Reviewerly/exHarmony huggingface dataset:
Effects of Virtual-real fusion on immersion, presence, and learning performance in laboratory education

Virtual-reality (VR) fusion techniques have become increasingly popular in recent years, and several previous studies have applied them to laboratory education. However, without a basis for evaluating the effects of virtual-real fusion on VR in education, many developers have chosen to abandon this expensive and complex set of techniques. In this study, we experimentally investigate the effects of virtual-real fusion on immersion, presence, and learning performance. Each participant was randomly assigned to one of three conditions: a PC environment (PCE) operated by mouse; a VR environment (VRE) operated by controllers; or a VR environment running virtual-real fusion (VRVRFE), operated by real hands. The analysis of variance (ANOVA) and t-test results for presence and self-efficacy show significant differences between the PCE*VR-VRFE condition pair. Furthermore, the results show significant differences in the intrinsic value of learning performance for pairs PCE*VRVRFE and VRE*VR-VRFE, and a marginally significant difference was found for the immersion group. The results suggest that virtual-real fusion can offer improved immersion, presence, and selfefficacy compared to traditional PC environments, as well as a better intrinsic value of learning performance compared to both PC and VR environments. The results also suggest that virtual-real fusion offers a lower sense of presence compared to traditional VR environments.
Understanding Visual Risk Communication Messages: An Analysis of Visual Attention Allocation and Think-Aloud Responses to Tornado Graphics

Abstract Online channels for communicating risk frequently include features and technological capabilities to support sharing images of risk. In particular, the affordances found in social media, such as Twitter, include the ability to attach maps, photographs, videos, and other graphical information. The inclusion of visual cues such as colors and shapes and their different sizes are important for making sense of approaching threats, populations at risk, the potential impacts, and ranges of associated uncertainty. The reception of and attention to these visual cues in messages about a potential threat is the necessary first stage to making a decision about protective actions. Understanding what visual features capture individual attention and how attention is directed to visual images of risk on social media has the potential to affect the design of risk communication messages and the protective actions that follow. In this paper we use eye-tracking methods to identify where people allocate attention to a series of tweets and qualitative “think alouds” to determine what features of the tweets people attend to in their visual field are salient to message receivers. We investigate visual attention to a series of tweets that depict an emerging tornado threat to identify areas of visual interest and the properties of those visual cues that elicit attention. We find the use of color, properties of text presentation, and contents of messages affect attention allocation. These findings could help practitioners as they design and disseminate their weather messages to inform the public of emerging threats.
A Fast Method of Visually Lossless Compression of Dental Images

A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.
The impact of term-weighting schemes and similarity measures on extractive multi-document text summarization

Automatic text summarization is currently a topic of great interest in many knowledge fields. Extractive multi-document text summarization methods aim to reduce the textual information from a document collection by covering the main content and reducing the redundant information. In the scientific literature, there are different approaches related to term-weighting schemes and similarity measures, which are necessary for implementing an automatic summary system. However, to the best of the authors' knowledge, there are no studies to analyze the performance of the different schemes and measures. In this paper, all possible combinations of the most common term-weighting schemes and similarity measures used in the extractive multi-document text summarization field have been implemented, compared, and analyzed. Experiments have been performed with Document Understanding Conferences (DUC) datasets, and the model performance has been assessed with eight Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the execution time. Results show that the best term-weighting scheme is the term-frequency inverse-sentence-frequency scheme, and the best similarity measure is the cosine similarity. Even more, the combination formed by both of them has obtained the best average results in 87.5% of ROUGE scores compared to the other combinations.
Post Quantum Cryptography(PQC) - An overview: (Invited Paper)

We discuss the Post Quantum Cryptography algorithms for key establishment under consideration by NIST for standardization. Three of these, Crystals- Kyber, Classic McEliece and Supersingular Isogeny based Key Encapsulation (SIKE), are representatives of the three classes of hard problems underlying the security of almost all 69 candidate algorithms accepted by NIST for consideration in round 1 of evaluation. For each algorithm, we briefly describe the hard problem underlying the algorithm's cryptographic strength, the algebraic structure i.e., the groups or finite fields, underlying the computations, the basic computations performed in these algorithms, the algorithm itself, and the performance considerations for efficient implementation of the basic algorithm on conventional many-core processors. For Crystals- Kyber and SIKE, we will discuss the potential solutions to improve their performance on many-core processors.
A Novel Algorithm for Detecting Pedestrians on Rainy Image

Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.
Masked Face Recognition with Identification Association

In the crime scene, criminals often consciously conceal their facial identity through face-masked disguise, which poses a huge challenge to identity recognition. Existing disguised face recognition techniques aiming for light even slight occlusions are completely invalid for face-masked identification. To this end, this paper proposes a masked face recognition method based on person re-identification association, which converts the masked face recognition problem into an association uncovering problem between the masked face and the appearing faces of the same person. Based on the characteristics that person re-identification technique does not rely solely on facial information, it first takes advantages of re-identification to establish the association between face-masked pedestrians and face-unveiled pedestrians. It further provides an effective face image quality assessment to select the most identifiable faces for subsequent recognition from a variety of appearing candidate faces. Finally, the selected high-quality recognizable faces are used to replace masked faces for identification. The comparison experiments with the existing disguise face recognition methods show its superiority in terms of accuracy.
A survey of embedding models of entities and relationships for knowledge graph completion

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true.This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations. It poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning methods with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing a novel alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify the proposed method yields significant improvements on robustness over state-of-the-art counterparts.
TextGraphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration

The 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating large detailed multi-fact explanations for standardized science exam questions.Given a question, correct answer, and knowledge base, models must rank each fact in the knowledge base such that facts most likely to appear in the explanation are ranked highest.Explanations consist of an average of 6 (and as many as 16) facts that span both core scientific knowledge and world knowledge, and form an explicit lexically-connected "explanation graph" describing how the facts interrelate.In this second iteration of the explanation regeneration shared task, participants are supplied with more than double the training and evaluation data of the first shared task, as well as a knowledge base nearly double in size, both of which expand into more challenging scientific topics that increase the difficulty of the task.In total 10 teams participated, and 5 teams submitted system description papers.The best-performing teams significantly increased state-of-the-art performance both in terms of ranking (mean average precision) and inference speed on this challenge task.
Graph Embeddings for Abusive Language Detection

Abusive behaviors are common on online social networks. The increasing frequency of antisocial behaviors forces the hosts of online platforms to find new solutions to address this problem. Automating the moderation process has thus received a lot of interest in the past few years. Various methods have been proposed, most based on the exchanged content, and one relying on the structure and dynamics of the conversation. It has the advantage of being languageindependent, however it leverages a hand-crafted set of topological measures which are computationally expensive and not necessarily suitable to all situations. In the present paper, we propose to use recent graph embedding approaches to automatically learn representations of conversational graphs depicting message exchanges. We compare two categories: node vs. whole-graph embeddings. We experiment with a total of 8 approaches and apply them to a dataset of online messages. We also study more precisely which aspects of the graph structure are leveraged by each approach. Our study shows that the representation produced by certain embeddings captures the information conveyed by specific topological measures, but misses out other aspects.
A low-cost IoT multi-spectral acquisition device

Color and lighting quality assessment allow users to reliably examine the quality of light sources and the changes in the lighting conditions. Technological advance has led to an increase in the development of intelligent systems that are permanently connected to the Internet and that allow access to real time-data. Smart lighting systems have become a subject of great interest given their potential in different application areas, such as optimization for object reflectance and damage reduction to artwork. It has become necessary to develop new sensors that are aligned with the technological needs, that are easily integrated into IoT (Internet of Things) or smart lightning systems, and that allow estimating lighting quality measurements in real time. Here, a low-cost IoT multi-spectral acquisition device with wireless communication is presented. Insights into the overall system design and a validation of its operation is provided to allow reproducibility and independent testing of the proposed device. The device can be used for light source spectrum recovery and estimation of light quality and color rendering measures. The total cost of the device is below 130 USD, and customizability provides a great advantage over commercially available devices.
Media Pembelajaran Matematika Konkret Versus Digital: Systematic Literature Review di Era Revolusi Industri 4.0

The development of technology in the age of the fourth industrial revolution affects almost every aspect of human lives, including education sector. Therefore, learning innovations are needed in order to meet learning objectives. The objectives are coaching people who are competitive and have 4C skills (communication, collaboration, critical thinking and problem solving, creativity and innovation). One of the learning innovations in mathematics is the application of concrete and digital learning media. To explore and summarize the latest facts related to the concrete and digital mathematics learning media during industry 4.0, the authors of this study conducted a research using Systematic Literature Review method. A total of 30 literatures from various databases were interpreted. A review was drawn on the uses, strengths, and weaknesses of digital and concrete mathematics learning media during industry 4.0. The concrete learning media has advantages which include improving students’ problem solving, creative thinking, and visual thinking skills, but it requires more attention and care. Meanwhile, the advantages of digital learning media include increasing the effectiveness and efficiency of learning mathematics, supporting distance learning, and not requiring special maintenance. Nevertheless, there are still students and teachers who are not familiar with technology, some students do not have gadgets, and there are also inadequate internet networks in some areas.
An Adaptive Biomedical Data Managing Scheme Based on the Blockchain Technique

A crucial role is played by personal biomedical data when it comes to maintaining proficient access to health records by patients as well as health professionals. However, it is difficult to get a unified view pertaining to health data that have been scattered across various health centers/hospital sections. To be specific, health records are distributed across many places and cannot be integrated easily. In recent years, blockchain has arisen as a promising solution that helps to achieve the sharing of individual biomedical information in a secure way, whilst also having the benefit of privacy preservation because of its immutability. This research puts forward a blockchain-based managing scheme that helps to establish interpretation improvements pertaining to electronic biomedical systems. In this scheme, two blockchains were employed to construct the base, whereby the second blockchain algorithm was used to generate a secure sequence for the hash key that was generated in first blockchain algorithm. This adaptive feature enables the algorithm to use multiple data types and also combines various biomedical images and text records. All data, including keywords, digital records, and the identity of patients, are private key encrypted with a keyword searching function so as to maintain data privacy, access control, and a protected search function. The obtained results, which show a low latency (less than 750 ms) at 400 requests/second, indicate the possibility of its use within several health care units such as hospitals and clinics.
Entanglement transfer, accumulation and retrieval via quantum-walk-based qubit–qudit dynamics

Abstract The generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies. Achieving such non-classical high-dimensional resources will potentially unlock enhanced capabilities for quantum cryptography, communication and computation. We propose a protocol that is able to attain entangled states of d -dimensional systems through a quantum-walk (QW)-based transfer &amp; accumulate mechanism involving coin and walker degrees of freedom. The choice of investigating QW is motivated by their generality and versatility, complemented by their successful implementation in several physical systems. Hence, given the cross-cutting role of QW across quantum information, our protocol potentially represents a versatile general tool to control high-dimensional entanglement generation in various experimental platforms. In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
Valence and Arousal-Infused Bi-Directional LSTM for Sentiment Analysis of Government Social Media Management

Private entrepreneurs and government organizations widely adopt Facebook fan pages as an online social platform to communicate with the public. Posting on the platform to attract people’s comments and shares is an effective way to increase public engagement. Moreover, the comment functions allow users who have read the posts to express their thoughts. Hence, it also enables us to understand the users’ emotional feelings regarding that post by analyzing the comments. The goal of this study is to investigate the public image of organizations by exploring the content on fan pages. In order to efficiently analyze the enormous amount of public opinion data generated from social media, we propose a Bi-directional Long Short-Term Memory (BiLSTM) that can model detailed sentiment information hidden in those words. It first forecasts the sentiment information in terms of Valence and Arousal (VA) values of the smallest unit in a text, and later fuses this into a deep learning model to further analyze the sentiment of the whole text. Experiments show that our model can achieve state-of-the-art performance in terms of predicting the VA values of words. Additionally, combining VA with a BiLSTM model results in a boost of the performance for social media text sentiment analysis. Our method can assist governments or other organizations to improve their effectiveness in social media operations through the understanding of public opinions on related issues.
Benefits management in software development: A systematic review of empirical studies

Considerable resources are wasted on software projects delivering less than the planned benefits. Herein, the objective is to synthesize empirical evidence of the adoption and impact of benefits management (BM) in software development, and to suggest directions for future research. A systematic review of the literature is performed and identified 4836 scientific papers of which the authors found 47 to include relevant research. While most organizations identify and structure benefits at the outset of a project, fewer organizations report implementing BM as a continuous process throughout the project lifecycle. Empirical evidence gives support for positive impact on project outcome from the following BM practices: identifying and structuring benefits, planning benefits realization, BM during project execution, benefits evaluation and the practice of having people responsible for benefits realization. The authors suggest four research directions to understand (1) why BM practices sometimes not are adopted, (2) BM in relation to other management practices, (3) BM in agile software development and (4) BM in the context of organizations' value creation logics.
Intelligent Tutoring Systems: Why Teachers Abandoned a Technology Aimed at Automating Teaching Processes

Expectations that technology will improve and streamline education are high.However, technology often introduces new problems.This study aims to explore the challenges mathematics teachers encounter when they implement a digital mathematics textbook with an integrated intelligent tutoring system.A formative intervention was conducted in a two-year project with 16 secondary school teachers.The method was based on activity theory and required the teachers to collaborate with researchers in analyzing their work activity when the new teaching tool was introduced.In this paper, we show that an intelligent tutoring system created systemic contradictions for the teachers.Those contradictions involved predictability, division of labor, individual versus collective learning, accountability, and expectations versus experience.The teachers all tried to resolve the contradictions, but eventually felt compelled to abandon the intelligent tutoring system.The findings contribute to a better understanding of teachers' responses to a technology aimed at automating teaching processes.