nils-herrmann/clean_scirepeval huggingface dataset:
From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems

Way beyond its industrial roots, robotics evolved to be a highly interdisciplinary field with a variety of applications in a smart world. The eRobotics methodology addresses this evolution by providing platforms where roboticist can exchange ideas and collaborate with experts from other disciplines for developing complex technical systems and automated solutions. Virtual Testbeds are the central method in eRobotics, where complex technical systems and their interaction with prospective working environments are first designed, programmed, controlled and optimized in simulation before commissioning the real system. On the other hand, Industry 4.0 concepts promote the notion of “Digital Twins”, virtual substitutes of real world objects consisting of virtual representations and communication capabilities making up smart objects acting as intelligent nodes inside the internet of things and services. Combining these two approaches, Virtual Testbeds and Digital Twins, leads to a new kind of “Experimentable Digital Twins” breaking new ground in the simulation-based development and operation of complex technical systems. In this contribution, we describe how such “Experimentable Digital Twins” can act as the very core of simulation-based development processes streamlining the development process, enabling detailed simulations at system level and realizing intelligent systems. Besides this, the multiple use of models and simulations in various scenarios significantly reduces the effort for the use of simulation technology throughout the life cycle of complex technical systems.
Deep metric learning for person Re-identification and De-identification

Large amounts of visual data are gathered from various surveillance systems across different places and times, and have to be processed in order to infer the current state of the world. One of the common problems in surveillance scenarios is person re-identification, the task of associating a person across different cameras. On the other hand, these scenarios raise privacy concerns, which lead to the need for person de-identification, i.e. concealing person identity. This task is related to the re-identification in two aspects: (i) multiple appearances of the same person could be de-identified in similar manner; and (ii) if we discover the features useful for re-identification, we could try to hide the identity by modifying those features. Re-identification can be addressed as a classification problem. The state-of-the-art classification methods are based on deep learning. In this paper we explore the applicability of the recently proposed Triplet network architecture to the person re-identification problem, by applying it on VIPeR dataset. We show that the network is able to learn useful feature-space embeddings, and analyze its benefits and limitations.
Estimation of the Cramer-Rao Bound for Radio Direction-Finding on the Azimuth and Elevation of the Cylindical Antenna Arrays

In this paper the problem of direction-of-arrival (DOA) estimation for conformal antenna arrays consisting of directive emitters for the azimuth and elevation cases is studied. The expression for the Cramer-Rao lower bound of the DOA estimates variance depending on the antenna directivity and the geometry is presented. The boundaries of the Cramer-Rao are evaluated across several scenarios, including the different signal sources locations in terms of the azimuth and elevation angles and signal-to-noise ratios using different antenna array configurations. The influence of the cylindrical conformal antenna array on the direction-of-arrival estimation accuracy is researched.
Dynamic Selection of Classifiers in Bug Prediction: An Adaptive Method

In the last decades, the research community has devoted a lot of effort in the definition of approaches able to predict the defect proneness of source code files. Such approaches exploit several predictors (e.g., product or process metrics) and use machine learning classifiers to predict classes into buggy or not buggy, or provide the likelihood that a class will exhibit a fault in the near future. The empirical evaluation of all these approaches indicated that there is no machine learning classifier providing the best accuracy in any context, highlighting interesting complementarity among them. For these reasons ensemble methods have been proposed to estimate the bug-proneness of a class by combining the predictions of different classifiers. Following this line of research, in this paper we propose an adaptive method, named ASCI (Adaptive Selection of Classifiers in bug prediction), able to dynamically select among a set of machine learning classifiers the one which better predicts the bug-proneness of a class based on its characteristics. An empirical study conducted on 30 software systems indicates that ASCI exhibits higher performances than five different classifiers used independently and combined with the majority voting ensemble method.
Historical vignettes of the thyroid gland.

Although "glands" in the neck corresponding to the thyroid were known for thousands of years, they were mainly considered pathological when encountered. Recognition of the thyroid gland as an anatomical and physiological entity required human dissection, which began in earnest in the 16th century. Leonardo Da Vinci is generally credited as the first to draw the thyroid gland as an anatomical organ. The drawings were subsequently "lost" to medicine for nearly 260 years. The drawings were probably of a nonhuman specimen. Da Vinci vowed to produce an anatomical atlas, but it was never completed. Michelangelo Buonarroti promised to complete drawings for the anatomical work of Realdus Columbus, De Re Anatomica, but these were also never completed. Andreas Vesalius established the thyroid gland as an anatomical organ with his description and drawings in the Fabrica. The thyroid was still depicted in a nonhuman form during this time. The copper etchings of Bartholomew Eustachius made in the 1560s were obviously of humans, but were not actually published until 1714 with a description by Johannes Maria Lancisius. These etchings also depicted some interesting anatomy, which we describe. The Adenographia by Thomas Wharton in 1656 named the thyroid gland for the first time and more fully described it. The book also attempted to assign a function to the gland. The thyroid gland's interesting history thus touches a number of famous men from diverse backgrounds.
Mining business topics in source code using latent dirichlet allocation

One of the difficulties in maintaining a large software system is the absence of documented business domain topics and correlation between these domain topics and source code. Without such a correlation, people without any prior application knowledge would find it hard to comprehend the functionality of the system. Latent Dirichlet Allocation (LDA), a statistical model, has emerged as a popular technique for discovering topics in large text document corpus. But its applicability in extracting business domain topics from source code has not been explored so far. This paper investigates LDA in the context of comprehending large software systems and proposes a human assisted approachbased on LDA for extracting domain topics from source code. This method has been applied on a number of open source and proprietary systems. Preliminary results indicate that LDA is able to identify some of the domain topics and isa satisfactory starting point for further manual refinement of topics
DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter

The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figurative Language in Twitter. The proposed approach employs syntactical and morphological features, which indicate sentiment polarity in both figurative and non-figurative tweets. These features were combined with others that indicate presence of figurative language in order to predict a fine-grained sentiment score. The method is supervised and makes use of structured knowledge resources, such as SentiWordNet sentiment lexicon for assigning sentiment score to words and WordNet for calculating word similarity. We have experimented with different classification algorithms (Naïve Bayes, Decision trees, and SVM), and the best results were achieved by an SVM classifier with linear kernel.
Development and field test of novel two-wheeled UAV for bridge inspections

This paper presents the development and field test of a novel unmanned aerial vehicle (UAV) for bridge inspections. The proposed UAV, which consists of a quadrotor, a cylindrical cage installed in it, and two spokeless wheels freely rotating around the cage, can climb and run on the bridge surface. Its structure improves durability and reduces air resistance compared with conventional UAVs with a cage or wheels. These advantages expand the uses of the UAV in various fields. This paper evaluates the effectiveness of the proposed UAV in real-world bridge inspection scenarios. We tested basic locomotion and measured air resistances. Experimental results on bridges demonstrated the ability to inspect various locations on bridges that are difficult for human inspectors to access.
RRT-Connect: An Efficient Approach to Single-Query Path Planning

A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.
System-Level Performance of Downlink Non-Orthogonal Multiple Access (NOMA) under Various Environments

Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for further improving the spectrum efficiency compared to that for orthogonal multiple access (OMA) in the 5th Generation (5G) mobile communication systems. All of the existing evaluations for NOMA focus on the macrocell deployment since NOMA fully utilizes the power domain and the difference in channel gains, e.g., path loss, between users, which is typically sufficiently large in macrocells. Currently, small cells are becoming important and being studied for future Long-Term Evolution (LTE) enhancements in order to improve further the system performance. Thus, it is of great interest to study the performance of NOMA for small cell deployment under various environments. This paper investigates the system level performance of NOMA in small cells considering practical assumptions such as the single user multiple-input multiple-output (SU-MIMO) technique, adaptive modulation and coding (AMC), feedback channel quality indicator (CQI). Some of the key NOMA specific functionalities, including multi-user paring and transmit power allocation are also taken into account in the evaluation. Based on computer simulations, we show that for both macrocell and small cell deployments, NOMA can still provide a larger throughput performance gain compared to that for OMA.
The Use of Information Retrieval Techniques for Intrusion Detection

Intrusion detection is a broad problem, and we need a greater range of tools than is currently available. In this article, we report a new approach. We have applied information retrieval techniques to index audit trails. These indexes can be extremely e cient at detecting attacks whose signature is an unusual combination of events, and they may consume only a very small additional amount of storage. This approach allows the intrusion detection community to adopt a wide range of techniques developed in applications ranging from library science to web search engines.
Fast Metric Tracking by Detection System: Radar Blob and Camera Fusion

This article proposes a system that fuses radar and monocular vision sensor data in order to detect and classify on-road obstacles, like cars or not cars (other obstacles). The obstacle detection process and classification is divided into three stages, the first consist in reading radar signals and capturing the camera data, the second stage is the data fusion, and the third step is the classify the obstacles, aiming to differentiate the obstacles types identified by the radar and confirmed by the computer vision. In the detection task it is important to locate, measure, and rank the obstacles to be able to take adequate decisions and actions (e.g. Generate alerts, autonomously control the vehicle path), so the correct classification of the obstacle type and position, is very important, also avoiding false positives and/or false negatives in the classification task.
Simulation, comparison and analysis of Wireless Sensor Networks protocols: LEACH, LEACH-C, LEACH-1R, and HEED

Wireless Sensor Networks (WSNs) are made up of tiny sensor nodes which sense the data and communicate to the base station via other nodes. These sensor nodes are inexpensive portable devices with limited processing power and energy resources which make them in need of smart clustering protocols. Many clustering and routing protocols were proposed in the literature to serve large networks of such tiny devices. In this paper, we have implemented and analyzed different clustering protocols, namely LEACH, LEACH-C, LEACH-1R, and HEED using MATLAB environment. These clustering protocols are compared in different terms such as residual energy, data delivery to the base station, maximum number of rounds and the number of live nodes. Experimental results showed a better performance of the LEACH protocols when compared to the different versions of HEED. Moreover, LEACH-1R proved to be efficient in terms of network lifetime.
Deep Multimetric Learning for Shape-Based 3D Model Retrieval

Recently, feature-learning-based 3D shape retrieval methods have been receiving more and more attention in the 3D shape analysis community. In these methods, the hand-crafted metrics or the learned linear metrics are usually used to compute the distances between shape features. Since there are complex geometric structural variations with 3D shapes, the single hand-crafted metric or learned linear metric cannot characterize the manifold, where 3D shapes lie well. In this paper, by exploring the nonlinearity of the deep neural network and the complementarity among multiple shape features, we propose a novel deep multimetric network for 3D shape retrieval. The developed multimetric network minimizes a discriminative loss function that, for each type of shape feature, the outputs of the network from the same class are encouraged to be as similar as possible and the outputs from different classes are encouraged to be as dissimilar as possible. Meanwhile, the Hilbert-Schmidt independence criterion is employed to enforce the outputs of different types of shape features to be as complementary as possible. Furthermore, the weights of the learned multiple distance metrics can be adaptively determined in our developed deep metric network. The weighted distance metric is then used as the similarity for shape retrieval. We conduct experiments with the proposed method on the four benchmark shape datasets. Experimental results demonstrate that the proposed method can obtain better performance than the learned deep single metric and outperform the state-of-the-art 3D shape retrieval methods.
Real-Time Fog using Post-processing in OpenGL

Fog is often used to add realism to a computer generated scene, but support for fog in current graphics APIs such as OpenGL is limited. The standard fog models are very simplistic, allowing for a uniform density fog with slight variations in attenuation functions. While recent extensions to the OpenGL standard provide height dependent, or layered fog, it does not correctly account for line-of-sight effects as the viewpoint moves through the fog. In this paper, we present a new, fast but simple method for generating heterogeneous fog as a post processing step. Using standard OpenGL Shading Language features, it requires only the depth buffer from the normal rendering process as input, evaluating fog integrals along line-of-sight to produce realistic heterogeneous fog effects in real-time.
HOPE: Enabling Efficient Service Orchestration in Software-Defined Data Centers

The functional scope of today's software-defined data centers (SDDC) has expanded to such an extent that servers face a growing amount of critical background operational tasks like load monitoring, logging, migration, and duplication, etc. These ancillary operations, which we refer to as management operations, often nibble the stringent data center power envelope and exert a tremendous amount of pressure on front-end user tasks. However, existing power capping, peak shaving, and time shifting mechanisms mainly focus on managing data center power demand at the "macro level" -- they do not distinguish ancillary background services from user tasks, and therefore often incur significant performance degradation and energy overhead.
 In this study we explore "micro-level" power management in SDDC: tuning a specific set of critical loads for the sake of overall system efficiency and performance. Specifically, we look at management operations that can often lead to resource contention and energy overhead in an IaaS SDDC. We assess the feasibility of this new power management paradigm by characterizing the resource and power impact of various management operations. We propose HOPE, a new system optimization framework for eliminating the potential efficiency bottleneck caused by the management operations in the SDDC. HOPE is implemented on a customized OpenStack cloud environment with heavily instrumented power infrastructure. We thoroughly validate HOPE models and optimization efficacy under various user workload scenarios. Our deployment experiences show that the proposed technique allows SDDC to reduce energy consumption by 19%, reduce management operation execution time by 25.4%, and in the meantime improve workload performance by 30%.
TORCS, The Open Racing Car Simulator

The open racing car simulator (TORCS ), is a modern, modular, highlyportable multi-player, multi-agent car simulator. Its high degree of modularity and portability render it ideal for artificial intelligence research. Indeed, a number of research-oriented competitions and papers have already appeared that make use of the TORCS engine. The purpose of this document is to introduce the structure of TORCS to the general artificial intelligence and machine learning community and explain how it is possible to tests agents on the platform. TORCS can be used to develop artificially intelligent (AI) agents for a variety of problems. At the car level, new simulation modules can be developed, which include intelligent control systems for various car components. At the driver level, a low-level API gives detailed (but only partial) access to the simulation state. This could be used to develop anything from mid-level control systems to complex driving agents that find optimal racing lines, react successfully in unexpected situations and make good tactical race decisions. Finally, for researchers that like a challenge and are also interested in visual processing, a 3d projection interface is available.
A High-Power Broadband Passive Terahertz Frequency Doubler in CMOS

To realize a high-efficiency terahertz signal source, a varactor-based frequency-doubler topology is proposed. The structure is based on a compact partially coupled ring that simultaneously achieves isolation, matching, and harmonic filtering for both input and output signals at f0 and 2f0. The optimum varactor pumping/loading conditions for the highest conversion efficiency are also presented analytically along with intuitive circuit representations. Using the proposed circuit, a passive 480-GHz frequency doubler with a measured minimum conversion loss of 14.3 dB and an unsaturated output power of 0.23 mW is reported. Within 20-GHz range, the fluctuation of the measured output power is less than 1.5 dB, and the simulated 3-dB output bandwidth is 70 GHz (14.6%). The doubler is fabricated using 65-nm low-power bulk CMOS technology and consumes near zero dc power.
Query preserving graph compression

It is common to find graphs with millions of nodes and billions of edges in, <i>e.g.</i>, social networks. Queries on such graphs are often prohibitively expensive. These motivate us to propose <i>query preserving graph compression</i>, to compress graphs <i>relative to</i> a class Λ of queries of users' choice. We compute a small <i>G</i><sub><i>r</i></sub> from a graph <i>G</i> such that (a) for <i>any</i> query <i>Q</i> Ε Λ <i>Q</i>, <i>Q</i>(<i>G</i>) = Q'(<i>G</i><sub><i>r</i></sub>), where <i>Q'</i> Ε Λ can be efficiently computed from <i>Q</i>; and (b) any algorithm for computing <i>Q</i>(<i>G</i>) can be <i>directly</i> applied to evaluating <i>Q'</i> on <i>G</i><sub><i>r</i></sub> <i>as is</i>. That is, while we cannot lower the complexity of evaluating graph queries, we reduce data graphs while preserving the answers to <i>all</i> the queries in Λ. To verify the effectiveness of this approach, (1) we develop compression strategies for two classes of queries: reachability and graph pattern queries via (bounded) simulation. We show that graphs can be efficiently compressed via a reachability equivalence relation and graph bisimulation, respectively, while reserving query answers. (2) We provide techniques for aintaining compressed graph <i>G</i><sub><i>r</i></sub> in response to changes Δ<i>G</i> to the original graph <i>G</i>. We show that the incremental maintenance problems are <i>unbounded</i> for the two lasses of queries, <i>i.e.</i>, their costs are not a function of the size of Δ<i>G</i> and changes in <i>G</i><sub><i>r</i></sub>. Nevertheless, we develop incremental algorithms that depend only on Δ<i>G</i> and <i>G</i><sub><i>r</i></sub>, <i>independent of</i> <i>G</i>, i.e., we do not have to decompress <i>G</i><sub>r</sub> to propagate the changes. (3) Using real-life data, we experimentally verify that our compression techniques could reduce graphs in average by 95% for reachability and 57% for graph pattern matching, and that our incremental maintenance algorithms are efficient.
Developing and validating an instrument for measuring user-perceived web quality

Many of the instruments to measure information and system quality were developed in the context of mainframe and PC-based technologies of yesteryears. With the proliferation of the Internet and World Wide Web applications, users are increasingly interfacing and interacting with web-based applications. It is, therefore, important to develop new instruments and scales, which are directly targeted to these new interfaces and applications. In this article, we report on the development of an instrument that captures key characteristics of web site quality from the userâ€TMs perspective. The 25item instrument measures four dimensions of web quality: specific content, content quality, appearance and technical adequacy. While improvements are possible, the instrument exhibits excellent psychometric properties. The instrument would be useful to organizations and web designers as it provides an aggregate measure of web quality, and to researchers in related web research. a 1, b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.