Reviewerly/exHarmony huggingface dataset:
Local Reasoning About Probabilistic Behaviour for Classical–Quantum Programs

Verifying the functional correctness of programs with both classical and quantum constructs is a challenging task. The presence of probabilistic behaviour entailed by quantum measurements and unbounded while loops complicate the verification task greatly. We propose a new quantum Hoare logic for local reasoning about probabilistic behaviour by introducing distribution formulas to specify probabilistic properties. We show that the proof rules in the logic are sound with respect to a denotational semantics. To demonstrate the effectiveness of the logic, we formally verify the correctness of non-trivial quantum algorithms including the HHL and Shor’s algorithms.
Sound Abstract Nonexploitability Analysis

Runtime errors that can be triggered by an attacker are sensibly more dangerous than others, as they not only result in program failure, but can also be exploited and lead to security breaches such as Denial-of-Service attacks or remote code execution. Proving the absence of exploitable runtime errors is challenging, as it involves combining classic techniques for safety with novel security analyses. While numerous approaches to statically detect runtime errors have been proposed, they lack the ability to classify program failures as potentially exploitable or not. In this paper, we bridge the gap between traditional safety properties and security hyperproperties by putting forward a novel definition of nonexploitability, which we leverage to propose a sound static analysis by abstract interpretation to prove the absence of exploitable runtime errors. While false alarms can occur, if our analysis determines that a program is nonexploitable, then there is a strong mathematical guarantee that it is impossible for an attacker to trigger a runtime error. Furthermore, our analysis reduces the noise generated from false positives by classifying each warning as security-critical or not. We implemented the first nonexploitability analyzer for a subset of C, and we evaluated it on a set of $$77$$ real-world programs taken from the GNU Coreutils package that are long up to $$4,188{}$$ lines of code. Our analysis was able to prove that more than 70% of the runtime errors previously reported ( $$3,498{}$$ over $$4,715{}$$ ) cannot be triggered by an attacker.
La prononciation du français au Burundi: influence du français de Belgique et du kirundi

Abstract French in Burundi offers an interesting case of language contact: speakers have Kirundi as their first language and French imported during the colonial era was the variety spoken in Belgium, which does not share all the features of reference French. In this study, we analyze a corpus of 12 speakers (including 4 women; mean age 38.5) producing different speaking styles collected according to the methodology of the Phonologie du Français Contemporain project: word reading, text reading, and free narration. The results of the pronunciation analysis concern vowels, consonants and schwa. We identify eight pronunciation features that differ from reference French. In addition, we analyze for each one the possible interferences with Kirundi or with Belgian French. In conclusion, we discuss which of these features can be considered pan-African.
The Influence of Virtual Reality on Purchase Intention: A Study of the Fashion Industry

This study examines how virtual reality (VR) technology affects consumers’ intentions to buy in the fashion sector. Data was gathered from a simple random sample of the Palestinian customers (324 respondents) who used VR technology in the context of online fashion purchasing using a survey-based technique. The findings of this paper indicated that the three major characteristics of VR technology (perceptual presence, behavioral interactivity, and technological embodiment) have positive effects on consumers’ purchase intentions. In the very competitive fashion business, these results provide marketers and retailers useful information they can use to improve customer shopping experiences and boost sales. This study's practical implications for fashion industry marketers and retailers make it significant. According to the results, integrating VR technology into the online buying process may encourage customers to make a purchase by raising their estimation of its value and fun.
Uncertainty-aware deep learning-based CAD system for breast cancer classification using ultrasound and mammography images

Breast cancer is one of the most common types of cancer in women. Early and accurate diagnosis of breast cancer can increase the treatment chances and decrease the mortality rate. Thus, the development of accurate and reliable Computer-Aided Diagnosis (CAD) systems using breast cancer images is an immediate priority for early diagnosis. Deep learning methods have had a widespread role in CAD systems. In spite of the advantages, most deep learning-based CADs cannot quantify the uncertainty of their predictions. Uncertainty in the predicted results from a CAD system might endanger people’s life in these crucial steps for breast cancer classification. Therefore, in this study, the Monte Carlo Dropout method is utilised as an uncertainty quantification method. Furthermore, differently from most existing studies, we present an uncertainty-aware deep learning model which can classify accurately from different types of images, namely, mammograms and ultrasound. The results show the proposed model achieved the accuracy of 99.95% in mammogram dataset, namely, DDSM and 98.40% and 91.34% in ultrasound datasets, namely, BUSM and BUSI, respectively. Finally, since we found the proposed model shows high uncertainty in misclassifications, we suggested to use the uncertainty values to find appropriate subsets for further inspection to increase the performance.
Simplifying Sentiment Analysis on Social Media: A Step-by-Step Approach

This tutorial presents a systematic guide to performing sentiment analysis on social media data, designed to be accessible to researchers and marketers with varying levels of data science expertise. We prioritise open science by providing comprehensive resources, including self-collected data, source code and guidelines, facilitating result reproduction. For marketing and business researchers without programming experience, this tutorial offers a robust resource for conducting sentiment analysis. Experienced data scientists can use it as a reference for evaluating cutting-edge approaches and streamlining the sentiment analysis process. Our work stands out in its unique perspective on the challenges and opportunities of sentiment analysis within the social media data domain. We delve into the potential of sentiment analysis for social media marketing, offering practical guidance and best practices for enhancing brand reputation and customer engagement. Notably, this tutorial advances beyond previous studies by comprehensively comparing a wide range of sentiment analysis methods, including state-of-the-art transfer learning approaches, filling a critical gap in the existing literature. Our commitment to transparency underscores our contribution, as we provide all necessary resources for result reproducibility. We make our resources available at the following address: https://tinyurl.com/SentimentTutorial .
The Missing Links in Neobanks

As the world transitions itself into the virtual era, the economic enterprises adopted virtual offerings in all facets of its operations. Customers at banks and other financial institutions are becoming more and more reliant on virtual services. The digital revolution is firmly established hence the investments in enabling technologies is predicted to increase significantly in the post-pandemic world. Neo-banks are the financial institutions that are entirely accessible online. Neo-bank’s products are tailored to the needs of the customer and supported by modern technology. This investigation involves a descriptive research design that incorporates qualitative and quantitative data from numerous secondary sources. The situation of Neo-Banks and Traditional Banks is also examined using a comparative approach. Neo banks are the new face of Indian banking technology. The purpose of this research article is to recognize the neo-banking structure and comprehend the ability.
Conceptual Research Paper on Financial Cybercrime Avoidance Behavior in Indonesia

In this digital era, human life is becoming increasingly intertwined with the development of cyber technology. As cyberspace users, we need a secure cyberspace. However, in reality, cyberspace is not secure, and there are still many cyber crimes that occur. This study aims to propose a conceptual model to explore the factors that influence financial cybercrime avoidance behavior. The method used in this research is the conceptual research method, which is described as a set of broad ideas and principles taken from relevant field investigations and used to develop and propose a framework model for further research. The main theory of this research is the Technology Threat Avoidance Theory, and the researchers expand the theory with the Regret Theory. This conceptual model is expected to provide an overview of the factors that influence financial cybercrime avoidance behavior.
Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification

Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient.
Impact of XR-Enabled Collaboration in Businesses—An Economic, Ecological, and Social Perspective

With increasing computing power and hardware diffusion, the number of possible use cases for Extended Reality (XR) in the business environment is increasing. Not least, due to globalization, the relevance of new forms of collaboration have furthermore gained relevance. This paper, therefore, investigates which different forms of collaboration are enabled with XR technologies and how they affect businesses. Various XR-enabled collaboration forms are demonstrated by distinguishing between synchronous, asynchronous, spatially co-located, and spatially separated XR collaboration. The impact of these new collaboration forms is then analyzed from a sustainability perspective, i.e., economic, ecological, and social. The analysis leads to five value dimensions illustrating the value-adding impact of XR-enabled collaboration on business activities. A subsequent discussion on concrete application scenarios from science demonstrates the value-adding potential in these dimensions and shows the limits of XR-enabled collaboration.
Digital Transformation Promotes the High-quality Development of Rural Education: Value, Dilemma and Strategy

The rapid development of digital technology has brought great opportunities for rural education. Systematically promoting the digital transformation of rural education is not only the inherent requirement of the national education digital strategic action, but also the inevitable choice to realize the education power, promote the integrated development of urban and rural compulsory education, and realize the comprehensive revitalization of rural education. Through field investigation, this paper deeply understands the value connotation of digital transformation of rural education, analyzes potential opportunities and challenges, and finally explores a feasible path of digital transformation of rural education. The study found that the introduction of digital technology will greatly enrich rural education resources, promote education equity, and cultivate students' innovative ability. However, in the process of digital transformation of rural education, there are still problems such as lack of digital education resources, cognitive impairment of digital education, homogenization of digital education programs, and insufficient digital literacy of teachers. In order to give full play to the advantages of digital technology in rural education, it is suggested to formulate comprehensive policy support, improve infrastructure construction, strengthen teacher training, and promote the popularization of digital literacy, so as to realize the sustainable development of digital transformation of rural education.
Application of Discussion Methods and Memory Board Games to Increase Student Activity and Learning Outcomes in History Learning

Purpose of the study: The aim of this research is to apply discussion methods and memory board games to increase student activity and learning outcomes in history learning.&#x0D; Methodology: This type of research is classroom action research. The samples in this research were class X history students and teachers in high school. The sampling technique uses purposive sampling. Data collection techniques through observation, interviews and tests. This research uses qualitative analysis and quantitative analysis.&#x0D; Main Findings: The results of the research can be concluded that the application of discussion methods and memory board games can increase the activity and learning outcomes of class X students in high school in learning history. Before the action was taken, the average activity level for class X was 36.16%. In cycle I the average percentage of student activity indicators was 79.46%, increasing in cycle II by 9.38% to 88.84%. The class average score in cycle I, namely the pretest, was 48.18 and increased in the posttest by 26.32 to 74.5. The average class score in cycle II, namely the pretest, was 46.82 and increased in the posttest by 33.72 to 80.54.&#x0D; Novelty/Originality of this study: As a novelty from previous research, this research will investigate the benefits of combining the use of discussion methods and memory board games in history learning. Then, through this research, the impact of memory board games on students in learning history will be known.
Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model

Health problems in older adults lead to situations where communication with peers, family and caregivers becomes challenging for seniors; therefore, it is necessary to use alternative methods to facilitate communication. In this context, Augmentative and Alternative Communication (AAC) methods are widely used to support this population segment. Moreover, with Artificial Intelligence (AI), and specifically, machine learning algorithms, AAC can be improved. Although there have been several studies in this field, it is interesting to analyze common phrases used by seniors, depending on their context (i.e., slang and everyday expressions typical of their age). This paper proposes a semantic analysis of the common phrases of older adults and their corresponding meanings through Natural Language Processing (NLP) techniques and a pre-trained language model using semantic textual similarity to represent the older adults’ phrases with their corresponding graphic images (pictograms). The results show good scores achieved in the semantic similarity between the phrases of the older adults and the definitions, so the relationship between the phrase and the pictogram has a high degree of probability.
Data-Driven Model Predictive Control for Uncalibrated Visual Servoing

This paper addresses the image-based visual servoing (IBVS) control problem with an uncalibrated camera, unknown dynamics, and constraints. A novel data-driven uncalibrated IBVS (UIBVS) strategy is proposed, incorporated with the Koopman-based model predictive control (KMPC) algorithm and the adaptive robust Kalman filter (ARKF). First, to alleviate the need for calibration of the camera’s intrinsic and extrinsic parameters, the ARKF with an adaptive factor is utilized to estimate the image Jacobian matrix online, thereby eliminating the laborious camera calibration procedures and improving robustness against camera disturbances. Then, a data-driven MPC strategy is proposed, wherein the unknown nonlinear dynamic model is learned using the Koopman operator theory, resulting in a linear Koopman prediction model. Only input–output data are used to construct the prediction model, and hence, the proposed approach is robust against model uncertainties. Furthermore, with a symmetric quadratic cost function, the proposed approach solves the quadratic programming problem online, and visibility constraints as well as joint torque constraints are taken into account. As a result, the proposed KMPC scheme can be implemented in real time, and the UIBVS performance degradation which arises from the control torque constraints can be avoided. Simulations and comparisons for a 2-DOF robotic manipulator demonstrate the feasibility of the proposed approach. Simulation results further validate that the computation time of the proposed approach is comparable to the one of kinematic-based methods.
Virtual Reality as Anxiety Management Tool

Virtual Reality technologies have been established as an effective tool for the treatment of a variety of mental health disorders. Despite those positive results, it remains unclear whether these findings can generalize to the healthy population. In the current study, we assessed to what degree a virtual scenario consisting of a tropical beach can be successfully applied for the reduction of state anxiety and negative mood in healthy individuals. The study was performed on 50 volunteers aged 18 to 45 that were individually exposed to virtual reality. Before and after the virtual reality session, the emotional state of the participants was measured through questionnaires. Using linear mixed effect models, we obtained evidence for the effectivity of a single virtual reality session for negative state reduction, namely, we observed a decrease in state anxiety and negative mood. Additionally, a positive mood of participants increased after the session, however, no statistical evidence was obtained. The results confirm our hypothesis that state anxiety and negative mood can be decreased using a low-cost virtual reality device. Based on our findings, we suggest that virtual scenarios can improve the emotional well-being and the quality of life of the general population.
A robust transformer GAN for unpaired data makeup transfer

Summary The objective of makeup transfer is to apply the makeup style of, thereby creating the similar appearance as if it was professionally done. This technique has significant practical applications in fashion, beauty, and video special effects industries. However, there are several challenges faced by current makeup transfer models: (1) Low‐resolution images can only achieve partial makeup transfer in mainstream models. (2) Difficulty arises in obtaining paired data consisting of both makeup and non‐makeup images. (3) Spatial displacement occurs due to differences in subject and pose between reference and source images, affecting corresponding feature regions. (4) Mainstream models primarily focus on local feature characteristics while lacking global feature perception. To address these challenges, this paper proposes a nonpaired data makeup transfer model based on swin transformer generative adversarial networks. Additionally, an improved progressive generative adversarial network model (PSC‐GAN), incorporating semantic perception and channel attention mechanisms, is proposed to enhance the effectiveness of makeup transfer.
Keyboard-type optical fiber musical interface

Musical instruments are complex multimodal interfaces combining audiovisual information and motivating researchers to design intuitive and creative ways of communication between the user and the digital system. We demonstrated a musical interface based on an optical fiber specklegram sensor (FSS) for one octave of C major scale. The setup comprises a laser source and a camera connected by a multimode optical fiber cable with foam pads marking the notes. The software recovers the pressed key by computing the cross-correlation coefficient between the immediate speckle pattern and the reference images from prior calibration, estimating their similarity, and taking the closest option as the output. Simultaneously, the software plays the identified key with a sound synthesizer. The experiments validated the proposed interface to play the calibrated notes, independent of the input sequence, with a response delay within 0.5 s, for a single fiber cable and further discuss the possibility of playing chords. With the musical interface processing the positional information of external stimuli, it simplifies the hardware configuration and allows different techniques of musical expression.