Reviewerly/exHarmony huggingface dataset:
Vehicle Detection and Tracking for 511 Traffic Cameras With U-Shaped Dual Attention Inception Neural Networks and Spatial-Temporal Map

This paper develops vehicle detection and tracking method for 511 camera networks based on the spatial-temporal map (STMap) as an add-on toolbox for the traveler information system platform. The U-shaped dual attention inception (DAIU) deep-learning model was designed, given the similarities between the STMap vehicle detection task and the medical image segmentation task. The inception backbone takes full advantage of diverse sizes of filters and the flexible residual learning design. The channel attention module augmented the feature extraction for the bottom layer of the UNet. The modified gated attention scheme replaced the skip connection of the original UNet to reduce irrelevant features learned from earlier encoder layers. The designed model was tested on NJ511 traffic cameras for different scenarios covering rainy, snowy, low illumination, and signalized intersections from a key, strategic arterial in New Jersey. The DAIU Net has shown better performance than other mainstream neural networks based on segmentation model evaluation metrics. The proposed scanline vehicle detection was also compared with the state-of-the-art solution for infrastructure-based traffic movement counting solution and demonstrates superior capability. The code for the proposed DAIU model and reference models has been made public with the labeled STMap data to facilitate future research.
A Machine Learning-based SDN Controller Framework for Drone Management

With the advancement of information and communication technology, Unmanned Aerial Vehicles (UAV), popularly known as drones, have also increased. The drones have been noted for their wide range of applications such as military, search and rescue operation, disaster detection and monitoring, agriculture, and delivery. Each type of drone has different characteristics and functionality based on its application, making them a security threat for some city zone. Therefore, there is an essential need for efficient drone management based on their type and application in different zones. To do this, we proposed a Machine learning (ML) based Software Defined Network (SDN) drone management framework. In this framework, the SDN controller uses ML with the drone’s radio frequency feature to detect its type and application and, according to its application, authenticate it and assign communication rules. SDN controller records authentication information in a DAG-based Distributed Ledger Technology (DLT) available for other SDN controllers. When a drone desires to migrate to another zone, the destination SDN controller can achieve authentication information by referring to DAG-based DLT, and there is no need for re-authentication. The experimental result shows authentication delay reduction in our proposed framework. Moreover, we adopted ML algorithms includes Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), to evaluate our proposed framework in drone’s type classification. The result shows that the RF algorithm shows the best performance with 92.81% accuracy in the classification of the drone’s type.
DeSVQ: Deep Learning Based Streaming Video QoE Estimation

The quality-of-experience (QoE) is a notable subjective quality metric to assess the efficiency of multimedia streaming services. Video streaming is a segment seeing notable growth in the past decade. It is affected by a mixed interplay of quality switching, compression, and buffering events that induce distortions that vary with time. Accurate streaming QoE prediction can help in adapting the content transmission to improve end viewers experience. In this work, we propose DeSVQ, a deep learning approach that uses an integrated framework consisting of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks that combine multiple feature processing stages, each of them effectively capturing the complex dependencies underlying the QoE prediction process. In one stage, the features are extracted per frame from the distorted videos by the CNN that are mapped sequentially to QoE scores by the LSTM network. In another stage, the LSTM network explores the temporal dependencies using the objective (numerical) features. The output from both the stages are linearly combined and fed to the decision trees. A cross-validation framework is used for evaluation. Our proposed model is shown to perform better QoE prediction over the existing approaches.
Underwater LiDAR Image Enhancement Using a GAN Based Machine Learning Technique

Robust underwater imaging sensors are essential in applications such as identifying and classifying marine animals in turbid waters, typically found around coastal monitoring sites. The Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) has been developed to capture images taken in degraded underwater environments and provides superior range and image contrast over conventional optical cameras. In contrast to traditional underwater LiDAR systems that employ blue-green lasers, UMLSI will not harm the vision of marine animals, which is an important aspect in such settings. As with any other underwater LiDAR sensors, improving the quality of images taken in highly turbid water is essential to the UMSLI system. This paper proposes a novel machine learning image enhancement technique based on a Generative Adversarial Network (GAN) framework. One main contribution of the method is the incorporation of a correntropy-based perceptual loss. This technique has shown to be effective in enhancing ground-based images and has been adapted for LiDAR image enhancement. Given the limitation of existing underwater LiDAR data, a method for simulating degraded data for training is also presented. This LiDAR technique was validated using LiDAR data captured by the UMSLI system within Florida Atlantic University&#x2019;s Harbor Branch Oceanographic Institute (FAU-HBOI) optical test facility and at the Marine and Coastal Research Laboratory at the Pacific Northwest National Laboratory (MCRL-PNNL). This image enhancement technique can be readily extended to other underwater LiDAR systems.
Personalizing label prediction for GitHub issues

Automated label prediction tools can help developers manage and categorize issues on GitHub. However, different open-source projects use various forms of labels with the same meaning. Previous label prediction methods mainly solve the problem of the synonymous labels by manual preprocessing rules, but these preprocessing rules can only identify synonyms with the same prefix or suffix. These factors inspire us to propose a method to identify these synonymous labels automatically and recommend personalized labels for different open-source projects. In this paper, we propose a Personalizing Label Prediction framework for Issues named PLPI. PLPI identifies labels with similar meanings by representing labels as semantic vectors and applying clustering methods . PLPI can predict personalized labels from the existing labels in the open-source project. We conduct a comprehensive study to compare seven commonly adopted labeling models with our approach. The experimental results demonstrate the advantages of our approach. Finally, we show some representative examples and discuss the visualization results of synonyms clustering by dimension reduction. The experimental results show that our method PLPI can improve label prediction performance and provide personalized label recommendation results for different open-source projects.
Learning event‐triggered control based on evolving data‐driven fuzzy granular models

Abstract This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event‐triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event‐triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event‐triggered learning mechanism is employed to determine the instants in which the event‐triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event‐triggered fuzzy control algorithm.
Application of optimal clustering and metric learning to patch-based anomaly detection

Anomaly detection in computer vision is a process of identifying observations or events deviated from the norm. Pixel- and patch-based approaches for locating abnormal regions suffer from a tremendously expensive computational complexity due to repeated calculations inherently required. The approach introduced in this letter aims to reducing such computational burden by employing an optimal clustering method for features extracted from a deep network-based encoder, and a metric learning which provides a way to bias the clusters found by the clustering algorithm. A set of loss functions is defined and used for training the encoder. After the training steps, the clustering result obtained from the training dataset is saved and used in the inference phase by measuring distances between the features of the input data and the clusters. The proposed approach has been applied to a patch-based anomaly detection, Patch-SVDD, to clarify the effectiveness of the idea. The experiment carried out with the implementation on MVTec-AD dataset results in improved detection speed by 10∼35% and better detection accuracy for many image classes, as well. In addition, the ablation study conducted proves the validity of the ideas introduced in the proposed approach.
Combining Utility AI and MCTS Towards Creating Intelligent Agents in Video Games, with the Use Case of Tactical Troops: Anthracite Shift

Tactical Troops: Anthracite Shift is a squad-based tactics game with complex environment. It is a commercial game released on the Steam platform. This paper is authored by its original developers. It presents how the AI -driven players, that are featured in the game, have been designed and implemented. Their procedure of operation is based on a hierarchical combination of Monte Carlo Tree Search and Utility AI. Although these methods alone have already been applied to video games, there have not been published works that employ such a combination. Solutions to creating the AI in commercial games are rarely open to the public, which motivated us even more. The quality of the AI agents has been assessed from two perspectives: playing strength and believability.
Comparing HDD to SSD from a Digital Forensic Perspective

Storage media has become more complex in structure. This matter has been observed during the past years and this was the result of the big expansion in the integrated circuits and electronics industry. Hard disk drives (HDDs) were traditionally used to store data. Solid-state drives (SSDs) are being used for the same purpose, but they can be represented as the new replacement of HDD. Even though the industry of storage media started with hard disk drives, every tool has its age, and for the current age, hard disk drives proved that they cannot afford the desired speed for users and for the whole industry in general. Since SSDs are still relatively new products, with new products come new challenges and new difficulties. Therefore, this paper deeply analyzes the results obtained from forensics tools on HDD and SSD to determine the difference between the two drives from a digital forensic perspective.
In the Year of Science — About Library Science

The article is devoted to the first in the domestic history International research and practical conference “Library Science in the 21st century: content, organization, digitalization and scientometrics”, organized by the Russian State Library, the National Library of Russia and the Russian Library Association. The conference was held on October 19—20, 2021 in Moscow — in the Year of Science and Technology and in the first year of implementation of the Strategy for development of librarianship in the Russian Federation for the period up to 2030, where one of the main priorities is scientific and methodological support of libraries. The aim of the conference is to update the topics and identify the factors that stimulate the study of the library sphere, discussion of issues of organization and evaluation of performance of library science, the exchange of experience in organizing scientific activities. The event was held in face-to-face and remote format; more than 180 directors and experts of federal, central regional and university libraries, cultural institutes, as well as other institutions of science, education and culture attended it from 14 regions of the Russian Federation and from foreign countries. During the online broadcast of the meetings, there were recorded over one and a half thousand connections. The program included more than 50 reports, focused on theoretical problems of science studies and practical issues of organization of scientific activity. Summing up, the conference participants noted the importance of library science and proposed a set of specific measures, the implementation of which should help strengthen the regulatory framework for scientific activities in libraries, increase personnel staff; stimulate scientific creativity and research initiative; promote the spread of new scientific knowledge. The conference has important scientific and practical value. It demonstrated the current state of library science, allowed to identify and comprehensively discuss the most acute problems of its development and to outline steps to eliminate them. The conference materials contain a rich empirical base for further study of library science itself.
MWFP-outlier: Maximal weighted frequent-pattern-based approach for detecting outliers from uncertain weighted data streams

Many outlier detection approaches have been proposed for identifying previously unknown outliers, therefore improving the credibility of data. However, previous outlier detection approaches have some problems. First, most approaches were designed for static precise datasets, thus, their detection accuracy is very low when processing uncertain data streams. Second, these approaches considered the importance (aka weight) of each pattern is the same, which could not accurately reflect some actual situations in real life. To solve these problems, we propose an efficient maximal weighted frequent-pattern-based outlier detection approach, called MWFP-Outlier, for accurately detecting potential outliers from uncertain data streams through two phases, namely pattern mining phase and an outlier detection phase. In the pattern mining phase, through fully considering the existential probabilities and weights for each pattern, we propose the MWFP-Mine approach to accurately and efficiently mine maximal weighted frequent patterns based on the designed tree structure, list structure, and pruning strategies. In the outlier detection phase, we design four deviation indices to accurately measure the deviation degree of each transaction, and then the transactions in the top k ranked are identified as potential outliers. Extensive experimental results demonstrate that the MWFP-Outlier approach can accurately detect the outliers from uncertain weighted data streams, as well as uses less time consumption.
How robust are discriminatively trained zero-shot learning models?

Data shift robustness has been primarily investigated from a fully supervised perspective, and robustness of zero-shot learning (ZSL) models have been largely neglected. In this paper, we present novel analyses on the robustness of discriminative ZSL to image corruptions. We subject several ZSL models to a large set of common corruptions and defenses. In order to realize the corruption analysis, we curate and release the first ZSL corruption robustness datasets SUN-C, CUB-C and AWA2-C. We analyse our results by taking into account the dataset characteristics, class imbalance, class transitions between seen and unseen classes and the discrepancies between ZSL and GZSL performances. Our results show that discriminative ZSL suffers from corruptions and this trend is further exacerbated by the severe class imbalance and model weakness inherent in ZSL methods. We then combine our findings with those based on adversarial attacks in ZSL, and highlight the different effects of corruptions and adversarial examples, such as the pseudo-robustness effect present under adversarial attacks. We also obtain new strong baselines for both models with the defense methods. Finally, our experiments show that although existing methods to improve robustness somewhat work for ZSL models, they do not produce a tangible effect.
Content‐based image retrieval for super‐resolutioned images using feature fusion: Deep learning and hand crafted

Abstract An innovative image retrieval agenda by concatenating deep learning features from GoogleNet and low‐level features from HSI and RGB color space is proposed in this article. Most of the CNN features suffer from loss of information due to image resize as a pre‐processing stage. To reduce this information loss super‐resolution technic is used for resizing images. An improved form of dot‐diffused block truncation coding is used for extracting RGB handcraft features. To discover the interdependencies between color and intensity component of an image, interchannel voting between hue, saturation, and intensity component is calculated as a color feature in HSI space. Histogram of orientated gradient (HOG) feature is used as shape feature. Five standard performance parameters, average precision rate, average recall rate, F‐Measure, Average Normalized Modified Retrieval Rank, and Total Minimum Retrieval Epoch, are applied on nine image datasets: Corel‐1K, Corel‐5K, Corel‐10K, VisTex, STex, ColorBrodatz and three subsets of ImageNet dataset for evaluation process of proposed method. For all dataset the best performance is achieved by the proposed method with respect to all performance parameters.
Voxceleb Enrichment for Age and Gender Recognition

VoxCeleb datasets are widely used in speaker recognition studies. Our work serves two purposes. First, we provide speaker age labels and (an alternative) annotation of speaker gender. Second, we demonstrate the use of this metadata by constructing age and gender recognition models with different features and classifiers. We query different celebrity databases and apply consensus rules to derive age and gender labels. We also compare the original VoxCeleb gender labels with our labels to identify records that might be mislabeled in the original VoxCeleb data. On modeling side, we design a comprehensive study of multiple features and models for recognizing gender and age. Our best system, using i-vector features, achieved an F1-score of 0.9829 for gender recognition task using logistic regression, and the lowest mean absolute error (MAE) in age regression, 9.443 years, is obtained with ridge regression. This indicates challenge in age estimation from in-the-wild style speech data.
Spatio-Temporal Dual-Branch Network With Predictive Feature Learning for Satellite Video Object Segmentation

Satellite video is an important new earth observation data source that can be used to acquire large-scale dynamic information. Satellite video object segmentation (SVOS) is aimed at separating the foreground and background of satellite video and is a fundamental processing task for satellite video. To date, the state-of-the-art research into SVOS has mainly focused on unsupervised target extraction methods through hand-crafted features and post-processing operations, which are prone to obtaining incomplete contours of the targets and result in foreground aperture problem. Furthermore, the small size of targets and appearance deformation make the SVOS more difficult. Therefore, in this article, a spatio-temporal dual-branch network is proposed with predictive feature learning for the SVOS task. The proposed model consists of a temporal coherence branch and a spatial segmentation branch. In the temporal coherence branch, the Wasserstein generative adversarial network (WGAN) architecture is utilized for the future frame prediction to exploit temporal information, which captures the dynamic appearance and motion cues from the unlabeled satellite video data through predictive feature learning module in an adversarial manner. As a result, the proposed method can obtain segmentation results with temporal consistency, while avoiding the generation of optical flow images. In the spatial segmentation branch, a fully convolutional network (FCN) is used to extract the high-level spatial information of the satellite video and achieve end-to-end SVOS, without any post-processing operations. In the network implementation, boundary loss is used to solve the highly unbalanced segmentation problem caused by small size of the targets. The two branches of the network are also mutually constrained, to improve the final object segmentation results. The visual and quantitative results of three experiments all demonstrate that the proposed method outperforms the other current SVOS models.
Developing a Personalised Learning Model Based on Interactive Novels to Improve the Quality of Mathematics Education

The lack of sufficiently developed methodological basis before graduation adversely affects the mathematical competency of future experts that are required by the modern economy. The study aims to investigate the features of the development of a personalized model of teaching mathematics by means of interactive novels to improve the quality of educational results. The main methods of research were the generalization of pedagogical experience and science literature, modelling, observation, analysis of results in AXMA Story Research methods. The chosen methods made it possible to justify the necessity of using interactive novels to personalize teaching mathematics and to confirm the qualitative improvements in the pedagogical system. Fisher's criterion was used to evaluate the results. 121 schoolchildren from Russian and Uzbek schools participated in the experiment. Individual educational routes supporting the studying of mathematical theory, problem solving in the interactive space AXMA Story Maker were developed. The peculiarities to be considered when designing a personalized model of mathematics learning by means of interactive novelties were formulated. These are the correlation between the didactic goal and the result of educational and research work in a nonlinear environment, the selection of the plot for a visual novel, the elaboration of the text component and the personalized learning track, etc. According to ANCOVA test results, the post-score of the experimental group is statistically higher than the control group. The results of the article can be used to develop ideas for personalising of learning in the digital school, as well as justification for the need for inclusion of digital interactive tools in mathematics education at all levels of education and training.