Reviewerly/exHarmony huggingface dataset:
Web-Based Expert System Application in Determining Facial Care

Abstract Along with the development of technology, also developed a system that is able to adopt technology and the human thinking process is an expert system which contains specific knowledge so that everyone can use it to solve a specific problem, in this case, the author tries to apply an expert system skincare for determining facial care by complaint to our face. The main objective of this paper is to build a knowledge-based system that skincare can determine facial care than mac that is shown in the form of a website using PHP programming alongside MySQL databased.
A New Clustering Algorithm Based on Rewarding Support Vectors

Support vector exhibits impressive behaviors in supervised learning tasks like classification and regression. However, they are embarrassed in unsupervised learning like clustering, due to the fact that support vectors produced by unsupervised learning process cannot provide sufficient information that facilitates label detection. Existing clustering algorithms based on support vector are often pay high cost to detect clusters but present moderate and unstable results. For that, this paper extends the geometric meaning of support vectors from describing cluster contours to describing both cluster contours and inner-cluster structures. That is achieved by adopting the individually-tuned scale parameters in support vector computation. Based on that, the spectrum analysis is done to find the number of clusters and grouping schemas. Finally, data are labelled. Such a process formulates a new Clustering algorithm based on Rewarding Support Vectors (CRSV). Empirical evidence on benchmark datasets and real dataset demonstrates the improvement of CRSV over traditional support vector clustering approach as well as the peer variants, and the competitiveness with some state of the art clustering methods.
Computer Vision Algorithms for Image Segmentation, Motion Detection, and Classification

This chapter describes several commonly used algorithms in computer vision. Algorithms discussed include adaptive Gaussian thresholding and mixture modeling for edge detection; cross correlation template matching for shape detection; the Viola Jones model for face detection; Gaussian mixture modeling for motion detection; and the histogram of oriented gradients feature descriptor and support vector machines (including directed acyclic graph multi-class support vector machines) for image classification. Several of these techniques also have wider application in other areas of machine learning.
Analysis of Information Flow in Hidden Layers of the Trained Neural Network by Canonical Correlation Analysis

Convolutional neural network (CNN) have been extensively applied for a variety of tasks. However, the internal processes of hidden units in solving problems are mostly unknown. In this study, we presented the use of canonical correlation analysis (CCA) to understand the information flow of the hidden layers in CNN. The proposed method analyzed and compared the information flow by measuring the correlations between a given feature vector and the activation pattern at each layer of the CNN. We quantified and analyzed specific information flows using the CCA to examine how the architecture works in the two experiments. In the first experiment, we analyzed the information flow of the U-net and auto-encoder architectures to remove the distorted light source information, and showed that the U-net works more efficiently for this task. In the second experiment, we analyzed the information flow of the architecture used for multitask learning, in which the classification of shifted characters in images and the estimation of the shift amount are performed simultaneously, and showed that it performed properly according to the task.
The Enforcement of Context Aware System Security Protocols with the Aid of Multi Factor Authentication

In this paper, an attempt has been made to describe Kerberos authentication with multi factor authentication in context aware systems. Multi factor authentication will make the framework increasingly secure and dependable. The Kerberos convention is one of the most generally utilized security conventions on the planet. The security conventions of Kerberos have been around for a considerable length of time for programmers and other malware to Figure out how to sidestep it. This has required a quick support of the Kerberos convention to make it progressively dependable and productive. Right now, endeavor to help explain this by strengthening Kerberos with the assistance of multifaceted verification.
Automatic Sign Language Manual Parameter Recognition (II): Comprehensive System Design

This chapter presents a comprehensive systems design for a practical video-based system for automatic recognition of sign language manual parameters. The system consists of two main components: hand retrieval and manual parameter representation and recognition. Hand retrieval has the following sub-components: input capture; hand, skin, and motion detection; and hand tracking. Manual parameter representation and recognition has the following sub-components: hold detection; hand segmentation; feature representation; hand orientation and shape classification; SignWriting lookup and transcription. The functions and other aspects of each of these sub-components are described.
Free view point generation based on depth maps computed by a single camera

Points of view generation allows to create virtual views between two or more cameras observing a scene. This field receives attention from multimedia markets, because sufficiently realistic points of view generation should allow to navigate freely between otherwise fixed points of observation. The new views must be interpolated between sampled data, aided by geometrical information relating real cameras poses, objects in the scene and desired point of view. Normally there are several steps involved, globally known as Structure from Motion (SfM) method. Our study focuses on the last stage; image interpolation based on the disparities between known cameras. In this paper, a new method is proposed that uses depth maps generated by a single camera, named SEBI, allowing a more efficient filling in presence of occlusions. Occlusions are considered during interpolation, creating an occlusion-map and an uncertainty-map using the depth information that SEBI cameras provide.
Residual Wavelet Coefficients Prediction using deep Convolutional Neural Network for Single Image Super-Resolution

This paper is about learning based super resolution (SR) by exploiting wavelet coefficients using convolutional neural network (CNN). The objective of SR is to generate a high quality and visually pleasing image from the low-resolution (LR) image(s) without extensive training. Wavelet coefficients are sparse in nature, this sparsity is further enhanced by learning residuals (difference of SR and LR wavelet sub-bands) of wavelet coefficients. Sparsity helps to speed up the training procedure as it contains significant information within only few coefficients. We train a CNN using residuals of wavelet sub-bands instead of raw image pixels. We learn an end-to-end mapping to predict unknown wavelet coefficients using CNN by reducing the root mean square error of wavelet coefficients from the four sub-bands. SR involves perfect reproduction of high frequency details like textures and sharp edges present at the original scale, which makes it as a severe inverse ill-posed problem. The proposed CNN is profited from the sparsity and is named as Residual Wavelet Super Resolution (RWSR) Net. The RWSR is approximately thirty times faster than the well-known SRCNN method and provides better performance. The performance of the proposed work is assessed through quantitative metrics and it is observed to give competitive results when compared to the existing state-of-the-art methods.
Building End-to-End Task-oriented Dialogue Systems Via CNNs and Attention Mechanisms

We put forward a general solution towards building task-oriented dialogue systems via convolutional neural networks (CNNs) and Attention Mechanisms. The dominant task-oriented dialogue models follow pipeline design and usually rely on complex recurrent neural networks(RNNs). In that case, its structure is complicated and the amount of parameters exceeds on million. In this paper, we propose a novel end-to-end task-oriented dialogue system with a clear structure, just based on CNNs and attention mechanisms, totally dispensing with recurrent neural networks (RNNs). Our model is based entirely on sequence-to-sequence (seq2seq) architecture and realizes the true meaning of end-to-end. Experimental results indicate that our model outperforms the state-of-the-art approaches on Cam-Rest676 and KVRET datasets in two evaluation indexes, namely, task completion and quality of language generation. Meanwhile, the training speed of our model is increased by 3-10 times and the amount of parameters is just 1/3.
Emergent design principles for prediction algorithms in health care

There has been a rise in the number of prediction algorithms developed for health care using machine learning (ML) technology. However, aside from a few exceptions, these novel prediction algorithms have not made it to the frontlines of health care. 1 Wiens J. Saria S. Sendak M. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019; 25: 1337-1340 Crossref PubMed Scopus (243) Google Scholar
A Method of Analyzing Data Linearly Plotted on 2D Hybrid Scale Graph Paper

混成対数正規分布(熊澤, 大橋, 1986)は，対数正規分布で予測されるより高い値の出現頻度を抑制するメカニズムの存在によって生成される確率分布である．これは，対数正規分布の本体と正規分布の右端を持つ分布であり，放射線作業に伴う線量累積を合理的に抑制する確率過程のリスク管理モデルとして導出された．同様の確率分布は，自然現象，工学，経済学，文化，スポーツ，社会統計で広く見られる．本論文では，混成対数正規分布の生成機構「量の増加抑制の合理的調整」を普遍的なリスク管理式として解釈，また，この管理式からのリスク出力は対数成分と線形成分の混成変動になることを前提にして，混成目盛（対数目盛と線形目盛の統合）および両軸に混成目盛を持つ2次元の両混成グラフ用紙上で混成変動を同定する考え方と適用例を示した．適用例では，両混成グラフ用紙上で直線グラフを与えるハイブリッドスケール（HS）モデルを用いて，核の構造安定性に関する（クーロン反発力源の）陽子数を超える（核力引力源の）中性子過剰数が線量管理から導出したリスク管理式と同じく増加抑制機能を示すことを検証した．
Detection and analysis of kidney artifacts using ultrasound imaging

The ultrasound imaging is a non-invasive methodological technique which is frequently employed for diagnosing purpose in the medical domain. Study and analysing of an evolving foetus, heart, blood vessels, muscles, tendons, abdominal and pelvic organs done by employing ultrasound imaging. This paper provides an understanding of automation which could be embedded with the ultrasound system resulting in identifying artefacts during ultrasound imaging itself. The correct conclusion regarding the various artefacts & disorders present in the ultrasound image of the respective body region can be diagnosed easily & effectively by utilizing this algorithm. An accurate course of treatment for the removal of artefacts, disorders etc. can be recommended to the affected person after utilizing this algorithm. The algorithm is designed by employing the MATLAB image processing tool. Ultrasound images are then analysed through this algorithm which accurately detects the artefacts present in images. The percentage accuracy of the proposed algorithm is 83.33 per cent. The presence of multi artefacts in tiny regions makes the algorithm based detection vulnerable. The accuracy of artefacts detection and less complex procedure of operation makes this algorithm a prominent contender for various health applications related to ultrasound imaging.
Secure Data Management Technology

In this chapter, we introduce data anonymization techniques for several types of datasets. Data anonymity of anonymized datasets is an index for estimating the (maximum) reidentification risk from anonymized datasets and is generally defined as a quantitative index based on adversary models. The adversary models are implicitly defined according to the attributes in the datasets, use cases, and anonymization techniques. We first review existing anonymization techniques and the adversary models behind the data anonymity definitions for anonymization techniques; then, we propose a common anonymity definition and its adversary model, which is applicable to several types of anonymization techniques. Furthermore, some extensions of the definition, which is optimized for specific types of datasets, are presented in the chapter.
Control Technology for Remotely Piloted Air Systems (RPAS)

Remotely piloted aircraft systems (RPAS) are capable to solve a wide range of application tasks, including ones with high requirements to the time limit of performance. These requirements are mitigated in case of simultaneous use of multiple vehicles of different type. It is expedient to develop and implement technologies that ensure automatic tasks fulfilment in order to improve efficiency of their use. This article describes typical missions for UAVs used in the Ministry of Emergency Situations (EMERCOM) and suggests the optimum RPAS. Next, it examines the design principles of swarm RPAS control and offers a control algorithm for UAV swarm based on the examined principles. Finally, the article makes a conclusion about perspective of the development of the proposed RPAS used in the Ministry of Emergency Situations of Russia and about the communication algorithm in the system. In this research article the briefing and typical tasks for UAV used in the EMERCOM are described and analysed. The design principles of RPAS swarm control are described, also the control strategy in swarm and functioning order of UAVs in dynamics and the protocol of local voting of the agents are given. Authors suggest the algorithm of control of the UAV swarm, based on the examined principles.
The Ecological Construction of the Basic Education Curriculum Under the Multicultural Perspective

The differences among nations, regions, ethnic groups or between urban and rural areas, which have been basis of cultural diversity and multiculturalism, are eliminated currently by developmentalists who are under great anxious of economic growth. The concept of multiculturalism is related to the curriculum reform closely, while the deep requirement of the curriculum reform is inheriting the cultural mission that behind the knowledge and skills of the course, exceeding the knowledge, technology and teaching mode that belonged to the curriculum diversity, constructing the curriculum ecology based on the multiculturalism and changing the problem of ‘just only have knowledge without culture’ in curriculum and teaching. It is necessary to enhance learner’s cultural confidence and ability of cross-cultural comprehension, critical thinking of the world-changing, independent choice and the capability of influencing the world changing. When individual-free development is achieved, it is necessary to promote world peace and sustainable development. China’s basic educational curriculum reform practice has not yet to reflect the cultural diversity attributes which behind the multicultural curriculum, and has not yet responded to the multicultural appeal of urban–rural integration and globalization. According to observing and participating experimental study of several rural schools, the author refreshes the relationships between curriculum diversity and cultural diversity and construct the basic educational curriculum ecology that reflecting and promoting the cultural diversity. Therefore, exploring the new path and new value of penetrating into the curriculum reform and launching multicultural education.
Meausuring the quality computer based test services using servqual method (case study admission system University of Janabadra)

This Computer Based Test (CBT) is one of the services provided at the new admissions system at a college. User satisfaction (prospective new students) is one of the important factors of successful development and implementation of this CBT. This study aims to measure the quality of CBT services by combining Servqual (Service Quality) model. Servqual model is used to measure service quality from user perception to five dimensions of service quality that is tangible, reliability, responsiveness, assurance, and empathy. The object of research is Janabadra University CBT system with the respondent is a new student academic year 2017/2018. The method used is determining the variables and dimensions to be measured, making and distributing questionnaires, validity and reliability test, processing the data of questionnaires by combining Servqual model, and analyzing data of questionnaire processing result. In this research, we will measure Gap 5 Servqual dimension that is the gap between service obtained with service expected by user. The results of this study can be the basis for development and improvement of CBT system on Universitas Janabadra in order to be more competitive in the future.
Security of Quantum Cryptography

Quantum cryptography is one of the most mature area of quantum information. In the past few decades, active research has taken place in theoretical as well as implementation security of a practical quantum key distribution system. This resulted in hardening of theoretical proofs for a robust implementation against implementation loopholes and realistic attacks on QKD system. Particularly, many hacking strategies on the source, link, and detection system have been demonstrated. This paved way to the development of robust engineering of QKD system. The emphasis of this paper is twofold, we bring forth the newer security techniques paving way for information-theoretic security proofs leading to high QKD performance for market deployment, and discuss the recent quantum hacking on different QKD protocols. This has led to the strengthening of the QKD system and enabled active monitoring against possible eavesdropping activities.
Android Apps in EFL Classroom Environment: Improving Students’ Learning Outcomes in Translation

Abstract This research aimed at improving students’ learning outcome on translation by using android smartphone. The media is a kind of software namely Edmodo. This research was conducted from March to May at the fourth semester of English Department of Education and Teachers Training Faculty of Universitas Lancang Kuning, Academic Year 2017/2018. The method of this research was Classroom Action Research (CAR) with some stages: plan, action, observation, and reflection. The research was conducted in a cycle with four meetings and a test. The data was taken by using a test of translation in which the students had to translate a news item text about Meghan Markle and Prince Harry’s Royal Wedding from English into bahasa Indonesia. After analysing the data, it is found that the students’ ability in translating the text from English into bahasa Indonesia was good. It is indicated that the students’ learning outcome on translation subject was improved.
Recursive LMMSE Centralized Fusion with Compressed Multi-Radar Measurements

For multi-sensor centralized fusion with linear measurements, simply stacking all measurements up and then applying the Kalman filter at the fusion center can give the optimal estimation performance. This optimal performance is independent of how the measurements from different sensors are stacked up. However, for centralized fusion with multiple radar measurements under the recursive LMMSE filtering framework, the performance really matters as to how to stack the measurements from different radars. In [1], we have shown that centralized fusion with stacked recombined multi-radar measurements outperforms the one with stacked original measurements under the recursive LMMSE filtering framework. In this paper, we further develop a new multi-radar centralized fusion approach by compressing all measurements first and then applying the recursive LMMSE filter with single radar measurements at the fusion center. Numerical examples show that the new centralized fusion with compressed measurements has better estimation accuracy and smaller noncredibility than the ones with stacked measurements.
Fuzzy Logic Using in Images Retrieval Depending on Their Features

Image processing has become one of the most important branches of computer science, especially after entering into several areas of life such as medicine, engineering and various sciences. In our current research, we have developed a system of image recognition based on image characteristics and some content information using the most important artificial intelligence algorithms, a fuzzy logic algorithm, to obtain complete image information using small values ranging from 0 to 1. The program was executed on a set of standard database called the WANG database. It holds the contents of 1000 images from the Corel stock photo database, in JPEG format. The system was evaluated using the recall method. This method calculates the proportion of correct results identified by the system as correct results with correct result identified by the classic system.
Ubiquitous Computing: From 5G to the Edge and Beyond

This is an invitation to a journey from our communication technology’s past to the technological borders of today and beyond to the unknown. We will jump back in time for about three generations to become aware of the major steps of progress we have achieved in the past 50 years. From there we will move forward in three main paths, covering the invention of the (inter-networking) network, the development of hard- and software, and the advance of mobile communications. We will have a look at how these three streams of development eventually merged into one and how it led us to the technological reality of today. If you have been part of this story yourself, or if you already know all about it, you may want to read it anyway with a smile of remembrance. If you do not want to repeat this part, please feel free to jump ahead a couple of pages to Sect. 4.
Bearings-Only Target Motion Analysis using Ray Tracing

Target angular information in a 3-dimensional space consists of an elevation angle and an azimuth angle. The azimuth angle indicates the direction of the target in a horizontal plane while the elevation angle indicates the direction of target signal in a vertical plane established from a passive line array sonar. The acoustic signals gathered by the line array sonar have different elevation angles as they propagate through multiple paths. The target angle measured by the line array sonar is, in fact, a conical angle that indicates the direction of incoming signal to the line array sonar system. Therefore, bottom bounce path produces an inaccurate target location if it is interpreted as the azimuth angle in the horizontal plane as is commonly used in exiting Target Motion Analysis (TMA) technologies. Therefore, it is necessary to consider the conical angle effect on bearings-only TMA (BO-TMA). In this paper, the conical angle of target that causes angular ambiguity will be simulated, and the BO-TMA method using Particle Swarm Optimization (PSO) is proposed to solve the angular ambiguity problem.
ADS4all: Democratizing Authenticated Data Structures

Bitcoin and Merkle trees are instances of Authenticated data structures (ADS). Unfortunately, such ADS’s are not widely used although they provide enormous security benefits for many distributed systems. This is because current tools and methods are not easy to use for system engineers. We present our ongoing work to create ADS4all, a framework that allows for the design and implementation of ADS’s that are tailored to specific domains.
A Progressive Formalization of Tacit Knowledge to Improve Semantic Expressiveness of Biodiversity Data

The majority of biodiversity data available on the Web are structured, lacking unstructured features such as tacit knowledge, images, audios, text documents, among others. Tacit knowledge can be used to add more expressiveness to ontologies. To achieve that, the knowledge needs to be elicited and formalized and further incorporated into an ontology. This paper aims to present a Progressive Formalization Schema (PFS) to formalize tacit knowledge into different levels of granularity.