Reviewerly/exHarmony huggingface dataset:
COVID-19 contact tracing applications: An analysis of individual motivations for adoption and use

In attempts to remediate the COVID-19 pandemic, governments internationally released contact tracing and exposure notification mobile applications seeking to limit the transmission of the disease by recording app user’s contact with other apps users. To address user adoption and usage challenges, we conducted a qualitative study of approximately 300 users and 300 non-users of one such contact tracing application, ABTraceTogether. This article highlights four of our main findings: having a sense of individual agency, trust in the application developers and sponsors, belief in the efficacy of the app, and altruism or pro-social beliefs. These findings are useful for application developers to target development and promotional issues to address adoption and usage challenges.
HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors

Human activity recognition (HAR) based on wearable sensors has developed as a new study topic in the domains of artificial intelligence and pattern recognition. HAR has a wide range of applications, including sports activity detection, smart homes, and health assistance, to name a few. Mobile device sensors such as accelerometers, gyroscopes, and magnetometers can generate time-series data for HAR. Computer Vision (CV) methods were previously utilised for HAR, which has a number of drawbacks, including mobility, ambient conditions, occlusion, higher cost, and, most importantly, privacy. Using sensor data instead of typical computer vision techniques has various advantages. Their work is believed to have overcome virtually all of the limitations of computer vision techniques. The use of Machine Learning (ML) and Deep Neural Networks (DNN) to recognise human activity from inertial sensor data is widely established in the literature. In this paper, we introduce HARResNeXT, a novel convolutional neural network inspired by ResNeXT. It classifies Human Activities based on inertial sensors data of smartphone. The presented model has been evaluated on a dataset by WISDM (Wireless Sensor Data Mining) Lab. We have achieved 97\% Precision, Recall and F1-score. Moreover, the average accuracy achieved is 96.62\%. Comparison with previous studies showed the presented model out-performed state-of-the-art.
Classification of Cervical Precancerous Cell of ThinPrep Images Based on Deep Learning Model AlexNet and InceptionV3

Cervical cancer is one of the deadliest diseases in the world, responsible for the greatest number of fatalities. Around 569.847 new cervical cancer cases are recorded every year. Efforts to prevent this condition can be conducted by early identification. There are several methods to detect cervical cancer, one of which is ThinPrep. In identifying cervical cancer, a neural network can be utilized as an alternative. AlexNet and InceptionV3 are neural network frequently applied to detect various diseases. In this study cervical cell images were classified based on cell severity, using deep learning models AlexNet and InceptionV3. The results it can be known that Inception V3 has a better performance based on the performance matrix analysis of the both models. The best performance matrix results for InceptionV3 are 89.80% for accuracy, 89.81% for precision, 91.17% for sensitivity, 94.49% for specificity, and 89.26% for F-score. However, AlexNet’s training time have much faster than InceptionV3, with an average training time 57 seconds and fastest training time 55 seconds.
A Hybrid Approach to Paraphrase Detection Based on Text Similarities and Machine Learning Classifiers

In the realm of natural language processing (NLP), paraphrase detection is a highly common and significant activity. Because it is involved in a lot of complicated and complex NLP applications like information retrieval, text mining, and plagiarism detection. The proposed model finds the best combination of the three types of similarity techniques that are string similarity, semantic similarity and embedding similarity. Then, inputs these similarity scores that range from 0 to 1, to the machine learning classifiers. This proposed model will be benchmarked on “the Microsoft research paraphrase corpus” dataset (MSRP) and from this approach for paraphrase detection problem, the accuracy acquired is 75.78% and F1-Score of 83.01 %.
Mobile learning application: Infusing critical thinking in the EFL classroom.

The emergence of mobile learning applications facilitates the pedagogical approach to developing students’ critical thinking. However, there is a scarcity of investigation on mobile learning applications’ impact on developing critical thinking as the learning outcome. Thus, this study reports the effect of a mobile learning application, ‘English with Noni’, designed to infuse critical thinking instruction in EFL classes on students’ critical thinking level by employing a sequential explanatory mixed-method approach. A quasi-experimental study was conducted to examine the critical thinking level of 65 students of a junior high school in Jakarta, Indonesia, by administering a post-test assessed using a SOLO rubric. Semi-structured interviews to explore students’ responses from using the ‘English with Noni’ application and class observation contributed to the qualitative findings. The quantitative result showed that the critical thinking level of the experimental group using this application improved significantly more than the controlled group did. The qualitative result suggested that the experimental group had positive responses to using it. They confirmed that it was interesting. They also admitted that it contributed to developing their critical thinking (i.e., predicting, providing reasons, expressing viewpoints, finding alternatives, and making conclusions), language skills (i.e., listening, reading, and writing), and sub-skill (vocabulary). The findings imply that critical thinking activities and learning affordances provided in the ‘English with Noni’ application is a potential tool to enhance students’ critical thinking infused in the EFL class, especially during the COVID-19 pandemic situation, by emphasizing self-regulated learning.
Facebook as a Flexible Ubiquitous Learning Space for Developing Speaking Skills

he research studied the effectiveness of the integration of Facebook as a flexible ubiquitous learning space into the educational process for speaking skills development of undergraduate students learning English as a second language. For this purpose teaching was organised via a specially created and moderated Facebook group where various media resources, uploaded materials, links to different applications and other social networking opportunities were accumulated. It was designed to achieve the educational programme objectives and address the specifics of digital age learning. A set of specially designed materials posted on the Facebook platform for language input, structured output, and communicative output activities was applied in experimental teaching to develop talk as transition, talk as interaction and mediation, and talk as performance. The results of the quasi-experiment (students’ speaking performance) were assessed in the form of the post-test with the data being analysed and interpreted based on descriptive and inferential statistics (independent samples t-test) by means of SPSS. The results revealed higher achievement scores of the experimental group in comparison to the control group in terms of expanding vocabulary, increasing English grammar literacy, developing interactive skills, discourse management, and pronunciation. The survey administered to find out the learners’ impressions of the successfulness of the FB-assisted activities revealed their overall positive attitude to the new methodology and usefulness for the development of all speaking qualifications checked.
Bitcoin USD Closing Price (BTC-USD) Comparison Using Simple Moving Average And Radial Basis Function Neural Network Methods

Bitcoin is a decentralized electronic money that is not controlled nor insured by a central authority. Because it is still a young system, the price of Bitcoin is extremely unpredictable, making Bitcoin users and investors uneasy. A typical difficulty for investors and traders is predicting the future movement of the value of Bitcoin electronic money based on historical data. Because investors and traders only notice swings in global currency prices and make Bitcoin buy/sell decisions instinctively, they frequently make the erroneous buy/sell decisions. Many investors and traders suffered significant losses as a result of this error. Losses can be reduced by employing an algorithm that predicts the movement of the value of Bitcoin electronic money. Using a comparison of two methodologies, the Simple Moving Average and RBFNN, we will anticipate the closing price of Bitcoin USD (BTC-USD) from January 1, 2021 to January 31, 2021. The results obtained using the simple moving average method MSE = 0.01 percent and MAPE = 36.67 percent, and the results obtained using the RBFNN method MSE = 9.97 x 10-7 and MAPE = 9.97 x 10-5, indicating that the RBFNN method with an accuracy rate of 99.9995 percent is better than the simple moving average method in forecasting the closing price of bitcoin.
Graph Neural Network Social Recommendation Algorithm Integrating Static and Dynamic Features

In recent years, the study of social-based recommender systems has become an active research topic. We incorporate a combination of static and dynamic interest characteristics to predict users’ real-time dynamic interests, which has rarely been considered in previous studies. In this paper, we propose a graph neural network social recommendation model that integrates static and dynamic feature relationships (FSDFR-GNNSR). The model uses a graph embedding algorithm to extract static features of users and movies, and takes the static features as input to gated recurrent unit (GRU), so that the model can take static features into consideration while modeling user dynamic behavior. Finally, we use graph attention networks to represent the dynamic influence of friends, simplify the update strategy of second-order neighbor nodes. We apply graph pooling operations to improve the generalization ability of the algorithm. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.
WeRLman

Blockchain technology is responsible for the emergence of cryptocurrencies, such as Bitcoin and Ethereum. The security of a blockchain protocol relies on the incentives of its participants. Selfish mining is a form of deviation from the protocol where a participant can gain more than her fair share. Previous analyses of selfish mining make easing, non-realistic assumptions. We introduce a more realistic model with varying block rewards in the form of transaction fees. However, this comes at the cost of an intractable state space. To solve the complex model, we introduce WeRLman, a novel method based on deep Reinforcement Learning (deep RL). Using WeRLman, we show reward variability can significantly hurt blockchain security.
Virtual education during COVID-19 in higher education: A systematic review

The objective of the present systematic review aimed to analyze studies linked to online teaching-learning, digital competence tutoring, and technological tools in virtual education during COVID-19 in higher education. The suggested methodology by the PRISMA declaration was pursued; the information search was conducted in Scopus, EBSCO, Springer Open, ProQuest, and One File. The target period ranged from May 10, 2021, to July 4, 2021; the identification, screening, eligibility, and inclusion for its progress were performed. The search produced a total of 230 studies, with 45 remaining. The inclusion criteria included original peer-reviewed research articles and qualitative and quantitative studies in higher education, comprising teachers and students addressing the study objective. In contrast, the exclusion criteria covered bibliographic reviews with procedural deficiencies, studies not exposed to a peer review process, and those not depicting a relationship with parts of the study. The results reveal that online teaching-learning, digital competence tutoring, and technological tools have been affirmative features expected to persist in higher virtual education engendered by the COVID-19 pandemic.&#x0D; Received: 5 October 2021Accepted: 22 April 2022
Exact Shapley values for local and model-true explanations of decision tree ensembles

Additive feature explanations using Shapley values have become popular for providing transparency into the relative importance of each feature to an individual prediction of a machine learning model. While Shapley values provide a unique additive feature attribution in cooperative game theory, the Shapley values that can be generated for even a single machine learning model are far from unique, with theoretical and implementational decisions affecting the resulting attributions. Here, we consider the application of Shapley values for explaining decision tree ensembles and present a novel approach to Shapley value-based feature attribution that can be applied to random forests and boosted decision trees. This new method provides attributions that accurately reflect details of the model prediction algorithm for individual instances, while being computationally competitive with one of the most widely used current methods. We explain the theoretical differences between the standard and novel approaches and compare their performance using synthetic and real data.
DRACO Stream Cipher

Stream ciphers are vulnerable to generic time-memory-data tradeoff attacks. These attacks reduce the security level to half of the cipher’s internal state size. The conventional way to handle this vulnerability is to design the cipher with an internal state twice as large as the desired security level. In lightweight cryptography and heavily resource constrained devices, a large internal state size is a big drawback for the cipher. This design principle can be found in the eSTREAM portfolio members Grain and Trivium.Recently proposals have been made that reduce the internal state size. These ciphers distinguish between a volatile internal state and a non-volatile internal state. The volatile part would typically be updated during a state update while the non-volatile part remained constant. Cipher proposals like Sprout, Plantlet, Fruit and Atom reuse the secret key as non-volatile part of the cipher. However, when considering indistinguishability none of the ciphers mentioned above provides security beyond the birthday bound with regard to the volatile internal state. Partially this is due to the lack of a proper proof of security.We present a new stream cipher proposal called Draco which implements a construction scheme called CIVK. In contrast to the ciphers mentioned above, CIVK uses the initial value and a key prefix as its non-volatile state. Draco builds upon CIVK and uses a 128-bit key and a 96-bit initial value and requires 23 % less area and 31 % less power than Grain-128a at 10 MHz. Further, we present a proof that CIVK provides full security with regard to the volatile internal state length against distinguishing attacks. This makes Draco a suitable cipher choice for ultra-lightweight devices like RFID tags.
Securing GPU via region-based bounds checking

Graphics processing units (GPUs) have become essential general-purpose computing platforms to accelerate a wide range of workloads, such as deep learning, scientific, and high-performance computing (HPC) applications. However, recent memory corruption attacks, such as buffer overflow, exposed security vulnerabilities in GPUs. We demonstrate that out-of-bounds writes are reproducible on an Nvidia GPU, which can enable other security attacks.
Design of multi-robot collaborative navigation and control system based on ROS and laser SLAM

Abstract Through in-depth research in the current robot field, a design scheme of cooperative navigation and control system for multiple robots based on ROS and laser SLAM is proposed. Compared with a single robot, multi-robot cooperation can improve the operation range and efficiency, and is suitable for more diversified scenarios. The system design includes multi-machine mapping, multi-machine global and local path planning, multi-machine navigation, real-time obstacle avoidance, multi-machine dynamic task assignment. Through the centralized control structure, the robot collects the lidar data in real time to build the map in real time and realizes the integration of multi-machine map building. Then, the path planning algorithm is called to plan the path. Combined with the dynamic task allocation algorithm, the efficiency of multi-robot cooperative operation is maximized and the execution experience is provided for the implementation of other similar projects.
Software Architecture Elements Applied to Software Test: View, Viewpoints and Containers

This paper presents definitions of test architecture views, viewpoints, and containers. The paper encourages debate within the modelling and architecture community and builds on previous work and papers. There are job postings for test architects. However, many testers and system architects do not consider test modeling and planning to include test architectures, views, viewpoints, and contents. The world of standards, particularly test standards, has not yet reached a consensus on software test architectures. This paper also presents definitions and considerations for architectural elements and concepts within a test perspective.
Development of a Sensing Platform Based on Hands-Free Interfaces for Controlling Electronic Devices

Hands-free interfaces are essential to people with limited mobility for interacting with biomedical or electronic devices. However, there are not enough sensing platforms that quickly tailor the interface to these users with disabilities. Thus, this article proposes to create a sensing platform that could be used by patients with mobility impairments to manipulate electronic devices, thereby their independence will be increased. Hence, a new sensing scheme is developed by using three hands-free signals as inputs: voice commands, head movements, and eye gestures. These signals are obtained by using non-invasive sensors: a microphone for the speech commands, an accelerometer to detect inertial head movements, and an infrared oculography to register eye gestures. These signals are processed and received as the user's commands by an output unit, which provides several communication ports for sending control signals to other devices. The interaction methods are intuitive and could extend boundaries for people with disabilities to manipulate local or remote digital systems. As a study case, two volunteers with severe disabilities used the sensing platform to steer a power wheelchair. Participants performed 15 common skills for wheelchair users and their capacities were evaluated according to a standard test. By using the head control they obtained 93.3 and 86.6%, respectively for volunteers A and B; meanwhile, by using the voice control they obtained 63.3 and 66.6%, respectively. These results show that the end-users achieved high performance by developing most of the skills by using the head movements interface. On the contrary, the users were not able to develop most of the skills by using voice control. These results showed valuable information for tailoring the sensing platform according to the end-user needs.
VoiceQuerySystem: A Voice-driven Database Querying System Using Natural Language Questions

With recent development in natural language processing (NLP) and automatic speech recognition (ASR), voice-based interfaces have become a necessity for applications such as chatbots, search engines, and databases. In this demonstration, we introduce VoiceQuerySystem, a voice-based database querying system that enables users to conduct data operations with natural language questions (NLQs). Different from existing voice-based interfaces such as SpeakQL or EchoQuery, which restricts the voice input to be an exact SQL or follow a pre-defined template, VoiceQuerySystem attempts to achieve data manipulation via common NLQs, and thus does not require the user's technical background in SQL language.
Developing an Augmented Reality Immersive Learning Design (AILeaD) Framework: A Fuzzy Delphi Approach

The use of augmented reality as a teaching aid among teachers is becoming more widespread. So, it is a must to create a holistic framework of augmented reality in order to enhance the teaching and learning context. Therefore, the purpose of this study is to develop an augmented reality immersive learning design (AILeaD) framework by using the Fuzzy Delphi Technique. The objective of this research is to gain experts’ consensus in building and identifying constructs and elements as well as their place in the AILeaD framework. The Fuzzy Delphi Technique is used as a systematic method to evaluate, verify as well as to make a decision in modifying all needed constructs and elements in order to create the AILeaD framework through the consensus of the experts. The research questionnaire is divided into three sections which are technology skills, instructional designs and type of augmented reality applications in a total of 36 items and using the Likert-7point scale. The sample of this research consists of 10 experts from educational technology, teaching technology, augmented reality and model development. The finding showed that the experts agreed and approved all of the constructs and elements where the three conditions were fulfilled which are the item value is less than or same with the threshold value (d) &lt; 0.2, experts' consensus percentage exceeded 75% and defuzzification value per item exceeded α-cut = 0.5. This study could contribute to the educational policy that focuses on the development of teaching quality with the improvement of existing skills among educators through the innovation and utilization of technology in the teaching and learning context. This is in line with Malaysia Education Blueprint known as Shift 7 (2013 – 2025) where it leverages the utilization of ICT to enhance the quality of education in Malaysia. The implication of this study is to make the AILeaD framework as a guideline for teachers, lecturers and instructional designers in producing the teaching aids in accordance with augmented reality and taking Gagne Nine Events, Cognitive Theory of Multimedia Learning dan Revised Bloom Taxonomy into account.
Growth algorithms in the phonological networks of second language learners: A replication of Siew and Vitevitch (2020a).

A recent study by Siew and Vitevitch (2020a) investigated word form lexica and their growth in children acquiring English and Dutch as first languages from a network perspective. They identified a unique developmental trajectory in network growth, with high-density neighborhoods becoming enriched through growth at early acquisition stages (the "preferential attachment" mechanism) but low-density neighborhoods gaining new neighbors at advanced acquisition stages (termed "inverse preferential attachment"). Their findings were confirmed for various languages, they fit with assumptions of cognitive efficiency in lexical memory and retrieval and are intriguing for second language research as well. The present study was designed as a replication of Siew and Vitevitch (2020a) study "An investigation of network growth principles in the phonological language network" with data of English-as-a-second-language learners. Results mirror findings by Siew and Vitevitch and demonstrate that preferential attachment is the main network growth algorithm driving lexical learning at early second-language proficiency stages, while inverse preferential attachment prevails at more advanced proficiency stages. The similar growth dynamics observed in phonological networks of first and second language users may indicate a universal cognitive principle underlying word learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).