Reviewerly/exHarmony huggingface dataset:
An Automated System for Generating Tactical Performance Statistics for Individual Soccer Players From Videos

The world of sports intrinsically involves fast and complex events that are difficult for coaches, trainers and players to analyze, and also for audiences to follow. In fast paced team sports such as soccer, keeping track of all the players and analyzing their performance after every match are very challenging. Current scenarios for identifying the best talents in soccer involve word-of-mouth and coaches/recruiters scouring through hours of manually annotated videos. This is a very expensive and laborious process and also biased by the nature of the recruiters. To alleviate these problems, this paper proposes an automated system that can detect, track, classify the teams of multiple players and identify the player controlling the ball in a video. The system generates three very important tactical statistics for a player: 1) duration of ball possession, 2) number of successful passes and 3) number of successful steals. This is done by training Convolutional Neural Networks (CNNs) to (a) localize and track the players on the field, (b) classify the team of a detected player, (c) identify the player controlling the ball and (d) pooling all the information extracted from (a), (b), and (c) to generate the statistics of players. To overcome the problem that the features learned from specific soccer matches do not necessarily generalize across different soccer matches, the paper proposes minimal amount of match-specific annotation and data augmentation, using a variant of Deep Convolutional Generative Adversarial Networks (DCGAN) to improve the accuracy. Experimental results and ablation studies show that the proposed approach outperforms the state-of-the-art approaches in terms of accuracy and processing speed.
Video Object Forgery Detection Algorithm Based on VGG-11 Convolutional Neural Network

In recent years, with the development of computer multimedia technology, video forgery has become more and more common. For the video object forgery can cover up some key evidence and it is hard to identify by the experts, the forgery detection technology for this class had always been a research hotspot. However, researchers mostly pay attention to traditional methods such as image processing and classifiers and rarely combine deep learning theory to the research. This paper proposes a video intra-frame forgery forensics algorithm based on the VGG-11 convolutional neural network, which can automatically detect video forgery frames. The algorithm first decompresses the video into a series of frames, calculates the motion residual map of each frame, and extracts the steganographic features. Then four different steganographic feature sample sets are used to construct as the training set and the test set to train and test model. The best-performing feature was selected by the comparison experiment. Finally, the forged frame was marked from the forgery video successfully. A series of experiments show that the proposed algorithm based on the VGG-11 convolutional neural network can automatically identify original or forgery frames in forgery video.
Automatic offensive language detection from Twitter data using machine learning and feature selection of metadata

The popularity of social networks has only increased in recent years. In theory, the use of social media was proposed so we could share our views online, keep in contact with loved ones or share good moments of life. However, the reality is not so perfect, so you have people sharing hate speech-related messages, or using it to bully specific individuals, for instance, or even creating robots where their only goal is to target specific situations or people. Identifying who wrote such text is not easy and there are several possible ways of doing it, such as using natural language processing or machine learning algorithms that can investigate and perform predictions using the meta-data associated with it. In this work, we present an initial investigation of which are the best machine learning techniques to detect offensive language in tweets. After an analysis of the current trend in the literature about the recent text classification techniques, we have selected Linear SVM and Naive Bayes algorithms for our initial tests. For the preprocessing of data, we have used different techniques for attribute selection that will be justified in the literature section. After our experiments, we have obtained 92% of accuracy and 95% of recall to detect offensive language with Naive Bayes and 90% of accuracy and 92% of recall with Linear SVM. From our understanding, these results overcome our related literature and are a good indicator of the importance of the data description approach we have used.
Allometric analysis using the multivariate shifted exponential normal distribution

Abstract In allometric studies, the joint distribution of the log‐transformed morphometric variables is typically elliptical and with heavy tails. To account for these peculiarities, we introduce the multivariate shifted exponential normal (MSEN) distribution , an elliptical heavy‐tailed generalization of the multivariate normal (MN). The MSEN belongs to the family of MN scale mixtures (MNSMs) by choosing a convenient shifted exponential as mixing distribution. The probability density function of the MSEN has a simple closed‐form characterized by only one additional parameter, with respect to the nested MN, governing the tail weight. The first four moments exist and the excess kurtosis can assume any positive value. The membership to the family of MNSMs allows us a simple computation of the maximum likelihood (ML) estimates of the parameters via the expectation‐maximization (EM) algorithm; advantageously, the M‐step is computationally simplified by closed‐form updates of all the parameters. We also evaluate the existence of the ML estimates. Since the parameter governing the tail weight is estimated from the data, robust estimates of the mean vector of the nested MN distribution are automatically obtained by downweighting; we show this aspect theoretically but also by means of a simulation study. We fit the MSEN distribution to multivariate allometric data where we show its usefulness also in comparison with other well‐established multivariate elliptical distributions.
Towards the semantic formalization of science

The past decades have witnessed a huge growth in scholarly information published on the Web, mostly in unstructured or semi-structured formats, which hampers scientific literature exploration and scientometric studies. Past studies on ontologies for structuring scholarly information focused on describing scholarly articles' components, such as document structure, metadata and bibliographies, rather than the scientific work itself. Over the past four years, we have been developing the Science Knowledge Graph Ontologies (SKGO), a set of ontologies for modeling the research findings in various fields of modern science resulting in a knowledge graph. Here, we introduce this ontology suite and discuss the design considerations taken into account during its development. We deem that within the next years, a science knowledge graph is likely to become a crucial component for organizing and exploring scientific work.
Hunter or Prey? Exploring the Situational Profiles that Define Repeated Online Harassment Victims and Offenders

Data collected from a sample of Spanish non-university students (N = 4174) were used to identify unique situational profiles of self-identified repeated online harassment victims and offenders, through a Conjunctive Analysis of Case Configurations (CACC). Repeat victim and offender profiles were constructed using individual-level factors and variables related to the cyber “places” where students go online and their personal information they share while there. Clustering analysis demonstrates that students spent their time online in few situational contexts where online harassment occurs. Dominant situational profiles of students are then provided, along with their associated probabilities for experiencing repeat victimization or committing repeat offending, identifying those at relatively higher and lower risk. Results show that composite profiles associated with victims of repeated online harassment are dissimilar to those associated with offenders of repeated online harassment, suggesting that each form of online harassment occurs in different situational contexts and therefore requires different preventative measures. Our findings are discussed in terms of criminological theory, future online harassment research, cybercrime prevention, and policy implications.
M-UNet: Modified U-Net Segmentation Framework with Satellite Imagery

In recent years, convolution neural network (CNN) has emerged as a dominant paradigm in machine learning for image processing application. In remote sensing, image segmentation is a very challenging task, and CNN has shown its worth over traditional image segmentation methods. U-Net structure is one of the simplified architectures used for image segmentation. However, it cannot extract promising spatial information from satellite data due to insufficient numbers of layers. In this study, a modified U-Net architecture has designed and proposed. This new architecture is deep enough to extract contextual information from satellite imagery. The study has formulated the downsampling part by introducing the DenseNet architecture, encourages the use of repetitive feature map, and reinforces the information propagation throughout the network. The long-range skip connections implemented between downsampling and upsampling. The quantitative comparison has performed using intersection over union (IOU) and overall accuracy (OA). The proposed architecture has shown 73.02% and 96.02% IOU and OA, respectively.
Comparative study of quality estimation of binary classification

The paper describes results of analytical and experimental analysis of seventeen functions used for evaluation of binary classification results of arbitrary data. The results are presented by 2×2 error matrices. The behavior and properties of the main functions calculated by the elements of such matrices are studied. Classification options with balanced and imbalanced datasets are analyzed. It is shown that there are linear dependencies between some functions, many functions are invariant to the transposition of the error matrix, which allows us to calculate the estimation without specifying the order in which their elements were written to the matrices. It has been proven that all classical measures such as Sensitivity, Specificity, Precision, Accuracy, F1, F2, GM, the Jacquard index are sensitive to the imbalance of classified data and distort estimation of smaller class objects classification errors. Sensitivity to imbalance is found in the Matthews correlation coefficient and Kohen’s kappa. It has been experimentally shown that functions such as the confusion entropy, the discriminatory power, and the diagnostic odds ratio should not be used for analysis of binary classification of imbalanced datasets. The last two functions are invariant to the imbalance of classified data, but poorly evaluate results with approximately equal common percentage of classification errors in two classes. We proved that the area under the ROC curve (AUC) and the Yuden index calculated from the binary classification confusion matrix are linearly dependent and are the best estimation functions of both balanced and imbalanced datasets.
The Python/C API: Evolution, Usage Statistics, and Bug Patterns

Python has become one of the most popular programming languages in the era of data science and machine learning, especially for its diverse libraries and extension modules. Python front-end with C/C++ native implementation achieves both productivity and performance, almost becoming the standard structure for many mainstream software systems. However, feature discrepancies between two languages can pose many security hazards in the interface layer using the Python/C API. In this paper, we applied static analysis to reveal the evolution and usage statistics of the Python/C API, and provided a summary and classification of its 10 bug patterns with empirical bug instances from Pillow, a widely used Python imaging library. Our toolchain can be easily extended to access different types of syntactic bug-finding checkers. And our systematical taxonomy to classify bugs can guide the construction of more highly automated and high-precision bug-finding tools.
Low-Rank Regularized Deep Collaborative Matrix Factorization for Micro-Video Multi-Label Classification

Deep matrix factorization can be regarded as an extension of traditional matrix factorization to help improve applications like social image tag refinement, image retrieval, and face clustering. Toward this tendency, in this letter, we proposed a low-rank regularized deep collaborative matrix factorization (LRDCMF) method to better tackle micro-video multi-label classification tasks. The proposed method aims to collaboratively learn two sets of factor matrices for characterization of latent attributes and two deep representations for instances and labels, respectively. During factorization process, the inverse covariance constraints are exploited to capture the latent correlation structures among latent attributes and labels and the low-dimensional intrinsic deep representations are ensured by further considering low-rank constraints. Moreover, a triplet term that connects instances representations, label representations, and labels is constructed to increase discrimination power of our method. Experimental results conducted on a large-scale micro-video dataset illustrate our model achieves superior performance in comparison with state-of-the-art methods.
Spatiotemporal variability of sunshine duration and influential climatic factors in mainland China during 1959–2017

Abstract Sunshine duration (SD) is a key index with which to quantitatively measure the intensity and duration of solar radiation. The exploration of spatiotemporal characteristics and potential influential factors for SD could help us better understand solar radiation variability. In this study, we first explore the spatiotemporal variability of SD across mainland China during 1959–2017, then identify the predominant influential climatic factors and detect their relative influence of temporal dynamic on SD, and finally discuss the relative influential rates of climatic factors and detect the dominating climatic factor on a spatial scale. The results show that: Firstly, the annual and seasonal SD gradually decreased from the northwest to southeast across mainland China; a significant decreasing trend ( p &lt; .05) was detected for annual SD at a rate of −2.7 h⋅a –1 , a turning point significantly occurred in 1986 ( p &lt; .05) in the year SD series, and oscillation periods of 2.4–3.8 years existed in mainland China and most sub‐regions. Secondly, the significant ( p &lt; .05) decrease of wind speed (Win), increase of precipitation (Pre), and increase of vapour pressure (Vp) were responsible for the decreasing trend of SD with relative influential rates of 39.9, 30.6, and 29.5%, respectively. Thirdly, temporally, the relative influential rate of each climatic variable changed over time; spatially, Win dominated most areas of mainland China (55.9%) during 1959–2017, followed by Pre (24.9%) and Vp (19.2%).
Temperature Sensor Denoising Algorithm Based on Curve Fitting and Compound Kalman Filtering

One of the most important ocean water parameters in world ocean observations is temperature. In the application of high-precision ocean sensors, there are often various interferences and random noises. These noises will cause the linearity of the sensor to change, and it is difficult to estimate the statistical characteristics, and the results will deviate from the real temperature. Aiming at the problems in the application, this paper proposes a compound Kalman smoothing filter (CKSF) algorithm based on least square curve fitting. This algorithm first analyzes the system model of the sensor, uses the least square method to fit the theoretical data and eliminate the non-linear factors caused by system itself, then estimates the statistical characteristics of the noise required by modeling, using the wavelet transform method to track the change of noise in real time and to accurately estimate the noise variance. Finally, a compound filtering method including wavelet transform and Kalman smoothing filtering is used as the main denoising algorithm, which is more accurate than a single Kalman filtering result. The algorithm is applied to the temperature measurement process of the ocean temperature sensor. The results show that the accuracy and stability of the sensor are improved.
Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs): A case study on bulk carrier's accident contributors

Statistical analysis of past maritime accidents may demonstrate the trends for certain contributing factors. However, there is a lack of a technique, which is capable of handling complex nature of maritime accidents by modelling interrelations between contributing factors. Due to the aforementioned complex interrelations and insufficient detail stored in accident databases about these contributors, it was not possible to quantify the importance of each factor in maritime accidents. This situation prevented researchers from considering these factors in risk assessments. Thus, in this research study, a technique for Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs) has been demonstrated. MALFCM employs fuzzy cognitive maps (FCMs) to model the relationships of accident contributors by using information directly from an accident database with the ability to combine expert opinion. Hence, the results can be considered more realistic and objective, which overcomes the main disadvantage of FCMs by eliminating or controlling the subjectivity in results. In this paper, FCMs were developed for bulk carriers with the aim of assessing the importance of contributing factors. For instance, in collision accidents in bulk carriers, situational awareness and inadequate communication were identified as the most critical factors, with a normalised importance weighting of 4.88% and 4.87% respectively.
Scalable Detection and Tracking of Extended Objects

This paper presents a factor graph formulation and sum-product algorithm (SPA) for scalable detection and tracking of extended objects that generate multiple measurements. The proposed method dynamically introduces newly detected objects into the state space and efficiently performs probabilistic multiple-measurement to object associations. We demonstrate performance advantages of our method numerically in a challenging tracking scenario with multiple closely-spaced extended objects.
Swarm optimized cluster based framework for information retrieval

This work explores the integrated power of swarm intelligence and advances in data mining techniques to solve the information retrieval (IR) problem of rapidly growing digital content on the World Wide Web. We propose a swarm optimized cluster based framework with frequent pattern mining techniques to retrieve user-specific knowledge from extensive document collections. In the pre-processing phase, we split the task into two sub-tasks. The first is to decompose the document collection into groups using a bio-inspired K-Flock clustering algorithm, while the second extracts frequent patterns from each cluster using a memory-efficient Recursive Elimination (RElim) algorithm. In the next phase, we implement a cosine similarity based probabilistic model to retrieve query-specific documents from clusters based on the matching scores between the closed frequent patterns of queries and clusters. The performance of a system is evaluated by conducting several experiments which are carried out on five well-known, diverse and variable size datasets viz- TREC 2014-15 CDS (Clinical Decision Support) datasets containing 733,138 records, OHSUMED dataset with 348,566 records from Medline database, NPL dataset with 11,429 records, LISA document collection of 6004 records, CACM (Collection of ACM) dataset of 3204 records. The results show that the proposed IR framework significantly outperforms the traditional sequential IR approach and other state-of-the-art IR approaches, both in terms of the quality of the returned documents and the time of execution.
A Study on Machine Learning Based Anomaly Detection Approaches in Wireless Sensor Network

Wireless sensor networks (WSN) became very popular in last few years. They are deployed in distributed manner for collecting variety of data. There are a lot of research issues and challenges in WSN viz; energy efficiency, security, localization etc. Outlier or anomaly detection is one of such area to prevent malicious attacks or reducing the errors and noisy data in millions of wireless sensor networks. Outlier detection models should not compromise with quality of data. We have to identify the anomalies in offline mode or online mode with accuracy, better performance and intake of minimal resources in the network. There are various machine learning techniques which have been used by several researchers these days to detect outliers. This paper presents a survey on outlier detection in WSN data using various machine learning techniques.
A Lightweight Data Aggregation Protocol With Privacy-Preserving for Healthcare Wireless Sensor Networks

In recent years, healthcare wireless sensor networks (HWSNs) have received extensive attention. Due to the direct participation of patients' personal health data, privacy issues, and security are very sensitive in HWSNs. How to aggregate the sensitive patient information securely and efficiently is still a challenging task to the HWSNs communication scenario. In this article, a lightweight data aggregation protocol with privacy-preserving is proposed to achieve enhancing security in HWSNs. With the proposed protocol, patients' personal health data collected by the data collect client can be aggregated by data aggregator and efficiently obtained by the medical service provider in a privacy-preserving manner. In particular, we first propose an efficient certificateless aggregate signcryption scheme, and then, design a data aggregation protocol for patient condition monitoring with information confidentiality, integrity, mutual authenticity, and public verifiability. Performance comparisons show that our newly proposed protocol significantly reduces computing overhead compared to the latest protocols in the field.