Reviewerly/exHarmony huggingface dataset:
Accessibility Degradation Prediction on LTE/SAE Network Using Discrete Time Markov Chain (DTMC) Model

In this paper, an algorithm for predicting accessibility performance on an LTE/SAE network based on relevant historical key performance indicator (KPI) data is proposed. Since there are three KPIs related to accessibility, each representing different segments, a method to map these three KPI values onto the status of accessibility performance is proposed. The network conditions are categorized as high, acceptable or low for each time interval of observation. The first state shows that the system is running optimally, while the second state shows that the system has deteriorated and needs full attention, and the third state indicates that the system has gone into degraded conditions that cannot be tolerated. After the state sequence has been obtained, a transition probability matrix can be derived, which can be used to predict future conditions using a DTMC model. The results obtained are system predictions in terms of probability values for each state for a specific future time. These prediction values are required for proactive health monitoring and fault management. Accessibility degradation prediction is then conducted by using measurement data derived from an eNodeB in the LTE network for a period of one month.
Understanding user intention in image retrieval: generalization selection using multiple concept hierarchies

Image retrieval is the technique that helps Users to find and retrieve desired images from a huge image database. The user has firstly to formulate a query that expresses his/her needs. This query may appear in textual form as in semantic retrieval (SR), in visual example form as in query by visual example (QBVE), or as a combination of these two forms named query by semantic example (QBSE). The focus of this paper lies in the techniques of analysing queries composed of multiple semantic examples. This is a very challenging task due to the different interpretations that can be drawn from the same query. To solve such a problem, we introduce a model based on Bayesian generalization. In cognitive science, Bayesian generalization, which is the base of most works in literature, is a method that tries to find, in one hierarchy of concepts, the parent concept of a given set of concepts. In addition and instead of using one single concept hierarchy, we propose a generalization so it can be used with multiple hierarchies where each one has a different semantic context and contains several abstraction levels. Our method consists in finding the optimal generalization by, firstly, determining the appropriate concept hierarchy, and then determining the appropriate level of generalization. Experimental evaluations demonstrate that our method, which uses multiple hierarchies, yields better results than those using only one single hierarchy.
Artificial Intelligence based Composition for E-Government Services

Given the complex nature of the public sector with several distributed governmental institutions and manifold semantic differences of interpretation, the achievement of interoperability and integration is a key challenge for a comprehensive and effective Electronic Government (e-Government).Promising technologies that could be used to tackle this issue are: the powerful concept of ontology and the advanced Artificial Intelligence (AI) systems.Ontologies contribute to a common understanding of heterogeneous resources, while AI techniques make process integration dynamic and automated.However, up until now the use of AI along with ontologies has been fairly limited in e-Government.There is still, then, untapped potential in this field which worth to be exploited.In this paper, we present a dynamic approach for semantically integrating e-Government Web services based on AI techniques.The overall objective of our approach is to improve the citizen centric e-Government vision by providing a conceptual framework for automatically discovering, composing and optimizing e-Government services.Within the proposed approach, special emphasis is put on personalization aspects and evaluation criteria for e-Government platform.
Neural

This paper introduces a new task – Chinese address parsing – the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence labelling approach, our observation is that there exist complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. We investigate neural structured prediction models with latent variables to capture such rich structural information within Chinese addresses. We create and publicly release a new dataset consisting of 15K Chinese addresses, and conduct extensive experiments on the dataset to investigate the model effectiveness and robustness. We release our code and data at http://statnlp.org/research/sp.
Bi-frequency temporal phase unwrapping using deep learning

In fringe projection profilometry (FPP), multi-frequency phase unwrapping, as a classical algorithm for temporal phase unwrapping (TPU), can eliminate the phase ambiguities and obtain the unwrapped phase with the aid of additional wrapped phase maps with different fringe periods. However, based on the principle of multi-frequency phase unwrapping, it needs multiple groups of fringe patterns with different fringe periods to eliminate the phase ambiguities of the wrapped phase with high-frequency, which is not suitable for high-speed 3D measurement. If two frequency fringe patterns are only projected, the reliability of multi-frequency phase unwrapping will be decreased significantly. Inspired by deep learning techniques, in this study, we demonstrate that the deep neural networks can learn to perform temporal phase unwrapping after appropriate training, which substantially improves the reliability of phase unwrapping compared with the traditional multi-frequency TPU approach even when high-frequency fringe patterns are used. In our experiment, a challenging problem in TPU is that the unwrapped phase of 64-period fringe patterns cannot be directly unwrapped by only using a single-frequency phase, but it can be easily resolved by our method. Experimental results demonstrate the temporal phase unwrapping method using deep learning provides the best unwrapping reliability to realize the absolute 3D measurement for objects with complex surfaces.
Karakterisasi Fisik dan Aktivitas Antioksidan Masker Wajah Gel Pell Off Yang Mengandung Sari Buah Naga (Hylocerus polyrhizus)

Damaged skin due to free radicals from ultraviolet radiation can be prevented using antioxidants. Red dragon fruit (Hylocereus polyrhizus) containing antioxidants can be made into a peel off gel face mask using gelatin as a gelling agent to be applied easily to the skin. The purpose of this study is to determine the effect of various gelatin concentrations on the physical properties and antioxidant activity of peel off gels containing red dragon fruit juice. The peel off gels face mask were made using various gelatin concentration i.e 7.5% w/w (F1); 10% w/w (F2); and 12.5% w/w and containing 10% w/w red dragon fruit. The peel off gels then evaluated for physical properties and antioxidant activity using DPPH. The result showed that the increasing of the gelatin concentration would increase the drying time and adhesive ability of the peel off gels. The escalation of gelatin concentration from 7.5% w/w (F1) to 10% w/w (F2) did not affect the spreading ability of the peel off gel. However, the spreading ability of the peel off gel decreased with the increasing concentration of gelatin from 10% w/w (F2) to 12.5% w/w (F3). The antioxidant test of peel off gel had an average percentage of inhibition of 9.101% (F1); 12.469% (F2); and 15.109% (F3). Based on the results of physical properties and antioxidant activity showed that formula 2 was recomended.
Detecting objects using Rolling Convolution and Recurrent Neural Network

At present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image.The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency.Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations.This is mainly because these approaches use coarse feature.Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance.Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers.Furthermore, we use a recurrent neural network of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects.Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCAL VOC 2007 and 72.3% mAP in PASCAL VOC 2012 dataset.It has been verified through many experiments that this method has reached a more advanced level of detection.
THE RELATIONSHIP BETWEEN STUDENTS’ MOTIVATION AND THEIR LEARNING ACHIEVEMENT

Among the factor influencing students learning, motivation becomes one of the momentous factors that may affect students learning achievement. The aim of this study is to investigate the relationship between students’ motivation and their learning achievement. The subject were 88 second year students at Science major class of an Islamic high school in Cimahi City in the second semester of the Academic year 2018. The research uses quantitative corelation as a method consisting of two variables, namely students motivation (independent) and students achievement (dependent). The writers uses questionnaire as an instrument to collect the data and the student English score from their English teacher. The data analyze use SPSS. Finding from this study indicate that there are any relationship between students motivation and their learning achievement. The relationship between students’ motivation and their English learning achievement was significantly and has Medium Correlation. When the result showing 0.580 appropriate with Pearson Correlation degree Value 0.41 s/d 0.60 = Medium Correlation. Keywords: Relationship, Motivation, Achievement
Natural Hand Gesture Recognition with an Electronic Textile Goniometer

Gesture recognition allows distinguishing specific user motions that intend to express a message.The recognized gestures can be used in various applications such as humancomputer interface (HCI), clinical practice including rehabilitation, and personal identification.We propose a method of recognizing upper-limb motion gestures for HCI using electronic textile sensors, which consist of a double-layered structure with complementary resistance characteristics.For gesture recognition, we apply dynamic time warping (DTW) as it exhibits a high performance with simple computations for dynamic signals.We verified the functional feasibility of the proposed method from the data of 10 subjects performing 6 HCI gestures.The gesture classification accuracy for all subjects was 85.4%, although each subject separately achieved a higher performance.In fact, six subjects achieved a perfect recognition performance (100% recognition accuracy); three subjects achieved an accuracy of 98.6%, and one achieved an accuracy of 97.2%.
New diagnostic procedure codes and reimbursement.

With the implementation of the new Medicare Physician Fee Schedule on January 1, 2019, it can be beneficial for all practitioners to grasp an understanding of how reimbursement is determined. With the new Physician Fee Schedule also came new relative value units (RVUs) and new billing codes. Biopsy codes, in particular, were changed to reflect the complexity of the sampling technique (ie, tangential, punch, incisional). In this article, we explain RVUs and how they determine reimbursement. This article also highlights changes and additions to billing codes, specifically for biopsies and telemedicine services.
A high-capacity coverless image steganography method based on double-level index and block matching.

Recently, a new information hiding technology called coverless information steganography (CIS) is proposed, which uses the original natural image as stego image for the transmission of secret information which can resist the detection of image steganalysis algorithm, so it received extensive attention and support. However, it is still a low hidden capacity of the CIS methods up to now. This paper proposes a high-capacity coverless information steganography technology. In which we divide the cover image into several image blocks and every image block can represent one bit secret information which improves the capacity greatly. Then we retrieve the image blocks for replacement from the image block database based on secret information and then synthesize them into stego image. The quality of the stego image is still high because the required image blocks are similar to cover image blocks and they are all from natural images. Moreover, in order to improve the retrieval efficiency, we have established a double-level index structure. The experimental results show that compared with the existing CIS methods, the proposed method has larger capacity and better visual quality.
Incremental Deep Neural Network Pruning Based on Hessian Approximation

In this paper, based on the Hessian approximation, an incremental pruning method is proposed to compress the deep neural network. The proposed method starts from the idea of using the Hessian to measure the "importance" of each weight in a deep neural network, and it mainly has the following key contributions. First, we propose to use the second moment in Adam optimizer as a measure of the "importance" of each weight to avoid calculating the Hessian matrix. Second, an incremental method is proposed to prune the neural network step by step. The incremental method can adjust the remaining non-zero weights of the whole network after each pruning to help boost the performance of the pruned network. Last but not least, the proposed method applies an automatically-generated global threshold for all the weights among all the layers, which achieves the inter-layer bit allocation automatically. Such a method can improve performance and save the complexity of adjusting the pruning threshold layer by layer. We perform a number of experiments on MNIST and ImageNet using commonly used neural networks such as AlexNet and VGG16 to show the benefits of the proposed algorithm. The experimental results show that the proposed algorithm is able to compress the network significantly with almost no loss of accuracy, which demonstrates the effectiveness of the proposed algorithm.
Mystery of deadly Indonesian tsunami cracked using social-media videos

The findings expose the deficiencies of tsunami warning systems, as well as highlighting the power of citizen science. The findings expose the deficiencies of tsunami warning systems, as well as highlighting the power of citizen science.
Conversation Initiation by Diverse News Contents Introduction

Satoshi Akasaki, Nobuhiro Kaji. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing

If we choose to compare computing technology to coral reef then cloud computing technology is its very live and growing end. Its challenges are new and demand innovative measure to bring the size of its expending data centers under calipers and bridle its energy consumptions. Reduction in the consumption of energy is to be brought about without compromising quality-of-service and efficacy. For this, we purpose a Hypercube based Genetic Algorithm (HBGA) for efficient VM migration for energy reduction in cloud computing under QoS (Quality-of-service) constraint. The proposed HBGA technique can be implemented in two phases. First, in a data center the physical machines organize themselves in such a way as to acquire a highly scalable structure called Hypercube. The hypercube imperceptibly grates itself up or dips low in sympathy with VM instances as they mount up or get depleted. Secondly on the basis of this representation model of the compute nodes, and given the hypercube topology in which they are organized we propose three algorithms: (a)Hypercube based Node Selection Algorithm to minimize energy consumption (b) Hypercube based VM Selection Algorithm which minimizes the number of VM to be migrated. (c) To solve the problem of VM Placement we propose Hypercube based Genetic algorithm.Experimental results of comparisons between the proposed HBGA method viz-a-viz the existing solutions show a marked reduction in energy consumption of cloud computing environment.
Design and Implementation of Herbal Therapy Knowledge Management System (HTKMS)

The recent influx of herbal mixtures in Nigeria today is gradually becoming alarming.It remains a major concern in the society especially if the right approach is used.Experts believe that the knowledge on herbal therapy is usually obtained from an implicit knowledge of an expert which is transcribed to document-based for reusable purposes.Nowadays, lack of a collaborative herbal therapy system and a massive introduction of different kinds of herbal mixtures in the market today remain a concern for deliberation.This study was designed as a result of lack of communicable herbal therapy platform in Nigeria and secondly, self-imposed solutions to health challenges.The concept of collaborative, user-friendly problem solving software was designed for the study, a package named Herbal Therapy Knowledge Management System (HTKMS).In this study, herbal experts provide expert analysis and solutions to ailments (problems) using the formidable approach of Case-Based Reasoning Model.Case forms, questionnaire and interview were used to gather information from herbal experts and users.Knowledge on Ailments causes and solutions gathered were represented using Entity Relationship Diagram.Case-Based Reasoning technique was adopted for information retrieval from herbal corporate memory (Case Forms) while cases not found were redirected to a collaborative forum for further discussion by herbal experts.Communication among the users (patients) and herbal experts were based on the principles of server-client network.The system was coded using PHP, HTML and CSS.The results of quantitative analysis of the questionnaire developed for the study showed that herbal experts strongly agreed that they diagnose patients manually (Mean Score: 1.90; Std Dev: 0.657), that they proffer solutions from personal experience and presumption (Mean Score 2.90; Std Dev: 0.852), they strongly agreed that they often do not seek for information from practicing colleagues or from a media (Mean Score: 1.85; Std Dev: 0.671) and lastly, they depended naturally on roots, stems, leaves and shrubs for herbal solutions as contained in their "account book" (Mean Score: 1.85; Std Dev: 0.745).Conclusively, the study was able to produce collaborative and reusable software which focused majorly on producing a more organized and reusable herbal knowledge for users.
Development of a Prototype Smart City System for Refuse Disposal Management

The future of modern cities largely depends on how well they can tackle problems that confront them by embracing the next era of digital revolution.A vital element of such revolution is the creation of smart cities. Smart city is an evolving paradigm that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis.To align concretely with the smart city revolution in the area of environmental cleanliness, this paper involves the development of a smart city system for refuse disposal management.The architecture of the proposed system is an adaptation of the Jalali reference smart city architecture.It features four essential layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers.A proof of concept prototype was implemented based on the designed architecture of the proposed system.The signal sensing and processing layer was implemented to produce a smart refuse bin that contains the Arduino microcontroller board, Wi-Fi/GSM transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components.The network layer provides interconnectivity among the layers via the internet.The intelligent user application layer was realized with non-browser client application, statistical feature extraction method and pattern classifiers.Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects.The sensors in the smart refuse bin generate multivariate dataset that corresponds to the status of refuse in the bin.Training and testing features were extracted from the dataset using first order statistical feature extraction method.Afterward, multilayer perceptron artificial neural network and support vector machine were trained and compared experimentally.The multilayer perceptron artificial neural network model gave the overall best accuracy of 98.0% and the least mean square error of 0.0036.
Construction, Visualization and Application of Knowledge Graph of Computer Science Major

To make full use of specialized vocabulary in computer science and discover relationships among these words, a Chinese knowledge graph of computer science major is constructed based on the internet web pages, and then the knowledge graph visualization and application for learning guidance based on it are developed. For the construction of computer science knowledge graph, a small amount of important specialized words in computer science are collected manually, and then these words are extended based on Baidu Baike (baike.baidu.com). Thus we get about 3000 specialized words (called entries). The similarity between two entries is calculated based on the Normalized Google Distance (NGD). Once the similarity is greater than a setting value, a link between the two entries is created. Finally the knowledge graph is constructed by these words and links between them. Here the relation type of link is ignored for simplicity. Furthermore the graph visualization is implemented by a tool called sigma.js, and an application for learning guidance is developed by J2EE. Through the application, students can get a visualized overview of computer science major and make a learning plan efficiently. Moreover the application and method of knowledge graph construction can be applied for other majors easily.