Reviewerly/exHarmony huggingface dataset:
Geometric simplification for reducing optic flow in VR

Today virtual reality (VR) technologies became more and more widespread and found strong applications in various domains. However the fear to experience motion sickness is still an important barrier for new VR users. Instead of moving physically, VR users experience virtual locomotion but their vestibular systems do not sense the self-motion that are visually induced by immersive displays. The mismatch in visual and vestibular senses causes sickness. Previous solutions actively reduce user's field-of-view, introduce intruder in the view or alter their navigation. In this paper we propose a passive approach that partially simplify the virtual environment according to user navigation. One manual simplification approach has been proposed and prototyped to simplify the scene seen in the peripheral field of view. The optic flow is analyzed on the rendered images seen by users. The result shows that the simplification reduces the perceived optic flow which is the main cause of the visually induced motion sickness (VIMS). This pilot study confirm the potential efficiency of reducing cybersickness through geometric simplification.
Sporthesia: Augmenting Sports Videos Using Natural Language

Augmented sports videos, which combine visualizations and video effects to present data in actual scenes, can communicate insights engagingly and thus have been increasingly popular for sports enthusiasts around the world. Yet, creating augmented sports videos remains a challenging task, requiring considerable time and video editing skills. On the other hand, sports insights are often communicated using natural language, such as in commentaries, oral presentations, and articles, but usually lack visual cues. Thus, this work aims to facilitate the creation of augmented sports videos by enabling analysts to directly create visualizations embedded in videos using insights expressed in natural language. To achieve this goal, we propose a three-step approach – 1) detecting visualizable entities in the text, 2) mapping these entities into visualizations, and 3) scheduling these visualizations to play with the video – and analyzed 155 sports video clips and the accompanying commentaries for accomplishing these steps. Informed by our analysis, we have designed and implemented Sporthesia, a proof-of-concept system that takes racket-based sports videos and textual commentaries as the input and outputs augmented videos. We demonstrate Sporthesia's applicability in two exemplar scenarios, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , authoring augmented sports videos using text and augmenting historical sports videos based on auditory comments. A technical evaluation shows that Sporthesia achieves high accuracy (F1-score of 0.9) in detecting visualizable entities in the text. An expert evaluation with eight sports analysts suggests high utility, effectiveness, and satisfaction with our language-driven authoring method and provides insights for future improvement and opportunities.
Scale development as a measuring tool of critical Javanese language’ awareness for pre-service Javanese teachers

This study aims to develop a valid and reliable tool for measuring critical awareness of the Javanese language. The test item developed refers to relevant literature studies and is also based on existing awareness measuring tools. The test items are re-examined by Javanese linguists and Javanese language education experts to produce a measuring scale. The scale of critical awareness of the Javanese language consists of phonological awareness, morphological awareness, syntactic awareness, semantic awareness, and Javanese cultural awareness. The scale is designed with a Likert scale type and is applied to 69 students. The result shows that the developed scale is a valid and reliable way to measure critical awareness of the Javanese language. The success of learning the Javanese language depends on how well prospective teachers have critical awareness of the language.
Jersey number detection using synthetic data in a low-data regime

Player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years and identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is challenging due to changing camera angles, low video resolution, small object size in wide-range shots, and transient changes in the player's posture and movement. In this paper, we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. We generate novel synthetic datasets of different complexities to mitigate the data imbalance and scarcity in the samples. To show the effectiveness of our synthetic data generation, we use a multi-step strategy that enforces attention to a particular region of interest (player's torso), to identify jersey numbers. The solution first identifies and crops players in a frame using a person detection model, then utilizes a human pose estimation model to localize jersey numbers in the detected players, obviating the need for annotating bounding boxes for number detection. We experimented with two sets of Convolutional Neural Networks (CNNs) with different learning objectives: multi-class for two-digit number identification and multi-label for digit-wise detection to compare performance. Our experiments indicate that our novel synthetic data generation method improves the accuracy of various CNN models by 9% overall, and 18% on low frequency numbers.
Evaluation of Crypto Assets and Their Adoption in the Business World: A Global Perspective of the COVID-19 Pandemic

The global adoption of cryptocurrency has soared during the COVID-19 pandemic across low-, middle-, and high-income countries. From private investors and public companies to underbanked individuals, everyone has realized the benefits of digital currencies. The market of virtual assets has grown exponentially by more than 2,300% since 2019 and 881% in the last year alone. During this period, Bitcoin has evolved from a small niche internet community into a well-known asset for investors and private firms nationwide. A number of US-based finance companies like JP Morgan Chase & Co. and Tudor Investment Corporation have cited inflation as one of the reasons for holding Bitcoin in their balance sheets. Many consider it to be an asset that remains liquid while also generating a return. Some of the business companies support Bitcoin for its ability to give economic empowerment to individuals. Our study aims to understand how the evolution of the crypto market is paving the way for technological innovation in global business organizations. As there are various kinds of crypto assets, people should study their structure and price movements before investing in them. The paper is divided into two parts. Part A aims to capture the basic structure and knowledge of cryptocurrency. We have also used some statistical analyses using the Stata software to examine the interrelationships among different crypto assets, where it has been found that the closing prices of crypto assets have long-running relationships. The investors should study the price movements of different crypto assets and make decisions wisely while choosing which crypto currency to invested in. Investing only in crypto currencies may be risky as they are subject to interrelated price volatility and shocks. Part B aims to study how global business firms have started entering this peer-to-peer financial system and adopting Bitcoin as their payment option, especially during the COVID-19 pandemic. The study revealed that various business firms around the globe have already moved into a more digital world while the pandemic has accelerated the process of the integration of cryptocurrencies, especially Bitcoin, into their balance sheets.
Influence of Voice Interactive Educational Robot Combined with Artificial Intelligence for the Development of Adolescents

In the context of multicultural information, to explore and analyze the use effect of voice interactive educational robot in the classroom of adolescent students, and the physical and mental impact of movie characters on adolescent students, and to lay the foundation for studying the positive development of adolescents, under the guidance of positive psychology theory, the relationship between positive psychology and adolescent mental health is analyzed, the application of adolescent educational robot is discussed, and the relationship between adolescent educational robot combined with movies characters and positive development of adolescent is analyzed. The questionnaire is used to collect data, including the questionnaire on the influence of movie characters on the positive development of adolescents, the questionnaire of pre- and postpopularization of artificial intelligence in primary and secondary schools, and the satisfaction questionnaire of voice interactive educational robot. The reliability and validity of the questionnaire are analyzed. The results show that students who watch more than 20 movies are 20% in grade one and that is only 10% in grade three. 79% of the students think that the movie characters have an impact on themselves. The distribution of the number of the grade two students exposed to the movie is relatively uniform, 40% of them watch 10-20 movies, and 82% of them think that the movie characters have an impact on themselves; the Cronbach coefficient of classroom satisfaction questionnaire is 0.929 > 0.9, the average value of the correlation of total correction items of the corresponding item is 0.612 > 0.5, the Kaiser-Meyer-Olkin measurement value is 0.812 > 0.6, and the Sig value is 0.000 < 0.05, indicating that the reliability and validity of the questionnaire are very high and 92.3% of the students are very satisfied with the classroom. This shows that voice interactive educational robot combined with movie characters can promote the positive development of adolescents.
Trace ratio optimization with an application to multi-view learning

A trace ratio optimization problem over the Stiefel manifold is investigated from the perspectives of both theory and numerical computations. Necessary conditions in the form of nonlinear eigenvalue problem with eigenvector dependency (NEPv) are established and a numerical method based on the self-consistent field (SCF) iteration with a postprocessing step is designed to solve the NEPv and the method is proved to be always convergent. As an application to multi-view subspace learning, a new framework and its instantiated concrete models are proposed and demonstrated on real world data sets. Numerical results show that the efficiency of the proposed numerical methods and effectiveness of the new orthogonal multi-view subspace learning models.
Complex Cases of Source Code Authorship Identification Using a Hybrid Deep Neural Network

This paper is a continuation of our previous work on solving source code authorship identification problems. The analysis of heterogeneous source code is a relevant issue for copyright protection in commercial software development. This is related to the specificity of development processes and the usage of collaborative development tools (version control systems). As a result, there are source codes written according to different programming standards by a team of programmers with different skill levels. Another application field is information security—in particular, identifying the author of computer viruses. We apply our technique based on a hybrid of Inception-v1 and Bidirectional Gated Recurrent Units architectures on heterogeneous source codes and consider the most common commercial development complex cases that negatively affect the authorship identification process. The paper is devoted to the possibilities and limitations of the author’s technique in various complex cases. For situations where a programmer was proficient in two programming languages, the average accuracy was 87%; for proficiency in three or more—76%. For the artificially generated source code case, the average accuracy was 81.5%. Finally, the average accuracy for source codes generated from commits was 84%. The comparison with state-of-the-art approaches showed that the proposed method has no full-functionality analogs covering actual practical cases.
Two Quantum Proxy Blind Signature Schemes Based on Controlled Quantum Teleportation

We present a scheme for teleporting an unknown, two-particle entangled state with a message from a sender (Alice) to a receiver (Bob) via a six-particle entangled channel. We also present another scheme for teleporting an unknown one-particle entangled state with a message transmitted in a two-way form between the same sender and receiver via a five-qubit cluster state. One-way hash functions, Bell-state measurements, and unitary operations are adopted in these two schemes. Our schemes use the physical characteristics of quantum mechanics to implement delegation, signature, and verification processes. Moreover, a quantum key distribution protocol and a one-time pad are adopted in these schemes.
Counterfactual Anonymous Quantum Teleportation in the Presence of Adversarial Attacks and Channel Noise

Hiding the identity of involved participants in the network, known as anonymity, is a crucial issue in some cryptographic applications such as electronic voting systems, auctions, digital signatures, and Byzantine agreements. This paper proposes a new anonymous quantum teleportation protocol based on counterfactual communication where no information-carrying particles pass through the channel. It is achieved by the distribution of a counterfactual entanglement among the participants in the network followed by the establishment of an anonymous entanglement between the sender and the receiver. Afterwards, the sender can anonymously teleport a quantum state to the receiver by utilizing the anonymous entanglement. However, the practicality of the anonymous quantum network mainly calls for two performance measures—robustness against adversarial attacks and noisy environments. Motivated by these demands, firstly, we prove the security of our proposed protocol and show that it achieves both the sender and receiver’s anonymity in the presence of active adversaries and untrusted parties. Along with anonymity, we also ensure the correctness of the protocol and the privacy of the teleported qubit. Finally, we analyze the robustness of our proposed protocol under the presence of channel noise and compare its fidelity with those of the conventional protocols. The main advantage of our proposed protocol is that it can provide useful anonymous quantum resources for teleportation under noisy environment with a higher security compared to previous protocols.
Numeracy of prospective elementary school teachers with low self-efficacy: A case study

Numeracy can develop the self-capacity of prospective elementary school teachers in the 21st century. One of the factors that influence success in numeracy is self-efficacy. Those with low self-efficacy will often avoid tasks and quickly give up when encountering problems. This study aims to describe the numeracy of prospective elementary school teachers with low self-efficacy. This study was qualitative research with a case study. Samples were selected using the purposive sampling technique, in which they were students majoring in Elementary School Teacher Education with low self-efficacy. The employed research instruments were valid and reliable numeracy tests and self-efficacy questionnaires and interview guidelines. Qualitative data were collected through tests and interviews. Furthermore, the validity of the obtained data was examined by source triangulation, researchers’ persistence, and attendance extension. The collected data were then analySed using the interpretative method, consisting of data condensation, data presentation, data validity checking, and verification. The results show that the numeracy of prospective elementary school teachers with low self-efficacy had been in the stage of being able to identify problems according to context and present problems even though they still made conceptual errors, calculation, and had difficulty in re-communicating the solution to the numeracy problems in context.&#x0D; &#x0D; Keywords: Elementary school teacher, numeracy, self-efficacy.
Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels

Generative adversarial networks,(GANs) have been trained to be professional artists able to create stunning artworks such as face generation and image style transfer. In this paper, we focus on a realistic business scenario: automated generation of customizable icons given desired mobile applications and theme styles. We first introduce a theme-application icon dataset, termed AppIcon, where each icon has two orthogonal theme and app labels. By investigating a strong baseline StyleGAN2, we observe mode collapse caused by the entanglement of the orthogonal labels. To solve this challenge, we propose IconGAN composed of a conditional generator and dual discriminators with orthogonal augmentations, and a contrastive feature disentanglement strategy is further designed to regularize the feature space of the two discriminators. Compared with other approaches, IconGAN indicates a superior advantage on the AppIcon benchmark. Further analysis also justifies the effectiveness of disentangling app and theme representations. Our project will be released at: https://github.com/architect-road/IconGAN.
Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation

Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short term memory (LSTM), and a bi-directional LSTM (BLSTM) were used to analyze the tagged data. By adding morphological elements and evolutionary word sense disambiguation, the suggested common character-word embedding-based NMT model gives a BLEU score of 38.58 which was higher than the others.
Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorporating this information for stereo image JPEG artifacts removal is a huge challenge, since the existing compression artifacts make pixel-level view alignment difficult. In this paper, we propose a novel parallax transformer network (PTNet) to integrate the information from stereo image pairs for stereo image JPEG artifacts removal. Specifically, a well-designed symmetric bi-directional parallax transformer module is proposed to match features with similar textures between different views instead of pixel-level view alignment. Due to the issues of occlusions and boundaries, a confidence-based cross-view fusion module is proposed to achieve better feature fusion for both views, where the cross-view features are weighted with confidence maps. Especially, we adopt a coarse-to-fine design for the cross-view interaction, leading to better performance. Comprehensive experimental results demonstrate that our PTNet can effectively remove compression artifacts and achieves superior performance than other testing state-of-the-art methods.
Comprehensive Assessment of Big Data in Recommendation Systems

Developments in web-based e-commerce platforms cause recommendation systems to gain increasing importance. Recommendation systems are systems developed to provide valuable and personalized recommendations for users. In the age of big data, existing recommendation systems face scalability and efficiency problems in the face of increasing numbers of users and products. Within the scope of this study, a comprehensive and comparative review of big data and recommendation systems has been made. Studies in which big data are used in recommendation systems have been examined in the literature. The necessary pre-processes and methods for applying big data to recommendation system with high performance and success have been discussed in detail.
Glare-Free Airport-Based Photovoltaic System via Optimization of Its Azimuth Angle

Photovoltaic modules and systems (PVs) play an important role in achieving self-sustainable airports. In particular, airport-based PVs (A-PVs) have access to their full potential because airports are typically located in open spaces. However, the reflection of solar light by A-PVs’ front glass is unavoidable and may cause an accident due to solar glare (SG). In this study, we theoretically calculated the risk of SG from A-PVs depending on their azimuthal installation orientation (θPV) and derived a general design rule for minimizing the SG. The simulation reveals that the SG from A-PVs facing the runway and potential flight path causes after-images in pilots and ground workers throughout the year (&gt;800 h/year). On the other hand, modifying their θPV, facing opposite runways and flight paths, significantly reduces the SG (&lt;1 h/year) by reflecting the incident light outside the aircraft route. Although the θPV is not southward, their annual energy generation with an optimized θPV decreases by only 5–7% compared with A-PVs facing southward. This universal design approach is verified at four other airports, confirming the model’s validity. We believe our study will contribute to more solar light harvesting at airports without glare hazards.
Fuzzy proximity-based robust data hiding scheme with interval threshold

Secret communication of sensitive data must progress in a trustworthy environment through data hiding. Using Mamdani fuzzy logic to identify color proximity at the block level and a shared secret key and post-processing system, this paper attempts to develop a robust data hiding scheme with similarity measures to ensure good visual quality, robustness, imperceptibility and enhance the security. In accordance with the Gestalt principle, proximity among the nearby objects is higher, whose value varies from expert to expert. Therefore, a possibility for type-I fuzzy logic to be used to evaluate proximity. Fuzzy proximity is computed by means of a difference in intensity (colordiff) and distance (closeness). Further, the block color proximity obtained from the proximity calculation network is graded using an interval threshold. Accordingly, data embedding is processed in the sequence generated by the shared secret keys. The tampering coincidence problem is solved through a post-processing approach to increase the quality and accuracy of the recovered secret message. The experimental analysis, steganalysis and comparisons clearly illustrate the effectiveness of the proposed scheme in terms of visual quality, structural similarity, recoverability and robustness.