nils-herrmann/clean_scirepeval huggingface dataset:
Employment of Diode Laser in the Treatment of Epulis Fissuratum. Case Report

Diode laser has become an important alternative technique for surgical treatment of oral lesions. Case report. It is reported a 73-years-old female patient case diagnosed of epulis fissuratum that was treated with FOX ® diode laser. Re-evaluation showed an appropriate healing with a total removal of the lesion. Diode laser can be used for the removal of soft tissue oral lesions with very good results due to its numerous advantages.
Auxetic metamaterial simplifies soft robot design

Soft materials are being adopted in robotics in order to facilitate biomedical applications and in order to achieve simpler and more capable robots. One route to simplification is to design the robot's body using `smart materials' that carry the burden of control and actuation. Metamaterials enable just such rational design of the material properties. Here we present a soft robot that exploits mechanical metamaterials for the intrinsic synchronization of two passive clutches which contact its travel surface. Doing so allows it to move through an enclosed passage with an inchworm motion propelled by a single actuator. Our soft robot consists of two 3D-printed metamaterials that implement auxetic and normal elastic properties. The design, fabrication and characterization of the metamaterials are described. In addition, a working soft robot is presented. Since the synchronization mechanism is a feature of the robot's material body, we believe that the proposed design will enable compliant and robust implementations that scale well with miniaturization.
Comparative methods of spike detection in epilepsy

Epilepsy is a common neurological condition which affects the central nervous system that causes people to have a seizure and can be assessed by electroencephalogram (EEG). Electroencephalography (EEG) signals reflect two types of paroxysmal activity: ictal activity and interictal paroxystic events (IPE). The relationship between IPE and ictal activity is an essential and recurrent question in epileptology. The spike detection in EEG is a difficult problem. Many methods have been developed to detect the IPE in the literature. In this paper we propose three methods to detect the spike in real EEG signal: Page Hinkley test, smoothed nonlinear energy operator (SNEO) and fractal dimension. Before using these methods, we filter the signal. The Singular Spectrum Analysis (SSA) filter is used to remove the noise in an EEG signal.
Artificial Intelligence and Software Engineering: Understanding the Promise of the Future

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.
Anti-jamming filtering for DRFM repeat jammer based on stretch processing

This paper addresses issues about the rejection of false jammer targets in the presence of digital radio frequency memory (DRFM) repeat jammer. An anti-jamming filtering technique is proposed that it can eliminate this type of jamming signal. By using a stretch processing with a particular selected reference signal, the presented method can fully separate the echoes being reflected from the true targets and the signals being re-transmitted by a jammer in frequency domain. Therefore, utilizing the nonoverlapping properties of the received signals, filters or suchlike techniques can be used to reject the undesired jamming signals. Particularly, this method does not require estimation of jamming signal parameters and does not involve a great computation burden. Simulations are given to show the validity of the introduced approach.
Integrating BIM and Safety : An Automated Rule-Based Checking System for Safety Planning and Simulation

Safety planning in the construction industry is generally done separately from the project execution planning. This separation creates difficulties for safety engineers to analyze what, when, why and where safety measures are needed for preventing accidents. Lack of information and integration of available data (safety plan, project schedule, 2D project drawings) during the planning stage often results in scheduling work activities with overlapping space needs that then can create hazardous conditions, for example, work above other crew. These space requirements are time dependent and often neglected due to the manual effort that is required to handle the data. Representation of project-specific activity space requirements in 4D models hardly happen along with schedule and work break-down structure. Even with full cooperation of all related stakeholders, current safety planning and execution still largely depends on manual observation and past experiences. The traditional manual observation is inefficient, error-prone, and the observed result can be easily effected by subjective judgments. This paper will demonstrate the development of an automated safety code checking tool for Building Information Modeling (BIM), work breakdown structure, and project schedules in conjunction with safety criteria to reduce the potential for accidents on construction projects. The automated safety compliance rule checker code builds on existing applications for building code compliance checking, structural analysis, and constructability analysis etc. and also the advances in 4D simulations for scheduling. Preliminary results demonstrate a computer-based automated tool can assist in safety planning and execution of projects on a day to day basis.
Adaptive Market Making via Online Learning

We consider the design of strategies for market making in an exchange. A market maker generally seeks to profit from the difference between the buy and sell price of an asset, yet the market maker also takes exposure risk in the event of large price movements. Profit guarantees for market making strategies have typically required certain stochastic assumptions on the price fluctuations of the asset in question; for example, assuming a model in which the price process is mean reverting. We propose a class of “spread-based” market making strategies whose performance can be controlled even under worst-case (adversarial) settings. We prove structural properties of these strategies which allows us to design a master algorithm which obtains low regret relative to the best such strategy in hindsight. We run a set of experiments showing favorable performance on recent real-world stock price data.
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks

Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchial feed-forward models (e.g., convolutional neural networks) showed promise in this direction, they are still difficult to train especially when few training examples are available. In this paper, we present a framework for training hierarchical feed-forward models for visual recognition, using transfer learning from pseudo tasks. These pseudo tasks are automatically constructed from data without supervision and comprise a set of simple pattern-matching operations. We show that these pseudo tasks induce an informative inverse-Wishart prior on the functional behavior of the network, offering an effective way to incorporate useful prior knowledge into the network training. In addition to being extremely simple to implement, and adaptable across different domains with little or no extra tuning, our approach achieves promising results on challenging visual recognition tasks, including object recognition, gender recognition, and ethnicity recognition.
Process of protein transport by the type III secretion system.

The type III secretion system (TTSS) of gram-negative bacteria is responsible for delivering bacterial proteins, termed effectors, from the bacterial cytosol directly into the interior of host cells. The TTSS is expressed predominantly by pathogenic bacteria and is usually used to introduce deleterious effectors into host cells. While biochemical activities of effectors vary widely, the TTSS apparatus used to deliver these effectors is conserved and shows functional complementarity for secretion and translocation. This review focuses on proteins that constitute the TTSS apparatus and on mechanisms that guide effectors to the TTSS apparatus for transport. The TTSS apparatus includes predicted integral inner membrane proteins that are conserved widely across TTSSs and in the basal body of the bacterial flagellum. It also includes proteins that are specific to the TTSS and contribute to ring-like structures in the inner membrane and includes secretin family members that form ring-like structures in the outer membrane. Most prominently situated on these coaxial, membrane-embedded rings is a needle-like or pilus-like structure that is implicated as a conduit for effector translocation into host cells. A short region of mRNA sequence or protein sequence in effectors acts as a signal sequence, directing proteins for transport through the TTSS. Additionally, a number of effectors require the action of specific TTSS chaperones for efficient and physiologically meaningful translocation into host cells. Numerous models explaining how effectors are transported into host cells have been proposed, but understanding of this process is incomplete and this topic remains an active area of inquiry.
Hyperthermic Intraperitoneal Chemotherapy (HIPEC) at the Time of Primary Curative Surgery in Patients with Colorectal Cancer at High Risk for Metachronous Peritoneal Metastases

Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) are maximally effective in early-stage colorectal cancer peritoneal metastases (CRC-PM); however, the use of HIPEC to treat subclinical-stage PM remains controversial. This prospective two-center study assessed adjuvant HIPEC in CRC patients at high risk for metachronous PM ( www.clinicaltrials.gov NCT02575859). During 2006–2012, a total of 22 patients without systemic metastases were prospectively enrolled to receive HIPEC simultaneously with curative surgery, plus adjuvant systemic chemotherapy (oxaliplatin/irinotecan-containing ± biologics), based on primary tumor-associated criteria: resected synchronous ovarian (n = 2) or minimal peritoneal (n = 6) metastases, primaries directly invading other organs (n = 4) or penetrating the visceral peritoneum (n = 10). A control group retrospectively included 44 matched (1:2) patients undergoing standard treatments and no HIPEC during the same period. The cumulative PM incidence was calculated in a competing-risks framework. Patient characteristics were comparable for all groups. Median follow-up was 65.2 months [95 % confidence interval (CI) 50.9–79.5] in the HIPEC group and 34.5 months (95 % CI 21.1–47.9) in the control group. The 5-year cumulative PM incidence was 9.3 % in the HIPEC group and 42.5 % in the control group (p = 0.004). Kaplan–Meier estimated 5-year overall survival (OS) was 81.3 % in the HIPEC group versus 70.0 % in the control group (p = 0.047). No operative death occurred. Grade 3–4 [National Cancer Institute Common Terminology Criteria for Adverse Events (NCI–CTCAE) version 4] morbidity rates were 18.2 % in the HIPEC group and 25 % in controls (p = 0.75). At multivariate analysis, HIPEC correlated to lower PM cumulative incidence [hazard ratio (HR) 0.04, 95 % CI 0.01–0.31; p = 0.002], and better OS (HR 0.25, 95 % CI 0.07–0.89; p = 0.039) and progression-free survival (HR 0.31, 95 % CI 0.11–0.85; p = 0.028). Adjuvant HIPEC may benefit CRC patients at high-risk for peritoneal failure. These results warrant confirmation in phase III trials.
An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis

Accurate diagnosis is crucial for successful treatment of the brain tumor. Accordingly in this paper, we propose an intelligent content-based image retrieval (CBIR) system which retrieves similar pathology bearing magnetic resonance (MR) images of the brain from a medical database to assist the radiologist in the diagnosis of the brain tumor. A single feature vector will not perform well for finding similar images in the medical domain as images within the same disease class differ by severity, density and other such factors. To handle this problem, the proposed CBIR system uses a two-step approach to retrieve similar MR images. The first step classifies the query image as benign or malignant using the features that discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features that distinguish the subclasses. In order to provide faster image retrieval, we propose an indexing method called clustering with principal component analysis (PCA) and KD-tree which groups subclass features into clusters using modified K-means clustering and separately reduces the dimensionality of each cluster using PCA. The reduced feature set is then indexed using a KD-tree. The proposed CBIR system is also made robust against misalignment that occurs during MR image acquisition. Experiments were carried out on a database consisting of 820 MR images of the brain tumor. The experimental results demonstrate the effectiveness of the proposed system and show the viability of clinical application.
Building Large Arabic Multi-domain Resources for Sentiment Analysis

While there has been a recent progress in the area of Arabic Sentiment Analysis, most of the resources in this area are either of limited size, domain specific or not publicly available. In this paper, we address this problem by generating large multi-domain datasets for Sentiment Analysis in Arabic. The datasets were scrapped from different reviewing websites and consist of a total of 33K annotated reviews for movies, hotels, restaurants and products. Moreover we build multi-domain lexicons from the generated datasets. Different experiments have been carried out to validate the usefulness of the datasets and the generated lexicons for the task of sentiment classification. From the experimental results, we highlight some useful insights addressing: the best performing classifiers and feature representation methods, the effect of introducing lexicon based features and factors affecting the accuracy of sentiment classification in general. All the datasets, experiments code and results have been made publicly available for scientific purposes.
Deconstructing the human algorithms for exploration

The dilemma between information gathering (exploration) and reward seeking (exploitation) is a fundamental problem for reinforcement learning agents. How humans resolve this dilemma is still an open question, because experiments have provided equivocal evidence about the underlying algorithms used by humans. We show that two families of algorithms can be distinguished in terms of how uncertainty affects exploration. Algorithms based on uncertainty bonuses predict a change in response bias as a function of uncertainty, whereas algorithms based on sampling predict a change in response slope. Two experiments provide evidence for both bias and slope changes, and computational modeling confirms that a hybrid model is the best quantitative account of the data.
Effects of Tourism Upon the Economy of Small and Medium-Sized European Cities . Cultural Tourists and “ The Others ”

The opinions expressed in this paper do not necessarily reflect the position of Fondazione Eni Enrico Mattei Corso Magenta, 63, 20123 Milano (I), web site: www.feem.it, e-mail: working.papers@feem.it Effects of Tourism Upon the Economy of Small and MediumSized European Cities. Cultural Tourists and “The Others” Barbara Del Corpo, Ugo Gasparino, Elena Bellini and William Malizia NOTA DI LAVORO 44.2008
Profiling Phishing Emails Based on Hyperlink Information

In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with who is (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVMto generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information.
Microduplication of 4p16.3 due to an unbalanced translocation resulting in a mild phenotype.

With the widespread clinical use of comparative genomic hybridization chromosomal microarray technology, several previously unidentified clinically significant submicroscopic chromosome abnormalities have been discovered. Specifically, there have been reports of clinically significant microduplications found in regions of known microdeletion syndromes. In general, these microduplications have distinct features from those described in the corresponding microdeletion syndromes. We present a 5½-year-old patient with normal growth, borderline normal IQ, borderline hypertelorism, and speech and language delay who was found to have a submicroscopic 2.3 Mb terminal duplication involving the two proposed Wolf-Hirschhorn syndrome (WHS) critical regions at chromosome 4p16.3. This duplication was the result of a maternally inherited reciprocal translocation involving the breakpoints 4p16.3 and 17q25.3. Our patient's features are distinct from those described in WHS and are not as severe as those described in partial trisomy 4p. There are two other patients in the medical literature with 4p16.3 microduplications of similar size also involving the WHS critical regions. Our patient shows clinical overlap with these two patients, although overall her features are milder than what has been previously described. Our patient's features expand the knowledge of the clinical phenotype of a 4p16.3 microduplication and highlight the need for further information about it.
ITIL maturity model

Information Technology Infra-structure Library (ITIL) is the most popular “best practices” framework for managing Information Technology (IT) services. However, implementing ITIL not only is very difficult but there also are no best practices for implementing ITIL. As a result, ITIL implementations are usually long, expensive, and risky. In this paper, we propose a maturity model to assess an ITIL implementation and provide a roadmap for improvement based on priorities, dependencies, and guidelines. We then demonstrate a practical application of the proposed model with a questionnaire to assess the ITIL Incident Management process that was evaluated in two real-world organizations.
Teacher adaptation to open learning spaces

The “open classroom” emerged as a reaction against the industrial-era enclosed and authoritarian classroom. Although contemporary school architecture continues to incorporate and express ideas of openness, more research is needed about how teachers adapt to new and different built contexts. Our purpose is to identify teacher reaction to the affordances of open space learning environments. We outline a case study of teacher perceptions of working in new open plan school buildings. The case study demonstrates that affordances of open space classrooms include flexibility, visibility and scrutiny, and a de-emphasis of authority; teacher reactions included collective practice, team orientation, and increased interactions and a democratisation of authority. We argue that teacher reaction to the new open classroom features adaptability, intensification of day-to-day practice, and intraand inter-personal knowledge and skills.
Modeling and simulation of PV module and zeta converter

Renewable Energy Sources (RES) plays a vital role today, owing to its excess availability and they are gaining importance because of the merits like pollution free, eco friendly, no maintenance, sustainability etc. Solar rays consist of photons, which are considered to be stream of energetic light particles. The solar cell or Photovoltaic cell (PV) converts photons present in solar rays to current. Intensity of sunlight striking the surface, efficiency and size of PV are the factors responsible for current generation from PV cell. However, change in irradiance, temperature or shadow effect will affect the overall PV performance. This will lead to decrease in output power from the solar cell. While modeling the PV in MATLAB, single diode model is given more importance over double diode model, because of the advantages like moderate complexity and acceptable accuracy. The PV is modeled in MATLAB considering the design equations of photon current and reverse saturation current. The design of power electronic converter is very important. To ensure reliability, safety, and to provide maximum efficiency to the PV system, selection and design of power electronic converters should be correct as well as optimal. The power converter is interfaced between PV panel and load. The Reference voltage is fixed based on the open circuit voltage available at the output of the converter. The benefits of zeta converter over other converters include non pulsating output current, lower settling time, adaptability, etc. In order to satisfy load voltage ripple requirement, and because of the non pulsating output current, zeta converter permits the use of small output capacitor. The objective of this paper is to maintain the constant output voltage, irrespective of change in irradiance. Change in irradiance, causes the change of output voltage from PV panel, which causes the duty cycle (D) to vary and duty cycle depends on both output voltage from PV module and reference voltage. Change in duty cycle makes the zeta converter to operate either in buck or boost mode.
Syntax and Data-to-Text Generation

With the development of the web of data, recent statistical, data-to-text generation approaches have focused on mapping data (e.g., database records or knowledge-base (KB) triples) to natural language. In contrast to previous grammar-based approaches, this more recent work systematically eschews syntax and learns a direct mapping between meaning representations and natural language. By contrast, I argue that an explicit model of syntax can help support NLG in several ways. Based on case studies drawn from KB-to-text generation, I show that syntax can be used to support supervised training with little training data; to ensure domain portability; and to improve statistical hypertagging.