Reviewerly/exHarmony huggingface dataset:
Methods of group dynamics as the instrument of the stuff potential development in libraries

The purpose of the article is to present the first results of application of group dynamics methods in the Irkutsk regional library network as a tool for developing libraries personnel potential. In September 2021, a group of specialists of the Irkutsk Regional State Library – representatives of the administration, the scientificmethodological department, the department of cultural programs and the user service department – united into a core group, which started working on methods of group dynamics. The group identified a range of problems of libraries in the Irkutsk region, set goals and tasks for the development of the regional library and the library network of the Irkutsk region, formulated the mission of its activities, created the image of the future of the regional library network, and developed a roadmap for achieving this image of the future. The group expects to work for three years. Having mastered the methods of group dynamics in the ontological school version of “T-System” company and developed shared goals, values and rules, the group members define their functional areas and then form the «group of groups»: specialists from the Irkutsk Regional State Library or from the network of regional libraries and together continue the transformation of the library network. The first results of the group work have been obtained: methods of group dynamics allows to settle to solve complex collective tasks, to create projects of higher quality and degree of elaboration, to hold high-quality operational meetings with a large number of discussed issues. The work on implementing methods of group dynamics into the library network of the Irkutsk region as the instrument of developing the library stuff potential will be continued.
Brief Announcement: A Parallel (Δ, Γ)-Stepping Algorithm for the Constrained Shortest Path Problem

We design a parallel algorithm for the Constrained Shortest Path (CSP) problem. The CSP problem is known to be NP-hard and there exists a pseudo-polynomial time sequential algorithm that solves it. To design the parallel algorithm, we extend the techniques used in the design of the Δ-stepping algorithm for the single-source shortest paths problem.
Flawed implemented cryptographic algorithm in the Microsoft ecosystem

Abstract With the continuous development in the electronic chip field, the requirements for the security of IT infrastructures are also increasing. The need for ever-increasing key lengths in cryptography to maintain security cannot grow indefinitely. One of the solutions in the field of cryptography for using shorter keys while maintaining security is cryptography based on the principle of elliptic curves. Asymmetric elliptic curve cryptosystems lies in solving the discrete logarithm problem on an elliptic curve. However, not only secure algorithm but also its correct implementation is important. In this paper, we discuss an incorrect implementation of the ECC algorithm in the crypt32.dll library (Microsoft Windows) and the possibilities of its misuse.
Simultaneous Real-Time Human Fall Detection and Reidentification Based on Multisensors Data

Fall detection and reidentification are active areas of research with a wide variety of applications in many fields. Nowadays, wearable devices capable of recording multisensors measurements are being increasingly introduced in people’s daily lives, such as smartphones and smartwatches. Often, these devices are used in healthcare rehabilitation scenarios to monitor patients activities and, eventually, detect important events like falls. In some cases, the equipped Inertial Measurements Unit (IMU) lacks of gyroscope and magnetometer, allowing to acquire only accelerations measures. In this conditions, the detection tasks become more complex and many false detections can arise. Additionally, other methods, such as monitoring cameras, are usually used in the environment to overcome this problem and further track the patients. But, due to the lack of personal identifiers in the camera tracking system, it is not straightforward to associate falls, when detected, to a particular person. In this paper, we propose a complete real-time system to detect falls reducing false positives and simultaneously reidentify the patients using 3D skeleton points, given by a tracking camera, and 3-axis accelerometer data. Firstly, the system performs fall detection by means of a mathematical analysis of the skeleton points evolution along different frames. At the same time, fall detection is computed on the acceleration data using a Finite State Machine approach. If a fall is detected with both mechanisms, reidentification is carried out to associate the skeleton with the wearable device. A dataset of fall sequences has been recorded and is available for testing purposes. The final accuracy of our fall detection and reidentification algorithm is 100% on our dataset.
Web-Based Text Structure Instruction on Ells’ High-Order Reading Comprehension Skills

The difficulties with developing high-order reading comprehension skills negatively impact academic success across fields. The current study investigated whether the text structure strategy instruction, delivered through the Intelligent Tutoring of Structure Strategy (ITSS) to adult English learners as a foreign language (EFL), can improve reading comprehension, especially their higher-order reading comprehension skills. With a quasi-experimental nonequivalent control group design, 61 adult Chinese EFL learners from two classes were assigned to experimental or control groups. The experimental group utilized the ITSS to support their English reading instruction, whereas the control group was provided with the traditional instruction. Our results indicated that the web-based text structure instruction had a statistically significant positive effect on Chinese EFL learners’ reading comprehension (β = 3.22, p < 0.05). Furthermore, we confirmed our hypothesis that text structure intervention was a significant predictor for inferential (β = 0.91, p < 0.05) and evaluative (β = 1.06, p < 0.05) subskills of comprehension, but not for literal subskills (β = 1.25, p > 0.05). Based on these results, research and pedagogical implications of this study are discussed.
Library’s role in communication of science and society

The purpose of the article is to reveal the communicative component of library’s activities within the framework of its participation in science and society communication (CSS) in Russian realities. The experience of popularizing science available in the practice of libraries does not allow us to declare its involvementin the processes of CSS. It needs critical refl tion and adaptation to the modern information and communication environment; an assessment of the effectiveness of the library as a participant is required. The article presents theoretical aspectsof library’sfunctions as CSS participants.Institutional characteristics are considered andanalyzed from the point of view of communicativefunction and communication processesoccurring in various social fields; and communicativepractices implemented by the library –with the provisions of the types of social communication(verbal, documentary, electronic).The library has obvious opportunities for implementationas a member of CSS, first of all, beingan element of documentary communications, itcan create an information environment, providingreaders with the high-quality reliablecontent; participate in forming the cognitiveactivity of readers and encourage and supporttheir self-education activities, including throughoral and hybrid forms of interaction, andinclusion in the electronic environment significantlyexpands the communication capabilities of libraries. The prospects for the participation of libraries in CSS as an intermediary are justified by the fact that they have their own contingent, platforms and specific technologies of communication impact. Hypothetically, libraries can use all existing forms and formats of CSS events,combining, expanding and supplementing them with their own (traditional and innovative), where a specialist – scientist, writer, scientifi journalist, scientifi communicator – will actas a popularizer. Thus, optimal conditions canbe establishing to satisfy people`s cognitive interest that forms the modern scientifi state and stimulate them to receive new scientific knowledge throughout their lives.
Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability

In multi-agent domains, dealing with non-stationary opponents that change behaviors (policies) consistently over time is still a challenging problem, where an agent usually requires the ability to detect the opponent’s policy accurately and adopt the optimal response policy accordingly. Previous works commonly assume that the opponent’s observations and actions during online interactions are known, which can significantly limit their applications, especially in partially observable environments. This paper focuses on efficient policy detecting and reusing techniques against non-stationary opponents without their local information. We propose an algorithm called Bayesian policy reuse with LocAl oBservations (Bayes-Lab) by incorporating variational autoencoders (VAE) into the Bayesian policy reuse (BPR) framework. Following the centralized training with decentralized execution (CTDE) paradigm, we train VAE as an opponent model during the offline phase to extract the latent relationship between the agent’s local observations and the opponent’s local observations. During online execution, the trained opponent models are used to reconstruct the opponent’s local observations, which can be combined with episodic rewards to update the belief about the opponent’s policy. Finally, the agent reuses the best response policy based on the updated belief to improve online performance. We demonstrate that Bayes-Lab outperforms existing state-of-the-art methods in terms of detection accuracy, accumulative rewards, and episodic rewards in a predator–prey scenario. In this experimental environment, Bayes-Lab can achieve about 80% detection accuracy and the highest accumulative rewards, and its performance is less affected by the opponent policy switching interval. When the switching interval is less than 10, its detection accuracy is at least 10% higher than other algorithms.
Brief Announcement

We present a new model for (asynchronous) byzantine reliable broadcast to investigate the potential of secret collusion between honest players. To model the collusion, we assume that each honest player has k > 1 distinct communication identities over which they can send and receive messages. A player can obtain these identities - for example - by joining a distributed system under several aliases.
Interlibrary service and electronic delivery of documents in the regional information and library system

The purpose of the article is to analyze and evaluate the activities of the Interlibrary Loan sector of the Central Scientific Library of the Federal Research Center “Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences” (CSL FRC KSC SB RAS) for the period of 2017–2021. CSL FRC KSC SB RAS concludes contracts for the provision of interlibrary loan services and electronic delivery of documents with the AllRussian Institute of Scientific and Technical Information RAS, State Public Scientific Technological Library of Russian Federation, State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences and with the State Universal Scientific Library of the Krasnoyarsk region. It also plans to cooperate with the Library of Natural Sciences RAS and the Russian State Library. The main indicators analyzed by the author: the number of received and completed orders by the year, their percentage, forms of the ordered information received by the library. Completed orders come from stockholders mainly in the form of electronic copies and originals, in some cases, there come paper copies and links to electronic resources. It is noted that not all structural divisions of the Krasnoyarsk FRC use ILL, – this requires further work on the presentation of the service. The participation of the shared automation center of SPSTL SB RAS in the automation of information and library processes of the Central Scientific Library of the Federal Research Center of the KSC SB RAS is shown. The developed ways to improve the efficiency of the user service in the ILL sector are offered.
Deployment of road-garbage detection solution based on YOLOv5

Because of changeable daylight and weather, the natural scenes of road are complicated. And the garbage on roads comes in polytropic shapes and sizes, especially small targets such as leaves. To detect the location and types of road-garbage in different scenarios quickly and accurately, we propose a road-garbage detection solution based on YOLOv5. The solution is finally applied to the garbage sweeper, where different deployment strategies are required for different modules. Firstly, to reduce the loss of image information caused by overexposure and underexposure, we design automatic exposure algorithm to adjust camera parameters in time and use OpenMP to accelerate it. Secondly, YOLOv5 algorithm based on object detection is trained for recognition and TensorRT framework is used for deployment. Taking into account the computing speed and accuracy, we choose FP16 computational precision for YOLOv5’s inference acceleration. Lastly, a low-cost computing platform named Jetson AGX Xavier is selected and the algorithm is optimized in combination with the characteristics of the hardware platform. Multithreading is also used to accelerate in software architecture. The results show that the average detection accuracy for various types of road-garbage reaches 70.4% under four different scenarios of self-built datasets. And the area of the smallest object detected accounts for 0.2‰ of the total area of the image. The speed of this road-garbage detection solution can reach 5fps when processing the 12-bit image of 2432*1226 size on the low-cost AGX Xavier computing platform.
A Cnn-Based Multi-Level Face Alignment Approach for Mitigating Demographic Bias in Clinical Populations

The investigation of demographic bias in facial analysis applications is a topic of growing interest with achievements in face recognition and gender classification. State-of-the-art convolutional neural networks (CNN) and traditional machine learning models for locating facial landmarks have reached overall performance levels close to human annotation. However, recent studies demonstrated that these models presented performance gaps when applied to different populations, characterizing bias led by demographic features. Nevertheless, few studies have addressed this problem in face alignment and facial landmarks localization methods. In this work, we propose a multi-level face alignment approach settled on CNN models to reduce performance gaps among different populations. We created facial subunit CNN models tied to a facial subunit detector at a higher level. The proposal seeks to improve bad results caused by facial impairment, guided by the following assumptions: facial unit landmarks localization does not require global texture, and combining different facial unit models can improve the final model’s variability. We applied the models in a balanced dataset mixing healthy controls (HC) and individuals with neurological disorders: amyotrophic lateral sclerosis (ALS) and post-stroke (PS). With fewer samples for training, our approach significantly reduced face alignment performance differences among those groups as compared to a state-of-the-art CNN model.
Development and Verification of Multi-class Data in Artificial Intelligence Model Group Based on Cloud Computing

Artificial intelligence (AI) has been widely studied. Due to the increasingly wide application of multi-class data, with the characteristics of rapid arrival, rapid change, huge quantity, and potential infinity, the original classification algorithm for the static database cannot adapt to the reality of multi-class data. In order to achieve reliable and accurate data dependency analysis by loop-level parallelism detection in large programs, this paper firstly designs and implements a parallel Naive Bayes (NB) multiclass data analysis algorithm. Then, we adjust the number and weight of classifiers as needed to maintain high classification accuracy and reduce the cost. Meanwhile, this paper establishes an optimal complexity model by realizing the self-organizing control of the data mining process. The results show that the parallel NB classification algorithm can bring excellent performance improvement and good scalability and can meet individuals’ performance requirements for massive data classification to a certain extent.
Action Recognition Based on Parallel Multi-Granularity Feature Refinement Networks

Video action recognition is an important research content in the field of computer vision. However, single feature motion information is under-represented and cannot completely describe the motion information. In this paper, an action recognition method based on parallel multi-granularity feature refinement network was propose to improve the action recognition accuracy. This method relaxes the requirement restriction on action recognition by describing the motion information of a video with multiple action class labels and shared features in the different action class group. Three action class granularity features was obtained by three action class label groups and integrate them to obtain the exact feature fusion of RGB image features and joint point skeleton information for action recognition. In order to verify the effective of our proposed network, a series of experiments were conducted on the UCF101 dataset. The experimental results show that the accuracy rate of proposed approach is higher than the traditional mainstream action recognition methods, which proves that the method is effective in action recognition.
An Optimization Method for Weight Computation in Hypergraph Transductive Learning Model

Compared with the graph structure, the hyper-graph structure can convey more complex and multivariate relationships. In addition, hypergraphs are more capable of portraying and mining higher-order nonlinear correlations among objects. Therefore, it has been widely used in feature selection, clustering classification, and addressing other issues, and hypergraph learning has also become an important branch in the field of machine learning. Hypergraph learning, however, is a relatively high time consuming learning algorithm, because in its iterative solving process, there are lots of high-dimension matrix computations with large-scale parameters. This paper proposes an optimization of the weights, which is constructed by Gaussian kernel, in hyper-graph transductive learning model the weight matrix is replaced by fixed parameters when constructing the hypergraph laplacian matrix, which reduces the time consuming of weights computation in iterative process. Extensive experimental with semi-supervised node classification experiment results on the UCI and Kaggle datasets show that the optimized model reduces the time consuming of the hypergraph model, while ensuring classification accuracy. After model optimization, the four accuracy indicators fluctuate on average 3.03%, the time consuming is reduced by 28.99% on average, and the maximum saving is 42.84%.
Interference Line Removal Using A Progressive Model

Interference lines in the text image greatly affects the recognition of the text, thus, removing interference lines can improve the text recognition accuracy of the text image and the robustness of the text recognition model. This paper proposes a progressive model for interference line removal based on a recurrent neural network. The model utilizes the cyclic structure of RNN to perform multiple stages of interference line removal, which are responsible for removing part of the interference lines. In each stage, dilated feature extraction module is adopted to gradually extract richer image features. The feature extraction module aggregates dilated convolutions with different dilation rates, which ensures the extraction of receptive field of various scales. To make full use of shallow feature and speed up network training, dense connectivity is added between feature extraction blocks. The experimental results show that the proposed model can effectively remove interference lines in text images, and achieves better performance compared to other models.
A deep-learning based diagnostic framework for Breast Cancer

In this paper, we present a deep-learning based diagnostic pipeline for breast cancer that has been designed in the H2020 INCISIVE project. The design of the pipeline has taken into consideration the needs of medical professionals and has been adapted to focus on early and accurate detection of malignant lesions to improve the patient’s survival rate. The main goal of our approach is to create a complete diagnostic service and bridge the gap towards real-world adoption of Artificial Intelligence on medical imaging. The pipeline will be offered as a service to medical professionals during the pilots of the project to evaluate its performance and assess the maturity of integrating such a service in a clinical workflow.
Use of Deep Neural Networks in Detecting Breast Cancer Lesion

This paper presents a deep neural network module for breast cancer lesion detection which is trained and tested over 569 datasets. The model produces an accuracy of 0.95 in predicting the benign and malignant lesions in the breast. This model gives high accuracy to few advancement in technology with multiple input views and optimised amongst many choices. A thorough analysis of the model’s performance on various populations is conducted. The python code used in the model uses tensor flow library and a seaborn visualisation library. The data sets used in the code are segregated as training and testing datasets. Training sets contain 80% of data and testing sets contain 20% of data. Histogram images are created to view all the parameters separately for training and testing. Later the accuracy and cost are found so that it can be implemented in real-world applications without any wrong predictions.
SinGAN-3D: towards unconditioned 3D shapes generation

We present SinGAN-3D, a variation of the deep neural network architecture presented originally by SinGAN, for the generation of 3D contents, starting from a single three-dimensional voxelized model. Our network uses a pyramid of 3D convolutional networks to model the third dimension and exploits periodic activation functions to capture the latent structure of the input model. The approach can synthesize contents at different resolutions and aspect ratios, and can be extended to implement super resolution. To evaluate the performances of the proposed model we use the Single Image Freche’t distance, and the multiscale structural similarity index. The metrics highlight the similarities between the synthesised-three dimensional assets and their corresponding original template. An additional user study has also been conducted to assess the quality of the generated shapes. The code can be found at: https://github.com/zenos4mbu/SinGAN3D