nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Recommendation Model Regularized with User Trust and Item Ratings

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.
A survey of shadow algorithms

The various types of shadows are characterized. Most existing shadow algorithms are described, and their complexities, advantages, and shortcomings are discussed. Hard shadows, soft shadows, shadows of transparent objects, and shadows for complex modeling primitives are considered. For each type, shadow algorithms within various rendering techniques are examined. The aim is to provide readers with enough background and insight on the various methods to allow them to choose the algorithm best suited to their needs and to help identify the areas that need more research and point to possible solutions.<<ETX>>
IoT based smart healthcare kit

The paper presents the design and implementation of an IOT-based health monitoring system for emergency medical services which can demonstrate collection, integration, and interoperation of IoT data flexibly which can provide support to emergency medical services like Intensive Care Units (ICU), using a INTEL GALILEO 2ND generation development board. The proposed model enables users to improve health related risks and reduce healthcare costs by collecting, recording, analyzing and sharing large data streams in real time and efficiently. The idea of this project came so to reduce the headache of patient to visit to doctor every time he need to check his blood pressure, heart beat rate, temperature etc. With the help of this proposal the time of both patients and doctors are saved and doctors can also help in emergency scenario as much as possible. The proposed outcome of the project is to give proper and efficient medical services to patients by connecting and collecting data information through health status monitors which would include patient's heart rate, blood pressure and ECG and sends an emergency alert to patient's doctor with his current status and full medical information.
Implications of Mobility : The Uses of Personal Communication Media in Everyday Life

This study examined the impact of mobile communications on interpersonal relationships in daily life. Based on a nationwide survey in Japan, landline phone, mobile voice phone, mobile mail (text messaging), and PC e-mail were compared to assess their usage in terms of social network and psychological factors. The results indicated that young, nonfamily-related pairs of friends, living close to each other with frequent faceto-face contact were more likely to use mobile media. Social skill levels are negatively correlated with relative preference for mobile mail in comparison with mobile voice phone. These findings suggest that mobile mail is preferable for Japanese young people who tend to avoid direct communication and that its use maintains existing bonds rather than create new ones.
An Estimate of an Upper Bound for the Entropy of English

We present an estimate of an upper bound of 1.75 bits for the entropy of characters in printed English, obtained by constructing a word trigram model and then computing the cross-entropy between this model and a balanced sample of English text. We suggest the well-known and widely available Brown Corpus of printed English as a standard against which to measure progress in language modeling and offer our bound as the first of what we hope will be a series of steadily decreasing bounds.
Electromagnetic brain mapping - IEEE Signal Processing Magazine

seen tremendous advances in our ability to produce images of human brain function. Applications of functional brain imaging extend from improving our understanding of the basic mechanisms of cognitive processes to better characterization of pathologies that impair normal function. Magnetoencephalography (MEG) and electroencephalography (EEG) (MEG/EEG) localize neural electrical activity using noninvasive measurements of external electromagnetic signals. Among the available functional imaging techniques, MEG and EEG uniquely have temporal resolutions below 100 ms. This temporal precision allows us to explore the timing of basic neural processes at the level of cell assemblies. MEG/EEG source localization draws on a wide range of signal processing techniques including digital filtering, three-dimensional image analysis, array signal processing, image modeling and reconstruction, and, more recently, blind source separation and phase synchrony estimation. In this article we describe the underlying models currently used in MEG/EEG source estimation and describe the various signal processing steps required to compute these sources. In particular we describe methods for computing the forward fields for known source distributions and parametric and imaging-based approaches to the inverse problem.  Functional brain imaging is a relatively new and multidisciplinary research field that encompasses techniques devoted to a better understanding of the human brain through noninvasive imaging of the electrophysiological, hemodynamic, metabolic, and neurochemical processes that underlie normal and pathological brain
A Survey of Ethernet LAN Security

Ethernet is the survivor of the LAN wars. It is hard to find an IP packet that has not passed over an Ethernet segment. One important reason for this is Ethernet's simplicity and ease of configuration. However, Ethernet has always been known to be an insecure technology. Recent successful malware attacks and the move towards cloud computing in data centers demand that attention be paid to the security aspects of Ethernet. In this paper, we present known Ethernet related threats and discuss existing solutions from business, hacker, and academic communities. Major issues, like insecurities related to Address Resolution Protocol and to self-configurability, are discussed. The solutions fall roughly into three categories: accepting Ethernet's insecurity and circling it with firewalls; creating a logical separation between the switches and end hosts; and centralized cryptography based schemes. However, none of the above provides the perfect combination of simplicity and security befitting Ethernet.
Analyzing Developmental Trajectories : A Semiparametric , Group-Based Approach

A developmental trajectory describes the course of a behavior over age or time. A group-based method for identifying distinctive groups of individual trajectories within the population and for profiling the characteristics of group members is demonstrated. Such clusters might include groups of "increasers." "decreasers," and "no changers." Suitably defined probability distributions are used to handle 3 data types—count, binary, and psychometric scale data. Four capabilities are demonstrated: (a) the capability to identify rather than assume distinctive groups of trajectories, (b) the capability to estimate the proportion of the population following each such trajectory group, (c) the capability to relate group membership probability to individual characteristics and circumstances, and (d) the capability to use the group membership probabilities for various other purposes such as creating profiles of group members.
Modeling a DFIG Wind Turbine System using PLECS

Variable speed operation is essential for large wind turbines in order to optimize the energy capture under variable wind speed conditions. Variable speed wind turbines require a power electronic interface converter to permit connection with the grid. The power electronics can be either partially-rated or fully-rated . A popular interface method for large wind turbines that is based on a partiallyrated interface is the doubly-fed induction generator (DFIG) system . In the DFIG system, the power electronic interface controls the rotor currents in order to control the electrical torque and thus the rotational speed. Because the power electronics only process the rotor power, which is typically less than 25% of the overall output power, the DFIG offers the advantages of speed control for a reduction in cost and power losses. This report presents a DFIG wind turbine system that is modeled in PLECS and Simulink. A full electrical model that includes the switching converter implementation for the rotor-side power electronics and a dq model of the induction machine is given. The aerodynamics of the wind turbine and the mechanical dynamics of the induction machine are included to extend the use of the model to simulating system operation under variable wind speed conditions. For longer simulations that include these slower mechanical and wind dynamics, an averaged PWM converter model is presented. The averaged electrical model offers improved simulation speed at the expense of neglecting converter switching detail.
Biharmonic diffusion curve images from boundary elements

There is currently significant interest in freeform, curve-based authoring of graphic images. In particular, "diffusion curves" facilitate graphic image creation by allowing an image designer to specify naturalistic images by drawing curves and setting colour values along either side of those curves. Recently, extensions to diffusion curves based on the biharmonic equation have been proposed which provide smooth interpolation through specified colour values and allow image designers to specify colour gradient constraints at curves. We present a Boundary Element Method (BEM) for rendering diffusion curve images with smooth interpolation and gradient constraints, which generates a solved boundary element image representation. The diffusion curve image can be evaluated from the solved representation using a novel and efficient line-by-line approach. We also describe "curve-aware" upsampling, in which a full resolution diffusion curve image can be upsampled from a lower resolution image using formula evaluated orrections near curves. The BEM solved image representation is compact. It therefore offers advantages in scenarios where solved image representations are transmitted to devices for rendering and where PDE solving at the device is undesirable due to time or processing constraints.
Cost-Sensitive Learning and the Class Imbalance Problem

Cost-Sensitive Learning is a type of learning in data mining that takes the misclassification costs (and possibly other types of cost) into consideration. The goal of this type of learning is to minimize the total cost. The key difference between cost-sensitive learning and cost-insensitive learning is that cost-sensitive learning treats the different misclassifications differently. Costinsensitive learning does not take the misclassification costs into consideration. The goal of this type of learning is to pursue a high accuracy of classifying examples into a set of known classes.
Multisensors realtime data fusion optimization for IOT systems

This study proposed an IoT (Internet of Things) system for the monitoring and control of the aquaculture platform. The proposed system is network surveillance combined with mobile devices and a remote platform to collect real-time farm environmental information. The real-time data is captured and displayed via ZigBee wireless transmission signal transmitter to remote computer terminals. This study permits real-time observation and control of aquaculture platform with dissolved oxygen sensors, temperature sensing elements using A/D and microcontrollers signal conversion. The proposed system will use municipal electricity coupled with a battery power source to provide power with battery intervention if municipal power is interrupted. This study is to make the best fusion value of multi-odometer measurement data for optimization via the maximum likelihood estimation (MLE).Finally, this paper have good efficient and precise computing in the experimental results.
A case for denoising before demosaicking color filter array data

Denoising algorithms are well developed for grayscale and color images, but not as well for color filter array (CFA) data. Consequently, the common color imaging pipeline demosaics CFA data before denoising. In this paper we explore the noise-related properties of the imaging pipeline that demosaics CFA data before denoising. We then propose and explore a way to transform CFA data to a form that is amenable to existing grayscale and color denoising schemes. Since CFA data are a third as many as demosaicked data, we can expect to reduce processing time and power requirements to about a third of current requirements.
Attending holistically versus analytically: comparing the context sensitivity of Japanese and Americans.

Much research indicates that East Asians, more than Americans, explain events with reference to the context. The authors examined whether East Asians also attend to the context more than Americans do. In Study 1, Japanese and Americans watched animated vignettes of underwater scenes and reported the contents. In a subsequent recognition test, they were shown previously seen objects as well as new objects, either in their original setting or in novel settings, and then were asked to judge whether they had seen the objects. Study 2 replicated the recognition task using photographs of wildlife. The results showed that the Japanese (a) made more statements about contextual information and relationships than Americans did and (b) recognized previously seen objects more accurately when they saw them in their original settings rather than in the novel settings, whereas this manipulation had relatively little effect on Americans.
SMSCrypto: A lightweight cryptographic framework for secure SMS transmission

Despite the continuous growth in the number of smartphones around the globe, Short Message Service (SMS) still remains as one of the most popular, cheap and accessible ways of exchanging text messages using mobile phones. Nevertheless, the lack of security in SMS prevents its wide usage in sensitive contexts such as banking and health-related applications. Aiming to tackle this issue, this paper presents SMSCrypto, a framework for securing SMS-based communications in mobile phones. SMSCrypto encloses a tailored selection of lightweight cryptographic algorithms and protocols, providing encryption, authentication and signature services. The proposed framework is implemented both in Java (target at JVM-enabled platforms) and in C (for constrained SIM Card processors) languages, thus being suitable
CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Designing and implementing efficient, provably correct parallel neural network processing is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. However, the diversity and large-scale data size have posed a significant challenge to construct a flexible and high-performance implementation of deep learning neural networks. To improve the performance and maintain the scalability, we present CNNLab, a novel deep learning framework using GPU and FPGA-based accelerators. CNNLab provides a uniform programming model to users so that the hardware implementation and the scheduling are invisible to the programmers. At runtime, CNNLab leverages the trade-offs between GPU and FPGA before offloading the tasks to the accelerators. Experimental results on the state-of-the-art Nvidia K40 GPU and Altera DE5 FPGA board demonstrate that the CNNLab can provide a universal framework with efficient support for diverse applications without increasing the burden of the programmers. Moreover, we analyze the detailed quantitative performance, throughput, power, energy, and performance density for both approaches. Experimental results leverage the trade-offs between GPU and FPGA and provide useful practical experiences for the deep learning research community.
Planar Dual U-Shaped Monopole Antenna With Multiband Operation for IEEE 802.16e

A novel compact design of a planar dual U-shaped monopole antenna with multiband operation for the IEEE 802.16e Worldwide Interoperability for Microwave Access (WiMAX) system is proposed. With the use of dual U-shaped monopole strips, multiresonant modes close to 2.6/3.5/5.5-GHz band are excited to meet the specifications of the WiMAX system (IEEE 802.16e) . The obtained impedance bandwidth across the operating bands can reach about 220/1660/1480 MHz for the 2.6/3.5/5.5-GHz bands, respectively. Only with the antenna size of 25×30×0.8 mm3, the proposed monopole antenna has the compact operation with more than 35% antenna size reduction. The measured peak gains and radiation efficiencies are about 3.5/4.1/5.5 dBi and 68%/66%/95% for the 2.6/3.5/5.5-GHz band, respectively, with nearly omnidirectional pattern in the xy plane.
Pixel-based methods for widget state and style in a runtime implementation of sliding widgets

Pixel-based methods offer unique potential for modifying existing interfaces independent of their underlying implementation. Prior work has demonstrated a variety of modifications to existing interfaces, including accessibility enhancements, interface language translation, testing frameworks, and interaction techniques. But pixel-based methods have also been limited in their understanding of the interface and therefore the complexity of modifications they can support. This work examines deeper pixel-level understanding of widgets and the resulting capabilities of pixel-based runtime enhancements. Specifically, we present three new sets of methods: methods for pixel-based modeling of widgets in multiple states, methods for managing the combinatorial complexity that arises in creating a multitude of runtime enhancements, and methods for styling runtime enhancements to preserve consistency with the design of an existing interface. We validate our methods through an implementation of Moscovich et al.'s Sliding Widgets, a novel runtime enhancement that could not have been implemented with prior pixel-based methods.
Empirical Study on Applications of Data Mining Techniques in Healthcare

The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree and Artificial Neural Network to massive volume of healthcare data. In particular we consider a case study using classification techniques on a medical data set of diabetic patients.
Concept-based analysis of scientific literature

This paper studies the importance of identifying and categorizing scientific concepts as a way to achieve a deeper understanding of the research literature of a scientific community. To reach this goal, we propose an unsupervised bootstrapping algorithm for identifying and categorizing mentions of concepts. We then propose a new clustering algorithm that uses citations' context as a way to cluster the extracted mentions into coherent concepts. Our evaluation of the algorithms against gold standards shows significant improvement over state-of-the-art results. More importantly, we analyze the computational linguistic literature using the proposed algorithms and show four different ways to summarize and understand the research community which are difficult to obtain using existing techniques.
RSVM: Reduced Support Vector Machines

An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation. To generate this nonlinear surface, the entire dataset is used as a constraint in an optimization problem with very few variables corresponding to the 1% of the data kept. The remainder of the data can be thrown away after solving the optimization problem. This is achieved by making use of a rectangular m×m̄ kernel K(A, Ā) that greatly reduces the size of the quadratic program to be solved and simplifies the characterization of the nonlinear separating surface. Here, the m rows of A represent the original m data points while the m̄ rows of Ā represent a greatly reduced m̄ data points. Computational results indicate that test set correctness for the reduced support vector machine (RSVM), with a nonlinear separating surface that depends on a small randomly selected portion of the dataset, is better than that of a conventional support vector machine (SVM) with a nonlinear surface that explicitly depends on the entire dataset, and much better than a conventional SVM using a small random sample of the data. Computational times, as well as memory usage, are much smaller for RSVM than that of a conventional SVM using the entire dataset.
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring

The classical approach to depth from defocus (DFD) uses lenses with circular apertures for image capturing. We show in this paper that the use of a circular aperture severely restricts the accuracy of DFD. We derive a criterion for evaluating a pair of apertures with respect to the precision of depth recovery. This criterion is optimized using a genetic algorithm and gradient descent search to arrive at a pair of high resolution apertures. These two coded apertures are found to complement each other in the scene frequencies they preserve. This property enables them to not only recover depth with greater fidelity but also obtain a high quality all-focused image from the two captured images. Extensive simulations as well as experiments on a variety of real scenes demonstrate the benefits of using the coded apertures over conventional circular apertures.
The return of Phineas Gage: clues about the brain from the skull of a famous patient.

When the landmark patient Phineas Gage died in 1861, no autopsy was performed, but his skull was later recovered. The brain lesion that caused the profound personality changes for which his case became famous has been presumed to have involved the left frontal region, but questions have been raised about the involvement of other regions and about the exact placement of the lesion within the vast frontal territory. Measurements from Gage's skull and modern neuroimaging techniques were used to reconstitute the accident and determine the probable location of the lesion. The damage involved both left and right prefrontal cortices in a pattern that, as confirmed by Gage's modern counterparts, causes a defect in rational decision making and the processing of emotion.