Reviewerly/exHarmony huggingface dataset:
Author Clustering with and Without Topical Features

Typically, the task of authorship attribution has been solved using supervised machine learning methods. It is only recently that unsupervised methods have been applied to authorship attribution, namely author clustering. Clustering could be useful in realistic scenario as it represents natural grouping of documents without a priori authorship information, although the problem of feature selection remains unsolved. That is particularly true for a cross-domain scenario. Studies have shown that in cross-domain settings some domain-specific text features cause noise in authorship attribution. In the current work we introduce a modification of unmasking technique aimed at selecting and removing the features most influenced by topic change. We apply the proposed technique to identify topical features and assess the quality of author clustering with different feature sets in a real-world dataset of forum texts in Russian. The main assumption is that the topical features result in topic-based text instead authorship-based clustering, and removing them could increase the performance of document clustering against authorship ground truth. We test this consideration by first clustering cross-topic documents with state-of-the-art authorship attribution features. Second, we remove the most significant topical features, and cluster texts with resulting feature set. Both clustering results are evaluated against ground truth authorship. The results demonstrate that the described approach of removing some topical features increases author clustering performance, however one should be cautious with the number of removed features.
A Decentralized File Sharing Framework for Sensitive Data

Access management of files which contain sensitive personal data such as electronic health records, financial history of individuals or legal documents is a challenge, because of the variety of participating entities such as legal institutions and privacy of the files. Trust among the entities are generally provided by a central authority such as a government agency. Blockchain technology and distributed file systems provide an opportunity to address this issue without having a central authority to rely on. The lack of a central authority provides a better security as no single entity can control the whole system. However, it brings up other issues. One of the main issues is to manage access rights of sensitive data in the system. In this work, we present a framework for sharing critical document using IPFS and Blockchain technology. Rather than providing framework specific to a single domain such as electronic health records or legal document sharing, we propose a general framework which can be tailored for the specific domains.
An End-to-End Approach for Extracting and Segmenting High-Variance References from PDF Documents

This paper addresses the problem of extracting and segmenting references from PDF documents. The novelty of the presented approach lies in its capability to discover highly varying references mainly in terms of content, length and location in the document. Unlike existing works, the proposed method does not follow the classical pipeline that consists of sequential phases. It rather learns the different characteristics of references to be used in a coherent scheme that reduces the error accumulation by following a probabilistic approach. Contrary to conventional references, mentioning the sources of information in some publications, such as those of social science, is not subject to the same specifications such as being located in a unique reference section. Therefore, the proposed method aims to extract references of highly varying reference characteristics by relaxing the restrictions of existing methods. Additionally, we present in this paper a new challenging dataset of annotated references in German social science publications. The main purpose of this work is to serve the indexation of missing references by extracting them from challenging publications such as those of German social science. The effectiveness of the presented methods in terms of both extraction and segmentation is evaluated on different datasets, including the German social science set.
MonoEye: Monocular Fisheye Camera-based 3D Human Pose Estimation

Wearable cameras have the potential to be used in various ways in combination with egocentric views such as action recognition, gesture input method for augmented/virtual reality (AR/VR) as well as lifelogger. Particularly, the pose of the camera wearer is one of the interesting factors of the egocentric view and various eccentric view-based pose estimation systems have been proposed; however, there is no balance between recognizable poses and enough egocentric views. In this work, we propose MonoEye, a system to provide wearer's estimated 3D pose and wide egocentric view. Our system's chest-mounted camera, equipped with the ultra-wide fisheye lens, covers the wearer's limbs and wide egocentric view; our pose estimation network estimates 3D body pose of the wearer from the camera's egocentric view. The proposed system not only can be used as an input interface of AR and VR through estimation of a various pose of the wearer but also has a potential to be used for action recognition by providing a wide egocentric view.
Push-pull optimization of quantum controls

Quantum optimal control involves setting up an objective function that evaluates the quality of an operator representing the realized process w.r.t. the target process. Here we propose a stronger objective function which incorporates not only the target operator but also a set of its orthogonal operators. We find significantly superior convergence of optimization routines with the combined influences of all the operators. We refer to this method as the $\textit{push-pull}$ optimization. In particular, we describe adopting the push-pull optimization to a gradient based approach and a variational-principle based approach. We carry out extensive numerical simulations of the push-pull optimization of quantum controls on a pair of Ising coupled qubits. Finally, we demonstrate its experimental application by preparing a long-lived singlet-order in a two-qubit system using NMR techniques.
The Cost of Complexity in Robot Bodies

The evolutionary cost of morphological complexity in biological populations remains an open question. This study investigates the impact of imposing a cost on morphological complexity given co-adapting behavior-morphology couplings in simulated robots. Specifically, we investigate the environmental and evolutionary conditions for which morphological complexity can be evolved without sacrificing behavioral efficacy. This study evaluates the relationship between between task difficulty (environment complexity) and evolved morphological complexity. We use multi-objective neuro-evolution to evolve robot controller-morphology couplings in task environments of increasing difficulty, where the objectives are to minimize the cost of (morphological) complexity and to maximize behavior quality (task performance) over evolutionary time. Results indicate that imposing a cost of complexity induces the evolution of simpler morphologies with negligible differences in behavior (task performance) across varying task environments. That is, with a cost of complexity, evolution maintained a constant selection pressure for morphological complexity across all environments.
A Competitive Algorithm for Online Multi-Robot Exploration of a Translating Plume

In this paper, we study the problem of exploring a translating plume with a team of aerial robots. The shape and the size of the plume are unknown to the robots. The objective is to find a tour for each robot such that they collectively explore the plume. Specifically, the tours must be such that each point in the plume must be visible from the field-of-view of some robot along its tour. We propose a recursive Depth-First Search (DFS)-based algorithm that yields a constant competitive ratio for the exploration problem. The competitive ratio is 2(S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> + S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> )(R+⌊log R⌋)/(S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> + S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> )(R+⌊log R⌋) where R is the number of robots, and Sr and Sp are the robot speed and the plume speed, respectively. We also consider a more realistic scenario where the plume shape is not restricted to grid cells but an arbitrary shape. We show our algorithm has 2(S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> + S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> )(18 R+⌊log R⌋)/(S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> + S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> )(1+⌊log R⌋) competitive ratio under the fat condition. We empirically verify our algorithm using simulations.
Meta-Learning via Weighted Gradient Update

Despite deep reinforcement learning has attained performance beyond human beings in many domains, including games, dialogue systems and robotics, sample inefficient is still a limitation in the application of deep reinforcement learning.This paper develops a novel and simple gradient-based meta learning method suitable for improving learning efficiency of deep reinforcement learning methods.Rather than designing complex network or adding excessive fine tuning parameters, the proposed method is simple and does not introduce any learned parameters for meta learning.Specifically, according to the characteristic of different trajectories, this paper proposes to weight every trajectory in model-agnostic meta-learning for meta updating gradient effectively.The key idea underlying this method is to take advantage of the relationship between different trajectories and the direction of parameter update.Additionally, an end-toend training approach is also introduced so that the proposed model can attain good results with a small amount of training data on new tasks.The statistical results of the experiments indicate that the proposed algorithm delivers state-of-the-art performance on both discrete and continuous control tasks.
Mining Spatial Co-Location Patterns Based on Overlap Maximal Clique Partitioning

Spatial co-location patterns are groups of spatial features whose instances are frequently located together in spatial proximity. Most existing algorithms of discovering spatial co-location patterns are based on the candidate-test model, which is computationally expensive. When the user adjusts the participation index (PI) threshold, these algorithms have to be re-executed from the size 2 co-location patterns. In this paper, we propose a novel spatial instance partition method for mining co-location patterns which called overlap maximal clique partitioning algorithm (OMCP). The OMCP co-location mining algorithm divides instances of an input spatial dataset into a set of overlap maximal cliques. Table instances of all colocation patterns are collected by the overlap maximal cliques. Prevalent co-location patterns are directly calculated without generating the candidate patterns. The OMCP algorithm only needs to execute once to get the PI of all patterns, without re-executing when the PI threshold is adjusted. Our algorithm is performed on both synthetic and real-world datasets to demonstrate that the OMCP algorithm improvements in efficiency of co-location pattern mining.
Hierarchical Text-Label Integrated Attention Network for Document Classification

Recurrent neural networks (RNN) and convolutional neural networks (CNN) have been extensively used on text classification to capture the local and long-range dependencies. Recent work has demonstrated the superiority of self-attention networks (SAN) owing to their highly parallelizable computation and excellent performance. However, SAN has difficulty capturing meaningful semantic relationships over very long sequences, and the memory requirement grows rapidly in line with the sequence length. To solve these limitations of SAN in processing long document sequence, this paper proposes four novel ideas and build a hierarchical text-label integrated attention network(HLAN). Firstly, a hierarchical architecture is introduced to map the hierarchy of document, which effectively shortens the sequence length of each process. Secondly, the attention weights are calculated in the joint embedding space of text and label. Thirdly, a multi-head soft attention is proposed to compress the sequence encoded by self-attention into a single vector. Finally, a loss term called class loss is given and combined with cross entropy loss. HLAN achieves competitive results over the compared strong baseline methods on 4 out of 5 benchmark datasets, which verifies the effectiveness of HLAN for document classification, in terms of both accuracy and memory requirement.
Releasing fast and slow: an exploratory case study at ING

The appeal of delivering new features faster has led many software projects to adopt rapid releases. However, it is not well understood what the effects of this practice are. This paper presents an exploratory case study of rapid releases at ING, a large banking company that develops software solutions in-house, to characterize rapid releases. Since 2011, ING has shifted to a rapid release model. This switch has resulted in a mixed environment of 611 teams releasing relatively fast and slow. We followed a mixed-methods approach in which we conducted a survey with 461 participants and corroborated their perceptions with 2 years of code quality data and 1 year of release delay data. Our research shows that: rapid releases are more commonly delayed than their non-rapid counterparts, however, rapid releases have shorter delays; rapid releases can be beneficial in terms of reviewing and user-perceived quality; rapidly released software tends to have a higher code churn, a higher test coverage and a lower average complexity; challenges in rapid releases are related to managing dependencies and certain code aspects, e.g., design debt.
Hospitality of chatbot building platforms

The temptation to be able to talk to a machine is not new. Recent advancements in the field of Natural Language Understanding has made it possible to build conversational components that can be plugged inside an application, similar to other components. These components, called chatbots, can be created from scratch or with the help of commercially available platforms. These platforms make it easier to build and deploy chatbots, often without writing a single line of code. However, similar to any other software component, chatbots also have quality concerns. Despite significant contributions in the field, an architectural perspective of building chatbots with desired quality requirements is missing in the literature.
A Unified Closed-Loop Motion Planning Approach For An I-AUV In Cluttered Environment With Localization Uncertainty

This paper presents a unified motion planning approach for an Intervention Autonomous Underwater Vehicle (I-AUV) in a cluttered environment with localization uncertainty. With the uncertainty being propagated by an information filter, a trajectory optimization problem closed by a Linear-Quadratic-Gaussian controller is formulated for a coupled design of optimal trajectory, localization, and control. Due to the presence of obstacles or complexity of the cluttered environment, a set of feasible initial I-AUV trajectories covering multiple homotopy classes are required by optimization solvers. Parameterized through polynomials, the initial base trajectories are from solving quasi-quadratic optimization problems that are linearly constrained by waypoints from RRTconnect, while the initial trajectories of the manipulator are generated by a null space saturation controller. Simulations on an I-AUV with a 3 DOF manipulator in cluttered underwater environments demonstrated that initial trajectories are generated efficiently and that optimal and collision-free I-AUV trajectories with low state uncertainty are obtained.
Improving sentiment analysis in Arabic: A combined approach

Sentiment analysis SA is concerned with determining users' opinions from text reviews. However, mining these opinions from massive amounts of unstructured and lengthy documents is a challenging task. In this paper, we propose methods that extract valuable opinions and values from online movie reviews to improve SA in Arabic. First, we propose a method that explores the role of n-gram and skip-n-gram models in opinion classification. Second, we study a method that exploits subjective words such as adjectives and nouns by applying Part-Of Speech tagging. Both of the methods are combined with a feature reduction technique to enhance SA results. Third, we present a method that seeks to extract relevant opinions such as review summaries and conclusion opinions. Then, a combined approach is proposed to augment opinion classification results. Forth, we introduce a method for analyzing customers' opinions by determining factors impacting their attitudes based on the costumer value model. Experimental results conducted on two datasets prove that our proposed methods are effective and provide better scores than baseline sentiment classifiers. The best obtained classification results reached 96% in F-Measure. These results indicate also that the aesthetic factor is the most influent factor in Arabic movie reviews.
Distance Estimation of Monocular Based on Vehicle Pose Information

In order to measure the distance between our vehicle and the target vehicle by monocular vision and eliminate the estimation error bring by changing of vehicle pose, we propose the distance estimation method based on the vehicle pose information, which can be used to eliminate the error of distance estimation effectively cause by the change of the pitch angle and roll angle of unmanned vehicle. In addition, the pose information could also help us estimate whether the vehicle is in a slope, thus engage in distance estimation for the vehicles. Several groups of data was collected for the experiments. And the result prove the validity of the algorithm in distance estimation.
A modality conversion approach to MV‐DRs and KV‐DRRs registration using information bottlenecked conditional generative adversarial network

Purpose As affordable equipment, electronic portal imaging devices (EPIDs) are wildly used in radiation therapy departments to verify patients’ positions for accurate radiotherapy. However, these devices tend to produce visually ambiguous and low‐contrast planar digital radiographs under megavoltage x ray (MV‐DRs), which poses a tremendous challenge for clinicians to perform multimodal registration between the MV‐DRs and the kilovoltage digital reconstructed radiographs (KV‐DRRs) developed from the planning computed tomography. Furthermore, the existent of strong appearance variations also makes accurate registration beyond the reach of current automatic algorithms. Methods We propose a novel modality conversion approach to this task that first synthesizes KV images from MV‐DRs, and then registers the synthesized and real KV‐DRRs. We focus on the synthesis technique and develop a conditional generative adversarial network with information bottleneck extension (IB‐cGAN) that takes MV‐DRs and nonaligned KV‐DRRs as inputs and outputs synthesized KV images. IB‐cGAN is designed to address two main challenges in deep‐learning‐based synthesis: (a) training with a roughly aligned dataset suffering from noisy correspondences; (b) making synthesized images have real clinical meanings that faithfully reflects MV‐DRs rather than nonaligned KV‐DRRs. Accordingly, IB‐cGAN employs (a) an adversarial loss to provide training supervision at semantic level rather than the imprecise pixel level; (b) an IB to constrain the information from the nonaligned KV‐DRRs. Results We collected 2698 patient scans to train the model and 208 scans to test its performance. The qualitative results demonstrate realistic KV images can be synthesized allowing clinicians to perform the visual registration. The quantitative results show it significantly outperforms current nonmodality conversion methods by 22.37% ( P = 0.0401) in terms of registration accuracy. Conclusions The modality conversion approach facilitates the downstream MV–KV registration for both clinicians and off‐the‐shelf registration algorithms. With this approach, it is possible to benefit the developing countries where inexpensive EPIDs are widely used for the image‐guided radiation therapy.
Sensor Bias Estimation for Track-to-Track Association

In this letter, we propose a heuristic method to address sensor bias estimation to improve track-to-track association accuracy. A novel multi-parameter cost function is derived from rigid transformation function and it is minimized by the covariance matrix adaptation evolution strategies algorithm. The proposed method is compared to other recognized methods under various simulation scenarios. The comparison results confirm that our approach accurately estimates sensor biases, provides higher correct association probability with low computational load compared to the competitor methods, and also it is robust to high missed and false track rates.
TimerTasks

short-paper Share on TimerTasks: Towards Time-driven Execution in Programmable Dataplanes Authors: Raj Joshi National University of Singapore National University of SingaporeView Profile , Ben Leong National University of Singapore National University of SingaporeView Profile , Mun Choon Chan National University of Singapore National University of SingaporeView Profile Authors Info & Claims SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and DemosAugust 2019 Pages 69–71https://doi.org/10.1145/3342280.3342310Published:19 August 2019Publication History 2citation243DownloadsMetricsTotal Citations2Total Downloads243Last 12 Months34Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Design and Testing of a Virtual Reality Enabled Experience that Enhances Engagement and Simulates Empathy for Historical Events and Characters

Our study uses Virtual Reality (VR) to transport high school students into the mountains of Papua New Guinea during World War Two - the Kokoda campaign - to witness first-hand the hardships faced by both Australian and Japanese soldiers. By using photogrammetry of real locations and artefacts, in combination with animated characters, Kokoda VR places the students in the centre of the action. Results from data collected in two Australian high schools has shown that a linear narrative in the VR condition increases feelings of empathy for the soldiers, over the 360° video desktop application. Students using VR also reported higher levels of engagement than students using 360° video and the study found a positive correlation between high engagement and increased empathy in VR.