Reviewerly/exHarmony huggingface dataset:
A neighborhood information-based adaptive differential evolution for solving complex nonlinear equation system model

Nonlinear equation systems (NESs) widely exist in diverse fields, which have multiple equivalent roots in the search space. While several improved evolutionary algorithms (EAs) have been designed to solving NESs in recent years, they are ineffective to exploit the neighborhood information for evolution. This article thus develops a neighborhood information-based adaptive differential evolution (NIADE) to detect and track different roots in one run. It includes the following advantages: (i) a dynamic neighborhood size mechanism is designed to adjust the neighborhood size according to the state of evolution; (ii) a novel mutation strategy based on neighborhood information is introduced, which is beneficial to a completed search of the decision space; (iii) the neighborhood information is combined with adaptive parameter adjustment to enhance the search efficiency. The performance of NIADE is verified by solving the benchmark functions with different roots and different characteristics. The experimental results shows that NIADE has the ability to identify multiple roots simultaneously. Moreover, It achieves the better results than a number of existing advanced methods in terms of the success rate and root ratio.
Mixed Reality Annotation of Robotic-Assisted Surgery videos with real- time tracking and stereo matching

Robotic-Assisted Surgery (RAS) is beginning to unlock its potential. However, despite the latest advances in RAS, the steep learning curve of RAS devices remains a problem. A common teaching resource in surgery is the use of videos of previous procedures, which in RAS are almost always stereoscopic. It is important to be able to add virtual annotations onto these videos so that certain elements of the surgical process are tracked and highlighted during the teaching session. Including virtual annotations in stereoscopic videos turns them into Mixed Reality (MR) experiences, in which tissues, tools and procedures are better observed. However, an MR-based annotation of objects requires tracking and some kind of depth estimation. For this reason, this paper proposes a real-time hybrid tracking–matching method for performing virtual annotations on RAS videos. The proposed method is hybrid because it combines tracking and stereo matching, avoiding the need to calculate the real depth of the pixels. The method was tested with six different state-of-the-art trackers and assessed with videos of a sigmoidectomy of a sigma neoplasia, performed with a Da Vinci® X surgical system. Objective assessment metrics are proposed, presented and calculated for the different solutions. The results show that the method can successfully annotate RAS videos in real-time. Of all the trackers tested for the presented method, the CSRT (Channel and Spatial Reliability Tracking) tracker seems to be the most reliable and robust in terms of tracking capabilities. In addition, in the absence of an absolute ground truth, an assessment with a domain expert using a novel continuous-rating method with an Oculus Quest 2 Virtual Reality device was performed, showing that the depth perception of the virtual annotations is good, despite the fact that no absolute depth values are calculated.
Robust Multi-Drone Multi-Target Tracking to Resolve Target Occlusion: A Benchmark

Multi-drone multi-target tracking aims at collabo- ratively detecting and tracking targets across multiple drones and associating the identities of objects from different drones, which can overcome the shortcomings of single-drone object tracking. To address the critical challenges of identity association and target occlusion in multi-drone multi-target tracking tasks, we collect an occlusion-aware multi-drone multi-target tracking dataset named MDMT. It contains 88 video sequences with 39,678 frames, including 11,454 different IDs of persons, bicycles, and cars. The MDMT dataset comprises 2,204,620 bounding boxes, of which 543,444 bounding boxes contain target occlusions. We also design a multi-device target association score (MDA) as the evaluation criteria for the ability of cross-view target association in multi-device tracking. Furthermore, we propose a Multi-matching Identity Authentication network (MIA-Net) for the multi-drone multi-target tracking task. The local-global matching algorithm in MIA-Net discovers the topological relationship of targets across drones, efficiently solves the problem of cross-drone association, and effectively complements occluded targets with the advantage of multiple drone view mapping. Extensive experiments on the MDMT dataset validate the effectiveness of our proposed MIA-Net for the task of identity association and multi-object tracking with occlusions.
SGNR: A Social Graph Neural Network Based Interactive Recommendation Scheme for E-Commerce

Interactive Recommendation (IR) formulates the recommendation as a multi-step decision-making process which can actively utilize the individuals' feedback in multiple steps and optimize the long-term user benefit of recommendation. Deep Reinforcement Learning (DRL) has witnessed great application in IR for e-commerce. However, user cold-start problem impairs the learning process of the DRL-based recommendation scheme. Moreover, most existing DRL-based recommendations ignore user relationships or only consider the single-hop social relationships, which cannot fully utilize the social network. The fact that those schemes can not capture the multiple-hop social relationships among users in IR will result in a sub-optimal recommendation. To address the above issues, this paper proposes a Social Graph Neural network-based interactive Recommendation scheme (SGNR), which is a multiple-hop social relationships enhanced DRL framework. Within this framework, the multiple-hop social relationships among users are extracted from the social network via the graph neural network which can sufficiently take advantage of the social network to provide more personalized recommendations and effectively alleviate the user cold-start problem. The experimental results on two real-world datasets demonstrate that the proposed SGNR outperforms other state-of-the-art DRL-based methods that fail to consider social relationships or only consider single-hop social relationships.
Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer

Personalized federated learning (PFL) aims to train model(s) that can perform well on the individual edge-devices' data where the edge-devices (clients) are usually IoT devices like our mobile phones. The participating clients for cross-device settings, in general, have heterogeneous system capabilities and limited communication bandwidth. Such practical properties of the edge-devices, however, are overlooked by many recent work in PFL, which use the same model architecture across all clients and incur high communication cost by directly communicating the model parameters. In our work, we propose a novel and practical PFL framework named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COMET</small> where clients can use heterogeneous models of their own choice and do not directly communicate their model parameters to other parties. Instead, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COMET</small> uses <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clustered codistillation</i> , where clients use knowledge distillation to transfer their knowledge to other clients with similar data distributions. This presents a practical PFL framework for the edge-devices to train through IoT networks by lifting the heavy communication burden of communicating large models. We theoretically show the convergence and generalization properties of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COMET</small> and empirically show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">COMET</small> achieves high test accuracy with several orders of magnitude lower communication cost while allowing client model heterogeneity compared to the other state-of-the-art PFL methods.
Comparison of Topic Modelling Approaches in the Banking Context

Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discovery has shown great performances, however, they are not consistent in their results as these approaches suffer from data sparseness and inability to model the word order in a document. Thus, this study presents the use of Kernel Principal Component Analysis (KernelPCA) and K-means Clustering in the BERTopic architecture. We have prepared a new dataset using tweets from customers of Nigerian banks and we use this to compare the topic modelling approaches. Our findings showed KernelPCA and K-means in the BERTopic architecture-produced coherent topics with a coherence score of 0.8463.
FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm

Canny edge detection is one of the most widely used edge detection algorithms due to its superior performance. However, it is a complex, time-consuming process and has a high hardware cost. To overcome these issues, an improved Canny algorithm is proposed in this paper. It uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware costs. Otsu’s algorithm is introduced to adaptively determine the image threshold. However, Otsu’s algorithm has division operations, and the division operation is complex and has low efficiency and slow speed. We introduce a logarithmic unit to turn the division into a subtraction operation that is easy to implement by hardware but does not affect the selection of the threshold. Experimental results show that the system can detect the edge of the image well without adjusting the threshold value when the external environment changes and requires only 1.231 ms to detect the edges of the 512 × 512 image when clocked at 50 MHz. Compared with existing FPGA implementations, our implementation uses the least amount of logical resources. Thus, it is more suitable for platforms that have limited logical resources.
Binary light spectrum optimizer for knapsack problems: An improved model

This paper presents a binary variant of a novel physics-based meta-heuristic optimization algorithm, namely Light spectrum optimizer (LSO), for tackling both the 0–1 knapsack (KP01) and multidimensional knapsack problems (MKP). Because of the continuous nature of the standard LSO that contradicts the knapsack problem's discrete nature, two various transfer functions: S-shaped and X-shaped, are used to convert the continuous values produced by LSO into discrete ones. Some binary solutions produced by the binary LSO (BLSO) may be infeasible, so an improvement-repair strategy is used to convert those solutions into feasible ones by making some improvements on them. Moreover, the classical LSO was modified in this study to propose a new binary variant, namely BMLSO, with better exploration and exploitation operators for overcoming the knapsack problems. Additionally, a novel method, which simulates the swarm intelligence behaviors and the simulated binary crossover (SBX) to accelerate the convergence speed with avoiding stuck into local minima, has been proposed for producing a new binary variant of MLSO known as BHLSO. To verify the performance of the proposed binary variants of LSO, 45 benchmark instances of KP01 and 30 benchmark instances of MKP used commonly in the literature have been used in our experiments. The experimental findings show the superiority of BHLSO for both KP01 and MKP compared with several well-known algorithms in terms of CPU time, convergence speed, and accuracy.
Delving Deep Into One-Shot Skeleton-Based Action Recognition With Diverse Occlusions

Occlusions areuniversal disruptions constantly present in the real world. Especially for sparse representations, such as human skeletons, a few occluded points might destroy the geometrical and temporal continuity critically affecting the results. Yet, the research of data-scarce recognition from skeleton sequences, such as one-shot action recognition, does not explicitly consider occlusions despite their everyday pervasiveness. In this work, we explicitly tackle body occlusions for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> keleton-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ne-shot <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> ction <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ecognition (SOAR). We mainly consider two occlusion variants: 1) random occlusions and 2) more realistic occlusions caused by diverse everyday objects, which we generate by projecting the existing IKEA 3D furniture models into the camera coordinate system of the 3D skeletons with different geometric parameters, (e.g., rotation and displacement). We leverage the proposed pipeline to blend out portions of skeleton sequences of the three popular action recognition datasets (NTU-120, NTU-60 and Toyota Smart Home) and formalize the first benchmark for SOAR from partially occluded body poses. This is the first benchmark which considers occlusions for data-scarce action recognition. Another key property of our benchmark are the more realistic occlusions generated by everyday objects, as even in standard recognition from 3D skeletons, only randomly missing joints were considered. We re-evaluate existing state-of-the-art frameworks for SOAR in the light of this new task and further introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trans4SOAR</i> – a new transformer-based model which leverages three data streams and mixed attention fusion mechanism to alleviate the adverse effects caused by occlusions. While our experiments demonstrate a clear decline in accuracy with missing skeleton portions, this effect is smaller with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trans4SOAR</i> , which outperforms other architectures on all datasets. Although we specifically focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">occlusions</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trans4SOAR</i> additionally yields state-of-the-art in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">standard</i> SOAR without occlusion, surpassing the best published approach by 2.85% on NTU-120.
A Systematic literature review on implementation of virtual reality for learning

Learning in this modern era can use various kinds of technology. One of them is virtual reality. Virtual Reality is an environment generated by a computer that makes the user feel immersed with the object that is generated in their surroundings. This paper presents a previous review to understand the implementation of virtual reality in learning. This paper uses SLR as a research method. The study concludes that there is one device for implementing virtual reality called Head Mounted Displays (HMD) and Google Cardboard, two types of user experience, and how far virtual reality has been applied in learning so far. To this day the implementation of virtual reality for learning is still a bit difficult to do because the lack of suffice facilities due to limited funds.
The impact of central bank digital currency variation on firm's implied volatility

The paper investigates whether a firm's implied volatility is affected by the volatility of central bank digital currencies. Our sample covers 2853 listed companies in the United States from 2014 to 2018. First, we find the variation of central bank digital currency has a positive impact on a firm’s implied volatility. Second, the healthier firms’ conditions can reduce the relationship between central bank digital currency variation and a firm’s implied volatility. Third, the positive relation between central bank digital currency and firm’s implied volatility still exists in investment-grade, speculative-grade, and unrated firms. Finally, to eliminate the endogeneity problem, we adopt simultaneous equation models (SEM) and find our results are still robust after excluding endogenous concerns. Our research provides a reminder for corporate managers and new implications for policymakers.
Synthetic Tabular Data Evaluation in the Health Domain Covering Resemblance, Utility, and Privacy Dimensions

Synthetic tabular data generation is a potentially valuable technology with great promise for data augmentation and privacy preservation. However, prior to adoption, an empirical assessment of generated synthetic tabular data is required across dimensions relevant to the target application to determine its efficacy. A lack of standardized and objective evaluation and benchmarking strategy for synthetic tabular data in the health domain has been found in the literature.The aim of this paper is to identify key dimensions, per dimension metrics, and methods for evaluating synthetic tabular data generated with different techniques and configurations for health domain application development and to provide a strategy to orchestrate them.Based on the literature, the resemblance, utility, and privacy dimensions have been prioritized, and a collection of metrics and methods for their evaluation are orchestrated into a complete evaluation pipeline. This way, a guided and comparative assessment of generated synthetic tabular data can be done, categorizing its quality into three categories ("Excellent," "Good," and "Poor"). Six health care-related datasets and four synthetic tabular data generation approaches have been chosen to conduct an analysis and evaluation to verify the utility of the proposed evaluation pipeline.The synthetic tabular data generated with the four selected approaches has maintained resemblance, utility, and privacy for most datasets and synthetic tabular data generation approach combination. In several datasets, some approaches have outperformed others, while in other datasets, more than one approach has yielded the same performance.The results have shown that the proposed pipeline can effectively be used to evaluate and benchmark the synthetic tabular data generated by various synthetic tabular data generation approaches. Therefore, this pipeline can support the scientific community in selecting the most suitable synthetic tabular data generation approaches for their data and application of interest.
An Improved Adam Optimization Algorithm Combining Adaptive Coefficients and Composite Gradients Based on Randomized Block Coordinate Descent

An improved Adam optimization algorithm combining adaptive coefficients and composite gradients based on randomized block coordinate descent is proposed to address issues of the Adam algorithm such as slow convergence, the tendency to miss the global optimal solution, and the ineffectiveness of processing high-dimensional vectors. The adaptive coefficient is used to adjust the gradient deviation value and correct the search direction firstly. Then, the predicted gradient is introduced, and the current gradient and the first-order momentum are combined to form a composite gradient to improve the global optimization ability. Finally, the random block coordinate method is used to determine the gradient update mode, which reduces the computational overhead. Simulation experiments on two standard datasets for classification show that the convergence speed and accuracy of the proposed algorithm are higher than those of the six gradient descent methods, and the CPU and memory utilization are significantly reduced. In addition, based on logging data, the BP neural networks optimized by six algorithms, respectively, are used to predict reservoir porosity. Results show that the proposed method has lower system overhead, higher accuracy, and stronger stability, and the absolute error of more than 86% data is within 0.1%, which further verifies its effectiveness.
Linear manipulator: Motion control of an n-link robotic arm mounted on a mobile slider

Linear manipulators are versatile linear robotics systems that can be reprogrammed to accommodate product changes quickly and are flexible to meet unique requirements. Such robotic systems tend to have higher accuracy, making them the perfect automation solution for those mundane, repetitious tasks. With the demand for linear systems in real-life applications expanding consistently, this paper addresses motion planning and control (MPC) of a new modified unanchored linear manipulator consisting of an n-link robotic arm mounted on a mobile slider along a rail. Using the method of the Lyapunov-based Control Scheme (LbCS), new centralized acceleration-based controllers are designed for the navigation of the system to an unreachable target. Via the scheme, the unanchored manipulator can perform assigned tasks with enhanced reachability. The limitations and singularities of the linear manipulator are treated as artificial obstacles in this motion control scheme. The robotic arm manipulator utilized in this research can reposition its base link to a desired location in the workplace due to changes in work requirements. The effectiveness of the motion planner and the resulting acceleration-based control laws are validated numerically using the Runge-Kutta Method and illustrated via computer simulations. The controllers devised in this research can solve specific and targeted motion control problems of smart cities' modern mechanical systems. The unanchored linear manipulator could be used in various disciplines where pick-and-place, assembly, material handling, and surgical procedures are required.
Fuzzy rough nearest neighbour methods for detecting emotions, hate speech and irony

Due to the ever-expanding volumes of information available on social media, the need for reliable and efficient automated text understanding mechanisms becomes evident. Unfortunately, most current approaches rely on black-box solutions rooted in deep learning technologies. In order to provide a more transparent and interpretable framework for extracting intrinsic text characteristics like emotions, hate speech and irony, we propose to integrate fuzzy rough set techniques and text embeddings. We apply our methods to different classification problems originating from Semantic Evaluation (SemEval) competitions, and demonstrate that their accuracy is on par with leading deep learning solutions.