Reviewerly/exHarmony huggingface dataset:
Interface, Pedagogical Agents and Chatbox in Virtual Learning Environments: An Eye Tracking Experiment

Scholars in education over the past few decades have discussed at length computer-based interactive learning through the conceptual framework of cognitive science. Yet, very few studies look at the design of visual content in technology-mediated learning and its effects on information dissemination. During the COVID-19 pandemic, the situation in online education revealed that the potential of visual and non-verbal design attributes in learning is underexplored. This study employed a 2 × 2 experimental design. An Eyelink 1000 plus eye-tracker was used to record student participants’ eye movements to determine how they looked at the interface layout, the chat box, and pedagogical agents with text cues. The participants’ learning performance were measured with retention and transfer tests. Results showed that the interface design, chat box and pedagogical agents interfere with students’ learning performance and visual attendance, which proves spatial contiguity effect, coherence effect and redundancy effect from the cognitive theory of multimedia learning. The present study results support the hypothesis that visual design is fundamental for effective virtual learning environments.
Organic Computing to Improve the Dependability of an Automotive Environment

The aim of our research is to implement and evaluate Organic Computing methods in a real vehicle environment and thus increase the reliability of the vehicle's steering system. For this purpose, the steering system is simulated using an Organic Computing approach, which is extended with explicit fault diagnosis capabilities. In order to achieve the goals set by the demands of the automotive industry, several development steps are necessary. This paper outlines the research gaps and potentials as well as the approaches we envision to tackle these challenges. The starting point is a general concept involving three Electronic Control Units (ECUs) that act as active redundancy in the event of a component failure, e.g., an ECU failure. Next, the Organic Computing middleware in terms of an Artificial Hormone System in combination with Artificial DNA will be implemented and examined on real automotive hardware, resulting in a highly-reliable, resource- and cost-efficient fail-operational system. Beyond the classical working principles of Organic Computing to overcome ECU failures, our project implements system-level fault diagnosis to additionally monitor ECU performance in the context of the steering system. Forecasting, detecting and identifying faults in the system enables fault-specific recovery actions, e.g., preventive service migration or degradation. Overall, we combine promising relative work to an Organic Computing approach for a vehicle steering system with an industrial partnership with the goal of a functional ECU. After successful deployment, tests will be documented with suitable vehicle hardware in a corresponding simulation environment. This paper provides insight into the early stages of our research project.
Tools for Online Politeness

In this chapter specific ways to study politeness in online contexts are presented, and the focus is on digital-mediated discourse analysis, which is a specialised approach for the collection and analysis of data produced through different types of digital channels. From a general point of view, studying politeness in online contexts does not display any specific features beyond those identified in politeness in other contexts.
Toxic Speech and Speech Emotions: Investigations of Audio-based Modeling and Intercorrelations

Content moderation (CM) systems have become essential following the monumental increase in multimodal and online social platforms; and while increasingly published work focuses on text-based solutions, there is still limited work on audio-based methods. In this study we aim to explore relation-ships between speech emotions and toxic speech, as part of a CM scenario. We first investigate an appropriate framework for combining speech emotion recognition (SER) and audio-based CM models. We then investigate which emotional aspects (i.e., attribute, sentiment, or attitude) could contribute the most in facilitating audio-based CM recognition platforms. Our experi-mental results indicate that conventional shared feature encoder approaches may fail to capture additional discriminative features for boosting audio-based CM tasks while utilizing SER learning. We further investigate performance trade-offs of late-fusion frameworks for combining SER and CM information. We argue that these observations could be attributed to an emotionally-biased distribution in the CM scenario, concluding that SER could in deed play a role in content moderation frameworks, given added application-specific emotional information.
Research on Classroom Interaction Behavior Analysis Algorithm based on Audio and Video

Classroom interaction behavior research is an important part of classroom teaching quality evaluation, which can effectively improve teaching quality. Traditional classroom interaction behavior research is mainly carried out in the form of expert lectures and student questionnaires. This method can neither make the best use of the large amount of data generated in the classroom scene, nor can it provide an objective and detailed evaluation of the teaching quality. However, in the context of educational informatization, using information technology to observe and analyze classroom interaction can make full use of teaching data and provide timely and objective feedback on the teaching situation. This paper focuses on the analysis of classroom interaction behavior in colleges and universities. In order to make full use of classroom audio and video data, a framework for classroom interaction behavior analysis based on audio and video is constructed. It divides classroom interaction behaviors into verbal and non-verbal categories, and uses deep learning technology to realize automated classroom interaction analysis. The main work and innovations are as follows: (1) Combined with the theoretical basis of traditional classroom interaction analysis and the requirements of efficient classrooms for classroom quality evaluation, this paper constructs an audio-video-based classroom interaction behavior analysis framework. (2) The speaker segmentation and clustering algorithm in the verbal classroom interaction behavior analysis task is improved, and a frame feature extraction network integrating LSTM and TDNN and a temporal pooling network based on the dual multi-head attention mechanism are proposed. Compared with the DIHARD III baseline network, the improved speaker segmentation clustering algorithm reduces the speaker separation error rate (DER) by 3.24%, 3.19%, 4.53% and 4.14%, respectively, on the four types of evaluation datasets. (3) For the face detection algorithm in the non-verbal classroom interactive behavior analysis task, a single-stage face detection network FDN is proposed, and a bidirectional feature fusion module FPN+PANet, a prediction branch IoU- aware and a loss function CIoU are designed. Compared with RetinaFace, the final FDN has the most obvious improvement, and the average precision (Average Precision, AP) on the verification and test set difficult targets has increased by 2.6% and 2.7%, respectively.
P2P-based Solution for the Cloud Availability

With the advent of the cloud computing as a new computing systems deployment model, big companies, scientific and even private communities benefit of the advantages of this paradigm to deport their resources and data. In this context, it becomes necessary to offer an irreproachable availability to the users, and avoid, at best, service breakdowns which could jeopardize the utility of cloud computing. Consequently, we suggest in this work a way of insuring service continuity to the users of a cloud community. We exploit the access logs to the cloud of each user while this one is available. When the cloud is not responding, the user uses these logs and contacts another cloud user (Peer to peer) to satisfy his request. In this paper a solution for the problem of cloud unavailability is given based on cloud user's cooperation to satisfy a particular user request.
Transformer-Based Cache Replacement Policy Learning

Cache replacement policy is critical in computer system. It determines which data to be evicted from the cache when new data is coming. A good cache replacement policy increases the cache hit rate and decreases system delay significantly. There are a few heuristic methods designed for specific access patterns, but they perform poorly on diverse and complex access patterns. In order to deal with complicated access patterns, we formulate the cache replacement problem as matching question answering and design a Transformer-based cache replacement (TBCR) model. TBCR learns access patterns based on a Transformer encoder, and this architecture performs well even on complex access patterns. We evaluated on six memory-intensive Standard Performance Evaluation Corporation (SPEC) applications. TBCR increases cache hit rates by 3 $$\%$$ over the state-of-the-art.
Does the Robot Show Empathy with Me? Talking vs. Musical Robot

A loneliness pandemic pervades our youth, especially in Hong Kong. A growing body of research suggests that robots with empathic capabilities are important for negative emotion release. However, few studies explained how sad youth perceive and feel the empathy expressed by a robot. This study extends a formalized model of Interactively Perceiving and Experiencing Fictional Characters (I-PEFiC) plus the additions from Theory of Affective Bonding (TAB) into a new framework that explains the effects of a robot acting empathically so to become friends, thus reducing loneliness. In the current study, we explore how musical robots may be an alternative to talking robots, given that current NLP is limited and (popular) music is appealing to youth. We present an experiment in which empathy is measured in youth being exposed to a musical or a talkative robot. In our discussion, insights for designing an empathetic robot and the limitations of the study are reflected upon.
Dual-Branch Network Fused with Attention Mechanism for Clothes-Changing Person Re-identification

Clothes-changing person re-identification is a hot issue in the current academic circle. The key to this work is to extract the inherent characteristics of people, such as gait and body shape. Most of the current methods assume that persons’ clothes will not change in a short period of time, so these methods are not applicable when changing clothes. Based on this situation, this paper proposes a dual-branch network clothes-changing person re-identification method fused with attention mechanism. The attention mechanism captures and aggregates persons semantic-related information in channels and spaces, and trains the clothes classification branch to suppress Sensitivity of the network to clothing features. In addition, the method in this paper does not use auxiliary means such as human skeleton, and the complexity of the model is greatly reduced. This paper conducts experiments on the popular clothes-changing person re-identification dataset PRCC, and the experimental results show that the method in this paper is more advanced than popular methods. This paper also conducts experiments on LaST, an ultra-large-scale cross-space-time dataset, and also achieves competitive result results.
Pendampingan Kegiatan Belajar di Rumah Melalui Media Pembelajaran Inovatif di Masa Pandemi COVID-19

Learning From Home (LFH) is the best solution during the COVID-19 pandemic. LFH is a challenge for teachers, especially in preparing materials, determining methods, and making appropriate learning media. For children with special needs, LFR is a new experience that is not easy to implement. The aims of this community service were: 1) to overcome problems and help the community, especially children with special needs who get obstacles in learning activities at home; 2) create interesting learning media for studying at home; and 3) evaluation. This service was carried out in May 2021 for three days with 3 children with special needs. The media created are science and mathematics media made from used goods such as making puzzles and toy phones. The results of this service were the help of online learning at home during COVID-19 pandemic for children with special needs, the existence of interesting learning media so that learning at home becomes fun, and the increased knowledge and motivation of learning for children with special needs after using learning media.
Multi-Source Feature Fusion for Object Detection Association in Connected Vehicle Environments

Improvements in vehicular perception systems over the last decade have enabled new levels of safety and awareness in modern production vehicles. However, achievable performance of these perception systems is bounded by sensor limitations, such as range, and environmental factors, such as occlusion. Collaborative perception circumvents these limitations by incorporating sensor data from multiple sources to fill in perception gaps experienced by an individual sources’ sensors. This paper explores one important aspect of collaborative perception: simultaneously associating objects detected by multiple individual vehicles with each other. This task is crucial as the inability to perform such object association accurately can lead to duplicate or missed detections, which can lead to unsafe driving behavior. This work proposes a graph neural network model for this task that achieves an average precision (AP) of 0.882 in a challenging virtual environment consisting of 25 unique, simultaneous, and mobile viewpoints. A simpler real-world scenario with two static viewpoints is also evaluated where the model achieves an AP of 0.998, showing that this model can readily transfer to real-world scenarios as well.
On Verified Automated Reasoning in Propositional Logic

As the complexity of software systems is ever increasing, so is the need for practical tools for formal verification. Among these are automatic theorem provers, capable of solving various reasoning problems automatically, and proof assistants, capable of deriving more complex results when guided by a mathematician/programmer. In this paper we consider using the latter to build the former. In the proof assistant Isabelle/HOL we combine functional programming and logical program verification to build a theorem prover for propositional logic. Finally, we consider how such a prover can be used to solve a reasoning task without much mental labor.
A Simple Overlapping Relation Extraction Method Based on Dropout

The relation extraction (RE) task of knowledge acquisition aims to identify all specific relations of two entities. Prior works had proved the effectiveness of tagging methods, especially on the overlapping triple extraction problem. In this work, we introduce a simple relation extraction method (DropRel) based on dropout. We propose a Dropout-Normalization layer to generate different vectors for one token pair. Our model also provides a unified perspective for entity boundary and relation tagging tasks. We define a novel dependence relation for relation triple to integrate boundary into relation. The empirical experiments show that the DropRel model outperforms previous state-of-the-art methods on NYT and WEBNLG datasets. And our ablation study shows the DropRel without the dependence relation is better on the WEBNLG dataset.
Measurement-Based Quantum Computing as a Tangram Puzzle

Measurement-Based Quantum Computing (MBQC), proposed in 2001 is a model of quantum computing that achieves quantum computation by performing a series of adaptive single-qubit measurements on an entangled cluster state. Our project is aimed at introducing MBQC to a wide audience ranging from high school students to quantum computing researchers through a Tangram puzzle with a modified set of rules played on an applet. The player is provided a quantum circuit which they have to map to MBQC using polyominos. Polyominos, the building blocks of our game, consist of square tiles joined edge-to-edge to form different shapes. Each tile represents a measurement basis, differentiated by its color. Polyominos rest on a square-grid playing board, which signifies a cluster state. We show that mapping a quantum circuit to MBQC is equivalent to arranging a set of polyominos—each corresponding to a gate in the circuit—on the playing board, subject to certain rules. We state the rules in simple terms with no reference to quantum computing. One such rule describes ways to deform a polyomino while it still correctly realizes a given quantum gate. The player has to place polyominos on the playing board conforming to the rules. Any correct solution creates a valid realization of the quantum circuit in MBQC. A higher-scoring correct solution fills up less space on the board, resulting in a lower-overhead embedding of the circuit in MBQC, a challenging research problem.
Text Summarization Clustered Transformer (TSCT)

Natural language processing has recently had a considerable reputation due to the quick increase in online and offline data worldwide. The Extractive text summarization grabs the sentence from the corpus using salient related information to produce a concise summary. However, most existing approach to extracting sentence feature engineering has not utilized related contextual information and relation among the sentence. We present clustered Transformer models to mitigate this issue, namely Text summarization using clustered Transformer models. Our proposal has the highest benefit. The utility of our frame-work working on contextual representation is to grab various linguistic context information. We also use surface features to improve our understanding of word and sentence elements. Another utility is that the hierarchical attention mechanism can capture the contextual relation from the word and sentence levels using the transform model. Also, we added clustering after the transformer model to capture the most similar sentence to improve the attentive quality for producing the extractive text summarization.
An Approach to Ontology-Based Smart Search in E-commerce

The article presents the implementation of a intelligence search system for an CS-Cart platform online store. The system is based on the formation of a domain products ontology. The system assumes the processing and expansion of the user request. This article describes the structure of an OWL ontology. The article describes the algorithm for processing custom queries, as well as the results of experiments.
Trusted Framework for Transitioning to Distributed Agile Development Environment – A Survey

In modern era of software development, distributed Agile development environment (DADE) is an important approach because it allows software development at geographically dispersed sites across the globe. There exist a lot of challenges in DADE environment. Different tools and frameworks have been proposed in the past to address these challenges. The frameworks suffer with problems related to trust, transparency, Integrity, authentication and security. As different people work at different locations in DADE environment, coordination among globally dispersed team members and the customer remain to be an important aspect. The paper provides a survey on the requirement of a robust framework for transitioning to DADE environment keeping in view the security aspect of the distributed Agile environment.
Adaptive Robust Least-Squares Smoothing Algorithm

The Kalman smoothing algorithm is widely use d in offline data processing in GNSS to improve filter calcu lation accuracy. When there are measurement outliers or b iased random models, the adaptive Kalman smoothing algo rithm can reduce their impact on the smoothing results to a certain extent. Nevertheless, the adaptive Kalman smoothi ng algorithm usually has difficulty obtaining optimal estim ation in a complex observation environment due to the inhe rent defects of the algorithm itself. Therefore, we propose a n adaptive robust least-squares smoothing algorithm. The row and column transformation of the least-square smooth noise covariance matrix is realized by constructing an orth ogonal space transformation matrix, and the noise covaria nce estimation problem of the Kalman filter is transformed into the problem of solving the variance covariance compo nent elements under a specific matrix structure. In additio n, by selecting the observation value with the largest stand ardized residual to determine whether it is gross error, we avoid the gross error misjudgment problem caused by the t wo-factor robust estimation method under least-square sm oothing, and further improve the smoothing accuracy. Fina lly, two experiments are studied to compare the performan ce of the new algorithm and the state-of-the-art algorithms. The proposed algorithm has demonstrated better perform ance in terms of robustness and estimation accuracy.
Experimental Research of Educational Content Tracking by Students Group for Distance Learning

The results of experimental testing of the developed software for matching the focus of the student’s gaze with the structure of the training content on a computer monitor are presented in this paper. The use of widespread equipment is assumed: a laptop with a built-in camera or one additional camera. Initial processing of the face image, selection of eye areas is carried out using the OpenCV library. An appropriate algorithm for calculating the center of the eye pupil and the point on the monitor corresponding to the current focus of the gaze has been developed. The influence of the system calibration process with different schemes of calibration point display, its delay time on the screen and location of the additional camera according to the accuracy of the calculation of the coordinates of the gaze focus is investigated. Based on the performed experiments, it was defined that the error of gaze focus recognition with using two cameras can be reduced to 4–10%. The experiment in order to improve the calibration processes and evaluate the capabilities of the developed software for use on a laptop with only one built-in camera involving a group of students was carried out. The proposed approach makes it possible for objective measurement of each student working time with one or another part of the content. The lecturer will have the opportunity to improve the content by highlighting significant parts that receive little attention and simplifying those elements that students process for an unreasonably big amount of time. It is planned to integrate the developed software into the LMS Moodle in the future.