OpenRaiser/Intern-Atlas huggingface dataset:
Online Knapsack Problems with a Resource Buffer
Venue: arXiv cs.DS 2019
Year: 2019

In this paper, we introduce online knapsack problems with a resource buffer. In the problems, we are given a knapsack with capacity $1$, a buffer with capacity $R\ge 1$, and items that arrive one by one. Each arriving item has to be taken into the buffer or discarded on its arrival irrevocably. When every item has arrived, we transfer a subset of items in the current buffer into the knapsack. Our goal is to maximize the total value of the items in the knapsack. We consider four variants depending on whether items in the buffer are removable (i.e., we can remove items in the buffer) or non-removable, and proportional (i.e., the value of each item is proportional to its size) or general. For the general&non-removable case, we observe that no constant competitive algorithm exists for any $R\ge 1$. For the proportional&non-removable case, we show that a simple greedy algorithm is optimal for every $R\ge 1$. For the general&removable and the proportional&removable cases, we present optimal algorithms for small $R$ and give asymptotically nearly optimal algorithms for general $R$.
Nonexistence of minimal-time solutions for some variations of the firing squad synchronization problem having simple geometric configurations
Venue: arXiv cs.FL 2019
Year: 2019

We prove nonexistence of minimal-time solutions for three variations of the firing squad synchronization problem (FSSP, for short). Configurations of these variations are paths in the two-dimensional grid space having simple geometric shapes. In the first variation a configuration is an L-shaped path such that the ratio of the length of horizontal line to that of the vertical line is fixed. The general may be at any position. In the second and the third variations a configuration is a rectangular wall such that the ratio of the length of the two horizontal walls to that of the two vertical walls is fixed. The general is at the left down corner in the second variation and may be at any position in the third variation. We use the idea used in the proof of Yamashita et al's recent similar result for variations of FSSP with sub-generals.
Active collaboration in relative observation for Multi-agent visual SLAM based on Deep Q Network
Venue: arXiv cs.AI 2019
Year: 2019

This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation. A task allocation algorithm based on deep reinforcement learning are proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent SLAM on it own initiative. By this way, the process of each agent SLAM will be interacted by the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique observation function which models the whole MAS is obtained. Secondly, a novel type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence between Q Value and state-action pair,abstract representation of agents in MAS are learned in the process of collaboration among agents. Finally, each agent must act with a certain degree of freedom according to MAS-DQN. The simulation results of comparative experiments prove that this mechanism improves the efficiency of cooperation in the process of multi-agent SLAM.
Route Packing: Geospatially-Accurate Visualization of Route Networks
Venue: arXiv cs.HC 2019
Year: 2019

We present route packing, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions. Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it. We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique. Our findings highlight the visual parameters under which the technique yields optimal performance.
IBFT 2.0: A Safe and Live Variation of the IBFT Blockchain Consensus Protocol for Eventually Synchronous Networks
Venue: arXiv cs.DC 2019
Year: 2019

In this work, we present IBFT 2.0 (Istanbul BFT 2.0), which is a Proof-of-Authority (PoA) Byzantine-fault-tolerant (BFT) blockchain consensus protocols that (i) ensures immediate finality, (ii) is robust in an eventually synchronous network model and (iii) features a dynamic validator set. IBFT 2.0, as the name suggests, builds upon the IBFT blockchain consensus protocol retaining all of the original features while addressing the safety and liveness limitations described in one of our previous works. In this paper, we present a high-level description of the IBFT 2.0 protocol and related robustness proof. Formal specification of the protocol and related formal proofs will be subject of a separate body of work. We also envision a separate work that will provide detailed implementation specifications for IBFT 2.0.
New Sets of Optimal Odd-length Binary Z-Complementary Pairs
Venue: arXiv cs.IT 2019
Year: 2019

A pair of sequences is called a Z-complementary pair (ZCP) if it has zero aperiodic autocorrelation sums (AACSs) for time-shifts within a certain region, called zero correlation zone (ZCZ). Optimal odd-length binary ZCPs (OB-ZCPs) display closest correlation properties to Golay complementary pairs (GCPs) in that each OB-ZCP achieves maximum ZCZ of width (N+1)/2 (where N is the sequence length) and every out-of-zone AACSs reaches the minimum magnitude value, i.e. 2. Till date, systematic constructions of optimal OB-ZCPs exist only for lengths $2^α \pm 1$, where $α$ is a positive integer. In this paper, we construct optimal OB-ZCPs of generic lengths $2^α10^β26^γ+1$ (where $α,~ β, ~ γ$ are non-negative integers and $α\geq 1$) from inserted versions of binary GCPs. The key leading to the proposed constructions is several newly identified structure properties of binary GCPs obtained from Turyn's method. This key also allows us to construct OB-ZCPs with possible ZCZ widths of $4 \times 10^{β-1} +1$, $12 \times 26^{γ-1}+1$ and $12 \times 10^β26^{γ-1}+1$ through proper insertions of GCPs of lengths $10^β,~ 26^γ, \text{and } 10^β26^γ$, respectively. Our proposed OB-ZCPs have applications in communications and radar (as an alternative to GCPs).
Pose Estimation for Texture-less Shiny Objects in a Single RGB Image Using Synthetic Training Data
Venue: arXiv cs.RO 2019
Year: 2019

In the industrial domain, the pose estimation of multiple texture-less shiny parts is a valuable but challenging task. In this particular scenario, it is impractical to utilize keypoints or other texture information because most of them are not actual features of the target but the reflections of surroundings. Moreover, the similarity of color also poses a challenge in segmentation. In this article, we propose to divide the pose estimation process into three stages: object detection, features detection and pose optimization. A convolutional neural network was utilized to perform object detection. Concerning the reliability of surface texture, we leveraged the contour information for estimating pose. Since conventional contour-based methods are inapplicable to clustered metal parts due to the difficulties in segmentation, we use the dense discrete points along the metal part edges as semantic keypoints for contour detection. Afterward, we exploit both keypoint information and CAD model to calculate the 6D pose of each object in view. A typical implementation of deep learning methods not only requires a large amount of training data, but also relies on intensive human labor for labeling the datasets. Therefore, we propose an approach to generate datasets and label them automatically. Despite not using any real-world photos for training, a series of experiments showed that the algorithm built on synthetic data perform well in the real environment.
The differing meanings of indicators under different policy contexts. The case of internationalisation
Venue: arXiv cs.DL 2019
Year: 2019

In this chapter we build upon Moed's conceptual contributions on the importance of the policy context when using and interpreting scientometric indicators. We focus on the use of indicators in research evaluation regarding internationalisation policies. The globalization of higher education presents important challenges to institutions worldwide, which are confronted with tensions derived from the need to respond both, to their local necessities and demands while participating in global networks. In this context, indicators have served as measures for monitoring and enforcing internationalisation policies, in many cases interpreting them regardless of the policy context in which they are enforced. We will analyse three examples of indicators related to internationalisation. The first one is about international collaborations, under the assumption that a greater number of internationally co-authored publications will benefit a national science system as it will result in higher citation impact. The second one relates to the promotion of English language as the dominant language of science. The third case analyses how different policy contexts shape the selection and construction of indicators, sometimes in a partial way which does not properly reflect the phenomenon under study. The examples illustrate that the interpretation and policy implications of the 'same' S&T indicators differ depending on specific contexts.
Human Synthesis and Scene Compositing
Venue: arXiv cs.CV 2019
Year: 2019

Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important for a variety of tasks ranging from photo editing, fashion virtual try-on, to special effects and image compression. In this paper, we propose HUSC, a HUman Synthesis and Scene Compositing framework for the realistic synthesis of humans with different appearance, in novel poses and scenes. Central to our formulation is 3d reasoning for both people and scenes, in order to produce realistic collages, by correctly modeling perspective effects and occlusion, by taking into account scene semantics and by adequately handling relative scales. Conceptually our framework consists of three components: (1) a human image synthesis model with controllable pose and appearance, based on a parametric representation, (2) a person insertion procedure that leverages the geometry and semantics of the 3d scene, and (3) an appearance compositing process to create a seamless blending between the colors of the scene and the generated human image, and avoid visual artifacts. The performance of our framework is supported by both qualitative and quantitative results, in particular state-of-the art synthesis scores for the DeepFashion dataset.
Modeling Fluid Mixing in Microfluidic Grids
Venue: arXiv cs.CE 2019
Year: 2019

We describe an approach for modeling fluid concentration profiles in grid-based microfluidic chips for fluid mixing. This approach provides an algorithm that predicts fluid concentrations at the chip outlets. Our algorithm significantly outperforms COMSOL finite element simulations in term of runtime while still producing results that closely approximate those of COMSOL.
Object Segmentation using Pixel-wise Adversarial Loss
Venue: arXiv cs.CV 2019
Year: 2019

Recent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adversarial networks, we present a generic end-to-end adversarial approach, which can be combined with a wide range of existing semantic segmentation networks to improve their segmentation performance. The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. In addition, we train our generator employing stochastic weight averaging fashion, which further enhances the predicted output label maps leading to state-of-the-art results. We show, that this combination of pixel-wise adversarial training and weight averaging leads to significant and consistent gains in segmentation performance, compared to the baseline models.
RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments
Venue: arXiv cs.CV 2019
Year: 2019

Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module has very few parameters, which helps to save memory. Thus, it can be flexibly plugged into other networks. Besides, a hybrid loss is introduced to train our network for addressing the class imbalance issue, which merges cross entropy and logarithms of Dice loss. A new dataset named Cata7 is constructed to evaluate our network. To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation. Based on this dataset, RAUNet achieves state-of-the-art performance 97.71% mean Dice and 95.62% mean IOU.
A Consolidated System for Robust Multi-Document Entity Risk Extraction and Taxonomy Augmentation
Venue: arXiv cs.CL 2019
Year: 2019

We introduce a hybrid human-automated system that provides scalable entity-risk relation extractions across large data sets. Given an expert-defined keyword taxonomy, entities, and data sources, the system returns text extractions based on bidirectional token distances between entities and keywords and expands taxonomy coverage with word vector encodings. Our system represents a more simplified architecture compared to alerting focused systems - motivated by high coverage use cases in the risk mining space such as due diligence activities and intelligence gathering. We provide an overview of the system and expert evaluations for a range of token distances. We demonstrate that single and multi-sentence distance groups significantly outperform baseline extractions with shorter, single sentences being preferred by analysts. As the taxonomy expands, the amount of relevant information increases and multi-sentence extractions become more preferred, but this is tempered against entity-risk relations become more indirect. We discuss the implications of these observations on users, management of ambiguity and taxonomy expansion, and future system modifications.
Informing a BDI Player Model for an Interactive Narrative
Venue: arXiv cs.AI 2019
Year: 2019

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.
On Model Stability as a Function of Random Seed
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general. We specifically perform a controlled study on the effect of random seeds on the behaviour of attention, gradient-based and surrogate model based (LIME) interpretations. Our analysis suggests that random seeds can adversely affect the consistency of models resulting in counterfactual interpretations. We propose a technique called Aggressive Stochastic Weight Averaging (ASWA)and an extension called Norm-filtered Aggressive Stochastic Weight Averaging (NASWA) which improves the stability of models over random seeds. With our ASWA and NASWA based optimization, we are able to improve the robustness of the original model, on average reducing the standard deviation of the model's performance by 72%.
Pelvis Surface Estimation From Partial CT for Computer-Aided Pelvic Osteotomies
Venue: arXiv cs.CV 2019
Year: 2019

Computer-aided surgical systems commonly use preoperative CT scans when performing pelvic osteotomies for intraoperative navigation. These systems have the potential to improve the safety and accuracy of pelvic osteotomies, however, exposing the patient to radiation is a significant drawback. In order to reduce radiation exposure, we propose a new smooth extrapolation method leveraging a partial pelvis CT and a statistical shape model (SSM) of the full pelvis in order to estimate a patient's complete pelvis. A SSM of normal, complete, female pelvis anatomy was created and evaluated from 42 subjects. A leave-one-out test was performed to characterise the inherent generalisation capability of the SSM. An additional leave-one-out test was conducted to measure performance of the smooth extrapolation method and an existing "cut-and-paste" extrapolation method. Unknown anatomy was simulated by keeping the axial slices of the patient's acetabulum intact and varying the amount of the superior iliac crest retained; from 0% to 15% of the total pelvis extent. The smooth technique showed an average improvement over the cut-and-paste method of 1.31 mm and 3.61 mm, in RMS and maximum surface error, respectively. With 5% of the iliac crest retained, the smoothly estimated surface had an RMS surface error of 2.21 mm, an improvement of 1.25 mm when retaining none of the iliac crest. This anatomical estimation method creates the possibility of a patient and surgeon benefiting from the use of a CAS system and simultaneously reducing the patient's radiation exposure.
Learning a Control Policy for Fall Prevention on an Assistive Walking Device
Venue: arXiv cs.RO 2019
Year: 2019

Fall prevention is one of the most important components in senior care. We present a technique to augment an assistive walking device with the ability to prevent falls. Given an existing walking device, our method develops a fall predictor and a recovery policy by utilizing the onboard sensors and actuators. The key component of our method is a robust human walking policy that models realistic human gait under a moderate level of perturbations. We use this human walking policy to provide training data for the fall predictor, as well as to teach the recovery policy how to best modify the person's gait when a fall is imminent. Our evaluation shows that the human walking policy generates walking sequences similar to those reported in biomechanics literature. Our experiments in simulation show that the augmented assistive device can indeed help recover balance from a variety of external perturbations. We also provide a quantitative method to evaluate the design choices for an assistive device.
Data Ordering Patterns for Neural Machine Translation: An Empirical Study
Venue: arXiv cs.CL 2019
Year: 2019

Recent works show that ordering of the training data affects the model performance for Neural Machine Translation. Several approaches involving dynamic data ordering and data sharding based on curriculum learning have been analysed for the their performance gains and faster convergence. In this work we propose to empirically study several ordering approaches for the training data based on different metrics and evaluate their impact on the model performance. Results from our study show that pre-fixing the ordering of the training data based on perplexity scores from a pre-trained model performs the best and outperforms the default approach of randomly shuffling the training data every epoch.
Suspicious Transactions in Smart Spaces
Venue: arXiv cs.CR 2019
Year: 2019

IoT systems have enabled ubiquitous communication in physical spaces, making them smart Nowadays, there is an emerging concern about evaluating suspicious transactions in smart spaces. Suspicious transactions might have a logical structure, but they are not correct under the present contextual information of smart spaces. This research reviews suspicious transactions in smart spaces and evaluates the characteristics of blockchain technology to manage them. Additionally, this research presents a blockchain-based system model with the novel idea of iContracts (interactive contracts) to enable contextual evaluation through proof-of-provenance to detect suspicious transactions in smart spaces.
Relational Learning for Joint Head and Human Detection
Venue: arXiv cs.CV 2019
Year: 2019

Head and human detection have been rapidly improved with the development of deep convolutional neural networks. However, these two tasks are often studied separately without considering their inherent correlation, leading to that 1) head detection is often trapped in more false positives, and 2) the performance of human detector frequently drops dramatically in crowd scenes. To handle these two issues, we present a novel joint head and human detection network, namely JointDet, which effectively detects head and human body simultaneously. Moreover, we design a head-body relationship discriminating module to perform relational learning between heads and human bodies, and leverage this learned relationship to regain the suppressed human detections and reduce head false positives. To verify the effectiveness of the proposed method, we annotate head bounding boxes of the CityPersons and Caltech-USA datasets, and conduct extensive experiments on the CrowdHuman, CityPersons and Caltech-USA datasets. As a consequence, the proposed JointDet detector achieves state-of-the-art performance on these three benchmarks. To facilitate further studies on the head and human detection problem, all new annotations, source codes and trained models will be public.
A Digital Library for Research Data and Related Information in the Social Sciences
Venue: arXiv cs.DL 2019
Year: 2019

In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored.