Reviewerly/exHarmony huggingface dataset:
The Accuracy and Faithfullness of AL-DLIME - Active Learning-Based Deterministic Local Interpretable Model-Agnostic Explanations: A Comparison with LIME and DLIME in Medicine

The goal of this paper is twofold. Firstly, it aims to introduce a novel eXplainable Artificial Intelligence (XAI) model, AL-DLIME (Active Learning-based Deterministic Local Interpretable Model-Agnostic Explanations), that integrates Active Learning (AL) into the DLIME framework. Secondly, it aims to perform a detailed comparison of LIME, DLIME, and AL-DLIME for medical diagnosis applications, with a focus on assessing the impact of DLIME and AL-DLIME’s deterministic behavior on their overall performance. For the purposes of this study, four datasets were selected within some of the areas of medicine that are considered to have the least accuracy in terms of diagnosis, oncology and cardiovascular diseases. As LIME, DLIME, and AL-DLIME are post-hoc XAI algorithms, Random Forest (RF) was selected to serve as the underlying black box model due to its popularity and overall good performance. Furthermore, we employ a Decision Tree (DT) model in order to address the accuracy-explainability tradeoff, more specifically, if the use of a black box model is strictly necessary. The performance of each model was evaluated using several metrics, including accuracy, F1-score, faithfulness to the black box model, single and incremental deletion, and stability measured through Jaccard’s distance. Our RF model outperformed DT on all accounts of both accuracy and F1-score, with its highest score of accuracy, 99%, being on par with other state of the art Machine Learning (ML) models. DLIME and AL-DLIME managed to outperform RF on several datasets, with AL-DLIME achieving the best results of accuracy and F1-score overall among all XAI models. However, LIME obtained the overall highest scores of faithfulness to RF, with results consistently above 60%. The study provides insights into the strengths and weaknesses of each XAI model and their suitability for medical diagnosis applications.
Reason to Explain: Interactive Contrastive Explanations (REASONX)

Many high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to develop tools to better understand their outputs. Popular explanation methods include contrastive explanations. However, they suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of interactivity. While (dialogue-like) interactivity is important to better communicate an explanation, background knowledge has the potential to significantly improve their quality, e.g., by adapting the explanation to the needs of the end-user. To close this gap, we present reasonx, an explanation tool based on Constraint Logic Programming (CLP). reasonx provides interactive contrastive explanations that can be augmented by background knowledge, and allows to operate under a setting of under-specified information, leading to increased flexibility in the provided explanations. reasonx computes factual and contrastive decision rules, as well as closest contrastive examples. It provides explanations for decision trees, which can be the ML models under analysis, or global/local surrogate models of any ML model. While the core part of reasonx is built on CLP, we also provide a program layer that allows to compute the explanations via Python, making the tool accessible to a wider audience. We illustrate the capability of reasonx on a synthetic data set, and on a well-developed example in the credit domain. In both cases, we can show how reasonx can be flexibly used and tailored to the needs of the user.
Tainted Love: a Systematic Literature Review of Online Romance Scam Research

Abstract Romance scams involve cybercriminals engineering a romantic relationship on online dating platforms for monetary gain. It is a cruel form of cybercrime whereby victims are left heartbroken, often facing financial ruin. We characterize the literary landscape on romance scams, advancing the understanding of researchers and practitioners by systematically reviewing and synthesizing contemporary qualitative and quantitative evidence. The systematic review establishes influencing factors of victimhood and explores countermeasures for mitigating romance scams. We searched 10 scholarly databases and websites using terms related to romance scams. The methodology followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines: a total of 279 papers were screened. One hundred seven papers were assessed for eligibility, and 53 were included in the final analysis. Three main contributions were identified: common profile features and techniques used by romance scammers, countermeasures for mitigating romance scams and factors predisposing an individual to become a scammer or a victim. Despite a growing corpus of literature, the total number of empirical or experimental examinations remained limited. The paper concludes with avenues for future research and victimhood intervention strategies for practitioners, law enforcement and the industry.
Airport Foreign Object Small Target Detection Dataset

To address the current issues in the field of airport runway foreign object detection, such as small data volume, low data quality, and limited data variety, a road foreign object dataset is proposed in this paper. The dataset is created through large-scale data collection and annotation, aiming to simulate the foreign object detection scenarios on airport runways and construct a diverse and detailed road foreign object dataset. This dataset consists of 12 categories of foreign objects, including metal devices, mechanical tools, and miscellaneous items. Furthermore, the dataset proposed in this paper was trained and tested using commonly used object detection algorithms. The experimental results demonstrated the significance of preprocessing the original dataset, and the Mean Average Precision(mAP) metric of the YOLO-V5-n algorithm cited in this paper outperformed other algorithms. The road debris dataset proposed in this paper provides a valuable database for the airport runway debris detection field.
Edge Computing for Efficient Storage and Low-Latency Video Streaming in Cloud Environments

Efficient storage and low-latency video streaming are critical for delivering high-quality multimedia experiences in cloud environments. This research explores the potential of edge computing as a solution to address these challenges. By strategically deploying computational resources at the network edge, edge computing minimizes latency and enhances storage capabilities, leading to an improved user experience. We propose a cutting-edge prototype system that integrates edge computing into the cloud infrastructure. Through rigorous experiments, we evaluate key performance metrics, including latency, bitrate, and storage utilization, the results show the effectiveness of the proposed approach for video streaming in the cloud. Particularly, it reduces the latency by up to 25% and storage utilization by up to 15%.
Creating Value with Blockchain for Organizations

The first appearance in public of the concept Blockchain has occurred in 2008, by an author named Satoshi Nakamoto. In the beginning, Blockchain was related to cryptocurrencies, but in recent years, blockchain has emerged as a disruptive technology with a massive potential in several industries, including energy, supply chain and healthcare. Many authors have studied the impact of Blockchain in a specific area and sector, but there are few that really dived on obtaining the primary advantages that Blockchain provides to organizations. This project, based on a systematic literature review that involved 35 articles previously chosen in a methodological way, aims to understand the advantages that organizations will recognize if they implement Blockchain in their business. As a distributed data network that contains a set of nodes, which use common communicational protocol, Blockchain will increase technological security in organizations among other benefits. Blockchain technology can help contract management and audit the origin of a product. It also can be used in voting platforms and managing titles and deeds. It can provide secure transactions, reduce compliance costs, and speed up data transfer processing. But there are some limitations regarded to Blockchain, such as security and privacy.
RUIESR: Realistic Underwater Image Enhancement and Super Resolution

Clear and high-resolution (HR) underwater images are indispensable in acquiring underwater information. However, existing underwater image enhancement and super-resolution (UIESR) networks achieve limited enhancement-super-resolution performance on real-world turbid low-resolution (LR) underwater images because (1) they assume that the resolution degradation is simple and known bicubic down-sampling, generating unrealistic training data for UIESR task; (2) they extract known priors from the underwater imaging model, which is meager to address complex UIESR problems caused by unknown mixed dual-degradation; and (3) they ignore the interaction between blurring and color casts in the RGB color space, leading to unsatisfactory correction results of two distortions. To address these issues, we propose a realistic UIESR network (RUIESR) consisting of three parts: a realistic LR image generation module (RLGM), a dual-degradation estimation module (DEM), and an enhancement and super-resolution module (ESRM). Firstly, RLGM aims to generate LR images obeying underwater LR image distribution by learning real LR properties from unpaired real LR-HR underwater images for training. Secondly, a contrast-driven learning strategy is proposed in the DEM to accurately estimate unknown dual-degradation priors that can aid the reconstruction task. Finally, ESRM is proposed to enhance textures and correct color casts, which includes a dual-branch structure to separate blurring and color casts distortions and utilizes specific priors for each distortion to assist reconstruction. Extensive experiments on real and synthetic underwater datasets show that the proposed RUIESR outperforms existing works regarding visual quality and quantitative metrics.
Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms

Match-3 puzzle games have garnered significant popularity across all age groups due to their simplicity, non-violent nature, and concise gameplay. However, the development of captivating and well-balanced stages in match-3 puzzle games remains a challenging task for game developers. This study aims to identify the optimal algorithm for reinforcement learning to streamline the level balancing verification process in match-3 games by comparison with Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms. By training the agent with these two algorithms, the paper investigated which approach yields more efficient and effective difficulty level balancing test results. After the comparative analysis of cumulative rewards and entropy, the findings illustrate that the SAC algorithm is the optimal choice for creating an efficient agent capable of handling difficulty level balancing for stages in a match-3 puzzle game. This is because the superior learning performance and higher stability demonstrated by the SAC algorithm are more important in terms of stage difficulty balancing in match-3 gameplay. This study expects to contribute to the development of improved level balancing techniques in match-3 puzzle games besides enhancing the overall gaming experience for players.
Multi-Label Radar Compound Jamming Signal Recognition Using Complex-Valued CNN with Jamming Class Representation Fusion

In the complex battlefield electromagnetic environment, multiple jamming signals can enter the radar receiver simultaneously due to the development of jammers and modulation technology. The received compound jamming signals aggravate the difficulty of recognition and subsequent counter-countermeasure. In the face of strong overlapping signals and unseen jamming signal combinations, the performance of existing recognition methods usually seriously degrades. In this paper, an end-to-end multi-label classification framework combining a complex-valued convolutional neural network (CV-CNN) and jamming class representations is proposed to automatically recognize the jamming signal components of compound jamming signals. A basic multi-label CV-CNN (ML-CV-CNN) is first designed to directly process time–domain complex signals and fully retain jamming signal information. Then, the jamming class representations are generated using prototype clustering implemented by learning vector quantization, and they are fused with the ML-CV-CNN using class decoupling implemented by the attention mechanism to construct a multi-label class representation CV-CNN (ML-CR-CV-CNN), which can better learn the class-related features required for recognition. Finally, an adaptive threshold calibration is adopted to obtain optimal recognition results by multi-threshold discrimination. Simulation results verify that the proposed method has superior recognition performance, which is reflected in the strong robustness to the varying jamming-to-noise ratio (JNR) and power ratio, faster convergence speed with high JNRs, and better generalization for unseen jamming signal combinations.
A Metaverse text recognition model based on character-level contrastive learning

Accurate and efficient text recognition can enhance the accuracy and time efficiency of human–computer interaction and information exchange in noise Metaverse scenarios. For example, it can improve the safety of digital twin-based intelligent transportation and the intelligence of Non-Player Characters. Robust features play a vital role in the performance of scene text recognition models in noise Metaverse situations. To extract robust features, and improve the accuracy and time efficiency of scene text recognition, we propose a Character level text recognition model in Metaverse applications, called MetaChara. It contains two main components: a lightweight text feature extraction module (LightFeature), and a robust character recognition module (RobChara). LightFeature leverages the advantage of global feature aggregation in the primitive representation learning network to handle irregular text images.RobChara incorporates the capability of contrastive learning from the momentum contrast method, improving the robustness of feature extraction in MetaChara. It structures a feature queue for organized storage. By optimizing the similarity of intra-character features and maximizing inter-character differences, it makes the model better adapted to scene text recognition tasks in Metaverse. Experiment results demonstrate that MetaChara is light with 29.14 million parameters and time efficient with an average recognition speed of 1.73 s. It also achieves excellent performance in terms of FLoating-point Operations (FLOPs), registering only 59.60 billion times for each operation. MetaChara achieves an average accuracy of 0.969 for character recognition. We present a case study where MetaChara quickly and accurately recognizes scene texts within the context of autonomous driving in the Metaverse. This demonstrates how MetaChara enhances safety and improves time efficiency for intelligent transportation systems.
Automatic Vulgar Word Extraction Method with Application to Vulgar Remark Detection in Chittagonian Dialect of Bangla

The proliferation of the internet, especially on social media platforms, has amplified the prevalence of cyberbullying and harassment. Addressing this issue involves harnessing natural language processing (NLP) and machine learning (ML) techniques for the automatic detection of harmful content. However, these methods encounter challenges when applied to low-resource languages like the Chittagonian dialect of Bangla. This study compares two approaches for identifying offensive language containing vulgar remarks in Chittagonian. The first relies on basic keyword matching, while the second employs machine learning and deep learning techniques. The keyword-matching approach involves scanning the text for vulgar words using a predefined lexicon. Despite its simplicity, this method establishes a strong foundation for more sophisticated ML and deep learning approaches. An issue with this approach is the need for constant updates to the lexicon. To address this, we propose an automatic method for extracting vulgar words from linguistic data, achieving near-human performance and ensuring adaptability to evolving vulgar language. Insights from the keyword-matching method inform the optimization of machine learning and deep learning-based techniques. These methods initially train models to identify vulgar context using patterns and linguistic features from labeled datasets. Our dataset, comprising social media posts, comments, and forum discussions from Facebook, is thoroughly detailed for future reference in similar studies. The results indicate that while keyword matching provides reasonable results, it struggles to capture nuanced variations and phrases in specific vulgar contexts, rendering it less robust for practical use. This contradicts the assumption that vulgarity solely relies on specific vulgar words. In contrast, methods based on deep learning and machine learning excel in identifying deeper linguistic patterns. Comparing SimpleRNN models using Word2Vec and fastText embeddings, which achieved accuracies ranging from 0.84 to 0.90, logistic regression (LR) demonstrated remarkable accuracy at 0.91. This highlights a common issue with neural network-based algorithms, namely, that they typically require larger datasets for adequate generalization and competitive performance compared to conventional approaches like LR.
Curvature-Based Machine Vision Method for Measuring the Dimension of Ball Screws

As a linear actuator, accurate dimension measurement is crucial to the transmission reliability and interchangeability of ball screws. However, most of the current approaches are ineligible for rapid ball screw in-situ inspections due to the installation condition requirement of the production line. In this research, a machine vision method is presented to achieve highly accurate measurements of crucial parameters (the center distance and raceway arcs) in ball screws using a curvature edge detection algorithm. To capture images of the immediate area surrounding the area of interest, a telecentric lens is used. Thereafter, the curvature-based edge detection algorithm is employed to extract the contours. The measurement location on the object is automatically chosen by using a shape-matching algorithm. Additionally, random noise is suppressed by using the multiple-measurement averaging technique. Based on the results of the experiments, it is concluded that the center distance and the two raceway arcs computed absolute errors are 0.0019 mm, 0.0055 mm, and 0.0059 mm, respectively.
An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentation accuracy needs to be improved. General neural networks are used to improve categorization accuracy, but they also caused significant losses to target scale and spatial features, and the traditional common features fusion techniques can only resolve some of the issues. A segmentation network has been designed to resolve the above-mentioned issues as well. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. This model has adopted a new Adaptive Multichannel Deeplabv3+ (AMC-Deeplabv3+) with the help of a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO). Here, the hyperparameters of Multichannel deeplabv3+ are optimized by the IBWO algorithm. The proposed model significantly enhances the performance of the overall system by measuring the accuracy and dice coefficient. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively with a computation time of 113.0123 seconds. The evolutional outcomes of the proposed model show significantly better than the state of the art techniques like CNN, MUnet and DFCNN models.
Minimax Rate-Distortion

We show the existence of variable-rate rate-distortion codes that meet the distortion constraint almost surely and are minimax, i.e., strongly, universal with respect to an unknown source distribution and a distortion measure that is revealed only to the encoder and only at runtime. If we only require minimax universality with respect to the source distribution and not the distortion measure, then we provide an achievable Õ(1/√ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ) redundancy rate, which we show is optimal. This is in contrast to prior work on universal lossy compression, which provides O(log <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ) redundancy guarantees for weakly universal codes under various regularity conditions. We show that either eliminating the regularity conditions or upgrading to strong universality while keeping these regularity conditions entails an inevitable increase in the redundancy to Õ(1/√ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ). Our construction involves random coding with non-i.i.d. codewords and a zero-rate uncoded transmission scheme. The proof uses exact asymptotics from large deviations, acceptance-rejection sampling, and the VC dimension of distortion measures.
Low-Artifact and Fast Backlit Image Enhancement Method Based on Suppression of Lightness Order Error

Many image enhancement methods have been proposed to improve the visibility of backlit images. Although these methods can effectively improve the visibility of the subject and background compared to standard image enhancement methods, they may result in image quality degradation owing to non-negligible artifacts. In many cases, such artifacts are caused by a significant change in the Lightness Order Error (LOE) between the original and processed images. To address this problem, this paper proposes a low-artifact and fast backlit image enhancement method to effectively improve the visibility of images by suppressing the LOE. The proposed method uses adaptive luminance correction to generate lightness-enhanced images of the dark and bright areas of the backlit image. These images are then fused based on a weight map to calculate the lightness of the output image with a lower LOE. The final output, i.e., the enhanced color image, is obtained by multiplying the input color image by the ratio of the lightness component of the input image to the enhanced lightness component. The experimental results demonstrate the superiority of the proposed method in terms of low artifacts, natural enhancement, and high processing speed based on straightforward processing.