Reviewerly/exHarmony huggingface dataset:
When Can Compliments and Humour Be Considered Hate Speech? A Perspective From Target Groups in Portugal

This article presents a qualitative study that examines hate speech (HS) from the perspectives of the most representative minority communities in Portugal, namely Afro-descendants, Roma, and LGBTQ+. The empirical research employed three focus groups, each with a representation of these communities (n=17), with the primary goal of investigating how members of these groups perceive and experience HS within the Portuguese social and geopolitical context. The results indicate that covert HS can be more detrimental than overt HS since the targeted individuals are often caught off guard and unsure how to respond, contributing to the continuation of systems of oppression and social decline. Additionally, our qualitative data show that covert forms of HS often manifest in the form of compliments and humour, common in various settings, such as the workplace, the media, and the online environment. These devices are communicative strategies anchored in both positive and negative stereotypes and may lead to the normalization of HS in Portuguese society. In this regard, this research draws attention to the significance of identifying covert forms of HS and devising strategies, such as counter-speech, to debunk them. Furthermore, it highlights the need for automatic detection of covert HS since these forms are prevalent in digital platforms.
Evaluating machine-generated explanations: a “Scorecard” method for XAI measurement science

Introduction Many Explainable AI (XAI) systems provide explanations that are just clues or hints about the computational models-Such things as feature lists, decision trees, or saliency images. However, a user might want answers to deeper questions such as How does it work?, Why did it do that instead of something else? What things can it get wrong? How might XAI system developers evaluate existing XAI systems with regard to the depth of support they provide for the user's sensemaking? How might XAI system developers shape new XAI systems so as to support the user's sensemaking? What might be a useful conceptual terminology to assist developers in approaching this challenge? Method Based on cognitive theory, a scale was developed reflecting depth of explanation, that is, the degree to which explanations support the user's sensemaking. The seven levels of this scale form the Explanation Scorecard. Results and discussion The Scorecard was utilized in an analysis of recent literature, showing that many systems still present low-level explanations. The Scorecard can be used by developers to conceptualize how they might extend their machine-generated explanations to support the user in developing a mental model that instills appropriate trust and reliance. The article concludes with recommendations for how XAI systems can be improved with regard to the cognitive considerations, and recommendations regarding the manner in which results on the evaluation of XAI systems are reported.
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis

Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.
A deep learning-based approach for emotional analysis of sports dance

There is a phenomenon of attaching importance to technique and neglecting emotion in the training of sports dance (SP), which leads to the lack of integration between movement and emotion and seriously affects the training effect. Therefore, this article uses the Kinect 3D sensor to collect the video information of SP performers and obtains the pose estimation of SP performers by extracting the key feature points. The Arousal-Valence (AV) emotion model, based on the Fusion Neural Network model (FUSNN), is also combined with theoretical knowledge. It replaces long short term memory (LSTM) with gate recurrent unit (GRU), adds layer-normalization and layer-dropout, and reduces stack levels, and it is used to categorize SP performers' emotions. The experimental results show that the model proposed in this article can accurately detect the key points in the performance of SP performers' technical movements and has a high emotional recognition accuracy in the tasks of 4 categories and eight categories, reaching 72.3% and 47.8%, respectively. This study accurately detected the key points of SP performers in the presentation of technical movements and made a major contribution to the emotional recognition and relief of this group in the training process.
Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.
Multidimensional Fourier series with quantum circuits

Quantum machine learning is the field that aims to integrate machine learning with quantum computation. In recent years, the field has emerged as an active research area with the potential to bring new insights to classical machine learning problems. One of the challenges in the field is to explore the expressibility of parametrized quantum circuits and their ability to be universal function approximators, as classical neural networks are. Recent works have shown that, with a quantum supervised learning model, we can fit any one-dimensional Fourier series, proving their universality. However, models for multidimensional functions have not been explored in the same level of detail. In this work, we study the expressibility of various types of circuit Ans\"atze that generate multidimensional Fourier series. We found that, for some Ans\"atze, the degrees of freedom required for fitting such functions grow faster than the available degrees in the Hilbert space generated by the circuits. For example, single-qudit models have limited power to represent arbitrary multidimensional Fourier series. Despite this, we show that we can enlarge the Hilbert space of the circuit by using more qudits or higher local dimensions to meet the degrees of freedom requirements, thus ensuring the universality of the models.
Automatic phase compensation of a continuous-variable quantum-key-distribution system via deep learning

In a practical continuous-variable quantum-key-distribution (CVQKD) system, it is vital to accurately evaluate and then compensate for the phase drifts of the signals, so that the involved system can achieve better performance and stability. In this paper, based on the long short-term memory network (LSTM) model, an automatic phase compensation approach of the CVQKD system is proposed. The LSTM model is first trained to predict the phase drift value of the quantum signal relative to the local oscillator over time. Then, the predicted phase drift value can be used by Alice to reconstruct her data. Finally, Alice and Bob can obtain the raw key, so that the CVQKD system can achieve enhancements in terms of performance and stability. The experimental results indicate that the proposed LSTM-based automatic phase compensation algorithm can accurately predict the phase drift value and perform phase compensation instead of real-time phase drift measurement, which improves the performance of the CVQKD system without requiring any additional quantum resources and extra experimental hardware.
The Effectiveness of Online Learning Using E-Learning During Pandemic Covid-19

Problems that often occur when online learning are, the material delivered by lecturers isn’t optimally taught due to lack of use good learning media. This study aims to find the effective use of online learning platforms in higher education. The research uses a qualitative approach, which aims to analyze, investigate and assess the effectiveness of online learning using e-learning in Indonesia during the Covid-19 pandemic. In addition, this study also compares which e-learning is easy to use, which has full-featured e-learning and which is economical. The subjects in this study were students enrolled in various universities in Indonesia with a total of 136 students. The method of data collection was done by distributing questionnaires via google forms to students. In this study, two analysis were used: descriptive analysis, and qualitative response analysis. The indicators used to see the effectiveness of e-learning are widely used, easy to use, full-featured, and economical. The most widely used e-learning in Indonesia are WhatsApp Group, Google Classroom, Ms. Teams, Schoology, Edmodo, and Moodle. From the results of the analysis it was found that the most effective e-learning used during a pandemic was Moodle. Because Moodle has complete and interesting features compared to other e-learning.
The Mediating Role of Marketing Innovation Between Internal Social Media Utilization and Business Performance of Smes in Saudi Arabia

Purpose: The usage of social media (SM) by small and medium enterprises (SMEs) received relatively less attention and the findings in the literature are mixed in terms of the effect of SM on SME performance. The purpose of this study is to examine the effect of SM utilization on SME business performance (SMEBP) in Saudi Arabia. Theoretical Framework: Based on the resource-based view, this study proposed that SM internal utilization (SMIU) affects positively the SMEBP and marketing innovation (MI). MI is expected to affect the SMEBP and mediate the effect of SMIU on SME performance (profitability (PR) and business growth (BG). Design/Methodology/Approach: The population of this study is the SMEs in Al-Riyadh city in Saudi Arabia. A simple random sampling is deployed. The data was collected using a questionnaire. A total of 451 respondents representing SMEs participated in this study. Findings: The finding was derived using Smart PLS version 4. The findings showed that SMIU affected positively PR and BG of SME and MI. MI affected positively the PR and BG of SME and mediated partially the effect of SMIU on PR and BG of SME. Research practical Implication: Decision makers are advised to deploy SM more effectively and to create a rewarding culture for innovative ideas to improve performance. Originality/Value: This study helps to enhance the understanding regarding the role of marketing innovation and social media internal usage in improving the performance of SMEs in emerging economies.
Load Balancing of Cloud Computing Service Model Empowered with Fuzzy Logic

Nowadays, cloud computing is a growing field in research and the marketplace, including virtualization, internet service, computer software, and web services. A cloud comprises multiple elements like a consumer's data center and servers. It provides high availability, efficiency, scalability, mobility, fault tolerance, decreased overhead of users, reduced expenses of ownership, on-demand services, etc. A Variety Of users need various Quality of Service (QoS) demands. Therefore, the cloud supplier needs to arrange the tasks to get maximum advantages regarding their services, and the consumer's quality of service demands are satisfied. Currently, the need for cloud is increasing day by day, people moved towards cloud simultaneously, so its scale is up that's why the service providers are required to deal with enormous requests. The biggest challenge is the availability of services and maintaining the performance equivalent or more effective whenever workload occurs. Multiple requests are processed simultaneously; that's why the workload increased. The load balancer uses to resolve that issue. Our research shows how to balance the load in a cloud environment using fuzzy logic. The processor speed, storage capacity, and assigned load of Virtual Machine (VM) utilize to balance the load in cloud computing through fuzzy logic to achieve better processing time and storage utilization.
A Kind of Water Surface Multi-Scale Object Detection Method Based on Improved YOLOv5 Network

Visual-based object detection systems are essential components of intelligent equipment for water surface environments. The diversity of water surface target types, uneven distribution of sizes, and difficulties in dataset construction pose significant challenges for water surface object detection. This article proposes an improved YOLOv5 target detection method to address the characteristics of diverse types, large quantities, and multiple scales of actual water surface targets. The improved YOLOv5 model optimizes the extraction of bounding boxes using K-means++ to obtain a broader distribution of predefined bounding boxes, thereby enhancing the detection accuracy for multi-scale targets. We introduce the GAMAttention mechanism into the backbone network of the model to alleviate the significant performance difference between large and small targets caused by their multi-scale nature. The spatial pyramid pooling module in the backbone network is replaced to enhance the perception ability of the model in segmenting targets of different scales. Finally, the Focal loss classification loss function is incorporated to address the issues of overfitting and poor accuracy caused by imbalanced class distribution in the training data. We conduct comparative tests on a self-constructed dataset comprising ten categories of water surface targets using four algorithms: Faster R-CNN, YOLOv4, YOLOv5, and the proposed improved YOLOv5. The experimental results demonstrate that the improved model achieves the best detection accuracy, with an 8% improvement in mAP@0.5 compared to the original YOLOv5 in multi-scale water surface object detection.
Cage4Deno: A Fine-Grained Sandbox for Deno Subprocesses

Deno is a runtime for JavaScript and TypeScript that is receiving great interest by developers, and is increasingly used for the construction of back-ends of web applications. A primary goal of Deno is to provide a secure and isolated environment for the execution of JavaScript programs. It also supports the execution of subprocesses, unfortunately without providing security guarantees.
NLPashto: NLP Toolkit for Low-resource Pashto Language

In recent years, natural language processing (NLP) has transformed numerous domains, becoming a vital area of research.However, the focus of NLP studies has predominantly centered on major languages like English, inadvertently neglecting low-resource languages like Pashto.Pashto, spoken by a population of over 50 million worldwide, remains largely unexplored in NLP research, lacking off-the-shelf resources and tools even for fundamental text-processing tasks.To bridge this gap, this study presents NLPashto, an open-source and publicly accessible NLP toolkit specifically designed for Pashto.The initial version of NLPashto introduces four state-of-the-art models for Spelling Correction, Word Segmentation, Part-of-Speech (POS) Tagging, and Offensive Language Detection.The toolkit also includes essential NLP resources like pre-trained static word embeddings, Word2Vec, fastText, and GloVe.Furthermore, we have pre-trained a monolingual language model for Pashto from scratch, using the Bidirectional Encoder Representations from Transformers (BERT) architecture.For the training and evaluation of all the models, we have developed several benchmark datasets and also included them in the toolkit.Experimental results demonstrate that the models exhibit satisfactory performance in their respective tasks.This study can be a significant milestone and will hopefully support and speed-up future research in the field of Pashto NLP.
Crowdsourcing Felt Reports Using the MyShake Smartphone App

Abstract MyShake is a free citizen science smartphone app that provides a range of features related to earthquakes. Features available globally include rapid postearthquake notifications, live maps of earthquake damage as reported by MyShake users, safety tips, and various educational features. The app also uses the accelerometer in the mobile device to detect earthquake shaking, and to record and submit waveforms to a central archive. In addition, MyShake delivers earthquake early warning alerts in California, Oregon, and Washington. In this study, we compare the felt shaking reports provided by MyShake users in California with the U.S. Geological Survey’s (USGSs) “Did You Feel It?” intensity reports. The MyShake app simply asks, “What strength of shaking did you feel?” and users report on a five-level scale. When the MyShake reports are averaged in spatial or time bins, we find strong correlation with the Modified Mercalli Intensity scale values reported by the USGS based on the DYFI surveys. The MyShake felt reports can therefore contribute to the creation of shaking intensity maps.
Is Industrial Tomography Ready for Augmented Reality? A Need-Finding Study of How Augmented Reality Can Be Adopted by Industrial Tomography Experts

Augmented Reality (AR) has grown into a well-established technique with a compelling potential for interactive visualization. In spite of its clear potential, this novel tool has not yet been widely embraced as an industrial solution. In this paper, we address AR for a specific domain: industrial tomography. Within this domain, we conducted a need-finding study featuring 14 surveyed participants, each with sufficient years of experience. A systematic survey study was designed as the main body of our approach. Using this survey, we collected answers helping us to establish findings and formulate novel insights. The study as a whole consisted of a pilot and a formal study for better robustness. Our findings uncovered the current status of AR being used in industrial tomography, and showed that the potential of AR in this domain was positively rated by the participants. Based on our findings, we present key challenges and propose potential for interdisciplinary synergies between AR and industrial tomography.
Alkaline: A Simplified Post-Quantum Encryption Algorithm for Classroom Use

This paper describes Alkaline, a size-reduced version of Kyber, which has recently been announced as a prototype NIST standard for post-quantum public-key cryptography. While not as simple as RSA, I believe that Alkaline can be used in an undergraduate classroom to effectively teach the techniques and principles behind Kyber and post-quantum cryptography in general. Classroom experiences with individual concepts used in Alkaline support this belief. In addition to cryptography, linear algebra and abstract algebra classes would be good candidates for the use of Alkaline. A few exercises suitable for use in these classes are included.
Specification and Verification of a Linear-Time Temporal Logic for Graph Transformation

We present a first-order linear-time temporal logic for reasoning about the evolution of directed graphs. Its semantics is based on the counterpart paradigm, thus allowing our logic to represent the creation, duplication, merging, and deletion of elements of a graph as well as how its topology changes over time. We then introduce a positive normal forms presentation, thus simplifying the actual process of verification. We provide the syntax and semantics of our logics with a computer-assisted formalisation using the proof assistant Agda, and we round up the paper by highlighting the crucial aspects of our formalisation and the practical use of quantified temporal logics in a constructive proof assistant.
Artificial Intelligence Techniques for SQL Injection Attack Detection

In recent years, web-based platforms and business applications have been rising in popularity deeming themselves indispensable as they constitute the main backbone of business processes and information sharing. However, the unprecedented increased number of cyber-attacks have been threatening their day-to-day operations. In particular, the Standard Query Language Injection Attack (SQLIA) remains one of the most prevalent cyber attacks targeting web-based applications. As a consequence, the SQLIA detection techniques need to be constantly revamped and stay up-to-date in order to achieve the full potential of mitigating such threats. In this paper, we propose an artificial intelligence model based on supervised machine learning techniques to detect SQLIA. As part of the proposed model, we introduce an input string validation technique as a primary anomaly identifier using pattern matching for SQL Query data with anomalies-injections. To evaluate our approach we injected one type of SQLIA that is tautology attacks and measured the performance of our model. We used three main classifiers in our model and our findings indicate a model prediction accuracy of 98.3605% for Support Vector Machine (SVM), 96.296% for K-Nearest Neighbors (KNN), and 97.530% for Random Forest. The approach proposed in this paper has the potential of being used to integrate an automated SQL Injection detection mechanism with Intrusion Detection Systems (IDS) and Intrusion Protection Systems (IPS).
Harnessing Large Language Models for Cognitive Assistants in Factories

As agile manufacturing expands and workforce mobility increases, the importance of efficient knowledge transfer among factory workers grows. Cognitive Assistants (CAs) with Large Language Models (LLMs), like GPT-3.5, can bridge knowledge gaps and improve worker performance in manufacturing settings. This study investigates the opportunities, risks, and user acceptance of LLM-powered CAs in two factory contexts: textile and detergent production. Several opportunities and risks are identified through a literature review, proof-of-concept implementation, and focus group sessions. Factory representatives raise concerns regarding data security, privacy, and the reliability of LLMs in high-stake environments. By following design guidelines regarding persistent memory, real-time data integration, security, privacy, and ethical concerns, LLM-powered CAs can become valuable assets in manufacturing settings and other industries.