nils-herrmann/clean_scirepeval huggingface dataset:
Dual-frequency antenna for HF/UHF handheld RFID reader

This paper presents a dual-frequency (HF and UHF) antenna for handheld radio frequency identification (RFID) reader to embrace the benefits offered by both the UHF and HF RFID systems. In the HF (13.56 MHz) band, a spiral-shaped loop antenna is used and placed at the outmost region of the available space. In the UHF (915 MHz) band, a quadrifilar spiral antenna (QSA) is used because it is electrically small and features a low frequency shift by platform size and is placed in the center of the HF antenna. Measurements revealed that the antenna provides a quality factor of 56.5 in the 13.56 MHz and a gain of 2.5 dBic in the 920 MHz band.
Long-Range Battery-Free UHF RFID With a Single Wire Transmission Line

We present a long-range passive (battery-free) radio frequency identification (RFID) and distributed sensing system using a single wire transmission line (SWTL) as the communication channel. A SWTL exploits guided surface wave propagation along a single conductor, which can be formed from existing infrastructure, such as power lines, pipes, or steel cables. Guided propagation along a SWTL has far lower losses than a comparable over-the-air (OTA) communication link; so much longer read distances can be achieved compared with the conventional OTA RFID system. In a laboratory-scale experiment with an ISO18000–6C (EPC Gen 2) passive tag, we demonstrate an RFID system using an 8 mm diameter, 5.2 m long SWTL. This SWTL has 30 dB lower propagation loss than a standard OTA RFID system at the same read range. We further demonstrate that the SWTL can tolerate extreme temperatures far beyond the capabilities of coaxial cable, by heating an operating SWTL conductor with a propane torch having a temperature of nearly 2000 °C. Extrapolation from the measured results suggest that a SWTL-based RFID system is capable of read ranges of over 70 m assuming a reader output power of +32.5 dBm and a tag power-up threshold of −7 dBm.
Review spam detection via time series pattern discovery

Online reviews play a crucial role in today's electronic commerce. Due to the pervasive spam reviews, customers can be misled to buy low-quality products, while decent stores can be defamed by malicious reviews. We observe that, in reality, a great portion (> 90% in the data we study) of the reviewers write only one review (singleton review). These reviews are so enormous in number that they can almost determine a store's rating and impression. However, existing methods ignore these reviewers. To address this problem, we observe that the normal reviewers' arrival pattern is stable and uncorrelated to their rating pattern temporally. In contrast, spam attacks are usually bursty and either positively or negatively correlated to the rating. Thus, we propose to detect such attacks via unusually correlated temporal patterns. We identify and construct multidimensional time series based on aggregate statistics, in order to depict and mine such correlation. Experimental results show that the proposed method is effective in detecting singleton review attacks. We discover that singleton review is a significant source of spam reviews and largely affects the ratings of online stores.
Hindi Subjective Lexicon : A Lexical Resource for Hindi Polarity Classification

With recent developments in web technologies, percentage web content in Hindi is growing up at a lighting speed. This information can prove to be very useful for researchers, governments and organization to learn what’s on public mind, to make sound decisions. In this paper, we present a graph based wordnet expansion method to generate a full (adjective and adverb) subjective lexicon. We used synonym and antonym relations to expand the initial seed lexicon. We show three different evaluation strategies to validate the lexicon. We achieve 70.4% agreement with human annotators and ∼79% accuracy on product review classification. Main contribution of our work 1) Developing a lexicon of adjectives and adverbs with polarity scores using Hindi Wordnet. 2) Developing an annotated corpora of Hindi Product Reviews.
A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

Intensity inhomogeneity often occurs in real-world images, which presents a considerable challenge in image segmentation. The most widely used image segmentation algorithms are region-based and typically rely on the homogeneity of the image intensities in the regions of interest, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a novel region-based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation. First, based on the model of images with intensity inhomogeneities, we derive a local intensity clustering property of the image intensities, and define a local clustering criterion function for the image intensities in a neighborhood of each point. This local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. In a level set formulation, this criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, by minimizing this energy, our method is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction). Our method has been validated on synthetic images and real images of various modalities, with desirable performance in the presence of intensity inhomogeneities. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known piecewise smooth model. As an application, our method has been used for segmentation and bias correction of magnetic resonance (MR) images with promising results.
Snake Charmer: Physically Enabling Virtual Objects

Augmented and virtual reality have the potential of being indistinguishable from the real world. Holographic displays, including head mounted units, support this vision by creating rich stereoscopic scenes, with objects that appear to float in thin air - often within arm's reach. However, one has but to reach out and grasp nothing but air to destroy the suspension of disbelief. Snake-charmer is an attempt to provide physical form to virtual objects by revisiting the concept of Robotic Graphics or Encountered-type Haptic interfaces with current commodity hardware. By means of a robotic arm, Snake-charmer brings physicality to a virtual scene and explores what it means to truly interact with an object. We go beyond texture and position simulation and explore what it means to have a physical presence inside a virtual scene. We demonstrate how to render surface characteristics beyond texture and position, including temperature; how to physically move objects; and how objects can physically interact with the user's hand. We analyze our implementation, present the performance characteristics, and provide guidance for the construction of future physical renderers.
Analysis and design of topological structure for DC solid-state circuit breaker

Existing mechanical circuit breakers can not satisfy the requirements of fast operation in power system due to noise, electric arc and long switching response time. Moreover the non-grid-connected wind power system is based on the Flexible Direct Current Transmission (FDCT) technique. It is especially necessary to research the Solid-State Circuit Breakers (SSCB) to realize the rapid and automatic control for the circuit breakers in the system. Meanwhile, the newly-developed Solid-State Circuit Breakers (SSCB) operating at the natural zero-crossing point of AC system is not suitable for a DC system. Based on the characteristics of the DC system, a novel circuit scheme has been proposed in this paper. The new scheme makes full use of ideology of soft-switching and current-commutation forced by resonance. This scheme successfully realizes the soft turn-on and fast turn-off. In this paper, the topology of current limiter is presented and analytical mathematical models are derived through comprehensive analysis. Finally, normal turn-on and turn-off experiments and overload delay protection test were conducted. The results show the reliability of the novel theory and feasibility of proposed topology. The proposed scheme can be applied in the grid-connected and non-grid-connected DC transmission and distribution systems.
Introduction to AI Robotics

This text covers all the material needed to understand the principles behind the AI approach to robotics and to program an artificially intelligent robot for applications involving sensing, navigation, planning, and uncertainty. Robin Murphy is extremely effective at combining theoretical and practical rigor with a light narrative touch. In the overview, for example, she touches upon anthropomorphic robots from classic films and science fiction stories before delving into the nuts and bolts of organizing intelligence in robots. Following the overview, Murphy contrasts AI and engineering approaches and discusses what she calls the three paradigms of AI robotics: hierarchical, reactive, and hybrid deliberative/reactive. Later chapters explore multiagent scenarios, navigation and path-planning for mobile robots, and the basics of computer vision and range sensing. Each chapter includes objectives, review questions, and exercises. Many chapters contain one or more case studies showing how the concepts were implemented on real robots. Murphy, who is well known for her classroom teaching, conveys the intellectual adventure of mastering complex theoretical and technical material. Robin R. Murphy is Associate Professor in the Department of Computer Science and Engineering, and in the Department of Psychology, at the University of South Florida, Tampa.
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is data-driven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. For validation, we use wind and solar times-series data from NREL integration data sets. We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training. For example, scenarios can be conditioned on weather events (e.g., high wind day, intense ramp events, or large forecasts errors) or time of the year (e.g., solar generation for a day in July). Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently without sophisticated sampling techniques.
Design considerations to maximize performance of an IPM motor for a wide flux-weakening region

Interior permanent magnet (IPM) synchronous machines result to be a valid motor topology in case of both high efficiency and high flux-weakening range. In industry applications the design of interior permanent magnet (IPM) synchronous machines requires to satisfy an increasingly number of limitations. For an IPM machine the key parameters that can be used for a performance maximization refer to many aspects: geometry, material property, cost, control strategy. According to the size and geometry limitations of an IPM motor for a very high flux-weakening speed range is required, the paper analyzes how to maximize the performance modifying the PM quantity. FE simulations, are firstly verified comparing the results with measurements on a prototype, and then they are used to evaluate the tradeoffs of the different cases.
A deep learning framework for character motion synthesis and editing

We present a framework to synthesize character movements based on high level parameters, such that the produced movements respect the manifold of human motion, trained on a large motion capture dataset. The learned motion manifold, which is represented by the hidden units of a convolutional autoencoder, represents motion data in sparse components which can be combined to produce a wide range of complex movements. To map from high level parameters to the motion manifold, we stack a deep feedforward neural network on top of the trained autoencoder. This network is trained to produce realistic motion sequences from parameters such as a curve over the terrain that the character should follow, or a target location for punching and kicking. The feedforward control network and the motion manifold are trained independently, allowing the user to easily switch between feedforward networks according to the desired interface, without re-training the motion manifold. Once motion is generated it can be edited by performing optimization in the space of the motion manifold. This allows for imposing kinematic constraints, or transforming the style of the motion, while ensuring the edited motion remains natural. As a result, the system can produce smooth, high quality motion sequences without any manual pre-processing of the training data.
On the Importance of Eliminating Errors in Cryptographic Computations

We present a model for attacking various cryptographic schemes by taking advantage of random hardware faults. The model consists of a black-box containing some cryptographic secret. The box interacts with the outside world by following a cryptographic protocol. The model supposes that from time to time the box is affected by a random hardware fault causing it to output incorrect values. For example, the hardware fault flips an internal register bit at some point during the computation. We show that for many digital signature and identification schemes these incorrect outputs completely expose the secrets stored in the box. We present the following results: (1) The secret signing key used in an implementation of RSA based on the Chinese Remainder Theorem (CRT) is completely exposed from a single erroneous RSA signature, (2) for non-CRT implementations of RSA the secret key is exposed given a large number (e.g. 1000) of erroneous signatures, (3) the secret key used in Fiat—Shamir identification is exposed after a small number (e.g. 10) of faulty executions of the protocol, and (4) the secret key used in Schnorr's identification protocol is exposed after a much larger number (e.g. 10,000) of faulty executions. Our estimates for the number of necessary faults are based on standard security parameters such as a 1024-bit modulus, and a 2 -40 identification error probability. Our results demonstrate the importance of preventing errors in cryptographic computations. We conclude the paper with various methods for preventing these attacks.
Robust design of electrostatic separation processes

The aim of this paper is to analyze the robustness of the electrostatic separation process control. The objective was to reduce variation in the process outcome by finding operating conditions (high-voltage level, roll speed), under which uncontrollable variation in the noise factors (granule size, composition of the material to be separated) has minimal impact on the quantity (and the quality) of the recovered products. The experiments were carried out on a laboratory roll-type electrostatic separator, provided with a corona electrode and a tubular electrode, both connected to a dc high-voltage supply. The samples of processed material were prepared from genuine chopped electric wire wastes (granule size >1 mm and <5 mm) containing various proportions of copper and PVC. The design and noise factors were combined into one single experimental design, based on Taguchi's approach, and a regression model of the process was fitted. The impact of the noise factors could be estimated, as well as the interactions between the design and noise factors. The conditions of industry application of Taguchi's methodology are discussed, as well as the possibility of adapting it to other electrostatic processes.
A Novel Dual-Stator Axial-Flux Spoke-Type Permanent Magnet Vernier Machine for Direct-Drive Applications

This paper proposes a novel double-stator axial-flux spoke-type permanent magnet vernier machine, which has a high torque density feature as well as a high-power factor at low speed for direct-drive systems. The operation principle and basic design procedure of the proposed machine are presented and discussed. The 3-D finite element method (3-D-FEM) is utilized to analyze its magnetic field and transient output performance. Furthermore, the analytical method and a simplified 2-D-FEM are also developed for the machine basic design and performance evaluation, which can effectively reduce the modeling and simulation time of the 3-D-FEM and achieve an adequate accuracy.
Frontal fibrosing alopecia: clinical presentations and prognosis.

Frontal fibrosing alopecia is an uncommon condition characterized by progressive frontotemporal recession due to inflammatory destruction of hair follicles. Little is known about the natural history of this disease.



To determine the clinical features and natural history of frontal fibrosing alopecia.



We studied the cases notes of patients diagnosed with frontal fibrosing alopecia from 1993 to 2008 at the Royal Hallamshire Hospital, Sheffield.



There were 18 patients aged between 34 and 71 years. Three were premenopausal. All had frontotemporal recession with scarring. This was associated with partial or complete loss of eyebrows in 15 patients while four had hair loss at other sites. One had keratosis pilaris-like papules on the face, and one had follicular erythema on the cheeks. Three patients had oral lichen planus, of whom two also had cutaneous lichen planus affecting other sites of the body. Treatments given included intralesional triamcinolone acetonide, 0.1% tacrolimus ointment and oral hydroxychloroquine. Progression of frontotemporal recession was seen in some patients, but not all. In one patient the hair line receded by 30 mm over 72 months, whereas in another patient there was no positional change in the hair line after 15 years.



Frontal fibrosing alopecia is more common in postmenopausal women, but it can occur in younger women. It may be associated with mucocutaneous lichen planus. Recession of the hair line may progress inexorably over many years but this is not inevitable. It is not clear whether or not treatment alters the natural history of the disease - the disease stabilized with time in most of the patients with or without continuing treatment.
Improving Credit Card Fraud Detection using a Meta- Classification Strategy

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These &quot;false alarms&quot; delay the detection of fraudulent transactions and can cause unnecessary concerns for customers. In this study, over 1 million unique credit card transactions from 11 months of data from a large Canadian bank were analyzed. A meta-classifier model was applied to the transactions after being analyzed by the Bank&apos;s existing neural network based fraud detection algorithm. This meta-classifier model consists of 3 base classifiers constructed using the decision tree, naïve Bayesian, and k-nearest neighbour algorithms. The naïve Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from the research show that when a meta-classifier was deployed in series with the Bank&apos;s existing fraud detection algorithm improvements of up to 28% to their existing system can be achieved.
Taking a HIT: Designing around Rejection, Mistrust, Risk, and Workers' Experiences in Amazon Mechanical Turk

Online crowd labor markets often address issues of risk and mistrust between employers and employees from the employers' perspective, but less often from that of employees. Based on 437 comments posted by crowd workers (Turkers) on the Amazon Mechanical Turk (AMT) participation agreement, we identified work rejection as a major risk that Turkers experience. Unfair rejections can result from poorly-designed tasks, unclear instructions, technical errors, and malicious Requesters. Because the AMT policy and platform provide little recourse to Turkers, they adopt strategies to minimize risk: avoiding new and known bad Requesters, sharing information with other Turkers, and choosing low-risk tasks. Through a series of ideas inspired by these findings-including notifying Turkers and Requesters of a broken task, returning rejected work to Turkers for repair, and providing collective dispute resolution mechanisms-we argue that making reducing risk and building trust a first-class design goal can lead to solutions that improve outcomes around rejected work for all parties in online labor markets.
Low Voltage Buffered Bandgap Reference

We propose a new bandgap reference topology for supply voltages as low as one diode drop (~0.8V). In conventional low-voltage references, supply voltage is limited by the generated reference voltage. Also, the proposed topology generates the reference voltage at the output of the feedback amplifier. This eliminates the need for an additional output buffer, otherwise required in conventional topologies. With the bandgap core biased from the reference voltage, the new topology is also suitable for a low-voltage shunt reference. We fabricated a 1V, 0.35mV/degC reference occupying 0.013mm2 in a 90nm CMOS process
Wheelchair control using an EOG- and EMG-based gesture interface

In our previous study, we presented a nonverbal interface that used biopotential signals, such as electrooculargraphic (EOG) and electromyographic (EMG), captured by a simple brain-computer interface. In this paper, we apply the nonverbal interface to hands-free control of an electric wheelchair. Based on the biopotential signals, the interface recognizes the operator's gestures, such as closing the jaw, wrinkling the forehead, and looking towards left and right. By combining these gestures, the operator controls linear and turning motions, velocity, and the steering angle of the wheelchair. Experimental results for navigating the wheelchair in a hallway environment confirmed the feasibility of the proposed method.
Robot Path Planning Based on Artificial Potential Field Approach with Simulated Annealing

The artificial potential field (APF) approach provides a simple and effective motion planning method for practical purpose. However, artificial potential field approach has a major problem, which is that the robot is easy to be trapped at a local minimum before reaching its goal. The avoidance of local minimum has been an active research topic in path planning by potential field. In this paper, we introduce several methods to solve this problem, emphatically, introduce and evaluate the artificial potential field approach with simulated annealing (SA). As one of the powerful techniques for escaping local minimum, simulated annealing has been applied to local and global path planning