nils-herrmann/clean_scirepeval huggingface dataset:
Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data

Data from the U.S. Defense Meteorological Satellite Program’s Operational Line-scan System are often used to map impervious surface area (ISA) distribution at regional and global scales, but its coarse spatial resolution and data saturation produce high inaccuracy in ISA estimation. Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) with its high spatial resolution and dynamic data range may provide new insights but has not been fully examined in mapping ISA distribution. In this paper, a new variable—Large-scale Impervious Surface Index (LISI)—is proposed to integrate VIIRS-DNB and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data for mapping ISA distribution. A regression model was established, in which LISI was used as an independent variable and the reference ISA from Landsat images was a dependent variable. The results indicated a better estimation performance using LISI than using a single VIIRS-DNB or MODIS NDVI variable. The LISI-based approach provides accurate spatial patterns from high values in core urban areas to low values in rural areas, with an overall root mean squared error of 0.11. The LISI-based approach is recommended for fractional ISA estimation in a large area. OPEN ACCESS Remote Sens. 2015, 7 12460
Node . js Paradigms and Benchmarks

The current environment of web applications demands performance and scalability. Several previous approaches have implemented threading, events, or both, but increasing traffic requires new solutions for improved concurrent service. Node.js is a new web framework that achieves both through server-side JavaScript and eventdriven I/O. Tests will be performed against two comparable frameworks that compare service request times over a number of cores. The results will demonstrate the performance of JavaScript as a server-side language and the efficiency of the non-blocking asynchronous model.
A Novel Doxorubicin Prodrug with Controllable Photolysis Activation for Cancer Chemotherapy

Doxorubicin (DOX) is a very effective anticancer agent. However, in its pure form, its application is limited by significant cardiotoxic side effects. The purpose of this study was to develop a controllably activatable chemotherapy prodrug of DOX created by blocking its free amine group with a biotinylated photocleavable blocking group (PCB). An n-hydroxy succunamide protecting group on the PCB allowed selective binding at the DOX active amine group. The PCB included an ortho-nitrophenyl group for photo cleavability and a water-soluble glycol spacer arm ending in a biotin group for enhanced membrane interaction. This novel DOX-PCB prodrug had a 200-fold decrease in cytotoxicity compared to free DOX and could release active DOX upon exposure to UV light at 350 nm. Unlike DOX, DOX-PCB stayed in the cell cytoplasm, did not enter the nucleus, and did not stain the exposed DNA during mitosis. Human liver microsome incubation with DOX-PCB indicated stability against liver metabolic breakdown. The development of the DOX-PCB prodrug demonstrates the possibility of using light as a method of prodrug activation in deep internal tissues without relying on inherent physical or biochemical differences between the tumor and healthy tissue for use as the trigger.
Sentence Relations for Extractive Summarization with Deep Neural Networks

Sentence regression is a type of extractive summarization that achieves state-of-the-art performance and is commonly used in practical systems. The most challenging task within the sentence regression framework is to identify discriminative features to represent each sentence. In this article, we study the use of sentence relations, e.g., Contextual Sentence Relations (CSR), Title Sentence Relations (TSR), and Query Sentence Relations (QSR), so as to improve the performance of sentence regression. CSR, TSR, and QSR refer to the relations between a main body sentence and its local context, its document title, and a given query, respectively.
 We propose a deep neural network model, Sentence Relation-based Summarization (SRSum), that consists of five sub-models, PriorSum, CSRSum, TSRSum, QSRSum, and SFSum. PriorSum encodes the latent semantic meaning of a sentence using a bi-gram convolutional neural network. SFSum encodes the surface information of a sentence, e.g., sentence length, sentence position, and so on. CSRSum, TSRSum, and QSRSum are three sentence relation sub-models corresponding to CSR, TSR, and QSR, respectively. CSRSum evaluates the ability of each sentence to summarize its local contexts. Specifically, CSRSum applies a CSR-based word-level and sentence-level attention mechanism to simulate the context-aware reading of a human reader, where words and sentences that have anaphoric relations or local summarization abilities are easily remembered and paid attention to. TSRSum evaluates the semantic closeness of each sentence with respect to its title, which usually reflects the main ideas of a document. TSRSum applies a TSR-based attention mechanism to simulate people’s reading ability with the main idea (title) in mind. QSRSum evaluates the relevance of each sentence with given queries for the query-focused summarization. QSRSum applies a QSR-based attention mechanism to simulate the attentive reading of a human reader with some queries in mind. The mechanism can recognize which parts of the given queries are more likely answered by a sentence under consideration. Finally as a whole, SRSum automatically learns useful latent features by jointly learning representations of query sentences, content sentences, and title sentences as well as their relations.
 We conduct extensive experiments on six benchmark datasets, including generic multi-document summarization and query-focused multi-document summarization. On both tasks, SRSum achieves comparable or superior performance compared with state-of-the-art approaches in terms of multiple ROUGE metrics.
Modeling laterally-contacted nipi-diode radioisotope batteries

A framework for modeling the power generation of laterally-contacted n-type / intrinsic / p-type / intrinsic (nipi) diodes coupled with an alpha-particle radioisotope source is developed. The framework consists of two main parts, the alpha-particle energy deposition profile (ADEP) and a lumped parameter equivalent circuit model describing the nipi device operation. Experimental measurements are used to verify the ADEP modeling approach which determines the spatially varying energy deposited within the device. Using these results, nipi-diode radioisotope batteries are simulated and the affects of the number of junctions, the thickness of the junction, and the alpha-particle flux on output voltage and power are investigated. The modeling results indicate that a 1 cm2 bi-layer device (consisting of one source and two adjacent nipi-diodes) with a source activity of 300 mCi can reach a power output of 2 mW.
Simulink based VoIP Analysis

Voice communication over internet not be possible without a reliable data network, this was first available when distributed network topologies were used in conjunction with data packets. Early network used single centre node network in which a single workstation (Server) is responsible for the communication. This posed problems as if there was a fault with the centre node, (workstation) nothing would work. This problem was solved by the distributed system in which reliability increases by spreading the load between many nodes. The idea of packet switching & distributed network were combined, this combination were increased reliability, speed & responsible for voice communication over internet. Voice-over-IP (VoIP) These data packets travel through a packet-switched network such as the Internet and arrive at their destination where they are decompressed using a compatible Codec (audio coder/decoder) and converted back to analogue audio. This paper deals with the Simulink architecture for VoIP network.  G.711, Wave file
Path Planning and Motion Coordination in Multiple Mobile Robot Teams

Autonomous robot An autonomous robot is a robot that can perform tasks in unstructured environments with minimal human guidance.  Complete path planner A complete path planner is an algorithm that is guaranteed to find a path, if one exists. Deadlocked path planning A deadlock is a situation in path planning in which a solution cannot be found, even though one exists. Typically, this is caused by robots blocking each other’s paths, and the planner being unable to find a solution in which robots move out of each other’s way.
emuARM: A tool for teaching the ARM assembly language

Technology has always enhanced learning as well as the overall teaching experience. With proper tools and resources in hand, we can easily integrate educational and information technologies into the academic environment. In this paper, we present a software tool to enhance the learning of microprocessors and computer architecture for students. We have developed an ARM instruction set emulator, emuARM, which is a Java based software tool for duplicating the functions of an ARMv5 microprocessor. Here, we present the internal design and features of emuARM. We present a comparison of the features of emuARM with other present ARM emulators in the market. At the end, we present the results of a survey that attests the pedagogical value of our tool.
Texture analysis and repacking for improved storage efficiency

Textures are widely used in modern computer graphics. Their size, however, is often a limiting factor. Considering the widespread adaptation of mobile virtual and augmented reality applications, efficient storage of textures has become an important factor.
 We present an approach to analyse textures of a given mesh and compute a new set of textures with the goal of improving storage efficiency and reducing memory requirements. During this process the texture coordinates of the mesh are updated as required. Textures are analysed based on the UV-coordinates of one or more meshes and deconstructed into per-triangle textures. These are further analysed to detect single coloured as well as identical per-triangle textures. Our approach aims to remove these redundancies in order to reduce the amount of memory required to store the texture data. After this analysis, the per-triangle textures are compiled into a new set of texture images of user defined size. Our algorithm aims to pack texture data as tightly as possible in order to reduce the memory requirements.
Wireless Sensor Based Remote Monitoring System for Agriculture Using ZigBee and GPS

The advanced development in wireless sensor networks can be used in monitoring various parameters in agriculture. Due to uneven natural distribution of rain water it is very difficult for farmers to monitor and control the distribution of water to agriculture field in the whole farm or as per the requirement of the crop. There is no ideal irrigation method for all weather conditions, soil structure and variety of crops cultures. Farmers suffer large financial losses because of wrong prediciton of weather and incorrect irrigation methods. In this context, with the evolution of miniaturized sensor devices coupled with wireless technologies, it is possible remotely monitor parameters such as moisure, temperature and humidity. In this paper it is proposed to design, develop and implement a wireless sensor network connected to a central node using ZigBee, which in turn is connected to a Central Monitoring Station (CMS) through General Packet Radio Service (GPRS) or Global System for Mobile (GSM) technologies. The system also obtains Global Positionting System (GPS) parameters related to the field and sends them to a central monitoring station. This system is expected to help farmers in evaluating soil conditions and act accordingly.
A coherent authentication framework for mobile computing based on homomorphic signature and implicit authentication

Mobile cloud computing is an extension of cloud computing that allow the users to access the cloud service via their mobile devices. Although mobile cloud computing is convenient and easy to use, the security challenges are increasing significantly. One of the major issues is unauthorized access. Identity Management enables to tackle this issue by protecting the identity of users and controlling access to resources. Although there are several IDM frameworks in place, they are vulnerable to attacks like timing attacks in OAuth, malicious code attack in OpenID and huge amount of information leakage when user’s identity is compromised in Single Sign-On. Our proposed framework implicitly authenticates a user based on user’s typing behavior. The authentication information is encrypted into homomorphic signature before being sent to IDM server and tokens are used to authorize users to access the cloud resources. Advantages of our proposed framework are: user’s identity protection and prevention from unauthorized access.
Video quality for face detection, recognition, and tracking

Many distributed multimedia applications rely on video analysis algorithms for automated video and image processing. Little is known, however, about the minimum video quality required to ensure an accurate performance of these algorithms. In an attempt to understand these requirements, we focus on a set of commonly used face analysis algorithms. Using standard datasets and live videos, we conducted experiments demonstrating that the algorithms show almost no decrease in accuracy until the input video is reduced to a certain critical quality, which amounts to significantly lower bitrate compared to the quality commonly acceptable for human vision. Since computer vision percepts video differently than human vision, existing video quality metrics, designed for human perception, cannot be used to reason about the effects of video quality reduction on accuracy of video analysis algorithms. We therefore investigate two alternate video quality metrics, blockiness and mutual information, and show how they can be used to estimate the critical video qualities for face analysis algorithms.
Application of the Artifical Neural Network in Predicting the Direction of Stock Market Index

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index, hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use indicators to forecast the direction of the stock market index. In this study, we applied two types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model.
The health burden of breast hypertrophy.

Women seeking consultation for the surgical relief of symptoms associated with breast hypertrophy have been the focus of many studies. In contrast, little is known about those women with breast hypertrophy who do not seek symptomatic relief. The purpose of this study was to describe the health burden of breast hypertrophy by using a set of validated questionnaires and to compare women with breast hypertrophy who seek surgical treatment with those who do not. In addition, this latter group was compared with a group of control women without breast hypertrophy. Women seeking consultation for surgery were recruited from 14 plastic-surgery practices. Control subjects were recruited by advertisements in primary-care offices and newspapers. Women were asked to complete a self-report questionnaire that included the European Quality of Life (EuroQol) questionnaire, McGill Pain Questionnaire, Multidimensional Body Self Relations Questionnaire (MBSRQ), the Short Form-36 (SF-36) questionnaire, and questions regarding breast-related symptoms, comorbidities, and bra size. Descriptive statistics were compiled for three groups of women: (1) hypertrophy patients seeking surgical care, (2) hypertrophy control subjects (those whose reported bra-cup size was a D or larger), and (3) normal control subjects (those whose reported bra-cup size was an A, B, or C). The multiple linear regression method was used to compare the health burdens across groups while adjusting for other variables. Two hundred ninety-one women seeking surgical care and 195 control subjects were enrolled in the study. The 184 control subjects with bra-cup information available were further separated into 88 hypertrophy control subjects and 96 normal control subjects. In the control group, bra-cup size was correlated with health-burden measures, whereas in the surgical candidates, it was not. When scores were compared across the three groups, significant differences were found in all health-burden measures. The surgical candidates scored more poorly on the EuroQol utility, McGill pain rating index, MBSRQ appearance evaluation, physical component scale of the SF-36, and on breast symptoms than did the two control groups. In addition, the hypertrophy control subjects scored more poorly than the normal control subjects. With multiple linear regression analysis incorporating important potential confounders, the poorer scores in the surgical candidates remained statistically significant. It was concluded that breast hypertrophy in those seeking surgical care and those not seeking surgery has a significant impact on women's quality of life as measured by validated and widely used self-report instruments including the EuroQol, MBSRQ, McGill Pain Questionnaire, and the SF-36. Likewise, a new assessment instrument for breast-related symptoms also demonstrated greater symptomatology in women with breast hypertrophy.
Design and analysis of position controlled eddy current based nonlinear magnetic levitation system using LMI

Magnetic levitation (Maglev) is becoming a popular transportation topic all around the globe. Maglev systems have been successfully implemented in many applications such as frictionless bearings, high-speed maglev trains, and fast tool servo systems. Due to the features of the instability and nonlinearity of the maglev system, the design of controllers for a maglev system is a difficult task. Literature shows, many authors have proposed various controllers to stabilize the position of the object in the air. The main drawback of these controllers is that symmetric constraints like stability conditions, decay rate conditions, disturbance rejection and eddy-current based force due to the motion of the levitated object are not taken into consideration. In this paper, a linear matrix inequality based fuzzy controller is proposed to reduce vibration in the object. The efficacy of the proposed controller tests on a maglev system considering symmetric constraints and the eddy current based force.
Effects of interprofessional education for medical and nursing students: enablers, barriers and expectations for optimizing future interprofessional collaboration – a qualitative study

To ensure high quality patient care an effective interprofessional collaboration between healthcare professionals is required. Interprofessional education (IPE) has a positive impact on team work in daily health care practice. Nevertheless, there are various challenges for sustainable implementation of IPE. To identify enablers and barriers of IPE for medical and nursing students as well as to specify impacts of IPE for both professions, the 'Cooperative academical regional evidence-based Nursing Study in Mecklenburg-Western Pomerania' (Care-N Study M-V) was conducted. The aim is to explore, how IPE has to be designed and implemented in medical and nursing training programs to optimize students' impact for IPC.



A qualitative study was conducted using the Delphi method and included 25 experts. Experts were selected by following inclusion criteria: (a) ability to answer every research question, one question particularly competent, (b) interdisciplinarity, (c) sustainability and (d) status. They were purposely sampled. Recruitment was based on existing collaborations and a web based search.



The experts find more enablers than barriers for IPE between medical and nursing students. Four primary arguments for IPE were mentioned: (1) development and promotion of interprofessional thinking and acting, (2) acquirement of shared knowledge, (3) promotion of beneficial information and knowledge exchange, and (4) promotion of mutual understanding. Major barriers of IPE are the coordination and harmonization of the curricula of the two professions. With respect to the effects of IPE for IPC, experts mentioned possible improvements on (a) patient level and (b) professional level. Experts expect an improved patient-centered care based on better mutual understanding and coordinated cooperation in interprofessional health care teams. To sustainably implement IPE for medical and nursing students, IPE needs endorsement by both, medical and nursing faculties.



In conclusion, IPE promotes interprofessional cooperation between the medical and the nursing profession. Skills in interprofessional communication and roles understanding will be primary preconditions to improve collaborative patient-centered care. The impact of IPE for patients and caregivers as well as for both professions now needs to be more specifically analysed in prospective intervention studies.
Coexistence and Transformation of Informal and Formal Structures: Requirements for More Flexible Hypermedia Systems

In this paper, we argue that some tasks (e.g., meeting support) require more flexible hypermedia systems and we describe a prototype hypermedia system, DOLPHIN, that implements more flexibility. As part of the argument, we present a theoretical design space for information structuring systems and locate existing hypertext systems within it. The dimensions of the space highlight a system's internal representation of structure and the user's actions in creating structure. Second, we describe an empirically derived range of activities connected to conducting group meetings, including the pre- and post-preparation phases, and argue that hyptetext systems need to be more flexible in order to support this range of activities. Finally, we describe a hypermedia prototype, DOLPHIN, which implements this kind of flexible support for meetings. DOLPHIN supports different degrees of formality (e.g., handwriting and sketches as well as typed nodes and links are supported), coexistence of different structures (e.g., handwriting and nodes can exist on the same page) and mutual transformations between them (e.g., handwriting can be turned into nodes and vice versa).