nils-herrmann/clean_scirepeval huggingface dataset:
Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification

Person re-identification is generally divided into two part: first how to represent a pedestrian by discriminative visual descriptors and second how to compare them by suitable distance metrics. Conventional methods isolate these two parts, the first part usually unsupervised and the second part supervised. The Bag-of-Words (BoW) model is a widely used image representing descriptor in part one. Its codebook is simply generated by clustering visual features in Euclidian space. In this paper, we propose to use part two metric learning techniques in the codebook generation phase of BoW. In particular, the proposed codebook is clustered under Mahalanobis distance which is learned supervised. Extensive experiments prove that our proposed method is effective. With several low level features extracted on superpixel and fused together, our method outperforms state-of-the-art on person re-identification benchmarks including VIPeR, PRID450S, and Market1501.
Mydriasis with Light-Near Dissociation in Fisher's Syndrome

Approximately 50% of patients with Fisher's syndrome show involvement of the pupillomotor fibers and present with mydriasis and light-near dissociation. However, it is uncertain whether this phenomenon is induced by an aberrant reinnervation mechanism as in tonic pupil, or is based on other mechanisms such as those associated with tectal pupils. We evaluated the clinical course and the pupillary responses in four of 27 patients with Fisher's syndrome who presented with bilateral mydriasis. The pupils of both eyes of the four patients were involved at the early stage of Fisher's syndrome. The pupils in patients 1 and 2 showed mydriasis with apparent light-near dissociation lasting for a significant period and had denervation supersensitivity to cholinergic agents. On the other hand, the pupils of patients 3 and 4 were dilated and fixed to both light and near stimuli. Our observations indicate that the denervated iris sphincter muscles, which are supersensitive to the cholinergic transmitter, may play an important role in the expression of light-near dissociation in Fisher's syndrome. Jpn J Ophthalmol 2007;51:224–227 © Japanese Ophthalmological Society 2007
Rule-based Korean Grapheme to Phoneme Conversion Using Sound Patterns

Grapheme-to-phoneme conversion plays an important role in text-to-speech applications and other fields of computational linguistics. Although Korean uses a phonemic writing system, it must have a grapheme-to-phoneme conversion for speech synthesis because Korean writing system does not always reflect its actual pronunciations. This paper describes a grapheme-to-phoneme conversion method based on sound patterns to convert Korean text strings into phonemic representations. In the experiment with Korean news broadcasting evaluation set of 20 sentences, the accuracy of our system achieve as high as 98.70% on conversion. The performance of our rule-based system shows that the rule-based sound patterns are effective on Korean grapheme-to-phoneme conversion.
MODAClouds: A model-driven approach for the design and execution of applications on multiple Clouds

Cloud computing is emerging as a major trend in the ICT industry. While most of the attention of the research community is focused on considering the perspective of the Cloud providers, offering mechanisms to support scaling of resources and interoperability and federation between Clouds, the perspective of developers and operators willing to choose the Cloud without being strictly bound to a specific solution is mostly neglected.
 We argue that Model-Driven Development can be helpful in this context as it would allow developers to design software systems in a cloud-agnostic way and to be supported by model transformation techniques into the process of instantiating the system into specific, possibly, multiple Clouds. The MODAClouds (MOdel-Driven Approach for the design and execution of applications on multiple Clouds) approach we present here is based on these principles and aims at supporting system developers and operators in exploiting multiple Clouds for the same system and in migrating (part of) their systems from Cloud to Cloud as needed. MODAClouds offers a quality-driven design, development and operation method and features a Decision Support System to enable risk analysis for the selection of Cloud providers and for the evaluation of the Cloud adoption impact on internal business processes. Furthermore, MODAClouds offers a run-time environment for observing the system under execution and for enabling a feedback loop with the design environment. This allows system developers to react to performance fluctuations and to re-deploy applications on different Clouds on the long term.
Golden retriever: a Java based open source image retrieval engine

Golden Retriever Image Retrieval Engine (GRire) is an open source light weight Java library developed for Content Based Image Retrieval (CBIR) tasks, employing the Bag of Visual Words (BOVW) model. It provides a complete framework for creating CBIR system including image analysis tools, classifiers, weighting schemes etc., for efficient indexing and retrieval procedures. Its eminent feature is its extensibility, achieved through the open source nature of the library as well as a user-friendly embedded plug-in system. GRire is available on-line along with install and development documentation on http://www.grire.net and on its Google Code page http://code.google.com/p/grire. It is distributed either as a Java library or as a standalone Java application, both GPL licensed.
Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria.

Aim of this review was to describe the main technologies for non-invasive glucose monitoring and the corresponding most relevant devices. The review tries to overcome the limitations of previous reviews on this topic, such as the lack of objective criteria for inclusion or exclusion of technologies or devices, and the poor organization of the information, which often does not allow easy comparison between technologies and devices. In this review, the information is concise and organized into specific categories, and hence it becomes easy to compare advantages and disadvantages of the different technologies and devices. For technologies, the categories of information considered are the technology name, the underlying physical principle, the technology limitations and the measurement sites on the human body. For devices, the categories of information are the device name, its approval condition (FDA Approval and/or CE Mark), the technology on which it is based, a device general description, the tests performed on the device, the corresponding results, safety information, aspects affecting usability, current status of the device and the manufacturer, an Internet reference for the device. A total of 14 technologies and 16 devices are included. Conclusions of the review were that, despite some interesting and promising technologies and devices, a satisfactory solution to the non-invasive glucose monitoring problem still requires further efforts.
Clinical classification of cauda equina syndrome for proper treatment

Cauda equina syndrome (CES) is a severe complication of lumbar spinal disorders; it results from compression of the nerve roots of the cauda equina. The purpose of this study was to evaluate the clinical usefulness of a classification scheme of CES based on factors including clinical symptoms, imaging signs, and electrophysiological findings.



The records of 39 patients with CES were divided into 4 groups based on clinical features as follows. Group 1 (preclinical): low back pain with only bulbocavernosus reflex and ischiocavernosus reflex abnormalities. Group 2 (early): saddle sensory disturbance and bilateral sciatica. Group 3 (middle): saddle sensory disturbance, bowel or bladder dysfunction, motor weakness of the lower extremity, and reduced sexual function. Group 4 (late): absence of saddle sensation and sexual function in addition to uncontrolled bowel function. The outcome including radiographic and electrophysiological findings was compared between groups.



The main clinical manifestations of CES included bilateral saddle sensory disturbance, and bowel, bladder, and sexual dysfunction. The clinical symptoms of patients with multiple-segment canal stenosis identified radiographically were more severe than those of patients with single-segment stenosis. BCR and ICR improved in groups 1 and 2 after surgery, but no change was noted for groups 3 and 4.



We conclude that bilateral radiculopathy or sciatica are early stages of CES and indicate a high risk of development of advanced CES. Electrophysiological abnormalities and reduced saddle sensation are indices of early diagnosis. Patients at the preclinical and early stages have better functional recovery than patients in later stages after surgical decompression.
Design of Low-Power CMOS Cell Structures Using Subthreshold Conduction Region

Business intelligence (BI) has been referred to as the process of making better decisions through the use of people, processes, data and related tools and methodologies. Data mining is the extraction of hidden stating information from large databases. It is a powerful new technology with large potential to help the company's to focus on the most necessary information in the data warehouse. This study gives us an idea of how data mining is applied in exhibiting business intelligence thereby helping the organizations to make better decisions. Business intelligence, data mining, database, information technology, management information system ——————————  ——————————
Finite-Difference Modeling of Very-Low-Frequency Propagation in the Earth-Ionosphere Waveguide

Simulations of Very-low-frequency (VLF) transmitter signals are conducted using three models: the long-wave propagation capability, a finite-difference (FD) time-domain model, and an FD frequency-domain model. The FD models are corrected using Richardson extrapolation to minimize the numerical dispersion inherent in these models. Using identical ionosphere and ground parameters, the three models are shown to agree very well in their simulated VLF signal amplitude and phase, to within 1 dB of amplitude and a few degrees of phase, for a number of different simulation paths and transmitter frequencies. Furthermore, the three models are shown to produce comparable phase changes for the same ionosphere perturbations, again to within a few degrees. Finally, we show that the models reproduce the phase data of existing VLF transmitter–receiver pairs reasonably well, although the nighttime variation in the measured phase data is not captured by the simplified characterization of the ionosphere.
African land-cover classification using satellite data.

Data from the advanced very-high-resolution radiometer sensor on the National Oceanic and Atmospheric Administration's operational series of meteorological satellites were used to classify land cover and monitor vegetation dynamics for Africa over a 19-month period. There was a correspondence between seasonal variations in the density and extent of green-leaf vegetation and the patterns of rainfall associated with the movement of the Intertropical Convergence Zone. Regional variations, such as the 1983 drought in the Sahel of westem Africa, were observed. Integration of the weekly satellite data with respect to time for a 12-month period produced a remotely sensed estimate of primary production based upon the density and duration of green-leaf biomass. Eight of the 21-day composited data sets covering an 11-month period were used to produce a general land-cover classification that corresponded well with those of existing maps.
Networks , Shocks , and Systemic Risk

This chapter develops a unified framework for the study of how network interactions can function as a mechanism for propagation and amplification of microeconomic shocks. The framework nests various classes of games over networks, models of macroeconomic risk originating from microeconomic shocks, and models of financial interactions. Under the assumption that shocks are small, the authors provide a fairly complete characterization of the structure of equilibrium, clarifying the role of network interactions in translating microeconomic shocks into macroeconomic outcomes. This characterization provides a ranking of different networks in terms of their aggregate performance. It also sheds light on several seemingly contradictory results in the prior literature on the role of network linkages in fostering systemic risk.
Anti-vaccination movements and their interpretations.

Over the last two or three decades, growing numbers of parents in the industrialized world are choosing not to have their children vaccinated. In trying to explain why this is occurring, public health commentators refer to the activities of an anti-vaccination 'movement'. In the light of three decades of research on (new) social movements, what sense does it make to attribute decline in vaccination rates to the actions of an influential anti-vaccination movement? Two sorts of empirical data, drawn largely from UK and The Netherlands, are reviewed. These relate to the claims, actions and discourse of anti-vaccination groups on the one hand, and to the way parents of young children think about vaccines and vaccination on the other. How much theoretical sense it makes to view anti-vaccination groups as (new) social movement organizations (as distinct from pressure groups or self-help organizations) is as yet unclear. In any event there is no simple and unambiguous demarcation criterion. From a public health perspective, however, to focus attention on organized opponents of vaccination is appealing because it unites health professionals behind a banner of reason. At the same time it diverts attention from a potentially disruptive critique of vaccination practices; the critique in fact articulated by many parents. In the light of current theoretical discussion of 'scientific citizenship' this paper argues that identifying anti-vaccination groups with other social movements may ultimately have the opposite effect to that intended.
Sparse distance learning for object recognition combining RGB and depth information

In this work we address joint object category and instance recognition in the context of RGB-D (depth) cameras. Motivated by local distance learning, where a novel view of an object is compared to individual views of previously seen objects, we define a view-to-object distance where a novel view is compared simultaneously to all views of a previous object. This novel distance is based on a weighted combination of feature differences between views. We show, through jointly learning per-view weights, that this measure leads to superior classification performance on object category and instance recognition. More importantly, the proposed distance allows us to find a sparse solution via Group-Lasso regularization, where a small subset of representative views of an object is identified and used, with the rest discarded. This significantly reduces computational cost without compromising recognition accuracy. We evaluate the proposed technique, Instance Distance Learning (IDL), on the RGB-D Object Dataset, which consists of 300 object instances in 51 everyday categories and about 250,000 views of objects with both RGB color and depth. We empirically compare IDL to several alternative state-of-the-art approaches and also validate the use of visual and shape cues and their combination.
Description of shape information for 2-D and 3-D objects

The description of the spatial characteristics of twoand three-dimensional objects, in the framework of MPEG-7, is considered. The shape of an object is one of its fundamental properties, and this paper describes an e$cient way to represent the coarse shape, scale and composition properties of an object. This representation is invariant to resolution, translation and rotation, and may be used for both two-dimensional (2-D) and three-dimensional (3-D) objects. This coarse shape descriptor will be included in the eXperimentation Model (XM) of MPEG-7. Applications of such a description to search object databases, in particular the CAESAR anthropometric database are discussed.
Automated Metareviewing: A Classifier Approach to Assess the Quality of Reviews

A review’s quality can be evaluated through metric-based automated metareview. But not all the metrics should be weighted the same when it comes to evaluating the overall quality of reviews. For instance, if a review identifies specific problems about the reviewed artifact, then even with a low score for other metrics it should be evaluated as a helpful review. To evaluate the usefulness of a review, we propose a use of decision-tree based classifier models computed from the raw score of metareview metrics, instead of using all the metrics, we can use a subset of them.
Early human communication helps in understanding language evolution.

Building a theory on extant species, as Ackermann et al. do, is a useful contribution to the field of language evolution. Here, I add another living model that might be of interest: human language ontogeny in the first year of life. A better knowledge of this phase might help in understanding two more topics among the "several building blocks of a comprehensive theory of the evolution of spoken language" indicated in their conclusion by Ackermann et al., that is, the foundation of the co-evolution of linguistic motor skills with the auditory skills underlying speech perception, and the possible phylogenetic interactions of protospeech production with referential capabilities.
May I talk to you? : -)" - Facial Animation from Text

We introduce a facial animation system that produces real-time animation sequences including speech synchronization and non-verbal speech-related facial expressions from plain text input. A state-of-the-art text-to-speech synthesis component performs linguistic analysis of the text input and creates a speech signal from phonetic and intonation information. The phonetic transcription is additionally used to drive a speech synchronization method for the physically based facial animation. Further high-level information from the linguistic analysis such as different types of accents or pauses as well as the type of the sentence is used to generate non-verbal speech-related facial expressions such as movement of head, eyes, and eyebrows or voluntary eye blinks. Moreover, emoticons are translated into XML markup that triggers emotional facial expressions.
Polygenic Modeling with Bayesian Sparse Linear Mixed Models

Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given dataset one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a "Bayesian sparse linear mixed model" (BSLMM) that includes both these models as special cases. We address several key computational and statistical issues that arise when applying BSLMM, including appropriate prior specification for the hyper-parameters and a novel Markov chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction. For PVE estimation, we demonstrate that BSLMM combines the advantages of both standard LMMs and sparse regression modeling. For phenotype prediction it considerably outperforms either of the other two methods, as well as several other large-scale regression methods previously suggested for this problem. Software implementing our method is freely available from http://stephenslab.uchicago.edu/software.html.
Memristor-CMOS hybrid integrated circuits for reconfigurable logic.

Hybrid reconfigurable logic circuits were fabricated by integrating memristor-based crossbars onto a foundry-built CMOS (complementary metal-oxide-semiconductor) platform using nanoimprint lithography, as well as materials and processes that were compatible with the CMOS. Titanium dioxide thin-film memristors served as the configuration bits and switches in a data routing network and were connected to gate-level CMOS components that acted as logic elements, in a manner similar to a field programmable gate array. We analyzed the chips using a purpose-built testing system, and demonstrated the ability to configure individual devices, use them to wire up various logic gates and a flip-flop, and then reconfigure devices.
Detecting web page structure for adaptive viewing on small form factor devices

Mobile devices have already been widely used to access the Web. However, because most available web pages are designed for desktop PC in mind, it is inconvenient to browse these large web pages on a mobile device with a small screen. In this paper, we propose a new browsing convention to facilitate navigation and reading on a small-form-factor device. A web page is organized into a two level hierarchy with a thumbnail representation at the top level for providing a global view and index to a set of sub-pages at the bottom level for detail information. A page adaptation technique is also developed to analyze the structure of an existing web page and split it into small and logically related units that fit into the screen of a mobile device. For a web page not suitable for splitting, auto-positioning or scrolling-by-block is used to assist the browsing as an alterative. Our experimental results show that our proposed browsing convention and developed page adaptation scheme greatly improve the user's browsing experiences on a device with a small display.
Recommendation Semantic of Services In Smart City

The interactions in the world of the web and the possibilities offered by information and communication technologies are becoming increasingly unlimited and at the same time produce large volumes of data. The management and classification of these data becomes difficult in Many areas. The smart city seeks nature to facilitate the complexity of urban life which faces the same problems and challenges posed by large data due to the diversity and plurality of sectors (political, economic, social, administrative, cultural, etc.), Identify the problem of producing and selecting relevant and customized services, real-time decision making is a problem for users.
 In this contribution, we propose semantic recommendation architecture for service in the smart city, based on the user profile in order to produce relevant services adapted to their preferences, using semantic Web technologies and the Recommendation system.