nils-herrmann/clean_scirepeval huggingface dataset:
The NASD Securities Observation , News Analysis & Regulation System ( SONAR )

The Securities Observation, News Analysis, and Regulation (SONAR) system was developed by NASD to monitor the Nasdaq, Over the Counter (OTC), and Nasdaq-Liffe (futures) stock markets for potential insider trading and fraud through misrepresentation. SONAR has been in operational use at NASD since December 2001, processing approximately 10,000 news wires stories and SEC filings, evaluating price/volume models for 25,000 securities, and generating 50-60 alerts (or “breaks”) per day for review by several groups of regulatory analysts and investigators. In addition, SONAR has greatly expanded surveillance coverage to new areas of the market and increased accuracy significantly over an earlier break detection system. SONAR makes use of several AI and statistical techniques, including NLP text mining, statistical regression, rule-based inference, uncertainty, and fuzzy matching. Sonar combines these enabling technologies in a system designed to deliver a steady stream of high-quality breaks to the analysts for further investigation. Additional components including visualization, text search agents, and flexible displays add to the system’s utility. Finally, SONAR is designed as a knowledge-based system. Domain knowledge is maintained by knowledge engineers working closely with the regulatory analysts. In this way, SONAR is adaptable to new market conditions, data sources, and regulatory concerns.
FUZZY SETS AND SYSTEMS *

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the "class of bald men," or the "class of numbers which are much greater than 10," or the "class of adaptive systems," etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between °and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to problems of this type is briefly sketched.
Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module

Electrochemical analysis of sweat using soft bioelectronics on human skin provides a new route for noninvasive glucose monitoring without painful blood collection. However, sweat-based glucose sensing still faces many challenges, such as difficulty in sweat collection, activity variation of glucose oxidase due to lactic acid secretion and ambient temperature changes, and delamination of the enzyme when exposed to mechanical friction and skin deformation. Precise point-of-care therapy in response to the measured glucose levels is still very challenging. We present a wearable/disposable sweat-based glucose monitoring device integrated with a feedback transdermal drug delivery module. Careful multilayer patch design and miniaturization of sensors increase the efficiency of the sweat collection and sensing process. Multimodal glucose sensing, as well as its real-time correction based on pH, temperature, and humidity measurements, maximizes the accuracy of the sensing. The minimal layout design of the same sensors also enables a strip-type disposable device. Drugs for the feedback transdermal therapy are loaded on two different temperature-responsive phase change nanoparticles. These nanoparticles are embedded in hyaluronic acid hydrogel microneedles, which are additionally coated with phase change materials. This enables multistage, spatially patterned, and precisely controlled drug release in response to the patient’s glucose level. The system provides a novel closed-loop solution for the noninvasive sweat-based management of diabetes mellitus.
Angular Quantization-based Binary Codes for Fast Similarity Search

This paper focuses on the problem of learning binary codes for efficient retrieval of high-dimensional non-negative data that arises in vision and text applications where counts or frequencies are used as features. The similarity of such feature vectors is commonly measured using the cosine of the angle between them. In this work, we introduce a novel angular quantization-based binary coding (AQBC) technique for such data and analyze its properties. In its most basic form, AQBC works by mapping each non-negative feature vector onto the vertex of the binary hypercube with which it has the smallest angle. Even though the number of vertices (quantization landmarks) in this scheme grows exponentially with data dimensionality d, we propose a method for mapping feature vectors to their smallest-angle binary vertices that scales as O(d log d). Further, we propose a method for learning a linear transformation of the data to minimize the quantization error, and show that it results in improved binary codes. Experiments on image and text datasets show that the proposed AQBC method outperforms the state of the art.
Behavior of MVC (Model View Controller) based Web Application developed in PHP and .NET framework

As the people are shifting from offline to online in every perspective of life, the dependency on Web Applications is becoming rampant. Web Applications serve the people by easing their tasks. There are ample web applications distributed over the internet. But what matters a lot is the performance shown by them. Performance means the request response time, page load time, etc. In this paper we have discussed the behavior of MVC architecture based on PHP and .NET framework. The comparison helps us to distinguish among them that which technology performs better. We have used one Web Application testing tool. This tool helps us to evaluate the web application based on various metrics.
Synthesis Design of a Wideband Bandpass Filter With Inductively Coupled Short-Circuited Multi-Mode Resonator

In this letter, a class of wideband bandpass filters (BPFs) with short-circuited multi-mode resonators (MMRs) is proposed and designed by a synthesis approach. The MMRs are formed by n sections of cascaded transmission lines, which are all set to be one quarter wavelength long with respect to the center frequency of the passband. Short-ended stubs are then added at the two ends of the MMR as inductive coupling elements. For the filter design, a transfer function is derived to characterize the frequency response of the proposed BPF, and it is enforced to apply to the equal-ripple in-band response. As such, the impedance value for each section is determined once the specifications are given. With this method, design curves of different orders and ripple factors are provided to aid the design of a class of wideband BPFs. As a design example, a fifth-order ultra-wide filter is designed, fabricated and measured in the end. The experimental results verify the design approach.
Cone Trees: animated 3D visualizations of hierarchical information

The task of managing and accessing large information spaces is a problem in large scale cognition. Emerging technologies for 3D visualization and interactive animation offer potential solutions to this problem, especially when the structure of the information can be visualized. We describe one of these Information Visualization techniques, called the Cone Tree, which is used for visualizing hierarchical information structures. The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure. Interactive animation is used to shift some of the user's cognitive load to the human perceptual system.
Effect of a Nontechnical Skills Intervention on First-Year Student Registered Nurse Anesthetists' Skills During Crisis Simulation.

Simulation-based education provides a safe place for student registered nurse anesthetists to practice non-technical skills before entering the clinical arena. An anesthetist's lack of nontechnical skills contributes to adverse patient outcomes. The purpose of this study was to determine whether an educational intervention on nontechnical skills could improve the performance of nontechnical skills during anesthesia crisis simulation with a group of first-year student registered nurse anesthetists. Thirty-two first-year students volunteered for this quasi-experimental study. Each subject was videotaped and rated as he or she performed 6 simulated crisis scenarios: 3 scenarios before the intervention and 3 after the intervention. Findings revealed that the nontechnical skills mean posttest score was greater than pretest scores: t (df = 31) = 1.99, P = .028. The mean gain in scores for standardized nontechnical skills were significantly greater than those for standardized technical skills: t (df = 30) = 1.81, P = .04. In conclusion, a 3-hour educational intervention on nontechnical skills resulted in significant improvement. Nontechnical skills therefore are not acquired through experience, but rather through instruction. An educational intervention using the Anaesthetists' Non-Technical Skills system is a valuable tool in the measurement of nontechnical skills assessment of first-year student registered nurse anesthetists.
A visual tool for ontology alignment to enable geospatial interoperability

In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query. Using our approach, querying can be easily extended to new data sources and, therefore, to new regions. In this paper, we describe the AgreementMaker, a tool that displays the ontologies, supports several mapping layers visually, presents automatically generated mappings, and finally produces the agreements. r 2007 Elsevier Ltd. All rights reserved.
Better Evaluation for Grammatical Error Correction

We present a novel method for evaluating grammatical error correction. The core of our method, which we call MaxMatch (M), is an algorithm for efficiently computing the sequence of phrase-level edits between a source sentence and a system hypothesis that achieves the highest overlap with the goldstandard annotation. This optimal edit sequence is subsequently scored using F1 measure. We test our M scorer on the Helping Our Own (HOO) shared task data and show that our method results in more accurate evaluation for grammatical error correction.
A MATLAB-Simulink-Based PV Module Model and Its Application Under Conditions of Nonuniform Irradiance

The performance of a photovoltaic (PV) module is mostly affected by array configuration, irradiance, and module temperature. It is important to understand the relationship between these effects and the output power of the PV array. This paper presents a MATLAB-Simulink-based PV module model which includes a controlled current source and an S-Function builder. The modeling scheme in S-Function builder is deduced by some predigested functions. Under the conditions of nonuniform irradiance, the model is practically validated by using different array configurations in testing platform. The comparison experiments indicate that I-V and P-V characteristic curves of simulation match the measurements from outdoor experiment well. Under the conditions of nonuniform irradiance, both simulation and experiment show that the output power of a PV array gets more complicated due to multiple peaks. Moreover, the proposed model can also simulate electric circuit and its maximum power point tracking (MPPT) in the environment of MATLAB-Simulink. The experiments show that the proposed model has good predictability in the general behaviors of MPPT under the conditions of both nonuniform and uniform irradiance.
Deep Q-Networks for Accelerating the Training of Deep Neural Networks

In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our approach, a deep RL agent (synonym for optimizer in this work) is used to automatically learn policies about how to schedule learning rates during the optimization of a DNN. The state features of the agent are learned from the weight statistics of the optimizee during training. The reward function of this agent is designed to learn policies that minimize the optimizee’s training time given a certain performance goal. The actions of the agent correspond to changing the learning rate for the optimizee during training. As far as we know, this is the first attempt to use deep RL to learn how to optimize a large-sized DNN. We perform extensive experiments on a standard benchmark dataset and demonstrate the effectiveness of the policies learned by our approach. All source code for reproducing the experiments can be downloaded from https://github.com/bigaidreamprojects/qan .
Virtual Dermatologist: An application of 3D modeling to tele-healthcare

In this paper, we present preliminary results towards the development of the Virtual Dermatologist: A 3D image and tactile database for virtual examination of dermatology patients. This system, which can be installed and operated by non-dermatologists in remotes areas where access to a dermatologist is difficult, will enhance and broaden the application of tele-healthcare, and it will greatly facilitate the surveillance and consequent diagnosis of various skin diseases. Unlike other systems that monitor the progress of skin diseases using qualitative data on simple baseline (2D) photography, the proposed system will also allow for the quantitative assessment of the progress of the disease over time (e.g. thickness, size, roughness, etc). In fact, the 3D model created by the proposed system will let the dermatologist perform dermatoscopic-like examinations over specially annotated areas of the 3D model of the patient's body (i.e. higher definition areas of the 3D model). As part of its future development, the system will also allow the dermatologist to virtually touch and feel the lesion through a haptic interface. In its current form, the system can detect skin lesions smaller than 1mm, as we demonstrate in the result section.
Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve, and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.
An Effective Scalable SQL Engine for NoSQL Databases

NoSQL databases were initially devised to support a few concrete extreme scale applications. Since the specificity and scale of the target systems justified the investment of manually crafting application code their limited query and indexing capabilities were not a major impediment. However, with a considerable number of mature alternatives now available there is an increasing willingness to use NoSQL databases in a wider and more diverse spectrum of applications and, to most of them, hand-crafted query code is not an enticing trade-off. In this paper we address this shortcoming of current NoSQL databases with an effective approach for executing SQL queries while preserving their scalability and schema flexibility. We show how a full-fledged SQL engine can be integrated atop of HBase leading to an ANSI SQL compliant database. Under a standard TPC-C workload our prototype scales linearly with the number of nodes in the system and outperforms a NoSQL TPC-C implementation optimized for HBase.
Performance Evaluation of NoSQL Databases

NoSQL databases have emerged as a backend to support Big Data applications. NoSQL databases are characterized by horizontal scalability, schema-free data models, and easy cloud deployment. To avoid overprovisioning, it is essential to be able to identify the correct number of nodes required for a specific system before deployment. This paper benchmarks and compares three of the most common NoSQL databases: Cassandra, MongoDB and HBase. We deploy them on the Amazon EC2 cloud platform using different types of virtual machines and cluster sizes to study the effect of different configurations. We then compare the behavior of these systems to high-level queueing network models. Our results show that the models are able to capture the main performance characteristics of the studied databases and form the basis for a capacity planning tool for service providers and service users.
Ultra broadband planar transmission line transformers with ferrite based bandwidth extension

This work describes the design and implementation of broadband broadside coupled impedance transformers with and without ferrite cores. Both transformers have the same dimensions, but in one of both two nanocrystalline cores around the broadside coupled 25 Ω transmission lines have been added. The cores have a very high permeability of 100000. The planar transformers are fabricated with RO4003 PCB material. The version without core has a VSWR at both ports of smaller than two and a insertion loss of lower than 1 dB over the whole bandwidth from 300 MHz to 8 GHz. For the same VSWR and insertion loss specifications the core loaded transformer achieves a bandwidth of 1.5 MHz to 8 GHz. A very good agreement between simulation and measurement can be observed.
Deep Neural Networks for Learning Graph Representations

In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the samplingbased method for generating linear sequences proposed by Perozzi et al. (2014). The advantages of our approach will be illustrated from both theorical and empirical perspectives. We also give a new perspective for the matrix factorization method proposed by Levy and Goldberg (2014), in which the pointwise mutual information (PMI) matrix is considered as an analytical solution to the objective function of the skipgram model with negative sampling proposed by Mikolov et al. (2013). Unlike their approach which involves the use of the SVD for finding the low-dimensitonal projections from the PMI matrix, however, the stacked denoising autoencoder is introduced in our model to extract complex features and model non-linearities. To demonstrate the effectiveness of our model, we conduct experiments on clustering and visualization tasks, employing the learned vertex representations as features. Empirical results on datasets of varying sizes show that our model outperforms other stat-of-the-art models in such tasks.
Industrial Applications of Pulsed Power Technology

A review of mainly the past two years is undertaken of the industrial applications of pulsed power. Repetitively operated pulsed power generators with a moderate peak power have been developed for industrial applications. These generators are reliable and have low maintenance. Development of the pulsed power generators helps promote industrial applications of pulsed power for such things as food processing, medical treatment, water treatment, exhaust gas treatment, ozone generation, engine ignition, ion implantation and others. Here, industrial applications of pulsed power are classified by application for biological effects, for pulsed streamer discharges in gases, for pulsed discharges in liquid or liquid- mixture, and for material processing.
3D AVM system for automotive applications

The car body structure and vision limitations of the rear view mirror may lead to inner wheel difference and blind spots, which results in traffic accidents. To solve this problem, many automakers develop the Around View Monitoring (AVM) actively. The AVM system provides the information around the car for drivers with videos. This paper is based on the 2D AMV and further develops the 3D AVM, which uses four fisheye cameras to set in the front, in the rear, and in the rear view mirrors of both sides to record videos. This paper applies fisheye correction, perspective transform, image stitching, and scale-invariant feature transform to synthesize four input videos into 2D AVM, and the 3D AVM is created by 2D AVM to 3D AVM algorithm. Compared to the 2D AVM in previous methods, the 3D AVM proposed in this paper can help drivers to determine the collision distance while crossing with another car in narrow roads and parking, which can prevent traffic accidents. Moreover, drivers can switch perspectives arbitrarily according to the requirements without the limitation of fixed overlooking view. Therefore, the driving safety is improved.