Reviewerly/exHarmony huggingface dataset:
Modulation Scheme for Biasing the Emotional Process of Autonomous Agents

Autonomous agents (AAs) are capable of evaluating their environment from an emotional perspective by implementing computational models of emotions (CMEs) in their architecture. A major challenge for CMEs is to integrate the cognitive information projected from the components included in the AA's architecture. In this chapter, a scheme for modulating emotional stimuli using appraisal dimensions is proposed. In particular, the proposed scheme models the influence of cognition on appraisal dimensions by modifying the limits of fuzzy membership functions associated with each dimension. The computational scheme is designed to facilitate, through input and output interfaces, the development of CMEs capable of interacting with cognitive components implemented in a given cognitive architecture of AAs. A proof of concept based on real-world data to provide empirical evidence that indicates that the proposed mechanism can properly modulate the emotional process is carried out.
High Performance Digital Algorithm for Calculating the Harmonic Spectrum Estimates of Complex Signals

The paper considers the high-performance digital algorithm for the harmonic analysis of complex signals. Binary sign analog-stochastic quantization formed the development basis of the algorithm. This type of quantization is used as the primary conversion of the analyzed signal to digital form. The discrete-event model of the binary sign analogue-stochastic quantization permitted the analytical calculation of integration operations in the transition to the computation of amplitude spectrum estimates in digital form. As a consequence, the developed harmonic analysis algorithm does not require performing digital multiplication operations in contrast to the classical algorithms based on the calculation of the direct Fourier transform. The elimination of digital multiplication operations increases in the computational efficiency of frequency spectrum estimation. Experimental research of the developed algorithm was carried out using simulation modeling. The simulation results showed that the algorithm allows calculating the amplitude spectrum of complex signals with high precision and frequency resolution in the presence of external additive noise. This algorithm can be used as a part of diagnosability provision for a wide range technical object.
Image Compression Using Run Length Encoding and Lempel Ziev Welch Method

Images are among the most common and popular representations of data. Digital images are used for professional and personal use ranging from official documents to social media. Thus, any Organization or individual needs to store and share a large number of images. One of the most common issues associated with using images is the potentially large file-size of the image. Advancements in image acquisition technology and an increase in the popularity of digital content means that images now have very high resolutions and high quality, inevitably leading to an increase in size. Storage limitations on any device mean that only a small number of such images can be stored. Moreover, transmitting and uploading larger images takes up more time and bandwidth. Image compression has thus emerged as a vital part of image processing. The need of the hour is to reduce the size of the image as much as possible ,while maintaining a high level of quality and preserving all the details in the image. Compressed images can also be transmitted faster and require less bandwidth. In this paper we will be discussing two compression techniques- "Lempel-Ziv-Welch (LZW)" and "Run length Encoding (RLE))" and how to implement them. We will then compare them based on certain parameters.
Fuzzy Reliability Measurement of Intelligent Substation Communication System

Science and technology promote the development of production and the transformation of society. It is also an important factor to highlight the country's overall national strength. Its wide application is bringing unprecedented convenience and prosperity to mankind.Intelligent substations have also replaced traditional substations in this trend, in order to achieve better and more efficient relay protection.Intelligent substations have improved in many performances and are also the main development direction of China in the future.This paper mainly measures the fuzzy reliability of intelligent substation communication system. Firstly, based on the relevant theory of fuzzy reliability, the relationship between fuzzy reliability, fuzzy failure rate and fuzzy average life is explained by formula.Secondly, the fuzzy reliability of intelligent substation process network is expounded and analyzed from the fuzzy reliability of typical system and the fuzzy reliability of single-interval process network. In order to solve the uncertainty of component failure probability and system failure mechanism, new problems are discussed. Research ideas.
A Theoretical Review: Risk Mitigation Through Trusted Human Framework for Insider Threats

This paper discusses the possible effort to mitigate insider threats risk and aim to inspire organizations to consider identifying insider threats as one of the risks in the company's enterprise risk management activities. The paper suggests Trusted Human Framework (THF) as the on-going and cyclic process to detect and deter potential employees who bound to become the fraudster or perpetrator violating the access and trust given. The mitigation's control statements were derived from the recommended practices in the “Common Sense Guide to Mitigating Insider Threats” produced by the Software Engineering Institute, Carnegie Mellon University (SEI-CMU). The statements validated via a survey which was responded by fifty respondents who work in Malaysia.
Formalized Description of Message Encryption in Messaging Apps Using Automata Theory

This article describes the method of end-to-end encryption of messages in messaging apps using the automata theory, which are necessary for the visual representation of this mechanism. Interpretation of end-to-end encryption on event-driven non-deterministic automaton allows to implement these methods not only by software, but also by hardware. Since at present there is a high probability of loss and falsification of users personal data and storing the correspondence in a decrypted form on the network, such software methods of message encryption as symmetric and asymmetrical are used now; their distinctive features were described in this article. On the basis of these encryption methods, two protocols, which are the most reliable and frequently used in modern messaging apps, were considered. A serious vulnerability was found in one of the methods. It is missing in another protocol due to the use of Double Ratchet and triple Diffie-Hellman. This vulnerability was described in this article. In the protocols analysis, the Diffie-Hellman algorithm and the AES-256 standard were detailed. The AES-256 standard is represented by a form of mathematical model using the automata theory, on the basis of which a hardware implementation of the encryption mechanism is possible.
Orthogonalizing Weights in Capsule Network Architecture

Scalar neural network algorithms are limited in their ability to understand scale, rotational, or affine transformations within images and resort to average or max-pooling techniques which result in translational invariance. In an attempt to overcome these limitations, Hinton et al. introduced vectorized capsule network frameworks which support equivariance while capturing spatial relationships between data points, thus enhancing predictive capabilities of networks. However, experimenting with activation functions, hyperparameters, and optimizers have proven faster convergence and orthogonalizing weights within the layers of capsules enhance performance by slashing associated average error rates.
Spatial Coordinate Trial : Converting Non-Spatial Data Dimension for DBSCAN

In big data, noise in data mining is a necessity. Its existence depends on data and algorithm, but it does not mean the algorithm caused noise. Although the advantages of the Density Based Spatial Clustering Application with Noise, DBSCAN algorithm, in executing spatial data (two-dimensional data) have been widely discussed, but it has not been convincing in executing non-spatial data. As an algorithm should perform well on any data for optimizing data mining, this research proposes a trial to convert dimensions of non-spatial data into 2 dimensions for executing with DBSCAN algorithm, and a different input value for epsilon to know about its minimum which begins arising noise in the execution. Method of analysis in trial is with considering the attributes of non-spatial data as variables that represent coordinate points, rather than cardinality. Technically, it is assumed that 2-dimensional coordinate axes as a spot point for coordinate with more than or equal 3 dimensions according to development of Cartesian coordinate system, by first paying attention to relationship of variables (attributes). This way is then called Spatial Coordinate. The different input values are with paying attention to numbers from non-zero minimum distance to the forth of epsilon where the epsilon is in integer. The results of trial and testing on clusters formed, with Silhouette Coefficient, point out that the clusters are well, strong, and quality enough. Therefore, this research gives a new way on how preprocessing non-spatial data for DBSCAN algorithm performance.
Text Localization and Extraction from Background with Texture and Noise in Digital Images Using Adaptive Thresholding and Convolutional Neural Network

Recently, the task of text localization attracts many attentions. In this paper, we propose a method to localize individual characters of text from background with texture and noise in digital images using adaptive thresholding, width-to-height ratio, and convolutional neural network. The proposed method consists of three main steps: preprocessing, text candidate localization and classification, and character candidate localization and classification. In preprocessing step, images are enhanced by average filter in order to reduce noise and make the texture background harmonious. Then text candidates in an image are localized and classified using adaptive thresholding and width-to-height ratio of bounding boxes. Finally, character candidates of each text are localized and a convolutional neural network is used to classify character candidates as either character or non-character. The experiments were conducted on a dataset obtained from ICDAR2013 containing the training set of 229 images and the testing set of 233 images. From the experimental results, the proposed method can correctly localize text with the accuracy of 71.87%.
A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting

We study channel number reduction in combination with weight binarization (1-bit weight precision) to trim a convolutional neural network for a keyword spotting (classification) task. We adopt a group-wise splitting method based on the group Lasso penalty to achieve over 50% channel sparsity while maintaining the network performance within 0.25% accuracy loss. We show an effective three-stage procedure to balance accuracy and sparsity in network training.
Quantum-behaved Pigeon-inspired optimization Algorithm based on Particle-best Mutation

Aiming at the problem that the Quantum-behaved Pigeon-inspired optimization Algorithm is easy to fall into the local optimum and is lack of fine search ability, a new algorithm based on particle-best mutation is proposed. Firstly, aiming at the map and compass operator characteristics of solve rough search, Cauthy mutation based on particle-best was adopted for the pigeons when they have not evolved at this stage, so as to enhance the global search ability. Then, aiming at the landmark operator characteristics of solve fine search, Gaussian mutation after updating by landmark operator was adopted for the pigeons when they have not evolved at this stage, so as to enhance the local search ability. Finally, model of pigeons' number factor which likes map and compass factor was built to keep the pigeons in the landmark operator at a certain amount, so as to get the optimal solution. The function test results show that the search speed and accuracy of the improved algorithm are better than other popular algorithms.
Long-Range Monitoring System with Haze Reducer Tool Based Digital Image and Video Processing

Haze is air vapor as a result of condensation that is still close to the ground that occurs due to rotating air or air, this causes the impact on the vision to decrease. it is a common happened on fire Mountain. The observers need video images of the crater activity to be analyzed. When the activity of fire mountain increases, the production of gas or haze coming out of the crater is also increasing, causing it to no longer be able to monitor the crater conditions directly. Of course, this will be very dangerous if there is a dangerous activity in a volcanic event. However, the officer cannot anticipate it. the development of a remote monitoring system based on video image with a haze reducer tool, the foggy image obtained from the camera could be reduced. It is expected that the monitor system can visualize video images that are clearer and better than the source image which is foggy.
3D Audio Post-Processing Module on DSP Platform

Object-based 3d audio has attracted many audio content makers and audio engineers. This kind of audio signal gives the listener a more realistic sound effect around him than, for instance, stereo or surround. An audio object is a type of signal which consists of two parts - PCM (pure audio content) and metadata (additional information on how objects should be rendered). A component that handles object-based signals is called object renderer which adjusts audio content based on metadata. In this paper, an evaluation of object renderer on DSP platform is reported with an example of assembly implementation within the MPEG-H system.
A Novel Chaotic based Privacy preserving machine learning model on large distributed client applications

With the rapid growth of data size, data storage and computational memory, it is essential to implement an advanced privacy preserving model on large datasets. Machine learning framework is used to extract essential hidden patterns which are plain text format for decision making in distributed applications. Privacy preserving data mining (PPDM) has emerged as an essential area for data confidential in terms of data exchange, decision making and data publication. Privacy preserving is a popular data privacy model for securing individual decision patterns from unauthorized access. As the decision-making patterns of the data owner are stored and distributed publicly, it leads to the misuse of information in distributed applications. Some privacy information about business organizations, industries and individuals has to be encoded before it is publicly shared or published. In this paper, a novel chaotic privacy preserving model is designed and implemented on the large distributed data for privacy preserving. Here, different traditional privacy preserving models are used to compare the proposed model to the traditional models.
The Feasibility of Credit Using C4.5 Algorithm Based on Particle Swarm Optimization Prediction

Credit is a belief that one is given to a person or other entity which is concerned in the future that will fulfill all the obligations previously agreed. The objective of the research is to do credit analysis to determine the feasibility of a credit crunch, through credit analysis results, to investigate whether the customer is feasible or not. The method used to predict credit worthiness is by using two models, i.e. C4.5 classification algorithm model and Particle Swarm Optimization (PSO)-based C4.5 classification algorithm model. After testing with these two models, it is found that the C4.5 classification algorithm generates a value of 90.99% accuracy and AUC value of 0.911 to the level diagnostics Classification Excellent, but after the optimization with C4.5 classification algorithm based on Particle Swarm Optimization accuracy values amounted to 91.18% and the AUC value of 0.913 to the level of diagnosis Excellent Classification. These both methods have different accuracy level of 0.18%.
Attribute-Based K-Means Algorithm

Clustering is a method to discover hidden natural structure in a dataset of a phenomenon. In this study, we have extended K-Means algorithm for spatiotemporal dataset by introducing attribute-based mass function to calculate center of mass of cluster instead of calculating geometry-based centroid in the dataset. The proposed modification in traditional K-Means algorithm produces more meaningful clusters and converges faster than traditional K-Means. In our study, we have used a real `fire dataset' to conduct experiments on the proposed approach for clustering.
Formal Approach for Compatibility Checking of Orchestrations of Composite Semantic Web Services

Web service composition is the process of combining Web services in order to offer value-added services that meet the increasing complex requirements of business processes. Besides simple Web services described with WSDL, OWL-S language allows to describe composite semantic Web services offering multiple operations using complex control structures. However, it becomes harder to manually check the consistency of orchestrations of such services in order to guarantee their correctness and avoid deadlock and erroneous statuses. In this paper, we first propose a formal method for translating OWL-S composite services into communicating automata using an iterative process which is driven by the control structure of that language. The same approach is also applied to the orchestrator whose description is given in WS-BPEL. Thereafter, we build up the synchronization product of the resulting automata and proceed to its consistency checking in order to unveil any forbidden statuses which depict flaws in that orchestration interaction protocol.
Rumor Blocking in Social Networks

Online social networks have many benefits as a medium for fast, widespread information dissemination. They provide fast access to large-scale news data, sometimes even before the mass media. They also serve as a medium to collectively achieve a social goal. For instance with the use of group and event pages in Facebook, events such as Day of Action protests reached thousands of protestors. While the ease of information propagation in social networks can be very beneficial, it can also have disruptive effects. One such example was observed in August 2012, thousands of people in Ghazni province left their houses in the middle of the night in panic after the rumor of earthquake. Another example is the fast spread of misinformation in twitter that the president of Syria is dead, leading to a sharp, quick increase in the price of oil. There are lots of similar examples. Although social networks are the main source of news for many people today, they are not considered reliable due to such problems.
Decision Support System to Discover Route and Time Spent at Waypoints for Cultural Tourism and Community Lifestyles through Participation of Communities

The main purpose of this research is to develop a web-based decision support system to discover cultural tourism routes and the length of time to spend at specific waypoints (stay time) in Khaosamokhon sub-district through the participation of communities. The system was developed by using a decision support system with rate and weight scoring rules. The development of the web-based system was done in HTML5 and used Leaflet, jQuery and the Google Map JavaScript API to provide friendly interactive maps. The community can input tourist information, which are the attractions and the weight of their categories with the communities in the system. In the system, tourists can specify preferred destinations and their category rate with desired departure and arrival times. The system calculates the route and stay time including destinations satisfying tourists' requirements with navigation instructions. The created maps are flexible in discovering routes. Five experts rated the overall performance of the system as high. Moreover, the overall satisfaction of 48 respondents was at a high level as well.
Feature Analysis for Fake Review Detection through Supervised Classification

Presently days, audit destinations are increasingly more defied with the spread of falsehood, i.e., assessment spam, which goes for advancing or harming some objective organizations, by deceiving either human peruses, or computerized feeling mining and opinion investigation frameworks. Thus, in the most recent years, a few information-driven methodologies have been proposed to survey the believability of client created content diffused through online life as on-line audits. Particular methodologies frequently think about various subsets of qualities, i.e., highlights, associated with the two audits and commentators, just as to the system structure connecting unmistakable elements on the survey site in test. This work goes for giving an examination of the fundamental audit and commentator driven highlights that have been proposed up to now in the writing to identify counterfeit surveys, specifically from those methodologies that utilize directed AI systems. These arrangements furnish when all is said in done better outcomes concerning simply unsupervised methodologies, which are frequently founded on chart-based strategies that think about social ties in audit destinations. besides, this work proposes and assesses.
Personal Verification System Using Thai ID Card and Face Photo for Cross-Age Face

Nowadays, the main personal verification system uses the photo in Thai ID card. However, the card can be used up to nine year. Consequently, there could be difference between the face on ID card and the current face of the cardholder. This article demonstrates the use of ArcFace + Face aging with IPCGAN to solve the problem of cross-age face comparison. Based on the experimental results, the proposed algorithm outperforms other algorithms. Moreover, to automatically extract text information from the ID card, the developed system applies OCR of Google document text detection. It was found that Thai text information obtained from the proposed system yielded 93.97% accuracy, which is higher than others.
PENGELOMPOKKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN INDIKATOR KESEJAHTERAAN DENGAN METODE KMEANS CLUSTER

Welfare is the thing that determines a development in an area. The government always tries to implement people's welfare as a program goal to be achieved. For this reason, this research will discuss the grouping of regencies / cities based on indicators of people's welfare using the K-Means cluster analysis. The aim of this research is to apply the K-Means cluster method to classify districts or cities based on the indicators of people's welfare in Central Java Province in 2017. The data in this study use secondary data from BPS publications in Central Java Province. The data used in this study are population density data (X1), open unemployment (X2), adjusted per capita expenditure (X3), life expectancy (X4), average length of schooling (X5), and the percentage of poor population (X6). From the results of the K-Means cluster analysis, the regencies / cities in Central Java are divided into 3 groups. Cluster 1 is a Regency / City group whose welfare is low (low). Cluster 2 is a regency / city group whose welfare is high (good). Cluster 3 is a Regency / City group whose welfare is quite good (moderate). Keywords: Kesejahteraan Rakyat, K-Means, Analisis Cluster.