Reviewerly/exHarmony huggingface dataset:
Multiresolution texture analysis of histopathologic images using ecological diversity measures

Texture descriptors have been quite popular in medical image analysis, particularly in histopathologic images (HIs), due to the variability of the texture found in such images and the tissue appearance due to irregularity in the staining process. Such variability may exist depending on differences in the staining protocol, such as fixation, inconsistency in the staining condition, and reagents, either between laboratories or in the same laboratory. However, extracting texture features for quantifying HI information in a discriminant way is challenging, given that the distribution of intrinsic properties of such images forms a non-deterministic complex system. This paper proposes a novel method that quantifies such intrinsic properties using ecological diversity measures and discrete wavelet transform. The experimental results on two HI datasets have shown that the proposed method overcomes state-of-the-art shallow and deep methods
A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER).
Investigating test content structure using multidimensional scaling

Although multidimensional scaling (MDS) has extensively been used in social and physical sciences to visualize and explore the latent dimensionality of observed data, few studies in language assessment have used MDS for the purpose of test construction, including construct and content validation. In this study, we use MDS to investigate and compare the similarity of writing prompts in the IELTS and TOEFL iBT tests. Random prompts from both tests were presented as item pairs and raters were asked to rate the similarity of the pairs in terms of content and cognitive complexity. The results showed the writing prompts in the TOEFL iBT test represented two major dimensions while those in the IELTS test demonstrated three domains.
Blockchain‐inspired lightweight trust‐based system in vehicular networks

Summary A decentralized application runs on the blockchain network without the intervention of a central authority. Transparency in transactions and security in vehicular networks are the issues for central systems. The proposed system uses blockchain‐based smart contracts, which eliminate the requirement for any third‐party verification. Additionally, with signature verification and reduced overhead, smart contracts also help in a fast and secure transaction. This study suggests a trust‐based system paradigm where certificate authority (CA) is employed for vehicle registration. We also propose a blockchain‐based system that provides efficient two‐way authentication and key agreement through encryption and digital signatures. The analysis of the proposed model reveals that it is an efficient way of establishing distributed trust management, which helps in preserving vehicle privacy. The proposed scheme is tested in Automated Validation of Internet Security‐sensitive Protocols (AVISPA), and security parameters verification in Network Simulator 2(NS2) also shows that the proposed scheme is more effective in comparison with existing schemes in terms of authentication cost, storage cost, and overhead.
Information security policies compliance in a global setting: An employee's perspective

Information security threats have a severe negative impact on enterprises. Organizations rely on employee compliance with information security policies to eliminate or reduce these hazards. The Unified Model of Information Security Policies Compliance (UMISPC) is employed to identify the factors that may affect employees' intention towards compliance with information systems security policy and reactance in a global setting. The study was assessed in two phases. The model's validity and measurement reliability were evaluated in the first phase, while in the second phase, all preliminary model relationships were appraised. This was achieved utilizing structural equation modelling to establish whether the proposed constructs, i.e. neutralization, response efficacy, fear, threat, habit and role values were good predictors for intention or reactance towards compliance with information systems security policy. Participants included 348 employees from 7 nations, i.e. the USA, the UK, Oman, India, Pakistan, Malaysia, and the Philippines. SmartPLS v. 3.3.9 was used for data analysis. The models' measurement reliability and validity were affirmed. Fear and role values have a significant influence on intention toward ISPC. RE significantly predicted threat which in turn significantly predicted fear, and the latter demonstrated a significant effect on reactance as well as Neutralization predicted reactance. In contrast, habit failed to reach a significant influence on intention towards ISPC. The implications are presented, together with proposals for further studies. Our findings are helpful for ISS literature and application by supporting the crucial functions of role values in encouraging employees to behave in a compliant manner. Additionally, it is regarded as the first empirical attempt to estimate intended compliance concerning ISPs in higher education from a worldwide viewpoint.
Evaluation of Dataflow through layers of convolutional neural networks in classification problems

This paper proposes a distance-based index to evaluate the input data and the data flowing through layers of a convolutional neural network in classification problems. This shape-less index is proposed based on the class separability and data complexity concepts which are explained in a classification problem. A dataset is complex if many of its samples are located in overlapping regions belonging to different classes, making the classification challenging. In addition, a class is separable if its samples are not located in the overlapped area. The proposed index counts the number of samples in the overlapped area normalized by the number of all samples, measuring the complexity of the dataset. The paper explains the idea of dividing features into exclusive and common features, and identifies the conditions for separability based on this concept. After that, probability evidence shows why the Separation index can be included in complexity measurement indices. The benefits of using the introduced index are shown through many case studies. In the first case study, the proposed index ranks some known supervised datasets from less to more challenging. In the second case study, the separability of Dataflow is evaluated through layers of two pre-trained VGG-16 networks in the classification of CIFAR-10, CIFAR-100, and Caltech-101. The evaluation of Dataflow is also shown through a trained ResNet-18 model for the Fashion-MNIST dataset in the subsequent case study. Finally, it is shown that the correct classification rate and the separation index are almost equivalent through layers, particularly when the separation index is increased.
Mining periodic high-utility itemsets with both positive and negative utilities

Mining high-utility patterns in databases containing items with both positive and negative profits is useful in market basket databases, since negative profits are common in the real world. Obviously, in the market basket database, patterns with stable long-term profits have more meaning. The discovery of itemsets with a consistent high frequency is known as periodic frequent pattern mining. Therefore, mining periodic high-utility patterns in a database containing items with both positive and negative profits is an interesting and useful task. However, this task has two main challenges. First, the utility measure does not have the download closure property. Second, the huge search space needs to be pruned more effectively. In this paper, we propose a vertical data structure-based algorithm called PHMN to discover periodic high-utility patterns (PHUPs) or itemsets in a transaction database with both positive and negative utilities. To be more efficient, we propose a new upper bound to prune the search space and an improved algorithm to discover the PHUPs. Finally, experiments are conducted to verify the effectiveness and efficiency of algorithms.
Numerical solution of multi-dimensional Itô Volterra integral equations by the second kind Chebyshev wavelets and parallel computing process

This paper presents a numerical method based on the least squares method and the second kind Chebyshev wavelets for solving the multi-dimensional Itô Volterra integral equations. The multi-dimensional Itô Volterra integral equation is converted to a non-square linear system of equations by Clenshaw-Curtis quadrature rules, then the least squares solution is obtained by the QR factorization method. As the multi-dimensional Itô Volterra integral equations are computationally intensive, we have used the parallel computing process to find the numerical solutions with reduced computation time. The convergence rate of the presented method is proven. Numerical results show the reliability and efficiency of the presented method.
Authentication methods for voice services on smart speakers – a multi-method study on perceived security and ease of use

Abstract With the increasing proliferation of security-critical voice-based services such as voice banking, user authentication on smart speakers is becoming a vital requirement. Prior research on verifying the speaker’s identity has been taken a technical perspective predominantly, while respective user-centered research is scarce. To investigate authentication methods for smart speakers from a user’s perspective, we conducted a multi-method experiment. In a comprehensive online survey ( n = 696) and a comparative lab study ( n = 18) with an advanced functional prototype we studied 6 authentication methods (spoken PIN, biometrics, app with button/voice confirmation, card reader, sound authentication) regarding their perceived security and ease of use. While token-based authentication approaches (in particular an authenticator app on a smartphone) typically are perceived as more secure, they are found inferior when it comes to the ease of use. The currently most frequently used authentication method for smart speakers, the spoken PIN method, seems to represent a compromise between security and ease of use. The sophisticated sound authentication was appreciated for its ease of use, however, was rated worst regarding the perceived security.
On the Semantics of Hybrid ASP Systems Based on Clingo

Over the last decades, the development of Answer Set Programming (ASP) has brought about an expressive modeling language powered by highly performant systems. At the same time, it gets more and more difficult to provide semantic underpinnings capturing the resulting constructs and inferences. This is even more severe when it comes to hybrid ASP languages and systems that are often needed to handle real-world applications. We address this challenge and introduce the concept of abstract and structured theories that allow us to formally elaborate upon their integration with ASP. We then use this concept to make the semantic characterization of clingo’s theory-reasoning framework precise. This provides us with a formal framework in which we can elaborate upon the formal properties of existing hybridizations of clingo, such as clingcon, clingo[dl], and clingo[lp].
Influence of teacher on student motivation: Opportunities to increase motivational factors during mobile learning

Amid the COVID-19 lockdown many countries have replaced traditional learning with remote or mobile one. It was noted that students' motivation has become much less with the transition to distance learning. This study analyzes how motivational processes influence mobile learning quality and aimed to analyze the factors boosted the motivation of students to learn in modern conditions of isolation and identify the major demotivating factors affecting the quality of mobile learning. Motivation is considered a key factor in improving students' involvement in distance learning. The author surveyed 200 students and 46 teachers of The University of Jordan and Jordan University of Science and Technology regarding the factors that influenced their motivation in terms of mobile learning. The results revealed that 178 out of 200 participants agreed that intrinsic motivation impacted their interest in mobile learning. Some 78% of the students approved of mobile learning, while the remaining 22% believe it is necessary to return to the traditional face-to-face education format. The importance of feedback and communication with teachers and its impact on the process of mobile learning is considered. The role of built-in mechanisms in information systems and the positive role of gamification is equally important. Plug-ins compatible with the convenient WordPress system, i.e. applications that help organize the educational process were examined in the scientific work. The specific recommendations for raising the motivation of students in the learning process, which can be used by relevant institutions worldwide presented.
The use of elements of negative logic in the construction of an energy-efficient combinational device

Definitions are given and the difference between positive and negative algebra of logic is indicated. It is noted that when switching from positive logic to negative, the elements of "Schaeffer's Stroke" and "Pierce's Arrow" change places. Similarly, conjunction changes with disjunction. The inverter retains its property regardless of its application in positive or negative logic. The laws and rules valid for the negative algebra of logic are presented. The method of transition from the positive algebra of logic to the negative one is presented. Elements of positive and negative logic are compared. On the basis of logic functions: (AᴧBᴧC)ᴧDvAᴧ(BᴧCᴧD)vAᴧ(BᴧC)ᴧDv(AᴧB)ᴧ(CᴧD), implemented by direct optimized, minimal, options matching device, as well as, the final options in bases 2-NOT-OR (2-AND-NOT), 4-NOT-OR (4-AND-NOT) for negative logic. The voltage table of K155LE1, K155LE3 microcircuits is presented. The truth table of K155LE1, K155LE3 microcircuits in negative logic algebra (as an element AND-NOT or NOT-OR) is presented. Truth tables of K176LE5, K176LE6 microcircuits are shown in positive logic algebra (OR-NOT). The voltage table of K176LE5, K176LE6 microcircuits is shown. The truth table of K176LE5, K176LE6 microcircuits is shown in negative logic algebra (as an AND-NOT or NOT-OR element). elements of negative logic. Conclusion about the results.
Real-time detection of deception attacks in cyber-physical systems

Detection of deception attacks is pivotal to ensure the safe and reliable operation of cyber-physical systems (CPS). Detection of such attacks needs to consider time-series sequences and is very challenging especially for autonomous vehicles that rely on high-dimensional observations from camera sensors. The paper presents an approach to detect deception attacks in real-time utilizing sensor observations, with a special focus on high-dimensional observations. The approach is based on inductive conformal anomaly detection (ICAD) and utilizes a novel generative model which consists of a variational autoencoder (VAE) and a recurrent neural network (RNN) that is used to learn both spatial and temporal features of the normal dynamic behavior of the system. The model can be used to predict the observations for multiple time steps, and the predictions are then compared with actual observations to efficiently quantify the nonconformity of a sequence under attack relative to the expected normal behavior, thereby enabling real-time detection of attacks using high-dimensional sequential data. We evaluate the approach empirically using two simulation case studies of an advanced emergency braking system and an autonomous car racing example, as well as a real-world secure water treatment dataset. The experiments show that the proposed method outperforms other detection methods, and in most experiments, both false positive and false negative rates are less than 10%. Furthermore, execution times measured on both powerful cloud machines and embedded devices are relatively short, thereby enabling real-time detection.
Quantum discriminative canonical correlation analysis

Discriminative canonical correlation analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists of two parts: (i) mean-centering that subtracts the sample mean from the sample and (ii) solving the generalized eigenvalue problem. The cost of DCCA is expensive when dealing with a large number of high-dimensional samples. To solve this problem, here we propose a quantum DCCA algorithm. Specifically, we devise an efficient method to compute the mean of all samples and then use block-Hamiltonian simulation and quantum phase estimation to solve the generalized eigenvalue problem. Our algorithm achieves a polynomial speedup in the dimension of samples under certain conditions over its classical counterpart.
A combined Blockchain and zero-knowledge model for healthcare B2B and B2C data sharing

The two main forms of healthcare data exchange among entities are business-to-business (B2B) and business-to-customer (B2C). The former uses the electronic data interchange (EDI) technology between healthcare institutions, while the latter is usually conducted by providing web-based interfaces for patients. This research argues that both forms have inherent security and privacy weaknesses. Furthermore, patients lack appropriate transparency and control over their own Personally Identifiable Information (PII). We explore the issues of medical record exchange, analyze them and suggest appropriate solutions in the form of a new model to mitigate them. The vulnerabilities, ranging from critical to minor, include the possibility of Man-in-The-Middle (MiTM) and supply chain attacks, weak cryptography, repudiable transactions, single points of failure (SPOF), and poor access controls. A novel model will be presented in this research for healthcare data sharing which applies the best security practices. The proposed unified model will counter the listed vulnerabilities. It automates the healthcare processes in decentralized architecture by utilizing the smart contracts for B2C transactions such as medicine purchase. The model is based on the Blockchain and zero-knowledge proofs. It is made with novel controls which represent the latest advancements in cybersecurity. It has the potential of setting a new cornerstone.
Deterministic solution of algebraic equations in sentiment analysis

Text mining methods usually use statistical information to solve text and language-independent procedures. Text mining methods such as polarity detection based on stochastic patterns and rules need many samples to train. On the other hand, deterministic and non-probabilistic methods are easy to solve and faster than other methods but are not efficient in NLP data. In this article, a fast and efficient deterministic method for solving the problems is proposed. In the proposed method firstly we transform text and labels into a set of equations. In the second step, a mathematical solution of ill-posed equations known as Tikhonov regularization was used as a deterministic and non-probabilistic way including additional assumptions, such as smoothness of solution to assign a weight that can reflect the semantic information of each sentimental word. We confirmed the efficiency of the proposed method in the SemEval-2013 competition, ESWC Database and Taboada database as three different cases. We observed improvement of our method over negative polarity due to our proposed mathematical step. Moreover, we demonstrated the effectiveness of our proposed method over the most common and traditional machine learning, stochastic and fuzzy methods.
End-to-End Database Software Security

End-to-end security is essential for relational database software. Most database management software provide data protection at the server side and in transit, but data are no longer protected once they arrive at the client software. In this paper, we present a methodology that, in addition to server-side security, protects data in transit and at rest on the application client side. Our solution enables flexible attribute-based and role-based access control, such that, for a given role or user with a given set of attributes, access can be granted to a relation, a column, or even to a particular data cell of the relation, depending on the data content. Our attribute-based access control model considers the client’s attributes, such as versions of the operating system and the web browser, as well as type of the client’s device. The solution supports decentralized data access and peer-to-peer data sharing in the form of an encrypted and digitally signed spreadsheet container that stores data retrieved by SQL queries from a database, along with data privileges. For extra security, keys for data encryption and decryption are generated on the fly. We show that our solution is successfully integrated with the PostgreSQL® database management system and enables more flexible access control for added security.