Reviewerly/exHarmony huggingface dataset:
Smart Bed Using Voice Recognition for Paralyzed Patient

Abstract Smart beds are a way to simplify homework, especially for the elderly (elderly) and those who have physical disabilities, disabilities or paralysis. They can move the height of the bed according to their needs and comfort. The proposed system consists of a speech recognition module, an arduino, a relay circuit, and an adjustable bed. Voice recognition modules need to be trained before they are used to recognize commands. After successfully recognizing the voice command, Arduino directs the charge according to the help of the relay circuit. Adjustable bed height can be adjusted in three different modes according to the user’s comfort and needs. The accuracy of the speech recognition module is measured in three bed height modes with different conditions Istirahat, Berbaring dan Bangun, Istirahat in a 145 degree position, Bangun 110 degrees and Berbaring 180 degrees. ON, OFF, Rest, Wake and Lie down. A total of ten experiments were carried out for each order listed in the table. Out of ten experiments, the speech recognition module correctly recognizes voice commands. The percentage accuracy of the voice recognition module in silence is 75%
Model of Data Bases of Veterinary Medical Journal of Apiary

In the work is proposed a model of structure for an automated database for a veterinary medical journal of an apiary. The structure of separate tables and their distribution are considered. Its show the relations between different tables and search capabilities are discussed.
Cryptocurrencies: A Futuristic Perspective or a Technological Strategy

Currently, there are several cryptocurrencies based on blockchain technology and a debate whether these kinds of assets are really a currency or if they are only an instrument for speculation is prevalent. Cryptocurrencies are technologies that allow sending value from person to person, without intermediaries. A cryptocurrency is digital money, which uses a type of encryption to protect transactions in a public and decentralized manner. This work argues that cryptocurrencies have intrinsic value and that their price is not only determined by supply and demand. It is concluded that this kind of digital asset contains some value drivers beyond the profitability that is generated through speculation. Some of the elements of value are cost of production associated with intellectual capacity, baggage of knowledge, time and resources invested in the creation process. Those resources can be found in electricity and sophisticated computer equipment, codes generating smaller units of measure, networks without physical barriers, artificial intelligence and human enhancement, and varied payment methods.
Estimating Sentence Final Tone Labels using Dialogue-Act Information for Text-to-Speech Synthesis within a Spoken Dialogue System

This paper proposes a novel sentence final tone labels estimation method using dialogue-act (DA) informationfor text-to-speech synthesis within a spoken dialogue system. Estimating appropriate sentence final tone labels isconsidered essential for communicating the exact system’s intentions to users by an utterance. In this paper, wepropose to utilize DA features as well as the conventional features, morphological information of the utterance text, toestimate the sentence final tone labels. For this study, we use the speech database with DA tags which we constructedin our previous study. We added sentence final tone labels to this database so that each utterance has all informationof utterance text, DA and sentence final tone label. Based on this database, we build the proposed sentence final toneestimation model. We evaluated the proposed method by comparing its performance with the conventional method.The evaluation results show that the proposed method overwhelms the conventional method in accuracy. We alsoanalyze the estimation results to investigate the efficacy and difficulty by the proposed method.
Semi-supervised domain adaptation using unlabeled data for end-to-end speech recognition

Recently, the neural network-based deep learning algorithm has dramatically improved performance compared to the classical Gaussian mixture model based hidden Markov model (GMM-HMM) automatic speech recognition (ASR) system.In addition, researches on end-to-end (E2E) speech recognition systems integrating language modeling and decoding processes have been actively conducted to better utilize the advantages of deep learning techniques.In general, E2E ASR systems consist of multiple layers of encoder-decoder structure with attention.Therefore, E2E ASR systems require data with a large amount of speech-text paired data in order to achieve good performance.Obtaining speech-text paired data requires a lot of human labor and time, and is a high barrier to building E2E ASR system.Therefore, there are previous studies that improve the performance of E2E ASR system using relatively small amount of speech-text paired data, but most studies have been conducted by using only speech-only data or text-only data.In this study, we proposed a semi-supervised training method that enables E2E ASR system to perform well in corpus in different domains by using both speech or text only data.The proposed method works effectively by adapting to different domains, showing good performance in the target domain and not degrading much in the source domain.
Correlation Analysis for Complex Equipment Systems Based on Multi-objective Association Rule Mining

Complex equipment systems are becoming common in both industrial production and social life, and complicated correlations exist between system components. With the development of information technology, data-based analysis methods have become an efficient approach to the correlation analysis, taking place the traditional model-based methods. Specifically, association rule mining is one of the most efficient correlation analysis methods. Hence, this paper presents a multi-objective association rule mining algorithm to extract hidden correlations between system components in the performance data generated by equipment systems. Based on the NSGA-III framework, five objectives are used to optimize the quantitative association rules comprehensively. Finally, a case study is carried out on a multivariate dataset generated by the propulsion plant system of a vessel. The results show that the proposed algorithm is competent to the correlation analysis of real complex equipment systems, and can assist in the fault warning and state analysis. This study has reference significance for the study on modelling, analysis, and optimization of complex equipment systems.
Scene Perception and Higher Cognitive Processing

情景とは, 周囲にある環境や場面, あるいはシーン (scene) のことをいう。情景には多くのものや情報が存在し, それらは相互の関連性をもって, シーンに位置づけられている。 情景を把握するには, 感覚-知覚という生理学的レベルと, どのような情景であるかを認識するという対象認知レベルの過程がある。さらに, 認識した情景を他者と共有するには, 把握した内容を言語と結びつける言語処理レベルも関与する。また, 知覚した情景はそのまま解釈されるのではなく, 記憶や知識などとの統合によるアクティブな処理を経て, 情景が認識される。 本稿では, 情景を認識するまでの感覚-知覚-認知という一連の過程に関する基本的な事柄や神経学的基盤を解説した。そして, 認知症高齢者における情景知覚と高次の情報処理過程の先行研究に基づき, 若干の考察を示した。
Research on the Computational Model of Problem-Based Tutoring

The existing intelligent tutoring still follows the traditional classroom teaching mode, with knowledge point as the center, which emphasizes the transfer of knowledge and neglects the problem-solving ability training. Problem-based learning can effectively solve the dilemma of the above teaching mode, but the existing intelligent tutoring technology still does not support it. To this end, this paper puts forward a new idea of problem-based intelligent tutoring and carries out the theoretical analysis about its computational feasibility. Firstly, we make a deep analysis about the cognitive activities of human teachers in the process of problem-based learning and abstract out the core elements in this process from the four aspects of concept, relationship, operation and control. After that, by using these core elements, we give an abstract description about the teachers’ cognitive process in the problem tutoring situation, so as to complete the construction of the cognitive model for problem-based tutoring. Based on this cognitive model, we firstly make the formal definitions about all of the conceptual and relational elements, then analyze the control schemes and the functional requirements about the operational elements completely, lastly use the pseudocode to describe the complete algorithm implementations of these operations. All of the above formal specifications about the concepts, relations and operations make up the computational model for problem-based tutoring, which lays the foundation for the system design and development int the future.
A Crypto Compression System Using ElGamal Public Key Encryption Algorithm and Even-Rodeh Codes

Abstract Confidentiality and size of data are some significant aspects in data exchange. Public key encryption algorithms, such as El-Gamal is known as to preserve confidentiality. On the other hand, public key encryption algorithms tend to increase the size of the encrypted data, making it difficult to transmit to the other party. This research will combine the ElGamal public key encryption algorithm with Even-Rodeh codes. ElGamal is used to secure data and Even-Rodeh codes are used to compress data. The parameters is being tested are the compression ratio and space savings. In this research, we will do an experiment that includes some data texts from Artificial Corpus. The results show that the crypto compression system can reduce the size of the transmitted data and the transmitted data could be revert back to the original data while still maintains its confidentiality.
DEVELOPMENT OF ALGORITHMIC THINKING IN PRESCHOOL AND PRIMARY SCHOOL STUDENTS AT PROGRAMMING AND ROBOTICS CLASSES

The article considers the features of the continuous development of algorithmic thinking in preschoolers and primary school students at programming and robotics classes. The expediency of using various software and hardware tools for preschoolers and primary school students, such as Gigo Kids First Coding &amp; Robotics, the freeware PictoMir, the LEGO Education WeDo robotic kit, and the object-oriented visual programming environment Scratch, is described. Practical tips for using these products are provided.
Plasmodium classification on red blood cells image using multiclass support vector machines

Abstract Classification methods have been frequently used in various aspects, including bioinformatics. One of it’s purpose of this classification is to determine phase level of a disease. This research will classify the phase of plasmodium falciparum parasite which causes malaria. The disease is spread by an infected female Anopheles mosquito, which contains Plasmodium . The result of this research could be used to determine Plasmodium parasite phase in infected people’s red blood cells. The purpose of this research is to discover the success rate of Multiclass Support Vector Machines method and analyze it in order to predict the parasite phase levels. The data of this study is image data of red blood cells which were infected by three kinds of Plasmodium falciparum parasite levels. In the process, this study will be using Canopy as Integration Development Environments of phyton programming language. From 112 trials, the highest number of accuracy is 87.5% for Multiclass Support Vector Machines one vs rest method which used the 4-fold cross-validation with C=1 as parameter for linear kernel.
Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

This paper presents an experiment on newsreaders’ behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user’s interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers’ perceived satisfaction.
Sememe Knowledge Representation

Abstract Linguistic Knowledge Graphs (e.g., WordNet and HowNet) describe linguistic knowledge in formal and structural language, which can be easily incorporated in modern natural language processing systems. In this chapter, we focus on the research about HowNet. We first briefly introduce the background and basic concepts of HowNet and sememe. Next, we introduce the motivations of sememe representation learning and existing approaches. At the end of this chapter, we review important applications of sememe representation.
An Animal Behavior State Estimation Method Using CCNN and BN Based System

The study of animal behavior can provide a basis for ecological researchers to help them formulate more reasonable and targeted ecological protection strategies. In this paper, the feature detection method based on cascade convolutional neural network is applied to the detection of animal features. By analyzing the general flow of convolutional neural networks, the researchers designed a cascade convolutional neural network with three hierarchical structures. Each hierarchical structure has multiple convolutional networks, which perform the same or different operations respectively. In order to make up for the defects of neural network in reasoning, this paper uses Bayesian network to infer and estimate in the analysis of features. Through experiments on the proposed method on a self-built data set, the results show that the overall accuracy of the animal behavior estimation method based on CCNN and BN proposed in this paper is more than 85%, and compared with general methods, this method has the advantages of fast learning speed, accurate feature location, and high accuracy of behavior estimation.
An adaptive and clustering-based steganographic method: OSteg

In this chapter, we propose a novel adaptive spatial steganographic method based on Otsu clustering method (hence named OSteg). It is based on clustering pixels values to restrict the embedding alteration only to rich-textured blocks. The security of the proposed data hiding method is enhanced through exploiting a nonlinear system, that is, Ikeda system. The embedding of a secret message is carried out in two steps, a preprocessing phase and an alteration phase. For the classification and the undetectability evaluation of OSteg, the spatial domain color rich model (SCRM) is used. ROC curves and the probability of classification error of OSteg are established and compared with four well-known embedding methods. The proposed method outperforms the current state-of-the-art schemes in terms of probability of error, which implies the high undetectability of OSteg for the tested payloads (9.1553e−05⩽payload⩽0.0209).
Development of a Chatbot Application for Cryogenic System

Abstract The vacuum group of NSRRC developed a task-oriented chatbot application based on LINE message platform in 2018. LINE message is a cross-platform communication software that can be easily used on mobile phone, tablets, and PC. This interactive user interface can not only reply the real-time status of sub-systems when the user has a query but also proactively report alert. The TPS cryogenic system also introduces this application which like a virtual assistant to help cryogenic engineer management system more efficiency. In this paper we present the configuration and operation of this task-oriented chatbot application for cryogenic system.
Remote Data Acquisition System for Measurement of Ambient Climatic Conditions and SPV Battery Status

The cost of solar PV (SPV) systems has reduced drastically over the years on account of efficient PV cell manufacturing technologies. Its suitability and economic viability for the installation site have strong correlation to local climatic resources. The access to data on solar insolation, wind speed and other climatic parameters is hence essential for site selection and SPV installation. The remotely accessible data of ambient temperature and solar irradiance at target location was acquired through Arduino-GSM hardware loaded with compatible software. The study showed that temperature and solar irradiance measured through developed system was within 2.23% and 5.83%, respectively, as compared to conventional measurement techniques. The novelty of the device is envisioned in its application to monitor small-scale as well as large-capacity renewable energy systems that have a strong dependence on climatic factors.
Advanced Monkey Testing for connected autonomous systems

Sensors, actuators, machine learning, communication and robotics are paving the way for the introduction of autonomous systems. Autonomous Systems in safety-critical applications require resilient operation of the intended functionality throughout the mission. Especially they must be safe and highly available. However, it is not possible to fully anticipate evolving threats, vulnerabilities and faults during the lifetime of those systems. This requires a resilient systems architecture of the autonomous system. Therefore, a thorough testing and evaluation of such systems is mandatory. In this paper, we present a monkey testing framework for evaluating resilience capabilities of autonomous systems. The framework contains a set of agents with specific role concepts and strategy sets. The framework can be applied to virtual, physical and hybrid testbeds. Due to its modularity the framework is extensible, scalable and also adaptable to different autonomous systems (e.g. mobile robot, manipulator). The monkey testing framework is able to work pseudo-randomized and thus reproducible on a connected system. A logging mechanism annotates the data so that the data can be used for machine learning (e.g. anomaly detection algorithm, selfhealing). We applied the framework on a mobile robotic system in virtual scenarios.
Diversified Clustering Methods and Algorithms in Data Mining used for Document Representation

In this World of Words the rate of arriving new born worlds are tremendously increase with respect to time zone, as it is obvious that it will create a lack of much better and reliable word management with us. Now to survive we possess good clusters of similar bunch of worlds and their making process which called clustering are available. In this paper will see various clustering methods, qualities of good clusters with various Algorithms used on ground of Data mining. This will help to refill the lacks in future development in this area.
Research on Face Recognition Algorithms and Application Based on PCA Dimension Reduction and LBP

In the face recognition system on campus, the influence of time and age change on face features can be neglected. This paper proposes a dimension reduction algorithm based on principal component analysis (PCA) algorithm and local binary patterns (LBP), and it is applied to campus face recognition APP. It is proved that the algorithm can significantly improve the speed and ensure its recognition accuracy in the application of small changes in the face and has certain reference value for practical application.
Traffic Scene Detection Based on YOLOv3

With the development of urbanization and the ongoing big data scene, intelligent object detection in traffic scenes has become a hot issue at the moment. In this paper, the largest computer vision algorithm evaluation data set in the world, the KITTI data set, is trained on the model of YOLOv3. K-means method is used to adjust anchor parameters to achieve more accurate results. The experimental results show that the traffic scene detection model trained in this paper has good detection effect.
Formation of Positive Emotions in Physical Exercises

The article discusses the impact of positive and negative emotions on the development and health of children. Special attention is paid to the methods of forming positive emotions. The process of mental activity taking into account features of formation of positive thinking is described. The article is addressed to teachers and teachers, students of pedagogical universities, Methodists and parents.
Comparison between Digital Tone-Mapping Operators and a Focal-Plane Pixel-Parallel Circuit

In a previous work, we proposed a color extension of an existing focal-plane tone-mapping operator (FPTMO) and introduced circuit modifications that led to smaller sensing array area and simpler off-chip white-balance operations. The proposed FPTMO chip was fabricated and its experimental test setup is under development. In the present work, we systematically compare the previously proposed FPTMOs with conventional digital tone-mapping operators (DTMOs), both in terms of overall image quality and execution time. To assess the image quality, we use the Tone-Mapped image Quality Index (TMQI) metric, the Blind Tone-Mapped Quality Index (BTMQI) metric, and an image colorfulness metric. By statistically comparing the results given by both metrics, we verify that the proposed FPTMOs achieve results similar to those obtained with DTMOs, occasionally outperforming them. To compare the tone-mapping operators execution times, we perform a detailed complexity analysis. Results show that system-level software FPTMO descriptions (FPTMO functionality described in software) rank among the fastest DTMOs and that the hardware FPTMO (described in software) speed-up depends on the target frame rate. For 30-frames-per-second color image generation, the fastest FPTMO is 15 times faster than the DTMO with highest TMQI and BTMQI metric scores, and 170 times faster than the DTMO with best colorfulness metric score.
A Virtual Mouse Interface for Supporting Multi-user Interactions

Traditionally, two approaches have been used to build intelligent room applications. Mouse-based control schemes allow developers to leverage a wealth of existing user-interaction libraries that respond to clicks and other events. However, systems built in this manner cannot distinguish among multiple users. To realize the potential of intelligent rooms to support multi-user interactions, a second approach is often used, whereby applications are custom-built for this purpose, which is costly to create and maintain. We introduce a new framework that supports building multi-user intelligent room applications in a much more general and portable way, using a combination of existing web technologies that we have extended to better enable simultaneous interactions among multiple users, plus speech recognition and voice synthesis technologies that support multi-modal interactions.