Reviewerly/exHarmony huggingface dataset:
LSB Steganography Using Pixel Locator Sequence with AES

Image steganography is a technique of hiding confidential data in the images. We do this by incorporating the LSB(Least Significant Bit) of the image pixels. LSB steganography has been there for a while, and much progress has been made in it. In this paper, we try to increase the security of the LSB steganography process by incorporating a random data distribution method which we call pixel locator sequence (PLS). This method scatters the data to be infused into the image by randomly picking up the pixels and changing their LSB value accordingly. This random distribution makes it difficult for unknowns to look for the data. This PLS file is also encrypted using AES and is key for the data encryption/decryption process between the two parties. This technique is not very space-efficient and involves sending meta-data (PLS), but that trade-off was necessary for the additional security. We evaluated the proposed approach using two criteria: change in image dynamics and robustness against steganalysis attacks. To assess change in image dynamics, we measured the MSE and PSNR values. To find the robustness of the proposed method, we used the tool StegExpose which uses the stego image produced from the proposed algorithm and analyzes them using the major steganalysis attacks such as Primary Sets, Chi-Square, Sample Pairs, and RS Analysis. Finally, we show that this method has good security metrics for best known LSB steganography detection tools and techniques.
Technical debt payment and prevention through the lenses of software architects

Architectural decisions are considered one of the most common sources of technical debt (TD). Thus, it is necessary to understand how TD is perceived by software architects, particularly, the practices supporting the elimination of debt items from projects, and the practices used to reduce the chances of TD occurrence. This paper investigates the most commonly used practices to pay off TD and to prevent debt occurrence in software projects from the architect's point of view. We used the available data from InsighTD, which is a globally distributed family of industrial surveys on the causes, effects, and management of TD. We analyze responses from a corpus of 72 software architects from Brazil, Chile, Colombia, and the United States. Results showed that refactoring (30.2%) was the main practice related to TD payment, followed by design improvements (14.0%). Refactoring, design improvements, and test improvements are the most cited payment practices among cases of code, design and test debt. Concerning the TD preventive practices, we find that having a well-defined architecture and design is the most cited practice (13.6%), followed by having a well-defined scope and requirements. This last practice is the most cited one for expert software architects. Finally, when comparing preventive practices among the three major roles derived from the survey (software architects, engineer roles, and management roles), we found that none of the roles shared the most cited practice, meaning that each role had its worries and focus on different strategies to reduce TD's presence in the software. The lists of TD payment and prevention practices can guide software teams by having a catalog of practices to keep debt controlled or reduced.
Resistance of Isogeny-Based Cryptographic Implementations to a Fault Attack

The threat of quantum computers has sparked the development of a new kind of cryptography to resist their attacks. Isogenies between elliptic curves are one of the tools used for such cryptosystems. They are championed by SIKE (Supersingular isogeny key encapsulation), an "alternate candidate" of the third round of the NIST Post-Quantum Cryptography Standardization Process. While all candidates are believed to be mathematically secure, their implementations may be vulnerable to hardware attacks. In this work we investigate for the first time whether Ti's 2017 theoretical fault injection attack is exploitable in practice. We also examine suitable countermeasures. We manage to recover the secret thanks to electromagnetic fault injection on an ARM Cortex A53 using a correct and an altered public key generation. Moreover we propose a suitable countermeasure to detect faults that has a low overhead as it takes advantage of a redundancy already present in SIKE implementations.
An automatic methodology for the quality enhancement of requirements using genetic algorithms

The set of requirements for any project offers common ground where the client and the company agree on the most important features and limitations of the project. Having a set of requirements of the highest possible quality is of enormous importance; benefits include improving project quality, understanding client needs better, reducing costs, and predicting project schedules and results with greater accuracy. This paper's primary goal is to create a methodology that can provide effective and efficient solutions for modifying poor requirements integrated into a full-fledged system, extracting the main features of each requirement, assessing their quality at an expert level, and, finally, enhancing the quality of the requirements. In the first step, a machine learning algorithm is implemented to classify requirements based on quality and identify those that are the likeliest to be problematic. In the second step, the genetic algorithm generated solutions to enhance the quality of the requirements identified as inferior. The results of the genetic algorithm are compared with the theoretically optimal solution. The paper demonstrates the significant flexibility of genetic algorithms, which create a wide variety of solutions and can adapt to any type of classifier . From the initial dataset of requirements, the genetic algorithm finds the optimal solution in 85% of cases after 10 iterations and achieves 59.8% success after only one iteration. Genetic algorithms are promising tools for requirements engineering by delivering benefits such as saving costs, automating tasks, and providing more solid and efficient planning in any project through the generation of new solutions.
Ensemble fuzzy radial basis function neural networks architecture driven with the aid of multi-optimization through clustering techniques and polynomial-based learning

This study is concerned with the concept of Ensemble Fuzzy Radial Basis Function Neural Networks (EFRBFNN) endowed with clustering techniques, polynomial-based LSE/WLSE learning as well as their optimization implemented by Multi-Objective Particle Swarm Optimization (MOPSO). The network architecture of the proposed EFRBFNN classifier is considered three types of clustering techniques and four forms of regression polynomials. The proposed classifier can not only fully capture the local distribution (feature) information contained in the data, but also choose effective network architecture for improving classification performance and compactness. Three classes of clustering techniques (such as iterative self-organizing data analysis techniques algorithm (ISODATA), affinity propagation (AP), and extended K-means) are considered to effectively produce membership degrees based on feature information extraction suitable to fit data characteristics. Least Square Error Estimation (LSE) and Weighted Least Square Error Estimation (WLSE)-based learning with four types of regression polynomials are considered to estimate to coefficients of polynomials. MOPSO is exploited for choosing the effective architecture among different clustering techniques, polynomials, and polynomial-based learning with multi-objective parametric optimization. MOPSO helps achieve a sound compromise between the preferred classification performance and the compactness realized with the aid of four objective functions such as a) classification accuracy (CA), b) complexity of clustering, c) complexity of polynomial and d) sum of squared coefficients (SSC). The improvement and effectiveness of the proposed network architecture are quantified on a basis of the comprehensive experimental results and also a comparative analysis is offered to demonstrate the superiority of the proposed classifier.
Digital Competence Analysis of University Students Using Latent Classes

The objective of this study is to characterize Latent Classes emerging from the analysis of the level of digital competences, use and consumption of applications and/or services through the Internet. For this purpose, the results of the survey Basic Digital Competences (Competencias Básicas Digitales-COBADI®) applied to university students, with more than 60 categorical variables, were considered. A total of 4762 undergraduate and graduate students from five Spanish universities participated in this survey: Complutense University of Madrid (UCM), Pablo de Olavide University (UPO), Almeria University (UAL), National University of Distance Education (UNED) and Rey Juan Carlos University (URJC). The application of the questionnaire was done through the Internet, from the Institute for Research in Social Sciences and Education of University of Atacama—Chile. The methodology used is mixed, because the questions of the questionnaire provide qualitative information that can be interpreted and elaborated from the results. It is also quantitative because basic statistical techniques are used for the exploratory analysis of the data, and later Latent Class Analysis (LCA), to complement the description of the data set and the variables considered in the study, thus allowing us to group the classes of variables that do not appear explicitly in the set of observed variables, but which nevertheless affect them. The results of the study show that regardless of the gender and age range of the participants, there are four clearly differentiated groups or classes in the use and consumption of ICTs in different ways for their activities, both personal and academic, which allows for identifying different developments of digital competences. This study allows establishing a baseline in order to be able to elaborate later, in the development of the digital competences currently needed, which should be developed by university students.
The Capacity of Private Information Retrieval Under Arbitrary Collusion Patterns for Replicated Databases

We study the private information retrieval (PIR) problem under arbitrary collusion patterns for replicated databases. We find a general characterization of the PIR capacity, which is the same as the capacity of the original PIR problem with the number of databases N replaced by a number S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> . S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> is the optimal solution to a linear programming problem that is a function of the incidence matrix of the collusion pattern. Hence, the essence of any collusion pattern can be distilled into one number S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> . In the proposed achievable scheme, databases are non-uniformly queried according to the optimal solution of a linear programming problem based on the collusion pattern. It can be seen that the databases who collude more with others are queried less. In the converse proof, Shearer's lemma is applied, in place of Han's inequality, to a linear combination of inequalities, where each inequality corresponds to one colluding set in the collusion pattern. The weights of the linear combination come from the optimal solution of another linear programming problem based on the collusion pattern. Finally, by noting the interesting fact that the two seemingly different linear programming problems, one used in the achievability proof and the other used in the converse proof, are in fact dual problems, we characterize the capacity of the PIR problem under arbitrary collusion patterns.
Generic Proactive IoT Cybercrime Evidence Analysis Model for Digital Forensics

With the widespread adoption of Internet of Things (IoT) applications around the world, security related problems become a challenge since the number of cybercrimes that must be identified and investigated increased dramatically. The volume of data generated and handled is immense due to the increased number of IoT applications around the world. As a result, when a cybercrime happens, the volume of digital data needs to be dealt with is massive. Consequently, more effort and time are needed to handle the security issues. As a result, in digital forensics, the analysis phase is an important and challenging phase. This paper proposes a generic proactive model for the cybercrime analysis process in the Internet of Things. The model is focused on the classification of evidences in advance based on its significance and relation to past crimes, as well as the severity of the evidence in terms of the probability occurrence of a cybercrime. This model is supposed to save time and effort during the automated forensic investigation process.
MTUNet: Few-shot Image Classification with Visual Explanations

Few-shot learning (FSL) approaches, mostly neural network-based, are assuming that the pre-trained knowledge can be obtained from base (seen) categories and transferred to novel (unseen) categories. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper its application in some risk-sensitive areas. In this paper, we reveal a new way to perform explainable FSL for image classification, using discriminative patterns and pairwise matching. Experimental results prove that the proposed method can achieve satisfactory explainability on two mainstream datasets. Code is available*.
Zeus: Improving Resource Efficiency via Workload Colocation for Massive Kubernetes Clusters

With the popularity of container-based microservices and cloud-native architectures, Kubernetes has established itself as the de facto standard for container orchestration. Kubernetes is known for its advantage in easy deployment and operations for applications; however, it suffers from low resource utilization, incurring high server provisioning and operational costs, due to following reasons. First, it is common practice for latency-sensitive services to be over-provisioned for the peak load: Kubernetes might consider such peak-load provisioning as constant, even though the actual resource utilization is low. Second, the isolation between different containers is poor and cannot prevent performance degradation when best-effort jobs and latency-sensitive services run together. Users may have to run these two classes of workloads on separate machines to avoid interference, resulting in higher provisioning costs. This paper presents a highly scalable cluster scheduling system named Zeus, which is designed based on Kubernetes extension mechanisms. Zeus achieves safe colocation of best-effort jobs and latency-sensitive services. Furthermore, Zeus schedules best-effort jobs based on the real server utilization and can adaptively allocate resources between the two classes of workloads. In addition, Zeus enhances container isolation by coordinating software and hardware isolation features. As a result, Zeus can effectively improve the resource utilization of Kubernetes clusters. We discuss the design and implementation of Zeus and evaluate its effectiveness using latency-sensitive services and best-effort jobs in a massive production environment. The results show that by colocating latency-sensitive services with best-effort jobs, Zeus can increase the average CPU utilization from 15% to 60% without SLO violations.
Key Guessing Strategies for Linear Key-Schedule Algorithms in Rectangle Attacks

When generating quartets for the rectangle attacks on ciphers with linear key-schedule, we find the right quartets which may suggest key candidates have to satisfy some nonlinear relations. However, some quartets generated always violate these relations, so that they will never suggest any key candidates. Inspired by previous rectangle frameworks, we find that guessing certain key cells before generating quartets may reduce the number of invalid quartets. However, guessing a lot of key cells at once may lose the benefit from the early abort technique, which may lead to a higher overall complexity. To get better tradeoff, we build a new rectangle framework on ciphers with linear key-schedule with the purpose of reducing overall complexity or attacking more rounds. In the tradeoff model, there are many parameters affecting the overall complexity, especially for the choices of the number and positions of key guessing cells before generating quartets. To identify optimal parameters, we build a uniform automatic tool on SKINNY as an example, which includes the optimal rectangle distinguishers for key-recovery phase, the number and positions of key guessing cells before generating quartets, the size of key counters to build that affecting the exhaustive search step, etc. Based on the automatic tool, we identify a 32-round key-recovery attack on SKINNY-128-384 in the related-key setting, which extends the best previous attack by 2 rounds. For other versions with n-2n or n-3n, we also achieve one more round than before. In addition, using the previous rectangle distinguishers, we achieve better attacks on round-reduced ForkSkinny, Deoxys-BC-384 and GIFT-64. At last, we discuss the conversion of our rectangle framework from related-key setting into single-key setting and give new single-key rectangle attack on 10-round Serpent.
Feature Selection based Artificial Intelligence Techniques for the Prediction of COVID like Diseases

Abstract Identification of disease from therapeutic statistical evidences area single confronted task which can make a point of importance in the field of medical science. But according to the literature survey, it has been seen that still there are some chances that this challenging task can be fulfilled. In this research a feature ranking algorithm Random Forest is used for ranked the features of the attributes &amp; later on four machine learning algorithm has been used i.e. Random forest, decision Tree, support Vector Machine &amp; XG Boost classification algorithm to classify similar disease datasets like Jaundice, Malaria, Covid, Common cold, Typhoid, Dengue &amp; Pneumonia. Comparison between the classifier is done on the basis of with ranking with feature selection &amp; ranking without feature selection with the help of parameters of confusion matrix, Matthews’s correlation coefficient (MCC), area under the curve (AUC), Receiver Operating Characteristics Curve (ROC) &amp; computational time. The results of the simulations shows the effectiveness of Covid like disease prediction is done by the feature selection ranking &amp;classification algorithm.
Software Requirements Classification using Machine Learning algorithm’s

The world is growing and developing rapidly, and the demand for software has been increasing speedily, any software has many steps for building a program and all the steps are important for software requirements. Requirements classification can be applied manually, which requires great effort, time, cost and the accuracy may vary. Therefore, many previous researchv has been proposed to automate the classification process, but the automation process of the classification was not sufficient. In this study, we will propose a technique to automatically classify software requirements using machine learning to represent text data from software requirements specification and classify requirement to group Functional Requirement and Non-Functional Requirement. The experimented dataset in this study was the PROMISE_exp, which includes labeled requirements. All the documents of software from the database were changed (cleaned) with a set of steps (normalization, extractions, selection any techniques that will be used. The BoW used SVM algorithm or KNN algorithm for classification. This study used data from the PROMISE_exp to do the work, the information of the steps used to re-performed the classification, and the Measurement BoW, when using SVM and KNN algorithms the classification of requirements making can serve as a way and resources for another study. It can be seen that the use of BoW with SVM is better than use KNN algorithms with an average F-measure of all cases of 0.74. In future work we intend to improve to technique with make merge and change some algorithms as Logiest Regression to improve the Accuracy ( Precision) of our model.
Designing for a Convenient In-Car Passenger Experience: A Repertory Grid Study

The driving experience has become one of the central decision factors when buying a car. In current manually driven cars, this experience is to a large extent influenced by driver-based infotainment functionalities. With the advent of rear-seat infotainment systems, manufacturers started to not only look at the driver’s perspective but also focus on passenger experiences. But passenger experiences can go beyond traditional aspects of user experience as they also include aspects of coziness and comfort in the context of riding, which we describe as passenger convenience. While insights about the design space and passenger’s needs are central when designing for an advanced level of passenger convenience, the body of knowledge in this area is limited. Therefore, we present the results from a repertory grid study (n = 32) where we investigated what makes a passenger ride in a manually driven car convenient. Based on three predefined and three participant-selected riding situations we accounted for common patterns and individual differences. The results confirm the importance of well-being, physical comfort, and safety. The interviews unveil that passengers strive for access to in-vehicle systems, the possibility to act as a co-driver, and the support for the integration of external technology, connectivity, and personalization. Based on our findings, we extracted a set of design recommendations to consider when designing automotive systems with passenger convenience and experience in mind.
SecureBallot: A secure open source e-Voting system

Voting is one of the most important acts through which a community can make a collective decision. In recent years, many works have focused on improving traditional voting mechanisms and, as a result, a wide range of electronic voting (e-Voting) systems have been proposed. Even though some approaches have achieved a proper level of usability, the main challenges of e-Voting are essentially still open: protect the privacy of participants, guarantee secrecy, anonymity, integrity, uniqueness, and authenticity of votes, while making e-Voting as trustful as voting. In order to address this issue, we present SecureBallot, a secure open-source e-Voting system that completely decouples the voter identification and voting phases by means of proven cryptographic technologies. The effectiveness of SecureBallot is demonstrated both theoretically, by presenting a formal verification of the whole protocol and assessing the security properties of its software components, and practically, by proposing a case study of university elections that contains all the challenges of a generic voting process.