Reviewerly/exHarmony huggingface dataset:
Current trends in TPACK research in science education: a systematic review of literature from 2011 to 2017

Abstract This paper reviews the research published from 2011 to 2017 that focuses on the characteristics and emerging topics on Technological Pedagogical Content Knowledge (TPACK) in Science education. A systematic search of the relevant research was undertaken of electronic databases including ERIC and Scopus using several key terms. Focussing on peer-reviewed journal articles, the screening process began with a perusal of the title and abstract, followed by the analysis of the purpose, method, findings, discussion and conclusion. The results showed that most of the research used pre-service teachers as the participants (56%). One-third used in-service teachers (31%) or both of them (13%). Furthermore, our results showed that the majority of studies use general science context (69%). Fewer studies focussed on specific subject domains, including biology (13%), Chemistry (19%), and Physics (6%). Based on the analysis of the current research, we found five key emerging trends, including the identification of teachers’ TPACK (100%), the relationship between TPACK and other components of technology integration (19%), strategies to developing preservice teachers TPACK (50%), how teacher implement TPACK (44%), and developing an instrument for TPACK (19%). Based on the analysis, we suggest some topics that need further investigation.
A Tertiary Systematic Literature Review on Software Visualization

Software visualization (SV) allows us to visualize different aspects and artifacts related to software, thus helping engineers understanding its underlying design and functionalities in a more efficient and faster way. In this paper, we conducted a tertiary systematic literature review to identify, classify, and evaluate the current state of the art on software visualization from 48 software visualization secondary studies, following three perspectives: publication trends, software visualization topics and techniques, and issues related to research field. Hence, we summarized the main findings among popular sub-fields of SV, identifying potential research directions and fifteen shared recommendations for developers, instructors and researchers. Our main findings are the lack of rigorous evaluation or theories support to assess SV tools effectiveness, the disconnection between tool design and their scope, and the dispersal of the research community.
Towards Digitization of Collaborative Savings Among Low-Income Groups

Rotating Savings and Credit Association (ROSCA) is a mechanism of informal collaborative savings that is widely used across the globe. Despite its popularity and prevalence, it is not well-studied from HCI and CSCW perspectives. The global increase in mobile penetration has created opportunities to serve the unbanked using mobile-based Digital Financial Services (DFS) for greater financial inclusion but there have not been any DFS-based interventions around ROSCAs. In this paper, we report a qualitative study involving 80 individuals to understand the dynamics of ROSCAs and opportunities for their digitization in the Pakistani context. We also present a smartphone-based Digital ROSCA platform designed on top of a simulated mobile money system. The platform was designed to be inclusive towards low-literate users. We present qualitative findings of its evaluation with 15 users (3 individual ROSCA groups). We find that digitization has the potential to support and strengthen traditional ROSCAs by mitigating issues like record-keeping, delayed payments, collection, distribution, and safety of money. It also allows the creation of payment history for individuals that can be used to score their financial credibility.
Distributed deep neural network training on edge devices

Deep Neural Network (Deep Learning) models have been traditionally trained on dedicated servers, after collecting data from various edge devices and sending them to the server. In recent years new methodologies have emerged for training models in a distributed manner over edge devices, keeping the data on the devices themselves. This allows for better data privacy and reduces the training costs. One of the main challenges for such methodologies is reducing the communication costs to and mainly from the edge devices. In this work we compare the two main methodologies used for distributed edge training: Federated Learning and Large Batch Training. For each of the methodologies we examine their convergence rates, communication costs, and final model performance. In addition, we present two techniques for compressing the communication between the edge devices, and examine their suitability for each one of the training methodologies.
Self-Supervised Contextual Data Augmentation for Natural Language Processing

In this paper, we propose a novel data augmentation method with respect to the target context of the data via self-supervised learning. Instead of looking for the exact synonyms of masked words, the proposed method finds words that can replace the original words considering the context. For self-supervised learning, we can employ the masked language model (MLM), which masks a specific word within a sentence and obtains the original word. The MLM learns the context of a sentence through asymmetrical inputs and outputs. However, without using the existing MLM, we propose a label-masked language model (LMLM) that can include label information for the mask tokens used in the MLM to effectively use the MLM in data with label information. The augmentation method performs self-supervised learning using LMLM and then implements data augmentation through the trained model. We demonstrate that our proposed method improves the classification accuracy of recurrent neural networks and convolutional neural network-based classifiers through several experiments for text classification benchmark datasets, including the Stanford Sentiment Treebank-5 (SST5), the Stanford Sentiment Treebank-2 (SST2), the subjectivity (Subj), the Multi-Perspective Question Answering (MPQA), the Movie Reviews (MR), and the Text Retrieval Conference (TREC) datasets. In addition, since the proposed method does not use external data, it can eliminate the time spent collecting external data, or pre-training using external data.
The Middleman Is Dead, Long Live the Middleman: The “Trust Factor” and the Psycho-Social Implications of Blockchain

Blockchain is widely regarded as a breakthrough innovation that may have a profound impact on the economy and society, of a magnitude comparable to the effects of the introduction of the Internet itself. In essence, a blockchain is a decentralized peer-to-peer network with no central authority figure, which adds information to the distributed database by collectively validating the accuracy of data. Since each node of the network participates in the review and confirmation of the new information before being accepted, the need for a trustworthy intermediary is eliminated. However, as trust plays an essential role in affecting decisions when transacting with one another, it is important to understand which implications the decentralized nature of blockchain may have on individuals' sense of trust. In this contribution, we argue that the adoption of blockchain is not only a technological, but foremostly a psychological challenge, which crucially depends on the possibility of creating a trust management approach that matches the underlying distributed communication system. We first describe the decentralization technologies and possibilities they hold for the near future. Next, we discuss the psycho-social implications of the introduction of decentralized processes of trust, examining some potential scenarios, and outline a research agenda.
Multivariate Myriad Filters Based on Parameter Estimation of Student-$t$ Distributions

The contribution of this study is twofold. First, we propose an efficient algorithm for the computation of the (weighted) maximum likelihood estimators for the parameters of the multivariate Student-$t$ distribution, which we call the generalized multivariate myriad filter. Second, we use the generalized multivariate myriad filter in a nonlocal framework for the denoising of images corrupted by different kinds of noise. The resulting method is very flexible and can handle heavy-tailed noise such as Cauchy noise, as well as the other extreme, namely Gaussian noise. Furthermore, we detail how the limiting case $\nu\to 0$ of the projected normal distribution in two dimensions can be used for the robust denoising of periodic data, in particular for images with circular data corrupted by wrapped Cauchy noise.
Caring for Alzheimer's Disease Caregivers

The number of informal caregivers for family members with Alzheimer's Disease (AD) is rising dramatically in the United States. AD caregivers disproportionately experience numerous health problems and are often isolated with little support. An active lifestyle can help prevent and mitigate physical and psychological health concerns amongst AD caregivers. Research has demonstrated how pervasive exergames can encourage physical activity (PA) in the general population, yet little work has explored how these tools can address the significant PA barriers that AD caregivers face. To identify opportunities for design, we conducted semi-structured interviews and participatory design sessions with 14 informal caregivers of family members with AD. Our findings characterize how becoming an AD caregiver profoundly impacts one's ability to be active, perspectives on being active, and the ways that exergames might best support this population.We discuss implications for design and howour findings challenge existing technological approaches to PA promotion.
Exploring Communal Technology Use in the Home

Vacuum cleaners, dish washers, and computers have had a lasting impact on ordinary life, and the last wave of ubiquitous technology, smart home technology, once again alters social order and practices in the home. Increasingly pervasive and internet-connected, domestic technology has become a community concern. Communal use of technology poses complex challenges for research and practice, requiring new approaches. Our investigation (36 interviews) of perceptions and considerations of communal device use illustrates how ordinary life evolves facing known and newly evolving challenges. We report four main themes around living with and sharing technology by relating aspects of technology considerations to different social groups. Using these insights, we illustrate participant considerations of personal characteristics, and discuss self-efficacy as a way to look at technology considerations, social groups, and personal characteristics. We outline in three ways how the concept of group-efficacy can help shape further investigation.
Qualifying and Quantifying Uncertainty in Digital Humanities

Research in digital humanities involves the need for conscious and explicit handling of data uncertainty. Recently, some initiatives have highlighted the importance of considering this uncertainty from the conceptual model to the final phases of implementation of software tools. Although the conceptual proposals for handling data uncertainty in the humanities have proliferated successfully, there is still a gap in bringing these proposals to actual implementations of software systems, especially due to the need to quantify this uncertainty and adopt more analytical paradigms based on margins of error, away from the conceptualization of data in the humanities. Trying to close this gap and avoid these paradigms, this paper presents a framework based on fuzzy logic that implements aspects of epistemic uncertainty (uncertainty related to our knowledge about the object of study) in digital humanities, providing algebraic and computational support to implementation. The framework proposes a solution to implement software systems that manage epistemic uncertainty, allowing comparisons, aggregations or reasoning with various levels of uncertainty in humanistic data. The solution is implemented in a real digital humanities project, illustrating its possibilities.
Partially Adversarial Learning and Adaptation

An image classification system for a specific target domain is usually trained with initialization from a source domain given with a large number of classes, particularly in an application of image recognition. The classes in target domain are usually seen as a subset in source domain. Partial domain adaptation aims to tackle this generalization issue where no labeled data are provided in target domain. This paper presents an adversarial learning for partial domain adaptation where a symmetric metric based on the Wasserstein distance is adopted in an adversarial learning objective. We build a Wasserstein partial transfer network where the Wasserstein adversarial objective is jointly optimized to partially transfer the relevance knowledge from source to target domains. The geometric property for optimal transport is assured to mitigate the gradient vanishing problem in adversarial training. The neural network components for feature extraction, relevance transfer, domain matching and task classification are jointly trained by solving a minimax optimization over multiple objectives. Experiments on image classification show the merits of the proposed partially adversarial domain adaptation over different tasks.
Isogeny-Based Cryptography: A Promising Post-Quantum Technique

Recent advances in quantum computing have made existing public key cryptosystems vulnerable to enormous security threats. Therefore, numerous efforts have been exploring post-quantum cryptographic techniques to address the emergence of quantum computing. We focus on one promising post-quantum cryptography known as “isogeny-based cryptography,” first by reviewing some concepts of the elliptic curve isogeny, and then, we present three major methods for constructing isogeny-based difficult problems. Then, we present some existing isogeny-based cryptographic primitives, such as signature and key exchange, and analyze their advantages and disadvantages. Finally, we discuss a few major challenges of isogeny research that, we hope, will attract more attention to isogeny-based cryptography.
Internet of Things in Blockchain Ecosystem from Organizational and Business Management Perspectives

This chapter focuses on Internet of Things (IoT) in blockchain ecosystems. IoT is a technological paradigm that bridges physical and digital worlds over a global network. There are a number of major challenges such as privacy and security. Blockchain can be a solution to these problems. In the literature, the subject is mostly discussed from technical and technological points of view. In this chapter, the topic is examined in a comprehensive manner including organization and business management perspectives. The chapter will begin with a comprehensive overview of IoT. Key features, different perspectives, developments and challenges about IoT will be described. The next section, blockchain based IoT as a solution to major challenges of IoT, will be explained. The relationship between IoT and blockchain will be described. Advantages of using blockchain for IoT, areas of usage, barriers and recommendations will be presented. Section 3.4 will be on its impact over businesses. Section 3.5 is devoted to emerging trends and future areas for research. The last section will conclude the chapter. Through the publication of this chapter, it is intended to be contributed to the literature on the Internet of Things and blockchain ecosystems. The chapter is expected to foster the research activities on the subject integrating organization and business management studies.
Bootstrap dual complementary hashing with semi-supervised re-ranking for image retrieval

With the rapid growth of multimedia data on the Internet, content-based image retrieval becomes a key technique for the Internet development. Hashing methods are efficient and effective for image retrieval. Dual Complementary Hashing (DCH) is one such method, which uses multiple hash tables and has good performance. However, DCH utilizes wrongly hashed image pairs to train the following hash table and discards correctly hashed image pairs. Therefore, the number of image pairs utilized for training the following hash tables will decrease rapidly. Moreover, each hash function in a hash table of DCH is trained by correcting the errors caused by its preceding one instead of holistically considering errors made by all previous hash functions. These restrictions significantly reduce the training efficiency and the overall performance of DCH. In this paper, we propose a new hashing method for image retrieval, Bootstrap Dual Complementary Hashing with semi-supervised Re-ranking (BDCHR). It is a semi-supervised multi-hashing method consisting of two parts: bootstrap DCH and semi-supervised re-ranking. The first part relieves the restrictions of DCH while the second part further enhances the image retrieval performance. Experimental results show that BDCHR yields better performance than other state-of-the-art multi-hashing methods.
Fuzzy cognitive maps (FCMs) for the analysis of team communication

Communication in teams plays a vital role in team success. This work proposes Fuzzy Cognitive Maps (FCMs) as a formalized, team communication methodology for the analysis of content and flow, simultaneously, of naturalistic team communication in a structured environment. Several methods of analysis of team communication exist. Few of them, however, analyze the flow and content of communication simultaneously, and none with teams using naturalistic language. Team communication data is coded for flow and content (through speech acts), then turned into FCMs for visual and statistical analysis. Results show that when using the FCM methodology both flow and content of communication can be diagrammed at the individual level, however, when assessing at the team level, what is illustrated is the flow of content. The application of different statistical analyses to the FCM output provides the opportunity to answer various questions on team communication. This study demonstrates that by using speech acts (SAs) as nodes, FCMs can illustrate and analyze team communication flow and content for naturalistic language. The FCM methodology is a powerful tool for studying team communication, and further incremental work could advance knowledge in team communication dynamics and provide contributions to graph theory. This method provides a visual overview of team communication dynamics in a naturalistic language setting, thus allowing for the study of intra-team dynamics and inter-team dynamics simultaneously.
A fine-grained requirement traceability evolutionary algorithm: Kromaia, a commercial video game case study

Context:Commercial video games usually feature an extensive source code and requirements that are related to code lines from multiple methods. Traceability is vital in terms of maintenance and content update, so it is necessary to explore such search spaces properly. Objective:This work presents and evaluates CODFREL (Code Fragment-based Requirement Location), our approach to fine-grained requirement traceability, which lies in an evolutionary algorithm and includes encoding and genetic operators to manipulate code fragments that are built from source code lines. We compare it with a baseline approach (Regular-LSI) by configuring both approaches with different granularities (code lines / complete methods). Method:We evaluated our approach and Regular-LSI in the Kromaia video game case study, which is a commercial video game released on PC and PlayStation 4. The approaches are configured with method and code line granularity and work on 20 requirements that are provided by the development company. Our approach and Regular-LSI calculate similarities between requirements and code fragments or methods to propose possible solutions and, in the case of CODFREL, to guide the evolutionary algorithm. Results:The results, which compare code line and method granularity configurations of CODFREL with different granularity configurations of Regular-LSI, show that our approach outperforms Regular-LSI in precision and recall, with values that are 26 and 8 times better, respectively, even though it does not achieve the optimal solutions. We make an open-source implementation of CODFREL available. Conclusions:Since our approach takes into consideration key issues like the source code size in commercial video games and the requirement dispersion, it provides better starting points than Regular-LSI in the search for solution candidates for the requirements. However, the results and the influence of domain-specific language on them show that more explicit knowledge is required to improve such results.
Boosting algorithms for a session-based, context-aware recommender system in an online travel domain

To keep up with a highly competitive the online hotel booking sector, it is necessary to develop fast and robust recommender systems. The 2019 RecSys Challenge is focused on ways we may use session-based and context-aware signals from users to improve the quality of hotel booking recommendations. In this paper, we present our approach to the challenge. We focus on the proper problem representation, feature extraction, and model blending. Our team achieved the 1st place out of 500 teams in the challenge, with the final MRR score of 0.685711.
FAIR Practices in Africa

This article investigates expansion of the Internet of FAIR Data and Services (IFDS) to Africa, through the three GO FAIR pillars: GO CHANGE, GO BUILD and GO TRAIN. Introduction of the IFDS in Africa has a focus on digital health. Two examples of introducing FAIR are compared: a regional initiative for digital health by governments in the East Africa Community (EAC) and an initiative by a local health provider (Solidarmed) in collaboration with Great Zimbabwe University in Zimbabwe. The obstacles to introducing FAIR are identified as underrepresentation of data from Africa in IFDS at this moment, the lack of explicit recognition of situational context of research in FAIR at present and the lack of acceptability of FAIR as a foreign and European invention which affects acceptance. It is envisaged that FAIR has an important contribution to solve fragmentation in digital health in Africa, and that any obstacles concerning African participation, context relevance and acceptance of IFDS need to be removed. This will require involvement of African researchers and ICT-developers so that it is driven by local ownership. Assessment of ecological validity in FAIR principles would ensure that the context specificity of research is reflected in the FAIR principles. This will help enhance the acceptance of the FAIR Guidelines in Africa and will help strengthen digital health research and services.