Reviewerly/exHarmony huggingface dataset:
A matrix adaptation evolution strategy for optimization on general quadratic manifolds

An evolution strategy design is presented that allows for an evolution on general quadratic manifolds. That is, it covers elliptic, parabolic, and hyperbolic equality constraints. The peculiarity of the presented algorithm design is that it is an interior point method. It evaluates the objective function only for feasible search parameter vectors and it evolves itself on the nonlinear constraint manifold. This is achieved by a closed form transformation of an individual's parameter vector, which is in contrast to iterative repair mechanisms. Results of different experiments are presented. A test problem consisting of a spherical objective function and a single hyperbolic/parabolic equality constraint is used. It is designed to be scalable in the dimension and it is used to compare the performance of the developed algorithm with other optimization methods supporting constraints. The experiments show the effectiveness of the proposed algorithm on the considered problems.
AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series

The ability to accurately and consistently discover anomalies in time series is important in many applications. Fields such as finance (fraud detection), information security (intrusion detection), healthcare, and others all benefit from anomaly detection. Intuitively, anomalies in time series are time points or sequences of time points that deviate from normal behavior characterized by periodic oscillations and long-term trends. For example, the typical activity on e-commerce websites exhibits weekly periodicity and grows steadily before holidays. Similarly, domestic usage of electricity exhibits daily and weekly oscillations combined with long-term season-dependent trends. How can we accurately detect anomalies in such domains while simultaneously learning a model for normal behavior? We propose a robust offline unsupervised framework for anomaly detection in seasonal multivariate time series, called AURORA. A key innovation in our framework is a general background behavior model that unifies periodicity and long-term trends. To this end, we leverage a Ramanujan periodic dictionary and a spline-based dictionary to capture both seasonal and trend patterns. We conduct experiments on both synthetic and real-world datasets and demonstrate the effectiveness of our method. AURORA has significant advantages over existing models for anomaly detection, including high accuracy (AUC of up to 0.98), interpretability of recovered normal behavior ( 100% accuracy in period detection), and the ability to detect both point and contextual anomalies. In addition, AURORA is orders of magnitude faster than baselines.
Image Encryption Algorithms Using Machine Learning and Deep Learning Techniques—A Survey

In this world, the present situation is coming from text to multimedia content transmission. In multimedia data, images play major role for transmission and it is very important to protect the image data while transmitting over network. This can be achieved by Image encryption. There are so many different techniques should be used to protect confidential image data from unauthorized access. In this paper, a survey has done on existing works which is used different techniques for image encryption by using machine learning and deep learning algorithms and it helps to analyze different algorithms for different images and also for image data sets.
Characterization of data compression across CPU platforms and accelerators

Abstract The ever increasing amount of generated data makes it more and more beneficial to utilize compression to trade computations for data movement and reduced storage requirements. Lately, dedicated accelerators have been introduced to offload compression tasks from the main processor. However, research is lacking when it comes to the system costs for incorporating compression. This is especially true for the influence of the CPU platform and accelerators on the compression. This work will show that for general‐purpose lossless compression algorithms following can be recommended: (1) snappy for high throughput, but low compression ratio; (2) zstandard level 2 for moderate throughput and compression ratio; (3) xz level 5 for low throughput, but high compression ratio. And it will show that the selected platforms (ARM, IBM or Intel) have no influence on the algorithm's performance. Furthermore, it will show that the accelerator's zlib implementation achieves a comparable compression ratio as zlib level 2 on a CPU, while having up to the throughput and utilizing over 80% less CPU resources. This suggests that the overhead of offloading compression is limited but present. Overall, this work will allow system designers to identify deployment opportunities for compression while considering integration constraints.
PEDAGOGICAL MANAGEMENT OF THE TRAJECTORY OF PROFESSIONAL ADAPTATION OF STUDENTS PARTICIPATING IN MILITARY OPERATIONS TO PRODUCTIVE ACTIVITIES IN THE FIELD OF SERVICES

The article proposes tools for effective management of the trajectory of professional adaptation of military students (participants in military operations) to productive activities at a higher institution as an innovative pedagogical technology. The article identifies the practical ways to build, update and apply a set of pedagogical mechanisms for effective management of individual professional adaptation line to productive activities of students (participants in military operations) during higher education. An individual educational route is a personal program of effective management of a specialist’s professional adaptation according to his/her age and skills, interests, incentives based on an educational and professional program. The article indicates the influence of educational, training, research and organizational activities on positive dynamics of the professional adaptation line to the productive activities in the field of services of students participating in military operations. This influence is shown and reasoned by a schedule of professional adaptation line management. The article determines the conceptual framework of an integrated approach to effective management of the professional adaptation line of students participating in military operations, which is confirmed by practical effect. In our opinion, the development of the theory of productive professional activity in the field of services of a student-participants in military operations will solve this problem. The article defines the functions of effective management and improvement of the professional adaptation line. The article uses the theoretical research methods: analysis of scientific-methodical and program documentation; empirical methods: questionnaires, testing, surveys and experimental approaches, graphic and schematic interpretations.
How Incomplete is Contrastive Learning? An Inter-intra Variant Dual Representation Method for Self-supervised Video Recognition.

Contrastive learning applied to self-supervised representation learning has seen a resurgence in deep models. In this paper, we find that existing contrastive learning based solutions for self-supervised video recognition focus on inter-variance encoding but ignore the intra-variance existing in clips within the same video. We thus propose to learn dual representations for each clip which (\romannumeral 1) encode intra-variance through a shuffle-rank pretext task; (\romannumeral 2) encode inter-variance through a temporal coherent contrastive loss. Experiment results show that our method plays an essential role in balancing inter and intra variances and brings consistent performance gains on multiple backbones and contrastive learning frameworks. Integrated with SimCLR and pretrained on Kinetics-400, our method achieves $\textbf{82.0\%}$ and $\textbf{51.2\%}$ downstream classification accuracy on UCF101 and HMDB51 test sets respectively and $\textbf{46.1\%}$ video retrieval accuracy on UCF101, outperforming both pretext-task based and contrastive learning based counterparts.
Self-Adaptive Asymmetrical Artificial Potential Field Approach Dedicated to the Problem of Position Tracking by Nonholonomic UAVs in Windy Enivroments

Abstract Artificial potential fields (APFs) are a popular method of planning and controlling the path of robot movement, including unmanned aerial vehicles (UAVs). However, in the case of nonholonomic robots such as fixed-wing UAVs, the distribution of velocity vectors should be adapted to their limited manoeuvrability to ensure stable and precise position tracking. The previously proposed local asymmetrical potential field resolves this issue, but it is not effective in the case of windy environments, where the UAV is unable to maintain the desired position and drifts due to the wind drift effect. This is reflected in the growth of position error, which, similar to the steady-state error in the best case, is constant. To compensate for it, the asymmetrical potential field approach is modified by extending definitions of potential function gradient and velocity vector field (VVF) with elements based on the integral of position tracking error. In the case of wind drift, the value of this integral increases over time, and lengths and orientations of velocity vectors will also be changed. The work proves that redefining gradient and velocity vector as a function of position tracking error integrals allows for minimisation of the position tracking error caused by wind drift.
Emotions in the Electronic Educational Space

The study of the role of emotions in education has already established traditions. The relevance of studying emotions in the electronic educational space stems from the expansion of distance education both in Russia and in other countries. System analysis allows us to identify many functions that emotions perform in learning, which include: motivational, meaning-formative, regulatory, communicative, directing, activating, the function of increasing creative potential, integrative, socializing, goal-setting. The retransmission of emotions in the electronic educational space has its own characteristics. Text information plays an important role in e-education. Therefore, the linguistics of emotions, discursive emotions and forms of verbalization of emotiongenic information are of great importance. The concepts of “emotional design” are of methodological importance for the study of emotions in the electronic educational space. The study revealed the different significance of cognitive and transactional emotiongenic factors of electronic space design for students. The most important cognitive emotiongenic factors were named: brightness and accessibility of visual signs, traditions and “customs”, feedback, reliability of the electronic system. The most significant transactional emotiongenic factors were: social communication, visualization, individualization in the design of the site, retransmission of values through the electronic educational space. The significance of emotiongenic factors differs on ZOOM, MOODLE, and LMS. The study of emotions when working in the electronic space allows us to identify areas of University information educational environment development.
Segmentation and Analysis of Brain MRI Images

Image segmentation plays a vital role in the field of medical imaging as it renders better diagnosis and early detection of diseases. The brain is one of the most important organs in the human body as it is responsible for all the actions and reactions in our body. It is the key factor that distinguishes us from other animals. Therefore, brain imaging is important in medical imaging because it helps the doctors to examine and understand the interior of the brain which is active. Magnetic Resonance Imaging is one of the brain imaging techniques. Brain image segmentation is used for measuring and anticipating brain anatomy so that we can figure out any changes in it. The brain tumor is any abnormal or uncontrolled growth of cells in our brain. This project illustrates the application of fuzzy logic in medical imaging, mainly for image segmentation. This uses FCM clustering for providing effective segmentation of blurred boundary areas of the brain. Thus, it segments any abnormalities in the MRI images. This work also uses morphological operations to detect the presence of any brain tumor. The segmentation and analysis of brain anatomy with the help of brain MRI images is the prime objective of this work.
Information accompaniment of the educational process of realization in the sustainable development interests at the University

This article talks about the opportunity of possibly using a specialized automated system for information support of the implementation of the educational process in the interests of sustainable development at university. The usage of the system will allow to increase the educational efficiency of the specialists in the field of ecology, sociology and economics using integration and implementation of resources, experience and readymade sustainable development projects into the educational process by experts, teachers, partners and groups of scientists. Besides, the organization of interaction in the system between all project participants will help develop and strengthen horizontal links between them. The relevance of the implementation of the social component in such systems is shown.
Facial Expression Recognition Based on Ensemble Convolutional Neural Network

To address high complexity of manually extracted features in traditional methods and insufficient classification of single convolutional neural network(CNN), this paper designs and implements an ensemble convolutional neural network-based model to improve the recognition rate. The ensemble network model is constructed by training two different subnetwork models and concatenating together the different features extracted from them. Experiments are conducted on the public dataset FER2013, and from the experimental results it is clear that the ensemble network enhances the recognition rate by about 0.4% over the individual network. It is shown that the convolutional neural network model based on the ensemble has a stronger recognition ability.
Data-driven deep density estimation

Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.
Assessing the Role of Boundary-Level Objectives in Indoor Semantic Segmentation

Providing fine-grained and accurate segmentation maps of indoor scenes is a challenging task with relevant applications in the fields of augmented reality, image retrieval, and personalized robotics. While most of the recent literature on semantic segmentation has focused on outdoor scenarios, the generation of accurate indoor segmentation maps has been partially under-investigated. With the goal of increasing the accuracy of semantic segmentation in indoor scenarios, we focus on the analysis of boundary-level objectives, which foster the generation of fine-grained boundaries between different semantic classes and which have never been explored in the case of indoor segmentation. In particular, we test and devise variants of both the Boundary and Active Boundary losses, two recent proposals which deal with the prediction of semantic boundaries. Through experiments on the NYUDv2 dataset, we quantify the role of such losses in terms of accuracy and quality of boundary prediction and demonstrate the accuracy gain of the proposed variants.
Integrity Protection for Research Artifacts using Open Science Chain’s Command Line Utility

Scientific data, its analysis, accuracy, completeness and reproducibility play a vital role in advancing science and engineering. Open Science Chain (OSC) is a cyberinfrastructure platform built using the Hyperledger Fabric (HLF) blockchain technology to address issues related to data reproducibility and accountability in scientific research. OSC preserves integrity of research datasets and enables different research groups to share datasets with the integrity information. Additionally, it enables quick verification of the exact datasets that were used for a particular published research and tracks its provenance.
Recommendation Model Based on Semantic Features and a Knowledge Graph

In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.
MoNET: no-reference image quality assessment based on a multi-depth output network

When deep convolutional neural networks perform feature extraction, the features computed at each layer express different abstractions of visual information. The earlier layers extract highly compact low-level features such as bandpass and directional primitives, whereas deeper layers extract structural features of increasing abstraction, similar to contours, shapes, and edges, becoming less effable as the depth increases. We propose a different kind of end-to-end no-reference (NR) image quality assessment (IQA) model, which is defined as a multi-depth output convolutional neural network (MoNET). It accomplishes this by mapping both shallow and deep features to perceived quality. MoNET delivers three outputs that express shallow (lower-level) and deep (high-level) features, and maps them to subjective quality scores. The multiple outputs are combined into a single, final quality score. MoNET does this by combining the responses of three learning machines, so it may be viewed as a form of ensemble learning. The experimental results on three public image quality databases show that our proposed model achieves better performance than other state-of-the-art NR IQA algorithms.
Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence

In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications.
Online flight path planning with flight time constraints for fixed-wing UAVs in dynamic environments

Purpose A major challenge for mission planning of aircraft is to generate flight paths in highly dynamic environments. This paper presents a new approach for online flight path planning with flight time constraints for fixed-wing UAVs. The flight paths must take into account the kinematic restrictions of the vehicle and be collision-free with terrain, obstacles and no-fly areas. Moreover, the flight paths are subject to time constraints such as predetermined time of arrival at the target or arrival within a specified time interval. Design/methodology/approach The proposed flight path planning algorithm is an evolution of the well-known RRT* algorithm. It uses three-dimensional Dubins paths to reflect the flight capabilities of the air vehicle. Requirements for the flight time are realized by skillfully concatenating two rapidly exploring random trees rooted in the start and target point, respectively. Findings The approach allows to consider static obstacles, obstacles which might pop up unexpectedly, as well as moving obstacles. Targets might be static or moving with constantly changing course. Even a change of the target during flight, a change of the target approach direction or a change of the requested time of arrival is included. Originality/value The capability of the flight path algorithm is demonstrated by simulation results. Response times of fractions of a second qualify the algorithm for real-time applications in highly dynamic scenarios.
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation

To date, most abstractive summarisation models have relied on variants of the negative loglikelihood (NLL) as their training objective.In some cases, reinforcement learning has been added to train the models with an objective that is closer to their evaluation measures (e.g.ROUGE).However, the reward function to be used within the reinforcement learning approach can play a key role for performance and is still partially unexplored.For this reason, in this paper, we propose two reward functions for the task of abstractive summarisation: the first function, referred to as RwB-Hinge, dynamically selects the samples for the gradient update.The second function, nicknamed RISK, leverages a small pool of strong candidates to inform the reward.In the experiments, we probe the proposed approach by fine-tuning an NLL pre-trained model over nine summarisation datasets of diverse size and nature.The experimental results show a consistent improvement over the negative loglikelihood baselines.