Reviewerly/exHarmony huggingface dataset:
Context-specific independencies in stratified chain regression graphical models

Graphical models are a useful tool with increasing diffusion. In the categorical variable framework, they provide important visual support to understand the relationships among the considered variables. Besides, particular chain graphical models are suitable to represent multivariate regression models. However, the associated parameterization, such as marginal log-linear models, is often difficult to interpret when the number of variables increases because of a large number of parameters involved. On the contrary, conditional and marginal independencies reduce the number of parameters needed to represent the joint probability distribution of the variables. In compliance with the parsimonious principle, it is worthwhile to consider also the so-called context-specific independencies, which are conditional independencies holding for particular values of the variables in the conditioning set. In this work, we propose a particular chain graphical model able to represent these context-specific independencies through labeled arcs. We provide also the Markov properties able to describe marginal, conditional, and context-specific independencies from this new chain graph. Finally, we show the results in an application to a real data set.
Randomized Sketching Algorithms for Low-Memory Dynamic Optimization

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 5 July 2019Accepted: 22 January 2021Published online: 10 May 2021KeywordsPDE-constrained optimization, matrix approximation, randomized algorithm, single-pass algorithm, sketching, adaptivity, trust-region method, flow control, Navier--Stokes equations, adjoint equationAMS Subject Headings49M37, 49L20, 68W20, 90C30, 90C39, 93C20Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8
Sports Video Annotation and Multi-Target Tracking using Extended Gaussian Mixture model

Video offers solutions to many of the traditional problems with coach, trainer, commenter, umpires and other security issues of modern team games. This paper presents a novel framework to perform player identification and tracking technique for the sports (Kabaddi) with extending the implementation towards the event handling process which expands the game analysis of the third umpire assessment. In the proposed methodology, video preprocessing has done with Kalman Filtering (KF) technique. Extended Gaussian Mixture Model (EGMM) implemented to detect the object occlusions and player labeling. Morphological operations have given the more genuine results on player detection on the spatial domain by applying the silhouette spot model. Team localization and player tracking has done with Robust Color Table (RCT) model generation to classify each team members. Hough Grid Transformation (HGT) and Region of Interest (RoI) method has applied for background annotation process. Through which each court line tracing and labeling in the half of the court with respect to their state-of-art for foremost event handling process is performed. Extensive experiments have been conducted on real time video samples to meet out the all the challenging aspects. Proposed algorithm tested on both Self Developed Video (SDV) data and Real Time Video (RTV) with dynamic background for the greater tracking accuracy and performance measures in the different state of video samples.
Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 4 November 2019Accepted: 16 February 2021Published online: 20 May 2021Keywordsparticle filters, state-space models, model error covariance, EM algorithmAMS Subject Headings62M05, 62M20, 60G25, 93E10, 93E11Publication DataISSN (online): 2166-2525Publisher: Society for Industrial and Applied MathematicsCODEN: sjuqa3
Impacto de la realidad aumentada en el rendimiento académico de los estudiantes de educación primaria en la enseñanza de las ciencias naturales

Augmented reality is a technology that allows combining realworld life with digital information, through other technologies such as computers, tablets or smartphones. Currently, it is used in diverse areas such as advertising, medicine, arts and education, among others. This research was carried out in the city of Ocosingo, Chiapas, Mexico, in a context where it is difficult to access technology and, above all, connectivity. It focused on using augmented reality in a didactic sequence for the teaching and learning process of the circulatory, respiratory and digestive systems in the Natural Sciences subject, in order to demonstrate whether the use of this technology as a didactic tool allows 4th grade students to improve their academic performance. The study was carried out under a quantitative, quasi-experimental and descriptive approach, using data collection tools such as tests designed with dichotomous responses (pre-test and post-test) and surveys with closed-ended questions under the Likert scale. The results show that using augmented reality in the teaching and learning process, within the classroom, significantly increases the academic performance of students compared to the traditional way of teaching these topics in the aforementioned subject.
Blind Deinterleaving of Signals in Time Series with Self-Attention Based Soft Min-Cost Flow Learning

We propose an end-to-end learning approach to address deinterleaving of patterns in time series, in particular, radar signals. We link signal clustering problem to min-cost flow as an equivalent problem once the proper costs exist. We formulate a bi-level optimization problem involving min-cost flow as a sub-problem to learn such costs from the supervised training data. We then approximate the lower level optimization problem by self-attention based neural networks and provide a trainable framework that clusters the patterns in the input as the distinct flows. We evaluate our method with extensive experiments on a large dataset with several challenging scenarios to show the efficiency.
Blockchain for COVID-19 Patient Health Record

In this digital era, where business operations and customer expectations involve harnessing and sharing the required information.In such scenarios, the trust and barrier for creating trusted partner networks can be resolved by Blockchain technologies.Blockchain has become vital for real-world applications involving business transactions where data security is predominant.Healthcare sector is another application of Blockchain gaining momentum.As countries across the globe grapple under the effect of the COVID-19 pandemic, the healthcare sector is the worst hit domain and hence, should scale like never before.During the current pandemic crisis, Blockchain technology could play a pivotal role in revolutionizing the way medical data can be handled and secured over its decentralized peer-to-peer network, thus enhancing the security of health-care information exchange.The infected patients serve as assets in the proposed Blockchain approach and their records include previous ailments if any, as blocks.In this paper a low-cost Blockchain approach has been proposed to store and view patient status along with transaction log details pertaining to their COVID-19 medical conditions.This secure data can only be viewed by the concerned government and local authorities for monitoring and further action.
The Future Of Accounting Profession in The Industrial Revolution 4.0: Meta-Synthesis Analysis

Various appearances of applied technology, such as Artificial Intelligence (AI), Internet of Things (IoT), and Big Data are all Industrial Revolution 4.0 characteristics that can change the business model. This research aims to explore how the advancement of Industrial Revolution 4.0 technology might disrupt the accounting profession. This research applied meta-synthesis method. The results of this study illustrated that the importance of development in the accounting sector is to keep pace with technological advances in industry 4.0. This must be balanced with the development of knowledge in the field of accounting so that all types of development can be carried out as well as possible. The findings showed that the appearance of various kinds new technology had a very significant impact in the development of the accounting profession. Further research should be able to discuss the Industrial Revolution 4.0 impact in more depth and can be linked to other accounting professions, such as internal auditors.&#x0D; Keywords: Accounting Profession; Industrial Revolution 4.0; Technology.
Towards Target-Dependent Sentiment Classification in News Articles

Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news as an essential information source in individual and societal decision making. This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles. Investigating characteristics of sentiment in news and contrasting them to popular TSC domains, we find that sentiment in the news is expressed less explicitly, is more dependent on context and readership, and requires a greater degree of interpretation. In an extensive evaluation, we find that the current state-of-the-art in TSC performs worse on news articles than on other domains (average recall $$AvgRec=69.8$$ on NewsTSC compared to $$AvgRev=\left[ 75.6, 82.2\right] $$ on established TSC datasets). Reasons include incorrectly resolved relation of target and sentiment-bearing phrases and off-context dependence. As a major improvement over previous news TSC, we find that BERT's natural language understanding capabilities capture the less explicit sentiment used in news articles.
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels

It is common for video-on-demand and music streaming services to adopt a user interface composed of several recommendation lists, i.e. widgets or swipeable carousels, each generated according to a specific criterion or algorithm (e.g. most recent, top popular, recommended for you, editors' choice, etc.). Selecting the appropriate combination of carousel has significant impact on user satisfaction. A crucial aspect of this user interface is that to measure the relevance a new carousel for the user it is not sufficient to account solely for its individual quality. Instead, it should be considered that other carousels will already be present in the interface. This is not considered by traditional evaluation protocols for recommenders systems, in which each carousel is evaluated in isolation, regardless of (i) which other carousels are displayed to the user and (ii) the relative position of the carousel with respect to other carousels. Hence, we propose a two-dimensional evaluation protocol for a carousel setting that will measure the quality of a recommendation carousel based on how much it improves upon the quality of an already available set of carousels. Our evaluation protocol takes into account also the position bias, i.e. users do not explore the carousels sequentially, but rather concentrate on the top-left corner of the screen. We report experiments on the movie domain and notice that under a carousel setting the definition of which criteria has to be preferred to generate a list of recommended items changes with respect to what is commonly understood.
Role of the Transformational Generative Grammar and other Language Learning Theories in English Language Teaching

In linguistics, Transformational –Generative Grammar (TGG) is the part of the theory of generative grammar, especially of natural native languages. Transformational Generative Grammar is also known as Transformational Grammar, which is a system of language analysis. It shows the relationship among the various elements of a sentence and among the possible sentences of the English language and process or rules which are called transformations of sentences to express semantics with the help of ‘surface structures’ and ‘deep structure’.While teaching English as second (L2), third (L3) or foreign language (FL), to the university students is an arduous task as they are socio-psychologically and linguistically preoccupied with his or her own native language (L1) competence which has its certain implications in the learning and teaching of any other language. This paper intends to explore some key concepts of Transformational Generative Grammar (TGG) The base Components, Transformational Component, Phonological Component, Semantic Component and their roles in contrastive analysis, one of the major instruments of language teaching in Applied Linguistics, of two languages of the same or different language families.In this article, our attempt is that TGG is an essential aspect of learning English but only for the students of advanced study of English what I find is that a fine fusion of all the there- traditional grammar, structural grammar and TGG is equally important. To be familiar with and learn the basics of grammar such as the knowledge of single and double parts of speech and such other items, traditional grammar (Newfield, and P. C. Wren & H. Martin) is a must; and for syntactic structures and sentence patterns (A. S. Hornby) structural grammar is needed and then a learner will easily learn the target language with the help of his native language (L1). He would enjoy learning English at the advanced stage through TGG (Chomsky).
Learning representative temporal features for action recognition

In this paper, a novel video classification method is presented that aims to recognize different categories of third-person videos efficiently. Our motivation is to achieve a light model that could be trained with insufficient training data. With this intuition, the processing of the 3-dimensional video input is broken to 1D in temporal dimension on top of the 2D in spatial. The processes related to 2D spatial frames are being done by utilizing pre-trained networks with no training phase. The only step which involves training is to classify the 1D time series resulted from the description of the 2D signals. As a matter of fact, optical flow images are first calculated from consecutive frames and described by pre-trained CNN networks. Their dimension is then reduced using PCA. By stacking the description vectors beside each other, a multi-channel time series is created for each video. Each channel of the time series represents a specific feature and follows it over time. The main focus of the proposed method is to classify the obtained time series effectively. Towards this, the idea is to let the machine learn temporal features. This is done by training a multi-channel one dimensional Convolutional Neural Network (1D-CNN). The 1D-CNN learns the features along the only temporal dimension. Hence, the number of training parameters decreases significantly which would result in the trainability of the method on even smaller datasets. It is illustrated that the proposed method could reach the state-of-the-art results on two public datasets UCF11, jHMDB and competitive results on HMDB51.
Deploying a smart queuing system on edge with Intel OpenVINO toolkit

Recent increases in computational power and the development of specialized architecture led to the possibility to perform machine learning, especially inference, on the edge. OpenVINO is a toolkit based on convolutional neural networks that facilitates fast-track development of computer vision algorithms and deep learning neural networks into vision applications, and enables their easy heterogeneous execution across hardware platforms. A smart queue management can be the key to the success of any sector. In this paper, we focus on edge deployments to make the smart queuing system (SQS) accessible by all also providing ability to run it on cheap devices. This gives it the ability to run the queuing system deep learning algorithms on pre-existing computers which a retail store, public transportation facility or a factory may already possess, thus considerably reducing the cost of deployment of such a system. SQS demonstrates how to create a video AI solution on the edge. We validate our results by testing it on multiple edge devices, namely CPU, integrated edge graphic processing unit (iGPU), vision processing unit (VPU) and field-programmable gate arrays (FPGAs). Experimental results show that deploying a SQS on edge is very promising.
Martial Arts Training Prediction Model Based on Big Data and MEMS Sensors

In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes’ posture is conducive to accurately judge sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes’ performance and reducing physical damage. The manufacturing level of MEMS sensors continues to improve, and status perception of assembly objects is becoming more and more abundant and accurate. The shape is small and can be worn, and data can be collected continuously without obstacles. The price is relatively low, the privacy protection is strong, and the advantages are clear and prominent. A considerable number of technicians choose to use MEMS sensors as the main tool for human behavior detection data collection. Therefore, this article designs multiple MEMS inertial sensors to form a human body lower limb capture device, and its core components are composed of accelerometer, gyroscope, and magnetometer. In order to make the obtained acceleration value, angular velocity value, and magnetometer value accurately reflect the movement state of the lower limb structure, different data fusion algorithms and magnetometer ellipsoid fitting calibration algorithms are studied to realize the calculation of the posture angle of each joint point and obtain martial arts posture big data. In addition, through big data analysis, this article designs a martial arts training performance and injury risk prediction model, which can provide guidance and suggestions for martial arts teaching tasks.
Scraping Airlines Bots: Insights Obtained Studying Honeypot Data

Airline websites are the victims of unauthorised online travel agencies and aggregators that use armies of bots to scrape prices and flight information. These so-called Advanced Persistent Bots (APBs) are highly sophisticated. On top of the valuable information taken away, these huge quantities of requests consume a very substantial amount of resources on the airlines' websites. In this work, we propose a deceptive approach to counter scraping bots. We present a platform capable of mimicking airlines' sites changing prices at will. We provide results on the case studies we performed with it. We have lured bots for almost 2 months, fed them with indistinguishable inaccurate information. Studying the collected requests, we have found behavioural patterns that could be used as complementary bot detection. Moreover, based on the gathered empirical pieces of evidence, we propose a method to investigate the claim commonly made that proxy services used by web scraping bots have millions of residential IPs at their disposal. Our mathematical models indicate that the amount of IPs is likely 2 to 3 orders of magnitude smaller than the one claimed. This finding suggests that an IP reputation-based blocking strategy could be effective, contrary to what operators of these websites think today.
Scaling Analysis of an Image Encryption Scheme Based on Chaotic Dynamical Systems

Despite that many image encryption systems based on chaotic or hyperchaotic systems have been proposed to protect different kinds of information, it has been crucial to achieve as much security as possible in such systems. In this sense, we numerically implement a known image encryption system with some variants, making special emphasis when two operations are considered in the scrambling stage. The variants of such an encryption system are based on some hyperchaotic systems, which generated some substitution boxes and the keys of the system. With the aim to have a more complete evaluation, some internal stages of the image encryption scheme have been evaluated by using common statistical tests, and also the scaling behavior of the encrypted images has been calculated by means of a two-dimensional detrended fluctuation analysis (2D-DFA). Our results show that the image encryption systems that include two operations or transformations in the scrambling stage present a better performance than those encryption systems that consider just one operation. In fact, the 2D-DFA approach was more sensitive than some common statistical tests to determine more clearly the impact of multiple operations in the scrambling process, confirming that this scaling method can be used as a perceptual security metric, and it may contribute to having better image encryption systems.
SRR-LGR: Local–Global Information-Reasoned Social Relation Recognition for Human-Oriented Observation

People’s interactions with each other form the social relations in society. Understanding human social relations in the public space is of great importance for supporting the public administrations. Recognizing social relations through visual data captured by remote sensing cameras is one of the most efficient ways to observe human interactions in a public space. Generally speaking, persons in the same scene tend to know each other, and the relations between person pairs are strongly correlated. The scene information in which people interact is also one of the important cues for social relation recognition (SRR). The existing works have not explored the correlations between the scene information and people’s interactions. The scene information has only been extracted on a simple level and high level semantic features to support social relation understanding are lacking. To address this issue, we propose a social relation structure-aware local–global model for SRR to exploit the high-level semantic global information of the scene where the social relation structure is explored. In our proposed model, the graph neural networks (GNNs) are employed to reason through the interactions (local information) between social relations and the global contextual information contained in the constructed scene-relation graph. Experiments demonstrate that our proposed local–global information-reasoned social relation recognition model (SRR-LGR) can reason through the local–global information. Further, the results of the final model show that our method outperforms the state-of-the-art methods. In addition, we have further discussed whether the global information contributes equally to different social relations in the same scene, by exploiting an attention mechanism in our proposed model. Further applications of SRR for human-observation are also exploited.