Reviewerly/exHarmony huggingface dataset:
Image Encryption Algorithm with Block Scrambling Based on Logistic Map

Objectives: To develop an efficient algorithm based on block-scrambling and chaotic maps to generate an encrypted image capable of protecting sensitive information.Methods: The proposed technique introduces the twopass Block-XOR operation column-wise and then swaps pixels within each block in an anti-clockwise crisscross pattern to separate and rearrange nearby pixels into distinct rows and columns.The strong correlations between nearby pixels are disrupted, making it challenging to identify the original image.Afterward, image diffusion using binarization is performed on the obtained scrambled image using a chaotic sequence generated by a logistic map.Findings: This scheme has accomplished an efficient encryption method based on block-scrambling and a chaotic map.The experimental results show that the system is competent for generating cipher text by encrypting images and can withstand malicious attacks and thus protect sensitive information.The histogram analysis results show that the proposed system displays adequate resistance to statistical attacks.The histogram of cipher text is uniformly distributed and bears no resemblance to the plaintext histogram.The differential analysis shows that the NPCR and UACI values are greater than 99 and 33, respectively, and are acceptable to conclude that the system is also resistant to differential attacks.The proposed technique experimented with using standard test images from the literature and the USC-SIPI image dataset.Novelty: A two-pass Block-XOR operation strengthens the scrambling by altering the value of the image pixel.Then an anti-clockwise crisscross pixel swapping algorithm is utilized for block scrambling.
Implementation and analysis of Web application security measures using OWASP Guidelines

Web application security is the most important area when it comes to developing a web application. Many web applications having vulnerabilities due to poor implementation of security measures. These web applications will be deployed without fixing the vulnerabilities thus becomes vulnerable to many cyber-attacks. Simple attacks like brute-force and NoSQL injection could give unauthorized access to the user accounts. This leads to user privacy issues which could create huge loss to the organizations. These vulnerabilities can be fixed by implementing the necessary security measures while developing the web application. OWASP (Open Web Application Security Project) is a non-profit organization which gives the severity, impact and prevention methods about Top 10 vulnerabilities in web applications. This research deals with the implementation of bestsecurity practices for Node.js web applications in detail. This research paper proposes the security mechanisms for attacks related to front-end, middleware and backend web development using OWASP suggestions. The main focus of this research paper is on prevention of Denial-of-service attack, Brute force attack, NoSQL injection attack and Unrestricted file upload vulnerability. The proposed prevention methods are implemented in a web application to test the defensive mechanisms against the mentionedvulnerabilities.
An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation

Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans’ exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm’s ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L’evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments’ outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.
Enhancing Quality Control in Web-based Participatory Augmented Reality Business Card Information System Design

The rapid development of information and communication technology has fostered a natural integration of technology and design. As a result, there is increasing interest in Augmented Reality (AR) business card systems that leverage digital media. This research aims to advance the design of an AR-based participatory business card information system in line with contemporary trends. Key aspects of this study include applying technology to acquire contextual information from paper business cards, transmitting it to a server, and delivering it to mobile devices; facilitating interactivity between users and content through a screen interface; providing multimedia business content (video, image, text, 3D elements) via image markers recognized by users on mobile devices, while also adapting the type and method of content delivery. The AR business card system designed in this research enhances traditional paper business cards by incorporating visual information and interactive elements and automatically generating buttons linked to phone numbers, location information, and homepages. This innovative approach enables users to interact and enriches their overall experience while adhering to strict quality control measures.
A Proprietary Language-Based Automatic Code Generation Expert System with Wider Business Applications

The study investigated a proprietary language-based automatic code generation expert system with wider business applications. The suggested system is an automated front-end Code Generator Expert System (CGES) that generates GUI with database source code for bigger commercial applications. Safeguarding data has always been important for efficient corporate operations. With the proposed CGES, a configurable database application may be created using a straightforward wizard with minimal effort and time investment. A database is a necessary prerequisite input for the CGES. The CGES shall employ approaches in accordance with the pre-defined procedure once the normalized database is featured with a diagram; the whole application with source code in various modules shall automatically create. By choosing the CGES solutions, an N-tier application with SQL server queries, Object Oriented features, and modules will be created. The test results demonstrate that the system's operating principles are written in MS SQL Server and on Visual Basic.NET source code produced by CGES.
Diagnostic Classifiers for Explaining a Neural Model with Hierarchical Attention for Aspect-based Sentiment Classification

The current models proposed for aspect-based sentiment classification (ABSC) are mainly developed with the purpose of providing high rates of accuracy, regardless of the inner working which is usually difficult to understand. Considering the state-of-art model LCR-Rot-hop++ for ABSC, we use diagnostic classifiers to gain insights into the encoded information of each layer. Starting from a set of various hypotheses, we test how sentiment-related information is captured by different layers of the model. Given the model architecture, information about the related words to the target is easily extracted. Also, the model is able to detect to some extent information about the sentiments of the words and, in particular, sentiments of the words related to the target. However, the model is less effective in extracting the aspect mentions associated with a word and the general structure of the sentence.
Performance Improvement of Face Recognition Method and Application for the COVID-19 Pandemic

In this paper, a novel framework is introduced by combining compressive sensing(CS) theory, digital curvelet transform, and Principal Component Analysis to improve the performance of face recognition method.CS is a highly attractive approach in the field of signal processing, which provides an efficient way of data sampling at a lower rate than the Nyquist sampling rate.CS offers numerous advantages, like less memory storage, less power consumption and higher data transmission rate etc.Here, CS is used on the face images, which offers reduction in storage space and computational time.The use of curvelet transform provides dual benefits: (i) sparse representation (ii) improvement on detailed content.To extract the feature vector, the Principal Component Analysis is then applied.The Performance of the proposed face recognition method is computed by applying cross-validation technique, compressive sensing based classifier, neural network, Naive Bayes and Support Vector Machine classifier.The proposed technique can efficiently perform the face recognition, at a low computational cost.Extensive experiments, on ORL and AR face databases, are conducted to validate our claim.The proposed technique also recognizes face images more efficiently than the traditional PCA, with a 1.5% higher recognition rate, if a person wears a face mask, as protection from
EVALUATING THE EFFICACY AND CAPABILITY OF DIFFERENT COVERT CHANNEL DETECTION TOOLS IN AN ANDROID APPLICATION

Covert channel attacks have been a significant concern in recent times.With the evolution of the internet and technology, users have adapted to it differently.Cyber security is one of the areas which has opened doors in terms of research and development.Smartphone users have grown considerably in the last decade.With this growth, on the other side, attackers have found an opportunity to create covert channels to leak sensitive information from android devices.Different attacks have been performed in the past, which can leak information such as contact numbers, sms, call logs, battery state, alarm state, and much more details similarly.Various security researchers have already worked in this domain and have built different static and dynamic detection tools to identify covert channels in android devices or applications.In this paper, the author has provided a systematic review of different detection tools used to identify such attacks.The author has executed various detection tools against an application that was able to leak sensitive information through a covert channel.The author also provides a comparative analysis of results by executing different tools in Android.At the end of this paper, the author concludes by giving limitations present in the existing tools.
Artificial Intelligence in Robotics: (Review Paper)

Abstract: This article offers an overview of the dynamic intersection of artificial intelligence, robotics, and their impact on economic and organizational dynamics. We delve into the burgeoning research streams that explore the multifaceted consequences of these cutting-edge technologies in the fields of economics and management. Drawing from the diverse approaches adopted by scholars in this field, we provide insights into the implications of artificial intelligence, robotics, and automation for organizational design and firm strategy. We call for increased attention and involvement by organizational and strategy researchers in these areas and outline promising avenues for future research endeavors
FORMATION OF PROFESSIONAL RESPONSIBILITY OF FUTURE JOURNALISTS AT THE BACHELOR'S LEVEL OF HIGHER EDUCATION: CRITERIA, INDICATORS AND LEVELS OF FORMATION

The article reveals the formation of professional responsibility of future bachelors of journalism and the need to verify the successful implementation of organized purposeful preparation for the professional activity of students of higher education institutions by teaching and nurturing a harmonious worldview in the conditions of an innovative space. That is, the ability to determine the level of formation of professional responsibility in future bachelors of journalism, since only a journalist who meets the requirements of modernity, the life challenges of today, is able to convey to individuals the positive experience of humanity in the optimal way.In accordance with the methodology of organizing and conducting a pedagogical experiment, the criteria, indicators and levels of formation of professional responsibility among future specialists were identified, and it was also clarified how successfully the formative process of increasing professional responsibility is carried out through the participation of subjects in the educational process of a higher education institution. Data characterizing the level of motivation, theoretical knowledge and practical skills of students in relation to the specified activity are needed to develop ways to increase the effectiveness of the formation of professional responsibility among future bachelors of journalism.The formation of professional responsibility in future journalists is determined by the need for a conscious attitude of students to their own professional choice and professional activity.The main task of higher education institutions is «the training of a qualified, competitive, competent, responsible specialist who is fluent in his profession, oriented in related areas of activity, capable of effective work in his profession at the level of world standards, ready for constant professional growth, social and professional mobility» [31, c. 22]. Today, an effective specialist is one who adequately responds to social expectations, is capable of professional self-improvement and creative growth, and of constant renewal of personal resources.
Scrutable AIED

This chapter describes scrutability in AIED. “Scrutable” systems are designed so that learners can answer key questions about the way those systems work. This is becoming increasingly important as AIED applications become more pervasive, and their impact more extensive. We show how “scrutability” fits in the landscape of many similar terms; it reflects a human-centred perspective and describes the effort a learner needs to invest to understand the way an advanced educational system works, even for the selected aspects they care about. We explain how scrutability should be integrated into the whole learning engineering enterprise. This includes the well-studied area of scrutability of learner models and the link with open learner models (OLMs). But it goes beyond that. It involves a scrutability design mindset for all elements in the architecture of AIED systems. We illustrate these ideas in two case studies. A key contribution is a set of benchmark questions – a scrutable AIED system should be designed so that a learner can answer at least some of these questions. These questions can guide the design and test-driven development of AIED systems. We discuss why scrutability is particularly important in education and how AIED makes an ideal context for scrutability research. We conclude with an agenda for future research on scrutability in AIED.
Towards a Reference Component Model of Edge-Cloud Continuum

Edge-cloud continuum (ECC) is a novel paradigm that seeks to blend the worlds of cloud computing and IoT into a continuous ecosystem capable of providing access to a range of previously impossible applications with significantly improved quality of service. However, while using the term ECC becomes increasingly common, there is still no clear and commonly accepted consensus on what the term entails and which properties the ECC environment must possess. Consequently, there is a lack of tools and examples for reasoning about applications in ECC and their specific properties. In this paper, we present the results of our literature study aimed at identifying the most common properties ascribed to ECC. Based on this, we outline a reference component model that can serve as a tool for reasoning about ECC systems and their properties.
Subcarrier wave continuous-variable quantum key distribution with Gaussian modulation: composable security analysis

In this paper, we continue the study of the quantum cryptographic GG02 protocol, performed using the approach based on the subcarrier waves. We modify the scheme via heterodyne detection and perform security analysis for the full trusted hardware noise model in the presence of collective attacks with finite-key effects. It is shown that the system can potentially distribute the key even if the level of losses in the channel is above 9 dB. This result is consistent with the general technical level and comply with modern standards of practical CV-QKD systems. Finally, the system under consideration fully meets the criterion of composability.
NIOT: A Novel Inference Optimization of Transformers on Modern CPUs

In the machine learning era, model inference efficiency is one of the most important issues for machine learning systems. It is a major challenge to find the optimal configuration in a huge search space as the combinations of kernel fusion, memory tiling, and thread allocation strategies result in highly variable and unpredictable inference performance. The problem is particularly pronounced in models with large parameter matrices such as Transformers. In this article, we aim to develop a general and powerful framework for inference optimization, called NIOT, to achieve desirable efficiency for the prevailing Transformer-like models on CPUs. To take full advantage of modern CPU features such as SIMD and cache hierarchy, NIOT employs various methods to provide promising strategies tailored to the target Transformer model. Our C++ implementation of NIOT shows significant performance improvements over popular well-optimized model-serving runtimes such as PyTorch and ONNXRuntime.
Natural language based malicious domain detection using machine learning and deep learning

Cyberattacks are still challenging since they are increasing day by day.Cybercriminals employ a variety of strategies to manipulate and exploit their targets vulnerabilities.Malicious URLs are one such strategy which is used to target large groups on various social media platforms.To draw internet users, these web addresses are disguised as being safe.Deliberate or inadvertent use of such URLs exposes the user or the organization in the cyberspace and opens the way for further attacks.Systems that use rules-based or machine learning algorithms to find malicious URLs usually rely on feature engineering.This requires domain expertise and experience.Sometimes, even after extracting features from a dataset, it may not completely leverage the potential of the dataset.The proposed method employs Natural Language Processing (NLP) approaches to vectorize the words in the URLs and applies machine learning and deep learning models for classification.Vectorization technique in NLP reduces the effort of feature engineering and maximizing the use of the dataset.For the experiment, two separate datasets are used.To vectorize the URL text, three different vectorization methods are used.To evaluate the performance of the proposed method, two different datasets (D1 and D2) that are regularly utilized in the research domain were used.The results demonstrate that the superior accuracy of 92.4 % with the D1 dataset is achieved by the Decision Tree (DT) with count vectorizer and the Random Forest (RF) with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer.With the D2 dataset, DT with TF-IDF vectorizer obtains a greater accuracy of 99.5 %.The Artificial Neural Network (ANN) model achieves 89.6 % accuracy with the D1 dataset and 99.2 % accuracy with the D2 dataset.
Type-Restricted Argument Ellipsis and Generalized Quantifiers

Theories of argument ellipsis based on PF deletion or LF copying do not generate predictions regarding possible constraints on the semantic type of the elided argument. Yet such constraints obtain, as documented in Landau 2022: only type-e arguments can be targeted by argument ellipsis. Focusing on quantificational arguments here, I show that when they yield readings expressible by type-e denotations, they may elide, but when they denote genuine generalized quantifiers, they may not. Utilizing the restricted range of interpretations made available by choice function binding and E-type pronouns, the analysis derives a number of peculiar scopal properties of indefinite NPs, quantifiers, and exceptive phrases under argument ellipsis.
Principles of Biostatistics

Journal Article Principles of Biostatistics Get access Principles of Biostatistics by Pagano Marcello, Gauvreau Kimberlee, Mattie Heather. June 7, 2022. 620 pp. $110.00. ISBN: 9780367355807 Amit K Chowdhry Amit K Chowdhry Wilmot Cancer Institute, University of Rochester, Rochester, USA amit_chowdhry@urmc.rochester.edu https://orcid.org/0000-0003-4051-5060 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad038, https://doi.org/10.1093/jrsssa/qnad038 Published: 12 April 2023
Breast cancer classification using a novel hybrid feature selection approach

Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results.