nils-herrmann/clean_scirepeval huggingface dataset:
Simple Test and Modeling of RFID Tag Backscatter

We consider here worst-case analysis of backscatter from passive radio frequency identification (RFID) tags. The basis is a figure of merit “B” to relate link power at reader ports to tag circuit parameters. A minimum bound for received monostatic backscatter can be determined by inspection from measured B. The bound is general for narrowband signals in any causal linear propagation. For an assembled tag, this minimum varies only with reader transmit power, tag antenna tuning, and chip power sensitivity of different commands. To validate this model, we propose a backscatter calibration device to enable measurements with estimated 0.5 dB uncertainty. We then demonstrate how the minimum bound can inform reader sensitivity specification to help ensure reliable inventory performance.
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases

The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection.
Do viewpoint-dependent mechanisms generalize across members of a class?

Evidence for viewpoint-specific image-based object representations have been collected almost entirely using exemplar-specific recognition tasks. Recent results, however, implicate image-based processes in more categorical tasks, for instance when objects contain qualitatively different 3D parts. Although such discriminations approximate class-level recognition. they do not establish whether image-based representations can support generalization across members of an object class. This issue is critical to any theory of recognition, in that one hallmark of human visual competence is the ability to recognize unfamiliar instances of a familiar class. The present study addresses this questions by testing whether viewpoint-specific representations for some members of a class facilitate the recognition of other members of that class. Experiment 1 demonstrates that familiarity with several members of a class of novel 3D objects generalizes in a viewpoint-dependent manner to cohort objects from the same class. Experiment 2 demonstrates that this generalization is based on the degree of familiarity and the degree of geometrical distinctiveness for particular viewpoints. Experiment 3 demonstrates that this generalization is restricted to visually-similar objects rather than all objects learned in a given context. These results support the hypothesis that image-based representations are viewpoint dependent, but that these representations generalize across members of perceptually-defined classes. More generally, these results provide evidence for a new approach to image-based recognition in which object classes are represented as cluster of visually-similar viewpoint-specific representations.
An Ultra-Low-Power 9T SRAM Cell Based on Threshold Voltage Techniques

This paper presents a new nine-transistor (9T) SRAM cell operating in the subthreshold region. In the proposed 9T SRAM cell, a suitable read operation is provided by suppressing the drain-induced barrier lowering effect and controlling the body–source voltage dynamically. Proper usage of low-threshold voltage (L-Vt) transistors in the proposed design helps to reduce the read access time and enhance the reliability in the subthreshold region. In the proposed cell, a common bit-line is used in the read and write operations. This design leads to a larger write margin without using extra circuits. The simulation results at 90nm CMOS technology demonstrate a qualified performance of the proposed SRAM cell in terms of power dissipation, power–delay product, writemargin, read access time and sensitivity to process, voltage and temperature variations as compared to the other most efficient low-voltage SRAM cells previously presented in the literature.
Torque estimation technique of robotic joint with harmonic drive transmission

A joint torque estimation technique utilizing the existing structural elasticity of robotic joints with harmonic drive transmission is proposed in this paper. Joint torque sensing is one of the key techniques for achieving high-performance robot control, especially for robots working in unstructured environments. The proposed joint torque estimation technique uses link-side position measurement along with a proposed harmonic derive model to realize stiff and sensitive torque estimation. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial torque sensor, and the results have attested the effectiveness of the proposed torque estimation technique.
Inferring Brain Networks through Graphical Models with Hidden Variables

Inferring the interactions between different brain areas is an important step towards understanding brain activity. Most often, signals can only be measured from some specific brain areas (e.g., cortex in the case of scalp electroencephalograms). However, those signals may be affected by brain areas from which no measurements are available (e.g., deeper areas such as hippocampus). In this paper, the latter are described as hidden variables in a graphical model; such model quantifies the statistical structure in the neural recordings, conditioned on hidden variables, which are inferred in an automated fashion from the data. As an illustration, electroencephalograms (EEG) of Alzheimer’s disease patients are considered. It is shown that the number of hidden variables in AD EEG is not significantly different from healthy EEG. However, there are fewer interactions between the brain areas, conditioned on those hidden variables. Explanations for these observations are suggested.
On the Significance of Process Comprehension for Conducting Targeted ICS Attacks

The exploitation of Industrial Control Systems (ICSs) has been described as both easy and impossible, where is the truth? PostStuxnet works have included a plethora of ICS focused cyber security research activities, with topics covering device maturity, network protocols, and overall cyber security culture. We often hear the notion of ICSs being highly vulnerable due to a lack of inbuilt security mechanisms, considered a low hanging fruit to a variety of low skilled threat actors. While there is substantial evidence to support such a notion, when considering targeted attacks on ICS, it is hard to believe an attacker with limited resources, such as a script kiddie or hacktivist, using publicly accessible tools and exploits alone, would have adequate knowledge and resources to achieve targeted operational process manipulation, while simultaneously evade detection. Through use of a testbed environment, this paper provides two practical examples based on a Man-In-The-Middle scenario, demonstrating the types of information an attacker would need obtain, collate, and comprehend, in order to begin targeted process manipulation and detection avoidance. This allows for a clearer view of associated challenges, and illustrate why targeted ICS exploitation might not be possible for every malicious actor.
Injuries in extreme sports

Extreme sports (ES) are usually pursued in remote locations with little or no access to medical care with the athlete competing against oneself or the forces of nature. They involve high speed, height, real or perceived danger, a high level of physical exertion, spectacular stunts, and heightened risk element or death.Popularity for such sports has increased exponentially over the past two decades with dedicated TV channels, Internet sites, high-rating competitions, and high-profile sponsors drawing more participants.Recent data suggest that the risk and severity of injury in some ES is unexpectedly high. Medical personnel treating the ES athlete need to be aware there are numerous differences which must be appreciated between the common traditional sports and this newly developing area. These relate to the temperament of the athletes themselves, the particular epidemiology of injury, the initial management following injury, treatment decisions, and rehabilitation.The management of the injured extreme sports athlete is a challenge to surgeons and sports physicians. Appropriate safety gear is essential for protection from severe or fatal injuries as the margins for error in these sports are small.The purpose of this review is to provide an epidemiologic overview of common injuries affecting the extreme athletes through a focus on a few of the most popular and exciting extreme sports.
Semiparametric Decolorization With Laplacian-Based Perceptual Quality Metric

While the RGB2GRAY conversion with fixed parameters is a classical and widely used tool for image decolorization, recent studies showed that adapting weighting parameters in a two-order multivariance polynomial model has great potential to improve the conversion ability. In this paper, by viewing the two-order model as the sum of three subspaces, it is observed that the first subspace in the two-order model has the dominating importance and the second and the third subspace can be seen as refinement. Therefore, we present a semiparametric strategy to take advantage of both the RGB2GRAY and the two-order models. In the proposed method, the RGB2GRAY result on the first subspace is treated as an immediate grayed image, and then the parameters in the second and the third subspace are optimized. Experimental results show that the proposed approach is comparable to other state-of-the-art algorithms in both quantitative evaluation and visual quality, especially for images with abundant colors and patterns. This algorithm also exhibits good resistance to noise. In addition, instead of the color contrast preserving ratio using the first-order gradient for decolorization quality metric, the color contrast correlation preserving ratio utilizing the second-order gradient is calculated as a new perceptual quality metric.
Location Privacy in Cognitive Radio Networks: A Survey

Cognitive radio networks (CRNs) have emerged as an essential technology to enable dynamic and opportunistic spectrum access which aims to exploit underutilized licensed channels to solve the spectrum scarcity problem. Despite the great benefits that CRNs offer in terms of their ability to improve spectrum utilization efficiency, they suffer from user location privacy issues. Knowing that their whereabouts may be exposed can discourage users from joining and participating in the CRNs, thereby potentially hindering the adoption and deployment of this technology in future generation networks. The location information leakage issue in the CRN context has recently started to gain attention from the research community due to its importance, and several research efforts have been made to tackle it. However, to the best of our knowledge, none of these works have tried to identify the vulnerabilities that are behind this issue or discuss the approaches that could be deployed to prevent it. In this paper, we try to fill this gap by providing a comprehensive survey that investigates the various location privacy risks and threats that may arise from the different components of this CRN technology, and explores the different privacy attacks and countermeasure solutions that have been proposed in the literature to cope with this location privacy issue. We also discuss some open research problems, related to this issue, that need to be overcome by the research community to take advantage of the benefits of this key CRN technology without having to sacrifice the users’ privacy.
Parental Socio-economic Status as a Determinant Factor on Academic Performance of Students in Regional Examination : Case of Dessie town ; Ethiopia Amogne

Parental socioeconomic status is among the widely cited factors that has strong association with academic performance of students. Explanatory research design was employed to assess the effects of parents’ socioeconomic status on the academic achievement of students in regional examination. To that end, regional examination result of 538 randomly selected students from thirteen junior secondary schools has been analysed using percentage, independent samples t-tests, Spearman’s rho correlation and one way ANOVA. The results of the analysis revealed that socioeconomic status of parents (particularly educational level and occupational status of parents) has strong association with the academic performance of students. Students from educated and better off families have scored higher result in their regional examination than their counterparts. Being a single parent student and whether parents are living together or not have also a significant impact on the academic performance of students. Parents’ age did not have a significant association with the performance of students.
Diversified radar micro-Doppler simulations as training data for deep residual neural networks

A key challenge in radar micro-Doppler classification is the difficulty in obtaining a large amount of training data due to costs in time and human resources. Small training datasets limit the depth of deep neural networks (DNNs), and, hence, attainable classification accuracy. In this work, a novel method for diversifying Kinect-based motion capture (MOCAP) simulations of human micro-Doppler to span a wider range of potential observations, e.g. speed, body size, and style, is proposed. By applying three transformations, a small set of MOCAP measurements is expanded to generate a large training dataset for network initialization of a 30-layer deep residual neural network. Results show that the proposed training methodology and residual DNN yield improved bottleneck feature performance and the highest overall classification accuracy among other DNN architectures, including transfer learning from the 1.5 million sample ImageNet database.
Bringing semantic structures to user intent detection in online medical queries

The Internet has revolutionized healthcare by offering medical information ubiquitously to patients via the web search. The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries. Aiming to better capture a focused picture of user's medical-related information search and shed insights on their healthcare information access strategies, it is challenging yet rewarding to detect structured user intentions from their diversely expressed medical text queries. We introduce a graph-based formulation to explore structured concept transitions for effective user intent detection in medical queries, where each node represents a medical concept mention and each directed edge indicates a medical concept transition. A deep model based on multi-task learning is introduced to extract structured semantic transitions from user queries, where the model extracts word-level medical concept mentions as well as sentence-level concept transitions collectively. A customized graph-based mutual transfer loss function is designed to impose explicit constraints and further exploit the contribution of mentioning a medical concept word to the implication of a semantic transition. We observe an 8% relative improvement in AUC and 23% relative reduction in coverage error by comparing the proposed model with the best baseline model for the concept transition inference task on real-world medical text queries.
Behçet’s disease physiopathology: a contemporary review

Behçet’s disease, also known as the Silk Road Disease, is a rare systemic vasculitis disorder of unknown etiology. Recurrent attacks of acute inflammation characterize Behçet’s disease. Frequent oral aphthous ulcers, genital ulcers, skin lesions and ocular lesions are the most common manifestations. Inflammation is typically self-limiting in time and relapsing episodes of clinical manifestations represent a hallmark of Behçet’s disease. Other less frequent yet severe manifestations that have a major prognostic impact involve the eyes, the central nervous system, the main large vessels and the gastrointestinal tract. Behçet’s disease has a heterogeneous onset and is associated with significant morbidity and premature mortality. This study presents a current immunological review of the disease and provides a synopsis of clinical aspects and treatment options.
Automatic Airway Segmentation in Chest CT Using Convolutional Neural Networks

Segmentation of the airway tree from chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity of airway structures. Recently, deep convolutional neural networks (CNNs) have become the state-of-the-art for many segmentation tasks, and in particular the so-called Unet architecture for biomedical images. However, its application to the segmentation of airways still remains a challenging task. This work presents a simple but robust approach based on a 3D Unet to perform segmentation of airways from chest CTs. The method is trained on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the influence of different loss functions and data augmentation techniques, and reach an average dice coefficient of 0.8 between the ground-truth and our automated segmentations.
RFID Estimation With Blocker Tags

With the increasing popularization of radio frequency identification RFID technology in the retail and logistics industry, RFID privacy concern has attracted much attention, because a tag responds to queries from readers no matter they are authorized or not. An effective solution is to use a commercially available blocker tag that behaves as if a set of tags with known blocking IDs are present. However, the use of blocker tags makes the classical RFID estimation problem much more challenging, as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose RFID estimation scheme with blocker tags REB, the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the EPC C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. REB conducts statistical inference from the two sets of responses and estimates the number of genuine tags. Rigorous theoretical analysis of parameter settings is proposed to guarantee the required estimation accuracy, meanwhile minimizing the time cost and energy cost of REB. We also reveal a fundamental tradeoff between the time cost and energy cost of REB, which can be flexibly adjusted by the users according to the practical requirements. Extensive experimental results reveal that REB significantly outperforms the state-of-the-art identification protocols in terms of both time efficiency and energy efficiency.
Vegetarianism . A blossoming field of study q

0195-6663/$ see front matter 2011  The author wishes to thank Ste Paul Rozin for their invaluable feedback on earlier dr E-mail address: matt@psych.ubc.ca Vegetarianism, the practice of abstaining from eating meat, has a recorded history dating back to ancient Greece. Despite this, it is only in recent years that researchers have begun conducting empirical investigations of the practices and beliefs associated with vegetarianism. The present article reviews the extant literature, exploring variants of and motivations for vegetarianism, differences in attitudes, values and worldviews between omnivores and vegetarians, as well as the pronounced gender differences in meat consumption and vegetarianism. Furthermore, the review highlights the extremely limited cultural scope of the present data, and calls for a broader investigation across non-Western cultures.
Growth through Heterogeneous Innovations ∗

We study how external versus internal innovations promote economic growth through a tractable endogenous growth framework with multiple innovation sizes, multi-product firms, and entry/exit. Firms invest in external R&D to acquire new product lines and in internal R&D to improve their existing product lines. A baseline model derives the theoretical implications of weaker scaling for external R&D versus internal R&D, and the resulting predictions align with observed empirical regularities for innovative firms. Quantifying a generalized model for the recent U.S. economy using matched Census Bureau and patent data, we observe a modest departure for external R&D from perfect scaling frameworks.
Key Lengths Contribution to The Handbook of Information Security

The key length used for a cryptographic protocol determines the highest security it can offer. If the key is found or ‘broken’, the security is undermined. Thus, key lengths must be chosen in accordance with the desired security. In practice, key lengths are mostly determined by standards, legacy system compatibility issues, and vendors. From a theoretical point of view selecting key lengths is more involved. Understanding the relation between security and key lengths and the impact of anticipated and unexpected cryptanalytic progress, requires insight into the design of the cryptographic methods and the mathematics involved in the attempts at breaking them. In this chapter practical and theoretical aspects of key size selection are discussed.
New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules

The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood–brain barrier (BBB) modeling. The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models. The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r 2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80–83% and leave-20%-out testing prediction accuracy between 80–82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs. Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood–brain barrier permeation in drug discovery.