nils-herrmann/clean_scirepeval huggingface dataset:
License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks

This work details Sighthoundś fully automated license plate detection and recognition system. The core technology of the system is built using a sequence of deep Convolutional Neural Networks (CNNs) interlaced with accurate and efficient algorithms. The CNNs are trained and fine-tuned so that they are robust under different conditions (e.g. variations in pose, lighting, occlusion, etc.) and can work across a variety of license plate templates (e.g. sizes, backgrounds, fonts, etc). For quantitative analysis, we show that our system outperforms the leading license plate detection and recognition technology i.e. ALPR on several benchmarks. Our system is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
Detection of human brain tumour using MRI image segmentation and morphological operators

Image Segmentation is an important and challenging factor in the field of medical sciences. It is widely used for the detection of tumours. This paper deals with detection of brain tumour from MR images of the brain. The brain is the anterior most part of the nervous system. Tumour is a rapid uncontrolled growth of cells. Magnetic Resonance Imaging (MRI) is the device required to diagnose brain tumour. The normal MR images are not that suitable for fine analysis, so segmentation is an important process required for efficiently analyzing the tumour images. Clustering is suitable for biomedical image segmentation as it uses unsupervised learning. This paper work uses K-Means clustering where the detected tumour shows some abnormality which is then rectified by the use of morphological operators along with basic image processing techniques to meet the goal of separating the tumour cells from the normal cells.
Author Identification Using Deep Learning

Authorship identification is the task of identifying the author of a given text from a set of suspects. The main concern of this task is to define an appropriate characterization of texts that captures the writing style of authors. Although deep learning was recently used in different natural language processing tasks, it has not been used in author identification (to the best of our knowledge). In this paper, deep learning is used for feature extraction of documents represented using variable size character n-grams. We apply A Stacked Denoising Auto-Encoder (SDAE) for extracting document features with different settings, and then a support vector machine classifier is used for classification. The results show that the proposed system outperforms its counterparts.
Optimized active-clamp circuit design for an isolated full-bridge current-fed DC-DC converter

This paper presents a new method for the capacitance selection of an active-clamp circuit to enhance the performance of an isolated current-fed full-bridge DC-DC converter. The developed approach can reduce the circulating energy through the bridge switches using an appropriate choice of the resonant frequency between the transformer leakage inductance and the clamp capacitance in relation to the switching frequency. Thus, current and the voltage stresses can be reduced and the converter efficiency improved. It has also been demonstrated that the use of a voltage-doubling output rectifier enhances the performance further. The optimized active-clamp circuit has been implemented on a 1.2 kW isolated current-fed full-bridge DC-DC converter and test results have confirmed the claimed improvements.
Managing to be ethical: debunking five business ethics myths

In the aftermath of recent corporate scandals, managers and researchers have turned their attention to questions of ethics management. We identify five common myths about business ethics and provide responses that are grounded in theory, research, and business examples. Although the scientific study of business ethics is relatively new, theory and research exist that can guide executives who are trying to better manage their employees' and their own ethical behavior. We recommend that ethical conduct be managed proactively via explicit ethical leadership and conscious management of the organization's ethical culture.
Brian: A Simulator for Spiking Neural Networks in Python

We present Brian, a new clock driven simulator for spiking neural networks which is available on almost all platforms. Brian is easy to learn and use, highly flexible and easily extensible. The Brian package itself and simulations using it are all written in the Python programming language, which is very well adapted to these goals. Python is an easy, concise and highly developed language with many advanced features and development tools, excellent documentation and a large community of users providing support and extension packages. Brian allows you to write very concise, natural and readable code for simulations, and makes it quick and efficient to play with these models (for example, changing the differential equations doesn't require a recompile of the code).  1 shows an example of a complete network implemented in Brian, a randomly connected network of integrate and fire neurons with exponential inhibitory and excitatory currents (the CUBA network from ). Defining the model, running from Seventeenth Annual Computational Neuroscience Meeting: CNS*2008 Portland, OR, USA. 19–24 July 2008
Fact Checking in Large Knowledge Graphs - A Discriminative Predicate Path Mining Approach

Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and show that a new model of the top discriminative predicate paths is able to understand the meaning of some statement and accurately determine its veracity. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.
Crowdsourced bug triaging

Bug triaging and assignment is a time-consuming task in big projects. Most research in this area examines the developers' prior development and bug-fixing activities in order to recognize their areas of expertise and assign to them relevant bug fixes. We propose a novel method that exploits a new source of evidence for the developers' expertise, namely their contributions to Q&A platforms such as Stack Overflow. We evaluated this method in the context of the 20 largest GitHub projects, considering 7144 bug reports. Our results demonstrate that our method exhibits superior accuracy to other state-of-the-art methods, and that future bug-assignment algorithms should consider exploring other sources of expertise, beyond the project's version-control system and bug tracker.
Column-based micro-beamformer for improved 2D beamforming using a matrix array transducer

In a 3-D medical ultrasound imaging system, a matrix array probe with 2-D positioning of the elements allows high resolution of ultrasound images due to its capability of two-dimensional dynamic focusing. However, the hundreds (up to thousands) of elements in the matrix array make the fabrication of the transducers and cables challenging. In this paper, to achieve high quality of 3-D ultrasound images with low hardware complexity, we introduce a column-based micro-beamformer (CMB) in which a column in the matrix array is considered as a sub-array and then elevational and lateral beamformings are sequentially conducted in the analog and digital stages, respectively. For the performance evaluation of the proposed beamformer, the beam pattern simulations are conducted and point-spread-functions are obtained. In addition, root-mean-square-errors (RMSEs) in round-trip time delays were measured over the depths. Compared to a conventional micro-beamformer, the CMB produced more tightly focused beam patterns and showed almost equivalent performance to that of fully sampled array. In the near field, the mean RMSEs of the proposed and conventional beamformers were 274 ns and 18.1 ns (improved 93.4% of delay accuracy), respectively.
Managing Congenitally Missing Lateral Incisors Part 2 : Tooth-Supported Restorations Learning

the most common congenitally missing teeth in the mouth.1,2 The replacement of these teeth raises several important treatment planning concerns. Therefore, it is beneficial to use an interdisciplinary treatment approach in order to get the most predictable outcome. As was previously discussed in Part 1, canine substitution can be an esthetic treatment alternative for the replacement of missing lateral incisors. However, there are many individuals who do not meet the qualifications necessary to be considered for canine substitution. In these patients, some form of restoration must be considered. The restorative treatment alternatives can be divided into two categories: a single-tooth implant or a tooth-supported restoration. The three primary types of tooth-supported restorations available today are a resin-bonded fixed partial denture, cantilevered fixed partial denture, or a conventional full-coverage fixed partial denture. The primary consideration among all these treatment options is conservation of tooth structure. Ideally, the treatment of choice should be the least invasive option that satisfies the expected esthetic and functional objectives. Many adolescent and adult patients lack sufficient space for a lateral incisor restoration. This is often due to ectopic eruption of the canine into the lateral incisor position. The orthodontist must move the canine distally into its appropriate position. This will ultimately aid in achieving alveolar ridge development and optimal final esthetics for the final restoration. Over the past several years, the single-tooth implant has become a popular method of replacing missing teeth.3,4 With the hard and soft tissue grafting procedures that are available, implant success rates as well as the final esthetic outcome have become increasingly predictable.5,6 However, there are still certain instances in which implants cannot be used, such as in the patient who is unwilling to undergo the necessary treatment to facilitate proper implant placement. In these situations some form of tooth-supported restoration must be used. Managing Congenitally Missing Lateral Incisors Part 2: Tooth-Supported Restorations
Direct Modulation at 56 and 50 Gb/s of 1.3- $\mu $ m InGaAlAs Ridge-Shaped-BH DFB Lasers

Direct modulation at 56 and 50 Gb/s of 1.3-μm InGaAlAs ridge-shaped-buried heterostructure (RS-BH) asymmetric corrugation-pitch-modulation (ACPM) distributed feedback lasers is experimentally demonstrated. The fabricated lasers have a low threshold current (5.6 mA at 85°C), high temperature characteristics (71 K), high slope relaxation frequency (3.2 GHz/mA1/2 at 85°C), and wide bandwidth (22.1 GHz at 85°C). These superior properties enable the lasers to run at 56 Gb/s and 55°C and 50 Gb/s at up to 80°C for backto-back operation with clear eye openings. This is achieved by the combination of a low-leakage RS-BH and an ACPM grating. Moreover, successful transmission of 56and 50-Gb/s modulated signals over a 10-km standard single-mode fiber is achieved. These results confirm the suitability of this type of laser for use as a cost-effective light source in 400 GbE and OTU5 applications.
Price , Quality , and Value : A Means-End Model

ions. They also presume that consumers carefully calculate the give and get components of value, an assumption that did not hold true for most consumers in the exploratory study. Price as a Quality Indicator Most experimental studies related to quality have focused on price as the key extrinsic quality signal. As suggested in the propositions, price is but one of several potentially useful extrinsic cues; brand name or package may be equally or more important, especially in packaged goods. Further, evidence of a generalized price-perceived quality relationship is inconclusive. Quality research may benefit from a de-emphasis on price as the main extrinsic quality indicator. Inclusion of other important indicators, as well as identification of situations in which each of those indicators is important, may provide more interesting and useful answers about the extrinsic signals consumers use.  An understanding of what quality and value mean to consumers offers the promise of improving brand positions through more precise market analysis and segmentation, product planning, promotion, and pricing strategy. The model presented here suggests the following strategies that can be implemented to understand and capitalize on brand quality and value. Close the Quality Perception Gap Though managers increasingly acknowledge the importance of quality, many continue to define and measure it from the company's perspective. Closing the gap between objective and perceived quality requires that the company view quality the way the consumer does. Research that investigates which cues are important and how consumers form impressions of qualConsumer Perceptions of Price, Quality, and Value / 17 ity based on those technical, objective cues is necessary. Companies also may benefit from research that identifies the abstract dimensions of quality desired by consumers in a product class. Identify Key Intrinsic and Extrinsic Attribute
Bilingual automatic text summarization using unsupervised deep learning

In the world of digitization, the growth of big data is raising at large scale with usage of high performance computing. The huge data in English and Hindi is available on internet and social media which need to be extracted or summarized in user required form. In this paper we are presenting Bilingual (Hindi and English) unsupervised automatic text summarization using deep learning. which is an important research area with in Natural Language Processing, Machine Learning and data mining, to improve result accuracy, we are using restricted Boltzmann machine to generate a shorter version of original document without losing its important information. In this algorithm we are exploring the features to improve the relevance of sentences in the dataset.
Class Imbalance Problem in Data Mining Review

In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes having more sample than other classes. The most of algorithm are more focusing on classification of major sample while ignoring or misclassifying minority sample. The minority samples are those that rarely occur but very important. There are different methods available for classification of imbalance data set which is divided into three main categories, the algorithmic approach, datapreprocessing approach and feature selection approach. Each of this technique has their own advantages and disadvantages. In this paper systematic study of each approach is define which gives the right direction for research in class imbalance problem.
Partial directed coherence: a new concept in neural structure determination

This paper introduces a new frequency-domain approach to describe the relationships (direction of information flow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models. We discuss its application and compare its performance to other approaches to the problem of determining neural structure relations from the simultaneous measurement of neural electrophysiological signals. The new concept is shown to reflect a frequency-domain representation of the concept of Granger causality.
Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

Analysis of a driver's head behavior is an integral part of a driver monitoring system. In particular, the head pose and dynamics are strong indicators of a driver's focus of attention. Many existing state-of-the-art head dynamic analyzers are, however, limited to single-camera perspectives, which are susceptible to occlusion of facial features from spatially large head movements away from the frontal pose. Nonfrontal glances away from the road ahead, however, are of special interest since interesting events, which are critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for head movement analysis, with emphasis on the ability to robustly and continuously operate even during large head movements. The proposed system tracks facial features and analyzes their geometric configuration to estimate the head pose using a 3-D model. We present two such solutions that additionally exploit the constraints that are present in a driving context and video data to improve tracking accuracy and computation time. Furthermore, we conduct a thorough comparative study with different camera configurations. For experimental evaluations, we collected a novel head pose data set from naturalistic on-road driving in urban streets and freeways, with particular emphasis on events inducing spatially large head movements (e.g., merge and lane change). Our analyses show promising results.
Probabilistic Relations between Words : Evidence from Reduction in Lexical Production

The ideas of frequency and predictability have played a fundamental role in models of human language processing for well over a hundred years (Schuchardt, 1885; Jespersen, 1922; Zipf, 1929; Martinet, 1960; Oldfield & Wingfield, 1965; Fidelholz, 1975; Jescheniak & Levelt, 1994; Bybee, 1996). While most psycholinguistic models have thus long included word frequency as a component, recent models have proposed more generally that probabilistic information about words, phrases, and other linguistic structure is represented in the minds of language users and plays a role in language comprehension (Jurafsky, 1996; MacDonald, 1993; McRae, Spivey-Knowlton, & Tanenhaus, 1998; Narayanan & Jurafsky, 1998; Trueswell & Tanenhaus, 1994) production (Gregory, Raymond, Bell, Fosler-Lussier, & Jurafsky, 1999; Roland & Jurafsky, 2000) and learning (Brent & Cartwright, 1996; Landauer & Dumais, 1997; Saffran, Aslin, & Newport, 1996; Seidenberg & MacDonald, 1999). In recent papers (Bell, Jurafsky, Fosler-Lussier, Girand, & Gildea, 1999; Gregory et al., 1999; Jurafsky, Bell, Fosler-Lussier, Girand, & Raymond, 1998), we have been studying the role of predictability and frequency in lexical production. Our goal is to understand the many factors that affect production variability as reflected in reduction processes such as vowel reduction, durational shortening, or final segmental deletion of words in spontaneous speech. One proposal that has resulted from this work is the Probabilistic Reduction Hypothesis: word forms are reduced when they have a higher probability. The probability of a word is conditioned on many aspects of its context, including neighboring words, syntactic and lexical structure, semantic expectations, and discourse factors. This proposal thus generalizes over earlier models which refer only to word frequency (Zipf, 1929; Fidelholz, 1975; Rhodes, 1992, 1996) or predictability (Fowler & Housum, 1987). In this paper we focus on a particular domain of probabilistic linguistic knowledge in lexical production: the role of local probabilistic relations between words.
An Intelligent V2I-Based Traffic Management System

Vehicles equipped with intelligent systems designed to prevent accidents, such as collision warning systems (CWSs) or lane-keeping assistance (LKA), are now on the market. The next step in reducing road accidents is to coordinate such vehicles in advance not only to avoid collisions but to improve traffic flow as well. To this end, vehicle-to-infrastructure (V2I) communications are essential to properly manage traffic situations. This paper describes the AUTOPIA approach toward an intelligent traffic management system based on V2I communications. A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed. The proposed solution was validated by an IEEE-802.11p-based communications study. The entire system showed good performance in testing in real-world scenarios, first by computer simulation and then with real vehicles.
Breaking the Myths of Rewards: An Exploratory Study of Attitudes about Knowledge Sharing

Many CEOs and managers understand the importance of knowledge sharing among their employees and are eager to introduce the knowledge management paradigm in their organizations. However little is known about the determinants of the individuals knowledge sharing behavior. The purpose of this study is to develop an understanding of the factors affecting the individuals knowledge sharing behavior in the organizational context. The research model includes various constructs based on social exchange theory, self-efficacy, and theory of reasoned action. Research results from the field survey of 467 employees of four large, public organizations show that expected associations and contribution are the major determinants of the individuals attitude toward knowledge sharing. Expected rewards, believed by many as the most important motivating factor for knowledge sharing, are not significantly related to the attitude toward knowledge sharing. As expected, positive attitude toward knowledge sharing is found to lead to positive intention to share knowledge and, finally, to actual knowledge sharing behaviors.
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an endto-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on OVERNIGHT dataset and gets competitive performance on GEO and ATIS datasets.