nils-herrmann/clean_scirepeval huggingface dataset:
Vehicle pose estimation in cluttered urban environments using multilayer adaptive Monte Carlo localization

In this contribution, we propose multilayer adaptive Monte Carlo localization (ML-AMCL) in combination with 3D point registration algorithms as a GPS-independent framework for precise global vehicle pose estimation in challenging urban environments. Scans from a 3D LIDAR sensor are split into a set of horizontal layers which are then used for localization with separate instances of an AMCL algorithm. A consistency check is performed for the obtained pose estimates in every time step and feasible results are fused. It is shown, that ML-AMCL is superior to existing localization approaches and is well suited as a prior for 3D point registration algorithms for the refinement of the pose estimate. Our key contributions are: i) proposal of ML-AMCL. ii) Incorporation of prior information from ML-AMCL into different pose refinement procedures, i.e. ICP variants and normal distribution transform (NDT), for precise vehicle localization. By means of experimental evaluation with a challenging real data set, performance of the reference localization system is verified. The proposed localization framework achieves a mean Euclidean measurement error of 0:2m under severe adverse environment conditions. The latter include high clutter densities in the sensor measurements and semi-static objects in the localization map.
Shrink Globally , Act Locally : Sparse Bayesian Regularization and Prediction

We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based on scale mixtures of normals. Two theorems are presented which characterize certain desirable properties of shrinkage priors for sparse problems. Next, we review some recent results showing how Lévy processes can be used to generate infinite-dimensional versions of standard normal scale-mixture priors, along with new priors that have yet to be seriously studied in the literature. This approach provides an intuitive framework both for generating new regularization penalties and shrinkage rules, and for performing asymptotic analysis on existing models.
An All-Textile Louis Vuitton Logo Antenna

An all-textile Louis Vuitton logo antenna is proposed. For proper antenna design, the electrical properties of various leathers are measured. The crossed LV-shaped logo is composed of two longer thin arms for the lower frequency band and two shorter thick arms for the higher frequency band. The proposed logo antenna is fabricated from a conductive textile and a leather substrate, and it has an appropriate logo size for practical bag utilization. The proposed antenna operates in the industrial, scientific, and medical 2.45 GHz band and in the 4.5 GHz band. The corresponding fractional -10-dB reflection coefficient bandwidths and maximum gains are 17.1% and -0.29 dBi, respectively, for the lower frequency band and 13% and 3.05 dBi, respectively, for the higher frequency band.
Static analysis tools as early indicators of pre-release defect density

During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring further testing. We present an empirical approach for the early prediction of pre-release defect density based on the defects found using static analysis tools. The defects identified by two different static analysis tools are used to fit and predict the actual pre-release defect density for Windows Server 2003. We show that there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance. Discriminant analysis shows that the results of static analysis tools can be used to separate high and low quality components with an overall classification rate of 82.91%.
Watertight surface reconstruction of caves from 3D laser data

The generation of accurate, watertight, three-dimensional models of environments are often crucial for the purposes of scientific study and infrastructure management. Most commonly, such models are acquired by using range sensors producing point clouds, and further processing steps are required for the construction of a surface model. We used a mobile lidar to map several kilometers of a natural cave system in order to obtain 3D volumetric models for use in scientific research studying the local palaeo-climatic record. For unstructured and GPS-denied environments, such as cave systems, the process of acquiring a complete map is difficult and further complicated by limited mobility within the cave. During the mapping process, many unwanted measurements occur due to occlusions from moving objects such as other people present in the cave. Most common point cloud surface reconstruction techniques are not designed to deal these occlusions; i.e., they require manual cleanup of the data set or are not capable of generating watertight surfaces. The large scale of the environments introduces the additional challenge of dealing with memory limitations. We propose a new volume-based approach to reconstruct a watertight surface from range measurements of enclosed environments without limitation on the scale of the collected data. Our approach carves all unoccupied voxels from the sensor to a triangulated and rasterized surface between successive scans, which is intended to fill in the missing data between the scan rays. The surface is then constructed from the isosurface between unoccupied and unknown cells. By decomposing the space, we are able to handle large-scale data without exceeding the memory limitation of a standard PC, at the cost of some additional computation time. The algorithm has been evaluated across several datasets within a variety of environments and observed to build more complete volumetric models than a simple space carving approach. We have mapped several kilometers of cave networks and, with the described method, produced watertight reconstructions suitable for further scientific analysis.
Supporting requirements traceability through refactoring

Modern traceability tools employ information retrieval (IR) methods to generate candidate traceability links. These methods track textual signs embedded in the system to establish relationships between software artifacts. However, as software systems evolve, new and inconsistent terminology finds its way into the system's taxonomy, thus corrupting its lexical structure and distorting its traceability tracks. In this paper, we argue that the distorted lexical tracks of the system can be systematically re-established through refactoring, a set of behavior-preserving transformations for keeping the system quality under control during evolution. To test this novel hypothesis, we investigate the effect of integrating various types of refactoring on the performance of requirements-to-code automated tracing methods. In particular, we identify the problems of missing, misplaced, and duplicated signs in software artifacts, and then examine to what extent refactorings that restore, move, and remove textual information can overcome these problems respectively. We conduct our experimental analysis using three datasets from different application domains. Results show that restoring textual information in the system has a positive impact on tracing. In contrast, refactorings that remove redundant information impact tracing negatively. Refactorings that move information among the system modules are found to have no significant effect. Our findings address several issues related to code and requirements evolution, as well as refactoring as a mechanism to enhance the practicality of automated tracing tools.
Democracy and Education Spending in Africa

While it is widely believed that electoral competition influences public spending decisions, there has been relatively little effort to examine how recent democratization in the developing world has resulted in changes in basic service provision. There have been even fewer attempts to investigate whether democracy matters for public spending in the poorest developing countries, where “weak institutions” may mean that the formal adoption of electoral competition has little effect on policy. In this article I confront these questions directly, asking whether the shift to multiparty competition in African countries has resulted in increased spending on primary education. I develop an argument, illustrated with a game-theoretic model, which suggests that the need to obtain an electoral majority may have prompted African governments to spend more on education and to prioritize primary schools over universities within the education budget. I test three propositions from the model using panel data on electoral competition and education spending in African countries. I find clear evidence that democratically elected African governments have spent more on primary education, while spending on universities appears unaffected by democratization.
Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. “Deep learning” methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.
An agent-based decision support system for wholesale electricity market

Application software has been developed for analyzing and understanding a dynamic price change in the US wholesale power market. Traders can use the software as an effective decision-making tool by modeling and simulating a power market. The software uses different features of a decision support system by creating a framework for assessing new trading strategies in a competitive electricity trading environment. The practicality of the software is confirmed by comparing its estimation accuracy with those of other methods (e.g., neural network and genetic algorithm). The software has been applied to a data set regarding the California electricity crisis in order to examine whether the learning (convergence) speed of traders is different between the two periods (before and during the crisis). Such an application confirms the validity of the proposed software.
Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.
Achieving High Performance Oxide TFT-Based Inverters by Use of Dual-Gate Configurations With Floating and Biased Secondary Gates

A dual-gate thin-film transistor (TFT) has two gate electrodes. In inverted, staggered TFTs, the primary gate is usually the bottom gate (BG) and the TFT's threshold voltage (VTH) can be adjusted by applying a constant bias to the secondary gate, the top gate (TG), while scanning the BG. Dual-gate TFTs are being used as the load TFT in oxide semiconductor-based inverters, where the TG is biased positively to shift the VTH negatively and, thereby improving the inverter's output swing. Here, we show that the same can be achieved by the use of a floating TG. Furthermore, we show that the noise margin of the inverter can also be controlled by adding a biased TG to the driving TFT.
How to Explain Individual Classification Decisions

David Baehrens∗ BAEHRENS@CS.TU-BERLIN.DE Timon Schroeter∗ TIMON@CS.TU-BERLIN.DE Technische Universit ä Berlin Franklinstr. 28/29, FR 6-9 10587 Berlin, Germany Stefan Harmeling∗ STEFAN.HARMELING@TUEBINGEN.MPG.DE MPI for Biological Cybernetics Spemannstr. 38 72076 T̈ubingen, Germany Motoaki Kawanabe† MOTOAKI .KAWANABE @FIRST.FRAUNHOFER.DE Fraunhofer Institute FIRST.IDA Kekulestr.7 12489 Berlin, Germany
Robust Real-Time Periodic Motion Detection, Analysis, and Applications

We describe new techniques to detect and analyze periodic mo t on as seen from both a static and moving camera. By tracking objects of interest, we compute a n object’s self-similarity as it evolves in time. For periodic motion, the self-similarity measure is a l o periodic, and we apply Time-Frequency analysis to detect and characterize the periodic motion. Th e periodicity is also analyzed robustly using the 2-D lattice structures inherent in similarity matrices . A real-time system has been implemented to track and classify objects using periodicity. Examples of o bject classification (people, running dogs, vehicles), person counting, and non-stationary periodici ty are provided.
A portable binaural beat device for learning stimulation

Binaural beats are created by presenting two different tones at slightly different frequencies separately into each ear. This auditory stimulation has been suggested to influence behavior and cognition via the process of cortical entrainment. The portable binaural beat device used in this study was designed to produce 500 and 515 Hz slightly different frequencies separately into each ear to form a given low 15 Hz binaural beat in brain hemisphere. In this study the generated binaural beat was 15 Hz as determined by digital storage oscilloscope. This portable device is 5x6x3 cm in size and weighs 100 g which can easily be taken to any laboratory. The device was examined by Digital Storage Oscilloscope to confirm its capacity in producing the mentioned beta wave frequencies prior to our application with four participants. Binaural beat audio was also studied as an alternative frequency source, for larger sample group that involve learning activities or moving around. The quantitative electroencephalograph (qEEG) method was used to convert the brainwave signal into brain mapping for apparent understanding of how frequency stimulation affect brainwave and to confirm the existence of the binaural beats created in brain hemisphere at 15 Hz. All participants are in age group of 26 40 years, who represent the majority of the chemical users’ age group in earlier research. The brain mapping evidently illustrated the result of this research that the 15 Hz binaural beats tremendously stimulated Brainwave Activity and neural connectivity in participants.  Binaural Beat; Electroencephalography (EEG); Chemical Safety; Brain Mapping.
Magnebike: A magnetic wheeled robot with high mobility for inspecting complex-shaped structures

This paper describes the Magnebike robot, a compact robot with two magnetic wheels in a motorbike arrangement, which is intended for inspecting the inner casing of ferromagnetic pipes with complex-shaped structures. The locomotion concept is based on an adapted magnetic wheel unit integrating two lateral lever arms. These arms allow for slight lifting off the wheel in order to locally decrease the magnetic attraction force when passing concave edges, as well as laterally stabilizing the wheel unit. The robot has the main advantage of being compact (180 × 130 × 220 mm) and mechanically simple: it features only five active degrees of freedom (two driven wheels each equipped with an active lifter stabilizer and one steering unit). The paper presents in detail design and implementation issues that are specific to magnetic wheeled robots. Low-level control functionalities are addressed because they are necessary to control the active system. The paper also focuses on characterizing and analyzing the implemented robot. The high mobility
Asymptotically Efficient Adaptive Allocation Rules for the Multiarmed Bandit Problem with Switching cost

We consider multiarmed bandit problems with switching cost, define uniformly good allocation rules, and restrict attention to such rules. We present a lower bound on the asymptotic performance of uniformly good allocation rules and construct an allocation scheme that achieves the bound. We discover that despite the inclusion of a switching cost the proposed allocation scheme achieves the same asymptotic performance as the optimal rule for the bandit problem without switching cost. This is made possible by grouping together the samples in a certain fashion. Finally, we illustrate an optimal allocation scheme for a large class of distributions which includes members of the exponential family.
A novel strong-motion seismic network for community participation in earthquake monitoring

The Quake-Catcher Network (QCN) is breaking new ground in seismology by combining new micro-electro-mechanical systems (MEMS) technology with volunteer seismic station distributed computing. Rather than distributing just computations, the QCN allows volunteers to participate in scientific data collection and computation. Using these innovative tools, QCN will increase the number of strong-motion observations for improved earthquake detection and analysis in California, and throughout the world. QCN's increased density of seismic measurements will revolutionize seismology. The QCN, in concert with current seismic networks, may soon provide advanced alerts when earthquakes occur, estimate the response of a building to earthquakes even before they happen, and generate a greater understanding of earthquakes for scientists and the general public alike. Details of how one can join the QCN are outlined. In addition, we have activities on our website that can be used in K-16 classrooms to teach students basic seismology and physics concepts.
Futures of elderly care in Iran: A protocol with scenario approach

The number of people aged 60 and older is increasing faster than other age groups worldwide. Iran will experience a sharp aging population increase in the next decades, and this will pose new challenges to the healthcare system. Since providing high quality aged-care services would be the major concern of the policymakers, this question arises that what types of aged care services should be organized in the coming 10 years? This protocol has been designed to develop a set of scenarios for the future of elderly care in Iran.  In this study, intuitive logics approach and Global Business Network (GBN) model were used to develop scenarios for elderly care in Iran. In terms of perspective, the scenarios in this approach are normative, qualitative with respect to methodology and deductive in constructing the process of scenarios. The three phases of GBN model are as follows: 1) Orientation: Identifying strategic levels, stakeholders, participants and time horizon; 2) Exploration: Identifying the driving forces and key uncertainties; 3) Synthesis: Defining the scenario logics and constructing scenario storyline.  Presently, two phases are completed and the results will be published in mid-2016.  This study delivers a comprehensive framework for taking appropriate actions in providing care for the elderly in the future. Moreover, policy makers should specify and provide the full range of services for the elderly, and in doing so, the scenarios and key findings of this study could be of valuable help.
Long-Term Temporal Convolutions for Action Recognition

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. Recent methods attempt to capture this structure and learn action representations with convolutional neural networks. Such representations, however, are typically learned at the level of a few video frames failing to model actions at their full temporal extent. In this work we learn video representations using neural networks with long-term temporal convolutions (LTC). We demonstrate that LTC-CNN models with increased temporal extents improve the accuracy of action recognition. We also study the impact of different low-level representations, such as raw values of video pixels and optical flow vector fields and demonstrate the importance of high-quality optical flow estimation for learning accurate action models. We report state-of-the-art results on two challenging benchmarks for human action recognition UCF101 (92.7%) and HMDB51 (67.2%).
A consensus-based approach for platooning with inter-vehicular communications

Automated and coordinated vehicles' driving (platooning) is gaining more and more attention today and it represents a challenging scenario heavily relying on wireless Inter-Vehicular Communication (IVC). In this paper, we propose a novel controller for vehicle platooning based on consensus. Opposed to current approaches where the logical control topology is fixed a priori and the control law designed consequently, we design a system whose control topology can be reconfigured depending on the actual network status. Moreover, the controller does not require the vehicles to be radar equipped and automatically compensates outdated information caused by network delays. We define the control law and analyze it in both analytical and simulative way, showing its robustness in different network scenarios. We consider three different wireless network settings: uncorrelated Bernoullian losses, correlated losses using a Gilbert-Elliott channel, and a realistic traffic scenario with interferences caused by other vehicles. Finally, we compare our strategy with another state of the art controller. The results show the ability of the proposed approach to maintain a stable string of vehicles even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.
Specifying IT Security Awareness

IT users are faced with various threats on a daily basis. Unfortunately, not all possible dangers are known to them, such that the users fall an easy victim to attacks. For this reason, IT specialists demand for higher IT security awareness. Although researchers and practitioners exercise ongoing efforts in this area, their work often lacks a concise definition of the term "security awareness". Since there is no agreement on the term, different (and sometimes not compatible) ways of raising and measuring security awareness exist. This paper is an effort to give an overview of this phenomenon and to show that awareness in IT security is not standardized yet.