Reviewerly/exHarmony huggingface dataset:
Application Of Trial Finalization System As Determinants Of Final Thesis Session Results

The objective assessment system and guiding assessment can be accessed online, but the examiner's assessment system in which there is a system of finalizing the trial still using the local network and not connected with the others. The trial appraisal system that was not mutually integrated made the examiner a little difficult. This is because as technology advances are utilized as well as possible by educational institutions in this case namely universities to be used in a comprehensive session of thesis students or final assignments. Then it is necessary to develop the examiner's assessment system in which there is a system of finalizing the trial. The research was conducted using the method of observation, interviews and literature studies to maximize this research. The researcher directly examines the congregation assessment system which still uses the local network to analyze how the system works so that when carried out development does not change the existing system flow. Interviews were also conducted by researchers to collect data from existing speakers, namely the board of examiners. Then the author searched the list of previous studies that are appropriate so that repetition does not occur. After the development of the examiner's assessment system along with the finalization of the session and making the system integrated with each other so as to facilitate the examiner chairman to use it in determining whether or not students are declared eligible to pass a comprehensive trial or not. This proved that the system that had been developed greatly helped the process of the trial.
Ball Detection using Local Binary Pattern in Middle Size Robot Soccer (ERSOW)

Detecting ball is the first competence that should be owned by robot soccer. In the case of wheeled-based robot soccer, the existence of a ball is commonly detected by using an omnidirectional camera and a monocular camera. Traditional wheeled-based robot soccer usually uses color thresholding technique to differentiate ball and other objects. However, the accuracy of the thresholding technique frequently decreasing due to the changing of lighting conditions. This condition causes the robot can't detect the ball correctly. In this paper, we address that problem by applying a Local Binary Pattern (LBP) based ball detection system. Our experimental results show the performance of our system has percentages of success of 96.97% in bright lighting conditions, 80.69% insufficient lighting conditions, and 98.74% in dimmed lighting conditions when the ball moves slowly. Whereas the percentage of successful detection becomes 70.74% during bright lighting conditions, 60.42% for sufficient lighting conditions, and 83.79% for minimum lighting conditions when the ball moves quickly. LBP-based ball detection has 92.4% accuracy in measuring the distance between the ball and the robot.
Hoax Web Detection For News in Bahasa Using Support Vector Machine

This research creates a web-based user-friendly system that aims to detect hoax and non-hoax news of Indonesian language news links. The input data is in the form of links and archive sites from the Forum Anti Fitnah Hasut dan Hoax (FAFHH), using 100 news for training data and 20 news for test data that is processed by crawling and then processed in the pre-processing phase, namely tokenizing, stop word and stemming. Next is the Term Frequency-Inverse Document Frequency (TF-IDF) stage to provide weighting data which will be input data at the classification stage using the Support Vector Machine (SVM) Algorithm with a linear kernel to detect hoax and non-hoax news. The experimental results show that the system can classify well with an accuracy of 85%.
Fuzzy directional enlacement landscapes for the evaluation of complex spatial relations

Structural spatial relations between image components are fundamental in the human perception of image similarity, and constitute a challenging topic in the domain of image analysis. By definition, some specific relations are ambiguous and difficult to formalize precisely by humans. In this work, we deal with the issue of evaluating complex spatial configurations, where objects can surround each other, potentially with multiple levels of depth. Based on a recently introduced spatial relation called enlacement, which generalizes the idea of surrounding for arbitrary objects, we propose a fuzzy landscape model that allows both to visualize and evaluate this relation directly in the image space, following different directions. Experiments on several characteristic examples highlight the interest and the behavior of this approach, allowing for rich interpretations of these complex spatial configurations.
Japanese sign language classification based on gathered images and neural networks

This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words.
Blockchain and Brand Loyalty Programs: A Short Review of Applications and Challenges

Blockchain is gaining ground on academic research and business applications as it is believed  to have the potential to change business models in numerous sectors of the economy. Blockchain applications characteristics (security, immutability of record keeping, efficiency, and disintermediation) provide solutions to a number of issues firms and organizations face. Until now most of the research until now deals with its financial applications and cryptocurrencies. There is however, an important field for developing blockchain application in marketing and most  specifically in brand loyalty programs. In this paper, we are going to review some of the applications of blockchain in marketing and marketing management, and the ways it could enhance brand loyalty and brand loyalty programs run by firms and brands. The main findings of our paper is that blockchain could help brand loyalty by building trust between the brand and consumers, by simplifying and reducing the cost of administering brand loyalty programs, and by providing more ways for consumers to derive benefits from these programs through tokenization or cryptocurrencies. Finally, we are proposing issues that should be considered for further research on the topic.
An L0 regularized cartoon-texture decomposition model for restoring images corrupted by blur and impulse noise

In this paper, we address the image restoration case that includes both blurring and impulse noise. To recover an image with abundant features, we propose an L0 regularized cartoon-texture model for the simultaneous deblurring and impulse noise removal problem. We propose an L0 regularized framelet-based sparse representation and L0 regularized discrete cosine transform (DCT)-based sparse approximation to model the cartoon and texture of images, respectively. Unlike other cartoon-texture decomposition based-restoration approaches, our method does not depend on local features but globally controls the important non-zero components of the cartoon and texture in the framelet and DCT domain. Furthermore, we develop an alternating half-quadratic splitting method to solve the proposed L0 regularized cartoon-texture deblurring and impulse noise removal model (L0_RCTDINR) by introducing an alternating algorithm into the half-quadratic method. Experiments show the effectiveness of L0_RCTDINR on deblurring and impulse noise removal compared with existing state-of-the-art methods.
One-Shot Face Recognition with Feature Rectification via Adversarial Learning

One-shot face recognition has attracted extensive attention with the ability to recognize persons at just one glance. With only one training sample which cannot represent intra-class variance adequately, one-shot classes have poor generalization ability, and it is difficult to obtain appropriate classification weights. In this paper, we explore an inherent relationship between features and classification weights. In detail, we propose feature rectification generative adversarial network (FR-GAN) which is able to rectify features closer to corresponding classification weights considering existing classification weights information. With one model, we achieve two purposes: without fine-tuning via back propagation as previous CNN approaches which are time consuming and computationally expensive, FR-GAN can not only (1) generate classification weights for new classes using training data, but also (2) achieve more discriminative test feature representation. The experimental results demonstrate the remarkable performance of our proposed method, as in MS-Celeb-1M one-shot benchmark, our method achieves 93.12% coverage at 99% precision with the introduction of novel classes and remains a high accuracy at 99.80% for base classes, surpassing most of the previous approaches based on fine-tuning.
Probabilistic Ensemble of Deep Information Networks

We describe a classifier made of an ensemble of decision trees, designed using information theory concepts. In contrast to algorithms C4.5 or ID3, the tree is built from the leaves instead of the root. Each tree is made of nodes trained independently of the others, to minimize a local cost function (information bottleneck). The trained tree outputs the estimated probabilities of the classes given the input datum, and the outputs of many trees are combined to decide the class. We show that the system is able to provide results comparable to those of the tree classifier in terms of accuracy, while it shows many advantages in terms of modularity, reduced complexity, and memory requirements.
WCCC 2019: The 25th World Computer Chess Championship

WCCC 2019, the 25th World Computer Chess Championship, continued the ICGA's longitudinal computer chess experiment begun in 1974. This event was held in Macau, featured six chess engines and was won by KOMODO which thereby retained its title of World Champion. CHIRON and SHREDDER were respectively second and third.
simode: R Package for Statistical Inference of Ordinary Differential Equations using Separable Integral-Matching

Systems of ordinary differential equations (ODEs) are commonly used for mathematical modeling of the rate of change of dynamic processes in areas such as mathematical biology (Edelstein-Keshet, 2005), biochemistry (Voit, 2000) and compartmental models in epidemiology (Anderson & May, 1992), to mention a few.Inference of ODEs involves the 'standard' statistical problems such as studying the identifiability of a model, estimating model parameters, predicting future states of the system, testing hypotheses, and choosing the 'best' model.However, dynamical systems are typically very complex: nonlinear, high dimensional and only partialy measured.Moreover, data may be sparse and noisy.Thus, statistical learning (inference, prediction) of dynamical systems is not a trivial task in practice.In particular, numerical application of standard estimators, like maximum-likelihood or least-squares, may be difficult or computationally costly.It typically requires solving the system numerically for a large set of potential parameters values, and choosing the optimal values using some nonlinear optimization technique.Starting from a random initial guess, the optimization can take a long time to converge to the optimal solution.Furthermore, there is no guarantee the optimization will converge to the optimal solution at all.
The EU Response to Criminal Misuse of Cryptocurrencies: The Young, already Outdated 5th Anti-Money Laundering Directive

Abstract Combatting criminal misuse of cryptocurrencies was at the core of the fatf agenda under the US Presidency, culminating in June 2019 with the thorough extension of international standards against money laundering over virtual assets’ markets. This echoed the first legislative measure regulating virtual currencies adopted by the EU a year before. Directive 2018/843, better known as the 5 th Anti-Money Laundering Directive, fails however to address key technological breakthroughs and new business models, which continuously make the ever-growing and fast-paced crypto economy evolve. Against this background, the present contribution investigates shortfalls and challenges that lay ahead in the light of the new fatf Recommendations. It ultimately argues that the preventive anti-money laundering measures cannot dispense with the establishment of a cross-border integrated supervisory and enforcement system.
Uniform Lyndon interpolation property in propositional modal logics

We introduce and investigate the notion of uniform Lyndon interpolation property (ULIP) which is a strengthening of both uniform interpolation property and Lyndon interpolation property. We prove several propositional modal logics including $$\mathbf{K}$$ , $$\mathbf{KB}$$ , $$\mathbf{GL}$$ and $$\mathbf{Grz}$$ enjoy ULIP. Our proofs are modifications of Visser’s proofs of uniform interpolation property using layered bisimulations (Visser, in: Hájek (ed) Gödel’96, logical foundations of mathematics, computer science and physics—Kurt Gödel’s legacy, Springer, Berlin, 1996). Also we give a new upper bound on the complexity of uniform interpolants for $$\mathbf{GL}$$ and $$\mathbf{Grz}$$ .
Virtual reality for elderly with breast cancer: Usefulness and acceptance

Because virtual reality seems to be increasingly considered in different medical fields, the objective of this study is to define the optimal conditions for its use in a population of elderly women with breast cancer. By using a questionnaire, we evaluated the interest but also the immersive aspirations and preferences of elderly women with regard to this innovative device, taking into account the possible changes with age (60-65 years; 66-69, 70 and over). Surprisingly, older people - whatever the age group - are particularly favorable to virtual reality without actually having experienced it. The patients primarily chose immersion in a natural environment (with a marked preference for mountain landscapes) accompanied by musical background or even guided relaxation. However, because they judged important to remain focused on the treatments, the patients especially expect VR to regulate their negative emotions (lowering anxiety) and to allow them to escape from the painful contingencies of the real. Thus, virtual reality must take them into a magical world, which is more salient than to distract them or make them laugh. The distracting power, commonly associated with VR, does not seem to be the most popular reason to use with seniors facing medical care.
ASPARTIX-V19 - An Answer-Set Programming Based System for Abstract Argumentation

We present ASPARTIX-V, a tool for reasoning in abstract argumentation frameworks that is based on answer-set programming (ASP), in its 2019 release. ASPARTIX-V participated in this year’s edition of the International Competition on Computational Models of Argumentation (ICCMA’19) in all classical (static) reasoning tasks. In this paper we discuss extensions the ASPARTIX suite of systems has undergone for ICCMA’19. This includes incorporation of recent ASP language constructs (e.g. conditional literals), domain heuristics within ASP, and multi-shot methods. In particular, with this version of ASPARTIX-V we partially deviate from an earlier focus on monolithic approaches (i.e., one-shot solving via a single ASP encoding) to further enhance performance. We also briefly report on the results achieved by ASPARTIX-V in ICCMA’19.
FPGA-Accelerated Searchable Encrypted Database Management Systems for Cloud Services

The use of database management systems (DBMSs) as a cloud service is rapidly expanding. Cloud DBMSs offer many advantages, such as easier management, lower costs, and greater scalability. However, there are still security concerns regarding attacks from adversaries. DBMSs that use searchable encryption have been investigated with regard to ensuring their security. Because searchable encryption allows query execution over encrypted data in the cloud, sensitive data can be securely stored there in the cloud. On the other hand, encrypted query processing is slower than query processing on plaintext data. In this article, we use a field-programmable gate array (FPGA) to accelerate query processing in a searchable encrypted DBMS. We also propose a new cache function to shorten the access time to database tables in a DBMS. According to an evaluation using basic queries, the proposed system has achieved up to 110.7 times speed-up compared with the central processing unit (CPU) processing of a single core. In addition, the proposed system can process queries faster than the plaintext processing on a CPU when processing large amounts of data.
Methods and formal models of intelligent analysis of weakly structured data

Abstract The article presents a formal presentation of four ontologies of knowledge bases of the software environment: ontology of a problem area, linguistic ontology, ontology of precedents, ontology of rules for analyzing texts in natural language. When developing the knowledge base of the software environment, it is necessary to formulate requirements for the model of knowledge representation, the ontology. The development of a unified environment for semantic analysis of flows of weakly structured information that implements modern intelligent algorithms for processing text information will greatly facilitate the decision-making process by a specialist in the time constraint mode, due to the possibility of using a single unified bank of expert knowledge in the work of the question-answer system, and will also allow an automated semantic verification of information flows in order to provide information ion safety organization.
Near-Lossless Post-Training Quantization of Deep Neural Networks via a Piecewise Linear Approximation

Quantization plays an important role in the energy-efficient deployment of deep neural networks on resource-limited devices. Post-training quantization is highly desirable since it does not require retraining or access to the full training dataset. The well-established uniform scheme for post-training quantization achieves satisfactory results by converting neural networks from full-precision to 8-bit fixed-point integers. However, it suffers from significant performance degradation when quantizing to lower bit-widths. In this paper, we propose a piecewise linear quantization (PWLQ) scheme to enable accurate approximation for tensor values that have bell-shaped distributions with long tails. Our approach breaks the entire quantization range into non-overlapping regions for each tensor, with each region being assigned an equal number of quantization levels. Optimal breakpoints that divide the entire range are found by minimizing the quantization error. Compared to state-of-the-art post-training quantization methods, experimental results show that our proposed method achieves superior performance on image classification, semantic segmentation, and object detection with minor overhead.
Implementation of database using python flask framework

This paper is aimed at development of a college database management system that can be used in any educational institution. Python flask framework is used to implement this application, that can be accessed throughout the institution or by any specific department of the institution. This system offers different features to the students and the members of the staff which includes attendance and marks of the students that can be accessed by both student and staff but can be only updated by staff of particular department . Students as well as staffs have separate logins that can be accessed by them easily . The system also includes a library management system which works in real time,a micro transport management system and a training and placement management system.Library management system keeps track of the books issued and also calculates the fine generated for delay in book returning and transport system maintains the record of each student i.e bus number ,route of that bus etc . A separate system for analysing the placement record of the college is also present . This system is developed to maintain and facilitate easy access to information to every member associated with this system. For this the users must be registered with the system after which they can access their accounts as well as modify data as per the permissions given to them.&#x0D; Python flask framework is the technology that we have used to develop the college management system. Flask is a web framework written in python. It can be classified into microframework because it does not require any particular tolls or libraries. However flask supports extension that can add application features . For frontend we have used HTML,CSS and javascript whereas for backend we have used python flask and mysql.This system helps college and universities to save time , money and resources.
Automatic Summarization and Keyword Extraction from Web Page or Text File

In this paper, we study the automatic summarization and keyword extraction techniques for web page and text file. First, we use the Readability algorithm to extract the text of the web page, and study the PageRank algorithm and TextRank algorithm, and then use the TextRank algorithm to extract keywords, key sentences and abstracts. We also develop the web application that processes web page and text file. The application can input URL, text file, or text paragraph, then application can complete the extraction of main content, abstract, keywords and key sentences.
CORRELATION BETWEEN STUDENT’S VOCABULARY MASTERY AND SPEAKING SKILL

The objective of the research was at measuring correlation between student’s mastery vocabulary and speaking skill of the eleventh grade at SMAN 6 Sidrap. Variables of the research were the student’s mastery vocabulary as independent variable (x) and speaking skill as dependent variable (y). In this study, the researcher used correlation design, where the researcher used this design to correlate two variables. The population of this research was all of the eleventh grade (XI2) students of IPA class at SMAN 6 Sidrap and the number of the subject was thirty-two students. In collecting the data, the researcher used some test was vocabulary test and interview. In this research, the researcher used cluster sampling technique to chosen classes. The researcher analyzed the data using Pearson Product Moment Correlation and Linear Regression with SPSS 21 program to test the hypothesis. The result of calculation using SPSS 21 program found that the rvalue 0.532 was also higher than rtable at 5% and 1% significant level or 0.349 &lt; 0.532 &gt; 0.449. It could be concluded that alternative hypothesis (H1) stating that there is a significant positive correlation between student’s mastery vocabulary and speaking skill of the eleventh grade at SMAN 6 Sidrap is accepted and null hypothesis (H0) stating there is no a significant positive correlation between student’s mastery vocabulary and speaking skill of eleventh grade at SMAN 6 Sidrap is rejected. The result of calculation of contribution between student’s mastery vocabulary (variable X) gave about 75.33% contribution to the speaking skill (variable Y) of the sample class and 25.66% was influenced by other aspects. Based on the research findings, in is shown that there is a very significant positive correlation between student’s mastery vocabulary and speaking skill of the eleventh grade at SMAN 6 Sidrap. It meant that the student who had lack of vocabulary