Reviewerly/exHarmony huggingface dataset:
Mathematical and Algorithmic Prevention of Biometric Data Leaks

Biometric methods are the most effective and accurate authentication methods. However, a significant drawback of such methods is the storage of authentication information in clear text. The article is devoted to solving this problem by means of symmetric encryption method and the method of dividing the memory space. The method of symmetric encryption ensures confidentiality during storage and transmission of biometric characteristics, the method of dividing the memory space provides an increase of information security level during processing of biometric characteristics.
Online High-Level Model Estimation for Efficient Hierarchical Robot Navigation

We would like to enable a robot to navigate efficiently and robustly in known, structured environments that are large enough to cause traditional planning approaches to incur considerable computational cost. Hierarchical planners are a promising way to increase planning efficiency in such environments because high-level abstract plans can be used to reduce the size of the search space over which detailed planning occurs. However, useful high-level representations of planning problems can be challenging to generate without prior domain knowledge. In this work, we propose a high-level planning representation which can be learned from previous plans considered in the environment and used online during hierarchical, multi-query robot navigation. We treat previous planning results as noisy measurements of high-level navigation properties, then update these properties over time using recursive estimation. We test our approach in standard and risk-aware hierarchical planning schemes, and demonstrate up to an 86% decrease in the number of nodes expanded and a 66% decrease in wallclock time as compared to a baseline A* planner while finding plans that are only 2-10% more expensive.
Interpretability Latent Space Method: Exploiting Shapley Representation to Explain Latent Space

Shapley values have become one of the most popular interpretation methods for feature attribution. These methods attribute the prediction of the input by the machine learning model to its basic feature. However, most of the previous work is based on Shapley's interpretation after the training is completed because of Shapley's calculation requirements (exponential time complexity), so that they can focus on post-hoc Shapley explanations. Therefore, we suggest trying to use Shapley value itself as a latent representation in the deep model to guide model training. First, we extract the latent space of the generation model as input of Shapley network. Then, we utilize Shapley network, which provides layer-wise transformation in the same forward pass and produce Shapley representation. Further, we apply Shapley representation to the calculation of the new model to explain the training process of the model. Finally, the experimental results show on the two datasets of MNIST and Fashion MNIST that our proposed method has certain interpretability.
Object Quality Guided Feature Fusion for Person Re-identification

Person re-identification (Re-ID) is an essential task in computer vision, which aims to match a person of interest across multiple non-overlapping camera views. It is a fundamental challenging task because of the conflicts between large variations of samples and the limited scale of training sets. Data augmentation method based on generative adversarial network (GAN) is an efficient way to relieve this dilemma. However, existing methods do not consider how to keep identity information and filter the noise of the generated auxiliary samples during Re-ID training. In this paper, we propose object quality guided feature fusion network for person re-identification, which consists of a self-supervised object quality estimation module and a feature fusion module. Specifically, the former evaluates the quality of the auxiliary data to filter the noise and the disturbing features, while the later accomplishes the feature fusion based on object quality estimation in the collection-to-collection recognition manner to make full use of auxiliary data. Extensive performance analysis and experiments are conducted on two benchmark datasets (Market-1501 and DukeMTMC-reID) to show that our proposed approach outperforms or shows comparable results to the existing best performed methods.
Research on the Method of Machine Learning Algorithms in Ecological Construction

Abstract In the rapid development of modern artificial intelligence, for the development of ecological construction, how to rationally use machine learning to promote the development of agricultural economy has become a focus of practice and scientific research. This paper takes ecological image recognition as an example to analyze how to use support vector machine in image processing technology and machine learning in deep learning to conduct in-depth research, and to optimize and improve the algorithm to build an ecological image recognition model.
Activity-Centered Design of Web User Experience: A One-Stop Application for Design Sprints

The design sprint is the process of designing, prototyping, and testing an idea in five days to get feedback from users to reduce the risks of ideas being incompatible with the target, thus saving resources. The short and systematic nature is one of design sprints’ advantages. One of the problems design sprint participants face is the lack of documentation caused by the media used during design sprints. The solution to that is to use digital media in design sprints. Digital media that can be used in design sprints are canvas applications. However, existing applications have drawbacks, such as not being user-friendly and too cluttered on one canvas. Therefore, a one-stop design sprint application is designed using activity-centered design (ACD), which is an activity-focused approach to help users carry out design sprint activities. One iteration of a low-fidelity prototype and two iterations of a high-fidelity prototype resulted in a high-fidelity prototype of Sprinster, an application that provides media and guides users in design sprints step by step. Usability testing is conducted to assess the achievement of the application’s usability and user experience goals, i.e., effective to use, efficient to use, easy to learn, and helpful. The results show that the prototype is effective to use with a success rate of 100%, efficient to use with a time-based efficiency (TBE) of 0.33 goals/second which is almost five times compared to physical media (0.07 goals/second), easy to learn with a Single Ease Question (SEQ) score of 6.59 out of 7, and helpful with an Intrinsic Motivation Inventory (IMI) value/usefulness subscale score of 6.55 out of 7.
Complaint Data Text Analysis Concerning the Apps Provided by Government Agency using Inference LDA

The rapid development of information technology and its applications with the emergence of internet media makes disseminating information more accessible, fast and creating huge data in any time. Government is one of important stakeholders that produce a big data every day. Big data is one combination with data analytics that plays an important role in data processing and new insight retrieving. In this study, text data from customers complaint regarding the services given by the Government organization namely Financial Monitoring and Development Agency was analyzed. Users can report various kinds of complaints related to the problems they experienced. In this study, new insights regarding the applications provided by the Government agency will be discussed. From the 15 thousand complaint data records, six groups of the most dominant complaint regarding the applications use were then categorized: SIMA applications, SIBIJAK applications, GDN applications, SADEWA applications, Lotus Notes, and Infrastructure. Latent Dirichlet Allocation (LDA) topic modeling with part-of-speech tagger techniques was used to disseminate information on the topics. The results showed that the SIMA application gave 52% of all complaints reports based on the method used. With the implementation of the LDA topic modeling, four topics were generated: complaints about using the SIMA application, the service and installation of the Lotus Notes and SADEWA application, and complaints related to the existing network infrastructure of Government Agency. In conclusion, inference LDA Topic modeling successfully provided insights to government organization regarding which aspects within organization that are needed to be improved.
EMOTIONAL BURNOUT AMONG THE REPRESENTATIVES OF THE PROFESSIONAL GROUP TEACHERS

The article discusses the concept and features of the formation of emotional burnout syndrome; empirical data of emotional burnout among representatives of the professional group «teachers» are given, conclusions are drawn based on the results of the study; general recommendations for the prevention of emotional burnout for representatives of the professional group «teachers» are given.
Investigating Cyber Security Factors Influencing The Perception Behavioral Intention of Small and Medium Enterprise

Technological developments have touched various fields of human life, including the economic field. Especially in the midst of the covid-19 pandemic, which has a big impact on micro, small and medium entrepreneurs (MSMEs) who have to look for innovations to stay in their business in the midst of a pandemic. Changes in behavior and habits that were initially far from technology, and the arrival of a pandemic that limited many things, especially in communicating between humans, made technology one of the best solutions. But in the use of technology, there is a danger that targets MSMEs, especially those who are new to using technological devices, namely cyber security. The main question is how SMEs perceive cyber security. This study aims to determine the perception of MSMEs on cyber security by using the Protection Motivation Theory (PMT) model. Data is collected through surveys. By using a quantitative approach, the data were analyzed using the Smart-PLS software. There are several variables raised in the study which are suspected of having an influence on Protective Behavioral Intention. Based on the results of the study indicate that the variables Perceived Severity (PS) and Self-Efficacy (SE) have a significant and positive effect on Protective Behavioral Intention (PBI). This study also shows that the use of information technology by MSMEs has begun to spread, but there is still no cyber security awareness found, so there is still no planning or mitigation of threats or risks (risk management).
Future Teachers’ Individual-Value Professional Picture as Reflection of Their Axiogenesis

The purpose of the study is to identify the interrelation and dialectical unity of the interdisciplinary notions “internalisation”, “axiogenesis”, “professional-value picture of personality” at the theoretical level. Delineating the conceptual foundations of the issue of future teachers’ professional and personal self-identification in the axiological aspect, the author examines the internal dependence of axiogenesis processes and the formation of a professional picture of a student’s personality. Scientific novelty lies in presenting the author’s definition of future teachers’ professional-value picture, in identifying its essence as an individual-personal entity, revealing the content and logical-structural characteristics of future teachers’ professional-value picture. As a result, the internal processes and mechanisms of introducing a future teacher to professional values have been described; the conceptual foundations of the axiogenesis process have been revealed; the theoretical prerequisites for scientific substantiation of future teachers’ individual-value professional picture in the logic of professional axiogenesis have been identified; the possible beginnings of a student’s professional axiogenesis at the stage of university education have been presented.
Iterative gradient encoding network using feature co-occurrence loss for single image reflection removal

Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. There is a dearth of literature for efficient methods concerning single image reflection removal, which can generalize well in large-scale reflection type. In this study, we proposed an iterative gradient encoding network for single image reflection removal. Next, to further supervise the network in learning the correlation between the transmission layer features, we proposed a feature co-occurrence loss. Extensive experiments on the public benchmark dataset of SIR2 demonstrated that our method can remove reflection favorably against the existing state of the art method on all imaging settings, including diverse backgrounds. Moreover, as the reflection strength increases, our method can still remove reflection even where other state of the art methods failed.
Digital transformation of management system of institutions of additional education of schoolchildren

The article focuses on studying the modern trends of digital transformation of the management system of institutions of additional education of schoolchildren. Theoretical and methodological provisions have been scientifically substantiated and practical recommendations have been developed to improve the management system of additional education of schoolchildren in the context of digital transformations. It is proved that the institution of extracurricular education is also a community characterized by dedication, formal and informal structures, autonomy, cyclicality, freedom of choice of extracurricular activities. For the first time it was proposed to use a system-cybernetic approach to the strategic management of institutions of additional education of schoolchildren based on the procedural model of strategic management and the information management system, tested in the management system of Russian Childrenʼs Center “Ocean”. It is proved that Management Information Systems (MIS) uses the information necessary for top managers at all levels in making operational, tactical and strategic decisions. The advantages and positive factors for institutions of the sphere of additional education of schoolchildren from the introduction of MIS within the boundaries of the procedural model of strategic management are highlighted. The characteristic features of the management activities of the subjects of management of institutions of additional education of schoolchildren are outlined. There are presented the management models, the knowledge of which will provide the heads of organizations of extracurricular education with data on selecting MIS. The importance of further development of the theory, methodology and practice in the strategic management of additional education of schoolchildren in the context of digital transformations has been proved.
Fault Detection and Isolation for Redundant Inertial Navigation System Based on Asymmetric Precision

For the redundant inertial navigation system (RINS) equipped by MEMS-based and FOG-based inertial navigation system (INS), a system architecture and fusion method will be introduced based on sequential Sage-Husa filter in this paper. The sequential probability ratio test (SPRT) utilizes piecewise method is employed for the fault detection program, the velocity numerical differentiation of each set of INS is considered in the fault diagnosis scheme. Simulations are conducted by adding soft and hard failures into the gyro of three axes of each set of INS with different values separately. Results show that there is a 2.42% improvement on positioning accuracy, the fault detection, diagnosis and isolation algorithm in this paper can detect failures correctly without false alarm under a short delay.
Recommendation of learning resources in social learning environment using data mining

with the growing number of learners in the social learning environments and social networks, and with the ever-growing volume of online content, learners are overwhelmed by the amount of available content. Recommender systems have been an effective strategy to deal with this challenge. In social learning environments, recommendation systems are used much more to locate the most suitable resources for learners, finding the right resources can help learners in their learning process. The proposed approach is based on the similarity calculation between a learner and his/her friends of the same frequent subnetwork by using Learner model. To approve this approach we developed a system that personalized to the requirements of learners.
NetANNS: A High-Performance Distributed Search Framework Based On In-Network Computing

Approximate nearest neighbor search (ANNS) is the most basic and important algorithm in Database, Machine Learning and other applications. With the expansion of cloud computing, the academia focuses on the study of how to optimize distributed frameworks based on approximate nearest neighbor search such as MapReduce, and Memcached. We implement a new distributed ANNS search framework (NetANNS). The main contributions of NetANNS are to accelerate the data preprocessing with programmable switch, and integrate a variety of efficient ANNS algorithms so that it can choose the most suitable algorithm for each datasets. The experiments show that the search efficiency of NetANNS is about 2x than the common distributed ANNS frameworks which are implemented based on the framework of MapReduce.
Benefits and Challenges of Developing Standards for Earth Observations

Data is the lifeblood of the geosciences.The acquisition, processing and interpretation of data all depend on established specifications describing the systems and procedures that were used in producing, describing and distributing that data.It can be said that technical standards underpin the entire scientific endeavour.This is becoming ever truer in the era of Big Data and Open, Transdisciplinary Science.It takes the dedicated efforts of many individuals to create a viable standard.This presentation will describe the experiences and status of standards development activities related to geoscience remote sensing technologies which are being carried out under the auspices of the IEEE Geoscience and Remote Sensing Society (GRSS).While the value and viability of community-developed principles and specifications have been amply demonstrated, a Standards Development Organization (SDO) exists to provide the environment, rules and governance that are needed to ensure the fair and equitable development of a standard, and to assist in the distribution and maintenance of the resulting standard.The GRSS sponsors projects with the IEEE Standards Association (IEEE-SA), which, like other SDOs such as ISO and OGC, has well-defined policies and procedures that help ensure the openness and integrity of the standards development process.Each participant in a standards working group typically brings specific interests as a producer, consumer or regulator of a product, process or service.Creating an environment that makes it possible to find consensus among competing interests is a primary role of an SDO.This presentation will include highlights and insights gained from the seven standards projects that the GRSS has initiated.These projects involve hyperspectral imagers, the spectroscopy of soils, data from synthetic aperture radars and GNSS reflectometry, calibration of microwave radiometers, and the characterization of radio frequency interference in protected geoscience bands.
Edge Detection in Natural Scenes Inspired by the Speed Drawing Challenge

Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10[Formula: see text]min, 1[Formula: see text]min and 10[Formula: see text]s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results.
Hybrid Project-Based Learning in Engineering Education

Technological advancement, global fostering innovative activities, digitization resulted in sustained demand for highly qualified engineers. Current engineering education and real-world requirements have drifted apart. The authors offer a hybrid framework with integrated edtech tools of online project-based learning. Russian and Tajikistan Universities were involved in the tandem project. The research provides the analysis of feedback of 35 tandem students majoring in engineering in 2020-2021 academic year. The research based on needs analysis, a questionnaire, and semi-structural interviews proved the efficiency of hybrid format for ESP training, revealed the importance of online course implementation – the students improved their communication and ESP skills. Diverse methodological, linguistic, and even psychological difficulties, faced by the students studied online were examined. The findings of this study promoted the development of a modern project-based online course for future engineers.
Avoiding Lookup Table in AES Algorithm

This article describes the AES encryption and decryption process without using lookup tables in the MixColumns transformation. The encryption process consists of transforming subbytes, shiftrows, mixcolumns and addroundkey. The process was carried out for 10 rounds, but in round 10 the mixcolumns transformation was not carried out. The decryption process consists of inverse mixcolumns, inverse shiftrows, inverse subbytes and addroundkey. In this study, the AES encryption and decryption process was carried out using two methods, namely based on the lookup table and without using the lookup table on the MC/IMC transformation. The method in this article is applied to Matlab software. The experimental results show that the encryption and decryption process using a lookup table is slower than the method without a lookup table. The encryption process without a lookup table on the MC transformation takes 0.091 seconds while using a lookup table takes 0.399 seconds. The decryption process without a lookup table on the IMC transformation takes 0.149 seconds while using a lookup table takes 0.206 seconds.