Reviewerly/exHarmony huggingface dataset:
Application Design of Intelligent Computer Aided Instruction System in Aerobics Teaching

Aerobics teaching is more practical, with complex movements, multiple changes, and strong composites. Therefore, it is necessary to use certain auxiliary teaching methods in the teaching process. In the new era, the emergence of intelligent computer-aided teaching systems has better played the role of demonstrating movements and can also be used as an auxiliary teaching method to provide students with all aspects of help. The application of intelligent computer-aided teaching system in aerobics teaching adapts to the needs of curriculum reform. Diversified teaching methods are extremely rich the content of teaching, stimulates students’ interest in learning, satisfies the individualized and diversified needs of the curriculum itself, better plays the role of aerobics teaching in the modern teaching system, and promotes the improvement of students’ comprehensive quality.
Detecting, identifying, and localizing radiological material in urban environments using scan statistics

A method is proposed, based on scan statistics, to detect, identify, and localize illicit radiological material using mobile sensors in an urban environment. Our method handles varying levels of background radiation that change according to an (unknown) environment. Our method can accurately determine if a source is present along a street segment as well as identify which of six possible sources generated the radiation. Our method can also localize the source, when detected, to within a few seconds. We have presented our results across a range of decision thresholds allowing stakeholders to evaluate the performance at different false alarm rates. Due to the simplicity of our approach, our models can be trained in a few minutes with very little training data and holds the potential to score a run in real-time. Our method was one of the top performing submissions in the Detecting Radiological Threats in Urban Areas competition.
Video Stream Gender Classification Using Shallow CNN

This paper describes the investigation results about the usage of shallow (limited by few layers only) convolutional neural networks (CNNs) to solve the video-based gender classification problem. Different architectures of shallow CNN are proposed, trained and tested using balanced and unbalanced static image datasets. The influence of diverse voting over confidences methods, applied for frame-by-frame gender classification of the video stream, is investigated for possible enhancement of the classification accuracy. The possibility of the grouping of shallow networks into ensembles is investigated; it has been shown that the accuracy may be more improved with the further voting of separate shallow CNN classification results inside an ensemble over a single frame or different ones.
FuzzyFeatureRank. Bringing order into fuzzy classifiers through fuzzy expressions

This work presents FuzzyFeatureRank, a new feature reduction method inspired on PageRank to reduce the dimensionality of the feature space in supervised classification problems. More precisely, as it relies on a weighted directed graph, it is ultimately inspired on TextRank, a PageRank based method that adds weights to the edges to express the strength of the connections between nodes. The method is based on dividing each original feature used to describe the data into a set of fuzzy predicates and then ranking all of them by their ability to differentiate among classes in the light of the training set. In order to do that, both the information gained by each predicate and their redundancy with other already selected predicates are taken into account. The fuzzy predicates with the best scores can then be used as a reduced input to construct fuzzy classifiers that consider only the preselected predicates to build the antecedents of the fuzzy rules. The novelty of the proposal relies on being an approach halfway between feature selection and feature extraction approaches, being able to improve the discrimination ability of the original features but preserving the interpretability of the new features in the sense that they are fuzzy expressions. The experimental results support the suitability of the proposal.
Ontology-Based Data Integration for Semantic Interoperability in Air Traffic Management

In global air traffic management (ATM), it is necessary to exchange various information related to aircraft operation such as the flight plans, surveillance, airports, weather, and restrictions, among different systems and operators. Hence, the concept of System Wide Information Management (SWIM) has been promoted by the International Civil Aviation Organization (ICAO) to implement interoperability and harmonization in global operation. In the SWIM environment, all the related stakeholders need to efficiently obtain the necessary ATM data with situational awareness from various information domains. However, this is difficult to realize in the current system, as different data are structured based on different data models. In this study, we construct domain ontologies based on flight, aeronautical, and weather information exchange models. Further, we develop a common ontology that uniformly defines the inconsistent representation in terms of the 4D, 3D, and 2D concepts and their inclusion relations and propose an integration method for heterogeneous domain ontologies. Moreover, we apply the proposed ontologies to a SWIM test system. The applicability and efficiency of the proposal are demonstrated through a case study in the SWIM environment.
Intellectualization Of Computation Based On Neural Networks For Mathematical Modeling

The problem of automation of efficient algorithm choice for solving linear algebra problems with a priori undefined structure of sparse matrices is considered. An approach of matrix classification and selection of an effective algorithm based on a visual portrait of a sparse matrix using a neural networks is proposed. Datasets for training of convolutional neural network "Sparse Matrix Vision" were prepared. Computational experiments, conducted in a scope of a current research showed that the algorithm chosen by neural network is effective for a specific problem.
Log-linear Chernoff Fusion for Distributed Particle Filtering

In large scale sensor networks the cross-correlations between nodes are rarely known. Failing to account for this detail in the design of distributed estimation schemes may lead to statistical inconsistencies and may even cause some estimators to diverge at various nodes within the network. Among the approaches that have been proposed to address this problem, covariance intersection is perhaps the most well-known. Other techniques rely on Chernoff fusion, of which covariance intersection is a special case. In a recent work we have introduced a technique, particles intersection, which is the application of Chernoff fusion using the empirical probability densities of two or more particle filters. This approach, however, may in some cases suffer from an excessive computational overhead. This issue is alleviated in this work by turning to a different yet related information fusion technique, a convex combination of probability densities. The utility of the new approach is demonstrated through simulations of cooperative robots localization, where it is compared with particles intersection and another state-of-the-art technique for distributed particle filtering. The results show a decisive advantage for the new technique both in terms of estimation accuracy and computational load. In addition, we provide an account of known techniques for tuning of the fusion parameters, and also suggest a new one based on the optimization of Chernoff information.
Children Immunization App (CImA), Low-Cost Digital Solution for Supporting Syrian Refugees in Zaatari Camp in Jordan — General Description

Mobile applications (apps) can improve health outcomes. In this study, we have described an app developed for documenting the history of vaccination among Syrian children in one of the largest refugees' camps in the Middle East region. This app includes health education information and automated reminders for parents, using a visual tool for parents with low literacy level. We have emphasized on the usability and technical concerns and have described the interdependency of technical and human considerations for such health app solution in a marginalized context.
X-vectors based Urdu Speaker Identification for short utterances

In context of commercial applications, robustness of a Speaker Identification (SI) system is adversely effected by short utterances. Performance of SI systems fairly depends upon extracted feature sets. This paper investigates the effect of various feature extraction techniques on performance of i-vectors and x-vectors based Urdu speakers' identification models. The scope of this paper is restricted to text independent speaker identification for short utterances (up to 4 seconds). SI systems demand for a large data covering sufficient inter-speaker and intra-speaker variability. Available Urdu speech corpus is used to measure performance of various feature sets on SI systems. A minimum percentage Equal Error Rate (%EER) of 0.113 is achieved using x-vectors with Linear Frequency Cepstral Coefficients (LFCCs) feature set.
Dynamic Attention Loss for Small-Sample Image Classification

Convolutional Neural Networks (CNNs) have been successfully used in various image classification tasks and gradually become one of the most powerful machine learning approaches. To improve the capability of model generalization and performance on small-sample image classification, a new trend is to learn discriminative features via CNNs. The idea of this paper is to decrease the confusion between categories to extract discriminative features and enlarge inter-class variance, especially for classes which have indistinguishable features. In this paper, we propose a loss function termed as Dynamic Attention Loss (DAL), which introduces confusion rate-weighted soft label (target) as the controller of similarity measurement between categories, dynamically giving corresponding attention to samples especially for those classified wrongly during the training process. Experimental results demonstrate that compared with Cross-Entropy Loss and Focal Loss, the proposed DAL achieved a better performance on the LabelMe dataset and the Caltech101 dataset.
Security and Electronic Identification Technologies in Latin Countries

The aggravation of the criminal situation in Latin America has led to the search for new technological solutions to the security problems of States and citizens. LA has become the largest market for biometric technologies. There are two types of biometric data: static, obtained by a person from birth, and dynamic, changing with time or under external influences. In the countries of LA, the identification by fingerprints is the most widespread among BT, the second most common is the set of biometric parameters of the face and the iris. The coverage of the BT identification of various areas of public life is as follows (in descending order): border control, critical infrastructure, the area of public and commercial services, digital signature, access to the election process. It is concluded that the experience of national implementation programs of electronic identification in LA countries indicates the growing international integration of BT identification, allowing to accelerate the exchange of information between States and improve the level of physical and information safety of countries and citizens.
Evaluating Nursing Students’ Perceptions of Using Quick Response Codes to Enhance Learning During Nursing Health Assessment

Abstract Providing engaging activities to supplement classroom learning can be a challenge for today’s nurse educator. Quick response (QR) code technology provides hands-on clinical experiences without the use of high-fidelity simulators. The purpose of this article is to evaluate how QR codes during a health assessment simulation activity enhanced learning for first-semester baccalaureate nursing students. The use of QR codes increased student confidence during health assessment with a form of technology they found engaging.
The Shortest Register With Non-Linear Update for Generating a Given Finite or Periodic Sequence

The use of feedback and feedforward functions in the construction of binary machines to generate a given finite or periodic sequence allows us to minimize the expected circuit-size. Using these functions a Register with Non-linear Update (RNLUs) is constructed which minimizes the expected circuit-size. However, from the practical standpoint, in order to construct a good stream cipher, the generated sequences must be periodic. It is, therefore, necessary to minimize the expected circuit-size of RNLUs which can generate the given random finite or periodic sequences. In this letter, the properties of feedback shift registers (FSRs) are studied for both random finite and periodic sequences to construct the shortest (minimal variables in the support set of feedback and feedforward functions) RNLUs, which can generate one or more than one-bit output at each clock cycle with asymptotically smaller expected circuit-size from all previously constructed algorithms for generating the given sequences.
Ball Game Controller

This research is based on a video game controller that focuses on being more interactive and intuitive interface by using the concept of Tangible user interface design. Here, the controller communicates with the game using an Arduino module and sonar sensors which tracks the motion of two balls on two axes horizontal and vertical and based on their movement players can control the ball movement on X and Y axes on a 2D ball game and X and Z axes in a 3D ball game. The goal of this research is to establish that this type of controllers can be more interactive, intuitive and entertaining as well as having a less steep learning curve than the traditional controllers.
Genetic Algorithm for Optimal Feature Vector Selection in Facial Recognition

Facial Recognition has a large number of real world applications in Security, Marketing, Healthcare, etc. In order for these applications to be feasible with respect to real time applications it's important that the feature vectors from these facial images be represented such that it is computationally efficient without compromising accuracy. Principal component analysis has often been used for dimensionality reduction. However using an evolutionary algorithm like genetic algorithm can ensure that only the best features are selected to increase efficiency and accuracy while ensuring overfitting doesn't take place.This paper demonstrates a way to effectively represent vectors of a face image using genetic algorithm and its comparison against other similar forms of representation.
File Fragmentation in the Wild: a Privacy-Friendly Approach

Digital forensic tooling should be based on reference data. Such reference data can be gathered by measuring a baseline, e.g. from volunteers. However, the privacy provisions in digital forensics tools are typically tailored for criminal investigations. This is not sufficient to ensure privacy obligations towards volunteer participants. Thus, privacy adaptations are necessary before such tooling can be used to establish or rejuvenate a baseline.(p)(/p)We illustrate the feasibility of this approach by rejuvenating a baseline for file carving, via a case study of file fragmentation. We derive a set of privacy requirements to prevent deanonymisation of individuals. Atypical properties of files can nevertheless still lead to plausible deanonymisation of the file. With regards to fragmentation, we find out-of-order fragmentation, where a later block is stored on disk before an earlier block of the same file, occurs in nearly half of all fragmented files. This is the first study to report on prevalence of this type of fragmentation. Its high rate of occurrence has implications for the practice of file carving.
The J-Orthogonal Square-Root NIRK-Based Extended-Unscented Kalman Filter for Nonlinear Continuous-Discrete Stochastic Systems

The paper presents a novel state estimation algorithm of the extended-unscented Kalman-like sort. In particular, this mixed-type filter employs the adaptive Nested Implicit Runge-Kutta (NIRK) method of order 6 and with an embedded automatic control of the numerical integration accuracy, which is used for prediction of the mean and covariance in its time-update step. Then, the filter's measurement update is grounded in the Unscented Transform (UT), i.e. it employs the measurement-update step of the famous Unscented Kalman Filter (UKF). Here, the principal novelty is the square-root fashion of the Accurate Continuous-Discrete Extended-Unscented Kalman Filter (ACD-EUKF) devised. Moreover, taking into account the negativity of some UT weights in continuous-discrete stochastic scenarios of large size we utilize the hyperbolic Householder transforms for designing the J-orthogonal square-root filtering algorithm, which is examined numerically in severe conditions of tackling the challenging radar tracking problem of size 7, where an aircraft executes a coordinated turn. It is also compared to the original non-square-root ACD-EUKF method within our stochastic target tracking scenario with ill-conditioned measurements.
Outperforming State-of-the-Art Systems for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is a very important problem with numerous applications. The three editions of SemEval's ABSA Shared Task have been instrumental in fostering the development in this field. One of its sub-tasks is the sentence-level ABSA. This sub-task has received a lot of attention and new techniques and better results are reported on it frequently. The purpose of this work is to achieve the highest accuracy for this problem. We follow a state-of-the-art (SOTA) approach that is based on multi-grain attention networks and infuse it with better embedding mechanisms in order to improve the results. For the famous SemEval's ABSA Shared Task, the results of the SOTA approaches reach 81.25 accuracy and 71.94 F1 score, whereas our approach surpasses them with 83.75 accuracy and 75.75 F1 score.
Basic issues and concepts

This chapter begins by discussing the relations between connectionist models and real neuronal systems, before proceeding to discuss functional properties of the models that link them to psychological issues such as the nature of learning, memory, and perception. It introduces the basic ideas of connectionism, while comparing it in a variety of ways with more conventional computational models. The basic computational process in a von Neumann machine, real or virtual, is a sequence of logical operations on symbols fetched from locations in memory, with the results of these operations being other symbols which are placed back in memory. Both Turing’s thesis and the physical symbol systems hypothesis support the view that the physical structure of the brain may have little bearing on its computational abilities. The chapter considers some of computational and psychological bases and reviews some of the basic properties of connectionist models that might make them attractive for people interested in modelling cognitive processes.
An Overview of Video Surveillance Approaches

In this paper, we present an overview of video surveillance approaches that are more related to the Internet-of-things (IoT) paradigm. We describe the first model-based architectures, dealing with anomaly detection; then, we illustrate approaches which are focused on the efficient implementation of database systems; subsequently, we describe architectures that exploit robotic systems, and eventually we mention the cognitive-based approaches.
Combine Discussion Mechanism and Chaos Strategy on Particle Swarm optimization Algorithm

Particle swarm optimization (PSO) is one of the artificial intelligence algorithms and has been proved to be an effective global optimization method. In this paper, a novel particle swarm optimization algorithm (DC-PSO) combining the discussion mechanism and chaos strategy is proposed to improve the standard PSO (SPSO) which is easy to trap into local optimum and has the problems of premature convergence. The four main strategies adapted in DC-PSO are chaos initializes the population, dynamic change of population, discussion mechanism of double populations and random variation of particles. Experiments are performed on eight benchmark functions to test the accuracy and performance of DC-PSO. Experiments show the proposed DC-PSO performs better than DEs intelligent algorithms.
Modified Binary Grey Wolf Optimizer

This chapter presents applications of the meta-heuristic known as grey wolf optimizer (GWO) to solve the NP-hard transmission network expansion planning (TNEP) problem. The computational intelligence technique known as GWO mathematically models the hunting technique and social hierarchy of grey wolves to solve optimization problems. In this context, the candidate solutions for the optimization problem are the preys, and the grey wolves adapt their positions in the search space to reach the optimal solutions. A pack of grey wolves is composed of four types of wolves: $$\alpha $$, $$\beta $$, $$\delta $$ and $$\omega $$. The three dominant wolves in a population are the $$\alpha $$, $$\beta $$ and $$\delta $$, and in an optimization context, they represent the three best solutions obtained in the searching process. The remaining wolves, called $$\omega $$, renovate their positions over the course of iterations by calculating the mean distance between their location and the position of the three dominant wolves, and after evaluation through an objective function, the pack hierarchy is updated. Originally, the GWO algorithm was designed for continuous problems, and however, the TNEP problem is of binary nature. Thus, the authors propose the use of a sigmoid transfer function to convert the optimization variables into binary values. At last, a simple modification at the local search component of the algorithm is incorporated to the problem to best suit its application to the TNEP problem and escape local minima.
Detection of Contrast Enhancement Forgery in Previously and Post Compressed JPEG Images

Nowadays the digital images become a very powerful source of communication. Digital images are consider to be proof in many investigation. So it is very important to provide security to these images, But with the advancement in the technology many photo editing tools like adobe photoshop etc. are used to enhance the features of original image which makes forged image. Thus it is important to detect this type of image forgery. Retouching is one of the forgery type in which contrast of images are enhanced or reduced. Our proposed system is about Contrast Enhancement Detection. The Contrast Enhancement Detection is done by Numerical Measures of image Estimation using Zigzag Zonal DCT Band. This algorithm are applicable for Previously and Post Compressed JPEG Images. According to these features SVM classifier gives detection result. Our proposed technique gives better accuracy and also detects contrast enhancement in all types of images.