Reviewerly/exHarmony huggingface dataset:
Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional methods. However, video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture factors. Considering that if the displays are more relevant, the results will more accurate, so ResNext, a kind of compact and efficient neural network, is firstly used as feature extractor, then an improved weighted frame unification method is adopted to obtain the key frame samples, finally the Discriminant correlation analysis (DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch (FEB) on static data. The public dataset named Depth based gesture recognition database (DGRD) is used in this paper, but the dataset is a little small and the class distribution is largely imbalance, and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training data. In order to conquer the disadvantages of limited dataset, a special loss function scheme combining the softmax loss and dice loss is proposed. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.
Digital Signal, Image and Video Processing for Emerging Multimedia Technology

Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology [...]
Study in Japan and the Motivation of Japanese Language Learners in Higher Educational Institutions in Indonesia

The number of Japanese learners in Indonesia ranks the second largest in the world after China. In addition to great interest in Japanese popular culture products, such as anime and manga, the enthusiasm of Japanese learners to study in Japan has become one of the main motivations for learning the Japanese language. The changing in educational policy implemented by the Japanese government has also offered a new possibility for study in Japan. This study aims to explain how the motivation to study in Japan was cultivated through the process of Japanese language learning in higher educational institutions in Indonesia. Researchers collected data using a set of a questionnaire distributed to Japanese language learners at designated universities and interviews with some of the respondents. This study focused on three issues: factors that motivate students to choose Japanese language study programs in university, language skills that learners want to develop in university, and their interest in studying in Japan. The result showed that interest in the Japanese language was the primary motivation for many learners to continue their study in higher educational institutions. The ability to communicate with native Japanese speakers was a significant achievement for a Japanese learner, but at the same time, it was a difficult task to achieve. Study in Japan provided an opportunity for learners to improve their Japanese language skills and to gain experience in Japan. These things were expected to increase the value of learners’ cultural capital and access to previously unobtainable resources.
THE EFFECT OF PROJECT 5-100 ON THE UNIVERSITY’S PUBLICATION ACTIVITY

The aim of the study is to estimate the effect of the Russian academic excellence project (Project 5-100) on the publication activity of the University of Tyumen, measured by the number of articles published in journals indexed in the Web of Science database. The hypothesis of the research is that there is a positive causal relationship between the number of published articles and the University’s participation in Project 5-100. This impact was estimated empirically for the University of Tyumen while the other university participants (31 universities) were taken as a control group. Counterfactual scenarios are used to find the number of articles that the university employees would have published in the absence of Project 5-100. Thus, it is shown that, despite the failure to meet the goal of entering the top 200 of world universities in the ranking for ecology and agricultural biology, the University of Tyumen benefited from its participation in Project 5-100: its number of publications, especially in journals of the first and second quartile, has been growing faster than in the control group of universities.
UJI SINERGIS KONSORSIA BAKTERI INDIGEN LCN DENGAN KONSORSIA BAKTERI TANAH DI KEBUN PERCOBAAN UNIVERSITAS MUHAMMADIYAH METRO SEBAGAI SUMBER BELAJAR BIOLOGI

Abstract: Antagonistic bacteria in the activities of one organism with other organisms compete with each other in fighting over places, air, water, food (nutrients). If the bacteria do not compete with each other but those bacteria can interact and synergize, and share the same nutritional sources and behave cooperatively between bacteria in their habitat. It is said to be synergisticThe goal is to find out the 15 consortia of LCN indigenous bacteria with soil bacteria can be synergistic so, it can be consortified. To utilize the synergistic test results of the consortia of LCN indigen bacteria with soil bacteria as a source of learning biology. The research design is a quantitative experiment . Synergistic test using Streak Plate Method on Nutrient Agar medium. The test results show that X2value 56.86&gt; X2table 23.68 with α 0.05 in the chi-square table, so it can be concluded that there is a synergy between LCN Indigenous Bacteria and Soil Bacteria. The validation results show that the microbiology practicum guide with 96% results has a very good qualification so, it is suitable as a biology learning source in the form of a microbiology practicum guide based on scientific approach.&#x0D;
Message Security Using Rivest-Shamir-Adleman Cryptography and Least Significant Bit Steganography with Video Platform

All over the world, information technology has developed into a critical communication medium. One of them is digital messaging. We can connect and share information in real-time using digital messages. Without us knowing it, advances in message delivery are not only followed by kindness. Message security threats are also growing. Many unauthorized parties try to intercept critical information sent for the benefit of certain parties. As a countermeasure, various message security techniques exist to protect the messages we send. One of them is cryptography and steganography. Cryptography is useful for converting our messages into coded text so that unauthorized parties cannot read them. Meanwhile, steganography is useful for hiding our encrypted messages into several media, such as videos. This research will convert messages into ciphertext using the Rivest-Shamir-Adleman method and then insert them into video media using the Least Significant Bit method. There are four types of messages tested with different sizes. All messages will be encrypted and embedding using the Python programming language. Then the video will be tested using the MSE, PSNR, and Histogram methods. So we get a value that shows which message gets the best results. So that the message sent is more guaranteed authenticity and reduces the possibility of message leakage.
La enseñanza remota no viene sin retos

Analysis of challenges faced by early childhood education teachers of 3 to 6 years old as a result of the suspension of face-to-face educational services due to COVID-19, and the implications in teacher education and professional development.
Sentiment Analysis towards Actionable Intelligence via Deep Learning

The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.
Multi-agent motion planning with sporadic communications for collision avoidance

A novel multi-vehicle motion planning and collision avoidance algorithm is proposed and analyzed in this paper. The algorithm aims to reduce the amount of onboard calculations and inter-agent communications needed for each vehicle to successfully navigate through an environment with static obstacles and reach their goals. To this end, each agent first calculates a path to the goal by means of an asymptotically optimal rapidly-exploring random tree (RRT*) with respect to the static obstacles. Then, other agents are treated as dynamic obstacles and potential collisions are determined by means of collision cones. Collision cones depend on the position and velocity from other agents and are grown conservatively between inter-agent communications. Based on the available information, each agent determines if a deconfliction maneuver is needed, if it can continue along its current path, or if communication is needed to make a decision about a conflict. With probability one, our algorithm guarantees that the agents keep from colliding with each other. Under an assumption on the existence of a solution for a vehicle to its goal, this algorithm also solves the planning problem with probability one. Simulations illustrate a group of agents successfully reaching their goal configurations and examine how the uncertainty affects the communication frequency of the multi-agent system.
Analisis Proses Pelaksanaan Pembelajaran Keterampilan Tata Boga Membuat “Kue Pastry” bagi Warga Belajar Paket C Kelas XI IPS di SPNF SKB Kota Samarinda

In general, the learning process is only related to academic learning. However, it is different from culinary skills which are generally only implemented if there is training or activities that lead to specific training. However, in SPNF SKB Samarinda City implements culinary skills as learning is routinely carried out even into the curriculum so that it has a Learning Implementation Plan (RPP). The purpose of this study was to determine the implementation of learning culinary skills to make a "Pastry Cake" for Residents Learning Package C Class XI IPS at SPNF SKB Samarinda City. This type of research is a Qualitative Research in which the focus of research is Learning Catering Skills Making "Pastry Cake". This research was conducted at the SPNF SKB Samarinda City with 7 personnel as the subject of research including the head of the institution, 2 tutors, and 4 learning residents. Data collection techniques using interviews as the main method and observation and documentation as a support. Data analysis techniques include data collection, data simplification, data presentation, and drawing conclusions. The results showed: in the implementation phase in the learning activities of culinary skills using the 2013 curriculum, using group practice methods and carried out on Sundays starting at 09.00 am to finish. From the results of the above research it can be concluded that the process of implementing culinary skills learning to make "pastry cakes" for residents learning package c class XI IPS at SPNF SKB Samarinda City must meet all stages of the implementation in order to be well implemented. Citizens learn also to get the skills they can get.
Tamil Paraphrase Detection Using Encoder-Decoder Neural Networks

Detecting paraphrases in Indian languages require critical analysis on the lexical, syntactic and semantic features. Since the structure of Indian languages differ from the other languages like English, the usage of lexico-syntactic features vary between the Indian languages and plays a critical role in determining the performance of the system. Instead of using various lexico-syntactic similarity features, we aim to apply a complete end-to-end system using deep learning networks with no lexico-syntactic features. In this paper we exploited the encoder-decoder model of deep neural network to analyze the paraphrase sentences in Tamil language and to classify. In this encoder-decoder model, LSTM, GRU units and gNMT are used as layers along with attention mechanism. Using this end-to-end model, there is an increase in f1-measure by 0.5% for the subtask-1 when compared to the state-of-the-art systems. The system was trained and evaluated on DPIL@FIRE2016 Shared Task dataset. To our knowledge, ours is the first deep learning model which validates the training instances of both the subtask-1 and subtask-2 dataset of DPIL shared task.
onIQ: An Ontology-Independent Natural Language Interface for Building SPARQL Queries

Publishing data in Linked Data tends to capture more and more space because this way of representation of data allows linking the data, which leads to an increase in the degree of representation and, why not, the quality of data. Unfortunately, Linked Data has a much more complex and heterogeneous structure than other ways of data representation, which means only a larger learning curve of SPARQL, the triplestore query language. In this paper, we propose a framework, Ontology-Independent Natural Language Query Interface for Building SPARQL Queries, for translating queries formulated in natural language into SPARQL queries, so as to facilitate the adoption of Linked Data. The framework uses spaCy library for applying specific NLP techniques for syntactic analysis and extraction of the elements needed to build the SPARQL query. Through WordNet, the synonyms for the words extracted from the user question are identified, and then, in order to map them with properties from the used ontologies, the degree of similarity is calculated. Finally, the SPARQL query is built and the user can use it for querying the triplestore.
Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge

The Cooking Activity Recognition Challenge tasked the competitors with recognizing food preparation using motion capture and acceleration sensors. This paper summarizes our submission to this competition, describing how we reordered the training data, relabeled it and how we handcrafted features for this dataset. Our classification pipeline first detected basic user actions (micro-activities); using them it recognized the recipe, and then used the recipe to refine the original micro-activity predictions. After the post-processing step using a Hidden Markov Model, we achieved the competition score of 95% on the training data with cross-validation.
Graph Reachability on Parallel Many-Core Architectures

Many modern applications are modeled using graphs of some kind. Given a graph, reachability, that is, discovering whether there is a path between two given nodes, is a fundamental problem as well as one of the most important steps of many other algorithms. The rapid accumulation of very large graphs (up to tens of millions of vertices and edges) from a diversity of disciplines demand efficient and scalable solutions to the reachability problem. General-purpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize algorithms that present a high degree of regularity. In this paper, we extend the applicability of GPU processing to graph-based manipulation, by re-designing a simple but efficient state-of-the-art graph-labeling method, namely the GRAIL (Graph Reachability Indexing via RAndomized Interval) algorithm, to many-core CUDA-based GPUs. This algorithm firstly generates a label for each vertex of the graph, then it exploits these labels to answer reachability queries. Unfortunately, the original algorithm executes a sequence of depth-first visits which are intrinsically recursive and cannot be efficiently implemented on parallel systems. For that reason, we design an alternative approach in which a sequence of breadth-first visits substitute the original depth-first traversal to generate the labeling, and in which a high number of concurrent visits is exploited during query evaluation. The paper describes our strategy to re-design these steps, the difficulties we encountered to implement them, and the solutions adopted to overcome the main inefficiencies. To prove the validity of our approach, we compare (in terms of time and memory requirements) our GPU-based approach with the original sequential CPU-based tool. Finally, we report some hints on how to conduct further research in the area.
Shopping Cart Analysis System in Product Layout Management with Apriori Algorithm

Coco Mart which is one of the retail network brands engaged in household needs is a company from PT. Bali Pawiwahan (Coco Group). As time goes on, transaction data continues to multiply and cannot be utilized by managerial parties to process transaction data that continues to grow every time. From transaction data, there is information or knowledge that can be extracted. The results of the research conducted, the construction of a shopping basket analysis system to process sales transaction data over the past four years, starting from 2013 with the aim of knowing consumer spending patterns. So that the Shopping Cart Analysis System can be a decisions support in business strategies to regulate the layout of goods and promote product combinations that are often purchased by Coco Mart Jimbaran customers.
Multi-Class Classification based on Relative Distribution of Class

Binarization of multi-class classification problem into two class problem is widely adopted in machine learning because of its simplicity and efficiency. It consists of dividing multiple classes into pairs of all possible combinations and learning the base classifiers on each pair of classes. Then, their outputs are combined to classify an instance. To improve the classification accuracy, several different combination schemes were studied previously. We proposed a new combination scheme based on relative distribution of each class. Instead of merely computing the distances of an instance to the nearest neighbors of each class, relative distances were measured considering the relative distribution of each class. Experimental results showed the proposed method outperforms previous methods both in terms of accuracy and kappa measures.
Adaptive Image Restoration via a Relaxed Regularization of Mean Curvature

In this paper, a new relaxation model based on mean curvature for adaptive image restoration is proposed. To solve the problem efficiently, an alternating direction method of multipliers (ADMMs) is proposed. Furthermore, a rigorous convergence theory of the proposed algorithm is established. We also give the complexity analysis of our proposed method. Experimental results are provided to demonstrate the effectiveness and efficiency of the proposed method over a state-of-the-art method on synthetic and natural images.
Models for Multicomponent Fuzzy Evaluation, with a Focus on the Assessment of Higher-Order Thinking Skills

The increased need to use distance and online forms of training in the modern world forces teachers to seek, develop and use new and diverse teaching models. The pursuit of adequate fair assessment of students' knowledge and skills, their active participation in the learning process, their independent work on projects and case studies has also posed new challenges for teachers. The article presents models for hierarchical multicomponent assessment of students, aimed at comprehensive assessment of various thinking skills of high and low order, theoretical knowledge and practical skills, etc. A fuzzy evaluation approach is used to formalize the assessor's subjective logic.
Limited sliding network: Fine‐grained target detection on electrical infrastructure for power transmission line surveillance

Summary Because of its small size, low local contrast, and much interference, the field image of fine‐grained equipment taken from power transmission line surveillance is hard to be sustained by the traditional small target detection technique, which requires the manual extraction of features, making it difficult to accurately detect micro‐fine‐grained equipment. The deep learning‐based algorithms have prospective application but require abundant data to guarantee performance and tackle the problem of foreground–background imbalance. This paper develops an effective pipeline, i.e., limited sliding network (LSNet), to detect the small and fine‐grained defects on equipment in power transmission line infrastructure. The model firstly performs the regional analysis on the entire image to determine the potential target locations. The feature extraction and classification on the potential location image blocks are further performed by the VGG‐style model for the dense target locations, and the nonmaximum suppression method is finally applied to locate the target. On the other hand, a specific training method is also developed to better deal with a wide range imbalances of positive and negative samples. The proposed method achieves the detection mean average precision (mAP) rate of 98.66% on the real datasets, while limiting the computational overhead of hardware.
Telepathology in Medical Education: Integration of Digital Microscopy in Distance Pathology Education during COVID-19 Pandemic

considered a clerkship that is best accomplished by face-to-face interaction.However, in the changing world,
‘We Were Scared of Catching the Virus’: Practices of Saudi College Students During the COVID-19 Crisis

College students in Saudi Arabia engaged in various English activities in digital spaces during the COVID-19 crisis, despite it being their second language. Drawing on the concept of digital literacies proposed by Jones and Hafner (2012), this paper identifies the digital literacy practices that occurred in the English language during COVID-19 crisis. Focus group interviews and individual interviews were conducted via WhatsApp with ten college students who had recently attended an intensive English course at a university in Saudi Arabia. Thematic analysis, assisted by ATLAS.ti, revealed that the college students engaged in complex digital literacy practices in English during the COVID-19 crisis to improve their competency in English, educating the community and oneself about COVID-19 as well as to cope with the boredom of remaining indoors. College students mainly used smartphone apps in their literacies, and their usage was guided by their feelings, commitment and the contextual events around them. The literacies these students drew on reflected their metacognitive awareness of the value of English to their everyday life experience. This paper concludes by encouraging college students to exploit the potential of smartphone apps to improve their capacity in English and incorporate apps into their everyday lives.