Reviewerly/exHarmony huggingface dataset:
The expanding circles of information behavior and human–computer interaction

This article examines the historical expansion and convergence of the fields of information behavior and human–computer interaction, primarily in terms of the philosophy underlying each field. Information behavior grew out of research in library service provision in the early 1900s, and human–computer interaction grew out of computer science and human factors engineering in the 1960s. While these two fields have had different origins, purposes, and discourses, in recent decades, they have begun to converge. In this article, we map this convergence and consider implications for the future of the information field. We conceptualize their scholarly paradigms as expanding circles, and we show that the circles of information behavior and human–computer interaction are expanding in terms of ontology, epistemology, and axiology—and moreover, they are beginning to overlap substantially. While the two fields continue to be largely separate in terms of scholarly discourses, we suggest that much could be gained by explicitly acknowledging their shared components. Some suggestions for this are discussed, and these are connected to the ongoing iSchool Movement.
Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction.
Stratified Sampling for Extreme Multi-label Data

Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data. Yet, there is no method for effectively generating stratified partitions of XML datasets. Instead, researchers typically rely on provided test-train splits that, 1) aren’t always representative of the entire dataset, and 2) are missing many of the labels. This can lead to poor generalization ability and unreliable performance estimates, as has been established in the binary and multi-class settings. As such, this paper presents a new and simple algorithm that can efficiently generate stratified partitions of XML datasets with millions of unique labels. We also examine the label distributions of prevailing benchmark splits, and investigate the issues that arise from using unrepresentative subsets of data for model development. The results highlight the difficulty of stratifying XML data, and demonstrate the importance of using stratified partitions for training and evaluation.
Parallelised ABox Reasoning and Query Answering with Expressive Description Logics

Automated reasoning support is an important aspect of logic-based knowledge representation. The development of specialised procedures and sophisticated optimisation techniques significantly improved the performance even for complex reasoning tasks such as conjunctive query answering. Reasoning and query answering over knowledge bases with a large number of facts and expressive schemata remains, however, challenging.We propose a novel approach where the reasoning over assertional knowledge is split into small, similarly sized work packages to enable a parallelised processing with tableau algorithms, which are dominantly used for reasoning with more expressive Description Logics. To retain completeness in the presence of expressive schemata, we propose a specifically designed cache that allows for controlling and synchronising the interaction between the constructed partial models. We further report on encouraging performance improvements for the implementation of the techniques in the tableau-based reasoning system Konclude.
The effect of external factors on industry performance: the case of Lalibela City micro and small enterprises, Ethiopia

Abstract The purpose of this study was to examine the effect of external factors on industry performance: in micro and small-scale enterprises. To achieve its objectives, the study employed a descriptive and explanatory research design and used quantitative research approach. The target population of the study was 395 MSEs owners working in manufacturing, trade, and service sectors. It used stratified and simple random sampling techniques and the required data have been collected from a sample size of 199 MSEs through standardized 5-point Likert-scale questionnaire. The study used both primary and secondary sources of data. For data analysis purpose, it employed descriptive and inferential statistical data analysis methods like correlation and multiple linear regression analysis. The finding of the study discovered the agreement of respondents to the positively significant relationship between external factors and industry performance in the study area. Specifically, marketing factors, financial factors, infrastructure, work premises factors, trade fair factors, and political-legal factors all have a positive effect on enterprises’ performance in the study area. In addition, the study result shows that financial factors, marketing factors, infrastructure, work premises factors, and trade fair factors have a positive significant effect on industry performance but political-legal factors do not significantly affect the dependent variable industry performance. From the predicting variables, infrastructure has more effect on industry performance than the rest of variables in the study area. The study recommends that the government/micro and small enterprises development offices should maintain different supportive trainings, decrease infrastructural problems, facilitate credit services, and create attractive environment to entice or create productive MSEs. In addition, the MSEs owners should design adoptive plans and programs with the external as well as internal environment to increase their performance.
Blockchain Aided Predictive Time Series Analysis in Supply Chain System

In the modern digital era, Artificial Intelligence (AI) and Blockchain Technology are the two most popular and promising research areas. AI helps in making intelligent decisions whereas blockchain technology automates the payment process by making it more secure, trusted and decentralized. Smart contracts help with interactions among participants over the network with no intermediate or a trusted third-party involvement. In this paper, we have considered a real-time application of tracking crops produced by farmers across different stages of the crops supply chain. We have proposed a secure and trusted supply chain model using blockchain technology and have done time series analysis for future prediction for the quantity of production of each crop in a season.
Monitoring Of Students' Soft Skills Development Within Foreign Language Learning Using Online Technologies

Digital transformation in the world poses new challenges for workers: employers now demand employees possessing excellent professional, management, and communication skills. But modern higher education is not ready for developing such soft skills due to several problems occurred: (1) a lack of a unified typology of soft skills; (2) differences in understanding of importance of developing soft skills between students, higher education professionals, and employers; (3) unavailability and the lack of focus of higher education institutions on the development of students' soft skills; (4) the lack of valid methodologies for developing soft skills. The author used a method of content analysis of scientific literature and found out that there is relevance and necessity of improving students' soft skills and for managing this process in digital environment pedagogical monitoring can be used as it provides opportunities for well-timed control and solving occurring problems.
A robust discriminative multi-atlas label fusion method for hippocampus segmentation from MR image

• The computational cost is reduced compared with classical “coarse-fine” registration stage by adopting the resampling algorithm in the coarse registration stage. • A patch embedding segmentation approach based on conditional random field (CRF) model potentially improves the segmentation performance of the hippocampus. • Furthermore, considering the label map rich in prior information is not fully used, the segmentation results are refined based on SPBM method. • Compared with state-of-the-art methods, our proposed method can obtain more accurate hippocampus segmentation results. Accurate and automatic segmentation of the hippocampus plays a vital role in the diagnosis and treatment of nervous system diseases. However, due to the anatomical variability of different subjects, the registered atlas images are not always perfectly aligned with the target image. This makes the segmentation of the hippocampus still face great challenges. In this paper, we propose a robust discriminative label fusion method under the multi-atlas framework. It is a patch embedding label fusion method based on conditional random field (CRF) model that integrates the metric learning and the graph cuts by an integrated formulation. Unlike most current label fusion methods with fixed (non-learning) distance metrics, a novel distance metric learning is presented to enhance discriminative observation and embed it into the unary potential function. In particular, Bayesian inference is utilized to extend a classic distance metric learning, in which large margin constraints are instead of pairwise constraints to obtain a more robust distance metric. And the pairwise homogeneity is fully considered in the spatial prior term based on classification labels and voxel intensity. The resulting integrated formulation is globally minimized by the efficient graph cuts algorithm. Further, sparse patch based method is utilized to polish the obtained segmentation results in label space. The proposed method is evaluated on IABA dataset and ADNI dataset for hippocampus segmentation. The Dice scores achieved by our method are 87.2 % , 87.8 % , 88.2 % and 88.9 % on left and right hippocampus on both two datasets, while the best Dice scores obtained by other methods are 86.0 % , 86.9 % , 86.8 % and 88.0 % on IABA dataset and ADNI dataset respectively. Experiments show that our approach achieves higher accuracy than state-of-the-art methods. We hope the proposed model can be transferred to combine with other promising distance measurement algorithms.
Federated Learning for Site Aware Chest Radiograph Screening

The shortage of Radiologists is inspiring the development of Deep Learning (DL) based solutions for detecting cardio, thoracic and pulmonary pathologies in Chest radiographs through multi-institutional collaborations. However, sharing the training data across multiple sites is often impossible due to privacy, ownership and technical challenges. Although Federated Learning (FL) has emerged as a solution to this, the large variations in disease prevalence and co-morbidity distributions across the sites may hinder proper training. We propose a DL architecture with a Convolutional Neural Network (CNN) followed by a Graph Neural Network (GNN) to address this issue. The CNN-GNN model is trained by modifying the Federated Averaging algorithm. The CNN weights are shared across all sites to extract robust features while separate GNN models are trained at each site to leverage the local co-morbidity dependencies for multi-label disease classification. The CheXpert dataset is partitioned across five sites to simulate the FL set up. Federated training did not show any significant drop in performance over centralized training. The site-specific GNN models also demonstrated their efficacy in modelling local disease co-occurrence statistics leading to an average area under the ROC curve of 0.79 with a 1.74% improvement.
Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems

We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We provide empirical evidence for the existence of such behavioral bias model through a controlled subject study with 145 participants. We then propose three learning techniques for enhancing decision-making in multi-round setups. We illustrate the benefits of our decision-making model through multiple interdependent real-world systems and quantify the level of gain compared to the case in which the defenders are behavioral. We also show the benefit of our learning techniques against different attack models. We identify the effects of different system parameters (e.g., the defenders' security budget availability and distribution, the degree of interdependency among defenders, and collaborative defense strategies) on the degree of suboptimality of security outcomes due to behavioral decision-making.
Belief Space Planning: a Covariance Steering Approach

A new belief space planning algorithm, called covariance steering Belief RoadMap (CS-BRM), is introduced, which is a multi-query algorithm for motion planning of dynamical systems under simultaneous motion and observation uncertainties. CS-BRM extends the probabilistic roadmap (PRM) approach to belief spaces and is based on the recently developed theory of covariance steering (CS) that enables guaranteed satisfaction of terminal belief constraints in finitetime. The CS-BRM algorithm allows the sampling of non-stationary belief nodes, and thus is able to explore the velocity space and find efficient motion plans. We evaluate CS-BRM in different planning problems and demonstrate the benefits of the proposed approach.
Face Recognition Using the Convolutional Neural Network for Barrier Gate System

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gate system.
High-Throughput Training of Deep CNNs on ReRAM-Based Heterogeneous Architectures via Optimized Normalization Layers

Resistive random-access memory (ReRAM)-based architectures can be used to accelerate convolutional neural network (CNN) training. However, existing architectures either do not support normalization at all or they support only a limited version of it. Moreover, it is common practice for CNNs to add normalization layers after every convolution layer. In this work, we show that while normalization layers are necessary to train deep CNNs, only a few such layers are sufficient for effective training. A large number of normalization layers do not improve prediction accuracy; it necessitates additional hardware and gives rise to performance bottlenecks. To address this problem, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepTrain</i> , a heterogeneous architecture enabled by a Bayesian optimization (BO) methodology; together, they provide adequate hardware and software support for normalization operations. The proposed BO methodology determines the minimum number of normalization operations necessary for a given CNN. Experimental evaluation indicates that the BO-enabled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepTrain</i> architecture achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> speedup compared to a conventional GPU for training CNNs with no accuracy loss while utilizing only a few normalization layers.
A Model with Iterative Trials for Correcting Logic Errors in Source Code

It is difficult for students and teachers to detect and correct logic errors in source code. Compilers and integrated development environments (IDEs) have the ability to detect and correct syntax errors but it is also difficult for them to detect and correct logic errors. Although many machine learning approaches have been proposed that can show correction candidates for logic errors, they do not provide guidance concerning how the user should fix them. In this paper, we propose a model for correcting logic errors in a given source code. The proposed model realizes debugging of multiple logic errors in the source code by iterative trials of identifying the errors, correcting the errors, and testing the source code. In this model, in the first stage, a list of correction candidates is provided by a deep learning model, and then the list is given to an editing operation predictor that predicts the editing operation for the correction candidate. To learn the internal parameters of the proposed model, we use a set of solution codes created to solve the corresponding programming tasks in a real e-learning system. To verify the usefulness of the proposed model, we apply it to 32 programming tasks. Experimental results show that the correction accuracy is, on average, 58.64% higher than that of the conventional model without iterative trials.
HisVA: A Visual Analytics System for Studying History

Studying history involves many difficult tasks. Examples include searching for proper data in a large event space, understanding stories of historical events by time and space, and finding relationships among events that may not be apparent. Instructors who extensively use well-organized and well-argued materials (e.g., textbooks and online resources) can lead students to a narrow perspective in understanding history and prevent spontaneous investigation of historical events, with the students asking their own questions. In this article, we proposed HisVA, a visual analytics system that allows the efficient exploration of historical events from Wikipedia using three views: event, map, and resource. HisVA provides an effective event exploration space, where users can investigate relationships among historical events by reviewing and linking them in terms of space and time. To evaluate our system, we present two usage scenarios, a user study with a qualitative analysis of user exploration strategies, and in-class deployment results.
Costa Rica’s educational scenario in times of COVID-19 pandemic

This article collected information that describes Costa Rican educational scenario during the closing of schools due to COVID-19 pandemic. It is described the strategies the Ministry of Public Education embraced to have students engaged in the learning process, and how teachers and parents were coping to provide a meaningful and quality education to the students despite the lack of resources and infrastructure. The strategy MEP implemented was "Learning at Home," which encompassed television and radio teaching, Microsoft Teams learning online platform, social media apps such as WhatsApp, digital resources, and autonomous didactic guides. Nonetheless, lack of the appropriate infrastructure, digital resources and digital skills hindered the continuation of the learning process for several pupils, exacerbating the existing social disparities that broadens the digital divide and educational gap.
Enabling a Secured Communication in Distributed IoT Using the Smart Systems Service Infrastructure

The Internet of Things builds a digital world where all assets including people, machines, human-machine interfaces (HMIs), processes, services, etc. are internetworked. Digital Twins provide 1-to-1 digital replica of physical assets and enable their networking and their goal-directed and standardized communication. The combination of assets and their corresponding Digital Twins are the “things”, the nodes of the IoT. With the rapid development of IoT and its pertinent technologies, more and more things will be mutually interconnected over the Internet. Hence, the security issues that come with it are gradually becoming sharper and sharper. In this paper, we focus on the topic of the secured communication among things - or more precisely their Digital Twins - and propose an approach related to OAuth2, asymmetrically encryption and digital signing to secure the communication within the newly developed Smart Systems Service Infrastructure (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> I).
How to Assess Coherent Beliefs: A Comparison of Different Notions of Coherence in Dempster-Shafer Theory of Evidence

Stemming from de Finetti’s work on finitely additive coherent probabilities, the paradigm of coherence has been applied to many uncertainty calculi in order to remove structural restrictions on the domain of the assessment. Three possible approaches to coherence are available: coherence as a consistency notion, coherence as fair betting scheme, and coherence in terms of penalty criterion. Due to its intimate connection with (finitely additive) probability theory, Dempster-Shafer theory allows notions of coherence in all the forms recalled above, presenting evident similarities with probability theory. In this chapter we present a systematic study of such coherence notions showing their equivalence.
3D Skeletal Joints-Based Hand Gesture Spotting and Classification

This paper presents a novel approach to continuous dynamic hand gesture recognition. Our approach contains two main modules: gesture spotting and gesture classification. Firstly, the gesture spotting module pre-segments the video sequence with continuous gestures into isolated gestures. Secondly, the gesture classification module identifies the segmented gestures. In the gesture spotting module, the motion of the hand palm and fingers are fed into the Bidirectional Long Short-Term Memory (Bi-LSTM) network for gesture spotting. In the gesture classification module, three residual 3D Convolution Neural Networks based on ResNet architectures (3D_ResNet) and one Long Short-Term Memory (LSTM) network are combined to efficiently utilize the multiple data channels such as RGB, Optical Flow, Depth, and 3D positions of key joints. The promising performance of our approach is obtained through experiments conducted on three public datasets—Chalearn LAP ConGD dataset, 20BN-Jester, and NVIDIA Dynamic Hand gesture Dataset. Our approach outperforms the state-of-the-art methods on the Chalearn LAP ConGD dataset.