Reviewerly/exHarmony huggingface dataset:
Detecting Out-of-Context Image-Caption Pair in News: A Counter-Intuitive Method

The growth of misinformation and re-contextualized media in social media and news leads to an increasing need for fact-checking methods. Concurrently, the advancement in generative models makes cheapfakes and deepfakes both easier to make and harder to detect. In this paper, we present a novel approach using generative image models to our advantage for detecting Out-of-Context (OOC) use of images-caption pairs in news. We present two new datasets with a total of 6800 images generated using two different generative models including (1) DALL-E 2, and (2) Stable-Diffusion. We are confident that the method proposed in this paper can further research on generative models in the field of cheapfake detection, and that the resulting datasets can be used to train and evaluate new models aimed at detecting cheapfakes. We run a preliminary qualitative and quantitative analysis to evaluate the performance of each image generation model for this task, and evaluate a handful of methods for computing image similarity.
Prediction of Cybercrime using the Avinashak Algorithm

The detection and prevention of phishing websites continue to be major obstacles in the continually changing field of cybersecurity. Phishing attacks continue to use sophisticated methods to exploit user vulnerabilities, thus it is vital to predict and identify these malicious websites. Traditional techniques for detecting phishing sites frequently rely on rule-based and domain-based approaches, which might not adequately capture the dynamic nature of phishing attacks. The Avinashak Crime Prediction Algorithm appears to be a proprietary or specialized algorithm not widely known in the machine learning community. Its details and working principles are not publicly available, which makes it challenging to provide a detailed explanation without additional information.
Accuracy Detection in Some Sports Training Using Computer Vision and Deep Learning Techniques

In this study, the performance of the MediaPipe Pose Estimation model in estimating body position in different sports activities was investigated in the light of biomechanical parameters. Additionally, the performance of the model was evaluated by comparing the real-time data obtained from the camera with different machine learning algorithms (regression, classification, etc.). The results showed that the MediaPipe Pose Estimation model is a suitable and effective tool for sports biomechanics. The model was able to estimate body position with high accuracy in different sports activities. Additionally, the performance of the model was improved by using different machine learning algorithms. This study is a pioneer research on the applicability of computer vision-supported deep learning techniques in sports training and pose estimation. The model has been developed into an application that can be used to improve the performance of athletes.
MMRec: Simplifying Multimodal Recommendation

This paper presents an open-source toolbox, MMRec for multimodal recommendation. MMRec simplifies and canonicalizes the process of implementing and comparing multimodal recommendation models. The objective of MMRec is to provide a unified and configurable arena that can minimize the effort in implementing and testing multimodal recommendation models. It enables multimodal models, ranging from traditional matrix factorization to modern graph-based algorithms, capable of fusing information from multiple modalities simultaneously. Our documentation, examples, and source code are available at https://github.com/enoche/MMRec.
diveXB: An Interactive Video Retrieval System for Beginners

We present an interactive video retrieval system called diveXB (diveXplore for Beginners), which is an enhancement of our diveXplore system used for the recent Video Browser Showdown competitions. In addition to a common free-text search with text-image embeddings, diveXB integrates a new exploration component that allows browsing the video collection without any query. For this purpose, we employ k-means clustering of image embeddings that are extracted from video summaries with state-of-the-art vision transformers and select the most coherent clusters for query-free browsing in our user interface. First qualitative experiments have shown that this feature allows users to easily get an overview of the content structure in the video collection and to find desired content without the need for any textual input.
NON-FUNGIBLE TOKENS AS A WAY TO PROTECT GLOBAL INTELLECTUAL PROPERTY

Blockchain technology is increasingly being recognized worldwide as a cornerstone of security and stability in the digital realm. The Internet, often viewed as a seemingly chaotic, unpredictable, and insecure environment, is experiencing a significant paradigm shift thanks to disruptive innovations such as blockchains, which provide a robust mechanism to ensure the integrity of the global information flow. While the original purpose of this technology was to facilitate cryptocurrencies, its applications have since broadened, permeating law, business, and more recently, sectors like art and gaming. In this context, Non-Fungible Tokens (NFTs) are on the cusp of becoming a mainstream asset class. These unique digital assets, intricately tied to Intellectual Property, present an unexplored avenue for legal and business applications. This paper assesses the potential uses of NFTs for to protect global IP. The study further suggests an analogous interpretation of existing norms that regulate the connection with NFTs and local regulations, thus laying the groundwork for an initial self-regulatory framework. In conclusion, NFTs are poised to revolutionize IP protection globally. However, from a legal perspective, it also represents the necessity to develop a regulated self-regulatory system to frame such technology. As part of future research, we propose the creation of legal standards to accept and introduce NFTs as part of the IP protection laws.
Risk factors for fraud victimization: The role of socio-demographics, personality, mental, general, and cognitive health, activities, and fraud knowledge

Fraud against individuals is a major and growing problem. Understanding why some people fall victim to fraud, while others do not, is crucial in developing effective prevention strategies. We therefore studied the effect of socio-demographics, personality traits, mental, general, and cognitive health, routine Internet activities, and prior fraud knowledge on general fraud victimization, susceptibility to fraud attempts, and exposure to fraud attempts. We modeled data from a Dutch fraud victimization survey, using an exhaustive fraud taxonomy and a representative sample for which an elaborate set of historical background variables were available. Results show that there is no clear personality or other profile of those most at risk for fraud, except for having low self-control, having a non-Western, immigrant background or being a frequent Internet user. Improving fraud knowledge could be an effective way to prevent fraud victimization by reducing susceptibility to attempts.
RETHINKING URBAN QUALITY OF LIFE: UNVEILING CAUSALITY LINKS USING COGNITIVE MAPPING, NEUTROSOPHIC LOGIC AND DEMATEL

Quality of life (QoL) is an important issue that reflects changes around the world caused not only by human population density, growth, and related initiatives but also by crises and pandemics. Concurrently, people's increasing tendency to live in urban areas has generated growing concerns about correctly assessing city QoL to facilitate the implementation of practical measures that favor both current and future generations’ well-being. Conducting accurate analyses in this context is a challenging endeavor due to the subjectivity and complexity intrinsic to QoL evaluations. Thus, this study develops a multicriteria model based on a constructivist and complementarity logic that helps decision makers evaluate urban QoL. The proposed analysis system combines cognitive mapping, neutrosophic logic, and the decision-making trial and evaluation laboratory (DEMATEL) method in order to address the limitations of previous studies. This model also enhances experts’ ability to decide which determining factors should be included in assessments of urban QoL. In addition, the system developed can help decision makers cope with uncertainty during evaluations because this holistic, realistic, and complete model fosters conscious decision making in urban contexts. The practical implications, advantages, and limitations of the proposed analysis system are also discussed.
Provenance Verification of Smart Contracts: Analysing the Cost of Ensuring Authenticity over the Logic Hosted in Blockchain Networks

The lack of sufficient guarantee about the authenticity of running smart contracts is a major entry barrier to blockchain networks. By authenticity,we refer to the smart contract ownership or provenance; this implies perfect matching between a published source-code and the corresponding running version of a given smart contract. Block verifiers are services that check the provenance authenticity of the logic contained in blockchain networks. Nevertheless, as a block verifier is an external verification service, it consumes time to use it; and the derived overhead may not comply with temporal requirements of time-sensitive domains like cyber-physical systems. Such systems require that the temporal cost of using external services is assessed prior to the final system deployment. To the best of our knowledge, there are no previous contributions on the determination of the temporal cost of the smart-contract provenance verification process. This paper presents the design and implementation of a middleware that assesses the temporal overhead of accessing the verification services; the middleware is hosted in the global ledger and runs the verification services over large sets of smart contracts. Our contribution is validated by providing an implementation on a real blockchain network, employing actual smart contract verifier logic, and analysing the temporal behavior of the overall system operations to comply with the time-sensitive requirements of cyber-physical systems.
Quantum Information with Integrated Photonics

Since the 1980s, researchers have taken giant steps in understanding how to use quantum mechanics for solving real problems—for example, making a computer that works according to the laws of quantum mechanics. In recent decades, researchers have tried to develop a platform for quantum information and computation that can be integrated into digital and telecom technologies without the need of a cryogenic environment. The current status of research in the field of quantum integrated photonics will be reviewed. A review of the most common integrated photonic platforms will be given, together with the main achievements and results in the last decade.
A Lightweight Man-Overboard Detection and Tracking Model Using Aerial Images for Maritime Search and Rescue

Unmanned rescue systems have become an efficient means of executing maritime search and rescue operations, ensuring the safety of rescue personnel. Unmanned aerial vehicles (UAVs), due to their agility and portability, are well-suited for these missions. In this context, we introduce a lightweight detection model, YOLOv7-FSB, and its integration with ByteTrack for real-time detection and tracking of individuals in maritime distress situations. YOLOv7-FSB is our lightweight detection model, designed to optimize the use of computational resources on UAVs. It comprises several key components: FSNet serves as the backbone network, reducing redundant computations and memory access to enhance the overall efficiency. The SP-ELAN module is introduced to ensure operational speed while improving feature extraction capabilities. We have also enhanced the feature pyramid structure, making it highly effective for locating individuals in distress within aerial images captured by UAVs. By integrating this lightweight model with ByteTrack, we have created a system that improves detection accuracy from 86.9% to 89.2% while maintaining a detection speed similar to YOLOv7-tiny. Additionally, our approach achieves a MOTA of 85.5% and a tracking speed of 82.7 frames per second, meeting the demanding requirements of maritime search and rescue missions.
Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.
Artificial Intelligence-Based Tools in Software Development Processes: Application of ChatGPT

Software development processes are continuously evolving and rapidly transforming alongside the rapid changes in technology. Recently, innovations in the field of Artificial Intelligence (AI) have led to significant changes in software development practices. AI tools can greatly enhance traditional software development processes by offering developers the ability to create projects more intelligently, swiftly, and effectively. These tools can be employed in various tasks, such as code generation, test automation, error analysis, and performance improvements. Particularly, ChatGPT, an AI-based language model that has had a profound impact on almost every domain, can assist software developers in writing code faster and in a more natural language manner. In this research article, essential information about the usage of ChatGPT in the software development process is presented. To evaluate some capabilities of ChatGPT in the software development context, applications were performed on a software project. For this purpose, a software development process was constructed based on the responses provided by ChatGPT. Various questions related to software development processes were formulated, and the responses generated by GPT were evaluated. The obtained results indicated that ChatGPT exhibited excellent performance in the software development process. Based on these findings, it was observed that AI-based models like ChatGPT could be effectively utilized as assisting tools in software development processes, accelerating traditional workflows. Furthermore, AI-based tools can automate testing processes, enhancing software quality while saving time and effort.
Integration of Game Theoretic and Tree Theoretic Approaches to Conway Numbers

Summary In this article, we develop our formalised concept of Conway numbers as outlined in [9]. We focus mainly pre-order properties, birthday arithmetic contained in the Chapter 1, Properties of Order and Equality of John Conway’s seminal book. We also propose a method for the selection of class representatives respecting the relation defined by the pre-ordering in order to facilitate combining the results obtained for the original and tree-theoretic definitions of Conway numbers.
Object Recognition System under Hazy Condition for Automated Driving Systems

We propose an object recognition system for vehicles controlled by "Automated Driving Systems (ADS)." In particular, the proposed system aims at operating "Minimum Risk Maneuver (MRM)" for the ADS only with image sensors. The image sensors, digital cameras for example, often face difficulties in acquiring images with foggy, rainy and/or misty weather conditions. Even in such "Hazy" conditions, the object recognition system should be reliable to perform the MRM strategy. The proposed system consists of two sub-systems: image "dehaze" processing, and object detection. The implemented system was validated by several sample movies recorded in hazy condition on real roads. Experimental results show that the proposed system is applicable to real-time applications.
Unsupervised Unlearning of Concept Drift with Autoencoders

Concept drift refers to a change in the data distribution affecting the data stream of future samples. Consequently, learning models operating on the data stream might become obsolete, and need costly and difficult adjustments such as retraining or adaptation. Existing methods usually implement a local concept drift adaptation scheme, where either incremental learning of the models is used, or the models are completely retrained when a drift detection mechanism triggers an alarm. This paper proposes an alternative approach in which an unsupervised and model-agnostic concept drift adaptation method at the global level is introduced, based on autoencoders. Specifically, the proposed method aims to “unlearn” the concept drift without having to retrain or adapt any of the learning models operating on the data. An extensive experimental evaluation is conducted in two application domains. We consider a realistic water distribution network with more than 30 models in-place, from which we create 200 simulated data sets / scenarios. We further consider an image-related task to demonstrate the effectiveness of our method.
Acceptability and Effectiveness Analysis of Large Language Model-Based Artificial Intelligence Chatbot Among Arabic Learners

This research stems from the broad use of AI based on Large Language Models (LLMs), which many academics find relevant and effective in higher education Arabic language learning. The goal is to confirm these views.This research is a mixed reseach that employs a both of qualitative and quantitative methodologies. The qualitative segment involves observations and literature reviews. Observations involved reviewing how participants used chatbots and carefully checking the accuracy and consistency of platform responses. The quantitative facet utilizes a paired experimental design, encompassing both classical and Bayesian Paired Sample t-Tests analysis. The research encompasses 45 individuals with a proficient understanding of Modern Standard Arabic and no hindrances in comprehending the material. These individuals are enrolled as students at Islamic College (STAI) Al-Anwar Rembang, Indonesia. The results show increased motivation and ease of use with the chatbot in Arabic language learning. However, concerns about the consistency of chatbot content have arisen, affecting participants' confidence in response accuracy of AI. This prompts an evaluation of effectiveness through classical and Bayesian tests, which fail to demonstrate statistically significant variances, even in the adaptive Bayesian probability analysis. These outcomes deviate from previous research on relevance and effectiveness and corroborate preceding studies on academic apprehensions and accuracy enhancements. The researchers advocate for further investigations, especially concerning the accuracy analysis of AI chatbots in Arabic pedagogical contexts.
ChatGPT Based Image Steganography (CGIS): A Novel Intelligent Information Hiding Approach to Achieve Secure Covert Communication

Covert communication refers to the practice of exchanging information or messages in a discreet or secretive manner, with the intent of keeping the communication hidden from unintended or unauthorized recipients. Steganography is a widely used, strategy for establishing covert communication, for maintaining the privacy and security of sensitive information. Steganography ensures that the message remains concealed from surveillance or eavesdropping. Artificial Intelligence (AI) plays a crucial role in enhancing security across various domains of covert communication. ChatGPT is an AI model, specifically a natural language processing (NLP) model. It belongs to the broader category of AI models which can comprehend and produce language for humans. In this paper, a new image steganographic model named CGIS is developed for covert communication using ChatGPT. Proposed steganographic method has achieved 3.0 bpp of hiding capacity. In situations where confidentiality is critical, such as in military operations, intelligence agencies, or corporate strategies, proposed method ensures that the message cannot be detected and or extracted by any surveillance or eavesdropper.
Designing an ASR Corpus for the Albanian Language

This paper reports on the creation of a corpus for the Albanian language that is intended for training and evaluating Automatic Speech Recognition (ASR) systems. The corpus comprises 100 h of audio recordings taken from 200 audiobooks and covers a wide range of topics with a rich vocabulary. The audio recordings were transcribed manually, strictly verbatim, and listened to carefully several times to ensure accuracy. The corpus was evaluated using various end-to-end models as well as Transformer-based architectures. The evaluation was conducted on both the training and testing sets, with Word Error Rate (WER) and Character Error Rate (CER) being considered as evaluation metrics. The results of the architectures trained with this corpus were compared with the results of the LibriSpeech corpus in English. The best architecture based on end-to-end models yielded 5% WER and 1% CER on the training set and 35% WER and 11% CER on the testing set. The transformer-based architecture yielded great results in the testing set, reaching a WER of 18%.