nils-herrmann/clean_scirepeval huggingface dataset:
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too defensive so that normal traffic flow is maintained. In this paper we apply deep reinforcement learning to the problem of forming long term driving strategies. We note that there are two major challenges that make autonomous driving different from other robotic tasks. First, is the necessity for ensuring functional safety — something that machine learning has difficulty with given that performance is optimized at the level of an expectation over many instances. Second, the Markov Decision Process model often used in robotics is problematic in our case because of unpredictable behavior of other agents in this multi-agent scenario. We make three contributions in our work. First, we show how policy gradient iterations can be used, and the variance of the gradient estimation using stochastic gradient ascent can be minimized, without Markovian assumptions. Second, we decompose the problem into a composition of a Policy for Desires (which is to be learned) and trajectory planning with hard constraints (which is not learned). The goal of Desires is to enable comfort of driving, while hard constraints guarantees the safety of driving. Third, we introduce a hierarchical temporal abstraction we call an “Option Graph” with a gating mechanism that significantly reduces the effective horizon and thereby reducing the variance of the gradient estimation even further. The Option Graph plays a similar role to “structured prediction” in supervised learning, thereby reducing sample complexity, while also playing a similar role to LSTM gating mechanisms used in supervised deep networks.
On the application of LoRa LPWAN technology in Sailing Monitoring System

Sailing Monitoring System operating in the sea area has the basic transmission requirement of low power and long range. LoRa Low Power Wide Area Network (LPWAN) technology can be seen as an alternative solution to address this problem. This study focuses on transmission performance of LoRa technology and attempts to apply LoRa technology to Sailing Monitoring System. Experiments of LoRa technology parameters' (e.g. spread factor and bandwidth) influences on data transmission time and coverage were set up. Then, the optimal parameters of LoRa technology are used in Sailing Monitoring System, and the evaluation of system's performances is given. The measurements were conducted in Brazil Olympics sailing venue for two cases, and system's performance of coverage and packet loss rate in sea area are analyzed. It shows that the system based on LoRa technology can achieve the intended purpose of system design and meet the basic requirement of system applications. In the concluding section, measurement results are summarized and the future research directions are presented.
SLA-Based Resource Provisioning for Heterogeneous Workloads in a Virtualized Cloud Datacenter

Efficient provisioning of resources is a challenging problem in cloud computing environments due to its dynamic nature and the need for supporting heterogeneous applications with different performance requirements. Currently, cloud datacenter providers either do not offer any performance guarantee or prefer static VM allocation over dynamic, which lead to inefficient utilization of resources. Earlier solutions, concentrating on a single type of SLAs (Service Level Agreements) or resource usage patterns of applications, are not suitable for cloud computing environments. In this paper, we tackle the resource allocation problem within a datacenter that runs different type of application workloads, particularly non-interactive and transactional applications. We propose admission control and scheduling mechanism which not only maximizes the resource utilization and profit, but also ensures the SLA requirements of users. In our experimental study, the proposed mechanism has shown to provide substantial improvement over static server consolidation and reduces SLA Violations.
A software architecture for Twitter collection, search and geolocation services

The substantial increase of social networks and their combination with mobile devices make rigorous analysis of the outcomes of such system of paramount importance for intelligence gathering and decision making purposes. Since the introduction of Twitter system in 2006, tweeting emerged as an efficient open social network that attracted interest from various research/commercial and military communities. This paper investigates the current software architecture of Twitter system and put forward a new architecture dedicated for semantic and spatial analysis of Twitter data. Especially, Twitter Streaming API was used as a basis for tweet collection data stored in MySQL like database. While Lucene system together with WordNet lexical database linked to advanced natural language processing and PostGIS platform were used to ensure semantic and spatial analysis of the collected data. A functional diversity approach was implemented to enforce fault tolerance for the data collection part where its performances were evaluated through comparison with alternative approaches. The proposal enables the discovery of spatial patterns within geo-located Twitter and can provide the user or operator with useful unforeseen
High-$Q$  Tunable Dielectric Resonator Filters Using MEMS Technology

This paper presents the design and implementation of a new class of high-Q tunable dielectric resonator (DR) filters based on microelectromechanical systems (MEMS) technology. The use of MEMS tuning elements results in the compact implementation of the proposed filters with high-Q and near to zero dc power consumption. The proposed filters consist of disk-shaped dielectric resonators with circular holes created in the center of each resonator. Three different filters are designed and measured based on different tuning elements. The first filter operates in TME mode at a center frequency of 4.72 GHz with a bandwidth of 21 MHz. MEMS contact-type switches are used as tuning elements for this filter. Measurement results demonstrate a tuning range of 160 MHz while the quality factor is above 510 (1200-510 over the tuning range). The other two implementations employ GaAs and MEMS varactors for tuning. The tunable filter with GaAs varactor has a continuous tuning range from 4.97 to 4.87 GHz with 65-MHz bandwidth and a Q value from 660 to 170. The MEMS varactor-tuned filter has a better tuning performance from 5.20 to 5.02 GHz with higher Q value from 800 to 550 over the tuning range. The proposed tuning approach is applicable to other modes at other frequencies of DR filters.
Evaluation methods for unsupervised word embeddings

We present a comprehensive study of evaluation methods for unsupervised embedding techniques that obtain meaningful representations of words from text. Different evaluations result in different orderings of embedding methods, calling into question the common assumption that there is one single optimal vector representation. We present new evaluation techniques that directly compare embeddings with respect to specific queries. These methods reduce bias, provide greater insight, and allow us to solicit data-driven relevance judgments rapidly and accurately through crowdsourcing.
Beyond Sigmoids: The NetTide Model for Social Network Growth, and Its Applications

What is the growth pattern of social networks, like Facebook and WeChat? Does it truly exhibit exponential early growth, as predicted by textbook models like the Bass model, SI, or the Branching Process? How about the count of links, over time, for which there are few published models?
 We examine the growth of several real networks, including one of the world's largest online social network, ``WeChat'', with 300 million nodes and 4.75 billion links by 2013; and we observe power law growth for both nodes and links, a fact that completely breaks the sigmoid models (like SI, and Bass). In its place, we propose NETTIDE, along with differential equations for the growth of the count of nodes, as well as links. Our model accurately fits the growth patterns of real graphs; it is general, encompassing as special cases all the known, traditional models (including Bass, SI, log-logistic growth); while still remaining parsimonious, requiring only a handful of parameters. Moreover, our NETTIDE for link growth is the first one of its kind, accurately fitting real data, and naturally leading to the densification phenomenon. We validate our model with four real, time-evolving social networks, where NETTIDE gives good fitting accuracy, and, more importantly, applied on the WeChat data, our NETTIDE forecasted more than 730 days into the future, with 3% error.
A Coarse-Fine Network for Keypoint Localization

We propose a coarse-fine network (CFN) that exploits multi-level supervisions for keypoint localization. Recently, convolutional neural networks (CNNs)-based methods have achieved great success due to the powerful hierarchical features in CNNs. These methods typically use confidence maps generated from ground-truth keypoint locations as supervisory signals. However, while some keypoints can be easily located with high accuracy, many of them are hard to localize due to appearance ambiguity. Thus, using strict supervision often fails to detect keypoints that are difficult to locate accurately To target this problem, we develop a keypoint localization network composed of several coarse detector branches, each of which is built on top of a feature layer in a CNN, and a fine detector branch built on top of multiple feature layers. We supervise each branch by a specified label map to explicate a certain supervision strictness level. All the branches are unified principally to produce the final accurate keypoint locations. We demonstrate the efficacy, efficiency, and generality of our method on several benchmarks for multiple tasks including bird part localization and human body pose estimation. Especially, our method achieves 72.2% AP on the 2016 COCO Keypoints Challenge dataset, which is an 18% improvement over the winning entry.
A Survey on Partial Retrieval of 3D Shapes

Content-based shape retrieval techniques can facilitate 3D model resource reuse, 3D model modeling, object recognition, and 3D content classification. Recently more and more researchers have attempted to solve the problems of partial retrieval in the domain of computer graphics, vision, CAD, and multimedia. Unfortunately, in the literature, there is little comprehensive discussion on the state-of-the-art methods of partial shape retrieval. In this article we focus on reviewing the partial shape retrieval methods over the last decade, and help novices to grasp latest developments in this field. We first give the definition of partial retrieval and discuss its desirable capabilities. Secondly, we classify the existing methods on partial shape retrieval into three classes by several criteria, describe the main ideas and techniques for each class, and detailedly compare their advantages and limits. We also present several relevant 3D datasets and corresponding evaluation metrics, which are necessary for evaluating partial retrieval performance. Finally, we discuss possible research directions to address partial shape retrieval.
Baby Tartanian8: Winning Agent from the 2016 Annual Computer Poker Competition

Imperfect-information games, where players have private information, pose a unique challenge in artificial intelligence. In recent years, Heads-Up NoLimit Texas Hold’em poker, a popular version of poker, has emerged as the primary benchmark for evaluating game-solving algorithms for imperfectinformation games. We demonstrate a winning agent from the 2016 Annual Computer Poker Competition, Baby Tartanian8.
Sparse mobile crowdsensing: challenges and opportunities

Sensing cost and data quality are two primary concerns in mobile crowd sensing. In this article, we propose a new crowd sensing paradigm, sparse mobile crowd sensing, which leverages the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated, thus lowering overall sensing cost (e.g., smartphone energy consumption and incentives) while ensuring data quality. Sparse mobile crowdsensing applications intelligently select only a small portion of the target area for sensing while inferring the data of the remaining unsensed area with high accuracy. We discuss the fundamental research challenges in sparse mobile crowdsensing, and design a general framework with potential solutions to the challenges. To verify the effectiveness of the proposed framework, a sparse mobile crowdsensing prototype for temperature and traffic monitoring is implemented and evaluated. With several future research directions identified in sparse mobile crowdsensing, we expect that more research interests will be stimulated in this novel crowdsensing paradigm.
A Generalization of Bayesian Inference

Procedures of statistical inference are described which generalize Bayesian inference in specific ways. Probability is used in such a way that in general only bounds may be placed on the probabilities of given events, and probability systems of this kind are suggested both for sample information and for prior information. These systems are then combined using a specified rule. Illustrations are given for inferences about trinomial probabilities, and for inferences about a monotone sequence of binomial pi. Finally, some comments are made on the general class of models which produce upper and lower probabilities, and on the specific models which underlie the suggested inference procedures.
Health monitoring and tracking system for soldiers using Internet of Things(IoT)

The paper reports an Internet of Thing (IoT) based health monitoring and tracking system for soldiers. The proposed system can be mounted on the soldier's body to track their health status and current location using GPS. These information will be transmitted to the control room through IoT. The proposed system comprise of tiny wearable physiological equipment's, sensors, transmission modules. Hence, with the use of the proposed equipment, it is possible to implement a low cost mechanism to protect the valuable human life on the battlefield.
Novel Empty Substrate Integrated Waveguide for High-Performance Microwave Integrated Circuits

Over the last years, a great number of substrate integrated circuits has been developed. These new circuits are a compromise between the advantages of classical waveguide technologies, such as high quality factor and low losses, and the advantages of planar circuits, such as low cost and easy compact integration. Although their quality factor and losses are better than for planar circuits, these characteristics are worse than in the case of waveguides, mainly due to the presence of the dielectric substrate. In order to improve the performance of the integrated circuits, a new methodology for manufacturing empty waveguides, without a dielectric substrate, but at the same time completely integrated in a planar substrate, is proposed in this work. A wideband transition with return losses greater than 20 dB in the whole bandwith of the waveguide allows the integration of the empty waveguide into the planar substrate so that the waveguide can be directly accessed with a microstrip line. Therefore, a microwave circuit integrated in a planar substrate, but at the same time with a very high quality factor (measured quality factor is 4.5 times higher than for the same filter in the substrate integrated waveguide), and very low losses is successfully achieved.
Collocated interaction with flying robots

We introduce a socially motivated interaction technique with collocated flying robots (a quadrotor in our current prototype). Instead of the traditional remote interaction controllers often used when interacting with flying robots and UAVs, we explore the collocated interaction space and suggest a direct interaction technique motivated by social human-robot interaction themes. Our approach is inspired by the types of interaction humans have with birds, specifically falconeering, and is facilitated by gestures-based interaction, while the user is within the field of view of the flying robot. This paper outlines our research goals, task examples, and our overall design approach. The paper also discusses our current prototyping efforts, as well as a preliminary evaluation of our approach, performed through two design critiques, studying our collocated interaction technique concept, and its potential, drawbacks and benefits for users.
Alternatives to Peer Review: Novel Approaches for Research Evaluation

In this paper we review several novel approaches for research evaluation. We start with a brief overview of the peer review, its controversies, and metrics for assessing efficiency and overall quality of the peer review. We then discuss five approaches, including reputation-based ones, that come out of the research carried out by the LiquidPub project and research groups collaborated with LiquidPub. Those approaches are alternative or complementary to traditional peer review. We discuss pros and cons of the proposed approaches and conclude with a vision for the future of the research evaluation, arguing that no single system can suit all stakeholders in various communities.
An ESPRIT-Based Approach for 2-D Localization of Incoherently Distributed Sources in Massive MIMO Systems

In this paper, an approach of estimating signal parameters via rotational invariance technique (ESPRIT) is proposed for two-dimensional (2-D) localization of incoherently distributed (ID) sources in large-scale/massive multiple-input multiple-output (MIMO) systems. The traditional ESPRIT-based methods are valid only for one-dimensional (1-D) localization of the ID sources. By contrast, in the proposed approach the signal subspace is constructed for estimating the nominal azimuth and elevation direction-of-arrivals and the angular spreads. The proposed estimator enjoys closed-form expressions and hence it bypasses the searching over the entire feasible field. Therefore, it imposes significantly lower computational complexity than the conventional 2-D estimation approaches. Our analysis shows that the estimation performance of the proposed approach improves when the large-scale/massive MIMO systems are employed. The approximate Cramér-Rao bound of the proposed estimator for the 2-D localization is also derived. Numerical results demonstrate that albeit the proposed estimation method is comparable with the traditional 2-D estimators in terms of performance, it benefits from a remarkably lower computational complexity.
A Survey on Open-source Cloud Computing Solutions

Cloud computing is an attractive computing model since it allows for resources to be provisioned according on a demand basis, i.e., cloud users can rent resources as they become necessary. This model motivated several academic and non-academic institutions to develop open-source cloud solutions. This paper presents and discusses the state-of-the of open-source solutions for cloud computing. The authors hope that the observation and classification of such solutions can leverage the cloud computing research area providing a good starting point to cope with some of the problems present in cloud computing environments.
Quantitatively Evaluating GANs With Divergences Proposed for Training

Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative methods for model assessment. Because of this, while many GAN variants are being proposed, we have relatively little understanding of their relative abilities. In this paper, we evaluate the performance of various types of GANs using divergence and distance functions typically used only for training. We observe consistency across the various proposed metrics and, interestingly, the test-time metrics do not favour networks that use the same training-time criterion. We also compare the proposed metrics to human perceptual scores.
Seamless Texture Atlases

Texture atlas parameterization provides an effective way to map a variety of color and data attributes from 2D texture domains onto polygonal surface meshes. However, the individual charts of such atlases are typically plagued by noticeable seams. We describe a new type of atlas which is seamless by construction. Our seamless atlas comprises all quadrilateral charts, and permits seamless texturing, as well as per-fragment down-sampling on rendering hardware and polygon simplification. We demonstrate the use of this atlas for capturing appearance attributes and producing seamless renderings.
Visualizing Group Structures in Graphs: A Survey

Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to indicate groups implicitly. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. The derived taxonomies for group structure and visualization types are also applied to group visualizations of edges. We survey group-only, group–node, group–link, and group–network tasks that are described in the literature as use cases of group visualizations. We discuss results from evaluations of existing visualization techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.
Nonparametric Bayes Pachinko Allocation

Recent advances in topic models have explored complicated structured distributions to represent topic correlation. For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). While PAM provides more flexibility and greater expressive power than previous models like latent Dirichlet allocation (LDA), it is also more difficult to determine the appropriate topic structure for a specific dataset. In this paper, we propose a nonparametric Bayesian prior for PAM based on a variant of the hierarchical Dirichlet process (HDP). Although the HDP can capture topic correlations defined by nested data structure, it does not automatically discover such correlations from unstructured data. By assuming an HDP-based prior for PAM, we are able to learn both the number of topics and how the topics are correlated. We evaluate our model on synthetic and real-world text datasets, and show that nonparametric PAM achieves performance matching the best of PAM without manually tuning the number of topics.