nils-herrmann/clean_scirepeval huggingface dataset:
Millimeter-wave microstrip-to-waveguide transition operating over broad frequency bandwidth

A broadband microstrip-to-waveguide transition is developed in the millimeter-wave band. Since novel geometrical features are applied in the printed pattern on the substrate, the proposed transition operates over a quite broad frequency bandwidth due to its double resonance. The two resonant frequencies can be controlled independently according to dimensions of the structure for required bandwidth and reflection level. Two versions of transition are designed and reliability is confirmed by measurements in the millimeter-wave band. The design frequency is 76.5 GHz. The bandwidth 12.9 GHz (16.8 %) is obtained for reflection level lower than -30 dB. In the other design for broadband, the bandwidth for reflection level lower than -20 dB results in 24.9 GHz (32.5%).
A Secure Interactive Architecture for Vehicular Cloud Environment

Cloud computing is gaining more and more popularity due to the wide use of the Internet. It can now be integrated into vehicle applications to enhance mobility, preventing accidents, improve safety, and improve driving comfort. In this paper, we propose a secure architecture of inter-vehicle communication which is obtained from a cloud services, including architecture level, communication level and associated security challenges. With the help of these cloud services, real time vehicle simulations are conducted to find the optimal route from a given source to the destination based on some factors such as distance, road width, traffic count, junction delay and velocity - all of which play an essential role in minimizing the travel time. Instead of deploying sensors and devices on each vehicle, authorized drivers will subscribe to the cloud-provided platform and applications as services on-demand with payment based on usage. On the other hand, this study tackles one of the most important and conical attacks in cloud vehicle offering services, which is the Shrew attack. A number of steps should be taken first in order to explore these attacks like activating a loss-based probe to identify the locations and capabilities of the current bottlenecks, making use the data centers low-tenancy in order to synchronize the attacks flow and analyzing the necessary and the sufficient traffic for an effective attack.
The 500MHz band low power rectenna for DTV in the Tokyo area

In this paper, the 500MHz band low power rectenna with high sensitivity characteristic is described. The rectenna is consisting of the folded dipole antenna with a 1.6kΩ impedance and the Cockcroft-Walton type rectifier for improvement of the rectification efficiency and the output dc voltage. Furthermore an L-Type LPF is added to reduce the negative influence due to the junction capacitance of the SBD. The developed rectennas (stage number: 1, 2) achieve the rectification efficiency of 48.9 % (stage number: 1) and 43.1 % (stage number: 2) at -15dBm of the input power. In addition to above, the top level efficiency of 8.7 % (stage number:1) at the input power of -30dBm can be achieved. In the Tokyo are, the output dc voltage of 0.22 V can be measured at the 25 km point from the antenna tower.
Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test

The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.
Body Area Sensor Networks: Requirements, Operations, and Challenges

This article provides an overview of body area sensor networks (BASNs), an application of wireless technology that changed health care to suit the comfort of the population. There are various applications of BASNs and attempts have been made to make the human body a channel for wireless communication. BASNs employ a three-tiered architectural system that requires various technical requirements for its optimal and efficient operation. Energy consumption is one of the major issues that is currently being addressed through self-harvesting and many other techniques. Even with the benefits at hand, there are various issues such as interference and eavesdropping that BASNs have to tackle. Biometrics is a widely used solution. Researchers are also working on various ambitious projects that deal with improving deep brain simulation, heart regulation, drug delivery, and prosthetic actuation to use BASN effectively.
Monte-Carlo Tree Search for the Game of "7 Wonders"

Monte-Carlo Tree Search algorithm, and in particular with the Upper Confidence Bounds formula, provided huge improvements for AI in numerous games, particularly in Go, Hex, Havannah, Amazons and Breakthrough. In this work we study this algorithm on a more complex game, the game of “7 Wonders”. This card game gathers together several known difficult features, such as hidden information, N-player and stochasticity. It also includes an inter-player trading system that induces a combinatorial search to decide which decisions are legal. Moreover it is difficult to hand-craft an efficient evaluation function since the card values are heavily dependent upon the stage of the game and upon the other player decisions. We show that, in spite of the fact that “7 Wonders” is apparently not so related to abstract games, a lot of known results still hold.
Cross-database mammographic image analysis through unsupervised domain adaptation

World Health Organization report shows 519,000 deaths due to breast cancer in 2014 and it was much more in 2008. Therefore, it is required to take early steps in detection and diagnosis of breast cancer to decrease the associated death rate. Computer Aided Diagnosis (CAD) is useful in mass screening of breast cancer datasets. Data mining and machine learning technologies have already achieved significant success in many knowledge engineering areas including classification, regression and clustering, and most recently, have been employed to assist the diagnosis of cancers with promising outcomes. Traditional machine learning models are characterized by training and testing data with the same input feature space and data distribution. But when distribution changes, most machine learning models need to be modified or rebuilt from scratch to work on newly collected data. In many real world applications, it is expensive or impossible to recollect the needed data and rebuild the models. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred as Transfer Learning. In this paper, we explore the usage of transfer learning, specially, unsupervised domain adaptation for breast cancer diagnosis to address the issues of fewer training data on target image dataset. On the strength of recent developed deep descriptors, we are able to adapt recent transfer learning methodologies, e.g., TCA (Transfer Component Analysis), CORAL (Correlation Alignment), BDA(Balanced Distribution Adaptation) to breast cancer diagnosis across multiple mammographic image databases including CBIS-DDSM, InBreast, MIAS, etc, and evaluate their performance. Experiments demonstrate that, without any labels in the target database, transfer learning is able to help improve the classification accuracy.
On the Influence of Emotional Valence Shifts on the Spread of Information in Social Networks

In this paper, we present a study on 4.4 million Twitter messages related to 24 systematically chosen real-world events. For each of the 4.4 million tweets, we first extracted sentiment scores based on the eight basic emotions according to Plutchik's wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the spread of information. We found that in general OSN users tend to conform to the emotional valence of the respective real-world event. However, we also found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted emotions in the corresponding tweets (i.e. positive messages). To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis postulates that positive emotions serve as an antidote during negative events.
VMotion: Designing a Seamless Walking Experience in VR

Physically walking in virtual reality can provide a satisfying sense of presence. However, natural locomotion in virtual worlds larger than the tracked space remains a practical challenge. Numerous redirected walking techniques have been proposed to overcome space limitations but they often require rapid head rotation, sometimes induced by distractors, to keep the scene rotation imperceptible. We propose a design methodology of seamlessly integrating redirection into the virtual experience that takes advantage of the perceptual phenomenon of inattentional blindness. Additionally, we present four novel visibility control techniques that work with our design methodology to minimize disruption to the user experience commonly found in existing redirection techniques. A user study (N = 16) shows that our techniques are imperceptible and users report significantly less dizziness when using our methods. The illusion of unconstrained walking in a large area (16 x 8m) is maintained even though users are limited to a smaller (3.5 x 3.5m) physical space.
Estimating the Kinematics and Structure of a Rigid Object from a Sequence of Monocular Images

The problem considered here involves the use of a sequence of noisy monocular images of a three-dimensional (3-D) moving object to estimate both its structure and kinematics. The object is assumed to be rigid, and its motion is assumed to be “smooth.” A set of object match points is assumed to be available, consisting of fixed features on the object, the image plane coordinates of which have been extracted from successive images in the sequence. Structure is defined as the 3-D positions of these object feature points, relative to each other. Rotational motion occurs about the origin of an object-centered coordinate system, while translational motion is that of the origin of this coordinate system. In previous work [SI-[8] we have developed a model based approach for motion/structure estimation using a long sequence of monocular images. This approach provides a great deal of flexibility, by allowing the use of arbitrarily many image frames and feature points, and each model can easily be modified or extended for different problems. Our earlier work involved assumptions about object structure and/or motion, were primarily tested on simulated imagery, and did not address the issue of uniqueness of the model parameters. In this paper, which is a continuation of the research started in [q, results of an experiment with real imagery are presented, involving estimation of 28 unknown translational, rotational, and structural parameters, based on 12 images with 7 feature points. Uniqueness results are summarized for the case of purely translational motion. A test based on a singular value decomposition is described that determines whether or not noise-free data from an image sequence uniquely determines the elements of any given parameter vector, and empirical support of this test is given.
Linked data on the web (LDOW2008)

The Web is increasingly understood as a global information space consisting not just of linked documents, but also of Linked Data. More than just a vision, the resulting Web of Data has been brought into being by the maturing of the Semantic Web technology stack, and by the publication of an increasing number of datasets according to the principles of Linked Data.
 The Linked Data on the Web (LDOW2008) workshop brings together researchers and practitioners working on all aspects of Linked Data. The workshop provides a forum to present the state of the art in the field and to discuss ongoing and future research challenges. In this workshop summary we will outline the technical context in which Linked Data is situated, describe developments in the past year through initiatives such as the Linking Open Data community project, and look ahead to the workshop itself.
Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras

An important aspect of collision avoidance and driver assistance systems, as well as autonomous vehicles, is the tracking of vehicle taillights and the detection of alert signals (turns and brakes). In this paper, we present the design and implementation of a robust and computationally lightweight algorithm for a real-time vision system, capable of detecting and tracking vehicle taillights, recognizing common alert signals using a vehicle-mounted embedded smart camera, and counting the cars passing on both sides of the vehicle. The system is low-power and processes scenes entirely on the microprocessor of an embedded smart camera. In contrast to most existing work that addresses either daytime or nighttime detection, the presented system provides the ability to track vehicle taillights and detect alert signals regardless of lighting conditions. The mobile vision system has been tested in actual traffic scenes and the results obtained demonstrate the performance and the lightweight nature of the algorithm.
Predictive coding: a fresh view of inhibition in the retina.

Interneurons exhibiting centre--surround antagonism within their receptive fields are commonly found in peripheral visual pathways. We propose that this organization enables the visual system to encode spatial detail in a manner that minimizes the deleterious effects of intrinsic noise, by exploiting the spatial correlation that exists within natural scenes. The antagonistic surround takes a weighted mean of the signals in neighbouring receptors to generate a statistical prediction of the signal at the centre. The predicted value is subtracted from the actual centre signal, thus minimizing the range of outputs transmitted by the centre. In this way the entire dynamic range of the interneuron can be devoted to encoding a small range of intensities, thus rendering fine detail detectable against intrinsic noise injected at later stages in processing. This predictive encoding scheme also reduces spatial redundancy, thereby enabling the array of interneurons to transmit a larger number of distinguishable images, taking into account the expected structure of the visual world. The profile of the required inhibitory field is derived from statistical estimation theory. This profile depends strongly upon the signal: noise ratio and weakly upon the extent of lateral spatial correlation. The receptive fields that are quantitatively predicted by the theory resemble those of X-type retinal ganglion cells and show that the inhibitory surround should become weaker and more diffuse at low intensities. The latter property is unequivocally demonstrated in the first-order interneurons of the fly's compound eye. The theory is extended to the time domain to account for the phasic responses of fly interneurons. These comparisons suggest that, in the early stages of processing, the visual system is concerned primarily with coding the visual image to protect against subsequent intrinsic noise, rather than with reconstructing the scene or extracting specific features from it. The treatment emphasizes that a neuron's dynamic range should be matched to both its receptive field and the statistical properties of the visual pattern expected within this field. Finally, the analysis is synthetic because it is an extension of the background suppression hypothesis (Barlow & Levick 1976), satisfies the redundancy reduction hypothesis (Barlow 1961 a, b) and is equivalent to deblurring under certain conditions (Ratliff 1965).
Near-Optimal Beam Selection for Beamspace MmWave Massive MIMO Systems

The recent concept of beamspace MIMO can utilize beam selection to reduce the number of required radio-frequency (RF) chains in mmWave massive MIMO systems without obvious performance loss. However, as the same beam in the beamspace is likely to be selected for different users, conventional beam selection schemes will suffer from serious multiuser interferences, and some RF chains may be wasted since they have no contribution to the sum-rate performance. To solve these problems, in this letter, we propose an interference-aware (IA) beam selection. Specifically, by considering the potential multiuser interferences, the proposed IA beam selection first classifies all users into two user groups, i.e., the interference-users (IUs) and noninterference-users (NIUs). For NIUs, the beams with large power are selected, while for IUs, the appropriate beams are selected by a low-complexity incremental algorithm based on the criterion of sum-rate maximization. Simulation results verify that IA beam selection can achieve the near-optimal sum-rate performance and higher energy efficiency than conventional schemes.
Lightweight IPS for port scan in OpenFlow SDN networks

Security has been one of the major concerns for the computer network community due to resource abuse and malicious flows intrusion. Before a network or a system is attacked, a port scan is typically performed to discover vulnerabilities, like open ports, which may be used to access and control them. Several studies have addressed Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) methods for detecting malicious activities, based on received flows or packet data analysis. However, those methods lead to an increase in switching latency, due to the need to analyze flows or packets before routing them. This may also increase network overhead when flows or packets are duplicated to be parsed by an external IDS. On the one hand, an IDS/IPS may be a bottleneck on the network and may not be useful. On the other hand, the new paradigm called Software Defined Networking (SDN) and the OpenFlow protocol provide some statistical information about the network that may be used for detecting malicious activities. Hence, this work presents a new port scan IPS for SDN based on the OpenFlow switch counters data. A non-intrusive and lightweight method was developed and implemented, with low network overhead, and low memory and processing power consumption. The results showed that our method is effective on detecting and preventing port scan attacks.
Effects of adaptive cruise control and highly automated driving on workload and situation awareness : A review of the empirical evidence

Department of BioMechanical Engineering, Faculty of Mechanical,  2628 CD Delft, The Netherlands Centre for Transport Studies, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands c TNO Human Factors, Kampweg 5, 3769 DE Soesterberg, The Netherlands d Transportation Research Group, Civil, Maritime, Environmental Engineering and Science, Engineering and the Environment, University of Southampton, United Kingdom
Firewalls and internet security: Repelling the wily hacker, 2nd ed. [Book Review]

General Interest How the Internet Works, 7th ed., Preston Gralla (Que, 2004, 368 pp., ISBN 0-78972973-3, $29.99; www.quepublishing.com) Preston Gralla is a well-known technology columnist and author. In this large-format book lavishly illustrated by Michael Troller, Gralla explains all of the concepts that most people are likely to encounter in connecting to the Internet. The explanations are not deep, but they systematically cover the basics. Most people, even experts, will probably find at least some of the explanations helpful. For example, I feel that I know quite a bit about the Internet, but Gralla’s explanation of proxy servers clarified the subject for me. The illustrations are colorful and attractive, but in many cases are merely decorative. Visual learners may find them helpful, though I doubt that anyone could get much from the book just by looking at the pictures. The text, on the other hand, can probably stand alone. If you know people who are just starting to use the Internet and electronic mail, consider giving them this book. It may save you from having to answer some difficult questions.
Neural representation of abstract and concrete concepts: a meta-analysis of neuroimaging studies.

A number of studies have investigated differences in neural correlates of abstract and concrete concepts with disagreement across results. A quantitative, coordinate-based meta-analysis combined data from 303 participants across 19 functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies to identify the differences in neural representation of abstract and concrete concepts. Studies that reported peak activations in standard space in contrast of abstract > concrete or concrete > abstract concepts at a whole brain level in healthy adults were included in this meta-analysis. Multilevel kernel density analysis (MKDA) was performed to identify the proportion of activated contrasts weighted by sample size and analysis type (fixed or random effects). Meta-analysis results indicated consistent and meaningful differences in neural representation for abstract and concrete concepts. Abstract concepts elicit greater activity in the inferior frontal gyrus and middle temporal gyrus compared to concrete concepts, while concrete concepts elicit greater activity in the posterior cingulate, precuneus, fusiform gyrus, and parahippocampal gyrus compared to abstract concepts. These results suggest greater engagement of the verbal system for processing of abstract concepts and greater engagement of the perceptual system for processing of concrete concepts, likely via mental imagery.
The Self-Report Method for Measuring Delinquency and Crime

The self-report technique is one of three major ways of measuring involvement in delinquent and criminal behavior. The basic approach of the self-report method is to ask individuals if they have engaged in delinquent or criminal behavior, and if so, how often they have done so. In this chapter, we review the origins of the self-report method in the 1950s, the growth and refinement of this measurement technique since then, and its role in criminological research, especially longitudinal research on the etiology of delinquent and criminal behavior. Particular attention is paid to assessing the reliability and validity of self-reported measures of delinquency. We also discuss specialized data collection methods, such as random response techniques and audio assisted computer-based interviewing, that have the potential to increase the accuracy of responses. Overall, we conclude that the psychometric quality of the self-report method has increased considerably since its inception in the 1950s. Although there is much room for continued improvement, self-report data appear acceptably valid and reliable for most research purposes. MEASUREMENT AND ANALYSIS OF CRIME AND JUSTICE