Reviewerly/exHarmony huggingface dataset:
Model selection in the space of Gaussian models invariant by symmetry

We consider multivariate centered Gaussian models for the random variable Z=(Z1,…,Zp), invariant under the action of a subgroup of the group of permutations on {1,…,p}. Using the representation theory of the symmetric group on the field of reals, we derive the distribution of the maximum likelihood estimate of the covariance parameter Σ and also the analytic expression of the normalizing constant of the Diaconis–Ylvisaker conjugate prior for the precision parameter K=Σ−1. We can thus perform Bayesian model selection in the class of complete Gaussian models invariant by the action of a subgroup of the symmetric group, which we could also call complete RCOP models. We illustrate our results with a toy example of dimension 4 and several examples for selection within cyclic groups, including a high- dimensional example with p=100.
Specifics of Receiving and Processing Phase Information in Voice Authentication Systems

The issues of improving the reliability of storing various resources, access to which is carried out using telecommunication networks, are considered. In this case, the first barrier in ensuring access reliability is the user authentication system. Lately, access systems based on biometric features of a user have been used. Initially, static biometric features of a user (facial image, finger papillary pattern and iris) were preferable, which did not meet the expectations of developers and users due to the simplicity of their falsification. Recently, the preference has been given to the dynamic (behavioral) biometric features of a user, namely voice authentication systems became more widely used. As it is known, voice authentication systems have several advantages, such as: simplicity, convenience, compactness, low cost, and a number of others. In addition, the passphrase can be quickly changed and expanded during the authentication process. However, the quality indicators of all biometric access systems do not meet the increasing requirements. The object of the study is the process of digital processing of voice signal during user authentication in access systems. In the process of voice authentication, the analysis of the amplitude-frequency spectrum of recording materials is performed. At the same time, the main research focuses on the use of estimates of formants, cepstral coefficients, mel-frequency cepstral coefficients, linear prediction coefficients, etc. as a user's template. On the basis of user's established patterns, admission decisions are made using Gaussian Mixture Models, Support Vector Machines, Hidden Markov Models or artificial neural networks. In the report, it is proposed to change the paradigm of digital processing of user voice signals and supplement the analysis of the amplitude-frequency spectrum with studies of phase data, which are traditionally ignored during the authentication. According to the authors, the latter is caused by the lack of effective procedures for the formation of phase data, the requirement of additional computational resources, which were not always available to researchers, and some features using the signal phase.
Improvement of Performance of Navigation System for Supporting Independence Rehabilitation of Wheelchair - Bed Transfer

Abstract The purpose of this research is to improve the position and angle estimation accuracy of the wheelchair guide system for Hemiplegic patients. The wheelchair guide system aims to assist the patients to transfer between their wheelchair and bed. This system prompts properly to user depending on the situation as same as care worker. Therefore, the system is developed to prompt the measured position and angle of wheelchair. The position estimator uses an AR marker to detect the wheelchair position. However, its estimation is contaminated by measurement noises and sometimes fails because the marker is out of the camera’s field of view. To improve the estimation of position and moving direction, we introduce the kinematic model of the wheelchair and utilize the Extended Kalman Filter with the model. In this paper, we describe the self-position estimation method of the navigation system and AR marker, with the estimation method using Kalman Filter.
Perspective template matching by differential evolution introducing the group remaking

Abstract In order to achieve correct perspective template matching on two‐dimensional general images, we propose a differential evolution (DE) introducing the group remaking. Since the perspective template matching has eight degree of freedom (DoF), acquisition of a global optimum, which is an area corresponding to a template in a target image, within limited calculation resource is difficult. In order to address this task, DE is utilized. This method can optimize some parameters simultaneously and acquire a good approximate solution within the limited calculation resources. The adopted DE divides the population into two equal groups, exploitation group for local search and exploration group for global search. By remaking the exploitation group every time, the best solution is found and updated, more effective search is possible. Also, linear group size changing scheme and initialization of parameters for crossover and mutation are introduced to acquire good performance. The experimental results using a general 2D image dataset show the lowest average error of the proposed method. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Routing Tensor Model with Providing Multimedia Quality

Routing tensor model with providing multimedia quality is proposed. The novelty of the proposed model lies in providing a given level of Quality of Experience regarding the multimedia quality indicator. Due to implementation of the tensornetwork analysis functional, it was possible to provide an analytical calculation of key parameters that influence the multimedia quality level: packet loss probabilities for audio and video flows, as well as the average end-to-end packet delay for the same flows transmitted within the multimedia session. The possibility of analytical and interconnected calculation of these QoS indicators allows to control the impact of desynchronization in time during delivery processes of packets of audio and video flows on multimedia quality, which is significant in the practical implementation of the proposed routing model.
Applied Behavior Analysis (ABA) as a Footprint for Tutoring Systems: A Model of ABA Approach Applied to Olfactory Learning

Applied Behavior Analysis (ABA) belongs to the analysis of behavior techniques introduced by the theorists of behaviorism in psychological fields. It deals with the application of behaviorism principles to guide the learning process. It can serve as a footprint to build artificial tutoring systems in environments for specific learning processes. In this paper, we delineate the pathway to build an artificial tutoring system following ABA footprints, named the ABA tutor. In implementing the ABA tutor, the techniques of ABA are reproduced. This paper also describes how to build a tutor based on ABA and how to use it to favor olfactory learning. In more detail, the ABA tutor is inserted in SNIFF, a system that combines a software and a hardware side to assess and practice the sense of smell exploiting gamification. A first experiment was run involving 90 participants, and the results indicated that the artificial tutoring system based on ABA principles can effectively promote olfactory learning. The implications of this approach are discussed.
Energy-efficient User-centric Dynamic Adaptive Multimedia Streaming in 5G Cellular Networks

5G multimedia mobile wireless network is designed to support on-demand encoding of rich mobile multimedia content for heterogeneous users. Due to the heterogeneity of the users, adaptive multimedia services are essential to provide a satisfactory Quality of Experience (QoE). In this paper, we propose a utility-based dynamic adaptive multimedia streaming scheme, named UDAS, for heterogeneous users that helps in extending the battery life of the low-battery users and also uses the bandwidth of the wireless channel efficiently. At each scheduling interval, the adaptation algorithm considers four utility functions of the user devices, namely, quality utility, power consumption utility, packet error ratio utility, and remaining battery utility to adapt the data rate dynamically. We formulate an optimization problem to maximize a joint utility function of these four utilities. The solution of the problem provides the best adaptive multimedia content that is selected for transmission to the end-users in every scheduling interval. The mobile edge computing (MEC) server situated at the base station performs an on-demand HEVC (high efficiency video coding) encoding of videos and select the best suitable videos for different users. Simulation results verify the improved performance of UDAS in terms of saved battery energy and the number of unserved low-battery users in comparison with state-of-the-art non-adaptive multimedia streaming schemes and a popular scheme ESDOAS from the literature.
A Learning Approach to Cooperative Communication System Design

The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values. It is also shown that training at a list of mixed SNR values can improve the error performance compared to training at a fixed SNR value. Moreover, to verify the robustness of the trained AE model, we test it under the effect of impulse-noise.
Supervised Learning Methods for Diversification of Image Search Results

We adopt a supervised learning framework, namely R-LTR [17], to diversify image search results, and extend it in various ways. Our experiments show that the adopted and proposed variants are superior to two well-known baselines, with relative gains up to 11.4%.
Sentiment Analysis for Software Code Assessment

This research explores the power of sentiment analysis, carried out through machine learning classifiers, for assessing the quality of code of existing machine learning code repositories. The paper provides sentiment attributes, extracted from git commit messages, referenced to the most popular open source frameworks, such as scikit-learn, TensorFlow and Theano. The purpose of this study is twofold. It proves that machine learning classifiers can be used in assessing the quality of software code. It also draws readers' attention to the fact that the same principle can be used for the evaluation of algorithms available in the field of machine learning and artificial intelligence.
Ensemble and Deep Learning for Real-time Sensors Evaluation of algorithms for real-time sensors with application for detecting brushing location

Real-time sensors on handheld devices place unique requirements on machine learning methods. In this study, ensemble and deep learning methods usage for real-time sensors are explored. The focus is on learning methods applied in smart toothbrush devices with the potential to improve overall health as dental caries is the most frequent chronical disease for children. For preventing cavities, smart toothbrushes are a valuable tool in guiding the user to which teeth and surface to brush. The core of a smart toothbrush is the algorithm to detect the location brushed in real-time. To improve the knowledge about promising methods for this task, various machine learning algorithms are examined, utilizing the data generated by real-time sensors on the toothbrush for the localization of the same in the mouth of the user. A final dataset of more than one million rows (1.44M), seven features and seventy-two classes is tested with KNN, Naïve Bayes, Support Vector Machines, Extra Trees Classifier and various voting classifiers. The results, compared across various dimensions such as prediction performance, model training, prediction overhead and model size, show that the ExtraTree classifier is the most promising method of the evaluated algorithms with an F1 score of 58.77%.
Using Hybridized techniques for Prediction of Software Maintainability using Imbalanced data

Maintainability is an essential dimension of software quality. Software Maintainability Prediction (SMP) is gaining the attention of researchers to develop maintainable software systems. Early prediction of software maintainability aid the software practitioners to focus on those software modules or classes that requires high maintainability effort in the maintenance phase. However, the imbalanced distribution of training data is a challenging and serious problem that is encountered while developing prediction models for software maintainability. This paper apply oversampling methods namely: Adaptive Synthetic Oversampling technique (AdaS), BorderlineSynthetic Minority Oversampling technique (BSMOTE), Synthetic Minority Oversampling technique (SMOTE), and SafeLevel Synthetic Minority Oversampling technique (SSMOTE) to treat the imbalanced data before learning the models for software maintainability. We also investigate the effectiveness of hybridized techniques for learning the prediction models using three popular Apache datasets. The outcome of the study supports the use of investigated oversampling methods with hybridized techniques to develop effective prediction models for software maintainability.
A Method for LDA-based Sina Weibo Recommendation

Sina Weibo is one of the most influential social platforms in China. Recommendation system helps user to find celebrities that they may interest in and thus helps to attract more users. User's Weibo contents reflect their personal preferences. In this paper we proposed an LDA topic modeling based recommendation method which can discover topics of user's Weibo contents and recommend celebrities that users are interest in. The comparison result shows that our method outperforms tf-idf-based recommendation method.
Natural Language Transfer Learning for Physiological Textual Similarity

Understanding textual and language information has always been one of the primary research concerns of artificial intelligence, as the crucial function it plays in communication. The biomedical domain has experienced a surge in the availability of data in the form of text. This collection of information has opened avenues to a plethora of automated applications. In this work, the nascent technique of Natural Language Transfer Learning is employed for Physiological Computing. This methodology measures the semantic similarity between medical text utilising pre-trained language models such as BERT and RoBERTa. Using the proposed methodology 90% accuracy over the BioSSES dataset has been obtained. Henceforth, transfer learning proves to be an effectual strategy for NLP tasks that belong to varied fields.
Prevention from Security Risks of Spyware by the use of Ai

This paper mainly tries to describe the importance and ways of preventing varied types of spywares present within the computers. This is done in order to maintain risk free computers within the network. Doing so, could be helpful for the organizations to maintain the business in a fruitful manner. However, to do so, artificial intelligence (AI) need to be used in order to improve the security measures of the computers in the future.
Improved Symbiotic Organism Search Based Approach for Scheduling Jobs in Cloud

Optimal task scheduling plays an important role in improving the performance of cloud computing. As it has been classified as a NP-complete problem, researchers are not able to get an exact solution for this issue. Symbiotic Organism Search (SOS) algorithm is the latest meta-heuristic technique that is widely used for finding the solution of optimization problems. Improved SOS (ISOS) algorithm is imitated from symbiotic relationships that exist among different organisms of an ecosystem. This work presents a scheduling algorithm based on ISOS Algorithm for the best possible mapping of various tasks on available cloud resources. The proposed algorithm is aimed to minimize two-objective functions that are makespan and cost. To validate the performance of presented work, it is compared with PSO algorithm. Simulation results show that ISOS algorithm gives 19.71–49.50% improvement in terms of makespan and 27.65–42.73% improvement in terms of cost over PSO algorithm when the number of tasks is varied from 100 to 500.
Enhanced Action Tubelet Detector for Spatio-Temporal Video Action Detection

Current spatio-temporal action detection methods usually employ a two-stream architecture, a RGB stream for raw images and an auxiliary motion stream for optical flow. Training is required individually for each stream and more efforts are necessary to improve the precision of RGB stream. To this end, a single stream network named enhanced action tubelet (EAT) detector is proposed in this work based on RGB stream. A modulation layer is designed to modulate RGB features with conditional information from the visual clues of optical flow and human pose. This network is end-to-end and the proposed layer can be easily applied into other action detectors. Experiments show that EAT detector outperforms traditional RGB stream and is competitive to existing two-stream methods while free from the trouble of training streams separately. By being embedded in a new three-stream architecture, the resulting three-stream EAT detector achieves impressive performances among the best competitors on UCF-Sports, JHMDB and UCF-101.
Recent Developments of Fireworks Algorithms

Fireworks Algorithm has been proposed for almost 10 years. Because of its basic but profound collaborative searching manner and advantages of universal effectiveness, hundreds of scholars have conducted and published a wide range of work on it. This chapter serves as a background description of fireworks algorithms' developments by introducing its detailed status of researches and applications. Specifically, it gives a brief summarization and analysis of published researches on Fireworks Algorithms since 2010 to clarify characteristics of its historical progress and future trend in detailed fields like algorithms improvements, theoretical analysis, and practical applications.
Residual Attention Network for Wavelet Domain Super-Resolution

Single-image super-resolution plays an important role in computer vision area. However, previous works using convolutional neural networks perform badly when reconstructing high frequency details, result in over-smooth and lacking of textural information in the output. At the same time, super-resolution computation always relays on convolutional neural networks with huge depth, which is super tricky to train and use. In this paper, we propose a novel network with better textural details in wavelet domain, which is composed of a feature extract layer, residual channel attention groups (RCAG) and a residual up-sampling layer based on inverse discrete wavelet transform. Channel attention and spatial attention layers are inserted into residual channel and spatial attention blocks (RCSAB), enhancing the learning of high frequency information with attention maps. Composed of a chain of RCSAB and a channel attention layer with short skip connection, RCAG is good at catching long-term high frequency information. Then the feature mapping component is composed of a chain of RCAG. Experiment shows that our method performs better than state-of-the-art methods on benchmark datasets in different scales.
Ontology-based sharing of structural health monitoring data

&lt;p&gt;A structural health monitoring system installed in a bridge produces a vast amount of sensor data that is analyzed and periodically reported to a bridge owner at an aggregate level. The data itself typically remains in the monitoring service of a service provider; it may be accessible to clients and third parties through a dedicated user interface and API. This paper presents an ontology to defining the monitoring model based on the Semantic Sensor Network Ontology by W3C. The goal is to enable an asset owner to utilize preferred tools to view and access monitoring data from different service providers, and in longer term, increase the utilization of monitoring data in facility management. The ultimate aim is to use BrIM as a digital twin of a bridge and to link external datasets to improve information management and maintenance over its lifecycle.&lt;/p&gt;
Object based forgery detection and localization in videos

Video forensics involving passive approach exploits the statistical patterns of the video sequence to measure the integrity of video and detect forgery if it is compromised. Digital videos are susceptible to tampering due to the availability of efficient video processing tools for malevolent purpose. Object based video tampering disturbs under lying natural pattern of the video sequence. Quantal parameters of the spatial moments of a frame in a video provides inherent clue when object based forgery happens. The scalar quantities representing global characteristics of the frames in a video have been exploited to detect suspected frames. Thresholding approach is adopted to distinguish forged frames and original frames in a video sequence. Experiments on the subset of benchmark dataset demonstrate the efficiency of our approach.
Representation Learning for Electronic Health Records: A Survey

Abstract With the wide application of Electronic Health Record (EHR) in hospitals in past few decades, researches that employ artificial intelligence (AI) and machine learning methods based on EHR data have been explosive. With such EHR data, one can engage in many tasks such as risk prediction, treatment recommendation, information imputation, etc. The performance of classification or prediction highly depends on the quality of data representation, i.e., representing original records into numerical vectors to facilitate further learning. However, there is little research that focuses on the representation learning techniques for EHR data at present, which makes it hard to understanding the development trend of EHR learning in a global map. In this paper, we bridge this gap by systematically investigating the related research efforts that apply the representation learning on EHR data. We analyze and conclude the techniques used in the typical representation learning approaches as well as the limitations and advantages of them. The survey would provide a comprehensive reference for further analysis and application in EHR research.
Self-Adaptive Feature Fool

Recently, deep neural networks (DNNs) are shown to be susceptible to data-agnostic quasi-imperceptible noises called Universal Adversarial Perturbations (UAPs). Moreover, the techniques to craft UAPs can be categorized into data-driven and data-free. However, data-free techniques craft UAPs without utilizing any data samples and therefore result in weaker attack capacity. In this paper, we propose a novel method to craft UAPs in the absence of data, via adaptively perturbing mid-layer outputs of the CNN. Based on our proposed self-adaptive attention mechanism, we explore the effects of feature correlation of the internal representations on generating UAPs for the first time. Experimental evaluation demonstrates that UAPs crafted by our Self-Adaptive Feature Fool (SAFF) approach achieve state-of-the-art performance in data-free scenarios.