nils-herrmann/clean_scirepeval huggingface dataset:
In Space Robotic Assembly of Large Telescopes
Year: 2020

Advances in our understanding of the universe have been enabled by ground and particularly by space based telescopes (e..g. the Hubble) free of interferences from Earth's atmosphere. However, current astronomical challenges in areas such as exoplanets, interstellar medium and structure of the universe, require larger telescope apertures. Using deployable structures and a segmented primary mirror, such as in the James Webb telescope, allows an increase of the aperture, but the maximum size of the telescope is anyways limited mainly by the fairing size of the launch vehicle. Further increasing the size of the telescope requires a technological change, to move toward space based assembly using autonomous robotic systems. This paper provides a survey of existing concepts for in space assembly of telescopes, and introduces PULSAR (Prototype of an Ultra Large Structure Assembly Robot) the latest European effort toward proving feasibility of the technologies required for autonomous robotic assembly of a telescope or a large spaceborne structure.
Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map
Venue: Nutrients
Year: 2018

An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model based or stereo based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items.
Lithium Ion Battery State of Health Monitoring Based on Ensemble Learning
Venue: IEEE Access
Year: 2019

State of health (SOH) estimation is critical for the battery management system. With autonomous ability and superior nonlinear mapping capability, machine learning is now a hot topic in this field. The training sample set of current machine learning methods based on single learner is often the regional data, which leads to a small range of data acquisition and affects the generalization ability of the model. Regard this issue, the idea of ensemble learning is considered, and by generating differential data samples and synthesizing the output of a series of base learners, a good learning performance can be achieved. Furthermore, gray relational analysis is used for feature correlation analysis. In this paper, NASA battery data sets are used to verify and validate the performance of the proposed method, which indicated an enhanced accuracy of the results based on the ensemble learning method. The proposed battery healthy assessment model based on ensemble learning can be concluded to provide highly accurate and stable SOH predictions.
Assessment of eating disorders with the diabetes eating problems survey revised (DEPS R) in a representative sample of insulin treated diabetic patients: a validation study in Italy
Venue: BMC Psychiatry
Year: 2017

BackgroundThe purpose of the study was to evaluate in a sample of insulin treated diabetic patients, with type 1 or type 2 diabetes, the psychometric characteristics of the Italian version of the DEPS R scale, a diabetes specific self report questionnaire used to analyze disordered eating behaviors.MethodsThe study was performed on 211 consecutive insulin treated diabetic patients attending two specialist centers. Lifetime prevalence of eating disorders (EDs) according to DSM IV and DSM 5 criteria were assessed by means of the Module H of the Structured Clinical Interview for DSM IV Axis I Disorder and the Module H modified, according to DSM 5 criteria. The following questionnaires were administered: DEPS R and the Eating Disorder Inventory 3 (EDI 3) Test/retest reproducibility was assessed on a subgroup of 70 patients. The factorial structure, internal consistency, test retest reliability and concurrent validity of DEPS R were assessed.ResultsOverall, 21.8% of the sample met criteria for at least one DSM 5 diagnosis of ED. A "clinical risk" of ED was observed in 13.3% of the sample. Females displayed higher scores at DEPS R, a higher percentage of at least one diagnosis of ED and a higher clinical risk for ED. A high level of reproducibility and homogeneity of the scale were revealed. A significant correlation was detected between DEPS R and the 3 ED risk scales of EDI 3.ConclusionsThe data confirmed the overall reliability and validity of the scale. In view of the significance and implications of EDs in diabetic patients, it should be conducted a more extensive investigation of the phenomenon by means of evaluation instruments of demonstrated validity and reliability.
Design, fabrication and control of soft robots
Venue: Nature
Year: 2015

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.
Syngas Redox (SGR) Process to Produce Hydrogen from Coal Derived Syngas
Year: 2007

The syngas redox (SGR) process to produce hydrogen from coal derived syngas is described. The process involves reduction of a metal oxide to metallic form with syngas and subsequent regeneration with steam to generate hydrogen in a cyclic operation. Metal oxides of Ni, Cu, Cd, Co, Mn, Sn, and Fe were evaluated for this process based upon thermodynamic equilibrium limitations. It was found that Fe2O3 provided the best conversion of syngas to combustion products CO2 and H2O along with a high conversion of steam to hydrogen. Other oxides provide high conversion of syngas but were found lacking in producing hydrogen from steam. Composite particles with Fe2O3 as the key ingredient were developed that undergo multiple redox cycles without loss of activity. Analysis of process economics with respect to particle recyclability showed that the particles should undergo at least a 100 redox cycles without diminishing its activity. Process modifications to address carbon formation on reaction of syngas with iron oxide.
Go Sanitizer: Bug Oriented Assertion Generation for Golang
Venue: 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)
Year: 2019

Go programming language (Golang) is widely used, and the security issue becomes increasingly important because of its extensive applications. Most existing validation techniques, such as fuzz testing and unit testing, mainly focus on crashes detection and coverage improvements. However, it is challenging for test engines to perceive common program bugs such as loss of precision and integer overflow. In this paper, we propose Go Sanitizer, an effective bug oriented assertion generator for Golang, which is able to achieve a better performance in finding program bugs. Firstly, we manually analyze the Common Weakness Enumeration (CWE) and summarize the applicabilities on Golang. Secondly, we design a generator to automatically insert several bug oriented assertions to the proper locations of the target program. Finally, we can utilize the traditional validation techniques such as fuzz and unit testing to test the programs with inserted assertions, and Go Sanitizer reports bugs via the failures of assertions. For evaluation, we apply Go Sanitizer to Badger, a widely used database software, and successfully discovers 12 previously unreported program bugs, which can not be detected by pure fuzzer such as Go Fuzz or unit testing methods.
Determination of Tetracyclines in Milk with a Molecularly Imprinted Polymer Based Microtiter Chemiluminescence Sensor
Year: 2019

A molecularly imprinted polymer (MIP) capable of recognizing five tetracyclines using minocycline as the template was synthesized for the first time. The MIP was employed as the recognition reagent to prepare a chemiluminescence sensor on a conventional microtiter plate. The light signal was initiated using the highly efficient bis(2,4,6 trichlorophenyl) oxalate hydrogen peroxide imidazole chemiluminescence system. After optimization of several appropriate factors, the sensor was employed to determine five tetracyclines in milk. The developed assay contained only one sample loading step, so each measurement was completed within 12 min. The limits of detection for these analytes were in the range from 0.5 to 2.0 pg/mL, while the recoveries from the fortified milk samples were between 78.1 and 105% In addition, the sensor was shown to be reusable for up to four measurements. Hence, this sensor has been demonstrated to be a simple, rapid, sensitive, and durable tool for the determination of tetracyclines in animal derived food.
Automatic Detection of Lung Nodules Using 3D Deep Convolutional Neural Networks
Venue: Journal of Shanghai Jiaotong University (Science)
Year: 2019

Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5 year survival rate of lung cancer, so the efficient and accurate detection of lung nodules, the potential precursors to lung cancer, is paramount. In this paper, a computer aided lung nodule detection system using 3D deep convolutional neural networks (CNNs) is developed. The first multi scale 11 layer 3D fully convolutional neural network (FCN) is used for screening all lung nodule candidates. Considering relative small sizes of lung nodules and limited memory, the input of the FCN consists of 3D image patches rather than of whole images. The candidates are further classified in the second CNN to get the final result. The proposed method achieves high performance in the LUNA16 challenge and demonstrates the effectiveness of using 3D deep CNNs for lung nodule detection.
Digital Labour and Karl Marx
Year: 2013

1.  An Introduction to Karl Marx's Theory 3.  Cultural Studies and Karl Marx 4. Dallas Smythe and Audience Labour Today 5. Capitalism or Information Society 6. Digital Slavery: Slave Work in ICT Related Mineral Extraction 7. Exploitation at Foxconn: Primitive Accumulation and the Formal Subsumption of Labour 8. The Division of Labour of the New Imperialism: Work in the Indian Software Industry 9. The Silicon Valley of Dreams and Nightmares of Exploitation: The Google Labour Aristocracy and its Context 10. Tayloristic, Housewifised Service Labour: The Example of Call Centre Work 11. Theorising Digital Labour on Social Media 12. Digital Labour and Struggles for Digital Work The Occupy Movement as a New Working Class Movement? Social Media as Working Class Social Media?
Investigating online complaint intention and service recovery expectations of clothing retail customers
Year: 2014

iii respondents pertaining to their attitude towards complaining, online complaint intention, service failure severity perception and strength of service recovery expectation. Finally with respect to an empirically tested theoretical model respondents" attitude towards complaining in general and their perceptions of the severity of the service failure experienced, significantly and positively influence their strength of service recovery expectations, while no significant positive influences were uncovered for paths linking the aforementioned constructs with online complaint intention. As a result, online complaint intention was omitted from the measurement model seeing that respondents who participated in this study did not indicate a high tendency to complain online. It is recommended that clothing retailers recognise the importance of in store complaint management programmes and ensure that in store complaint channels are easily accessible, efficient and pleasant to use. Clothing retailers should actively educate customers regarding alternative mechanisms and channels available for complaining. In order to increase customers" online complaint intention, clothing retailers should provide a visible complaint portal on their website and communicate the benefits of online complaining during in store advertising. Clothing retailers should furthermore provide the appropriate strength of service recovery for particular levels of service failure severity when managing customer complaints. Consequently, it is recommended that clothing retailers provide their employees with sufficient training that will educate them regarding their interaction with customers and the best practice thereof, the difference between minor and major service failures, and the selection of an appropriate service recovery strategy during different service failure severity encounters. It is recommended that this study is extended to other service settings in order to uncover online complaint intention and strength of service recovery expectations of customers in other industries. Finally, seeing that respondents" strength of service recovery expectations vary according to the severity of the service failure, scenarios representing different levels of service failure severity can be used as basis to measure the key constructs measured in this study.
Teaching experiences during COVID 19 pandemic: Narratives from ResearchGate
Venue: 2020 XV Conferencia Latinoamericana de Tecnologias de Aprendizaje (LACLO)
Year: 2020

The COVID 19 pandemic has remarkably affected every sphere of our lives and education inclusive. It greatly disrupted the teaching and learning process, necessitating alternative approaches at different times to ameliorate the situation. In the new normal, various pedagogical tactics and teaching strategies are employed for content delivery. More so, different platforms are utilized to enable learning as well as assessment techniques. This study, therefore, sought to explore the teaching experiences during the pandemic. It also highlights the success factors of online teaching during the COVID 19 semester and the best assessment strategies used during the pandemic. The methodology adopted in the study is narrative and, precisely, the Naturalist narrative. The data utilized are the discussions and responses from questions posed on ResearchGate. The retrieved responses reveal both positive and negative experiences, but more of the challenges encountered dominated the reviews. Many factors account for the negative experiences such as internet issues including its cost, student's participation rate, insufficient media instructions, lack of students preparation, and preference for face to face class. The responses further show that various platforms enable teaching to continue remotely during the COVID 19 pandemic, and the preference of tools used by individuals differs based on interest, audience, location, the content of the course, accessibility, among others. We gave a conclusion and offered a useful suggestion for future study.
Quantifying the radiant exposure and effective dose in patients treated for actinic keratoses with topical photodynamic therapy using daylight and LED white light.
Venue: Physics in medicine and biology
Year: 2018

Daylight photodynamic therapy (dl PDT) is as effective as conventional PDT (c PDT) for treating actinic keratoses but has the advantage of reducing patient discomfort significantly. Topical dl PDT and white light PDT (wl PDT) differ from c PDT by way of light sources and methodology. We measured the variables associated with light dose delivery to skin surface and influence of geometry using a radiometer, a spectral radiometer and an illuminance meter. The associated errors of the measurement methods were assessed. The spectral and spatial distribution of the radiant energy from the LED white light source was evaluated in order to define the maximum treatment area, setup and treatment protocol for wl PDT. We compared the data with two red LED light sources we use for c PDT. The calculated effective light dose is the product of the normalised absorption spectrum of the photosensitizer, protoporphyrin IX (PpIX) the irradiance spectrum and the treatment time. The effective light dose from daylight ranged from 3 0.4 to 44 6 J cm 2due to varying weather conditions. The effective light dose for wl PDT was reproducible for treatments but it varied across the treatment area between 4 0.1 J cm 2 at the edge and 9 0.1 J cm 2 centrally. The effective light dose for the red waveband (615 645 nm) was 0.42 0.05 J cm 2 on a clear day, 0.05 0.01 J cm 2 on an overcast day and 0.9 0.01 J cm 2 using the white light. This compares with 0.95 0.01 and 0.84 0.01 J cm 2 for c PDT devices. Estimated errors associated with indirect determination of daylight effective light dose were very significant, particularly for effective light doses less than 5 J cm 2 (up to 83% for irradiance calculations) The primary source of error is in establishment of the relationship between irradiance or illuminance and effective dose. Use of the O'Mahoney model is recommended using a calibrated logging illuminance meter with the detector in the plane of the treatment area.
Stabilising a cart inverted pendulum system using pole placement control method
Venue: 2017 15th International Conference on Quality in Research (QiR) International Symposium on Electrical and Computer Engineering
Year: 2017

A cart inverted pendulum system is one of the most common case to be considered for testing many control algorithms, since it has some challenging problems associated with non linearity, complexity and underactuated system model. In fact, non linearity behaviour of the inverted pendulum can be observed easily. Different pendulum angle response can be obtained by giving the same velocity in the cart. The cart inverted pendulum can be understood as an under actuated system since the system has a lower number of actuator than the degrees of freedom. One of the most convenient method to model the inverted pendulum system is to use Lagrange's equation. However, at present, many presented inverted pendulum models have been derived using simplified physical model. This simplified model may lead to problems for the implementation of the control algorithm in a real physical inverted pendulum system. In this paper, an inverted pendulum system model is presented, where a mechanical transmission system and a motor models have been included in the derivation of the inverted pendulum model. Hence, the problems for the control implementation in a real system can be minimized. The mathematical model of the inverted pendulum was derived using Lagrange's equation. The determination of the pole zero of the system is discussed. A simple method of pole placement is proposed to stabilise the pendulum at the desired position of the cart. Matlab simulation results show the effectiveness of the proposed method. And yet, this intuitive approach can lead to better understanding of the control behaviours.
The Effect of Connected Speech Teaching on Listening Comprehension of Iranian EFL Learners
Year: 2017

Listening comprehension is the most problematic skill among Iranian EFL learners even for those who passed high level of proficiency. The present study investigated the effects of teaching explicit instruction of connected speech on Iranian EFL learners' listening comprehension. It also investigated learners' attitudes towards this kind of training. For the purpose of the study, 52 male subjects aged 14 to 19 were purposefully selected. The Iranian EFL learners were given explicit instruction of connected speech features for five weeks. Listening comprehension tests and dictation tests were used before and after the treatment to determine if such instruction enhanced listening comprehension, and also, to determine whether the subjects were able to recognize individual words in connected speech. The subjects were asked to complete a questionnaire after the treatment in order to determine their attitudes toward the instruction. The results of the study showed significant improvement in learners' ability to recognize individual words in connected speech through the instruction of connected speech feature. On the question of listening comprehension, the instruction of connected speech features significantly enhanced listening comprehension. The learners expressed that they were able to recognize English prosody including connected speech features better after the instruction. They had positive attitude toward this instruction. This study clearly presented the importance and usefulness of teaching connected speech features. The finding of this study could help teachers and curriculum designers in the field to employ new ways to improve learners' listening comprehension.
Design Architecture Of An Integrated Student Activities Management System For Higher Education
Year: 2021

This paper proposes a technological platform that provides a holistic and user friendly interface for the management of information pertaining to co curricular activities handled by the student bodies in Universiti Teknologi MARA Cawangan Melaka (UiTMCM) The handling of any students activities lie in the collective processes of submitting the proposal, securing the budget, getting the approval, promoting and handling the event, collecting feedbacks, reporting and post mortem discussion The management of these activities has been a major concern with the involvement of over 100 student clubs that carry out more than 20 events per week that are vying for a budget Acquiring the approval for the event proposal, tracking the running of the activities and reporting are some of the factors that created a backlog from the management aspect These procedures require a physical approach Therefore, an Interactive Student Activities Management System (iSAMS) was designed and developed through the Waterfall methodology as a one stop centre to help the UiTMCM Student Affairs Division facilitate the management of the students' activities This web based system is able to track, display and store all the activities information from the proposal application stage until the end which includes tracking of attendance, progress of the event, collecting feedback, reporting of completed activities, storing supporting documents and generating reports for administrative purposes All of these are done and managed via a secure, online interface in the Student Affairs Division website without physical presence of the students and the administrators Even though iSAMS is yet to be completely implemented, the study has great potential in the form of a holistic management of students' co curricular activities Going by that, in the end it is hoped that there is going to be a more efficient management of students' co curricular activities (c) 2021 Karadeniz Technical University All rights reserved