Reviewerly/exHarmony huggingface dataset:
An Intelligent Assessment System of Teaching Competency for Pre-service Teachers Based on AHP-BP Method

The Education Informatization 2.0 Action Plan issued by the Chinese Ministry of Education calls for further improvement on teachers’ teaching competency in the era of "Internet+ Education", which also brings new requirement for the training of pre-service teachers who are enrolled in university teacher education courses. This study put forward three principles to construct a new framework to assess the teaching competency for university students majoring in teacher education. Developed on the basis of the Analytic Hierarchy Process (AHP) methods, the assessment system included six dimensions contributed by 21 indicators. This system is supported by Back Propagation (BP) neural network and data label technology to realize intelligent assessment of teaching competency. The trial results of real data samples showed that the system could meet the requirements of large-scale assessment for Pre-service students in higher education sectors. The assessment framework could well cover pre-service teachers’ qualification standards, facilitate the assessment of their teaching competency, and provide a clear guidance for effective trainings for the pre-service teachers to meet the requirement of future teaching supported by the Internet technology.
Discrete mission planning algorithm for air-sea integrated search model

Abstract The selection of optimal search effort for air-sea integrated search has become the most concerned issue for maritime search and rescue (MSAR) departments. Helicopters play an important role in maritime search because of their strong maneuverability and hovering ability. In this work, the requirements of maritime search were analyzed, from which a global optimization model with quantitative constraints for vessels and aircraft was developed by setting the least search time as single-objective optimization problem; then the improved Dinkelbach algorithm was used to solve the continuous programming problem, and the discrete mission planning algorithm was used to improve the calculation accuracy of search time and area. A case study shows that the errors in calculating search time and area decrease from 12–18 min to 36 s and from 76.5 to 0.45 n mile 2 , respectively. The results obtained from the discrete mission planning algorithm can provide better guidance for MASR departments in selecting optimal search scheme.
Option for optimal extraction to indicate recognition of gestures using the self-improvement of the micro genetic algorithm

The hearing-impaired community uses gestures to communicate. Gestures can also be used in interactions between man and computer. However, gestures become increasingly complicated in a comparatively complex environment. A recognition algorithm with a choice of function based on the improved genetic algorithm is proposed to improve the ability to identify gestures. The recognition process includes retailing, extraction, and feeding functions before classifying the neural network. After learning gestures, the proposed method is compared with traditional methods that use the classic genetic algorithm. The proposed method demonstrates the effect of optimization and sensitivity of the function.
A blockchain based approach for improving transparency and traceability in silk production and marketing

Abstract Blockchain technology may be described as a distributed digital ledger database for transparent and immutable recording of transactions occurring between a groups of nontrusting parties. Transactions can be implemented without any intermediaries. Given this potential, it can play a great role in enhancing Supply chain management. Silk production and marketing has exponentially increased and spanned across the world in recent years. To redefine current silk production and marketing industry, there is a need to strengthen its supply chain management process. With Blockchain, government, farmers, weavers, and sellers can collaborate on a single system without any inconvenience. They create an immutable chain of transactions which can be verified by any of the parties. There is an immutable shared ledger that no one can modify. This research presents a private and permissioned application that uses Blockchain and aims to automate the shipping processes among different participants in the supply chain ecosystem. Data in this private ledger is governed with the participants’ invocation of their smart contracts. The performance of the proposed work is evaluated in terms of the transaction throughput, transaction average latency, resource utilization with a fixed and variable rate of transactions.
Predicting the Breed of Dogs and Cats with Fine-Tuned Keras Applications

The images classification is one of the most common applications of deep learning. Images of dogs and cats are mostly used as examples for image classification models, as they are relatively easy for the human eyes to recognize. However, classifying the breed of a dog or a cat has its own complexity. In this paper, a fine-tuned pre-trained model of a Keras’ application was built with a new dataset of dogs and cats to predict the breed of identified dogs or cats. Keras applications are deep learning models, which have been previously trained with general image datasets from ImageNet. In this paper, the ResNet-152 v2, Inception-ResNet v2, and Xception models, adopted from Keras application, are retrained to predict the breed among the 21 classes of dogs and cats. Our results indicate that the Xception model has produced the highest prediction accuracy. The training accuracy is 99.49%, the validation accuracy is 99.21%, and the testing accuracy is 91.24%. Besides, the training time is about 14 hours and the predicting time is about 18.41 seconds.
Knowledge Distillation From End-To-End Image Compression To Vvc Intra Coding For Perceptual Quality Enhancement

In the current hybrid coding schemes, mean-squared-error is widely used for the rate-distortion optimization, which leads to high peak signal-to-noise ratio but sub-optimal perceptual quality. Although human perception-related measures, like multi-scale structural similarity (MS-SSIM), have been proposed, plugging them into the hybrid coding schemes may be computationally expensive. Recently, end-to-end optimized image compression has demonstrated the advantage of perceptual quality-oriented optimization by simply changing the training loss function. Inspired by this, we propose to distill the “perceptual” knowledge from end-to-end image compression and use the knowledge to enhance the perceptual quality for Versatile Video Coding (VVC) intra coding. For an input image, we obtain the block-level bit allocation via end-to-end image compression, and use the bit allocation to adjust the quantization parameter of VVC intra coding. Being compatible to the VVC standard, our method achieves on average 9.32% BD-rate reduction on the Kodak image set when evaluated by MS-SSIM, compared to the VVC reference software.
Identification of Dynamic Models by Using Metaheuristic Algorithms

A modified versions of metaheuristic algorithms are presented to compare their performance in identifying the structural dynamic systems. Genetic algorithm, biogeography based optimization algorithm, ant colony optimization algorithm and artificial bee colony algorithm are heuristic algorithms that have robustness and ease of implementation with simple structure. Different algorithms were selected some from evolution algorithms and other from swarm algorithms to boost the equilibrium of global searches and local searches, to compare the performance and investigate the applicability of proposed algorithms to system identification; three cases are suggested under different conditions concerning data availability, different noise rate and previous familiarity of parameters. Simulation results show these proposed algorithms produce excellent parameter estimation, even with little measurements and a high noise rate.
Classification of the Korean Sign Language Alphabet Using an Accelerometer with a Support Vector Machine

Recognition and understanding of sign language can aid communication between nondeaf and deaf people. Recently, research groups have developed sign language recognition algorithms using multiple sensors. However, in everyday life, minimizing the number of sensors would still require the use of a sign language interpreter. In this study, a sign language classification method was developed using an accelerometer to recognize the Korean sign language alphabet. The accelerometer is worn on the proximal phalanx of the index finger of the dominant hand. Triaxial accelerometer signals were used to segment the sign gesture (i.e., the time period when a user is performing a sign) and recognize the 31 Korean sign language letters (producing a chance level of 3.2%). The vector sum of the accelerometer signals was used to segment the sign gesture with 98.9% segmentation accuracy, which is comparable to that of previous multisensor systems (99.49%). The system was able to classify the Korean sign language alphabet with 92.2% accuracy. The recognition accuracy of this approach was found to be higher than that of a previous work in the same sign language alphabet classification task. The findings demonstrate that a single-sensor accelerometer with simple features can be reliably used for Korean sign language alphabet recognition in everyday life.
Application of NLP:Design of Chatbot for New Research Scholars

Development of Intelligent Conversational Agent using Artificial Intelligence and Machine Learning technique is an interesting problem in the field of natural language processing (NLP). Conversational agents, widely known as chatbots, are being used as virtual assistant in many different domains including business, healthcare and government organizations in attempt to improve the quality of service provided.This paper aims at using NLP towards the development of chatbot to assist the new research scholars. New research scholars often find it difficult where to start, how to start and have some common queries about fundamental concepts in research, research publications, data sources, funding agencies, indexing services etc.  The paper addresses the need of virtual assistant to the new researcher scholars and describes the design of the chatbot model that would help them get the answers to the research queries at their fingertips. The database used in the study is generated by collecting data from web as well as through personal interactions with new research scholars. We have designed retrieval based text chatbot using Keras sequential model with Adagrad optimizer that is optimized for learning rate. The research also reveals the relationship between learning rate and accuracy for five optimizers. It was found that accuracy of Adam and RMSprop optimizers is inversely proportional to the learning rate whereas the accuracy of Adadelta optimizer is directly proportional to the learning rate. The model designed in this work is simple, computationally inexpensive and has succeeded to provide answers to user queries satisfactorily. The paper gives important directions in which research can further be extended.
Lossy Image Compression Techniques

In recent years, domain image processing plays a most significant role in real-time applications.Due to reserved bandwidth and capacity, images are needed to be compressed, enhance, and noise-free to reduce the storage space as well as the transmission time.Compression overcomes the problem such as insufficient bandwidth of network and storage of memory device.Nowadays, many techniques are used to compress an image with a good quality.This paper discussed about the concept of image compression and reviews the techniques which are used in lossy image compression.This technique losses some data by using transform codes.Two codes are used to compress an image such as wavelet and fractal.The techniques scalar and vector quantization are used to quantize an input and partition into sub-images.And also it discussed about the 2D-Discrete wavelet transform codes with examples which were done by Matlab software.
Deposit Decision Model for Data Brokers in Distributed Personal Data Markets Using Blockchain

Since personal data becomes a valuable asset for IT industries, the necessity of data ecosystems with personal data trading methodologies is continuously increased, and the concept of data brokers is now widely used in the market. Moreover, utilizing the advantages of blockchain, distributed personal data markets have recently been considered because all participants behave selfishly for their profits in the markets. Since the participants may behave maliciously, it is necessary to prevent such behavior, and one of the considered approaches is putting additional deposits as a penalty into blockchain smart contracts. Since data brokers give various advantages to maintain personal data trading markets, many studies have considered both data brokers and blockchain at the same time. However, those studies have mainly focused on putting deposits into data buyers/sellers but not data brokers. However, since data brokers are also major players in the market, it is necessary to consider deposit decision models for data brokers. Hence, this paper proposes a deposit decision model for data brokers in distributed personal data markets using blockchain. Particularly, this paper proposes a profit model with deposits depending on their behavior for handling contracts and a credit level model that puts fewer deposits for a data broker with a higher credit level to motivate the data brokers’ truthful behavior. With the analysis of the proposed models, this paper shows that the models are feasible to motivate data brokers’ truthful behavior by allocating deposits for not only a large enough penalty but also a fair enough incentive.
Using the Stochastic Gradient Descent Optimization Algorithm on Estimating of Reactivity Ratios

This paper describes an improved method of calculating reactivity ratios by applying the neuronal networks optimization algorithm, named gradient descent. The presented method is integral and has been compared to the following existing methods: Fineman–Ross, Tidwell–Mortimer, Kelen–Tüdös, extended Kelen–Tüdös and Error in Variable Methods. A comparison of the reactivity ratios that obtained different levels of conversions was made based on the Fisher criterion. The new calculation method for reactivity ratios shows better results than these other methods.
Perancangan Sistem Informasi Retensi Rekam Medis Pasien Rawat Inap Menggunakan Visual Studio 2010 di Rumah Sakit Jasa Kartini Tasikmalaya

The hospital is a place to organize health efforts, namely every activity to maintain and improve health and aims to realize optimal levels of health for the community. This study aims to design an information system for inpatient medical record retention using Microsoft Visual Studio 2010 at the Jasa Kartini Hospital, Tasikmalaya. The research method used is a qualitative method with a descriptive approach. Data collection techniques are using observation, interviews and literature studies related to the subject matter of the research. As for the software development method using the waterfall. Based on the research conducted, various problems were found, including the medical record files that had been retained were not well organized at the time of storage so that they were not sequentially according to the medical record number, the lack of storage racks so that the medical records were not well organized and the retention process was slow. Therefore, it is necessary to design a medical record retention information system in the form of an application. The system design method uses Flowmap, DFD (Data Flow Diagram) and ERD (Entity Relation Diagram) and is implemented with Microsoft Visual Studio 2010 programming language with Microsoft Access database. Testing is done using the black box with the results of each successful form according to the design procedure.
Designing the Neural Model for POS Tag Classification and Prediction of Words from Ancient Stone Inscription Script

POS (Part-of-Speech) Tagging is essential to indicate labeling the words in thecorpus into grammatical categories in text analysis and marking up linguistic words in atext. According to the inflections and combinations in the words of Tamil language, thereis still difficulty in POS Tagging classification and prediction of Tags of the words as theautomated tools are very rare compared to the aspects of rich English language. As if thereare tools for modern Tamil language there is a lack of such statistical methods andtechniques for the Ancient Tamil language such as the texts from inscriptions and scriptsof stone where the words are lengthy and combined without splitting up into morphemes orlemmas. Package supportiveness and availability also considerably have some issues indealing with it as the words of Ancient Tamil script differs from modern Tamil. Theproposed work overcomes the complexity of classifying ancient words. The proposed workis based on designing the Neural Model for POS Tag Classification and Prediction ofWords from the Ancient 11th century stone inscription script. Bi-LSTM model isimplemented with the embedding layer of vectors of words for training the POS Taggingmodel based on pattern generation of regular expressions and classifying the words intotags and prediction of Tags of words for any novel script given that involves syntactic tagassigning and predicting tag for concerning words efficiently. The proposed modelprovides 88.88% accuracy compared to the existing works in the stream.
Factors Behind the Effectiveness of an Unsupervised Neural Machine Translation System between Korean and Japanese

Korean and Japanese have different writing scripts but share the same Subject-Object-Verb (SOV) word order. In this study, we pre-train a language-generation model using a Masked Sequence-to-Sequence pre-training (MASS) method on Korean and Japanese monolingual corpora. When building the pre-trained generation model, we allow the smallest number of shared vocabularies between the two languages. Then, we build an unsupervised Neural Machine Translation (NMT) system between Korean and Japanese based on the pre-trained generation model. Despite the different writing scripts and few shared vocabularies, the unsupervised NMT system performs well compared to other pairs of languages. Our interest is in the common characteristics of both languages that make the unsupervised NMT perform so well. In this study, we propose a new method to analyze cross-attentions between a source and target language to estimate the language differences from the perspective of machine translation. We calculate cross-attention measurements between Korean–Japanese and Korean–English pairs and compare their performances and characteristics. The Korean–Japanese pair has little difference in word order and a morphological system, and thus the unsupervised NMT between Korean and Japanese can be trained well even without parallel sentences and shared vocabularies.
Evaluasi Pelaksanaan Tantangan Bebras untuk Siswa di Biro Universitas Kristen Maranatha pada tahun 2017 - 2018 untuk Edukasi Computational Thinking

International Bebras Community is an organization which its purpose is educating informatics and computational thinking for students. Since 2016, Komunitas Bebras Indonesia consistently once a year hold Bebras Challenge. Faculty of Information Technology Maranatha Christian University has participated as a Bebras partner since 2016. As a Bebras Bureau, Faculty of Information Technology performs Teacher Workshop and Bebras Challenge in Bandung. For continuous support, Maranatha Bureau evaluates the performance for these. Schools, teachers, and students who participated in the Teacher Workshop and Bebras Challenge at Maranatha Bureau show increasing numbers from 2016 until 2018. The top scorer at Maranatha Bureau also included in the National ranking since 2017 for Elementary School and Junior High School. Teachers involvement in Teacher Workshop is important to help students follow the Bebras Challenge.
Performance Analysis On Bangla Handwritten Digit Recognition Using CNN And Transfer Learning

A computer can read and interpret intelligible handwritten input from sources like paper, photos, and other devices, known as Handwriting recognition (HWR).Besides, handwritten recognition is an interesting challenge in machine learning and deep learning.Because several strategies and approaches have been followed already to solve this challenge, machine learning and deep learning provided the best results.Handwritten digit recognition is a part of HWR.It is getting popular day by day because many applications could be made using this system like OCR, postal code recognition, license plate recognition, bank checks recognition, etc. Besides, the importance of recognizing the Bangla digit from the document is increasing.But the works available in Bangla handwritten digit recognition are very few.Similarly, none of them are robust, and some of them are overfitted.Therefore, we need to make some improvements to this system considering its importance.This paper explores the presentation of transfer learning with the help of some best-in-class profound CNN strategies for the acknowledgment of manually written Bangla digits.It considers two deep CNN architectures, such as Mobile Net and Residual Network (Reset) based on performance and accuracy.This model was trained and tested with the CMATERdb dataset.The study suggests that transfer learning provides 97% accurate results, where traditional CNN provides 86-92%.
SuperClass: A Deep Duo-Task Learning Approach to Improving QoS in Image-driven Smart Urban Sensing Applications

Image-driven smart urban sensing (ISUS) has emerged as a powerful sensing paradigm to capture abundant visual information about the urban environment for intelligent city monitoring, planning, and management. In this paper, we focus on a Classification and Super-resolution Coupling (CSC) problem in ISUS applications, where the goal is to explore the interdependence between two critical tasks (i.e., classification and super-resolution) to concurrently boost the Quality of Service (QoS) of both tasks. Previous efforts often focus on solving the two tasks individually and ignore the opportunity to explore the interdependence between them. In this paper, we develop SuperClass, a deep duo-task learning framework, to effectively integrate the classification and super-resolution tasks into a holistic network design that jointly optimizes the QoS of both tasks. The evaluation results on a real-world ISUS application show that SuperClass consistently outperforms state-of-the-art baselines by simultaneously achieving better land usage classification accuracy and higher reconstructed image quality.
Additive Phoneme-Aware Margin Softmax Loss for Language Recognition

This paper proposes an additive phoneme-aware margin softmax (APM-Softmax) loss to train the multi-task learning network with phonetic information for language recognition. In additive margin softmax (AM-Softmax) loss, the margin is set as a constant during the entire training for all training samples, and that is a suboptimal method since the recognition difficulty varies in training samples. In additive angular margin softmax (AAM-Softmax) loss, the additional angular margin is set as a costant as well. In this paper, we propose an APM-Softmax loss for language recognition with phoneitc multi-task learning, in which the additive phoneme-aware margin is automatically tuned for different training samples. More specifically, the margin of language recognition is adjusted according to the results of phoneme recognition. Experiments are reported on Oriental Language Recognition (OLR) datasets, and the proposed method improves AM-Softmax loss and AAM-Softmax loss in different language recognition testing conditions.