Reviewerly/exHarmony huggingface dataset:
Multi-Robot Motion Planning With Dynamics via Coordinated Sampling-Based Expansion Guided by Multi-Agent Search

This letter combines sampling-based motion planning with multi-agent search to efficiently solve challenging multi-robot motion-planning problems with dynamics. This idea has shown promise in prior work that developed a centralized approach to expand a motion tree in the composite state space of all the robots along routes obtained by multi-agent search over a discrete abstraction. Still, the centralized expansion imposes a significant bottleneck due to the curse of dimensionality associated with the high-dimensional composite state space. To improve efficiency and scalebility, we propose a coordinated expansion of the motion tree along routes obtained by the multi-agent search. We first develop a single-robot sampling-based approach to closely follow a given route σ . The salient aspect of the proposed coordinated expansion is to invoke the route follower one robot at a time, ensuring that robot i follows σ while avoiding not only the obstacles but also robots 1, . . . , i - 1. In the next iteration, the motion tree could be expanded from another state along other routes. This enables the approach to progress rapidly and achieve significant speedups over a centralized approach.
A Top-Down Approach to Articulated Human Pose Estimation and Tracking

Both the tasks of multi-person human pose estimation and pose tracking in videos are quite challenging. Existing methods can be categorized into two groups: top-down and bottom-up approaches. In this paper, following the top-down approach, we aim to build a strong baseline system with three modules: human candidate detector, single-person pose estimator and human pose tracker. Firstly, we choose a generic object detector among state-of-the-art methods to detect human candidates. Then, cascaded pyramid network is used to estimate the corresponding human pose. Finally, we use a flow-based pose tracker to render keypoint-association across frames, i.e., assigning each human candidate a unique and temporally-consistent id, for the multi-target pose tracking purpose. We conduct extensive ablative experiments to validate various choices of models and configurations. We take part in two ECCV'18 PoseTrack challenges ( https://posetrack.net/workshops/eccv2018/posetrack_eccv_2018_results.html ): pose estimation and pose tracking.
Research challenges and opportunities in blockchain and cryptocurrencies

The blockchain is the underlying technology of the Bitcoin cryptocurrency, and it has created much excitement in the technology and research communities. A blockchain is a distributed ledger collectively maintained by a peer‐to‐peer network of participants who in Bitcoin are known as miners. This key innovation enables cryptocurrencies such as Bitcoin to operate in a decentralized manner with no intermediaries such as financial institutions. But the blockchain can be used to record things other than cryptocurrency transactions. While many of the concepts of Bitcoin build on what have been around since the 1980s and 1990s, the designer(s) of it have made important assumptions that make it work along with the use of an incentive protocol, leading to a major breakthrough from traditional academic thinking. In this paper, we present the state‐of‐the‐art of blockchain and cryptocurrencies along with research challenges and opportunities that would be of interest to researchers getting into this exciting field.
Data Science for Child Health

Data science has revolutionized industry and academic fields including marketing, 1 Adomavicius G. Tuzhilin A. Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans on Data and Knowledge Engineering. 2005; 17: 734-749 Crossref Scopus (6127) Google Scholar astronomy, 2 Altman M.C. Busse W.W. A Deep dive into asthma transcriptomics. Lessons from U-BIOPRED. Am J Respir Crit Care Med. 2017; 195: 1279-1280 Crossref PubMed Scopus (8) Google Scholar and computer vision. 3 Krizhevsky A. Sutskever I. Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks. Dec 12-17, 2011 Google Scholar It has not yet impacted medicine and biomedical research to the same degree. However, many observers 4 Sanchez-Pinto L.N. Luo Y. Churpek M.M. Big data and data science in critical care. Chest. 2018; 15: 1239-1248 Abstract Full Text Full Text PDF Scopus (67) Google Scholar , 5 Murdoch T.B. Detsky A.S. The inevitable application of big data to health care. JAMA. 2013; 309: 1351-1352 Crossref PubMed Scopus (911) Google Scholar , 6 Naylor C.D. On the prospects for a (deep) learning health care system. JAMA. 2018; 320: 1099-1100 Crossref PubMed Scopus (87) Google Scholar believe that data science will improve the ability of health care systems to deliver personalized medicine, 7 Wong H.R. Intensive care medicine in 2050: precision medicine. Intensive Care Med. 2017; 43: 1507-1509 Crossref PubMed Scopus (14) Google Scholar population health, 8 Zhu L. Zheng J.W. Informatics, data science, and artificial intelligence. JAMA. 2018; 320: 1103-1104 Crossref PubMed Scopus (11) Google Scholar and public health. 9 Dolley S. Big data's role in precision public health. Front Public Health. 2018; 6: 68 Crossref PubMed Scopus (0) Google Scholar
Negative Binomial Models for Visual Fixation Counts on Test Items

With the development of technology-enhanced learning platforms, eye-tracking biometric indicators can be recorded simultaneously with students item responses. In the current study, visual fixation, an essential eye-tracking indicator, is modeled to reflect the degree of test engagement when a test taker solves a set of test questions. Three negative binomial regression models are proposed for modeling visual fixation counts of test takers solving a set of items. These models follow a similar structure to the lognormal response time model and the two-parameter logistic item response model. The proposed modeling structures include individualized latent person parameters reflecting the level of engagement of each test taker and two item parameters indicating the visual attention intensity and discriminating power of each test item. A Markov chain Monte Carlo estimation method is implemented for parameter estimation. Real data are fitted to the three proposed models, and the results are discussed.
Large-Scale Empirical Study of Important Features Indicative of Discovered Vulnerabilities to Assess Application Security

Existing research on vulnerability discovery models shows that the existence of vulnerabilities inside an application may be linked to certain features, e.g., size or complexity, of that application. However, the applicability of such features to demonstrate the relative security between two applications is not well studied, which may depend on multiple factors in a complex way. In this paper, we perform the first large-scale empirical study of the correlation between various features of applications and the abundance of vulnerabilities. Unlike existing work, which typically focuses on one particular application, resulting in limited successes, we focus on the more realistic issue of assessing the relative security level among different applications. To the best of our knowledge, this is the most comprehensive study of 780 real-world applications involving 6498 vulnerabilities. We apply seven feature selection methods to nine feature subsets selected among 34 collected features, which are then fed into six types of machine learning models, producing 523 estimations. The predictive power of important features is evaluated using four different performance measures. This paper reflects that the complexity of applications is not the only factor in vulnerability discovery and the human-related factors contribute to explaining the number of discovered vulnerabilities in an application.
Secondary gold mineralization in the Amani Placer Gold Deposit, Tanzania

The Amani Placer Gold Deposit (APGD) is located in a braided river valley in southwestern Tanzania and is characterised by large gold nuggets with anomalously fine macroscopic textural features as well as significant levels of silver depletion (i.e., high fineness gold) in the nugget rim. The study site thus represents an ideal natural laboratory in which to explore the controls on the low-temperature biogeochemical cycle of gold. Using X-ray Fluorescence (XRF) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) geochemical data, this study evaluated the sediment and pore-water chemistry of the river sediment profile, and using Scanning Electron Microscopy (SEM) coupled these results with a high resolution morphological and chemical investigation of the ambient gold nuggets collected from the auriferous gravel horizon. Down-profile metal distributions indicate a steep redox gradient and observed oximorphic mottling textures along with slumped river terraces, reveal that the redox conditions fluctuate as a function of seasonality and episodic fluvial incision events, respectively. Fluctuating physico-chemical conditions during early diagenesis result in changes to the stability of ambient gold bearing complexes (e.g., Au(S2O3)23− in oxidising condition and Au(HS)2− in reducing conditions), thereby leading to nanophase gold precipitation on clay minerals, organic matter, and importantly on pre-existing gold nugget surfaces. The diagenetic biogeochemical processes operating in the APGD thus formed supergene-enriched gold nuggets characterized by delicate textures and low Ag (<6 wt% Ag) rims. We predict that fluctuating diagenetic conditions may impact gold nugget chemistry at other placer gold deposits, and that the chemical signature arising from these processes may be measurable in paleoplacer deposits such as the supergiant Witwatersrand Au deposit.
Material and sensory dimensions of everyday news use

This article seeks to capture material and sensory dimensions of everyday news use that usually remain unexplored. To that end, we developed a two-sided-ethnography, filming people while they use news, allowing both researchers and participants to look in and reflect on their news use. Tapping into news users’ embodied, tacit knowledge, we found that the materiality of devices and platforms and the ways users physically handle and navigate them impact how they engage with news, in ways they themselves had not realized. We also deepened our understanding of previously found news user practices, and identified the distinct practice scrolling, which is characterized by an embodied urge to keep up the movement of the hand, even when the user finds content appealing. Finally, we show how people actively ‘make’ place and time through their news practices, using coping strategies that mediate between the comfortability of ritual news use and the disruptiveness of news content. We conclude by discussing the theoretical, methodological, and epistemological implications of our research, which include a call for a more in-situ, real-time, and non-news-centric approach to studying everyday news use.
A sequential learning method with Kalman filter and extreme learning machine for regression and time series forecasting

In many regression and time series forecasting problems, the input data is not fully available at the beginning of the training phase. Conventional machine learning methods for batch data are not able to handle this problem. The sequential version of ELM, called Online Sequential Extreme Learning Machine (OS-ELM), addresses this problem through the least squares recursive solution for updating the network output weights. However, the implementation of OS-ELM and its extensions suffer from the problem of multicollinearity and its side effect on the variance of the weight estimates. This paper introduces a new method of sequential learning for handling the effects of multicollinearity. The proposed method, called Kalman Learning Machine (KLM), uses the Kalman filter to sequentially update the output weights of a Single Layer Feedforward Network (SLFN) based on OS-ELM. An extension of the proposed method, called Extended Kalman Learning Machine (EKLM), is presented in order to address the problem of nonlinear data. The proposed method was compared with some of the most recent and effective methods for handling the effects of multicollinearity in sequential learning problems. The experiments performed showed that the proposed method performs better than most state-of-the-art methods considering both the prediction error and training time.
Robotic hand illusion with tactile feedback: Unravelling the relative contribution of visuotactile and visuomotor input to the representation of body parts in space

The rubber hand illusion describes a phenomenon in which participants experience a rubber hand as being part of their body by the synchronous application of visuotactile stimulation to the real and the artificial limb. In the recently introduced robotic hand illusion (RobHI), a robotic hand is incorporated into one's body representation due to the integration of synchronous visuomotor information. However, there are no setups so far that combine visuotactile and visuomotor feedback, which is expected to unravel mechanisms that cannot be detected in experimental designs applying this information in isolation. We developed a robotic hand, controlled by a sensor glove and equipped with pressure sensors, and varied systematically and separately the synchrony for motor feedback (MF) and tactile feedback (TF). In Experiment 1, we implemented a ball-grasping task and assessed the perceived proprioceptive drift of one's own hand as a behavioral measure of the spatial calibration of body coordinates as well as explicit embodiment experiences by a questionnaire. Results revealed significant main effects of both MF and TF for proprioceptive drift data, but we only observed main effects for MF on perceived embodiment. Furthermore, for the proprioceptive drift we found that synchronous feedback in one factor compensates for asynchronous feedback in the other. In Experiment 2, including a new sample of naïve participants, we further explored this finding by adding unimodal conditions, in which we manipulated the presence or absence of MF and/or TF. These findings replicated the results from Experiment 1 and we further found evidence for a supper-additive multisensory effect on spatial body representation caused by the presence of both factors. Results on conscious body perception were less consistent across both experiments. The findings indicate that sensory and motor input equally contribute to the representation of spatial body coordinates which for their part are subject to multisensory enhancing effects. The results outline the potential of human-in-the-loop approaches and might have important implications for clinical applications such as for the future design of robotic prostheses.
Which Commits Can Be CI Skipped?

Continuous Integration (CI) frameworks such as Travis CI, automatically build and run tests whenever a new commit is submitted/pushed. Although there are many advantages in using CI, e.g., speeding up the release cycle and automating the test execution process, it has been noted that the CI process can take a very long time to complete. One of the possible reasons for such delays is the fact that some commits (e.g., changes to readme files) unnecessarily kick off the CI process. Therefore, the goal of this paper is to automate the process of determining which commits can be CI skipped. We start by examining the commits of 58 Java projects and identify commits that were explicitly CI skipped by developers. Based on the manual investigation of 1,813 explicitly CI skipped commits, we first devise an initial model of a CI skipped commit and use this model to propose a rule-based technique that automatically identifies commits that should be CI skipped. To evaluate the rule-based technique, we perform a study on unseen datasets extracted from ten projects and show that the devised rule-based technique is able to detect and label CI skip commits, achieving Areas Under the Curve (AUC) values between 0.56 and 0.98 (average of 0.73). Additionally, we show that, on average, our technique can reduce the number of commits that need to trigger the CI process by 18.16 percent. We also qualitatively triangulated our analysis on the importance of skipping the CI process through a survey with 40 developers. The survey results showed that 75 percent of the surveyed developers consider it to be nice, important or very important to have a technique that automatically flags CI skip commits. To operationalize our technique, we develop a publicly available prototype tool, called CI-Skipper, that can be integrated with any git repository and automatically mark commits that can be CI skipped.
Impact of DNI forecasting on CSP tower plant power production

In the context of energy policies focusing on minimizing power plant emissions, concentrating solar power (CSP) technology plays an important role in the energy mix. These plants require a high level of direct normal irradiance to work properly and profitably. Over-sizing of plant capacity is frequently employed in order to store part of the energy produced, to extend the operating time throughout the day, and also to manage cloud transients. Forecasting the energy delivered by the plant is very important in plant operational strategies to ensure dispatchability as much as possible. This work presents an analysis of energy forecasting in solar tower plants by combining a short-term solar irradiation forecasting scheme with a solar tower plant model using the System Advisor Model (SAM), as the modeling tool for computing plant production throughout the year. Satellite images were used to predict Direct Normal Irradiance (DNI) on an intra-hour time-scale (up to three hours). The predictions were introduced into SAM to simulate the behavior of the Gemasolar and Crescent Dunes plants, placed on Spain and Nevada, respectively). The results show that the best outcomes appear for the 90-mins horizon, where the Mean Bias was about −10% and the RMSE near to 23%.
Spiders like Onions: on the Network of Tor Hidden Services

Tor hidden services allow offering and accessing various Internet resources while guaranteeing a high degree of provider and user anonymity. So far, most research work on the Tor network aimed at discovering protocol vulnerabilities to de-anonymize users and services. Other work aimed at estimating the number of available hidden services and classifying them. Something that still remains largely unknown is the structure of the graph defined by the network of Tor services. In this paper, we describe the topology of the Tor graph (aggregated at the hidden service level) measuring both global and local properties by means of well-known metrics. We consider three different snapshots obtained by extensively crawling Tor three times over a 5 months time frame. We separately study these three graphs and their shared "stable" core. In doing so, other than assessing the renowned volatility of Tor hidden services, we make it possible to distinguish time dependent and structural aspects of the Tor graph. Our findings show that, among other things, the graph of Tor hidden services presents some of the characteristics of social and surface web graphs, along with a few unique peculiarities, such as a very high percentage of nodes having no outbound links.
Click Feedback-Aware Query Recommendation Using Adversarial Examples

Search engine users always endeavor to formulate proper search queries during online search. To help users accurately express their information need during search, search engines are equipped with query suggestions to refine users' follow-up search queries. The success of a query suggestion system counts on whether we can understand and model user search intent accurately. In this work, we propose Click Feedback-Aware Network (CFAN) to provide feedback-aware query suggestions. In addition to modeling sequential search queries issued by a user, CFAN also considers user clicks on previous suggested queries as the user feedback. These clicked suggestions, together with the issued search query sequence, jointly capture the underlying search intent of users. In addition, we explicitly focus on improving the robustness of the query suggestion system through adversarial training. Adversarial examples are introduced into the training of the query suggestion system, which not only improves the robustness of system to nuisance perturbations, but also enhances the generalization performance for original training data. Extensive experiments are conducted on a recent real search engine log. The experimental results demonstrate that the proposed method, CFAN, outperforms competitive baseline methods across various situations on the task of query suggestion.
Adaptive Multivariable Control for Multiple Resource Allocation of Service-Based Systems in Cloud Computing

Service-based systems resource allocation in cloud computing is a key method of meeting service requests because service request workloads and resource demands change over time. When coping with dynamic fluctuating service requests and resource demands, adaptive resource allocation to ensure the quality of service (QoS) with the lowest resource consumption becomes challenging. In cloud computing, services share the same resource pool and compete for critical resources, such as CPU and memory resources. Because services need arbitrary resource combinations, focusing on a single resource may lead to excessive or deficient resource allocations or even service request failures. Due to the shared nature of cloud computing, QoS may be impacted by interference with co-hosted services. In this paper, we propose an adaptive control approach for resource allocation that adaptively reacts to dynamic request workloads and resource demands. The multivariable control is adopted to allocate multiple resources for multiple services according to the dynamic fluctuating requests and considers the interference between co-hosted services, thereby ensuring QoS even if the resource pool is insufficient. The comparative experiments show that the proposed approach can meet service requests and can improve resource utilization regardless of whether the resource pool is sufficient.
Citation Recommendation as Edge Prediction in Heterogeneous Bibliographic Network: A Network Representation Approach

With an increasing number of scholarly publications, accessing and retrieving appropriate papers is becoming an essential task for researchers. Citation recommendation, which can automatically provide a reference list based on a text segment, can overcome this problem. In this paper, we first construct a heterogeneous bibliographic network and deem citation recommendation as edge prediction problem, and then we develop a network representation-based edge prediction (NREP) model, which can simultaneously learn the edge prediction knowledge and the predictive representation for efficient citation recommendation. For personalized recommendation, we incorporate author information. We conduct extensive experiments on two datasets; the experimental results show that the NREP-based approach outperforms the other four state-of-the-art baseline approaches in terms of recall, mean average precision, and normalized discounted cumulative gain.