Reviewerly/exHarmony huggingface dataset:
Crowdsourced Verification for Operating Calving Surveillance Systems at an Early Stage

This study attempts to use crowdsourcing to facilitate the operation of pattern-recognition-based video surveillance systems at an early stage. Target events (i.e. events to be detected during surveillance) are not frequently observed in recorded video, so achieving reliable surveillance on the basis of machine learning requires a sufficient amount of target data. Acquiring sufficient data is time-consuming. However, operating unreliable surveillance systems can induce many false alarms. Crowdsourcing is introduced to address this problem by verifying the unreliable results in data-driven surveillance. Experimental simulation conducted using monitoring video of Japanese black beef cattle demonstrates that crowdsourced verification successfully reduced false alarms in calving detection systems.
Waveform Analysis and Perception of Harpsichord Sounds with Various Articulations and Ornaments

This study investigates the diversity in sound of the harpsichord, including articulation created by different ways of touching, duration and accentuation. Examined also are ornaments that are often added by the performer, as discussed in several historical treatises. Rolled arpeggios at various speeds can also be executed to either make a chord sound gentler or more brilliant. To investigate the unique sound characteristics of the harpsichord, sound analysis is conducted on a replica of a two-manual Blanchet-harpsichord in comparison with a Steinway-grand piano. Temporal and spectral waveform analysis of single tones, chords, short passages with various articulations and an excerpt from Johann Sebastian Bach's Partita no. 2 in C minor, BWV 826, demonstrates a variety of differences in tones, timbres and phrasings. These results provide scientific ways to describe perception of sound and the art of playing on the harpsichord.
A Data Management Method Based on Blockchain Technology

With the advent of the era of big data, data management technology has been applied in various important fields, and even become an important factor in the competitiveness of enterprises and even the country. Due to the problems of excessive concentration, data abuse, lack of trust and privacy leakage in the traditional data management mode, this paper proposes a data management method based on blockchain technology, which is decentralized, unforgeable and traceable, solve the bottleneck problem of traditional data management methods. Firstly, this paper analyzes and introduces the current development of blockchain technology, and describes the characteristics and architecture of blockchain technology in detail. Then, based on the analysis and comparison of the problems existing in the traditional data management methods, this paper summarizes the progress of the application of blockchain technology in data management methods. Finally, the shortcomings of the current research are analyzed and the future research direction is prospected.
Education In The Semantic Dimension: State Of Issues And Prospects

The article is devoted to the study of the problems and prospects of the education system development from the standpoint of semantic logic.The paper provides a critical assessment of the education system in pre-revolutionary Russia and the Soviet period.It is revealed how the Unified State Exam (USE) was introduced under the innovations of education in the new liberal Russia.There are presented examples from the modern educational system confirming the signs of its decay, from the field: the Russian language, chemistry, physics, rhetorical and semantic logic and other areas.The signs of destruction and ascent in the education system are determined.Mystical evidence of the demand for education is allocated.Metamorphoses of public consciousness and education are manifested.The formula of cognition is derived; the essence of the religious worldview is shown.The mechanism of progress towards awakening is indicated, awakening is correlated with globalization.Using the principle of the Universal Semantic Code (USC), it has been proved that any unmotivated word is a psychotronic generator that induces a sleep of the mind if an illogical explanation is given to the word.It was revealed that humanity is in a sleepy state due to the nondiscrimination of words.The goal of education is to bring it out of this state.It has been established that all words have one sourcethe semantic plasma of RA (Russian and Arabic).
[DC] Psychophysical Effects of Augmented Reality Experiences

My Ph.D. research aims to explore to which extent Augmented Reality (AR) experiences can evoke measurable physiological, psychological, and neurological responses in the user's body. During the first year of my Ph.D. studies, we have developed an AR experience that enables users to see and hear their own hands burning while looking through a Video See-Through Head-Mounted Display (VST-HMD).In a pilot study, half of the participants reported a heat-sensation on the affected left hand. All participants experienced a significant increase in skin conductance during the experiment. Moreover, participants who experienced a heat sensation had a higher skin conductance response. In a follow up study, we could show that this experience also influences the micro-circulatory blood flow of the hand on some participants.Our research continues on this with several planned follow-up experiments, to explore more deeply how AR experience can have measurable effects on the user's body and mental state. This allows deeper insights into the perceptual and cognitive effects unique to AR experiences. Insights from our experiment may be of significance in a neuroscientific or medical context.
TexelBlocks: Dynamic Surfaces for Physical Interactions

We present TexelBlocks, modular blocks that dynamically change their surfaces to support rich physical interactions. The core component of each block is a motorized crawler belt with a set of material patches attached to it, allowing each of the TexelBlocks to change their surface independently and be concatenated into flat surfaces of various sizes. We implemented a working system that controls 24 TexelBlocks to form 6 × 4 dynamic surfaces. With the working prototype, we developed five applications such as context-aware surface adaptation, real-time tactile feedback, immersive storytelling with dynamic background, real-time embodied platform game, and a surface-changing board game to demonstrate rich physical interactions of dynamic surfaces.
Fast streaming translation using machine learning with transformer

Machine Translation is the usage of machine learning techniques in translation from one language to another. It has recently been applied to streaming translation, also known as automatic subtitling. The most common challenge in this area is the trade-off between correctness and speed. Due to its real-time feature, streaming translation needs high speed as it has strict playtime constraints. This paper proposes an enhanced Transformer model for fast streaming translation. The proposed machine-learning method is described, implemented, and evaluated based on a common German-English bilingual dataset. The evaluation results have shown that the proposed system successfully achieved a good speed in the training phase, and a high speed in the actual translating phrase that is fast enough for real-time applications, while also maintaining robust correctness. We believe the proposed Transformer model is a significant contribution to natural-language processing, and would be useful for other real-time translation applications.
The Economy of Ghana and Tanzania

This chapter attempts to trace and connect the current economic structure, patterns of trade and the significance of the informal sector in Ghana and Tanzania to socio-economic and historical factors. The chapter then compares and contrasts both countries based on key economic indicators, arguing that colonization and the economic recovery programme (ERP) and the structural adjustment programme (SAP) are the two main factors that have defined the economic structures in both economies.
Work-in-Progress-WebXR to Support Student Wellbeing and Anxiety

The COVID-19 pandemic social distancing measures had immense evidenced impact on student life in higher education affecting their mental health in many ways. In addition, remote working measures taken by Higher Education organizations to protect students and staff created an additional barrier for students seeking support at a stage they feel the most vulnerable. This paper presents a work-in-progress study that focuses on investigating ways of designing an online system for self-assessment symptoms of anxiety based on which available support is provided in a personalized and emotionally engaging manner. The project builds and compares three prototypes: a conventional web site; a VR immersive environment with a single virtual human playing the role of a student life advisor; and an immersive environment with more than one virtual humans interacting with the user aiming to study which system engages and assists vulnerable students more effectively, contributing to a better user experience. The paper presents that project motivations, its aims and objectives, the proposed research methodology and the expected contributions to knowledge.
Locally Linear Support Vector Machines for Imbalanced Data Classification

Classification of imbalanced data is one of most challenging aspects of machine learning. Despite over two decades of progress there is still a need for developing new techniques capable to overcome numerous difficulties embedded in the nature of imbalanced datasets. In this paper, we propose Locally Linear Support Vector Machines (LL-SVMs) for effectively handling imbalanced datasets. LL-SVMs is a lazy learning approach which trains a local classifier for each new test instance using its k nearest neighbors. This way, we are able to maximize the margin in the original input features space and obtain a better adaptation to complex class boundaries. We combine LL-SVMs with local oversampling and cost-sensitive approaches to make them skew-insensitive. Working only in the local neighborhood significantly improves the generalization over the minority class and tackles instance-level difficulties, such as class overlapping, borderline and noisy instances, as well as small disjuncts. An extensive experimental study shows that our local models are able to outperform their global counterparts, especially when handling difficult, borderline, and noisy imbalanced datasets.
Taming the Wild Etext: Managing, Annotating, and Sharing Tibetan Corpora in Open Spaces

Digital text is quickly becoming essential to modern daily life. The article you are reading right now is born digital; unlike texts of the not-so-distant past, it may never be printed at all. Worldwide, the trend is clear: Digital text is on the way in, and print is on its way out. Year-by-year, more and more readers are turning to ebooks, internet news, and other forms of ereading, while generation by generation, print is becoming less and less relevant. 1 1 Pew research shows 50% of Americans have a dedicated ereading device, with yearly gains in ereadership [1]; industry research, too, shows a definite trend toward ereading and non-traditional publishing, with ebooks making up 50% of fiction reading in 2016 [2], while journalism is also trending online [3]. These trends are not unique to English—to meet the demands and expectations of today's readers, Tibetan texts, too, are being digitized by many organizations and institutions with a shared appreciation for the Tibetan literary heritage. They include a variety of secular publishers, monastic institutions, and Buddhist foundations, among others. But while these organizations share common goals for common texts, their work is all too frequently completely disconnected from the community at large. This situation negatively impacts what is already a minoritized and under-resourced language. While competition—from other languages, as well as other publishers in the Tibetan etext world—has been a driver of innovation in the adoption of ereading technology, we believe that a rich, shared data source is not only in everyone's best interest but also the only practical way forward when we consider the time, effort, expertise, and money that quality digitization takes. That is why we have designed OpenPecha to be a public, open platform for collaborative etext curation and annotation sharing. Its aim is providing a wide range of users with the latest version of the exact “view” of any text needed, while maintaining the integrity of the text and its annotations and simultaneously allowing for community improvements and additions. In this article, we explore the details of how the project came to be, what it is, and how it works, while also presenting a few common use cases.
Visual Dialog Agent Based On Deep Q Learning and Memory Module Networks

In the past many years, it has been observed that there has been an increase in methods to solve problems and the solution involves a combination of Computer Vision and Natural Language Processing. New algorithms and systems are emerging and are being developed every day to solve the above-mentioned kind of problems. Visual Dialog Agent is one of them. This kind of system utilizes both Computer Vision and Natural Language Processing algorithms. With this technology many variants of Visual Dialog Agents have been designed till date and many exclusive algorithms are created for Visual Dialog Agent. In this paper we propose an idea to create a Visual Dialog Agent which utilizes the present state of art End to End Memory Module Networks along with Reinforcement Learning Policies to answer the questions prompted by the user and as well understand the inclination of the user in the conversation which it holds. The goal of the proposed Visual Dialog Agent is to have a more engaging conversation with the highest user inclination.
Bibliographical theory as the phenomenon of Russia library and information science. (On the occasion of Oleg P. Korshunov 95<sup>th</sup> anniversary)

P. Korshunov’s contribution to the theory of bibliography is discussed; the discipline structure and development stages are analyzed. The main principles of Oleg P. Korshunov’s theory are outlined: dialectical method of rising from the abstract to the concrete applied to bibliographic phenomena studies; the view of document communication system as a metasystem of bibliographic functioning. The theory also sees the contradictions/information barriers existing in the system of document communications as an intrinsic reason for the emergence of mediating social institutions (libraries, archives, bibliography, etc.), which mission is to establish correspondence between documents and end users. Oleg P. Korshunov also developed the concept of bibliographic information as an intrinsic distinction of bibliography from other types of information activity; he derived the modes and functions of bibliographic information and common patterns of bibliographic activity. The author specifies several rival bibliography theories: information-cultural (M. G. Vokhrysheva, N. B. Zinovieva), ideographic (N. A. Sliadneva), management (A. A. Grechikhin), semiotic (G. Ya. Uzilevskiy), and synergetic (T. A. Novozhenova). The author points to the conceptual diversity of the theory of bibliography in Russia and current unavailability of the single theory. The author demonstrates the achievements and challenges of the theory of bibliography within the framework of academic studies, as well as maturity of bibliography subdisciplines, i. e. theory and methodology of bibliography, history of bibliography, management of bibliographic activities, bibliographic methods and technologies. The prospects of theory of bibliography are specified as stated by Russian scholars on the basis of Thomas S. Kuhn’s theory of scientific revolutions. They unanimously connect development history of theory of bibliography to the bibliological and documentological paradigms (in different terminologies), while interpreting the future of bibliography in different ways though always with dominating technological issues.
Efficient High-Resolution High-Level-Semantic Representation Learning for Human Pose Estimation

Ensuring that features contain both high-resolution and high-level-semantic information is important for human pose estimation, while most existing methods suffer from spatial information loss or semantic information mismatch when extracting high-resolution high-level-semantic features. To efficiently address these issues, we propose a novel Dilation Pyramid Module (DPM), which can enlarge the receptive field multiplicatively to extract high-level-semantic information as subsampling without reducing spatial resolution. DPM is composed of several consecutive dilated convolution layers of which dilation radius is specially designed to enlarge the receptive field multiplicatively and avoid the gridding issue of dilated convolution. Based on DPM, the Dilation Pyramid Net (DPN) is proposed to efficiently extract high-resolution high-level-semantic features. We experimentally demonstrate the effectiveness and efficiency of the proposed DPN with competitive performance to the state-of-the-art methods over two challenging benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.
Protection of Personal Data in High Performance Computing Platform for Scientific Research Purposes

The Open Science projects are also aimed at strongly encouraging the use of Cloud technologies and High Performance Computing (HPC), for the benefit of European researchers and universities. The emerging paradigm of Open Science enables an easier access to expert knowledge and material; however, it also raises some challenges regarding the protection of personal data, considering that part of the research data are personal data thus subjected to the EU’s General Data Protection Regulation (GDPR). This paper investigates the concept of scientific research in the field of data protection, with regard both to the European (GDPR) and national (Luxembourg Data Protection Law) legal framework for the compliance of the HPC technology. Therefore, it focuses on a case study, the HPC platform of the University of Luxembourg (ULHPC), to pinpoint the major data protection issues arising from the processing activities through HPC from the perspective of the HPC platform operators. Our study illustrates where the most problematic aspects of compliance lie. In this regard, possible solutions are also suggested, which mainly revolve around (1) standardisation of procedures; (2) cooperation at institutional level; (3) identification of guidelines for common challenges. This research is aimed to support legal researchers in the field of data protection, in order to help deepen the understanding of HPC technology’s challenges and universities and research centres holding an HPC platform for research purposes, which have to address the same issues.
An Axiomatic Approach to Detect Information Leaks in Concurrent Programs

Realizing flow security in a concurrent environment is extremely challenging, primarily due to non-deterministic nature of execution. The difficulty is further exacerbated from a security angle if sequential threads disclose control locations through publicly observable statements like print, sleep, delay, etc. Such observations lead to internal and external timing attacks. Inspired by previous works that use classical Hoare style proof systems for establishing correctness of distributed (real-time) programs, in this paper, we describe a method for finding information leaks in concurrent programs through the introduction of leaky assertions at observable program points. Specifying leaky assertions akin to classic assertions, we demonstrate how information leaks can be detected in a concurrent context. To our knowledge, this is the first such work that enables integration of different notions of non-interference used in functional and security context. While the approach is sound and relatively complete in the classic sense, it enables the use of algorithmic techniques that enable programmers to come up with leaky assertions that enable checking for information leaks in sensitive applications.
Survey On Credit Card Fraud Detection using Face Authentication

With the popularization of on-line looking, dealings fraud is growing seriously. Therefore, the study on fraud detection is attention-grabbing and important. a vital manner of detective work fraud is to extract the behavior profiles (BPs) of users supported their historical dealings records, so to verify if AN incoming dealings may be a fraud or not seeable of their bits per second. Markov process models square measure widespread to represent bits per second of users that is effective for those users whose dealings behaviors square measure stable comparatively. However, with the event and popularization of on-line looking, it's a lot of convenient for users to consume via the net that diversifies the dealings behaviors of users. Therefore, Markov process models square measure unsuitable for the illustration of those behaviors. During this paper, we have a tendency to propose logical graph of BP (LGBP) that may be a total order-based model to represent the relation of attributes of dealings records. Supported LGBP and users dealings records, we are able to cipher a path-based transition chance from AN attribute to a different one. Here we are able to find Face by mistreatment viola jones and LBP acknowledge formula for face detection as we use invisible keyword sequence for authentication of OTP. The keyword sequence modification when. At constant time, we have a tendency to outline. A data entropy-based diversity constant so as to characterize the variety of dealings behaviors of a user. We have a tendency to additionally track fraud user with location by mackintosh address of the user laptop portable computer} or computer that have last dealings with success. Additionally, we have a tendency to outline a state transition chance matrix to capture temporal options of transactions of a user. Consequently, we are able to construct a BP for every user so use it to verify if AN incoming dealings may be a fraud or not. Our experiments over a true information set illustrate that our methodology is healthier than 3 progressive ones
On the Black-Hole Numbers of the Iteration of Digital Biquadratic Sum in the Quanternary, Octonary or Hexadecimal System

There are different periodic and fixed points under the iteration of biquadratic sum of digits of positive integer in different carry systems. There are three fixed points and one 2-circle in quanternary, seven fixed points in octonary, two fixed points, one 2-circle and one 6-circle in hexadecimal system.
Decision Snippet Features

Decision trees excel at interpretability of their prediction results. To achieve required prediction accuracies, however, often large ensembles of decision trees - random forests - are considered, reducing interpretability due to large size. Additionally, their size slows down inference on modern hardware and restricts their applicability in low-memory embedded devices. We introduce Decision Snippet Features, which are obtained from small subtrees that appear frequently in trained random forests. We subsequently show that linear models on top of these features achieve comparable and sometimes even better predictive performance than the original random forest, while reducing the model size by up to two orders of magnitude.