Reviewerly/exHarmony huggingface dataset:
Autonomous Apex Detection and Micro-Expression Recognition using Proposed Diagonal Planes

Micro-expression as the main way of non-verbal communication occurs quickly and subtle in highrisk situations. Since it cannot be misleading, it discloses the real human aim. Nonetheless, feature extraction is an arduous task due to its two particular features. To resolve this problem in this paper, we propose Local Binary Pattern on Four Diagonal Planes. In fact, we utilize it after motion magnification for feature extraction in apex detection task. Also, we combine it and optical flow for micro-expression recognition. Simulation results show that automatic micro-expression apex frame detection and micro-expression recognition is promising using our method on CASME2 and CASME in comparison with other methods.
Innovation and Economic Growth: Does Internet Matter?

We analyze the relationship between economic growth and innovation taking into consideration the importance of the internet. To do so, we use a panel ARDL model, with data on a sample of 76 developed and developing countries in different geographic regions for the 1995–2016 period. Our findings provide empirical evidence of the positive role of innovation and internet in economic growth and the positive role of economic growth and internet in innovation. From these results, we derive several basic policy conclusions.
Dual Reinforcement-Based Specification Generation for Image De-Rendering

Advances in deep learning have led to promising progress in inferring graphics programs by de-rendering computer-generated images. However, current methods do not explore which decoding methods lead to better inductive bias for inferring graphics programs. In our work, we first explore the effectiveness of LSTM-RNN versus Transformer networks as decoders for order-independent graphics programs. Since these are sequence models, we must choose an ordering of the objects in the graphics programs for likelihood training. We found that the LSTM performance was highly sensitive to the sequence ordering (random order vs. pattern-based order), while Transformer performance was roughly independent of the sequence ordering. Further, we present a policy gradient based reinforcement learning approach for better inductive bias in the decoder via multiple diverse rewards based both on the graphics program specification and the rendered image. We also explore the combination of these complementary rewards. We achieve state-of-the-art results on two graphics program generation datasets.
Goods with Embedded Software: Consumer Protection 2.0 in Times of Digital Content?

Software has acquired a central role in the lives of almost anyone taking part in modern society. People buy watches measuring their blood pressure and heart pulsation, cars slowing down automatically when getting to close to the car in front and computers with pre-installed software. However, digital content is not only dispersed via tangible media. People also acquire digital content and digital services apart from a tangible medium (mostly online). Streaming music and downloading movies are just two of the many examples.
Toward deep neural networks: Mirror extreme learning machines for pattern classification

In this paper, a novel type of feed-forward neural network with a simple structure is proposed and investigated for pattern classification. Because the novel type of forward neural network?s parameter setting is mirrored with those of the Extreme Learning Machine (ELM), it is termed the mirror extreme learning machine (MELM). For the MELM, the input weights are determined by the pseudoinverse method analytically, while the output weights are generated randomly, which are completely different from the conventional ELM. Besides, a growing method is adopted to obtain the optimal hidden-layer structure. Finally, to evaluate the performance of the proposed MELM, abundant comparative experiments based on different real-world classification datasets are performed. Experimental results validate the high classification accuracy and good generalization performance of the proposed neural network with a simple structure in pattern classification.
Non-autoregressive Mandarin-English Code-switching Speech Recognition with Pinyin Mask-CTC and Word Embedding Regularization.

Mandarin-English code-switching (CS) is frequently used among East and Southeast Asian people. However, the intra-sentence language switching of the two very different languages makes recognizing CS speech challenging. Meanwhile, the recent successful non-autoregressive (NAR) ASR models remove the need for left-to-right beam decoding in autoregressive (AR) models and achieved outstanding performance and fast inference speed. Therefore, in this paper, we took advantage of the Mask-CTC NAR ASR framework to tackle the CS speech recognition issue. We propose changing the Mandarin output target of the encoder to Pinyin for faster encoder training, and introduce Pinyin-to-Mandarin decoder to learn contextualized information. Moreover, we propose word embedding label smoothing to regularize the decoder with contextualized information and projection matrix regularization to bridge that gap between the encoder and decoder. We evaluate the proposed methods on the SEAME corpus and achieved exciting results.
Comparative evaluation of thin smear, thick smear and antigen detection test in the diagnosis of malaria

Background: Malaria is a serious and sometimes fatal disease caused by a parasite that commonly infects a certain type of mosquito which feeds on humans. There are many Techniques available for Detection of malarial parasite from the blood.Objective: The objective of this study is to determine efficacy of different methods for detection of malaria parasite.Methodology: Total 5344 blood samples that came to pathology laboratory are investigated for malarial parasite by different technique like thin smear and Thick smear that is stained by Gimsa stain and Rapid Diagnostic Test (RDT) for detection of malarial parasite.Result: Among total 5354 samples were collected 305(5.7%) were found to be positive for malaria of the positive samples 300(98.36%) were positive by Thick smear, 221(72.45%) were positive by Thin smear and 281(92.13%) were positive by Antigen detection method. Among them 211(69.18%) cases of P. Vivax, 77(25.24%) cases of P. falciparum and 17(5.6%) had a mixed infection. Sensitivity of Thick smear is 98.36%, thin smear 72.45% and for antigen detection method it was 92.13%.Conclusion: Detection of malarial parasite can be best done by combination of Antigen detection method and by Microscopic Examination of Thick smear.Keywords: Plasmodium vivax, Plasmodium falciparum, Rapid test for antigen detection Sensitivity, Thick smear, Thin smear
Tree-based Mining of Semantically Related Words in Tamil Biomedicine

Unsupervised mining of unstructured data is a vast field open for a number of research ideas. This paves way for identifying methods with minimum computational complexities but maximum consequential yields. One such method is to mine data purely based on Spanning Tree traversal. In this work, we concentrate on identifying semantically related and not similar entities from a wide collection of Tamil Siddha medicinal data. The proposed work converts the unstructured terms into a single graph with an adjacency matrix, dissects them into small closely knit subunits and constructs a maximum spanning tree from which word association information is mined. The breaking down into subunits involves two types of cliques, the biggest maximal cliques and vertex-oriented cliques, both giving agreeable results on tree traversal for any given context.
HIGHER EDUCA HIGHER EDUCATION MANAGEMENT FEATURES BASED ON TURES BASED ON INNOVATION

The article lists the features of innovative management of higher education. The higher education system has the principles for the implementation of scientific and innovative activities, specific problems of the effective development of innovative activities and the problems that universities must solve. It is also noted that the university is designing the development of innovative higher education services in two areas (systematically important projects of the university; innovative projects in the priorities of the university).
IMPLEMENTASI MEDIA PEMBELAJARAN BUSY BOOK UNTUK MENINGKATKAN KOGNITIF ANAK USIA DINI DALAM MENGENAL KONSEP BILANGAN DIKELOMPOK A RA INSAN MUBAROK

Cognitive abilities are abilities that place more emphasis on human intellectual potential. Cognitive development of children aged 4-5 years in recognizing the concept of numbers, namely: 1) counting and calling the sequence 1-10. 2) counting 1-10 by showing the objects. 3) shows the sequence of numbers 1-10 with objects. 4) connect or pair the symbols of numbers 1-10 with objects. 5) denotes two sets which are equal, unequal, more, and less. Busy book learning media is a book made of cloth and contains simple game activities designed creatively as props. This study aims to improve children’s cognitive abilities in recognizing the concept of numbers in group A RA Insan Mubarok Tegalsari for the 2019-2020 academic year. This research is a qualitative descriptive study with research subjects, namely: A group of 13 children. Data collection techniques that researchers use  are observation, interviews, and documentation. The result of research in improving cognitive abilities to recognize the concept of numbers through busy book learning media show that there is an increase in early childhood cognitive abilities in recognizing the concept of numbers in group A (ages 4-5 years) RA Insan Mubarok Tegalsari after learning to recognize the concept of numbers by implementing busy book learning media.
Mouse Not Working Windows 10: Most Effective Solutions

When youre utilizing a new operating system, for example, Windows 10, there can generally be some hardware compatibility issues. However, today, we ...
Paper ID IJMS0519-007 DESIGN AND DEVELOPMENT OF GIS-ENABLED TOOL FOR HUMAN ASSETS MAPPING

For economic survival, an individual must have human capital and social capital, both comprise the humanassets. This study is a design and development of a GIS-enabled human assets mapping (GIS-HAM) tool. It is aimedat mapping-out human assets to easily identify the different skills of the community members which are needed forfuture employment by various job sectors. The study used the waterfall model as Software Development Life Cyclein designing and developing the GIS-HAM prototype. The GIS-HAM tool was tested in Barangay Manlin, Buug,Zamboanga Sibugay, among randomly selected household heads (n=176) who answered the researcher-madequestionnaire and were subjected to focus group discussions and interview. Research has shown that unemploymentdue to skills and educational qualification mismatch in the labor market is one of the causes of poverty. Majority ofthe respondents and key informants (82%) were high school level, acquired skills by experience (66%), and hadskills in farming (37%). The system has the improved interoperability functions of the Google API, such as GoogleMap and Visual Analytics, which map and locate the addresses of skilled persons while showing their individualeducational attainment, skills, occupation and contact number. Both employer (skills searcher) and employee (jobseeker) can match skills to job types. Respondents’ evaluation yielded the system being overall functional in termsof content, user-friendliness, and performance. Study recommended that the GIS-HAM be implemented in mobileapps for accessibility of data at any time and be developed using multimedia software to enhance its user interfacedesign.
Construction of a Large-scale Japanese ASR Corpus on TV Recordings

This paper presents a new large-scale Japanese speech corpus for training automatic speech recognition (ASR) systems. This corpus contains over 2,000 hours of speech with transcripts built on Japanese TV recordings and their subtitles. We develop herein an iterative workflow to extract matching audio and subtitle segments from TV recordings based on a conventional method for lightly-supervised audio-to-text alignment. We evaluate a model trained with our corpus using an evaluation dataset built on Japanese TEDx presentation videos and confirm that the performance is better than that trained with the Corpus of Spontaneous Japanese (CSJ). The experiment results show the usefulness of our corpus for training ASR systems. This corpus is made public for the research community along with Kaldi scripts for training the models reported in this paper.
Unexpected Effects of Online no-Substitution $k$-means Clustering

Offline k-means clustering was studied extensively, and algorithms with a constant approximation are available. However, online clustering is still uncharted. New factors come into play: the ordering of the dataset and whether the number of points, n, is known in advance or not. Their exact effects are unknown. In this paper we focus on the online setting where the decisions are irreversible: after a point arrives, the algorithm needs to decide whether to take the point as a center or not, and this decision is final. How many centers are needed and sufficient to achieve constant approximation in this setting? We show upper and lower bounds for all the different cases. These bounds are exactly the same up to a constant, thus achieving optimal bounds. For example, for k-means cost with constant k>1 and random order, Theta(log n) centers are enough to achieve a constant approximation, while the mere a priori knowledge of n reduces the number of centers to a constant. These bounds hold for any distance function that obeys a triangle-type inequality.
Improving Word Alignment with Contextualized Embedding and Bilingual Dictionary

Word alignment is a natural language processing task that identifies the relationship of the among words of multiword units in a bitext. Large pre-trained models can generate significantly improved contextual word embedding. However, Statistical methods are still preferred choices. In this paper, we utilize bilingual dictionaries and contextualized word embeddings generated by pre-trained models in word alignment. We use statistical methods to generate rough alignment first, then use bilingual dictionaries to modify the alignment to make it more accurate. We use this alignment as training data and leverage this in training to optimize alignment. We demonstrate that our approach produces better or comparable performance compared to statistical approaches.
A Word + Character Embedding Based Relation Extraction Frame for Domain Ontology of Natural Resources and Environment

Building domain ontology is a challenging problem, and there are many different approaches for domain ontology construction. However, most of these approaches are still mainly using manual methods [1]. Ontology enrichment is a fairly standard approach in domain ontology construction, in which semi-automated methods and automated methods of ontology learning from a derived ontology. Relation extraction is one of the ways for ontology enrichment. Relation extraction techniques include law-based techniques, machine learning-based techniques with three typical methods: supervised learning, semi-supervised learning, and unsupervised learning. This paper proposes a word + character embedding-based relation extraction frame for the Vietnamese domain ontology of natural resources and environment. The model’s effect was demonstrated by experiments in the domain of natural resources and the environment and achieving promising results.
New Directions in Cloud Programming.

Nearly twenty years after the launch of AWS, it remains difficult for most developers to harness the enormous potential of the cloud. In this paper we lay out an agenda for a new generation of cloud programming research aimed at bringing research ideas to programmers in an evolutionary fashion. Key to our approach is a separation of distributed programs into a PACT of four facets: Program semantics, Availablity, Consistency and Targets of optimization. We propose to migrate developers gradually to PACT programming by lifting familiar code into our more declarative level of abstraction. We then propose a multi-stage compiler that emits human-readable code at each stage that can be hand-tuned by developers seeking more control. Our agenda raises numerous research challenges across multiple areas including language design, query optimization, transactions, distributed consistency, compilers and program synthesis.
Kejadian Hiperbilirubinemia Ditinjau dari Berat Badan Lahir

Hyperbilirubinemia is one of the clinical phenomena most often found in neonates occurring in the first week of life, which is also one of the factors causing infant death is influenced by the immature liver function of the baby to process erythrocytes (red blood cells), resulting in the accumulation of bilirubin. The purpose of this study was to determine the relationship of birth weight of infants with the incidence of hyperbilirubinemia in RSUD dr. Ibnu Soetowo Baturaja Ogan Komering Ulu Regency in 2019. This study uses analytic methods with a cross-sectional approach. The study population was all infants aged 0-7 days in the neonatal room at RSUD dr. Ibnu Soetowo Baturaja Ogan Komering Ulu Regency in 2019, with a random sampling. Data analysis uses univariate analysis and bivariate analysis using distribution tables and Chi-Square statistical tests, with a 95% confidence level. In the univariate analysis, of 203 respondents found 26.5% had hyperbilirubinemia and those without hyperbilirubinemia 72.5%, 24.6% of infants with LBW and non-LBW infants 75.4%. Bivariate analysis showed that there was an LBW relationship with the incidence of hyperbilirubinemia (p-value 0,000).
Adaptación al cambio en tiempos de pandemia el caso de una Universidad Pública en México

From an organizational learning perspective as a process of change to adapt to a dynamic and complexenvironment. The purpose of this article is to expose the adaptations to change at an organizational,technological, academic and operational management level, in teachers and students as a result of thechallenges that one of the Higher Education Institutions ies Publica de Mexico has faced since the emergenceof the Coronavirus SARS-CoV2 (COVID-19) pandemic. The reaction capacity to give continuity to academicactivities framed in healthy distance protocols is shown and the vicissitudes to face and coexist successfullyin the face of crisis and uncertainty are raised. During the individual and collective experience, through thecontinuous monitoring strategy within the University, emerging areas of opportunity are observed that,when well channeled, can become an incentive to promote learning at an organizational level. Althoughthe changes were not planned in advance and occurred in a short period of time, they are part of the selflearningof Higher Education Institutions in unprecedented times of pandemic.
ADAPTATION OF THE COURSE THEORY OF STRUCTURES TO ONLINE TEACHING DUE TO THE COVID-19 PANDEMIC. ANALYSIS AND RESULTS

ADAPTATION OF THE COURSE THEORY OF STRUCTURES TO ONLINE TEACHING DUE TO THE COVID-19 PANDEMIC. ANALYSIS AND RESULTS
Determination of Age, Gender, Dress Color and Type of a Person by Convolutional Neural Network (CNN)

Artificial Intelligence is a soft computing technique that mimics the human intelligence which interprets and percepts based on the inputs given to it, for the output generation or prediction. The input image is processed by the neural network along with the help of the algorithms of Machine Learning making it advantageous to process inputs with reduced use of hardware and the complexities of implementation irrespective of number of models are needed to be developed and deployed. Determining a person’s age, gender and color and pattern of the dress is done by the help of the convolutional neural network and Computer Vision for video analytics such that they can be determined individually and in a combined manner by it at a good value of accuracy in training and validation. The existing works could determine only one but not with the combination of the parameters, so pre-trained models like the Caffe, minivggnet models are used to determine them along with the use of openCV. The coding is done in python language, wherein packages like tensorflow, keras, scikit and openCV are used. Opencv is used for the video analytics such it can be used for the trend analysis or the surveillance purposes.
A Brief Python Tutorial

This chapter provides a short tutorial of the Python programming language. This chapter briefly covers basic concepts of Python, including variables, expressions, control flow, functions, and classes. The goal is to provide a reference for the Python constructs used in the book. A full introduction to Python is provided by resources such as those listed at the end of the chapter.
Boolean functions, vectorial functions, and error-correcting codes

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Conceptualizing Multidimensional Reputation Evaluation Model for Crowdsourcing Participants: Blockchain Design Perspective

Given the weak mechanisms to display real reputation status of crowdsourcing participants (MTurkers), the existing reputation evaluation models of crowdsourcing platforms have several problems arising from centralized and simple evaluation systems and poor differentiation ability. We conceptualize a multidimensional reputation evaluation using concepts of Blockchain and collect indexes of crowdsourcing participants from the perspective of rewards and punishments based on four dimensions: initial reputation dimension, transaction dimension, evaluation dimension and fraud penalty dimension. Our findings based on the empirical analysis of data of participants from the “Mechanical Turk” platform verifies the comprehensiveness, dynamicity, and accuracy than the existing models of crowdsourcing platforms.
Self-sovereign Identity as Trusted Root in Knowledge Based Systems.

In this paper we continue to speak about the concept of Self-Sovereign Identity (SSI), but not in the cases of IoT devices as it was in previous works [1]. The main purpose of this research is the usage of digital identity in two cases: a) SSI of a single person in Knowledge based system “Experts Ledger” and b) SSI of a company and candidate in HR matching systems. Though these two systems are developed for different issues, the idea of SSI in both is similar. The overview of these systems is done, and the pros and cons of using SSI with the relation of Zero-Knowledge Proof (ZKP) in each of them is made.