nils-herrmann/clean_scirepeval huggingface dataset:
Flexible Near-Field Wireless Optoelectronics as Subdermal Implants for Broad Applications in Optogenetics

In vivo optogenetics provides unique, powerful capabilities in the dissection of neural circuits implicated in neuropsychiatric disorders. Conventional hardware for such studies, however, physically tethers the experimental animal to an external light source, limiting the range of possible experiments. Emerging wireless options offer important capabilities that avoid some of these limitations, but the current size, bulk, weight, and wireless area of coverage is often disadvantageous. Here, we present a simple but powerful setup based on wireless, near-field power transfer and miniaturized, thin, flexible optoelectronic implants, for complete optical control in a variety of behavioral paradigms. The devices combine subdermal magnetic coil antennas connected to microscale, injectable light-emitting diodes (LEDs), with the ability to operate at wavelengths ranging from UV to blue, green-yellow, and red. An external loop antenna allows robust, straightforward application in a multitude of behavioral apparatuses. The result is a readily mass-producible, user-friendly technology with broad potential for optogenetics applications.
Automatic Extraction and Classification of Footwear Patterns

Identification of the footwear traces from crime scenes is an important yet largely forgotten aspect of forensic intelligence and evidence. We present initial results from a developing automatic footwear classification system. The underlying methodology is based on large numbers of localized features located using MSER feature detectors. These features are transformed into robust SIFT or GLOH descriptors with the ranked correspondence between footwear patterns obtained through the use of constrained spectral correspondence methods. For a reference dataset of 368 different footwear patterns, we obtain a first rank performance of 85% for full impressions and 84% for partial impressions.
Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification

http://dx.doi.org/10.1016/j.knosys.2014.02.003 0950-7051/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author.  Zhejiang University, Hangzhou 310027, China.  E-mail addresses: xlzheng@zju.edu.cn (X. Zheng), nblin@zju.edu.cn (Z. Lin), alexwang@zju.edu.cn (X. Wang), klin@ece.uci.edu (K.-J. Lin), mnsong@bupt.edu.cn (M. Song). 1 http://www.yelp.com/. Xiaolin Zheng a,b,⇑, Zhen Lin , Xiaowei Wang , Kwei-Jay Lin , Meina Song e
Web-Service Clustering with a Hybrid of Ontology Learning and Information-Retrieval-Based Term Similarity

Organizing Web services into functionally similar clusters, is an efficient approach to discovering Web services efficiently. An important aspect of the clustering process is calculating the semantic similarity of Web services. Most current clustering approaches are based on similarity-distance measurement, including keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to grouping Web services into functionally similar clusters by mining Web service documents and generating an ontology via hidden semantic patterns present within the complex terms used in service features to measure similarity. If calculating the similarity using the generated ontology fails, the similarity is calculated by using an information-retrieval-based term-similarity method that adopts term-similarity measuring techniques used by thesaurus and search engines. Another important aspect of high performance in clustering is identifying the most suitable cluster center. To improve the utility of clusters, we propose an approach to identifying the cluster center that combines service similarity with the term frequency-inverse document frequency values of service names. Experimental results show that our clustering approach performs better than existing approaches.
Can Good Principals Keep Teachers in Disadvantaged Schools ? Linking Principal Effectiveness to Teacher Satisfaction and Turnover in Hard-to-Staff Environments

High rates of teacher turnover likely mean greater school instability, disruption of curricular cohesiveness, and a continual need to hire inexperienced teachers, who typically are less effective, as replacements for teachers who leave. Unfortunately, research consistently finds that teachers who work in schools with large numbers of poor students and students of color feel less satisfied and are more likely to turn over, meaning that turnover is concentrated in the very schools that would benefit most from a stable staff of experienced teachers. Despite the potential challenge that this turnover disparity poses for equity of educational opportunity and student performance gaps across schools, little research has examined the reasons for elevated teacher turnover in schools with large numbers of traditionally disadvantaged students.  This study hypothesizes that school working conditions help explain both teacher satisfaction and turnover. In particular, it focuses on the role effective principals in retaining teachers, particularly in disadvantaged schools with the greatest staffing challenges.  The study conducts quantitative analysis of national data from the 2003-04 Schools and Staffing Survey and 2004-05 Teacher Follow-up Survey. Regression analyses combat the potential for bias from omitted variables by utilizing an extensive set of control variables and employing a school district fixed effects approach that implicitly makes comparisons among principals and teachers within the same local context.  Descriptive analyses confirm that observable measures of teachers‘ work environments, including ratings of the effectiveness of the principal, generally are lower in schools with large numbers of disadvantaged students. Regression results show that principal effectiveness is associated with greater teacher satisfaction and a lower probability that the teacher leaves the school within a year. Moreover, the positive impacts of principal effectiveness on these teacher outcomes are even greater in disadvantaged schools. These findings suggest that policies focused on getting the best principals into the most challenging school environments may be effective strategies for lowering perpetually high teacher turnover rates in those schools.
Optimal control of a reverse osmosis desalination plant using multi-objective optimization

In this contribution, the control of a reverse osmosis desalination plant by using an optimal multi-loop approach is presented. Controllers are assumed to be players of a cooperative game, whose solution is obtained by multi-objective optimization (MOO). The MOO problem is solved by applying a genetic algorithm and the final solution is found from this Pareto set. For the reverse osmosis plant a control scheme consisting of two PI control loops are proposed. Simulation results show that in some cases, as for example this desalination plant, multi-loop control with several controllers, which have been obtained by join multi-objective optimization, perform as good as more complex controllers but with less implementation effort.
Prediction of Stock Price Movements Based on Concept Map Information

Visualization of textual data may reveal interesting properties regarding the information conveyed in a group of documents. In this paper, we study whether the structure revealed by a visualization method can be used as inputs for improved classifiers. In particular, we study whether the locations of news items on a concept map could be used as inputs for improving the prediction of stock price movements from the news. We propose a method based on information visualization and text classification for achieving this. We apply the proposed approach to the prediction of the stock price movements of companies within the oil and natural gas sector. In a case study, we show that our proposed approach performs better than a naive approach and a bag-of-words approach
Phighting the Phisher: Using Web Bugs and Honeytokens to Investigate the Source of Phishing Attacks

This paper presents a summary of research findings for a new reacitve phishing investigative technique using Web bugs and honeytokens. Phishing has become a rampant problem in today 's society and has cost financial institutions millions of dollars per year. Today's reactive techniques against phishing usually involve methods that simply minimize the damage rather than attempting to actually track down a phisher. Our research objective is to track down a phisher to the IP address of the phisher's workstation rather than innocent machines used as intermediaries. By using Web bugs and honeytokens on the fake Web site forms the phisher presents, one can log accesses to the honeytokens by the phisher when the attacker views the results of the forms. Research results to date are presented in this paper
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.10.027 ⇑ Corresponding author.  fax E-mail address: melekacar@yahoo.com (M. Acar B Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).
Analysis of handover performance improvement in cloud-RAN architecture

With the surge of mobile internet traffic, Cloud RAN (C-RAN) becomes an innovative architecture to help mobile operators maintain profitability and financial growth as well as to provide better services to the customers. It consists of Base Band Units (BBU) of several base stations, which are co-located in a secured place called Central Office and connected to Radio Remote Heads (RRH) via high bandwidth, low latency links. With BBU centralization in C-RAN, handover, the most important feature for mobile communications, could achieve simplified procedure or improved performance. In this paper, we analyze the handover performance of C-RAN over a baseline decentralized RAN (D-RAN) for GSM, UMTS and LTE systems. The results indicate that, lower total average handover interrupt time could be achieved in GSM thanks to the synchronous nature of handovers in C-RAN. For UMTS, inter-NodeB soft handover in D-RAN would become intra-pool softer handover in C-RAN. This brings some gains in terms of reduced signalling, less Iub transport bearer setup and reduced transport bandwidth requirement. For LTE X2-based inter-eNB handover, C-RAN could reduce the handover delay and to a large extent eliminate the risk of UE losing its connection with the serving cell while still waiting for the handover command, which in turn decrease the handover failure rate.
Prediction of cyclosporine A blood levels: an application of the adaptive-network-based fuzzy inference system (ANFIS) in assisting drug therapy

Therapeutic drug monitoring (TDM) is a procedure in which the levels of drugs are assayed in various body fluids with the aim of individualizing the dose of critical drugs, such as cyclosporine A. Cyclosporine A assays are performed in blood. We proposed the use of the Takagi and Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the concentration of cyclosporine A in blood samples taken from renal transplantation patients. We implemented the ANFIS model using TDM data collected from 138 patients and 20 input parameters. Input parameters for the model consisted of concurrent use of drugs, blood levels, sampling time, age, gender, and dosing intervals. Fuzzy modeling produced eight rules. The developed ANFIS model exhibited a root mean square error (RMSE) of 0.045 with respect to the training data and an error of 0.057 with respect to the checking data in the MATLAB environment. ANFIS can effectively assist physicians in choosing best therapeutic drug dose in the clinical setting.
Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf

Athletes in any sports can greatly benefit from feedback systems for improving the quality of their training. In this paper, we present a golf swing training system which incorporates wearable motion sensors to obtain inertial information and provide feedback on the quality of movements. The sensors are placed on a golf club and athlete’s body at positions which capture the unique movements of a golf swing. We introduce a quantitative model which takes into consideration signal processing techniques on the collected data and quantifies the correctness of the performed actions. We evaluate the effectiveness of our framework on data obtained from four subjects and discuss ongoing research.
DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.
An Easy to Deploy Street Light Control System Based on Wireless Communication and LED Technology

This paper presents an intelligent streetlight management system based on LED lamps, designed to facilitate its deployment in existing facilities. The proposed approach, which is based on wireless communication technologies, will minimize the cost of investment of traditional wired systems, which always need civil engineering for burying of cable underground and consequently are more expensive than if the connection of the different nodes is made over the air. The deployed solution will be aware of their surrounding's environmental conditions, a fact that will be approached for the system intelligence in order to learn, and later, apply dynamic rules. The knowledge of real time illumination needs, in terms of instant use of the street in which it is installed, will also feed our system, with the objective of providing tangible solutions to reduce energy consumption according to the contextual needs, an exact calculation of energy consumption and reliable mechanisms for preventive maintenance of facilities.
Technical Brief: Agreement, the F-Measure, and Reliability in Information Retrieval

Information retrieval studies that involve searching the Internet or marking phrases usually lack a well-defined number of negative cases. This prevents the use of traditional interrater reliability metrics like the kappa statistic to assess the quality of expert-generated gold standards. Such studies often quantify system performance as precision, recall, and F-measure, or as agreement. It can be shown that the average F-measure among pairs of experts is numerically identical to the average positive specific agreement among experts and that kappa approaches these measures as the number of negative cases grows large. Positive specific agreement-or the equivalent F-measure-may be an appropriate way to quantify interrater reliability and therefore to assess the reliability of a gold standard in these studies.
Text extraction from natural scene image: A survey

With the increasing popularity of portable camera devices and embedded visual processing, text extraction from natural scene images has become a key problem that is deemed to change our everyday lives via novel applications such as augmented reality. Text extraction from natural scene images algorithms is generally composed of the following three stages: (i) detection and localization, (ii) text enhancement to variations in the font size and color, text alignment, illumination change and reflections. This paper aims to classify and assess the latest algorithms. More specifically, we draw attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist. This paper offers to the researchers a link to public image database for the algorithm assessment of text extraction from natural scene images. & 2013 Elsevier B.V. All rights reserved.
CARLOC: Precise Positioning of Automobiles

Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lane-level positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location of estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.
Differentially Fed Dual-Band Implantable Antenna for Biomedical Applications

A novel differentially fed dual-band implantable antenna is proposed for the first time for a fully implantable neuro-microsystem. The antenna operates at two center frequencies of 433.9 MHz and 542.4 MHz, which are close to the 402-405 MHz medical implant communication services (MICS) band, to support sub-GHz wideband communication for high-data rate implantable neural recording application. The size of the antenna is 480.06 mm3 (27 mm × 14 mm × 1.27 mm). The simulated and measured bandwidths are 7.3% and 7.9% at the first resonant frequency, 5.4% and 6.4% at the second resonant frequency. The specific absorption rate (SAR) distribution induced by the implantable antenna inside a tissue-mimicking solution is evaluated. The performance of the communication link between the implanted antenna and external half-wavelength dual-band dipole is also examined.
Automated Brain Tumor Detection and Identification Using Image Processing and Probabilistic Neural Network Techniques

In this paper, modified image segmentation techniques were applied on MRI scan images in order to detect brain tumors. Also in this paper, a modified Probabilistic Neural Network (PNN) model that is based on learning vector quantization (LVQ) with image and data analysis and manipulation techniques is proposed to carry out an automated brain tumor classification using MRI-scans. The assessment of the modified PNN classifier performance is measured in terms of the training performance, classification accuracies and computational time. The simulation results showed that the modified PNN gives rapid and accurate classification compared with the image processing and published conventional PNN techniques. Simulation results also showed that the proposed system out performs the corresponding PNN system presented in , and successfully handle the process of brain tumor classification in MRI image with 100% accuracy when the spread value is equal to 1. These results also claim that the proposed LVQ-based PNN system decreases the processing time to approximately 79% compared with the conventional PNN which makes it very promising in the field of in-vivo brain tumor detection and identification.  Probabilistic Neural Network, Edge detection, image segmentation, brain tumor detection and identification
FACE spoofing detection using LDP-TOP

In this paper, we propose a novel approach for face spoofing detection using the high-order Local Derivative Pattern from Three Orthogonal Planes (LDP-TOP). The proposed method is not only simple to derive and implement, but also highly efficient, since it takes into account both spatial and temporal information in different directions of subtle face movements. According to experimental results, the proposed approach outperforms state-of-the-art methods on three reference datasets, namely Idiap REPLAY-ATTACK, CASIA-FASD, and MSU MFSD. Moreover, it requires only 25 video frames from each video, i.e., only one second, and thus potentially can be performed in real time even on low-cost devices.
Back-EMF Harmonic Analysis and Fault-Tolerant Control of Flux-Switching Permanent-Magnet Machine With Redundancy

A flux-switching permanent-magnet (PM) (FSPM) machine is a new class of stator-PM brushless machines, which offers the advantages of high reliability, high power density, and high efficiency. In this paper, by employing redundant winding in the existing FSPM machine topology, a new redundant FSPM (R-FSPM) machine is proposed for a fault-tolerant operation of critical applications. The redundant winding configuration is analyzed, and the corresponding suitable control strategy for the postfault operation is proposed. The electromagnetic performance of the R-FSPM motor drive under normal and fault conditions is analyzed by using a cosimulation method coupling magnetic and electrical circuit solvers. Finally, a 12-stator-tooth/10-rotor-pole R-FSPM machine is prototyped for verification. Both simulation and experimental results are presented, verifying the fault-tolerant characteristic of the proposed motor drive.