Reviewerly/exHarmony huggingface dataset:
Prediksi Jumlah Kunjungan Wisata Mancanegara Dengan Algoritma Backpropagation

Bukittinggi City is known as a tourist destination that is very attractive in foreign tourist interest. Diverse types of tours are presented naturally and man-made the beauty of mountains, valleys and the beauty of the existing architectural buildings is Bukittinggi Clock Tower. Not only that, the type of culinary tourism and traditional market snacks are also an attraction for foreign tourists to travel in the city of Bukittinggi. In this study, the problem that will be discussed is the process of predicting tourist visits conducted by foreign tourists to the city of Bukittinggi. The prediction process uses the concept of artificial neural network backpropagation algorithm. The data set that will be used as a discussion is the data foreign tourist visits recorded in the Tourism Office of Bukittinggi City from 2018 to 2019. The prediction results generated with the concept of artificial neural network backpropagation algorithm produce output numbers of number of visits with an accuracy value of 95,64% and level value the resulting error is 4,36%. The benefits generated from this research are helping the government of the city of Bukittinggi especially the Tourism Office in providing input to manage the tourism sector.
Interactive Analytics of Massive Spatial Vector Data via Display-Driven Computing

In the big data era, real-time analysis of large-scale spatial data remains a challenging problem since the computational scales of conventional data-driven methods expand rapidly with data volumes. This paper presents display-driven computing (DisDC), a new computing model for interactive analysis of large-scale spatial vector data. The computing units in DisDC are screen pixels for display rather than spatial objects in traditional data-driven methods, and the core task of DisDC is to judge the spatial relationships between pixels and spatial objects. As the number of pixels for display is stable, DisDC is less sensitive to data volumes and can be used to provide real-time data analysis of large-scale spatial vector data. Experiments show that DisDC is capable of handling billion-scale spatial vector data, and an online demonstration is provided (https://github.com/MemoryMmy/DisDC).
The Effectiveness of Using a Mobile Messenger as an Educational Supporting Tool among Nursing Students

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The evolution of information technology has exerted great influence on nursing education via new pedagogy of knowledge delivery without time and place restriction. Mobile technology revolutionises nursing education and clinical practice via empowering skills of critical thinking and clinical decision-making through learning. The aim of this study is to evaluate the effectiveness of using mobile messenger (Whatsapp) as an educational supporting tool among nursing students. The study design used is a Cluster Randomized Control Trail. Two nursing colleges were selected. Sample size was 93 participants, 48 from the Kuala Terengganu Nursing College Kuala Terengganu as the intervention group while the control group were recruited among 45 participants from UniSZA Nursing College. There is a significant difference in the level of knowledge between pre and posttest among intervention group (mean difference was -8.70 with a standard deviation 8.42, p-value&lt; 0.001) and 93.8 percent of the respondents perceived the usefulness of using WhatsApp mobile messenger to enhance learning. This demonstrates that learning through mobile messenger (WhatsApp) enhances learning and is well received as a new method of learning by almost all students.&#x0D; &#x0D; Keywords: Mobile learning, WhatsApp messenger, Social Interaction
Webometric Analysis and Link Relationship of Computer Science and Information Technology Websites of Malaysia Universities

This paper aims to investigate 18 web domains of computer science and information technology academic websites of Malaysia universities.We collected more than two million web pages. A webometric analysis was used to explore the number of web pages, inbound links, the web impact factor (WIF) and link relationships. The results show Fakulti Teknologi dan Sains Maklumat (FTSM), Universiti Kebangsaan Malaysia (UKM) has the highest number of webpages while Fakulti Teknologi Kreatif dan Warisan (FTKW), Universiti Malaysia Kelantan (UMK) has the largest WIF score. Pearson’s rank correlation coefficient was used to detect the relationship between institutions subdomain age and WIF. Correlations point out that there is scant relationship between subdomain age and WIF score across all 18 Malaysia selected schools [ r =−.076, n = 18, p &lt; .0005]. This is due to WIF are highly dependent on the quality of the content to attract backlinks and Google crawler algorithm that changes from time to time for the number of web pages. Subdomain age is independent to the year of establishment of the schools. These findings can be used as a guide to the implementation of university web content strategy.
Planning of the coverage route for multi-UAVs based on section optimization

<p indent=0mm>Route planning coverage is commonly used in vacuuming, mapping, and tracking of the area. A group of programmable unmanned aerial vehicles (UAVs) is an ideal choice for such missions to increase coverage efficiency. In this paper, for the large-scale environmental coverage challenge, a novel route planning approach is proposed, which gathers abundant information per unit length and ensures full coverage for the entire environment. The proposed approach is based on the method of section optimization, as it is extremely difficult to obtain a coverage route directly for the large-scale setting with accurate and reliable information. Next, the atmosphere is divided into several small sub-regions that conform to the sensor’s detection distance. Based on this, the optimal coverage route for each sub-region is calculated. For clarity, the route in each sub-region is termed as a partial path. Under certain specific constraints, the obtained partial routes are connected to constitute an integral path. It is worth noting that the higher steering frequency and the greater turning angle will result in more time and energy consumption. The proposed approach is also aimed at reducing these two significant indexes. Another benefit of the proposed approach is that even if the area in charge is changed, it is easy to adjust. To validate the performance of the proposed planning scheme, simulation, and experimental results are provided.
Application of Block Chain Technology in Power Information Asset Lifecycle Management

The amount of data needed to be recorded for the whole life cycle management of power communication assets and the traceability of data requirements provide a good application background for the application of block chain in this field. In this paper, based on the characteristics of the whole life cycle management of power assets, the basic requirements of the application of block chain in this field are put forward. On this basis, the overall plan of the whole life management of communication assets based on block chain and the intelligent contract mechanism used are designed to provide the academic research foundation for the later practical application.
s-SCMM: A Simplified Software Configuration Management Model for Software Services in Public University

Software Configuration Management (SCM) is a process that supports Software Development Life Cycle (SDLC), which benefits project management, development activities, maintenance activities, quality assurance activities, and the end users of the software product. However, findings from series of interviews have proven that SCM is difficult to operate and to be implemented by the practitioners in public universities specifically in Malaysia. Some of the SCM activities could be aided by using the SCM automated tools, however not all practitioners opted that due to inflexibility of the tools. Theoretical and empirical study have identified and revealed the current practices, success factors, essential elements, and components of SCM that were used frequently and beneficial among software practitioners. The empirical study also found that the main components of s-SCMM are process, SCM tool, and human. The s-SCMM was proposed and developed by considering the main components: process, tools, human, and success factors. The s-SCMM model was validated through expert review. As the result, a Simplified SCM Model (or s-SCMM) was proposed and developed to help the execution and implementation of SCM in public university environment. The s-SCMM model is simplified by focusing on change management, version control, system building, and release management. Therefore, s-SCMM will become a platform to assist total implementation of SCM in organization. By using this model, the preparation and usage of SCM artefacts could become more effective, efficient, and systematic.
Continuous information support of the curriculum life cycle within the Digital University

Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region 2019: Proceedings of 2019 XVIII Russian Scientific and Practical Conference on Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region (PTES)
Man with a Movie Camera: Economy of Movement and ASMR Videos on Youtube

The article examines the ASMR phenomena through the perspective of early USSR philosophy of technic, especially, of the constructivist movement (Gan, Ginzburg) and of works of film director Dziga Vertov. Autonomous sensory meridian response (ASMR) is a tingling sensation that typically begins on the scalp and moves down the back of the neck and upper spine. The aim of research is to reconstruct the notion of «movement», «machine» and «technique» in constructivist writings and to transfer this notion on contemporary media culture. For the analysis authors use ASMR videos on Youtube in order to discover new possible connotations of the term «movement» in a constructivist perspective. The ASMR phenomena could be considered as a merge of Haraway’s cyborg metaphor and the constructivist conception of domestic life, movement and technique.
Exploring Undergraduate Students’ Motivation-regulation Strategies in Thesis Writing

Self-regulation is an ability that helps the students to maintain their effort in achieving their goals. In self-regulation, students can plan, monitor, and evaluate their learning process. Self-regulation encompasses many aspects. One of them is motivation. Motivation is considered as one of the important aspects of the learning process. Concerning the educational context in Indonesia, thesis writing course is a compulsory course. However, students are required to do the task independently. Therefore, students have to be self-regulated learners during the process of thesis writing. This study aims to investigate students’ motivation-regulation strategies in thesis writing. There were 9 participants involved in this study. The participants were students of English Language and Education Study Program of Sanata Dharma University. This study belongs to mixed-method research. Questionnaire and interview were used as instruments to collect the data. The results of this study revealed that the participants used some motivation-regulation strategies, namely, self-consequating, environmental structuring, goal-oriented self-talk, interest enhancement, and attribution control. Additionally, the majority of the participants were apt to procrastinate writing their thesis. The implications are provided for undergraduate students, thesis advisor, and future researchers.
Image and Video Compression Artifact Reduction at Low Bit Rates Using Local Cosine Transform in Conjunction with Significance Map Coding

In this study, we present an image and video compression method, which is efficient at low bit rates and includes local cosine transform (LCT) and significance map coding. Low bit rate image or video coding causes distortion and artifacts such as blocking effect, which is induced by block-based discrete cosine transform (DCT). On the other hand, another block-based method, LCT, which implements DCT, decreases blocking artifact, because it folds contiguous blocks before applying DCT. Significance map coding (SMC) achieves better image/video quality and decreases artifacts when compared to the conventional DCT coding approach. We apply SMC in conjunction with LCT for further improvement as shown by the experiments.
Reduced-Dimensional Capture of High-Dynamic Range Images with Compressive Sensing

The range of light illumination in real scenes is very large, and ordinary cameras can only record a small part of this range, which is far lower than the range of human eyes’ perception of light. High-dynamic range (HDR) imaging technology that has appeared in recent years can record a wider range of illumination than the perceptual range of the human eye. However, the current mainstream HDR imaging technology is to capture multiple low-dynamic range (LDR) images of the same scene with different exposures and then merge them into one HDR image, which greatly increases the amount of data captured. The advent of single-pixel cameras (compressive imaging system) has proved the feasibility of obtaining and restoring image data based on compressive sensing. Therefore, this paper proposes a method for reduced-dimensional capture of high dynamic range images with compressive sensing, which includes algorithms for front end (capturing) and back end (processing). At the front end, the K-SVD dictionary is used to compressive sensing the input multiple-exposure image sequence, thereby reducing the amount of data transmitted to the back end. At the back end, the Orthogonal Matching Pursuit (OMP) algorithm is used to reconstruct the input multiple-exposure image sequence. A low-rank PatchMatch algorithm is proposed to merge the reconstructed image sequence to obtain an HDR image. Simulation results show that, under the premise of reducing the complexity of the front-end equipment and the amount of communication data between the front end and the back end, the overall system achieves a good balance between the amount of calculation and the quality of the HDR image obtained.
Interlanguage Translation Utility with Integrated Machine Learning Algorithms

Same program can be written in different programming languages and different ways.One programming language will have advantages and disadvantages compared to another; hence sometimes it is needed to rewrite the code into another language to support this or that functionality.This paper demonstrates an algorithm of interlanguage translator, which is using intended machine learning (ML) algorithms to ensure higher translation rate.In the scope of research, tool for interlanguage translation is created and tested with two programming languages: Python and Java.Comparison results for translation accuracy are calculated, about 53% without AI algorithms and about 65% with the use of AI algorithms.
An online isotonic separation with cascade architecture for binary classification

Isotonic separation (IS) is a non-parametric classification technique which constructs an isotonic function from ordered data. The rationale is to convert partially isotonic data into isotonic using a linear programming problem (LPP) and partition the input space into isotonic and non-isotonic regions to make predictions easier. Despite the widespread applications of IS in diverse domains where monotonicity exists, it has certain limitations: Firstly, computing time and the constraints of the LPP in isotonic separation increase polynomially as size of the data increases and it is highly complex to solve the LPP and obtain the model on large data sets. In order to support dynamic stream data and address the computational overhead and size of the LPP issues, this paper proposes an online isotonic separation algorithm called Cascade-IS (CIS) for binary classification. The rationale behind CIS is that it splits the data set into a sequence of partitions and models are obtained and combined in cascade. Statistical and experimental analysis are done on datasets with isotonic properties and the results prove that CIS is superior to its variants in terms of training time, performance measures and number of constraints in the LPP.
EXPERIENCE-TECHNOLOGIES IN THE DEVELOPMENT OF INFORMATION READINESS OF PEDAGOGICAL EMPLOYEES FOR THE PERFORMANCE OF THEIR PROFESSIONAL ACTIVITIES

The article features an innovative approach to the development of information readiness of pedagogical staff for the performance of their professional activities in full-time distance postgraduate learning. The essence and components (motivational, cognitive, operative and personal) of pedagogical staff's readiness for professional activity are characterized. Indicators of the distribution of teachers by levels of their information readiness (low, medium, high) for professional activity are given. The article also highlights methodology and results of the empirical study of the levels of pedagogical staff's information readiness for their professional activity, testifying to the insufficient level of development of such readiness with a considerable number of educators. It is shown that a high level of such readiness is peculiar to only one-tenth of the participated educators, who have deep and complete knowledge, skills and experience of work with information. They are characterized by developed personal qualities that provide active character and responsible attitude to the professional activity in the conditions of uncertainty. Other respondents have a partial (or nonexistence) awareness of possible negative consequences from the use of information, insufficient willingness to bear responsibility, as well as an inability to understand the regularities of information processes in the context of uncertainty, etc. The essence of experience technologies of the development of information readiness of pedagogical staff to professional activity in conditions of part-time distance teaching in postgraduate education is revealed. It also highlights the specific features of the implementation of experience technologies regarded as a tool for specialist training on the basis of practical experience and their application in the process of professional development. It emphasizes the effectiveness of experience technologies in non-linear forms of training, which create the conditions for learners to make a conscious choice of learning content and forms of their own professional development.
Study of a Machine Learning Model for Face Detection, Age Detection, and Gender Recognition

Machine learning offers different models for age and gender recognition such as convolutional neural networks, feed forward neural network, and recurrent neural network. In our review we will be describing our system that takes input from the user and gives age and gender as output. User can provide input through browsing an image or through web camera. Our model is able to accept real time input through web camera. For the training and testing we are using structured data and we split data into testing and training date of the model. We are using train-test-split ratio as 3:7 i.e, 30% of the data will be usedfor training of the model and 70% of the data will be used for testing of the model .In this review we will be describing our algorithms and tools used and also methods for improving their accuracy.
Retrieval of source documents in a text reuse system

The architecture of the text-reuse detection system consists of three main modules, i.e., source retrieval, text analysis, and knowledge-based postprocessing. Each module plays an important role in the accuracy rate of the detection outputs. Therefore, this research focuses on developing the source retrieval system in cases where the source documents have been obfuscated in different levels. Two steps of term weighting were applied to get such documents. The first was the local-word weighting, which has been applied to the test or reused documents to select query per text segments. The tf-idf term weighting was applied for indexing all documents in the corpus and as the basis for computing cosine similarity between the queries per segment and the documents in the corpus. A two-step filtering technique was applied to get the source document candidates. Using artificial cases of text reuse testing, the system achieves the same rates of precision and recall that are 0.967, while the recall rate for the simulated cases of reused text is 0.66.
Professional AI Chess Board Autonomous Chess Board (Human Vs. Robot)

Chess -the classic game has come a long way from ancient times to modern days but yet remains a mind boggling challenge of intense strategic play and patience.Artificial intelligence is booming branch in technology which can be simply defined as "Any device /software that perceives its environment and takes actions that maximize its chance of successfully achieving its goals which is often compared to the 'cognitive' functions that humans associate with human minds, such as 'learning' and 'problem solving'."The project aims to combine both these entities to create a selfplaying chess board which has the capacity to stand on its own as a playing unit for a single person without any opponent.The pieces on the board will automatically be moved on the board according to AI so as to beat the person in front and thus create an unparalleled experience for a chess player.The strong structure consisting of various parts and sensors along with a fast processing unit.The introduction of AI brings a completely new dynamic into the game as the machine will improve its game strategies with time opening a completely new prospect in the game.A motor based system, along with smart magnetic detection provides a low noise and friction product at lower power consumption along with less wear and tear and even more effectively brings the cost of the product down sizably It helps in providing unique features such as hassle free motion of pieces along with unprecedented detection of moves are just some of the features the project aims to cover against the currently available similar products available in today's market.Also an improved size of the board along with aesthetically pleasing pieces is one of the feature the project aims to complete over its other competitors.
Gated Multiple Feedback Network for Image Super-Resolution

The rapid development of deep learning (DL) has driven single image super-resolution (SR) into a new era. However, in most existing DL based image SR networks, the information flows are solely feedforward, and the high-level features cannot be fully explored. In this paper, we propose the gated multiple feedback network (GMFN) for accurate image SR, in which the representation of low-level features are efficiently enriched by rerouting multiple high-level features. We cascade multiple residual dense blocks (RDBs) and recurrently unfolds them across time. The multiple feedback connections between two adjacent time steps in the proposed GMFN exploits multiple high-level features captured under large receptive fields to refine the low-level features lacking enough contextual information. The elaborately designed gated feedback module (GFM) efficiently selects and further enhances useful information from multiple rerouted high-level features, and then refine the low-level features with the enhanced high-level information. Extensive experiments demonstrate the superiority of our proposed GMFN against state-of-the-art SR methods in terms of both quantitative metrics and visual quality. Code is available at this https URL.