Reviewerly/exHarmony huggingface dataset:
Stereoscopic Video Quality Assessment Using Oriented Local Gravitational Force Statistics

We develop a new no-reference (NR) stereoscopic video quality assessment model that adopts oriented local gravitational force (OLGF) statistics in the space-time domain. The OLGF is a novel extension of an existing local gravitational force descriptor and includes two new components: relative local gravitational force magnitude and relative local gravitational force orientation. The resulting algorithm, called Stereoscopic Video Integrity Predictor using OLGF Statistics (SVIPOS), first uses our previous work to generate a cyclopean image and a product image in spatial domain from a stereoscopic video sequence pair, and calculates a frame difference image in temporal domain from a left video sequence. Then the OLGF model is deployed to compute various local gravitational force responses on the cyclopean image, the product image and the frame difference image. Finally, a support vector regression is used to map statistical features extracted from the computed gravitational force responses to stereoscopic video quality predications. SVIPOS is thoroughly tested on three publicly stereoscopic video quality databases. The experimental results show that SVIPOS outperforms state-of-the-art stereoscopic video quality methods.
Creation of Interactive Dollhouse with Projection Mapping and Measurement of Distance and Pressure Sensors

In the growth process of children, playing with dolls contributes to the formation of the self, creativity, and social development. In general dollhouse play, children move the dolls and imagine the world of the dollhouse. In this study, we installed sensors of distance and pressure in a dollhouse, and projected interactive images according to the measured values by projection mapping. It became possible to expand the doll play by generating the image corresponding to the movement of the dolls and hand in real time and reflecting it in the doll house.
Channel Recurrent Attention Networks for Video Pedestrian Retrieval

Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional network, termed channel recurrent attention network, for the task of video pedestrian retrieval. The main attention unit, channel recurrent attention, identifies attention maps at the frame level by jointly leveraging spatial and channel patterns via a recurrent neural network. This channel recurrent attention is designed to build a global receptive field by recurrently receiving and learning the spatial vectors. Then, a set aggregation cell is employed to generate a compact video representation. Empirical experimental results demonstrate the superior performance of the proposed deep network, outperforming current state-of-the-art results across standard video person retrieval benchmarks, and a thorough ablation study shows the effectiveness of the proposed units.
Research on User Enhanced Experience for Public Digital Cultural Services

On the basis of analyzing the needs of modern public digital culture and maximizing the value of personalized information, it is oriented to the "National Library Open Course" platform and resolves user intention mining from the two directions of analyzing user portraits and resource data. This paper proposes a personalized information recommendation service model oriented to "National Library Open Course ", and gives a solution to the personalized service recommendation system based on user behavior characteristics, which can effectively solve the problem of unsatisfactory recommendation performance caused by sparse matrix data. Experiments were conducted on the "National Library Open Course" of the National Library, and the comparative experiment achieved good results. The research results obtained effectively improved the user experience and cultural resource utilization efficiency of the National Library Open Course.
End-to-end recognition of streaming Japanese speech using CTC and local attention

Many end-to-end, large vocabulary, continuous speech recognition systems are now able to achieve better speech recognition performance than conventional systems. Most of these approaches are based on bidirectional networks and sequence-to-sequence modeling however, so automatic speech recognition (ASR) systems using such techniques need to wait for an entire segment of voice input to be entered before they can begin processing the data, resulting in a lengthy time-lag, which can be a serious drawback in some applications. An obvious solution to this problem is to develop a speech recognition algorithm capable of processing streaming data. Therefore, in this paper we explore the possibility of a streaming, online, ASR system for Japanese using a model based on unidirectional LSTMs trained using connectionist temporal classification (CTC) criteria, with local attention. Such an approach has not been well investigated for use with Japanese, as most Japanese-language ASR systems employ bidirectional networks. The best result for our proposed system during experimental evaluation was a character error rate of 9.87 %.
A segmentation processing method for effectively solving the problem of partial covering of angiography images

In this paper, an image segmentation method is proposed to deal with the partial contamination or partial covering of the region of interest. This method can be used to segment and extract the vessels or the catheter of the X-ray coronary angiography sequence image. The main steps of this method include: firstly, image preprocessing. Preprocessing includes several steps such as image filtering, Fourier transform, and image enhancement. The live images and masks of angiography images require different processing methods. For masks that are not covered by contrast agents, the routine edge detection is sufficient. However, in the case of live film, the catheter or blood vessel may be covered by the contrast agent. The segmentation method is as follows: after image preprocessing, select the pixel with the lowest gray value as the seed point to grow the region of specific elements. Using morphological processing to make the growing area more fully and connected. Based on this connected domain, it expands twice to form an expanded area. Make the extended area cover the spilled contrast agent as much as possible to determine the contrast agent range. After that, the gray value is decreased for the growth area, and the gray value is increased for the contrast medium area, and the gray difference is enlarged. Using the bottom-hat transformation, the covered part of the blood vessel or catheter is more prominent, which is convenient for segmentation. Finally, the canny operator is used for edge detection, and the largest connected domain is retained, so that a complete blood vessel or catheter can be extracted. The experiment shows that the method can effectively solve the problem of vessel or catheter segmentation and extraction in the covered film with good controllability and robustness.
Anonymous Traffic Networks

Anonymous traffic networks help anonymizing the identity and privacy of the user and their uses. There has been a great amount of interest in the research community for these implementations. Tor network, based on onion routing, has been a big volunteer-driven network and is being used worldwide. Other than Tor, implementations such as Garlic, I2P, Bitcoin, etc., use the principal concept of anonymous network. There has been plenty of implementations which tried to address the issue of anonymity and privacy has been in practice and evolving. The evolution of the decentralized web, zero-knowledge authentications, in a way, advocate the work toward anonymity and privacy. The usage of the Internet is evolving and so the traffic patterns of a user. This chapter will try to address the Idea of Anonymous Traffic Networks (ATN), Types of implementations of ATN, Challenges of ATN, Tools and Technologies around ATN, Relevance of anonymity and privacy of users for ATN, ATN—Projects and Opportunities
Regularized Multiset Neighborhood Correlation Analysis for Semi-paired Multiview Learning

Canonical correlation analysis (CCA) is a popular and powerful technique for two-view dimension reduction and feature extraction. But, CCA is not able to directly handle more than two view data and has a rigorous assumption that all the samples from two different views are paired. However, practical multiple view data are often semi-paired. To address this problem, we in this paper propose a novel semi-paired multiview dimension reduction approach, which takes cross-view neighborhood relationship among semi-paired data and within-view global structure information into consideration. The proposed approach can not only deal with multiview (more than two) data, but also take sufficient advantage of unpaired multiview data and then mitigate overfitting effectively caused by the limited paired data. Experimental results on two benchmark data sets demonstrate the effectiveness of our proposed method.
Programming in Style with bach

Several programming systems for computer music are based upon the data-flow family of programming paradigms. In the first part of this article, we shall introduce the general features and lexicon of data-flow programming, and review some specific instances of it with reference to computer music applications. We shall then move the discussion to Max’s very peculiar take on data-flow, and evaluate its motivation and shortcomings. Subsequently, we shall show how the bach library can support different programming styles within Max, improving the expression, the readability and the maintenance of complex algorithms. In particular, the latest version of bach has introduced bell, a small textual programming language embedded in Max and specifically designed to facilitate programming tasks related to manipulation of symbolic musical material.
Formative assessment techniques as a tool for the development of the behavioral-reflexive component of the future teacher’s professional culture

This paper discusses current issues of future teachers training, in particular their professional culture development. There are five components in the teachers professional culture. The problem of its behavioral-reflexive component development is posed. The authors analyze the federal state educational standard of higher education as well as highlight prerequisites for the development of the behavioral-reflective component of future teachers professional culture. The paper considers techniques of formative assessment as an important element of future teachers professional training. Formative assessment strategies are analyzed; they are divided into five groups. The authors argue that the use of formative assessment techniques for future teachers training at universities contributes to the development of the behavioral-reflexive component of their professional culture. The analysis of educational and methodical literature allowed the authors to single out the techniques of formative assessment used in the process of future teachers training. The methodologies identified by the authors formed the basis of the developed educational-methodical complex of the course Professional culture of the teacher aimed at future teachers professional culture development. The paper presents the results of the experiment aimed at development of the behavioral-reflexive component of future teachers professional culture.
Rigidity of Superdense Coding

The famous superdense coding protocol of Bennett and Wiesner demonstrates that it is possible to communicate two bits of classical information by sending only one qubit and using a shared EPR pair. Our first result is that an arbitrary protocol for achieving this task (where there are no assumptions on the sender's encoding operations or the dimension of the shared entangled state) is locally equivalent to the canonical Bennett-Wiesner protocol. In other words, the superdense coding task is rigid. In particular, we show that the sender and receiver only use additional entanglement (beyond the EPR pair) as a source of classical randomness. We also investigate several questions about higher-dimensional superdense coding, where the goal is to communicate one of $d^2$ possible messages by sending a $d$-dimensional quantum state, for general dimensions $d$. Unlike the $d=2$ case (i.e. sending a single qubit), there can be inequivalent superdense coding protocols for higher $d$. We present concrete constructions of inequivalent protocols, based on constructions of inequivalent orthogonal unitary bases for all $d > 2$. Finally, we analyze the performance of superdense coding protocols where the encoding operators are independently sampled from the Haar measure on the unitary group. Our analysis involves bounding the distinguishability of random maximally entangled states, which may be of independent interest.
Practs in First Official Speeches of Presidents Muhammadu Buhari and Nana Akufo-Addo on Coronavirus Pandemic

This paper examined first official speeches of presidents of Nigeria and Ghana on coronavirus pandemic.The study adopted content analytical method which involves qualitative and quantitative method of data analysis and presentation.Applying concepts from Pragmatics act, the study revealed different practs through which both presidents made their intentions known towards combating the virus.It was discovered that President Muhammadu Buhari deployed 95 practs in order to achieve five pragmatic functions namely: to reveal achievements and further action (53.69%), to instruct and direct (28.41%), to commend (4.21%), to pacify/encourage (10.53%), and to persuade (3.16%).President Nana Akufo-Addo deployed 39 practs to achieve six pragmatic functions: to reveal achievements and further action (56.41%), to instruct and direct (25.64%), to pacify (7.69%), to commendation (5.13%), persuasion (2.56%), and to warn (2.56%).It was discovered that shared situational knowledge, reference and inference enable the citizens of both countries to understand what both presidents wish to communicate especially where both presidents used words and phrases to refer to coronavirus.The paper concluded that through the analysis of different practs in speeches, thematic focus of a speech can be discerned.
Modeling and control of 3-omni wheel Robot using PSO optimization and Neural Network

Omni mobile robots are one of the mobile robots that interact with humans in many areas where it is needed to be collaborative and accurate. Committing robotics with artificial intelligence-based controllers became nowadays mandatory for more association of these robots with distinct environments. This paper proposes the distinction of the 3WD Omni Vision feedback model between Simscape and actual information to obtain a surmised model. Study applying some artificial control procedures on this model for path planning and speed control as the artificial neural system and PSO optimization techniques for PID error and contrast these outcomes and compare them with conventional PID and Fuzzy Control techniques.
Survey on Captcha Recognition Using Deep Learning

With the increasing dependency of people and evolving technology, security has become an issue on online platforms. Earlier days CAPTCHA was introduced to provide protection and security in web but lately due to the new bulk of web bots, now security becomes a major concern as it could be easily breached. This paper discusses about the technique involved that can help in optimizing the performance of a captcha breaking model and also provide a moot that can help to withstand the guard of captchas.
Adoption of Ensemble Empirical Mode Decomposition Algorithm and Back Propagation Neural Network in Net Surface Solar Radiation Prediction

Abstract To improve the prediction accuracy (PA) of net surface solar radiation (NSSR), a net surface solar radiation (NSSR) prediction model, named EEMD-BPNN, is proposed by adopting the ensemble empirical mode decomposition (EEMD) algorithm along with back propagation neural network (BPNN). In this paper, EEMD is used to extract the signals to reduce the influence of noise with physical significance from the time series of the original NSSR, so as to obtain the intrinsic mode functions and residual terms of different frequencies. As well as BPNN is used to establish a corresponding prediction model for each component of mode function. The proposed model has been applied and verified in test the daily total NSSR in Aksu region, Xinjiang. In the case study, the mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE), and correlation coefficient are taken as evaluation indexes, and the accuracy and applicability of the EEMD-BPNN for NSSR prediction are analyzed by comparing the prediction results of the EEMD-BPNN with the BPNN and H-S model. The results show that the predicted values (PVs) of EEMD-BPNN are closer to the actual data and have better correlation coefficient (R2=0.9615) compared with the prediction results of BPNN (R2=0.8703) and H-S model (R2=0.8373), and the error analysis indexes of the predicted results are all small. It indicates that the PA of EEMD-BPNN is improved obviously, which means the EEMD-BPNN has superiority in NSSR prediction and provides a new reference method for NSSR prediction.
CIMLA: Checkpoint Interval Message Logging Algorithm in Kafka Pipeline Architecture

In big data era, the reliability of real-time messages is the most important point for many business needs. Real-time messaging system establishes to handle real-time requirements and reliability requirements in business applications. Apache Kafka is a highly scalable publish-subscribe messaging system. The effect of reliability in Apache Kafka impact on the different use cases. Fault tolerance becomes the main factor to improve the reliability of Apache Kafka. Apache Kafka needs to handle the challenges of the message loss rate in server failure. To improve the message recovery, Kafka requires to manage the message delivery process. This paper introduces CIMLA (Checkpoint Interval Message Logging Algorithm) to enhance the message delivery in Apache Kafka. The system evaluates the message loss rate, throughput and latency in the server failure case. The system achieves the message recovery process by using CIMLA which effectively controls the lost messages with identification. The experimental results emphasize the improvement of message recovery and verify the reliability of message delivery in Apache Kafka.
Analysis of XR Research in Brazil from 21 Years of SVR Publications

Although Virtual Reality technology was first developed almost sixty years ago, there has been little survey work giving an overview on how research in VR, AR and MR evolved in Brazil along with their future trends. We provide such analysis by reviewing the development made since the first WRV event, back in 1997, until the last SVR, in 2019. The 21 event editions may help understand how the area was explored and provide a roadmap for future research. The 557 full papers analyzed were compiled into an open catalog, available on the internet. It features important filter capabilities, enabling the quick selection of papers based on citation number, conference topic, area of application, user experiments, statistical analysis and so on. We hope this tool to be of great value to the field, and also for helping researchers decide which topics should be explored when they are beginning their own studies in the area.
Constructing an Information Search Platform Using Data Mining to Improve Student Learning

There has been an ongoing proliferation of online articles and other materials on the World Wide Web for e-learning. Although a generic search engine can be used to find materials in a subject domain (for example, computer science,) the search results often have advertising, media, and news mixed in. To improve the search quality, in this study an information search platform based on data mining technology was constructed. Using term frequency-inverse document frequency (TF-IDF), this platform calculates all terms in each web article to automatically filter out non-computer science category keywords and articles. The search platform enables students quickly find and read information in articles for a given set of search keywords. The experimental results show improved learning performance with increased computer science knowledge and concepts and more computer science articles found using the information search platform by filtering out articles in non-computer science categories.
Enhancement of properties in Mizar

A “property” in the Mizar proof-assistant is a construction that can be used to register chosen features of predicates (e.g., “reflexivity”, “symmetry”), operations (e.g., “involutiveness”, “commutativity”) and types (e.g., “sethoodness”) declared at the definition stage. The current implementation of Mizar allows using properties for notions with a specific number of visible arguments (e.g., reflexivity for a predicate with two visible arguments and involutiveness for an operation with just one visible argument). In this paper we investigate a more general approach to overcome these limitations. We propose an extension of the Mizar language and a corresponding enhancement of the Mizar proof-checker which allow declaring properties of notions of arbitrary arity with respect to explicitly indicated arguments. Moreover, we introduce a new property—the “fixedpoint-free” property of unary operations—meaning that the result of applying the operation to its argument always differs from the argument. Results of tests conducted on the Mizar Mathematical Library are presented.
Melayu Grid: Sharing Language Service to Bridge Cultural Difference

Today grid computing powered by web service technology has been a research interest for decades. Several grids of language services are available in Internet as part of daily activity. Melayu Grid is proposed to share Indonesian, Malay and regional language resources. To ease the sharing, each language is located as a distinctive concept in language service ontology and eventually measured its semantic closeness to other languages. By utilizing semantic closeness, it is expected that the endangered language resource that has less amount of its speaker can take advantage by replacing it with other language resource that has better resources and speakers. This project implements service grid architecture as a culture grid for Indonesia and Malaysia to balance cultural differences and conflicts in these two neighboring countries. Multicriteria optimization is utilized to adapt the multi culture feature of several language services. It substitutes several regional languages services that are coming from the same language families or having similar linguistic vocabularies, syntax, morphology and semantics.
Storage management of educational scenarios modeled with the Recursive Entity Modelling Method

The Recursive Entity Modelling Method (REMM) is a pedagogical modelling formalism. It was conceived in order to overcome the limitations raised into existing pedagogical modelling languages, which consist in the fact that they were not able: - to describe in detail the unfolding of an educational sequence - to model the scenarios adopting active pedagogical strategies - to personalize and reuse the learning scenarios according to different context... After REMM test through case studies of pedagogical scenarios adopting different pedagogical strategies, some corrections have been made to improve its quality. In this work phase, we aim to convert REMM into a notation language susceptible to run on ICT platforms. To do this, we will start this work by designing a database ensuring the storage of educational scenarios modeled with REMM.