Reviewerly/exHarmony huggingface dataset:
Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning

The intelligent identification of coal gangue on industrial conveyor belts is a crucial technology for the precise sorting of coal gangue. To address the issues in coal gangue detection algorithms, such as high false negative rates, complex network structures, and substantial model weights, an optimized coal gangue detection algorithm based on YOLOv5s is proposed. In the backbone network, a feature refinement module is employed for feature extraction, enhancing the capability to extract features for coal and gangue. The improved BIFPN structure is employed as the feature pyramid, augmenting the model’s capability for cross-scale feature fusion. In the prediction layer, the ESIOU is utilized as the bounding box regression loss function to rectify the misalignment issue between predicted and actual box angles. This approach expedites the convergence speed of the network while concurrently enhancing the accuracy of coal gangue detection. Channel pruning is implemented on the network to diminish model computational complexity and weight, consequently augmenting detection speed. The experimental results demonstrate that the refined YOLOv5s coal gangue detection algorithm outperforms the original YOLOv5s algorithm, achieving a notable accuracy enhancement of 2.2% to reach 93.8%. Concurrently, a substantial reduction in model weight by 38.8% is observed, resulting in a notable 56.2% increase in inference speed. These advancements meet the detection requirements for scenarios involving mixed coal gangue.
Discussing the Formal Components of Material Objects: A New Reply to Bennett

Abstract Recently mereological hylomorphism, the theory in which form and matter are considered to be proper parts of objects, has become very important among contemporary metaphysicians. The present work aims to analyse and dismantle Bennett’s criticism regarding the existence of formal proper parts. To do this, I will start by presenting Koslicki’s mereological hylomorphism. Next, I will focus on Bennett’s critique which seeks to deny the existence of formal proper parts. Finally, I will analyse critically the Bennett’s criticism focusing on the scenario of lump of clay and statue. I will show Bennett’s proposal is not explanatorily better than Koslicki’s hylomorphism because she needs to accept a counter-intuitive thesis. Therefore, we should prefer Koslicki’s mereological hylomorphism.
Dialogue System for Casual and Continuous Word Learning

Facilitating causal and continuous learning in individuals with low motivation presents a formidable challenge owing to the diverse methods and systems available for vocabulary acquisition. Therefore, this study developed a non‐task‐oriented dialogue system to facilitate casual and continuous word learning. Specifically, dialogue systems capable of processing text‐based inputs, encompassing words registered by learners, were formulated. Learners could engage in the acquisition of these words through informal conversation. The evaluation results demonstrated the effectiveness of the proposed method for fostering casual and continuous learning. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Beyond the pages: unleashing holograms and holographic technology in Caribbean academic libraries

Purpose This paper is intended to spark discourse on the impact of holograms and holographic technology on libraries and the possibility of their usage in Caribbean academic libraries for assisting users, especially those from the physically challenged community, as well as distance library users. Design/methodology/approach A thorough assessment of literature was done of all academic and higher institution libraries across the English-speaking Caribbean to determine the effective use of holograms and holographic technology in Caribbean libraries. Literature from several developed countries was studied, paying keen attention to the emerging holograms and holographic technology and the plausibility for inclusion and exploitation by academic libraries. Findings Revealing the possibilities for fostering great levels of efficacy, as well as saving time and reducing the possibility for inefficiencies in the system through smoother check-in and check-out while offering more accountability. Research limitations/implications This research focused primarily on holograms and holographic technology that can assist libraries and their users in the near future. It further attested to the need for there to be greater consideration for physically challenged individuals and their access to material from the library. Originality/value This paper is the genesis for discussion surrounding holograms and holographic technology in the English-speaking Caribbean. It sought to bring awareness to the possible uses of holograms while exploring the practical implications of its use in libraries across the English-speaking Caribbean.
Siri as an interactive pronunciation coach: its impact on EFL learners

Language is a powerful tool that facilitates communication, knowledge acquisition, and negotiation of meaning, but it is a complex skill for English as foreign language (EFL) learners who often encounter various challenges and complexities. Among the critical language skills, pronunciation holds a significant place. As much as it is challenging, many EFL teachers find it necessary to teach it explicitly, so exploring innovative approaches to address this challenge is essential. This preliminary study investigated how Siri, a voice assistant tool, can be integrated with in-class instruction to improve the pronunciation of EFL learners. The study demonstrated that Siri could lead to better learning gains for students than using only in-class instructions and feedback when combined with teacher instructions. Forty EFL learners participated; twenty-five who did not have iPhones compatible with Siri were treated as the control group, whereas fifteen with iPhone devices compatible with Siri were treated as the experimental group. Each group was assigned an experienced English teacher to help give instructions and feedback. The primary data collection method involved students’ reading the assigned tasks, recording, and sending them to their respective teachers and peers for feedback and peer review, respectively. Cohen’s kappa coefficient and independent t-test were used to analyze the data. The results showed that Siri improved the students’ pronunciation skills more than in-class instruction.
Optimal trajectory planning of robot energy consumption based on improved sparrow search algorithm

In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.
WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model

This paper proposes a novel weight-enriched ranking-based baseline model, WeightedSLIM, aiming to provide more accurate top-N recommendations from implicit user feedback. The model utilizes ItemRank to calculate the ranking score of each item, which is then used as an item weight within the Sparse Linear Model (SLIM), while using Bayesian Personalized Ranking (BPR) to optimize the item similarity matrix. Experiments, conducted for performance comparison of the proposed model with existing recommendation models, demonstrate that it can indeed provide better recommendations and can be used as a strong baseline for top-N recommendations.
<i>Clingraph</i>: A System for ASP-based Visualization

Abstract We present the Answer Set Programming (ASP)-based visualization tool clingraph , which aims at visualizing various concepts of ASP by means of ASP itself. This idea traces back to the aspviz tool and clingraph redevelops and extends it in the context of modern ASP systems. More precisely, clingraph takes graph specifications in terms of ASP facts and hands them over to the graph visualization system graphviz . The use of ASP provides a great interface between logic programs and/or answer sets and their visualization. Also, clingraph offers a Python application programming interface (API) that extends this ease of interfacing to clingo ’s API and in turn to connect and monitor various aspects of the solving process.
TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm.
Enhancing Human Action Recognition with 3D Skeleton Data: A Comprehensive Study of Deep Learning and Data Augmentation

Human Action Recognition (HAR) is an important field that identifies human behavior through sensor data. Three-dimensional human skeleton data extracted from the Kinect depth sensor have emerged as a powerful alternative to mitigate the effects of lighting and occlusion of traditional 2D RGB or grayscale image-based HAR. Data augmentation is a key technique to enhance model generalization and robustness in deep learning while suppressing overfitting to training data. In this paper, we conduct a comprehensive study of various data augmentation techniques specific to skeletal data, which aim to improve the accuracy of deep learning models. These augmentation methods include spatial augmentation, which generates augmented samples from the original 3D skeleton sequence, and temporal augmentation, which is designed to capture subtle temporal changes in motion. The evaluation covers two publicly available datasets and a proprietary dataset and employs three neural network models. The results highlight the impact of temporal augmentation on model performance on the skeleton datasets, while exhibiting the nuanced impact of spatial augmentation. The findings underscore the importance of tailoring augmentation strategies to specific dataset characteristics and actions, providing novel perspectives for model selection in skeleton-based human action recognition tasks.
COVID-19 vaccine confidence index and economic uncertainty indices: empirical evidence from the payment-based system cryptocurrency market

Purpose We investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns. Design/methodology/approach We used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrency’s countries and the data spans from 22 March 2021 to 6 May 2022. Findings This research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figures’ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns. Originality/value This study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returns’ new determinant.
A Multibranch Deep Neural Network for the Superresolution of Solar Magnetograms

Abstract The existing superresolution (SR) models for solar magnetograms are mostly borrowed from the SR models for natural images. They are less effective for processing solar magnetograms with a very large dynamic range and very rich image features. In this paper, a multibranch superresolution (MBSR) model is specially designed for solar magnetograms. First, we split a low-resolution magnetogram into a group of overlapping image patches, and classify them into three categories according to magnetic flux intensity, namely simple, medium, and complex. Then, image patches of each category are fed into the corresponding branch of the MBSR network, the lightweight branch for simple image patches and the heavyweight one for complex image patches. The advantage of such a strategy is twofold. On the one hand, active regions are allocated more computational resources to train a heavyweight branch more fully, while quiet regions are allocated fewer computational resources to train a lightweight branch for saving computational resources. On the other hand, a lightweight network with a simple nonlinear function is preferable to simple regions, while a heavyweight one may be underfitting. Additionally, to verify the effectiveness of the proposed model, a magnetic field structure similarity metric is proposed to measure the artifacts of the generated high-resolution (HR) magnetograms. Experimental results show that the proposed MBSR model generates HR magnetograms highly consistent with the HMI ones, and achieves the best performance over five objective metrics, including peak signal-to-noise ratio and structure similarity, etc.
Deep learning for cancer cell detection: do we need dedicated models?

Abstract This article proposes a novel concept for a two-step Ki-67/lymphocytes classification cell detection pipeline on Ki-67 stained histopathological slides utilizing commonly available and undedicated, in terms of the medical problem considered, deep learning models. Models used vary in implementation, complexity, and applications, allowing for the use of a dedicated architecture depending on the physician’s needs. Moreover, generic models’ performance was compared with the problem-dedicated one. Experiments highlight that with relatively small training datasets, commonly used architectures for instance segmentation and object detection are competitive with a dedicated model. To ensure generalization and minimize biased sampling, experiments were performed on data derived from two unrelated histopathology laboratories.
Circumventing Stragglers and Staleness in Distributed CNN using LSTM

INTRODUCTION: Using neural networks for these inherently distributed applications is challenging and time-consuming. There is a crucial need for a framework that supports a distributed deep neural network to yield accurate results at an accelerated time.&#x0D; METHODS: In the proposed framework, any experienced novice user can utilize and execute the neural network models in a distributed manner with the automated hyperparameter tuning feature. In addition, the proposed framework is provided in AWS Sage maker for scaling the distribution and achieving exascale FLOPS. We benchmarked the framework performance by applying it to a medical dataset. &#x0D; RESULTS: The maximum performance is achieved with a speedup of 6.59 in 5 nodes. The model encourages expert/ novice neural network users to apply neural network models in the distributed platform and get enhanced results with accelerated training time. There has been a lot of research on how to improve the training time of Convolutional Neural Networks (CNNs) using distributed models, with a particular emphasis on automating the hyperparameter tweaking process. The study shows that training times may be decreased across the board by not just manually tweaking hyperparameters, but also by using L2 regularization, a dropout layer, and ConvLSTM for automatic hyperparameter modification.&#x0D; CONCLUSION: The proposed method improved the training speed for model-parallel setups by 1.4% and increased the speed for parallel data by 2.206%. Data-parallel execution achieved a high accuracy of 93.3825%, whereas model-parallel execution achieved a top accuracy of 89.59%.
Sequence-Aware Graph Neural Network Incorporating Neighborhood Information for Session-Based Recommendation

Abstract Session-based recommendation is an important part of many e-commerce websites. Its purpose is to make recommendations based on the interaction behavior of anonymous users in a short period of time. Graph neural network can capture complex interactions in sessions, and they are a class of methods with better performance among existing session-based recommendation models. However, most existing models only learn item features for a single session. At the same time, GNNs are not good at capturing long-distance dependencies in a session, which leads to limited performance improvements for them. To address this deficiency, we propose sequence-aware graph neural network incorporating neighborhood information, named SAN-GNN. We construct a session graph and a neighborhood graph to learn item representations. For neighborhood graph, we propose a neighborhood Information extractor for the neighborhood graph to learn the neighbor information of nodes on the neighborhood graph. For the graph model of the session layer, we propose a session graph attention(SGA) module to learn the item representation of the target session. SGA uses Ta-LSTM to learn sequential dependencies in the target session and uses GCN with an integrated attention mechanism to learn node feature relevant to the target item. Exhaustive experiments on three public real-world datasets show that SAN-GNN outperforms the most advanced existing session-based method.
Hybrid time-spatial video saliency detection method to enhance human action recognition systems

Abstract Since digital media has become increasingly popular, video processing has expanded in recent years. Video processing systems require high levels of processing, which is one of the challenges in this field. Various approaches, such as hardware upgrades, algorithmic optimizations, and removing unnecessary information, have been suggested to solve this problem. This study proposes a video saliency map based method that identifies the critical parts of the video and improves the system’s overall performance. Using an image registration algorithm, the proposed method first removes the camera’s motion. Subsequently, each video frame’s color, edge, and gradient information are used to obtain a spatial saliency map. Combining spatial saliency with motion information derived from optical flow and color-based segmentation can produce a saliency map containing both motion and spatial data. A nonlinear function is suggested to properly combine the temporal and spatial saliency maps, which was optimized using a multi-objective genetic algorithm. The proposed saliency map method was added as a preprocessing step in several Human Action Recognition (HAR) systems based on deep learning, and its performance was evaluated. Furthermore, the proposed method was compared with similar methods based on saliency maps, and the superiority of the proposed method was confirmed. The results show that the proposed method can improve HAR efficiency by up to 6.5% relative to HAR methods with no preprocessing step and 3.9% compared to the HAR method containing a temporal saliency map.
Bayesian active learning with model selection for spectral experiments

Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments. Therefore, we proposed an active learning with model selection method using multiple parametric models as learning models. Important points for model selection and its parameter estimation were actively measured using Bayesian posterior distribution. The present study demonstrated the effectiveness of our proposed method for spectral deconvolution and Hamiltonian selection in X-ray photoelectron spectroscopy.
Underactuated unmanned surface vehicles formation of obstacle avoidance and assembly: A disturbed fluid-based solution

Unmanned surface vehicles (USVs) are highly manoeuvrable and autonomous, and hold significant potential for both military and civilian applications, particularly in formation operations. However, because of their underactuated nature, USVs struggle to navigate in complex maritime conditions during formation. At present, most of the technology is devoted to Unmanned Areial Vehicles and ground robots; these methods cannot be well applied to underactuated USVs. Moreover, the rationality of local path planning decision-making for underactuated USVs formation is still lacking. This study proposes an interfered fluid dynamic system (IFDS)-based local path planning method, called USV-IFDS, specifically designed for the formation of underactuated USVs. This method incorporates the IFDS obstacle avoidance approach, while adapting it through modifications and the inclusion of the kinematic constraints of USVs, thereby enhancing its applicability to the maritime environment. By decomposing the flow field velocity vector and implementing a formation control strategy, we effectively address the challenges in forming underactuated USVs and enhance the efficiency of USV formation local path planning. The proposed formation technique is predicated on the highly robust virtual structure method. Simulations of formation local path planning indicate that our method produces smooth paths, therefore validating its practical applicability to underactuated USV formations.