Reviewerly/exHarmony huggingface dataset:
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation

Model-based 3D pose and shape estimation methods reconstruct a full 3D mesh for the human body by estimating several parameters. However, learning the abstract parameters is a highly non-linear process and suffers from image-model misalignment, leading to mediocre model performance. In contrast, 3D keypoint estimation methods combine deep CNN network with the volumetric representation to achieve pixel-level localization accuracy but may predict unrealistic body structure. In this paper, we address the above issues by bridging the gap between body mesh estimation and 3D keypoint estimation. We propose a novel hybrid inverse kinematics solution (HybrIK). HybrIK directly transforms accurate 3D joints to relative body-part rotations for 3D body mesh reconstruction, via the twistand-swing decomposition. The swing rotation is analytically solved with 3D joints, and the twist rotation is derived from the visual cues through the neural network. We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods. Without bells and whistles, the proposed method surpasses the state-of-the-art methods by a large margin on various 3D human pose and shape benchmarks. As an illustrative example, HybrIK outperforms all the previous methods by 13.2 mm MPJPE and 21.9 mm PVE on 3DPW dataset. Our code is available at https://github.com/Jeff-sjtu/HybrIK.
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction

In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data. To this end, we propose a novel objective that incorporates representation learning and data recovery into a unified framework from the view of information theory. To be specific, the informative and consistent representation is learned by maximizing the mutual information across different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy of different views through dual prediction. To the best of our knowledge, this could be the first work to provide a theoretical framework that unifies the consistent representation learning and cross-view data recovery. Extensive experimental results show the proposed method remarkably outperforms 10 competitive multi-view clustering methods on four challenging datasets. The code is available at https://pengxi.me.
An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system

The accurate and real-time detection of moving ships has become an essential component in maritime video surveillance, leading to enhanced traffic safety and security. With the rapid development of artificial intelligence, it becomes feasible to develop intelligent techniques to promote ship detection results in maritime applications. In this work, we propose to develop an enhanced convolutional neural network (CNN) to improve ship detection under different weather conditions. To be specific, the learning and representation capacities of our network are promoted by redesigning the sizes of anchor boxes, predicting the localization uncertainties of bounding boxes, introducing the soft non-maximum suppression, and reconstructing a mixed loss function. In addition, a flexible data augmentation strategy with generating synthetically-degraded images is presented to enlarge the volume and diversity of original dataset to train learning-based ship detection methods. This strategy is capable of making our CNN-based detection results more reliable and robust under adverse weather conditions, e.g., rain, haze, and low illumination. Experimental results under different monitoring conditions demonstrate that our method significantly outperforms other competing methods (e.g., SSD, Faster R-CNN, YOLOv2 and YOLOv3) in terms of detection accuracy, robustness and efficiency. The ship detection results under poor imaging conditions have also been implemented to demonstrate the superior performance of our learning method.
Rethinking and Improving Relative Position Encoding for Vision Transformer

Relative position encoding (RPE) is important for transformer to capture sequence ordering of input tokens. General efficacy has been proven in natural language processing. However, in computer vision, its efficacy is not well studied and even remains controversial, e.g., whether relative position encoding can work equally well as absolute position? In order to clarify this, we first review existing relative position encoding methods and analyze their pros and cons when applied in vision transformers. We then propose new relative position encoding methods dedicated to 2D images, called image RPE (iRPE). Our methods consider directional relative distance modeling as well as the interactions between queries and relative position embeddings in self-attention mechanism. The proposed iRPE methods are simple and lightweight. They can be easily plugged into transformer blocks. Experiments demonstrate that solely due to the proposed encoding methods, DeiT [21] and DETR [1] obtain up to 1.5% (top-1 Acc) and 1.3% (mAP) stable improvements over their original versions on ImageNet and COCO respectively, without tuning any extra hyperparameters such as learning rate and weight decay. Our ablation and analysis also yield interesting findings, some of which run counter to previous understanding. Code and models are open-sourced at https://github.com/microsoft/Cream/tree/main/iRPE.
Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.
SCSTCF: Spatial-Channel Selection and Temporal Regularized Correlation Filters for visual tracking

Recently, combining multiple features into discriminative correlation filters to improve tracking representation has shown great potential in object tracking. Existing trackers apply fixed weights to fuse features or fuse response maps, which cannot adapt to the object drift well. Moreover, in the tracking algorithm, using cyclic shift to obtain training samples always cause boundary effect, resulting in dissatisfied tracking effect. Therefore, we first design a multiple features fusion method. Various handcrafted features are fused with the same weight, then the fused handcrafted features and deep features are fused by adaptive weights, which considerably improves the representation ability of the tracking object. Second, we propose a correlation filter object function model called Spatial-Channel Selection and Temporal Regularized Correlation Filters. We perform the grouping features selection from the dimensions of channel, spatial and temporal, so as to establish the relevance between the multi-channel features and the correlation filter. Finally, we transform the objective function of the model with equality constraint to augmented Lagrangian multiplier formula without constraint, which is divided into three subproblems with closed-form solutions. The optimal solution is obtained by iteratively solving three subproblems using Alternating Direction Multiplier Method (ADMM). We conduct extensive experiments in four public datasets, OTB-2013, OTB-2015, TC128, UAV123, and VOT2016. The experimental results represent our proposed tracker performs favorably against other prevailing trackers in success rate and precision.
TAM: Temporal Adaptive Module for Video Recognition

Video data is with complex temporal dynamics due to various factors such as camera motion, speed variation, and different activities. To effectively capture this diverse motion pattern, this paper presents a new temporal adaptive module (TAM) to generate video-specific temporal kernels based on its own feature map. TAM proposes a unique two-level adaptive modeling scheme by decoupling the dynamic kernel into a location sensitive importance map and a location invariant aggregation weight. The importance map is learned in a local temporal window to capture short-term information, while the aggregation weight is generated from a global view with a focus on long-term structure. TAM is a modular block and could be integrated into 2D CNNs to yield a powerful video architecture (TANet) with a very small extra computational cost. The extensive experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently, and achieves the state-of-the-art performance under the similar complexity. The code is available at https://github.com/liu-zhy/temporal-adaptive-module.
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

Neural networks achieve the state-of-the-art in image classification tasks. However, they can encode spurious variations or biases that may be present in the training data. For example, training an age predictor on a dataset that is not balanced for gender can lead to gender biased predicitons (e.g. wrongly predicting that males are older if only elderly males are in the training set). We present two distinct contributions:
Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection

Prostate cancer is a highly incident malignant cancer among men. Early detection of prostate cancer is necessary for deciding whether a patient should receive costly and invasive biopsy with possible serious complications. However, existing cancer diagnosis methods based on data mining only focus on diagnostic accuracy, while neglecting the interpretability of the diagnosis model that is necessary for helping doctors make clinical decisions. To take both accuracy and interpretability into consideration, we propose a stacking-based ensemble learning method that simultaneously constructs the diagnostic model and extracts interpretable diagnostic rules. For this purpose, a multi-objective optimization algorithm is devised to maximize the classification accuracy and minimize the ensemble complexity for model selection. As for model combination, a random forest classifier-based stacking technique is explored for the integration of base learners, i.e., decision trees. Empirical results on real-world data from the General Hospital of PLA demonstrate that the classification performance of the proposed method outperforms that of several state-of-the-art methods in terms of the classification accuracy, sensitivity and specificity. Moreover, the results reveal that several diagnostic rules extracted from the constructed ensemble learning model are accurate and interpretable.
Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification

Imbalanced data classification poses a major challenge in data mining community. Although standard support vector machine can generally show relatively robust performance in dealing with the classification problems of imbalanced data set, it is a typical overall accuracy-oriented algorithm which results in the final decision boundary biasing toward the majority class. Some ensemble methods have emerged as meta-techniques for improving the generalization performance of existing learning algorithms. In this paper, we propose a novel self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification. In the proposed approach, to guarantee the consistency of optimization objectives between weak learners and boosting scheme, we not only apply cost-sensitive SVMs as basic weak leaner but also simultaneously modify the standard boosting scheme to cost-sensitive ones. In order to ensure more training minority instances for successive classifiers, especially borderline minority instances, we also present a self-adaptive sequential misclassification cost weights determination method. The method can self-adaptively consider the different contribution of minority instances to the form of SVM classifiers at each iteration based on the preceding obtained classifier during boosting, which can allow it to produce diverse classifiers and thus improve its generalization performance. In the experiments, we analyze and discuss the effect of different parameters on the performance and some suggestions are also provided. The extensive experimental results on the different imbalanced datasets demonstrate that the proposed approach can achieve better generalization performance in terms of G-Mean and F-Measure as compared to the other existing imbalanced dataset classification techniques.
Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study

Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch.
Traceability of counterfeit medicine supply chain through Blockchain

The main issues with drug safety in the counterfeit medicine supply chain, are to do with how the drugs are initially manufactured. The traceability of right and active pharmaceutical ingredients during actual manufacture is a difficult process, so detecting drugs that do not contain the intended active ingredients can ultimately lead to end-consumer patient harm or even death. Blockchain's advanced features make it capable of providing a basis for complete traceability of drugs, from manufacturer to end consumer, and the ability to identify counterfeit-drug. This paper aims to address the issue of drug safety using Blockchain and encrypted QR(quick response) code security.
Credit Card Fraud Detection - Machine Learning methods

Credit card fraud refers to the physical loss of credit card or loss of sensitive credit card information. Many machine-learning algorithms can be used for detection. This research shows several algorithms that can be used for classifying transactions as fraud or genuine one. Credit Card Fraud Detection dataset was used in the research. Because the dataset was highly imbalanced, SMOTE technique was used for oversampling. Further, feature selection was performed and dataset was split into two parts, training data and test data. The algorithms used in the experiment were Logistic Regression, Random Forest, Naive Bayes and Multilayer Perceptron. Results show that each algorithm can be used for credit card fraud detection with high accuracy. Proposed model can be used for detection of other irregularities.
Avatar characteristics induce users’ behavioral conformity with small-to-medium effect sizes: a meta-analysis of the proteus effect

Over a decade of research on the Proteus effect in numerous contexts suggests that people conform in behavior and attitudes to their avatars’ characteristics. In order to provide clarity about the reliability and size of the Proteus effect, a meta-analysis was conducted with 46 quantitative experimental studies in which avatars with specific characteristics were randomly assigned to participants. Results indicate a relatively consistent effect size (between .22 and .26, depending on subset of studies examined) and nearly all variance explained. Unexplained variance differed between studies that used behavioral or attitudinal measures, while studies which examined potential moderators explained all variance. Overall, this research suggests that the Proteus effect is a reliable phenomenon, with a small-but-approaching-medium effect size according to a traditional rule of thumb, but is relatively large compared to other digital media effects examined in previous meta analyses.
Value of social robots in services: social cognition perspective

Purpose The technological revolution in the service sector is radically changing the ways in which and with whom consumers co-create value. This conceptual paper considers social robots in elderly care services and outlines ways in which their human-like affect and cognition influence users’ social perceptions and anticipations of robots’ value co-creation or co-destruction potential. A future research agenda offers relevant, conceptually robust directions for stimulating the advancement of knowledge and understanding in this nascent field. Design/methodology/approach Drawing from service, robotics and social cognition research, this paper develops a conceptual understanding of the value co-creation/destruction potential of social robots in services. Findings Three theoretical propositions construct an iterative framework of users’ evaluations of social robots in services. First, social robots offer users value propositions leveraging affective and cognitive resources. Second, users’ personal values become salient through interactions with social robots’ affective and cognitive resources. Third, users evaluate social robots’ value co-creation/destruction potential according to social cognition dimensions. Originality/value Social robots in services are an emerging topic in service research and hold promising implications for organizations and users. This relevant, conceptually robust framework advances scholarly understanding of their opportunities and pitfalls for realizing value. This study also identifies guidelines for service managers for designing and introducing social robots into complex service environments.
Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images

The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.
A Survey of Tools for Analyzing Ethereum Smart Contracts

Smart contracts are at the heart of many decentralized applications, encapsulating core parts of the business logic. They handle the exchange of valuable assets like crypto-currencies or tokens in a transparent, decentralized manner. Being computer programs, they are also prone to programming errors, which have already lead to spectacular losses. Therefore, methods and tools have emerged to support the development of secure smart contracts and to aid the analysis of deployed ones. Assessing the quality of such tools turns out to be difficult. There are academic tools, tools developed by companies, and community tools in open repositories, but no comprehensive survey that may serve as a guide. Most discussions of related work in research papers are not helpful either, as they concentrate on methods rather than tools, base their review on publications about the tools rather than the tools themselves, or disregard tools outside of academia. Our survey aims at filling this gap by considering tools regardless of their provenance and by installing and testing them. It is meant as a guide for those who intend to analyze already deployed code, want to develop secure smart contracts, or plan to teach a related subject. We investigate 27 tools for analyzing Ethereum smart contracts regarding availability, maturity level, methods employed, and detection of security issues.
Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers

Variational algorithms are a promising paradigm for utilizing near-term quantum devices for modeling electronic states of molecular systems. However, previous bounds on the measurement time required have suggested that the application of these techniques to larger molecules might be infeasible. We present a measurement strategy based on a low rank factorization of the two-electron integral tensor. Our approach provides a cubic reduction in term groupings over prior state-of-the-art and enables measurement times three orders of magnitude smaller than those suggested by commonly referenced bounds for the largest systems we consider. Although our technique requires execution of a linear-depth circuit prior to measurement, this is compensated for by eliminating challenges associated with sampling non-local Jordan-Wigner transformed operators in the presence of measurement error, while enabling a powerful form of error mitigation based on efficient postselection. We numerically characterize these benefits with noisy quantum circuit simulations for ground state energies of strongly correlated electronic systems.