Reviewerly/exHarmony huggingface dataset:
Part-of-Speech Annotation Challenges in Marathi

Part of Speech (POS) annotation is a significant challenge in natural language processing. The paper discusses issues and challenges faced in the process of POS annotation of the Marathi data from four domains viz., tourism, health, entertainment and agriculture. During POS annotation, a lot of issues were encountered. Some of the major ones are discussed in detail in this paper. Also, the two approaches viz., the lexical (L approach) and the functional (F approach) of POS tagging have been discussed and presented with examples. Further, some ambiguous cases in POS annotation are presented in the paper.
Convergence Speed up Using Convolutional Neural Network Combining with Long Short-Term Memory for American Sign Language Alphabet Recognition

In sign language alphabet recognition problem, the scope of study limits only static hand gestures which not cover all gestures of sign language. This paper aims to find an approach for recognizing the static and dynamic gestures of American Sign Language (ASL) alphabet and apply GANs to generates synthetic images to increase dataset size. The proposed method combines convolutional neural networks (CNN) with long short-term memory (LSTM) networks to extract the features and classify images of the American Sign Language alphabet along various dimensions. With two consecutive images, this proposed method has an accuracy of over 97% and on 1D vector images, accuracy reaches 90% in large batch size when were tested on various batch sizes and epochs. Thus, this method is more appropriate for two consecutive images than on 1D vector images. For dynamic features, the performance of the proposed CNN-LSTM on two consecutive images is lower than the simple CNN at the beginning epoch, but the accuracy converged quickly, and finally, it reaches to the accuracy of simple CNN in a few epochs. Our proposed approach offers good results and better than simple CNN for dynamic ASL alphabet gestures, especially on 1D vector images.
PerioClust: A Simple Hierarchical Agglomerative Clustering Approach Including Constraints

PerioClust is a hierarchical agglomerative clustering (HAC) method including temporal (resp. spatial) ordering constraints. This new semi-supervised learning algorithm is designed to consider two potentially error-prone sources of information associated with the same observations. One reflects dissimilarities in the “feature space” and the other the temporal (resp. spatial) constraint structure between the observations. A distance-based approach is adopted to modify the distance measure in the classical HAC algorithm using a convex combination to take into account the two initial dissimilarity matrices. The choice of the mixing parameter is, therefore, the key point. We define a criterion based on cophenetic distances, as well as a resampling procedure to ensure the good robustness of the proposed clustering method. The dendrogram associated with this HAC can be interpreted as the result of a compromise between each source of information analysed separately. We illustrate our clustering method on two real data sets: (i) an archaeological one containing temporal information, (ii) a socio-economical one containing geographical information.
SISTEM PENJADWALAN MENGAJAR GURU BERBASIS ANDROID PADA YAYASAN PERGURUAN HARAPAN STABAT

The arrangment of teaching schedule at Yayasan Perguruan Harapan Stabat is still not effective as it is done manually. The scheduling process is less accurate, takes a long time in terms of scheduling and delivering information to teachers and students. There are a number of teaching schedules for teachers that are not suitable and clashes occur. In this research, an Android and web application-based system is designed to solve these problems. The system is designed using Android Studio to build Android-based applications, while on the backend for schedule management a web-based system is provided using the PHP programming language and MySQL database. The new system designed can be accessed online by teachers and students so that the delivery of teaching schedules is more effective, fast and accurate.
Literary Work as Teaching Materials:A Study of Students and Lecturers Needs Analysis

— The orientation of learning English in higher education is to prepare professionals students andskilled workers who have language skills applicants for job positions in the public and private sectors.Looking at the above phenomenon, English lecturers in higher education are expected to have the ability toanalyze student needs so that they can design appropriate teaching and learning processes, especially inEnglish language skills (Listening, Speaking, Reading, and Writing) and components in English (Grammar& Vocabulary). The purpose of this study is to identify some factual information about the needs of highereducation students in learning English and analyze the suitability of the content in English textbooks withstudents in higher education in terms of student needs (students' needs). The participants of this course werethe first semester students of Education Study Program in the 2017/2018 academic year total fifty-twostudents and three English lecturers. The descriptive qualitative data of this study were obtained by doing aninterview and distributing a questionnaire to the participants. The data obtained were about the problemsusually faced by the students and the needs or preferences during the English language course at AlAsyariah Mandar University of Indonesia. The data from the needs analysis were used to design the syllabusand to develop the materials. The data finding in this study shows most of the students and lecturers believeby using literary work film, song, short story, poem, novel and drama improve their English competence inlearning English as a foreign language.
Discussion of How to Reach a Root Failure Point in Open Systems Problem Using Function Chain

Discussion how to reach a root failure point in open systems problem. In most case, the analyzing process depends on the experiences of support engineers, but there is no known way for trouble shooting in such circumstance. The function chain method enables to draw down a problem visually to show the direction of analyzing process. It also provides efficient manner to drill down the problem to detail of failure point. Secondary we propose the method how to judge whether the components on the function chain have been failed or safe. It stands on to compare the log information that captured pre and post of the problem. Finally, we show the efficiency of function chain by analyzing the actual open systems problem reports. The result proofs that there is a superiority of the function chain with comparison method rather than Fault Tree Analysis. Also, it shows the function chain method reduces the number of analyzed components. There is a possibility that one function chain has multiple failure points. However, the majority of function chains have a single failure point. Therefore, in this study, we only discuss the single failure problem.
Identity Theft and Prevention

E-commerce users are exposed to identity theft, which is perpetrated in many ways: through unauthorized intrusions such as phishing, spoofing, use of a Trojan-horse, and injection attacks. Very few initiatives have been taken to introduce intensive measures to educate people about the dangers of identity theft. By employing theoretical integration, this research identified risk behaviors and protective behaviors that influence the likelihood of being a victim of identity theft and proposed a new model to minimize the occurrence of identity theft for e-commerce users. The model was evaluated using quantitative methods on 386 university students in Malaysia. Structural equation modeling (SEM) was used to determine how 92 human risk and protective behaviors influence identity theft. The result indicated that offline risk and account monitoring are the most influential factors in the occurrence of identity theft.
Solar gain augmentation using planar reflectors on the west and east sides of a flat plate collector

With widespread calls for improved development of clean energy sources, solar thermal collectors are continuously receiving renewed attention being one of the easiest means of converting solar radiation to heat. Planar reflectors are often employed in flat plate collectors in order to boost solar radiation captured. Significant improvement in efficiency can be achieved if reflectors are positioned optimally. A model that predicts the additional solar gain using reflectors on the eastern and western side of a collector was developed using engineering equation solver (EES) and transient system simulation (TRNSYS) software. Â Simulations were carried out for various reflector angles using Zaria weather data and the monthly reflector tilts for optimum solar gain were obtained through parametric studies. The results show that solar radiation received by collector can be enhanced with the reflectors throughout the year except for the month of July for both reflectors and August for the eastern reflector. Also, the magnitude of contribution from reflector on the western side of the collector is higher.
Construction of an Efficient Divided/Distributed Neural Network Model Using Edge Computing

Modern deep learning has significantly improved performance and has been used in a wide variety of applications. Since the amount of computation required for the inference process of the neural network is large, it is processed not by the data acquisition location like a surveillance camera but by the server with abundant computing power installed in the data center. Edge computing is getting considerable attention to solve this problem. However, edge computing can provide limited computation resources. Therefore, we assumed a divided/distributed neural network model using both the edge device and the server. By processing part of the convolution layer on edge, the amount of communication becomes smaller than that of the sensor data. In this paper, we have evaluated AlexNet and the other eight models on the distributed environment and estimated FPS values with Wi-Fi, 3G, and 5G communication. To reduce communication costs, we also introduced the compression process before communication. This compression may degrade the object recognition accuracy. As necessary conditions, we set FPS to 30 or faster and object recognition accuracy to 69.7% or higher. This value is determined based on that of an approximation model that binarizes the activation of Neural Network. We constructed performance and energy models to find the optimal configuration that consumes minimum energy while satisfying the necessary conditions. Through the comprehensive evaluation, we found that the optimal configurations of all nine models. For small models, such as AlexNet, processing entire models in the edge was the best. On the other hand, for huge models, such as VGG16, processing entire models in the server was the best. For medium-size models, the distributed models were good candidates. We confirmed that our model found the most energy efficient configuration while satisfying FPS and accuracy requirements, and the distributed models successfully reduced the energy consumption up to 48.6%, and 6.6% on average. We also found that HEVC compression is important before transferring the input data or the feature data between the distributed inference processes.
Design and Implementation of Universal Platform for Teaching Experimental of Microcomputer Relay Protection

In view of the problem that the microcomputer relay protection teaching experiment needs to use multiple devices to teach separately, this paper develops a universal platform for implementing various types of microcomputer relay protection experiments. The platform structure is based on the modular principle. It can be programmed to input various microprocessor relay protection device parameters and control logic, so as to be compatible with the virtual and real connectors of the microcomputer relay protection device. The operator can simulate various microcomputer relay protection devices in the platform by selecting different modules and configuring different parameters. The platform reduces teaching investment and enhances the operator's awareness of the experimental principle of microcomputer relay protection.
Structural and geomechanical models of gold deposits in the Amur region (Russia)

A large number of gold deposits are concentrated in the Russian part of the Amur river basin. Gold units are characterized by various formation conditions, and, due to this, by a variety of structural factors, as well as stability of rock masses. Structural features, qualities and properties of ore-bearing rocks determine the specifics of choosing mining systems for the units. Geological and geomechanical data obtained in the course of exploration and exploitation of objects allowed us to identify groups of deposits with similar genetic conditions and form common features of their development through the development of appropriate models. There are five major groups of gold deposits. They are represented by deposits formed in epiplatform conditions of young volcanism, in intrusive-tectonic and intrusive-dome structures of Cretaceous age, in metamorphic domes, as well as in tectonic wedges. The selected groups of deposits are localized in masses with different rock stability. In accordance with these features, we developed structural and geomechanical models of gold deposits in the Amur region. The models are used for correcting the methodology of studying deposits and justifying rational methods of their development.
Contemporary Research Challenges and Applications of Service Oriented Architecture

Service Oriented Architecture (SOA) is distributed architectural framework that provides service-based solutions for improving the effectiveness of enterprise’s IT infrastructure. In this framework, technical and business processes are implemented as services. A service is an independent software application that has been designed to perform a specific function with emphasis on loose coupling between interacting services and their components. SOA permits developers to utilize many of the resources from existing services to form the distributed applications. This study has investigated to highlight the emerging issues of SOA such as service structures advancement, requirements of evolution for current age applications like mobile-cloud, medical and mechanism for interoperable operations. The paper also uncovers the practical application domains of SOA. It has identified research attentions in these domains with detection of issues to carry further research to overcome constraints in current scenarios.
KEBERKESANAN PEMBELAJARAN MODUL BOROS MAAN BERASASKAN MULTIMEDIA INTERAKTIF TERHADAP PENCAPAIAN MURID DALAM KOSA KATA [THE EFFECTIVENESS OF INTERACTIVE MULTIMEDIA-BASED BOROS MAAN LEARNING MODULE OVER STUDENT VOCABULARY]

Vocabulary is a very important element in learning the Kadazandusun language. Without sufficient vocabulary, students will not only have the difficulty to understand the language either in written text or when communicating during the Kadazandusun Language teaching and learning session, but will also have difficulty in mastering other language skills. Therefore, this study was conducted to study the effectiveness of interactive multimedia based boros maan modules on student vocabulary accessibility. This study was a quasi-experimental study. Quantitative approaches are used in conjunction with pre- and post-test instruments. A total of 20 Year Four pupils at a school in Tambunan district were sampled for this study. The mean of the post-test was 8.15. Implications of the study show that using a multimedia interface based on interactive multimedia can serve as a platform for students to learn vocabulary and can help teachers to add teaching aids to Kadazandusun subjects. In conclusion, interactive multimedia based wasteful modules have a positive effect as vocabulary learning tools.
Towards Personalized Aesthetic Image Caption

Image captioning (IC) is a commonly-used technique for generating textual image description, which finds its applications on semantic image retrieval and multi-modal image understanding, among many others. This paper focuses on an important IC method specialized for generating aesthetic descriptions of images, i.e., aesthetic image captioning (AIC). Despite some effectiveness of initial work on AIC, their performances are inherently limited due to a lack of consideration of user preferences on aesthetics and better aesthetic feature, making it unusable for real-world applications where human users present a large variation on evaluating visual aesthetics of images. To tackle this, we propose a novel personalized aesthetic image caption (PAIC) approach for capturing and incorporating user preferences for AIC tasks. Our approach mainly contains Aesthetic feature Extraction Network(AEN), User Encoder network(UEN) and a personalized image caption model. AEN is designed to extract more expressive feature, UEN is introduced for learning the user vector from the limited information in our AVA-PCap dataset. Personalized image caption model is constructed to generate the caption when given the user id and photo pairs. The experimental results show that our methods outperform baselines by 10% , which is encouraging for a first step towards personalized aesthetic image caption.
Demonstration of 8-Step Single-Photon Quantum Walk using 32 x 32 Reconfigurable Silicon Photonics Switch

We implemented a reconfigurable quantum-walk circuit on a 32*32 silicon photonics switch system. The 1 to 8-step quantum walk exhibited a long-term stability over 45 hours with clear single-photon interference for 10-nm-bandwidth heralded single photons.
Online Persian/Arabic Writer Identification using Gated Recurrent Unit Neural Networks

Conventional methods in writer identification mostly rely on hand-crafted features to represent the characteristics of different handwritten scripts. In this paper, we propose an end-to-end model for online text-independent writer identification on Persian/Arabic online handwritten scripts by using Gated Recurrent Unit (GRU) neural networks. The method does not require any specific knowledge for handwriting data analysis. Because of the exclusive ability of deep neural networks, we just represented our data by Random Strokes (RS) representations, which are differential horizontal and vertical coordinates extracted from different handwritings with a predefined length. This representation is a context independent representation. Therefore, this writer identification at RS level is more general than character level or word level in identification systems, which require character or word segmentation. The RS representation is then fed to a GRU neural network to represent the sequence for final classification. All RS features of a writer are then classified independently, and in the final stage, the posterior probabilities are averaged to make the final decision. Experiments on KHATT database, which consists of online handwritings of Arabic writers, gave us 100% accuracy on 10 writers and 76% accuracy on 50 writers, which is much better than previous works on online Persian/Arabic writer identification.
A thermal quantum classifier

We find that the additivity of quantum information channels enables one to introduce a quantum classifier or a quantum decision maker. Proper measurement and sensing of temperature are of central importance to the realization of nanoscale quantum devices. Minimal classifiers may constitute the basic units for the physical quantum neural networks. We introduce a binary temperature classifier quantum model that operates in a thermal environment. In the present study, first the mathematical model was introduced through a two-level quantum system weakly coupled to the thermal reservoirs and it was demonstrated that the model faithfully classifies the temperature information of the reservoirs in the thermal steady state limit. A physical model by superconducting circuits composed of transmon qubits was also suggested.
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization

Conventional DNN training paradigms typically rely on one training set and one validation set, obtained by partitioning an annotated dataset available for the purpose of training, namely gross training set, in a certain way. The training set is used for training the model while the validation set is used to estimate the generalization performance of the trained model as the training proceeds to avoid over-fitting. There exist two major issues in this training paradigm. Firstly, the validation set may hardly guarantee an unbiased estimate of the generalization performance due to potential mismatching with the test data. Secondly, training a DNN corresponds to solve a complex optimization problem, which is prone to getting trapped into inferior local optima and thus leads to the undesired training result. To address these issues, we propose a novel DNN training framework. It generates multiple pairs of training and validation sets from the gross training set via random splitting, trains a DNN model of a pre-specified network structure on each pair while making the useful knowledge (e.g., promising network parameters) obtained from one model training process to be transferred to other model training processes via multi-task optimization (i.e., a recently emerging optimization paradigm), and outputs the best one, among all trained models, which has the overall best performance across the validation sets from all pairs. The knowledge transfer mechanism featured in this new framework can not only enhance training effectiveness by helping the model training process to escape from local optima but also improve on generalization performance via implicit regularization imposed on one model training process from other model training processes. We implement the proposed framework, parallelize the implementation on a GPU cluster, and apply it to train several widely used DNN models. Experimental results on several classification datasets of different nature demonstrate the superiority of the proposed framework over the conventional training paradigm.
Malaria parasite density estimation using actual and assumed white blood cell counts in Plasmodium falciparum-infected patients in Dak Lak province, Vietnam

Estimating malaria parasite density is necessary for disease management, clinical trials and drug efficacy studies. This study was conducted to compare the malaria parasite density among patients using actual white blood cell (WBC) and the assumed WBC counts (8.0 ×109/l). A cross-sectional study was conducted in Dak Lak, Vietnam. WBC and asexual malaria parasite counts were performed on blood films. Eighty patients were enrolled. The mean (±SD) of WBCs was 5.84±1.63 ×109/l. The median (IQR) of parasite density using the assumed WBCs (8000 cells/µl) (11057.50 [2388.50-34210.75] rings/µl) was significantly higher than that estimation based on the actual WBC count (6898.61 [1892.12-24623.10] rings/µl) (p
An obstacle avoidance design of UAV based on genetic algorithm

Abstract In order to improve the effect of obstacle avoidance, a PID control algorithm combined with genetic algorithm is proposed. By establishing the mathematical model of UAV, the auxiliary control system of UAV is analyzed, and then the control system is optimized by genetic algorithm. Taking STM32 as the control platform, the obstacle avoidance experiment was carried out by building the experimental platform. The experimental results show that the algorithm can avoid obstacles effectively.