nils-herrmann/clean_scirepeval huggingface dataset:
Theme-Weighted Ranking of Keywords from Text Documents Using Phrase Embeddings

Keyword extraction is a fundamental task in natural language processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases. Keywords from text documents are primarily extracted using supervised and unsupervised approaches. In this paper, we present an unsupervised technique that uses a combination of theme-weighted personalized PageRank algorithm and neural phrase embeddings for extracting and ranking keywords. We also introduce an efficient way of processing text documents and training phrase embeddings using existing techniques. We share an evaluation dataset derived from an existing dataset that is used for choosing the underlying embedding model. The evaluations for ranked keyword extraction are performed on two benchmark datasets comprising of short abstracts (Inspec), and long scientific papers (SemEval 2010), and is shown to produce results better than the state-of-the-art systems.
Social People-Tagging vs. Social Bookmark-Tagging

Tagging has been widely used and studied in various domains. Recently, people-tagging has emerged as a means to categorize contacts, and is also used in some social access control mechanisms. In this paper, we investigate whether there are differences between people-tagging and bookmark-tagging. We show that the way we tag documents about people, who we do not know personally, is similar to the way we tag online documents (i.e., bookmarks) about other categories (i.e., city, country, event). However, we show that the tags assigned to a document related to a friend, differ from the tags assigned to someone we do not know personally. We also analyze whether the age and gender of a taggee a person, who is tagged by others have influences on social people-tags (i.e., people-tags assigned in social Web 2.0 platforms).
Primary penile cancer organ sparing treatment

Surgical treatment of penile cancer is usually associated with mutilation; alterations in self-esteem and body image; affecting sexual and urinary functions; and declined health-related quality of life. Recently, organ sparing treatment has appeared and led to limiting these complications.



An extensive review of the literature concerning penile-preserving strategies was conducted. The focus was put on indications, general principles of management, surgical options and reconstructive techniques, the most common complications, as well as functional and oncological outcomes.



Analyzed methods, e.g.: topical chemotherapy, laser ablation therapy, radiotherapy, Moh's microscopic surgery, circumcision, wide local excision, glans resurfacing and glansectomy are indicated in low-stage tumors (Tis, Ta-T2). After glansectomy, reconstruction is also possible.



Organ sparing techniques may achieve good anatomical, functional, and psychological outcomes without compromising local cancer control, which depends on early diagnosis and treatment. Penile sparing strategies are acceptable treatment approaches in selected patients with low-stage penile cancer after establishing disease-risk and should be considered in this population.
Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

2016  2017 Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job. Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.
KnowMe and ShareMe: understanding automatically discovered personality traits from social media and user sharing preferences

There is much recent work on using the digital footprints left by people on social media to predict personal traits and gain a deeper understanding of individuals. Due to the veracity of social media, imperfections in prediction algorithms, and the sensitive nature of one's personal traits, much research is still needed to better understand the effectiveness of this line of work, including users' preferences of sharing their computationally derived traits. In this paper, we report a two- part study involving 256 participants, which (1) examines the feasibility and effectiveness of automatically deriving three types of personality traits from Twitter, including Big 5 personality, basic human values, and fundamental needs, and (2) investigates users' opinions of using and sharing these traits. Our findings show there is a potential feasibility of automatically deriving one's personality traits from social media with various factors impacting the accuracy of models. The results also indicate over 61.5% users are willing to share their derived traits in the workplace and that a number of factors significantly influence their sharing preferences. Since our findings demonstrate the feasibility of automatically inferring a user's personal traits from social media, we discuss their implications for designing a new generation of privacy-preserving, hyper-personalized systems.
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.
A robust method for PRI modulation recognition

One of the main problems of radar identification in electronic warfare support system is analysis of pulse repetition interval (PRI) modulation. In this paper a robust method of recognizing the PRI modulation type of radar signals is proposed to improve robustness of previous recognition methods as well as to reduce the complexity. Simulation results show that the proposed method achieves much better error recognition rate in case of missing and spurious pulses than other methods such as autocorrelation based techniques and the Noone method.
Translation UML activity diagram into colored Petri net with inscription

UML activity diagram is widely used to describe the behavior of the software system. Unfortunately, there is still no practical tool to verify the UML diagrams automatically. This paper proposes an alternative to translate UML activity diagram into a colored petri nets with inscription. The model translation rules are proposed to guide the automatic translation of the activity diagram with atomic action into a CPN model. Moreover, the relevant basic arc inscriptions are generated without manual elaboration. The resulting CPN with inscription is correctly verified as expected.
Orthodontic aspects of the use of oral implants in adolescents: a 10-year follow-up study.

The aim of the present study was to evaluate the long-term effect of implants installed in different dental areas in adolescents. The sample consisted of 18 subjects with missing teeth (congenital absence or trauma). The patients were of different chronological ages (between 13 and 17 years) and of different skeletal maturation. In all subjects, the existing permanent teeth were fully erupted. In 15 patients, 29 single implants (using the Brånemark technique) were installed to replace premolars, canines, and upper incisors. In three patients with extensive aplasia, 18 implants were placed in various regions. The patients were followed during a 10-year period, the first four years annually and then every second year. Photographs, study casts, peri-apical radiographs, lateral cephalograms, and body height measurements were recorded at each control. The results show that dental implants are a good treatment option for replacing missing teeth in adolescents, provided that the subject's dental and skeletal development is complete. However, different problems are related to the premolar and the incisor regions, which have to be considered in the total treatment planning. Disadvantages may be related to the upper incisor region, especially for lateral incisors, due to slight continuous eruption of adjacent teeth and craniofacial changes post-adolescence. Periodontal problems may arise, with marginal bone loss around the adjacent teeth and bone loss buccally to the implants. The shorter the distance between the implant and the adjacent teeth, the larger the reduction of marginal bone level. Before placement of the implant sufficient space must be gained in the implant area, and the adjacent teeth uprighted and paralleled, even in the apical area, using non-intrusive movements. In the premolar area, excess space is needed, not only in the mesio-distal, but above all in the bucco-lingual direction. Thus, an infraoccluded lower deciduous molar should be extracted shortly before placement of the implant to avoid reduction of the bucco-lingual bone volume. Oral rehabilitation with implant-supported prosthetic constructions seems to be a good alternative in adolescents with extensive aplasia, provided that craniofacial growth has ceased or is almost complete.
Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks

Automatic detection of lymphocyte in H&E images is a necessary first step in lots of tissue image analysis algorithms. An accurate and robust automated lymphocyte detection approach is of great importance in both computer science and clinical studies. Most of the existing approaches for lymphocyte detection are based on traditional image processing algorithms and/or classic machine learning methods. In the recent years, deep learning techniques have fundamentally transformed the way that a computer interprets images and have become a matchless solution in various pattern recognition problems. In this work, we design a new deep neural network model which extends the fully convolutional network by combining the ideas in several recent techniques, such as shortcut links. Also, we design a new training scheme taking the prior knowledge about lymphocytes into consideration. The training scheme not only efficiently exploits the limited amount of free-form annotations from pathologists, but also naturally supports efficient fine-tuning. As a consequence, our model has the potential of self-improvement by leveraging the errors collected during real applications. Our experiments show that our deep neural network model achieves good performance in the images of different staining conditions or different types of tissues.
Multiobjective evolutionary algorithms for dynamic social network clustering

The main focus of this paper is to propose integration of dynamic and multiobjective algorithms for graph clustering in dynamic environments under multiple objectives. The primary application is to multiobjective clustering in social networks which change over time. Social networks, typically represented by graphs, contain information about the relations (or interactions) among online materials (or people). A typical social network tends to expand over time, with newly added nodes and edges being incorporated into the existing graph. We reflect these characteristics of social networks based on real-world data, and propose a suitable dynamic multiobjective evolutionary algorithm. Several variants of the algorithm are proposed and compared. Since social networks change continuously, the immigrant schemes effectively used in previous dynamic optimisation give useful ideas for new algorithms. An adaptive integration of multiobjective evolutionary algorithms outperformed other algorithms in dynamic social networks.
Gender differences in depression.

It is commonly suggested that a female preponderance in depression is universal and substantial. This review considers that proposition and explanatory factors. The view that depression rates are universally higher in women is challenged with exceptions to the proposition helping clarify candidate explanations. 'Real' and artefactual explanations for any such phenomenon are considered, and the contribution of sex role changes, social factors and biological determinants are overviewed. While artefactual factors make some contribution, it is concluded that there is a higher order biological factor (variably determined neuroticism, 'stress responsiveness' or 'limbic system hyperactivity') that principally contributes to the gender differentiation in some expressions of both depression and anxiety, and reflects the impact of gonadal steroid changes at puberty. Rather than conclude that 'anatomy is destiny' we favour a diathesis stress model, so accounting for differential epidemiological findings. Finally, the impact of gender on response to differing antidepressant therapies is considered briefly.
Nuglets : a Virtual Currency to Stimulate Cooperation in Self-Organized Mobile Ad Hoc Networks

In mobile ad hoc networks, it is usually assumed that all the nodes belong to the same authority; therefore, they are expected to cooperate in order to support the basic functions of the network such as routing. In this paper, we consider the case in which each node is its own authority and tries to maximize the bene ts it gets from the network. In order to stimulate cooperation, we introduce a virtual currency and detail the way it can be protected against theft and forgery. We show that this mechanism ful lls our expectations without signi cantly decreasing the performance of the network.
Physiological and pathological roles for microRNAs in the immune system

Mammalian microRNAs (miRNAs) have recently been identified as important regulators of gene expression, and they function by repressing specific target genes at the post-transcriptional level. Now, studies of miRNAs are resolving some unsolved issues in immunology. Recent studies have shown that miRNAs have unique expression profiles in cells of the innate and adaptive immune systems and have pivotal roles in the regulation of both cell development and function. Furthermore, when miRNAs are aberrantly expressed they can contribute to pathological conditions involving the immune system, such as cancer and autoimmunity; they have also been shown to be useful as diagnostic and prognostic indicators of disease type and severity. This Review discusses recent advances in our understanding of both the intended functions of miRNAs in managing immune cell biology and their pathological roles when their expression is dysregulated.
GdR MACS et GdR Robotique Programme de la journée : jeudi 26 mai 2011 ENSAM , Salle GAMMA 9 h 30 : The Navigation and Control technology inside the AR . Drone micro UAV

This paper exposes the Navigation and Control technology embedded in a recently commercialized micro Unmanned Aerial Vehicle (UAV), the AR.Drone, which cost and performance are unprecedented among any commercial product for mass markets. The system relies on state-of-the-art indoor navigation systems combining low-cost inertial sensors, computer vision techniques, sonar, and accounting for aerodynamics models.
Review of Nurses’ Knowledge of Delirium, Dementia and Depressions (3Ds): Systematic Literature Review

This paper aims at reviewing literature on nurses’ knowledge of delirium, dementia and depression (3Ds) which are rapidly increasing worldwide as the population ages, and to identify interventions that have shown effectiveness in improving nurses’ knowledge level of these diseases. Nurses’ knowledge of delirium, dementia and depression is essential to providing quality patient care. To access the literature, online databases including Medline (OVID), CINAHL (EBSCO), Nursing and Allied Health Source (ProQuest), and Health and Medicine (ProQuest), in addition to Google scholar search engine, were searched using key words “delirium”, “dementia”, “depression”, “nurse*”, “knowledge” and their alternative words. Overall, 20 articles were found to meet the criteria for inclusion in the review. The study found that nurses’ knowledge of the 3Ds was generally low, and they were not particularly able to differentiate between the three diseases. It is important that health care systems are adequately resourced to meet this growing challenge. Nurses should receive appropriate training about the 3Ds, and their knowledge be reinforced through continuing education.
Uniform Embedding for Efficient JPEG Steganography

Steganography is the science and art of covert communication, which aims to hide the secret messages into a cover medium while achieving the least possible statistical detectability. To this end, the framework of minimal distortion embedding is widely adopted in the development of the steganographic system, in which a well designed distortion function is of vital importance. In this paper, a class of new distortion functions known as uniform embedding distortion function (UED) is presented for both side-informed and non side-informed secure JPEG steganography. By incorporating the syndrome trellis coding, the best codeword with minimal distortion for a given message is determined with UED, which, instead of random modification, tries to spread the embedding modification uniformly to quantized discrete cosine transform (DCT) coefficients of all possible magnitudes. In this way, less statistical detectability is achieved, owing to the reduction of the average changes of the first- and second-order statistics for DCT coefficients as a whole. The effectiveness of the proposed scheme is verified with evidence obtained from exhaustive experiments using popular steganalyzers with various feature sets on the BOSSbase database. Compared with prior arts, the proposed scheme gains favorable performance in terms of secure embedding capacity against steganalysis.
A framework for adapting agile development methodologies

Department of Information Technology and Decision Sciences, College of Business and Public Administration, Old Dominion University Nortfolk, VA, U.S.A.; Department of Statistics and Computer Information Systems, Zicklin School of Business, Baruch College, City University of New York, New York, NY, U.S.A.; Department of Management Science and Information Systems, College of Management, University of Massachusetts Boston, Boston, MA, U.S.A.; Board of Advisors Professor of Computer Information Systems, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, U.S.A.
[Imperforate hymen: undiagnosed at birth, surgical emergency in adolescence].

Imperforate hymen is a malformation that is easy to diagnose, even in countries with limited health care coverage. Unrecognized at birth, it becomes evident at puberty because of the development of a hematocolpos, which requires surgical intervention. This situation can be avoided with a complete examination of the infant at birth. This case report describes four patients whom we saw from 1995 through 2001 at the Bangui (Central African Republic) Pediatric Center and Community Hospital.
Randomised trial of old and new antihypertensive drugs in elderly patients: cardiovascular mortality and morbidity the Swedish Trial in Old Patients with Hypertension-2 study

The efficacy of new antihypertensive drugs has been questioned. We compared the effects of conventional and newer antihypertensive drugs on cardiovascular mortality and morbidity in elderly patients.



We did a prospective, randomised trial in 6614 patients aged 70-84 years with hypertension (blood pressure > or = 180 mm Hg systolic, > or = 105 mm Hg diastolic, or both). Patients were randomly assigned conventional antihypertensive drugs (atenolol 50 mg, metoprolol 100 mg, pindolol 5 mg, or hydrochlorothiazide 25 mg plus amiloride 2.5 mg daily) or newer drugs (enalapril 10 mg or lisinopril 10 mg, or felodipine 2.5 mg or isradipine 2-5 mg daily). We assessed fatal stroke, fatal myocardial infarction, and other fatal cardiovascular disease. Analysis was by intention to treat.



Blood pressure was decreased similarly in all treatment groups. The primary combined endpoint of fatal stroke, fatal myocardial infarction, and other fatal cardiovascular disease occurred in 221 of 2213 patients in the conventional drugs group (19.8 events per 1000 patient-years) and in 438 of 4401 in the newer drugs group (19.8 per 1000; relative risk 0.99 [95% CI 0.84-1.16], p=0.89). The combined endpoint of fatal and non-fatal stroke, fatal and non-fatal myocardial infarction, and other cardiovascular mortality occurred in 460 patients taking conventional drugs and in 887 taking newer drugs (0.96 [0.86-1.08], p=0.49).



Old and new antihypertensive drugs were similar in prevention of cardiovascular mortality or major events. Decrease in blood pressure was of major importance for the prevention of cardiovascular events.
Automatic extraction of relations between medical concepts in clinical texts

A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records.



A single support vector machine classifier was used to identify relations between concepts and to assign their semantic type. Several resources such as Wikipedia, WordNet, General Inquirer, and a relation similarity metric inform the classifier.



The techniques reported in this paper were evaluated in the 2010 i2b2 Challenge and obtained the highest F1 score for the relation extraction task. When gold standard data for concepts and assertions were available, F1 was 73.7, precision was 72.0, and recall was 75.3. F1 is defined as 2*Precision*Recall/(Precision+Recall). Alternatively, when concepts and assertions were discovered automatically, F1 was 48.4, precision was 57.6, and recall was 41.7.



Although a rich set of features was developed for the classifiers presented in this paper, little knowledge mining was performed from medical ontologies such as those found in UMLS. Future studies should incorporate features extracted from such knowledge sources, which we expect to further improve the results. Moreover, each relation discovery was treated independently. Joint classification of relations may further improve the quality of results. Also, joint learning of the discovery of concepts, assertions, and relations may also improve the results of automatic relation extraction.



Lexical and contextual features proved to be very important in relation extraction from medical texts. When they are not available to the classifier, the F1 score decreases by 3.7%. In addition, features based on similarity contribute to a decrease of 1.1% when they are not available.