Reviewerly/exHarmony huggingface dataset:
Argumentation and explanation in the law

This article investigates the conceptual connection between argumentation and explanation in the law and provides a formal account of it. To do so, the methods used are conceptual analysis from legal theory and formal argumentation from AI. The contribution and results are twofold. On the one hand, we offer a critical reconstruction of the concept of legal argument, justification, and explanation of decision-making as it has been elaborated in legal theory and, above all, in AI and law. On the other hand, we propose some definitions of explanation in the context of formal legal argumentation, showing a connection between formal justification and explanation. We also investigate the notion of stable normative explanation developed elsewhere in Defeasible Logic and extend some complexity results. Our contribution is thus mainly conceptual, and it is meant to show how notions of explanation from literature on explainable AI and legal theory can be modeled in an argumentation framework with structured arguments.
Ground-Based Hyperspectral Retrieval of Soil Arsenic Concentration in Pingtan Island, China

The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson’s correlation coefficients (e.g., abs(Pearson’s correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson’s correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide.
Bridge Displacement Measurement Using the GAN-Network-Based Spot Removal Algorithm and the SR-Based Coarse-to-Fine Target Location Method

Image-based bridge displacement measurement still suffers from certain limitations in outdoor implementation. Each of these limitations was addressed in this study. (1) The laser spot is difficult to identify visually during the object distance (OD: mm) measurement using a laser rangefinder, which makes the scale factor (SF: mm/pixel) calibration tricky. To overcome this issue, a stereovision-based full-field OD measurement method using only one camera was suggested. (2) Sunlight reflected by the water surface during the measurement causes light spot interference on the captured images, which is not conducive to target tracking. A network for light spot removal based on a generative adversarial network (GAN) is designed. To obtain a better image restoration effect, the edge prior was novelly designed as the input of a shadow mask-based semantic-aware network (S2Net). (3) A coarse-to-fine matching strategy combined with image sparse representation (SR) was developed to balance the subpixel location precision and efficiency. The effectiveness of the above innovations was verified through algorithm evaluation. Finally, the integrated method was applied to the vibration response monitoring of a concrete bridge impacted by the traffic load. The image-based measurement results show good agreement with those of the long-gauge fiber Bragg grating sensors and lower noise than that of the method before improvement.
Reinforcement learning based path planning using a topological map for mobile service robot

A mobile service robot is a class of robots that operates in a dynamic environment with other robots and human beings. The service environment is often considered large and unknown, and the robot is expected to operate lifelong. The static path planning system that directly uses maps or explores an unknown environment will work fine if the environment does not change over time. But for a service environment, there can be changes in the environment over time, i.e., new routes can be generated, or old routes are permanently blocked. Considering these facts, it is evident that the path planning system for a service robot should be an artificial intelligence-based system that will be able to plan the path given less or no information about the environment and cope with the environment’s changes for a lifelong. Our work aims at developing a path planning framework using reinforcement learning for a service environment. The proposed path planning algorithm uses a Q-Learning algorithm to learn the paths using a topological map of the environment. The system is trained and tested for two different service environments. The experiment results show that the system can learn and generate efficient routes based on the topological maps with an accuracy of 95%. This artificial intelligence-based framework can extend the path planning system’s capability to adapt to long-term route changes.
Face Mask Detection Using ResNet50 Model and fine tuning it on various hyperactive parameters

The COVID-19 that took all the nations for a toll has increased interest in the logical field of facemask placement. This research determines if a person is appropriately wearing a face mask or is at danger of illness using computer vision and machine learning algorithms. The study uses multiple social datasets that show people wearing face coverings or not in photographs or videos. These datasets are used to create and implement machine learning models that can determine whether or not a person is wearing a face covering. In this work, faces covered under coverings are recognised using a ResNet50 Model. Next, it is improved for a number of hyper characteristics to forecast face photographs with an accuracy of 99 percent, aiding in the advancement of the investigation and the development of humanistic technology that will enhance sustainable development.
Quantum Computing - The Next Big Leap in the World of Computation

With the rapid onset of the Quantum Computing (QC) paradigm in the field of computer science, the QC approach could enable better outcomes to solve some of the classical problems. It is plausible to speculate that quantum computers could outperform classical computers in machine learning tasks because quantum systems exhibit peculiar patterns that conventional systems are assumed to be incapable of producing. This work introduces the QC approach to Machine Learning solutions in computer science and provides a comparison of the performances of QC-based approaches against the classical ones. In addition, an attempt to discuss the potential of QC in solving classical computer science problems such as Shor’s algorithm, the Travelling Salesman problem(TSP), and the prime factorization problem is made through the empirical study of the same.In conclusion, we analyze the outcomes of our practical investigation and devise potential improvements that can be made to the QC methods employed by the solutions.
Breast Cancer Prognosis and Prediction through Gene Expression Analysis and the Hybrid Model of SVM and Logistic Regression

Breast cancer is one of the most challenging issues in the health industry today. It takes many lives per year around the globe. It must be diagnosed at the early stages so that it can be cured timely. Several hypotheses and research works have been proposed to date, however many of them do not handle large data sets, and some of them do not provide promising results. On the other side, gene expression analysis for breast cancer prediction is a rapidly evolving field with great potential for future advancements. This paper proposes a novel approach for breast cancer detection using gene expression analysis. It implements machine learning techniques such as SVM and logistic regression to detect methylated DNA and breast cancer classification, respectively. It predicts whether the patient with methylated DNA has breast cancer or not, and it achieved an accuracy of 94.73%.
A Detailed Analysis of Image Forgery Detection Techniques and Tools

Nowadays, we can see there is a surge in image tampering. In addition, to remove any traces of tampering, post-processing is carried out, which makes it challenging for the analyzer to find the fake. With the goal of leaving no signs for human visual detection, image forging can be carried out in a variety of ways by conducting different operations such as adding, removing, or copying specific elements of a picture within an image or to another image. Image tampering can be used to dilute, defame, and slander an individual or organization. Since digital images are frequently used as evidence in the media, in court, and for long-term record keeping, it is crucial to develop approaches to detect forgeries in images used by the mainstream press, in court hearings, and for maintaining visual records. The proposed paper focuses on various image forgery detection methods and tools. It also gives a comparative analysis between the various methods, their pros and cons, and the results obtained using various tools.
Copy and Move Forged Image Detection by Deep Learning

Copy-Move forgery detection (CMFD) is one of the most active study topics in blind image forensics. Most known algorithms are based on block and key-point approaches or a mix. Recently, various deep convolutional neural network approaches have been utilised in image classification, image forensics, image hashing retrieval, and other areas, and they have outperformed the previous way. The paper proposes a unique copy-move forgery detection system based on a convolutional neural network. The suggested technique takes a trained model from an extensive database such as ImageNet and minimally modifies the net structure using tiny training examples. The experimental findings indicate that the approach we presented outperforms the counterfeit picture created automatically by the computer using a simple image copy-move operation.
Predict Success of a Zomato Restaurant using Machine Learning

Making an informed decision when choosing a restaurant has grown more challenging as the number of restaurants in Bangalore has increased. To assist in decision-making, websites like Zomato offer restaurant ratings and reviews. However, the quality of reviews can be subjective and biased. Therefore, predicting restaurant ratings can provide a more accurate representation of a restaurant’s quality and aid consumers in their decision-making process. This research paper aims to predict restaurant ratings in Bangalore using Zomato data. We will utilize machine learning methods like Linear Regression, Random Forest, and KNN to predict the rating of a restaurant. The study will also identify the features that contribute to higher ratings, such as location, price range, and type of cuisine. This research can assist both consumers in choosing the best restaurants and restaurant owners in identifying areas for improvement and increasing their ratings.
Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks Using a Malware Dataset: A Comparative Analysis

The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security-sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models' performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNN's accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.
Handwritten Chinese Character Recognition Based on Morphology and Transfer Learning

Handwritten Chinese character recognition is a research hotspot in the field of computer vision, aiming at the problem that the inaccurate segmentation of adhesive characters in reality, resulting in the low accuracy of convolutional networks in unbalanced character recognition tasks, this paper designs a handwritten Chinese character recognition method based on morphology and transfer learning. Firstly, the adhesive character is morphologically processed to facilitate cutting; Then, the ResNet101 model based on transfer learning is used to extract and classify the features of the cut characters. Experiments on the CASIA-HWDB dataset showed that the proposed model effectively completed the segmentation task of adhesive character, and exceeded other commonly used classification models in the accuracy of handwritten Chinese character recognition, reaching 92.16 percent Top-1 accuracy.
A Survey of Explainable Artificial Intelligence Approaches for Sentiment Analysis

Nowadays, the problems of sentiment analysis, opinion mining and fake news detection are very important. Artificial intelligence methods are widely used to analyze opinions in social media and to obtain the results in an efficient manner and with high accuracy. The most common approaches are ML methods using nonlinear models and complex structures, e.g. deep neural networks, SVM or random forest. These methods have only one disadvantage: they work as black-boxes so it is hard to understand how they predict the results and lowers trust to such methods. In this paper we present a survey of explainable artificial intelligence methods that are used in sentiment analysis area, analyze the differences between XAI in SA and feature selection methods and indicate trends and challenges in this area.
Creating High-Resolution Adversarial Images Against Convolutional Neural Networks with the Noise Blowing-Up Method

Convolutional Neural Networks (CNNs) are widely used for image recognition tasks but are vulnerable to attacks. Most existing attacks create adversarial images of a size equal to the CNN's input size; mainly because creating adversarial images in the high-resolution domain leads to substantial speed, adversity, and visual quality challenges. In a previous work, we developed a method that lifts any existing attack working efficiently in the CNN's input size domain to the high-resolution domain. This method successfully addressed the first two challenges but only partially addressed the third one. The present article provides a crucial refinement of this strategy that, while keeping all its other features, substantially increases the visual quality of the obtained high-resolution adversarial images. The refinement amounts to a blowing-up to the high-resolution domain of the adversarial noise created in the low-resolution domain. Adding this blown-up noise to the clean original high-resolution image leads to an almost indistinguishable high-resolution adversarial image. The noise blowing-up strategy is successfully tested on an evolutionary-based black-box targeted attack against VGG-16 trained on ImageNet, with 10 high-resolution clean images.
Scheduling Deep Learning Training in GPU Cluster Using the Model-Similarity-Based Policy

Training large neural networks with huge amount of data using multiple Graphic Processing Units (GPUs) became widespread with the emergence of Deep Learning (DL) technology. It is usually operated in datacenters featuring multiple GPU clusters, which are shared amongst users. However, different GPU architectures co-exist on the market and differ in training performance. To maximise the utilisation of a GPU cluster, the scheduler plays an important role in managing the resources by dispatching the jobs to the GPUs. An efficient scheduling strategy should take into account that the training performance of each GPU architecture varies for the different DL models. In this work, an original model-similarity-based scheduling policy is introduced that takes into account the GPU architectures that match with the DL models. The results show that using the model-similarity-based scheduling policy for distributed training across multiple GPUs of a DL model with a large batch size can reduce the makespan.
Extracting Top-k High Utility Patterns from Multi-level Transaction Databases

Several approaches have been introduced to solve the problem of high utility pattern mining (HUPM). However, the proposed algorithms require a minimum utility threshold before execution. This task is impractical for end users as they do not know utility distributions in the transaction datasets. The output will contain too many patterns if this value is too low. In contrast, if the threshold is set too high, the result would be empty or insufficient for analysis. Recently, HUPM was extended to work with hierarchical transaction datasets. With the search space of the mining task expanded, selecting a proper threshold is far more challenging. To address this issue, we propose a top- $$k$$ high utility pattern mining method from multi-level transactions databases. The users only need to specify a $$k$$ value, denotes the desired number of patterns of interest. To the best of our knowledge, the method proposed in our work is the first to address this mining topic. Experiments on both real and synthetic hierarchical datasets were extensively conducted to evaluate the performance of the proposed algorithm.
Autonomous Landing Algorithm for UAV on a Mobile Robotic Platform with a Fractal Marker

This article describes experiments related to the simulation of automatic landing of UAV on a mobile robotic platform using computer vision and a control system based on PID and polynomial regulators within the Gazebo environment. Algorithm has been developed to generate control inputs for maintaining the velocities of the UAV based on the data from the computer vision system and feedback from onboard sensor devices. A series of experiments were conducted at altitudes ranging from 5 to 20 m, which allowed for identifying limitations that affect the successful landing of the UAV. Measurements were taken of the landing time and landing error, calculated as the distance between the platform center and the UAV’s center of mass upon completion of the landing. The average landing time and error ranged from 19.64 s and 0.16 m at an initial altitude of 5 m to 121.01 s and 0.27 m at an initial altitude of 20 m. Analysis of the obtained results revealed that both the average error and landing time increase with the initial altitude, which is associated with the accuracy of marker recognition at altitudes above 15 m. The obtained results can be valuable for further improvement of systems for automatic landing of UAVs on mobile platforms.
Metrics for Evaluating Explainable Recommender Systems

Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, internal state, and capabilities, that are in line with their actual design. Explanations and transparent behaviour of the system should inspire trust and, ultimately, lead to more persuasive recommendations. Here, explanations convey reasons why a recommendation is given or how the system forms its recommendations. This paper focuses on the question how such claims about effectiveness of explanations can be evaluated. Accordingly, we investigate various models that are used to assess the effects of explanations and recommendations. We discuss objective and subjective measurement and argue that both are needed. We define a set of metrics for measuring the effectiveness of explanations and recommendations. The feasibility of using these metrics is discussed in the context of a specific explainable recommender system in the food and health domain.