nils-herrmann/clean_scirepeval huggingface dataset:
DPIL@FIRE2016: Overview of the Shared task on Detecting Paraphrases in Indian language

This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.
Graph Data Management and Mining: A Survey of Algorithms and Applications

Graph mining and management has become a popular area of research in recent years because of its numerous applications in a wide variety of practical fields, including computational biology, software bug localization and computer networking. Different applications result in graphs of different sizes and complexities. Correspondingly, the applications have different requirements for the underlying mining algorithms. In this chapter, we will provide a survey of different kinds of graph mining and management algorithms. We will also discuss a number of applications, which are dependent upon graph representations. We will discuss how the different graph mining algorithms can be adapted for different applications. Finally, we will discuss important avenues of future research
Towards deep anchor learning

Anchor learning is a promising framework for electronic health record phenotyping and for predicting clinical state variables. In particular, anchor learning alleviates the problem of having to manually annotate (label) a large dataset (time consuming and expensive) by transforming certain key clinical observations (anchors) into noisy labels. Thus far, the predictive models in anchor learning have utilized rather simple features for the analysis. We hypothesize that anchor learning may benefit hugely from more powerful feature extraction. Re­cent advances in deep learning have illustrated neural networks' exceptional ability to learn powerful feature representations from data. In this paper, we propose the deep anchor learning framework, which uses a deep autoencoder to obtain stronger features for use in anchor learning. A support vector machine trained on the autoencoder features achieves state-of-the art performance on the task of postoperative delirium prediction from anchors.
Fear and Loathing across Party Lines : New Evidence on Group Polarization

When defined in terms of social identity and affect toward copartisans and opposing partisans, the polarization of the American electorate has dramatically increased. We document the scope and consequences of affective polarization of partisans using implicit, explicit, and behavioral indicators. Our evidence demonstrates that hostile feelings for the opposing party are ingrained or automatic in voters’ minds, and that affective polarization based on party is just as strong as polarization based on race. We further show that party cues exert powerful effects on nonpolitical judgments and behaviors. Partisans discriminate against opposing partisans, doing so to a degree that exceeds discrimination based on race. We note that the willingness of partisans to display open animus for opposing partisans can be attributed to the absence of norms governing the expression of negative sentiment and that increased partisan affect provides an incentive for elites to engage in confrontation rather than cooperation.
Deep Learning for Natural Language Processing

1Student,Dept. of Computer Engineering, VESIT, Maharashtra, India ---------------------------------------------------------------------------***-------------------------------------------------------------------Abstract Deep Learning has come into existence as a new area for research in Machine Learning. It aims to act like a human brain, having the ability to learn and process from complex data and also tries solving intricate tasks as well. Due to this capability, its been used in various fields like text, sound, images etc. Natural language process has started to being impacted by the deep learning techniques. This research paper highlights Deep Learning’s recent developments and applications in Natural Language Processing.
Short to mid-range night fire detection

Computer vision methods used for night-time fire detection are limited. Existing works are for detection of distant night fires recorded from watch towers. In this paper, detection of short to mid-range night fires from video cameras are aimed. Flames in short distance flicker, grow and move more rapidly compared to ones in long distance. Features obtained by taking advantage of these distinctions let us detect fire over 90% accuracy on average in videos containing deceptive light sources like common city lights and headlights of vehicles.
Design Issues and Challenges in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed self-organized wireless ad hoc networks which comprise of a large number of resource constrained sensor nodes. The major areas of research in WSN is going on hardware, and operating system of WSN, deployment, architecture, localization, synchronization, programming models, data aggregation and dissemination, database querying, architecture, middleware, quality of service and security. This paper study highlights ongoing research activities and issues that affect the design and performance of Wireless Sensor Network.
Smart clothing: Perceived benefits vs. perceived fears

Smart textile technologies integrate computer functionality into textiles. Since a few years, smart clothing has been coming up in the sport and health sector and is increasingly implemented in everyday objects within private spaces. Especially the use of textiles for medical reasons and their potential use within Ambient Assisted Living-Concepts (AAL) make it necessary to understand users' perspectives on these technologies and the willingness to use them. Today, the understanding in which way individual attitudes and emotional and cognitive abilities, may impact the acceptance of pervasive health care technologies, is restricted. This research is focused on the users' hopes and fears towards smart clothing and examines perceived benefits and barriers. As women have a higher life expectancy and will dominate the group of old people in the future - gender was chosen as one central factor of interest. As the second factor we examined technical experience in order to learn if the acceptance for smart clothing is connected to the degree of users previous experience with technology. Outcomes revealed both factors - gender and technical experience - to be decisive factors for the acceptance of smart clothing. Generally, women and persons with low technical experience show considerable caveats towards the usage of smart clothing technologies what becomes most evident in the perceived barriers and fears connected to the usage of this new technology.
Testing using Log File Analysis: Tools, Methods, and Issues

Large software systems often keep log files of events. Such log files can be analyzed to check whether a run of a program reveals faults in the system. We discuss how such log files can be used in software testing. We present a framework for automatically analyzing log files, and describe a language for specifying analyzer programs and an implementation of that language. The language permits compositional, compact specifications of software, which act as test oracles; we discuss the use and efficacy of these oracles for unitand system-level testing in various settings. We explore methodological issues such as efficiency and logging policies, and the scope and limitations of the framework. We conclude that testing using log file analysis constitutes a useful methodology for software verification, somewhere between current testing practice and formal verification methodologies.
Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images

The large-scale hyperspectral image (HSI) clustering problem has attracted significant attention in the field of remote sensing. Most traditional graph-based clustering methods still face challenges in the successful application of the large-scale HSI clustering problem mainly due to their high computational complexity. In this letter, we propose a novel approach, called fast spectral clustering with anchor graph (FSCAG), to efficiently deal with the large-scale HSI clustering problem. Specifically, we consider the spectral and spatial properties of HSI in the anchor graph construction. The proposed FSCAG algorithm first constructs anchor graph and then performs spectral analysis on the graph. With this, the computational complexity can be reduced to <inline-formula> <tex-math notation="LaTeX">$O(ndm)$ </tex-math></inline-formula>, which is a significant improvement compared to conventional graph-based clustering methods that need at least <inline-formula> <tex-math notation="LaTeX">$O(n^{2}d)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> are the number of samples, features, and anchors, respectively. Several experiments are conducted to demonstrate the efficiency and effectiveness of the proposed FSCAG algorithm.
Hepta-Band Internal Antenna for Personal Communication Handsets

Modern personal communication handsets are shrinking in size and are required to operate at multiple frequency bands for enhanced functionality and performance. This poses an important challenge for antenna designers to build multiband antennas within the limited allowable space. In this paper, an internal antenna covering seven frequency bands is presented for personal communication handsets. The proposed antenna operates at GSM (880-960 MHz), DCS (1710-1880 MHz), PCS (1880-1990 MHz), UMTS (1900-2170 MHz), WiBro (2300-2390 MHz), Bluetooth (2.4-2.48 GHz), and WLAN (5.0-5.5 GHz) frequency bands. Measured input return loss of the antenna is better than dB at all the frequency bands with reasonable radiation performance. Antenna volume is 30 mm times15 mm times 4.0 mm (1.8 cm) that makes it attractive for modern multiband and multifunctional slim handsets.
Probabilistic Principal Component Analysis

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Multi-dimension semantic dictionary for online intelligence

The proposed online intelligence (OI) is to provide popular and well-formatted reports for direct decision support based on online data. Completeness, preciseness, conciseness and smartness are proposed as the basic technical indicators to evaluate an OI application. OI requires similar data processing flow and reporting tools with traditional business intelligence (BI). However, OI collects data basically from online other than provided by enterprise application systems. The online data is usually non-structural or semi-structural. Semantics techniques are required by OI to find and process online data into decision-making knowledge automatically in many cases. To this end, a multi-dimension semantic dictionary is proposed to divide online objects' semantics into four semantic aspects, i.e., information semantics, business semantics, application semantics and structural semantics. The four semantic aspects of the semantic dictionary are developed separately and interlinked as a multi-dimension semantic matrix to conduct matchmaking when find and process online data. In this work, the related concepts and implementation framework of the multi-dimension semantic dictionary are discussed and a real application for illegal drug tracing is provided to illustrate how OI make sense and semantics are developed.
Flattening Gamma: Radiometric Terrain Correction for SAR Imagery

Enabling intercomparison of synthetic aperture radar (SAR) imagery acquired from different sensors or acquisition modes requires accurate modeling of not only the geometry of each scene, but also of systematic influences on the radiometry of individual scenes. Terrain variations affect not only the position of a given point on the Earth's surface but also the brightness of the radar return as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover induced backscatter differences, and comparison of backscatter from multiple satellites, modes, or tracks loses meaning. The ASAR & PALSAR sensors provide state vectors and timing with higher absolute accuracy than was previously available, allowing them to directly support accurate tie-point-free geolocation and radiometric normalization of their imagery. Given accurate knowledge of the acquisition geometry of a SAR image together with a digital height model (DHM) of the area imaged, radiometric image simulation is applied to estimate the local illuminated area for each point in the image. Ellipsoid-based or sigma naught (σ0) based incident angle approximations that fail to reproduce the effect of topographic variation in their sensor model are contrasted with a new method that integrates terrain variations with the concept of gamma naught (γ0) backscatter, converting directly from beta naught (β0) to a newly introduced terrain-flattened γ0 normalization convention. The interpretability of imagery treated in this manner is improved in comparison to processing based on conventional ellipsoid or local incident angle based σ0 normalization.
Uncovering Temporal Context for Video Question and Answering

In this work, we introduce Video Question Answering in temporal domain to infer the past, describe the present and predict the future. We present an encoder-decoder approach using Recurrent Neural Networks to learn temporal structures of videos and introduce a dual-channel ranking loss to answer multiple-choice questions. We explore approaches for finer understanding of video content using question form of “fill-in-the-blank”, and managed to collect 109,895 video clips with duration over 1,000 hours from TACoS, MPII-MD, MEDTest 14 datasets, while the corresponding 390,744 questions are generated from annotations. Extensive experiments demonstrate that our approach significantly outperforms the compared baselines.
A Fully Convolutional Neural Network for Speech Enhancement

In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a ‘mapping’ between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using fully Convolutional Neural Networks, which consist of lesser number of parameters than fully connected networks. The proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates that a convolutional network can be 12 times smaller than a recurrent network and yet achieves better performance, which shows its applicability for an embedded system: the hearing aids.
24/7 Characterization of petascale I/O workloads

Developing and tuning computational science applications to run on extreme scale systems are increasingly complicated processes. Challenges such as managing memory access and tuning message-passing behavior are made easier by tools designed specifically to aid in these processes. Tools that can help users better understand the behavior of their application with respect to I/O have not yet reached the level of utility necessary to play a central role in application development and tuning. This deficiency in the tool set means that we have a poor understanding of how specific applications interact with storage. Worse, the community has little knowledge of what sorts of access patterns are common in today's applications, leading to confusion in the storage research community as to the pressing needs of the computational science community. This paper describes the Darshan I/O characterization tool. Darshan is designed to capture an accurate picture of application I/O behavior, including properties such as patterns of access within files, with the minimum possible overhead. This characterization can shed important light on the I/O behavior of applications at extreme scale. Darshan also can enable researchers to gain greater insight into the overall patterns of access exhibited by such applications, helping the storage community to understand how to best serve current computational science applications and better predict the needs of future applications. In this work we demonstrate Darshan's ability to characterize the I/O behavior of four scientific applications and show that it induces negligible overhead for I/O intensive jobs with as many as 65,536 processes.
Ultra-Wideband $E$-Plane Monopulse Antenna Using Vivaldi Antenna

An ultra-wideband (UWB) E-plane monopulse antenna working in the frequency range from 3.1 to 10.6 GHz is proposed. The UWB E-plane two-element array is constructed using Vivaldi antennas. By shortening the adjacent tapered lines of the two Vivaldi antennas and using corrugated edges, sum and difference characteristics of the array are improved. The UWB monopulse comparator is a multilayer planar magic T consisting of an UWB 90° directional coupler and two UWB 45° phase shifters in series. By integrating the array and the comparator, the UWB E-plane monopulse antenna is designed and fabricated with the low-cost PCB process. In the frequency range from 3.1 to 10.6 GHz, the sum gain is measured as more than 10.1 dBi, the sidelobe level less than -13.1 dB, and the null depth greater than 22.5 dB. The good sum and difference characteristics obtained make the proposed UWB E-plane monopulse antenna desirable for target detecting and tracking.
The art of deep learning ( applied to NLP )

In many regards, tuning deep-learning networks is still more an art than it is a technique. Choosing the correct hyper-parameters and getting a complex network to learn properly can be daunting to people not well versed in that art. Taking the example of entailment classification, this work will analyze different methods and tools that can be used in order to diagnose learning problems. It will aim at providing an deep understanding of some key hyper-parameters, sketch out their effects and explain how to identify recurrent problems when tuning them.
Information Security Policy Compliance: The Role of Information Security Awareness

Compliance and systems misuse has been the focus of researchers in the last couple of years. However, given that voids in this area is still significant and systems abuse is a pressing issue likely to persist in the future, more investigation is needed in this area. Toward this end, we conducted a research study to help understand factors motivating compliance behavior intentions. Drawing on Theory of Planned Behavior, we investigated the role of users’ self-learning and knowledge of security issues in shaping their attitudes toward compliance with information security policies (ISPs). We collected data from nine financial organizations to test the proposed research model. Results show that employees’ previous knowledge of security issues and technologies have significant positive impact on their attitudes toward compliance with ISPs. This study sheds light on the importance of users’ general awareness of security issues and technologies in shaping their attitudes to comply with ISPs.
Miniaturization of Circular Patch Antennas Using MNG Metamaterials

Miniaturized circular patch antennas loaded by helices, which operate as μ-negative (MNG) metamaterials, is reported. Two antennas having different helix parameters have been fabricated. The patch radius for both antennas is 42 mm. The first fabricated antenna shows a resonance at 1.126 GHz, which corresponds to a 40% size reduction. The second antenna has an even better size reduction of 60%. Measured results of return loss and radiation pattern at the subwavelength resonances are compared to these parameters at the conventional resonance of the patch antenna.
Discovery-driven graph summarization

Large graph datasets are ubiquitous in many domains, including social networking and biology. Graph summarization techniques are crucial in such domains as they can assist in uncovering useful insights about the patterns hidden in the underlying data. One important type of graph summarization is to produce small and informative summaries based on user-selected node attributes and relationships, and allowing users to interactively drill-down or roll-up to navigate through summaries with different resolutions. However, two key components are missing from the previous work in this area that limit the use of this method in practice. First, the previous work only deals with categorical node attributes. Consequently, users have to manually bucketize numerical attributes based on domain knowledge, which is not always possible. Moreover, users often have to manually iterate through many resolutions of summaries to identify the most interesting ones. This paper addresses both these key issues to make the interactive graph summarization approach more useful in practice. We first present a method to automatically categorize numerical attributes values by exploiting the domain knowledge hidden inside the node attributes values and graph link structures. Furthermore, we propose an interestingness measure for graph summaries to point users to the potentially most insightful summaries. Using two real datasets, we demonstrate the effectiveness and efficiency of our techniques.
Smart Hospital based on Internet of Things

In this paper, we propose an architecture and a scheme of smart hospital based on Internet of Things (IOT) in order to overcome the disadvantages of the present hospital information system, such as the fixed information point, inflexible networking mode and so on. The key technologies and construction of smart hospital is presented based on understanding of the connotation and architecture of smart hospital. Furthermore, taking a third grade-A hospital as an example, a scheme of smart hospital is given, and its logic structure, application framework, the construction of basic network environment etc. are described in detail. Experiment proves that deployment of smart hospital can effectively solve the prominent problems existing the diagnosis and treatment of hospital and it brings a positive and profound effect for the present diagnosis and treatment mode in hospital.
The HiBench benchmark suite: Characterization of the MapReduce-based data analysis

The MapReduce model is becoming prominent for the large-scale data analysis in the cloud. In this paper, we present the benchmarking, evaluation and characterization of Hadoop, an open-source implementation of MapReduce. We first introduce HiBench, a new benchmark suite for Hadoop. It consists of a set of Hadoop programs, including both synthetic micro-benchmarks and real-world Hadoop applications. We then evaluate and characterize the Hadoop framework using HiBench, in terms of speed (i.e., job running time), throughput (i.e., the number of tasks completed per minute), HDFS bandwidth, system resource (e.g., CPU, memory and I/O) utilizations, and data access patterns.