nils-herrmann/clean_scirepeval huggingface dataset:
Mobile Cloud Robotics as a Service with OCCIware

We have recently witnessed the emerging of cloud computing on one hand and robotics platforms on the other hand. Naturally, these two visions have been merging to give birth to the Cloud Robotics paradigm in order to offer even more remote services. But such a vision is still in its infancy. Architectures and platforms are still to be defined to efficiently program robots so they can provide different services, in a standardized way masking their heterogeneity. This paper introduces Open Mobile Cloud Robotics Interface (OMCRI), a Robot-as-a-Service vision based platform, which offers a unified easy access to remote heterogeneous mobile robots. OMCRI encompasses an extension of the Open Cloud Computing Interface (OCCI) standard and a gateway hosting mobile robot resources. We then provide an implementation of OMCRI based on the open source model-driven Eclipse-based OCCIware tool chain and illustrates its use for three off-the-shelf mobile robots: Lego Mindstorm NXT, Turtlebot, and Parrot AR. Drone.
Orthostatic hypotension in older adults. The Cardiovascular Health Study. CHS Collaborative Research Group.

The purpose of the present study was to assess the prevalence of orthostatic hypotension and its associations with demographic characteristics, cardiovascular risk factors and symptomatology, prevalent cardiovascular disease, and selected clinical measurements in the Cardiovascular Health Study, a multicenter, observational, longitudinal study enrolling 5,201 men and women aged 65 years and older at initial examination. Blood pressure measurements were obtained with the subjects in a supine position and after they had been standing for 3 minutes. The prevalence of asymptomatic orthostatic hypotension, defined as 20 mm Hg or greater decrease in systolic or 10 mm Hg or greater decrease in diastolic blood pressure, was 16.2%. This prevalence increased to 18.2% when the definition also included those in whom the procedure was aborted due to dizziness upon standing. The prevalence was higher at successive ages. Orthostatic hypotension was associated significantly with difficulty walking (odds ratio, 1.23; 95% confidence interval, 1.02, 1.46), frequent falls (odds ratio, 1.52; confidence interval, 1.04, 2.22), and histories of myocardial infarction (odds ratio, 1.24; confidence interval, 1.02, 1.50) and transient ischemic attacks (odds ratio, 1.68; confidence interval, 1.12, 2.51). History of stroke, angina pectoris, and diabetes mellitus were not associated significantly with orthostatic hypotension. In addition, orthostatic hypotension was associated with isolated systolic hypertension (odds ratio, 1.35; confidence interval, 1.09, 1.68), major electrocardiographic abnormalities (odds ratio, 1.21; confidence interval, 1.03, 1.42), and the presence of carotid artery stenosis based on ultrasonography (odds ratio, 1.67; confidence interval, 1.23, 2.26). Orthostatic hypotension was negatively associated with weight. We conclude that orthostatic hypotension is common in the elderly and increases with advancing age. It is associated with cardiovascular disease, particularly those manifestations measured objectively, such as carotid stenosis. It is associated also with general neurological symptoms, but this link may not be causal. Differences in prevalence of and associations with orthostatic hypotension in the present study compared with others are largely attributed to differences in population characteristics and methodology.
RTL implementation for AMBA ASB APB protocol at system on chip level

In today's era AMBA (advanced microcontroller bus architecture) specifications have gone far beyond the Microcontrollers. In this paper, AMBA (Advanced Microcontroller Bus Architecture) ASB APB (Advanced system bus - Advanced Peripheral Bus) is implemented. The goal of the proposed paper is to synthesis, simulate complex interface between AMBA ASB and APB. The methodology adopted for the proposed paper is Verilog language with finite state machine models designed in ModelSim Version 10.3 and Xilinx-ISE design suite, version 13.4 is used to extract synthesis, design utilization summary and power reports. For the implementation APB Bridge, arbiter and decoder are designed. In AMBA ASB APB module, master gets into contact with APB bus. Arbiter determines master's status and priority and then, starts communicating with the bus. For selecting a bus slave, decoder uses the accurate address lines and an acknowledgement is given back to the bus master by the slave. An RTL view and an extracted design summary of AMBA ASB APB module at system on chip are shown in result section of the paper. Higher design complexities of SoCs architectures introduce the power consumption into picture. The various power components contribute in the power consumptions which are extracted by the power reports. So, power reports generate a better understanding of the power utilization to the designers. These are clocks total power which consumes of 0.66 mW, hierarchy total power which consumes of 1.05 mW, hierarchy total logical power which consumes of 0.30 mW and hierarchy total signal power which consumes of 0.74 mW powers in the proposed design. Graph is also plotted for clear understanding of the breakdown of powers.
Forward dual-active-bridge solid state transformer for a SiC-based cascated multilevel converter cell in solar applications

Central inverters based on conventional topologies are the current preferred solution in solar farms because of their low cost and simplicity. However, such topologies have some disadvantages as poor maximum power tracking, use of bulky filters and low frequency transformers. A good alternative in this case is the SiC-based Cascaded Multilevel Converter (CMC), which provides a distributed MPPT control with reduced footprint and high flexibility. Each cell of a CMC usually has as an intermediate stage a solid-state transformer based on a Dual-Active-Bridge (DAB) DC-DC Converter. Due to the unidirectional power flow characteristic of the photovoltaic application and aiming further reduction in the converter footprint, this work proposes a Forward Dual-Active-Bridge (F-DAB) topology, which reduces the number of active switches. This paper shows through analytical, simulation and experimental results that the cell using an F-DAB is superior to other unidirectional topologies in two aspects: greater power density with the available power modules and simplicity of control.
Design of 64-bit low power parallel prefix VLSI adder for high speed arithmetic circuits

The addition of two binary numbers is the basic and most often used arithmetic operation on microprocessors, digital signal processors and data processing application specific integrated circuits. Parallel prefix adder is a general technique for speeding up binary addition. This method implements logic functions which determine whether groups of bits will generate or propagate a carry. The proposed 64-bit adder is designed using four different types prefix cell operators, even-dot cells, odd-dot cells, even-semi-dot cells and odd-semi-dot cells; it offers robust adder solutions typically used for low power and high-performance design application needs. The comparison can be made with various input ranges of Parallel Prefix adders in terms power, number of transistor, number of nodes. Tanner EDA tool was used for simulating the parallel prefix adder designs in the 250nm technologies.
Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs

We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefined characteristic scoring function for evaluating the correctness of KG triples. These scoring functions distinguish correct triples (high score) from incorrect ones (low score). However, their performance vary across different datasets. In this work, we demonstrate that a simple neural network based score function can consistently achieve near start-of-the-art performance on multiple datasets. We also quantitatively demonstrate biases in standard benchmark datasets, and highlight the need to perform evaluation spanning various datasets.
Fast camera self-calibration for synthesizing Free Viewpoint soccer Video

Recently, non-fixed camera-based free viewpoint sports video synthesis has become very popular. Camera calibration is an indispensable step in free viewpoint video synthesis, and the calibration has to be done frame by frame for a non-fixed camera. Thus, calibration speed is of great significance in real-time application. In this paper, a fast self-calibration method for a non-fixed camera is proposed to estimate the homography matrix between a camera image and a soccer field model. As far as we know, it is the first time to propose constructing feature vectors by analyzing crossing points of field lines in both camera image and field model. Therefore, different from previous methods that evaluate all the possible homography matrices and select the best one, our proposed method only evaluates a small number of homography matrices based on the matching result of the constructed feature vectors. Experimental results show that the proposed method is much faster than other methods with only a slight loss of calibration accuracy that is negligible in final synthesized videos.
Choosing a Selection Technique for a Virtual Environment

Bearing in mind the difficulty required to create virtual environments, a platform for Setting-up Interactive Virtual Environments (pSIVE) was created to help non-specialists benefit from virtual applications involving virtual tours where users may interact with elements of the environment to extract contextual information. The platform allows creating virtual environments and setting up their aspects, interaction methods and hardware to be used. The construction of the world is done by loading 3D models and associating multimedia information (videos, texts or PDF documents) to them. A central interaction task in the envisioned applications of pSIVE is the selection of objects that have associated multimedia information. Thus, a comparative study between two variants of the ray-tracing selection technique was performed. The study also demonstrates the flexibility of the platform, since it was easily adapted to serve as a test environment.
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds

Many online social networks thrive on automatic sharing of friends' activities to a user through activity feeds, which may influence the user's next actions. However, identifying such social influence is tricky because these activities are simultaneously impacted by influence and homophily. We propose a statistical procedure that uses commonly available network and observational data about people's actions to estimate the extent of copy-influence---mimicking others' actions that appear in a feed. We assume that non-friends don't influence users; thus, comparing how a user's activity correlates with friends versus non-friends who have similar preferences can help tease out the effect of copy-influence.
 Experiments on datasets from multiple social networks show that estimates that don't account for homophily overestimate copy-influence by varying, often large amounts. Further, copy-influence estimates fall below 1% of total actions in all networks: most people, and almost all actions, are not affected by the feed. Our results question common perceptions around the extent of copy-influence in online social networks and suggest improvements to diffusion and recommendation models.
Prevention of calcification in bioprosthetic heart valves: challenges and perspectives.

Surgical replacement with artificial devices has revolutionised the care of patients with severe valvular diseases. Mechanical valves are very durable, but require long-term anticoagulation. Bioprosthetic heart valves (BHVs), devices manufactured from glutaraldehyde-fixed animal tissues, do not need long-term anticoagulation, but their long-term durability is limited to 15 - 20 years, mainly because of mechanical failure and tissue calcification. Although mechanisms of BHV calcification are not fully understood, major determinants are glutaraldehyde fixation, presence of devitalised cells and alteration of specific extracellular matrix components. Treatments targeted at the prevention of calcification include those that target neutralisation of the effects of glutaraldehyde, removal of cells, and modifications of matrix components. Several existing calcification-prevention treatments are in clinical use at present, and there are excellent mid-term clinical follow-up reports available. The purpose of this review is to appraise basic knowledge acquired in the field of prevention of BHV calcification, and to provide directions for future research and development.
Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs

Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It requires a lot of memory storage and computational power. There are different approaches to compute the solution of convolution and reduce its computational complexity. In this paper, a matrix multiplication-based convolution (ConvMM) approach is fully parallelized using concurrent resources of GPU (Graphics Processing Unit) and optimized, considerably improving the performance of the image classifiers and making them applicable to real-time embedded applications. The flow of this CUDA (Compute Unified Device Architecture)-based scheme is optimized using unified memory and hardware-dependent acceleration of matrix multiplication. Proposed flow is evaluated on two different embedded platforms: first on an Nvidia Jetson TX1 embedded board and then on a Tegra K1 GPU of an Nvidia Shield K1 Tablet. The performance of this optimized and accelerated convolutional layer is compared with its sequential and heterogeneous versions. Results show that the proposed scheme significantly improves the overall results including energy efficiency, storage requirement and inference performance. In particular, the proposed scheme on embedded GPUs is hundreds of times faster than the sequential version and delivers tens of times higher performance than the heterogeneous approach.
Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery

Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both network structures and contents. To deal with network structures, most existing works assume a given or enumerable set of meta-paths and then leverage them for the computation of meta-path-based proximities or network embeddings. However, expert knowledge for given meta-paths is not always available, and as the length of considered meta-paths increases, the number of possible paths grows exponentially, which makes the path searching process very costly. On the other hand, while there are often rich contents around network nodes, they have hardly been leveraged to further improve similarity modeling. In this work, to properly model node similarity in content-rich heterogeneous networks, we propose to automatically discover useful paths for pairs of nodes under both structural and content information. To this end, we combine continuous reinforcement learning and deep content embedding into a novel semi-supervised joint learning framework. Specifically, the supervised reinforcement learning component explores useful paths between a small set of example similar pairs of nodes, while the unsupervised deep embedding component captures node contents and enables inductive learning on the whole network. The two components are jointly trained in a closed loop to mutually enhance each other. Extensive experiments on three real-world heterogeneous networks demonstrate the supreme advantages of our algorithm.
Fiber-bound nitrogen in gorilla diets: implications for estimating dietary protein intake of primates.

Protein is essential for living organisms, but digestibility of crude protein is poorly understood and difficult to predict. Nitrogen is used to estimate protein content because nitrogen is a component of the amino acids that comprise protein, but a substantial portion of the nitrogen in plants may be bound to fiber in an indigestible form. To estimate the amount of crude protein that is unavailable in the diets of mountain gorillas (Gorilla beringei) in Bwindi Impenetrable National Park, Uganda, foods routinely eaten were analyzed to determine the amount of nitrogen bound to the acid-detergent fiber residue. The amount of fiber-bound nitrogen varied among plant parts: herbaceous leaves 14.5+/-8.9% (reported as a percentage of crude protein on a dry matter (DM) basis), tree leaves (16.1+/-6.7% DM), pith/herbaceous peel (26.2+/-8.9% DM), fruit (34.7+/-17.8% DM), bark (43.8+/-15.6% DM), and decaying wood (85.2+/-14.6% DM). When crude protein and available protein intake of adult gorillas was estimated over a year, 15.1% of the dietary crude protein was indigestible. These results indicate that the proportion of fiber-bound protein in primate diets should be considered when estimating protein intake, food selection, and food/habitat quality.
EFFECT OF EXAMINATION STRESS ON THE PLASMA CORTISOL LEVEL

Medical profession has been always a noble and prestigious path but the endeavour behind it has been truly known by the persons who undergone the training of becoming a doctor. Medical students face many stresses in their academic life. This study is carried out to provide data and re-establish the effect of academic examination stress on the plasma cortisol levels.  A longitudinal follow up study was carried out on the first MBBS medical students who were appearing for their first credit examination by measuring their plasma cortisol levels in pre-examination and post-examination stage in fasting condition. Serum Cortisol was estimated by using Byer’s Advia Centuse advanced Chemiluminescence’s technique with inbuilt calibrators and controls; the results obtained were statistically analysed using paired ‘t’ test.  On statistically analysing the results of our study we found that medical students in stage – I had significantly higher values of plasma cortisol than when they were in stage – II.  The results cover a significant correlation of examination stress factors to changes in plasma cortisol values. It is important for medical students to use stress reducing measures, or reduce them as much as possible in order to avoid factors that can affect themselves and their patients in stressful way.
The Link between Individual and Organizational Learning

All organizations learn, whether they consciously choose to or not-it is a fundamental requirement for their sustained existence. Some firms deliberately advance organizational learning, developing capabilities that are consistent with their objectives; others make no focused effort and, therefore, acquire habits that are counterproductive. Nonetheless, all organizations learn. But what does it mean that an organization learns? We can think of organizational learning as a metaphor derived from our understanding of individual learning. In fact, organizations ultimately learn via their individual members. Hence, theories of individual learning are crucial for understanding organizational learning. Psychologists have studied individual learning for decades, but they are still far from fully understanding the workings of the human mind. Likewise, the theory of organizational learning is still in its embryonic stage. The purpose of this paper is to build a theory about the process through which individual learning advances organizational learning. To do this, we must address the role of individual learning and memory, differentiate between levels of learning, take into account different organizational types, and specify the transfer mechanism between individual and organizational learning. This transfer is at the heart of organizational learning: the process through which individual learning becomes embedded in an organization's memory and structure. Until now, it has received little attention and is not well understood, although a promising interaction between organization theory and psychology has begun. To contribute to our understanding of the nature of the learning organization, I present a framework that focuses on the crucial link between individual learning and organizational learning. Once we have a clear understanding of this transfer process, we can actively manage the learning process to make it consistent with an organization's goals, vision, and values.
Touch the virtual reality: using the leap motion controller for hand tracking and wearable tactile devices for immersive haptic rendering

The complexity of the world around us is creating a demand for novel interfaces that will simplify and enhance the way we interact with the environment. The recently unveiled Android Wear operating system addresses this demand by providing a modern system for all those companies that are now developing wearable devices, also known as "wearables". Wearability of robotic devices will enable novel forms of human intention recognition through haptic signals and novel forms of communication between humans and robots. Specifically, wearable haptics will enable devices to communicate with humans during their interaction with the environment they share. Wearable haptic technology have been introduced in our everyday life by Sony. In 1997 its DualShock controller for PlayStation revolutionized the gaming industry by introducing a simple but effective vibrotactile feedback. More recently, Apple unveiled the Apple Watch, which embeds a linear actuator that can make the watch vibrate. It is used whenever the wearer receives an alert or notification, or to communicate with other Apple Watch owners.