Reviewerly/exHarmony huggingface dataset:
FREQUENCY CEPSTRAL COEFFICIENT AND LEARNING VECTOR QUANTIZATION METHOD FOR OPTIMIZATION OF HUMAN VOICE RECOGNITION SYSTEM

Speaker verification is the process of verifying a speaker, where the identity of the speaker is previously known based on the data that has been inputted. To be able to perform speaker verification, voice data will go through the process of extracting voice characteristics to obtain the information contained in the voice data. In previous studies, the Mel Frequency Cepstral Coefficients (MFCC) method has the highest accuracy rate with a recognition rate of 85.3% and the fastest feature extraction time compared to other feature extraction methods, so the MFCC method is a good method for feature extraction in speech recognition. As a classifier, the Learning Vector Quantization (LVQ) neural network is used. LVQ is a method for conducting learning or training at a supervised competitive layer. Before the voice data is processed, first the voice data is extracted using the MFCC method. The results of feature extraction using the MFCC method are used for the matching process, where this matching process will compare the results of feature extraction from the test data with the results of feature extraction from the training data contained in the database. Data from feature extraction were classified using LVQ. LVQ performs learning on vectors from the results of the MFCC filter. The sound matching process can be done by measuring the closest distance using the Euclidean distance to find out how similar the sound from the test data is to the pattern data in the knowledge base in the database
Research on the current situation and development of intelligent campus construction in private universities

This paper analyzes the smart campus, expounds the status quo of smart campus construction at home and abroad, explores the problems existing in the construction of smart campus from many aspects, and puts forward the construction strategies of smart campus, which has certain reference significance for the development of smart campus construction.
The Relationship Between Dancer’s Self-Management and Resilience and Passion

The purpose of this study was to analyze the structural relationship between dancers’ self-management, resilience and passion. The purpose of this study is to provide a positive effect to dancers and to provide basic data to dance leaders and educators. The subjects of this study were dancers working at 4 universities in Seoul with dance departments and dancers working in dance troupes. A total of 300 questionnaires were distributed and 280 copies were collected. A total of 257 questionnaires were analyzed except those that were answered twice or were found to be difficult to use in the study. Data processing was analyzed by frequency analysis, factor analysis, reliability analysis, correlation analysis, and multiple regression analysis using SPSS 18.0 for Windows. In addition, confirmatory factor analysis were performed using AMOS 18.0. The results of this study are as follows. First, self-management had a partially significant effect on resilience. Second, self-management had a significant effect on passion in part. Third, resilience has a significant effect on passion in partly influenced.
DuctTake: Spatiotemporal Video Compositing

DuctTake is a system designed to enable practical compositing of multiple takes of a scene into a single video. Current industry solutions are based around object segmentation, a hard problem that requires extensive manual input and cleanup, making compositing an expensive part of the film-making process. Our method instead composites shots together by finding optimal spatiotemporal seams using motion-compensated 3D graph cuts through the video volume. We describe in detail the required components, decisions, and new techniques that together make a usable, interactive tool for compositing HD video, paying special attention to running time and performance of each section. We validate our approach by presenting a wide variety of examples and by comparing result quality and creation time to composites made by professional artists using current state-of-the-art tools.
Effectiveness of acoustic AR-TA agent using localised footsteps corresponding to audience members' participating attitudes

We investigated an acoustic augmented reality teaching assistant agent (AATA) that walks according to the participating attitudes of the audience members in one-to-many communications. If participants are not focused on the lecture, it is difficult to make them pay attention. To improve such audience members' attitudes, or to draw their attention to the appropriate target, we propose an AATA expressed by moving the localised footsteps using a direction-controllable parametric speaker. We conducted the experiment to evaluate the effectiveness of the AATA. As the results, the participants felt as though someone, the teaching assistant or the lecturer, was walking around them when they perceived the movement of the localised footsteps, and they felt the changes in the AATA's attention and intention to them when the AATA's walking pattern was changed. Accordingly, it is proposed that the AATA's movement can deliver an implicit message to the audience members.
Keynote Talk 2: Eye Movement Detection Sensors, Biometrics, and Health Assessment

The usage of eye tracking sensors is expected to grow in virtual (VR) and augmented reality (AR) platforms. In my talk I will discuss the past and present status of eye tracking sensors, along with my vision for future development. I will also discuss applications that necessitate the presence of such sensors in VR/AR devices, along with applications that would have the power to benefit society on a large scale when VR/AR solutions are widely adopted.
The Effect of Dance Major Student’s Physical Self-Efficacy on Their Self-Determination Motivation and Dance Emotional Expressiveness

The purpose of this study is to identify the effects of dance major student’s physical self-efficacy on their self-determination motivation and dance emotional expressiveness. A total of 467 dance major students agreed to participate in this study. Four types of questionnaires were utilized: physical self-efficacy questionnaire, self-determination motivation questionnaire, and dance emotional expressiveness test. The data were analyzed using the frequency analysis, the correlation analysis, and the multiple regression analysis. The results are as follows: first, the level of physical self-efficacy positively and negatively affect the level of self-determination motivation. Second, the level of physical self-efficacy positively affects the level of dance emotional expressiveness. Lastly, the level of self-determination motivation positively and negatively affects the level of dance emotional expressiveness. The findings of this study suggest that dance field needs to offer systematic useful program which student effectively increase their level of physical self-efficacy. In addition, it is essential to create new types of education method in dance field so that they can reinforce their level of intrinsic motivation.
EL SOFTWARE LIBRE COMO UNA HERRAMIENTA PARA UNA EDUCACIÓN DE CALIDAD EN LATINOAMÉRICA

This paper presents a documentary research about Free Software, their background, main features, principles, successful experiences, advantages, disadvantages and valuation as the best choice for the Latin American educational sector. The methodology used was the electronic information application, and the review of literature on the subject. It exposes a set of considerations and sociocultural-economic reasons that justify the use of the liberating information technologies in the education sector in Latin America, mainly free software both in the processes of teaching and learning as in administrative processes, against the option of using proprietary software. Through an historical and conceptual ov erview of free software that lead to conclude that free Software hasbecome in the future a safe, economical and efficient computerization of the Latin American educational system, showing as a tool to achieve a shift towards one quality education, at the same time that is moving toward the consolidation of the technological independence of the region.
Towards Augmenting Lexical Resources for Slang and African American English

Researchers in natural language processing have developed large, robust resources for understanding formal Standard American English (SAE), but we lack similar resources for variations of English, such as slang and African American English (AAE). In this work, we use word embeddings and clustering algorithms to group semantically similar words in three datasets, two of which contain high incidence of slang and AAE. Since high-quality clusters would contain related words, we could also infer the meaning of an unfamiliar word based on the meanings of words clustered with it. After clustering, we compute precision and recall scores using WordNet and ConceptNet as gold standards and show that these scores are unimportant when the given resources do not fully represent slang and AAE. Amazon Mechanical Turk and expert evaluations show that clusters with low precision can still be considered high quality, and we propose the new Cluster Split Score as a metric for machine-generated clusters. These contributions emphasize the gap in natural language processing research for variations of English and motivate further work to close it.
English Use In Al-Amien Islamic Boarding School Prenduan Sumenep Madura

The research discusses English Use in Al-Amien Islamic Boarding School Prenduan Sumenep Madura. The research says that the VI class students of Marhalah Aliyah TMI Putra Al-Amien Islamic Boarding School Prenduan Sumenep Madura do not pronounce the given aimed words in the sentence bases word stressed syllables of English. The research is designed as qualitative research with descriptive method. Within, the researcher takes observation, interview, and test for instrument of data collection. Here, the data is taken from Marhalah Aliyah TMI Putra Al-Amien Islamic Boarding School Prenduan Sumenep Madura students. There are 204 students from VI class: four classes for Dirasah Islamiyah Wal Arabiyah (DIA), one class for Matematika dan IPA (MIPA) and another one class for IPS dan Bahasa Inggris (IPSI). Five students are taken from each class as respondents through purposive sampling and another Six interviewees for primary data. In conclusion, the research says that the VI class students of Marhalah Aliyah TMI Putra Al-Amien Islamic Boarding School Prenduan Sumenep Madura do not pronounce the given aimed words in the sentence bases word stressed syllables of English.
Tacoma Community College Library: ABE 69: HS 21+ Fitness (PE): Citing your sources - MLA

This guide provides links to the most useful TCC Library databases and resources for the PE credit module in the ABE 69 HS 21+ portfolio course.
POSSIBILITY OF USING ELEMENTS OF MATHEMATICAL LIBRARY FOR A PARALLEL LANGUAGE IN CONSTRUCTION MATRIX PROGRAMS

Today, due to problems in improving computing performance, parallel programming continues to evolve. There are many different languages in which you can write parallel programs. One of them is the functional-threading parallel programming language Pifagor, which in turn is very specific and allows you to write a program with maximum parallelism, as well as it is designed to solve the portability problem of parallel programs. Tools and a library of functions continue to be developed for this language. This study is devoted to the development of elements of the mathematical library and the search for the most effective mathematical parallel algorithms. The following methods are considered and used in the work: sequential, recursive (left and right recursion), factorization, and pairwise comparisons. As a result of the study, a number of mathematical functions were developed, and a study was made of the possibility of using these functions in the development of programs for multiplying large-dimensional matrices. The work demonstrates the effectiveness of using the developed simple functions implemented by different methods in matrix multiplication programs. The prospects of further work in this direction are noted, having in mind the analysis of the possibility of using artificial intelligence methods to increase efficiency and facilitate the development of parallel programs with large-sized matrices.
Google Posts and its Benefits

What is google posts and how to use google posts? Google posts are of 100-300 words that can also have an image or video along with it. Google gives the ...
Pengaplikasian Sistem Admin Pada Pembayaran Nota Produk Paguyuban Tenongan Boyolali Menggunakan Microsoft Access Dan Excel

Tenongan Pengging Association is a capital-free business forum involving producers and traders, which provides a variety of market snacks. The producer entrusts the product to the merchant and the merchant pays for the product from the producer. With the increasing number of traders and producers, the amount of money involved in the Pengging Tenongan Association is small, which creates several undesirable problems. The purpose of this research is to help tenongan actors so that when payment of money to producers is more efficient there will be no problems. This study aims to solve the problem by applying the admin system to the non-payment of the Boyolali tenongan community using Microsoft Access and Excel. The creation of this system will focus on the payment of producer notes. The results obtained from the research were notes per product containing the amount of deposits, salaries, and money per trader. After testing it for 14 days, the application of the admin system increases the effectiveness and efficiency of product payments.
DOES COMPENSATION AFFECT TASK PERFORMANCE? A CASE FROM PUBLIC SECTOR HOSPITALS OF KARACHI – PAKISTAN

The performance of human resources is regarded as a contributory factor to the performance of an organization. Nowadays compensation is considered the most influential factor to motivate human resources to accomplish the designated tasks. This study examines the effect of salary and bonus as a form of direct compensation while employee recognition and flexible working hours as a form of indirect compensation on the task performance. A deductive approach was employed to determine the causal link with a sample of 370 employees working in public sector hospitals while the developed hypotheses have been examined by structural equation modeling. The results of the study revealed that salary, bonus, employee recognition, and flexible working hours have a positive effect on task performance. So, managers of public sector hospitals need to seriously deliberate when formulating the compensation strategy as it directly links with the performance of employees which will finally be deciphered into better delivery of services.
Technology-based services for deaf and dumb people

Nowadays, the deaf and dumb category represents a large number of people in our society, especially in Saudi Arabia. The sign language is considered to be the main way to interconnect with deaf and dumb, but most of the normal people have no ideas about this language. For this reason, there is a growing need to find alternative solutions for communicating with them. In this paper, we propose three different methods that can help deaf and dumb people to have a better and easy life. First, the "ABSHER for Deaf" service can help deaf people to use their account for the Ministry of Interior which called ABSHER. Second, the "Keyboard for Deaf" feature can support the sign language images and symbols in the keyboard as a different feature to convert between the normal person language and the deaf language. Finally, "Be Aware of your medicine" that can help deaf people to understand all the information about the medicine given to them.
The Reader's Mindreading of Realist, Modernist, and Postmodern Fiction: A Comparative Study

This article discusses the reader's mindreading when exposed to realist, modernist, and postmodern fiction. It has been argued that the reader of fiction practices his mindreading competence by trying to attribute mental states to fictional characters and predict their next action (Zunshine). We argue that this applies most of all to the reader of realist texts written in the objective-realist mode. By contrast, readers of realist fiction that extensively informs the reader about the characters' mental states and readers of modernist fiction cannot freely practice mindreading in this way, but instead have an opportunity to enrich their folk psychology or practice their imagination. Also, the metafictional and antimimetic character of postmodern fiction partly undermines the possibility of readerly simulations of a character's mental experience or readerly ascriptions of mental states. We illustrate our argumentation with detailed analyses of three short stories, each representing a different convention. When interpreting the results of our analyses, we refer to three models of mindreading proposed in contemporary cognitive studies: the theory-of-mind theory, the simulation theory, and the interaction theory. This text is meant to contribute to the current debate on the impact of mindreading in which the reader of fiction is engaged on his mindreading competence in real life; in particular, we draw attention to the variety of modes in which literary texts involve readers as well as to the variety of ways in which currently debated theories of mindreading interpret this involvement.
Effectiveness of structured teaching program on knowledge regarding emergency management of head injury among the staff nurses

The current study aimed to evaluate the effectiveness of structured teaching program on knowledge regarding emergency management of head injury among the staff nurses. To assess and compare knowledge regarding the emergency management of head injury among Staff Nurses before and after the Structured Teaching Program. To determine the association between the level of knowledge with selected sample characteristics. The research design used for the Present study was Pre Experimental One Group Pre-Test Post-Test Research Design conducted among 50 staff nurses which were selected using non probability purposive sampling technique. The data were collected by using Sample Characteristics Performa and self-structured knowledge questionnaire. The finding of the study revealed that the mean score of post-test knowledge (23.62) was significantly higher than the mean score of the pre-test knowledge (16.68). The pre-test knowledge of the staff nurse were having significant association with the age (χ2=15.29), and working experience (χ2=26.66). Therefore, it was concluded that the structured teaching programme improved the knowledge regarding emergency management of head injury among the staff nurses.
The Effect of In-Domain Word Embeddings for Chemical Named Entity Recognition

Research articles and patents contain information in the form of text. Chemical named entity recognition (ChemNER) refers to the process of extracting chemical named entities from research articles or patents. Chemical information extraction pipelines have ChemNER as its first step. Existing ChemNER methods rely on rule-based, dictionary-based, or feature-engineered based approaches. More recently, deep learning-based approaches have been used to approach ChemNER. Deep-learning based methods utilize pre-trained word embeddings such as word2vec and Glove. Previously, we have used embedded language models (ELMo) with Bi-LSTM-CRF to learn the effect of contextual information for ChemNER. In this paper, we further experiment to learn the impact of using in-domain (large unlabelled corpora of chemical patents) pre-trained ELMo for ChemNER and compare it with ELMo pre-trained on biomedical corpora. We report the results on three benchmark corpora and conclude that in-domain embeddings statistically significantly improve F1-score on patent corpus but do not lead to any performance gains for chemical articles corpora.
Generating Inflectional Errors for Grammatical Error Correction in Hindi.

Automated grammatical error correction has been explored as an important research problem within NLP, with the majority of the work being done on English and similar resource-rich languages. Grammar correction using neural networks is a data-heavy task, with the recent state of the art models requiring datasets with millions of annotated sentences for proper training. It is difficult to find such resources for Indic languages due to their relative lack of digitized content and complex morphology, compared to English. We address this problem by generating a large corpus of artificial inflectional errors for training GEC models. Moreover, to evaluate the performance of models trained on this dataset, we create a corpus of real Hindi errors extracted from Wikipedia edits. Analyzing this dataset with a modified version of the ERRANT error annotation toolkit, we find that inflectional errors are very common in this language. Finally, we produce the initial baseline results using state of the art methods developed for English.
Universal Dependency Treebank for Xibe

We present our work of constructing the first treebank for the Xibe language following the Universal Dependencies (UD) annotation scheme. Xibe is a low-resourced and severely endangered Tungusic language spoken by the Xibe minority living in the Xinjiang Uygur Autonomous Region of China. We collected 810 sentences so far, including 544 sentences from a grammar book on written Xibe and 266 sentences from Cabcal News. We annotated those sentences manually from scratch. In this paper, we report the procedure of building this treebank and analyze several important annotation issues of our treebank. Finally, we propose our plans for future work.
Keynote Talk 3: Security and Privacy Challenges in Learning Learning-enabled IoT Systems

Innovative edge devices, pervasive wireless connectivity, and powerful cloud computing are leading to a new generation of learning learning-enabled IoT systems. Unlike their precursors that primarily focused on collecting sensor data for off off-line knowledge discovery and simple control, this new generation of IoT systems harness machine learning (ML) to make rich inferences about the state of natural, eng ineered, and human systems; to comprehend and project it to the future; and to decide on actions that influence and nudge that state in a desired and timely manner. While data data-driven ML algorithms are helping equip these emerging IoT systems with intellig ence and autonomy, they also introduce vulnerabilities to a variety of security and privacy problems. This talk will cover research in my group on (i) efficient black black-box attacks on sensor data and machine learning models that cause these systems to make i ncorrect inferences and control actions, (ii) defense mechanisms that help detect and mitigate these attacks, and (iii) protecting against privacy loss arising from inference attacks on high high-dimensional sensory data being shared by differentially differentially-private s ynthetic sensor data generation generation.