Reviewerly/exHarmony huggingface dataset:
CaFe DBSCAN: A Density-based Clustering Algorithm for Causal Feature Learning

Causal Feature Learning (CFL) infers macro-level causes (e.g., an aggregation of pixels in a traffic light image) from micro-level data (e.g., pixels of the image) by clustering the predicted probabilities of effect states (e.g., state of the traffic light). The current method for CFL uses a two-step procedure. First, a classifier for the effect states is trained, and afterwards, the predicted effect state probabilities are clustered. With CaFe DBSCAN, we present a novel density-based clustering method that conducts CFL directly by estimating conditional probabilities during clustering. To this end, we introduce the notion of clustering regions with similar conditional probabilities of the effect states given their micro-level data points. Our single-step approach has the following benefits: (1) CaFe DBSCAN introduces a comprehensive approach to Causal Feature Learning. Unlike existing methods, CaFe DBSCAN uses a probabilistic framework and does not require separate classification and clustering steps implemented by different algorithms relying on various assumptions, parameter settings, and optimization goals. (2) We do not need to train and tune a classifier first, hence the algorithm is more runtime-efficient than the current approach. (3) Due to the properties of density-based clustering algorithms, CaFe DBSCAN is robust against noise and outliers, which leads to purer clusters. (4) Our algorithm automatically infers a reasonable number of clusters, i.e., macro-level causes. We demonstrate the benefits of CaFe DBSCAN on synthetic and real-world data.
STG-Siam: siamese object tracking based on sparse self attention and graph information interaction

Aiming at the problems of insufficient foreground and background discrimination ability of the tracking algorithm in complex scenes, and limited ability to cope with the situation of partial occlusion of the target, we propose a target tracking algorithm based on sparse self-attention interaction with graph information (STG-Siam). To deal with background interference during tracking, a sparse self-attention mechanism is designed to make the model focus more on foreground information and suppress attention to background information. To better handle partial occlusions during tracking, template features and search region features are mapped to graph structures, and local information interaction between graph nodes is used to generate higher quality feature response maps. Experiments on the publicly available datasets VOT2018 and UAV123 demonstrate the effectiveness of the proposed STG-Siam, which can achieve accurate and robust tracking in complex scenarios.
An object detection method based on YOLOv3 on infrared images

In order to improve the accuracy and speed of object detection on infrared images, an object detection method based on YOLOv3 is proposed. First, the rectangle filling, Mosaic data augmentation and adaptive anchors are used for data preprocessing, which lays the foundation for subsequent network training. Secondly, the convolution calculation layer and the cross stage partial module are used for the lightweight design to achieve high-speed detection while maintaining high-accurate. Then, spatial pyramid pooling is used to improve the learning ability of the network by enhancing the receptive field. Finally, bottom-up path augmentation is used to improve multi-layer feature fusion, which improves the transmission speed and utilization rate of low-level feature information. The experimental results show that the proposed method can detect cars accurately. Compared YOLOv3 methods, the proposed method has higher accuracy and faster speed, which meets the requirements of accurate and rapid detection on infrared images.
Tracking of moving human in different overlapping cameras using Kalman filter optimized

Abstract Tracking objects is a crucial problem in image processing and machine vision, involving the representation of position changes of an object and following it in a sequence of video images. Though it has a history in military applications, tracking has become increasingly important since the 1980s due to its wide-ranging applications in different areas. This study focuses on tracking moving objects with human identity and identifying individuals through their appearance, using an Artificial Neural Network (ANN) classification algorithm. The Kalman filter is an important tool in this process, as it can predict the movement trajectory and estimate the position of moving objects. The tracking error is reduced by weighting the filter using a fuzzy logic algorithm for each moving human. After tracking people, they are identified using the features extracted from the histogram of images by ANN. However, there are various challenges in implementing this method, which can be addressed by using Genetic Algorithm (GA) for feature selection. The simulations in this study aim to evaluate the convergence rate and estimation error of the filter. The results show that the proposed method achieves better results than other similar methods in tracking position in three different datasets. Moreover, the proposed method performs 8% better on average than other similar algorithms in night vision, cloud vision, and daylight vision situations.
High Quality and Resilient Historical Vector Data

While digitized georeferenced maps have become increasingly available to researchers over the past years, astonishingly little progress has been made in the realm of the production of historical vector data. In contrast to the well documented workflows for georeferencing and curation of digitized historical maps, the challenges and costs of spatiotemporal data modelling and creation seem to pose a high barrier for individual researchers and institutions alike.
Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users

Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users' experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via a blanket filtering operation, many websites are increasingly denying access if an ad blocker is active. Moreover, ad blockers often do not filter out internal ads injected by the websites themselves. Therefore, we devised an algorithm to automatically identify contextually deceptive ads on a web page. Specifically, we built a detection model that leverages a multi-modal combination of handcrafted and automatically extracted features to determine if a particular ad is contextually deceptive. Evaluations of the model on a representative test dataset and 'in-the-wild' random websites yielded F1 scores of 0.86 and 0.88, respectively.
TCCCD: Triplet-Based Cross-Language Code Clone Detection

Code cloning is a common practice in software development, where developers reuse existing code to accelerate programming speed and enhance work efficiency. Existing clone-detection methods mainly focus on code clones within a single programming language. To address the challenge of code clone instances in cross-platform development, we propose a novel method called TCCCD, which stands for Triplet-Based Cross-Language Code Clone Detection. Our approach is based on machine learning and can accurately detect code clone instances between different programming languages. We used the pre-trained model UniXcoder to map programs written in different languages into the same vector space and learn their code representations. Then, we fine-tuned TCCCD using triplet learning to improve its effectiveness in cross-language clone detection. To assess the effectiveness of our proposed approach, we conducted thorough comparative experiments using the dataset provided by the paper titled CLCDSA (Cross Language Code Clone Detection using Syntactical Features and API Documentation). The experimental results demonstrated a significant improvement of our approach over the state-of-the-art baselines, with precision, recall, and F1-measure scores of 0.96, 0.91, and 0.93, respectively. In summary, we propose a novel cross-language code-clone-detection method called TCCCD. TCCCD leverages the pre-trained model UniXcode for source code representation and fine-tunes the model using triplet learning. In the experimental results, TCCCD outperformed the state-of-the-art baselines in terms of the precision, recall, and F1-measure.
Trustworthy Artificial Intelligence Methods for Users’ Physical and Environmental Security: A Comprehensive Review

Artificial Intelligence is an indispensable element of the modern world, constantly evolving and contributing to the emergence of new technologies. We meet it in everyday applications, primarily using intelligent systems that aim to improve our lives. Artificial Intelligence techniques must inspire users’ trust because they significantly impact virtually every industry and person. For this reason, systems using Artificial Intelligence are subject to many requirements to verify their trustworthiness in various aspects. This review focused on users’ physical and environmental security, considering the safety and robustness dimensions of Trustworthy Artificial Intelligence. We examined these Trustworthy Artificial Intelligence solutions and dimensions because security is one of the most-critical aspects of human life and can be considered in many different contexts. We examined the trustworthiness of Artificial Intelligence techniques in systems supporting road safety and securing computer network users. Also, we analyzed the challenges and requirements of the newly designed solutions using Trustworthy Artificial Intelligence methods. Verifying Trustworthy Artificial Intelligence solutions and their practical use will increase users’ physical and environmental security.
Trajectory-Driven Metaheuristic Approach Using a Second-Order Model

To obtain the optimal solution in an optimization problem where multiple local solutions exist, one must navigate the search space using an objective function and effective search patterns.
Introduction to Metaheuristic Schemes: Characteristics, Properties, and Importance in Solving Optimization Problems

In this chapter, the primary ideas behind metaheuristic schemes are introduced. The goal of this chapter is to provide an overview of the features and qualities of these approaches. Additionally, an important aim of this chapter is to acknowledge the significance of metaheuristic methods in addressing optimization problems that cannot be effectively solved using conventional techniques.
Exploring the Potential of Agent Systems for Metaheuristics

Agent-based modeling is a way of simulating complex systems by creating individual agents that follow simple rules. The interactions between these agents can lead to complex global patterns that were not explicitly programmed. In recent years, there has been a growing interest in using agent-based modeling for optimization problems.
Multi-objective Optimization of Anisotropic Diffusion Parameters for Enhanced Image Denoising

Noise often corrupts images during their acquisition, storage, and transmission processes, resulting in a degradation of image quality. The main challenge in image denoising is to remove the corrupted information while preserving the fine details of the original image. Anisotropic Diffusion (AD) is a well-established method for noise removal in digital images that effectively preserves the image edges. However, selecting the appropriate parameters for the AD operation significantly influences the filtering outcomes. Despite its importance, the automatic determination of AD parameters based on image requirements has received limited attention in the literature. This chapter introduces a novel multi-objective approach for obtaining optimal AD parameters that yield effective filtering results. The presented methodology aims to strike the best possible balance between two conflicting objectives: minimizing noise content in the image while maximizing contrast. Improving one objective without compromising the other is not feasible. To address this, the presented approach employs the Non-dominated Sorting Genetic Algorithm based on reference points (NSGA-III), which is known as a robust and powerful algorithm for multi-objective optimization. The final solution is determined by analyzing the Optimal Pareto front. Experimental results demonstrate that the presented method outperforms existing filtering algorithms in terms of both visual quality and standard performance metrics.
Collaborative Hybrid Grey Wolf Optimizer: Uniting Synchrony and Asynchrony

The Grey Wolf Optimizer (GWO) is a relatively new metaheuristic approach that has shown promising results in solving continuous optimization problems. The Grey Wolf Optimizer (GWO) is a relatively new metaheuristic approach that has shown promising results in solving continuous optimization problems. However, it faces challenges when applied to various optimization formulations that require different exploration–exploitation rates. This chapter introduces a modified version of GWO called the Group-based Synchronous-Asynchronous Grey Wolf Optimizer (GSAGWO). GSAGWO divides the population into three groups, each containing an equal number of candidate solutions. The optimization process employs a control strategy that allows for adjusting the exploration–exploitation balance. Additionally, the algorithm incorporates a mechanism to enhance population diversity by exchanging elements among the groups. The performance of GSAGWO is evaluated using the CEC2017 benchmark and several real-world engineering problems. Comparative analyses with other state-of-the-art metaheuristic algorithms demonstrate the effectiveness and accuracy of the discussed technique.
Efficient Image Contrast Enhancement by Using the Moth Swarm Algorithm

Image Contrast Enhancement (ICE) plays a vital role in various applications related to image processing and computer vision. Its primary aim is to enhance the quality of visual information in processed images. However, the presence of noise and small pixel sets in images not only hampers their visualization but also hinders the effectiveness of ICE algorithms. The inclusion of irrelevant information disrupts the appropriate distribution of significant pixel intensities, leading to issues such as undesirable artifacts, noise amplification, over saturation, and compromised human visual perception in most existing ICE methods.
Dynamic Multimodal Function Optimization: An Evolutionary-Mean Shift Approach

Numerous metaheuristic-based dynamic optimization algorithms have emerged recently. While these algorithms excel in locating a single global optimum, they fall short in identifying multiple optimal solutions. The primary objective of dynamic multimodal optimization is to identify several optimal solutions for a problem where the objective function changes over time. The discovery of multiple optimal solutions, both global and local, holds significant importance for numerous applications. The best implementable alternative may not always be the optimal solution due to practical limitations. Surprisingly, the literature has paid scant attention to the problem of dynamic multimodal optimization using evolutionary principles. Conversely, mean shift, a nonparametric iterative process, is capable of detecting local maxima in a density function represented by a sample set. Mean shift exhibits adaptive qualities that enable it to identify local maxima in dynamic environments. In this chapter, we present a mean shift approach to detect global and local optima in dynamic optimization problems. The presented method modifies the search strategy of mean shift by considering both density and fitness values of candidate solutions. To expedite the convergence process, a competitive memory and dynamic strategy are incorporated to leverage valuable information from previous environments. Consequently, the present approach effectively identifies a significant number of global and local optima in dynamic environments. To showcase its performance, we compare the present algorithm with well-known dynamic optimization methods using the CEC competition benchmark generator. Experimental results demonstrate the present scheme's high accuracy and robustness, positioning it as a highly competitive solution.
Formant and Voice Source Characteristics of Vowels in Chinese National Singing and Bel Canto. A Pilot Study

There have been numerous reports on the acoustic characteristics of singers' vowel articulation and phonation, and these studies cover many phonetic dimensions, such as fundamental frequency (F0), intensity, formant frequency, and voice quality.Taking the three representative vowels (/a/, /i/, /u/) in Chinese National Singing and Bel Canto as the research object, the present study investigates the differences and associations in vowel articulation and phonation between Chinese National Singing and Bel Canto using acoustic measures, for example, F0, formant frequency, long-term average spectrum (LTAS).The relationship between F0 and formant indicates that F1 is proportional to F0, in which the female has a significant variation in vowel /a/. Compared with the male, the formant structure of the female singing voice differs significantly from that of the speech voice. Regarding the relationship between intensity and formant, LTAS shows that the Chinese National Singing tenor and Bel Canto baritone have the singer's formant cluster when singing vowels, while the two sopranos do not.The systematic changes of formant frequencies with voice source are observed. (i) F1 of the female vowel /a/ has undergone a significant tuning change in the register transition, reflecting the characteristics of singing genres. (ii) Female singers utilize the intrinsic pitch of vowels when adopting the register transition strategy. This finding can be assumed to facilitate understanding the theory of intrinsic vowel pitch and revise Sundberg's hypothesis that F1 rises with F0. A non-linear relationship exists between F1 and F0, which adds to the non-linear interaction of the formant and vocal source. (iii) The singer's formant is affected by voice classification, gender, and singing genres.
A Model Personalization-based Federated Learning Approach for Heterogeneous Participants with Variability in the Dataset

Federated learning is an emerging paradigm that provides privacy-preserving collaboration among multiple participants for model training without sharing private data. The participants with heterogeneous devices and networking resources decelerate the training and aggregation. The dataset of the participant also possesses a high level of variability, which means the characteristics of the dataset change over time. Moreover, it is a prerequisite to preserve the personalized characteristics of the local dataset on each participant device to achieve better performance. This article proposes a model personalization-based federated learning approach in the presence of variability in the local datasets. The approach involves participants with heterogeneous devices and networking resources. The central server initiates the approach and constructs a base model that executes on most participants. The approach simultaneously learns the personalized model and handles the variability in the datasets. We propose a knowledge distillation-based early-halting approach for devices where the base model does not fit directly. The early halting speeds up the training of the model. We also propose an aperiodic global update approach that helps participants to share their updated parameters aperiodically with server. Finally, we perform a real-world study to evaluate the performance of the approach and compare with state-of-the-art techniques.
2D Shape Detection for Solving Geometry Word Problems

The increasing scale of education has decreased the teacher–student ratio, making it difficult to keep track of student's progress individually. Therefore, teachers are leveraging artificial intelligence tools such as smart tutoring digital tools like learning management systems, and automated solvers for various subjects. Mathematics is one of the most fundamental subjects in school education. This work deals with facilitating the automatic solving of geometry math word problems (MWPs) at the elementary school level. To solve such problems, the information from the text as well as the accompanying figure needs to be interpreted. The preliminary task in solving such problems is to first identify which geometric shape is given in the question. Most existing systems or geometry solvers give the shape as one of the inputs to their systems to solve it further. In this work, the focus is on detecting the shape automatedly and proposing a technique for the same using traditional image processing methods. In addition, further, the triangles and quadrilaterals are classified into fine-grained classifications by taking into account their geometrical properties. Performance metrics like precision, recall, accuracy, and F1-score are used to present the results of this approach. The testing is done on various datasets that are readily available. The results show that on Geometry3K, GeoQA, and GeoS datasets the proposed technique attains an accuracy of 77.8%, 93.38%, and 95.02% and an F1 score of 60%, 71%, and 91.97% respectively. Like all other techniques, our system also has a limitation in that it does not detect all inscribed shapes.
A Survey of Software Implementations for the Number Theoretic Transform

This survey summarizes the software implementation knowledge of the Number Theoretic Transform (NTT)—a major subroutine of lattice-based cryptosystems. The NTT is a special type of Fast Fourier Transform defined over finite fields, and as such, NTT enables faster polynomial multiplication. There have been over a decade of implementations of NTT following different design methods (e.g., CPU vs. GPU), aiming different optimization goals (e.g., memory-footprint vs. high-throughput), and proposing different styles of optimizations at different abstraction levels (e.g., arithmetic vs. assembly). At the same time, there are several techniques for evaluating and mitigating implementation attacks on NTT. Yet there is no quick guideline to help new developers/practitioners or future researchers given the continuing industry and academic efforts on NTT implementations. Our goal in this paper is to provide an overview of a decade of work. To that end, we survey NTT software implementations and categorize them based on their target platforms, optimization goals, and implementation security enhancements. We furthermore provide an executive summary of the key ideas proposed in related works. We hope this paper to be a designer pit stop into the NTT world and help them navigate to their destination.
A Brief History of General‐to‐specific Modelling*

We review key stages in the development of general‐to‐specific modelling ( Gets ). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, Gets applications faced many criticisms, especially from accusations of ‘data mining’—no longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non‐stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables.