nils-herrmann/clean_scirepeval huggingface dataset:
Defection Detection : Measuring and Understanding the Predictive Accuracy of Customer Churn Models

(May 2006), 204–211 204 Scott A. Neslin is Albert Wesley Frey Professor of Marketing, Tuck School of Business, Dartmouth College (e-mail: scott.neslin@dartmouth. edu). Sunil Gupta is Meyer Feldberg Professor of Business, Graduate School of Business, Columbia University (e-mail: sg37@columbia.edu). Wagner Kamakura is Ford Motor Company Professor of Global Marketing, Fuqua School of Business, Duke University (e-mail: kamakura@duke. edu). Junxiang Lu is Vice President of Comerica Bank (e-mail: jlu@ comerica.com). Charlotte H. Mason is Associate Professor of Marketing, Kenan-Flagler Business School, University of North Carolina (e-mail: charlotte_mason@unc.edu). The authors express their gratitude to Sanyin Siang (Managing Director, Teradata Center for Customer Relationship Management at the Fuqua School of Business, Duke University); research assistants Sarwat Husain, Michael Kurima, and Emilio del Rio; and an anonymous wireless telephone carrier that provided the data for this study. The authors also thank participants in the Tuck School of Business, Dartmouth College, Marketing Workshop, for comments and the two anonymous JMR reviewers for their constructive suggestions. Finally, the authors express their appreciation to former editor Dick Wittink (posthumously) for his invaluable insights and guidance. SCOTT A. NESLIN, SUNIL GUPTA, WAGNER KAMAKURA, JUNXIANG LU, and CHARLOTTE H. MASON
Patch-Based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI

This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis (MS) lesion segmentation in magnetic resonance images (MRI) of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images.
Understanding the GPU Microarchitecture to Achieve Bare-Metal Performance Tuning

In this paper, we present a methodology to understand GPU microarchitectural features and improve performance for compute-intensive kernels. The methodology relies on a reverse engineering approach to crack the GPU ISA encodings in order to build a GPU assembler. An assembly microbenchmark suite correlates microarchitectural features with their performance factors to uncover instruction-level and memory hierarchy preferences. We use SGEMM as a running example to show the ways to achieve bare-metal performance tuning. The performance boost is achieved by tuning FFMA throughput by activating dual-issue, eliminating register bank conflicts, adding non-FFMA instructions with little penalty, and choosing proper width of global/shared load instructions. On NVIDIA Kepler K20m, we develop a faster SGEMM with 3.1Tflop/s performance and 88% efficiency; the performance is 15% higher than cuBLAS7.0. Applying these optimizations to convolution, the implementation gains 39%-62% performance improvement compared with cuDNN4.0. The toolchain is an attempt to automatically crack different GPU ISA encodings and build an assembler adaptively for the purpose of performance enhancements to applications on GPUs.
Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions

We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product databases. Such a representation is useful for a variety of tasks where treating a product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include product recommendations, product comparison, and demand forecasting. We formulate the extraction as a classification problem and use a semi-supervised algorithm (co-EM) along with (Naı̈ve Bayes). The extraction system requires very little initial user supervision: using unlabeled data, we automatically extract an initial seed list that serves as training data for the supervised and semi-supervised classification algorithms. Finally, the extracted attributes and values are linked to form pairs using dependency information and co-location scores. We present promising results on product descriptions in two categories of sporting goods.
DATA WAREHOUSING, DATA MINING, OLAP AND OLTP TECHNOLOGIES ARE ESSENTIAL ELEMENTS TO SUPPORT DECISION-MAKING PROCESS IN INDUSTRIES

This paper provides an overview of Data warehousing, Data Mining, OLAP, OLTP technologies, exploring the features, applications and the architecture of Data Warehousing. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.
Organismically-inspired robotics : homeostatic adaptation and teleology beyond the closed sensorimotor loop

In 1972, and later in 1979, at the peak of the golden era of Good Old Fashioned Artificial Intelligence (GOFAI), the voice of philosopher Hubert Dreyfus made itself heard as one of the few calls against the hubristic programme of modelling the human mind as a mechanism of symbolic information processing (Dreyfus, 1979). He did not criticise particular solutions to specific problems; instead his deep concern was with the very foundations of the programme. His critical stance was unusual, at least for most GOFAI practitioners, in that it did not rely on technical issues, but on a philosophical position emanating from phenomenology and existentialism, a fact contributing to his claims being largely ignored or dismissed for a long time by the AI community. But, for the most part, he was eventually proven right. AI’s over-reliance on worldmodelling and planning went against the evidence provided by phenomenology of human activity as situated and with a clear and ever-present focus of practical concern – the body and not some algorithm is the originating locus of intelligent activity (if by intelligent we understand intentional, directed and flexible), and the world is not the sum total of all available facts, but the world-as-it-is-for-this-body. Such concerns were later vindicated by the Brooksian revolution in autonomous robotics with its foundations on embodiment, situatedness and de-centralised mechanisms (Brooks, 1991). Brooks’ practical and methodological preoccupations – building robots largely based on biologically plausible principles and capable of acting in the real world – proved parallel, despite his claim that his approach was not “German philosophy”, to issues raised by Dreyfus. Putting robotics back as the acid test of AI, as oppossed to playing chess and proving theorems, is now often seen as a positive response to Dreyfus’ point that AI was unable to capture true meaning by the summing of meaningless processes. This criticism was later devastatingly recast in Searle’s Chinese Room argument (1980), and extended by Harnad’s Symbol Grounding Problem (1990). Meaningful activity – that is, meaningful for the agent and not only for the designer – must obtain through sensorimotor grounding in the agent’s world, and for this both a body and world are needed. Following these developments, work in autonomous robotics and new AI since the 1990s rebelled against pure connectionism because of its lack of biological plausibility and also because most of connectionist research was carried out in vacuo – it was compellingly argued that neural network models as simple input/output processing units are meaningless for modelling the cognitive capabilities of insects, let alone humans, unless they are embedded in a closed sensorimotor loop of interaction with a world (Cliff, 1991).  meaning, that is meaningful internal states and states of the world, can only obtain in an embodied agent whose effector and sensor activities become coordinated
Gnort: High Performance Network Intrusion Detection Using Graphics Processors

The constant increase in link speeds and number of threats poses challenges to network intrusion detection systems (NIDS), which must cope with higher traffic throughput and perform even more complex per-packet processing. In this paper, we present an intrusion detection system based on the Snort open-source NIDS that exploits the underutilized computational power of modern graphics cards to offload the costly pattern matching operations from the CPU, and thus increase the overall processing throughput. Our prototype system, called Gnort, achieved a maximum traffic processing throughput of 2.3 Gbit/s using synthetic network traces, while when monitoring real traffic using a commodity Ethernet interface, it outperformed unmodified Snort by a factor of two. The results suggest that modern graphics cards can be used effectively to speed up intrusion detection systems, as well as other systems that involve pattern matching operations.
Smart Parking System ( SPS ) Architecture Using Ultrasonic Detector

With the increase in vehicle production and world population, more and more parking spaces and facilities are required. In this paper a new parking system called Smart Parking System (SPS) is proposed to assist drivers to find vacant spaces in a car park in a shorter time. The new system uses ultrasonic (ultrasound) sensors to detect either car park occupancy or improper parking actions. Different detection technologies are reviewed and compared to determine the best technology for developing SPS. Features of SPS include vacant parking space detection, detection of improper parking, display of available parking spaces, and directional indicators toward vacant parking spaces, payment facilities and different types of parking spaces (vacant, occupied, reserved and handicapped) through the use of specific LEDs. This paper also describes the use of an SPS system from the entrance into a parking lot until the finding of a vacant parking space. The system is designed for a four-level parking lot with 100 parking spaces and five aisles on each floor. The system architecture defines the essential design features such as location of sensors, required number of sensors and LEDs for each level, and indoor and outdoor display boards.
A robust method of fingertip detection in complex background

In this paper, we propose a robust and accurate method to detect fingertips of hand palm with a down-looking camera mounted on an eyeglass for the utilization of hand gestures for user interaction between human and computers. To ensure consistent performance under unconstrained environments, we propose a novel method to precisely locate fingertips by combing both statistical information of palm edge distribution and structure information of convex null analysis on palm contour. Briefly, first SVM (support vector machine) with a statistical nine-bin based HOG (histogram of oriented gradient) features is introduced for robust hand detection from video stream. Then, binary image regions are segmented out by an adaptive Cg-Cr model on detected hands. With the prior information of hand contour, it takes a global optimization approach of convex hull analysis to locate hand fingertip. The experimental results have demonstrated that the proposed approach performs well because it can well detect all hand fingertips even under some extreme environments.
Prediction of parking space availability in real time

0957-4174/$ see front matter 2012  ⇑ Corresponding author.  E-mail addresses: felix.caicedo@ucv.cl (F. C (C. Blazquez), pablo.miranda@ucv.cl (P. Miranda). 1 Tel.: +56 (2) 661 5863. 2 Tel.: +56 (32) 227 3701. Intelligent parking reservation (IPR) systems allow customers to select a parking facility according to their preferences, rapidly park their vehicle without searching for a free stall, and pay their reservation in advance avoiding queues. Some IPR systems interact with in-vehicle navigation systems and provide users with information in real time such as capacity, parking fee, and current parking utilization. However, few of these systems provide information on the forecast utilization at specific hours – a process that requires the study of the competition between parking alternatives for the market share. This paper proposes a methodology for predicting real-time parking space availability in IPR architectures. This methodology consists of three subroutines to allocate simulated parking requests, estimate future departures, and forecast parking availability. Parking requests are allocated iteratively using an aggregated approach as a function of simulated drivers’ preferences, and parking availability. This approach is based on a calibrated discrete choice model for selecting parking alternatives. A numerical comparison between a one-by-one simulation-based forecast and the proposed aggregated approach indicates that no significant discrepancies exists, validating and suggesting the use of the less time consuming proposed aggregated methodology. Results obtained from contrasting predictions with real data yielded small average error availabilities. The forecast improves as the system registers arrivals and departures. Thus, the forecast is adequate for potential distribution in real-time using different media such as Internet, navigation systems, cell phones or GIS.
Architecture Principles for Cloud RAN

Cloud Radio Access Networks (Cloud RAN) is an emerging architectural paradigm that attempts to exploit operational efficiencies through centralization of baseband functions, pooling efficiencies for RAN baseband processing, and air-interface performance gains by fast-time-scale multi-cell coordination. In this paper, we aim to derive insights underlying key technologies and tradeoffs that drive Cloud RAN. We highlight principles that explain the impact of Cloud RAN architectures and functional splits on multi-cell coordination gains. We show that the ability to extract pooling gains can differ significantly between RAN functions, depending on whether they are per-user or per-cell functions. We propose a method for elastic scaling of RAN functions that takes into account both real-time and non-real-time needs. We conclude with a proposal for a methodology for operators to evaluate the overall tradeoffs they will face in deciding whether to adopt Cloud RAN.
Regulation of anterior insular cortex activity using real-time fMRI

Recent advances in functional magnetic resonance imaging (fMRI) data acquisition and processing techniques have made real-time fMRI (rtfMRI) of localized brain areas feasible, reliable and less susceptible to artefacts. Previous studies have shown that healthy subjects learn to control local brain activity with operant training by using rtfMRI-based neurofeedback. In the present study, we investigated whether healthy subjects could voluntarily gain control over right anterior insular activity. Subjects were provided with continuously updated information of the target ROI's level of activation by visual feedback. All participants were able to successfully regulate BOLD-magnitude in the right anterior insular cortex within three sessions of 4 min each. Training resulted in a significantly increased activation cluster in the anterior portion of the right insula across sessions. An increased activity was also found in the left anterior insula but the percent signal change was lower than in the target ROI. Two different control conditions intended to assess the effects of non-specific feedback and mental imagery demonstrated that the training effect was not due to unspecific activations or non feedback-related cognitive strategies. Both control groups showed no enhanced activation across the sessions, which confirmed our main hypothesis that rtfMRI feedback is area-specific. The increased activity in the right anterior insula during training demonstrates that the effects observed are anatomically specific and self-regulation of right anterior insula only is achievable. This is the first group study investigating the volitional control of emotionally relevant brain region by using rtfMRI training and confirms that self-regulation of local brain activity with rtfMRI is possible.
An adaptive write buffer management scheme for flash-based SSDs

Solid State Drives (SSD's) have shown promise to be a candidate to replace traditional hard disk drives. The benefits of SSD's over HDD's include better durability, higher performance, and lower power consumption, but due to certain physical characteristics of NAND flash, which comprise SSD's, there are some challenging areas of improvement and further research. We focus on the layout and management of the small amount of RAM that serves as a cache between the SSD and the system that uses it. Of the techniques that have previously been proposed to manage this cache, we identify several sources of inefficient cache space management due to the way pages are clustered in blocks and the limited replacement policy. We find that in many traces hot pages reside in otherwise cold blocks, and that the spatial locality of most clusters can be fully exploited in a limited time period, so we develop a hybrid page/block architecture along with an advanced replacement policy, called BPAC, or Block-Page Adaptive Cache, to exploit both temporal and spatial locality. Our technique involves adaptively partitioning the SSD on-disk cache to separately hold pages with high temporal locality in a page list and clusters of pages with low temporal but high spatial locality in a block list. In addition, we have developed a novel mechanism for flash-based SSD's to characterize the spatial locality of the disk I/O workload and an approach to dynamically identify the set of low spatial locality clusters. We run trace-driven simulations to verify our design and find that it outperforms other popular flash-aware cache schemes under different workloads. For instance, compared to a popular flash aware cache algorithm BPLRU, BPAC reduces the number of cache evictions by up to 79.6% and 34% on average.
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system

In this study, three popular signal processing techniques (Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the decomposition of Electroencephalography (EEG) Signals in Brain Computer Interface (BCI) system for a classification task. Publicly available BCI competition III dataset IVa, a multichannel 2-class motor-imagery dataset, was used for this purpose. Multiscale Principal Component Analysis method was applied for the purpose of noise removal. In addition, different sets of features were formed to examine the effect of a particular group of features. The parameter selection process for signal decomposition methods was thoroughly explained as well. Our results show that the combination of Multiscale Principal Component Analysis de-noising and higher order statistics features extracted from wavelet packet decomposition sub-bands resulted in highest average classification accuracy of 92.8%. Our study is one among very few that provides a comprehensive comparison between signal decomposition methods in combination with higher order statistics in classification of BCI signals. In addition, we stressed the importance of higher frequency ranges in improving the classification task for EEG signals in Brain Computer Interface Systems. Obtained results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair. It can also further enhance the current rehabilitation therapies where appropriate feedback is delivered once the individual executes the correct movement. In that way, motor rehabilitation outcomes may improve over time.
Internet use and video gaming predict problem behavior in early adolescence.

In early adolescence, the time spent using the Internet and video games is higher than in any other present-day age group. Due to age-inappropriate web and gaming content, the impact of new media use on teenagers is a matter of public and scientific concern. Based on current theories on inappropriate media use, a study was conducted that comprised 205 adolescents aged 10-14 years (Md = 13). Individuals were identified who showed clinically relevant problem behavior according to the problem scales of the Youth Self Report (YSR). Online gaming, communicational Internet use, and playing first-person shooters were predictive of externalizing behavior problems (aggression, delinquency). Playing online role-playing games was predictive of internalizing problem behavior (including withdrawal and anxiety). Parent-child communication about Internet activities was negatively related to problem behavior.
Computer Virus-Antivirus Coevolution

A S RECENTLY AS SIX YEARS AGO, COMPUTER viruses were considered an urban myth by many. At the time, only a handful of PC viruses had been written and infection was relatively uncommon. Today the situation is very different. As of November 1996, virus writers have programmed more than 10,000 DOS-based computer viruses. In addition to the sheer increase in the number of viruses, the virus writers have also become more clever. Their newer creations are significantly more complex and difficult to detect and remove. These “improvements” can be at least partially attributed to the efforts of antivirus producers. As antivirus products improve and detect the “latest and greatest” viruses, the virus authors invent new and more devious ways to hide their progeny. This coevolution has led to the creation of the most complex class of virus to date: the polymorphic computer virus. The polymorphic virus avoids detection by mutating itself each time it infects a new program; each mutated infection is capable of performing the same tasks as its parent, yet it may look entirely different. These cunning viruses simply cannot be detected costeffectively using traditional antivirus scanning algorithms. Fortunately, the antivirus producers have responded, as they have in the past, with an equally creative solution to the polymorphic virus threat. Many antivirus programs are now starting to employ a technique known as generic decryption to detect even the most complex polymorphic viruses quickly and cost effectively. A computer virus is a self-replicating computer pro-