nils-herrmann/clean_scirepeval huggingface dataset:
Vehicle Applications of Controller Area Network

The Controller Area Network (CAN) is a serial bus communications protocol developed by Bosch in the early 1980s. It defines a standard for efficient and reliable communication between sensor, actuator, controller, and other nodes in real-time applications. CAN is the de facto standard in a large variety of networked embedded control systems. The early CAN development was mainly supported by the vehicle industry: CAN is found in a variety of passenger cars, trucks, boats, spacecraft, and other types of vehicles. The protocol is also widely used today in industrial automation and other areas of networked embedded control, with applications in diverse products such as production machinery, medical equipment, building automation, weaving machines, and wheelchairs. In the automotive industry, embedded control has grown from stand-alone systems to highly integrated and networked control systems [11, 7]. By networking electro-mechanical subsystems, it becomes possible to modularize functionalities and hardware, which facilitates reuse and adds capabilities. Fig. 1 shows an example of an electronic control unit (ECU) mounted on a diesel engine of a Scania truck. The ECU handles the control of engine, turbo, fan, etc. but also the CAN communication. Combining networks and mechatronic modules makes it possible to reduce both the cabling and the number
601 Applying Data Mining Classification Techniques for Employee ’ s Performance Prediction

The valuable knowledge can be discovered through data mining process. In data mining, classification is one of the major tasks to impart knowledge from huge amount of data. This technique is widely used in various fields, but it has not attracted much attention in Human Resource Management (HRM). This article presents a study on the implementation of data mining approach for employee development regarding to their future performance. By using this approach, the performance patterns can be discovered from the existing database and will be used for future performance prediction in their career development. In the experimental phase, we have used selected classification techniques to propose the appropriate technique for the dataset. An experiment is carried out to demonstrate the feasibility of the suggested classification techniques using employee’s performance data. Thus, the experiment results, we suggest the potential classification techniques and the possible prediction model for employee’s performance forecasting.
Exploiting vulnerability to secure user privacy on a social networking site

As (one's) social network expands, a user's privacy protection goes beyond his privacy settings and becomes a social networking problem. In this research, we aim to address some critical issues related to privacy protection: Would the highest privacy settings guarantee a secure protection? Given the open nature of social networking sites, is it possible to manage one's privacy protection? With the diversity of one's social media friends, how can one figure out an effective approach to balance between vulnerability and privacy? We present a novel way to define a vulnerable friend from an individual user's perspective is dependent on whether or not the user's friends' privacy settings protect the friend and the individual's network of friends (which includes the user). As a single vulnerable friend in a user's social network might place all friends at risk, we resort to experiments and observe how much security an individual user can improve by unfriending a vulnerable friend. We also show how privacy weakens if newly accepted friends are unguarded or unprotected. This work provides a large-scale evaluation of new security and privacy indexes using a Facebook dataset. We present and discuss a new perspective for reasoning about social networking security. When a user accepts a new friend, the user should ensure that the new friend is not an increased security risk with the potential of negatively impacting the entire friend network. Additionally, by leveraging the indexes proposed and employing new strategies for unfriending vulnerable friends, it is possible to further improve security and privacy without changing the social networking site's existing architecture.
Processing capacity defined by relational complexity: implications for comparative, developmental, and cognitive psychology.

Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A binary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by "segmentation" or "conceptual chunking." In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are "collapsed" to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed.
Evidence for sensory prediction deficits in schizophrenia.

Patients with schizophrenia experiencing delusions and hallucinations can misattribute their own actions to an external source. The authors test the hypothesis that patients with schizophrenia have defects in their ability to predict the sensory consequences of their actions.



The authors measured sensory attenuation of self-produced stimuli by patients with schizophrenia and by healthy subjects.



Patients with schizophrenia demonstrated significantly less sensory attenuation than healthy subjects.



Patients with a diagnosis of schizophrenia have a dysfunction in their predictive mechanisms.
Pex-White Box Test Generation for .NET

Pex automatically produces a small test suite with high code coverage for a .NET program. To this end, Pex performs a systematic program analysis (using dynamic symbolic execution, similar to pathbounded model-checking) to determine test inputs for Parameterized Unit Tests. Pex learns the program behavior by monitoring execution traces. Pex uses a constraint solver to produce new test inputs which exercise different program behavior. The result is an automatically generated small test suite which often achieves high code coverage. In one case study, we applied Pex to a core component of the .NET runtime which had already been extensively tested over several years. Pex found errors, including a serious issue.
Surround-screen projection-based virtual reality: the design and implementation of the CAVE

Several common systems satisfy some but not all of the VR definition above. Flight simulators provide vehicle tracking, not head tracking, and do not generally operate in binocular stereo. Omnimax theaters give a large angle of view , occasionally in stereo, but are not interactive. Head-tracked monitors [4][6] provide all but a large angle of view. Head-mounted displays (HMD) [7][13] and BOOMs  use motion of the actual display screens to achieve VR by our definition. Correct projection of the imagery on large screens can also create a VR experience, this being the subject of this paper. This paper describes the CAVE (CAVE Automatic Virtual Environment) virtual reality/scientific visualization system in detail and demonstrates that projection technology applied to virtual-reality goals achieves a system that matches the quality of workstation screens in terms of resolution, color, and flicker-free stereo. In addition, this format helps reduce the effect of common tracking and system latency errors. The off-axis perspective projection techniques we use are shown to be simple and straightforward. Our techniques for doing multi-screen stereo vision are enumerated, and design barriers, past and current, are described. Advantages and disadvantages of the projection paradigm are discussed, with an analysis of the effect of tracking noise and delay on the user. Successive refinement, a necessary tool for scientific visualization, is developed in the virtual reality context. The use of the CAVE as a one-to-many presentation device at SIGGRAPH '92 and Supercomputing '92 for computational science data is also mentioned. Previous work in the VR area dates back to Sutherland [12], who in 1965 wrote about the “Ultimate Display.” Later in the decade at the University of Utah, Jim Clark developed a system that allowed wireframe graphics VR to be seen through a headmounted, BOOM-type display for his dissertation. The common VR devices today are the HMD and the BOOM. Lipscomb  showed a monitor-based system in the IBM booth at SIGGRAPH '91 and Deering  demonstrated the Virtual Portal, a closets ized three-wal l project ion-based system, in the Sun Microsystems' booth at SIGGRAPH '92. The CAVE, our projection-based VR display , also premiered at SIGGRAPH '92. The Virtual Portal and CAVE have similar intent, but different implementation schemes.
Behavior Wizard: A Method for Matching Target Behaviors with Solutions

We present a method for matching target behaviors with solutions for achieving those behaviors. Called the Behavior Wizard, this method first classifies behavior change targets into one of 15 types. Later stages focus on triggers for the target behaviors and on relevant theories and techniques. This new approach to persuasive design, as well as the terminology we propose, can lead to insights into the patterns of behavior change. The Behavior Wizard can also increase success rates in academic studies and commercial products. The most current version of this method is at www.BehaviorWizard.org.
Kqueue - A Generic and Scalable Event Notification Facility

ApplicationsrunningonaUNIX platformneedto benotified whensomeactivity occurson asocketor otherdescriptor, andthis is traditionallydonewith theselect()or poll() systemcalls. However, it hasbeenshown that the performanceof thesecallsdoesnotscalewell with anincreasingnumberof descriptors.Theseinterfacesarealso limited in therespecthatthey areunableto handleother potentiallyinterestingactivitiesthatanapplicationmight be interestedin, thesemight includesignals,file system changes,and AIO completions. This paperpresentsa genericevent delivery mechanism,which allows an application to selectfrom a wide rangeof event sources, andbenotifiedof activity on thesesourcesin a scalable andefficient manner . The mechanismmay be extended to coverfutureeventsourceswithoutchangingtheapplicationinterface.
SAP HANA - From Relational OLAP Database to Big Data Infrastructure

SAP HANA started as one of the best-performing database engines for OLAP workloads strictly pursuing a main-memory centric architecture and exploiting hardware developments like large number of cores and main memories in the TByte range. Within this paper, we outline the steps from a traditional relational database engine to a Big Data infrastructure comprising different methods to handle data of different volume, coming in with different velocity, and showing a fairly large degree of variety. In order to make the presentation of this transformation process more tangible, we discuss two major technical topics–HANA native integration points as well as extension points for collaboration with Hadoop-based data management infrastructures. The overall of goal of this paper is to (a) review current application patterns and resulting technical challenges as well as to (b) paint the big picture for upcoming architectural designs with SAP HANA database as the core of a SAP Big Data infrastructure.
A CORDIC processor for FFT computation and its implementation using gallium arsenide technology

In this paper, the architecture and the implementation of a complex fast Fourier transform (CFFT) processor using 0.6 m gallium arsenide (GaAs) technology are presented. This processor computes a 1024-point FFT of 16 bit complex data in less than 8 s, working at a frequency beyond 700 MHz, with a power consumption of 12.5 W. The architecture of the processor is based on the COordinate Rotation DIgital Computer (CORDIC) algorithm, which avoids the use of conventional multiplicationand-accumulation (MAC) units, but evaluates the trigonometric functions using only add and shift operations. Improvements to the basic CORDIC architecture are introduced in order to reduce the area and power of the processor. This together with the use of pipelining and carry save adders produces a very regular and fast processor. The CORDIC units were fabricated and tested in order to anticipate the final performance of the processor. This work also demonstrates the maturity of GaAs technology for implementing ultrahigh-performance signal processors.
Cache-aware Roofline model: Upgrading the loft

The Roofline model graphically represents the attainable upper bound performance of a computer architecture. This paper analyzes the original Roofline model and proposes a novel approach to provide a more insightful performance modeling of modern architectures by introducing cache-awareness, thus significantly improving the guidelines for application optimization. The proposed model was experimentally verified for different architectures by taking advantage of built-in hardware counters with a curve fitness above 90%.
The Business Value of Business inTelligence

This paper focuses on the influence of BI on manufacturing and distribution organizations, although much of what’s covered in it can apply to businesses in non-related industries, as well. To kick it off, the chart below illustrates the primary value propositions of business intelligence and the types of business analytics (e.g., Sales, Inventory, Supplier Performance analysis) that support each value area. The effect of BI on each of these business value areas are then discussed in more detail in the following sections.
A Broadband Center-Fed Circular Patch-Ring Antenna With a Monopole Like Radiation Pattern

A center-fed circular microstrip patch antenna with a coupled annular ring is presented. This antenna has a low profile configuration with a monopole like radiation pattern. Compared to the center-fed circular patch antenna (CPA), the proposed antenna has a large bandwidth and similar radiation pattern. The proposed antenna is fabricated and tested. It resonates at 5.8 GHz, the corresponding impedance bandwidth and gain are 12.8% and 5.7 dBi, respectively. Very good agreement between the measurement and simulation for the return loss and radiation patterns is achieved.
Abstractive Multi-document Summarization with Semantic Information Extraction

This paper proposes a novel approach to generate abstractive summary for multiple documents by extracting semantic information from texts. The concept of Basic Semantic Unit (BSU) is defined to describe the semantics of an event or action. A semantic link network on BSUs is constructed to capture the semantic information of texts. Summary structure is planned with sentences generated based on the semantic link network. Experiments demonstrate that the approach is effective in generating informative, coherent and compact summary.
A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System

The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge.


MAIN BODY
This narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not currently in widespread use within biomechanics.



This review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.
Feature selection for nonlinear models with extreme learning machines

In the context of feature selection, there is a trade-off between the number of selected features and the generalisation error. Two plots may help to summarise feature selection: the feature selection path and the sparsity-error trade-off curve. The feature selection path shows the best feature subset for each subset size, whereas the sparsity-error trade-off curve shows the corresponding generalisation errors. These graphical tools may help experts to choose suitable feature subsets and extract useful domain knowledge. In order to obtain these tools, extreme learning machines are used here, since they are fast to train and an estimate of their generalisation error can easily be obtained using the PRESS statistics. An algorithm is introduced, which adds an additional layer to standard extreme learning machines in order to optimise the subset of selected features. Experimental results illustrate the quality of the
Overview of Permanent-Magnet Brushless Drives for Electric and Hybrid Electric Vehicles

With ever-increasing concerns on our environment, there is a fast growing interest in electric vehicles (EVs) and hybrid EVs (HEVs) from automakers, governments, and customers. As electric drives are the core of both EVs and HEVs, it is a pressing need for researchers to develop advanced electric-drive systems. In this paper, an overview of permanent-magnet (PM) brushless (BL) drives for EVs and HEVs is presented, with emphasis on machine topologies, drive operations, and control strategies. Then, three major research directions of the PM BL drive systems are elaborated, namely, the magnetic-geared outer-rotor PM BL drive system, the PM BL integrated starter-generator system, and the PM BL electric variable-transmission system.
Diagnosis, management, and prevention of catheter-associated urinary tract infections.

Catheter-associated urinary tract infection (CAUTI) is common, costly, and causes significant patient morbidity. CAUTIs are associated with hospital pathogens with a high propensity toward antimicrobial resistance. Treatment of asymptomatic patients with CAUTI accounts for excess antimicrobial use in hospitals and should be avoided. Duration of urinary catheterization is the predominant risk for CAUTI; preventive measures directed at limiting placement and early removal of urinary catheters have an impact on decreasing CAUTI rates. The use of bladder bundles and collaboratives, coupled with the support and active engagement from both hospital leaders and followers, seem to help prevent this common problem.
Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models

The Kullback Leibler (KL) divergence is a widely used tool in statistics and pattern recognition. The KL divergence between two Gaussian mixture models (GMMs) is frequently needed in the fields of speech and image recognition. Unfortunately the KL divergence between two GMMs is not analytically tractable, nor does any efficient computational algorithm exist. Some techniques cope with this problem by replacing the KL divergence with other functions that can be computed efficiently. We introduce two new methods, the variational approximation and the variational upper bound, and compare them to existing methods. We discuss seven different techniques in total and weigh the benefits of each one against the others. To conclude we evaluate the performance of each one through numerical experiments.
Multi-shot Imaging: Joint Alignment, Deblurring, and Resolution-Enhancement

The capture of multiple images is a simple way to increase the chance of capturing a good photo with a light-weight hand-held camera, for which the camera-shake blur is typically a nuisance problem. The naive approach of selecting the single best captured photo as output does not take full advantage of all the observations. Conventional multi-image blind deblurring methods can take all observations as input but usually require the multiple images are well aligned. However, the multiple blurry images captured in the presence of camera shake are rarely free from mis-alignment. Registering multiple blurry images is a challenging task due to the presence of blur while deblurring of multiple blurry images requires accurate alignment, leading to an intrinsically coupled problem. In this paper, we propose a blind multi-image restoration method which can achieve joint alignment, non-uniform deblurring, together with resolution enhancement from multiple low-quality images. Experiments on several real-world images with comparison to some previous methods validate the effectiveness of the proposed method.
Using The Matrix Ridge Approximation to Speedup Determinantal Point Processes Sampling Algorithms

Determinantal point process (DPP) is an important probabilistic model that has extensive applications in artificial intelligence. The exact sampling algorithm of DPP requires the full eigenvalue decomposition of the kernel matrix which has high time and space complexities. This prohibits the applications of DPP from large-scale datasets. Previous work has applied the Nyström method to speedup the sampling algorithm of DPP, and error bounds have been established for the approximation. In this paper we employ the matrix ridge approximation (MRA) to speedup the sampling algorithm of DPP, showing that our approach MRA-DPP has stronger error bound than the Nyström-DPP. In certain circumstances our MRA-DPP is provably exact, whereas the Nyström-DPP is far from the ground truth. Finally, experiments on several real-world datasets show that our MRA-DPP is more accurate than the other approximation approaches.