Reviewerly/exHarmony huggingface dataset:
Location-independent adversarial patch generation for object detection

Object detection models are at the core of various computer vision tasks and have shown excellent performance on public datasets, but they also inherit the disadvantage of neural networks that they are vulnerable to adversarial example attacks. Adversarial patches are specific forms of adversarial examples that, as shown in previous studies, can only make specific objects (such as pedestrians and traffic signs), but not all objects, disappear. In addition, a patch must be placed on every object to deceive the detector. To solve the above problems, we propose a location-independent adversarial patch generation method that can attack objects in the range to be detected with a single patch. By attacking the confidence loss of the object detector, we creatively assign a greater weight to the foreground region, which makes its confidence decrease faster and effectively guides the convergence direction of the adversarial patch in the training process. Furthermore, we glue the patches randomly on the images to make them less sensitive to location during patch training. Experimental results indicate that the patches generated using our proposed method are not restricted to specific areas of the image and provide a minimum recall of 29.5%.
Genetic Algorithms for Quadratic Equations

A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furthermore, the utility of the evolutionary algorithm has been demonstrated by programming it in Python code and comparing the outcomes to conventional intuitions.
Improved structured light system based on generative adversarial networks for highly-reflective surface measurement

Gray code pattern structured light projection technology is widely used in industrial inspection due to its good robustness and anti-noise performance. Gray code pattern technology projects a sequence of encoded fringe patterns with black and white strips onto the scanned object in order to measure its height distribution. However, if the scanned object has strong specular reflection properties, the acquired encoded fringe images tend to miss significant amounts of local area information. As a result, the measured three-dimensional point clouds contain many missing points, and hence the measurement accuracy is severely degraded. To address this problem, the present study proposes a novel fringe-inpainting system based on a generative adversarial network framework, to repair the fringe features in the regions of the scanned surface in which the local information is lost. The performance of the proposed fringe-inpainting system is compared with that of several other advanced highly-reflective surface measurement technologies reported in the literature. The experimental results show that the proposed method significantly outperforms these techniques and yields an excellent encoded fringe inpainting for all of the considered objects.
Analysis of Extractive Text Summarization Methods as a Binary Classification Problem

One of the critical challenges for natural language processing methods is the issue of automatic content summarization. The enormous increase in the amount of data delivered to users by news services leads to an overload of information without meaningful content. There is a need to generate an automatic text summary that contains as much essential information as possible while keeping the resulting text smooth and concise. Methods of automatic content summarization fall into two categories: extractive and abstractive. This work converts the task of extractive summarization to a binary classification problem. The research focused on analyzing various techniques for extracting the abstract in a supervised learning manner. The results suggest that this different view of text summarization has excellent potential.
Phishing URL Detection with Prototypical Neural Network Disentangled by Triplet Sampling

Phishing attacks continue to pose a significant threat to internet security, with phishing URLs being among the most prevalent attacks. Detecting these URLs is challenging, as attackers constantly evolve their tactics. Few-shot learning has emerged as a promising approach for learning from limited data, making it ideal for the task of phishing URL detection. In this paper, we propose a prototypical network (DPN) disentangled by triplet sampling that learns disentangled URL prototypes to improve the accuracy of phishing detection with limited data. The key idea is to capture the underlying structure and characteristics of URLs, making it highly effective in detecting phishing URLs. This method involves sampling triplets of anchor, positive, and negative URLs to train the network, which encourages the embedding space to be more separable between phishing and benign URLs. To evaluate the proposed method, we have collected and assessed a real-world dataset consisting of one million URLs, and additionally utilized two benchmark URL datasets. Our method outperforms the state-of-the-art models, achieving accuracies of 98.0% in a 2-way 50-shot task and 98.32% in a 2-way 5000-shot task. Moreover, the experiments highlight the advantages of using a disentangled representation of URLs, where t-SNE visualizations reveal distinct and well-separated URL prototypes.
Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation

The quality of university teaching is essential for the success of students and the academic excellence of an educational institution. The purpose of this work is to provide a methodology based on the Association Rule technique using the Apriori algorithm to analyze the results obtained from the student evaluation process regarding their satisfaction with the teaching received. This methodology has been applied in programming courses of students of several courses both in the Computer Engineering and Health Engineering degrees at University of Seville, Spain. The proposed methodology can serve as a starting point for a self-improvement process that clearly identifies strengths and weaknesses.
A Machine-Learning Based Approach to Validating Learning Materials

In this paper, we propose a Machine Learning-based approach to validate suggested learning materials. Learning material validation is an essential part of the learning process, ensuring that learners have access to relevant and accurate information. However, the process of manual validation can be time-consuming and may not be scalable. Traditional learning contents are often only updated or changed in the yearly course revisions. This can be presented with some challenges, especially to courses on emerging subjects and catering to diversified learners, which includes the ability to provide adaptive and updated learning contents to the learners, and the opportunity to continually incorporate feedback. We present a solution and framework that utilizes machine learning algorithms to validate learning materials in an open learning content creation platform. Our approach involves pre-processing the data using Natural Language Processing techniques, creating vectors using TF-IDF and training a Machine Learning model to classify the subject of the learning material. We then calculate the similarity with existing materials for the given course to make sure there is not an existing mate-rial with same content and the new material will add new value. Using an augmented TF-IDF score, we check if the suggested learning materials satisfies the key phrases for the course. We evaluate our approach by comparing the Machine-Learning based approach to manual validation. Not only does the machine-learning based approach reduce the time and effort needed for validation, but it also achieves high accuracy in detecting duplicates and similarity matches.
QuantumSolver Composer: Automatic Quantum Transformation of Classical Circuits

This paper describes the implementation of a software module that allows the generation of quantum circuits from the definition of their classical analog logic circuits. This tool confers a great power of abstraction to the user, who does not need to know any concept of quantum computing to implement quantum algorithms or quantum protocols. Thus, the proposal achieves its main objective by obtaining the quantum equivalent of several classical circuits in an intuitive and didactic way. Additionally, this composer module has been added to a library developed by the authors for quantum development. This is part of a work in progress so that the implementation of some relevant cryptographic protocols is planned to demonstrate the pedagogical and abstraction potential of the developed tool.
Systematic Literature Review of Methods Used for SQL Injection Detection Based on Intelligent Algorithms

In this work, a systematic literature review of methods used for SQL injection detection based on intelligent algorithms, especially deep learning or machine learning, is carried out. We identify the main methods used, the algorithms that have been tested and the main techniques used to process the data. In order to make the study, a quantitative review has been performed following the SLR methodology which includes identification of the need for the review, definition of the search strategy, search and selection of articles, and data extraction and analysis. This work aims to prove if it is possible to apply intelligent algorithms in this field and to collect the methods used so far.
A Simulation Platform for Testing Negotiation Strategies and Artificial Intelligence in Higher Education Courses

Teaching Artificial Intelligence in higher education develops critical thinking, problem-solving, and computational skills. Negotiation is a crucial aspect of multi-agent systems, enabling agents to achieve their goals through communication and collaboration. In this area, simulation platforms provide a flexible and safe way to experiment, leading to improvements in negotiation and decision-making in a wide range of scenarios. Board games, which include interaction and negotiation between players, provide a low-cost and low-risk way to experiment with different negotiation strategies. In this context, we present a novel simulation platform based on the rules of Catan, a popular boardgame that entails both strategic thinking and negotiation. This platform is oriented to teach negotiation in artificial intelligence and multi-agent systems. The platform allows students to develop and program intelligent agents to play autonomously, improving their technical skills and applying their understanding of Artificial Intelligence concepts.
Accountability and Explainability in Robotics: A Proof of Concept for ROS 2- And Nav2-Based Mobile Robots

Using mobile robots in environments with humans has become increasingly common, raising concerns over security and safety. The lack of capabilities to justify their decisions and behaviours to non-expert users poses a significant challenge in gaining their trust, especially in safety-critical situations. Therefore, explaining why a robot carries out a specific and unexpected action is crucial to comprehend the cause of a failure when attempting to achieve a goal or complete a task. Besides, it is essential to make such explanations understandable to people, providing appropriate, effective, and natural responses to their queries. This work depicts a Proof of Concept of an accountability and explicability engine on ROS-based mobile robots. Our solution has two components: first, a black box-like component to provide accountability, and another for generating natural language explanations from data in the black box. Initial results show that it is possible to get accountable data from robot actions to obtain understandable explanations.
A combined deep neural network and semi-supervised clustering method for sports injury risk prediction

Sports injury risk prediction plays an important role in evaluating the stability of the body in the process of sports. Based on deep neural network and semi-supervised clustering theory, this paper constructs a sports injury risk prediction model, and designs a recognition algorithm based on feature sequence classification combined with Bayesian filtering, which solves the problems of false alarms and missed alarms caused by relying on local spatial attribute features. The FMS (functional movement screen) kit was used to test seven movements (squat, hurdle step, straight leg squat, shoulder flexibility, active straight leg lift, trunk stability push-up, and body rotation) of 29 athletes. In the simulation process, Pareto analysis method was used to evaluate the overall risk of athletes' after-school physical exercise, and the FMS score, receiver operating characteristic (ROC) curve and Youden index were calculated. The results showed that the best cut-off points of the total score of FMS for athletes as a whole, male athletes and female athletes were 15.5, 15.5 and 16.5, respectively, which effectively verified the generalization performance of the above sports prediction model.
MALICIOUS UAVS CLASSIFICATION USING VARIOUS CNN ARCHITECTURES FEATURES AND MACHINE LEARNING ALGORITHMS

Aircraft are used in many fields such as engineering, logistics, transportation and disaster management. With the development of drones, aerial vehicles have become more widely used for entertainment purposes. However, in addition to its useful applications, its malicious use is also becoming widespread. It has become a necessity to eliminate this problem, especially since it poses a significant danger to other aircraft. In order to identify the aircraft and solve this problem quickly, in this study, five different aircraft were classified based on images. In the study, a five-class dataset containing aeroplane, bird, drone, helicopter and malicious UAV (Unnamed Aerial Vehicle) images was used. Three different CNN (Convolutional Neural Network) models were employed to extract the images of features. Image features extracted with SqueezeNet, VGG16, VGG19 models were classified with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR) machine learning methods. As a result of the experiments, the most accuracyful result, 92%, was obtained from the classification of the features extracted with the SqueezeNet model with ANN. The models proposed in the study will be integrated into various systems and used in the field of aviation to detect malicious UAVs and take necessary precautions.
The cold-start problem in nascent AI strategy: Kickstarting data network effects

While many artificial intelligence (AI) strategies are successful, countless others fail. Why do some strategies succeed while others fail? We adopt a network effects (NEs) perspective to conceptualize AI strategies, highlighting the AI context’s specifics. We argue that nascent AI strategies’ success depends on data NEs: companies establishing a functional “running system” to capitalize on these effects. However, this presents a challenge known as the cold-start problem (CSP), which involves initiating and accelerating a virtuous cycle: more data benefits the AI system, enhancing performance, which then attracts more data. In this paper, we examine the CSP in nascent AI strategy, exploring how it can be understood in terms of its technological and business dimensions and ultimately be overcome to kick-start a virtuous cycle of data NEs. By drawing insights from existing literature and practitioner interviews, we present a research agenda to encourage further investigation into overcoming the CSP.
Social Presence, Negative Emotions, and Self-Protective Behavioral Intentions of Nonsmokers in Response to Secondhand Smoking in Virtual Reality: Quasi-Experimental Design

The application of virtual reality (VR) in health care has grown rapidly in China, where approximately half of the population is directly exposed to secondhand smoke (SHS). As VR headsets have become increasingly popular and short video platforms have incorporated 360° videos in China, new formats and opportunities for health campaigns about SHS have emerged.In a simulated environment of exposure to SHS, this study aims to explore the emotional and behavioral responses to enhanced social presence brought about by VR in contrast to flat-screen videos. It also aims to examine whether and to what extent video modality (360° video vs flat-screen video) and contextual cues (high threat vs low threat) influence psychometric and intentional variables among viewers.A total of 245 undergraduate and graduate students who were nonsmokers and from a large university in China participated in this study between October 2020 and January 2021. This study created 4 different versions of a SHS experience in a café with a 2 (360° video on a head-mounted display vs flat-screen display) × 2 (high threat vs low threat) experimental design. It developed and tested a path model examining the effects of experience modality and threat levels on social presence, emotions (anger and disgust), and eventually behavioral intentions (staying away and asking for help).We found that both video modality (P<.001) and threat level (P=.005) significantly influenced social presence, whereas the interaction of video modality and threat level did not have a statistically significant effect on social presence (P=.55). Negative emotions mediated the relationships between social presence and SHS-related self-protective behaviors. Specifically, anger positively predicted the intention to ask smokers to stop smoking through the waitress (P<.001). Disgust and fear both positively predicted the intention to stay away from the SHS environment (P<.001 for disgust; P=.002 for fear).This study explored the potential mediating mechanisms that influence individuals' responses to the risks of SHS in public areas. The results demonstrated that social presence and negative emotions are 2 important mediators that underlie the relationship between video modality and behavioral intention regarding SHS in a VR setting. These findings suggest that an immersive environment could be a better stimulator of anti-SHS emotions and behaviors than flat-screen videos.
A Decision-Based Image Merging Technique for Server-Side Redundancy Reduction

In a client-server architecture, plenty of images are uploaded to the server, of which many are redundant. Hence, unnecessarily consuming the memory space of the server. It also results in computation overhead for any kind of information extraction. Freeing up those spaces would result in memory availability for more images. The images could be similar to each other in terms of viewpoint etc. Merging them efficiently to produce an equivalent image reduces the quantity to a certain extent. Although few existing works combine images, they are limited to some conditions. So, we primarily design an algorithm that decides whether to merge images or not. Later, a suitable merging method is applied to merge those images. Performance of the proposed scheme is compared with the existing works. The results show that the proposed scheme is superior in terms of running time than most state-of-the-art techniques, keeping the finest quality of merging.
Low-Memory Pedestrian Detection Using Binarized Neural Networks

Pedestrian detection is a very active topic in study because of its wide applications in fields such as automotive, security, and surveillance, as well as diverse use cases like traffic control and intruder detection. Although there has been great progress in this area of research, there are still many obstacles to overcome in terms of real-time utilization. In recent years, deep learning and convolutional neural networks have provided significant improvements in terms of accuracy, beating prior standard image processing algorithms. However, they face challenges and difficulties while installing for the very reason of large size of the models. Addressing this problem statement, binarization of the parameters and lessening intermediate operations could yield encouraging outcomes. Previous works with regards to quantization of weight values and activations have been proposed for the development of applications for the less power hungry applications. In this paper, an architecture for the pedestrian recognition and localization has been developed using quantization processes, with the goal of making them simple to implement in embedded devices or portable assisted devices. This model will suffer from accuracy degradation in comparison with the existing deep networks, but it will be more flexible to utilize in multiple modes. This paradigm will eliminate the need for external servers for computation, making the systems self-contained and secure. In addition to it, it will consume less power which would be beneficial for the healthy battery life. Finally, diverse datasets have been evaluated, and statistical analyses suggest its feasibility for utilizing it.
The Role of Electronic Management in Promoting Organizational Creativity: A Case Study of Orange Telecom Company/Jordan

The study aimed to investigate the concept of electronic management and organizational creativity and to test the impact of the dimensions of electronic management (electronic management, electronic devices, electronic applications, electronic networks, and databases) on enhancing organizational creativity at Orange Communications Company. On the questionnaire, the sample is convenient, consisting of (120) male and female employees working in senior administrative positions at Orange Telecom Company. The statistical analysis program, SPSS, was used to analyze the data collected through the study tool. The descriptive analytical approach was used in this study; because it is the most appropriate to achieve the study objectives with its dimensions and description of its characteristics. The study’s results showed the existence of a role for electronic management in promoting organizational creativity in Orange Telecom/Jordan. The study recommended several recommendations, including the necessity of activating electronic management in various sectors because of its positive impact on organizational creativity and holding courses and lectures by telecommunications companies to familiarize employees with technologies, applications, networks, and rules. Data on electronic management, conducting more studies on the dimensions of electronic management and other variables to measure their impact on business development, and developing administrative systems in line with electronic management.
A Combined Approach of Color Correction and Homomorphic Filtering for Enhancing Underwater Images

Underwater imaging is crucial for marine biology, oceanography, and underwater archeology, but low-quality images often result from issues like speckle noise, backscatter noise, and blur. To address the difficulties posed by these challenges, a recent study presents an effective methodology for enhancing the visual quality of underwater images. The approach comprises color correction, contrast enhancement, homomorphic filtering, and fusion. Contrast stretching is also used to improve contrast based on the range of intensity values. The proposed method is evaluated through qualitative and quantitative assessments, demonstrating its effectiveness in improving image details, enhancing global contrast, and exposing dark areas. According to the results, the proposed methodology surpasses several state-of-the-art techniques currently in use. The ultimate objective is to enhance the visual quality of underwater images, and the outcome of the study is a strong methodology that effectively tackles the challenges of underwater imaging, leading to improved image quality.