nils-herrmann/clean_scirepeval huggingface dataset:
Macromastia: a review of presentation and management.

Macromastia is a condition of abnormal enlargement of the breast tissue in excess of the normal proportion. The condition may be caused by glandular hypertrophy, excessive fatty tissue or combination of both. The usual physiologic enlargement of female breast occurs over 3 to 5 years and the female breast size is related to body habitus and hereditary characteristics.



A review of the literatures of Macromastia was undertaken with emphasis on presentation and management.



The aetiology of macromastia is usually undetermined, however, hormonal excesses and hypersensitivity of the target organ have been found in some cases. Cases of hyperprolactinaemia has been reported by some workers. Immunological risk factors for development of macromastia have also been seen in some groups of patients with Myasthenia gravis, Chronic arthritis, and Hashimoto thyroiditis. Ultrasonography may show no breast parenchyma abnormalities, while mammography may be indicated in some patients who are 40 years or older. Hormonal assay can be done but its value in treatment is doubtful. Drugs are only marginally effective in reversing gigantomastia, therefore surgery remains the mainstay of treatment.



Management of macromastia can be physically, socially and psychologically satisfying to both the patients and the surgeons.
Structural Architectures for a Deployable Wideband UHF Antenna

This paper explores concepts for a wideband deployable antenna suitable for small satellites. The general approach taken was to closely couple antenna theory and structural mechanics, to produce a deployable antenna that is considered efficient in both fields. Approaches that can be deployed using stored elastic strain energy have been favored over those that require powered actuators or environmental effects to enforce deployment. Two types of concepts were developed: thin shell structure and pantograph. These concepts cover four antenna topologies: crossed log periodic, conical log spiral, helical and quadrifilar helix. Of these, the conical log spiral antenna and the accompanying deployment concepts are determined to be the most promising approaches that warrant further study.
A Semantic Web Methodology for Situation-Aware Curative Food Service Recommendation System

Recently curative food is becoming more popular, as more people realize its benefits. Based on the theory of Chinese medicine, food itself is medicine. The Curative food which is an ideal nutritious food can help to loss weight, increase immunity and is also good for curative effects in patients. In this paper, we propose a new curative food service (CFS) recommendation system, the designed Situation-aware Curative Food Service Recommendation System (SCFSRS), which provides the cooperation web-based platform for all related mobile users (MUs) and curative food service providers (CFSPs), could strengthen the ability of CFS suggestion. SCFSRS is a five-tier system composed of the MUs, UDDI registries (UDDIRs), CFSPs, curative food services server (CFSS), and database server (DS).
Distributed Neural Networks for Internet of Things: The Big-Little Approach

Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback for neural networks. In order to exploit the apparent trade-off between utilizing the available though limited embedded computing power of the IoT devices at high speed/stable latency and the seemingly unlimited computing power of Cloud computing at the cost of higher and variable latency, we propose a Big-Little architecture for deep neural networks. A small neural network trained to a subset of prioritized output classes can be used to calculate an output on the embedded devices, while a more specific classification can be calculated in the Cloud only when required. We show the applicability of this concept in the IoT domain by evaluating our approach for state of the art neural network classification problems on popular embedded devices such as the Raspberry Pi and Intel Edison.
DeExcelerator: a framework for extracting relational data from partially structured documents

Of the structured data published on the web, for instance as datasets on Open Data Platforms such as data.gov, but also in the form of HTML tables on the general web, only a small part is in a relational form. Instead the data is intermingled with formatting, layout and textual metadata, i.e., it is contained in partially structured documents. This makes transformation into a true relational form necessary, which is a precondition for most forms of data analysis and data integration. Studying data.gov as an example source for partially structured documents, we present a classification of typical normalization problems. We then present the DeExcelerator, which is a framework for extracting relations from partially structured documents such as spreadsheets and HTML tables.
The Impact of Power Allocation on Cooperative Non-orthogonal Multiple Access Networks With SWIPT

In this paper, a cooperative non-orthogonal multiple access (NOMA) network is considered, where a source communicates with two users through an energy harvesting relay. The impact of two types of NOMA power allocation policies, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), on the considered cooperative simultaneous wireless information and power transfer (SWIPT) system is investigated. In particular, closed-form expressions for the outage probability and their high SNR approximations are derived to characterize the performance of SWIPT-F-NOMA and SWIPT-CR-NOMA. These developed analytical results demonstrate that the two power allocation policies realize different tradeoffs between the reception reliability, user fairness and system complexity. Compared with the conventional SWIPT relaying networks with orthogonal multiple access (OMA), the proposed NOMA schemes can effectively reduce the outage probability, although all of them realize the same diversity gain.
THE SUSTAINABILITY BALANCED SCORECARD – LINKING SUSTAINABILITY MANAGEMENT TO BUSINESS STRATEGY

The Balanced Scorecard of Kaplan and Norton is a management tool that supports the successful implementation of corporate strategies. It has been discussed and considered widely in both practice and research. By linking operational and non-financial corporate activities with causal chains to the firm’s long-term strategy, the Balanced Scorecard supports the alignment and management of all corporate activities according to their strategic relevance. The Balanced Scorecard makes it possible to take into account non-monetary strategic success factors that significantly impact the economic success of a business. The Balanced Scorecard is thus a promising starting-point to also incorporate environmental and social aspects into the main management system of a firm. Sustainability management with the Balanced Scorecard helps to overcome the shortcomings of conventional approaches
Layer-wise Relevance Propagation for Explainable Recommendations

In this paper, we tackle the problem of explanations in a deeplearning based model for recommendations by leveraging the technique of layer-wise relevance propagation. We use a Deep Convolutional Neural Network to extract relevant features from the input images before identifying similarity between the images in feature space. Relationships between the images are identified by the model and layer-wise relevance propagation is used to infer pixel-level details of the images that may have significantly informed the model’s choice. We evaluate our method on an Amazon products dataset and demonstrate the efficacy of our approach.
Broadband antenna array aperture made of tightly couple printed dipoles

This study reports a novel tight-coupled dipole array antenna to operate with up to six octave bandwidth and 60 degrees scanning. The array was designed through full-wave EM simulations by employing the current sheet array radiator concept that was advanced by a novel integrated feed network. Several prototypes of planar and conformal arrays across 0.3–20 GHz have been fabricated and tested with good agreement observed between all predicted and measured terminal and radiation features. The exemplified arrays have been designed for 1.2–6 GHz with relative radiator height 0.12 of maximum operational wavelength.
Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier Hayder

Cervical cancer threatens the lives of many women in our world today. In 2014, the number of women infected with this disease in the United States was 12,578, of which 4,115 died, with a death rate of nearly 32%. Cancer data, including cervical cancer datasets, represent a significant challenge data mining techniques because absence of different costs for error cases. The proposed model present a cost sensitive classifiers that has three main stages; the first stage is prepressing the original data to prepare it for classification model which is build based on decision tree classifier with cost selectivity and finallyevaluation the proposed model based on many metrics in addition to apply a cross validation.The proposed model provides more accurate result in both binary class and multi class classification. It has a TP rate (0.429) comparing with (0.160) for typical decision tree in binary class task.
Silicon-on-Glass Dielectric Waveguide—Part I: For Millimeter-Wave Integrated Circuits

A low-cost Silicon-on-Glass (SOG) integrated circuit technology is proposed for millimeter-wave (mmW) applications, for the first time. In the proposed technology, all mmW passive components are made of high-resistivity Silicon (Si) on a glass substrate. The proposed technique leads to a high-precision and low-cost fabrication process, which eliminates the need for costly assembly of the complex structures. This is achieved by photolithography and dry etching of the entire integrated passive circuit through the Si layer of the SOG wafer. Silicon-on-Glass dielectric waveguide, as the basic component of the SOG integrated circuit, is theoretically and experimentally investigated. A test setup is designed to measure propagation characteristics of the proposed SOG waveguide. Measured dispersion diagrams of the SOG dielectric waveguide show average attenuation constants of 0.63 dB/cm, 0.28 dB/cm, and 0.53 dB/cm over 55-65 GHz, 90-110 GHz, and 140-170 GHz, respectively.
Road-marking Analysis for Autonomous Vehicle Guidance

Driving an automobile autonomously on rural roads requires knowledge about the geometry of the road. Furthermore, knowledge about the meaning of each lane of the road is needed in order to decide which lane should be taken and if the vehicle can do a lane change. This paper addresses the problem of extracting additional information about lanes. The information is extracted from the types of road-markings. The type of lane border markings is estimated in order to find out if a lane change is allowed. Arrows, which are painted on the road, are extracted and classified in order to determine the meaning of a lane such as a turn off lane.
Process Invariant Biasing of Ring Amplifiers Using Deadzone Regulation Circuit

In this paper, process invariant biasing is proposed for robust operation of ring amplifiers. The ring amplifier is an efficient solution for high accuracy amplification in sub-micron CMOS process. A traditional ring amplifier structure requires an external control voltage, defined as the deadzone voltage, to set the optimum quiescent current at the output stage. Other structures of ring amplifiers use on-chip resistors or current starved inverters to set the deadzone voltage. Since the deadzone voltage is a function of the threshold voltage of transistors in the output stage inverter, ring amplifiers are susceptible to process variations. In this paper, a deadzone regulation circuit is proposed that utilizes a constant current source and a negative feedback loop to regulate the quiescent current of the output stage inverter across process corners. Transistor level simulations are used to validate the operation of the proposed deadzone regulation technique across FF, TT and SS corners in a 65nm CMOS process. The design example uses a current starved inverter based ring amplifier in a switched capacitor amplifier to achieve a closed loop gain of 4 with a settling accuracy of ≤ 0.05% and operated at a sampling rate of 125MHz.
A new blended learning concept for medical students in otolaryngology.

To evaluate students' overall assessment and effectiveness of the web-based blended learning conception "Unified Patient Project" (UPP) for medical students rotating on their otolaryngology internship (ear, nose, and throat [ENT] tertiary).



Prospective comparison group design of the quasiexperimental type.



Medical education.



The experimental group (preintervention test [pretest], intervention, and postintervention test [posttest]) comprised 117 students, and the comparison group (pretest, alternative intervention, and posttest), 119.



In the experimental group, lecturing of case studies was replaced by the blended learning concept UPP. A standardized questionnaire evaluated students' overall assessment of teaching otolaryngology. A pretest and posttest using multiple choice questions was administered to clarify whether the UPP has led to a knowledge gain.



The comparison group was more satisfied with their teaching; however, this was not statistically significant (P = .26) compared with the UPP. Students with higher preknowledge benefitted from the UPP, while students with lower preknowledge did not (P = .01). On average, posttest results in the experimental group exceeded those of the comparison group by 8.7 percentage points for a 75% preknowledge of the maximum attainable score, while they fell below those of the comparison group by 8.1 percentage points for a 25% preknowledge.



Students' satisfaction with the blended learning concept UPP was lower than in the face-to-face teaching, although this was not statistically significant. The new web-based UPP leads to an improved knowledge in clinical otolaryngology for all students. Students with lower preknowledge benefitted more from face-to-face teaching than from the UPP, while students with higher preknowledge benefitted more from the UPP. This implies students with poor preknowledge need special promotion programs.
Optimizing Multipath Routing With Guaranteed Fault Tolerance in Internet of Things

Internet of Things (IoTs) refers to the rapidly growing network of connected objects and people that are able to collect and exchange data using embedded sensors. To guarantee the connectivity among these objects and people, fault tolerance routing has to be significantly considered. In this paper, we propose a bio-inspired particle multi-swarm optimization (PMSO) routing algorithm to construct, recover, and select  $k$ -disjoint paths that tolerates the failure while satisfying the quality of service parameters. Multi-swarm strategy enables determining the optimal directions in selecting the multipath routing while exchanging messages from all positions in the network. The validity of the proposed algorithm is assessed and results demonstrate high-quality solutions compared with the canonical particle swarm optimization (CPSO). Our results indicate the superiority of the multi-swarm and fully PMSO with constriction coefficient, which record an average improvement over CPSO equal to 88.45% in terms of sensors’ count, and 89.15% and 86.51% under the ring and mesh topologies, respectively.
College students' electronic victimization in friendships and dating relationships: anticipated distress and associations with risky behaviors.

This study investigated college students' reports of electronic victimization in friendships and dating relationships. We examined 22 items representing four categories of electronic victimization: hostility, humiliation, exclusion, and intrusiveness. Nearly all participants (92%) reported some electronic victimization in the past year, with males reporting more victimization and females anticipating more distress. Both females and males anticipated more distress from electronic victimization in dating relationships than friendships. More actual experience with electronic victimization related to lower anticipated distress. Electronic victimization was associated with females' alcohol use, even after controlling for other victimization experiences. Discussion focuses on the contextualized nature of electronic victimization, and on the importance of understanding what makes electronic victimization highly distressing for some individuals.
Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections

Carolina Becatti, Guido Caldarelli, 2, 3, 4 Renaud Lambiotte, and Fabio Saracco IMT School for Advanced Studies, P.zza S. Francesco 19, 55100 Lucca (Italy) Istituto dei Sistemi Complessi (CNR) UoS Università Sapienza, P.le A. Moro 2, 00185 Rome (Italy) European Centre for Living Technology, Università di Venezia Ca’ Foscari, S. Marco 2940, 30124 Venice (Italy) Catchy srl, Talent Garden Poste Italiane Via Giuseppe Andreoli 9, 00195 Rome (Italy) Mathematical Institute, University of Oxford, Oxford (UK)
Morbidity in patients with hidradenitis suppurativa.

Although skin diseases are often immediately visible to both patients and society, the morbidity they cause is only poorly defined. It has been suggested that quality-of-life measures may be a relevant surrogate measure of skin disease. Hidradenitis suppurativa (HS) leads to painful eruptions and malodorous discharge and is assumed to cause a significant degree of morbidity. The resulting impairment of life quality has not previously been quantitatively assessed, although such an assessment may form a pertinent measure of disease severity in HS.



To measure the impairment of life quality in patients with HS.



In total, 160 patients suffering from HS were approached. The following data were gathered: quality-of-life data (Dermatology Life Quality Index, DLQI questionnaire), basic demographic data, age at onset of the condition and the average number of painful lesions per month.



One hundred and fourteen patients participated in the study. The mean +/- SD age of the patients was 40.9 +/- 11.7 years, the mean +/- SD age at onset 21.8 +/- 9.9 years and the mean +/- SD duration of the disease 18.8 +/- 11.4 years. Patients had a mean +/- SD DLQI score of 8.9 +/- 8.3 points. The highest mean score out of the 10 DLQI questions was recorded for question 1, which measures the level of pain, soreness, stinging or itching (mean 1.55 points, median 2 points). Patients experienced a mean of 5.1 lesions per month.



HS causes a high degree of morbidity, with the highest scores obtained for the level of pain caused by the disease. The mean DLQI score for HS was higher than for previously studied skin diseases, and correlated with disease intensity as expressed by lesions per month. This suggests that the DLQI may be a relevant outcome measure in future therapeutic trials in HS.
MultiVis: Content-Based Social Network Exploration through Multi-way Visual Analysis

With the explosion of social media, scalability becomes a key challenge. There are two main aspects of the problems that arise: 1) data volume: how to manage and analyze huge datasets to efficiently extract patterns, 2) data understanding: how to facilitate understanding of the patterns by users? To address both aspects of the scalability challenge, we present a hybrid approach that leverages two complementary disciplines, data mining and information visualization. In particular, we propose 1) an analytic data model for content-based networks using tensors; 2) an efficient high-order clustering framework for analyzing the data; 3) a scalable context-sensitive graph visualization to present the clusters. We evaluate the proposed methods using both synthetic and real datasets. In terms of computational efficiency, the proposed methods are an order of magnitude faster compared to the baseline. In terms of effectiveness, we present several case studies of real corporate social networks.
Mixing Coins of Different Quality: A Game-Theoretic Approach

Cryptocoins based on public distributed ledgers can differ in their quality due to different subjective values users assign to coins depending on the unique transaction history of each coin. We apply game theory to study how qualitative differentiation between coins will affect the behavior of users interested in improving their anonymity through mixing services. We present two stylized models of mixing with perfect and imperfect information and analyze them for three distinct quality propagation policies: poison, haircut, and seniority. In the game of perfect information, mixing coins of high quality remains feasible under certain conditions, while imperfect information eventually leads to a mixing market where only coins of the lowest quality are mixed.
A Comprehensive Review on Handwritten Digit Recognition using various Neural Network Approaches

Handwritten digit recognition has capability of the computer to receive information from sources such as paper documentation, camera, tablets and other devices. Handwritten digits are used in different types of data like cheque, post-mail, address on home’s name plate and exam form which also filled by hand etc. In recent years, handwritten digit recognition plays a main role for user authentication applications. In the proposed work, we will be developed gradient descent back propagation using neural network based on digit recognition system. Here introducing four phase: preprocessing, segmentation, feature extraction, classification. MNIST dataset contains CSV file from which image can be obtained by reading it. And this image acts as an input to the procedure. After that this image is preprocessed using RGB converter into binary form and contains some noise ,this noise will be remove using median filter and then convert into filtered image and also various techniques such as skeletonization, centering, and thresholding, are used for the preprocessing the image numerals If the characteristics for one class are distinct and remain constant with variations in this class, then we can term the features as good and desired. Neural Network Technique is used for classification and recognition of handwritten digits through training and testing.
Beyond Big and Little : The Four C Model of Creativity

Most investigations of creativity tend to take one of two directions: everyday creativity (also called “little-c”), which can be found in nearly all people, and eminent creativity (also called “Big-C”), which is reserved for the great. In this paper, the authors propose a Four C model of creativity that expands this dichotomy. Specifically, the authors add the idea of “mini-c,” creativity inherent in the learning process, and Pro-c, the developmental and effortful progression beyond little-c that represents professional-level expertise in any creative area. The authors include different transitions and gradations of these four dimensions of creativity, and then discuss advantages and examples of the Four C Model.