Miracle-dz/UniFAR-FTUs huggingface dataset:
Industrial Robot : An International Journal Introducing data analytics to the robotic drilling process

Purpose – This paper presents a method for extracting the geometric primitives of a circle in a three-dimensional space from a discrete point cloud data set obtained by a laser stripe sensor. This paper aims to first establish a reference frame for the robotic drilling process by detecting the position and orientation of a reference hole on structural parts in a pre-drilling step, and second, to perform quality inspection of the hole in a post-drilling step. Design/methodology/approach – The method is divided into the following steps: a plane is initially fitted on the data by evaluating the principle component analysis using singular value decomposition; the data points or measurements are then rotated around an arbitrary axis using the Rodrigues’ rotation formula such that the normal direction of the estimated plane and the z-axis direction is parallel; the Delaunay triangulation is constructed on the point cloud and the confidence interval is estimated for segmenting the data set located at the circular boundary; and finally, a circular profile is fitted on the extracted set and transformed back to the original position. Findings – The geometric estimation of the circle in three-dimensional space constitutes of the position of the center, the diameter and the orientation, which is represented by the normal vector of the plane that the circle lives in. The method is applied on both simulated data set with the addition of several noise levels and experimental data sets. The main purpose of both the tests is to quantify the accuracy of the estimated diameter. The results show good accuracy (mean relative error< 1 per cent) and high robustness to noise. Research limitations/implications – The proposed method is applied here to estimate the geometric primitives of only one circle (the reference hole). If multiple circles are needed, an addition clustering procedure is required to cluster the segmented data into multiple data sets. Each data set represents a circle. Also, the method does not operate efficiently on a sparse data sets. Dense data are required to cover the hole (at least ten scans to cover the hole diameter). Practical implications – Researchers and practitioners can integrate this method with several robotic manufacturing applications where high accuracy is required. The extracted position and orientation of the hole are used to minimize the positioning and alignment errors between the mounted tool tip and the workpiece. Originality/value – The method introduces data analytics for estimating the geometric primitives in the robotic drilling application. The main advantage of the proposed method is to register the top surface of the workpiece with respect to robot base frame with a high accuracy. An accurate workpiece registration is extremely necessary in the lateral direction (identifying where to drill), as well as in the vertical direction (identifying how far to drill).
Communicative Listener Feedback in Human-Agent Interaction: Artificial Speakers Need to Be Attentive and Adaptive

In human dialogue, listener feedback is a pervasive phenomenon that serves important functions in the coordination of the conversation, both in regulating its flow, as well as in creating and ensuring understanding between interlocutors. This make feedback an interesting mechanism for conversational human-agent interaction. In this paper we describe computational models for an attentive speaker' agent is able to (1) interpret the feedback behaviour of its human interlocutors by probabilistically attributing listening-related mental states to them; (2) incrementally adapt its ongoing language and behaviour generation to their needs; and (3) elicit feedback from them when needed. We present a semi-autonomous interaction study, in which we compare such an attentive speaker agent with agents that either do not adapt their behaviour to their listeners' needs, or employ highly explicit ways of ensuring understanding. The results show that human interlocutors interacting with the attentive speaker agent provided significantly more listener feedback, felt that the agent was attentive to, and adaptive to their feedback, attested the agent a desire to be understood, and rated it more helpful in resolving difficulties in their understanding.
A First Study on What MDL Can Do for FCA

Formal Concept Analysis can be considered as a classification engine able to build classes of objects with a description or concepts and to organize these concepts within a concept lattice. The concept lattice can be navigated for selecting significant concepts. Then the problem of finding significant concepts among the potential exponential number of concepts arises. Some measures exist that can be used for focusing on interesting concepts such as support, stability, and other. MDL (mini-mum description length) is also a good candidate that was rarely used in FCA by now for such objective. In this paper we argue that MDL can give FCA practitioners a good measure for selecting significant and representative concepts.
Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series

Advances in technology coupled with the availability of low‐cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group‐by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube‐based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space‐efficient cube data structures allow for updates. Thus, the cube must be re‐computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory‐efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web‐based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.
Networks, Percolation, and Consumer Demand

Understanding diffusion processes is key to market strategies as well as innovation and sustainability policies. In promoting new products and technologies, firms and governments need to understand the conditions favouring successful spread of these products. We propose a generic diffusion model based on percolation theory. Our reference is a new product diffusion in a social network through word-of-mouth. Given that consumers differ in their reservation prices, a critical price exists that defines a phase transition from a no-diffusion to a diffusion regime. As consumer surplus is maximised just below a productâ€™s critical price, one can systematically compare the economic efficiency of network structures by investigating their critical price. Networks with low clustering were the most efficient, because clustering leads to redundant information flows hampering effective product diffusion. We further showed that the more equal a society, the more efficient the diffusion process.
The Limits of Maxing, Ranking, and Preference Learning

We present a comprehensive understanding of three important problems in PAC preference learning: maximum selection (maxing), ranking, and estimating all pairwise preference probabilities, in the adaptive setting. With just Weak Stochastic Transitivity, we show that maxing requires Ω(n) comparisons and with slightly more restrictive Medium Stochastic Transitivity, we present a linear complexity maxing algorithm. With Strong Stochastic Transitivity and Stochastic Triangle Inequality, we derive a ranking algorithm with optimalO(n log n) complexity and an optimal algorithm that estimates all pairwise preference probabilities.
The Effect of User Embodiment in AV Cinematic Experience

Virtual Reality (VR) is becoming a popular medium for viewing immersive cinematic experiences using 360 ◦ panoramic movies and head mounted displays. There are previous research on user embodiment in real-time rendered VR, but not in relation to cinematic VR based on 360 panoramic video. In this paper we explore the effects of introducing the user’s real body into cinematic VR experiences. We conducted a study evaluating how the type of movie and user embodiment affects the sense of presence and user engagement. We found that when participants were able to see their own body in the VR movie, there was signiﬁcant increase in the sense of Presence, yet user engagement was not signiﬁcantly affected. We discuss on the implications of the results and how it can be expanded in the future.
Subpixel Edge Localization Based on Adaptive Weighting of Gradients

This paper presents a subpixel edge localization method based on the adaptive weighting of gradients (AWG). The method first finds the optimal power factors to be used by the AWG through an error propagation scheme. Then, with the goal of fast implementation of the method, a process that directly relates the edge width to the optimal powers is proposed. A method that uses the squared weighting of gradients (SWG) is also proposed, which applies a power of 2 to the gradients of an edge profile to calculate the edge location with subpixel accuracy. Next, an enhanced version of the AWG that combines the SWG and AWG selectively is proposed to obtain the best localization. At present, a fitting method based on the error function (Erf) is considered to be the most accurate method among current state-of-the-art methods, but it has a very high computational cost. The experimental results show that the proposed method is computationally much less expensive and more accurate than the Erf-fitting method.
Object-Oriented Curriculum Generation for Reinforcement Learning

Autonomously learning a complex task takes a very long time for Reinforcement Learning (RL) agents. One way to learn faster is by dividing a complex task into several simple subtasks and organizing them into a Curriculum that guides Transfer Learning (TL) methods to reuse knowledge in a convenient sequence. However, previous works do not take into account the TL method to build specialized Curricula , leaving the burden of a careful subtask selection to a human. We here contribute novel procedures for: (i) dividing the target task into simpler ones under minimal human supervision; (ii) automatically generating Curricula based on object-oriented task descriptions; and (iii) using generated Curricula for reusing knowledge across tasks. Our experiments show that our proposal achieves a better performance using both manually given and generated subtasks when compared to the state-of-the-art technique in two different domains.
Deduplication Analyses of Multimedia System Images

The availability and usage of embedded systems increases permanently and the industry drives the IoT to become more and more relevant in daily life. Factory lines, planes and cars, traffic lights, or even clothes are equipped with sensors and small computers constantly communicating with the outside world. One challenge in maintaining those devices is updating their software. Due to slow connections or only because of the huge amount of devices data transfers can be problematic. Data compression algorithms can be applied to reduce the amount of data that must be transferred. A data reduction technique that provides high efficiency, but which has not been considered so far for embedded systems is data deduplication. In this work we present the results of a long term study for updating a car multimedia system. The results show that deduplication can achieve significantly better results than commonly used data compression techniques.
The Compact Balanced Filtering Power Divider With In-Phase or Out-of-Phase Output Using H-Shape Resonators

In this paper, the compact balanced filtering power dividers using four coupled H-shape resonators and two resistors are proposed. The theoretic coupling topologies of the proposed designs are given and analyzed. Meanwhile, the phase feature of the filtering structure formed by the electric or magnetic coupling is studied. The main advantages are compact size, both in-phase and out-of-phase output, wideband isolation, and wideband out-of-band suppression when compared with the reported designs. For demonstration, one in-phase prototype with the size of <inline-formula> <tex-math notation="LaTeX">$0.36\,\,\lambda _{g} \times 0.22\,\,\lambda _{g}$ </tex-math></inline-formula> and one out-of-phase prototype with the size of <inline-formula> <tex-math notation="LaTeX">$0.34\,\,\lambda _{g} \times 0.22\,\,\lambda _{g}$ </tex-math></inline-formula> are fabricated and measured (<inline-formula> <tex-math notation="LaTeX">$\lambda _{g}$ </tex-math></inline-formula> is the guided wavelength at the center frequency). The in-phase (out-of-phase) design exhibits the insertion loss of 2.2 dB (2 dB) at the center frequency, the 3-dB bandwidth of 6.6% (7.4%), the 20-dB stopband of <inline-formula> <tex-math notation="LaTeX">$2. 7f_{0}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$2.3f_{0}$ </tex-math></inline-formula>), and the 15-dB isolation band of <inline-formula> <tex-math notation="LaTeX">$4. 9f_{0}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$4.74f_{0}$ </tex-math></inline-formula>).
Towards intelligent geospatial data discovery: a machine learning framework for search ranking

ABSTRACT Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date). This approach largely fails to take account of users’ multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset. This study reports a machine learning framework to address the ranking challenge, the fundamental obstacle in geospatial data discovery, by (1) identifying a number of ranking features of geospatial data to represent users’ multidimensional preferences by considering semantics, user behavior, spatial similarity, and static dataset metadata attributes; (2) applying a machine learning method to automatically learn a ranking function; and (3) proposing a system architecture to combine existing search-oriented open source software, semantic knowledge base, ranking feature extraction, and machine learning algorithm. Results show that the machine learning approach outperforms other methods, in terms of both precision at K and normalized discounted cumulative gain. As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain, we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.
NBSL: A Supervised Classification Model of Pull Request in Github

A lot of Pull Requests (PRs) appear in Github everyday, and thus it is a very important work to review these PRs quickly in Github. Labeling PRs according to the PRs classification can improve the success rate and the review efficiency. However, recent research works have shown that most of the PRs are not labeled, and if the PR is labeled, it is done manually. To solve this problem, we propose a supervised classification model combined with supervised topics model and Naive Bayes classifier, which can make the PR be classified automatically. The method creates a one-one relationship between labels and PRs, and the approach classifies most PRs automatically with the only label which record the closest topic of PRs. The experimental results show that the proposed model can reach a precision of 60% in majority situation. The proposed model may support a better result via adjusting the parameters in case.
EPTI: Efficient Defence against Meltdown Attack for Unpatched VMs

The Meltdown vulnerability, which exploits the inherent out-of-order execution in common processors like x86, ARM and PowerPC, has shown to break the fundamental isolation boundary between user and kernel space. This has stimulated a non-trivial patch to modern OS to separate page tables for user space and kernel space, namely, KPTI (kernel page table isolation). While this patch stops kernel memory leakages from rouge user processes, it mandates users to patch their kernels (usually requiring a reboot), and is currently only available on the latest versions of OS kernels. Further, it also introduces non-trivial performance overhead due to page table switching during user/kernel crossings. In this paper, we present EPTI, an alternative approach to defending against the Meltdown attack for unpatched VMs (virtual machines) in cloud, yet with better performance than KPTI. Specifically, instead of using two guest page tables, we use two EPTs (extended page tables) to isolate user space and kernel space, and unmap all the kernel space in user's EPT to achieve the same effort of KPTI. The switching of EPTs is done through a hardware-support feature called EPT switching within guest VMs without hypervisor involvement. Meanwhile, EPT switching does not flush TLB since each EPT has its own TLB, which further reduces the overhead. We have implemented our design and evaluated it on Intel Kaby Lake CPU with different versions of Linux kernel. The results show that EPTI only introduces up to 13% overhead, which is around 45% less than KPTI.
State Tracking Networks for Dialog State Tracking

Dialog state tracking is to accurately infer a compact representation of the dialog status up to the current turn, it needs to summarize all the dialog history information and user’s goals. In a successful spoken dialog system, dialog state tracker is one of the most important components of the pipelines. Yet until recently, there are no general, ﬂexible, accurate and truly end to end dialog state tracking models. In this paper, we pro-pose a novel model named state tracking networks that can perform dialog state tracking in a natural efﬁcient and elegant way. It uses an explicit gate to model the state updating mechanism and can be trained end to end in a deterministic manner using standard backpropagation techniques or stochastically by reinforcement learning. Our model can both deal with ASR and text input without any modiﬁcation. We perform experiments on the Second Dialog State Tracking Challenge dataset(DSTC2) and get performance matching the state-of-the-art models. Furthermore, the qualitative analysis reveals that the gating mechanism learned by our model agree well with intuition.
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems

This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark on standard datasets and unified experimental environment. In this special session, we will release human-annotated conversational data in three domains (movie-ticket booking, restaurant reservation, and taxi booking), as well as an experiment platform with built-in simulators in each domain, for training and evaluation purposes. The final submitted systems will be evaluated both in simulated setting and by human judges.
Comparison Study On UAV Movement for Adapting to Multimedia Burst in Post-Disaster Networks

Unmanned aerial vehicles (UAVs) can provide important communication advantages to ground-based wireless mesh networks with multiple connected routers. Their usage in civilian areas has become commonplace, especially more promising recently as emergency communication networks for rescuing victims, dispatching resources and recovering communications to bridge the gap between the post-disaster areas and outsides. First, usually a flash crowd of victims will attempt to make a video or VoIP connection with outsides. Furthermore, it results in a large volume of traffic shift among mesh routers due to high migration of people such as leaving and joining in the refuges. Thus, in this paper we consider the optimization of the location and movement of a single UAV to improve the network throughput by means of high mobility of the UAV to adapt to heavy fluctuation of network traffic. Though there are several methodologies to the UAV movement, it is still indistinct which one is better, especially while employing different routing protocols. A detailed comparison between two typical schemes based on the mean value and the collision domain is made for UAV-assisted wireless mesh networks with multimedia burst. Specifically, we designed two schemes accordingly to select the UAV position and the mesh router as the gateway to connect the UAV. A numerical experiments with different routing protocols are evaluated and compared in a realistic simulation environment of NS-3 and show that they both have advantages and disadvantages with respect to QoS when employing different routing protocols in the considered scenario.
Impact of ambulatory cardiac rehabilitation on cardiovascular outcomes: a long-term follow-up study

AIMS To evaluate the long-term clinical impact of the application of cardiac rehabilitation (CR) early after discharge in a real-world population. METHODS AND RESULTS We analysed the 5-year incidence of cardiovascular mortality and hospitalization for cardiovascular causes in two populations, attenders vs. non-attenders to an ambulatory CR program which were consecutively discharged from two tertiary hospitals, after ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, coronary artery bypass graft, or planned percutaneous coronary intervention. A primary analysis using multivariable regression model and a secondary analysis using the propensity score approach were performed. Between 1 January 2009 and 31 December 2010, 839 patients attended a CR program planned at discharged, while 441 patients were discharged from Cardiovascular Department without any program of CR. During follow-up, the incidence of cardiovascular mortality was 6% in both groups (P = 0.62). The composite outcome of hospitalizations for cardiovascular causes and cardiovascular mortality were lower in CR group compared to no-CR group (18% vs. 30%, P < 0.001) and was driven by lower hospitalizations for cardiovascular causes (15 vs. 27%, P < 0.001). At multivariable Cox proportional hazard analysis, CR program was independent predictor of lower occurrence of the composite outcome (hazard ratio 0.58, 95% confidence interval 0.43-0.77; P < 0.001), while in the propensity-matched analysis CR group experienced also a lower total mortality (10% vs. 19%, P = 0.002) and cardiovascular mortality (2% vs. 7%, P = 0.008) compared to no-CR group. CONCLUSION This study showed, in a real-world population, the positive effects of ambulatory CR program in improving clinical outcomes and highlights the importance of a spread use of CR in order to reduce cardiovascular hospitalizations and cardiovascular mortality during a long-term follow-up.