Reviewerly/exHarmony huggingface dataset:
The Instantaneous Accuracy: a Novel Metric for the Problem of Online Human Behaviour Recognition in Untrimmed Videos

The problem of Online Human Behavior Recognition in untrimmed videos, aka Online Action Detection (OAD), needs to be revisited. Unlike traditional off-line action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find few works and no consensus on evaluation protocols to be used. In this paper we introduce a novel online metric, the Instantaneous Accuracy (IA), that exhibits an online nature, solving most of the limitations of the previous (off-line) metrics. We conduct a thorough experimental evaluation on the TVSeries dataset, comparing the performance of various baseline methods with the state of the art. Our results confirm the problems of the previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario for human behaviour understanding.
Image denoising based on nonsubsampled shearlet transform domain Laplacian mixture model and bilateral filter and its method noise thresholding

In this paper, we present a spatially-adaptive non subsampled shearlet transform (NSST) based Bayesian technique for denoising the natural images. The NSST can provide better directional selectivity and give nearly optimal approximation for a piecewise smooth function and is approximately shift invariant. The NSST coefficients of the images are modeled as 2-state Laplacian mixture(LM) distribution that uses local parameters. Through Kolomogrov-Smirnov (KS) goodness of fit test, it is shown that a 2-state LM distribution is highly appropriate for modeling the NSST image coefficients. This prior distribution is then employed to obtain a maximum a posteriori (MAP) estimator. An adaptive bilateral filtering (ABF) is applied on the MAP output to smooth out the visual artifacts in the homogeneous regions while preserving the textures. The NSST domain MAP estimator with LMM prior followed by ABF is finally implemented in its method noise thresholding framework for image denoising. The performance of proposed denoising framework is validated on a variety of benchmark images at different noise levels. The proposed framework achieves highly encouraging results in terms of fine detail preservation and exhibit less distortion compared with some recent relevant methods and BM3D method.
Splitting the fitness and penalty factor for temporal diversity increase in practical problem solving

In this paper, we propose an optimization system based on an evolutionary algorithm developed to solve a real-life optimization problem related to the optimization of the computer networks. In particular, we focus on an NP-hard flow allocation problem in computer networks related to network survivability and addressing various constraints specific to computer networks. The constrained problems are usually handled by evolutionary methods by the introduction of the so-called penalty factor. However, such techniques raise difficulties regardless of their simple working principle. The more oppressive the penalty factor is, the more likely the evolutionary method is to stuck in the local optima with feasible solutions and propose final results of low-quality. On the other hand, a low penalty factor may lead the method to regions of high-rated solutions that violate the constraints and are useless due to their infeasibility. Therefore, we address the issue of penalty handling by proposing Ranking-based Fitness and Penalty Weighting (RFPW). RFPW is inspired by a theory-driven penalty parameter estimation using a bi-objective and weighted sum approach. It separates the objective and penalty functions and removes the necessity of hand-made weighting. RFPW was introduced to the recent proposition of an evolutionary method dedicated to solving hard NP-complete practical problem. The performed experiments related to the flow allocation problem confirm that employing RFPW may lead to a significant improvement in results quality. In our opinion, the proposed RFPW framework of the penalty function handling has the potential to be adapted to a wide range of optimization systems that utilize the idea of penalty function. RFPW allows the optimization method to reduce or increase the strength of a convergence automatically. Therefore, RFPW has the potential of results quality improvement and may replace expensive commonly-used diversity preservation techniques (e.g., island models).
Effectiveness of web-based health education and consultation on health promotion behaviors of adolescents

Aims:Adolescence is a critical period for many health-risk behaviours.The purpose of this study was to examine the effectiveness of web-based health education and consultation on health promotion behaviors of adolescence and their e-health literacy. Methods:This randomized controlled study consisted of 252 adolescence, of whom 120 were in the control group and 132 comprised the intervention group.Students in the intervention group received web based health education and consultation program based on health promotion theory.No procedures were applied to the control group.The results were reevaluated three months after the intervention.Measurements included the e-HEALS: The Health Literacy Scale, Adolescent Lifestyle Questionnaire, Information Assessment Form for the Content of Health Education Program and Satisfaction Evaluation Form.Results:Results indicated that total Adolescent Lifestyle Questionnaire (p= 0.004), e-health literacy (p= 0.001) differences were greater in the intervention group than in the control group, and the differences were statistically significant.Conclusions:Web-based education and consultation demonstrated positive contributions for adolescents'health promotion behaviors and e-health literacy who are the biggest group of internet users, in promoting healthy lifestyle choices.
Context-Aware Collaborative Object Recognition For Distributed Multi Camera Time Series Data

Recent research shows that the multi-view system for object recognition outperforms the single-view point system. When viewpoints are added, additional communication cost and cost to deploy the viewpoints are also added. However, prior work has shown that not all of the views are useful, and poor viewpoints can be excluded.
Stimulated emission tomography for entangled photon pairs with different detection spectral ranges

Frequency nondegenerate entangled photon pairs have been employed in quantum communication, imaging, and sensing. To characterize quantum entangled states with long-wavelength [infrared (IR) or even terahertz (THz)] photons, one needs to either develop the single-photon detectors at the corresponding wavelengths or use a novel tomography technique that does not rely on single-photon detections, such as stimulated emission tomography (SET). We use standard quantum state tomography and SET to measure the density matrix of entangled photon pairs, with one photon at 1550 nm and the other one at 810 nm, and obtain highly consistent results, showing the reliability of SET. Our work paves the way for efficient measurement of entangled photons with highly dissimilar frequencies, even to the frequencies where single-photon detections are not available.
TangiBoard: A Toolkit to Reduce the Implementation Burden of Tangible User Interfaces in Education

The use of Tangible User Interfaces (TUI) as an educational technology has gained sustained interest over the years with common agreement on its innate ability to engage and intrigue students in active-learning pedagogies. Whilst encouraging results have been obtained in research, the widespread adoption of TUI architectures is still hindered by a myriad of implementation burdens imposed by current toolkits. To this end, this paper presents an innovative TUI toolkit: TangiBoard, which enables the deployment of an interactive TUI system using low-cost, and presently available educational technology. Apart from curtailing setup costs and technical expertise required for adopting TUI systems, the toolkit provides an application framework to facilitate system calibration and development integration with GUI applications. This is enabled by a robust computer vision application that tracks a contributed passive marker set providing a range of tangible interactions to TUI frameworks. The effectiveness of this toolkit was evaluated by computer systems developers with respect to alternate toolkits for TUI design. Open-source versions of the TangiBoard toolkit together with marker sets are provided online through research license.
A Content-Based Model for Tag Recommendation in Software Information Sites

Abstract Developers use software information sites such as Stack Overflow to get and give information on various subjects. These sites allow developers to label content with tags as a short description. Tags, then, are used to describe, categorize and search the posted content. However, tags might be noisy, and postings may become poorly categorized since people tag a posting based on their knowledge of its content and other existing tags. To keep the content well organized, tag recommendation systems can help users by suggesting appropriate tags for their posted content. In this paper, we propose a tag recommendation scheme that uses the textual content of already tagged postings to recommend suitable tags for newly posted content. Our approach combines multi-label classification and textual similarity techniques to improve the performance of tag recommendation. We evaluate the performance of the proposed scheme on 11 software information sites from the Stack Exchange network. The results show a significant improvement over TagCombine, TagMulRec and FastTagRec, which are well-known tag recommendation systems. On average, the proposed model outperforms TagCombine, TagMulRec and FastTagRec by 26.2, 15.9 and 13.8% in terms of Recall@5 and by 16.9, 12.4 and 9.4% in terms of Recall@10, respectively.
Removal of pollutants from chicken slaughterhouse wastewater using constructed wetland system

The growth of the poultry industry has a significant impact on the environment especially in the slaughtering stage that will create a high quantity of wastewater. This wastewater has high organic content that requires efficient and low-cost treatment technology alternatives, such as by Constructed Wetlands system. The purpose of this research was to determine the ability of plants to reduce the content of Chemical Oxygen Demand (COD), and the ammonia content of chicken slaughterhouse wastewater. The wetland pond dimension was 6x20x0.8m (WxLxH) and filled with coarse sand and then planted with cattail grass (Typha latifolia), umbrella plant (Cyperus alternifolius) and vetiver (Vetiveria zizanioides L. Nash). The results showed that the plants in the wetland had a good performance in treating chicken slaughterhouse wastewater. The efficiency of decreasing the total COD can reach up to 85%, while the highest ammonium removal efficiency was 80%. Meanwhile, the most efficient plant for reducing COD was shown by umbrella plant (Cyperus alternifolius).
Cross Fusion for Egocentric Interactive Action Recognition

The characteristics of egocentric interactive videos, which include heavy ego-motion, frequent viewpoint changes and multiple types of activities, hinder the action recognition methods of third-person vision from obtaining satisfactory results. In this paper, we introduce an effective architecture with two branches and a cross fusion method for action recognition in egocentric interactive vision. The two branches are responsible to model the information from observers and inter-actors respectively, and each branch is designed based on the multimodal multi-stream C3D networks. We leverage cross fusion to establish effective linkages between the two branches, which aims to reduce redundant information and fuse complementary features. Besides, we propose variable sampling to obtain discriminative snippets for training. Experimental results demonstrate that the proposed architecture achieves superior performance over several state-of-the-art methods on two benchmarks.
Software Engineering Framework for Software Defect Management Using Machine Learning Techniques with Azure

TheAzure presence of bugs in a software release has become inevitable. The loss incurred by a company due to the presence of bugs in a software release is phenomenal. Modern methods of testingTesting and debugging have shifted focus from ‘detecting’ to ‘predicting’ bugs in the code. The existing models of bug prediction have not been optimized for commercial use. Moreover, the scalability of these models has not been discussed in depth yet. Taking into account the varying costs of fixing bugs, depending on which stage of the software development cycle the bug is detected in, this chapter uses two approaches—one model which can be employed when the ‘cost of changing code’ curve is exponential and the other model can be used otherwise. The cases where each model is best suited are discussed. This chapter proposes a model that can be deployed on a cloud platform for software development companies to use. The model in this chapter aims to predict the presence or absence of a bug in the code, using machine learningMachine learning classification models. Using Microsoft Azure’sMicrosoft Azure machine learning platform, this model can be distributed as a web service worldwide, thus providing bug predictionBug Prediction as a service (BPaaS)Bug Prediction as a service.
VAMD: Visual Analytics for Multimodal Data

From social media to smart traffic signals, digital records of the state of the world are being produced at unprecedented rates. These data may contain critical information regarding patterns of life or unfolding events, but the necessary pieces may be dispersed across multiple disparate sources. To make matters worse, the data produced is by nature massive, heterogeneous, and imperfect. To deal with this, current solutions typically analyze each data source within its own class. This reduces the problem's complexity but forfeits the potential benefits of discovering nuanced connections and details encompassing an entire situation. In order to exploit these potentially rich (but frequently one-sided) data sources, it is imperative that there exists an environment and supporting tooling that allows for the analysis of heterogeneous data sources within a combined view. This paper presents VAMD, a visual analytics system focused on dealing with large heterogeneous datasets. The design is supported with a use case focused around a disaster response scenario using a curated social media dataset featuring both text and accompanying images.
The Application of Reinforcement Learning in Amazons

Computer game is a challenging and active research field. The success of AlphaGo has attracted people's attention and enthusiasm in this field, its following version, AlphaZero, provides a general solution for complete information game. However, different kinds of game have different rules, how to apply this framework to specific games is a problem worthy of study. This paper applies AlphaZero in Amazons game, using a convolutional neural network to provide evaluation and prior probability for current board which make up for the Monte Carlo tree search. Its improvement depends self-play entirely. Experiments show that the method is effective.
Machine Learning and Artificial Intelligence

Interest in artificial intelligence for use in healthcare and health services research is growing as the amount of data available is ever increasing. In this chapter, we define the terminology surrounding artificial intelligence. Machine learning methods are the building blocks for artificial intelligence, and we provide an overview of selected methods useful in healthcare and health services research. Cutting edge “deep learning” hold particular promise for image analysis and natural language processing. We review two examples to illustrate the features of deep learning that make it a powerful tool for research and clinical applications.
A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point

Clustering is widely used as an unsupervised learning algorithm. However, it is often necessary to manually enter the number of clusters, and the number of clusters has a great impact on the clustering effect. At present, researchers propose some algorithms to determine the number of clusters, but the results are not very good for determining the number of clusters of data sets with complex and scattered shapes. To solve these problems, this paper proposes using the Gaussian Kernel density estimation function to determine the maximum number of clusters, use the change of center point score to get the candidate set of center points, and further use the change of the minimum distance between center points to get the number of clusters. The experiment shows the validity and practicability of the proposed algorithm.
Statistical measurement of trees’ similarity

Abstract Diagnostic theories are fundamental to Information Systems practice and are represented in trees. One way of creating diagnostic trees is by employing independent experts to construct such trees and compare them. However, good measures of similarity to compare diagnostic trees have not been identified. This paper presents an analysis of the suitability of various measures of association to determine the similarity of two diagnostic trees using bootstrap simulations. We find that three measures of association, Goodman and Kruskal’s Lambda, Cohen’s Kappa, and Goodman and Kruskal’s Gamma (J Am Stat Assoc 49(268):732–764, 1954) each behave differently depending on what is inconsistent between the two trees thus providing both measures for assessing alignment between two trees developed by independent experts as well as identifying the causes of the differences.
Deep Motion-Appearance Convolutions for Robust Visual Tracking

Visual tracking is a challenging task due to unconstrained appearance variations and dynamic surrounding backgrounds, which basically arise from the complex motion of the target object. Therefore, the information and the correlation between the target motion and its resulting appearance should be considered comprehensively to achieve robust tracking performance. In this paper, we propose a deep neural network for visual tracking, namely the Motion-Appearance Dual (MADual) network, which employs a dual-branch architecture, by using deep two-dimensional (2D) and deep three-dimensional (3D) convolutions to integrate the local and global information of the target object's motion and appearance synchronously. For each frame of a tracking video, 2D convolutional kernels of the deep 2D branch slide over the frame to extract its global spatial-appearance features. Meanwhile, 3D convolutional kernels of the deep 3D branch are used to collaboratively extract the appearance and the associated motion features of the visual target from successive frames. By sliding the 3D convolutional kernels along a video sequence, the model is able to learn the temporal features from previous frames, and therefore, generate the local patch-based motion patterns of the target. Sliding the 2D kernels on a frame and the 3D kernels on a frame cube synchronously enables a better hierarchical motion-appearance integration, and boosts the performance for the visual tracking task. To further improve the tracking precision, an extra ridge-regression model is trained for the tracking process, based not only on the bounding box given in the first frame, but also on its synchro-frame-cube using our proposed Inverse Temporal Training method (ITT). Extensive experiments on popular benchmark datasets, OTB2013, OTB50, OTB2015, UAV123, TC128, VOT2015 and VOT2016, demonstrate that the proposed MADual tracker performs favorably against many state-of-the-art methods.
Ensemble Kalman Filter and Extended Kalman Filter for State-Parameter Dual Estimation in Mixed Effects Models Defined by a Stochastic Differential Equation

The biological processes that occur in the real world have complex dynamics. Mathematical models that try to describe these phenomena have nonlinear structures, observations are made at discrete time points and include measurement errors, and are difficult to estimate. In particular, when modeling dynamics of repeated measurements on individuals or objects, they are analyzed by mixed-effects diffusion models. The standard estimation methods in these cases are: maximum likelihood, EM, SAEM, Newton Raphson, among others. In this paper we propose a specific inference methodology for models. Apply extended Kalman Filter (EKF) and the ensemble Kalman filter (EnKF) to the estimation of both states and parameters of nonlinear state-space models. To illustrate the methodology, the states and parameters of an Ornstein- Uhlembeck (O-U) mixed-effects model were estimated, obtaining precise estimates with small standard deviations. To measure the estimation quality of the algorithms was used as a measure of goodness-of-fit known as the square root of the mean quadratic error, obtaining very small errors.
Argument evaluation in multi-agent justification logics

Abstract Argument evaluation , one of the central problems in argumentation theory, consists in studying what makes an argument a good one. This paper proposes a formal approach to argument evaluation from the perspective of justification logic. We adopt a multi-agent setting, accepting the intuitive idea that arguments are always evaluated by someone. Two general restrictions are imposed on our analysis: non-deductive arguments are left out and the goal of argument evaluation is fixed: supporting a given proposition. Methodologically, our approach uses several existing tools borrowed from justification logic, awareness logic, doxastic logic and logics for belief dependence. We start by introducing a basic logic for argument evaluation, where a list of argumentative and doxastic notions can be expressed. Later on, we discuss how to capture the mentioned form of argument evaluation by defining a preference operator in the object language. The intuitive picture behind this definition is that, when assessing a couple of arguments, the agent puts them to a test consisting of several criteria (filters). As a result of this process, a preference relation among the evaluated arguments is established by the agent. After showing that this operator suffers a special form of logical omniscience, called preferential omniscience, we discuss how to define an explicit version of it, more suitable to deal with non-ideal agents. The present work exploits the formal notion of awareness in order to model several informal phenomena: awareness of sentences, availability of arguments and communication between agents and external sources (advisers). We discuss several extensions of the basic logic and offer completeness and decidability results for all of them.
Improved Rule Engine Based on RETE Algorithm for Smart-Home Environment

The rapid development of Internet of Things (IoT) has promoted many of technologies. Smart-Home is an important part of IoT. Nowadays, many homes have used Smart-Home system. To achieve true user-free intelligence, not just which user using their phone to control the device, rule engine needs to be introduced. Rule engine allows users to customize the rules of devices and the personalized services in advance. And Due to the development of 5G, the implementation of remote rule engine has become more realistic. The remote rule engine can access a large number of rules. But because of the limitations of rule engine algorithms in large scale rules, it cannot be used in Smart-Home environment well. Thus, in the paper, fuzzy processing and B+ tree structure are proposed. Fuzzy processing uses fuzzy set to transform precise data into fuzzy concepts to facilitate user customize rules. And B+ tree structure speeds up traversal of non-empty nodes. These proposed approaches reduce the matching time of rule engine. The experimental results verify the proposed approaches.