Reviewerly/exHarmony huggingface dataset:
Multi-View Scholar Clustering With Dynamic Interest Tracking

Scholar clustering has garnered increasing attention due to the explosive growth of scholar data. Although researchers have proposed many algorithms to cluster scholars, they typically focus on clustering scholars from the intrinsic view (scholars’ contents). These algorithms may lead to inaccurate and biased clustering results because they ignore the extrinsic view (scholar's specialty) and the changeability of scholars’ interest in each view. In this paper, we propose a multi-view scholar clustering topic model (MSCT), which integrates complementary information from both intrinsic and extrinsic views while considering dynamic scholar interests. Specifically, MSCT involves two novel schemes. The first one is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-view integration</i> , where MSCT collaboratively tracks scholars’ time-varying topic distribution from two views: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> intrinsic view</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extrinsic view</i> . The former exploits the details of different academic degrees in the title and information in the abstract; the latter leverages the specialty of different categories in the corresponding research field and research discipline. The second one is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic interest tracking</i> , which dynamically models each scholar's interest distribution in terms of the current scholar texts and previously estimated distribution through a newly designed collapsed Gibbs sampling algorithm. Experimental results demonstrate that MSCT can significantly outperform state-of-the-art algorithms.
Dual Fusion-Propagation Graph Neural Network for Multi-View Clustering

Deep multi-view representation learning focuses on training a unified low-dimensional representation for data with multiple sources or modalities. With the rapidly growing attention of graph neural networks, more and more researchers have introduced various graph models into multi-view learning. Although considerable achievements have been made, most existing methods usually propagate information in a single view and fuse multiple information only from the perspective of attributes or relationships. To solve the aforementioned problems, we propose an efficient model termed Dual Fusion-Propagation Graph Neural Network (DFP-GNN) and apply it to deep multi-view clustering tasks. The proposed method is designed with three submodules and has the following merits: a) The proposed view-specific and cross-view propagation modules can capture the consistency and complementarity information among multiple views; b) The designed fusion module performs multi-view information fusion with the attributes of nodes and the relationships among them simultaneously. Experiments on popular databases show that DFP-GNN achieves significant results compared with several state-of-the-art algorithms.
SVF-RRT*: A Stream-Based VF-RRT* for USVs Path Planning Considering Ocean Currents

In a large-scale oceanic environment with spatially variable ocean currents, it is vital for unmanned surface vehicles (USVs) to navigate with a safe and energy-efficient path. In this letter, a stream-based VF-RRT* (SVF-RRT*) is proposed for path planning in ocean current fields defined by the stream function. Firstly, SVF-RRT* quantifies the extent to which a feasible path goes against currents with a parameter termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upstream coefficient</i> . Secondly, a heuristic interval is constructed based on the stream function and adjusted by the coefficient. Then, SVF-RRT* employs biased sampling and tree pruning to facilitate convergence. Sampling nodes possessing a stream value outside the interval are rejected with an adaptive probability. Meanwhile, nodes in the exploration tree outside the interval or with a cost higher than the path are discarded. Furthermore, SVF-RRT* utilizes the upstream coefficient to update the extended direction of the tree. Finally, simulation results show that SVF-RRT* accelerates the convergence of upstream cost and generates a smoother path for USVs.
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Federated Learning

Federated learning (FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive communication costs, limited resources, and data heterogeneity. In this paper, we propose SPATL, an FL method that addresses these issues by: (a) introducing a salient parameter selection agent and communicating selected parameters only; (b) splitting a model into a shared encoder and a local predictor, and transferring its knowledge to heterogeneous clients via the locally customized predictor. Additionally, we leverage a gradient control mechanism to further speed up model convergence and increase robustness of training processes. Experiments demonstrate that SPATL reduces communication overhead, accelerates model inference, and enables stable training processes with better results compared to state-of-the-art methods. Our approach reduces communication cost by up to 86.45%, accelerates local inference by reducing up to 39.7% FLOPs on VGG-11, and requires 7.4× less communication overhead when training ResNet-20. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at: https://github.com/yusx-swapp/SPATL
LightSeq2: Accelerated Training for Transformer-Based Models on GPUs

Transformer-based neural models are used in many AI applications. Training these models is expensive, as it takes huge GPU resources and long duration. It is challenging because typical data like sentences have variable lengths, and Transformer's computation patterns are more complex than convolutional neural networks. Existing systems either only focus on model inference or optimization for only BERT-like encoder models. In this paper, we present LightSeq2, a system to accelerate training for a general family of Transformer models on GPUs. We propose a series of GPU optimization techniques tailored to the specific computation flow and memory access patterns of Transformer models. LightSeq2 supports many model architectures, including BERT (encoder-only), GPT (decoder-only), Transformer (encoder-decoder), and vision Transformer. Our experiments for a variety of models and benchmarks show that LightSeq2 is consistently faster (1.4-3.5 x) than previous systems on different GPUs. In particular, it gains 308 % training speedup compared with existing systems on a large public machine translation benchmark (WMTI4 English-German).
FRE-Net: Full-region enhanced network for nuclei segmentation in histopathology images

Accurate nuclei segmentation is a critical step for physicians to achieve essential information about a patient’s disease through digital pathology images, enabling an effective diagnosis and evaluation of subsequent treatments. Since pathology images contain many nuclei, manual segmentation is time-consuming and error-prone. Therefore, developing a precise and automatic method for nuclei segmentation is urgent. This paper proposes a novel multi-task segmentation network that incorporates background and contour segmentation into the nuclei segmentation method and produces more accurate segmentation results. The convolution and attention modules are merged with the model to increase its global focus and enhance good segmentation results indirectly. We propose a reverse feature enhance module for contour extraction that facilitates feature integration between auxiliary tasks. The multi-feature fusion module is embedded in the final decoding branch to use different levels of features from auxiliary segmentation branches with varying concerns. We evaluate the proposed method on four challenging nuclei segmentation datasets. The proposed method achieves excellent performance on all four datasets. We found that the Dice coefficient reached 0.8563±0.0323, 0.8183±0.0383, 0.9222±0.0216, and 0.9220±0.0602 on the TNBC, MoNuSeg, KMC, and Glas. Our method produces better boundary accuracy and less sticking than other end-to-end segmentation methods. The results show that our method can perform better than other proposed state-of-the-art methods.
Fast density estimation for density-based clustering methods

Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning. They can deal with non-hyperspherical clusters and are robust to outliers. However, the runtime of density-based algorithms is heavily dominated by neighborhood finding and density estimation which is time-consuming. Meanwhile, the traditional acceleration methods using indexing techniques such as KD-tree may not be effective when the dimension of the data increases. To address these issues, this paper proposes a fast range query algorithm, called Fast Principal Component Analysis Pruning (FPCAP), with the help of the fast principal component analysis technique in conjunction with geometric information provided by the principal attributes of the data. Based on FPCAP, a framework for accelerating density-based clustering algorithms is developed and successfully applied to accelerate the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the BLOCK-DBSCAN algorithm, and improved DBSCAN (called IDBSCAN) and improved BLOCK-DBSCAN (called BLOCK-IDBSCAN) are then obtained, respectively. IDBSCAN and BLOCK-IDBSCAN preserve the advantage of DBSCAN and BLOCK-DBSCAN, respectively, while greatly reducing the computation of redundant distances. Experiments on seven benchmark datasets demonstrate that the proposed algorithm improves the computational efficiency significantly.
A novel MR remote collaboration system using 3D spatial area cue and visual notification

In remote collaboration for emergency maintenance or training, since local workers may lack relevant experience and knowledge, it is very important to support remote experts to annotate 3D spatial areas for local workers in their workspace, such as risky spatial areas that need to be avoided for safe operation when assembling replacement parts. Recently, many researchers have studied various visual communication cues for expressing spatial information in Mixed Reality (MR) remote collaboration systems for physical tasks. However, the expression and transmission of 3D spatial area information have not been well explored, especially in tasks that need to annotate specific spatial areas in 3D space (such as risky 3D spatial areas). In the study, we developed a novel MR remote collaboration system that supports remote experts using 3D spatial area cues and visual notification to create Augmented Reality (AR) instructions for local workers on the shared 3D stereoscopic scene of the local workspace in the Virtual Reality (VR) space. We performed a user study to explore the effect of three interfaces on remote collaborative physical assembly tasks in the workspace with risky 3D spatial areas: 3D sketch cue (3DS) which is the baseline solution, 3D sketch cue combines the novel 3D spatial area cue (3DSA), and 3D sketch cue combines 3D spatial area cue and visual notification (3DSAN). We found the 3DSAN can significantly improve collaboration efficiency, reduce operation errors, and improve the usability and collaboration experience (especially the level of awareness), among industrial remote collaborative physical tasks requiring 3D spatial area reference.
Bayesian asymmetric quantized neural networks

This paper develops a robust model compression for neural networks via parameter quantization. Traditionally, quantized neural networks (QNN) were constructed by binary or ternary weights where the weights were deterministic. This paper generalizes QNN in two directions. First, M-ary QNN is developed to adjust the balance between memory storage and model capacity. The representation values and the quantization partitions in M-ary quantization are mutually estimated to enhance the resolution of gradients in neural network training. A flexible quantization with asymmetric partitions is formulated. Second, the variational inference is incorporated to implement the Bayesian asymmetric QNN. The uncertainty of weights is faithfully represented to enhance the robustness of the trained model in presence of heterogeneous data. Importantly, the multiple spike-and-slab prior is proposed to represent the quantization levels in Bayesian asymmetric learning. M-ary quantization is then optimized by maximizing the evidence lower bound of classification network. An adaptive parameter space is built to implement Bayesian quantization and neural representation. The experiments on various image recognition tasks show that M-ary QNN achieves similar performance as the full-precision neural network (FPNN), but the memory cost and the test time are significantly reduced relative to FPNN. The merit of Bayesian M-ary QNN using multiple spike-and-slab prior is investigated.
An adaptive position-guided gravitational search algorithm for function optimization and image threshold segmentation

Gravitational search algorithm is a population-based optimization method. To address its low search performance and premature convergence, a novel variant called adaptive position-guided gravitational search algorithm is proposed. It utilizes the best, worst and other particles’ position information to adaptively determine the Kbest particles which provide a good movement direction. The gravitational force is reinforced by Kbest particles and new constructed Dbest particles to improve the exploration and exploitation abilities. Various particles’ position information jointly provide the effective search guideline and accelerate the convergence rate. Validations are conducted to firstly discuss the parameters and strategies of the proposed algorithm. Then, compared with several state-of-the-art gravitational search algorithm variants on CEC2017 benchmark functions, the proposed algorithm proves its superiority. Finally, the proposed algorithm exhibits the good segmentation effect on image threshold segmentation problems.
Localization Distillation for Object Detection

Previous knowledge distillation (KD) methods for object detection mostly focus on feature imitation instead of mimicking the prediction logits due to its inefficiency in distilling the localization information. In this paper, we investigate whether logit mimicking always lags behind feature imitation. Towards this goal, we first present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Second, we introduce the concept of valuable localization region that can aid to selectively distill the classification and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and the absence of localization distillation is a critical reason for why logit mimicking under-performs for years. The thorough studies exhibit the great potential of logit mimicking that can significantly alleviate the localization ambiguity, learn robust feature representation, and ease the training difficulty in the early stage. We also provide the theoretical connection between the proposed LD and the classification KD, that they share the equivalent optimization effect. Our distillation scheme is simple as well as effective and can be easily applied to both dense horizontal object detectors and rotated object detectors. Extensive experiments on the MS COCO, PASCAL VOC, and DOTA benchmarks demonstrate that our method can achieve considerable AP improvement without any sacrifice on the inference speed. Our source code and pretrained models are publicly available at https://github.com/HikariTJU/LD.
SAR image near-shore ship target detection method in complex background

Due to background clutter in synthetic aperture radar (SAR) images, the detection of dense ship targets suffers from a low detection rate, high false alarm rate, and high missed detection rate. To address this issue, an FSM-DFF-YOLOv5+Confluence algorithm is proposed in this paper for the detection of near-shore ship targets in SAR images with complex backgrounds. First, based on the YOLOv5 target detection algorithm, two improvements are made in the feature extraction network: feature refinement and multi-feature fusion; in the feature extraction network, deformable convolutional neural networks are adopted to change the position of the target sampling points of the convolution to improve the feature extraction capability of the target and the detection rate of ship targets in SAR images with a complex background; in the multi-feature fusion network structure, cascading and parallel pyramids are used in the multi-feature fusion network to realize feature fusion at different levels; the visual perceptual field of feature extraction is expanded by using null convolution to enhance the adaptability of the network to detect near-shore multi-scale ship targets with complex backgrounds and reduce the false alarm rate of ship target detection in SAR images with complex environments. In this way, the DFF-YOLOv5 near-shore ship target detection algorithm is established. Meanwhile, to address the problem of missed detection in near-shore dense ship target detection, this paper adds rectangular convolution kernels to the convolution of the feature extraction network to better realize the feature extraction of dense ship targets in SAR images with complex backgrounds. Besides, the Confluence algorithm instead of non-maximum suppression is used in the prediction stage. Through experiments on the constructed complex background near-shore ship detection dataset, it is indicated that the average accuracy of the FSM-DFF-YOLOv5+Confluence detection algorithm reaches 88.96%, and the recall rate reaches 88.80%.
Prediction of the customers' interests using sentiment analysis in e-commerce data for comparison of Arabic, English, and Turkish languages

In the business world, large companies that can achieve continuity in innovation gain a significant competitive advantage. The sensitivity of these companies to follow and monitor news sources in e-commerce, social media, and forums provides important information to businesses in the decision-making process. With the large amount of data shared in these resources, sentiment analysis can be made from people's comments about services and products, users' emotions can be extracted and important feedback can be obtained. All of this is of course possible with accurate sentiment analysis. In this study, new data sets were created for Turkish, English, and Arabic, and for the first time, comparative sentiment analysis was performed from texts in three different languages. In addition, a very comprehensive study was presented to the researchers by comparing the performances of both the pre-trained language models for Turkish, Arabic, and English, as well as the deep learning and machine learning models. Our paper will guide researchers working on sentiment analysis about which methods will be more successful in texts written in different languages, which contain different types and spelling mistakes, which factors will affect the success, and how much these factors will affect the performance.
Intelligent time-series forecasting framework for non-linear dynamic workload and resource prediction in cloud

The industrial revolution 4.0 (I4.0), internet of things developments, and the expansion of online web services have caused exponential growth and deployment in the number of cloud data centers(CDC). Cloud computing is a paradigm that enables tenants to use storage and computing resources in the pay-per-use model. Cloud service providers maximize their profits by distributing the tenant’s demands to the reserved storage and computing servers, minimizing the reservation cost with the satisfaction of the tenant’s quality of service level agreement. Workload prediction and resource management are fundamental and critical problems due to cloud-distributed infrastructure and nonlinear dynamic workload conditions. Conventional prediction techniques in a cloud environment provide the one-dimensional output. Existing solutions mostly forecast resources, such as CPU and memory usage, each as a single output. However, the one-dimensional output in the form of resource provision and usage is not able to capture the relationship of application requirements of multiple resources such as CPU, memory, CPU cores, Disk, and network, which result in inaccurate prediction results and limited information. Efficient resource management requires predicting multiple resource parameters using multivariate state variables for efficient resource allocation. This study proposes an intelligent computing framework based on multivariate time-series bidirectional long short-term memory (BiLSTM) forecasting for predicting cloud virtual machine resources. We consider multi-dimensional resources such as CPU provisioned and usage, memory provisioned and usage, CPU cores, Disk write and read throughput, and network receives and transmit throughput. We investigate several deep learning techniques the proposed multivariate BiLSTM, LSTM, stacked BiLSTM, stacked BiLSTM with LSTM, and BiLSTM auto-encoder. Furthermore, we evaluate the effectiveness of the proposed framework on two real workload traces: Bitbrains traces fastStorage and Rnd. The performance metrics used to evaluate forecasting accuracy are the root mean square error, mean absolute error and mean absolute percentage error. Furthermore, we observe the training-testing data size and the historical window size variation effects on these models.
Promoting musical instrument learning in virtual reality environment: Effects of embodiment and visual cues

While virtual reality (VR) provides a great potential for musical instrument learning, little attention has been paid to the instructional design in creating a VR musical instrument. Previous research has suggested that high embodied interaction or added visual cues (e.g., distinctive colors, flashing areas) on VR-based musical instrument may aid students' learning. In this study, we investigated the feasibility and efficacy of the embodied design (low and high level of embodiment; LoEmb and HiEmb) and visual cues (low and high level of visual cues; LoViz and HiViz) on students' musical instrument learning. Four corresponding virtual Chinese dulcimers (Yangqin) were thereby designed. A sample of 112 university students participated in our study, and they were randomly assigned into the four conditions (LoEmb & LoViz, LoEmb & HiViz, HiEmb & LoViz, and HiEmb & HiViz). Results showed that the LoEmb design benefited students' completion rate, the HiViz improved students' playing rhythmic accuracy. Both LoEmb and HiViz decreased students' playing errors and improved their overall performance. Moreover, we found that the HiViz and HiEmb designs had a combined effect on reducing students’ cognitive load and improving the instructional efficiency of learning material. These findings collectively reveal that the design of VR learning materials should carefully consider the trade-off between the level of embodiment and visual cues.