Reviewerly/exHarmony huggingface dataset:
Evaluation of Hypermedia Tools in Terms of Usability Heuristics for English Language Teaching

The technology-assisted teaching and learning process has seen a spurt in growth in the last two decades. The education technology field has rapidly embraced new tools and techniques to enhance the student learning experience. With the evolution of multimedia elements such as digital images, audio, video, graphics, and animation, the learning supported by technology has made learning flexible and accessible in terms of time and place. With Wi-Fi enabled campuses, it is much easier for students to learn using their smart devices enabled by hypermedia content. Hypermedia, also known as active media, is the multimedia content that brings in interactivity, where the user can interact with the system, rather than viewing the content in passive mode. This helps in generating a dialogue between the system and user, sustaining user interest. Some examples of hypermedia are interactive quizzes, games, interactive videos, etc. This study aims to investigate and evaluate four interactive tools, namely FluentU, Duolingo, Livemocha, and Hello English, which are designed for language learning. A qualitative assessment of the applications, including a review of past literature on language learning using tools, was undertaken. The expert evaluation or assessment was done using Jakob Nielsen’s ten heuristics or design guidelines. The objective was to compare the applications by measuring their usability against the standard heuristics. The goal of any usability study is user satisfaction. Through this interface evaluation, the researchers have concluded for designers that could be considered during future development of hypermedia-based tools.
A design science approach to developing an integrated mobile app forensic framework

As our society becomes increasingly interconnected and reliant on technologies, the need for digital forensics is no longer restricted to cybercriminal investigations. For example, when a private organization’s system is compromised due to unauthorized access or espionage, digital forensic investigations can help to reveal the ‘what, why, how, who, when, and where’ relating to the incident, and the findings can inform decision-making and mitigation strategy formulation. Taking an interdisciplinary lens, this paper uses the design science approach to guide the development of an integrated digital forensic framework. It is then demonstrated how the framework can be used by a forensic investigator to examine Android applications, using a series of case studies (also referred to as “instantiations” of the method artifact in the design science literature).
Intelligibility Issues Faced by Smart Speaker Enthusiasts in Understanding What Their Devices Do and Why

Studies of smart speakers highlight issues people face with understanding why unexpected behaviour occurs and with recovering from mistakes due to uninformative responses. Yet, our understanding of such intelligibility issues in smart speakers — difficulties in understanding the device's behaviour — remains limited, in particular, for long-term and frequent smart speaker users who may encounter more complex situations than first-time users. We conducted an online survey and interviews with smart speaker enthusiasts to investigate how they form an understanding of the device's behaviour and what strategies they use to recover from breakdowns. We identified seven different breakdown recovery strategies and found that enthusiasts particularly struggled with breakdowns in their IoT infrastructure. Informed by our results, we propose three research directions: infrastructural breakdowns as learning opportunities for understanding the smart speaker's behaviour; leveraging aspects of non-verbal communication as opportunities for design; and considering passive users' intelligibility and control needs.
The nexus between global disruption due to the covid-19 and performance of the construction project

In the current COVID-19 pandemic, this paper attempts to empirically find out the increasing workplace bullying effect on project performance with employee deviant behaviour as a mediator, internal, and external locus of control as moderators. The random sampling technique was used to gather data from 777 employees, working in construction companies in Pakistan. The examination was established using WarpPLS software version 7.0. The results revealed that workplace bullying is positively associated with employee deviant behaviour which in return negatively affects the project performance. It can be inferred from the results that the “moderating role” of internal “locus of control” is insignificant; whereas the external “locus of control” significantly moderates the relationship between workplace bullying &amp; employee deviant behaviour. The proposed research and hypotheses support social exchange theory and social behaviour exchanges. This study has clarified some unique points that are equally important for the managers, employees, and project-based construction organization as a whole. It is also recommended that once the pandemic COVID-19 passes, it would be the dire need of that time to re-evaluate that, what has been changed in terms of professional thinking, working &amp; behaving as for as their project work and communication between the team members is concerned.
Artificial Intelligence Aided Course Scheduling System

Abstract College and department courses are broad, elegant and diversified. The demand for interdisciplinary elective courses is increasing, and the teaching management work is becoming increasingly heavy. Therefore, the administrative work of arranging and adjusting courses cannot be increased. We should carry out reform according to the actual difficulties. In the preliminary study of different educational administration work between colleges, the action research case study was started. This paper discusses how to apply the thinking of software engineering in educational administration system, innovate the current operation procedure, find out the solution, and realize the Artificial Intelligence Aided course scheduling system. This paper discusses the difficulties and difficulties of the current course scheduling system, and designs the new core principle and algorithm. Based on the problems collected by action research, the entity relationship between system analysis and system design is discussed, and the correct relation diagram is drawn to realize the function module of system design. After discussion, the group decided to adopt browser / server mode architecture, run the prototype of Artificial Intelligence Aided course scheduling system, and gradually test and eliminate problems, so as to realize the feasibility of the system and share the successful experience.
A Wavelet-Based Asymmetric Convolution Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution(SISR) and obtained remarkable performance. However, most of the existing CNN-based SISR methods tend to produce over-smoothed outputs and miss some textural details. To address these issues, we propose a wavelet-based asymmetric convolution network (WACN). Different from conventional CNN methods that directly infer HR images, our approach firstly learns to predict the LR’s corresponding series of HR’s wavelet coefficients before reconstructing HR images from them. This helps to capture more structural information in images to preserve texture information and avoid artifacts. To enhance the ability of feature extraction, we propose an asymmetric convolution block (ACB) structure to form a very deep network. In the training phase, ACB can provide different receptive fields to enrich feature information. In the inference phase, ACB’s asymmetric convolution kernel can be equivalently fused into the standard square-kernel layers, such that no extra computational burdens are introduced in the inference phase. Furthermore, we propose a variance-based channel attention (VCA) mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experimental results demonstrate the superiority of the proposed WACN in comparison with the state-of-the-art methods.
Voluntary Disclosure of Intellectual Capital: The Case Of Family And Non-Family Businesses In Jordan

The purpose of this paper is to examine the effect of firm’s characteristics (family vs. non- family firms) on disclosure behaviour regarding intellectual capital. The sample is composed of 201 firms from Amman Stock Exchange in 2018. First, this study differentiates three categories of intellectual capital: structural capital, relational capital, and human capital. Second, it examines the influence of the size, the profitability, the leverage, and the industry on the importance of intellectual capital disclosure. The study shows that family firms disclose more intellectual capital information than non- family firms. The results also show that the industry and the size have a significant and positive influence, that profitability and the leverage have a significant and negative influence on capital intellectual disclosure. Furthermore, Jordan family firms seem to disclose more about intellectual capital than Jordan non-family firms do.
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods

Current work in explainable reinforcement learning generally produces policies in the form of a decision tree over the state space. Such policies can be used for formal safety verification, agent behavior prediction, and manual inspection of important features. However, existing approaches fit a decision tree after training or use a custom learning procedure which is not compatible with new learning techniques, such as those which use neural networks. To address this limitation, we propose a novel Markov Decision Process (MDP) type for learning decision tree policies: Iterative Bounding MDPs (IBMDPs). An IBMDP is constructed around a base MDP so each IBMDP policy is guaranteed to correspond to a decision tree policy for the base MDP when using a method-agnostic masking procedure. Because of this decision tree equivalence, any function approximator can be used during training, including a neural network, while yielding a decision tree policy for the base MDP. We present the required masking procedure as well as a modified value update step which allows IBMDPs to be solved using existing algorithms. We apply this procedure to produce IBMDP variants of recent reinforcement learning methods. We empirically show the benefits of our approach by solving IBMDPs to produce decision tree policies for the base MDPs.
A STUDY ON MODALITY IN ENGLISH-MEDIUM RESEARCH ARTICLES

The present paper contributes to the increasing investigation into the lexico-grammatical features of the English-medium research articles (RAs). The study investigated the use of modality in the RAs both as a whole and across the sections, and compared these features between two subsets - RAs from an internationally established journal and those from a non-indexed journal published in Vietnam. Data for the study was 30 RAs over a three-year time span from 2017 to 2019 from English for Specific Purposes and VNU Journal of Foreign Studies. The findings indicate a small disproportion in the frequency between these two groups of authors, with the international subset having a slightly higher normalized frequency. Modality distribution across sections suggests the same decreasing order for both subsets, which starts from Conclusion to Results and Discussion, Literature review, Introduction and ends with Method, with Conclusion being the section with the highest frequency, and Method with the lowest. Additionally, the international subset consistently has a slightly higher normalized frequency in all sections than that in the Vietnamese subset. It is expected that the issues unfolded from this study could theoretically contribute to a better understanding of modality in research papers in general and in those in the discipline of Applied Linguistics in particular; practically, the thesis is also hoped to promote the Vietnamese researchers in their endeavor to join the international academic community.
STUDENTS’ INDIVIDUAL FEATURES AS A FACTOR OF ONLINE LEARNING EFFICIENCY

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Digital co-Hopf spaces

In this work, we deal with co-Hopf space structure of digital images. We prove that a pointed digital image having the same digital homotopy type as a digital co-Hopf space is itself a digital co-Hopf space. We conclude that a k-deformation retract of a digital co-Hopf space is a digital co-Hopf space. We also show that the digital equivalences are digital co-Hopf homomorphisms.
Analysis of Cognitive Complexity with Cyclomatic Complexity Metric of Software

The complexity of a software can be derived by using software complexity metrics which determines various software attributes quantitatively.The cognitive complexity metric, which is considering as a prominent factor of calculating the complexity of a software, evaluates how the human brain processes the given software with respective to different aspects, which involves the concept of cognitive Informatics.The McCabe's cyclomatic complexity is currently using as a standard complexity metric to determine the software complexity in terms of the number of linear independent paths.Thus, a broad analysis is carried on how the cognitive complexity derived based on Cognitive Information Complexity Measure (CICM) and the McCabe's cyclomatic complexity relates and varies with the computation of the given software, resulting that the cognitive complexity value becomes high with respective to its cyclomatic complexity.The cognitive complexity computation beyond the CICM value does not have a strong linear relation of the computation with cyclomatic complexity, which may be derived with a certain combination of relationships based on the factors involved within the cognitive complexity determination.
Detection of Forgery and Fabrication in Passports and Visas using Cryptography and QR Codes

In this paper, we present a novel solution to detect forgery and fabrication in passports and visas using cryptography and QR codes. The solution requires that the passport and visa issuing authorities obtain a cryptographic key pair and publish their public key on their website. Further they are required to encrypt the passport or visa information with their private key, encode the ciphertext in a QR code and print it on the passport or visa they issue to the applicant. The issuing authorities are also required to create a mobile or desktop QR code scanning app and place it for download on their website or Google Play Store and iPhone App Store. Any individual or immigration uthority that needs to check the passport or visa for forgery and fabrication can scan its QR code, which will decrypt the ciphertext encoded in the QR code using the public key stored in the app memory and displays the passport or visa information on the app screen. The details on the app screen can be compared with the actual details printed on the passport or visa. Any mismatch between the two is a clear indication of forgery or fabrication. Discussed the need for a universal desktop and mobile app that can be used by immigration authorities and consulates all over the world to enable fast checking of passports and visas at ports of entry for forgery and fabrication
PEOPLE COUNTING FOR PUBLIC TRANSPORTATIONS USING YOU ONLY LOOK ONCE METHOD

People counting have been widely used in life, including public transportations such as train, airplane, and others. Service operators usually count the amount of passengers manually using a hand counter. Nowadays, in an era that most of human-things are digital, this method is certainly consuming enough time and energy. Therefore, this research is proposed so the service operator doesn't have to count manually with a hand counter, but using an image processing with You Only Look Once (YOLO) method. This project is expected that people counting is no longer done manually, but already based on computer vision. This Final Project uses YOLOv4 that is the latest method in detecting untill 80 classes of object. Then it will use transfer learning as well to change the number of classes to 1 class. This research was done by using Python programming language with various platforms. This research also used three training data scenarios and two testing data scenarios. Parameters measured are accuration, precision, recall, F1 score, Intersection of Union (IoU), and mean Average Precision (mAP). The best configurations used are learning rate 0.001, random value 0, and sub divisions 32. And the best accuration for this system is 69% with the datasets that has been trained before. The pre-trained weights have 72.68% of accuracy, 77% precision, and 62.88% average IoU. This research has resulted a proper performance for detecting and counting people on public transportations.
Efficient Concurrent Execution of Smart Contracts in Blockchain Sharding

Throughput performance is a critical issue in blockchain technology, especially in blockchain sharding systems. Although sharding proposals can improve transaction throughput by parallel processing, the essence of each shard is still a small blockchain. Using serial execution of smart contract transactions, performance has not significantly improved, and there is still room for improvement. A smart contract concurrent execution strategy based on concurrency degree optimization is proposed for performance optimization within a single shard. This strategy is applied to each shard. First, it characterizes the conflicting contract feature information by executing a smart contract, analyzing the factors that affect the concurrent execution of the smart contracts, and clustering the contract transaction. Second, in shards with high transaction frequency, considering the execution time, conflict rate, and available resources of contract transactions, finding a serializable schedule of contract transactions by redundant computation and a Variable Shadow Speculative Concurrency Control (SCC-VS) algorithm for smart contract scheduling is proposed. Finally, experimental results show that the strategy increases the concurrency of smart contract execution by 39% on average and the transaction throughput of the whole system by 21% on average.
Finance-growth nexus in sub-Saharan Africa

Background: There is now significant empirical literature suggesting that finance is good for growth only up to a threshold level of financial development, becoming harmful after that level, in developed and developing countries. Aim: This study extends this literature that investigates non-linearities on the finance-growth link, by testing the inverted U -shape hypothesis in sub-Saharan African countries, which are among the least developed ones. Setting: 36 countries from sub-Saharan Africa over the period 1980–2015. Method: Estimation of quadratic dynamic panel data models by system-generalised method of moments. Results: Empirical results show that there is a hump-shaped relationship between financial development and economic growth in sub-Saharan African countries. Conclusion: Results suggest that the hypothesis of ‘too much finance harms economic growth’ also holds for low-income and less developed countries, but for much lower threshold levels of financial development than those of more developed and higher-income countries. As for policy implications, measures to strengthen finance quality and other growth-enhancing strategies need to be undertaken, rather than increasing finance size.
An Efficient FPGA Implementation of Richardson-Lucy Deconvolution Algorithm for Hyperspectral Images

This paper proposes an implementation of a Richardson-Lucy (RL) deconvolution method to reduce the spatial degradation in hyperspectral images during the image acquisition process. The degradation, modeled by convolution with a point spread function (PSF), is reduced by applying both standard and accelerated RLdeconvolution algorithms on the individual images in spectral bands. Boundary conditions are introduced to maintain a constant image size without distorting the estimated image boundaries. The RL deconvolution algorithm is implemented on a field-programmable gate array (FPGA)-based Xilinx Zynq-7020 System-on-Chip (SoC). The proposed architecture is parameterized with respect to the image size and configurable with respect to the algorithm variant, the number of iterations, and the kernel size by setting the dedicated configuration registers. A speed-up by factors of 61 and 21 are reported compared to software-only and FPGA-based state-of-the-art implementations, respectively.
Adoption of Mobile Assisted Language Learning (MALL) in Saudi Arabian EFL Classrooms

This study aimed to examine EFL teachers' concerns about the adoption of Mobile Assisted Language Learning (MALL) in Saudi Arabia. The Concerns-Based Adoption Model (CBAM) was used to analyze the participants' stages of concern (SoC) about MALL. The study also investigated whether some specific demographic and technographic variables (gender, age, teaching experience, and professional development) had any statistically significant effect on EFL teachers’ stages of concern about MALL. The participants in this study were (130) Saudi EFL public school teachers. The results revealed that the participants had high concerns at the Informational, Personal, and Management stages and minimal concerns at the Awareness, Refocusing, Collaboration, and Consequence stages. The MANOVA analysis revealed no significant difference among EFL teachers in terms of their MALL stages of concerns in relation to their gender, age, and teaching experience. Such findings indicate that Saudi EFL teachers' gender, age, and teaching experience have no effect on their concerns about using and implementing MALL. However, The MANOVA analysis yielded a significant difference among EFL teachers in terms of their MALL stages of concerns in relation to their professional development. These results entail that Saudi EFL teachers' professional development has a significant effect on their concerns about MALL. The study concluded that technology-related professional development could help decrease teachers’ self-concerns and increase their impact-concerns. The study recommended providing EFL teachers with technology-related professional development to ensure successful MALL adoption.