nils-herrmann/clean_scirepeval huggingface dataset:
Value of magnetic resonance imaging in the evaluation of sex-reassignment surgery in male-to-female transsexuals*

We investigated the value of magnetic resonance imaging (MRI) in the evaluation of sex-reassignment surgery in male-to-female transsexual patients.  Ten male-to-female transsexual patients who underwent sex-reassignment surgery with inversion of combined penile and scrotal skin flaps for vaginoplasty were examined after surgery with MRI. Turbo spin-echo T2-weighted and spin-echo T1-weighted images were obtained in sagittal, coronal, and axial planes with a 1.5-T superconductive magnet. Images were acquired with and without an inflatable silicon vaginal tutor. The following parameters were evaluated: neovaginal depth, neovaginal inclination in the sagittal plane, presence of remnants of the corpus spongiosum and corpora cavernosa, and thickness of the rectovaginal septum.  The average neovaginal depth was 7.9 cm (range = 5–10 cm). The neovagina had a correct oblique inclination in the sagittal plane in four patients, no inclination in five, and an incorrect inclination in one. In seven patients, MRI showed remnants of the corpora cavernosa and/or of the corpus spongiosum; in three patients, no remnants were detected. The average thickness of the rectovaginal septum was 4 mm (range = 3–6 mm).  MRI allows a detailed assessment of the pelvic anatomy after genital reconfiguration and provides information that can help the surgeon to adopt the most correct surgical approach.
Plastics and environmental health: the road ahead.

Plastics continue to benefit society in innumerable ways, even though recent public focus on plastics has centered mostly on human health and environmental concerns, including their endocrine-disrupting properties and the long-term pollution they represent. The benefits of plastics are particularly apparent in medicine and public health. Plastics are versatile, cost-effective, require less energy to produce than alternative materials like metal or glass, and can be manufactured to have many different properties. Due to these characteristics, polymers are used in diverse health applications like disposable syringes and intravenous bags, sterile packaging for medical instruments as well as in joint replacements, tissue engineering, etc. However, not all current uses of plastics are prudent and sustainable, as illustrated by the widespread, unwanted human exposure to endocrine-disrupting bisphenol A (BPA) and di-(2-ethylhexyl)phthalate (DEHP), problems arising from the large quantities of plastic being disposed of, and depletion of non-renewable petroleum resources as a result of the ever-increasing mass production of plastic consumer articles. Using the health-care sector as example, this review concentrates on the benefits and downsides of plastics and identifies opportunities to change the composition and disposal practices of these invaluable polymers for a more sustainable future consumption. It highlights ongoing efforts to phase out DEHP and BPA in the health-care and food industry and discusses biodegradable options for plastic packaging, opportunities for reducing plastic medical waste, and recycling in medical facilities in the quest to reap a maximum of benefits from polymers without compromising human health or the environment in the process.
Personalization in Goal-Oriented Dialog

The main goal of modeling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios. End-to-end trained neural dialog systems are an important line of research for such generalized dialog models as they do not resort to any situation-specific handcrafting of rules. However, incorporating personalization into such systems is a largely unexplored topic as there are no existing corpora to facilitate such work. In this paper, we present a new dataset of goal-oriented dialogs which are influenced by speaker profiles attached to them. We analyze the shortcomings of an existing end-toend dialog system based on Memory Networks and propose modifications to the architecture which enable personalization. We also investigate personalization in dialog as a multi-task learning problem, and show that a single model which shares features among various profiles outperforms separate models for each profile.
A secure IoT management architecture based on Information-Centric Networking

This paper explores the use of Information-Centric Networking (ICN) to support management operations in IoT deployments, presenting the design of a flexible architecture that allows the appropriate operation of IoT devices within a delimited ICN network domain. Our architecture has been designed with special consideration to naming, interoperation, security and energy-efficiency requirements. We theoretically assess the communication overhead introduced by the security procedures of our solution, both at IoT devices and clients. Additionally, we show the potential of our architecture to accommodate enhanced management applications, focusing on a specific use case, i.e. an information freshness service level agreement application. Finally, we present a proof-of-concept implementation of our architecture over an Arduino board, and we use it to carry out a set of experiments that validate the feasibility of our solution. & 2016
Radar: Residual Analysis for Anomaly Detection in Attributed Networks

Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various types of anomalies in real-world attributed networks coexist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.
Modélisation et manipulation des systèmes OLAP : de l'intégration des documents à l'usager

Enfin, je ne peux terminer ces quelques lignes sans remercier ma famille qui dans l'ombre m'apporte depuis si longtemps tout le soutien qui m'est nécessaire, et spécialement, mes pensées vont à Magaly. Un peu d'histoire… Dès les années 60, les données informatisées dans les organisations ont pris une importance qui n'a cessé de croître. Les systèmes informatiques gérant ces données sont utilisés essentiellement pour faciliter l'activité quotidienne des organisations et pour soutenir les prises de décision. La démocratisation de la micro-informatique dans les années 80 a permis un important développement de ces systèmes augmentant considérablement les quantités de données 1 2 informatisées disponibles. Face aux évolutions nombreuses et rapides qui s'imposent aux organisations, la prise de décision est devenue dès les années 90 une activité primordiale nécessitant la mise en place de systèmes dédiés efficaces [Inmon, 1994]. A partir de ces années, les éditeurs de logiciels ont proposé des outils facilitant l'analyse des données pour soutenir les prises de décision. Les tableurs sont probablement les premiers outils qui ont été utilisés pour analyser les données à des fins décisionnelles. Ils ont été complétés par des outils facilitant l'accès aux données pour les décideurs au travers d'interfaces graphiques dédiées au « requêtage » ; citons le logiciel Business Objects qui reste aujourd'hui encore l'un des plus connus. Le développement de systèmes dédiés à la prise de décision a vu naître des outils E.T.L. (« Extract-Transform-Load ») destinés à faciliter l'extraction et la transformation de données décisionnelles. Dès la fin des années 90, les acteurs importants tels que Microsoft, Oracle, IBM, SAP sont intervenus sur ce nouveau marché en faisant évoluer leurs outils et en acquérant de nombreux logiciels spécialisés ; par exemple, SAP vient d'acquérir Business Objects pour 4,8 milliards d'euros. Ils disposent aujourd'hui d'offres complètes intégrant l'ensemble de la chaîne décisionnelle : E.T.L., stockage (S.G.B.D.), restitution et analyse. Cette dernière décennie connaît encore une évolution marquante avec le développement d'une offre issue du monde du logiciel libre (« open source ») qui atteint aujourd'hui une certaine maturité (Talend 3 , JPalo 4 , Jasper 5). Dominée par les outils du marché, l'informatique décisionnelle est depuis le milieu des années 90 un domaine investi par le monde de la recherche au travers des concepts d'entrepôt de données (« data warehouse ») [Widom, 1995] [Chaudhury, et al., 1997] et d'OLAP (« On-Line Analytical Processing ») [Codd, et al., 1993]. D'abord diffusées dans …
WordSup: Exploiting Word Annotations for Character Based Text Detection

Imagery texts are usually organized as a hierarchy of several visual elements, i.e. characters, words, text lines and text blocks. Among these elements, character is the most basic one for various languages such as Western, Chinese, Japanese, mathematical expression and etc. It is natural and convenient to construct a common text detection engine based on character detectors. However, training character detectors requires a vast of location annotated characters, which are expensive to obtain. Actually, the existing real text datasets are mostly annotated in word or line level. To remedy this dilemma, we propose a weakly supervised framework that can utilize word annotations, either in tight quadrangles or the more loose bounding boxes, for character detector training. When applied in scene text detection, we are thus able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e.g. ICDAR15 [19] and COCO-text [39]. The character detector acts as a key role in the pipeline of our text detection engine. It achieves the state-of-the-art performance on several challenging scene text detection benchmarks. We also demonstrate the flexibility of our pipeline by various scenarios, including deformed text detection and math expression recognition.
Biologically oriented preparation technique (BOPT) 
for implant-supported fixed prostheses

A patient of 58 years of age without medical problems came to the clinic due to missing teeth in the upper posterior region and to change the partial fixed prosthesis in the upper anterior area. Proposed treatment: surgical phase of three conical shape tapering implants with prosthetic platform in occlusal direction with mechanize collar tissue level with fixtures to place implant-supported metal-ceramic restorations. In the anterior area, a zirconium oxide fixed partial prosthesis was vertical preparation of the tooth's. When preparing teeth to receive fixed prostheses, the definition and shape of finish lines has been a subject of endless discussion, modification, and change ever since the beginnings of restorative prosthetic dentistry. The BOPT technique (biologically oriented preparation technique) was first described in the context of tooth-supported restorations but has recently been applied to dental implants with the aim of ensuring healthy peri-implant tissue and creating the possibility of modeling the peri-implant sulcus by modifying prosthetic emergence profiles. Vertical preparation of teeth and abutments without finish line on implants is a technique which was found to be adequate for ensuring the remodeling and stability of peri-implant tissues. Peri-implant tissue health, shoulderless abutments.
Bags of Local Convolutional Features for Scalable Instance Search

This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an assignment map, a compact representation that relates regions of an image with a visual word. We use the assignment map for fast spatial reranking, obtaining object localizations that are used for query expansion. We demonstrate the suitability of the BoW representation based on local CNN features for instance retrieval, achieving competitive performance on the Oxford and Paris buildings benchmarks. We show that our proposed system for CNN feature aggregation with BoW outperforms state-of-the-art techniques using sum pooling at a subset of the challenging TRECVid INS benchmark.
Fast defect detection in textured surfaces using 1 D Gabor filters

In this paper, we present a fast machine vision method for the automatic inspection of defects in textured surfaces. Traditional 2D Gabor filtering schemes have shown to be very effective for detecting local anomalies in textured surfaces of industrial materials. However, they are computationally expensive and sensitive to image rotation. In order to alleviate the limitations of 2D Gabor filtering, we first use the 1D ring-projection transformation to compress a 2D graylevel image to a 1D pattern, and then employ a 1D Gabor filter to detect defects embedded in a homogeneous texture. Given a problem with image size N x N and filter window W x W, the computational complexity can be significantly reduced from O(WN) in the 2D Gabor space to O(WN) in the 1D Gabor space, and the detection results are invariant to rotation changes of a texture. The experiments on structural textures such as wooden surface, LCD display and machined surface, and statistical textures such as granite, leather and sandpaper have shown the efficiency and effectiveness of the proposed method.
Thermal Feasibility of Die-Stacked Processing in Memory

Processing in memory (PIM) implemented via 3D die stacking has been recently proposed to reduce the widening gap between processor and memory performance. By moving computation that demands high memory bandwidth to the base logic die of a 3D memory stack, PIM promises significant improvements in energy efficiency. However, the vision of PIM implemented via 3D die stacking could potentially be derailed if the processor(s) raise the stack’s temperature to unacceptable levels. In this paper, we study the thermal constraints for PIM across different processor organizations and cooling solutions and show the range of designs that are viable under different conditions. We also demonstrate that PIM is feasible even with low-end, fanless cooling solutions. We believe these results help alleviate PIM thermal feasibility concerns and identify viable design points, thereby encouraging further exploration and research in novel PIM architectures, technologies, and use cases.
Guided Search 2.0 A revised model of visual search.

An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.
Nearest-neighbour classifiers in natural scene analysis

It is now well-established that k nearest-neighbour classi"ers o!er a quick and reliable method of data classi"cation. In this paper we extend the basic de"nition of the standard k nearest-neighbour algorithm to include the ability to resolve con#icts when the highest number of nearest neighbours are found for more than one training class (model-1). We also propose model-2 of nearest-neighbour algorithm that is based on "nding the nearest average distance rather than nearest maximum number of neighbours. These new models are explored using image understanding data. The models are evaluated on pattern recognition accuracy for correctly recognising image texture data of "ve natural classes: grass, trees, sky, river re#ecting sky and river re#ecting trees. On noise contaminated test data, the new nearest neighbour models show very promising results for further studies. We evaluate their performance with increasing values of neighbours (k) and discuss their future in scene analysis research.
peHash: A Novel Approach to Fast Malware Clustering

Data collection is not a big issue anymore with available honeypot software and setups. However malware collections gathered from these honeypot systems often suffer from massive sample counts, data analysis systems like sandboxes cannot cope with. Sophisticated self-modifying malware is able to generate new polymorphic instances of itself with different message digest sums for each infection attempt, thus resulting in many different samples stored for the same specimen. Scaling analysis systems that are fed by databases that rely on sample uniqueness based on message digests is only feasible to a certain extent. In this paper we introduce a non cryptographic, fast to calculate hash function for binaries in the Portable Executable format that transforms structural information about a sample into a hash value. Grouping binaries by hash values calculated with the new function allows for detection of multiple instances of the same polymorphic specimen as well as samples that are broken e.g. due to transfer errors. Practical evaluation on different malware sets shows that the new function allows for a significant reduction of sample counts.
SourceData: a semantic platform for curating and searching figures

Entities are further categorized into those that are the object of the measurement (‘assayed components’) and those, if any, that are subjected to targeted and controlled experimental interventions (‘perturbations/interventions’). These two core categories are related to the concepts ‘perturbagen’ and ‘target’ in the Bioassay Ontology (BAO2) and capture an important aspect of the design of experiments where multiple conditions are compared with each other in order to test whether a given perturbation (e.g., the presence or absence of a drug), causes a given response (e.g., a change in gene expression). Additional categories include ‘experimental variables’, ‘reporters’, ‘normalizing components’ and generic ‘biological components’ (Supplementary Data). We developed a web-based tool with a graphical user interface that allows computer-assisted manual extraction of the metadata model described above at the level of individual figure panels based on the information provided in figure legends and in the images. Files that contain raw or minimally processed data, when available, can furthermore be linked or uploaded and attached to the figure. As proof of principle, we have curated a compendium of over 18,000 experiments published across 23 journals. From the 721 papers processed, 381 papers were related to the field of autophagy, and the rest were annotated during the publication process of accepted manuscripts at four partner molecular biology journals. Both sets of papers were processed identically. Out of the 18,157 experimental panels annotated, 77% included at least one ‘intervention/assayed component’ pair, and this supported the broad applicability of the perturbation-centric SourceData model. We provide a breakdown of entities by categories in  We note that the presence of a perturbation is not a requirement for the model. As such, the SourceData model is also applicable in cases such as correlative observations. The SourceData model is independent of data type (i.e., image-based or numerical values) and is well suited for cell and molecular biology experiments. 77% of the processed entities were explicitly mentioned in the text of the legend. For the remaining entities, curators added the terms based on the labels directly displayed on the image of the figure.  a semantic platform for curating and searching figures
Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO

In recent years, several noteworthy large, cross-domain, and openly available knowledge graphs (KGs) have been created. These include DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Although extensively in use, these KGs have not been subject to an in-depth comparison so far. In this survey, we provide data quality criteria according to which KGs can be analyzed and analyze and compare the above mentioned KGs. Furthermore, we propose a framework for finding the most suitable KG for a given setting.
Cell phone-induced failures of visual attention during simulated driving.

This research examined the effects of hands-free cell phone conversations on simulated driving. The authors found that these conversations impaired driver's reactions to vehicles braking in front of them. The authors assessed whether this impairment could be attributed to a withdrawal of attention from the visual scene, yielding a form of inattention blindness. Cell phone conversations impaired explicit recognition memory for roadside billboards. Eye-tracking data indicated that this was due to reduced attention to foveal information. This interpretation was bolstered by data showing that cell phone conversations impaired implicit perceptual memory for items presented at fixation. The data suggest that the impairment of driving performance produced by cell phone conversations is mediated, at least in part, by reduced attention to visual inputs.
Contextual Gamification of Social Interaction - Towards Increasing Motivation in Social E-learning

In current e-learning studies, one of the main challenges is to keep learners motivated in performing desirable learning behaviours and achieving learning goals. Towards tackling this challenge, social e-learning contributes favourably, but it requires solutions that can reduce side effects, such as abusing social interaction tools for ‘chitchat’, and further enhance learner motivation. In this paper, we propose a set of contextual gamification strategies, which apply flow and self-determination theory for increasing intrinsic motivation in social e-learning environments. This paper also presents a social e-learning environment that applies these strategies, followed by a user case study, which indicates increased learners’ perceived intrinsic motivation.