Reviewerly/exHarmony huggingface dataset:
Building Real-Time Translation of Arabic Sign Language System using Mechatronic Approach

Each individual use language to communicate with others.The normal person uses phonetic language but the deaf uses sign language.So, all of them are using different language and having trouble dealing with the other.This paper aims to assist the deaf people to communicate with normal people by building real-time translation Arabic sign language system (ArSL).This system has two parts.The first part translates ArSl to phonetic language by using mechatronic approach.A Data Glove (DG) was built by flex sensors to get the hand gesture of ArSL and convert it to digital data.This data is interrupted to speech via speech knowledge base for Arabic phonetic language.The second part translates Arabic phonetic language to ArSL by using speech recognition for Arabic language to translate speech to text, and then translate text to ArSL via video knowledge base for Arabic language.This system is characterized by mobile, real-time translation, easy in usage and its support for Arabic language.
RANCANG BANGUN PENGAMAN MATA DENGAN LAYAR TV BERBASIS ARDUINO UNO

Television is an electronic device which now the majority is owned by all Indonesians. Besides the use of this electronic device, there is a negative impact or thing that is contained in this electronic device. When watching television, eyes often experience tired and dry eyes, this is caused by television monitors that emit radiation rays and cause several eye problems. The radiation beam emitted from each television differs depending on the size of the monitor. This has been proven by tests that have been carried out by related institutions that the distance to watch television for each monitor is different. "The eye has the most important role for the development of human intelligence". Therefore a tool was designed from the problems that arise which will set the ideal distance for watching television using the PIR sensor to determine the distance of the audience from the television which will be forwarded to Arduino UNO as data processing, the output of the tool is in the form of LED, Buzzer and executed by Rellay as Automatic shutdown where the television will be turned off automatically when the audience enters a distance of less than 3 meters and within 1 minute with the LCD indicator displaying text data in the form of "Danger" and the television will not be turned off automatically if the viewing distance is more than 3 meters and the LCD will display text data in the form of "SAFE".
Analysis and design of a GIS-enabled virtual public meeting space using UML for participatory municipal planning

The main aim of this research is to develop and test Web-based Public Participation Geographical Information Systems (WebPPGIS) to enable public involvement and participation in municipal planning and decision making. This objective is based on the belief that by providing citizens with access to information and data in the form of maps and visualisations they can make better informed decisions and it can immerse them into the spatial decision making process. This thesis presents a prototype implementation serving for spatially related discussions which is based on the GeoVPMS (GIS-based Virtual Public Meeting Space) model introduced by (Li et al., 2007). Moreover, a prototype has been analysed, designed and implemented using UML (Unified Model Language) approach to demonstrate a Web GIS-based architecture with utilization of various open source GIS and other OSS (Open Source Software) tools. In addition, it depicts a cost effective model of n-tier (multi-tier) Web integrated application prototype that can facilitate online public participation in municipal planning and development processes. Its components include online GIS-based participation forum as well as notification system enhance communication during spatially-related discussions in municipal planning and manage all kinds of notice among members as well as general public participants. Furthermore, the spatial data handling components used in this prototype is designed to help the public to explore the spatial contexts related to the issues under planning with and without addressing the form, whereas this contribution makes the protytope more effective and successful. In addition, the prototype is demonstrated with a scenario of public participation in spatial planning using Region of Peel's data.
Power Quality Recommendations in Mushroom Farm

Mushroom development is one of the most gainful agribusiness that can be begun with least speculation and space. Mushroom cultivating in India is developing consistently as an elective wellspring of salary. Here is the finished direction on paddy straw mushroom, shellfish mushroom and catch mushroom generation in India. This paper proposes power quality recommendations in small scale mushroom farm located in Sulur, Coimbatore
Pengaruh Molaritas dalam Teknik Spray terhadap Performa Fotodetektor Berbasis ZnO

ZnO:Fe thin films has been successfully carried out by a simple spray pyrolysis method. The thin film was grown on ITO (Indium Tin Oxide) substrate with a metal-semiconductor-metal (MSM) structure. ZnO:Fe thin film characters as photodetector application include morphological structure, and electrical properties. SEM image results show the molarity difference can affect the particle size. The Current-Voltage (I-V) characterization shows that different solvent effects and molarity give different sensitivity. Sample ZnO:Fe 0.5M Ethanol has the highest sensitivity compared to other samples because it has a fairly low current and high bright current.
Voice Pathology Identification using Deep Neural Networks

The human voice construction is a complex biological mechanism capable of Changing pitch and volume. Some Internal or External factors frequently damage the vocal cords and change quality of voice or do some alteration in the voice modulation. The effects are reflected in expression of speech and understanding of information said by the person. So it is important to examine problem at early stages of voice change and overcome from this problem. ML play a major role in identifying whether voice is pathological or normal in nature. Voice features are extracted by Implementing Mel-frequency Cepstral Coefficients (MFCC) method, and examined on the Convolutional Neural Network (CNN) to identify the category of voice
Sistem Informasi Peminjaman Laboratorium pada Cross-Platform dengan Metode Prototyping (Studi Kasus: Politeknik Caltex Riau)

The Laboratory information system is one of the information systems that was built by the BSTI of Polytechnic Caltex Riau to provide information about laboratory lending. This system is also being intended to the person who is in charge of approving laboratory lending such as the head of the laboratory, and laboratory assistant in case of monitoring some of the laboratories that are lent. However, the features provided by this information system are not enough to support the process of laboratory lending. Therefore, a mobile application with the quality of Cross-Platform was built, in other words, it can be used on iOS and Android platforms. One of the technologies that can be used to build a cross-platform application is the React Native. This application was built with a prototype method that is used to get more perspective points from users with interviews and can simplify the development of this application to be effective. This application was developed with Laravel, Firebase Authentication, and MySQL. Because there are several problems with the old system, an application that offers several solutions consists of avasisten instruktur laboratoriumbility in the lending laboratory, receiving legal paper of laboratory lending, and receiving notification were developed in this study. The main feature provided in this application is the evidence letter of laboratory lending that doesn’t have to be printed so that can reduce paper usage. As a result, 94.1% of respondents stated that this application’s features can run well and make it easy for college students to propose laboratory lending.
Proactive Fault Tolerance using Heartbeat Strategy for Fault Detection

Failure is something which causes services on the cloud to go down for some time period. Most of the times instead of recovery and repair, we opt for virtual machine migration where failover of the failed service is done on some other running virtual server so that the service is revived. Virtual migrations and recovery mechanisms consume a lot of energy and many approaches are implemented to make them energy efficient. Failure Detection is a topic of equal importance and comes under fault tolerance. Failure detection if done properly can be more effective and energy/cost saving than fault recovery. Heartbeat strategy is one such failure detection approach where live processes send an “I am alive” message to the host device at some pre-defined fixed intervals which ensures that the process is running fine. In this paper, we propose to mark the nodes whose processes have failed to send the heartbeat message and prepare a count (confidence factor, α) for the same. In primary testing, if this confidence factor reaches a specific threshold then that particular node is sent for confidence testing (second level failure detection testing using a different time sequence of heartbeat message arrival) and later marked for failure recovery (if found faulty). Fault recovery techniques are then applied to it so that it can be corrected and reused and the current jobs can be migrated to the better node during the recovery period. If the confidence factor, α is below the threshold value then no action is taken and only network parameters and connections can be rechecked. This method will re-ensure the trust on heartbeat strategy for fault detection and save the device from failure.
Integrating SIFT Features with Multivariate Gaussian Distribution to Enhance CBIR Performance

CBIR gained substantial importance in the recent fifteen years.Variety of modern systems have designed to effectively retrieve images from database and recognition of the objects in computer vision.Among all methods, Histogram of Gaussian (HOG) and Scale Invariant Feature Transform (SIFT) are standard procedures for attaining the features of the image.The image features achieved by SIFT are helpful in several applications, but these features prone to zero-order statistics as they derived from histogram quantities.This results in; there lacks the natural mechanism of high descriptiveness of image features.A novel method is developed based on Multivariate Gaussian distribution which establishes the relation among SIFT features in the neighbourhood.The exciting part of this approach lies in representing the non-linear Gaussian space into linear space as the Gaussian space is in Riemannian manifold.The Gaussian space can be mapped into linear Euclidean space using Lie group theory.The tests were conducted systematically on Caltech-101 and ZANG image databases to endorse this approach.The experiments demonstrated that a significant amount of the CBIR system performance has improvised with the proposed approach.
Cops, robbers, and barricades

Cops, Robbers, and Barricades is a new variant of the game on graphs, Cops and Robbers. In this variant, the robber may build barricades that restrict the movements of the cops. The minimum number of cops required to capture the robber on a graph G is called the barricade-cop number, denoted cB(G). If cB(G) = 1, then G is called barricade-cop-win. The game can be generalized so that the robber may build b(k)-many barricades on vertices during her kth turn, in accordance with barricade rules that dictate the permissible positions of these barricades. The barricade-cop number is determined exactly for complete graphs, cycles, and paths, and we provide bounds on trees and locally-path-like graphs. We compare and contrast variants on the barricade rules, and give an algorithmic characterization of barricade-cop-win graphs with any set of barricade rules.
Improving Fuzzy Network Models For the Analysis of Dynamic Interacting Processes in the State Space

Most promising approaches that deal with dynamics of interacting processes do not take into consideration the fuzzy properties of all components and the state space of the model, the set of parameters and features of the subject domain. This leads us to create integrated systematic approach to solve these problems as a sole task for developing new effective mathematical fuzzy models that take into account the advantages of existing models, formal criteria and approaches, modern technologies, methods and tools for solving theoretical and practical tasks. The extended fuzzy Petri Networks and their further development are proposed with new capabilities to solve the analysis and adaptation problems depending on the peculiarities of the processes of the subjected area. The proposed approach which is mainly based on some general provisions for the development of color Petri nets and fuzzy colored Petri dishes, tends to create a new class of fuzzy Petri nets to effectively solve complex tasks while reducing the dimension of the model and complexity. The implementation of the model is focused on the use of modern information technologies. Colored Petri nets can be considered an effective tool for determining the size of network models. Limitations on the homogeneity of logical operations are presented taking into account fragments of fuzzy network models. This can be done by practical use of the presented models..
Recurrent Neural Network for Classifying of HPC Applications

This paper presents a novel approach using deep neural networks in HPC software application domain. The approach uses Recurrent Neural Network (RNN) and more specifically Long Short-Term Memory (LSTM) models to classify jobs running on HPC clusters into different application families. This is a mandatory step in order to optimize their run time environment for our future work. Our approach was inspired by the recent advancement in Deep Learning in the AI area. We implemented our models on a HPC cluster and took advantage of the GPU to accelerate the training process. The evaluation of our models on a database of synthetic jobs (with more than 500 runs) gave good results for the classification of short duration jobs (less than 1 hour). In comparison with the conventional approaches such as Support Vector Machine and Hidden Markov Model, the stateful LSTM implementation improves significantly the classification accuracy for long duration jobs (more than 2 hours).
THE FORMATION OF THE FUTURE SPECIALIST PROFESSIONAL CULTURE IN THE EDUCATIONAL SPACE OF THE UNIVERSITY

The article highlights the theoretical foundations on the formation of future specialist professional culture the in the field of professions «person-person».It is established that this is a purposeful, specially organized influence on the individual to improve his/her motivational sphere, value orientations, professional knowledge, skills and abilities, ability to empathy and reflection.This problem was studied by the author from the view of several scientific approaches: axiological, cultural, competence, activity.Based on the definitive analysis, the «professional culture of a specialist» concept is formulated, which is considered as a combination of professional and personal values and qualities of a specialist, his/her professional competence, which ensure highly effective fulfilment of professional duties based on reflection and self-development.Structural and semantic analysis allowed identifying components of the professional culture of future professionals i.e. value, cognitive, personal, and reflexive.The experimental research diagnostic tools of a formation condition of the future specialist professional culture in educational space of university are developed and tested: criteria, indicators, levels, techniques which reflect integrity of the investigated phenomenon structural components.The organizational and pedagogical conditions for the formation of the professional culture of the future specialist in the field of professions «person-person», which were implemented in the educational environment of the university, have been developed and theoretically grounded.An experimental study was fulfilled and the effectiveness of the proposed organizational and pedagogical conditions was proved.
A bidirectional associative memory based on cortical spiking neurons using temporal coding

In this thesis, we proposed a spiking bidirectional associative memory (BAM) using temporal coding. The information processing in biological neurons is beyond of[sic] that applied in the current Artificial Neural Networks (ANNs). The coding scheme used in ANNs known as “mean firing rate” cannot answer the fast and complex computations occurring in the cortex. In biological neural networks the information is coded and processed based on the timing of action potentials. To improve the biological plausibility of the standard BAM, we employed spiking neurons for its processing units, and information is presented to the BAM in the form of temporal coding. The neurons employed in the model are heterogeneous, and being able to generate various spike-timing patterns. Genetic Algorithm and Co-evolution are used for training, and the experiment results of the proposed BAM are compared to those of the standard BAM. The results show improvements in recall, storage capacity and convergence which are of interest to design a BAM.
Defining the Engine Design Process

Shortening design lead times while maintaining product quality requires careful planning of the design process. This paper describes a technique used at a major automobile manufacturer to create a simple, flexible process to do just that for their engine design group. The design organization's communications infrastructure was tailored to assure that design information was reliably transmitted through the organization. The mechanism used to achieve this accounts for (a) both component and system perspectives, and (b) the unpredictable nature of the flow of design information by treating unpredictability as an acceptable, even desirable, state of affairs.
Introduction to the Minitrack on Text Mining in Big Data Analytics

This mini-track recognizes the reality that global collaboration systems, social media, and information systems of all types, generate enormous amounts of unstructured textual data, including: system logs, email archives, websites, blog posts, meeting transcripts, speeches, annual reports, published material, and social media posts.While this unstructured textual data is readily available, it presents tremendous challenges to researchers trying to analyze these large bodies of text with traditional methods.Text mining in big data analytics is an increasingly important technique for an interdisciplinary group of scholars, practitioners, government officials, and international organizations.For example, the American Association for the Advancement of Science (AAAS) launched a new competition in 2014 on Big Data and Analytics within its highly competitive senior executive branch fellowship program.
A Factory Analysis on the Proportion of Defects Reduction in The Animal Feed Pellet Production Process using Design of Experiment Analysis

The aim of this research is to reduce a number of defects during a feed pellet production process to improve customer satisfaction. A factory case study produces the feed pellets for several species such as food for pigs, chickens, and ducks. Production data from January to June 2017 manufacturing found that the manufacturing defects rate were about 3.32%. The data showed that the overall defects originated from different problems; 1) cracked or broken food; 2) high humidity; 3) distorted of product color; and 4) an ingredient error, respectively. Statistical methods, design analysis, and cause analysis techniques e.g. the Ishikawa diagram, Pareto chart, and FMEA (Failure Mode and Effects Analysis) were applied to help the factory to identify the main root cause of the defects and the potential failure modes of the factory case study. Due to an increasing number of complaints, this study only concentrated on the duck feed pellet production process. The study was divided into two parts: finding the root cause of the defects, which are the most critical factors for further analysis, and applying an experimental statistical design to decrease the number of defects during the duck pellet production process. The problem with cracked or broken pellets (dust) was found as the main factor affecting the production defects. Results showed that the main factors contributing to the amount of dusk in the duck feed productions came from three factors as follow: the thickness of die, distance between compression rollers and die, and time and temperature of mill machine needed during compressing the duck feed pellet production. Both the fractional factorial experimental design, 2k and 3k, were used to evaluate the influence of each factor on the duck feed production defects. The results by using the factorial 2K experimental show that the most important variable in duck pellets production were thickness of the die, distance between compression rollers and die, and temperature of mill machine needed during compressing the duck feed pellet production while time was not an interaction effect in this problem. The 3k factorial design was used to determine the interaction effects for the duck pellets production process. The experiment was ran and tested for 3 months. The final outcomes showed a significant reduction of defects from 2.51% to 1.09% (P&lt;0.01). The results indicated that thickness -20 mm. of the die, 0.05 mm of distance between compression rollers and die, and 95 degree Celsius of temperature of mill machine needed during compressing the duck feed pellet production would be the most appropriate set of pelleting machine for the duck production process case study.
Da Internet alla galleria d’arte, ovvero gli esperimenti traduttivi con gli emoji

The article presents various forms of intersemiotic and intrasemiotic translations which use emoji signs, i.e. iconic characters employed in digital communication by users of popular Internet applications and platforms (e.g.Twitter, Facebook and Messenger).The use of emojis is becoming more and more widespread, as evidenced by the increasing number of text messages and works of art which consist exclusively of these characters.At the same time, attempts are being made to translate more extensive verbal texts (including literary works) into the nonverbal system of emojis.One of the feats such efforts have so far accomplished is an experimental translation of Carlo Collodi's Pinocchio (La storia di un burattino), a classic of children's literature, from Italian to emojitaliano (a system codified by the participants of the experiment), which was developed on Twitter and appeared in print in 2017.Emoji-based works of art have also found their way to art galleries.The Garden of Emoji Delights, a triptych by Carla Gannis, is a conspicuous example of intrasemiotic translation (from a non-verbal code to another non-verbal code) involving Hieronymus Bosch's celebrated painting.Such applications of emoji are associated with paradigmatic changes in the humanities, which are related to the digital, iconic and performative turns and to today's remix culture (Lawrence Lessig, Lev Manovich).