Reviewerly/exHarmony huggingface dataset:
A <scp>lattice‐based</scp> blind ring signature scheme for sensitive data protection in blockchain applications

Summary Every transaction is made public and verified by a third party, and the transparency of blockchain applications leads to privacy leaks. Everyone can access the transaction, making it easier for network analysis to identify the user's identity, which is a major problem with the blockchain. Financial institutions have become hesitant to adopt blockchain technology; as a result, preventing its spread. To confirm that encryption and security are maintained for transactions in the blockchain, this research introduces a zero‐knowledge proof (ZKP) based transaction validation scheme. To increase the security of blockchain applications, the proposed lattice‐based blind ring signature (LBRS) scheme integrates blind and ring signatures and is applied to sensitive data. The data are encrypted using this encryption scheme before being stored with the transactional information in the blocks. The encrypted data is then stored in the blocks, which users can retrieve after verification. The encrypted data is then stored in blocks, which users can retrieve after verification. A verification scheme based on the ZKP is used to prevent unauthorized access and changes to the data. The proposed LBRS scheme meets the security analysis requirement. The performance of an LBRS scheme is compared to other relevant models in terms of execution time, encryption time, and decryption time. Overall, the results are more encouraging and reassuring than other relevant works of interest. Finally, this model provides better computational security and satisfies the requirements like correctness and security analysis.
Distributed Trajectory Planning for a Group of UGVs Carrying a Load Considerting Terrain Properties

The article studies a trajectory planning task for a group of UGVs with a consideration of wheels-terrain adhesion variation. Within this paper a brief analysis devoted to existing trajectory planning is done. It outcomes with a conclusion of a necessity to produce additional research within this topic. This paper suggests to use a Sampling-based method to solve this trajectory planning task. An algorithm of rapidly exploring random tree (RRT) is used as a basic algorithm. An advantage of this method (typical for Sampling-based methods) is a simplicity of various non-linear restrictions introduction (e.g. obstacles, differential restrictions etc.). In addition we should mention good potential for algorithm parallelization, because of tree structure of the algorithm. However there exists a shortage of the proposed methods — a high consumption of computational resources, and as an outcome a long calculus duration. This paper proposes to overcome this shortage via distributing of computation among UGVs — actors of a group. This is followed by a comparative analysis of distributed and centralized methods. Analysis shows that the main advantage of proposed method is that it can use almost all models of interaction between wheel and terrain. The latter can act a component for calculation of restrictions for motion acceleration over certain types of terrain. Within this paper we did not study models of interaction between wheel and terrain, but instead used empirical data of allowed values of tangential and normal accelerations for specific UGVs in particular conditions. In final part we present results of simulation witch confirm effectiveness of proposed methods.
Current Directions of Education in Russia at the Present Stage

The article discusses current trends and approaches for the modern educational process concerning educational standards, the involvement of practitioners in the educational process and the education of persons with disabilities. The purpose of this article is to consider modern problems arising in the process of teaching disciplines at a university, to determine the means, conditions and methods of improving the competence of university graduates.
Principles of Development of Educational Materials for Learning in an E-Learning Environment

This paper deals with the problem associated with the definition of the principles for the development of educational materials designed for learning in an electronic educational environment. The result of a critical analysis of the basic concepts is presented. The dominant of independent work and independence of the student as a specific feature of learning in the electronic educational environment is considered. The essence and purpose of educational materials for the organization of cognitive activity of students in the conditions of an electronic educational environment are disclosed. Particular attention is paid to the construction of a system of principles underlying the development of educational materials designed to train students in an electronic information and educational environment.
Methodology for Studying the PHD Study Program in Statistical Analysis

The article describes the content of the discipline "Methods of statistical analysis", developed for the preparation of postgraduate students of the Moscow State Academy of Physi-cal Culture in the scientific specialty 5.8.7. "Methodology and technology of vocational educa-tion". The relevance substantiated and the tasks of the discipline defined. Methods of statistical analysis that are effective for the specified specialty are given, and features of statistical pro-cessing of data from pedagogical research are noted. The specifics of the issues under consider-ation and the volume of the studied material are reflected in the main learning outcomes (to know, to be able, to master), which graduate students must demonstrate during certification in the discipline.
Fusion-based Representation Learning Model for Multimode User-generated Social Network Content

As mobile networks and APPs are developed, user-generated content (UGC), which includes multi-source heterogeneous data like user reviews, tags, scores, images, and videos, has become an essential basis for improving the quality of personalized services. Due to the multi-source heterogeneous nature of the data, big data fusion offers both promise and drawbacks. With the rise of mobile networks and applications, UGC, which includes multi-source heterogeneous data including ratings, marks, scores, images, and videos, has gained importance. This information is very important for improving the calibre of customized services. The key to the application's success is representational learning of fusing and vectorization on the multi-source heterogeneous UGC. Multi-source text fusion and representation learning have become the key to its application. In this regard, a fusion representation learning for multi-source text and image is proposed. The convolutional fusion technique, in contrast to splicing and fusion, may take into consideration the varied data characteristics in each size. This research proposes a new data feature fusion strategy based on the convolution operation, which was inspired by the convolutional neural network. Using Doc2vec and LDA model, the vectorized representation of multi-source text is given, and the deep convolutional network is used to obtain it. Finally, the proposed algorithm is applied to Amazon's commodity dataset containing UGC content based on the classification accuracy of UGC vectorized representation items and shows the feasibility and impact of the proposed algorithm.
A web crawler-based method for collecting information on investment promotion enterprises

Data acquisition is a prerequisite for performing big data analytics. However, as the diversity and timeliness of data increase, the complexity of data collection also increases. In this paper, we take enterprise data on a big data investment platform as the research object, and design two data collection models, static data collection based on incremental crawlers and dynamic data collection based on query topic crawlers, for the static and dynamic characteristics of this data. In the experiments, this paper tests the effectiveness of these two web crawler methods and proves that they can collect static and dynamic investment data comprehensively and accurately. Thus, this study provides an effective data collection scheme that helps improve the accuracy and reliability of big data analysis.
IoT device multi-classification using traffic behavior analysis

The management of Internet of Things (IoT) devices is becoming increasingly complex. One of the reasons is that IoT device manufacturers are different, and there are different degrees of heterogeneity in service, technology, protocol and other aspects. Accurate identification IoT devices connected to the organization network is an effective way to maintain the organization network security. In this paper, we propose a two-stage machine learning method to identify IoT devices by analyzing network traffic. This method uses the futures of the original traffic advance to classify the types of IoT devices. The method is tested on two public datasets, our method classified IoT devices with an accuracy rate of over 99%.
Reconstruction performance analysis of discriminative infinite fuzzy restricted Boltzmann machine

The existing traditional neural network reconstruction models have some questions, including the high training epochs, low recognition rate, and complex structure. The restricted Boltzmann machine (RBM) is an excellent generative learning model for feature extraction, which is a simple model compared to other deep neural networks. The Discriminative Fuzzy Restricted Boltzmann Machine (DFRBM) is proposed by extending its parameters from natural numbers to fuzzy ones. Then we introduced the random permutation (RP) algorithm, with the hidden units random permutation, Discriminative Infinite Fuzzy Restricted Boltzmann Machine (Dis-iFRBM) is proposed. Dis-iFRBM is a better RBM model than DFRBM and Classical RBMs.We further investigate and compare the generative ability of the Dis-iFRBM on image reconstruction. First, we transform the MSTAR SAR piece to HRRPs images. Then the Dis-iFRBM, DFRBM, and Classical RBMs are compared in detail under optimal conditions on the HRRPs images that transformed from MSTAR data sets. Specifically, they can be trained by relatively limited datasets into excellent stand alone classifiers and retain satisfactory generative capability simultaneously. The comparison of experimental images shows that the Dis-iFRBM possesses better generative capability than the Discriminative Fuzzy Restricted Boltzmann Machine (DFRBM). Meanwhile, surveillance images in the city, license plate number recognition, and other scenarios need damaged image restoration. Dis-iFRBM as a model can save computational resources of terminal image devices that deploy in the Urban Internet of Things. The experiment results indicate that the Dis-iFRBM outperforms image restoration. It can achieve smaller average reconstruction errors (AREs) while given a small number of hidden units. Finally, experimentation over several classical RBMs revealed the proposed approach’s preferable reconstruction capability.
Of Social Discipline and Control

Abstract Recent scholarship on disinformation points to a range of adverse effects on society, including its potential to fragment institutions and incite violence. Despite this, little research has looked into the stratified effects of fake news in society – particularly on how minorities are disproportionately victimised by the problem. This study sets out to analyse the qualitative impact of fake news on racial, ethnic and sexual minority communities in Indonesia through the theory of gendered and stratified misinformation. Three research questions are addressed: first, what disinformation campaigns affecting minority groups have emerged in Indonesia? Next, how have these campaigns influenced and shaped social behavior of minorities? Lastly, how is the state implicated in the problem, and to what extent are state responses considered adequate? Drawing upon primary data collected through interviews with Indonesian citizens belonging to major minority groups, this study’s findings reveal the disproportionate impact fake news has on minority communities in Indonesia. It sheds light on how fake news has been weaponised by hegemonic groups in society to both amass various forms of political and religious capital, as well as to socially control and discipline minority groups. Furthermore, it shows how the state is implicated through politicised policing of fake news.
Blockchain Based Access Control Systems

While accessing data through the Internet of Things (IoT), privacy and safety are the most important features. IoT developers have a challenging job of securing and protecting the data of the users during the development process due to the complicated nature of decentralisation and heterogeneity of the environment present in IoT. There are several access control models for the IoT available in the literature which rely on centralized architecture and raises security concerns because IoT devices can spontaneously and dynamically interact with one another. The need for secure and distributed access control architecture to overcome the single point of failure problem of a centralized entity becomes a big challenge when coupled with scalability and lightweight features. It is possible to achieve this through the use of Blockchain technology, which has recently been used to provide access control services. IoT device management would be used to manage distribution, heterogeneity, scalability, the ability to tolerate failure, security and privacy aspects of IoT devices at scale in the near future as it is useful.
Design A Resilient Network Infrastructure Security Policy Framework

The information security policy development lifecycle tends to lack focus on the use of standard terms and semantics. This results in blurred outlines for monitoring, evaluation, and enforcement of the security policy for the employees confusing adhering to and implementing it, which leads to a lack of a process of publishing from the security policy, end-user awareness, translation of high-level policy to lowest level component configuration plans and actions to take in time of crisis. This leads to the critical need to design an empirically tested, comprehensive security policy. This chapter proposes bridging the gap between the high-level information security policy descriptions and low-level network infrastructure security implementation. With new and innovative technologies, such as Cloud, Remote Computing, Enterprise Mobility, and e-commerce on the rise, network security has remained an ever-increasing challenge. This chapter presents a security framework to bridge the gap between high.level specification requirements and the low-level implementation phase for network infrastructure security using the network architecture model with the security policies associated with the network components required to be enforced. An architectural model and a set of design-level security policies are considered to achieve the framework design. Also discussed are the advantages and desired characteristics of the model, relating to existing processes worked in the design area, and future research directions are pointed.
#2654 USE OF SUPERVISED DEEP LEARNING ALGORITHM TO PREDICT RISK OF RENAL REPLACEMENT THERAPY(RRT) IN PATIENTS WITH CHRONIC KIDNEY DISEASE (CKD)

Abstract Background and Aims Chronic kidney disease (CKD) is one of the most common causes of mortality, affecting around 10% adults worldwide [1]. Although statistical models for predicting risk of renal replacement therapy (RRT) have been developed for a decade [2], referral of patients with CKD to nephrology service is often based on clinical experience of primary care physicians. The use of deep learning algorithms (DLAs) allow clinicians to capture complex, multidimensional, non-linear relationships instead of relying on linear relationships between independent variables and outcome. Our study aims to develop a DLA that has at least non-inferior performance compared to the Kidney Failure Risk Equation (KFRE), which is a well-established and validated risk prediction tool [3]. Method This is a retrospective cohort study carried out in 3 major acute hospitals in Hong Kong providing a total of 3000 beds from Jan 1, 2009 to Mar 31, 2022. All patients aged &amp;gt; 18 with an estimated glomerular filtration rate (eGFR)&amp;lt;30ml/min/1.73 m2 according to the CKD-EPI formula, who attended follow-up for at least 3 months in nephrology clinic of the 3 hospitals, were recruited. Those who were on chronic RRT, received renal transplantation or had an eGFR&amp;lt;15 ml/min/1.73 m2 before referral were excluded. Supervised DLAs of different structures and their combinations were created and trained. Data in the test set were fed into models to predict the risk of requiring RRT in 2 years and 5 years. Predictive performance of these models were compared with that of KFRE [3]. Results 4992 patients were recruited in the study. Data of 499 patients were isolated as test set and were not used for model training. 1576 patients progressed to stage 5 CKD during their follow-up, and 989 patients required initiation of RRT during the study timeframe. When compared with KFRE (4-variable: ROC-AUC = 0.84, 95%CI:0.835-0.852; 8-variable: ROC-AUC = 0.84, 95%CI:0.830-0.847), almost all DLAs showed statistically significant superior robustness in predicting the risk of requiring RRT (Figure 1). No significant difference was found between performance of DLAs combining different neural network layers and those with single structures (CNN+LSTM+ANN layers ROC-AUC = 0.90, 95%CI:0.896-0.902; CNN ROC-AUC = 0.91, 95%CI:0.907-0.914) (Table 1). Conclusion The use of DLAs provided better prediction in the risk of requiring RRT when compared with KFRE.
What Are Recurrent Expansion Algorithms? Exploring a Deeper Space than Deep Learning

Machine learning applications usually become a subject of data unavailability, complexity, and drift, resulting from massive and rapid changes in data volume, velocity, and variety (3V). Recent advances in deep learning have brought many improvements to the field, providing generative modeling, nonlinear abstractions, and adaptive learning to address these challenges. In fact, deep learning aims to learn from representations that provide a consistent abstraction of the original feature space, which makes it more meaningful and less complex. However, data complexity related to different distortions, such as higher levels of noise, remains difficult to overcome. In this context, recurrent expansion (RE) algorithms have recently been developed to explore deeper representations than ordinary deep networks, providing further improvement in feature mapping. Unlike traditional deep learning, which extracts meaningful representations through inputs abstraction, RE enables entire deep networks to be merged into another one consecutively, allowing the exploration of inputs, maps, and estimated targets (IMTs) as primary sources of learning; three sources of information that provide additional knowledge about their interactions in a deep network. In addition, RE makes it possible to study IMTs of several networks and learn significant features, improving its accuracy with each round. In this context, this paper presents a general overview of RE, its main learning rules, advantages, disadvantages, and its future opportunities, while reviewing the state-of the art and providing some illustrative examples.
Lenslet Image Coding With SAIs Synthesis via 3D CNNs-Based Reinforcement Learning With a Rate Reward

The deep learning-based coding schemes for lenslet images combine coding standards and view synthesis through Deep Learning (DL) models, where the compression efficiency is heavily influenced by the coding structure and quality of synthesized views. To exploit the inter-view redundancy among Sub-Aperture Images (SAIs), this paper proposes a hybrid closed-loop coding system that uses a novel coding structure based on checkerboard interleaving at a frame level. The frame-wise checkerboard interleaving method partitions an Original SAIs’ Set (OSS) of images into two mutually exclusive subsets, each consisting of alternating rows and columns of SAIs. We utilize the video coding standard Versatile Video Coding (VVC) to encode one subset while proposing a novel rate constraint-reinforced 3D Convolutional Neural Networks (CNNs) to predict the other subset, referred to as the complement subset. The rate constraint-reinforced 3D CNNs is newly designed with a gradient loss and reinforced rate cost to improve synthesized SAIs’ image quality and bit cost saving simultaneously. Experimental results on the light field image dataset demonstrate that the proposed hybrid coding system outperforms both HEVC_LDP and the previous state-of-the-art (SOTA), achieving an average BD-Bitrate savings of 41.58% and 23.31%, respectively.
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks

In this paper, we present AnalogVNN, a simulation framework built on PyTorch that can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural network accelerators. We use this framework to train and optimize linear and convolutional neural networks with up to nine layers and ∼1.7 × 106 parameters, while gaining insights into how normalization, activation function, reduced precision, and noise influence accuracy in analog photonic neural networks. By following the same layer structure design present in PyTorch, the AnalogVNN framework allows users to convert most digital neural network models to their analog counterparts with just a few lines of code, taking full advantage of the open-source optimization, deep learning, and GPU acceleration libraries available through PyTorch.
A descriptive problem of clitic pronouns and pronominal verbs in Italian

Pronominal verbs are confused with reflexive verbs in Italian. Reflexive verbs refer to those with a pattern in which the effect of the action of the subject returns to the agent of the verb. Pronominal verbs are similar to reflexive verbs in morphological form, but differ in grammatical function and act as a word. Even though the two types of verbs exhibit the same morphological form, they demonstrate different syntactic behavior. However, most grammar books explain the two different verbs in the same section as reflexive verbs, leading to confusion among learners. This study asserts that when reflexive and pronominal verbs are completely different in the nature of how they are used, they should be presented seperately, this will enhance learners’ learning ability.