network modeling and analysis of cryptocurrency transaction records are common methods in blockchain data analysis. The analysis of attribute graphs can provide insights into various economic indicators, illegal activities, and general Internet security, among others. Accordingly, this article aims to summarize and analyze the literature on cryptocurrency transaction data from the perspective of complex networks. To provide systematic guidance for researchers, we put forward a blockchain data analysis framework based on the introduction of the relevant background and reviewed the work from five aspects: blockchain data model, data acquisition on blockchains, existing analysis tools, available insights, and common analysis methods. For each aspect, we introduce the research problems, summarize the methods, and discuss the results and findings. Finally, we present future research points and several open questions in the study of cryptocurrency transaction networks.
Adaptive Spatio-Temporal Query Strategies in Blockchain

In various applications of blockchain, how to index spatio-temporal data more efficiently has become a subject of continuous attention. The existing spatio-temporal data query in the blockchain is realized by adding additional external storage or fixed spatio-temporal index in the block, without considering the distribution of the spatio-temporal query itself and the proof performance accompanying the query. We propose an adaptive spatio-temporal blockchain index method, called Verkle AR*-tree, which adds the verification of time and location in the blockchain without additional storage and realizes the spatio-temporal index with an encrypted signature. Verkle AR*-tree further provides an adaptive algorithm, which adjusts the tree structure according to the historical query to produce the optimized index structure. The experimental results based on the pokeman dataset show that compared with the existing static spatio-temporal index, our method can effectively increase the performance of the spatio-temporal query and the spatio-temporal commitment in the blockchain.
Meaningful involvement of children and young people in health technology development

The anatomical, physiological, and developmental changes which arise as children mature through childhood and adolescence support the need to develop new health technologies that meet the specific requirements of children and young people (CYP). Failing to involve CYP during the development of technology increases the risk that the outcome falls short of their expectations and needs, leading to rejection of novel interventions. Through participation in health technology development, CYP and their families can provide context, insight, personal experience and tacit knowledge to ensure that the end-product is usable, acceptable, and can be integrated into its intended environment. A nuanced, balanced understanding of the methods that can be used to facilitate participation will support researchers in choosing an effective approach to involving CYP in health technology development. Methodological approaches include patient and public involvement and engagement, co-design, and experienced based co-design. These methods can be used in isolation or in combination, to facilitate meaningful involvement of CYP and encourage the development of impactful solutions, in consideration of the context, stakeholders, and objectives of the project. We provide the rationale and justification for involving CYP in health technology design and development, an explanation of the methods supporting meaningful involvement, and case studies exemplifying real world application of these methods with positive outputs.
Strategies for counteracting social engineering attacks

As the result of a five-year project investigating social engineering attacks, researchers at Tsinghua University, China have identified a number of strategies that organisations can use to protect themselves. However, they warn that there is no single strategy that works for all forms of attack. And when choosing the right methods it's essential to consider the context, the environment and the available facilities.
PROSPECT AND PROMISE IN INTEGRATING MULTILITERACY PEDAGOGY IN THE ENGLISH LANGUAGE CLASSROOM IN INDONESIA

This article discusses the multiliteracy idea to give reasons in favor of multiliteracy implementation in Indonesian classrooms. Multiliteracy offers fresh perspectives on language education and learning. The usage of multiliteracy in Indonesia provides potential benefits, such as the growth of pupils' literacy levels. Multiliteracy adds a new dimension to education and allows instructors to take an alternative approach to teach, moving away from the conventional teacher-centered method towards a more student-centered approach. Furthermore, it places the emphasis on critical thinking, cultural awareness, and fresh insight. Students are encouraged and expected to be more curious when studying multiliteracy since they will be able to explore and learn on their own. Educators in Indonesia, on the other hand, should not be unfamiliar with the notion of multiliteracy. It should be welcomed, and while it may appear challenging at first, it can be a rewarding and exciting experience for instructors and students.
Edge preserving noise robust deep learning networks for vehicle classification

Summary For controlling and managing the traffic and to help traffic surveillance, the vehicles classification is a matter of great importance. In the last few decades, vehicle classification systems based on pattern recognition have been utilized to enhance the efficiency for traffic monitoring systems. In the literature many deep learning networks are suggested for vehicle classification. Even though deep learning algorithms are fascinating and growing research area. However, there are several barriers that slow down its progress. The greatest factor that reduces the progress of deep learning systems is the quality of the image. The available vehicle image datasets are affected by noise, weather, and illumination variations. To overcome these issues, we suggest a robust deep learning system by combining bilateral filter individually with three different networks for the improvement of the robustness of vehicle classification in real‐time application. For validation the suggested networks are assessed on CompCars dataset. The study of literature has reported many Suggested CNN models, some of are discussed here. Lee et al. have employed SqueezeNet model and achieved accuracy of 0.963. Wang et al. have achieved accuracy of 0.989 through H‐squeezeNet model. By employing Faster‐RCNN Inception and SSD MobileNet‐v2 Giron et al. have achieved accuracy of 0.864. Zhang et al. have used Inception‐v3 and attained an accuracy of 0.974. In this article, the proposed CNN bilateral integrated models have accomplished the accuracies as 0.999, 0.997, and 0.988 for Inception‐v3, MobileNet‐v2, SqueezeNet networks, respectively. The results demonstrate the recommended techniques outperform the traditional deep learning networks.
Are cryptocurrencies safe havens during the COVID-19 pandemic? A threshold regression perspective with pandemic-related benchmarks

The paper employs a threshold regression framework conditioned by two COVID-19 related proxies, to investigate whether Bitcoin and Ether exhibit short-term safe haven or diversifier features for stock and bond markets. Both cryptocurrencies fulfil a diversifier role for the responsible investments represented by sustainable stock market indices, a safe haven role for major bond markets and a mixed role for a selection of representative stock market indices. Furthermore, in times characterized by an increasing number of COVID-19 daily cases or deaths the statistical relationship between both cryptocurrencies and the main financial market determinants weakens.
The Guidance System of Gamification and Augmented Reality in a Museum Space

Gamification is the application of game-oriented design approaches or game-inspired mechanics to otherwise non-game contexts.Mobile guiding system is the design process of information interactions.It is the integration of information design, interaction design, and sensorial design.The e-learning system of mobile guide is able to be loaded gamification concepts and let mobile learning interestingly, diversely, and validly.The problem of the research was if we combined the concept of gamification design into museum guide services with augmented reality for non-commercial purposes, it also provided the same benefits to the promotion of museum learning and knowledge, integrating mobile devices as navigation media.It would improve more users to participate in a museum and use the guide system actively, and then arise their interest and achievement.The result was to establish a preliminary model for developing a museum mobile guide system of gamification design and augmented reality.