Reviewerly/exHarmony huggingface dataset:
The Architecture of Tourism Recommendation System Based on Context-Awareness and Two-Way Relationship

Increasing and growing of destinations in the tourism sector cause excessive information loads. Due to overloaded information, it will be difficult for tourists to determine the appropriate destination. In recent years, the recommendation system has been developed to overcome these problems. This study about a tourist attraction recommendation system model designed with context-awareness and two-way relationships to produce recommendations based on tourist preferences in real-time. The Analytical Hierarchy Process (AHP) algorithm used in weight input from users in determining location recommendations. The algorithm composes an alternative sequence of decisions and selects the best alternative when making decisions by weighting certain criteria. The system considers contextual information (e.g., weather, time, and traffic flow), and a two-way relationship, i.e., feedback (e.g., rating and comment) from other users regarding a particular destination which notified the active users in real time. Further, contextual information and feedback from other users will affect active user to change the list of recommendations that have been given or remain on the recommendations given by the system.
Development of Image Processing Simulator: Case Study on Robotic Soccer

This paper presents an image processing simulator for robotic soccer, which allows robot soccer developers (students/lecturers) to take values on objects used by robot soccer. Therefore, this simulator provides several tasks in image processing by providing a comparison of methods. The fourth tasks is a color conversion that compares the selection of RGB and HSV; blurring that compares the median method and the gaussian method; edge detection that compares the methods of sobel, canny and hough transformation; and morphology that compares the methods of erosion, dilation, opening, and closing. Specifically, this simulator is used to take value, and method comparison will be used in subsequent experiments. Take values can be in the form of color values or image results from several methods used. In order for our simulators to be used by robot soccer developers, testing is needed. The test was conducted in three stages, that are validity test, small group test, and large group test. In the first stage, the validation test results were 92.59% and 99.07%. In the second stage, the results of the small group test amounted to 82.24%. In the third stage, the results of a large group test amounted to 87.10%, while the evaluation phase is carried out at each stage.
Performance of Different Optimizers for Traffic Sign Classification

The use of traffic signs is vital especially when travelling in the highways or the hills in adverse environmental conditions. The pace of advancements in the field of Machine learning has opened doors for scopes of improvement in the performance of Convolutional Neural Network Architectures dedicated to classification of traffic signs. Speed is as important as accuracy for such problems in the field of Advance Driver Assistance Systems and the use of GPU instead of CPU gives the benefit of parallel processing. Gradient Descent helps navigating towards the minima of the loss function. Purpose of various gradient descent optimizing algorithms is to help in quicker convergence. This proposed algorithm comprises of a compact Convolutional Neural Network architecture that was trained on GPU using RMSProp, Adam and Nadam optimizers on the BelgiumTS dataset. RMSProp and Adam caused either underfitting or over-fitting that was resolved by Nadam used with an appropriate dropout with 97.51 training accuracy and 96.78 testing accuracy. The predictions on test images convey that the architecture trained using Nadam works perfectly for blurry images, positionally challenging images and images with uneven illumination.
Monitoring Logistics Through Blockchain

According to a conceptual business aspect, logistics is the flow of things between origin and consumption point of a product to meet customers or corporation’s requirements. The problems prevailing in current logistics are inefficiency, expensive, and vulnerability. Blockchain is a distributed ledger technology maintained by anonymous consensus. It records transactions between parties in a secure and permanent way. We aim to build a system enabling identity management protocols inside the creation of identities, the validation of their binding to real-world entities, their secure and reliable storage along with protection, distribution, verification, and use. The proposed system will entail four key properties concerning all materials and consumables it covers: nature, quality, quantity, and ownership. Any interested party with the goal to explore a particular step to perform the quality assessment will be able to audit the network in which every step is verified by the relevant performing party.
A Trellis for Novice AI Practitioners

It is commonly said that all models are wrong, but some are useful, yet the utility of models does not come easily. As the allure of AI attracts new practitioners it is crucial for sound technique to be modeled and freely made available. This is especially true in fields such as information security, where manual human observation is impossible, the consequences for failure are high, and the existing workforce is competent enough in computer programming to read documentation, troubleshoot through frustrations and ambiguity, and, in worst-case scenarios, succeed at getting an algorithm they do not know much about to output a model they do not understand with unfounded and unbridled confidence. Where previous generations of statisticians mentored new practitioners, passing along processes that protected them from the dangers of making a living predicting an unknowable future, today's workforce often queries in isolation. In a culture where algorithms decide who gets interviewed for jobs, who gets flagged for an IRS audit, who warrants increased scrutiny at immigration checkpoints, who should be approved for a home or auto loan, and what the terms of that load should be - and so on ad infinitum - there should be no doubt that the quality of our society's data science apparatus must be excellent to avoid catastrophic ramifications. For this purpose, we present a tool to help information security students and professionals transition into the world of data science. A carefully chosen data set, selected for its anticipated familiarity and suitability for quality work, gives context to our training exercises. As entrants gain confidence and experience the tool will evolve with them, self-organizing into a couture tool-kit that reflects their specialty, preferences, and even their personality. Built upon solid data science processes and andragogical foundations this learning tool will increase safety from mortal error during the dangerous initial formative period of a developer's transition to data science practitioner.
Restricted Boltzmann Machine as Image Pre-processing Method for Deep Neural Classifier

The paper presents a novel approach to image preprocessing for feature extraction that is designed for reduction of dimensionality of the classifier which is in this case the convolutional neural network (CNN). The proposed method uses Restricted Boltzmann Machine(RBM) as an Aggregation Method (AM) for binary feature descriptors. The assumption of this technique is that the RBM is performing an dimension expansion of the feature space. Also the type of the data undergoes the transformation from binary to floating point. The conventional approach in convolutional neural networks uses as an input the image that consists of one (grayscale) or three channels (RGB). The method presented herein allows to have the number of channels configurable, as it depends on the size of the Restricted Boltzmann Machine (RBM). The size of the entire network and its parallel implementation makes the architecture usable in real-time systems with reduced memory size.
The Use of Short Stories with Hidden Pictures to Improve Students’ Vocabulary Achievement at Fifth Grade of MI Gempolan IIKediri.

Abstract This study was conducted to improve the students’ vocabulary achievement through short stories with hidden pictures. Short stories with hidden pictures is a media in which some nouns, adjectives and verbs contained in the story text were replaced with folded paper hiding the pictures that give clues about the meaning of the words. This study used Classroom Action Research (CAR) as the research design which was conducted in one cycle consisting of planning, implementing, observing and reflecting stages. The instruments for the data collection were vocabulary test, observation checklist, field notes, and questionnaire. The data gained from the vocabulary test after the implementation of the media showed that the result had achieved the criteria of success which stated that at least 80% of the students gained score above the minimum passing grade which was 65. None of sixteen students got score below 65. In addition, the data gained from the field note and observation checklist showed that more than 80% of the students gave positive responses toward the implementation of the media by participating actively. Based on the result of this study, it is suggested the short stories with hidden pictures be used by the English teacher as an alternative media to improve other language skills or components with different strategy of teaching. It is also important to pay attention to the weaknesses of this media. First, the development of it takes a lot of time. Second, the media is not practical and durable. Keywords : short stories, hidden pictures, vocabulary achievement 1 1 1 1 MicrosoftInternetExplorer4 0 2 DocumentNotSpecified 7.8 磅 Normal 0
Social Workers’ Thriving at Work: Thriving beyond Surviving

본 연구는 기존에 충분한 논의가 부족했던 사회복지사 직무수행의 긍정적 경험으로서 직무번영감을 탐색하는 것을 목적으로 한다. 이를 위해 본 연구주제에 적합하다고 판단한 전국 사회복지사 30명을 선정하여 심층인터뷰를 실시하고 그 결과를 근거이론에 기반하여 분석하였다. 분석결과 직무번영감으로서 유능해진다고 느낌, 일과의 합일을 경험 등의 2가지 개념을 도출하였다. 직무번영감의 엔진에 해당하는 주도적 직무수행경험으로는 일에 대한 몰입감, 탐험과 탐색, 관계의 확장 등의 3가지 개념을 도출하였다. 주도적 직무수행경험은 개인의 자원과 조직의 자원이 전제될 때 발생하는 것으로 확인하였고, 이를 토대로 사회복지사의 긍정적 직무수행 경험인 직무번영감의 이론적 모델을 제시하였다. 사회복지사가 노동현장에서 생존하는 것을 넘어 직무번영감을 경험할 수 있는 맥락을 제시함으로써 사회복지실천에 관한 함의를 제시하였다.The purpo se of this s tudy i s to explore t hriving at w ork of so cial wo rkers as a p o sitive experience in job. Thirty social workers who were suitable for the study purpose were selected for in-depth interviews and analyzed based on the grounded theory. Agentic work behavior, which is regarded as engine of thriving at work, is consisted with three concepts: task focus, exploration, and heedful relating. Agentic work behavior was found to occur when social workers’ individual and organizational resources are satisfied. Based on this results, the theoretical model of social workers’ thriving at work was suggested. Also, the implications are suggested by the mechanism of thriving at work in social work field.
Historische taalkunde en Digital Humanities: samen naar een mooie toekomst

Both historical linguistics and Digital Humanities have made great progress in recent decades. However, technology and research have still remained separate worlds, and as a result the digital infrastructures and corpora are not optimally equipped for diachronic linguistic research and take too little account of the theoretical insights. In this article I show on the basis of a concrete research question what requirements the data, metadata and infrastructure should meet in order to make the best use of the theoretical insights, and how far advanced we are at this moment. I demonstrate which research possibilities the available infrastructures offer and what more is needed, and I conclude with a description of the ideal infrastructure for a historical linguist and how to achieve this.
Image Classification with Visual Relationship

While traditional image classification has shown great success propelled by deep convolutional neural networks (CNNs), but computers perform poorly on cognitive tasks, such as connecting computer vision and natural language. In this paper, we propose a framework for image classification base on semantic labels which are generated by visual relationship. The task of visual relationship is to seek the most likely "relationship" between objects in a given image. The proposed method provides a means of extracting the rich meaning embedded in the images. We utilize Long Short-Term Memory (LSTM) to gather the visual relationships of an image to yield a semantic label. Experimental results on Visual Genome and Microsoft COCO demonstrate that the proposed architecture achieves a good performance.
People Flow Presentation by Vibrotactile Stimulation to Assist Dynamic Environment Recognition for Visually Impaired People

According to a 2018 World Health Organization investigation, worldwide, there are around 253 million visually impaired persons. Technological support is required to help such people walk safely. This paper focuses on the fact that many pedestrians are forming a flow in the actual walking environment. We believe that behavior decisions based on people flow lead to safe and reliable walking of the visually impaired people. The purpose of this study is to assist a visually impaired person’s recognition of surrounding dynamic environments by presenting people flow. To realize this, it is necessary to establish methods of extracting surrounding people flow information from sensing data and presenting the information to a user. In this paper, we propose a method of people flow presentation using vibrotactile stimulation. After identifying the vibration perception characteristics of the presentation positions on a human body, we design and verify the vibrotactile stimulation for people flow presentation.
Optimized Channel Allocation Scheme with Selective Noise Avoiding in DWDM-QKD System

To reduce impairments on quantum signals in DWDM-QKD system, we propose a channel allocation scheme which selectively avoids FWM according to degeneracy or non-degeneracy. Simulations show that our scheme can effectively suppress noise.
A Blockchain based Investment and Collective Support Mapping for Emerging Businesses

Traditional small-scale business framework takes a centralized approach, with the necessary financing being done by local, independent institutions or individuals. The scenario can be improved with a decentralized platform that aids in finding investors for startups, agriculture, small scale industries and businesses. A decentralized platform that supports non-banking financial institutions to collaborate with entrepreneurs is also practically scarce. The proposed model aims at solving this problem by providing a secure, decentralized platform that brings together entrepreneurs, small scale businessmen, farmers and investors. It would create an extensive market that would satisfy the financial needs of small business and would encourage them and entrepreneurs alike. In this paper, we describe a Financial Mapping(FinMap) model, a revolutionary financial mechanism that uses blockchain technology to map investors, entrepreneurs, and the beneficiaries together in a secure manner. This model expects to devise a globally available, self-sustaining business environment where people with different needs work together.
Keynote Speakers Biography

Shamsul Sahibuddin 38 years involvement in the Information Technology started in 1982 when the PC industry was at its infancy. His interest now is in the area of Software Engineering, focusing on Internet of Things (IoT) and Cyber-Physical System (CPS) and its application in the 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Industrial Revolution (4IR). Shamsul obtained his Ph.D. in Software Engineering from Aston University, U.K.. Prior to that, he graduated in Masters of Science in Computer Science from Central Michigan University. His undergraduate qualification is Bachelor of Science in Computer Science from Western Michigan University. He joined Mesiniaga Sdn. Bhd., sole agent dealer for IBM in Malaysia, in 1988 as Programmer Analyst. His responsibility was to design and develop software for the personal computer platform. In 1989 he joined Universiti Teknologi Malaysia (UTM) to begin his teaching career. Currently he is the Pro-Vice Chancellor of UTM, responsible for the Kuala Lumpur Campus. Previously, Shamsul was the Dean of Advanced Informatics School (AIS) for seven years, in which during his tenure, three new programmes were introduced. The programmes are Masters of Science in Computer System Engineering, which focuses on IoT, Masters of Science in Business Intelligence and Analytics, and Masters of Science in Informatics, which focuses on the management and operational issues of Informatics especially in the era of 4IR. Shamsul was also the Director of Centre for Advanced Software Engineering (CASE) for the duration of six years. Among the focuses was post graduate programme, training, and consultation in the area of Software Engineering and Information Security. During his 31 years with UTM, he has been involved in 25 consultancy and research projects with the value of RM4 million. Shamsul also have authored and co-authored more than 180 papers and book chapters, and successfully supervised 20 Ph.D. graduates.
Face Alignment Method Based on Mouth Constrain

Mouth shape detection can effectively solve the problem of complex environments such as semi-occlusion encountered in the face key point detection. In this paper, a method based on feature point detection in the local area of the mouth is proposed. By determining 20 feature points in the mouth area and then using the regression algorithm, the position of the feature points in the mouth area is accurately determined, and then matched and predicted the feature points of the face are located. according to the distance and other characteristics. The feature points of the entire face are positioned to achieve the effect of face alignment. In this paper, the improved ESR algorithm effectively solves the problem of local feature point localization. At the same time, since we only locate the feature points of the mouth area and then calculate other feature points, we can improve the detection efficiency.
Living in Flow: The Science of Synchronicity and How Your Choices Shape Your World by Sky Nelson-Isaacs

While Sky Nelson-Isaacs is not the first physicist to be interested in the phenomenon of synchronicity—others who come to mind are Wolfgang Pauli, F. David Peat, and Walter von Lucadou—Nelson-Isaacs’ new book Living in Flow is notable for its engaging and highly readable presentation of his particular theory about the relationship between quantum physics and synchronicity. Like the great idealist philosophers before him, Nelson-Isaacs takes mind to be the primary reality, and his theory explains how the contents of our minds—in particular, the qualities of the experiences we anticipate having—shape the evolution of the physical world through the process of “meaningful history selection.” Nelson-Isaacs also links his theory to psychologist Mihaly Csikszentmihalyi’s concept of flow, positing that we are best able to shape the evolution of the world in accordance with our desires when we are in the state of flow. Nelson-Isaacs begins his book with the psychological component of his theory and works up to the quantum physics near the end, but I’m going to take the opposite approach here.
Hydropower Optimization Test-Case Solved with Nature-Inspired Algorithms

The computational efficiency of various nature-inspired algorithms is compared here for a specific hydropower optimization problem, selected as test-case due to its accurate reference solution yielded by the Newton-Raphson method. The above test-case is attached to a hydropower development in Romania, bounded by Vidraru Reservoir (on Arges river) and Vidraru Hydro-Power Plant. The results obtained in this paper using four algorithms, namely Genetic Algorithm, Particle Swarm Optimization (PSO), Flower Pollination Algorithm, and Shark Smell Optimization, are compared with previous results obtained on the same test-case using other algorithms (Honey Bees Mating Optimization Algorithm, Firefly Algorithm, Cuckoo Search Algorithm, Bat-Inspired Algorithm). It turned out that PSO is the most efficient algorithm among the 8 tested algorithms.
The Independence of Monosyllabic Words

Based on the characteristics of Chinese characters, Chinese characters is the intersection of speech and grammar in Chinese with stable speech performance. One Chinese character is a syllable with a tone as a sign. Moreover, Chinese characters have a tenacious meaning, which makes the meaning of words not easy to lose. In other words, monosyllabic words can always maintain strong independence. But between different word classes, its independence also shows differences. In this paper we take the component of antonymous compounds as the entry point, and find that monosyllabic nouns have the strongest independence, followed by verbs, and adjectives have the weakest independence.
Sensor Technology for Unsupervised Monitoring of Real World Activities

Rapid advancement of technologies in the area of sensors, ultralow power embedded systems with wireless communications, enables the rapid development of the wearable sensors. In this paper, we are discussing about the development of self-designed low-cost wearable sensor for continuous monitoring of human and sports activities. Developed prototype offers unobtrusive, inexpensive and unsupervised monitoring of activities for longer durations of the period. Unsupervised monitoring is crucial component in the areas of health care for patient rehabilitation, sport activities for player movement etc., The wearable sensor was developed by designing a custom hardware using inertial sensors and Bluetooth Low Energy (BLE) SoC.
CN-StartEnd: A Chinese Event Base Recording Start and End for Everything

Start and end are very important attributes in knowledge graphs. The entities or relations in a knowledge graph often have their validity periods represented by start timestamps and end timestamps. For example, Obama’s birthday is the start time of his life and the departure time is the end of his president’s career. We need to refer to the start timestamps or end timestamps when dealing with temporal tasks such as temporal question answering. The existing Chinese knowledge graphs, with popular examples including CN-DBpedia, Zhishi.me and PKU-PIE, contain some unprocessed start timestamps and end timestamps for their entities. While in Chinese, a large number of descriptions about the beginning or end of entities, relations and states lie in events. In this paper we introduce our work in constructing a Chinese event base which focus on start-events and end-events. We extract more than 3 million event-temporal cases from infoboxes and natural texts of Chinese encyclopedias. After selection and matching, these event-temporal cases are reconstructed into a large-scale knowledge base that incorporates over 2.3 million start-events and 700 thousand end-events. Events describing the same object and match our start-end templates are merged into more than 150 thousand start-end pairs. Dumps for CN-StartEnd are available at: http://eventkg.cn/cn_StartEnd .