nils-herrmann/clean_scirepeval huggingface dataset:
A 1.2-pJ/bit 16-Gb/s 60-GHz OOK Transmitter in 65-nm CMOS for Wireless Network-On-Chip

This paper presents a high-efficiency 60-GHz on-off keying (OOK) transmitter (TX) designed for wireless network-on-chip applications. Aiming at an intra-chip communication distance of 20 mm, the TX consists of a drive amplifier (DA), a high-speed OOK modulator, and a transformer-coupled voltage-controlled oscillator. For high efficiency, a common-source topology with a drain-to-gate neutralization technique is chosen for the DA. A detailed mathematical design methodology is derived for the neutralization technique. The bulk-driven OOK modulator employs a novel dual feedthrough cancellation technique, resulting in a 30-dB on-off ratio. Fabricated in a 65-nm bulk CMOS process, the TX consumes only 19 mW from a 1-V supply, and occupies an active area of 0.077 mm2. A maximum modulation data rate of 16 Gb/s with 0.75-dBm output power is demonstrated through measurements, which translates to a bit-energy efficiency of 1.2 pJ/bit.
A Fast Implementation of the Isodata Clustering Algorithm

Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times. A preliminary version of this paper appeared in  Toulouse, France, 2003, Vol. 111, 2057-2059. +NASA Goddard Space Flight Center, Architecture and Automation Branch, Greenbelt, MD 20771 and Department of Computer Science, University of Maryland, College Park, Maryland, 20742.  The work of this author was supported by the Science Foundation under grant CCR-0098151.  mount@cs.umd.edu. l ~ e ~ a r t m e n t of Computer Science, Bar-Ilan University, Ramat-Gan 52900, Israel, and Center for Automation Research, University of Maryland, College Park, Maryland, 20742.  nathanOcs.biu.ac.il. NASA Goddard Space Flight Center, previously Applied Information Sciences Branch, currently Advanced Architectures and Automation Branch, Greenbelt, MD 20771.  2017-12-21T21:41:30+00:00Z
Data Model Development for Fire Related Extreme Events: An Activity Theory Approach

Post-analyses of major extreme events reveal that information sharing is critical for effective emergency response. The lack of consistent data standards for current emergency management practice, however, hinders efficient critical information flow among incident responders. In this paper, we adopt a third-generation activity theory guided approach to develop a data model that can be used in the response to fire-related extreme events. This data model prescribes the core data standards to reduce information interoperability barriers. The model is validated through a three-step approach including a request for comment (RFC) process, case application, and prototype system test. This study contributes to the literature in the area of interoperability and data modeling; it also informs practice in emergency response system design.
Evaluation Of Pavement Surface Distress Using Digital Image Collection And Analysis

Local Authority road networks commonly include roads with different functional characteristics and a variety of construction types, which require maintenance solutions tailored to their needs. Given this background, on local road network, pavement management is founded on the experience of the agency engineers and is often constrained by low budgets and a variety of environmental and external requirements. This paper forms part of a research work that investigates the use of digital techniques for obtaining field data in order to increase safety and reduce labour cost requirements using a semi-automated distress collection and measurement system. More specifically, a definition of a distress detection procedure is presented which aims at producing a result complying more closely to the distress identification manuals and protocols. The process comprises the following two steps: Automated pavement image collection. Images are collected using the high speed digital acquisition system of the Mobile Laboratory designed and implemented by the Department of Civil and Environmental Engineering of the University of Catania; Distress Detection. By way of the Pavement Distress Analyser (PDA), a specialised software, images are adjusted to eliminate their optical distortion. Cracks, potholes and patching are automatically detected and subsequently classified by means of an operator assisted approach. An intense, experimental field survey has made it possible to establish that the procedure obtains more consistent distress measurements than a manual survey thus increasing its repeatability, reducing costs and increasing safety during the survey. Moreover, the pilot study made it possible to validate results coming from a survey carried out under normal traffic conditions, concluding that it is feasible to integrate the procedure into a roadway pavement management system.
A Hybrid Tabu Search Algorithm for the Nurse Rostering Problem

This paper deals with the problem of nurse rostering in Belgian hospitals. This is a highly constrained real world problem that was (until the results of this research were applied) tackled manually. The problem basically concerns the assignment of duties to a set of people with different qualifications, work regulations and preferences. Constraint programming and linear programming techniques can produce feasible solutions for this problem. However, the reality in Belgian hospitals forced us to use heuristics to deal with the over constrained schedules. An important reason for this decision is the calculation time, which the users prefer to reduce. The algorithms presented in this paper are a commercial nurse rostering product developed for the Belgian hospital market, entitled Plane.
Product recommendation and rating prediction based on multi-modal social networks

Online Social Rating Networks (SRNs) such as Epinions and Flixter, allow users to form several implicit social networks, through their daily interactions like co-commenting on the same products, or similarly co-rating products. The majority of earlier work in Rating Prediction and Recommendation of products (e.g. Collaborative Filtering) mainly takes into account ratings of users on products. However, in SRNs users can also built their explicit social network by adding each other as friends. In this paper, we propose Social-Union, a method which combines similarity matrices derived from heterogeneous (unipartite and bipartite) explicit or implicit SRNs. Moreover, we propose an effective weighting strategy of SRNs influence based on their structured density. We also generalize our model for combining multiple social networks. We perform an extensive experimental comparison of the proposed method against existing rating prediction and product recommendation algorithms, using synthetic and two real data sets (Epinions and Flixter). Our experimental results show that our Social-Union algorithm is more effective in predicting rating and recommending products in SRNs.
Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution.
Eigenvector method for maximum-likelihood estimation of phase errors in synthetic-aperture-radar imagery

Received November 25, 1992; revised manuscript received April 14, 1993; accepted May 11, 1993 We develop a maximum-likelihood (ML) algorithm for estimation and correction (autofocus) of phase errors induced in synthetic-aperture-radar (SAR) imagery. Here, M pulse vectors in the range-compressed domain are used as input for simultaneously estimating M 1 phase values across the aperture. The solution involves an eigenvector of the sample covariance matrix of the range-compressed data. The estimator is then used within the basic structure of the phase gradient autofocus (PGA) algorithm, replacing the original phase-estimation kernel. We show that, in practice, the new algorithm provides excellent restorations to defocused SAR imagery, typically in only one or two iterations. The performance of the new phase estimator is demonstrated essentially to achieve the Cramer-Rao lower bound on estimation-error variance for all but small values of target-toclutter ratio. We also show that for the case in which M is equal to 2, the ML estimator is similar to that of the original PGA method but achieves better results in practice, owing to a bias inherent in the original PGA phaseestimation kernel. Finally, we discuss the relationship of these algorithms to the shear-averaging and spatialcorrelation methods, two other phase-correction techniques that utilize the same phase-estimation kernel but that produce substantially poorer performance because they do not employ several fundamental signal-processing steps that are critical to the algorithms of the PGA class.
Detection and localization of persons behind obstacles using M-sequence through-the-wall radar

and is made available as an electronic reprint (preprint) with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.  We describe the architecture and design of a through-the-wall radar. The radar is applied for the detection and localization of people hidden behind obstacles. It implements a new adaptive processing technique for people detection, which is introduced in this article. This processing technique is based on exponential averaging with adopted weighting coefficients. Through-the-wall detection and localization of a moving person is demonstrated by a measurement example. The localization relies on the time-of-flight approach.
Neural Circuits Involved in the Recognition of Actions Performed by Nonconspecifics: An fMRI Study

Functional magnetic resonance imaging was used to assess the cortical areas active during the observation of mouth actions performed by humans and by individuals belonging to other species (monkey and dog). Two types of actions were presented: biting and oral communicative actions (speech reading, lip-smacking, barking). As a control, static images of the same actions were shown. Observation of biting, regardless of the species of the individual performing the action, determined two activation foci (one rostral and one caudal) in the inferior parietal lobule and an activation of the pars opercularis of the inferior frontal gyrus and the adjacent ventral premotor cortex. The left rostral parietal focus (possibly BA 40) and the left premotor focus were very similar in all three conditions, while the right side foci were stronger during the observation of actions made by conspecifics. The observation of speech reading activated the left pars opercularis of the inferior frontal gyrus, the observation of lip-smacking activated a small focus in the pars opercularis bilaterally, and the observation of barking did not produce any activation in the frontal lobe. Observation of all types of mouth actions induced activation of extrastriate occipital areas. These results suggest that actions made by other individuals may be recognized through different mechanisms. Actions belonging to the motor repertoire of the observer (e.g., biting and speech reading) are mapped on the observer's motor system. Actions that do not belong to this repertoire (e.g., barking) are essentially recognized based on their visual properties. We propose that when the motor representation of the observed action is activated, the observer gains knowledge of the observed action in a personal perspective, while this perspective is lacking when there is no motor activation.
Predicting Success in Goal-Driven Human-Human Dialogues

In goal-driven dialogue systems, success is often defined based on a structured definition of the goal. This requires that the dialogue system be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal. However, in many human-human dialogues the diversity of goals makes it infeasible to define success in such a way. To address this scenario, we consider the task of automatically predicting success in goal-driven human-human dialogues using only the information communicated between participants in the form of text. We build a dataset from stackoverflow.com which consists of exchanges between two users in the technical domain where groundtruth success labels are available. We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition. We show this model outperforms rule-based heuristics and other baselines as it is able to detect patterns over the course of a dialogue and capture notions such as gratitude.
Heterogeneous Defect Prediction

Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets. To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance.
Ontology Learning from Incomplete Semantic Web Data by BelNet

Recent years have seen a dramatic growth of semantic web on the data level, but unfortunately not on the schema level, which contains mostly concept hierarchies. The shortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learning from semantic web data becomes an increasingly pressing issue. In this paper we propose a novel schemas learning approach -BelNet, which combines description logics (DLs) with Bayesian networks. In this way BelNet is capable to understand and capture the semantics of the data on the one hand, and to handle incompleteness during the learning procedure on the other hand. The main contributions of this work are: (i)we introduce the architecture of BelNet, and corresponding lypropose the ontology learning techniques in it, (ii) we compare the experimental results of our approach with the state-of-the-art ontology learning approaches, and provide discussions from different aspects.
Analysis and reduction of common mode EMI noise for resonant converters

In this paper, the common mode (CM) EMI noise characteristic of three popular topologies of resonant converter (LLC, CLL and LCL) is analyzed. The comparison of their EMI performance is provided. A state-of-art LLC resonant converter with matrix transformer is used as an example to further illustrate the CM noise problem of resonant converters. The CM noise model of LLC resonant converter is provided. A novel method of shielding is provided for matrix transformer to reduce common mode noise. The CM noise of LLC converter has a significantly reduction with shielding. The loss of shielding is analyzed by finite element analysis (FEA) tool. Then the method to reduce the loss of shielding is discussed. There is very little efficiency sacrifice for LLC converter with shielding according to the experiment result.
Flare: Native Compilation for Heterogeneous Workloads in Apache Spark

The need for modern data analytics to combine relational, procedural, and map-reduce-style functional processing is widely recognized. State-of-the-art systems like Spark have added SQL front-ends and relational query optimization, which promise an increase in expressiveness and performance. But how good are these extensions at extracting high performance from modern hardware platforms? While Spark has made impressive progress, we show that for relational workloads, there is still a significant gap compared with best-of-breed query engines. And when stepping outside of the relational world, query optimization techniques are ineffective if large parts of a computation have to be treated as user-defined functions (UDFs). We present Flare: a new back-end for Spark that brings performance closer to the best SQL engines, without giving up the added expressiveness of Spark. We demonstrate order of magnitude speedups both for relational workloads such as TPC-H, as well as for a range of machine learning kernels that combine relational and iterative functional processing. Flare achieves these results through (1) compilation to native code, (2) replacing parts of the Spark runtime system, and (3) extending the scope of optimization and code generation to large classes of UDFs.
DCT-Based Iris Recognition

This paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent correct recognition rate (CRR) and perfect receiver-operating characteristic (ROC) curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the false acceptance rate (FAR) and false rejection rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical equal error rate (EER) is predicted to be as low as 2.59 times 10-1 available data sets
Interacting with eye movements in virtual environments

Eye movement-based interaction offers the potential of easy, natural, and fast ways of interacting in virtual environments. However, there is little empirical evidence about the advantages or disadvantages of this approach. We developed a new interaction technique for eye movement interaction in a virtual environment and compared it to more conventional 3-D pointing. We conducted an experiment to compare performance of the two interaction types and to assess their impacts on spatial memory of subjects and to explore subjects' satisfaction with the two types of interactions. We found that the eye movement-based interaction was faster than pointing, especially for distant objects. However, subjects' ability to recall spatial information was weaker in the eye condition than the pointing one. Subjects reported equal satisfaction with both types of interactions, despite the technology limitations of current eye tracking equipment.
The Elliptic Curve Digital Signature Algorithm (ECDSA)

The Elliptic Curve Digital Signature Algorithm (ECDSA) is the elliptic curve analogue of the Digital Signature Algorithm (DSA). It was accepted in 1999 as an ANSI standard and in 2000 as IEEE and NIST standards. It was also accepted in 1998 as an ISO standard and is under consideration for inclusion in some other ISO standards. Unlike the ordinary discrete logarithm problem and the integer factorization problem, no subexponential-time algorithm is known for the elliptic curve discrete logarithm problem. For this reason, the strength-per-key-bit is substantially greater in an algorithm that uses elliptic curves. This paper describes the ANSI X9.62 ECDSA, and discusses related security, implementation, and interoperability issues.
An overview on the existing language models for prediction systems as writing assistant tools

The prediction task in national language processing means to guess the missing letter, word, phrase, or sentence that likely follow in a given segment of a text. Since 1980s many systems with different methods were developed for different languages. In this paper an overview of the existing prediction methods that have been used for more than two decades are described and a general classification of the approaches is presented. The three main categories of the classification are statistical modeling, knowledge-based modeling, and heuristic modeling (adaptive).
Part Of Speech Tagging and Chunking with HMM and CRF

In this paper we propose an approach to Part of Speech (PoS) tagging using a combination of Hidden Markov Model and error driven learning. For the NLPAI joint task, we also implement a chunker using Conditional Random Fields (CRFs). The results for the PoS tagging and chunking task are separately reported along with the results of the joint task.
Extracting moods from pictures and sounds: towards truly personalized TV

This paper considers how we feel about the content we see or hear. As opposed to the cognitive content information composed of the facts about the genre, temporal content structures and spatiotemporal content elements, we are interested in obtaining the information about the feelings, emotions, and moods evoked by a speech, audio, or video clip. We refer to the latter as the affective content, and to the terms such as happy or exciting as the affective labels of an audiovisual signal. In the first part of the paper, we explore the possibilities for representing and modeling the affective content of an audiovisual signal to effectively bridge the affective gap. Without loosing generality, we refer to this signal simply as video, which we see as an image sequence with an accompanying soundtrack. Then, we show the high potential of the affective video content analysis for enhancing the content recommendation functionalities of the future PVRs and VOD systems. We conclude this paper by outlining some interesting research challenges in the field