Reviewerly/exHarmony huggingface dataset:
Feature Hourglass Network for Skeleton Detection

Geometric shape understanding provides an intuitive representation of object shapes. Skeleton is typical geometrical information. Lots of traditional approaches are developed for skeleton extraction and pruning, while it is still a new area to investigate deep learning for geometric shape understanding. In this paper, we build a fully convolutional network named Feature Hourglass Network (FHN) for skeleton detection. FHN uses rich features of a fully convolutional network by hierarchically integrating side-outputs with a deep-to-shallow manner to decrease the residual between the prediction result and the ground-truth. Experiment data shows that FHN achieves better performance compared with baseline on both Pixel SkelNetOn and Point SkelNetOn datasets.
Minimizing Tardiness for Data-Intensive Applications in Heterogeneous Systems: A Matching Theory Perspective

The increasing data requirements of Internet applications have driven a dramatic surge in developing new programming paradigms and complex scheduling algorithms to handle data-intensive workloads. Due to the expanding volume and the variety of such flows, their raw data are often processed on Intermediate Processing Nodes (IPNs) before being sent to servers. However, the intermediate processing constraint is rarely considered in existing flow computing models. This paper aims to minimize the tardiness of data-intensive applications in the presence of intermediate processing constraint. Motivating cases show that the tardiness is affected by both IPN locations and flow dispatching strategies. Based on the observation that dispatching flows to IPNs is essentially building a matching between flows and IPNs, a novel solution is proposed based on matching theory. In the deployment phase, a tardiness-aware deferred acceptance algorithm is developed to optimize IPN locations. In the operation phase, the Power-of-D paradigm and matching theory are combined together to dispatch flows efficiently. Evaluation results show that our solution effectively minimizes the total tardiness of data-intensive applications in heterogeneous systems.
Empirical Evaluation of Fault Localisation Using Code and Change Metrics

Fault localisation aims to reduce the debugging efforts of human developers by highlighting the program elements that are suspected to be the root cause of the observed failure. Spectrum Based Fault Localisation (SBFL), a coverage based approach, has been widely studied in many researches as a promising localisation technique. Recently, however, it has been proven that SBFL techniques have reached the limit of further improvement. To overcome the limitation, we extend SBFL with code and change metrics that have been mainly studied in defect prediction, such as size, age, and churn. FLUCCS, our fault learn-to-rank localisation technique, employs both existing SBFL formulæ and these metrics as input. We investigate the effect of employing code and change metrics for fault localisation using four different learn-to-rank techniques: Genetic Programming, Gaussian Process Modelling, Support Vector Machine, and Random Forest. We evaluate the performance of FLUCCS with 386 real world faults collected from Defects4J repository. The results show that FLUCCS with code and change metrics places 144 faults at the top and 304 faults within the top ten. This is a significant improvement over the state-of-art SBFL formulæ, which can locate 65 and 212 faults at the top and within the top ten, respectively. We also investigate the feasibility of cross-project transfer learning of fault localisation. The results show that, while there exist project-specific properties that can be exploited for better localisation per project, ranking models learnt from one project can be applied to others without significant loss of effectiveness.
Online Maximum-Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes

We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter.The estimation is to be done online, i.e., the parameter estimate should be updated recursively based on the observation filtration.We provide a theoretical analysis of the stochastic gradient ascent algorithm on the incomplete-data log-likelihood.The convergence of the algorithm is proved under suitable conditions regarding the ergodicity of the process consisting of state, filter, and tangent filter.Additionally, our parameter estimation is shown numerically to have the potential of improving suboptimal filters, and can be applied even when the system is not identifiable due to parameter redundancies.Online parameter estimation is a challenging problem that is ubiquitous in fields such as robotics, neuroscience, or finance in order to design adaptive filters and optimal controllers for unknown or changing systems.Despite this, theoretical analysis of convergence is currently lacking for most of these algorithms.This paper sheds new light on the theory of convergence in continuous time.
An in-depth comparison of s-t reliability algorithms over uncertain graphs

Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging applications, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is the s-t reliability, which measures the probability that a target node t is reachable from a source node s in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Due to the inherent complexity of the s-t reliability estimation problem (#P-hard), various sampling and indexing based efficient algorithms were proposed in the literature. However, since they have not been thoroughly compared with each other, it is not clear whether the later algorithm outperforms the earlier ones. More importantly, the comparison framework, datasets, and metrics were often not consistent (e.g., different convergence criteria were employed to find the optimal number of samples) across these works. We address this serious concern by re-implementing six state-of-the-art s-t reliability estimation methods in a common system and code base, using several medium and large-scale, real-world graph datasets, identical evaluation metrics, and query workloads. Through our systematic and in-depth analysis of experimental results, we report surprising findings, such as many follow-up algorithms can actually be several orders of magnitude inefficient, less accurate, and more memory intensive compared to the ones that were proposed earlier. We conclude by discussing our recommendations on the road ahead.
Unexpected Tokens

Diagnostic messages generated by compilers and interpreters such as syntax error messages have been researched for decades. Unfortunately these messages which include error, warning, and runtime messages, present substantial difficulty and could be more effective, particularly for novices. Recent years have seen increased number of papers in the area including studies on the effectiveness of these messages, improving or enhancing them, and their usefulness as a part of programming process data that can be used to predict student performance. Despite this increased interest, the long history of literature is quite scattered and has not been brought together in any digestible form. We argue that in order to help the community proceed with more work on diagnostic messages, the literature needs to be presented in a state-of-the-art report. In addition we will synthesize and present the existing evidence for these messages including the difficulties they present and their effectiveness. We will also formulate a set of guidelines based on this evidence that can be used when designing or enhancing diagnostic messages. This work can serve as a starting point for those who wish to conduct research on such messages, those who wish to design better messages or those that aim to measure their effectiveness, more effectively.
Trace-free counterfactual communication with a nanophotonic processor

Abstract In standard communication information is carried by particles or waves. Counterintuitively, in counterfactual communication particles and information can travel in opposite directions. The quantum Zeno effect allows Bob to transmit a message to Alice by encoding information in particles he never interacts with. A first remarkable protocol for counterfactual communication relied on thousands of ideal optical operations for high success rate performance. Experimental realizations of that protocol have thus employed post-selection to demonstrate counterfactuality. This post-selection, together with arguments concerning a so-called “weak trace” of the particles traveling from Bob to Alice, have led to a discussion regarding the counterfactual nature of the protocol. Here we circumvent these controversies, implementing a new, and fundamentally different, protocol in a programmable nanophotonic processor, based on reconfigurable silicon-on-insulator waveguides that operate at telecom wavelengths. This, together with our telecom single-photon source and highly efficient superconducting nanowire single-photon detectors, provides a versatile and stable platform for a high-fidelity implementation of counterfactual communication with single photons, allowing us to actively tune the number of steps in the Zeno measurement, and achieve a bit error probability below 1%, without post-selection and with a vanishing weak trace. Our demonstration shows how our programmable nanophotonic processor could be applied to more complex counterfactual tasks and quantum information protocols.
A Single Paradigm for Implicit and Statistical Learning

Implicit learning generally refers to the acquisition of structures that, like knowledge of natural language grammar, are not available to awareness. In contrast, statistical learning has frequently been related to learning language structures that are explicitly available, such as vocabulary. In this paper, we report an experimental paradigm that enables testing of both classic implicit and statistical learning in language. The paradigm employs an artificial language comprising sentences that accompany visual scenes that they represent, thus combining artificial grammar learning with cross-situational statistical learning of vocabulary. We show that this methodology enables a comparison between acquisition of grammar and vocabulary, and the influences on their learning. We show that both grammar and vocabulary are promoted by explicit information about the language structure, that awareness of structure affects acquisition during learning, and awareness precedes learning, but is not distinctive at the endpoint of learning. The two traditions of learning-implicit and statistical-can be conjoined in a single paradigm to explore both the phenomenological and learning consequences of statistical structural knowledge.
Path-Invariant Map Networks

Optimizing a network of maps among a collection of objects/domains (or map synchronization) is a central problem across computer vision and many other relevant fields. Compared to optimizing pairwise maps in isolation, the benefit of map synchronization is that there are natural constraints among a map network that can improve the quality of individual maps. While such self-supervision constraints are well-understood for undirected map networks (e.g., the cycle-consistency constraint), they are under-explored for directed map networks, which naturally arise when maps are given by parametric maps (e.g., a feed-forward neural network). In this paper, we study a natural self-supervision constraint for directed map networks called path-invariance, which enforces that composite maps along different paths between a fixed pair of source and target domains are identical. We introduce path-invariance bases for efficient encoding of the path-invariance constraint and present an algorithm that outputs a path-variance basis with polynomial time and space complexities. We demonstrate the effectiveness of our formulation on optimizing object correspondences, estimating dense image maps via neural networks, and 3D scene segmentation via map networks of diverse 3D representations. In particular, our approach only requires 8\% labeled data from ScanNet to achieve the same performance as training a single 3D semantic segmentation network with 30\% to 100\% labeled data.
Stage Delay Scheduling

To increase the resource utilization of datacenters, big data analytics jobs are commonly running stages in parallel which are organized into and scheduled according to the Directed Acyclic Graph (DAG). Through an in-depth analysis of the latest Alibaba cluster trace and our motivation experiments on Amazon EC2, however, we show that the CPU and network resources are still under-utilized due to the unwise stage scheduling, thereby prolonging the completion time of a DAG-style job (e.g., Spark). While existing works on reducing the job completion time focus on either task scheduling or job scheduling, stage scheduling has received comparably little attention. In this paper, we design and implement DelayStage, a simple yet effective stage delay scheduling strategy to interleave the cluster resources across the parallel stages, so as to increase the cluster resource utilization and speed up the job performance. With the aim of minimizing the makespan of parallel stages, DelayStage judiciously arranges the execution of stages in a pipelined manner to maximize the performance benefits of resource interleaving. Extensive prototype experiments on 30 Amazon EC2 instances and complementary trace-driven simulations show that DelayStage can improve the cluster resource utilization by up to 81.8% and reduce the job completion time by up to 41.3%, in comparison to the stock Spark and the state-of-the-art stage scheduling strategies, yet with acceptable runtime overhead.
Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning

Class imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers has been reported to yield promising results. However, the majority of ensemble methods applied to imbalance learning are static ones. Moreover, they only deal with binary imbalanced problems. Hence, this paper presents an empirical analysis of Dynamic Selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and 14 Dynamic Selection schemes. Our experiments conducted on 26 multi-class imbalanced problems show that the dynamic ensemble improves the AUC and the [Formula: see text]-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases.
Effectiveness of Using WhatsApp for Grade 12 Learners in Teaching Mathematics in South Africa

The growth of social media and its usage has been witnessed in many sectors. Nowadays social media has become one of the effective means of communication. Within the education sector platforms such as Facebook, Youtube and WhatsApp have gained more popularity. Despite the growth and usage on WhatsApp, less research has been done on its effectiveness and how it could be used for teaching and learning Mathematics. Mathematics remains one of the challenging subjects for many learners. The aim of the study was to assess the current methods that are being used for teaching and learning Mathematics and to create a WhatsApp group for teaching and learning Mathematics. We conducted a study to prove if WhatsApp could be in effective to teaching and learning Mathematics. To collect the data a mixed approach was used where both qualitative and quantitative methods were considered. A WhatsApp group of 10 learners and one (1) Mathematics teacher were engaged in the study. We engaged the participants to explore the effectiveness of using WhatsApp for Grade 12 learners in teaching and learning Mathematics at Phakamani High School in the Eastern Cape Province of South Africa. Results show that, WhatsApp motivates, encourages and allows learners and teachers to share information on the subject. Learners were free to ask and discuss critical topics and issues on the Mathematics group. From the comments given by the participants, it was evident that WhatsApp could be one of the means to improve Mathematics interest and performance among learners engaged.
How to Combine Tree-Search Methods in Reinforcement Learning

Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success. Usually, the lookahead policies are implemented with specific planning methods such as Monte Carlo Tree Search (e.g. in AlphaZero). Referring to the planning problem as tree search, a reasonable practice in these implementations is to back up the value only at the leaves while the information obtained at the root is not leveraged other than for updating the policy. Here, we question the potency of this approach. Namely, the latter procedure is non-contractive in general, and its convergence is not guaranteed. Our proposed enhancement is straightforward and simple: use the return from the optimal tree path to back up the values at the descendants of the root. This leads to a $\gamma^h$-contracting procedure, where $\gamma$ is the discount factor and $h$ is the tree depth. To establish our results, we first introduce a notion called \emph{multiple-step greedy consistency}. We then provide convergence rates for two algorithmic instantiations of the above enhancement in the presence of noise injected to both the tree search stage and value estimation stage.
Towards passive analysis of anycast in global routing

Anycast has been widely adopted by today's Internet services, including DNS, CDN, and DDoS protection, in which the same IP address is announced from distributed locations and clients are directed to the topologically-nearest service replica. Prior research has focused on various aspects of anycast, either its usage in particular services such as DNS or characterizing its adoption by Internet-wide active probing methods. In this paper, we first explore an alternative approach to characterize anycast based on previously collected global BGP routing information. Leveraging state-of-the-art active measurement results as near-ground-truth, our passive method without requiring any Internet-wide probes can achieve 90% accuracy in detecting anycast prefixes. More importantly, our approach uncovers anycast prefixes that have been missed by prior datasets based on active measurements. While investigating the root causes of inaccuracy, we reveal that anycast routing has been entangled with the increased adoption of remote peering, a type of layer-2 interconnection where an IP network may peer at an IXP remotely without being physically present at the IXP. The invisibility of remote peering from layer-3 breaks the assumption of the shortest AS paths on BGP and causes an unintended impact on anycast performance. We identify such cases from BGP routing information and observe that at least 19.2% of anycast prefixes have been potentially impacted by remote peering.
Crime Event Embedding with Unsupervised Feature Selection

We present a novel event embedding algorithm for crime data that can jointly capture time, location, and the complex free-text component of each event. The embedding is achieved by regularized Restricted Boltzmann Machines (RBMs), and we introduce a new way to regularize by imposing a ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> penalty on the conditional distributions of the observed variables of RBMs. This choice of regularization performs feature selection and it also leads to efficient computation since the gradient can be computed in a closed form. The feature selection forces embedding to be based on the most important keywords, which captures the common modus operandi (M.O.) in crime series. Using numerical experiments on a large-scale crime dataset, we show that our regularized RBMs can achieve better event embedding and the selected features are highly interpretable from human understanding.
An Enhanced Optimization Scheme Based on Gradient Descent Methods for Machine Learning

A The learning process of machine learning consists of finding values of unknown weights in a cost function by minimizing the cost function based on learning data. However, since the cost function is not convex, it is conundrum to find the minimum value of the cost function. The existing methods used to find the minimum values usually use the first derivative of the cost function. When even the local minimum (but not a global minimum) is reached, since the first derivative of the cost function becomes zero, the methods give the local minimum values, so that the desired global minimum cannot be found. To overcome this problem, in this paper we modified one of the existing schemes—the adaptive momentum estimation scheme—by adding a new term, so that it can prevent the new optimizer from staying at local minimum. The convergence condition for the proposed scheme and the convergence value are also analyzed, and further explained through several numerical experiments whose cost function is non-convex.
FMT: fusing multi-task convolutional neural network for person search

Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person re-identification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1.
Artificial Intelligence based Camera Calibration

Camera calibration technique plays a vital role in three dimensional computer vision systems. The aim of this technique is to calibrate the camera in order to collect more precise three dimensional data from the images to utilize for robot navigation, three dimensional reconstruction, biomedical, virtual reality and visual surveillance. In camera calibration one of the major issue is to search out the set of image parameters describing the mapping between three dimensional images reference coordinates and two-dimensional images reference coordinates. Currently, MATLAB toolbox and OpenCv are the most popular tools used by the researchers for camera calibration. We utilize the concept of deep learning to recognize the chessboard corners. Our presented technique is a convolutional neural network (CNN) trained on a huge number of chessboard images. The network is trained on different datasets: noisy and with high lens malformation images. The proposed scheme is more accurate than the conventional MATLAB algorithm technique. The presented technique is more accurate against the different sort of ruination present in the training set. Results reaffirmed the correctness and effectiveness of our proposed CNN technique.