Reviewerly/exHarmony huggingface dataset:
Fast and Secure Back-Propagation Learning Using Vertically Partitioned Data with IoT

The use of the cloud computing system is expanding. As the number of terminals (things) connected to the cloud system increases, the limit of the capability is also becoming apparent. It leads to significant processing time delay. Edge (or fog) computing system is known as a method for improving a conventional cloud system. The basic idea is to consider a system that places edges (servers) between the cloud and the terminals (things). The capacity of each edge may not be so high, but many edges cooperate to execute tasks to achieve high processing power. Then, how should machine learning be realized on the edge system? Fast and secure learning methods are desired for machine learning. The use of a cryptographic system does not seem to be necessarily suitable for machine learning. Therefore, a safe system using distributed processing has attracted attention. SMC (Secure Multiparty Computation) is one of the typical models of them. Horizontally and vertically partitioned data are known for SMC. There have been proposed some methods for realizing machine learning on the cloud using SMC. Also, some methods of machine learning using horizontally partitioned data of SMC on the edge system have been proposed. On the other hand, little studies have been done on machine learning using the vertically partitioned data. In this paper, a fast and secure BP (Back-Propagation) neural network learning on vertically partitioned data with edge system is proposed. The effectiveness is shown by numerical simulation.
Object Detection from Mongolian Nomadic Environmental Images

Mongolian historical and cultural monuments on settlement areas of stone inscriptions, stone images, rock-drawings, remains of cities, architecture are still telling us their stories.These monuments depict the understanding of the word, philosophical and artistic outlook, beliefs, religion, national art, language, culture and traditions of Mongols [1].Nowadays computer science, especially computer vision is applying in the other science fields.The main problem is how to apply and which algorithm can detect and classify the objects correctly.In this paper, we propose a method to detect object from Mongolian nomadic environment images.This work proposes a method for object detection that is the combination of the binary operations in the edge detection results.We found out the best method and parameters of state-of-the-art machine learning algorithms.In experimental result, we evaluate our results with 10-fold cross validation and split 66% strategies.
A Novel Approach to Fuzzy-Based Facial Feature Extraction and Face Recognition

Generalized two-dimensional Fisher’s linear discriminant (G-2DFLD) is an effective feature extraction technique that maximizes class separability along row and column directions simultaneously. In this paper, we propose a fuzzy logic-based feature extraction technique, called fuzzy generalized two-dimensional Fisher’s linear discriminant analysis (FG-2DLDA) method which is extended version of the G-2DFLD method. This paper also explores the use of the proposed method for face recognition using radial basis function (RBF) neural network as a classifier. Fuzzy membership matrix values are calculated by fuzzy k -nearest neighbour (F k- NN) algorithm for the training samples. These fuzzy membership values are combined with the training samples to generate global mean and class-wise mean training images. Thereafter, the global and class-wise mean images are used to generate fuzzy within- and between-class scatter matrices along the both directions. Finally, by solving the Eigen value problems of these scatter matrices, we find the optimal fuzzy projection vectors, which actually used to generate more discriminant features. Our proposed method has been evaluated on the four public face databases using RBF neural network and establish that the proposed FG-2DLDA method provides favourable recognition rates than some contemporary face recognition methods.
Learning and Development of Home Service Robots’ Service Cognition Based on a Learning Mechanism

In order to improve the intelligence of home service robots and resolve their inability to develop service cognition skills in an autonomous, human-like manner, we propose a method for home service robots to learn and develop skills that allow them to perform their services appropriately in a dynamic and uncertain home environment. In a context model built with the support of intelligent sensors and Internet of Things (IoT) technology in a smart home, common-sense information about environmental comfort is recorded into the logical judgment of the robot as a reward provided by the environment. Our approach uses a reinforcement learning algorithm that helps train the robot to provide appropriate services that bring the environment to the user’s comfort level. We modified the incremental hierarchical discriminant regression (IHDR) algorithm to construct an IHDR tree from the discrete part of the data in a smart home to store the robot’s historical experience for further service cognition. Poor adaptive capacity in a changeable home environment is avoided by additional user guidance, which can be inputted after the decision is made by the IHDR tree. In the early development period, when robots make an inappropriate service decision because they lack historical experience, the user can help fix this decision. Then, the IHDR tree is updated incrementally with fixed decisions to enrich the robot’s empirical knowledge and realize the development of its autonomic cognitive ability. The experimental results show that the robot accumulates increasingly more experience over time, and this experience plays an important role in its future service cognition, similar to the process of human mental development.
Media Information Literacy to Produce Collaborative Social Capital

In the ubiquitous cyber communication society, vernacular creators participate a creative community on social media and use collaborative methods of production that are different from traditional industrial modes.This study investigates advanced media information literacy (MIL) competency for collaborative creation.A well-known case was examined, and the analysis provides a deeper understanding of interrelated factors in mixed media creation.We also extracted disruptive technologies that blur the borders between vernacular and professional production in quality.The results show that creators have more fun in making work themselves than in making profit.Users also show benevolence and make contributions to expand the community.There is importance in understanding entrepreneurship and the community in which creators cooperate with industries.We conclude that social capital resides in the connection of users.The competency of socialized creation as an advanced MIL competency is also discussed in the context of four pillars of items: socialized creation, collaborative creativity, critical eye, and building affordance.
Long Run Return and Survival Factors of ICO

The cryptocurrency market is young and booming. The growth of this market is to a large extent associated with an increase in the number of initial coin offerings, also known as ICOs, as well as the volume of funds raised during the placement of new cryptocurrencies. In this article, we consider the relationship between the returns of cryptocurrencies emerged through ICOs and traded on specialized exchanges and various market factors as well as we examine the risk factors associated with the failure of cryptocurrency projects expressed in the delisting of their tokens or in a critical drop in their value. As part of the regression analysis, we showed that, in full accordance with the available literature, the returns of cryptocurrencies are not related to traditional factors of the financial market. However, sentiments in the cryptocurrency market itself, proxied by Bitcoin's returns, have a significant impact on long-term profitability, as well as sentiment in the market of Internet companies stocks. Moreover, the latter play an important role in relation to new tokens entering the market. We also showed that sentiment in the cryptocurrency market is important for the survival of cryptocurrencies. The token entering the market during periods of rising Bitcoin and increasing number of ICOs significantly reduces the chances of its survival. The results of this article are also relevant for the Russian market, since Russian companies together occupy the fourth place in the world in the number of ICOs conducted.
Expected Income of New Currency in Blockchain Based on Data-Mining Technology

In order to realize the understanding of expected returns after issuance of blockchain new currency initial coin offerings (ICO) and maximize investment returns, in this study, the Semantic Orientation Pointwise Mutual Information (SO-PMI) algorithm is used to create a customer emotional dictionary of blockchain new currency, and collect users’ online comments based on blockchain currency before ICO. The Support Vector Machine (SVM) algorithm is used to construct an evaluation model, analyze and judge users’ comments, make accurate prediction of the expected return of ICO issuing new currency, improve investment operations, and maximize the return of investment. The results show that the combination of the SO-PMI and SVM algorithms can accurately evaluate the price after the issuance of new currency, and then realize the judgment of expected return and obtain the expected return of investment. It can be seen that the combination of algorithms based on data-mining technology is applied to the study of the expected return of new currency issuance in blockchain, which achieves the goal of revenue anticipation and greatly reduces the investment risk of new currency issuance in blockchain.
Speech Analysis in Legal Proceedings: Urgent Needs, Problems and Ways to Solve Them

The article describes the problems in legal proceedings that impede effective use of special knowledge of speech necessary to qualify offenses committed by means of speech actions. Among them the imperfection of the current legislation on forensic expert activity is mentioned, where there are no uniform professional qualifications and requirements for state forensic experts and persons who are not the employees of state forensic institutions and who are quite often involved in the conduct of linguistic expertise, authorship and phonogram examinations. It is stated that due to the lack of professional training in forensic specialties of such non-state experts, the quality of their speech analysis does not meet the need of the legal community and civil society in objective and effective examinations, the results of which could be used in trial. If the incompetence of non-state experts is revealed, their opinions are recognized as an inadmissible evidence, reexaminations are assigned to the state forensic institutions, which significantly delays the proceedings. It is proposed to legislate the mandatory certification for nonstate experts, to unify the procedure of additional professional education in the field of specific expert’s specialties for state and non-state forensic experts. In order to solve the problems mentioned above it is proposed to develop and implement a passport for each forensic specialty, a single nomenclature of forensic specialties corresponding to the types of forensic speech examinations, to unify additional professional retraining and professional development programs.
An Adaptive Image Inpainting Method Based on the Weighted Mean

Imaging inpainting is the process of digitally filling-in missing pixel values in images and requires carefully crafted image analysis tools. In this work, we propose an adaptive image inpainting method based on the weighted mean. The weighted mean is assessed to be better than the median because, for the case of the weighted mean, we can exclude the values of the corrupted pixels from evaluating values to fill those corrupted pixels. In the experiments, we implement the algorithm on an open dataset with various corrupted masks and we also compare the inpainting result by the proposed method to other similar inpainting methods – the harmonic inpainting method and the inpainting by directional median filters – to prove its own effectiveness to restore small, medium as well as fairly large corrupted regions. This comparison will be handled based on two of the most popular image quality assessment error metrics, such as the peak signal to noise ratio, and structural similarity. Further, since the proposed inpainting method is non-iterative, it is suitable for implementations to process big imagery that traditionally require higher computational costs, such as the large, high-resolution images or video sequences.
The use of 3D software on plant anatomy courses for prospective Biology teachers

Abstract The use of computer-based learning media is something that is commonly done in the era of the revolution industry 4.0 today. The aim of this research is to describe the results of three-dimensional images created using 3D Blender. The research method used is a descriptive research. the collected data of the results of the assessment of the creation of the three-dimensional tissue form in plants. The criteria created by students are viewed based on form, cell details, size, differentiation, layout and caption. The population in this study are prospective Biology teachers who took the courses of plant anatomy in the academic year 2018/2019 in Biology Education department, One of the universities in East Priangan, Indonesia. Sample research used only one class with sampling technique is purposive sampling, regarding their homogenous sampling of having the relatively same ability and enough computer specification. The results showed an average of the 3-dimensional form of the assessment made on the ground tissues (2.84), tissue vessels (2.68) and dermal tissues (3.32). The average value obtained by prospective Biology teachers tends to be low, it is due to lack of mastery of the concept and at least the time for training software use.
Object Detection and Classification Using GPU Acceleration

In order to speed up the image processing for self-driving cars, we propose a solution for fast vehicle classification using GPU computation. Our solution uses Histogram of Oriented Gradients (HOG) for feature extraction and Support Vector Machines (SVM) for classification. Our algorithm achieves a higher processing rate in frames per second (FPS) by using multi-core GPUs without compromising on its accuracy. The implementation of our GPU programming is in OpenCL, which is a platform independent library. We used a dataset of images of cars and other non-car objects on road to feed it to the classifier.
Intention mining: A deep learning-based approach for smart devices

Smartwatches have become increasingly popular due to their ability to track human activities. The tracked information can be shared with other devices, such as smartphones, and used for scheduling, time management, and health management. Although several studies have focused on developing technique s for natural language text, users intention-to-recommend smartwatches have never been investigated. Consequently, the manufacturers, as well as potential buyers cannot get a holistic view of users’ perception of the smart device of their interest. Also, the non-availability of publicly available benchmark corpus has thwarted the development of intention mining techniques. Retrospectively, this study has proposed an approach for mining users’ intention to recommend smartwatches. In particular, we have employed an innovative approach, involving a screening processing and annotation guidelines, to develop the first-ever manually annotated corpus for mining intention-to-recommend smartwatches. Furthermore, we have performed experiments using two deep-learning techniques and five types of word embeddings to evaluate their effectiveness for intention mining. Finally, the recommendation sentences are synthesized to develop a deeper understanding of the user feedback on the selected products.
An inverse procedure for characterization of material parameters of passive skeletal muscle using FEM and experimental data

This work develops an inverse procedure which combines an improved niche genetic algorithm, finite element models and experimental data to identify material parameters of biological tissues behaving like hyperelastic materials.A novel objective function is proposed with two coefficients, which controls the contributions in a well-balanced fashion, respectively, for the small deformation stage and the large deformation stage.This allows us to obtain a global minimizer (of material constants) for the error between FEM solutions and experimental data.Moreover, simple uniaxial compression tests at two different angles (0 • and 90 • ) with respect to the muscle fiber orientation are performed on fresh muscle tissues.This enables us to obtain anisotropic properties of the muscle tissue from the present inverse procedure.The result shows that the proposed inverse procedure is stable and reliable to determine material constants in hyperelastic models for soft biological tissues like skeletal muscles considering anisotropy.
Estimating Dynamic Characteristics of Gravel-Tire Chips Mixtures Using Artificial Intelligence Techniques

Scrap Tire Derived Geo-Materials (TDGM) mixed with soil are often being used as environmentally friendly granular materials in sustainable construction of civil engineering projects for reducing dynamic loads acting on geo-structures and soil liquefaction remediation purposes. Predicting dynamic properties of TDGM-soil mixture is a complicated task due to the numbers of factor involved in soil- TDGM mixture. This study presents application of artificial intelligence technique in estimating dynamic characteristics of granular mixture of Gravel and Tire chips (GTCM). Support Vector Regression (SVR) and Artificial Neural Networks (ANN) were used for predicting shear modulus and damping ratio of GTCM. Shear modulus and damping ratio models were developed using ANN and AVR techniques. The models were trained and tested using a database that included results from a set of laboratory tests on the GTCM. Stress controlled cyclic triaxial tests were conducted on specimens of gravel and tire chips with different volumetric portions of gravel in mixture (GF). The tests were performed on GTCM specimens at an initial relative density of 50% under different initial effective confining pressures. Test results have shown that shear modulus and damping ratio of the granular mixtures are remarkably influenced by volumetric fraction of gravel in GTCM. Furthermore, shear modulus was found to increase with the mean effective confining pressure and gravel fraction in the mixture. It was found that a feed-forward multilayer perceptron model with back-propagation training algorithm have better performance in predicting complex dynamic characteristics of granular mixture than SVR one.
On the Number of Shortest Weighted Paths in a Triangular Grid

Counting the number of shortest paths in various graphs is an important and interesting combinatorial problem, especially in weighted graphs with various applications. We consider a specific infinite graph here, namely the honeycomb grid. Changing to its dual, the triangular grid, paths between triangle pixels (we abbreviate this term to trixels) are counted. The number of shortest weighted paths between any two trixels of the triangular grid is discussed. For each trixel, there are three different types of neighbor trixels, 1-, 2- and 3-neighbours, depending the Euclidean distance of their midpoints. When considering weighted distances, the positive values α, β and γ are assigned to the ‘steps’ to various neighbors. We gave formulae for the number of shortest weighted paths between any two trixels in various cases by the respective weight values. The results are nicely connected to various numbers well-known in combinatorics, e.g., to binomial coefficients and Fibonacci numbers.
PENGARUH POLA ASUH ORANG TUA TERHADAP MOTIVASI BELAJAR SISWA KELAS VII DI SMP KRISTEN 2 SALATIGA

This study aims to determine the significance of the effect of parenting patterns in the learning motivation of students grade VII in Christian Middle School 2 Salatiga. This type of research is comparative causal research or it can be called ex post facto research, this research carried out in Christian Middle School 2 Salatiga. Subject of this research subjects were class VII students, amounting to 87 people from class VII A totaling 22 students, class VII B totaling 21 students, class VII C totaling 22 students and class VII D totaling 22 students. The methods for collects the data is by spreading questionnaires about parenting pattern based on Baumrind’s theory amounted to 30 questions, and learning motivation questionnaires based on Uno theory amounted to 25 questions. The data analysis technique used is simple linear regression with the help of the SPSS for Windows version 20. Computer program. Based on the results of the study there is no significant influence on parenting pattern towards the learning motivation of grade VII students in Christian Middle School 2 Salatiga, with parenting pattern only contributing influence of 3.9% with Sig 0.097&gt; 0.050 on learning motivation. While the remaining 96.1% is influenced by other factors not examined in this study.Keyword: Parenting Pattern, Learning Motivation
Software code analysis using ensemble learning techniques

Ensuing the advent of advancements in software systems, the probability of them containing high severity defects is exponentially on the rise. With each technological addition, the complexity of software is increasing. Reproduction and rectification of a defect requires time and effort. Current state of the art analysis tools cater to the investigation of static aspects of a production level code. However, it is imperative to assess the dynamic development process of a system so as to be able to timely detect erroneous components early on in the development life cycle of a software. A novel automated defect prediction feature enhancement is proposed that analyses the static structure of the current code and state of the software in past releases to extract relevant static and dynamic feature sets. Data generated is modelled for defect trends in the future release of the software by four ensemble classifiers. Results demonstrate the superiority of Voting algorithm for the problem of defect prediction.
Segmentation Based Preprocessing Techniques for Predicting the Cervix Type Using Neural Networks

Cervical cancer is a disease condition which makes cells on the human organ Cervix grow out of control. Among all women, it is the second most occurring type of cancer. Human papillomavirus is the cause of this disease. Early stage detection of cancer helps in finalizing correct medication at the appropriate time. Diagnosis of cervical cancer involves human expertise to a greater extend. Advanced medical imaging enables computerized methods to identify the cancerous cells at beginning stage. Detecting the cervix type is very important as the type of treatment depends on the cervix type. We intend to propose a classification technique to predict the cervix type using Neural networks. From our earlier works, it is understood that the lack of powerful preprocessing techniques for finding Region of Interest(ROI) is missing in the literature. We have developed a Neural Network(UNet) based segmentation technique for cutting the ROI from the input image set. Finally, a CNN model is designed, developed and trained for classifying the cervix type. The applicability of transfer learning to cervix type prediction is also tried. A accuracy of 70% is obtained.