Reviewerly/exHarmony huggingface dataset:
Integrated crossing pooling of representation learning for Vision Transformer

In recent years, transformer technology such as ViT, has been widely developed in the field of computer vision. In the ViT model, a learnable class token parameter is added to the head of the token sequence. The output of the class token through the whole transformer encoder is looked as the final representation vector, which is then passed through a multi-layer perception (MLP) network to get the classification prediction. The class token can be seen as an information aggregation of all other tokens. But we consider that the global pooling of tokens can aggregate information more effective and intuitive. In the paper, we propose a new pooling method, called cross pooling, to replace class token to obtain representation vector of the input image, which can extract better features and effectively improve model performance without increasing the computational cost. Through extensive experiments, we demonstrate that cross pooling methods achieve significant improvement over the original class token and existing global pooling methods such as average pooling or maximum pooling.
Automatic pre- and postconditions for partial differential equations

Based on an automata-theoretic and algebraic framework, we study equational reasoning for Initial Value Problems (ivps) of polynomial Partial Differential Equations (pdes). We first characterize the solutions of a pde system Σ in terms of the final morphism from a coalgebra induced by Σ to the coalgebra of formal power series (fps). fps solutions conservatively extend the classical analytic ones. To express ivps in their general form, we then introduce stratified systems, where the specification of a function can be decomposed into distinct subsystems of pdes. We lift the existence and uniqueness result of fps solutions to stratified systems. We then give a relatively complete algorithm to compute weakest preconditions and strongest postconditions for such systems. To some extent, this result reduces equational reasoning on pde initial value problems to algebraic reasoning. We illustrate some experiments conducted with a proof-of-concept implementation of the method.
Conceptualizing the Domain and an Empirical Analysis of Operations Security Management

Operations security management integrates the activities of all the information systems security controls. It ensures that the entire computing environment is adequately secured. This chapter conducts an in-depth review of scholarly and practitioner works to conceptualize the domain of operations security management. Drawing upon the existing information systems security literature, the chapter classifies operations security management into 10 domains. Following, the chapter performs an empirical analysis to investigate the state-of-practice of operations security management in organizations. The findings show that the maturity level of operations security management is at the Level 3 (well-defined). The maturity levels range from Level 0 (not performed) to Level 5 (continuously improving). The results indicate that operations security processes are documented, approved, and implemented organization-wide. Backup and malware management are the most applied operations security controls, while logging, auditing, monitoring, and reviewing are the least implemented controls.
Implementation BRQI grayscale image model on IBM Quantum Experience

Techniques for exploiting visual information play an important and topical role. Image processing is an area of Artificial Intelligence, dealing with how to represent, reconstruct, classify, recognition and analysis of images using computers. The idea behind this study is to follow how the quantum properties of superposition and entanglement are used by the Bit-plane Representation of Quantum Images BRQI model, grayscale image, to map and store the image as qubits. For this, we used the IBM Quantum Experience platform that provides the opportunity to implement and run programs with quantum applications.
Towards an OLAP Cubes Recommendation Approach in Cloud Computing Environment

The Cloud Computing technology is constantly evolving in terms of service provision. Recently, many companies that use OLAP systems for decision-making are deploying their OLAP cubes as services in a cloud environment. The potential of the exploited cubes is growing significantly. Therefore, consumers find difficulties in selecting relevant cubes especially when their needs are dispersed across multiple cubes. Hence, we propose in this paper an approach that allows relevant cubes recommen-dation among the deployed cubes in the cloud as well as the construction of new cubes if the need cannot be met by a single cube. In order to validate our approach, a tool called “Cube-RS” is developed. An experimental study that evaluates our proposal in terms of relevance and performance is presented.
The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.
Research on Information Security Behavior of Smart Agriculture Internet of Things Based on Protection Motivation Theory

[Purpose/Meaning] Explore the factors that influence the willingness of users of smart agricultural Internet of Things to conduct information security, to help the government, agricultural organizations or related organizations take effective measures, provide targeted information security education and training, and help train users The awareness of information security precautions promotes the willingness of information security behaviors among users of the intelligent agricultural Internet of Things. [Process/Method] This research is based on the theory of protection motivation, combined with the theory of social cognition, to establish a model that affects the information security behavior of users of the intelligent agricultural Internet of Things, conduct surveys for users, and finally use the structural equation model to analyze and explore the relationship between variables. [Results/Conclusions] The study found that threat assessment, response assessment, control tendencies, subjective norms, and security habits have a significant positive impact on user information security behavior, response costs have a negative impact on user behavior willingness, and perceived ease of use has a negative impact on user behavior. The influence of behavior intention is not significant.
Binary neural network computing architecture

In this chapter, we focus on reducing the memory cost and energy efficiency from the aspect of BNN hardware accelerators design. We introduce the bagged binary neural network accelerator (BBNA), which is a fully pipelined BNN accelerator with bagging ensemble unit for aggregating several BNN pipelines to achieve better model accuracy. In other words, the proposed architecture provides an opportunity for embedded devices to obtain acceptable accuracy with smaller ensemble BNNs. As a result, compared to other works, our design achieves 1.9× better energy efficiency with better performance, and the ensemble method saves more than 79% and 94% memory footprint, respectively, with nearly accuracy on MNIST dataset.1
LINAC: A Spatially Linear Accelerator for Convolutional Neural Networks

We first propose a method to utilize the spatially linear correlation between activations and weights of CNN models and accelerate the inference process via linear regression. With the proposed linear regression-based acceleration method, stronger bit-sparsity is excavated to reduce ineffectual computations during the inference in CNN acceleration systems without introducing any errors to the algorithm. We also propose the corresponding hardware accelerator based on the linear convolution mechanism: LINAC, which implements the linear convolution that exploits bit-sparsity to boost performance and energy efficiency significantly. In our experiments, LINAC boosts the inference performance by 11× on average over the present value-agnostic accelerator and outperforms other state-of-the-art bit-sparse accelerators.
Sentiment Analysis of Restaurants Customer Reviews on Twitter

With the spread of using the Internet around the world and a huge number of social media users, social media platforms have become a popular place to share customers’ opinions and experiences on a variety of services and products, such as restaurants and different consumables etc. These data can be utilized to assist in decision-making. However, collecting and managing customers’ opinions on different social media platforms is a complicated and challenging task. The customers express their opinions in their colloquial language that contains spelling errors and repetition of letters, so the preprocessing stage solves these problems and enhances the quality of data to become ready to conduct sentiment polarity and calculate reputation scores. In this paper, the Saudi customers’ tweets are collected for four restaurants in Madinah. The Polarity Lexicon for the Saudi dialect (PLSD) and Emoji Lexicon for Sentiment Analysis (ELSA) are developed as a core component in our system to classify the extracted tweets into positive, negative, and neutral. Next, the Net Brand Reputation (NBR) is used to derive the reputation score from the lexicon feedback. Experimental evaluation demonstrates that the results from the proposed approach were compatible with the results extracted from Foursquare, an application that measures restaurants’ reputations based on customer ratings and comments.
Perfect Secrecy in the Bounded Storage Model

In this paper, we propose a new provably secure cryptosystem for two party communication that provides security in the face of new technological breakthroughs. Most of the practical cryptosystems in use today can be breached in the future with new sophisticated methods. This jeopardizes the security of older but highly confidential messages. Our protocol is based on the bounded storage model first introduced in [1]. The protocol is secure as long as there is bound on the storage, however large it may be. We also suggest methods to extend the protocol to unbounded storage models where access to adversary is limited. Our protocol is a substantial improvement over previously known protocols and uses short key and optimal number of public random bits size of which is independent of message length. The smaller and constant length of key and public random string makes the scheme more practical. The protocol generates key using elements of the additive group <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{Z}_{\mathrm{n}}$</tex> . Our protocol is very generalized and the protocol in [1] is a special case of our protocol. Our protocol is a step forward in making provably secure cryptosystems practical. An important open problem raised in [2] was designing an algorithm with short key and size of public random string <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\mathcal{B})$</tex> where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{B}$</tex> bounds the storage of adversary. Our protocol satisfies the conditions and is easy to implement.
A hybrid descriptor to improve kidney pathologies classification

The importance of glomerular function in kidney physiology characterizes glomerular diseases as the main problem in nephrology. So finding and classifying glomerular disorders are fundamental steps for diagnosing many kidney diseases. This paper conducted an extensive study to determine the best set of features for glomerular image representation. Our feature extraction methodology, which includes clinical data, texture, and global descriptors, resulted in 8486 features. Besides, we compared four classifiers to propose a method that helps the specialist define a renal pathology diagnosis. The proposed method achieved an accuracy of 98.46% and a Kappa index of 98.42% using the Random Forest Classifier. We concluded that a combination of clinical data and global image features facilitates accurate disease classification.
A lightweight approach for sound call graph approximation

Interprocedural analysis refers to gathering information about the entire program rather than for a single procedure only, as in intraprocedural analysis. It enables a more precise analysis; however, it is complicated due to the difficulty of constructing an accurate program call graph. Algorithms for constructing sound call graphs must trade-off precision against scalability. Many precise call graph techniques are complex and are difficult to scale due to the kind of type-inference analysis they use, in particular the use of some variations of points-to analysis. This forces use cases that require both soundness and scale such as vulnerability propagation analysis to resort to simpler variants such as Class Hierarchy Analysis. These kinds of analyses have no sound equivalent for dynamically typed languages such as Python and JavaScript that gained more popularity over recent years. To address this problem, we propose NoCFG, a new sound and scalable method for approximating a call graph that supports a wide variety of programming languages. A key property of NoCFG is that it works on a coarse abstraction of the program, discarding many of the programming language constructs. Due to the coarse program abstraction, extending it to support also other languages is easy. We evaluate NoCFG for real-world projects written in both Python and C# and the results demonstrate a high precision rate of ≥ 89% and scalability through a security use-case over projects with up to 2 million lines of code.
3D reconstruction based on KAZE quasi-dense match algorithm

The traditional matching algorithm is easy to lose the detail information in the complex background, which is disadvantageous to the 3D reconstruction. To solve these problems, a quasi-dense algorithm based on KAZE feature detection is proposed in this paper, which is helpful to more accurate 3D reconstruction. Firstly, the method uses the KAZE Algorithm to extract the feature points, constructs the feature vector to match, and uses the random sample consistency algorithm to remove the mismatched points. Then the region growth is realized by the polar distance constraint criterion based on the adaptive window diffusion method. The experimental results show that the proposed algorithm can get satisfactory quasi- dense matching results and good disparity map, and can reflect the real scene.
Analysis and prediction of Huawei brand search index based on R language time series

With the arrival of the era of big data, scientific and rational use of historical data has become one of the convenient ways to earn information. Search index records hundreds of millions of search attention and demand, for enterprises to grasp the market to provide the necessary database. This analysis takes the search index of Huawei brand on the Baidu Index website as an example. Based on R-STUDIO, the monthly search index of the brand is extracted as historical data, and the time series is used for analysis and prediction.
Energy-Aware VM Scheduler

Cost is the backbone of any business model to sustain and growth in the competitive business market. Considering this, higher energy consumption may affect the operational cost of any industry. Cloud computing is one of the most popular IT business model for service provider and its users. It is observed in many research, that the average cost of the services are highly dependent upon the run time and power consumption. More power consumption effect the environmental things as well. In this review, more than 100 research articles are considered to evaluate the energy consumption methods, issues and challenges from 2009 to 2021. Based on the study, the energy-aware load balancing methods are classified into four major clusters. The most cited methods are compared based on method used, platform where it is deployed, different evaluation parameters like cost, run time, Virtual Machine utilization, Service-level-agreement violation rate, and Quality-of-service QoS. The result of this review is presented as open research issues and challenges for forthcoming research.
An Improved BERT and Syntactic Dependency Representation Model for Sentiment Analysis

Text representation of social media is an important task for users' sentiment analysis. Utilizing the better representation, we can accurately acquire the real semantic information expressed by online users. However, existing works cannot achieve the best results. In this paper, we construct and implement a sentiment analysis model based on the improved BERT and syntactic dependency. Firstly, by studying the word embeddings of BERT, we have ameliorated the embeddings representation. Attention mechanism is added to the word embeddings, sentence embeddings, and position embeddings. Secondly, we have exploited the dependency syntax analysis of the text, and the dependency relationship of different syntactic components will be obtained. For different syntactic components, the hierarchical attention mechanism is used to construct the phrase embeddings or block embeddings. Finally, we splice the syntactic blocks for sentiment analysis. Extensive experiments show that the proposed model has a stronger ability than the baselines on two standard data sets.
A Conflict-Free Redactable Blockchain

To cope with the challenges that posed by illegal data on the immutable blockchain, and to meet the requirements of privacy regulations, the redactable blockchains are proposed. The biggest shortcoming of the state-of-the-art redactable blockchain protocols is that they fail to consider the appearance of multiple conflicting redactions at the same time. When the protocol faces multiple conflicting requests for a specific block submitted almost simultaneously, their method will lead to conflicts.We put forward a redactable blockchain protocol that packages requests into a conflict-free set for vote-based consensus to eliminate the possibility of two different edited versions of the chain and ensure the consistency of the system. Moreover, our protocol, unbinding the redaction processing from the extension of the blockchain, can be easily integrable in blockchain systems based on proof of work and proof of stake. Furthermore, our protocol, resisting against a fully adaptive adversary, confirms the edited blocks at a constant rate.
Density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy

The density peaks clustering algorithm (DPC) has been widely concerned since it was proposed in 2014. There is no need to specify in advance and only one parameter required. However, some disadvantages are still witnessed in DPC: (1) Requiring repeated experiments for choosing a suitable calculation method of the local density due to the variations in the scale of the dataset, which will lead to additional time cost. (2) Difficulty in finding an optimal cutoff distance threshold, since different parameters not only impact the selection of cluster centers but also directly affect the quality of clusters. (3) Poor fault tolerance of the allocation strategy, especially in manifold datasets or datasets with uneven density distribution. Targetting solutions to these problems, a density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy (LF-DPC) is proposed in this paper. First, to obtain a more balanced local density, two classic local density calculation methods are combined in the algorithm to calculate the local fair density through the optimization function with the smallest local density difference. Second, a robust two stage remaining points allocation strategy is designed. In the first stage, k-nearest neighbors are used to quickly and accurately allocate points from the cluster center. In the second stage, to further improve the accuracy of allocation, a fuzzy k-nearest neighbors membership method is designed to allocate the remaining points. Finally, the LF-DPC algorithm has been experimented based on several synthetic and real-world datasets. The results prove that the proposed algorithm has obvious advantages compared with the other five ones.
A New Zero-Trust Aided Smart Key Authentication Scheme in IoV

With the development of 5G networking technology on the Internet of Vehicle (IoV), there are new opportunities for numerous cyber-attacks, such as in-vehicle attacks like hijacking occurrences and data theft. While numerous attempts have been made to protect against the potential attacks, there are still many unsolved problems such as developing a fine-grained access control system. This is reflected by the granularity of security as well as the related data that are hosted on these platforms. Among the most notable trends is the increased usage of smart devices, IoV, cloud services, emerging technologies aim at accessing, storing and processing data. Most popular authentication protocols rely on knowledge-factor for authentication that is infamously known to be vulnerable to subversions. Recently, the zero-trust framework has drawn huge attention; there is an urgent need to develop further the existing Continuous Authentication (CA) technique to achieve the zero-trustiness framework. In this paper, firstly, we develop the static authentication process and propose a secured protocol to generate the smart key for user to unlock the vehicle. Then, we proposed a novel and secure continuous authentication system for IoVs. We present the proof-of-concept of our CA scheme by building a prototype that leverages the commodity fingerprint sensors, NFC, and smartphone. Our evaluations in real-world settings demonstrate the appropriateness of CA scheme and security analysis of our proposed protocol for digital key suggests its enhanced security against the known attack-vector.