nils-herrmann/clean_scirepeval huggingface dataset:
A cost driven 24GHz Doppler radar sensor development for automotive applications

This paper deals with a low-cost 24GHz Doppler radar sensor for traffic surveillance. The basic building blocks of the transmit/receive chain, namely the antennas, the balanced power amplifier (PA), the dielectric resonator oscillator (DRO), the low noise amplifier (LNA) and the down conversion diode mixer are presented underlining the key technologies and manufacturing approaches by means the required performances can be attained while keeping industrial costs extremely low.
A novel five-phase pancake shaped switched reluctance motor for hybrid electric vehicles

Switched reluctance motors (SRMs) have been gaining increasing popularity and emerging as an attractive alternative to traditional electrical motors in hybrid vehicle applications due to their numerous advantages. However, large torque ripple and acoustic noise are its major disadvantages. This paper presents a novel five-phase 15/12 SRM which features higher power density, very low level of vibration with flexibility in controlling the torque ripple profile. This design is classified as an axial field SRM and hence it needs three-dimensional finite-element analysis model. However, an alternative two-dimensional model is presented and some design features and result are discussed in this paper.
DAUGHMAN ‟ S ALGORITHM METHOD FOR IRIS RECOGNITION-A BIOMETRIC APPROACH

Iris recognition is considered to be the most reliable and accurate biometric identification system available. Iris recognition system captures an image of an individual’s eye, the iris in the image is then meant for the further segmentation and normalization for extracting its feature. The performance of iris recognition systems depends on the process of segmentation. Segmentation is used for the localization of the correct iris region in the particular portion of an eye and it should be done accurately and correctly to remove the eyelids, eyelashes, reflection and pupil noises present in iris region. In our paper we are using Daughman’s Algorithm segmentation method for Iris Recognition. Iris images are selected from the CASIA Database, then the iris and pupil boundary are detected from rest of the eye image, removing the noises. The segmented iris region was normalized to minimize the dimensional inconsistencies between iris regions by using Daugman’s Rubber Sheet Model. Then the features of the iris were encoded by convolving the normalized iris region with 1D Log-Gabor filters and phase quantizing the output in order to produce a bit-wise biometric template. The Hamming distance was chosen as a matching metric, which gave the measure of how many bits disagreed between the templates of the iris. Index Terms —Daughman’s Algorithm, Daugman’s Rubber Sheet Model, Hamming Distance, Iris Recognition, segmentation.
From high heels to weed attics: a syntactic investigation of chick lit and literature

Stylometric analysis of prose is typically limited to classification tasks such as authorship attribution. Since the models used are typically black boxes, they give little insight into the stylistic differences they detect. In this paper, we characterize two prose genres syntactically: chick lit (humorous novels on the challenges of being a modern-day urban female) and high literature. First, we develop a top-down computational method based on existing literary-linguistic theory. Using an off-the-shelf parser we obtain syntactic structures for a Dutch corpus of novels and measure the distribution of sentence types in chick-lit and literary novels. The results show that literature contains more complex (subordinating) sentences than chick lit. Secondly, a bottom-up analysis is made of specific morphological and syntactic features in both genres, based on the parser’s output. This shows that the two genres can be distinguished along certain features. Our results indicate that detailed insight into stylistic differences can be obtained by combining computational linguistic analysis with literary theory.
Toward Interactive Grounded Language Acqusition

This paper addresses the problem of enabling robots to interactively learn visual and spatial models from multi-modal interactions involving speech, gesture and images. Our approach, called Logical Semantics with Perception (LSP), provides a natural and intuitive interface by significantly reducing the amount of supervision that a human is required to provide. This paper demonstrates LSP in an interactive setting. Given speech and gesture input, LSP is able to learn object and relation classifiers for objects like mugs and relations like left and right. We extend LSP to generate complex natural language descriptions of selected objects using adjectives, nouns and relations, such as “the orange mug to the right of the green book.” Furthermore, we extend LSP to incorporate determiners (e.g., “the”) into its training procedure, enabling the model to generate acceptable relational language 20% more often than the unaugmented model.
Protein disorder prediction: implications for structural proteomics.

A great challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Disordered regions in proteins often contain short linear peptide motifs (e.g., SH3 ligands and targeting signals) that are important for protein function. We present here DisEMBL, a computational tool for prediction of disordered/unstructured regions within a protein sequence. As no clear definition of disorder exists, we have developed parameters based on several alternative definitions and introduced a new one based on the concept of "hot loops," i.e., coils with high temperature factors. Avoiding potentially disordered segments in protein expression constructs can increase expression, foldability, and stability of the expressed protein. DisEMBL is thus useful for target selection and the design of constructs as needed for many biochemical studies, particularly structural biology and structural genomics projects. The tool is freely available via a web interface (http://dis.embl.de) and can be downloaded for use in large-scale studies.
Non-Local Recurrent Network for Image Restoration

Many classic methods have shown non-local self-similarity in natural images to be an effective prior for image restoration. However, it remains unclear and challenging to make use of this intrinsic property via deep networks. In this paper, we propose a non-local recurrent network (NLRN) as the first attempt to incorporate non-local operations into a recurrent neural network (RNN) for image restoration. The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood. (2) We fully employ the RNN structure for its parameter efficiency and allow deep feature correlation to be propagated along adjacent recurrent states. This new design boosts robustness against inaccurate correlation estimation due to severely degraded images. (3) We show that it is essential to maintain a confined neighborhood for computing deep feature correlation given degraded images. This is in contrast to existing practice [43] that deploys the whole image. Extensive experiments on both image denoising and super-resolution tasks are conducted. Thanks to the recurrent non-local operations and correlation propagation, the proposed NLRN achieves superior results to state-of-the-art methods with many fewer parameters. The code is available at https://github.com/Ding-Liu/NLRN.
Accurate and scalable nearest neighbors in large networks based on effective importance

Nearest neighbor proximity search in large graphs is an important analysis primitive with a variety of applications in graph data from different domains. We propose a novel proximity measure for weighted graphs called Effective Importance which incorporates multiple paths between nodes and captures the inherent structural clusters within a network. We develop effective bounds on the EI value using a modified small subnetwork around a query node, enabling scalable exact nearest neighbor (NN) search at query time. Our NN search does not require heavy offline analysis or holistic knowledge of the graph, making our method suitable for very large dynamically changing networks or composite network overlays.
 We employ our NN search algorithm on social, information and biological networks and demonstrate the effectiveness and scalability of the approach. For million-node networks, our method retrieves the exact top 20 neighbors using less than $0.2%$ of the network edges in a fraction of a second on a conventional desktop machine. We also evaluate the effectiveness of our proximity measure and NN search for three applications, namely (i) finding good local clusters, (ii) network sparsification and (iii) prediction of node attributes in information networks. The EI measure and NN search method outperform recent counterparts from the literature in all applications.
A Resource-Based View of Strategic IT Alignment: How Knowledge Sharing Creates Competitive Advantage

A critical decision problem for top management, and the focus of this study, is whether the CEO (chief executive officer) and CIO (chief information officer) should commit their time to formal planning with the expectation of producing an information technology (IT)-based competitive advantage. Using the perspective of the resource-based view, a model is presented that examines how strategic IT alignment can produce enhanced organizational strategies that yield competitive advantage. One hundred sixty-one CIOs provided data using a postal survey. Results supported seven of the eight hypotheses. They showed that information intensity is an important antecedent to strategic IT alignment, that strategic IT alignment is best explained by multiple constructs which operationalize both process and content measures, and that alignment between the IT plan and the business plan is significantly related to the use of IT for competitive advantage. Study results raise questions about the effect of CEO participation, which appears to be the weak link in the process, and also about the perception of the CIO on the importance of CEO involvement. The paper contributes to our understanding of how knowledge sharing in the alignment process contributes to the creation of superior organizational strategies, provides a framework of the alignment-performance relationship, and furnishes several new constructs.  Competitive Advantage, Information Systems Planning, Knowledge Sharing, Resource-Based View, Strategic Planning, and Structural Equation Modeling.
Malware fuzzy ontology for semantic web

The term malware, in the area related to computer science, is used to define malicious code which is designed and written to execute attacks on software systems. In this paper, after a quick review of malact ontology, malware ontology is presented. Malwares include viruses, worms, botnets, spywares, backdoors, trojan horses, rootkits and exploits. In this paper malwares are grouped based on four attributes. These attributes include: Objective, operational status, establishment status and communication status. Then by analyzing malware and their characteristics, we propose malware ontology. The ontology is used to represent the concepts and their relationships in network security. One of the usages of ontology is information sharing and reuse in semantic web. In this paper, by proposing malware ontology, we presented the semantic relation map between concepts of this area which is used in semantic based search engines in incident area and CERT portals. Because malwares have similar characteristics, there is no clear boundary between their concepts so fuzzy logic is used to represent malware relationships. Malware relationships are presented in five categories: very weak relations, weak relations, moderate relations, good relations and very good relations and weights are assigned to them. If search is done on any concept (nodes) in the graph, the amount of the relationship with other concepts is calculated and based on the search demand and level of relationship, search is done on other related concepts.
Creation of a neovagina by Davydov's laparoscopic modified technique in patients with Rokitansky syndrome.

The purpose of this study was to assess anatomic and functional results after the laparoscopic Davydov procedure for the creation of a neovagina in Rokitansky syndrome.



Thirty patients with Rokitansky syndrome underwent the laparoscopic Davydov technique from June 2005-August 2008. Mean follow-up time lasted 30 months (range, 6-44 months) and included clinical examinations and evaluation of the quality of sexual intercourse; vaginoscopy, Schiller's test, and neovaginal biopsies were performed after 6 and 12 months. Functional results were assessed with the use of Rosen's Female Sexual Function Index and were compared with age-matched normal control subjects.



No perioperative complications occurred. At 6 months, anatomic success was achieved in 97% of the patients (n = 29); functional success and optimal results for the Female Sexual Function Index questionnaire were obtained in 96% of patients. Vaginoscopy and biopsy results showed a normal iodine-positive vaginal epithelium.



The Davydov technique seems to be a safe and effective treatment for vaginal agenesis in patients with Rokitansky syndrome.
Blind equalization and multiuser detection in dispersive CDMA channels

The problem of blind demodulation of multiuser information symbols in a high-rate code-division multiple-access (CDMA) network in the presence of both multiple-access interference (MAI) and intersymbol interference (ISI) is considered. The dispersive CDMA channel is first cast into a multipleinput multiple-output (MIMO) signal model framework. By applying the theory of blind MIMO channel identification and equalization, it is then shown that under certain conditions the multiuser information symbols can be recovered without any prior knowledge of the channel or the users’ signature waveforms (including the desired user’s signature waveform), although the algorithmic complexity of such an approach is prohibitively high. However, in practice, the signature waveform of the user of interest is always available at the receiver. It is shown that by incorporating this knowledge, the impulse response of each user’s dispersive channel can be identified using a subspace method. It is further shown that based on the identified signal subspace parameters and the channel response, two linear detectors that are capable of suppressing both MAI and ISI, i.e., a zeroforcing detector and a minimum-mean-square-errror (MMSE) detector, can be constructed in closed form, at almost no extra computational cost. Data detection can then be furnished by applying these linear detectors (obtained blindly) to the received signal. The major contribution of this paper is the development of these subspace-based blind techniques for joint suppression of MAI and ISI in the dispersive CDMA channels.
GOP primary season on twitter: "popular" political sentiment in social media

As mainstream news media and political campaigns start to pay attention to the political discourse online, a systematic analysis of political speech in social media becomes more critical. What exactly do people say on these sites, and how useful is this data in estimating political popularity? In this study we examine Twitter discussions surrounding seven US Republican politicians who were running for the US Presidential nomination in 2011. We show this largely negative rhetoric to be laced with sarcasm and humor and dominated by a small portion of users. Furthermore, we show that using out-of-the-box classification tools results in a poor performance, and instead develop a highly optimized multi-stage approach designed for general-purpose political sentiment classification. Finally, we compare the change in sentiment detected in our dataset before and after 19 Republican debates, concluding that, at least in this case, the Twitter political chatter is not indicative of national political polls.
The German hospital malnutrition study.

Malnutrition is frequently observed in chronic and severe diseases and associated with impaired outcome. In Germany general data on prevalence and impact of hospital malnutrition are missing.



Nutritional state was assessed by subjective global assessment (SGA) and by anthropometric measurements in 1,886 consecutively admitted patients in 13 hospitals (n=1,073, university hospitals; n=813, community or teaching hospitals). Risk factors for malnutrition and the impact of nutritional status on length of hospital stay were analyzed.



Malnutrition was diagnosed in 27.4% of patients according to SGA. A low arm muscle area and arm fat area were observed in 11.3% and 17.1%, respectively. Forty-three % of patients 70 years old were malnourished compared to only 7.8% of patients <30 years. The highest prevalence of malnutrition was observed in geriatric (56.2%), oncology (37.6%), and gastroenterology (32.6%) departments. Multivariate analysis revealed three independent risk factors: higher age, polypharmacy, and malignant disease (all P<0.01). Malnutrition was associated with an 43% increase of hospital stay (P<0.001).



In German hospitals every fourth patient is malnourished. Malnutrition is associated with increased length of hospital stay. Higher age, malignant disease and major comorbidity were found to be the main contributors to malnutrition. Adequate nutritional support should be initiated in order to optimize the clinical outcome of these patients.
Characterizing digital image acquisition devices

Despite the growing popularity of digital imaging devices, the problem of accurately estimating the spatial frequency response or optical transfer function (OTF) of these devices has been largely neglected. Traditional methods for estimating OTFs were designed for film cameras and other devices that form continuous images. These traditional techniques do not provide accurate OTF estimates for typical digital image acquisition devices because they do not account for the fixed sampling grids of digital devices . This paper describes a simple method for accurately estimating the OTF of a digital image acquisition device. The method extends the traditional knife-edge technique''3 to account for sampling. One of the principal motivations for digital imaging systems is the utility of digital image processing algorithms, many of which require an estimate of the OTF. Algorithms for enhancement, spatial registration, geometric transformations, and other purposes involve restoration—removing the effects of the image acquisition device. Nearly all restoration algorithms (e.g., the
Looking for Gender : Gender Roles and Behaviors Among Online Gamers

Several hypotheses regarding the importance of gender and relationships were tested by combining a large survey dataset with unobtrusive behavioral data from 1 year of play. Consistent with expectations, males played for achievement-oriented reasons and were more aggressive, especially within romantic relationships where both partners played. Female players in such relationships had higher general happiness than their male counterparts. Contrary to stereotypes and current hypotheses, it was the female players who played the most. Female players were also healthier than male players or females in the general population. The findings have implications for gender theory and communication-oriented methods in games and online research—most notably for the use of self-reported time spent, which was systematically incorrect and different by gender.
Mental health consumers' experience of stigma.

The extent to which mental health consumers encounter stigma in their daily lives is a matter of substantial importance for their recovery and quality of life. This article summarizes the results of a nationwide survey of 1,301 mental health consumers concerning their experience of stigma and discrimination. Survey results and followup interviews with 100 respondents revealed experience of stigma from a variety of sources, including communities, families, churches, coworkers, and mental health caregivers. The majority of respondents tended to try to conceal their disorders and worried a great deal that others would find out about their psychiatric status and treat them unfavorably. They reported discouragement, hurt, anger, and lowered self-esteem as results of their experiences, and they urged public education as a means for reducing stigma. Some reported that involvement in advocacy and speaking out when stigma and discrimination were encountered helped them to cope with stigma. Limitations to generalization of results include the self-selection, relatively high functioning of participants, and respondent connections to a specific advocacy organization-the National Alliance for the Mentally Ill.
Developing a grounded theory approach: a comparison of Glaser and Strauss.

Novice qualitative researchers are often unsure regarding the analysis of their data and, where grounded theory is chosen, they may be uncertain regarding the differences that now exist between the approaches of Glaser and Strauss, who together first described the method. These two approaches are compared in relation to roots and divergences, role of induction, deduction and verification, ways in which data are coded and the format of generated theory. Personal experience of developing as a ground theorist is used to illustrate some of the key differences. A conclusion is drawn that, rather than debate relative merits of the two approaches, suggests that novice researchers need to select the method that best suits their cognitive style and develop analytic skills through doing research.
The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics

Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the impact of CACC on traffic-flow characteristics. It uses the traffic-flow simulation model MIXIC that was specially designed to study the impact of intelligent vehicles on traffic flow. The authors study the impacts of CACC for a highway-merging scenario from four to three lanes. The results show an improvement of traffic-flow stability and a slight increase in traffic-flow efficiency compared with the merging scenario without equipped vehicles
Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent

First-order methods play a central role in large-scale machine learning. Even though many variations exist, each suited to a particular problem, almost all such methods fundamentally rely on two types of algorithmic steps: gradient descent, which yields primal progress, and mirror descent, which yields dual progress. We observe that the performances of gradient and mirror descent are complementary, so that faster algorithms can be designed by linearly coupling the two. We show how to reconstruct Nesterov’s accelerated gradient methods using linear coupling, which gives a cleaner interpretation than Nesterov’s original proofs. We also discuss the power of linear coupling by extending it to many other settings that Nesterov’s methods cannot apply to.