nils-herrmann/clean_scirepeval huggingface dataset:
Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data

trillion a year. Driven by the market size, health care has become an important and fast growing application domain for data analytics. McKinsey’s influential report on big data analytics (Manyika et al. 2011) lists health care as the top most promising application domain. One significant problem of health care is the loss of health-care expenditures to fraud, waste, and abuse (FWA).  1 lists the amount of improper payment in U.S. government expenditure. In 2012, improper payments totaled about $120 billion. Health-carerelated programs such as Medicaid, Medicare fee for service (FFS) and parts C and D contribute significantly, representing more than half of the total. A separate report from the Institute of Medicine (IOM) estimates the annual loss to FWA in the health-care domain to be $750 billion (Institute of Medicine 2012). The magnitude of the fraud problem has attracted many efforts from the health-care industry, the data-analytics industry, and research communities to develop fraud-detection systems.
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space—if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher’s Linear Discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed “Fisherface” method has error rates that are lower than those of the Eigenface technique for tests on the Harvard and Yale Face Databases. Index Terms —Appearance-based vision, face recognition, illumination invariance, Fisher’s linear discriminant. —————————— ✦ ——————————
A Survey on Data Mining Algorithm for Market Basket Analysis

s Association rule mining identifies the remarkable association or relationship between a large set of data items. With huge quantity of data constantly being obtained and stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis.  Association Rule Mining, Apriori Algorithm, Market Basket Analysis GJCST Classification : H.2.8 A Survey on Data Mining Algorithm for Market Basket Analysis Strictly as per the compliance and regulations of: Global Journal of Computer Science and Technology Volume 11 Issue 11 Version 1.0 July 2011  Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 &
An experimental investigation of model-based parameter optimisation: SPO and beyond

This work experimentally investigates model-based approaches for optimising the performance of parameterised randomised algorithms. We restrict our attention to procedures based on Gaussian process models, the most widely-studied family of models for this problem. We evaluated two approaches from the literature, and found that sequential parameter optimisation (SPO)  offered the most robust performance. We then investigated key design decisions within the SPO paradigm, characterising the performance consequences of each. Based on these findings, we propose a new version of SPO, dubbed SPO+, which extends SPO with a novel intensification procedure and log-transformed response values. Finally, in a domain for which performance results for other (model-free) parameter optimisation approaches are available, we demonstrate that SPO+ achieves state-of-the-art performance.
AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis

Most research on determining the strength of subjective expressions in a sentence or document uses single, specific parts of speech such as adjectives, adverbs, or verbs. To date, almost no research covers the development of a single comprehensive framework in which we can analyze sentiment that takes all three into account. The authors propose the AVA (adjective verb adverb) framework for identifying opinions on any given topic. In AVA, a user can select any topic t of interest and any document d. AVA will return a score that d expresses topic t. The score is expressed on a –1 (maximally negative) to +1 (maximally positive) scale.
A Type-Based Compiler for Standard ML

Compile-time type information should be valuable in efficient compilation of statically typed functional languages such as Standard ML. But how should type-directed compilation work in real compilers, and how much performance gain will type-based optimizations yield? In order to support more efficient data representations and gain more experience about type-directed compilation, we have implemented a new type-based middle end and back end for the Standard ML of New Jersey compiler. We describe the basic design of the new compiler, identify a number of practical issues, and then compare the performance of our new compiler with the old non-type-based compiler. Our measurement shows that a combination of several simple type-based optimizations reduces heap allocation by 36%; and improves the already-efficient code generated by the old non-type-based compiler by about 19% on a DECstation 500.
Plan explicability and predictability for robot task planning

Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave in unexpected ways. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in “natural ways”, and work that investigated legible motion planning, there is no general solution for high level task planning. To address this issue, we introduce the notions of plan explicability and predictability. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with a physical robot to demonstrate the effectiveness of our approach.
ONOS: towards an open, distributed SDN OS

We present our experiences to date building ONOS (Open Network Operating System), an experimental distributed SDN control platform motivated by the performance, scalability, and availability requirements of large operator networks. We describe and evaluate two ONOS prototypes. The first version implemented core features: a distributed, but logically centralized, global network view; scale-out; and fault tolerance. The second version focused on improving performance. Based on experience with these prototypes, we identify additional steps that will be required for ONOS to support use cases such as core network traffic engineering and scheduling, and to become a usable open source, distributed network OS platform that the SDN community can build upon.
Business processes--attempts to find a definition

Definitions of business process given in much of the literature on Business Process Management are limited in depth and their related models of business processes are correspondingly constrained. After giving a brief history of the progress of business process modeling techniques from production systems to the office environment, this paper proposes that most definitions are based on machine metaphor type explorations of a process. While these techniques are often rich and illuminating it is suggested that they are too limited to express the true nature of business processes that need to develop and adapt to today’s challenging environment.
Visualizing Deep Convolutional Neural Networks Using Natural Pre-images

Image representations, from SIFT and bag of visual words to convolutional neural networks (CNNs) are a crucial component of almost all computer vision systems. However, our understanding of them remains limited. In this paper we study several landmark representations, both shallow and deep, by a number of complementary visualization techniques. These visualizations are based on the concept of “natural pre-image”, namely a natural-looking image whose representation has some notable property. We study in particular three such visualizations: inversion, in which the aim is to reconstruct an image from its representation, activation maximization, in which we search for patterns that maximally stimulate a representation component, and caricaturization, in which the visual patterns that a representation detects in an image are exaggerated. We pose these as a regularized energy-minimization framework and demonstrate its generality and effectiveness. In particular, we show that this method can invert representations such as HOG more accurately than recent alternatives while being applicable to CNNs too. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.
Set of X-band distributed absorptive limiter GaAs MMICs

A set of X-band absorptive limiter GaAs MMICs has been designed and realised using both the PPH25x foundry process from UMS and the PP50-10 process from WIN semiconductors. The innovative limiter concepts have been extensively characterised by both pulsed and CW measurements. Both passive and active topologies have been implemented. The passive limiter design has a typical small-signal insertion loss of 1.5 dB at 10 GHz, and it can withstand (absorb) up to 4 Watts (36 dBm) of CW power without degradation or damage, while keeping the output power level below 100 mW (20 dBm). The active limiter handles up to 10 Watts of CW input power, at the cost of higher small-signal insertion loss. For all designs the input reflection remains low for any input power level. The used GaAs surface ranges from 2 to 3 mm2 .
On the importance of accurate weak classifier learning for boosted weak classifiers

Recent work has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.
Autonomous robotic exploration using occupancy grid maps and graph SLAM based on Shannon and Rényi Entropy

In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation

The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences.
Maximum Complex Task Assignment: Towards Tasks Correlation in Spatial Crowdsourcing

Spatial crowdsourcing has gained emerging interest from both research communities and industries. Most of current spatial crowdsourcing frameworks assume independent and atomic tasks. However, there could be some cases that one needs to crowdsource a spatial complex task which consists of some spatial sub-tasks (i.e., tasks related to a specific location). The spatial complex task's assignment requires assignments of all of its sub-tasks. The currently available frameworks are inapplicable to such kind of tasks. In this paper, we introduce a novel approach to crowdsource spatial complex tasks. We first formally define the Maximum Complex Task Assignment (MCTA) problem and propose alternative solutions. Subsequently, we perform various experiments using both real and synthetic datasets to investigate and verify the usability of our proposed approach.
Enriching Wayfinding Instructions with Local Landmarks

Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method.
Proactive Network Fault Detection

The increasing role of communication networks in today’s society results in a demand for higher levels of network availability and reliability. At the same time, fault management is becoming more dificult due to the dynamic nature and heterogeneity of networks. We propose an intelligent monitoring system using adaptive statistical techniques. The system continually learns the normal behavior of the network and detects deviations from the norm. Within the monitoring system, the measurements are segmented, and features extracted from the segments are used to describe the normal behavior of the measurement variables. This information is combined in the structure of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. Experimental results on real network data demonstrate that the proposed system can detect abnormal behavior before a fault actually occurs.
Algorithm 980: Sparse QR Factorization on the GPU

Sparse matrix factorization involves a mix of regular and irregular computation, which is a particular challenge when trying to obtain high-performance on the highly parallel general-purpose computing cores available on graphics processing units (GPUs). We present a sparse multifrontal QR factorization method that meets this challenge and is significantly faster than a highly optimized method on a multicore CPU. Our method factorizes many frontal matrices in parallel and keeps all the data transmitted between frontal matrices on the GPU. A novel bucket scheduler algorithm extends the communication-avoiding QR factorization for dense matrices by exploiting more parallelism and by exploiting the staircase form present in the frontal matrices of a sparse multifrontal method.
Exemplar Guided Unsupervised Image-to-Image Translation

Image-to-image translation task has become a popular topic recently. Most works focus on either one-to-one mapping in an unsupervised way or many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner and cross-domain variations. To alleviate these issues, we propose the Exemplar Guided UNsupervised Image-to-image Translation (EGUNIT) network which conditions the image translation process on an image in the target domain. An image representation is assumed to comprise both content information which is shared across domains and style information specific to one domain. By applying exemplar-based adaptive instance normalization to the shared content representation, EG-UNIT manages to transfer the style information in the target domain to the source domain. Experimental results on various datasets show that EG-UNIT can indeed translate the source image to diverse instances in the target domain with semantic consistency.
Symmetry in locomotor central pattern generators and animal gaits.

Animal locomotion is controlled, in part, by a central pattern generator (CPG), which is an intraspinal network of neurons capable of generating a rhythmic output. The spatio-temporal symmetries of the quadrupedal gaits walk, trot and pace lead to plausible assumptions about the symmetries of locomotor CPGs. These assumptions imply that the CPG of a quadruped should consist of eight nominally identical subcircuits, arranged in an essentially unique matter. Here we apply analogous arguments to myriapod CPGs. Analyses based on symmetry applied to these networks lead to testable predictions, including a distinction between primary and secondary gaits, the existence of a new primary gait called 'jump', and the occurrence of half-integer wave numbers in myriapod gaits. For bipeds, our analysis also predicts two gaits with the out-of-phase symmetry of the walk and two gaits with the in-phase symmetry of the hop. We present data that support each of these predictions. This work suggests that symmetry can be used to infer a plausible class of CPG network architectures from observed patterns of animal gaits.
Curriculum Learning for Facial Expression Recognition

Over the past few years, there has been an increased interest in machine understanding and recognition of affective states based on facial expressions. While great progress has been made, there are still a lot of challenges facing automatic emotion recognition, namely: generalizability of models across datasets, accounting for individual differences, and recognition of subtle expressions. While deep learning techniques enabled a large amount of progress in many areas of computer vision, this progress has not yet been fully translated to emotion recognition. Our work attempts to partly address that by presenting a novel learning technique for deep learning methods that leads to better generalization for emotion recognition from facial expressions.
Registration and Integration of Textured 3-D Data

In general, multiple views are required to create a complete 3-D model of an object or of a multi-roomed indoor scene. In this work, we address the problem of merging multiple textured 3-D data sets, each of which corresponds to a different view of a scene or object. There are two steps to the merging process: registration and integration. To register, or align, data sets we use a modified version of the Iterative Closest Point algorithm; our version, which we call color ICP, considers not only 3-D information, but color as well. We show experimentally that the use of color decreases registration error significantly. Once the 3-D data sets have been registered, we integrate them to produce a seamless, composite 3-D textured model. Our approach to integration uses a 3-D occupancy grid to represent likelihood of spatial occupancy through voting. In addition to occupancy information, we store surface normal in each voxel of the occupancy grid. Surface normal is used to robustly extract a surface from the occupancy grid; on that surface we blend textures from multiple views.