nils-herrmann/clean_scirepeval huggingface dataset:
An artificial intelligence tool for heterogeneous team formation in the classroom

Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organize their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimized. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin’s role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student’s predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction.
A Kinect-Based Real-Time Compressive Tracking Prototype System for Amphibious Spherical Robots

A visual tracking system is essential as a basis for visual servoing, autonomous navigation, path planning, robot-human interaction and other robotic functions. To execute various tasks in diverse and ever-changing environments, a mobile robot requires high levels of robustness, precision, environmental adaptability and real-time performance of the visual tracking system. In keeping with the application characteristics of our amphibious spherical robot, which was proposed for flexible and economical underwater exploration in 2012, an improved RGB-D visual tracking algorithm is proposed and implemented. Given the limited power source and computational capabilities of mobile robots, compressive tracking (CT), which is the effective and efficient algorithm that was proposed in 2012, was selected as the basis of the proposed algorithm to process colour images. A Kalman filter with a second-order motion model was implemented to predict the state of the target and select candidate patches or samples for the CT tracker. In addition, a variance ratio features shift (VR-V) tracker with a Kalman estimation mechanism was used to process depth images. Using a feedback strategy, the depth tracking results were used to assist the CT tracker in updating classifier parameters at an adaptive rate. In this way, most of the deficiencies of CT, including drift and poor robustness to occlusion and high-speed target motion, were partly solved. To evaluate the proposed algorithm, a Microsoft Kinect sensor, which combines colour and infrared depth cameras, was adopted for use in a prototype of the robotic tracking system. The experimental results with various image sequences demonstrated the effectiveness, robustness and real-time performance of the tracking system.
CUSPARC IP processor: Design, characterization and applications

In this paper, we introduce the design of an IP processor core code-named CUSPARC for Cairo university SPARC processor. This core is a 32 bit pipelined processor that conforms to SPARC v8 ISA. It is complete with 4 register windows, I and D caches, SRAM and flash memory controller, resolution hardware for the data and branch hazards, interrupts and exception handling, instructions to support I/O transfers, and two standard WISHBONE buses to support high speed and low speed IO transfers. The design was downloaded and tested on different FPGA platforms, in addition to 0.35µm and 0.13µm ASIC technologies. CUSPARC has a promising metric of 0.9663 DMIPS/MHz. A novel debugger tool was developed for validating CUSPARC. This tool facilitates the testing of the processor running complex software loads by invoking Mentor's MODELSIM simulator in the background while maintaining a “simulator-like” GUI in the foreground.
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain

We study the problem of hypothesis testing between two discrete distributions, where we only have access to samples after the action of a known reversible Markov chain, playing the role of noise. We derive instance-dependent minimax rates for the sample complexity of this problem, and show how its dependence in time is related to the spectral properties of the Markov chain. We show that there exists a wide statistical window, in terms of sample complexity for hypothesis testing between different pairs of initial distributions. We illustrate these results in several concrete examples.
Multiport Bidirectional SRM Drives for Solar-Assisted Hybrid Electric Bus Powertrain With Flexible Driving and Self-Charging Functions

The hybrid electric bus (HEB) presents an emerging solution to exhaust gas emissions in urban transport. This paper proposes a multiport bidirectional switched reluctance motor (SRM) drive for solar-assisted HEB (SHEB) powertrain, which not only improves the motoring performance, but also achieves flexible charging functions. To extend the driving miles and achieve self-charging ability, photovoltaic (PV) panels are installed on the bus to decrease the reliance on fuelsbatteries and charging stations. A bidirectional front-end circuit with a PV-fed circuit is designed to integrate electrical components into one converter. Six driving and five charging modes are achieved. The dc voltage is boosted by the battery in generator control unit (GCU) driving mode and by the charge capacitor in battery driving mode, where the torque capability is improved. Usually, an extra converter is needed to achieve battery charging. In this paper, the battery can be directly charged by the demagnetization current in GCU or PV driving mode, and can be quickly charged by the PV panels and GCUAC grids at SHEB standstill conditions, by utilizing the traction motor windings and integrated converter circuit, without external charging converters. Experiments on a three-phase 128 SRM confirm the effectiveness of the proposed drive and control scheme.
Physical Activity and Sedentary Behavior Patterns Before and During Pregnancy in a Multi-ethnic Sample of Asian Women in Singapore

To describe physical activity (PA) and sedentary behavior (SB) patterns before and during pregnancy among Chinese, Malay and Indian women. In addition, to investigate determinants of change in PA and SB during pregnancy. The Growing Up in Singapore Towards healthy Outcomes cohort recruited first trimester pregnant women. PA and SB (sitting time and television time) before and during pregnancy were assessed as a part of an interview questionnaire at weeks 26–28 gestational clinic visit. Total energy expenditure (TEE) on PA and time in SB were calculated. Determinants of change in PA and SB were investigated using multiple logistic regression analysis. PA and SB questions were answered by 94 % (n = 1171) of total recruited subjects. A significant reduction in TEE was observed from before to during pregnancy [median 1746.0–1039.5 metabolic equivalent task (MET) min/week, p < 0.001]. The proportion of women insufficiently active (<600 MET-min/week) increased from 19.0 to 34.1 % (p < 0.001). Similarly, sitting time (median 56.0–63.0 h/week, p < 0.001) and television time (mean 16.1–16.7 h/week, p = 0.01) increased. Women with higher household income, lower level of perceived health, nausea/vomiting during pregnancy and higher level of pre-pregnancy PA were more likely to reduce PA. Women with children were less likely to reduce PA. Women reporting nausea/vomiting and lower level of pre-pregnancy sitting time were more likely to increase sitting time. Participants substantially reduced PA and increased SB by 26–28 weeks of pregnancy. Further research is needed to better understand determinants of change in PA and SB and develop effective health promotion strategies.
Valproic acid as epigenetic cancer drug: preclinical, clinical and transcriptional effects on solid tumors.

Among many anticancer drugs collectively named "targeted or molecular therapies" epigenetic drugs are clearly promising. Differently from other agents targeting a single gene product, epigenetic drugs have chromatin as their target through inhibition of histone deacetylases (HDACs) and DNA methyltransferases (DNMTs) therefore, yet unspecific, they may act upon most or all tumor types, as deregulation of the methylation and deacetylation machinery are a common hallmark of neoplasia. In the last years, valproic acid (VPA) as emerged as a promising drug for cancer treatment. VPA has shown potent antitumor effects in a variety of in vitro and in vivo systems, and encouraging results in early clinical trials either alone or in combination with demethylating and/or cytotoxic agents. In addition, whole genome expression by microarray analysis from the primary tumors of patients treated with VPA show significant up-regulation of hundred of genes belonging to multiple pathways including ribosomal proteins, oxidative phosphorylation, MAPK signaling; focal adhesion, cell cycle, antigen processing and presentation, proteasome, apoptosis, PI3K, Wnt signaling, calcium signaling, TGF-beta signaling, and ubiquitin-mediated proteolysis among others. Despite in general, industry is not particularly interested in funding the clinical development of VPA, -at least in comparison to novel HDAC inhibitors-, existing preclinical and preliminary clinical data strongly suggest that VPA could be a drug that eventually will be used in combination therapies, either with classical cytotoxics, other molecular-targeted drugs or radiation in a number of solid tumors.
The ideal romantic partner personality

Most contemporary social psychological studies on inter-personal attraction have independently explored the competing concepts of similarity and complementarity. Incorporating evolutionary principles associated with assortative mating, two studies were conducted that examined individual difference preferences using the five-factor model (FFM) of human personality as assessed by the NEO-FFI. The first study (N = 104) examined the degree to which individuals showed an absolute or relative preference in an ‘‘ideal romantic partner’’ when compared to self-rated personality. The second study (N = 161) extended this by incorporating personality ratings for self, ‘‘ideal’’ romantic partner, and ‘‘actual’’ romantic partner, and perceptions of mate value for each. Overall, findings supported both evolutionary and social psychological theories of inter-personal attraction and support both relative and absolute preferences in romantic partners. Individuals sought mates that were matches of themselves to some degree (a concept that we termed aspirational positive assortative mating) but also sought mates that were somewhat higher in Conscientiousness, Extraversion, Agreeableness, and Mate Value, but lower in Neuroticism than themselves.
3'-minor groove binder-DNA probes increase sequence specificity at PCR extension temperatures.

DNA probes with conjugated minor groove binder (MGB) groups form extremely stable duplexes with single-stranded DNA targets, allowing shorter probes to be used for hybridization based assays. In this paper, sequence specificity of 3'-MGB probes was explored. In comparison with unmodified DNA, MGB probes had higher melting temperature (T(m)) and increased specificity, especially when a mismatch was in the MGB region of the duplex. To exploit these properties, fluorogenic MGB probes were prepared and investigated in the 5'-nuclease PCR assay (real-time PCR assay, TaqMan assay). A 12mer MGB probe had the same T(m)(65 degrees C) as a no-MGB 27mer probe. The fluorogenic MGB probes were more specific for single base mismatches and fluorescence quenching was more efficient, giving increased sensitivity. A/T rich duplexes were stabilized more than G/C rich duplexes, thereby leveling probe T(m)and simplifying design. In summary, MGB probes were more sequence specific than standard DNA probes, especially for single base mismatches at elevated hybridization temperatures.
System based on machine vision for translation of fingerspelling alphabet to latin alphabet

On the nowadays society exist a lot of communication problems, particularly when the persons has sensory disabilities as deafness or blindness. This problem take place at the moment of interpreting the sign language. The present paper shows the development of a current research project that integrates an intelligent system in the recognition of images and its reproduction in hardware interpretations ends. For those purposes, a system of image acquisition with a webcam and an interface was implemented in Matlab, through which the video was displayed in real time, the image of the point gained, together with the translation of Colombian sign language. This system was trained to recognize 4-letter alphabet, obtaining an average error of 2%, concluding that such application was effective for translating letters acquired both the right hand or the left; similarly concluded that the type of radial neural network proves to be very useful for this type of operation and the higher classification have this training, the results will be more accurate cast. Finally it is important to note that the system integrates hardware with Arduino system that displays real-time translation in this system was trained to recognize 4 letters of the alphabet, giving an average error of 2%, the same way the PNN neural network proves to be very useful for this type of sorting operations.
The linkage of allergic rhinitis and obstructive sleep apnea.

Rhinitis can be divided into allergic and non-allergic rhinitis. Rhinitis, particularly allergic rhinitis, has been shown to be associated with obstructive sleep apnea; a condition characterized by repetitive upper airway obstruction during sleep. Allergic rhinitis increases the risk of developing obstructive sleep apnea by two major mechanisms: 1) increase in airway resistance due to higher nasal resistance and 2) reduction in pharyngeal diameter from mouth breathing that moves the mandible inferiorly. Other inflammatory mediators including histamine, CysLTs, IL 1β and IL-4 found in high levels in allergic rhinitis, have also been shown to worsen sleep quality in obstructive sleep apnea. Prior studies have shown that treatment of allergic rhinitis, particularly when intranasal steroid are used, improved obstructive sleep apnea. Leukotriene receptor antagonists were also associated with positive results on obstructive sleep apnea in adult patients with concomitant allergic rhinitis but current data are limited in the case of children.
Energy-Efficient Floating-Point Unit Design

Energy-efficient computation is critical if we are going to continue to scale performance in power-limited systems. For floating-point applications that have large amounts of data parallelism, one should optimize the throughput/mm2 given a power density constraint. We present a method for creating a trade-off curve that can be used to estimate the maximum floating-point performance given a set of area and power constraints. Looking at FP multiply-add units and ignoring register and memory overheads, we find that in a 90 nm CMOS technology at 1 W/mm2, one can achieve a performance of 27 GFlops/mm2 single precision, and 7.5 GFlops/mm double precision. Adding register file overheads reduces the throughput by less than 50 percent if the compute intensity is high. Since the energy of the basic gates is no longer scaling rapidly, to maintain constant power density with scaling requires moving the overall FP architecture to a lower energy/performance point. A 1 W/mm2 design at 90 nm is a "high-energy" design, so scaling it to a lower energy design in 45 nm still yields a 7× performance gain, while a more balanced 0.1 W/mm2 design only speeds up by 3.5× when scaled to 45 nm. Performance scaling below 45 nm rapidly decreases, with a projected improvement of only ~3x for both power densities when scaling to a 22 nm technology.
The microbiota in adaptive immune homeostasis and disease

In the mucosa, the immune system's T cells and B cells have position-specific phenotypes and functions that are influenced by the microbiota. These cells play pivotal parts in the maintenance of immune homeostasis by suppressing responses to harmless antigens and by enforcing the integrity of the barrier functions of the gut mucosa. Imbalances in the gut microbiota, known as dysbiosis, can trigger several immune disorders through the activity of T cells that are both near to and distant from the site of their induction. Elucidation of the mechanisms that distinguish between homeostatic and pathogenic microbiota–host interactions could identify therapeutic targets for preventing or modulating inflammatory diseases and for boosting the efficacy of cancer immunotherapy.
Influencing factors on multimodal interaction during selection tasks

When developing multimodal interactive systems it is not clear which importance should be given to which modality. In order to study influencing factors on multimodal interaction, we conducted a Wizard of Oz study on a basic recurrent task: 53 subjects performed diverse selections of objects on a screen. The way and modality of interaction was not specified nor predefined by the system, and the users were free in how and what to select. Natural input modalities like speech, gestures, touch, and arbitrary multimodal combinations of these were recorded as dependent variables. As independent variables, subjects’ gender, personality traits, and affinity towards technical devices were surveyed, as well as the system’s varying presentation styles of the selection. Our statistical analyses reveal gender as a momentous influencing factor and point out the role of individuality for the way of interaction, while the influence of the system output seems to be quite limited. This knowledge about the prevalent task of selection will be useful for designing effective and efficient multimodal interactive systems across a wide range of applications and domains.
Large-Scale Parallel Multibody Dynamics with Frictional Contact on the Graphical Processing Unit

In the context of simulating the frictional contact dynamic s of large systems of rigid bodies, this paper reviews a novel method for solving large cone complementarity proble ms by means of a fixed-point iteration algorithm. The method is an extension of the Gauss-Seidel and Gauss-Jacobi methods with overrelaxation for symmetric convex linear complementarity problems. Convergent under fairly standa rd assumptions, the method is implemented in a parallel framework by using a single instruction multiple data compu tation paradigm promoted by the Compute Unified Device Architecture library for graphical processing unit progra mming. The framework supports the simulation of problems with more than 1 million bodies in contact. Simulation thus b ecomes a viable tool for investigating the dynamics of complex systems such as ground vehicles running on sand, pow der composites, and granular material flow.
Randomized requantization with local differential privacy

In this paper we study how individual sensors can compress their observations in a privacy-preserving manner. We propose a randomized requantization scheme that guarantees local differential privacy, a strong model for privacy in which individual data holders must mask their information before sending it to an untrusted third party. For our approach, the problem becomes an optimization over discrete mem-oryless channels between the sensor observations and their compressed version. We show that for a fixed compression ratio, finding privacy-optimal channel subject to a distortion constraint is a quasiconvex optimization problem that can be solved by the bisection method. Our results indicate interesting tradeoffs between the privacy risk, compression ratio, and utility, or distortion. For example, in the low distortion regime, we can halve the bit rate at little cost in distortion while maintaining the same privacy level. We illustrate our approach for a simple example of privatizing and recompressing lowpass signals and show that it yields better tradeoffs than existing approaches based on noise addition. Our approach may be useful in several privacy-sensitive monitoring applications envisioned for the Internet of Things (IoT).
A model free automatic tuning method for a restricted structured controller by using Simultaneous Perturbation Stochastic Approximation (SPSA)

A model free auto tuning algorithm is developed by using simultaneous perturbation stochastic approximation (SPSA). For such a method, plant models are not required. A set of closed loop experiments are conducted to generate data for an online optimization procedure. The optimum of the parameters of the restricted structured controllers will be found via SPSA algorithm. Compared to the conventional gradient approximation methods, SPSA only needs the small number of measurement of the cost function. It will be beneficial to application with high dimensional parameters. In the paper, a cost function is formulated to directly reflect the control performances widely used in industry, like overshoot, settling time and integral of absolute error. Therefore, the proposed auto tuning method will naturally lead to the desired closed loop performance. A case study of auto tuning of spool position control in a twin spool two stage valve is conducted. Both simulation and experimental study in TI C2000 target demonstrate effectiveness of the algorithm.
CAS-CNN: A deep convolutional neural network for image compression artifact suppression

Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image — a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.