Reviewerly/exHarmony huggingface dataset:
Robust collaborative clustering approach with adaptive local structure learning

Different from one-sided clustering technique based on sample similarity, collaborative clustering captures the sample clustering structure and feature clustering structure by mining the duality between samples and features. Non-negative Matrix Tri-Factorization (NMTF) has become one of the most commonly used frameworks for collaborative clustering due to its excellent geometric significance. Unfortunately, NMTF-based approaches generally suffer from the following drawbacks: (1) Based on a fixed and fallible affinity matrix to explore the spatial intrinsic geometry structure, which limits the spatial exploration ability of the model. (2) Based on the L2 loss function to measure the reconstruction error, which weakens the robustness of the model. (3) Based on strict constraints to preserve the orthogonality of the factor matrix, which increases the burden of model solving. To overcome the aforementioned shortcomings, we propose a novel NMTF-based unsupervised collaborative clustering approach. In particular, we first propose a new adaptive local structure learning strategy to enhance the spatial exploration ability of the model. Secondly, we adopt the L1 loss function to improve the robustness of the model. Thirdly, we relax the orthogonal constraints of the factor matrix as a regularization term to reduce the burden of model solving. Finally, we propose a special four-step iterative algorithm to solve the corresponding model, and prove the convergence of the algorithm. The numerical performance on 3 synthetic data sets and 8 benchmark databases demonstrates that the proposed approach outperforms the state-of-the-art collaborative clustering algorithm.
Moving Multi-Object Detection and Tracking Using MRNN and PS-KM Models

On grounds of the advent of real-time applications, like autonomous driving, visual surveillance, and sports analysis, there is an augmenting focus of attention towards Multiple-Object Tracking (MOT).The tracking-by-detection paradigm, a commonly utilized approach, connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the similarities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment, a Pearson Similarity-centred Kuhn-Munkres (PS-KM) algorithm was proposed in the present study.In this light, the input videos were, initially, gathered from the MOT dataset and converted into frames.The background subtraction occurred which filtered the inappropriate data concerning the frames after the frame conversion stage.Then, the extraction of features from the frames was executed.Afterwards, the higher dimensional features were transformed into lower-dimensional features, and feature reduction process was performed with the aid of Information Gaincentred Singular Value Decomposition (IG-SVD).Next, using the Modified Recurrent Neural Network (MRNN) method, classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identified that the recognized objects were tracked.Finally, the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97% accuracy with a low false positive rate (FPR) of 2.3%.It was also proved that the present techniques viz.RNN, CNN, and KNN, were effective with regard to the existing models.
The possibility of an impetus heuristic

Evidence consistent with a belief in impetus is drawn from studies of naïve physics, perception of causality, perception of force, and representational momentum, and the possibility of an impetus heuristic is discussed. An impetus heuristic suggests the motion path of an object that was previously constrained or influenced by an external source (e.g., object, force) appears to exhibit the same constraint or influence even after that constraint or influence is removed. Impetus is not a valid physical principle, but use of an impetus heuristic can in some circumstances provide approximately correct predictions regarding future object motion, and such predictions require less cognitive effort and resources than would predictions based upon objective physical principles. The relationship of an impetus heuristic to naïve impetus theory and to objective physical principles is discussed, and use of an impetus heuristic significantly challenges claims that causality or force can be visually perceived. Alternatives to an impetus heuristic are considered, and potential boundary conditions and falsification of the impetus notion are discussed. Overall, use of an impetus heuristic offers a parsimonious explanation for findings across a wide range of perceptual domains and could potentially be extended to more metaphorical types of motion.
VisLitE: Visualization Literacy and Evaluation

With the widespread advent of visualization techniques to convey complex data, visualization literacy (VL) is growing in importance. Two noteworthy facets of literacy are user understanding and the discovery of visual patterns with the help of graphical representations. The research literature on VL provides useful guidance and opportunities for further studies in this field. This introduction summarizes and presents research on VL that examines how well users understand basic and advanced data representations. To the best of our knowledge, this is the first tutorial article on interactive VL. We describe evaluation categories of existing relevant research into unique subject groups that facilitate and inform comparisons of literacy literature and provide a starting point for interested readers. In addition, the introduction also provides an overview of the various evaluation techniques used in this field of research and their challenging nature. Our introduction provides researchers with unexplored directions that may lead to future work. This starting point serves as a valuable resource for beginners interested in the topic of VL.
Weakly Supervised Learning for Textbook Question Answering

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multi-modal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://github.com/dr-majie/WSTQ.
Personalized query techniques in graphs: A survey

Graph is a famous data structure that has prevalent applications in the real world, including social networks, biological networks, and computer networks. In these applications, graph management operators are powerful tools for mining important information hidden in large-scale graphs. As important graph data management operators, personalized graph queries are playing an increasingly significant role in providing users with effective decision support. In particular, the purpose of personalized graph queries is to compute personalized results which can meet the preferences of different users from the three aspects of specified query vertices, structures, and attributes. In this paper, we conduct a survey to offer a comprehensive view of the current personalized graph queries which need users to specify query vertices over simple and attributed graphs, respectively. These queries fall into three categories, including point-related, path-related, and subgraph-related graph queries, whose query results have distinct structures. We analyze existing approaches to personalized graph queries and highlight current challenges. In addition, we also offer guidelines for future graph queries.
Transfer-Learning-Based Gaussian Mixture Model for Distributed Clustering

Distributed clustering based on the Gaussian mixture model (GMM) has exhibited excellent clustering capabilities in peer-to-peer (P2P) networks. However, more iterative numbers and communication overhead are required to achieve the consensus in existing distributed GMM clustering algorithms. In addition, the truth that it cannot find a closed form for the update of parameters in GMM causes the imprecise clustering accuracy. To solve these issues, by utilizing the transfer learning technique, a general transfer distributed GMM clustering framework is exploited to promote the clustering performance and accelerate the clustering convergence. In this work, each node is treated as both the source domain and the target domain, and these nodes can learn from each other to complete the clustering task in distributed P2P networks. Based on this framework, the transfer distributed expectation-maximization algorithm with the fixed learning rate is first presented for data clustering. Then, an improved version is designed to obtain the stable clustering accuracy, in which an adaptive transfer learning strategy is adopted to adjust the learning rate automatically instead of a fixed value. To demonstrate the extensibility of the proposed framework, a representative GMM clustering method, the entropy-type classification maximum-likelihood algorithm, is further extended to the transfer distributed counterpart. Experimental results verify the effectiveness of the presented algorithms in contrast with the existing GMM clustering approaches.
Extended : Learning with Mixed Closed-set and Open-set Noisy Labels

The noise transition matrix T, reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and build statistically consistent classifiers. The traditional transition matrix is limited to model closed-set label noise, where noisy training data have true class labels within the noisy label set. It is unfitted to employ such a transition matrix to model open-set label noise, where some true class labels are outside the noisy label set. Therefore, when considering a more realistic situation, i.e., both closed-set and open-set label noises occur, prior works will give unbelievable solutions. Besides, the traditional transition matrix is mostly limited to model instance-independent label noise, which may not perform well in practice. In this paper, we focus on learning with the mixed closed-set and open-set noisy labels. We address the aforementioned issues by extending the traditional transition matrix to be able to model mixed label noise, and further to the cluster-dependent transition matrix to better combat the instance-dependent label noise in real-world applications. We term the proposed transition matrix as the cluster-dependent extended transition matrix. An unbiased estimator (i.e., extended T-estimator) has been designed to estimate the cluster-dependent extended transition matrix by only exploiting the noisy data. Comprehensive experiments validate that our method can better cope with realistic label noise, following its more robust performance than the prior state-of-the-art label-noise learning methods.
Double-frequency grating shearing interferometer with built-in phase-shifting function for robust multi-level phase retrieval

In this paper, we propose and demonstrate a novel interferometer and signal process to retrieve two-dimensional signals with multilevel phases. The interferometer is based on a shearing interferometry with double-frequency grating, and phase-shifting interferometry is derived as a built-in function of the lateral displacement of the grating. The interferometer not only retrieves the multilevel phase signals but also eliminates slow-varying phase errors wherever they occur. Owing to the common path algorithm, the new interferometer is more robust in dynamic circumstances for optical testing and data processing. We propose a pre-integral signal process for two-dimensional (2D) data processing to prevent post-phase-integral due to shearing interferometry. The simulation and experiment showed that the proposed interferometer with a pre-integral process has various advantages in signal processing for multilevel phase retrieval, and will be useful for higher data rates in optical data storage and free-space communication.
Big Data and Knowledge Graph Based Fault Diagnosis for Electric Power Systems

Fault detection plays an important role in the daily maintenance of power electric system. Big data and knowledge graph (KG) have been proposed by researchers to solve many problems in industrial Internet of Things, which also give lots of potentials in improving the performance of fault detection for electric power systems. In particular, this paper analyzes a distributed knowledge graph framework for fault detection in the electric power systems, where multiple devices train their local detection models used for fault detection assisted with a central server. Each device owns its local data set composed of historical fault information and current device state, which can be used to train a local model for fault detection. To enhance the detection performance, the distributed devices interact with each other in the KG framework, where the devices ought to achieve the regional computation in addition to the model aggregation within a specified latency threshold. Through searching for the vibrant qualities together with determined ability at the devices, we enhance the knowledge graph framework by the optimum variety of energetic devices together with the restriction of latency as well as data transmission. Particularly, two data transmission bandwidth allocation (BA) schemes are developed for the distributed knowledge graph framework, through which scheme I is actually bared after the instantaneous device state information (DSI), and scheme II utilizes particle swarm optimization (PSO) technique along with the statistical DSI. The results of simulation on the examination as well as convergence are lastly demonstrated to show the advantages of the proposed distributed KG framework in the fault detection for the electric power systems.
Research on UAV Path Planning Based on Improved Artificial Potential Field Method

The path planning based on the traditional artificial potential field method ignores the shape or influence range of obstacles, and the path planning results are difficult to be used to solve practical problems. In order to solve this problem, this article combines the UAV's obstacle avoidance rules, introduces variables such as relative speed and relative acceleration, and proposes a path planning model based on the improved artificial potential field method. Under the same conditions, the path planning results of the traditional artificial potential field method and the algorithm in this paper are compared. The simulation results show that the improved artificial potential field method proposed in this paper can effectively solve the UAV obstacle avoidance problem.
Eyes versus Eyebrows: A Comprehensive Evaluation Using the Multiscale Analysis and Curvature-Based Combination Methods in Partial Face Recognition

This work aimed to find the most discriminative facial regions between the eyes and eyebrows for periocular biometric features in a partial face recognition system. We propose multiscale analysis methods combined with curvature-based methods. The goal of this combination was to capture the details of these features at finer scales and offer them in-depth characteristics using curvature. The eye and eyebrow images cropped from four face 2D image datasets were evaluated. The recognition performance was calculated using the nearest neighbor and support vector machine classifiers. Our proposed method successfully produced richer details in finer scales, yielding high recognition performance. The highest accuracy results were 76.04% and 98.61% for the limited dataset and 96.88% and 93.22% for the larger dataset for the eye and eyebrow images, respectively. Moreover, we compared the results between our proposed methods and other works, and we achieved similar high accuracy results using only eye and eyebrow images.
Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

Background . There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications . Methods . We systematically searched over five databases to find relevant articles that applied knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis. Results . We looked at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identified the potential future directions according to the identified limitations, including employing semisupervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability. Conclusions . We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging to encourage future research.
Object Detection of Furniture and Home Goods Using Advanced Computer Vision

Object Detection Technology has been a subject to much research and development due to increasing use of images and videos as data sources and their huge number of applications. Traditional models for object detection had limitations in training and did not use transfer learning for their benefit. With the evolution of deep learning and Neural networks, newer and powerful tools have made way to achieve Object Detection in real-time, with the added advantage of transfer learning and detection of multiple instances of different classes of interest in the given image context. The proposed system is an Object Detection model based on the Single Shot Detector (SSD) algorithm trained with MobileNetV2 feature extraction that can be utilized and integrated in e-commerce, hospitality industry, security and surveillance, real estate, self-driving cars and floor inventory management.
Key Technology of the Medical Image Wise Mining Method Based on the Meanshift Algorithm

Mean-shift originally refers to the mean vector of the offset. The algorithm idea is to assume that the data sets of different clusters conform to different probability density distributions, and the area with high sample density corresponds to the center of the cluster. With the wide application of hospital information system, especially the popularity of the meanshift algorithm in the outpatient system, it has greatly improved the efficiency of medical staff. Medical imaging refers to the technology and process of obtaining internal tissue images of the human body or a certain part of the human body in a noninvasive manner for medical treatment or medical research. It contains the following two relatively independent research directions: medical imaging system and medical image processing. In this paper, we expect to improve the mining ability of medical image information with the help of the meanshift algorithm based on the key technology of the medical image intelligent mining algorithm. This paper proposes a method to enhance image feature extraction and data mining and how to apply relevant analysis rules for mining. Applying this integrated algorithm to extract simplified rules is more beneficial to people’s understanding than the raw data and helps doctors quickly understand the patient’s condition.
Private decentralized crowdsensing with asynchronous blockchain access

Because of the popularity of mobile devices, crowdsensing has emerged as a data sensing paradigm for collecting large-scale data. Blockchain technology is promising to address problems of privacy and trust existing in centralized crowdsensing systems. However, it is challenging for crowdsensing applications to use a public blockchain to collect real-time or large-scale data since current blockchain systems lack of scalability. We propose a crowdsensing scheme that combines Trusted Execution Environments (TEE) with a public blockchain, achieving high efficiency with guarantees of privacy and trust. The scheme is a layer-two blockchain solution that supports off-chain multi-round sensing-data evaluations inside TEE enclaves, so that the sensing data needs not be propagated over the blockchain network. Besides, the scheme prevents false-reporting and free-riding for workers and requesters without reliance on a trusted third party. Moreover, the scheme secures the sensing data, letting only the worker and the corresponding requester know the data. Evaluations of our experimental prototype demonstrate the efficiency of our designs, as well as the reasonable on-chain monetary cost of running a task's smart contract and performing token payments using Ethereum.
Rethinking graph data placement for graph neural network training on multiple GPUs

Graph partitioning is commonly used for dividing graph data for parallel processing. While they achieve good performance for the traditional graph processing algorithms, the existing graph partitioning methods are unsatisfactory for data-parallel GNN training on GPUs. In this work, we rethink the graph data placement problem for large-scale GNN training on multiple GPUs. We find that loading input features is a performance bottleneck for GNN training on large graphs that cannot be stored on GPU. To reduce the data loading overhead, we first propose a performance model of data movement among CPU and GPUs in GNN training. Then, based on the performance model, we provide an efficient algorithm to divide and distribute the graph data onto multiple GPUs so that the data loading time is minimized. For cases where data placement alone cannot achieve good performance, we propose a locality-aware neighbor sampling technique to further reduce the data movement overhead without losing accuracy. Our experiments with graphs of different sizes on different numbers of GPUs show that our techniques not only achieve smaller data loading time but also incur much less preprocessing overhead than the existing graph partitioning methods.