Reviewerly/exHarmony huggingface dataset:
Universal Vector Neural Machine Translation with Effective Attention

Neural Machine Translation (NMT) leverages one or more trained neural networks for the translation of phrases. Sutskever introduced a sequence to sequence based encoder-decoder model which became the standard for NMT based systems. Attention mechanisms were later introduced to address the issues with the translation of long sentences and improving overall accuracy. In this paper, we propose a singular model for Neural Machine Translation based on encoder-decoder models. Most translation models are trained as one model for one translation. We introduce a neutral/universal model representation that can be used to predict more than one language depending on the source and a provided target. Secondly, we introduce an attention model by adding an overall learning vector to the multiplicative model. With these two changes, by using the novel universal model the number of models needed for multiple language translation applications are reduced.
3D Pose Estimation using RGB Camera for Karate Motions

The objective of our research is to develop the pose estimation algorithm for Karate motions using RGB Camera. Kata is the representation of Karate’s self-defense techniques strung together into a performance routine. Kata is judged based on several technical and physical criteria; including speed and strength. For this reason, quality evaluation for Karate motions by technology is difficult. In this research, we created a data set targeting karate movement. We also developed the pose estimation algorithm for Karate motions by deep learning using this data set.
Static analysis method for detecting cross site scripting vulnerabilities

These days, the internet has turned into the favoured stage for clients to complete many activities of their everyday lives, including activities that include delicate data, for example, e-trade, e-administration, e-saving money, shopping portals and that is only the tip of the iceberg. Web applications have turned out to be unavoidable in all parts of life in view of the simplicity of remote availability for its clients. Yet, as the use of web builds each day, it has likewise brought into light the perilous side of HTML. Security has, along these lines end up noticeably one of the significant concerns with respect to the internet. In this paper, we concentrate on the particular issue of cross site scripting (XSS) assaults. We exhibit a scientific categorisation think about on cross site scripting assaults. We have additionally examined different sorts of vulnerabilities present and dangers delivered for this assault. We have also proposed a static analysis-based system for the detection and removal of the XSS vulnerabilities.
Development of Range Exploring Genetic Algorithms

In mixed-variable problems, various types of variables are mixed, and it is necessary to handle multiphysics such as thermal analysis and fluid analysis. For this problem, the effectiveness of Adaptive Range Genetic Algorithms (ARGA) and Genetic Range Genetic Algorithms (GRGA) has been shown. In the former algorithm, parameter setting is complicated and it is difficult to obtain effective parameter setting. The latter algorithm is less burdensome than the former, but is not comparable to the former in which effective parameter settings are made in terms of convergence and accuracy. Therefore, we examined the development of a new genetic algorithm that makes use of both characteristics. plurality of search ranges are prepared, a search range having the best value is fixed, and solution search is performed. In this study, three things were verified. First, we compare it with PSO, another optimization method, and confirmed that the proposed method is better. Second, we could find the best solution even as the number of variables increased. Finally, we confirmed that the proposed method is superior to ARGA.
Cognitive and motor Analysis of the Tennis Serve Return Expertise under Virtual Environment

In tennis performance analysis, it is important to understand differences between advanced and beginners in order to train beginners in an efficient manner. To understand these differences within the game of tennis, we developed a virtual environment to analyze the responses of advanced and beginners to services. By capturing actual service motions of an expert, it is possible to reproduce virtualized services in the environment and we can make special services by combining ball motion and server motion in addition to usual services. We did two experiments on types of services, cognitive and motor analysis. As a result of motor analysis, there is a difference in the initial response of take-back between the top group of advanced and the other groups. In cognitive analysis, we found a difference between advanced and beginners in anticipation skill. We consider the more skilled players are, the more important the anticipation when playing become.
A Hybrid Model for Detecting Anomalous Ozone Values

One class Support Vector Machine (OCSVM) is remarkably an efficient semi-supervised learning method for classifying one class anomaly in the applications such as fault detection in hardware, document classification, novelty detection, etc. However, many studies showed that due to the presence of anomalies in the training data the boundary measured by OCSVM is biased towards anomalies and thereby results in to low classification accuracy. Classifying ozone measurements obtained from the environment is one such application where the dataset composes huge number of anomalies due to irregularities in the deployed sensors. To this end, this paper presents a technique to improve the anomaly classification accuracy of OCSVM using Deep Belief Networks (DBN). First, the data are pre-processed and then DBN is used for extracting linearly separable data. This outcome is then given to the OCSVM for classification of anomalous ozone measurements in the next step. It is observed from the simulation results that the proposed method shows better classification and achieved high accuracy of 92.71%.
Automatic Article Summary with the Term Frequency-Inverse Document Frequency Algorithm for Information on Elderly Health

Elderly is someone whose age ranges from 60–74 years. At that age, one’s health tends to decline. Various programs have been provided by the Indonesian government, such as providing information, giving brochures, and giving announcements on the health service website. But this counselling is not optimal because of the elderly, tend to be lazy to read this because the eyes have started to farsight. So that the health information provided by dina health can be optimal, we try to make a model that is used to summarize an article so that the article is easily understood by the elderly. To summarize the article, this study uses the TF-IDF algorithm. By using the TF-IDF algorithm, it is expected that the elderly will be easier to understand health articles.
Applying low carbon landscapes at the Premier Polytechnic of Sultan Salahuddin Abdul Aziz Shah, Shah Alam, Malaysia

Campus physical development plan is an efficient tool in shaping campus life to promote a sustainable living community, especially for students. The purpose of this study is to enhance the quality of campus outdoor environment by applying low carbon to green landscapes and sustainable development. Premier Polytechnic of Sultan Salahuddin Abdul Aziz Shah (PSA) was selected as a proposed model for the government's planning to turn the polytechnic into a green campus. Applying and practising green and low carbon development in the PSA campus areas has reached a total reduction of 12.51% carbon sequestration via tree planting. This finding will help to produce a physical model of outdoor planning and design in generating low carbon campuses in Malaysian polytechnics.
An extensible and scalable cloud information monitoring middleware

With the increasing complexity of cloud systems, how to efficiently manage cloud infrastructures has become a challenging task, while an effective monitoring service plays a crucial role to achieve this goal. In this paper, we present an extensible and scalable cloud monitoring middleware called extensible cloud monitoring service (ECMS), which is designed for offering online monitoring service to different monitoring requests from either cloud users or resource providers. In ECMS, a layered monitoring framework is introduced with aiming to cover different aspects of cloud monitoring requests. The implementation of ECMS is evaluated in a real-world cloud test-bed and the results indicate that it can efficiently provide online monitoring services to different kinds of clients and exhibits better scalability and robustness comparing with other monitoring systems.
Language in data processing and ontology engineering for journalism

Trigonometry in algorithms with NLP (Natural Language Processing) can sort word connotations. The Triple structure of grammar in the RDF also extends to semantics in machine learning and big-data processing, but ontologies and a metamodel are essential for meaningful relations across data. They should inform the design of new journalism systems. Major processing platforms used by Facebook and Yahoo are distributed systems, like Hadoop, with resource negotiation features and computations applied to text. NLP used by Google also uses cosine vectors for connotations of words. Data processing already works across structured data for online news tags and unstructured data, like social media tags, with folksonomy characteristics, but social media also uses structured data. However, journalism is yet to come up with semantic systems, from an ontological base. For that end, ontologies across journalism, social media, and public relations and a little OWL to reason about resources can inform AI sub-systems and wider system perspectives.
Studies on Development of Posture Estimation Method for Wear Installing Inertial Sensors and Motion Measurement by Load Control Type Treadmill

This paper proposes the new posture estimation method based on quaternion by inertial sensor system and the motion analysis of load control treadmill. The treadmill is installed the force plate, and the belt speed is controlled by the force feedback. Hence, this treadmill can provide the load to subject. We conducted the measurement experiment and analysis using the inertial sensor system by changing the target load values of the treadmill control. The analysis results indicated the difference of posture by the changing of target load value. The posture estimation method consists of the method for estimating the quaternion on Roll and Pitch angles component and the method for estimating the quaternion on Yaw angle component. We estimated the right femur posture of subject walking on treadmill, and the posture information was indicated. The posture estimation method can be used by limiting the posture information in Roll and Pitch angles component in the environment generating the magnetic disturbance, and the estimation of posture information in Yaw angle component do not use the dip angle. Hence, the posture estimation method can be used to motion measurement and analysis using inertial sensor system.
Artificial Locust Swarm Routing Algorithm: Decision Making in Path Search Problem

The good known problems and solutions nowadays require to implement some new approaches due to changing circumstances. The primary factor of these changes is a drastic growth of data volume and transition from simple data processing models and algorithms to big data ones. According to a limited computing resources (memory, CPU load, time boundaries etc.) there is a need arises to follow required limits but still solve problems involving some larger amount of data. This paper considers the problem of a path search on a graph (also known as a problem of routing on a network) with respect to a large amount of vertices and edges. Difficulty of the problem lies in both many thousands of data entries describing graph topology and necessity to satisfy reasonable time boundaries to perform the whole search on-the-fly. Such complexity requires developing a new and more efficient approaches in decision making during path search to avoid combinatorial explosion (burst) within known approaches to path search. The artificial locust swarm routing algorithm with special assessment procedures was offered as a solution that enables less time and memory consuming approach. Case study analysis was performed with navigation data of airways network.
Exercise instruction support application using 3D pose estimation with monocular camera

This research is aimed at developing a simple support application that allows general users to easily approximate the ideal motion. The application performs 3D pose estimation using “Lifting from the Deep” from monocular camera images that can be easily acquired by the user, and evaluates whether the target motion is close to the ideal teacher motion. The evaluation is performed based on the DTW distance of the difference data of the target time series parameter. The proposed application has been shown to be useful for supporting exercise guidance in rowing experiments.
Onboard ROI selection for aerial surveillance using a high resolution, high framerate camera

With the latest advances in image sensor technology, cameras are able to generate video with tens of megapixels per frame. These high resolution videos streams offer great potential to be used in the surveillance domain. For ground based systems, gigapixel streams are already used with great effect as illustrated by the ICME 2019 crowd counting challenge. However, for Unmanned Aerial Vehicles (UAVs), this vast stream of data exceeds the limit of transmission bandwidth to send this data back to the ground. On board data analysis and selection is thus required to use and benefit from high resolution cameras. This paper presents a result of the CAVIAR project, where a combination of hardware and algorithms was designed to answer the question: ‘how to exploit a high resolution high frame rate camera on board a UAV?’. With the associated size, weight and power limitations, we implement data reduction by deploying deep learning on hardware to find the relevant information and transmit it to an operator station. The proposed solution aims at employing the high resolution potential of the sensor only onto objects of interest. We encode and transmit the identified regions containing those objects of interest (ROI) at the original resolution and framerate, while also transmitting the downscaled background to provide context for an operator. We demonstrate using a 35 fps, 65 Megapixel camera that this set-up indeed saves considerable bandwidth while retaining all important video data at high quality at the same time.
Compilation the Type of Exercise: “Using the Information from the Text” to Improve Reading Comprehension Skill in Teaching Chemistry in English at High School

Reading comprehensions skill is one of the basic skills that is cared about the process of teaching and learning foreign languages, especially scientific subjects. Chemistry is one of them. That determines whether the learner understands the content of the or not, at what level. Improving reading comprehension skills is one of the most important purposes of the study, which helping students to be able to reach suitable content for their level and age by reading books, newspapers, and documents written in English. That helps them to have the opportunity to improve their English skills, especially the English academy. Thereby, students can have habits of learning actively, lifelong learning; practice English academic skills; have confidence in English academic communication and international integration in the future. The paper researches to design the type of exercise: Using the information from the text to practice reading comprehension skills in teaching chemistry in English in high school. The paper also focuses on analyzing students' expressions and assessing the level of using chemical reading comprehension skills in English through specific examples of this type, to develop students' skills more effectively.
The Changing World of Scholarly Books

Digital scholarly book technologies, especially print-on-demand (POD), directly impacted authors, readers, libraries, and publishers. It was possible to significantly reduce traditional lithographic (often called litho, offset, or photo-offset) print runs and rely on small runs, which were more likely to be sold, and then utilize POD for additional sales. This chapter addresses the impact of technological changes on print and digital scholarly books, including patron-driven access, and the impact on academic library collection development and electronic reserves. Attention is paid to the movement for open access scholarly books, libraries publishing academic books, and the growing threats and costs of book piracy to publishers, including Sci-Hub. Scholarly book revenues are presented and discussed.
Remote sensing performance enhancement due to quantum + classical cooperative sensor

Within the last decade, the field of quantum remote sensing has garnered a lot of interest from the radar and communication community. Many papers on this topic have compared the performance of a classical system versus a quantum system. However, the concept of a system using both classical and quantum components in conjunction has not been explored thoroughly. This paper documents the design and simulation of a quantum + classical cooperative remote sensing design in the optical regime. The arrangement uses quantum correlations created by entangled photons in addition to conventional classical waveform correlations. We show that the composite quantum + classical system exhibits increased performance compared to a pure classical system alone.
Theories as Sets of Propositions

In this chapter, the so-called ‘Syntactic Approach’ is outlined that takes theories to be sets of propositions. This account has come under fire for its perceived inadequacy in capturing various aspects of scientific practice. However, recent developments in this approach have indicated how it may have the resources to accommodate these features. These developments are presented and the nature of propositions themselves explored, as abstract entities and fictions. The former raises obvious concerns about ontological inflation, whereas the latter requires changes in how we talk about the practices of scientists. A more economical view is offered by eliminativism and thus this chapter lays the groundwork for discussion of these issues in the rest of the book.
The level of pedagogical culture as the basis of professional activity of the head of the internal Affairs body of the Russian Federation

the article deals with the management activities of the head of the internal affairs bodies of the Russian Federation. The main professionally important qualities of the head influencing his professional activity are highlighted. An ideal model of the pedagogically cultural personality of the head of the internal affairs body of the Russian Federation is proposed, which is a set of those properties that must be formed by the head. This model consists of a number of subsystems that are the main dialectically interconnected and interdependent structural elements; pedagogical skills, active life position, knowledge and understanding of pedagogical phenomena, pedagogical conviction. The article discusses the views of a number of scientists regarding the pedagogical culture of the leader. The pedagogical culture is considered as an integral element of the set of professionally important qualities of the head of the internal affairs body of the Russian Federation. The authors conclude that the mere presence of professionally important qualities in a person cannot ensure the high efficiency of managerial activity if, in the process of training and preparation for managerial activity, they were not developed to the highest level - professionalism.
Pelatihan Keterampilan Berkomunikasi Bahasa Inggris Bagi Pramusaji Kedai di Kawasan Wisata Aik Berik

The English communication skill training for shop waiters in the tourism area of Aik Berik aims to provide theoretical and practical knowledge about the use of a variety of tourism English, expressions and vocabulary commonly used in providing services especially to foreign tourists. This training activity involved the shop waiters around the tourism area of Aik Berik. The mentors of this training were lectures and students majoring in English who had proficient English skills. The training activity took a place around the shop location in the tourism area of Aik Berik, Central Lombok. The training method included material presentation including greetings, showing the way, kinds of food and beverage, menus, price lists, fruits, expressions, mini conversations and the practice of making short conversations among participants regarding the materials that had been taught. The results of the evaluation during the training showed high interest and enthusiasm of the shop waiters in participating in the whole series of training activities ranging from socialization to the implementation of the training. The shop waiters also showed an increase in the capability of using the language, expressions and vocabularies as well as better self-confidence in providing services for tourists. Participants also gave high appreciation to the presence of lectures and students as trainers and hoped that similar activities could continue to be carried out in order to enrich insight and improve communication skills and confidence of tourism activists, especially shop waiters in the Aik Berik tourism area.
Statistical evaluation of the information content of attributes for the task of searching for semantically close sentences

The paper presents the results of evaluating the informative value of quantitative and binary signs to solve the problem of finding semantically close sentences (paraphrases). Three types of signs are considered in the article: those built on vector representations of words (according to the Word2Vec model), based on the extraction of numbers and structured information and reflecting the quantitative characteristics of the text. As indicators of information content, the percentage of paraphrases among examples with a characteristic, and the percentage of paraphrases with a attribute (for binary characteristics), as well as estimates using the accumulated frequency method (for quantitative indicators) are used. The assessment was conducted on the Russian paraphrase corps. The set of features considered in the work was tested as input for two machine learning models for defining semantically close sentences: reference vector machines (SVMs) and a recurrent neural network model. The first model accepts only the considered set of signs as input parameters, the second - the text in the form of sequences and the set of signs as an additional input. The quality of the models was 67.06% (F-measure) and 69.49% (accuracy) and 79.85% (F-measure) and 74.16% (accuracy), respectively. The result obtained in the work is comparable with the best results of the systems presented in 2017 at the competition for the definition of paraphrase for the Russian language (the second result for the F-measure, the third result for accuracy). The results proposed in the work can be used both in the implementation of search models for semantically close fragments of texts in natural language, and for the analysis of Russian-language paraphrases from the point of view of computer linguistics.