Reviewerly/exHarmony huggingface dataset:
Learning Interpretation with Explainable Knowledge Distillation

Knowledge Distillation (KD) has been considered as a key solution in model compression and acceleration in recent years. In KD, a small student model is generally trained from a large teacher model by minimizing the divergence between the probabilistic outputs of the two. However, as demonstrated in our experiments, existing KD methods might not transfer critical explainable knowledge of the teacher to the student, i.e. the explanations of predictions made by the two models are not consistent. In this paper, we propose a novel explainable knowledge distillation model, called XDistillation, through which both the performance the explanations’ information are transferred from the teacher model to the student model. The XDistillation model leverages the idea of convolutional autoencoders to approximate the teacher explanations. Our experiments shows that models trained by XDistillation outperform those trained by conventional KD methods not only in term of predictive accuracy but also faithfulness to the teacher models.
Fuzzy Active Learning to Detect OpenCL Kernel Heterogeneous Machines in Cyber Physical Systems

Cyber-physical systems (CPS) consist of a variety of multicore architectures, including central processing units (CPU) and graphical processing units (GPU). In general, programmers assign sequential programs to the CPU while parallel applications are assigned to the GPU. This article provides a method for mapping an OpenCL application to a heterogeneous multicore architecture using active fuzzy learning to determine the adequacy and processing capabilities of the application. During learning, subsamples are created by developing a machine learning-based device suitability classifier that predicts which processors would have excessive computational compatibility for running OpenCL programs. In addition, this study integrates an active learning model based on entropy with a fuzzification model to find nonoverlapping patterns. To minimize rule generation, the fuzzification-based weighted probabilistic technique is presented. The defuzzification process is optimized by using uncertainty values in conjunction with classification probability. In addition, 20 different features are proposed for extraction using the newly developed LLVM-based static analyzer. The correlation analysis approach is used to determine the optimal subset of features. The synthetic minority oversampling approach with and without feature selection is used to differentiate the class imbalance problem. Instead of manually modifying the machine learning classifier, a tree-based pipeline construction approach is used to determine the optimal classifier and associated hyperparameters. Experiments are then conducted on a set of benchmarks to verify the performance of the designed model. The results show that by increasing the number of training examples and including an entropy uncertainty measure, the proposed model is able to support and improve decision boundaries. We achieved a high F-measure of 0.77 and a ROC of 0.92 by optimizing and reducing the feature subsets.
Mobile learning and humanistic education: a science mapping approach

Scientific research on mobile learning and its implications on humanistic education has grown tremendously. Within the context of an overview of its forerunners (i.e. educational technology) and critical characteristics concerning the humanistic perspective in education, bibliometric findings on mobile learning publications in humanistic education are explained. Between 2000 and 2021, the phrase mobile learning was given attention in education databases through bibliometric analysis of 766 peer-reviewed works on mobile learning in humanistic education. This study takes a novel analytical approach, classifying the most significant authors, publications, and journals into seven theme clusters: pedagogy, learning approach, learning tools, support, real-time learning, technology acceptance, and self-directed learning. This bibliometric analysis uses four metrics: citation analysis, co-citation analysis, bibliographic coupling, and co-occurrences of keywords. The most significant contributions are addressed in each cluster. Current knowledge gaps, promising future research fields, and directions are discussed.
How heated is it?

Although issues of open source software are created to discuss and solve technical problems, conversations can become heated, with discussants getting angry and/or agitated for a variety of reasons, such as poor suggestions or violation of community conventions. To prevent and mitigate discussions from getting heated, tools like GitHub have introduced the ability to lock issue discussions that violate the code of conduct or other community guidelines. Despite some early research on locked issues, there is a lack of understanding of how communities use this feature and of potential threats to validity for researchers relying on a dataset of locked issues as an oracle for heated discussions. To address this gap, we (i) quantitatively analyzed 79 GitHub projects that have at least one issue locked as too heated, and (ii) qualitatively analyzed all issues locked as too heated of the 79 projects, a total of 205 issues comprising 5,511 comments. We found that projects have different behaviors when locking issues: while 54 locked less than 10% of their closed issues, 14 projects locked more than 90% of their closed issues. Additionally, locked issues tend to have a similar number of comments, participants, and emoji reactions to non-locked issues. For the 205 issues locked as too heated, we found that one-third do not contain any uncivil discourse, and only 8.82% of the analyzed comments are actually uncivil. Finally, we found that the locking justifications provided by maintainers do not always match the label used to lock the issue. Based on our results, we identified three pitfalls to avoid when using the GitHub locked issues data and we provide recommendations for researchers and practitioners.
Development of NavIC synchronized fully automated inter-building QKD framework and demonstration of quantum secured video calling

Quantum key distribution (QKD) is a revolutionary communication technology that promises ultimate security assurance by exploiting the fundamental principles of quantum mechanics. In this work, we report design and development of a fully automated inter-building QKD framework for generation and distribution of cryptographic keys, securely and seamlessly, by executing weak coherent pulse based BB84 protocol. This framework is experimentally validated by establishing a quantum communication link between two buildings separated by ~300m of free-space atmospheric channel. A novel synchronization technique enabled with indigenous NavIC (IRNSS) constellation is developed and implemented. This QKD system demonstrates generation of secure key rate as high as 300 Kbps with QBER< 3% for mean photon no. per pulse (${\mu}$) of 0.15. The intercept-resend eavesdropping attack has been emulated within the system and evaluated during experiment. A novel quantum secured end-to-end encrypted video calling app (QuViC) is also developed and integrated with QKD framework to demonstrate unconditionally secure two-way communication over Ethernet, functioning alongside with quantum communication.
Price, quality, and market dynamics of malaria rapid diagnostic tests: analysis of Global Fund 2009–2018 data

Rapid diagnostic tests (RDTs) for malaria are a vital part of global malaria control. Over the past decade, RDT prices have declined, and quality has improved. However, the relationship between price and product quality and their larger implications on the market have yet to be characterized. This analysis used purchase data from the Global Fund together with product quality data from the World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) Malaria RDT Product Testing Programme to understand three unanswered questions: (1) Has the market share by quality of RDTs in the Global Fund's procurement orders changed over time? (2) What is the relationship between unit price and RDT quality? (3) Has the market for RDTs financed by the Global Fund become more concentrated over time?Data from 10,075 procurement transactions in the Global Fund's database, which includes year, product, volume, and price, was merged with product quality data from all eight rounds of the WHO-FIND programme, which evaluated 227 unique RDT products. To describe trends in market share by quality level of RDT, descriptive statistics were used to analyse trends in market share from 2009 to 2018. A generalized linear regression model was then applied to characterize the relationship between price and panel detection score (PDS), adjusting for order volume, year purchased, product type, and manufacturer. Third, a Herfindahl-Hirschman Index (HHI) score was calculated to characterize the degree of market concentration.Lower-quality RDTs have lost market share between 2009 and 2018, as have the highest-quality RDTs. No statistically significant relationship between price per test and PDS was found when adjusting for order volume, product type, and year of purchase. The HHI was 3,570, indicating a highly concentrated market.Advancements in RDT affordability, quality, and access over the past decade risk stagnation if health of the RDT market as a whole is neglected. These results suggest that from 2009 to 2018, this market was highly concentrated and that quality was not a distinguishing feature between RDTs. This information adds to previous reports noting concerns about the long-term sustainability of this market. Further research is needed to understand the causes and implications of these trends.
Predicting Hospital Admission for Emergency Department Patients: A Machine Learning Approach

The objective of this study was to establish a machine learning model and to evaluate its predictive capability of admission to the hospital. This observational retrospective study included 3204 emergency department visits to a public tertiary care hospital in Greece from 14 March to 4 May 2019. We investigated biochemical markers and coagulation tests that are routinely checked in patients visiting the Emergency Department (ED) in relation to the ED outcome (admission or discharge). Among the most popular classification techniques of the scikit-learn library through a 10-fold cross-validation approach, a GaussianNB model outperformed other models with respect to the area under the receiver operating characteristic curve.
ANNA”:" Enhanced Language Representation for Question Answering

Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks.Most existing models performing state-of-theart results have shown their approaches in the separate perspectives of data processing, pretraining tasks, neural network modeling, or fine-tuning.In this paper, we demonstrate how the approaches affect performance individually, and that the language model performs the best results on a specific question answering task when those approaches are jointly considered in pre-training models.In particular, we propose an extended pre-training task, and a new neighbor-aware mechanism that attends neighboring tokens more to capture the richness of context for pre-training language modeling.Our best model achieves new state-of-the-art results of 95.7% F1 and 90.6% EM on SQuAD 1.1 and also outperforms existing pre-trained language models such as RoBERTa, ALBERT, ELECTRA, and XLNet on the SQuAD 2.0 benchmark.
STSM: Spatio-Temporal Shift Module for Efficient Action Recognition

The modeling, computational complexity, and accuracy of spatio-temporal models are the three major foci in the field of video action recognition. The traditional 2D convolution has low computational complexity, but it cannot capture the temporal relationships. Although the 3D convolution can obtain good performance, it is with both high computational complexity and a large number of parameters. In this paper, we propose a plug-and-play Spatio-Temporal Shift Module (STSM), which is a both effective and high-performance module. STSM can be easily inserted into other networks to increase or enhance the ability of the network to learn spatio-temporal features, effectively improving performance without increasing the number of parameters and computational complexity. In particular, when 2D CNNs and STSM are integrated, the new network may learn spatio-temporal features and outperform networks based on 3D convolutions. We revisit the shift operation from the perspective of matrix algebra, i.e., the spatio-temporal shift operation is a convolution operation with a sparse convolution kernel. Furthermore, we extensively evaluate the proposed module on Kinetics-400 and Something-Something V2 datasets. The experimental results show the effectiveness of the proposed STSM, and the proposed action recognition networks may also achieve state-of-the-art results on the two action recognition benchmarks.
Enhanced Obfuscation for Software Protection in Autonomous Vehicular Cloud Computing Platforms

Nowadays, sensors, communications connections, and more powerful computing capabilities are added to automobiles, making them more intelligent.The primary goal was to eliminate the need for human control, making them Autonomous Vehicles (AVs).Consequently, researchers thought to put all that newly added computational power to use for other endeavors.Hence, Autonomous Vehicular Cloud Computing (AVCC) models were introduced.Nevertheless, this goal is not an easy undertaking, the dynamic nature of autonomous vehicles introduces a critical challenge in the development of such a distributed computing platform.Furthermore, it presents far complicated issues as far as security and protection of services associated with this framework.In this paper, we center around securing programs running on AVCC.Here, we focus on timing side-channel attacks which aim to leak information about running code, which can be utilized to reverse engineer the program itself.We propose to mitigate these attacks via obfuscated compilation.In particular, we change the control flow of an input program at the compiler level, thereby changing the program's apparent behavior and accompanying physical manifestations to hinder these attacks.We improve our previous ARM-based implementation to address its limitations and provide more comprehensive coverage for different programs.Our solution is software-based and generically portablefitting different hardware platforms and numerous input program languages at the source level.Our findings prove a considerable improvement over our previous technique, which may provide more defense against timing side-channels.
Measuring Attribution in Natural Language Generation Models

With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world. In this work, we present a new evaluation framework entitled Attributable to Identified Sources (AIS) for assessing the output of natural language generation models, when such output pertains to the external world. We first define AIS and introduce a two-stage annotation pipeline for allowing annotators to appropriately evaluate model output according to AIS guidelines. We empirically validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset) via human evaluation studies that suggest that AIS could serve as a common framework for measuring whether model-generated statements are supported by underlying sources. We release guidelines for the human evaluation studies.
The Perceptions of Smallholder Farmers on the Use of Mobile Technology: A Naturalistic Inquiry in Zimbabwe

This research is an inquiry into the (under)utilisation of mobile phones by smallholder farmers in their agricultural activities in Zimbabwe. Through a naturalistic enquiry, the research established that agricultural extension officers are vital in the adoption and use of mobile phones for agricultural purposes. Those extension officers who were not skilled in productively using mobile phone technology had technophobia, which was the primary reason they did not use the technology in their interactions with farmers. Yet, a sizeable number of farmers used their mobile phones to receive information from agricultural extension officers, officials, other farmers, and market vendors. Interviewed farmers perceived the mobile phone as a tool for education in agricultural matters. However, very few used their phones effectively for agricultural purposes. The research identified two main inhibitors to the acceptance and use of mobile phones by farmers: a lack of the necessary digital skills, and lacking information to use mobile phones. Other inhibiting factors included semi-literacy, old age, insufficient infrastructure, socioeconomic status, excessive cost, and lack of support from telecommunications companies and other service providers. This research contributes a novel perspective to the body of knowledge regarding mobile technology adoption for agricultural activity in marginalised communities.
Classification and action rules in identification and self-care assessment problems

Disability, especially in children, is a very important and current problem. Lack of proper diagnosis and care increases the difficulty for children to adapt to disabilities. Disabled children have many problems with basic activities of daily living. Therefore, it is very important to support diagnosticians and physiotherapists in recognizing self-care problems in children.The aim of this paper is to extract classification and action rules, useful for those who work with children with disabilities.First, features and their impact on the accuracy of classification are determined. Then, two models are built: one with all features and one with selected ones. For these models the classification rules are extracted. Finally, action rules are mined and the next step in treatment process is predicted.Seventeen features with the greatest impact on classifying a child into a particular group of self-care problems were identified. Based on the implemented algorithms, decision and action rules were obtained.The obtained model, selected attributes and extracted classification and action rules can support the work of therapists and direct their work to those areas of disability where even a minimal reduction of features would be of great benefit to the children.
A Short Introduction To The Symbolic Turiyam Vector Spaces and Complex Numbers

This paper is dedicated to define for the first time the concept of Symbolic Turiyam vector space as a generalization of the corresponding neutrosophic one by using the algebra of symbolic Turiyam set. Also, we illustrate many examples to show and clarify the validity of this work.
An Extensive Study of User Identification via Eye Movements across Multiple Datasets

Several studies have reported that biometric identification based on eye movement characteristics can be used for authentication. This paper provides an extensive study of user identification via eye movements across multiple datasets based on an improved version of method originally proposed by George and Routray. We analyzed our method with respect to several factors that affect the identification accuracy, such as the type of stimulus, the IVT parameters (used for segmenting the trajectories into fixation and saccades), adding new features such as higher-order derivatives of eye movements, the inclusion of blink information, template aging, age and gender.We find that three methods namely selecting optimal IVT parameters, adding higher-order derivatives features and including an additional blink classifier have a positive impact on the identification accuracy. The improvements range from a few percentage points, up to an impressive 9 % increase on one of the datasets.
Human-Machine Communication Scholarship Trends: An Examination of Research From 2011 to 2021 in Communication Journals

Despite a relatively short history, the modern-day study of communication has grown into multiple subfields. To better understand the relationship between Human-Machine Communication (HMC) research and traditional communication science, this study examines the published scholarship in 28 communication-specific journals from 2011–2021 focused on human-machine communication (HMC). Findings suggest limited prior emphasis of HMC research within the 28 reviewed journals; however, more recent trends show a promising future for HMC scholarship. Additionally, HMC appears to be diverse in the specific context areas of research in the communication context. Finally, we offer future directions of research and suggestions for the development of HMC.
A Secure and Trusted Fog Computing Approach based on Blockchain and Identity Federation for a Granular Access Control in IoT Environments

Fog computing is a new computing paradigm that is an extension of the standard cloud computing model, which can be adopted as a cost effective strategy for managing connected objects , by enabling real-time computing and communication for analytical and decision making.Nonetheless, even though Fog-based Internet of Things networks optimize the standard architecture by moving computing, storage, communication, and control decision closer to the edge network, the technology becomes open to malicious attackers and remains many business risks that are not yet resolved.In fact, access control, privacy as well as trust risks present major challenges in Internet of Things environments based on Fog computing due to the large scale distributed nature of devices at the Fog layer.In addition, the traditional authentication methods are not adequate in Fog-based Internet of Things contexts since they consume significantly more computation power and incur high latency.To deal with these gaps, we present in this paper a secure and trusted Fog Computing approach based on Blockchain and Identity Federation technologies for a granular access control in IoT environments.The proposed scheme uses Smart Contract concept and Attribute-Based Access Control model to ensure the level of security and scalability required for data integrity without resorting to a central authority to make an access decision.
Validating The Framework of The Accounting Information Systems Components and Firm Performance: A Conceptual Study

The aim of this research is to propose a conceptual framework that links the Accounting Information System components with the Firm Performance. The framework contained the Availability, the Security and the Integrity, the Confidentiality and Privacy, and the System Quality as independent variable, with Firm financial and non-financial performance among the Jordanian Firms as dependent variable. The researcher followed the quantitative research methodology by testing the measurement model of the conceptual framework by checking the convergent and discriminant virility of the framework. The researcher used the mean of survey questionnaire as a research instrument, on which the researcher developed a 31 items questionnaire and distributed 350 questionnaires, and received 263 fully answered questionnaire. The findings of this study revealed that the scores of factor loadings and AVE did not achieve the recommended level of 0.4 and 0.5 respectively, which required a modification on the research model in the second run, on which the researcher achieve a satisfactory level of Factor loadings, Composite Reliability, Cronbach Alpha, and AVE. However, the scores of the Fornell and Larcker Criterion and HTMT which confirmed the discriminant validity. This study was limited to the measurement model analysis only, an empirical study with both the measurement and structural model will be a great addition to the future studies.