Reviewerly/exHarmony huggingface dataset:
Classification using the C4.5 Algorithm in Predicting Students Organizational Status Amikom University Yogyakarta

Participation in an organization as a college student is an important way to selfimprovement.This research analyzes students' learning in an organization and whether the student is active or not active in an organization using the Decision Tree C4.5 algorithm.Attributes analyzed were out-of-campus organization, previous organization experience, public speaking, problem-solving, confidence level, and personality.From a question from propagating on October 2021, the researcher collect 203 raw data, with 167 processes ones that were used in this research.The test conducted results in a tree or the decision tree that could be used to decide how active a student is in an organization.The accuracy value of this test using cross-validation resulted in a score of 59.27% or in comparison to the data training: data testing of 4:1 or 80%: 20%.
ASVM: Adaboost with SVM-based Classifier Implementation for Aspect-based Opinion Mining to Appraise Products

Product evaluations can be extremely beneficial to both customers and manufacturers. The number of evaluations could range from hundreds to thousands, with each one presenting a different point of view. It becomes increasingly difficult to analyse and extract information from existing evaluations as a result of these developments. The method of evaluating the intensity of opinionated texts is known as Sentiment Analysis. A large number of social networking sites are being utilised by users for the purpose of expressing their ideas and opinions and rating things. These user-generated opinions are much unstructured in nature, necessitating the employment of a variety of natural language processing techniques to extract meaning from them. When performing entity-based sentiment classification, the many characteristics of a item are detected throughout the training activity. For example, the picture quality, size, resolution, and memory of a camera are all important considerations. The Ensemble AdaBoost with support vector machine classifier (ASVM) is used to do the quantitative examination of each aspect of the study. In the majority of earlier publications, a product review is examined as a whole rather than taking into account each individual part of it. Due to the difficulty in identifying individual features, aspect-based opinion mining can be time-consuming and labour-intensive to perform.
Una vida conjunta entre la Tecnología Educativa en la Ingeniería y la Educación y la Tecnología

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Modified K-Nearest Neighbour Using Proposed Similarity Fuzzy Measure for Missing Data Imputation on Medical Datasets (MKNNMBI)

Early disease diagnosis is a burning problem in health sector, medical domain and disease management. During analysis, quality of the data can be achieved only if the data is complete. Missing values reduces the efficiency of data analysis task. Researchers proposed various imputation methods but always there was a need for a better imputation method. This paper objective is to propose a method for imputation using proposed similarity fuzzy measure through which we can impute missing values by finding k similar instances called as Modified k-Nearest Neighbour for imputation of missing data (MKNNMBI). The proposed imputation method outperformed when compared with other existing imputation methods MV EM, MV BPCA, MV Ignore, MV KMeans, MV FKMeans, MV KNN, MV MC, MV WKNNimpute, MV SVDimpute, MV SVMimpute, CBC-IM-FUZZY. These imputation methods were studied on different benchmark datasets and tested for performance on different classifiers like C4.5, SVM, kNN, NB and found that the proposed method leads to accurate imputation and improves the accuracy.
Synthesis of Java Deserialisation Filters from Examples

Java natively supports serialisation and deserialisation, features that are necessary to enable distributed systems to exchange Java objects. Deserialisation of data from malicious sources can lead to security exploits including remote code execution because by default Java does not validate deserialised data. In the absence of validation, a carefully crafted payload can trigger arbitrary functionality. The state-of-the-art general mitigation strategy for deserialisation exploits in Java is deserialisation filtering that validates the contents of an object input stream before the object is deserialised using user-provided filters. In this paper we describe a novel technique called ds-prefix for automatic synthesis of deserialisation filters (as regular expressions) from examples. We focus on synthesis of allowlists (permitted behaviours) as they provide a better level of security. ds-prefix is based on deserialisation heuristics and specifically targets synthesis of deserialisation allowlists. We evaluate our approach by executing ds-prefix on popular open-source systems and show that ds-prefix can produce filters preventing real CVEs using a small number of training examples. We also compare our approach with other synthesis tools which demonstrates that ds-prefix outperforms existing tools and achieves better F1-score.
AN ACTIVE CONTOUR MODEL FOR TEXTURE IMAGE SEGMENTATION USING RÉNYI DIVERGENCE MEASURE

This paper proposes an efficient method for active unsupervised texture segmentation. A new descriptor for texture features extractions based on Gaussian and mean curvature is constructed. Then the optimization of a functional who uses the R´enyi divergence measure and our descriptor is proposed in order to design an active contour model for texture segmentation. To get a global solution and efficient, fast algorithm, the optimization problem is redefined. The algorithm associated with this last optimization problem avoids local minimums and the run-time consuming compared to the level-set representation of our active contour model. In order to illustrate the performance of the technique, some results are presented showing the effectiveness and robustness of our approach.
Voting Classification Approach for Sentiment Analysis of Twitter Data

This research work focuses on performing SA (sentiment analysis) from the text data. The sentiments are analyzed in different stages such to pre-process the data, extract the attributes and classify the data. Initially, SVM (Support Vector Machine) is implemented to analyze the sentiments. Subsequently, the integration of SVM is done with KNN (K-Neural Network) to classify the data. 5 diverse datasets are executed to test this integrated model. Eventually, the voting classification technique is put forward in order to analyze the sentiments. In this technique, GNB (gaussian naive bayes), RF (random forest) and SVM algorithms are integrated. The introduced techniques are implemented on Python. The outcomes are evaluated on the basis of several metrics including accuracy and execution time. The accuracy acquired from the introduced approach is superior and the execution time is lower in comparison with SVM model to analyze the sentiments.
Smart Traffic Light System Time Prediction Using Binary Images

In current circumstances, congestion in traffic is a major issue. Due to traffic congestion, many people didn&#x2019;t reach their destination place in a pre-defined time. This may lead to loss of job opportunity, miss of journey, etc. Many algorithms had been developed for reducing the congestion problem in traffic. They are working well in day times but those algorithms didn&#x2019;t give precise results at night times. So a new algorithm is developed by using image processing in this paper that will give precise results at night times also. This is done by isolating the RGB image into its individual channels and converting each channel to binary images with different thresholds for vehicle identification. Based on the results, the density of traffic is estimated and compared with the reference one with no traffic to get the allowed time for the green light to glow. So the allowed time is completely dependent on the density of the traffic.
Perancangan sistem informasi pengarsipan berbasis website

The rapid development of technology causes an increase in the need for information. The problems encountered in the system that has been running as it is today. The correspondence archiving system at the Leadership Administration Bureau still uses the manual method, so that the processing and data retrieval process becomes long and less efficient. The results of the analysis of the system that will be designed in accordance with the formulation of existing problems and discuss the results of observations with employees at the Leadership Administration Bureau that use this filing application flexibly in completing their tasks. Such as importing incoming letters, looking for letters, and borrowing letters easily, quickly, and efficiently. Therefore, this archiving information system is made in order to help agencies or companies to increase effectiveness in processing archive data by minimizing the possibilities of errors that will occur and so that information security can be guaranteed.
An Empirical Study on the Quality of Entropy Sources in Linux Random Number Generator

Random numbers are essential for communications security, as they are widely employed as secret keys and other critical parameters of cryptographic algorithms. The Linux random number generator (LRNG) is the most popular open-source software-based random number generator (RNG). The security of LRNG is influenced by the overall design, especially the quality of entropy sources. Therefore, it is necessary to assess and quantify the quality of the entropy sources which contribute the main randomness to RNGs. In this paper, we perform an empirical study on the quality of entropy sources in LRNG with Linux kernel 5.6, and provide the following two findings. We first analyze two important entropy sources: jiffies and cycles, and propose a method to predict jiffies by cycles with high accuracy. The results indicate that, the jiffies can be correctly predicted thus contain almost no entropy in the condition of knowing cycles. The other important finding is the failure of interrupt cycles during system boot. The lower bits of cycles caused by interrupts contain little entropy, which is contrary to our traditional cognition that lower bits have more entropy. We believe these findings are of great significance to improve the efficiency and security of the RNG design on software platforms.
Industrial Part Image Retrieval Combining Convolution and Attention Features

The image retrieval of industrial parts aims at ranking parts of different classes based on the similarity of the content, so as to solve the common problems of informatization management in industrial production and improve production efficiency. Generally speaking, the images are ranked by comparing the similarity between the representation vector of the query image and the gallery image for retrieval. Therefore, a compact and rich feature representation has become the key to image retrieval. In this paper, we propose a new image retrieval framework, which combines the features extracted by convolutional neural network (CNN) and Transformer, and retains local and global information to the greatest extent through convolution and self-attention. Specifically, we co-training CNN and Transformer to generate image feature descriptors, and combine multiple metric learning loss functions to train the model. Experiments show that this method achieves high accuracy in image retrieval. For example, we obtained a rank-1 score of 87.1% on the button Re-ID dataset, which is significantly better than existing methods.
Classification of Screenshot Image Captured in Online Meeting System

Owing to the spread of the COVID-19 virus, the online meeting system has become popular. From the security point of view, the protection against information leakage is important, as confidential documents are often displayed on a screen to share the information with all participants through the screen sharing function. Some participants may capture their screen to store the displayed documents in their local devices. In this study, we focus on the filtering process and lossy compression applied to the video delivered over an online meeting system, and investigate the identification of screenshot images using deep learning techniques to analyze the distortion caused by such operations. In our experimental results for Zoom applications, we can obtain more than 92.5% classification accuracy even if the captured image is intentionally edited to remove the traces of screen capture.
Undirected data analysis using factors, empirical orthogonal functions, and clusters

This chapter, Undirected data analysis using factors, empirical orthogonal functions, and clusters, introduces tools that are helpful in finding patterns within a dataset about which little is initially known. The advantage to this undirected approach is that the patterns are drawn from the data themselves, as contrasted to being imposed by the analyst, and may bring out unanticipated features that reflect the physical processes of the system. Three undirected methods, factor analysis, empirical orthogonal function analysis, and cluster analysis, are treated in depth. Factor analysis characterizes data as a mixture of a few significant patterns. It is applied to the Atlantic Rocks dataset, with the result of identifying mixing between several rock end-members. Empirical orthogonal function (EOF) analysis extends factor analysis with strategies for dealing with natural ordering of data in time and space. It is applied in a case study to a sequence of images of Pacific Ocean sea surface temperature, with the result of “rediscovering” the El Niño Southern Ocean (ENSO) climate oscillation. Cluster analysis characterizes data in term of subsets, each associated with a cluster mean and k-mean algorithm is introduced as a way of estimating the cluster means. Clustering is applied to the Atlantic Rocks dataset, and the results compared and contrasted to those of the factor analysis.
Sequencing of Autonomous Network functions using eXplainable AI Methods

Autonomous network functions (ANFs) are activated to achieve a specific objective. (e.g.: Load balancing, coverage and capacity optimization, energy saving across the network). Many times, activating the ANFs does not meet the specific objective predominantly due to external factors [1]. This paper introduces how explainable AI (xAI) methods such as feature impact analysis, dependency plot and other interpretable machine learning algorithms can be used for identifying such external factors and in turn sequencing the ANFs for meeting the objective. The paper concludes by introducing counterfactual and recourse algorithms as further research possibilities that goes beyond xAI for getting favorable outcome from ANFs.
Track planning and obstacle avoidance of wave glider based on improved artificial potential field algorithm

The research on track planning and obstacle avoidance methods of wave glider is an indispensable ability for its smooth work in the ocean. As a real-time track planning and obstacle avoidance algorithm, artificial potential field algorithm has attracted extensive attention. Aiming at the problem that the target point can not be reached in the traditional artificial potential field algorithm, the distance factor between the wave glider and the target point is introduced into the repulsive potential field; The relative velocity potential field function is introduced and the influence of velocity potential field is enhanced to solve the threat of dynamic obstacles to wave gliders. Then, based on the motion characteristics of wave gliders, the influence of stable waves in a certain range on track planning and obstacle avoidance is analyzed. Finally, the simulation analysis is carried out. The simulation results show that this method has good track planning and obstacle avoidance effect, and the generated path is smooth. Fixed the problem of not being able to reach the target.
AMSNet: Attention-based Multi-Scale Network for Image SR Reconstruction

Image super-resolution reconstruction (SR) aims to find the mapping relationship between a low-resolution (LR) image and the corresponding high-resolution (HR) image. At present, the SR methods based on deep learning still has the problem of blurred edges and loss of details in the reconstruction results. To address these issues, we propose an attention-based multi-scale SR network (AMSNet), In order to better learn the global and local features of images, a multi-scale network structure is designed to reconstruct richer image features. In the multi-scale structure, cascaded residual blocks (CRB) are used to extract image features. Additionally, a Squeeze-and-Excitation-based residual upsampling block (SERUB) is designed to enhance important features that are beneficial to image reconstruction. Extensive experiments show that our network has better performance in image reconstruction compared to some other state-of-the-art models and is able to reconstruct image texture details better.
A Novel Platform Orientation System for Proportional-Integral-Derivative-Controlled Ball-Catching Robot

Abstract Ball catching by a robot is one of the challenging and complex control tasks that is extensively studied to achieve human-like skills in robots. Over the last decade, several ball-catching robot designs have attained benchmarks in visual tracking and control algorithms. However, the coordination between the ball’s path tracking and the robot’s motion planning remains highly sensitive to environmental parameter changes. In general, ball-catching robots require a noise-free background with good lighting and multiple off-board tracking cameras. Also, a common failing point of these systems is the short flight time (or high-speed) of the ball and the uncertainties of throwing direction. To address these issues, in this study, we propose a ball-catching platform system that can rapidly orient the platform towards the throwing direction by utilizing two onboard cameras with multi-threading. A graphical user interface platform has been developed to implement the orientation algorithm and mask the ball with high accuracy. Our experimental results show that the proposed orientation platform system can be used in a low-light noisy background, and the overall ball-catching rate increases from 50% to 90% compared to the baseline design. The new system can also avoid erratic platform movements when masking is done in a noisy environment.
PEDAGOGICAL СONDITIONS AND PRINCIPLES OF FORMATION OF FUTURE TECHNICAL SPECIALISTS’ PROFESSIONAL VALUES PROFILE

The article substantiates the pedagogical conditions and principles of formation of professional values of future technical specialists. Based on the analysis of existing systems of universal and professional values in general and value systems of specialists of various profiles, it is established that purposeful research of the system of professional values of future technical specialists is not given enough attention, although there is significant potential to improve their spiritual outlook. Pedagogical conditions are formulated as determining the content of the axiological component of professional training; providing motivation for the formation of professional values; development of an active professional position on the basis of the axiological component of professional training; development of a positive attitude to professional values as part of professional training; awareness of their own professional values. These conditions are based on such leading positions as the idea of integration of special and axiological components as a goal-setting factor in training; identification and consideration of factors influencing the formation of professional values due to the specifics of professional activity; continuity of formation of professional values as a gradual inclusion of professional values in the system of universal values of the individual, and each subsequent stage is a logical continuation of the previous one. An instrumental tool for the formation of professional values is an integrative approach at the methodological stage and a differentiated approach at the stage of building models and specifying the conceptual framework for a particular specialization. Relevant principles are highlighted: humanization (formation of professional values of the future technical specialist contributes to the formation and development of the humanistic paradigm of vocational education), acmeological (formation of professional values of the specialist can significantly improve the quality of training). values of the future technical specialist should be focused on the professional and practical needs of students, their interests and needs and serve as an effective means of improving the quality of training), integrated goal setting (special training and professional personality formation is an integral goal of professional education axiological component of training).
Research on Fault Diagnosis of Power Communication Network Based on Improved Convolutional Neural Network

With the increasing complexity of power grid construction, traditional power maintenance methods are difficult to meet the current grid maintenance needs. Based on the needs of new smart grid communication system maintenance, this paper proposes a power communication network maintenance method based on improved convolutional neural networks. First, establish a model of power communication network failure, analyze modules with more failure probability, and propose a quantitative model to reduce the risk cost of communication failure, and improve it based on the long-short memory deep neural network, and determine the power grid communication based on the output of the model. The strategy of equipment maintenance provides a certain strategy for the construction of smart communication grid.