Reviewerly/exHarmony huggingface dataset:
Security Attacks and Key Challenges in Blockchain Technology: A survey

A blockchain is a decentralized, distributed, and open ledger that is used to record transactions across several computer clusters. Without a change in subsequent blocks, the record cannot be changed. Blockchain is a well-known phrase in the IT industry. There are several issues with Blockchain technology that need to be taken into account, despite the fact that it has many applications in the fields of information technology, banking, and other industries. In this paper, we discussed the major challenges and issues associated with Blockchain technology
An Efficient Storage Solution for Healthcare Management Systems Using Cloud

E –Healthcare is a blooming field in the intersection of communal health and business, relating to health amenity and information. This has been used to store medical records and other personal information on the patient's medical history and recovery. This has been a reason for the development of a new relationship between the patients and the doctors, enabling information exchange through online portals. Though E – healthcare and Healthcare records have been very useful in maintaining patient history, there are a few things that are still under developed. Over the past several years, there has been a dramatic trend toward the use of electronic health records (EHRs) in cloud environments, where many devices are synced with cloud computing to improve the sharing of patient information. However, concerns concerning data privacy and security have been raised in light of this recent shift in the environment. We came up with an EHR sharing framework that combines block chain and decentralized interplanetary system (IFPS) with an application that can be used in any application. We present a model that provides a practical solution for reliable data exchange being turned on cloud with protecting the sensitive information provided by the user The patients can also upload their PHIs (Private health information statistics) to the cloud for later reviews. This paper deals with an application that is very useful for maintaining the healthcare records of patients and their doctors and other necessary information using cloud. Today, most the data are stored in cloud. The data may vary from normal data to very personal and sensitive data like healthcare records of a patient. In the proposed system, a sanitizer is used to summarize blocks of data corresponding to sensitive file details. The proposed system is based on Identity based cryptography, which simplifies complex certificate management. The proposed system is based on the pseudorandom function and BLS signature.
Automated construction of an Object-Oriented Bayesian Network (OOBN) Class Hierarchy

Bayesian networks (BNs) are a widely used probabilistic modelling tool for reasoning under uncertainty, though scaling them up for complex real-world problems can be challenging. Object-Oriented Bayesian Networks (OOBNs) have been proposed to address this challenge, providing modellers with the ability to define hierarchies of classes and use these classes to construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. The object-oriented concept of inheritance supports reuse of existing components, but comes with the challenge of building, and then maintaining, an efficient hierarchy of classes. This paper proposes a supergraph based method that constructs class inheritance hierarchies from a set of OOBN classes; this can be used either to form an initial inheritance hierarchy, or to reform an existing hierarchy into a more efficient one. We also present heuristics to convert a BN to an OOBN class, measures to evaluate a constructed hierarchy and empirical analyses of the proposed approach on synthetic hierarchies and on a real-world OOBN project; results show the algorithm works well in practice.
Method of Vehicle Behavior Analysis for Real-Time Video Streaming Based on Mobilenet-YOLOV4 and ERFNET

This paper presents a vehicle behavior analysis method of the real-time video streaming based on MobilenetYOLOV4 and ERFNet. Firstly, an image dataset SYGData based on roadside camera is established, which contains a variety of vehicles and vehicle behavior to keep consistent with the actual traffic scene. Secondly, we present a new deep learning model Mobilenet-YOLOV4 which backbone is Mobilenet network to improve detection accuracy and speed. Compared with YOLOV4, its mAP increased from 77.79% to 84.53%, and inference speed increased from 4 frames per second to 25 frames per second. The results demonstrate its performance improvement in the accuracy and real-time over YOLOV4. Thirdly, we present a FFmpegOpenVINO framework that supports multi-model driving. It loads Mobilenet-YOLOV4 and ERFNet to detect vehicles and lane lines. Finally, the detection data are used to analyze vehicle behavior, including vehicle tracking, over-speed detection, lane change detection and traffic statistics. The results show that this method is feasible. It not only improves the detection accuracy and speed but also can analyze various vehicle behaviors on an intelligent roadside device.
CILP: Co-Simulation-Based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments

Intelligent Virtual Machine (VM) provisioning is central to cost and resource efficient computation in cloud computing environments. As bootstrapping VMs is time-consuming, a key challenge for latency-critical tasks is to predict future workload demands to provision VMs proactively. However, existing AI-based solutions tend to not holistically consider all crucial aspects such as provisioning overheads, heterogeneous VM costs and Quality of Service (QoS) of the cloud system. To address this, we propose a novel method, called CILP, that formulates the VM provisioning problem as two sub-problems of prediction and optimization, where the provisioning plan is optimized based on predicted workload demands. CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores. We extend the neural network to also act as an imitation learner that dynamically decides the optimal VM provisioning plan. A transformer based neural model reduces training and inference overheads while our novel two-phase decision making loop facilitates in making informed provisioning decisions. Crucially, we address limitations of prior work by including resource utilization, deployment costs and provisioning overheads to inform the provisioning decisions in our imitation learning framework. Experiments with three public benchmarks demonstrate that CILP gives up to 22% higher resource utilization, 14% higher QoS scores and 44% lower execution costs compared to the current online and offline optimization based state-of-the-art methods.
Trajectory Planning for Autonomous Valet Parking in Narrow Environments With Enhanced Hybrid A* Search and Nonlinear Optimization

This article focuses on the problem of autonomous valet parking trajectory planning in complex environments. The task normally can be well described by an optimal control problem (OCP) for the rapid, accurate, and optimal trajectory generation. Appropriate initial guesses obtained by sampling or searching-based methods are important for the numerical optimization procedure. Still, in highly complex environments with narrow passages, it may incur high computational costs or fail to find the proper initial guess. To address this challenge, an enhanced hybrid A* (EHA) algorithm is proposed to address the issue. The EHA includes four steps. The first step is to quickly obtain the global coarse trajectory using a traditional A* search. The second step is constructing a series of driving corridors along the rough trajectory, then evaluating and extracting nodes from each wide or narrow passage based on the length of the box's side. The third step is to extract each passage's boundary points. The final step is connecting boundary points by hybrid A* and generating a feasible initial guess for OCP. To reduce safety risks, vehicles in particular areas (Fig. 1(b)) should travel as slowly as possible. The global and local speed restrictions are distinct, and local restrictions are only activated when the vehicle enters a particular area. This “if-else” structure makes the optimization problem difficult. A novel approximation formulation for these local state constraints is introduced to overcome this issue. The experimental results demonstrate that the proposed method for trajectory planning is effective and robust.
Differentially Private Distributed Optimization With an Event-Triggered Mechanism

This study concentrates on the differential private distributed optimization problem with an event-triggered mechanism, whose goals include preserving the privacy of agents’ initial states and local cost functions and improving communication efficiency. A distributed event-triggered mechanism is integrated into the differentially private subgradient-push distributed optimization algorithm and then a new algorithm named as DP-ETSP is designed, where the real-time information propagation among agents is avoided. Additionally, under the proposed event-triggered mechanism, an analysis of mean-square consensus and optimality over time-varying directed networks is made when the added Laplace noises meet some specific decaying conditions. Convergence rate results are further established under a specific stepsize, which are equal to the rate of stochastic gradient-push algorithm without event-triggered communication. Moreover, the differential privacy preservation performance is analyzed and the rule for selecting privacy level is discussed. Finally, the feasibility and effectiveness of DP-ETSP are verified in two simulation cases.
Blockchain-enabled carbon emission management system in a multi-tier supply chain

To abate global carbon emissions, there has been an unprecedented push for carbon transparency in the manufacturing industry. However, with products becoming increasingly complex, they contain sub-assemblies with components that have sub-components supplied by different manufacturers. This creates a multi-tier supply chain that complicates the propagation of sustainability information necessary to compute the product's carbon emission due to the lack of oversight and potential loss of information from manufacturers in the multi-tier supply chain. Blockchain technology, characterized by its immutability, visibility, and traceability, is a promising medium to propagate sustainability information. Despite the fact that many papers have proposed a system that is viable for products with a multi-tier supply chain in the literature, we have not seen its proof-of-concept yet. This work first proposes a blockchain-enabled system that enables the propagation of environmental sustainability information in a multi-tier supply chain. This paper describes the architecture of the proposed blockchain system and the data structure of each block in the blockchain. Based on the defined architecture, we implemented the proposed blockchain system with Ethereum and tested it using the pseudo-multi-tier supply chain of a torchlight.
Low-Light Enhancer for UAV Night Tracking Based on Zero-DCE++

Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor contrast, and low signal-to-noise ratio.This letter presents an enhanced low-light enhancer for UAV nighttime tracking based on Zero-DCE++ due to its advantages of low processing cost and quick inference.We developed a light-weight UCBAM capable of integrating channel information and spatial features and offered a fully considered curve projection model in light of the low signalto-noise ratio of night scenes.This method significantly improved the tracking performance of the UAV tracker in night situations when tested on the public UAVDark135 and compared to other cutting-edge low-light enhancers.By applying our work to different trackers, this search shows how broadly applicable it is.
Concentration Inequalities and Optimal Number of Layers for Stochastic Deep Neural Networks

We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow us to introduce an expected classifier (EC), and to give probabilistic upper bound for the classification error of the EC. We also state the optimal number of layers for the SDNN via an optimal stopping procedure. We apply our analysis to a stochastic version of a feedforward neural network with ReLU activation function.
Applying a proven filtering method to adjust the training sample of neural networks

The article notes the complexity and duration of the process of forming a training sample of a neural network, since the correctness of the sample is checked by assessing the quality of the network after its training, it also notes the negative impact of the commonly used formal method of forming a training sample of a neural network without taking into account the physical processes of data and signal transformation in real devices on the quality of the network when filtering the noise of the speech signal. Methods and means for filtering the noise of speech signals are described. To solve the filtering problem, a sequence of main stages of processing a speech signal containing noise is presented, and their description is given. The article proposes to choose a filtering method based on the analysis of noise characteristics, while it is recommended to distinguish between homogeneous (monotonic) and dynamically changing (random) noise, for which filtering methods are different. When choosing a filtering method, it is proposed to take into account the degree of correspondence between the frequency range of the noise and the speech signal. As the main way to reduce noise, an approach is proposed based on the use of an improved and proven method for filtering noise by subtracting the spectral components of noise from the spectrum of a signal containing noise. This approach is proposed to be used for the formation and correction of a training set for a neural network designed to reduce noise in a speech signal. The results of the practical application of the proven filtration method confirmed the feasibility of its application. An important result of the work presented in the article is the possibility of evaluating the feasibility of specific corrective changes in the neural network training set by comparing it with the filtering results of the modified and tested method.
An Intensional Formalization of Generic Statements

Abstract A statement is generic if it expresses a generalization about the members of a kind, as in, ’Pear trees blossom in May,’ or, ’Birds lay egg’. In classical logic, generic statements are formalized as universally quantified conditionals: ‘For all x, if..., then....’ We want to argue that such a logical interpretation fails to capture the intensional character of generic statements because it cannot express the generic statement as a simple proposition in Aristotle’s sense, i.e., a proposition containing only one single predicate. On the contrary, we want to show that typed lambda-abstraction can help us transform the classical, non-simple and extensional expression of generic statements into a new, simple and intensional formalization, through the introduction of an abductively defined operator ALL *. This new operator allows for the possibility of a single predication, e.g. fly (), because it builds, out of a concept like ‘bird’, a concrete universal, e.g. ‘birds’, upon which the single predicate can be applied to authentically formalize a generic statement, e.g. ‘birds fly’.
Graph Signal Sampling Under Stochastic Priors

We propose a generalized sampling framework for stochastic graph signals. Stochastic graph signals are characterized by graph wide sense stationarity (GWSS) which is an extension of wide sense stationarity (WSS) for standard time-domain signals. In this paper, graph signals are assumed to satisfy the GWSS conditions and we study their sampling as well as recovery procedures. In generalized sampling, a correction filter is inserted between sampling and reconstruction operators to compensate for non-ideal measurements. We propose a design method for the correction filters to reduce the mean-squared error (MSE) between original and reconstructed graph signals. We derive the correction filters for two cases: The reconstruction filter is arbitrarily chosen or predefined. The proposed framework allows for arbitrary sampling methods, i.e., sampling in the vertex or graph frequency domain. We also show that the graph spectral response of the resulting correction filter parallels that for generalized sampling for WSS signals if sampling is performed in the graph frequency domain. Furthermore, we reveal the theoretical connection between the proposed and existing correction filters. The effectiveness of our approach is validated via experiments by comparing its MSE with existing approaches.
Applying Graph Neural Networks to the Decision Version of Graph Combinatorial Optimization Problems

In recent years, there has been a significant increase in the application of graph neural networks on a wide range of different problems.A specially promising direction of research is on graph convolutional neural networks (GCN).This work focuses on the application of GCNs to graph combinatorial optimization problems (GCOPs), specifically on their decision versions.GCNs are applied on the family of GCOPs in which the objective function is directly related to the number of vertices in a graph.The selected problems are the minimum vertex cover, maximum clique, minimum dominating set and graph coloring.In this work, a framework is proposed for solving problems of this type.The performance of this approach is explored for standard multi-layer GCNs using different types of convolutional layers.On top of this, we propose a new structure that is based on graph isomorphism networks.Computational experiments show that this new structure is significantly more efficient than basic structures.To further improve the performance of this method, the use of GCN ensembles is also explored.When using GCNs to generate solution values for GCOPs, they often correspond to non-feasible solutions because they violate the problem boundaries.This issue is addressed by defining an asymmetric loss function that is used during the GCN training.The proposed method is evaluated on a large set of training data consisting of graph solution pairs that can also be accessed online.
EC2Net: Efficient Attention-Based Cross-Context Network for Near Real-Time Salient Object Detection

The development of salient object detection is crucial in ubiquitous applications. Existing state-of-the-art models tend to have complex designs and a significant number of parameters, prioritizing performance improvement over efficiency. Hence, there pose significant challenges to deploying them in edge devices. The intricacy in these models stems from the complicated encoder-decoder that aims to effectively generate and integrate coarse and semantic features. To address this problem, we introduced EC2Net, an efficient attention-based cross-context network for salient object detection. To start with, we introduce the shallow crossed-context aggregation (SCCA) mechanism to enhance and preserve object boundaries for shallow layers. We introduced a deep cross-context aggregation (DCCA) mechanism to enhance semantic features in deep layers. Subsequently, we introduced the dual cross-fusion module (DCFM) to efficiently merge shallow and deep features. The proposed modules complement each other, enabling EC2Net to accurately detect salient objects with reduced computational overhead. Through experiments on five standard datasets, the proposed method demonstrated competitive performance while utilizing fewer parameters, FLOPS, and memory storage than other resource-intensive models.
Deep Learning Using Context Vectors to Identify Implicit Aspects

Aspects extraction is the key task in the sentiment analysis problem, which includes extraction of both explicit and implicit aspects. Identifying implicit aspects is not a new task in sentiment analysis, but it still presents many challenges. Numerous studies have addressed the issue and offered different approaches, but there are still fundamental challenges as follows: solving the context word problem; the domain-specific problem; and the implicit aspects represented by sentiment words that do not appear explicitly in the text. This paper proposes a method for identifying implicit aspects of sentiment words. To address these above challenges, the system is built on the foundation of deep learning with context vectors, dependency grammar, anaphora coreference resolution, and sentiment ontology. The dependency parser and the entity coreference resolver are adopted to filter out the sentiment–aspect pairs data generated from embedding context words. Then, a fine-tuned technique is applied, and the classifier corresponding to each sentiment word is built to identify implicit aspects. This combination has produced positive results, reaching almost 90% accuracy.
Intercultural communication methodology: teaching sciences and engineering students

The problem area of this research paper focuses on the priority functions of the language sphere of the being mentality necessarily reflecting the state documents on education, methodology of scientific and methodical support for the implementation of educational programs of language education for training students of non-linguistic departments of universities in line with the axiology within the current educational program research of the eco-system spaces of international language education in Russia. Current Scientific Scholarship in terms of the intercultural approach needs to be created for linguistic security via making the outmost of the language programs for SP. The goal of this article is threefold: a) to design Knowledge Content, Technology and Evaluation Concepts of the target Model, b) to substantiate the integration characteristics of intercultural communication at universities (Natural Sciences and Engineering), c) to clarify the integration characteristics of intercultural communication within the innovations of national education in the third millennium, based on topical issues of the polyphony of the eco-system spaces. The latter reflects the priority dominant values due to the cognitive, developmental and educational functions in line with positive trends requiring thorough attention of both the university pedagogical community and teachers of general education organizations.