Reviewerly/exHarmony huggingface dataset:
Transfer Learning for Semi-supervised Classification of Non-stationary Data Streams

In the scenario of data stream classification, the occurrence of recurring concept drift and the scarcity of labeled data are very common, which make the semi-supervised classification of data streams quite challenging. To deal with these issues, a new classification algorithm for partially labeled streaming data with recurring concept drift is proposed. CAPLRD maintains a pool of concept-specific classifiers and utilizes historical classifiers to label unlabeled data, in which the unlabeled data are labeled by a weighted-majority vote strategy, and concept drifts are detected by automatically monitoring the threshold of classification accuracy on different data chunks. The experimental results illustrate that the transfer learning from historical concept-specific classifiers can improve labeling accuracy significantly, the detection of concept drifts and classification accuracy effectively.
A CNNs-based Model for Knowledge Graph Completion

Knowledge Graph Completion (KGC) aims to find the missing relationships between entities. In this paper, we introduce a novel embedding method based on Convolutional Neural Networks (CNNs) for KGC task. Different from the existing model ConvKB, we design three different shapes of filters to produce more useful features, rather than extracting the features by the filters with the same shape of 1 × 3. Experiments show that our model obtains better link prediction results than the previous models on two benchmark datasets WN18RR and FB15k-237.
Moodle options in the process of scientific events preparation and organization

The article is devoted to the automation of scientific events preparation through the information technologies introduction. The authors analyzed the current state of the problem, identified the advantages and disadvantages of some information platforms of scientific conferences, substantiated the advantages of free LMS Moodle. A review of scientific literature is presented, three main directions of using Moodle in the educational process are identified and characterized. It is established that the experience of using Moodle in the preparation and organization of scientific and other events is practically not reflected in the publications. The characteristic of the preparatory work algorithm and the procedures for using Moodle system in the preparation of a scientific event with an undetermined number of participants is given, the stages and content of custom blocks are described in detail on the example of a real event. Much attention is given to the creation of a module for collecting and filling out applications, in which the Moodle element Databases is selected. The problem of protecting the participant's personal data and his intellectual work has been solved with the use of different database blanks for participants and for organizers through the Presets tab. The authors describe the main functions that Moodle can solve for organizing events. You can get acquainted with the implementation of the system considered in the article on the Portal of Scientific Conferences of Vologda State Dairy Farming Academy by N. V. Vereshchagin, located at: https:// moodle.molochnoe.ru/sci/
Identifying software problems in Java applications using ViMC

Modern software applications are large and complex, and their maintenance has become non-trivial. In this context, having proper tools that help the development process has become a necessity. A distinct disadvantage in large software applications is that they feature complex dependencies on other projects and frameworks, which can lead to an even higher maintenance effort. This is why development and analysis tools need to be able to provide features such as the identification of the level of coupling between projects, and between projects and the frameworks they are based on. In this paper we present how identifying the cyclic dependencies and the degree of coupling between a framework and a project is done using our Java-based tool, called ViMC.
Perancangan Sistem Informasi Electronic New Student Registration Studi Kasus Lembaga Bahasa Dan Pendidikan Profesional (LBPP) LIA Pramuka Jakarta

The rapid development of information technology can meet a variety of human activities and needs, the use of computers can provide the effectiveness of a good life for humans, besides that with a computer someone is able to create a useful technological system. Today's internet technology has the privilege of finding information in other parts of the world without being hindered by distance, time and place. Based on the background stated above, with this the author tries to design a new student registration website, so that it can partially replace the role of the new student registration officer. The design of this web application will provide many conveniences in the process of registering new students, especially for students or parents who have a busy schedule so that registration of prospective students can be done online. This system provides an information that is registration activities for prospective new students, LIA profiles, verification and announcements of level placement at the Institute of Language and Professional Education (LBPP) LIA Pramuka Jakarta.
A Survey of College Students’ Recognition of Live Broadcast Platform

The development of Internet technology provides strong technical support for the network live broadcast platform. The wide use of live network platform has become a new social phenomenon. Based on the analysis of College Students' recognition of the live broadcast platform, this survey explores how to improve the recognition of college students to the live broadcast platform.
Cell Phones and Classroom Management

This chapter outlines and analyzes various classroom management strategies for teachers to regulate cell phone use. Beginning with the background of why cell phones are known to cause distractions in the classroom, the chapter then focuses on using both positive and negative reinforcement strategies for teachers to structure and encourage proper mobile phone usage. The author also includes the benefits and challenges imposed by various classroom management systems in order to guide teachers in making the right decisions regarding cell phone use in their classrooms.
Innovation and Practice Study on the System of “General Education Electives” for Application-oriented Universities from the Perspective of “First-class Courses” in the Era of Artificial Intelligence

The importance of strengthening general education in the era of artificial intelligence is increasingly prominent. General education can help promote well-rounded and long-term human development, and the main way to implement general education is to offer “general education electives”. The system of “general education electives” should be established via the standard of “first-class courses”, because it is of pivotal importance to contribute to the cultivation of top talents. Application-oriented universities are considered as the main force of general undergraduate colleges. The paper, based on real cases, aims to explore the innovation and practice of the system of “general education electives” for application-oriented universities from the perspective of “first-class courses” in the era of artificial intelligence, so as to provide the paths and methods for the cultivation of top applied talents.
Machine Learning for evaluating Kaizens in Volkswagen Production System - An Industrial Case study

In this paper, we classify Kaizen (production process related best practices) from Volkswagen internal knowledge database into their adaptability status over other Volkswagen production plants with help of supervised machine learning. Different criteria's like Return on Investment, implementation time-frame, impact on various other key performance indicators, plant-specific technical details, etc. are used to evaluate Kaizens and eventually cluster them into two categories. Empirical results show that the Decision tree model can predict the degree of adaptability (Success/Denied) with 85% accuracy.
DisCANTree: A Distributed Algorithm for Incremental Frequent Itemset Mining based on MapReduce

Abstract Frequent itemset mining is one of the most important data mining tasks. Classical frequent itemset mining algorithms need to store data in a centralized way and run in a batch way, which cannot meet the requirements of fast updating big data mining. In this paper, we propose a distributed incremental frequent itemset mining algorithm, DisCANTree, which uses CANTree to store the conditional database, achieves the load balance between nodes by grouping all items, updates the new transaction to the existing CANTree to avoid the load of tree reconstruction, and uses the efficient FPGrowth algorithm to mine CANTree to generate frequent itemsets. The popular distributed programming model MapReduce and its open source system Hadoop are used to implement the DisCANTree algorithm. The experimental results show that the DisCANTree algorithm has more advantages than the most popular PFP algorithm in performance as well as the number of transferred records between nodes, and especially suits for the fast updating sparse big data.
Real-Time Access Control System Method Using Face Recognition

Face recognition has been widely studied and studied for many years, but most PC-based face recognition systems have very limited portability and mobility. Face recognition is a process of dynamically capturing facial features through a camera connected to a computer and simultaneously comparing the captured facial features with the facial features previously entered into the personnel library. Face recognition-based person authentication system has been popular among other biometrics recently. This technology can be applied to important departments such as public security, banking, and customs to provide convenient and efficient detection methods. In this paper, we discuss a method of access control system using face recognition technology for entrance limited places. Some face recognition technologies that have shown state-of-the-art performance at their time are discussed. Here we present a method for access control system using facial recognition technology.
TOC generation in PDF Document for Smart Automated Compliance Engine

Portable Document Format (PDF) is a commonly used format for the scientific publication. Currently, an input document is used to test the compliance and relevance of the document or text in Automated Compliance Engines and Natural Language Processing(NLP) based system. The whole document text is used for searching the compliance rules which is computationally expensive and slow process. For speeding up the compliance checking process and making it cost efficient, this paper purposes a method based on Table of Content(TOC) Data Structure. This work proposed the PDFparser which performs Data Indexing, separate headings text, and non-heading text, create hierarchy of headings and generates TOC to reduce the semantic-based string searching time and space. Furthermore, in the NLP based system, mostly semantic-based string matching used. The proposed PDFparser uses the Cosine Similarity method for computing semantic based similarity. Our purposed method performs 47.2% better than the previous approach of searching in the non-indexed whole document and decreases the search time and space. In the worst-case scenario, where no string match found, our purposed method performs 20.5 % better.
Label Selection of Inflectional Word Gestures for Indonesian Sign System known as SIBI (Sistem Isyarat Bahasa Indonesia)

Sistem Isyarat Bahasa Indonesia (abbreviated as SIBI) is a sign language that follows Indonesian grammatical rules. SIBI has two components in its application, namely the component gesture for giving meaning (hand gesture) and the component gesture to support the meaning (lip motion). In previous study, it was demonstrated that lip motion as the component gesture to support the meaning has low accuracy in determining prefix (21.64%), root word (23.87%) and suffix (22.61%). This is because the lip motion follows data label that relies on the hand gesture instead. Therefore, this study tried to select the appropriate data label by conducting experiments using skeleton, hand and lip features and labeling that refers to hand gesture and lip motion. The largest accuracy difference obtained from experiments conducted for skeleton, hand, and lip features were on the root words, denoting (36.76%), (23.36%), and (17.33%), respectively. From these differences, it was found that the most suitable of the data label for SIBI inflectional word is the hand gesture. Nevertheless, the accuracy of lip feature using lip motion label is better than the lip feature using hand gesture label; moreover, the skeleton feature and hand feature using hand gesture label are better than the same feature using lip motion label.
Anchor-Free Detector and Re-Identification with Joint Loss for Person Search

Abstract Person search aims at matching a target person from a gallery of panorama images. Its performance depends on the localization accuracy and the recall rate of the pedestrian detector. FoveaBox detector which outperforms others is utilized in our framework. It generates high-quality region proposals for following re-identification (re-id). A joint loss function is proposed to train the network effectively. It is made up of on-the-fly Online Instance Matching (OIM) and proposal pair double margin contrastive (PPDMC) loss. We propose offset guided erasing instead of random erasing to solve the occlusion problem in person search task preferably. Experiments show that our method performs more effectively against the state-of-the-art methods on two widely used person search datasets.
Application of Walsh filters and subsequent angular-stitching for edge enhancement

Abstract In common edge-enhancement techniques, mostly employed in digital image processing, all the edges of an object are enhanced simultaneously. Selective edge enhancement, a well-known technique in optics, on the other hand, is useful for controlled image representation and structural analysis of the images with applications in biology and chemistry. A simple yet versatile method for selective edge enhancement using a spatial bunch of filters, known as Walsh filters, applied in the Fourier domain has been presented in this paper. The method has been tested on different types of images such as camera images, and scanning electron microscope images. Experimental results with a half-circular object have been presented using reflective spatial light modulators (SLMs). After obtaining selective edges, Walsh filters have been rotated for ‘angular stitching’ to enhance edges in all possible radial directions. This makes this method superior to all previous selective edge enhancement methods using SLMs since it can be tuned to provide both selective or overall edge enhancements depending upon the situation.
Context-Aware Multi-agent Coordination with Loose Couplings and Repeated Interaction

Coordination between multiple agents can be found in many areas of industry or society. Despite a few recent advances, this problem remains challenging due to its combinatorial nature. First, with an exponentially scaling action set, it is challenging to search effectively and find the right balance between exploration and exploitation. Second, performing maximization over all agents’ actions jointly is computationally intractable. To tackle these challenges, we exploit the side information and loose couplings, i.e., conditional independence between agents, which is often available in coordination tasks. We make several key contributions in this paper. First, the repeated multi-agent coordination problem is formulated as a multi-agent contextual bandit problem to balance the exploration-exploitation trade-off. Second, a novel algorithm called MACUCB is proposed, which uses a modified zooming technique to improve the context exploitation process and a variable elimination technique to efficiently perform the maximization through exploiting the loose couplings. Third, two enhancements to MACUCB are proposed with improved theoretical guarantees. Fourth, we derive theoretical bounds on the regrets of each of the algorithms. Finally, to demonstrate the effectiveness of our methods, we apply MACUCB and its variants to a realistic cloudlet resource rental problem. In this problem, cloudlets must coordinate their computation resources in order to optimize the quality of service at a low cost. We evaluate our approaches on a real-world dataset and the results show that MACUCB and its variants significantly outperform other benchmarks.
An Improved Least Significant Bit Image Steganography Method

Information security concerns are evolving and expanding in recent years due to the Internet era where people often transmit private information through the Internet. For this reason, steganography gained much attention and played an emerging role in digital communications. In steganographic technique, the concealed secret information can be carried through several carriers like images, text files, video files, and audio files. In this paper, we handled steganography in digital images since they are the most popular carriers used on the Internet. The main well known steganographic methods for images are: spatial domain and transform domain. The performance and efficiency of these methods are measured and evaluated considering different parameters such as data undetectability, robustness and payload. In this paper, we proposed a solution to improve the image security through appropriate modification of the traditional Least Significant Bit (LSB) technique. The proposed solution improves the performance in terms of security and robustness. Moreover, we have used Huffman Encoding to compress the secret message before it is embedded. In this technique, the message is compressed using both random and selective pattern and at the same time the performance is enhanced using an improved RC4 method. The evaluation of the proposed method demonstrates better results for a bigger message compared to other techniques.
Fishing activity sensing and visualization system using sensor-equipped fishing rod

In recent years, many studies and development of Cyber Physical Systems (CPS) have been carried out to feed back analysis results to human users in a physical space by using machine learning, aiming to analyze a huge amount of information obtained from physical space in a cyber space. To apply CPS to sports, a lot of studies have been conducted on sensing and recognizing actions and movements of athletes using machine learning. In this study, we focus on fishing as a sport, and propose a fishing CPS that recognizes anglers' actions in real-time and provides information on the past useful actions that are linked to fishing results depending on time and place as a decision support when the anglers do not make catch. In addition, this paper reports on the development of an IoT (Internet of Things) device that acquires positional information, acceleration and gyroscope information, and a web system that displays results of real-time activity recognition along with the place and time by animation for realizing the fishing CPS. We have evaluated the developed IoT device and web system from the viewpoint of practical use. As a result, we have confirmed that the GPS and acceleration sensors, in the actual breakwater environment, were constantly transmitting data to a server via UDP communication for 4 hours and 40 minutes.
A Genetic Algorithm for the Design of Active Filters with Standard-Value Components

Genetic algorithms (GAs) have been used effectively for decades to search large parameter spaces of discrete sets. Despite some success in designing small analog circuits, they are still not widely-used for that purpose. Recent papers have used GAs to design active filters using floating point encoding, resulting in non-standard values which must be approximated for implementation. In this paper, a GA is presented with integer parameter encoding that selects industry standard resistor and capacitor values for active filters. Comparison is made against a ninth-order Chebyshev lowpass filter designed using classical standard-value rounding techniques. It is shown that a high-order filter with standard-value components can be obtained which exhibits very small deviation from the desired ideal response.
Integration of ALOS PALSAR and Landsat-7 ETM+ data for buried lineaments extraction at the Farafra Oasis, Egypt

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Integration of ALOS PALSAR and Landsat-7 ETM+ data for buried lineaments extraction at the Farafra Oasis, EgyptAuthorsNohaIsmail MedhatiDM.-YYamamotoK.MansourA.MAbbasM.Abdel ZaherSee all authors Noha Ismail MedhatiDCorresponding Author• Submitting AuthorKochi University of Technology, 185, Miyanokuchi, Tosayamada, Kami, Kochi, 782-8502, JapanNational Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, EgyptiDhttps://orcid.org/0000-0002-5380-4569view email addressThe email was not providedcopy email addressM.-Y YamamotoKochi University of Technology, 185, Miyanokuchi, Tosayamada, Kami, Kochi, 782-8502, Japanview email addressThe email was not providedcopy email addressK. MansourNational Research Institute of Astronomy and GeophysicsHelwanCairo 11421Egyptview email addressThe email was not providedcopy email addressA.M AbbasNational Research Institute of Astronomy and GeophysicsHelwanCairo 11421Egyptview email addressThe email was not providedcopy email addressM. Abdel ZaherNational Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, Egyptview email addressThe email was not providedcopy email address
Open Government Data in the Time of the Pandemic

This policy paper investigates the state of open government data in the Philippines and compares access to health information during the COVID-19 pandemic. It takes the lessons from the creation of the health data hub related to the pandemic and models it as a way forward for more systematic data sharing in the future.
A Hybrid of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Decision Tree for Drought Forecasting

Drought is one of the triggers for forest fires due to depletion of surface water reserves. Along with the frequent drought, the incidence of forest fires has also increased. Therefore, it is important to know or forecast drought to take precautions. In this study, drought forecasting was carried out by applying the concept of data mining classification methods and forecasting methods. This classification uses the decision tree (CART) method, which is a method that aims to see the rules resulting from the classification of existing data. While forecasting uses the SARIMA method, this method is used to predict the factors that cause drought (temperature, humidity, and rainfall). Furthermore, the rule of the classification results is used to classify the results of forecasts. Based on the implementation of the CART algorithm which is evaluated using a confusion matrix is able to achieve an accuracy of 91.33%. Based on the implementation of the SARIMA method, a model is obtained for each variable to build forecasting. Each model was selected based on AIC criteria, and evaluated using MSE. The optimal model for temperature (Tx) is SARIMA (1, 1, 0) x (0, 1, 1, 12) with the MSE value of 0.15. While the selected model for humidity (RH_avg) is SARIMA (0, 1, 1) x (1, 1, 1, 12) with the MSE value of 3.85, and the optimal model for rainfall (RR) is SARIMA (0, 1, 1) x (0, 1, 1, 12) with the MSE value of 8.61.