nils-herrmann/clean_scirepeval huggingface dataset:
Flying object detection for automatic UAV recognition

With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object classification. In this work, we investigate the suitability of a number of flying object detection approaches for the task of UAV detection based on video data from static and moving cameras. We compare approaches based on image differencing with object proposal detectors which are learned from data. Finally, we classify each detection by a convolutional neural network (CNN) into the classes UAV or clutter. Our approach is evaluated on six sequences of challenging real world data which contain multiple UAVs, birds, and background motion.
A human parietal face area contains aligned head-centered visual and tactile maps

Visually guided eating, biting and kissing, and avoiding objects moving toward the face and toward which the face moves require prompt, coordinated processing of spatial visual and somatosensory information in order to protect the face and the brain. Single-cell recordings in parietal cortex have identified multisensory neurons with spatially restricted, aligned visual and somatosensory receptive fields, but so far, there has been no evidence for a topographic map in this area. Here we mapped the organization of a multisensory parietal face area in humans by acquiring functional magnetic resonance images while varying the polar angle of facial air puffs and close-up visual stimuli. We found aligned maps of tactile and near-face visual stimuli at the highest level of human association cortex—namely, in the superior part of the postcentral sulcus. We show that this area may code the location of visual stimuli with respect to the face, not with respect to the retina.*NOTE: In the version of this article initially published online, there was an error in the affiliation in the html version. The first affiliation should read Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA. The error has been corrected online.
How ontologies benefit enterprise applications

This paper contributes an argumentation line for how technological features of ontologies lead to benefits for enterprise applications. Although many features are also available in precursory or alternative technologies, we claim that combinations of specific features are uniquely provided by ontologies. A careful elicitation of the available features therefore is a prerequisite for the argumentation line. As a second contribution, this paper reports on several challenges that frequently occur when trying to adopt ontologies in existing enterprise settings. These challenges have to be contrasted with the often overstressed benefits in Semantic Web literature. Together with reports for several SAP Research case studies, this paper channels back experiences to the Semantic Web community. As a third contribution, we give several recommendations for future research directions based on the gathered experiences.
Designs for broad-band microstrip vertical transitions using cavity couplers

Two designs for broad-band vertical transitions using microstrip-fed cavity couplers are presented in this paper. Both designs feature a cavity in an electrically thick ground plane between two parallel back-to-back microstrip lines terminated by open-circuited stubs. The cavity in the first design can be treated as a section of a waveguide, whereas the second design has the cavity operate in nonresonant modes. The electrically thick ground plane can provide isolation between modules, thermal dissipation for on-board active devices, as well as structural support for thin substrates. The finite-element method is chosen to carry out the required simulations for the proposed designs because of its accuracy. Both rectangular and circular cavity couplers are investigated. More than 100% fractional bandwidth can be achieved. The validity of the simulation results is verified experimentally. For applications at millimeter-wave frequencies, sensitivity study is conducted to demonstrate that the proposed design is robust and not susceptible to potential errors in dimension and misalignment, which may occur during the fabrication process of the vertical transitions
Training Sources 3 D Normalized Pose Space 2 D Normalized Pose Space KD-Tree Input Image 2 D Pose Estimation 3 D Pose Reconstruction

In this work we address the challenging problem of 3D human pose estimation from single images. Recent approaches learn deep neural networks to regress 3D pose directly from images. One major challenge for such methods, however, is the collection of training data. Specifically, collecting large amounts of training data containing unconstrained images annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of accurate 3D motion capture data, and the second source consists of unconstrained images with annotated 2D poses. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient 3D pose retrieval. To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data. During inference, we estimate the 2D pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a mapping from the 3D pose space to the image and reconstruct the 3D pose. We provide a comprehensive evaluation of the proposed method and experimentally demonstrate the effectiveness of our approach, even when the skeleton structures of the two sources differ substantially.
Forensic Application-Fingerprinting Based on File System Metadata

While much work has been invested in tools for aquisition and extraction of digital evidence, there are only few tools that allow for automatic event reconstruction. In this paper, we present a generic approach for forensic event reconstruction based on digital evidence from file systems. Our approach applies the idea of fingerprinting to changes made by applications in file system metadata. We present a system with which it is possible to automatically compute file system fingerprints of individual actions. Using NTFS timestamps as an example, we show that with our approach it is possible to automatically reconstruct actions performed by different applications even if the set of files accessed by those actions overlap.
The orbitofrontal cortex and reward.

The primate orbitofrontal cortex contains the secondary taste cortex, in which the reward value of taste is represented. It also contains the secondary and tertiary olfactory cortical areas, in which information about the identity and also about the reward value of odors is represented. The orbitofrontal cortex also receives information about the sight of objects and faces from the temporal lobe cortical visual areas, and neurons in it learn and reverse the visual stimulus to which they respond when the association of the visual stimulus with a primary reinforcing stimulus (such as a taste reward) is reversed. However, the orbitofrontal cortex is involved in representing negative reinforcers (punishers) too, such as aversive taste, and in rapid stimulus-reinforcement association learning for both positive and negative primary reinforcers. In complementary neuroimaging studies in humans it is being found that areas of the orbitofrontal cortex (and connected subgenual cingulate cortex) are activated by pleasant touch, by painful touch, by rewarding and aversive taste, and by odor. Damage to the orbitofrontal cortex in humans can impair the learning and reversal of stimulus- reinforcement associations, and thus the correction of behavioral responses when these are no longer appropriate because previous reinforcement contingencies change. This evidence thus shows that the orbitofrontal cortex is involved in decoding and representing some primary reinforcers such as taste and touch; in learning and reversing associations of visual and other stimuli to these primary reinforcers; and in controlling and correcting reward-related and punishment-related behavior, and thus in emotion.
Nonparametric estimation of the precision-recall curve

The Precision-Recall (PR) curve is a widely used visual tool to evaluate the performance of scoring functions in regards to their capacities to discriminate between two populations. The purpose of this paper is to examine both theoretical and practical issues related to the statistical estimation of PR curves based on classification data. Consistency and asymptotic normality of the empirical counterpart of the PR curve in sup norm are rigorously established. Eventually, the issue of building confidence bands in the PR space is considered and a specific resampling procedure based on a smoothed and truncated version of the empirical distribution of the data is promoted. Arguments of theoretical and computational nature are presented to explain why such a bootstrap is preferable to a "naive" bootstrap in this setup.
Integrated decision support systems: A data warehousing perspective

Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. Data warehouses have emerged to meet this need. They serve as an integrated repository for internal and external data—intelligence critical to understanding and evaluating the business within its environmental context. With the addition of models, analytic tools, and user interfaces, they have the potential to provide actionable information resources—business intelligence that supports effective problem and opportunity identification, critical decision-making, and strategy formulation, implementation, and evaluation. Four themes frame our analysis: integration, implementation, intelligence, and innovation. D 2005 Elsevier B.V. All rights reserved.
An Attentive Neural Architecture for Fine-grained Entity Type Classification

In this work we propose a novel attentionbased neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts. Our model achieves state-of-theart performance with 74.94% loose micro F1score on the well-established FIGER dataset, a relative improvement of 2.59% . We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine-grained category memberships of an entity.
Geometric intersection problems

We develop optimal algorithms for forming the intersection of geometric objects in the plane and apply them to such diverse problems as linear programming, hidden-line elimination, and wire layout. Given N line segments in the plane, finding all intersecting pairs requires O(N2) time. We give an O(N log N) algorithm to determine whether any two intersect and use it to detect whether two simple plane polygons intersect. We employ an O(N log N) algorithm for finding the common intersection of N half-planes to show that the Simplex method is not optimal. The emphasis throughout is on obtaining upper and lower bounds and relating these results to other problems in computational geometry.
Text-to-3 D Scene Generation using Semantic Parsing and Spatial Knowledge with Rule Based System

Scene Generation plays an important role in digital media to represent a news or a specific domain to the viewers. It’s not easy to produce a scene from a text. Text may not completely express the whole situation in digital media. Most of the people are not conscious about the news until it's not visualized to them. Text to 3D scene generation is a process where people do not need to read a news. The 3D Scene will represent the situation. It will help people to conscious about their life. In this paper, we introduce a rule-based framework where scene generated from text using semantic parsing and spatial knowledge. Semantic parsing has identified the templates, objects, and constraints and spatial knowledge have built the relation between object and template. Our rule based framework has identified the uncountable noun and some spatial relations to generate 3D scenes.
Knowledge Discovery in Databases (KDD) with Images: A Novel Approach toward Image Mining and Processing

We are in an age often referred to as the information age. In this information age, because we believe that information leads to power and success, from the technologies such as computers, satellites, etc., we have been collecting tremendous amounts of information. Our ability to analyze and understand massive datasets lags far behind our ability to gather and store data. Image and video data contains abundant, rich information for data miners to explore. On one hand, the rich literature on image and video data analysis will naturally provide many advanced methods that may help mining other kinds of data. On the other hand, recent research on data mining will also provide some new, interesting methods that may benefit image and video data retrieval and analysis. Today, a lot of data is available everywhere but the ability to understand and make use of that data is very less. Whether the context is business, medicine, science or government, the datasets themselves are of little value. What is of value is the knowledge that can be inferred from the data and put to use. We need systems which would analyze the data for us. This paper basically aims to find out important pixels of an image using one of the classification technique named as decision tree (ID-3). Our aim is to separate the important and unimportant pixels of an image using simple rules. Further one of the compression techniques named as Huffman algorithm is applied for image compression. Finally, resultant image is stored with lesser space complexity .
Efficient Edge Detection on Low-Cost FPGAs

Improving the efficiency of edge detection in embedded applications, such as UAV control, is critical for reducing system cost and power dissipation. Field programmable gate arrays (FPGA) are a good platform for making improvements because of their specialised internal structure. However, current FPGA edge detectors do not exploit this structure well. A new edge detection architecture is proposed that is better optimised for FPGAs. The basis of the architecture is the Sobel edge kernels that are shown to be the most suitable because of their separability and absence of multiplications. Edge intensities are calculated with a new 4:2 compressor that consists of two custom-designed 3:2 compressors. Addition speed is increased by breaking carry propagation chains with look-ahead logic. Testing of the design showed it gives a 28% increase in speed and 4.4% reduction in area over previous equivalent designs, which demonstrated that it will lower the cost of edge detection systems, dissipate less power and still maintain highspeed control. edge detection; FPGA; compressor; low-cost;
Clickonomics: Determining the Effect of Anti-Piracy Measures for One-Click Hosting

Piracy is a mass phenomenon on the Internet today. Various file sharing platforms offer free access to unauthorised copies of copyrighted works such as media content and software. Copyright holders are using a range of legal and technical methods to protect their rights, and they are lobbying for legislation that would give them additional ways of enforcing their copyright online. However, little is known about how effective current forms of copyright enforcement are and how enduring the effect of proposed new measures can be. In this paper, we report on the results of a largescale measurement study of the piracy ecosystem that has emerged around One-Click Hosters or “cyberlockers” such as Rapidshare, Megaupload, Mediafire, and Hotfile. Our data shows that current anti-piracy efforts are visible, but their overall impact appears to be rather limited. Furthermore, our analysis of the file sharing ecosystem suggests that future antipiracy measures that are currently under discussion may not be as successful as their proponents might expect. Ongoing legal proceedings and efforts by payment processors, however, may force hosters to increase their own anti-piracy efforts.
A Ka-band YIG-based local oscillator for astronomical heterodyne receiver

A Ka-band local oscillator generator based on YIG-tuned oscillator is designed, fabricated and measured for astronomical telescope applications. The optimized bandwidth analysis can overcome high phase noise problems caused by poor microwave photonic source noise and to improve the outband phase noise of LO by at least 10.5 dB. The experimental results show that the 33.8 GHz LO phase noise of -107 dBc/Hz is achieved at offset frequency of 700 kHz and exhibits a reference spurs of -71 dBc.
A Single-Layer CPW-FED Active Patch Antenna

A single-layer CPW-fed active patch antenna oscillator at 2.76 GHz is presented. The patch antenna acts both as a resonator and a radiator. Electromagnetic coupling is utilized for providing the appropriate closed-loop positive feedback. The active antenna is built around a commercially available GaAs MMIC on a low-permittivity = 2 33 Duroid 5870 substrate. The measured effective isotropic radiated power (EIRP) is 20 dBm, whereas the front-to-back ratio is about 12 dB with the cross-polarized fields better than 15 dB. The measured phase noise is 87.5 dBc/Hz at a 100-kHz offset away from the carrier. The structure only requires a single substrate, is compatible with uniplanar technology, and results into a low-component count.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
RF Energy Harvesting Wireless Powered Sensor Networks for Smart Cities

This paper investigates the optimal energy beamforming and time assignment in radio frequency (RF) energy harvesting (EH) wireless powered sensor networks for smart cities, where sensor nodes (SNs) first harvest energy from a sink node, and then transmit their collected data to the sink node via time-division-multiple-access (TDMA) manner by using the harvested energy. In order to achieve green system design, we formulate a problem to minimize the energy requirement of the sink node to support transmission between the sink node and the SNs under data amount constraint and EH constraint. For practical design, the energy consumed by circuit and information processing is also considered. Since the problem is non-convex, we use semidefinite relaxation (SDR) method to relax it into a convex optimization problem and then solve it efficiently. We theoretically prove that when the number of SNs are not greater than two, the relaxed problem guarantees rank-one constraint and when the number of SNs exceeds two, our obtained results are very close to the optimal ones. Simulation results show that when the data amount is relatively small, the energy consumed by circuit and information processing affects the system performance greatly, but for a relatively large data amount, the energy requirement of the sink node on its own signal processing is affected very limited and the system energy requirement is dominated by the transmit power consumption at the SNs. Furthermore, we also discuss the effects of the other parameters on the system performance, which provide some useful insights in future smart city planning.
Determining Semantic Similarity among Entity Classes from Different Ontologies

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent, entity classes.
On the limits of steganography

In this paper, we clarify what steganography is and what it can do. We contrast it with the related disciplines of cryptography and traffic security, present a unified terminology agreed at the first international workshop on the subject, and outline a number of approaches—many of them developed to hide encrypted copyright marks or serial numbers in digital audio or video. We then present a number of attacks, some new, on such information hiding schemes. This leads to a discussion of the formidable obstacles that lie in the way of a general theory of information hiding systems (in the sense that Shannon gave us a general theory of secrecy systems). However, theoretical considerations lead to ideas of practical value, such as the use of parity checks to amplify covertness and provide public key steganography. Finally, we show that public key information hiding systems exist, and are not necessarily constrained to the case where the warden is passive.  Cryptography, Copyright protection, Data compression, Image registration, Jitter, Motion pictures, Multimedia systems, Music, Observability, Pseudonoise coded communication, Redundancy, Spread spectrum communication, Software protection