nils-herrmann/clean_scirepeval huggingface dataset:
Silicon MEMS Disk Resonator Gyroscope With an Integrated CMOS Analog Front-End

We present a 2-mm diameter, 35-μm-thick disk resonator gyro (DRG) fabricated in <;111> silicon with integrated 0.35-μm CMOS analog front-end circuits. The device is fabricated in the commercial InvenSense Fabrication MEMSCMOS integrated platform, which incorporates a wafer-level vacuum seal, yielding a quality factor (Q) of 2800 at the DRGs 78-kHz resonant frequency. After performing electrostatic tuning to enable mode-matched operation, this DRG achieves a 55 μV/°/s sensitivity. Resonator vibration in the sense and drive axes is sensed using capacitive transduction, and amplified using a lownoise, on-chip integrated circuit. This allows the DRG to achieve Brownian noise-limited performance. The angle random walk is measured to be 0.008°/s/√(Hz) and the bias instability is 20°/h.
A Compensation Technique for Smooth Transitions in Non-inverting Buck-Boost Converter

With the advent of battery-powered portable devices and the mandatory adoptions of power factor correction (PFC), non-inverting buck-boost converter is attracting numerous attentions. Conventional two-switch or four-switch non-inverting buck-boost converters choose their operation modes by measuring input and output voltage magnitudes. This can cause higher output voltage transients when input and output are close to each other. For the mode selection, the comparison of input and output voltage magnitudes is not enough due to the voltage drops raised by the parasitic components. In addition, the difference in the minimum and maximum effective duty cycle between controller output and switching device yields the discontinuity at the instant of mode change. Moreover, the different properties of output voltage versus a given duty cycle of buck and boost operating modes contribute to the output voltage transients. In this paper, the effect of the discontinuity due to the effective duty cycle derived from device switching time at the mode change is analyzed. A technique to compensate the output voltage transient due to this discontinuity is proposed. In order to attain additional mitigation of output transients and linear input/output voltage characteristic in buck and boost modes, the linearization of DC-gain of large signal model in boost operation is analyzed as well. Analytical, simulation, and experimental results are presented to validate the proposed theory.
XHOUND: Quantifying the Fingerprintability of Browser Extensions

In recent years, researchers have shown that unwanted web tracking is on the rise, as advertisers are trying to capitalize on users' online activity, using increasingly intrusive and sophisticated techniques. Among these, browser fingerprinting has received the most attention since it allows trackers to uniquely identify users despite the clearing of cookies and the use of a browser's private mode. In this paper, we investigate and quantify the fingerprintability of browser extensions, such as, AdBlock and Ghostery. We show that an extension's organic activity in a page's DOM can be used to infer its presence, and develop XHound, the first fully automated system for fingerprinting browser extensions. By applying XHound to the 10,000 most popular Google Chrome extensions, we find that a significant fraction of popular browser extensions are fingerprintable and could thus be used to supplement existing fingerprinting methods. Moreover, by surveying the installed extensions of 854 users, we discover that many users tend to install different sets of fingerprintable browser extensions and could thus be uniquely, or near-uniquely identifiable by extension-based fingerprinting. We use XHound's results to build a proof-of-concept extension-fingerprinting script and show that trackers can fingerprint tens of extensions in just a few seconds. Finally, we describe why the fingerprinting of extensions is more intrusive than the fingerprinting of other browser and system properties, and sketch two different approaches towards defending against extension-based fingerprinting.
Recognition of weeds with image processing and their use with fuzzy logic for precision farming

Yang, C.-C., Prasher, S.O., Landry, J.-A., Perret, J. and Ramaswamy, H.S. 2000. Recognition of weeds with image processing and their use with fuzzy logic for precision farming. Can. Agric.  Herbicide use can be reduced if the spatial distribution of weeds in the field is taken into account. This paper reports the initial stages of development of an image capture/processing system to detect weeds, as well as a fuzzy logic decision-making system to determine where and how much herbicide to apply in an agricultural field. The system used a commercially available digital camera and a personal computer. In the image processing stage, green objects in each image were identified using a greenness method that compared the red, green, and blue (RGB) intensities. The RGB matrix was reduced to a binary form by applying the following criterion: if the green intensity of a pixel was greater than the red and the blue intensities, then the pixel was assigned a value of one; otherwise the pixel was given a value of zero. The resulting binary matrix was used to compute greenness area for weed coverage, and greenness distribution of weeds (weed patch). The values of weed coverage and weed patch were inputs to the fuzzy logic decision-making system, which used the membership functions to control the herbicide application rate at each location. Simulations showed that a graduated fuzzy strategy could potentially reduce herbicide application by 5 to 24%, and that an on/off strategy resulted in an even greater reduction of 15 to 64%.
Active Learning for Dialogue Act Classification

Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English humanhuman Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result.
Sparse PCA: Extracting Multi-scale Structure from Data

Sparse Principal Component Analysis (S-PCA) is a novel framework for learning a linear, orthonormal basis representation for structure intrinsic to an ensemble of images. S-PCA is based on the discovery that natural images exhibit structure in a low-dimensional subspace in a sparse, scale-dependent form. The S-PCA basis optimizes an objective function which trades off correlations among output coefficients for sparsity in the description of basis vector elements. This objective function is minimized by a simple, robust and highly scalable adaptation algorithm, consisting of successive planar rotations of pairs of basis vectors. The formulation of S-PCA is novel in that multi-scale representations emerge for a variety of ensembles including face images, images from outdoor scenes and a database of optical flow vectors representing a motion class.
A sensitive approach to grasping

Experimental results in psychology have shown the important role of manipulation in guiding infant development. This has inspired work in developmental robotics as well. In this case, however, the benefits of this approach have been limited by the intrinsic difficulties of the task. Controlling the interaction between the robot and the environment in a meaningful and safe way is hard especially when little prior knowledge is available. We push the idea that haptic feedback can enhance the way robots interact with unmodeled environments. We approach grasping and manipulation as tasks driven mainly by tactile and force feedback. We implemented a grasping behavior on a robotic platform with sensitive tactile sensors and compliant actuators; the behavior allows the robot to grasp objects placed on a table. Finally, we demonstrate that the haptic feedback originated by the interaction with the objects carries implicit information about their shape and can be useful for learning.
Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification

The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain-computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper.
A simulation study of arti " cial neural networks for nonlinear time-series forecasting

This study presents an experimental evaluation of neural networks for nonlinear time-series forecasting. The e!ects of three main factors * input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while traditional linear methods are not as competent for this task. The number of input nodes is much more important than the number of hidden nodes in neural network model building for forecasting. Moreover, large sample is helpful to ease the over"tting problem.
Test Stimulus Compression Based on Broadcast Scan With One Single Input

In this paper, a novel test compression technique is proposed that can achieve very high test compression ratio with low area overhead and only one single test input. An inverter and a series of D flip-flops together with a configurable switch logic are inserted between the single input and the scan chains so as to convert the input patterns to the test data required by each scan chain. All scan chains are divided into some scan groups such that scan chains in the same group can share the same test data and the switch logic only needs to connect each group to an appropriate data provider. Hence the total area overhead is quite small. A novel algorithm is developed to determine the required test configurations and corresponding test patterns for 100% testable fault coverage. Experimental results show that on average this method can achieve data reduction factors of 23×, 124×, and 394× with 3.77%, 0.95%, and 0.03% area overhead for ISCAS'89, IWLS'05 OpenCores, and IWLS'05 Gaisler Research benchmark circuits, respectively. These results indicate that the reduction factor increases with the sizes of circuits; it even reaches 464× for a circuit containing 2.07 million gates with very small area overhead. As all test and control data can be provided by a single input, great reduction on test channel requirement is also achieved.
A Hospital Healthcare Monitoring System Using Wireless Sensor Networks

Body sensor network systems can help people by providing healthcare services such as medical monitoring, memory enhancement, medical data access, and communication with the healthcare provider in emergency situations through the SMS or GPRS . Continuous health monitoring with wearable  or clothing-embedded transducers  and implantable body sensor networks [5] will increase detection of emergency conditions in at risk patients. Not only the patient, but also their families will benefit from these. Also, these systems provide useful methods to remotely acquire and monitor the physiological signals without the need of interruption of the patient’s normal life, thus improving life quality [6,7].
Consumer perception of interface quality, security, and loyalty in electronic commerce

Customer interface quality, perceived security, and customer loyalty are critical factors for success of an e-commerce website; however, the relationships among them are not fully understood. We proposed a model for testing the relationships among them and the important outcomes of the site: switching costs and customer loyalty. Data was collected to test the model using a web-based survey, and empirical analyses were performed using SEM. The analytical results demonstrated that customer interface quality and perceived security positively affected customer satisfaction and switching costs, and thus customer loyalty to an e-commerce website. Specifically, our study showed that switching costs positively moderated the effect of customer satisfaction on customer loyalty; this moderating effect is discussed.  Corresponding author. Tel.: +886 6 2757575x53326; fax: +886 6 2757575x53326. E-mail addresses: easyhhc@mail.ncku.edu.tw (H.H. Chang), swchen@npic.edu.tw (S.W. Chen).
From user requirements to UML class diagram

The transition from user requirements to UML diagrams is a difficult task for the designer espec ially when he handles large texts expressing these needs. Modelin g class Diagram must be performed frequently, even during t he development of a simple application. This paper prop oses an approach to facilitate class diagram extraction from textual requirements using NLP techniques and domain ontolog y. Keywords-component; Class Diagram, Natural Language Processing, GATE, Domain ontology, requirements.
Indirect Back-EMF Zero Crossing Detection for Sensorless BLDC Motor Operation

Brushless DC (BLDC) Motors are widely used in industrial and consumer appliances due to their high efficiency, high power density, low maintenance and silent operation. However the need for position sensors makes the drive less reliable and expensive. Sensorless control is therefore gaining importance now-a-days. This paper proposes an indirect way of detecting the zero crossing instant of the back-EMF from the three terminal voltages without using the neutral potential. Inaccuracies due to device drops have been eliminated. This scheme is simple to implement. Simulation results show the validity of this scheme.
SenaaS: An event-driven sensor virtualization approach for Internet of Things cloud

In this paper, we propose an Internet of Things (IoT) virtualization framework to support connected objects sensor event processing and reasoning by providing a semantic overlay of underlying IoT cloud. The framework uses the sensor-as-aservice notion to expose IoT cloud's connected objects functional aspects in the form of web services. The framework uses an adapter oriented approach to address the issue of connectivity with various types of sensor nodes. We employ semantic enhanced access polices to ensure that only authorized parties can access the IoT framework services, which result in enhancing overall security of the proposed framework. Furthermore, the use of event-driven service oriented architecture (e-SOA) paradigm assists the framework to leverage the monitoring process by dynamically sensing and responding to different connected objects sensor events. We present our design principles, implementations, and demonstrate the development of IoT application with reasoning capability by using a green school motorcycle (GSMC) case study. Our exploration shows that amalgamation of e-SOA, semantic web technologies and virtualization paves the way to address the connectivity, security and monitoring issues of IoT domain.
Measuring Online Learning Systems Success: Applying the Updated DeLone and McLean Model

Based on a survey of 232 undergraduate students, this study used the updated DeLone and McLean information systems success model to examine the determinants for successful use of online learning systems (OLS). The results provided an expanded understanding of the factors that measure OLS success. The results also showed that system quality, information quality, and service quality had a significant effect on actual OLS use through user satisfaction and behavioral intention to use OLS.
Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks

Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior ability of preserving sequential information over time. Traditionally, the NLU module tags semantic slots for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, natural language exhibits linguistic properties that provide rich, structured information for better understanding. This paper introduces a novel model, knowledge-guided structural attention networks (K-SAN), a generalization of RNN to additionally incorporate non-flat network topologies guided by prior knowledge. There are two characteristics: 1) important substructures can be captured from small training data, allowing the model to generalize to previously unseen test data; 2) the model automatically figures out the salient substructures that are essential to predict the semantic tags of the given sentences, so that the understanding performance can be improved. The experiments on the benchmark Air Travel Information System (ATIS) data show that the proposed K-SAN architecture can effectively extract salient knowledge from substructures with an attention mechanism, and outperform the performance of the state-of-the-art neural network based frameworks.
"Got You!": Automatic Vandalism Detection in Wikipedia with Web-based Shallow Syntactic-Semantic Modeling

Discriminating vandalism edits from non-vandalism edits in Wikipedia is a challenging task, as ill-intentioned edits can include a variety of content and be expressed in many different forms and styles. Previous studies are limited to rule-based methods and learning based on lexical features, lacking in linguistic analysis. In this paper, we propose a novel Web-based shallow syntacticsemantic modeling method, which utilizes Web search results as resource and trains topic-specific n-tag and syntactic n-gram language models to detect vandalism. By combining basic task-specific and lexical features, we have achieved high F-measures using logistic boosting and logistic model trees classifiers, surpassing the results reported by major Wikipedia vandalism detection systems.
Smart Elderly Home Monitoring System with an Android Phone

Falls, heart attack and stroke are among the leading causes of hospitalization for the elderly and illness individual. The chances of surviving a fall, heart attack or stroke are much greater if the senior gets help within an hour. In this project, a smart elderly home monitoring system (SEHMS) is designed and developed. An Android-based smart phone with 3-axial accelerometer is used as the telehealth device which could detect a fall of the carrier. The smart phone is then connected to the monitoring system by using the TCP/IP networking method via Wi-Fi. A graphical user interface (GUI) is developed as the monitoring system which exhibits the information gathered from the system. In addition, the concept of a remote panic button has been tested and implemented in this project by using the same android based smart phone. With the developed system, elderly and chronically ill patients could stay independently in their own home with care facilities and secure in the knowledge that they are being monitored.
The acquisition of gender labels in infancy: implications for gender-typed play.

Two aspects of children's early gender development-the spontaneous production of gender labels and gender-typed play-were examined longitudinally in a sample of 82 children. Survival analysis, a statistical technique well suited to questions involving developmental transitions, was used to investigate the timing of the onset of children's gender labeling as based on mothers' biweekly telephone interviews regarding their children's language from 9 through 21 months. Videotapes of children's play both alone and with mother during home visits at 17 and 21 months were independently analyzed for play with gender-stereotyped and gender-neutral toys. Finally, the relation between gender labeling and gender-typed play was examined. Children transitioned to using gender labels at approximately 19 months, on average. Although girls and boys showed similar patterns in the development of gender labeling, girls began labeling significantly earlier than boys. Modest sex differences in play were present at 17 months and increased at 21 months. Gender labeling predicted increases in gender-typed play, suggesting that knowledge of gender categories might influence gender typing before the age of 2.
Cryptographic schemes based on elliptic curve pairings

Cryptographic Schemes based on Elliptic Curve Pairings: Contributions to Public Key Cryptography and Key Agreement Protocols This thesis introduces the concept of certificateless public key cryptography (CLPKC). Elliptic curve pairings are then used to make concrete CL-PKC schemes and are also used to make other efficient key agreement protocols. CL-PKC can be viewed as a model for the use of public key cryptography that is intermediate between traditional certificated PKC and ID-PKC. This is because, in contrast to traditional public key cryptographic systems, CL-PKC does not require the use of certificates to guarantee the authenticity of public keys. It does rely on the use of a trusted authority (TA) who is in possession of a master key. In this respect, CL-PKC is similar to identity-based public key cryptography (ID-PKC). On the other hand, CL-PKC does not suffer from the key escrow property that is inherent in ID-PKC. Applications for the new infrastructure are discussed. We exemplify how CL-PKC schemes can be constructed by constructing several certificateless public key encryption schemes and modifying other existing ID based schemes. The lack of certificates and the desire to prove the schemes secure in the presence of an adversary who has access to the master key or has the ability to replace public keys, requires the careful development of new security models. We prove that some of our schemes are secure, provided that the Bilinear Diffie-Hellman Problem is hard. We then examine Joux’s protocol , which is a one round, tripartite key agreement protocol that is more bandwidth-efficient than any previous three-party key agreement protocol, however, Joux’s protocol is insecure, suffering from a simple man-in-the-middle attack. We shows how to make Joux’s protocol secure, presenting several tripartite, authenticated key agreement protocols that still require only one round of communication. The security properties of the new protocols are studied. Applications for the protocols are also discussed.