Reviewerly/exHarmony huggingface dataset:
Concepts and Models of Semantic Technologies

The paper discusses the main distinctions of semantic information systems from other types of intelligent software. We analyse conceptual aspects of semantic technologies realised by semantic applications with use of semantic resources and knowledge bases and try to define terminology used in this sphere. Ontological analysis is used as an instrument for representation of domain knowledge and formalisation of relations between main components of semantic technologies. Examples of semantic software designed by us are used for testing of proposed approach.
Narrowband IoT Signal Identification in LTE Networks Using Convolutional Neural Networks

Narrowband Internet of Things (NB-IoT) is an emerging standard serving massive wireless communications devices. It is implemented based on the legacy long-term evolution (LTE) technology and thus shares many system configurations with it. In fact, NB-IoT is deployed on some of the spectrum allocated to LTE. This sometimes introduces interference to the frequency bands with LTE transmission. Signal identification has been found as a critical and effective method for spectrum awareness and the improvement of resource allocation performance thus mitigating potential interference. While not been studied in previous work, this article fills the hole in identifying NB-IoT transmissions in different communications scenarios. In particular, we develop a convolutional neural network (CNN)-based signal identification method to distinguish NB-IoT signals from other cellular signals. The performance of the signal identification method is tested with different CNN training setups. Our experiments demonstrate that the proposed model can successfully identify the existence of NB-IoT signal with 98.13% accuracy in the Rayleigh fading channel where the signal-to-noise ratio (SNR) = 10 dB. Our results also show that the proposed signal identification method is able to provide promising identification accuracy under various and unknown SNR environments in both additive white Gaussian noise (AWGN) and Rayleigh fading channels.
The Impact of the Distance Sensors Orientation on the Obstacle Avoidance Ability of the Robot

In these few years, intelligent robotics technology becomes more and more popular, they bring big convenience for people. And how to further advance the efficiency of the robot becomes a focus topic. The objective of this paper explores the way to improve robot efficiency in the way to find an optimum orientation for the sensors on the two-wheeled robots. Based on the Matlab Robotics Playground, the research found different mazes, adjusted the original angle of the sensors, and then collected the necessary data for the further survey. From the following experiment, can find that the sensors whose original orientation angle is set at a small value (from 0 degree to 30 degrees) have a higher efficiency than those sensors with a big original orientation angle. Furthermore, when the orientation angle is 0 degree, the robot could have the most optimum efficiency. This paper attempts to seek how the orientation of the sensors could influence the movement process and the efficiency of the robot, provides a feasible way for the installation of sensors on robot.
The potential of universities as resource centers in the agile teacher training for organizing STEM projects for school students

The paper considers pedagogical and engineering universities as potential sources of resources for developing school teachers' practical skills in STEM projects activities. The interdisciplinary project organization for schools' students may become a basis for interaction and pedagogical collaboration. Universities can effectively implement teacher training for STEM project organizing at cooperation schools with the undergraduate and graduate students' participation. Blended upskilling for representatives of school staff teams combines classroom intensive courses and distance learning, seminars, or consultations at the workplace, in schools, or university pedagogical centers. Possession of distant communication technologies, project activities support tools, computer modeling environments is optimal. Opportune needs and difficulties identification serves the basis for personalizing and implementing agile learning for long-term learning support programs and short-term professional development courses for school teachers.
Target Tracking Based on Kalman Filtering Techniques

Target tracking techniques aim at estimating desired target states online overtime. This paper introduces and derives algorithms for both single and multiple targets tracking, most of which are based on Kalman filtering technique. We demonstrate that multiple target tracking is more complicated that single target tracking in that data association techniques are required to match estimate target tracks with true targets. We present simulation examples of applications of both single and multiple target tracking in MAT LAB to demonstrate and analysis the performance of our introduced algorithms. In both tests, moving targets are successfully tracked via RGB camera with object recognition algorithms to extract the target features.
Electronic information and educational environment as a means of organizing distance learning

Constantly changing and emerging new conditions for the implementation of the educational process in the Russian Federation should not affect the level and quality of classes in universities. On the contrary, they should represent the initial data for solving problems in the field of providing innovative forms of the educational process, such as, for example, distance learning, but only at a higher level, and using modern information and communication technologies. At the same time, it is necessary to take into account the latest achievements of scientific and technical progress, and be based on the principle of remote electronic communication between a teacher and a student. For these purposes, many universities began to develop so-called electronic information and educational environments that allow organizing and adjusting the independent work of students. As such a platform, it is proposed to use the modern LMS Moodle system, which provides a platform for developing e-courses and conducting distance learning. The role of this system in the educational activities of students has been determined, the effectiveness and efficiency of LMS Moodle in the course of distance learning has been substantiated.
Active Testing for an Emerging Epidemic

Identifying disease carriers is a key barrier to effectively control an epidemic outbreak, especially when many carriers are asymptomatic, have minor symptoms, or have a delayed symptom onset. Current isolation policies largely operate at the two ends of the spectrum: isolate almost everyone (lock-down) or isolate only those with severe symptoms. This leads to high misclassification costs. To address this issue, we develop an active learning approach. Active learning is useful when labeling is expensive and there is a limited budget; an active learning algorithm selects which data points to label in order to build the best training dataset for machine learning. We present the novel Active Testing protocol to combine 1) an online, disease-carrier classification model trained on symptom data paired with 2) an active learning based disease testing policy, that results in lower misclassification costs than either of the two extreme isolation policies. Coupling these two components enables our protocol to pick the best testing kit allocation policy to train the carrier classification model and minimize the total decision-theoretic, isolation misclassification cost. We accomplish this with a novel, cost-aware active learning algorithm, and demonstrate its effectiveness compared to existing algorithms in the class-imbalanced setting of disease-carrier classification.
Peningkatan Kemampuan Menulis Laporan Observasi Melalui Model Pembelajaran Kontekstual pada Mahasiswa D-III Sekretari Universitas Pamulang

The purpose of this research was to determine the increase in the student’ ability of Secretary Diploma at Pamulang University in writing observation reports through a contextual learning model. The subjects of this research were Secretary Diploma’ students of semester VI regular C class 644 with a total of 36 students. The method used in this research is CAR (Classroom Action Research) with a quantitative analysis approach. Data collection techniques in the form of written tests, and documentation studies. The results of the research in the initial test there were no students who succeeded in accordance with the minimum completeness score of 70 with an assessment result of 51.16, then in the final test one only eleven students who had succeeded according to the minimum completeness score with an assessment result of 67.48, then on the final test two, all students had succeeded in accordance with the minimum completeness score with an assessment result of 82.29. The conclusion of this research is through the contextual learning model, Indonesian language learning and students' ability to write reports can be improved.Keywords: contextual learning; writing ability; report; observation
Multilabel Text Classification Using Multilayer DGAT

Text classification is one of the most fundamental tasks in text data analysis. Generally, textual data are extensive in scale and complex in the inherent relationship, leading to low accuracy in traditional classification models. This paper proposes a multi-label classification model based on multi-layer neural network architecture. We first construct a dual-attention mechanism graph neural network (named DGAT for short) to fuse the typological and informational features of the target node and the connected nodes. Secondly, we also build a multi-layer network architecture with multiple DGATs to expand the range of neighborhood nodes participating in the feature fusion to meet the needs of classifying different datasets. To ensure the learning ability of the model, we also use the residual network to solve the problem of error rise and gradient descent caused by multi-layer network architecture. Finally, we conducted a large number of experiments on five benchmark datasets. The results show that the accuracy of the proposed model is significantly better than that of the traditional models, and that there is a noticeable improvement when compared with other deep learning methods.
Mining the ethereum blockchain platform

Ethereum is the most popular blockchain platform that supports smart contracts. Smart contracts are computing programs that constitute the building blocks of decentralized applications (DApps). DApps are revolutionary and have led to the creation of entirely new businesses (e.g., marketplaces for digital collectibles). Nonetheless, developing and maintaining DApps lead to entirely new research challenges. Empirical research demands high-quality data, which can be obtained by carefully mining Ethereum. In this tutorial, I will discuss best practices and pitfalls associated with mining Ethereum. The tutorial will be organized into three main parts: (i) a brief introduction to Ethereum and its fundamental concepts, (ii) a hands-on mining session, and (iii) a final Q&A session.
Weakly Supervised Object Localization Using Long-Range Semantic Foreground Activation

Weakly supervised object localization (WSOL) is a challenging task to find the object location using only image-level supervision. Previous works using CNN architectures stack in finding only the most discriminative parts. Transformer-based methods expand the finding areas but they still fail to take full advantage of attention information. To address this problem, we propose the Long-range Semantic Foreground Activation (LSFA) method. We demonstrate that localization maps should be generated by attention parameters under the guidance of non-discriminative foreground features. In LSFA, a visual transformer model is first used to generate attention information. Then, a long-range dependency activation module is constructed to help the learned attention maps to focus more on global information by weighting them with different parameters. Finally, a semantic foreground activation module is built to use non-discriminative foreground regions learned from attention maps as indices to activate token semantic areas. Experiments on two benchmark datasets CUB-200-2011 and ILSVRC demonstrate the superiority of our LSFA method in comparison with other state-of-the-art WSOL approaches.
Detecting Fall Actions of Videos by Using Weakly-Supervised Learning and Unsupervised Clustering Learning

A framework has been proposed for detecting fall actions from videos to solve the problem of imbalance between fall action data and Activity of Daily Life (ADL) data. In the framework, a 3D-convolutional variational auto-encoder (VAE) was used to reconstruct ADL videos, and reconstruction errors were used to recognize fall actions. In this paper, we propose an improved method using unsupervised clustering learning to cluster fall actions. The 3D-convolutional VAE extracts representations from videos, and additionally proposed fully-connected VAE to gather those representations into two clusters, where representations of fall actions are distinguished from distribution of ADL data. The experimental results showed that our method achieved a promising level of accuracy and better generalization ability compared to methods using supervised learning with well-labeled data. We further show visualization results of latent variables during unsupervised clustering, which showed the representations were clustered into two distinct clusters.
Failure Sources in Machine Learning for Medicine—A Study

Machine learning (ML) inherently suffers from at least a small amount of inaccuracy. Typically, these errors are acceptable in trade for either speed to an answer or the ability to find an answer at all. For high consequence domains, such as medicine where a wrong diagnosis can mean the difference between catching a disease early or not or prescribing debilitating treatment when it may not be needed, certain kinds and types errors are less acceptable. In a study attempting to reproduce ML for medicine research, many difficulties are encountered. These difficulties highlight both the need for higher standards to achieve reproducible ML in general and especially when it comes to high-stakes domains. This paper explores some of those difficulties with a focus on the error sources and discussions about how they may be addressed.
CORDIS Partner Matching Algorithm for Recommender Systems

The purpose of this paper is developing a method for recommending business and scientific partners matchmaking with use of a deep learning model based on historical data on previously completed European Union projects. The paper starts with an introduction to recommender systems, followed by a systematic literature review on the subject. The next part describes the course of the research and its implementation of two deep learning approaches: (1) the entity embeddings of organisations and (2) the embedding space of keywords. The paper ends with a summary of the entity embedding-based recommendation characterized by coverage, average accuracy, low Gini index and the entropy measure.
The Handie system: Hand signs interaction with autonomous, mobile cyber-physical systems

Autonomous, mobile cyber-physical systems are becoming popular in the transportation, manufacturing and industry sectors, thanks to their ability to provide smart and twenty-four-seven services to society, production industries and companies. A smart service is, for example, autonomous transportation of people in traffic or goods in manufacturing industries without human control. The use of autonomous, mobile cyber-physical systems has led to changes and challenges. Especially, dynamic obstacles are affecting the cyber-physical systems and these must be handled to provide robust environments. Although the mobile cyber-physical systems are interacting and acting with the other moveable cyber-physical systems, they also need to handle interactions with human beings. These human beings' interactions can be verbal conversations with the system. However, this communication may struggle with language barriers where a cyber-physical system, for example, only works with one or two natural languages. These interaction limitations may not be obvious nor even known to human beings. In addition, the human being may not be able to handle verbal communication with the cyber-physical systems, therefore hand signs are required to communicate with the systems to provide a trustworthy and secure environment together with several cyber-physical systems. This paper presents Handie, a system handling hand sign interactions with autonomous mobile cyber-physical systems, aka mobile robots. The hand signs are simple, i.e., signs for "ok", "thumbs-up" and "stop" that constitute commands to the robot. Moreover, this non-verbal interaction includes human moods, with facial expressions like happiness, surprise, and fear. Both hand signs and facial expressions are handled by a deep learning recognition component in a humanoid, programmable robot Softbank Robotics' NAO robot v6. Results from testing the system show that the cyber-physical system can recognize hand signs and facial expressions to an acceptable extent to be useful, although the interaction is time-consuming and slow. Also, currently, the communication back to the end users is made verbally and not visually. The next step of Handle's will be to expand the number of hand signs and improve the interaction with the NAO robot.
Learning Zero Noise Extrapolation for Deterministic Quantum Circuits

In the current era of noisy quantum computers the reduction or correction of noise is one of the most important factors in achieving quantum supremacy. Zero-noise extrapolation (ZNE) uses noise amplification to imitate results with higher noise rates, which are then used to extrapolate a result with zero noise. In this work, we provide an intuitive explanation for ZNE by analogy of archery in windy conditions. We show how gate repetition can be used effectively for noise amplification. This is hardware- and platform-agnostic as it requires only gate-level control. We then show how machine learning may be combined with gate repetition to achieve ZNE. We define a class of quantum circuits, called deterministic, for which this method works. We compare the performance of neural networks with other extrapolation methods. Our technique is easy to implement and likely to scale well for large quantum systems.
Machine-Learning Based Physical Exercise Identification with Heuristic Optimized Features Prioritization

Human Activity Recognition (HAR) is a sensor-based observation of human action. HAR is used to monitor the health issues associated with human activities. HAR is used to monitor human health, observation of elderly people, and fitness tracking activity. HAR is an advanced application of Machine Learning (ML), and Artificial Intelligence is used to detect various human-related activities. In this study, we have described a few physical exercises, using Random Forest (RF) classifier. In that case, we have obtained an accuracy of 99%. We have also used a few feature selection algorithms such as Equilibrium Algorithm (EQ), Marine Predators algorithm (MPA), Tree Growth Algorithm (TGA), Artificial Butterfly Algorithm (ABO), and Bat Algorithm (BA) to reduce computational complexity and time. These algorithms were tested using a KU-HAR dataset, which provided 97%, 98%, 99.8%, 97%, and 97% accuracy in classification for the dataset used for testing. This study aims to understand the extent to which supervised machine learning algorithms can achieve well and how it affects the accuracy of sensor recognition. This approach will help researchers conduct more research in the future on the recognition of human exercise activities.
Weakly Supervised Distribution Discrepancy Minimization Learning With State Information for Person Re-Identification

Weakly supervised person re-identification (Re-ID) is appealing to handle real-world tasks by using state information that is available without manual annotation. At present, most methods perform unsupervised cross domain (UCD) learning by transferring the knowledge from the labeled source domain to the unlabeled target domain, which results in poor performance due to the severe shift. To address this problem, in this paper, we utilize the tracklet and camera information as weak supervision to propose a distribution discrepancy minimization learning (DDML) model for UCD person Re-ID. In addition to aligning data distributions from the perspective of domain adaptation learning, two losses are developed from the view of neighborhood invariance exploration to optimize matching results. Specifically, to bridge the gap between domains, we propose a camera-distribution-based (CDB) loss to align pair-wise distance distributions. Furthermore, to alleviate the biased search within the target domain, we propose a ranking-confidence-based (RCB) loss to perform the mined neighborhood for intra-camera and inter-camera separately to explore a high degree of confidence neighbor relations. Extensive experiments on three challenging datasets demonstrate that applying our method to unlabeled target domain outperforms current weakly supervised methods for person Re-ID.
Video Background Subtraction Algorithms for Object Tracking

Surveillance applications may requires tracking of multiple moving objects in the videos. The Background Subtraction (BS) is the prime stage of tracking methods used for separation of foreground and background. Paper addressed various design issues of BS for the object detection. As the varying illumination, shadows and large interfere motions may affect the efficiency of the BS for objet detection. Performance may also vary in the case of stationary and dynamic background of video frames. Therefore in the paper three different approaches of the BS including pixel differencing, morphological opening, and the moving average methods are evaluated. Initially paper classifies the various BS methods and presets the brief review of recent works. The results of BS in the tri color RGB space are presented using the adaptive thresolding techniques. Paper considers the basic kalman filter for tracking. The efficiency of the background substation is evaluated under the presence of the multiple fast moving objects in the scene. The proposed moving average based BS method out performs in all cases
A new association rule mining method and its application in mental health data analysis

A lot of employees are plagued by all kinds of mental health problems. Mental health problems brought a lot of negative effects to them. Therefore, it is particularly important to find key laws by analyzing mental health data. Association rule algorithm can mine the potential relationship between the elements hidden in massive data, which is often used in data analysis in various fields. In this paper, we apply it to the analysis of mental health data in order to mine the correlation between various symptom factors and provide data-based scientific support for psychologists or relevant practitioners. However, traditional association rule mining algorithms will still produce a large number of redundant results even when the support or confidence is high enough, which undoubtedly adds obstacles to decision-makers in discovering key information. Therefore, in this paper, we propose a freely filterable association rule mining method, which can better locate the target results. Experimental results show that our method can mine association rules accurately among the various mental factors, and these rules can play a guiding role in the psychological construction of various organizations for their employees.