Reviewerly/exHarmony huggingface dataset:
Articulated deformable structure approach to human motion segmentation and shape recovery from an image sequence

The aim of this study is to perform motion segmentation and three‐dimensional shape recovery of a dynamic human body from an image sequence. The authors note that human body motion generally consists of large articulations between different body parts and small local deformations within each body part. On the basis of this notion, they develop an integrated framework that combines articulated structure from motion and non‐rigid SFM to estimate human body motion and shape as an articulated deformable structure. Unlike existing approaches that apply a low‐rank subspace method for motion segmentation, they use a metric constraint for identifying rigid subsets, which is more robust and, therefore, allow a more relaxed error threshold to be set for fitting rigid subsets, catering for small deformations within individual rigid subsets. They provide an automated statistical procedure for setting the aforementioned error threshold. The rigid subsets are then linked into articulated kinematic chains by minimum spanning tree search in a graph of joint costs. Finally, the blend‐shape method is applied to model local deformations of each individual subset. Experimental results show that the proposed method provides better performance for human motion segmentation and shape recovery compared with existing methods.
Probabilistic Rank-One Discriminant Analysis via Collective and Individual Variation Modeling

Linear discriminant analysis (LDA) is a classical supervised subspace learning technique that has wide applications. However, it is designed for vector only, which cannot exploit the tensor structures and may lead to suboptimal results when dealing with tensorial data. To address this problem, several multilinear LDA (MLDA) methods have been proposed to learn the subspaces from tensors. By exploiting the tensor structures, they achieve compact subspace representations, reduced parameter sizes, and improved robustness against the small sample size problem. However, existing MLDA methods do not take data uncertainty into account, fail to converge properly, or have to introduce additional tuning parameters for good convergence properties. In this paper, we therefore solve these limitations by proposing a probabilistic MLDA method for matrix inputs. Specifically, we propose a new generative model to incorporate structural information into the probabilistic framework, where each observed matrix is represented as a linear combination of collective and individual rank-one matrices. This provides our method with both the expressiveness of capturing discriminative features and nondiscriminative noise, and the capability of exploiting the 2-D tensor structures. To overcome the convergence problem of existing MLDAs, we develop an EMtype algorithm for parameter estimation, which has closed-form solutions with convergence guarantees. Experimental results on real-world datasets show the superiority of the proposed method to other probabilistic and MLDA variants.
Efficient Mining Template of Predictive Temporal Clinical Event Patterns From Patient Electronic Medical Records

Exploring the temporal relationship among events in patient electronic medical records (EMR) is an important problem in biomedical informatics and the results can reveal patients' impending disease conditions. In this paper, we investigate the problem of mining patterns from a sequence of point events, i.e., we only have the information on when the event happens but no duration or numerical value available. We propose a whole pipeline, including event preprocessing, pattern mining, and outcome analysis to mine the patterns and evaluate their effectiveness and discriminative power. Finally, we treat those mined patterns as additional features and evaluate them in a predictive modeling task for the early detection of congestive heart failure. On a real-world EMR data warehouse, we found that by adding those sequential pattern features, the prediction performance could be significantly improved approximately 0.1.
Monte Carlo Continual Resolving for Online Strategy Computation in Imperfect Information Games

Online game playing algorithms produce high-quality strategies with a fraction of memory and computation required by their offline alternatives. Continual Resolving (CR) is a recent theoretically sound approach to online game playing that has been used to outperform human professionals in poker. However, parts of the algorithm were specific to poker, which enjoys many properties not shared by other imperfect information games. We present a domain-independent formulation of CR applicable to any two-player zero-sum extensive-form games (EFGs). It works with an abstract resolving algorithm, which can be instantiated by various EFG solvers. We further describe and implement its Monte Carlo variant (MCCR) which uses Monte Carlo Counterfactual Regret Minimization (MCCFR) as a resolver. We prove the correctness of CR and show an $O(T^-1/2 )$-dependence of MCCR's exploitability on the computation time. Furthermore, we present an empirical comparison of MCCR with incremental tree building to Online Outcome Sampling and Information-set MCTS on several domains.
DeepSigns: An End-to-End Watermarking Framework for Protecting the Ownership of Deep Neural Networks

Deep Learning (DL) models have created a paradigm shift in our ability to comprehend raw data in various important fields, ranging from intelligence warfare and healthcare to autonomous transportation and automated manufacturing. A practical concern, in the rush to adopt DL models as a service, is protecting the models against Intellectual Property (IP) infringement. DL models are commonly built by allocating substantial computational resources that process vast amounts of proprietary training data. The resulting models are therefore considered to be an IP of the model builder and need to be protected to preserve the owner’s competitive advantage. We propose DeepSigns, the ﬁrst end-to-end IP protection framework that enables developers to systematically insert digital watermarks in the pertinent DL model before distributing the model. DeepSigns is encapsulated as a high-level wrapper that can be leveraged within common deep learning frameworks. The libraries in DeepSigns work by dynamically learning the probability density function (pdf) of activation maps obtained in different layers of a DL model. DeepSigns uses the low probabilistic regions within a deep neural network to gradually embed the owner’s signature (watermark) while minimally affecting the overall accuracy and/or training overhead. DeepSigns can demonstrably withstand various removal and transformation attacks, including model pruning, model ﬁnetuning, and watermark overwriting. We evaluate DeepSigns performance on a wide variety of DL architectures including Wide Residual Networks, Convolution Neural Networks, and Multi-Layer Perceptrons with MNIST, CIFAR10, and ImageNet data. Our extensive evaluations corroborate DeepSigns’ effectiveness and applicability. Our highly-optimized accompanying API further facilitates training watermarked neural networks with an extra overhead as low as 2.2%.
Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Recommender systems have become indispensable for online services since they alleviate the information overload problem for users. Some work has been proposed to support the personalized recommendation by utilizing collaborative filtering to learn the latent user and item representations from implicit interactions between users and items. However, most of existing methods simplify the implicit frequency feedback to binary values, which make collaborative filtering unable to accurately learn the latent user and item features. Moreover, the traditional collaborating filtering methods generally use the linear functions to model the interactions between latent features. The expressiveness of linear functions may not be sufficient to capture the complex structure of users’ interactions and degrades the performance of those recommender systems. In this paper, we propose a neural personalized ranking model for collaborative filtering with the implicit frequency feedback. The proposed method integrates the ranking-based poisson factor model into the neural networks. Specifically, we firstly develop a ranking-based poisson factor model, which combines the poisson factor model and the Bayesian personalized ranking. This model adopts a pair-wise learning method to learn the rankings of uses’ preferences between items. After that, we propose a neural personalized ranking model on top of the ranking-based poisson factor model, named NRPFM, to capture the complex structure of user-item interactions. NRPFM applies the ranking-based poisson factor model on neural networks, which endows the linear ranking-based poisson factor model with a high level of nonlinearities. Experimental results on two real-world datasets show that our proposed method compares favorably with the state-of-the-art recommendation algorithms.
Uniaxial Static Stress Estimation for Concrete Structures Using Digital Image Correlation

This paper proposes a static stress estimation method for concrete structures, using the stress relaxation method (SRM) in conjunction with digital image correlation (DIC). The proposed method initially requires a small hole to be drilled in the concrete surface to induce stress relaxation around the hole and, consequently, a displacement field. DIC measures this displacement field by comparing digital images taken before and after the hole-drilling. The stress level in the concrete structure is then determined by solving an optimization problem, designed to minimize the difference between the displacement fields from DIC and the one from a numerical model. Compared to the pointwise measurements by strain gauges, the full-field displacement obtained by DIC provides a larger amount of data, leading to a more accurate estimation. Our theoretical results were experimentally validated using concrete specimens, demonstrating the efficacy of the proposed method.
OpinionLink: Leveraging user opinions for product catalog enrichment

A vast number of user opinions are available from reviews posted on e-commerce websites. Although these opinions are a valuable source of knowledge for both manufacturers and customers, they provide volumes of information that exceeds the human cognitive processing capacity, which can be a major bottleneck for their effective use. To address this problem, a number of opinion-summarization methods have been proposed to organize these opinions by grouping them around aspects. However, these methods tend to generate an excessive number of aspect groups that are frequently overly generic and difficult to interpret. We argue that a superior alternative would be to organize opinions around product attributes as defined in a product catalog. Typically, product attributes correspond to the most important characteristics of the products. Furthermore, they are common to all products in a given category and thus, form a more stable set than aspects. In this paper, we propose a novel approach called OpinionLink to products in a catalog at the attribute granularity level with opinions extracted from product reviews. The proposed approach is divided into two phases. In the first phase, OpinionLink uses a classifier to identify opinionated sentences in the reviews on a particular product. In the second phase, another classifier is used to map the opinions that were previously extracted from the user reviews to the attributes of the products in the product catalog. We performed a series of experiments on these phases. For the first phase, our experiments indicated that using classifiers with the proposed features achieved an average of 0.87 in terms of F1 measure for the task of identifying opinionated sentences. In the second phase, the method we proposed for the opinion-mapping task achieved an average of 0.85 in terms of F1. Further, we verified the effectiveness of the proposed approach as a realistic end-to-end application, indicating that we can use OpinionLink in a real setting. Finally, we empirically demonstrate the feasibility of using the proposed approach with an extremely large volume of opinions available in a collection of more than 600,000 real reviews. We also set forth a number of directions for future research.
Application of Improved BQGA in Robot Kinematics Inverse Solution

In view of the problem that Bloch Quantum Genetic Algorithm (BQGA) is easy to fall into local optimum, an improved BQGA is proposed. The algorithm can control the step size and the mutation probability in real time in the iterative process, avoiding over the optimal solution and guaranteeing search efficiency. In addition, the improved algorithm further completes the anti-degradation mechanism, which maintains the diversity of the population while preserving the dominant gene to the maximum extent, so that the algorithm is not easy to fall into the local extremum and finally approaches the global optimal solution. The application in the inverse solution of robot kinematics shows that the improved BQGA effectively avoids the premature problem and accelerates the convergence of understanding and the search result is close to the complete solution, which provides a new idea for solving complex nonlinear and multivariate functional equations.
Introduction to Personalisation in Cyber-Physical-Social Systems

In this paper we introduce the notion of personalisation in Cyber-Physical-Social Systems (CPSS). A CPSS is an extension of Cyber-Physical systems involving cyberspace and physical space, in which humans, machines and objects interact adding to the complexity of the system, especially due to the dynamics of human behaviour that is not yet fully understood. We propose Personalisation to address this complexity. Since the development of CPSS is still at its infancy, we first cover a brief overview on the existing conceptualizations of CPSS and present the perspective we take for our work. Then we discuss the benefits of introducing Personalisation in CPSS and formalize the notion. The discussion further illustrates research challenges for Personalisation in CPSS with examples on possible use cases, taken from preceding works as well as future ones.
Towards a Gateway-based Context-Aware and Self-Adaptive Security Management Model for IoT-based eHealth Systems

IoT-based systems have considerable dynamic behavior and heterogeneous technology participants.The corresponding threats and security operations are also complex to handle.Traditional security solutions may not be appropriate and effective in such ecosystems as they recognize and assess a limited context, they work well only with high-end and specific computing platforms, and implement manual response mechanisms.We have identified the security objectives of a potential IoT-eHealth system and have proposed a security model that can efficiently achieve them.The proposed model is a context-aware and self-adaptive security management model for IoT, in eHealth perspective that will monitor, analyze, and respond to a multitude of security contexts autonomously.As these operations are planned at the gateway level, the model exploits the advantages of computing in the Fog Layer.Moreover, the proposed model offers flexibility and open connectivity to allow any smart device or thing to be managed irrespective of their native design.We have also explained how our model can establish and serve the essential security objectives of an IoT-based environment.
Towards Controlled Transformation of Sentiment in Sentences

An obstacle to the development of many natural language processing products is the vast amount of training examples necessary to get satisfactory results. The generation of these examples is often a tedious and time-consuming task. This paper this paper proposes a method to transform the sentiment of sentences in order to limit the work necessary to generate more training data. This means that one sentence can be transformed to an opposite sentiment sentence and should reduce by half the work required in the generation of text. The proposed pipeline consists of a sentiment classifier with an attention mechanism to highlight the short phrases that determine the sentiment of a sentence. Then, these phrases are changed to phrases of the opposite sentiment using a baseline model and an autoencoder approach. Experiments are run on both the separate parts of the pipeline as well as on the end-to-end model. The sentiment classifier is tested on its accuracy and is found to perform adequately. The autoencoder is tested on how well it is able to change the sentiment of an encoded phrase and it was found that such a task is possible. We use human evaluation to judge the performance of the full (end-to-end) pipeline and that reveals that a model using word vectors outperforms the encoder model. Numerical evaluation shows that a success rate of 54.7% is achieved on the sentiment change.
Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials

Maintaining high medication adherence is essential for achieving desired efficacy in clinical trials, especially prevention trials. However, adherence is traditionally measured by self-reports that are subject to reporting biases and measurement error. Recently, electronic medication dispenser devices have been adopted in several HIV pre-exposure prophylaxis prevention studies. These devices are capable of collecting objective, frequent, and timely drug adherence data. The device opening signals generated by such devices are often represented as regularly or irregularly spaced discrete functional data, which are challenging for statistical analysis. In this paper, we focus on clustering the adherence monitoring data from such devices. We first pre-process the raw discrete functional data into smoothed functional data. Parametric mixture models with change-points, as well as several non-parametric and semi-parametric functional clustering approaches, are adapted and applied to the smoothed adherence data. Simulation studies were conducted to evaluate finite sample performances, on the choices of tuning parameters in the pre-processing step as well as the relative performance of different clustering algorithms. We applied these methods to the HIV Prevention Trials Network 069 study for identifying subgroups with distinct adherence behavior over the study period.
Identification-Method Research for Open-Source Software Ecosystems

In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework.
Gold Prospects in the Western Segment of the Russian Arctic: Regional Metallogeny and Distribution of Mineralization

Location of the deposits and occurrences of gold mineralization in metamorphic complexes of the Kola region is controlled by tectonic zones at the regional scale at the boundaries of major segments of the Fennoscandian Shield. Three zones are the most important: (1) the system of Neoarchean greenstone belts Kolmozero–Voron’ya–Ura-guba along the southern boundary of the Murmansk craton; (2) the suture, delineating the core of the Lapland–Kola orogeny in the north; and (3) the series of overthrusts and faults at the eastern flank of the Salla–Kuolajarvi belt. Gold deposits and occurrences are located within greenstone belts of Neoarchean and Paleoproterozoic age, and hosted by rocks of different primary compositions (mafic metavolcanics, diorite porphyry, and metasedimentary terrigenous rocks). The grade of metamorphism varies from greenschist to upper amphibolite facies, but the mineralized rocks are mainly lower amphibolite metamorphosed, close to the transition from greenschist to amphibolite facies. Gold deposits and occurrences in the northeastern part of the Fennoscandian Shield formed during two periods: the Neoarchean 2.7–2.6 Ga and the Paleoproterozoic 1.9–1.7 Ga. According to paleo-geodynamic reconstructions, these were the periods of collisional and accretionary orogeny in the region. Those Archean greenstone belts, which were reworked in the Paleoproterozoic (e.g., Strel’na and Tiksheozero belts), can contain gold deposits of Paleoproterozoic age.
Adaptive visual target tracking algorithm based on classified-patch kernel particle filter

We propose a high-performance visual target tracking (VTT) algorithm based on classified-patch kernel particle filter (CKPF). Novel features of this VTT algorithm include sparse representations of the target template using the label-consistent K-singular value decomposition (LC-KSVD) algorithm; Gaussian kernel density particle filter to facilitate candidate template generation and likelihood matching score evaluation; and an occlusion detection method using sparse coefficient histogram (ASCH). Experimental results validate superior performance of the proposed tracking algorithm over state-of-the-art visual target tracking algorithms in scenarios that include occlusion, background clutter, illumination change, target rotation, and scale changes.
Human actions recognition in video scenes from multiple camera viewpoints

Internet of Video Things (IoVT) has been proposed and studied as a scenario where video cameras are ubiquitous and continuously acquiring data from their surroundings. In order to enable handling of a large amount of data generated in IoVT architectures, robust autonomous video processing must be performed. An important application is the recognition of different actions performed by humans in the context of security. This research evolves a previously published work, by reducing the input dimensionality to the recognition system, making it more robust to variations in the position of the body in each video frame, and by using a Multilayer Perceptron Artificial Neural Network whose hyperparameters are here optimized by a Genetic Algorithm. Significant improvements in the recognition rate have been obtained, despite the use of a more straightforward pre-processing phase and the increase in the number of viewpoints from the video cameras.