nils-herrmann/clean_scirepeval huggingface dataset:
Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach

Segmentation of lungs with (large) lung cancer regions is a nontrivial problem. We present a new fully automated approach for segmentation of lungs with such high-density pathologies. Our method consists of two main processing steps. First, a novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs. The initial position of the RASM is found by means of a rib cage detection method. Second, an optimal surface finding approach is utilized to further adapt the initial segmentation result to the lung. Left and right lungs are segmented individually. An evaluation on 30 data sets with 40 abnormal (lung cancer) and 20 normal left/right lungs resulted in an average Dice coefficient of 0.975±0.006 and a mean absolute surface distance error of 0.84±0.23 mm, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models.
BamTools: a C++ API and toolkit for analyzing and managing BAM files

Analysis of genomic sequencing data requires efficient, easy-to-use access to alignment results and flexible data management tools (e.g. filtering, merging, sorting, etc.). However, the enormous amount of data produced by current sequencing technologies is typically stored in compressed, binary formats that are not easily handled by the text-based parsers commonly used in bioinformatics research.



We introduce a software suite for programmers and end users that facilitates research analysis and data management using BAM files. BamTools provides both the first C++ API publicly available for BAM file support as well as a command-line toolkit.


AVAILABILITY
BamTools was written in C++, and is supported on Linux, Mac OSX and MS Windows. Source code and documentation are freely available at http://github.org/pezmaster31/bamtools.
Possibilistic interest discovery from uncertain information in social networks

User generated content on the microblogging social network Twitter continues to grow with significant amount of information. The semantic analysis offers the opportunity to discover and model latent interests’ in the users’ publications. This article focuses on the problem of uncertainty in the users’ publications that has not been previously treated. It proposes a new approach for users’ interest discovery from uncertain information that augments traditional methods using possibilistic logic. The possibility theory provides a solid theoretical base for the treatment of incomplete and imprecise information and inferring the reliable expressions from a knowledge base. More precisely, this approach used the product-based possibilistic network to model knowledge base and discovering possibilistic interests. DBpedia ontology is integrated into the interests’ discovery process for selecting the significant topics. The empirical analysis and the comparison with the most known methods proves the significance of this approach.
Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting

The aim of this paper is to present deep neural network architectures and algorithms and explore their use in time series prediction. Existing and novel input variable selection algorithms and deep neural networks are applied for ultra-short-term wind prediction. Since gradient-based optimization starting from random initialization often appears to get stuck in poor solutions, recent research effort aimed at training methods for such deep networks is summarized. Shallow and deep neural networks coupled with two input variable selection algorithms are compared on a ultra-short-term wind prediction task. Initial results show that deep neural networks outperform shallow ones. Depth adds additional computational cost and input variable selection use reduces it. Deep Neural Networks, Input Variable Selection, ultrashort-term, wind forecasting
An axial flux-focusing magnetically geared motor

In this paper an axial flux magnetic gear (AFMG) is proposed that has an integrated outer stator. This axial flux magnetically geared motor (AFMGM) is unique in that the stator shares the high-speed rotor with the AFMG, so there is no need for a separate rotor. The high speed and low speed rotors use a flux-focusing typology. The stator is mounted outside the AFMG and this makes the design mechanically less complex. It also enables the stator to be cooled more easily.
Study on Battery Management System and Lithium-ion Battery

This paper mainly discusses a distributed battery management system (BMS) that used for hybrid electrical vehicle (HEV) and the research on Lithium-ion battery based on the model of PNGV. The BMS presented is composed with the distributed and integrated module, the distributed modules(DM) takes charge of measuring the parameters of voltage and temperature accurately and in time, communicate with the integrated module(IM) by CAN bus. The IM takes charge of estimating Lithium-ion batteries’ state of charge (SOC) based on the data of DM and PNGV model. According to the complicated electromagnetism condition the anti-jamming methods are used between every part of the BMS to have the characters of robustness and reliability. According to the non-liner feature of Lithium-ion battery, PNGV model has the high accuracy to reflect the open circuit voltage (OCV) and SOC. Results show that the BMS has the characteristics of easy to expansion, high precision, working reliability and the PNGV model is suitable for Lithium-ion battery.
Continuous cuffless blood pressure monitoring based on PTT

Blood pressure (BP) is one of the important vital signs that need to be monitored for personal healthcare. This paper describes the method developed by the authors to measure systolic blood pressure from pulse transit time (PTT) Pulse transit time is the time taken for the arterial pulse pressure wave to travel from the aortic valve to a peripheral site. It is usually measured from the R wave on the electrocardiogram to a photoplethysmography signal. PTT is inversely proportional to blood pressure. This method does not require an air cuff and only a minimal inconvenience of attaching electrodes and LED/photo detector sensors on a subject. Twenty three healthy subjects (age 18–60 yrs) were studied. Blood pressure measurement is carried out using pulse transit time and is compared with sphygmomanometry, the reference standard and the oscillometric based automatic BP measuring machine. The results show that the standard deviation of their differences was around 3 mmHg. The developed pulse transit time based method can be used as a noninvasive and cuffless alternative to the conventional occluding-cuff approaches for long-term and continuous monitoring of blood pressure.
Can social media help us reason about mental health?

Millions of people each year suffer from depression, which makes mental illness one of the most serious and widespread health challenges in our society today. There is therefore a need for effective policies, interventions, and prevention strategies that enable early detection and diagnosis of mental health concerns in populations. This talk reports some findings on the potential of leveraging social media postings as a new type of lens in understanding mental illness in individuals and populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements of behavior over time while radically expanding population sample sizes. The talk highlights how this research direction may be useful in developing tools for identifying the onset of depressive disorders, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from mental illness to be more proactive about their mental health.
Camera Processing With Chromatic Aberration

Since the refractive index of materials commonly used for lens depends on the wavelengths of light, practical camera optics fail to converge light to a single point on an image plane. Known as chromatic aberration, this phenomenon distorts image details by introducing magnification error, defocus blur, and color fringes. Though achromatic and apochromatic lens designs reduce chromatic aberration to a degree, they are complex and expensive and they do not offer a perfect correction. In this paper, we propose a new post-capture processing scheme designed to overcome these problems computationally. Specifically, the proposed solution is comprised of chromatic aberration-tolerant demosaicking algorithm and post-demosaicking chromatic aberration correction. Experiments with simulated and real sensor data verify that the chromatic aberration is effectively corrected.
Privacy Leakage of Location Sharing in Mobile Social Networks: Attacks and Defense

Along with the popularity of mobile social networks (MSNs) is the increasing danger of privacy breaches due to user location exposures. In this work, we take an initial step towards quantifying location privacy leakage from MSNs by matching the users’ shared locations with their real mobility traces. We conduct a three-week real-world experiment with 30 participants and discover that both direct location sharing (e.g., Weibo or Renren) and indirect location sharing (e.g., Wechat or Skout) can reveal a small percentage of users’ real points of interests (POIs). We further propose a novel attack to allow an external adversary to infer the demographics (e.g., age, gender, education) after observing users’ exposed location profiles. We implement such an attack in a large real-world dataset involving 22,843 mobile users. The experimental results show that the attacker can effectively predict demographic attributes about users with some shared locations. To resist such attacks, we propose SmartMask, a context-based system-level privacy protection solution, designed to automatically learn users’ privacy preferences under different contexts and provide a transparent privacy control for MSN users. The effectiveness and efficiency of SmartMask have been well validated by extensive experiments.
Intelligent Accident Detection System

Recently technological and population development, the usage of vehicles are rapidly increasing and at the same time the occurrence accident is also increased. Hence, the value of human life is ignored. No one can prevent the accident, but can save their life by expediting the ambulance to the hospital in time. A new vivid scheme called Intelligent Accident detection system is introduced. The objective of this scheme is to minimize the delay caused by traffic congestion and to provide the smooth flow of emergency vehicles. The concept of this scheme is to green the traffic signal in the path of ambulance automatically with the help of RF module. So that the ambulance can reach the spot in time and human life can be saved and the accident location is identified sends the accident location immediately to the main server. The main server finds the nearest ambulance to the accident zone and sends the exact accident location to the emergency vehicle. The control unit monitors the ambulance and provides the shortest path to the ambulance at the same time it controls the traffic light according to the ambulance location and thus arriving at the hospital safely. This scheme is fully automated, thus it locates the accident spot accurately, controls the traffic lights, provide the shortest path to reach the location and to the hospital in time. KeywordGlobal System for Mobile Communications (GSM), Global Positioning System (GPS), Global Packet Radio Service (GPRS), Short Message Service (SMS), Radio Frequency (RF) __________________________________________________________________________________________________
ZEUS: A Toolkit and Approach for Building Distributed Multi-Agent Systems

The innate difficulty of constructing multi-agent systems has motivated agent developers to move away from developing point solutions to point problems in favour of developing methodologies and toolkits for building distributed multi-agent systems. This philosophy led to the development of the ZEUS Agent Building Toolkit, which facilitates the rapid development of collaborative agent applications through the provision of a library of agent-level components and an environment to support the agent building process. The ZEUS toolkit is a synthesis of established agent technologies with some novel solutions to provide an integrated collaborative agent building environment.
THE THREE-DIMENSIONAL EXTENSION OF SPACE SYNTAX 048

The purpose of this study is to develop a new space syntax technique by adding third dimension in analysis of urban structure that is important to understand the formation of the cities. The theoretical framework of the study is put forward by integrating space syntax with the theory of the image of city. This entails the essential elucidation of spatial cognition pattern and image and the integration of space syntax and the image of city. The paper analyzes and demonstrates the prerequisite and feasibility of the mutual complementarily between space syntax and the image of city. The threedimensional space syntax integrated with the image of city is different from the traditional syntax that is only limited to non-figurative two-dimensional spatial cognition. It also emphasizes the important influences of the three-dimensional image points on people's behaviour in space and it is quite close to the accurate description of “natural movement”. Based on the three-dimensional theory, a concept model of the threedimensional syntax is constructed, and its content includes how to describe forming process of the three-dimensional spatial image pots during the cognition activity, how to construct the objective evaluation model for the three-dimensional image pots and how to put forward the concept of weighted-integration to calculate the concept mathematical model. In the end, the paper carries through three experimental analyses. The paper argues that the extended three-dimensional syntactical model is much closer to people's spatial illation and cognition. It should be more accurate to use the model to describe and grasp the urban configuration.
Development of Single-Transistor-Control LDO Based on Flipped Voltage Follower for SoC

The design issues of a single-transistor-control (STC) low-drop-out (LDO) based on flipped voltage follower is discussed in this paper, in particular the feedback stability at different conditions of output capacitors, equivalent series resistances (ESRs) and load current. Based on the analysis, an STC LDO was implemented in a standard 0.35-mum MOS technology. It is proven experimentally that the LDO provides stable voltage regulation at a variety of output-capacitor/ESR conditions and is also stable in no output capacitor condition. The preset output voltage, minimum unregulated input voltage, maximum output current at a dropout voltage of 200 mV, ground current and active chip area are 1 V, 1.2 V, 50 mA, 95 muA, and 140 mum times 320 mum, respectively. The full-load transient response in the no output capacitor case is faster than a micro second and is about 300 ns.
The effects of feedback on energy conservation: A meta-analysis.

Feedback has been studied as a strategy for promoting energy conservation for more than 30 years, with studies reporting widely varying results. Literature reviews have suggested that the effectiveness of feedback depends on both how and to whom it is provided; yet variations in both the type of feedback provided and the study methodology have made it difficult for conclusions to be drawn. The current article analyzes past theoretical and empirical research on both feedback and proenvironmental behavior to identify unresolved issues, and utilizes a meta-analysis of 42 feedback studies published between 1976 and 2010 to test a set of hypotheses about when and how feedback about energy usage is most effective. Results indicate that feedback is effective overall, r = .071, p < .001, but with significant variation in effects (r varied from -.080 to .480). Several treatment variables were found to moderate this relationship, including frequency, medium, comparison message, duration, and combination with other interventions (e.g., goal, incentive). Overall, results provide further evidence of feedback as a promising strategy to promote energy conservation and suggest areas in which future research should focus to explore how and for whom feedback is most effective.
Dual port UWB diversity/MIMO antenna with dual band-notch characteristics

A compact two-port diversity/MIMO antenna with dual band-notch characteristics for ultra wideband (UWB) applications is presented in this paper. The antennas exhibit a good impedance match over frequency band of 3.1 GHz–12 GHz, while exhibiting high isolation. Enhanced isolation is achieved with a decoupling structure having circular slots. The decoupling structure provides isolation of more than 20 dB for whole UWB spectrum. By introducing U-shaped slot in the main radiator and horizontal stubs in ground plane, band-notch characteristic is achieved in proposed MIMO design. Two notches are achieved at 5.150–5.825 GHz (WLAN) and 7.25–7.75 GHz (downlink of X-band satellite). The antenna is miniaturized having dimensions of 22mm × 36mm. The results suggests that the proposed diversity/MIMO antenna design can be suitable for portable UWB applications.
Knowledge sharing in organisational contexts: a motivation-based perspective

Facilitating knowledge sharing within organisations is a difficult task: the willingness of individuals to share and integrate their knowledge is one of the central barriers. This paper aims to develop a motivation-based perspective to explore how organisations resolve the social dilemma of knowledge sharing.  The analysis builds on a three-category taxonomy of motivation, adding ‘‘hedonic’’ motivation to the traditional dichotomy of ‘‘extrinsic’’ and ‘‘intrinsic’’ motivation. It uses case studies gleaned from the literature to explore the interactive effects between the different motivators in two different types of knowledge-intensive organisations: professional bureaucracy and operating adhocracy.  Within a professional bureaucracy, the social dilemma of knowledge sharing may be overcome through normative motivation, with provision of hedonic motivation through extrinsic incentives such as training and career progression. In an operating adhocracy where interdependent teamwork is vital, it may be overcome through normative alignment reinforced by intensive socialisation. Extrinsic motivators that align with hedonic motivation may also reinforce the propensity for knowledge sharing. In both organisational types, financial extrinsic incentives do not appear to be relevant on their own, and may ‘‘crowd out’’ other motivators.  The cases reported were chosen from the existing literature and, although many were not designed specifically to address motivational issues, suggestive conclusions are drawn. Most of the cases were drawn from organisations rooted in the Anglo-American context and thus care would be needed in generalising the findings to organisations in other contexts.  The paper represents the first attempt to apply a three-category taxonomy of motivation to examine knowledge-sharing behaviour in organisations. It highlights the interaction between the different motivators and provides a basis to integrate further the work of social psychologists and socio-economists on incentives and motivation in the context of knowledge sharing.
A Deep Learning Approach for Wi-Fi Based People Localization

People localization is a key building block in many applications. In this paper, we propose a deep learning based approach that significantly improves the localization accuracy and reduces the runtime of Wi-Fi based localization systems. Three variants of the deep learning approach are proposed, a sub-task architecture, an end-to-end architecture, and an architecture that incorporates prior knowledge. The performance of the three architectures under different conditions is evaluated and the significant improvement of the three architectures over existing approaches is demonstrated.
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks

Five billion people in the world lack access to quality surgical care. Surgeon skill varies dramatically, and many surgical patients suffer complications and avoidable harm. Improving surgical training and feedback would help to reduce the rate of complications—half of which have been shown to be preventable. To do this, it is essential to assess operative skill, a process that currently requires experts and is manual, time consuming, and subjective. In this work, we introduce an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos, leveraging region-based convolutional neural networks. In order to study this problem, we also introduce a new dataset, m2cai16-tool-locations, which extends the m2cai16-tool dataset with spatial bounds of tools. While previous methods have addressed tool presence detection, ours is the first to not only detect presence but also spatially localize surgical tools in real-world laparoscopic surgical videos. We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection. We further demonstrate the ability of our method to assess surgical quality through analysis of tool usage patterns, movement range, and economy of motion.
User Acceptance of Hedonic Information Technologies: A Perceived Value Perspective

Hedonic information technologies do not focus on productivity-oriented applications, but rather, on lifestyle-augmentation and entertainment. As such users of hedonic IT are provided with benefits related to general enjoyment as opposed to gains in organizational efficiency. The main purpose of this study is to investigate this burgeoning phenomenon by measuring several value drivers for downloading mobile phone ringtones. Hypothesis testing was performed with PLS techniques applied to data collected from 119 ringtone users. Results confirmed that the overall value of hedonic IT is a third-order construct, which successfully predicted behavioral usage intentions and positive word of mouth.
Large Scale Urban Scene Modeling from MVS Meshes

In this paper we present an eicient modeling framework for large scale urban scenes. Taking surface meshes derived from multiview-stereo systems as input, our algorithm outputs simpliied models with semantics at diferent levels of detail (LODs). Our key observation is that urban building is usually composed of planar roof tops connected with vertical walls. There are two major steps in our framework: segmentation and building modeling. The scene is irst segmented into four classes with a Markov random ield combining height and image features. In the following modeling step, various 2D line segments sketching the roof boundaries are detected and slice the plane into faces. Through assigning each face with a roof plane, the inal model is constructed by extruding the faces to the corresponding planes. By combining geometric and appearance cues together, the proposed method is robust and fast compared to the state-of-the-art algorithms.