Reviewerly/exHarmony huggingface dataset:
Multi-Object Tracking With Spatial-Temporal Topology-Based Detector

Multi-object tracking is a challenging task due to the occlusion of different targets. Existing methods focus on inferring a robust and discriminative feature for data association based on the targets generated by the existing detector. Unlike existing methods that consider each target independently during generating the trajectories, we propose a novel Spatial-Temporal Topology-based Detector (STTD) algorithm that treats the target and its nearest neighbors as a cluster and introduces a topology structure to describe the dynamics of moving targets belonging to the same cluster. With the public detections and the tracked objects in the previous frame, STTD firstly refines them by regression of detector to obtain the candidate proposals in the current frame. After that, the temporal topology constraint is proposed to recover the missed objects by considering the continuity and consistency of the topological structure. Based on the assumption that the targets belonging to the same topology should have a consistent characteristic, the spatial topology constraint is proposed to remove the inaccurate targets. Then we can obtain new candidate objects and construct the cost matrix used for data association. The evaluations on three MOTChallenge benchmarks verify the effectiveness of the proposed method.
Deep low-rank matrix factorization with latent correlation estimation for micro-video multi-label classification

Currently, micro-videos are becoming an increasingly prevailing form of user-generated contents (UGCs) on various social platforms. Several studies have been conducted to explore the semantics of micro-videos and the behavior of individuals for various tasks, such as venue categorization, popularity prediction, and personalized recommendation. However, few studies have been dedicated to solving micro-video multi-label classification. More importantly, learning intrinsic and robust feature representations for micro-videos is still a complicated and challenging problem. In this paper, we propose a deep matrix factorization with latent correlation estimation (DMFLCE) for micro-video multi-label classification. In DMFLCE, we develop a deep matrix factorization component constrained by a low-rank constraint to learn the lowest-rank representations for micro-videos and the intrinsic characterizations for latent attributes simultaneously. To explicitly exhibit the dependencies of the learned latent attributes and labels for improved classification performance, we construct two inverse covariance estimation components to automatically encode correlation patterns with respect to the latent attributes and labels. Experiments conducted on a publicly available large-scale micro-video dataset demonstrate the effectiveness of our proposed method compared with state-of-the-art methods.
Research on Design, Calibration and Real-Time Image Expansion Technology of Unmanned System Variable-Scale Panoramic Vision System

This paper summarized the research status, imaging model, systems calibration, distortion correction, and panoramic expansion of panoramic vision systems, pointed out the existing problems and put forward the prospect of future research. According to the research status of panoramic vision systems, a panoramic vision system with single viewpoint of refraction and reflection is designed. The systems had the characteristics of fast acquisition, low manufacturing cost, fixed single-view imaging, integrated imaging, and automatic switching depth of field. Based on these systems, an improved nonlinear optimization polynomial fitting method is proposed to calibrate the monocular HOVS, and the binocular HOVS is calibrated with the Aruco label. This method not only improves the robustness of the calibration results, but also simplifies the calibration process. Finally, a real-time method of panoramic map of multi-function vehicle based on vcam is proposed.
A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

Automated diagnosis systems aim to reduce the cost of diagnosis while maintaining the same efficiency. Many methods have been used for breast cancer subtype classification. Some use single data source, while others integrate many data sources, the case that results in reduced computational performance as opposed to accuracy. Breast cancer data, especially biological data, is known for its imbalance, with lack of extensive amounts of histopathological images as biological data. Recent studies have shown that cascade Deep Forest ensemble model achieves a competitive classification accuracy compared with other alternatives, such as the general ensemble learning methods and the conventional deep neural networks (DNNs), especially for imbalanced training sets, through learning hyper-representations through using cascade ensemble decision trees. In this work, a cascade Deep Forest is employed to classify breast cancer subtypes, IntClust and Pam50, using multi-omics datasets and different configurations. The results obtained recorded an accuracy of 83.45% for 5 subtypes and 77.55% for 10 subtypes. The significance of this work is that it is shown that using gene expression data alone with the cascade Deep Forest classifier achieves comparable accuracy to other techniques with higher computational performance, where the time recorded is about 5 s for 10 subtypes, and 7 s for 5 subtypes.
The boundary node method for <scp>multi‐robot multi‐goal</scp> path planning problems

Abstract In this paper, we investigate the multi‐goal path planning problem to find the shortest collision‐free path connecting a given set of goal points located in the robot working environment. This problem combines two sub‐problems: first, optimize the sequence of the goal points located in the free workspace; second, compute the shortest collision‐free path between goal points. In this study, a genetic algorithm is used to optimize the sequence of the goal points that the robot should visit. Once the sequence of the goal points is available, our developed method called Boundary Node Method ( BNM ) is applied to generate an initial collision‐free path between every pair of the sequenced goal points. Subsequently, an additional developed method called Path Enhancement Method ( PEM ) is used to find an optimal or near‐optimal path by reducing the overall initial path length. In the BNM , the robot and its immediate surroundings are simulated by a nine‐node quadrilateral element, where the centroid node represents the robot's position. The robot moves between goals with boundary nodes in the working environment depending on the boundary node's potential value. The potential value of each point in the working environment is calculated based on the proposed potential function. Additionally, this article investigates the multi‐goal path planning problem for multi‐robot systems, when each goal reached by several robots. Finally, simulations and experiments are performed on a real mobile robot to demonstrate the effectiveness of the developed methods.
What identifies different age cohorts in Yahoo! Answers?

For different kinds of online platforms, understanding demographics has shown to be instrumental in improving user experience, especially for personalizing and contextualizing content. Needless to say, there has been a number of studies delving into demographics in online social media platforms including Facebook and Twitter. However, only a mere handful of works have explored demographic factors behind community question-answering platforms despite their massive amount of members. For this reason, we decided to undertake a study of Yahoo! Answers members, namely as it relates to age demographics. To this end, we automatically built and annotated a large-scale corpus comprising metadata and textual inputs produced by ca. 650,000 community fellows. We profit from this collection by conducting both an exploratory/statistical analysis and predictive modelling. In the former, we explored the correlation between distinct age groups and some variables that, intuitively, can seem to be highly correlated with some cohorts. Interestingly enough, this analysis revealed that Millennials are answering questions prompted by their succeeding age group (GEN Z). In the latter, we assessed the prediction rate of various traditional statistical methods and neural networks classifiers coupled with numerous combinations of assorted textual and metadata features. Overall, best classifiers finished with an MRR of up to 0.862, and were modelled by means of FastText and Maximum Entropy (MaxEnt). In terms of informative attributes, user asking/answering activity patterns and sentimentally charged words provide telltale clues about which age group a community peer belongs to.
CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes

Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images. However, garment transfer between images with heavy misalignments or severe occlusions still remains as a challenge. In this work, we propose Complementary Transfering Network (CT-Net) to adaptively model different levels of geometric changes and transfer outfits between different people. In specific, CT-Net consists of three modules: i) A complementary warping module first estimates two complementary warpings to transfer the desired clothes in different granularities. ii) A layout prediction module is proposed to predict the target layout, which guides the preservation or generation of the body parts in the synthesized images. iii) A dynamic fusion module adaptively combines the advantages of the complementary warpings to render the garment transfer results. Extensive experiments conducted on DeepFashion dataset demonstrate that our network synthesizes high-quality garment transfer images and significantly outperforms the state-of-art methods both qualitatively and quantitatively. Our source code will be available online.
Semi 3D-TENet: Semi 3D network based on temporal information extraction for coronary artery segmentation from angiography video

Coronary artery interventional therapy is a clinically effective minimally invasive surgery for coronary artery disease. Extracting effective coronary vascular structures from coronary angiography videos is essential for the safe navigation of coronary interventional equipment and for the doctor to observe the location of the lesion. This paper proposes a new semi 3D architecture that uses the temporal information of video to segment coronary arteries from angiography video. We combine the 3D U-Net and 2D U-Net through a dimension conversion layer and a context extracting module. The input of the 3D encoder is a set of coronary video sequences. After the extracted three-dimensional features pass through the dimension conversion layer and the context information extraction module, the valuable features are input into the 2D decoder module. Finally, a clearer and more complete coronary is extracted to help the doctor to observe the vascular status better. We tested this method and the comparison methods on the coronary angiography video data set we made before. We can see from the experimental results that even in coronary angiography video sequences with poor quality, our method can achieve better results than the other methods. The accuracy of our results can reach 98.60%, which shows that in the vessel video segmentation task, the extraction of temporal information is helpful to extract a more complete vascular structure.
Exploiting Aliasing for Manga Restoration

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screen-tone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative two-stage method to restore quality bitonal manga from de-graded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SE-Net) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method.
A mixed data clustering algorithm with noise-filtered distribution centroid and iterative weight adjustment strategy

Clustering is an important technology for data analysis. Cluster analysis for mixed data remains challenging. This paper proposes a mixed data clustering algorithm with noise-filtered distribution centroid and iterative weight adjustment strategy. The proposed algorithm defines noise-filtered distribution centroid for categorical attributes. We combine both mean and noise-filtered distribution centroid to represent the cluster center with mixed attributes, the noise-filtered distribution centroid records the frequency of occurrences for each possible value of the categorical attributes in a cluster more accurately. Furthermore, because the "noise values" are filtered, the measure to calculate the dissimilarity between data objects and cluster centers could be improved. In addition, the algorithm introduces an iterative weight adjustment strategy with combined intra-cluster and inter-cluster information. The unified weight measurement method is used for refining numeric attributes and categorical attributes. Then attributes with higher intra-cluster homogeneity and inter-clusters heterogeneity are considered as attributes with higher priority. They tend to be assigned with relatively heavier weights during clustering. Experimental results on different datasets from the UCI repository show that the MCFCIW algorithm outperforms the existing partition-based clustering algorithm and clustering algorithm based on data conversion for mixed data on both cluster validity indices and convergence speed.
Single Image Deraining Integrating Physics Model and Density-Oriented Conditional GAN Refinement

Although advanced single image deraining methods have been proposed, their generalization ability to real-world images is usually limited, especially when dealing with rain patterns of different densities, shapes, and directions. In order to improve the robustness and generalization of these deraining methods, we propose a novel density-aware single image deraining method with gated multi-scale feature fusion, which consists of two stages. In the first stage, a sophisticated physics model is leveraged for initial deraining and a network branch is utilized for rain density estimation to guide the subsequent refinement. The second stage of model-independent refinement is realized using conditional Generative Adversarial Network (cGAN), attempting to eliminate artifacts and improve the restoration quality. Extensive experiments have been conducted on the representative synthetic rain datasets and real rain scenes, demonstrating the superiority of our method in terms of effectiveness and generalization ability, which outperforms the state-of-the-arts.
Why bad coffee? Explaining BDI agent behaviour with valuings

An important issue in deploying an autonomous system is how to enable human users and stakeholders to develop an appropriate level of trust in the system. It has been argued that a crucial mechanism to enable appropriate trust is the ability of a system to explain its behaviour. Obviously, such explanations need to be comprehensible to humans. Due to the perceived similarity in functioning between humans and autonomous systems, we argue that it makes sense to build on the results of extensive research in social sciences that explores how humans explain their behaviour. Using similar concepts for explanation is argued to help with comprehensibility, since the concepts are familiar. Following work in the social sciences, we propose the use of a folk-psychological model that utilises beliefs, desires, and "valuings". We propose a formal framework for constructing explanations of the behaviour of an autonomous system, present an (implemented) algorithm for giving explanations, and present evaluation results.
Living on the Edge

Voluminous time-series data streams produced in continuous sensing environments impose challenges pertaining to ingestion, storage, and analytics. In this study, we present a holistic approach based on data sketching to address these issues. We propose a hyper-sketching algorithm that combines discretization and frequency-based sketching to produce compact representations of the multi-feature, time-series data streams. We generate an ensemble of data sketches to make effective use of capabilities at the resource-constrained edge devices, the links over which data are transmitted, and the server pool where this data must be stored. The data sketches can be queried to construct datasets that are amenable to processing using popular analytical engines. We include several performance benchmarks using real-world data from different domains to profile the suitability of our design decisions. The proposed methodology can achieve up to ∼ 13 × and ∼ 2, 207 × reduction in data transfer and energy consumption at edge devices. We observe up to a ∼ 50% improvement in analytical job completion times in addition to the significant improvements in disk and network I/O.
Demo: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

Automatic Lane Centering (ALC) systems are convenient and widely deployed today, but they are also highly security and safety critical. Recent research shows that Dirty Road Patch (DRP) attack can successfully attack DNN-based ALC systems. In this work, we will demonstrate the DRP attack on a production-level simulator and a physical-world setup.
A coarse-to-fine collective entity linking method for heterogeneous information networks

Linking ambiguous entity mentions in a text with their true mapping entities in a heterogeneous information network (HIN) is important. Most of existing entity linking methods with HINs assume that the entities in a text are independent while ignoring the relationships between the entities in context. Recent studies have shown that collective entity linking methods are more effective than traditional independent entity linking methods because they consider the relationships between different entities in the same text. However, few studies focus on collective entity linking for HINs. Most of collective entity linking methods rely largely on special features in Wikipedia, and may not be suitable for the HINs that are not mapped to Wikipedia. Moreover, existing collective entity linking methods may have high time complexity. Therefore, a Coarse-to-Fine collective Entity Linking algorithm (called CFEL) is proposed for the case the Wikipedia cannot be used. CFEL is composed of a coarse-grained model and a fine-grained model. In the coarse-grained model, a pruning strategy motivated by the human cognition mechanism, is adopted to reduce the number of candidates for each entity mention in texts. The candidates in HINs that are inconsistent with the type of entity mentions can be deleted. In the fine-grained model, we present a probabilistic method that combines the semantic information in a text with the structural information in HINs. The experimental results on four real-world datasets verify the effectiveness of our algorithm compared to the baselines.
POMP++: Pomcp-based Active Visual Search in unknown indoor environments

In this paper, we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning strategy that introduces a novel formulation on top of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to allow training-free online policy learning in unknown environments. We present a new belief reinvigoration strategy that enables the use of POMCP with a dynamically growing state space to address the online generation of the floor map. We evaluate our method on two public benchmark datasets, AVD that is acquired by real robotic platforms and Habitat ObjectNav that is rendered from real 3D scene scans, achieving the best success rate with an improvement of >10% over the state-of-the-art methods.
GA-based multi-objective optimization technique for medical image denoising in wavelet domain

Medical images that are acquired with reduced radiation exposure or following the administration of imaging agents with a low dose, are often known to experience problems by the noise stemming from acquisition hardware as well as psychological sources. This noise can adversely affect the quality of diagnosis, but also prevent practitioners from computing quantitative functional information. With a view to overcoming these challenges, the current paper puts forward optimization of multi-objective for denoising medical images within the wavelet domain. This proposed technique entails the use of genetic algorithm (GA) to get the threshold optimized within the denoising framework of wavelets. Two purposes are associated with this technique: First, its ability to adapt with different noise types of noise in the image without requiring prior information about the imaging process per se. In addition, it balances relevant diagnostic details’ preservation against the reduction of noise by considering the optimization of the error factor of Liu and SNR as the foundation of objective function. According to the implementation of this method on magnetic resonance (MR) and ultrasound (US) images of the brain, a better performance has been observed as compared to the existing wavelet-based denoising methods with regard to quantitative and qualitative metrics.
Differentially private empirical risk minimization for AUC maximization

Area under the ROC curve (AUC) is a widely used performance measure for imbalanced classification. Oftentimes, the ubiquitous imbalanced data such as financial records from fraud detection or genomic data from cancer diagnosis contains sensitive information, and therefore it is of practical and theoretical importance to develop privacy-preserving AUC maximization algorithms. In this paper, we propose differentially private empirical risk minimization (ERM) for AUC maximization, and systematically study their privacy and utility guarantees. In particular, we establish guarantees on the generalization (utility) performance of the proposed algorithms with fast rates. The technical novelty contains fast rates for the regularized ERM in AUC maximization, which is established using the peeling techniques for Rademacher averages [1] and properties of U-Statistics [2,3] to handle statistically non-independent pairs of examples in the objective function, and a new error decomposition to handle strongly smooth losses (e.g. least square loss). In addition, we revisit the private ERM with pointwise loss [4,5] and show optimal rates can be obtained using the uniform convergence approach.