Reviewerly/exHarmony huggingface dataset:
Graphical Password Authentication For Child Personal Storage Application

Abstract Mobile applications are very popular among the children nowadays as children use it for many purposes such as entertainment, learning or education. Keeping hand written notes in hard copy is not a good choice as the accumulated papers would mess up child’s study space. Although there is high adoption of mobile application among children nowadays, but children are not educated to practice password to secure their personals in their primary school syllabus. Therefore, an application which can provide personal storage for children to store their notes in softcopy forms called Graphical Password Authentication for Child Personal Storage Application was proposed. The adoption of graphical password authentication in the proposed system is intended to cultivate children practice password to secure their files in an interesting method. The proposed system is developed in Android mobile application platform and the Object-oriented Mobile Application Development is applied as methodology of the project. The proposed system’s functional requirements are implemented according to user’s requirements and the application interfaces implementation also based on user’s characteristics that has been studied in literature reviews. The significance of the project is awareness on secure file keeping among the children. Children can use graphical password in their childhood age would be the advantage for them in the future because they would make a sense to protect privacy by setting up password. In terms of information security, the proposed system has mentioned some values that would be achieved which are authentication, confidentiality and availability.
Hyperspectral image sparse reconstruction model based on collaborative multidimensional correlation

In recent years, the sparse reconstruction of hyperspectral images based on compressed sensing theory has attracted great attention of the research community. How to better explore the sparsity priori that conforms to the statistical properties and structural properties of the original hyperspectral images is one of the key problems. To address this, in this paper, a novel hyperspectral image sparse reconstruction model based on collaborative multidimensional correlations is proposed. First, considering the high-dimensional and high-redundancy properties of hyperspectral data, the correlations of hyperspectral images are studied from multiple dimensions, except the “spectral correlation” between hyperspectral bands and the “local spatial correlation” within a single band, the “overall nonlocal correlation” is novel defined in band groups with highly correlated spectral features, where the “nonlocal correlation” of the key band in the band group can be used to guide the “nonlocal correlation” of other bands within the group. Then, the sparse measurement models are provided corresponding to the above correlations in different dimensions, at the same time, a band group collaborative sparse model which integrates the multidimensional correlations has been proposed. Finally, the proposed band group collaborative sparse model is used as the sparse constraint to construct the hyperspectral sparse reconstruction framework. Experimental results demonstrate that, the newly defined “global non-local correlation” can effectively enhance the detail information in the reconstruction process of hyperspectral images, which provides the possibility for better analyzing land-cover objects in the subsequent applications.
Robust node embedding against graph structural perturbations

Despite achieving superior performance for many graph-related tasks, recent works have shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks on graph structures. In particular, by adding or removing a small number of carefully selected edges in a graph, an adversary can maliciously manipulate a GNNs-based classifier. The vulnerability to adversarial attacks poses numerous concerns for employing GNNs in real-world applications. Previously research aims to overcome the negative impact from adversarial edges with graph-based regularization of some heuristic properties. However, the real-world graph data is far more intricate, and these defense mechanisms do not fully utilize comprehensive semantic information of graph data. In this work, we present a novel defense method, Holistic Semantic Constraint Graph Neural Network (HSC-GNN), which approaches the joint modeling of the node features, labels, and the graph structure to mitigate the effects of malicious perturbations. Extensive experimental evaluation under various graph datasets demonstrates that our approach results in more robust node embedding and better performance than existing models.
Nonclassical atomic system dynamics time-dependently interacts with finite entangled pair coherent parametric converter cavity fields

We consider a time-dependent model that describes a qubit time-dependently interacting with a cavity containing finite entangled pair coherent parametric converter fields. The dynamics of some quantum phenomena, as: phase space information, quantum entanglement and squeezing, are explored by atomic Husimi function, atomic Wehrl entropy, variance, and entropy squeezing. The influences of the unitary qubit-cavity interaction, the difference between the two-mode photon numbers, the initial atomic coherence, and the time-dependent qubit location are investigated. It is found that the regularity, the amplitudes and the frequency of the quantum phenomena can be controlled by the physical parameters. For the initial atomic pure state, the qubit-cavity entanglement, the qubit phase space information, and atomic squeezing can be generated strongly compared to those of the initial atomic mixed state. The time-dependent location parameters enhance the generated quantum phenomena, and their effect can be enhanced by the parameters of the two-mode photon numbers and the initial atomic coherence.
Outreach marketing may be a successful strategy for NHS libraries

Abstract This dissertation study investigates the ways that NHS libraries are currently marketing their services within their organisation and was submitted as part of the MA Library and Information Management at the University of Sheffield in 2019. This paper presents the findings from twelve semi‐structured interviews carried out with NHS library managers in the East of England to identify the most and least successful methods, and in comparison with that which is currently in the general marketing literature. The study found that outreach marketing was the most effective and that librarians are currently conducting marketing to the best of their ability, but they lack time and funding to be able to make the most of their promotional campaigns. F.J.
Slicing, Tokenization, and Encryption Based Combinational Approach to Protect Data-at-Rest in Cloud Using TF-Sec Model

Data in the Cloud, which applies to data as a cloud service provider (CSP), transmits stores, or manages it. The company will enforce the same definition of data usage while the data is resident within the enterprise and thus extend the required cryptographic security criteria to data collected, exchanged, or handled by CSP. The CSP Service Level Agreements cannot override the cryptographic access measures. When the data is transferred securely to CSP, it can be securely collected, distributed, and interpreted. Data at the rest position applies to data as it is processed internally in organized and in the unstructured ways like databases and file cabinets. The Data at the Rest example includes the use of cryptography for preserving the integrity of valuable data when processed. For cloud services, computing takes multiple forms from recording units, repositories, and many unstructured items. This paper presents a secure model for Data at rest. The TF-Sec model suggested is planned for use with Slicing, Tokenization, and Encryption. The model encrypts the given cloud data using AES 256 encryption, and then the encrypted block is sliced into the chunks of data fragments using HD-Slicer. Then it applies tokenization algorithm TKNZ to each chunk of data, applies erasure coding technique to tokens, applies the data dispersion technique to scramble encrypted data fragments, and allocates to storage nodes of the multiple CSP. In taking the above steps, this study aims to resolve the cloud security problems found and to guarantee the confidentiality of their data to cloud users due to encryption of data fragments would be of little benefit to a CSP.
Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems

This paper presents to estimating studies of the torque data of the Electric Vehicle (EV) motor using Adaptive-Network Based Fuzzy Inference Systems (ANFIS). The real-time data set of the Outer-Rotor Permanent Magnet Brushless DC (ORPMBLDC) motor which was designed and manufactured for using in ultra-light EV, was used in these estimation process. The current, the power and the motor speed parameters are defined as input variables, and the torque parameter defined as output variable. Five distinct ANFIS models were designed for torque estimation process and the performances of each model were compared. The most effective model for testing data set among the ANFIS models was anfis: 2 with 98 nodes and 36 fuzzy rules, and the worst model was anfis: 5 with 286 nodes and 125 fuzzy rules. Performance results of all designed models were presented in tables and graphs.
A General Algorithm of Association Rule-Based Machine Learning Dedicated for Text Classification

Abstract Many data mining techniques and machine learning algorithms have been developed to classify textual data involving decision tree, support vector machine, K-Nearest neighbour, in addition to machine learning-based algorithms. Association rules based machine learning is accomplished in two phases; training phase and testing phase that may be reinforced to enhance the classification accuracy according to new minimum support and confidence. Association rules mining/processing, in its various applications, passes through two massive computation steps; frequent itemsets mining and association rules extraction. This paper presents a general algorithm for association rules-based machine learning dedicated to text classification. To verify the efficiency of the algorithm, different text datasets were used such as tweets dataset for sentiment classification, pdf documents and HTML documents. Experiments of sentiment classification showed that the classifier constructed according to minsup threshold =%700 and minconf threshold =50% gives the best performance with F1 = 0.9861811 while the experiments of HTML and PDF appeared accurate classification equal to (94%).
Hardware-Based Detection of Spectre Attacks: A Machine Learning Approach

The Spectre vulnerability, which has been found in a variety of processors, enables attackers to take advantage of speculative execution in modern computer architectures to access unauthorized memory content through temporal side channels. Herein, we propose a hardware-based defense mechanism that can detect Spectre attacks by utilizing the profile of a small fraction of malicious program execution. More specifically, we focus on malicious attempts to read data from theoretically inaccessible memory space. The corresponding instruction sequence is divided into consecutive windows, from which a performance counter-based tracker extracts descriptive features. These features are, then, processed by a trained machine learning model to analyze program behaviors and identify suspicious ones. Our experiment shows that Spectre attacks on thirteen vulnerable purpose-built victim code patterns can be detected by our system. Additionally, testing with benign benchmarks demonstrates that our framework is able to distinguish Spectre attacks from normal behavior.
Hybridization between Neural Computing and Nature-Inspired Algorithms for a Sentence Similarity Model Based on the Attention Mechanism

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.
WITHDRAWN: Integration of blockchain technology for security and privacy in internet of things

Now days, the Blockchain Technology is in focus because of its enormous features associated with privacy, security and integrity of the transactions in the network environment. A blockchain refers to a chain of records which are interconnected with each other and highly secured because of the dynamic hash and cryptography implemented at every phase of the transaction. The blockchain technology avoids the possibilities of hacking or cracking the transactions by intentional or accidental attempts. Blockchain can be used for the calculation of sensor data and avoid replication of other malicious data. IoT system implementations can be challenging and a distributed ledge is ideal to identify, authenticate and switch IoT devices seamlessly safely. This paper is presenting the usage patterns of Wireless Body Area Networks with the implementation patterns of Blockchain Technology using advanced scripts of Solidity and embedded programming. The presented integration of implementation is giving the effectual results with the blockchain technology as compared to the traditional approach of security using cryptography.
Formal security analysis for software architecture design: An expressive framework to emerging architectural styles

Analysing security in the architecture design of modern software systems is a challenging task. Emerging technologies utilised in building software systems may pose security threats, so software engineers need to consider both the structure and behaviour of architectural styles that employ these supporting technologies. This paper presents an automated approach to security analysis that helps to identify security characteristics at the architectural level. Key techniques used by our approach include the use of metrics, vulnerability identification and attack scenarios. Our modelling is expressive in defining architectural styles and security characteristics. Our analysis approach gives insightful results that allow software engineers to trace through the design to find parts of the system that may be impacted by attacks. We have developed an analysis tool that allows user to seamlessly model the software architecture design and analyse security. The evaluation has been conducted to assess the accuracy and performance of our approach. The results show that our analysis approach performs reasonably well to analyse the security in the architectural design.
An Alternate Approach to Improve Access Time for Defining Frequent Item Set Through ‘A-Apriori’ in Textual Data Set

Today so many data mining algorithms exist for classification through association rules, among them the most popular classification algorithm is the Apriori algorithm. Basically Apriori algorithm is used to define the frequent itemsets from big transactional data set by scanning the whole database looking for k-element frequent item set. As per the Apriori algorithm here we analyze and going to reduce access time which consumes in scanning the database for k-times looking for k-element frequent item set. In this paper, we are going to analyze and compare our proposed Ameliorated-Apriori Algorithm (A-Apriori) with original Apriori algorithm which concludes the experimental result to calculate frequent items on several groups of transactions with minimum support (for both Apriori & A-Apriori) and improve its performance by reducing the time consumption in accessing the database by 67%. Our proposed A-Apriori algorithm is an Improved version of Apriori algoritam[4] and working much better at every parameter which we include in concluding the results.
Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer

Selecting the most suitable candidates for interviews is an important process for organizations that can affect their overall work performance. Typically, recruiters check Curriculum Vitae (CV), shortlist them and call candidates for interviews which have been the way of recruiting new employees for a long time. To minimize the time spent on the above process, pre-screening mechanisms are nowadays implemented by organizations. However, those mechanisms need sufficient information to evaluate the candidate. For example, in case of a software engineer, the recruiters are interested on the programming ability, academic performance as well as personality traits of potential candidates. In this research, a pre-screening solution is proposed to screen the applicants for the post of Software Engineer where candidates are screen based on an initial call transcript, GitHub profile, LinkedIn profile, CV, Academic transcript and, Recommendation letters. This approach extracts textual features of different dimensions based on Natural Language Processing to identify the Big Five personality traits, CV and GitHub insights, candidate's skills, background, and capabilities from Recommendation letters as well as programming skills and knowledge from Academic transcript and Linked Profile. The results obtained from the different areas are presented and shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.
Security vulnerability and encryption technology of computer information technology data under big data environment

Abstract In recent years, the traditional encryption storage technology and management methods have been difficult to satisfy the demand of big data in terms of speed, capacity, storage efficiency and security, along with the rapid increase of data volume. Data security is facing great challenges and challenges. To solve this problem, this paper proposes a de-redundancy method based big data encryption algorithm. Firstly, bloom filter technology is used to decrease the dimension of big data. Based on its remarkable advantages, bloom filter space and query efficiency and variable-length block detection scheme with less additional overhead, duplicate data detection and deletion algorithm suitable for large data is designed and implemented. Secondly, based on eliminating redundancy of big data, combined with the characteristics of the elliptic curve encryption algorithm and AES block cipher mode in terms of computing speed, parallelism and security, an encryption approach based on data de-redundancy technology is presented. According to the evaluation system of the encryption algorithm, the encryption time, keyspace and security of the new algorithm are evaluated. Experimental results reveal that the scheme has better security, and the presented approach can increase the speed of the encryption process, and decrease the encryption time and consumption.
NoVT: Eliminating C++ Virtual Calls to Mitigate Vtable Hijacking

The vast majority of nowadays remote code execution attacks target virtual function tables (vtables). Attackers hijack vtable pointers to change the control flow of a vulnerable program to their will, resulting in full control over the underlying system. In this paper, we present NoVT, a compiler-based defense against vtable hijacking. Instead of protecting vtables for virtual dispatch, our solution replaces them with switch-case constructs that are inherently control-flow safe, thus preserving control flow integrity of C++ virtual dispatch. NoVT extends Clang to perform a class hierarchy analysis on C++ source code. Instead of a vtable, each class gets unique identifier numbers which are used to dispatch the correct method implementation. Thereby, NoVT inherently protects all usages of a vtable, not just virtual dispatch. We evaluate NoVT on common benchmark applications and real-world programs including Chromium. Despite its strong security guarantees, NoVT improves runtime performance of most programs (mean overhead −0.5%, −3.7% min, 2% max). In addition, protected binaries are slightly smaller than unprotected ones. NoVT works on different CPU architectures and protects complex C++ programs against strong attacks like COOP and ShrinkWrap.
The image of the digital future of society through the prism of the pandemic

The Covid19 pandemic turned out to be a challenge that forced civilization to demonstrate how modern technologies are able to protect human biological vulnerability. Considered by the authors as the main object of research, digital technologies have taken on a huge burden of maintaining public relations and processes. The analysis of social processes and cases made in the article revealed the main trends, opportunities and dangers. The threat to existence has accelerated the process of digitalization many times, leaving virtually no options for dissenters, forced to expand the use of existing information and communication technologies, accelerate the creation of new ones. Constant presence on the web becomes an absolute psychological and in many cases an objective necessity. For many organizations, the choice was between ceasing to exist or going digital. Humanity has largely been prepared for the "digitalization" of its existence from the point of view of technological solutions, and to a much lesser extent from the point of view of psychological and social. The article identifies three main functions of information and communication technologies that united people during the pandemic: information/infodemia, digitalization of activities and control. Information and communication technologies set the agenda, enable a person to work, study, have fun, and communicate. Physical connections between people are being replaced by digital ones. The information circulating in the system can both contribute to the harmonious interaction of people, and poison and infect the most vulnerable parts of society. A variety of digital solutions from centralized total control to self-regulation based on mobile information, adopted in different countries to control and prevent the spread of the virus, demonstrate options for possible management of the emerging bioinformatic system.