Reviewerly/exHarmony huggingface dataset:
DC-SHADE-IF: An infeasible–feasible regions constrained optimization approach with diversity controller

To address constrained optimization problems (COPs), there are two crucial trade-offs in preeminent constrained evolutionary algorithms: (1) the trade-off between exploration and exploitation, and (2) the one between the constraints and the objectives. Moreover, the problem of inaccurate diversity regulation in constrained optimization evolutionary algorithms has yet to be completely solved. To achieve the first trade-off, this paper designs a diversity controller (DC) based on a small-world network. Then, the fitness distance correlation information is applied to adjust the reconnection probability of the small-world network to dynamically control the diversity. To achieve the second trade-off, we propose an infeasible–feasible regions constraint handling method (IF). There are two stages in the IF method. The first stage is to search the boundary between the infeasible and feasible regions, while the second stage adopts the self-adaptive Epsilon constraint handling method. Furthermore, combining the DC and IF constraint handing methods into the success-history-based parameter adaptive differential evolution (SHADE) algorithm, a DC-SHADE-IF algorithm is proposed. Twenty-eight constrained optimization problems provided in the CEC2017 competition on constrained real parameter optimization are employed to compare the performance between the proposed DC-SHADE-IF and seven state-of-the-art algorithms. Besides that, two real-world constrained optimization problems are employed to examine the performance of the proposed DC-SHADE-IF in real-world problems. Experimental results show the superior performance of the proposed algorithm in terms of accuracy and convergence.
Empirical research in software architecture — Perceptions of the community

Previous research highlighted concerns about empirical research in software engineering (e.g., reproducibility, applicability of findings). It is unclear how these concerns reflect views of those who conduct and evaluate research. Focusing on software architecture, one subfield of software engineering, we study perceptions of the research community on (1) how empirical research is applied, (2) human participants, (3) internal and external validity, and (4) replications. We collected responses from 105 key players in architecture research via a survey; we analyzed data quantitatively and qualitatively. Although respondents do generally not prefer either quantitative or qualitative research, around 40% express a preference for various reasons. Professionals are the preferred participants; there is no consensus on the value of student participants. Also, there is no consensus on when to focus on internal or external validity. Most respondents value replications, but acknowledge difficulties. A comparison with published research shows differences between how the community thinks research should be done. We provide evidence that consensus about empirical research is limited. Findings have implications for conducting and reviewing empirical research (e.g., training researchers and reviewers), and call for reflection on empirical research (e.g., to resolve conflicts). We outline actions for the future.
Performance Evolution of Different SYCL Implementations based on the Parallel Least Squares Support Vector Machine Library

In machine learning and scientific computing, some of the biggest challenges are efficient and performant portable computing. With our Parallel Least Squares Support Vector Machine (PLSSVM) library, we have not only developed an unrivaled Support Vector Machine (SVM) implementation for huge dense data sets, but we have also created a representative benchmark for a frequently encountered task in scientific computing, a (implicit) matrix-vector multiplication. PLSSVM supports multiple backends—OpenMP, CUDA, HIP, OpenCL, and SYCL—to be able to target the most widely used hardware platforms in machine learning and scientific computing.
Generating Proof Certificates for a Language-Agnostic Deductive Program Verifier

Previous work on rewriting and reachability logic establishes a vision for a language-agnostic program verifier, which takes three inputs: a program, its formal specification, and the formal semantics of the programming language in which the program is written. The verifier then uses a language-agnostic verification algorithm to prove the program correct with respect to the specification and the formal language semantics. Such a complex verifier can easily have bugs. This paper proposes a method to certify the correctness of each successful verification run by generating a proof certificate. The proof certificate can be checked by a small proof checker. The preliminary experiments apply the method to generate proof certificates for program verification in an imperative language, a functional language, and an assembly language, showing that the proposed method is language-agnostic.
A Verification Methodology for the Arm® Confidential Computing Architecture: From a Secure Specification to Safe Implementations

We present Arm's efforts in verifying the specification and prototype reference implementation of the Realm Management Monitor (RMM), an essential firmware component of Arm Confidential Computing Architecture (Arm CCA), the recently-announced Confidential Computing technologies incorporated in the Armv9-A architecture. Arm CCA introduced the Realm Management Extension (RME), an architectural extension for Armv9-A, and a technology that will eventually be deployed in hundreds of millions of devices. Given the security-critical nature of the RMM, and its taxing threat model, we use a combination of interactive theorem proving, model checking, and concurrency-aware testing to validate and verify security and safety properties of both the specification and a prototype implementation of the RMM. Crucially, our verification efforts were, and are still being, developed and refined contemporaneously with active development of both specification and implementation, and have been adopted by Arm's product teams. We describe our major achievements, realized through the application of formal techniques, as well as challenges that remain for future work. We believe that the work reported in this paper is the most thorough application of formal techniques to the design and implementation of any current commercially-viable Confidential Computing implementation, setting a new high-water mark for work in this area.
A new code‐based digital signature based on the McEliece cryptosystem

Abstract Digital signature schemes are used for the authentication and verification of signatures. The Courtois–Finiasz–Sendrier (CFS) digital signature is a well‐known code‐based digital signature scheme based on the Niederreiter cryptosystem. However, it is not widely used due to the computation time of the signing algorithm. Most code‐based digital signature schemes are based on the Niederreiter cryptosystem. This paper proposes a new code‐based digital signature that is based on the McEliece cryptosystem. Key generation, signing, and verification algorithms are presented. The key generation algorithm constructs a public key using random inverse matrices. The signing algorithm has lower complexity and requires less computation time than the CFS scheme to sign a document. The verification algorithm is able to detect forgeries. It is shown that the proposed scheme is secure against public key structural attacks.
Conditional generative positive and unlabeled learning

The quantity of data generated increases daily, which makes it difficult to process. In the case of supervised learning, labeling training examples may represent an especially tedious and costly task. One of the aims of positive and unlabeled (PU) learning is to train a binary classifier from partially labeled data, representing a strategy for combining supervised and semi-supervised learning and alleviating the cost of labeling data fully. Still, the main strength of PU learning arises when the negative data are not directly available or too diverse. Although the generative approaches have shown promising results in this field, they also bring shortcomings, such as high computational cost, training instability, and inability to generate fully labeled datasets. In the paper, we propose a novel Conditional Generative PU framework (CGenPU) with a built-in auxiliary classifier. We develop a novel loss function to learn the distribution of positive and negative examples, which leads to a unique, desirable equilibrium under a nonparametric assumption. Our CGenPU is evaluated against existing generative approaches using both synthetic and real data. The characteristics of various methods, including ours, are depicted with different toy examples. The results demonstrate the state-of-the-art performance on standard positive and unlabeled learning benchmark datasets. Given only ten labeled CIFAR-10 examples, CGenPU achieves classification accuracy of 84%, while current state-of-the-art D-GAN framework achieves 54%. On top of that, CGenPU is the first single-stage generative framework for PU learning.
An Image-Based Hashtag Recommendation System as a Social Media Workflow Tool

Social Media is an obligatory choice to make for businesses in today's age to grow their brand. Attaining maximum reach on social media is pivotal for any business to gain consumers. This growth is directly proportional to the manner in which the business handles their social media. Hashtags are supposed to be extremely vital on social media platforms like Twitter, Instagram for easy retrieval, categorization and engagement of posts. But most businesses fail to make most of the benefits offered by usage of appropriate hashtags. Hence, in this research work we suggest a novel approach to recommend hashtags for image-based posts on social platforms using Deep Learning techniques. We also provide an in-depth methodology to build the essential dataset from scratch and develop the model for a real time web-based application. The proposed methodology has also been compared to a few other alternative approaches that we took. The experimental results showcase that our proposed approach is greatly significant in terms of providing hashtags for best reach as well as most suited to the post's image.
Prediction of Online Academic dishonesty Using the Voting Ensemble Machine Learning Method

Due to pandemics, remote tests have grown in popularity, and with the benefit of remote working circumstances, the majority of enterprises and institutions employ online platforms for assessment. Despite the high degree of test monitoring, it is extremely simple for a student to acquire third-party assistance during the exam. To defend these sorts of operations, there are relatively few models that can anticipate the zone where the likelihood of fraud is greatest. In this research, we describe an academic dishonesty detection method for online tests that is based on machine learning. This algorithm predicts if a student is cheating or acting ethically based on a study of exam data performed after the exam. In this study, we analyze a large variety of variables for predicting unethical behavior during tests, including exam mode, number of questions completed within ideal time, observed time, accurate questions within ideal time, pass criteria, likelihood of question recurrence, and exam traversal. However, for initial modelling, only eleven elements are used. To increase the accuracy, we include all remaining components. The model predicts suspicious behavior when the number of right tries within the allotted time exceeds the threshold established based on the total number of exam questions. The link between time-based parameters, accuracy, and pass criteria gov-erns the machine learning model used to forecast fraud and real situations. Combining the findings of Extreme Random Forest, Logistic Regression, Random Forest, and XGBoost Classifier models, the suggested solution employs the “Voting Ensemble” approach of machine learning to increase the accuracy of prediction.
Face Recognition Using KD-tree Algorithm for Fraud Detection

Fraud or Malpractice occurs when someone who is not supposed to be in an exam room passes off as someone else. This can lead to students being marked incorrectly, students being given extra points, or failing grades because they were given answers or answers given to them by someone other than their teacher or instructor. The only way we will be able to prevent this sort of thing from happening is through machine learning and AI technology that can identify people based on their appearance and behaviour patterns. Face recognition has been the subject of much research and development. Lately, biometric systems have been improved by incorporating features from object recognition in addition to human behaviour analysis. However, some methods are not effective for large-scale databases. The KD-tree algorithm-based acceleration technique for a large-scale face recognition system is proposed in this paper. Face recognition takes a long time because it needs to process numerous photographs one at a time when working with a huge database. We use the KD-tree algorithm to address this issue. K means algorithm combined with the KD-tree method is another potential solution to the mentioned issue. We use the k-means clustering algorithm to group facial features to be more precise. In particular, the data in each cluster are kept as KD- trees, and closest neighbour searches based on KD trees are used to match faces to features. Studies using self-collected databases demonstrate that our suggested method outperforms other various face recognition algorithms.
Handwritten Telugu Vowel Character Classification Using Modified 25-Layer AlexNET With Transfer Learning

Due to remarkable performance, deep neural networks have gained popularity in computer vision and machine learning applications such as regression, segmentation, classification, detection, and pattern recognition over the past few years. This paper examines the categorization of handwritten telugu vowel characters using a modified 25-layer AlexNET and transfer learning approach. The six-class Handwritten Telugu vowel dataset from Kaggle is utilized in this experiment. To accommodate the required amount of vowel dataset classes, the 23rd and 25th layers of pre-trained AlexNET are adjusted. Training makes advantage of stochastic gradient descent using momentum optimizer. The proposed model accurately classified handwritten telugu vowel characters with a remarkable 99.72 % accuracy.
Devanagari Script Digit Classification using modified AlexNet with Transfer Learning

Digitization of ancient scripts is more important to make available for future generations. Devanagari script is the most popular ancient Indian literature of India. Over the last decade the researcher focusing on Devanagari script digit classification. This paper presents Devanagari script digit classification using modified AlexNet with transfer learning. The pre-trained AlexNet 23rd and 25th layers modified for the requirement of Devanagari script digit data. The proposed model employs a dataset from the Kaggle and Stochastic Gradient Descent with a momentum optimizer employed for training. The proposed model shows impressive results in terms of accuracy of 93.64%.
Identification of Cybercriminals in Social Media using Machine Learning

The significant growth in online social media has allowed users worldwide to communicate freely and share their ideas. The emerging social media is also becoming a powerful communication tool for businesses and organizations. Due to this rapid use of technology, performing crime in newer ways has also emerged. By using social networking sites, criminals can extract the information for criminal activity. Such activities include cyber frauds, cyberbullying, cyberstalking, hacking etc., which may harm an online user. So, identifying such proficient cybercriminals is of utmost importance to mitigate these cyberattacks. With growing technology, these cybercriminals have also become advanced and hide in large underground forums. Majorly, the crime-related activities are in text format, which makes identifying the cybercriminal a tedious task. Detection and discovering the crime and identifying a cybercriminal is the primary task of the crime analysis process. The article proposes a method to solve this problem by combining user-generated content analysis and user network analysis. Machine learning algorithms such as Multinomial Naïve Bayes, K-Nearest Neighbour, Random Forest, Multilayer Perceptron and ID3 can be used. Also, text mining approaches are used to detect and predict criminal activities effectively. These techniques continuously check for suspicious users and create a graph to find the suspected user.
A Hybrid Approach for Automatic Text Summarization and Translation based On Luhn, Pegasus, and Textrank Algorithms

Text summarization equipment is in high demand these days due to the fact the primary goal of a textual content summarization gadget is to become aware of the most crucial facts from a given textual content and gift it to stop customers. In this project, we deployed a web application that accepts any generic paragraph as input to provide a summary version of that particular paragraph by identifying textual features that are of high significance and also translating the generated summarized text into any language. To summarize the text, we proposed a hybrid model that is based on the Luhn and Textrank algorithms, which are extractive summarization techniques, and the Pegasus model, which is an abstractive summarization technique. This hybrid model was also compared with BERT, XLNet, and GPT2 based on their ROGUE scores. The generated optimal paragraph is sent as input to the translator, which translates the summarized text into any language. In this project, we also tried to get better results from the proposed hybrid model by comparing it with other existing models. Firstly, the TextRank algorithm is used for arranging the sentences based on their priority, and then abstractive summarization is performed on this paragraph using the Pegasus model to generate a new summary with good context, which is then fed to the Luhn algorithm, which generates the final optimal paragraph. The proposed hybrid model achieved higher ROGUE scores in comparison with other existing models such as BERT, GPT2, and XLNet.
Performances of Sorting Algorithms in Popular Programming Languages

In modern days we see disciplines like data sciences, data Analytics, and machine learning are emerging to promote Information Technology and in bringing up Internet of things. Algorithms like sorting and searching plays a important role, as efficient sorting with searching is important for other algorithms. The emerging areas are using Python and Java languages for its implementation and have become most popular ones than C and C++. Also it is seen less asymptotic analysis is made on popular sorting algorithms using Python and Java. Sufficient asymptotic analysis and results using C/C++ are seen in research articles. This paper makes performance measures by implementing Popular Sorting Algorithms(PSAs) using Java, Python and C++ languages. We conducted our experiments on random data to make average case analysis and on ordered data to make worst case analysis. Our results have shown Merge sort performing well for Python implementation than Quick sort. Quick sort remains excellent in performance for Java implementation for random data. C++ implementation verified the behaviors of PSAs. Finally, we have indicated the miss behaviors made by Java and Python as observed in the results.
Neural Symbolic AI For POMDP Games

This paper demonstrates the difficulties of solving partially observable Markov decision process (POMDP) games with pure deep reinforcement learning. And shows that combining the strengths of deep reinforcement learning and symbolic reasoning can achieve improved results with less training time. Deep reinforcement learning excels at exploring states and optimizing actions based on those observations, but struggle to reason when facing uncertainty. So we propose an agent to use deep reinforcement learning to force exploration on the game states and its corresponding values, and then use reasoning on the given observations to deduce winning actions when arriving at high-value states, to enhance the performance compared to a pure deep reinforcement learning agent.
Choosing Suitable Object Detection Method to Examine Pigment Powders of Similar Shape

In recent years, a variety of paints have been developed in response to the diversification of color design. Due to the powder manufacturing process, different sizes of powders may randomly attach to each powder. Since the objects attached to larger ones affect the quality of the product, they are specifically referred to as asteroid powder. Asteroid powders are similar in shape have many problems and it difficult to detect. This paper investigates whether an object detection method can automatically detect asteroid powders. It also tries to choose a suitable detection method. The paper compared two typical object detection methods YOLO and SSD. As a result, it has turned out YOLO works far better than SSD.
Augmented Reality as an Educational Tool and Assistive Technology for People with Intellectual Disabilities: Scoping Review

Many people with intellectual disability seek opportunities to develop their self-determination, personal development, interpersonal relationships, and well-being. Emerging technologies, such as augmented reality (AR), present an opportunity for new approaches to supporting inclusion and personal development. AR interactively integrates digital information and the real world, and has been increasingly used as a tool for intervention, education, or as an assistive technology. However, there has been little attention to how AR applications can engage and support people with intellectual disability. This paper presents a scoping review of seventeen studies in the past decade and discusses the benefits highlighted by the authors. Most studies conducted with people with intellectual disability have shown that AR performs a positive and effective role as an instructional or assistive tool in the areas of education, daily living, or health. AR provides an opportunity to help people acquire new knowledge (e.g., foreign language and numeracy), form new habits (e.g., teeth brushing technique), and experience environments inclusively (e.g., arrive to the classroom with the support of an AR navigation app on the smartphone). Hence, this paper provides a comprehensive overview of known useful aspects of AR technology in the support and inclusion of people with intellectual disabilities.
Employing AR/MR Mockups to Imagine Future Custom Manufacturing Practices

Versatile augmented reality (AR)/mixed reality (MR) technologies align with custom manufacturers’ resource constraints and support their requirement for agility in responding to unique Industry 4.0 challenges. However, for Australian custom manufacturers, AR/MR uptake remains low. This modest-sized case study seeks to support resource-constrained custom manufacturers by exploring current AR/MR adoption challenges and potentials. Underpinned by a Research-through-Design (RtD) methodology and building upon Situated and Participative Enactment of Scenarios (SPES) methods, we reflect on using novel Microsoft HoloLens 2 AR/MR mockups to support in-situ enactments with domain experts to collaboratively imagine and design more productive and efficient augmented fabrication and assembly practices. In exploring new ways of making and doing through AR/MR, we find promising pathways for Australian custom manufacturers to add value across a product's lifecycle. Our findings identify five key areas for further research, which will be explored and developed through workshops around each identified AR/MR application area.