Reviewerly/exHarmony huggingface dataset:
A Case Study of Innovative Teaching Methods with Problem-Based Learningin Practical Training Seminar Courses in Beauty Design

The introduction of the 4th industrial revolution technology has brought about changes in various fields such as the economy, technology, and society. Responding to changes in external environmental factors, universities strive to nurture skills and knowledge necessary for the future by improving the existing education system and innovating overall education, including teaching methods and contents. This study aims to introduce PBL (Problem-Based Learning) cases applied to practical training seminars, a major in the Department of Beauty Design, in detail and to provide learning effects accordingly, thereby presenting PBL activities as suitable activities for field-oriented curriculum operation. It provides primary data for development. The subjects of this study are 4-year college students in Gyeonggi-do who have learned majors such as makeup/hair/skin/nails in advance and are 4-year learners taking beauty practice seminars or lectures. Most of the students have the basic skills required to submit presentations and design proposals, and the application period was six weeks (9 to 14 weeks), 20 students, and three hours once a week. Through this PBL curriculum, learners were found to be very satisfied with the results derived. It is a teaching method for improving the practical ability of cooperative learning, and it can be a plan for operating a field-oriented curriculum. Through this, it can be used as basic data to establish a PBL innovative teaching method class operation support system, and its significance is to strengthen the creative capabilities needed in the future society by applying it as a field-oriented curriculum necessary for the beauty industry.
Socially Fair Matching: Exact and Approximation Algorithms

Matching problems are some of the most well-studied problems in graph theory and combinatorial optimization, with a variety of theoretical as well as practical motivations. However, in many applications of optimization problems, a “solution” corresponds to real-life decisions that have major impact on humans belonging to diverse groups defined by attributes such as gender, race, or ethnicity. Due to this motivation, the notion of algorithmic fairness has recently emerged to prominence. Depending on specific application, researchers have introduced several notions of fairness. In this paper, we study a problem called Socially Fair Matching, which combines the traditional Minimum Weight Perfect Matching problem with the notion of social fairness that has been studied in clustering literature [Abbasi et al., and Ghadiri et al., FAccT, 2021]. In our problem, the input is an edge-weighted complete bipartite graph, where the bipartition represent two groups of entities. The goal is to find a perfect matching as well as an assignment that assigns the cost of each matched edge to one of its endpoints, such that the maximum of the total cost assigned to either of the two groups is minimized. Unlike Minimum Weight Perfect Matching, we show that Socially Fair Matching is weakly NP-hard. On the positive side, we design a deterministic PTAS for the problem when the edge weights are arbitrary. Furthermore, if the weights are integers and polynomial in the number of vertices, then we give a randomized polynomial-time algorithm that solves the problem exactly. Next, we show that this algorithm can be used to obtain a randomized FPTAS when the weights are arbitrary.
Quantum cryptographic protocol implementation using a highly-efficient cold-atom-based quantum memory

We report on a highly-efficient cold-atom-based quantum memory and its use to perform a quantum money protocol. We also simulate the multi-mode capacity of this quantum memory using Hermite-Gaussian modes of light.
Nested Binary Classifier as an Outlier Detection Method in Human Activity Recognition Systems

The present article is devoted to outlier detection in phases of human movement. The aim was to find the most efficient machine learning method to detect abnormal segments inside physical activities in which there is a probability of origin from other activities. The problem was reduced to a classification task. The new method is proposed based on a nested binary classifier. Test experiments were then conducted using several of the most popular machine learning algorithms (linear regression, support vector machine, k-nearest neighbor, decision trees). Each method was separately tested on three datasets varying in characteristics and number of records. We set out to evaluate the effectiveness of the models, basic measures of classifier evaluation, and confusion matrices. The nested binary classifier was compared with deep neural networks. Our research shows that the method of nested binary classifiers can be considered an effective way of recognizing outlier patterns for HAR systems.
Integrated photonic-electronic in-memory computing platforms

We present our recent progress in integrating photonic in-memory computing platforms with electronics to provide easier controls and better computing performance, specifically by integrating tunable phase shifters and doped silicon microheaters into the platform.
A Method for Detecting and Correcting Specular Highlights in Capsule Endoscope Images Based on Independent Cluster Distribution

Highlights of capsule endoscope images can significantly interfere with tasks such as three-dimensional reconstruction of the digestive tract and lesion recognition. Because the red channel pixel values in the non highlight regions of the capsule endoscope image are higher than the green and blue channel values, while the three channel pixel values in the highlight regions are similar, this paper proposes a specular highlight detection and correction method for capsule endoscope images based on independent cluster distribution. Firstly, the method determines the detection threshold based on the difference between the red channel and other channels, solves the RGB channel ratio, and identifies and detects the highlight region; Then, the detected highlight regions are divided into independent clusters based on a disconnected partitioning strategy, and based on the surrounding color, filtering and mask decay are used to compensate for each clustered highlight region; Finally, more detailed results are obtained through Gaussian blurring and mask decay. The experimental results showed that the proposed method improved the accuracy of highlight detection by 29.81% - 41.38% compared to the existing well-known color model based methods, improved the recall rate by 28.83% compared to the adaptive RPCA method, and reduced the NIQE index by 34.9905% compared to the well-known filtering method in terms of correction effect.
Text-Guided Generative Adversarial Network for Image Emotion Transfer

In recent years, image emotion computing has attracted widespread attention from researchers in the computer vision field due to its demonstrated potential in various domains. The main research direction in this field has been extended from image emotion classification and recognition to tasks such as image emotion transformation and generation. However, the semantic gap between high-level semantic image emotion and low-level image features has led to biases in existing image style transfer models’ representation of image emotion, resulting in inconsistencies in the generated images’ emotional features and semantic content. Inspired by text-guided image generation models, we first introduced the guiding role of text information into the research of emotional image transformation. The proposed model has achieved significant advantages in the performance of text-based editing of image emotions compared to existing text-guided image generation models. By leveraging the inherent connection between text and image emotion and content, we improved the accuracy of image emotion transformation and generated more natural and realistic images compared with existing image style transfer models and image emotion transfer models.
influence of using MEDBOGI (media boneka gigi) on knowledge of how to brush teeth in mild mental mental disability children at SLB Tunas Mulya Surabaya

It was found in a preliminary study conducted on mild mentally retarded children aged 9-12 years at SLB Tunas Mulya Surabaya, most of them had poor knowledge of how to brush their teeth. This is caused by the use of media that is less effective in the learning process. Efforts to increase knowledge of dental and oral health maintenance can be carried out by using MEDBOGI (Media Boneka Gigi). This study aimed to determine the effect of using MEDBOGI on knowledge of how to brush teeth in children with mild mental retardation at SLB Tunas Mulya Surabaya. The research method uses analytic research with quick experimental and one-group pretest-posttest design. The research sample was 30 mentally retarded children aged 9-12 years at SLB Tunas Mulya Surabaya, taking samples using a total sampling technique. The data collection instrument uses a questionnaire sheet. The data analysis technique uses the Mann-Whitney test. Results: based on the Man-Whitney test, the result is P (0.000) &lt;0.05. This value indicates that H0 is rejected, so there is an effect of using MEDBOGI on knowledge of how to brush teeth in children with mild mental retardation at SLB Tunas Mulya Surabaya.
Pengaruh Bahan Ajar Bermuatan Folklor (Cerita Rakyat) Angkola Terhadap Menulis Cerita Rakyat Siswa Kelas X SMK Negeri 1 Batang Angkola

The learning process has one component that supports the success of the learning process, namely teaching materials. Teaching materials are all forms of materials used by an educator in the learning process to achieve completeness of competence. Teaching materials are also a set of learning tools or tools that contain learning materials, methods, boundaries, and ways of evaluating which are systematically designed and interesting in order to achieve the expected goals in achieving competencies or sub-competencies with all their complexity in the learning process. In addition, teaching materials can be said to be systematically compiled lecture materials or materials that educators and students or lecturers and students use in the learning process. One way to encourage educators to always update knowledge in folkloric learning. The purpose of this study is to describe the influence of teaching materials containing folklore (folklore) in Angkola on writing folklore of class X students of SMK Negeri 1 Batang Angkola. Research results There is an increase in the influence of the influence of Angkola's Folklore (Folklore) Teaching Materials on Writing Folklore for Class X Students of SMK Negeri 1 Batang Angkola. This can be seen in the hypothesis test carried out on the data found, where the hypothesis obtained the value of rxy (productmoment correlation correlation between variable X and variable Y) is 0.889 so that when compared with r table i.e. (0.669&gt;0.45 then the hypothesis in this study is accepted.
Online Unsupervised Anomaly Detection in Stream Data with Spiking Neural Networks Using Dynamic Scoring

Unsupervised anomaly discovery in stream data is a challenging task, as it involves detecting unusual patterns in data that is constantly evolving over time. Online data-stream outlier detection can indeed be more difficult and challenging. This is because new data points are continuously arriving, and the outlier detection algorithm must process them in real-time. Our idea is to use online evolving spiking neural network classifier and dynamic outlier score normalization to determine whether the current data point is an outlier. The evolutionary algorithm helps the network adapt to changing patterns in the data over time, while the dynamic outlier score normalization adjusts the outlier score based on the distribution of data points over a moving window of time. Our approach is found superior in terms of effectiveness than the other solutions provided in the literature when applied to data streams from the Numenta Anomaly Benchmark repository.
Pengembangan Game Edukasi Matematika Berbasis Aplikasi Android untuk Meningkatkan Kemampuan Literasi Numerasi Siswa SMP

Interactive learning media can be used as a learning tool to improve the numeracy literacy skills of junior high school students, one of which is educational games. Educational games are games that contain educational content and are used to make it easier for students to understand the material provided. Development research using the ADDIE model was used to develop numeracy literacy educational games for junior high school students on statistics material. The population of this study were students of class VIII SMP Kesatrian 1 Semarang. The sample of this research is class VIII A, VIII C, and VIII D. The research design is Posttest Only Control Design. The data collection used is the observation method, the test method, the documentation method. The results of this study indicate that math educational games to improve numeracy literacy skills in statistical material are valid and practical to use based on validation assessments by media experts, material experts, and student response questionnaires. Furthermore, the developed educational game is effectively used in learning mathematics to improve the numeracy literacy skills of junior high school students based on the right-tail side t test, the percentage of student activity, the teacher's ability to manage learning, and student completeness in learning. Android-based math educational games are said to be valid, practical, and effective for improving junior high school students' mathematical numeracy literacy skills in statistics material.
Sentiment Analysis and Classification of Forest Fires in Indonesia

Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%.
Classification of Dog and Cat Images using the CNN Method

Blind people can be defined as those people who are unable to see objects or pictures around them with their eyes. This inability becomes an issue for them when dealing with objects or images in front of them. These problems lead to the novelty of this study that is to recognize objects or images around blind people with the CNN algorithm. Dogs and cats were used as objects in this study. These object recognitions used Deep Learning, a relatively new science in the field of machine learning. Deep learning works like the human brain's ability to recognize an object. In this study, the objects that were used were pictures of a dog and a cat. This study used 3 types of data, namely training, validation, and testing data. The data training consisted of dog data with a total of 1000 images and cat data with a total of 1000 images. Data validation consisted of 500 dog data and 500 cat data. The CCN architecture employed 3 convolution layers. The layer was convolution 1 using 16 filters of kernel size 3x3, the second convolution using 32 filters of kernel size 3x3 and the third using 64 filters of kernel size 3x3. While the data testing consisted of 51dog data and 27 cat data. The method used to analyze the image was CNN. The input was an image with a size of 150x150 pixels with 3 channels, namely R, G, and B. This classification went through a performance test with the Confusion Matrix and it obtained 45% precision, 45% recall and 45% f1-score. From these results it can be concluded that the accuracy values should be improved.
Politics of “Localised Legitimacy”, Vigilantism, Non-State Policing and Counter-Banditry in Northwest Nigeria: Evidence from the Epicenter

Northwest Nigeria has been devastated by armed banditry, with Zamfara state being the epicenter of the crisis. The use of vigilantes in non-state policing against armed banditry has accorded legitimacy to these groups. However, their activities also pose significant challenges. Where scholarly work has examined the legitimacy of vigilantes, the focus has mostly been on legitimacy derived from the nation-state. This paper, contributes to the literature by interrogating the legitimacy of vigilantism as a non-state policing tool toward countering armed banditry, derived from affected local communities in Zamfara state.
Ultra-Low Power Multidomain Secure Implementation for In-Vehicle Systems and Its Evaluation

In recent years, many electronic control units called ECUs have been installed in automobiles to perform various functions such as automatic driving technology and automatic safety devices. ECUs send and receive data using a communication protocol called CAN. It has been reported that it is possible to disable the brakes and hijack the gas pedal by sending unauthorized messages. Therefore, it is essential to take countermeasures against attacks such as eavesdropping, analysis, and spoofing attacks. This paper proposes low power consumption and secure CAN communication system. The proposed system adopted Midori-OTR which combines the lightweight block cipher Midori with authenticated encryption OTR. To improve security, countermeasures against DoS attacks will be implemented. Evaluation experiments using microcontroller boards verify the validity of the proposed system.
Proposal for the Publication of Linked Open Bibliographic Data

Linked Open Data (LOD) are a set of principles for publishing structured, connected data available for reuse under an open license. The objective of this paper is to analyze the publishing of bibliographic data such as LOD, having as a product the elaboration of theoretical-methodological recommendations for the publication of these data, in an approach based on the ten best practices for publishing LOD, from the World Wide Web Consortium. The starting point was the conduction of a Systematic Review of Literature, where initiatives to publish bibliographic data such as LOD were identified. An empirical study of these institutions was also conducted. As a result, theoretical-methodological recommendations were obtained for the process of publishing bibliographic data such as LOD.
Visualizing Cyber-Threats in Underground Forums

In this paper, we develop a language-agnostic methodology to extract features of interest to an analyst from forum posts and visualize them in a way which facilitates identification and stratification of areas of interest in the forums, as well as further manual analysis of the text. We then apply this methodology to a specific Russian underground forum. The visualization acts as a ‘thumbnail’ for individual posts, conveying semantic metadata of post contents. By viewing the thumbnail, an analyst is provided with an immediate ‘sense’ of post length and key features present within a post, as well as their frequency and spatial arrangement. Using the generated visualizations of posts from the underground forum we speed up analyst identification of post subject matter by up to 72%.As a key novelty, we propose that the image output of our method has fractal properties that can be exploited when sorting threats and extracting highly technical posts. Thus, we use a method based on the Minkowski-Bouligand fractal dimension to prioritize analysis of posts which represent more sophisticated threats.
Multi-robot Path Planning using Modified Particle Swarm Optimization

Multi-robot path planning in a shared environment is a critical research area in robotics. The objective is to find a collision-free path for each robot to reach its destination while optimizing various performance metrics. This paper proposes a novel multi-robot path planning algorithm based on Modified Particle Swarm Optimization (MPSO). The proposed MPSO algorithm introduces a new path planning scheme for determining the robot’s waypoints. Unlike the standard PSO algorithm, which initializes the particle swarm at the robot’s starting position and iteratively determines each waypoint until a completed path is generated, the MPSO algorithm initializes the particle swarm within a predefined search space and searches for the global best position within it to determine a specific robot waypoint through iteration updates. Moreover, a combination of global and local path planning methods is introduced to cope with a dynamic environment. The PSO algorithm functions as a global path planner, determining the complete path for each robot, whereas an obstacle avoidance algorithm serves as a local planner to avoid collision with dynamic obstacles during navigation. This study compares the performance of MPSO with normal PSO using MATLAB simulations in two scenarios. The results demonstrate MPSO’s superiority in terms of total path length and execution time.
VISUAL SERVOING IN VIRTUALISED ENVIRONMENTS BASED ON OPTICAL FLOW LEARNING AND CONSTRAINED OPTIMISATION, 1-10.

In this paper, we describe a visual servoing method for object picking.We propose a new architecture for generating robotic manipulator motions approaching a target object for grasping.The architecture consists of two convolutional neural networks (CNNs), one generating goal-directed motion and one collision avoidance motion.The networks' outputs are combined, along with additional constraints, such as motion ranges of the joints, by means of quadratic programming (QP).One issue with learning-based approaches is that large amounts of training data are required.We devise an operation strategy that reduces the amount of training data required using a physics simulator.This method enables visual servoing that is unaffected by texture and colour variation in real environments.We show the effectiveness of the proposed method in experiments using simple shapes as target objects.
Video data security: analysis, relevance and open challenges

With the start of digitalization, huge amount of information is being transmitted over the internet every day, thereby increasing the need to secure it.Thus, in order to secure the transmission, the concept of information security has been introduced, which is a set of policies for managing the tools, processes to keep the information and its associated systems safe from the unauthentic use, leak of confidential information or alteration in the existing data.It works in two major parts i.e., system security which focuses on the legitimacy of the accurate action to be performed irrespective of the data stored as well as executed by the computer system while system assurance focuses on providing security to the information[13].It continuously linked with the organizational function in order to guard the data from unknown as well as to fabricate the data as and when required by an authorized individual.The essential characteristics of standard information security are the availability, integrity, authenticity and confidentiality.