nils-herrmann/clean_scirepeval huggingface dataset:
Modified SVPWM Algorithm for Three Level VSI With Synchronized and Symmetrical Waveforms
Venue: IEEE Transactions on Industrial Electronics
Year: 2007

The objective of the present work is to improve the output waveform of three level inverters used in high power applications, where the switching frequency is very low. This is achieved by maintaining the synchronization, half wave symmetry, quarter wave symmetry, and three phase symmetry in the pulsewidth modulation (PWM) waveforms. The principles of achieving synchronization and symmetries in terms of space vectors for three level inverters are presented. A novel synchronized space vector pulsewidth modulation (SVPWM) algorithms is proposed and verified experimentally. The experimental waveforms of the inverter output voltage and motor no load current for different operating conditions of the drive are presented. The performance measure in terms of the weighted total harmonic distortion (THD) of the line voltage is computed for the linear modulation region of the drive for the proposed algorithm and compared with that of synchronized SVPWM and synchronized sine triangle pulsewidth modulation (SPWM) technique. The comparative results show that consideration of synchronization and symmetry results in improved THD. Another significant feature of the proposed algorithm is that the symmetry and synchronization leads to self balancing of the direct current (dc) bus capacitor voltages over every one third cycle of the fundamental
Fluid MPC: Secure Multiparty Computation with Dynamic Participants
Venue: IACR Cryptol. ePrint Arch.
Year: 2020

Existing approaches to secure multiparty computation (MPC) require all the participants to commit to the entire duration of the protocol. As interest in MPC continues to grow, it is inevitable that there will be a desire to use it to evaluate increasingly complex functionalities on massive datasets, resulting in computations spanning several hours or days. Such scenarios call for a dynamic participation model for MPC where participants have the flexibility to go offline as needed and (re)join when they have available computational resources. Such a model would also democratize access to privacy preserving computation by facilitating an "MPC asa service" paradigm the deployment of MPC in volunteer operated networks that perform computation on behalf of clients. In this work, we initiate the study of fluid MPC, where parties can dynamically join and leave the computation. The minimum commitment required from each participant is referred to as fluidity, measured in the number of rounds of communication that it must stay online. Our contributions are threefold: We provide a formal treatment of fluid MPC, exploring various possible modeling choices. We construct information theoretic fluid MPC protocols in the honest majority setting. Our protocols achieve maximal fluidity, meaning that a party can exit the computation after receiving and sending messages in one round. We implement our protocol and test it in multiple network settings.
Economie de la donnee et plateforme en ligne essai sur la pertinence d'un regime juridique contractuel
Year: 2019

Le developpement de l'Internet et du numerique a engendre une revolution ayant conduit a l'avenement de l'economie numerique, fondee sur les donnees et dominee par les plateformes en ligne. Les donnees se caracterisent par leur omnipresence sur l'Internet. Elles constituent le nouvel or noir du XXIeme siecle, la matiere premiere necessaire au developpement des plateformes en ligne. Quant a ces dernieres, elles se sont developpees jusqu'a devenir des acteurs incontournables de l'Internet et de son economie. Bien que frequemment employees, les notions de donnee et de plateforme en ligne n'en demeurent pas moins floues et leurs contours restent a determiner. Cela est d'autant plus vrai que les plateformes en ligne et les donnees sont intimement liees. En effet, les premieres se servent des secondes afin d'attirer davantage d'internautes et, ainsi, prosperer. Or, les internautes tout comme les editeurs de contenus et les ayants droit constituent les collaborateurs des plateformes en leur fournissant les donnees dont elles ont besoin pour croitre. Le lien unissant les donnees aux plateformes en ligne pose la question cruciale du partage de la valeur. Ainsi, des interrogations demeurent les donnees mises en ligne ou issues de la navigation des internautes sont elles appropriables Dans l'affirmative, quels en sont les proprietaires La presente these tente d'apporter une reponse en se placant sous le prisme du contrat, outil juridique de regulation de l'exploitation des donnees par les plateformes en ligne. Ainsi, un nouveau regime juridique contractuel est propose afin d'encadrer les relations liant les plateformes en ligne a ses partenaires, relations dont l'objet est la donnee.
Digital Transformation and Governance Innovation for Public Biobanks and Free/Libre Open Source Software Using a Blockchain Technology.
Venue: Omics a journal of integrative biology
Year: 2020

Digitalization and digital health are transforming research practices, while economic growth is increasingly driven by the information commons. In the case of biological sciences, information commons, such as public biobanks and free/libre open source software (FLOSS) are of paramount importance for both research and the bioeconomy. In a time of digitalization, however, information commons are vulnerable to violations, such as the free rider problem, that render the commons unsustainable. Consequently, it has been argued that the enclosure of the informational common resources is the only means to effectively exploit them. Given the social and economic importance of the information commons, the new digital environment in biology and health requires governance innovation that will regulate the social embedding of the commons and their relationship to the free market, that is, a new political economy is needed. In this context, the need for a core common infrastructure, stretching from the physical to the logical and content layer of the information environment, that will guarantee the protection of the commons from both violations and enclosures, has been highlighted. Focusing on the interaction between two biological/bioinformatics commons, namely public biobanks and the FLOSS, we have set up an ecosystem relying on a blockchain technology. The proposed governance mechanism protects the information commons from the free rider problem and guarantees their sustainability without hampering their operational framework. Our model demonstrates the interdependence and protection of the information commons not as an abstract theoretical exercise, but rather as a physical reality on the digital ontological matrix.
Inventory of Carbon Dioxide (CO2) Emissions at Pacific Northwest National Laboratory
Year: 2009

The Carbon Management Strategic Initiative (CMSI) is a lab wide initiative to position the Pacific Northwest National Laboratory (PNNL) as a leader in science, technology and policy analysis required to understand, mitigate and adapt to global climate change as a nation. As part of an effort to walk the talk in the field of carbon management, PNNL conducted its first carbon dioxide (CO2) emissions inventory for the 2007 calendar year. The goal of this preliminary inventory is to provide PNNL staff and management with a sense for the relative impact different activities at PNNL have on the lab's total carbon footprint.
PIFuHD: Multi Level Pixel Aligned Implicit Function for High Resolution 3D Human Digitization
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020

Recent advances in image based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi level architecture that is end to end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. This provides context to an fine level which estimates highly detailed geometry by observing higher resolution images. We demonstrate that our approach significantly outperforms existing state of the art techniques on single image human shape reconstruction by fully leveraging 1k resolution input images.
A survey of computational kits for young children
Venue: IDC
Year: 2018

This paper presents a survey of computational kits that enable young children (ages 7 years old and under) to explore computing ideas and practices. We examined physical, virtual, and hybrid kits across three different perspectives: how they are designed, how they support children to explore computational concepts and practices, and how they enable children to engage in a range of projects and activities. Based on our analysis, we present design suggestions and opportunities to expand the possibilities in how children can engage in computing, what kinds of projects children can make, and what kinds of computational ideas children can explore.
Traffic speed prediction using deep learning method
Venue: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2016

Successful traffic speed prediction is of great importance for the benefits of both road users and traffic management agencies. To solve the problem, traffic scientists have developed a number of time series speed prediction approaches, including traditional statistical models and machine learning techniques. However, existing methods are still unsatisfying due to the difficulty to reflect the stochastic traffic flow characteristics. Recently, various deep learning models have been introduced to the prediction field. In this paper, a deep learning method, the Deep Belief Network (DBN) model, is proposed for short term traffic speed information prediction. The DBN model is trained in a greedy unsupervised method and fine tuned by labeled data. Based on traffic speed data collected from one arterial in Beijing, China, the model is trained and tested for different prediction time horizons. From experiment analysis, it is concluded that the DBN can outperform Back Propagation Neural Network (BPNN) and Auto Regressive Integrated Moving Average (ARIMA) for all time horizons. The advantages of DBN indicate that deep learning is promising in traffic research area.
Being 'really real' on YouTube: authenticity, community and brand culture in social media entertainment
Year: 2017

This article develops an analytical framework to understand the modes of address of native to online content types (gameplay, do it yourself (DIY) beauty, personality vlogging) These types of content differ sharply from established screen entertainment and are constituted from intrinsically interactive audience centricity and appeal to authenticity and community in a commercialising space which we call 'social media entertainment' The article offers a revisionist analysis of the shaping and disciplining of brand culture through the twinned discourses of authenticity and community. The significance of social media entertainment lies in that, for a great many especially young viewers, this is what television is, now.
The effect of STEM application on 21st century skills of middle school students and student experiences
Year: 2020

The aim of this study is to reveal the effect of STEM approach on 21st century skills of middle school students and to determine their experiences about STEM. This application, which lasted 6 weeks with 7th grade students, was realized with 35 students. In the study, by using the explanatory mixed method, firstly, quantitative data was taken and qualitative data were tried to be explained. In the first stage of the study, the pre test and post test results were examined in the group by using dependent groups t test. "Multidimensional 21st Century Skills Scale" was used as a pre test and post test tool. In the second stage of the study, student experiences for STEM application were revealed using the semi structured interview form, which is one of the qualitative data collection methods. In the findings of the study, it was seen in the results of the analysis that the STEM approach contributed positively to 21st century skills. In addition, a significant difference was observed between the pre test and post test in five sub dimensions in the scale. However, in the results of the interviews, it was stated that the students were enjoyable to study with STEM, increased communication within the group, and encouraged the student to research and think. It was also observed that some students had difficulties during the application, complained about the lack of time and had difficulty in designing.
Semantic Accessibility to E learning Web Services
Venue: MTSR
Year: 2013

Semantic web technologies are all the more relevant in modern e learning environments that are built upon existing web resources, applications and services, which cannot be completely hosted and managed by a centralised web based system. This paper shows how semantic web and linked data technologies can improve the interoperability between virtual learning environments and external web resources. The ReST architectural principles were applied and a semantically enhanced access interface was defined to enable a richer exploitation of the services provided by external web applications for the aim of the e learning environment. Following this methodology, several open source web applications have been integrated with a popular course management system, in order to improve web services accesibility from the e learning system.
Fasting, Circadian Rhythms, and Time Restricted Feeding in Healthy Lifespan.
Venue: Cell metabolism
Year: 2016

Most animals alternate periods of feeding with periods of fasting often coinciding with sleep. Upon >24 hr of fasting, humans, rodents, and other mammals enter alternative metabolic phases, which rely less on glucose and more on ketone body like carbon sources. Both intermittent and periodic fasting result in benefits ranging from the prevention to the enhanced treatment of diseases. Similarly, time restricted feeding (TRF) in which food consumption is restricted to certain hours of the day, allows the daily fasting period to last >12 hr, thus imparting pleiotropic benefits. Understanding the mechanistic link between nutrients and the fasting benefits is leading to the identification of fasting mimicking diets (FMDs) that achieve changes similar to those caused by fasting. Given the pleiotropic and sustained benefits of TRF and FMDs, both basic science and translational research are warranted to develop fasting associated interventions into feasible, effective, and inexpensive treatments with the potential to improve healthspan.
Modeling habitat suitability for occurrence of highly pathogenic avian influenza virus H5N1 in domestic poultry in Asia: a spatial multicriteria decision analysis approach.
Venue: Spatial and spatio temporal epidemiology
Year: 2013

Risk maps are one of several sources used to inform risk based disease surveillance and control systems, but their production can be hampered by lack of access to suitable disease data. In such situations, knowledge driven spatial modeling methods are an alternative to data driven approaches. This study used multicriteria decision analysis (MCDA) to identify areas in Asia suitable for the occurrence of highly pathogenic avian influenza virus (HPAIV) H5N1 in domestic poultry. Areas most suitable for H5N1 occurrence included Bangladesh, the southern tip and eastern coast of Vietnam, parts of north central Thailand and large parts of eastern China. The predictive accuracy of the final model, as determined by the area under the receiver operating characteristic curve (ROC AUC) was 0.670 (95% CI 0.667 0.673) suggesting that, in data scarce environments, MCDA provides a reasonable alternative to the data driven approaches usually used to inform risk based disease surveillance and control strategies.
Generation of induced pluripotent stem cells from urine.
Venue: Journal of the American Society of Nephrology JASN
Year: 2011

Forced expression of selected transcription factors can transform somatic cells into embryonic stem cell (ESC) like cells, termed induced pluripotent stem cells (iPSCs) There is no consensus regarding the preferred tissue from which to harvest donor cells for reprogramming into iPSCs, and some donor cell types may be more prone than others to accumulation of epigenetic imprints and somatic cell mutations. Here, we present a simple, reproducible, noninvasive method for generating human iPSCs from renal tubular cells present in urine. This procedure eliminates many problems associated with other protocols, and the resulting iPSCs display an excellent ability to differentiate. These data suggest that urine may be a preferred source for generating iPSCs.
Artificial reefs do increase secondary biomass production: mechanisms evidenced by stable isotopes
Year: 2014

A large complex of artificial reefs was deployed in the Bay of Marseilles, North Western Mediterranean, for the enhancement of commercial fisheries stocks. Carbon and nitrogen stable isotope and stomach content analyses were performed on 23 fish species collected on the artificial reefs to assess their trophic position and feeding behaviour. Results indicated that fish diets were not modified on the artificial reefs compared to natural environments, nor was the structure of their trophic network. Artificial reefs, with their complex design, provide diverse and abundant food sources for fishes. Ranges of d13C and d15N of artificial reef fishes were comparable to those recorded in natural Mediterranean environments, with a similar trophic organization. However, some discrepancies appeared when comparing fish trophic level based on isotopic or diet results, which calls for a careful interpretation of stable isotope values as direct indicators of trophic level.
Joint Common Sense and Relation Reasoning for Dense Relational Captioning
Venue: 2020 15th IEEE International Conference on Signal Processing (ICSP)
Year: 2020

Relation reasoning between objects plays a vital role in image captioning. A joint common sense and relation reasoning model is proposed, which has the ability to generate more informative and diverse sentences for dense relational captioning. Our proposed model consists of two stages, region features extraction and relational captioning generation. The features of regions including object, union and subject are encoded by Region Proposal Network (RPN) Afterwards, they are fed to triple stream Long Short Term Memory (LSTM) respectively where object predicate subject categories are used as the prior information to lead the proper sequence of words in the descriptions generation. Moreover, we present the memory augmented union operation where the image features and union features are leveraged to learn common sense and relation reasoning. Our experimental results on VG relationship captioning datasets demonstrate the validity of the joint common sense and relation reasoning model, which achieves competitive performance in dense relational captioning.
Performance comparative of OpenCV Template Matching method on Jetson TX2 and Jetson Nano developer kits
Venue: 2020 10th Annual Computing and Communication Workshop and Conference (CCWC)
Year: 2020

Template Matching is a widely used method for object detection in digital images, it requires great processing power since it is an exhaustive method that compares the intensity levels of a source image pixel to pixel with a template image that contains the object to identify. Nowadays there are dedicated embedded systems that provide high processing capabilities, such as the NVIDIA Jetson family. This work presents the experimentation and the performance comparison between the Jetson Nano and Jetson TX2 development kits, when implementing the Template Matching method, in order to get an evaluation criterion to select one of them in image processing projects. It was carried out to six images with different sizes and two variants in terms of the size of the template image. The processing times for the sequential implementation using the CPUs and the parallel implementation with the GPUs were obtained quantitatively. It was observed that the processing times using the parallel versions on average doubled those of the sequential versions and that the Jetson TX2 exceeded the Jetson Nano in execution speeds.
Effects of Trunk Control Exercise Performed on an Unstable Surface on Dynamic Balance in Chronic Stroke Patients
Year: 2016

This study compared the effectiveness of trunk control exercise performed on an unstable surface with that of general balance exercise on dynamic balance in the patients with chronic stroke.  The persons of this study were thirty seven chronic stroke patients were recruited and randomly divided into 2 groups; (1) those who performed trunk control exercise on a foam roll and (2) those who performed general balance exercise. The exercises were performed 5 times a week for 4 weeks. To determine the effectiveness of the 2 types of exercises, we measured dynamic balance at the beginning of the exercises and again after 4 weeks at the completion of exercises program.
FPGA based generic RO TRNG architecture for image confusion
Venue: Multimedia Tools and Applications
Year: 2020

True Random Number Generator (TRNG) has become a central element of today's secure communication. TRNGs developed through FPGA implementation have a significant role in a number of applications in multimedia communication. Ring Oscillator (RO) has been adopted extensively for developing device independent TRNG structures. The proposed design is a generic TRNG architecture based on an arrangement of ROs with post processing. This TRNG was initially tested on Altera Cyclone II FPGA which consumed only 64 inverters to produce good randomness. Also, the device independent capability of this design has been verified by implementing it on other two FPGA families Xilinx Artix 7 and Microsemi Smart Fusion2. True randomness was also verified by conducting restart experiment, and the statistical properties of TRNG were evaluated through entropy analysis and NIST SP 800 22 tests. The proposed TRNG yields 26.640650 Mbps as throughput with a sampling clock of 27 MHz. Going beyond the regular key generation utility, the evolving random bits of TRNG were packed suitably to perform confusion of grayscale images producing a near zero correlation of pixels, thereby extending the application of TRNG to image encryption.
The evolution of seasonal influenza viruses
Venue: Nature Reviews Microbiology
Year: 2018

Despite decades of surveillance and pharmaceutical and non pharmaceutical interventions, seasonal influenza viruses continue to cause epidemics around the world each year. The key process underlying these recurrent epidemics is the evolution of the viruses to escape the immunity that is induced by prior infection or vaccination. Although we are beginning to understand the processes that underlie the evolutionary dynamics of seasonal influenza viruses, the timing and nature of emergence of new virus strains remain mostly unpredictable. In this Review, we discuss recent advances in understanding the molecular determinants of influenza virus immune escape, sources of evolutionary selection pressure, population dynamics of influenza viruses and prospects for better influenza virus control.