Reviewerly/exHarmony huggingface dataset:
Sharing Data Through Specialized Corpus-Based Tools: The Case of GramatiKat

Abstract This paper presents a specialized corpus tool GramatiKat in the context of Open Science principles, namely data sharing, which offers opportunities for original research and facilitates verifiability of research and building on previous research. The tool is designed primarily for examining grammatical categories from the quantitative point of view. It offers grammatical profiles of particular lemmas (currently 14 thousand Czech nouns) and the proportion of individual grammatical categories within a part of speech, i.e., the standard behavior of a word class. The data in GramatiKat are pre-processed, statistically evaluated, and presented in charts and tables for clarity, and they are available to other linguists, especially from fields of morphology and lexicography. This article is aimed at providing inspiration and support to corpus and non-corpus linguists with utilization and enhanced use of the existing tools and with the creation of new specialized tools available to other users.
Redundancy-Controlled Feature Selection for Fuzzy Neural Networks

Group Lasso term has the group compression role on the value of the corresponding variables. In the fuzzy rule based system, for realizing fuzzification operation each feature corresponds to different number of membership functions. Inspired by the Group Lasso idea, the width of Gaussian membership function based Group Lasso regularization is designed to realize feature selection in the multiple inputs multiple outputs (MIMO) fuzzy neural network (FNN). To take into account the linear/nonlinear redundancy between features. Using the Pearson&#x2019;s correlation coefficient, a redundancy-controlled regularizer is also added into the traditional error function to further improve the performance of feature selection, i.e., control redundancy between features. We demonstrate the effectiveness of the strategy on three benchmark classification data sets.
Multi-Scale-Average-Filter-Assisted Level Set Segmentation Model with Local Region Restoration Achievements

Segmentation of noisy images having light in the background it is a challenging task for the existing segmentation models. In this paper, we propose a new variational model for joint restoration and segmentation of noisy images having intensity inhomogeneity in the presence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centred at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and solved by alternating direction minimization method of multipliers. Through experiments, we have tested our proposed model on different types of synthetic and real images in the presence of intensity inhomogeneity and demonstrate the accuracy and the robustness of the proposed model. In addition, the results are compared with state of the art two-phase and multiphase methods and show that our method has superiority for images in the presence of noise and inhomogeneity.
DARUD: Detecting and Arresting Rogue USB Devices in the V2X Ecosystem

Vehicle-to-Everything (V2X) is a cutting-edge technology in intelligent transportation systems (ITS). In V2X, various entities communicate and cooperate each other to ensure road safety and efficiency. However, such communications and cooperation also pose various security risks to the transportation system. In particular, due to the involvement of several unattended roadside devices, such as roadside units (RSUs), sensors and controllers, the V2X ecosystem is highly vulnerable to malware attacks. Attackers may use rogue USB devices to inject various types of malware to the V2X system via the USB ports of the roadside devices. Such attacks may result in a debilitating impact on the safety and efficiency of road traffic. Although a wide-range of approaches have been proposed against USB-based attacks, most of them have several limitations, especially when applied in the V2X environment. For example, the widely adopted approaches against USB-based attacks are scanning USB devices using anti-malware/antivirus tools (which is often not effective against zero-day malware), disabling USB ports (security-through-obscurity has already several drawbacks), whitelisting USB devices using certain attributes of the USB devices (which is often bypassed by brute-force attacks and not effective against dishonest USB users). Furthermore, most of the existing solutions are implemented only for general-purpose computing devices (e.g., computers and servers), hence might not be suitable for sensors and tiny IoT devices involved in the V2X environment. Moreover, the configuration and update processes of most existing solutions requires physical access to the devices, which might not be feasible in V2X where devices and sensors are dispersed across various roadside locations. In this work, we propose and implement DARUD - a lightweight and automated toolkit that dynamically detects and prevents rogue USB devices in V2X. This is achieved by constructing a USB authorization policy based on kernel-level USB rules and fingerprints. The proposed solution can also be configured and updated to the roadside devices over-the-air via a secured VPN tunneling. This avoids the hassle of physically configuring or updating the USB-security solutions on each roadside device. The effectiveness of our proposed approach is also tested using a realistic V2X infrastructure.
FORMATION OF MORAL CONSCIOUSNESS PRIMARY SCHOOL CHILDREN BASED ON COMPETENCE APPROACH

The article examines some aspects of the formation of the moral consciousness of modern schoolchildren on the basis of one of the leading methodological principles - the competence-based approach.&#x0D; Large-scale changes taking place in various spheres of society, new social and economic reality, developing market relations, digitalization, modernization of Russian education require a revision of the strategy and tactics of educating the younger generation in terms of solving the problems of forming high moral qualities of the personality of students, including primary school age. But is a modern teacher competent enough to successfully cope with the multitasking of real pedagogical reality, including the moral education of schoolchildren?&#x0D; The study of the problem under consideration in theory and in practice shows that moral education is significant, first of all, because it permeates all aspects of education: mental, aesthetic, physical, labor, environmental, etc. Hence, its backbone nature is obvious. Any human action presupposes a moral aspect, including the regulatory function of morality.&#x0D; All of the above served as the basis for choosing the topic of the article. According to the authors, the results and conclusion of this article can, to a certain extent, actualize the problem of the formation of the moral consciousness of primary schoolchildren from the standpoint of the competence-based approach and outline ways to solve it.
Location Privacy-Preserving Mechanism - A Data-Driven Approach

Location privacy is defined as the right of individuals to decide how, when, and for what purpose their location information could be released to other parties. Location-based services (LBSs) are emerging rapidly with the proliferation of mobile application, and the problems with privacy are growing with them. Location information is one of the most fundamental pieces of contextual data, and it is used to drive an extensive variety of applications. So, when location systems track users automatically and continuously, a huge amount of potentially sensitive information is generated which may be used in profiling & revealing user identity based on their location updates. Users do not necessarily intend to stop complete access to their location information, since some applications may need this information to provide useful services. However, the users would want to have control over the details of location used. Encryption/cryptographic techniques on location data & Location obfuscation which can be done reversibly or irreversibly using Trusted-Third Party (TTP) servers are the most common approaches explored which can give protection against third party attacks but it cannot provide protection against privacy threats on the server which can still obtain user location and use it for maleficent purposes. The lack of location privacy protection could be exploited by adversaries to perform various attacks. In this paper, we propose a technique by which the user can be provided control over the precision with which user can provide access to its location information and propose a mechanism enabling granular control of privacy for any LBS application without limiting its serviceability.
Influence of cultural and educational traditions on critical thinking formation practices

We consider the cultural and philosophical aspects of critical thinking and scientific approaches to them in the classical and postclassical philosophy of education. We characterize the specific features of thinking and requirements for it within the framework of the traditional and new models of educational values. We note that in the culturological value context an important place is occupied not by individual models as an intrinsic value, but by the presence of continuity, the implementation of the principles of complementarity and interdependence of “traditional” and “modern”. We analyze the concepts of “culture of thinking” and “culture of education”. We pro-vide definitions and levels of critical thinking in their relation to universal human cultural values, taking into account the awareness of the multipolarity of world. We propose an approach to solv-ing problematic issues related to the formation of critical thinking, in accordance not only with his-torically established philosophical doctrines about thinking, but also with cultural and educational traditions. As an example of the traditions influence of culture of thinking on the critical thinking formation, such educational practice as “Philosophy for Children” is given. We substantiate the choice of programs and methods of philosophizing with children based on cultural reality and spi-ritual problems (intellectual, political, ethical, etc.), characteristic of a particular state. We con-clude that there is a close relationship between cultural and educational traditions and key aspects of the critical thinking formation.
HIANet: Hierarchical Interweaved Aggregation Network for Crowd Counting

Aiming at scale variation in crowd counting, we consider that an optimal solution is to take full advantage of the complementarity between multi-scale features. To implement this idea, we devise a Hierarchical Interweaved Aggregation Network (HIANet) in this paper. Instead of directly using the traditional concatenation method to aggregate multi-level features, our Hierarchical Interweaved Aggregation Module (HIAM) utilizes an innovative multiplication aggregation strategy to facilitate sufficient multi-scale feature fusion, which can help HIANet recover more structural and detail information. Furthermore, our HIANet employs a Gated Passing Mechanism (GPM) to selectively control the passing of feature information on each level, thus further to suppress the useless background information. Extensive experiments in different crowd scenes well demonstrate that our method has clear advantages under different evaluation metrics.
An Advanced Approach for Optical Large Size Colored Image Compression Using RGB Laser Beams: Simulation Results

Theoretical image compression could have the ability to effectively reduce the data size by eliminating undesirable information to consequently get more space as well as time for hastening transmission. The objective of the proposed approach is to introduce a new algorithm for color image compression using RGB laser beams and reduce the time required to compress/decompress an image due to coherent optics. Research methodology used laser beams as a solution to simulate, implement, analyze, and evaluate images. Succinctly, the advantages of this approach are simple, fast, straightforward, and can be done numerically or optically. An optical setup for compression/decomposition of colored images has been actually indicated. In addition, we provided illustrative simulations to validate our proposed method and assess its performance on various types of color images. The aim of this method is oriented towards large-sized images wherever the image is processed as a whole without being divided into blocks. The significance and outcome of the proposed approach are to reduce the time required to process an image and to hasten transmission. The most important recommendation of this paper is to implement optically the proposed implementation and evaluate the real-time obtained results.
R2Net: Recurrent Recalibration Network for Medical Image Segmentation

We analyze the prediction confidence heat map of current state-of-the-art models in medical image segmentation. We find that low confidence usually comes with a high probability of error. Based on this observation, we propose a novel Recurrent Recalibration Network (R2Net) for medical images segmentation. Specifically, the decoder will forward multiple times in a recurrent manner while the encoder forward only once to extract features during training time. Except for the first time, each decoder input features at time t will be recalibrated by the confidence heat maps which were output from the decoder at previous time t −1. As a result, R2Net can learn to focus on the regions which are error-prone to segment. The recurrent manner makes the feature reusable and saves the parameters. We validate the proposed models on two benchmark datasets: multiorgan segmentation and cardiac organ segmentation datasets. The experimental results show that our Recurrent Recalibration scheme consistently improves the segmentation performance of various models while preserving computational efficiency.
Blockchain-Based Mechanism for Electronic Healthy Records Sharing Using Fine-grained Authorization

Nowadays, there is a growing demand for sharing electronic health records among many people and industries, particularly among hospitals and patients. The sharing of health records necessitates dealing with massive amounts of data, including sensitive information, as well as data search. In our work, we present a blockchain scheme which combines ciphertext policy attribute based encryption (CP-ABE) and IPFS(Inter Planetary File System)to encrypt and store health records. Our solution is mainly suitable for large hospitals to share a number of medical records. So the data owner has the ability to generate and distribute the secret key and encrypt the records. Meanwhile, we combine smart contracts and bloom-filter to search and hide the keywords. The experiments have proven the proposed scheme is feasibility and efficiency.
Swear Words Replacement Suggestion System

While giving the reviews the customers intentionally/unintentionally use the swear/bad words to express their disappointment to the service provider. Customer care representatives spend each day of their working lives dealing with a lot of angry customers and the casual use of curse words. Even when it is not any fault of their own, they have to be patient and help resolve issues with composure. This kind of interaction affects the mental health of the employees who have to work despite these unkind words, and it does not solve the problem rather it makes the world of the internet an unkind place. So, we want to make the internet savvy millennial aware that with a little effort, we could speak kindly to strangers, even in stressful scenarios. This paper proposes an application which helps users respect the person on the other side of a chat by offering an autocorrect option that replaces the swear word with the name of the related item for which user is complaining about.
Redefining Knowledge Management in the Era of COVID-19: A Concept Analysis Approach in the Context of Higher Education

Higher education institutions are frequently using knowledge management to achieve their mission. Due to COVID-19, education has moved to online, this has led to an increasing need for knowledge management and thus put under scrutiny to obtain the best results. However, successful knowledge management requires definition as the first step. Knowledge Management is a complex and multi-dimensional concept making its definition hard to craft. The lack of well-defined universally accepted definition is a problem associated with KM is due to the diversity of areas it exists in, lack of consensus and others. In this paper, an objective concept analysis was undertaken to examine the attributes, characteristics and uses of KM for the purpose of defining KM in the higher educational institution. Therefore, this paper aims at deriving a definition of the term knowledge management with the help of concept analysis which is a recognized method for the terminological studies. The study concluded with a definition based on the results of the analysis
UAV-based cross-view geo-localization fusion spatial attention mechanism and Netvlad

The purpose of cross-view image geo-localization is to retrieve the same geographic target from images with different views acquired from different platforms. Facing the great differences in the appearance of images from different viewpoints, we propose an image retrieval method (spatial-Netvlad-siamese net, SNSnet) that fuses the trainable local aggregation descriptor vector (Netvlad) and the spatial attention mechanism. SNSnet can simultaneously process two different viewpoint images, and achieve image retrieval and matching by increasing the distance between mismatched image pairs and decreasing the distance between matched image pairs. We conducted experiments on mutual retrieval between satellite viewpoint images and UAV viewpoint images and achieved good results.
Efficient Compression Method Based on Object Model for Massive Real-time Data in Power IOT

Abstract With rapid development of the Internet of things (IOT) in power field, equipment of various majors began to access the smart IOT system on a large scale. In this paper, a new data compression algorithm based on object model (CABM) is proposed to solve the problems of large-scale applications of power Internet of Things, such as high-frequency transmission of large quantities, real-time short data can not be effectively compressed, resulting in low efficiency of cloud edge information transmission and serious waste of bandwidth resources. The CABM adds a compression and decompression module between the IOT management platform and the edge agent; the IOT management platform automatically generates a compression dictionary based on the object model file and saves it synchronously in the edge agent. During data transmission, both the sender and receiver complete automatic compression and decompression according to the compression dictionary. The experimental results show that for real-time message data below 1KB, the compression rate using CABM algorithm is 3-5 times higher than that using GZIP compression algorithm; and the compression time is 10-20 times lower than that using GZIP; the efficiency using CABM algorithm is 4.5 times higher than that using GZIP algorithm; that is, CABM can reduce redundant data transmission between cloud edges and improve the efficiency of cloud edge transmission.
Toward Self-Intelligence: A New Foundation

Artificial Intelligence is regaining its wide popularity in recent years. On one hand, the importance of Artificial Intelligence is due to the availability of big data which is the result of the formation of large systems that are interconnected by various networks. On the other hand, robots and machines of tomorrow are urgently in need of gaining self-intelligence so as to be able to efficiently perform difficult tasks, and even to free people from doing dangerous jobs. With the rise of Artificial Intelligence in the public minds or eyes, people are putting a very high level of interests on knowing the scientific solutions behind the understanding and capabilities of self-skills and self-intelligence. In this invited paper, I will go through the following key points: What is the past paradigm on computerized intelligence? What is the present paradigm on machine learning? What is the new foundation toward achieving machines’ self-intelligence?
Planning on an Empty Stomach: On Agents with Projection Bias

People often believe that their future preferences will be similar to their current ones. For example, people who go hungry to the supermarket, often buy less healthy food items than when they go on a full stomach. Loewenstein et al. [10] coined the term projection bias to capture this and similar behaviors. Our first contribution is a generalization of the restricted model of Loewenstein et al. by considering agents with projection bias that traverse a state graph for time horizon t. Our generalization allows us to capture more complex planning scenarios, such as a student that plans his occupational path. We analyze the planning behavior of biased agents and show that their loss due to their projection bias may be unbounded. Obviously, agents who do not suffer from projection bias at all will be able to traverse the graph optimally. We show–perhaps surprisingly–that agents that exhibit a strong projection bias sometimes fare better than agents that exhibit projection bias to a smaller extent. Similarly, we show that agents that plan for a longer time horizon do not necessarily fare better than agents that plan for a shorter time horizon. We then provide bounds on the number of these “non-monotonicity” points in a given state graph. Among other results, we prove a hardness result for computing a subgraph that maximizes the utility of the biased agent.
Preserving healthcare data

With the rise of e-health services, cloud-based storage of health data became an integral part of the system. However, cloud-based system is prone to privacy issues and frequent cyberattacks. Therefore, efficient encryption of health-related data is required. Evolution of preserving healthcare data is constantly occurring from traditional encryption models to cognitive-based deep learning models. Various such encryption algorithms exist, and in this study, works related to application of encryption in clinical sector are discussed. Importance of encryption in healthcare domain along with its associated challenges is highlighted in this work. Different encryption algorithms including cognitive-enabled deep learning–based method used in modern healthcare are presented here along with their benefits and limitations. Experimental evaluation demonstrating the performance of some of the algorithms is discussed and a comparative analysis among the algorithms is done based on several performance metrics to figure out their suitability in cloud-based systems assisting healthcare data security.
ПОЗААУДИТОРНА ДІЯЛЬНІСТЬ ЗДОБУВАЧІВ ВИЩОЇ ОСВІТИ У КЛУБНИХ ОБ’ЄДНАННЯХ ПЕДАГОГІЧНИХ ЗАКЛАДІВ ВИЩОЇ ОСВІТИ

The article is devoted to the analysis of extracurricular activities of higher education students in the form of club associations. The author identifies important tasks performed by clubs. Namely, the multifaceted development of the future teacher’s personality and the creation of a free informal atmosphere of communication