Reviewerly/exHarmony huggingface dataset:
Joint gaze estimation and facial expression for student engagement prediction in collaborative learning

Estimating and improving students' engagement in a collaborative learning environment is an important component in the field of learning research. Collaborative learning is a strategy of learning activities employed by small groups in which cooperative learning behaviors are closely related to other members or objects in the group. Researchers showed that students who are actively involved in class learn more. Therefore, gaze behavior and facial expression are important nonverbal indicators in cooperative learning environments. In this paper, we proposed a multimodal deep neural network (MDNN) to solve the engagement prediction problem in collaborative learning. We combined facial expression and gaze direction as individual streams of MDNN to predict engagement levels in collaborative learning environments. Our multi-modal solution was evaluated in a real collaborative environment with a significant accuracy of 74%. The results show that the model can accurately predict students' performance in the collaborative learning environment.
A Blockchain-Based User Identity Authentication Method for 5G

The massively connected 5G IoT scene brings new challenges to identity authentication. Existing identity authentication methods have problems such as high time delay and poor efficiency when mass access is performed, and it is difficult to meet the situation of the rapid increase in the number of users. In this paper, we proposed a blockchain-based user identity authentication method for 5G IoT scenarios. This method builds the mapping relationship between user attributes and identity, and utilizes blockchain technology to realize the authentication requirements of mass users in 5G scenarios. In addition, we also deployed a prototype system to verify the performance of the proposed method. Experimental results show that the identity authentication method proposed in this paper has significant advantages in delay and processing efficiency compared with traditional centralized authentication methods and common distributed authentication methods.
Exploring Life in Concentration Camps through a Visual Analysis of Prisoners’ Diaries

Diaries are private documentations of people’s lives. They contain descriptions of events, thoughts, fears, and desires. While diaries are usually kept in private, published ones, such as the diary of Anne Frank, show that they bear the potential to give personal insight into events and into the emotional impact on their authors. We present a visualization tool that provides insight into the Bergen-Belsen memorial’s diary corpus, which consists of dozens of diaries written by concentration camp prisoners. We designed a calendar view that documents when authors wrote about concentration camp life. Different modes support quantitative and sentiment analyses, and we provide a solution for historians to create thematic concepts that can be used for searching and filtering for specific diary entries. The usage scenarios illustrate the importance of the tool for researchers and memorial visitors as well as for commemorating the Holocaust.
Determinants of student performance with mobile‐based assessment systems for English as a foreign language courses

Abstract Background Mobile‐based assessment has been an active area of research in the field of mobile learning. Prior research has demonstrated that mobile‐based assessment systems positively affect student performance. However, it is still unclear why and how these systems positively affect student performance. Objectives This study aims to identify the determinants of student performance during students' use of a mobile‐based assessment application in a formative assessment activity as part of English as a Foreign Language courses in higher education. Methods A structural model based on hypotheses will be validated using partial least squares‐structural equation modelling with data from the interaction of around 100 students of English as a Foreign Language (EFL) courses from the A1 and A2 levels of English that used a mobile‐based assessment system for a period of 4 weeks. Results and Conclusions This registered report describes the related work, hypotheses development, methodology, and proposed analysis to validate the structural model based on hypotheses. No results or conclusions have been obtained yet.
Feature Selection using Modified Null Importance

Some difficult feature selection tasks have a property of 'small <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> . ‘Feature selection methods using variable importance measures that were defined in tree-based ensemble methods have reportedly shown a relatively good performance even in 'small <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> ‘tasks. Among them, this study focuses on the feature selection method using the null importance. Through our preliminary experiments, we found that this feature selection method often fails to detect input variables that weakly affect the output. We should note that some practical feature selection tasks require to find all of the input variables that actually affect the output. In order to find even input variables that weakly affect the output, we thus modify the feature selection method using the null importance. Specifically, our method, what we call the feature selection method using the modified null importance, normalizes an importance score of each of the input variables by using those of random variables, and then uses the normalized score in order to determine whether or not the variable is relevant to the output. Through numerical experiments, we finally show that the modified method is capable of detecting even input variables that weakly affect the output.
Developing Interpretable Machine Learning for Forward Kinematics of Robotic Arms

Machine learning (ML) is becoming increasingly sought after in diverse domains. Unfortunately for this objective, most ML research has focused on improving performance on evaluation metrics such as accuracy. However, to make important decisions, ML models need to be interpretable. We propose an approach to approximate kinematics of a robotic arm using interpretable artificial neural networks (ANNs). This work is based on approximating nonlinear functions where domain knowledge and visually observable features of the data are used to design ANNs. After analyzing existing work, we present a feasibility study approximating the kinematics of a simplified robotic arm and extend the work to multiple hidden layers. We generalize the existing work and extend its use for a different application, noting the challenges that arise while extending this work to multiple hidden layers.
Distributed Trust and Reputation Services in Pervasive Internet-of-Things Deployments

Cyberprotection in the context of Internet-of-Things (IoT) includes three basic areas: security, privacy and trust. Security and privacy technologies are related to mechanisms such as cryptography or authentication protocols that have been extensively explored and adapted to IoT requirements. However, new risks such as cyber-physical attacks and novel distributed, and pervasive architectures reveal new weaknesses where trust and reputation issues are the main challenges to be addressed. Nevertheless, both, trust and reputation, are intrinsically subjective and distributed, and algorithms guaranteeing a dynamic and efficient management of these properties tend to be computationally heavy and complex. In this context, new trust and reputation services for pervasive IoT deployments are needed. Therefore, in this paper we propose a new distributed architecture for the provision of trust and reputation services in IoT systems. The architecture is based on Blockchain technologies and the composition of different conceptual models (cognitive, computational, neurological, and game-theoretical) using stochastic functions. The resulting probability may be employed by nodes to create their own trustworthy subsystem. In order to validate the performance and usability of the proposal, an experimental validation based on simulation technologies is provided.
KubFBS: A fine‐grained and balance‐aware scheduling system for deep learning tasks based on kubernetes

Abstract The past decade witnessed a remarkable increase in deep learning (DL) workloads which require GPU resources to accelerate the training process. However, the existing coarse‐grained scheduling mechanisms are agnostic to information other than the number of GPUs or GPU memory, which results in performance degradation of DL tasks. Moreover, the common assumption held by the existing balance‐aware DL task scheduling strategies, a DL task consumes resources once it starts, fails to reduce resource contention, and further limits execution efficiency. To address these problems, this article proposes a fine‐grained and balance‐aware scheduling model (FBSM) which considers the resource consumption characteristic of the DL task. Based on FBSM, we propose customized GPU sniffer (GPU‐S) and balance‐aware scheduler (BAS) modules to construct a scheduling system called KubFBS. The experimental results demonstrate KubFBS accelerates the execution of DL tasks while improving the load balancing capability of the cluster.
On the Use of Social Networks in Teachers’ Career Guidance Activities

The article reveals the essence of professional orientation as a system of interaction between a young person and a educator (teacher, teaching staff, society), which is aimed at meeting the needs of the person in professional self-determination.The authors make a content analysis of Internet sources to identify resources that can be used to support career guidance activities and present a classification of Internet resources by types of career guidance work: diagnostic (online testing), agitation (university and interested institution sites), advisory (career guidance sites, network services), information and orientation (professions directories, job sites), accompanying (support development software), organizational (chats, e-platforms for communication), and by types of career guidance activities: sites for meetings (network services), for immersion (specialized software), for consulting (online tests, career guidance sites), for excursions (institution sites), for projects (educational resources).Emphasis is placed on social networks and possibilities of their use in career guidance activities due to the large number of users, the informal nature of communication within the network, which is more interactive (almost one-time exchange of ideas and resources).The practical state of teachers' readiness to use social networks in career guidance was studied and it was found that despite the 100% inclusion of pre-service and service teachers in social networks, their readiness to involve them in career guidance is insufficient.
Solving Explicit Arithmetic Word Problems via Using Vectorized Syntax-Semantics Model

This paper presents an algorithm for solving explicit arithmetic word problems by using and fusing new and effective methods. The proposed algorithm is based two novel ideas. First, it bases on the newly built syntax-semantics method. This method is very effective because it significantly reduces the difficulty caused by the variety of semantics expressions. Second, it uses a vector computing method to enhance the syntax-semantics method. The proposed algorithm consists of four steps. It first encodes the problem text into a vector sequence. Second, it generates the matching candidates of the problem. Third, it computes matching scores between the vectorized models and the candidates. Finally, it decodes quantity relations and their positions in the original problem text. Experimental results show that the proposed algorithm outperforms the baseline algorithms on the benchmark datasets.
Finding Age Path of Self-Paced Learning

Self-paced learning (SPL) is an emerging research topic in recent machine learning research which is often formulated as a bi-convex problem. The choice of the age parameter in SPL can control the learning pace and is crucial to achieve optimal performance. Traditionally, the age parameter is programmed to increase in a fixed rate while solving the SPL problem using the alternative optimization strategy (AOS). However, this simple heuristic is likely to miss the optimal age parameter especially when efficiency is a major concern. To address this problem, we propose a solution path method, APSPL, which can track the optimal solutions of SPL with respect to the change of age parameter (age path). Specifically, we use the difference of convex (DC) formulation to replace the original biconvex problem, which enables us to derive the path-following algorithm. For better efficiency, our algorithm uses a decremental and incremental training strategy to avoid retraining several times at different age values. We theoretically prove that the solutions produced by APSPL are the same as those generated by traditional SPL solvers. We also provide the finite time convergence proof of APSPL. To demonstrate the applicability of APSPL, we provide an extension of APSPL for semi-supervised classification. To the best of our knowledge, APSPL is the first solution path algorithm for self-paced learning. Experimental results on a variety of benchmark datasets not only verify the effectiveness and efficiency of APSPL over traditional SPL, but also show the advantage of using the optimal age parameter.
Instance Correlation Graph for Unsupervised Domain Adaptation

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application fields. Due to the expensive cost of manual labeling efforts, it is important to transfer knowledge from a label-rich source domain to an unlabeled target domain. The core problem is how to learn a domain-invariant representation to address the domain shift challenge, in which the training and test samples come from different distributions. First, considering the geometry of space probability distributions, we introduce an effective Hellinger Distance to match the source and target distributions on statistical manifold. Second, the data samples are not isolated individuals, and they are interrelated. The correlation information of data samples should not be neglected for domain adaptation. Distinguished from previous works, we pay attention to the correlation distributions over data samples. We design elaborately a Residual Graph Convolutional Network to construct the Instance Correlation Graph (ICG). The correlation information of data samples is exploited to reduce the domain shift. Therefore, a novel Instance Correlation Graph for Unsupervised Domain Adaptation is proposed, which is trained end-to-end by jointly optimizing three types of losses, i.e., Supervised Classification loss for source domain, Centroid Alignment loss to measure the centroid difference between source and target domain, ICG Alignment loss to match Instance Correlation Graph over two related domains. Extensive experiments are conducted on several hard transfer tasks to learn domain-invariant representations on three benchmarks: Office-31, Office-Home, and VisDA2017. Compared with other state-of-the-art techniques, our method achieves superior performance.
OpsSC: Decentralized Blockchain Network Operation Workflow for Hyperledger Fabric

Enterprises have paid attention to permissioned blockchains like Hyperledger Fabric, which is one of the most promising platforms, for efficient decentralized transactions without depending on any particular organization. A permissioned blockchain-based system will be typically built across multiple organizations. In such blockchain-based systems, system operations across multiple organizations in a decentralized manner are essential to maintain the value of introducing permissioned blockchains. Decentralized system operations have recently been becoming realistic with the evolution of permissioned blockchains. For instance, the release of Hyperledger Fabric v2.x, in which individual operational tasks for a blockchain network, such as command execution of configuration change of channels (Fabric's sub-networks) and upgrade of chaincodes (Fabric's smart contracts), can be partially executed in a decentralized manner. However, the operational workflows also include the preceding procedure of pre-sharing, coordination, and pre-agreeing the operational information (e.g., configuration parameters) among organizations, after which operation executions can be conducted, and this preceding procedure relies on costly manual tasks. To achieve efficient decentralized operations, we have developed a decentralized inter-organizational operations method that we call Operations Smart Contract (OpsSC), which defines an operational workflow as a smart contract. Our prior research proposed the concept and was focused mainly on a simple framework of OpsSC to execute a sequence of commands and prototyping with Hyperledger Fabric v1.x to validate the concept, and its applicability to the aforementioned underlying blockchain network-level operational workflows (e.g., update of channels and chaincodes) with v2.x has not sufficiently been explored yet. In this paper, we redesign and implement OpsSC for blockchain network operations with Hyperledger Fabric v2.x. This paper presents OpsSC for operating channels and chaincodes, which are essential for managing the blockchain networks, through clarifying detailed workflows of those operations. A cost evaluation based on an estimation model shows that the total operational cost for executing a typical operational scenario to add an organization to a blockchain network having ten organizations could be reduced by 54 percent compared with a conventional script-based method. The implementation of OpsSC has been open-sourced and registered as one of Hyperledger Labs projects, which hosts experimental projects approved by Hyperledger.
Merging Datasets for Emotion Analysis

Context.Applying sentiment analysis is in general a laborious task.Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated.Objective.We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets.Method.We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance.We analyzed the results according to precision, recall, F1score and accuracy.Results.The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset.Conclusions.Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data.This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.
Humanistics in the Digital Age: A New Renaissance?

The article is devoted to contemporary problems of humanistics. It comprehends the essence of humanistics (the term was introduced by the modern philosopher, culturologist and linguist M. Epstein) as a totality of the humanities, completed by humanitarian technologies and humanitarian invention. There is discussed the importance of breaking the trend towards total technicism to the detriment of humanism in human, which is associated with the total onset of modern technologies for the development of artificial intelligence. The author expresses the idea of the importance of reviving the relevance of humanism in human, since the uncontrolled development of information and communication technologies can lead, according to modern humanitarian researchers (Yu. Harari), to a pessimistic scenario of the regressive evolution of humankind up to its disappearance. The article emphasizes the importance of the parity development of natural and artificial in modern society. The author draws attention to the need to revive the importance of such humanitarian sciences as cultural studies, aesthetics, and to stimulate the development of new humanitarian areas. A special place today should be occupied by the renewal of understanding of the importance of socio-cultural and artistic practices from the point of view of both enriching spiritual production and developing skills for mastering and comprehending their meanings by wide audiences to create the necessary conditions for the formation of a “complex person”. The article presents the origins of the discussion of “physicists and lyricists”. There are demonstrated various positions of researchers on the issues of passivity and activity of humanitarians to explain the current state of humanism. There is a paradoxical discrepancy between attitudes towards pragmatism, technological innovation and the development of a creative class, the essence of which is close collaboration with creative professions. Leaders of innovative information industries (S. Jobs, B. Gates, M. Zuckerberg, etc.) show their close relationship with humanitarian knowledge and values. The article presents and analyzes examples of humanitarian technologies and inventions that are recognized by the world community and continue to be in demand until the present time, even though they were created in different cultural and historical epochs and in different countries. The author pays attention to socio-cultural inventions in the field of pedagogy, especially artistic.
Dense Pedestrian Detection Based on YOLO-V4 Network Reconstruction and CIoU Loss Optimization

Abstract Online object detection is a fundamental problem in time-critical video analysis applications. Due to the performance limitations of one-stage object detection algorithms in dense pedestrian occlusion, we have improved YOLO-V4 in this paper, including network structure optimization, more efficient multi-scale feature fusion strategy formulation, and more specialized network loss function design. First, a single-output YOLO-V4 network structure is proposed, which integrates image information from multiple scales through the designed ladder fusion strategy. This not only ensures that the aspect ratio estimation of anchors is still driven by training data but also solves the invalid anchor distribution problem of the original network for objects with approximate size. Second, we adjust the resolution ratio of the network output feature map to the original input image to reduce the label rewriting cases of training samples. Finally, the concept of repulsive force is introduced to optimize the bounding box regression loss function, which improves the robustness of the model to the detection of densely occluded pedestrians, and enhances the practical value of YOLO-V4 in actual application scenarios.