Reviewerly/exHarmony huggingface dataset:
Knowledge Graph enhanced Multimodal Learning for Few-shot Visual Recognition

Few-shot learning (FSL) aims to learn a classifier for novel classes with only a few labeled samples per category available. The mainstream FSL approaches fall in the meta-learning paradigm, where a meta-learner is used to learn transferable knowledge and generalize to new tasks. However, these approaches usually only leverage information from a single modality (e.g., visual image) and fail to explore the information from other modalities (e.g., the knowledge graph). Since the labeled samples are scarce in FSL, increasing the information for each example is a possible solution to improve the performance. This motivates us to develop a new meta-learning framework for few-shot visual recognition termed Knowledge Graph enhanced FSL (KGFSL), which combines the information from multiple modalities: 1) the visual information in images and 2) the rich semantics and structural information in a knowledge graph (KG). Specifically, KGFSL exploits the word embedding of the category and its relationship to other categories to improve the visual-based models. A graph convolutional network (GCN) is first introduced to learn the semantic embeddings for each node (a visual category) in KG. The visual and semantic embeddings are then aligned and combined for final prediction. Finally, the whole framework is trained in an end-to-end manner. We conduct extensive experiments on two widely-used FSL benchmarks: miniImageNet and tieredImageNet. Experimental results demonstrate the effectiveness of the multimodal information for few-shot learning, and our proposed method can significantly outperform the state-of-the-art approaches.
USE OF DIGITAL TECHNOLOGIES IN PROFESSIONAL TRAINING OF MASTERS OF PHYSICAL CULTURE

The transition of the modern economy to Industry 4.0 forces the main branches of the state's life to adapt to the realities of modern digitalization. This entails digital transformations in all areas, including the field of physical culture and sports. The article analyses the experience of digital transformation in the field of education in general, and in organizations in the field of physical culture and sports, in particular. The authors studied the experience of an educational organization of higher education in training personnel in the field of digital competencies. The research objectives were to identify competencies aimed at the formation and development of digital competence of future specialists in the field of physical culture; to substantiate the levels of the development of this competence; to identify digital services, the use of which will allow students to develop their digital competence; to develop methodological recommendations on the use of digital technologies in the performance of individual tasks performed by students. To solve the research tasks, the methods of analysis of the normative base of the issue under study, analysis of the literature and generalization were used. The landmarks of digital transformation were studied, normative documents were analyzed, the analysis and generalization of studies on the topic of digital transformation of education in the field of physical culture were carried out, the experience of organizing higher education in training personnel for the field of physical culture and sports with digital competence was studied. As a result, a competence was identified that enables to master digital competence by students. The levels of the competence formation were determined. The authors developed guidelines where they recommended digital services, the use of which will allow students to develop their digital competence.
Collision Warning in Vehicular Networks Based on Quantum Secure Multiparty Computation

Quantum Secure Multiparty Computation (QSMC) is a technology that takes the advantage of quantum features allowing multiple parties to communicate in a secure and efficient manner while preserving their privacy. Using QSMC technology, we implement a collision warning use case in which vehicles can freely broadcast information while preserving the privacy of their confidential data. We integrate two quantum technologies namely Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution (QOKD) with the Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to implement a secure and efficient QSMC platform. This quantum approach significantly improves efficiency and security when we compare it with the classical implementation as both used quantum technologies (QKD and QOKD) are robust against quantum computer attacks.
The Impact of the 3-2-1 Reading Strategy on Iraqi EFL University Students' Achievement in Reading Comprehension

The present study aims at measuring the effect of the 3-2-1 reading strategy on Iraqi EFL University students in Reading ComprehensionThe sample of the study consists of second year university students / English Department / College of Education for Humanities / Tikrit University, during the academic year 2019-2020. It has been randomly chosen and has divided into two groups, the control group with (35) students and the experimental group with (35) students also. The two groups are equalized according to certain variables. The 3-2-1 strategy is used with the experimental group, whereas the control group is not trained on this strategy, i.e, by using the conventional method. After instruction for six weeks, both groups completed the same reading comprehension test. The obtained results have shown that there are statistically significant differences between the achievements of the students of the two groups in favor of the experimental group due to the use of the 3-2-1 strategy.
Tackling Dataset Bias With an Automated Collection of Real-World Samples

The early 21st-century technological advancements tilted the scales towards data-driven learning. Thus, modern machine-learning systems rely heavily on data to learn complex models to efficiently provide relevant predictions. Data-driven learning suffers from overfitting, a situation in which the learning process seems to have converged into a model that, unfortunately, lacks generalization power. One way to withstand overfitting is to expand the training dataset with more diverse samples. Typically, this is implemented (particularly in computer vision research, which is of interest in this study) by multiplying the original sample using several transformations. Although this strategy might seem straightforward, it does not affect any preexisting dataset bias because the initial distribution remains more or less similar. Ideally, new samples of unseen data must be found, but the cost of acquiring them individually is high. This study presents a novel pipeline that combines state-of-the-art modules to automatically create new thematic datasets with low bias. The proposed method was able to acquire and allocate more than 880K previously unseen images to produce a data collection, that InceptionV3 classified it with 72% accuracy and achieved 0.0008 performance variance when testing on similar datasets.
VLSP 2021 - TTS Challenge: Vietnamese Spontaneous Speech Synthesis

Text-To-Speech (TTS) was one of nine shared tasks in the eighth annual international VLSP 2021 workshop. All three previous TTS shared tasks were conducted on reading datasets. However, the synthetic voices were not natural enough for spoken dialog systems where the computer must talk to the human in a conversation. Speech datasets recorded in a spontaneous environment help a TTS system to produce more natural voices in speaking style, speaking rate, intonation... Therefore, in this shared task, participants were asked to build a TTS system from a spontaneous speech dataset. This 7.5-hour dataset was collected from a channel of a famous youtuber "Giang ơi..."and then pre-processed to build utterances and their corresponding texts. Main challenges at this task this year were: (i) inconsistency in speaking rate, intensity, stress and prosody across the dataset, (ii) background noises or mixed with other voices, and (iii) inaccurate transcripts. A total of 43 teams registered to participate in this shared task, and finally, 8 submissions were evaluated online with perceptual tests. Two types of perceptual tests were conducted: (i) MOS test for naturalness and (ii) SUS (Semantically Unpredictable Sentences) test for intelligibility. The best SUS intelligibility TTS system had a syllable error rate of 15%, while the best MOS score on dialog utterances was 3.98 over 4.54 points on a 5-point MOS scale. The prosody and speaking rate of synthetic voices were similar to the natural one. However, there were still some distorted segments and background noises in most of TTS systems, a half of which had a syllable error rate of at least 30%.
A Cryptosystem analysis for text messages using Homomorphic Transform

Information is as important as it has always been, but it is even more so now since we are living in the Information Era. Data security is an essential aspect. The concerns that have been found and the action plans that have been recommended in this study are based on testing. The goal of this work is to provide actionable intelligence about the security of the cypher text we were given. Only in those circumstances where it was thought necessary, this threat intelligence report is based on analysis to validate the accuracy and authenticity of the information gathered. The tests carried out during the limited period for doing such an activity are mostly used to identify the issues in this work. Though the work has taken sufficient care to cover the principal scenarios, the analysis' result may not be exhaustive or represent all possibilities.
A new approach for machine learning security risk assessment

We propose a new security risk assessment approach for Machine Learning-based AI systems (ML systems). The assessment of security risks of ML systems requires expertise in ML security. So, ML system developers, who may not know much about ML security, cannot assess the security risks of their systems. By using our approach, a ML system developers can easily assess the security risks of the ML system. In performing the assessment, the ML system developer only has to answer the yes/no questions about the specification of the ML system. In our trial, we confirmed that our approach works correctly.
Telaah Problematika Anak Slow Learner dalam Pembelajaran

This study reviews the problems of slow learners in learning. Literary references slow learners to teachers providing learning and parents in providing assistance to children at home during online learning. Based on the results of the study that has been carried out, it is concluded that slow learner children are children who experience learning delays. This can be shown in situations where a child has not been able to master the learning information conveyed by the teacher within a certain time limit. The characteristics of these children are one level above mentally retarded children so they need teacher responses and regular assistance to help them develop themselves. Teachers must observe and identify children who are slow learners to be given counseling assistance that can stimulate cognitive, affective, physical, and intuitive aspects. Counseling should also educate children to use devices in a positive way during online learning.
K-Focal Search for Slow Learned Heuristics (Extended Abstract)

Learned heuristics, though inadmissible, can provide very good guidance for bounded-suboptimal search. Given a single search state s and a learned heuristic h, evaluating h(s) is typically very slow relative to expansion time, since state-of-the-art learned heuristics are implemented as neural networks. However, by using a Graphics Processing Unit (GPU), it is possible to compute heuristics using batched computation. Existing approaches to batched heuristic computation are specific to satisficing search and have not studied the problem in the context of bounded-suboptimal search. In this paper, we present K-Focal Search, a bounded suboptimal search algorithm that in each iteration expands K nodes from the FOCAL list and computes the learned heuristic values of the successors using a GPU. We experiment over the Rubik's Cube domain using DeepCubeA, a very effective inadmissible learned heuristic. Our results show that K-Focal Search benefits both from batched computation and from the diversity in the search introduced by its expansion strategy. Over standard FS, it improves runtime by a factor of 6, expansions by up to three orders of magnitude, and finds better solutions, keeping the theoretical guarantees of Focal Search.
Flying STAR2, a Hybrid Flying Driving Robot With a Clutch Mechanism and Energy Optimization Algorithm

This paper presents FSTAR2, an improved FSTAR design, and a near optimal energy-based algorithm for a flying-driving robot that can navigate in environments crowded with obstacles. The robot can be used for tasks such as package deliveries, industrial inspection, and search and rescue applications. FSTAR2 is fitted with a clutch mechanism that separates the propellers from the motors when driving. Experiments show that the clutch reduces the driving energy consumption by up to 52% and enables driving at higher speeds. To optimize its energy consumption (reducing its normalized energy cost) the FSTAR2 exploits its flying and driving capabilities by implementing a new approach which encloses the search space of the weighted A* algorithm and finds a new weight for the heuristic function, yielding a near optimal solution. Given that both flying and driving speeds are subject to environmental restrictions, the algorithm favors driving whenever possible (driving is almost three times more energy efficient at the considered speed – 2 m/s). Multiple simulations in different environments were conducted and the findings were compared to Dijkstra’s algorithm to estimate the quality of the results. In general, the weighted A* algorithm reduced the number of iterations vs the A* algorithm and produced very similar values to Dijkstra’s Algorithm. The experimental robot can fly like a regular drone, penetrate (25 cm/10 inches diameter) pipes and drive at speeds of up to 4.5 m/s, which is an increase of 50% compared to FSTAR.
Constructing of Three-dimensional Visual Maps of Generalized Computational Experiment

This paper continues the series of publications of the authors' research materials in the field of developing an approach to dynamic planning and control of a generalized computational experiment based on the application of visualization and visual analytics methods. A generalized computational experiment implies a multiple solution of the numerical simulation problem for different sets of values of defining model parameters, and such an approach allows for obtaining a solution at once for a certain class of problems of mathematical modeling given in a multidimensional space of defining parameters. The paper considers an extension of the existing author's approach to the construction of visual maps of the generalized computational experiment, based on the application of visualization metaphors that can display not only individual images, but also their relationships. A method for constructing visual maps of a generalized computational experiment is proposed, which is focused on visualizing relationships between single computational experiments in threedimensional space. The method is based on the mechanism of formalization of relations between single computational experiments, as well as on the notion of a graph model visualization metaphor, which defines a prototype of a visual map. A description of the software system for the construction and analysis of three-dimensional visual maps of a generalized computational experiment is given, and examples of its application in assessing the accuracy of numerical models of the OpenFOAM software platform for the three-dimensional problem of inviscid flow around a cone are considered.
Pengaruh Model Sugesti – Imajinasi dengan Media Lagu Bunda terhadap Kemampuan Menulis Cerpen Siswa Kelas XI SMK Swasta YPK Medan

&#x0D; &#x0D; &#x0D; &#x0D; This study aims to describe the ability to write short stories using the Suggestion-Imagination learning model with the media song of class XI students of YPK Private Vocational School Medan,&#x0D; to describe the ability to write short stories using the Suggestion-Imagination learning model with video media for class XI students at YPK Private Vocational School Medan,&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; To describe whether there is a significant effect on the ability to write short stories using the Suggestion-Imagination learning model with the media of song for class XI students of YPK Private Vocational School Medan. The sample in this study was 60 students. From the results of data processing, the average value of the experimental class was 71.6 while the control class was 65.5. Thus it can be said that the results of learning to write short stories in the experimental class are higher than the control class. After testing the hypothesis, the value of tcount = 3.144 was then adjusted to ttable at a significant level of = 5% with dk = (n1+n2) – 2 = (30+30 – 2) = 58, then obtained a significant level of 5% = 2.002 ( by interpolation). Then compared between tcount and ttable, it was obtained tcount 3.144 &gt; ttable 2.002 so that Ho (Nil Hypothesis) was rejected and Ha (Alternative Hypothesis) was accepted.
Perancangan Expert System Development Life Cycle pada Sistem Pakar Forward Chaining Sebagai Media Pembelajaran

The purpose of this research is to create and develop learning media in the form of applications used for plant identification in The Division of Pinophyta. The approach used is conventional approach with Expert System Development Life Cycle (ESDLC) method from Durkin, the inference mechanism used is Forward Chaining, while the software, used for Data Base Management System (DBMS) is MySQL, Unified Modeling Language (UML), Personal Home Page (PHP). Based on the results of the application assessment test in general obtained results that this expert system application deserves to be used as a learning media with criteria for Quality of end-user interface design (55.5%), Engagement (52.3%), Interactivity (64.3 %) and Tailorability (52.4%). Conclusion, application made by Expert System Development Life Cycle (ESDLC) method, with Forward Chaining inference mechanism worthy to be used as a learning medium of plant identification in Divisio Pinophyta.&#x0D; &#x0D; Keywords: Application, Plant Identification, Learning Media, Expert System, Forward Chaining
Using Authorship Verification to Mitigate Abuse in Online Communities

Social media has become an important method for information sharing. This has also created opportunities for bad actors to easily spread disinformation and manipulate public opinion. This paper explores the possibility of applying Authorship Verification on online communities to mitigate abuse by analyzing the writing style of online accounts to identify accounts managed by the same person. We expand on our similarity-based authorship verification approach, previously applied on large fanfictions, and show that it works in open-world settings, shorter documents, and is largely topic-agnostic. Our expanded model can link Reddit accounts based on the writing style of only 40 comments with an AUC of 0.95, and the performance increases to 0.98 given more content. We apply this model on a set of suspicious Reddit accounts associated with the disinformation campaign surrounding the 2016 U.S. presidential election and show that the writing style of these accounts are inconsistent, indicating that each account was likely maintained by multiple individuals. We also apply this model to Reddit user accounts that commented on the WallStreetBets subreddit around the 2021 GameStop short squeeze and show that a number of account pairs share very similar writing styles. We also show that this approach can link accounts across Reddit and Twitter with an AUC of 0.91 even when training data is very limited.
Medical Image Compression and Encryption Using Adaptive Arithmetic Coding, Quantization Technique and RSA in DWT Domain

Nowadays, the advances in information and communication technologies open the wide door to realize the digital world’s dream. Besides, within the clear scientific scope in all fields, especially the medical field, it has become necessary to harness all the scientific capabilities to serve people, especially in medical-related services. The medical images represent the basis of clinical diagnosis and the source of telehealth and teleconsultation processes. The exchange of these images can be subject to several challenges, such as transmission bandwidth, time delivery, fraud, tampering, modifying, privacy, and more. This paper will introduce an algorithm consisting a combination of compression and encryption techniques to meet such challenges in the medical image field. First, compression is done by applying the Adaptive Arithmetic Coding (AAC) technique and controllable frequency quantization process in the Discrete Wavelet Transform. After that, the encryption process is applied using RSA and SHA-256 algorithms to encrypt the compressed file and to create the digital signature. The performance analysis has shown that the algorithm can produce high compression ratio with good image quality, whereas range of PSNR near 45 dB and SIM is 0.88 as average values. For the security analysis, we have adopted data encryption and digital signature to guarantee the main data security services including integrity, authentication, and confidentiality, making the algorithm secure against passive or active attacks.
Feature Selection and Sensitivity Analysis of Oversampling in Big and Highly Imbalanced Bank's Credit Data

Machine learning has evolved as a multidisciplinary study in the last few years and gains more popularity in big data analytics, including in the banking industry. Numerous methods can be used in predictive analytics through supervised machine learning, either for regression or classification problems. In the banking industry, credit quality is one of the core focuses, since it is one of the main areas that is reviewed regularly by regulators and impacts banks' profitability. This research is intended to give recommendations on how to select appropriate machine learning technique, perform feature selection and sensitivity analysis on bank's credit data with more than one million records and highly imbalanced, i.e., 97.5% of data is at one category. By using several supervised machine learning classification methods including the application of SMOTE (synthetic minority oversampling technique), computational results are compared and summarized, resulting in recommendations on the most appropriate technique for big and extremely imbalanced datasets, i.e., the Tree Ensemble method with SMOTE, with the computational issue is solved through data sampling, without significantly reducing its accuracy. It is also concluded that optimum number of features will increase model accuracy, however significant reduction of number of features will not necessarily increase model accuracy. The research is expected to be useful for the banking industry, especially in credit portfolio analytics, or other industries with a big and imbalanced dataset, to perform predictive analytics to support business objectives. Further research is possible, to cover more in-depth analytics for the decision-making process in banking.