Reviewerly/exHarmony huggingface dataset:
Bayesian Inference with Certifiable Adversarial Robustness

We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Networks (BNNs) with certifiable guarantees. We rely on techniques from constraint relaxation of non-convex optimisation problems and modify the standard cross-entropy error model to enforce posterior robustness to worst-case perturbations in $\epsilon$-balls around input points. We illustrate how the resulting framework can be combined with methods commonly employed for approximate inference of BNNs. In an empirical investigation, we demonstrate that the presented approach enables training of certifiably robust models on MNIST, FashionMNIST and CIFAR-10 and can also be beneficial for uncertainty calibration. Our method is the first to directly train certifiable BNNs, thus facilitating their deployment in safety-critical applications.
Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.
The Impacts of Covid-19 Pandemy in Term of Technology Literacy Usage on Students Learning Experience

This study aims at investigating the positive impact of the Covid-19 pandemic which has changed the learning process from offline to online. The research focuses on the use of the internet as a means of technological literacy which is the reference for research on student learning experiences. This study uses a comparative descriptive method by comparing internet usage before and during the pandemic related to the learning process. A structural questionnaire link using google form was sent to students through WhatsApp. A total of 36 students provided complete information regarding the survey. The simple percentage distribution was used to assess the learning status of the study participants. The results showed that there was a significant increase in the use of technology and internet literacy where downloading, uploading, attending workshops, webinars, and online competitions increased by using many online learning applications. The positive impact that can be seen is the increase in digital skills of students and lecturers as shown by their creativity and willingness to be more active, which of course supports online learning
Image Segmentation Based on Fuzzy C Means Clustering Algorithm and Morphological Reconstruction

The purpose of segmentation is to depict an original picture in something easier to interpret. Generally, in image processing watershed algorithm is used essentially for segmentation purposes which is fast and simple method and requires low computation time. But, it has disadvantages causing excessive segmentation and this method is sensitive of falsifying edges. The fuzzy c means (FCM) technique is extremely successful when segmenting images. Fuzzy c means clustering's biggest advantage is the high identification rate and the lower false location rate. Nevertheless, the fuzzy c means algorithm is noise-sensitive. To overcome these problems, an improved image segmentation algorithm based on morphological reconstruction and fuzzy c means algorithm is presented in order to improve the performance of the segmentation. Firstly, principle component analysis method is applied to reduce number of variables in data by extracting important one from large pool. Secondly, morphological reconstruction operation is introduced which guarantees the immunity to noise. Thirdly, fuzzy c means algorithm is applied. Finally, digital images are segmented by using this proposed method. Segmented findings indicate that better segmentation efficiency than watershed algorithm and fuzzy c means algorithm were obtained with proposed approach.
METHOD OF MULTI-MODAL VIDEO ANALYSIS OF HAND MOVEMENTS FOR AUTOMATIC RECOGNITION OF ISOLATED SIGNS OF RUSSIAN SIGN LANGUAGE

Abstract. This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed method, due to the extraction and analysis of spatio-temporal data, makes it possible to identify more informative features of signs, which leads to an increase in the accuracy of SL recognition.
The concept of teaching the history course for students of noncore specialties

This work is aimed at disclosure of features of history teaching in conformity with modernization processes in Russian higher education. The purpose of this concept is quality improvement of historical education and development of cognitive and socially important knowledge, skills and habits of students of noncore specialties (nonhistory students), economy profile first of all. The concept is based on competence and activity approaches to education. The relevance of development of this concept is dictated by high requirements of modern society to quality of history education, increased public interest to facts of Russian and foreign history, development of historical studies, accumulation of new historical knowledge. In the course of this work, the recommendations have been given regarding development of content and education plan of History discipline, which are universal and after certain adaptation can be used by teachers of other humanitarian and social disciplines. A fresh point of view is proposed on historical education as a tool of formation of system thinking of students, creation of consistent concepts of world history process, including the role of Russia and its development as a part of human civilization.
Environment humidity and temperature prediction in agriculture using Mamdani inference systems

&lt;span&gt;This paper presents the results of a humidity and temperature prediction model in the environment for agriculture, using diffuse sets and optimizing their parameters by heuristic methods, such as genetic algorithms, and exact methods such as Quasi-Newton. It has been identified that non-specialized users could have difficulties in understanding the system dynamics and the behavior of variables over time. The aim of this research is obtain models with a high level of interpretability and accuracy that allows predicting the temperature and humidity values for the environment. The use of fuzzy logic to present a solution has great advantages as this system is highly rated for interpretability. Furthermore, by relating the obtained values for environment humidity and temperature to qualitative categories as high, medium or low, it allows non-specialized users to have a better understanding of the system dynamics. Two optimization techniques are applied to two different diffuse sets that allow the prediction of the humidity and temperature. It is found that the best implementation involves a Mamdani fuzzy inference system optimized with Quasi-Newton algorithm that uses a set of initial values attained through a previous optimization process with a genetic algorithm.&lt;/span&gt;
PolyMorph-2D: An open-source GIS plug-in for morphometric analysis of vector-based 2D polygon features

This paper introduces a free and open-source geographic information system (GIS) plug-in, named PolyMorph-2D, for morphometric analysis of vector-based polygon features (PFs) in OpenJUMP. Written in Java™ programming language, the plug-in allows researchers from various earth and spatial science-related disciplines to compute morphometric parameters of 2D input vector features forming a polygon (i.e., a closed curve) which may represent outer boundaries or contours of various natural and/or man-made entities that differ in size, shape, complexity and drawing scale (e.g., craters, sinkholes, cirques, drainage basins, land parcels, buildings, lakes, ponds, peaks, pits, playas, pockmarks, coral mounds, seamounts, landslides, drumlins, islands, atolls, granular materials, mineral crystals, fossils, etc.). Using this plug-in, a number of basic tasks (e.g., calculations related to location, length, width, perimeter, radius, area, and orientation) can be performed and derived parameters (i.e., shape and size measures) of PFs (e.g., axis ratios, ellipticity, circularity, compactness, dispersion, complexity, elongation, regularity, convexity, concavity, solidity, rectangularity, roundness, and sphericity) are computed and written to input polygon layer's attribute table, through a simple point-and-click interface. The derived parameters calculated by the plug-in are invariant to the PF's location, scale (size), and rotation. The plug-in is also capable of performing aforementioned morphometric calculations on overlapping PFs with or without holes, provided they are topologically correct. The extensive data matrix (consisting of 39 parameters) obtained from this route can be further subjected to a number of multivariate statistical analysis methods to decipher underlying processes and/or environmental factors responsible for formation of these shapes and to classify them into homogeneous subtypes or typologies featuring similar characteristics.
A critical comparison on attitude estimation: From gaussian approximate filters to coordinate‐free dual optimal control

This paper conveys attitude and rate estimation without rate sensors by performing a critical comparison, validated by extensive simulations. The two dominant approaches to facilitate attitude estimation are based on stochastic and set-membership reasoning. The first one mostly utilizes the commonly known Gaussian-approximate filters, namely the EKF and UKF. Although more conservative, the latter seems to be more promising as it considers the inherent geometric characteristics of the underline compact state space and accounts—from first principles—for large model errors. The set-theoretic approach from a control point of view is addressed, and it is shown that it can overcome reported deficiencies of the Bayesian architectures related to this problem, leading to coordinate-free optimal filters. Lastly, as an example, a modified predictive filter is derived on the tangent bundle of the special orthogonal group TSO ( 3 ) .
Automotive Vulnerability Disclosure: Stakeholders, Opportunities, Challenges

Since several years, the overall awareness for the necessity to consider a vehicle as a potentially vulnerable system is facing accelerated growth. In 2015, the safety relevant exploitability of vulnerabilities through cyber attacks was exposed to a broader public for the first time. Only a few months after this attack has reached public awareness, affected manufacturer implemented one of the first bug bounty programs within the automotive field. Since then, many others followed by adapting some of ITs good practices for handling and responsibly disclose found and reported vulnerabilities for the automotive field. Nevertheless, this work points out that much remains to be done concerning quantity and quality of these measures. In order to cope with this, this present paper deals with what can be learned from IT and which conclusions can be drawn from these findings in the light of special conditions in the automotive environment. Furthermore, current handling and challenges regarding the disclosure process of vulnerabilities in the automotive sector are presented. These challenges are addressed by discussing desirable conditions for a beneficial disclosure culture as well as requirements and responsibilities of all parties involved in the disclosure process.
Arabic Quizzes Game to Improve Arabic Vocabulary

Learning media has experienced many variations along with the development of technology. Learning and playing are activities that are not only carried out by children but also by adults. Educational games as visual media are games that are packaged to support and increase users' thinking and concentration. The use of educational game technology in the teaching and learning process is an interactive learning technique. Mastery of Arabic vocabulary is very important, seeing from the many educational games that use this international language. This research was conducted to determine the effectiveness and benefits of educational games as well as learning designs using educational game content. In this study, researchers used quantitative methods that were supported by population and sampling. The development of this educational game was carried out by collecting data in the form of interviews as the main technique, documentation and observation as supporting techniques. The results in this study state that educational games are an effective and interactive medium in increasing Arabic vocabulary at the higher education level. This is because educational games can increase learning motivation and can make it easier for students to understand the material.
Optimization Hydro-Thermal-Wind-PV Solar Using MOPSO Algorithm Applied to Economic/ Environmental Dispatch

The purpose in this present work exposure the application of MOPSO multiple-objective particle swarm optimization algorithm to solver dynamic economic emission dispatch problems with ramp rate.The proposed MOPSO algorithm is applied in economic emission dispatch to fin the best solutions of hydro plant, thermal units, wind and PV solar generation scheduling powers and then calculate the cost, emission functions for SOx and NOx gas pollution, and combined the three function.The dynamic economic emission distribution (DEED) is an important subject in electrical power systems network.This consists of a valve point effect(VPE), transmission powers losses, a load, an equilibration power system and generator constraints.The proposed algorithm was exanimate on ten unit test power systems.All simulations results are thorough on the MATLAB-Simulinck space.
THE EFFECTIVENESS OF USING PECHA KUCHA TECHNIQUE TO PROMOTE STUDENTS’ SPEAKING SKILL

The aimed of this research is to test the effectiveness of using Pecha Kucha Technique in students’ speaking performance at fourth semester of English Department in Muhammadiyah University of Metro. Pecha Kucha is a presentation form of 20 pictures for 20 seconds and it is also called 20x20 presentations.The entire presentation always lasts for exactly 6 minutes and 40 seconds. The research method used in this research is an experimental research. The subjects of this research are the students at fourth semester of English Department in Muhammadiyah University of Metro academic year 2020. There are 21 students of control group and 21 students of experimental group. The research instrument used to collect the data in this research is an oral test. From the two classes, the experimental and control group, the measurement shows that increasing line of the speaking ability of the experimental group is higher than the control group. The result of pre-test score shows that the average score of the experimental group and the control group are 52.38 and 56.19. From the score of both groups, the result shows that the control group is better than experimental group. Then, the result of the post test of the experimental group is higher than control group; the average scores are 70,71 for experimental group and 62.38 for control group. It can be concluded that there is significant influence of using Pecha Kucha Technique in students’ speaking skill.
Cyber-criminology defense in pervasive environment: A study of cybercrimes in Malaysia

The growth of technologies; infrastructures and platforms with less or no security protection in emerging big data and internet of things (IoT) trends increase the likelihood of cybercrime attacks. With the rise of coronavirus disease-2019 (Covid-19) pandemic towards mankind, more cybercrimes are designed to penetrate one’s cognitive mind in revealing sensitive details. In this paper; an exploration of cybercrime threats in Southeast Asia country; Malaysia from year 2008 up to 2020 and its hike trends and impacts will be discussed. An investigation revolving the study of cyber-criminology and the reasoning behind the growth in terms of technological advancement will be presented. The findings suggest that the consequences and impacts of the cyberspace attacks are beyond the loss of money and reputations. It now becomes the failure of the global systemic altogether. As a mechanism to handle this would be to focus on protecting mission critical applications in pervasive environment. In this paper, a comprehensive authentication and authorization framework in safeguarding applications and users in the pervasive environment will be presented.
Graph convolution network with node feature optimization using cross attention for few-shot learning

Graph convolution network (GCN) is an important method recently developed for few-shot learning. The adjacency matrix in GCN models is constructed based on graph node features to represent the graph node relationships, according to which, the graph network achieves message-passing inference. Therefore, the representation ability of graph node features is an important factor affecting the learning performance of GCN. This paper proposes an improved GCN model with node feature optimization using cross attention, named GCN-NFO. Leveraging on cross attention mechanism to associate the image features of support set and query set, the proposed model extracts more representative and discriminative salient region features as initialization features of graph nodes through information aggregation. Since graph network can represent the relationship between samples, the optimized graph node features transmit information through the graph network, thus implicitly enhances the similarity of intra-class samples and the dissimilarity of inter-class samples, thus enhancing the learning capability of GCN. Intensive experimental results on image classification task using different image datasets prove that GCN-NFO is an effective few-shot learning algorithm which significantly improves the classification accuracy, compared with other existing models.
Improving Student’s Speaking Skill Using Youtube Video as Media: An Action Research

&lt;p&gt;&lt;span lang="EN-US"&gt;This research is an action research that aims to improve students' speaking skills using Youtube media. Discussing how Youtube as a learning medium can improve students' speaking skills. During the Covid 19 pandemic, formal schools are still carrying out the learning process online and only courses are allowed to study face-to-face because there are fewer students in one class, therefore this research was conducted in the Easy English course. Students' speaking ability on the Easy English course is still low especially at the level of children. English course students have difficulty in speaking skills. The students are bored with the old method of improving their speaking skills. The data were collected through speaking assessment and interview&lt;/span&gt;&lt;span lang="EN-US"&gt;in classroom action research. The writer used Youtube video and students watched and practiced it. Each class consisted of 7 to 10 students, the writer made three cycles and gave a score for each cycle according to the speaking size. The results of the research point out that the students could improve their speaking skills by using Youtube videos. In the first cycle, the students' average score is 65 points, the second cycle is 75 points, and the last cycle is 85 points.&lt;/span&gt;&lt;/p&gt;
Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection

Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprise-wide data storage capabilities that support the use of artificial intelligence (AI) and machine learning (ML) approaches to stay one step ahead of criminals. AI is best suited to defend against today’s fast-changing and complex bank fraud, where new threats are under development every day. Approaches relying on fragmented and siloed data, rules-based approaches or traditional point-solutions are no longer acceptable. These approaches are not only ineffective, but they are extremely costly to banks and financial services firms because they force legal and compliance teams to spend a lot of time trying to gain access to the data they need. By relying on advanced analytics and AI and ML capabilities, fraud and compliance units can spend their time working on more-complex fraud issues. Manual investigation can be reduced through the use of complex algorithms powered by ML, often in conjunction with rules, a combination that offers significant advantages over purely based -rules fraud detection. In this paper, we have included different machine learning algorithms used to detect credit card frauds and also provide a comparative study between different algorithms.
HW/SW Architecture Exploration for an Efficient Implementation of the Secure Hash Algorithm SHA-256

Hash functions are used in the majority of security protocol to guarantee the integrity and the authenticity. Among the most important hash functions is the SHA-2 family, which offers higher security and solved the insecurity problems of other popular algorithms as MD5, SHA-1 and SHA-0. However, theses security algorithms are characterized by a certain amount of complex computations and consume a lot of energy. In order to reduce the power consumption as required in the majority of embedded applications, a solution consists to exploit a critical part on accelerator (hardware). In this paper, we propose a hardware/software exploration for the implementation of SHA256 algorithm. For hardware design, two principal design methods are proceeded: Low level synthesis (LLS) and high level synthesis (HLS). The exploration allows the evaluation of performances in term of area, throughput and power consumption. The synthesis results under Zynq 7000 based-FPGA reflect a significant improvement of about 80% and 15% respectively in FPGA resources and throughput for the LLS hardware design compared to HLS solution. For better efficiency, hardware IPs are deduced and implemented within HW/SW system on chip. The experiments are performed using Xilinx ZC 702-based platform. The HW/SW LLS design records a gain of 10% to 25% in term of execution time and 73% in term of power consumption.