nils-herrmann/clean_scirepeval huggingface dataset:
AISeL ) All Sprouts Content Sprouts 10-3-2008 Understanding Changes in Consumer Payment Habits-Do Mobile Payments Attract Consumers ?

Mobile payment services have, thus far, failed to entice consumers. An apparent conclusion is that these services have failed to meet consumers' payment needs. Deeper understanding of consumer adoption motivations is thus needed to be able to develop and launch mobile payment services successfully. For more than a decade researchers have tried to uncover the generic determinants of technology adoption common to most if not all technologies. Even when armed with this knowledge mobile payment services have fallen short of gaining customer bases large enough to sustain them. This suggests that the current academic knowledge is either ignored by practitioners or that the knowledge of consumer adoption is far from clear. Our paper seeks to find out if the generic technology adoption models are sufficient to explain factors consumers consider when they decide whether or not to adopt mobile payment services. In particular, we develop two models in the payment context. One of them models the determinants of the mobile payments services adoption while the other models the determinants of electronic invoicing adoption. The comparison of the model structures suggests that perceived ease of use seems to be the least common denominator for consumer adoption of these information technology based services while the context of technology adoption determines both the non-differentiating and the differentiating determinants of technology adoption.
Detection, Tracking and Classification of Road Signs in Adverse Conditions

In this paper a complete automatic system for road-sign detection, tracking, and classification is presented and evaluated. The processing of video frames in the L*a*b color space improves significantly the sign detection rate by processing the same frame in different normalized color spaces. The tracking module reduces significantly the processing time by transferring the sign detection information in the next frames and processing different radii signs in parallel. The proposed system is evaluated in normal, raining and night driving conditions. In a total number of 266 road-sign recordings, the complete system track and recognize successfully 216. The main source of system fault appears in city night driving due to the presence of a great number of light sources
A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem

The rapid urbanization of developing countries coupled with explosion in construction of high rising buildings and the high power usage in them calls for conservation and efficient energy program. Such a programme require monitoring of end-use appliances energy consumption in real-time. The worldwide recent adoption of smart-meter in smart-grid, has led to the rise of Non-Intrusive Load Monitoring (NILM); which enables estimation of appliance-specific power consumption from building’s aggregate power consumption reading. NILM provides households with cost-effective real-time monitoring of end-use appliances to help them understand their consumption pattern and become part and parcel of energy conservation strategy. This paper presents an up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem. This is followed by the review of the state-of-the art NILM algorithms. Furthermore, we review several performance metrics used by NILM researcher to evaluate NILM algorithms and discuss existing benchmarking framework for direct comparison of the state of the art NILM algorithms. Finally, the paper discuss potential NILM use-cases, presents an overview of the public available dataset and highlight challenges and future research directions. ∗sambaiga@gmail.com †shubi.kaijage@nmaist.ac.tz ‡kisangiri.michael@nmaist.ac.tz §nhmvungi@udsm.ac.tz
Gamut Mapping to Preserve Spatial Luminance Variations

A spatial gamut mapping technique is proposed to overcome the shortcomings encountered with standard pointwise gamut mapping algorithms by preserving spatially local luminance variations in the original image. It does so by first processing the image through a standard pointwise gamut mapping algorithm. The difference between the original image luminance Y and gamut mapped image luminance Y’ is calculated. A spatial filter is then applied to this difference signal, and added back to the gamut mapped signal Y’. The filtering operation can result in colors near the gamut boundary being placed outside the gamut, hence a second gamut mapping step is required to move these pixels back into the gamut. Finally, the in-gamut pixels are processed through a color correction function for the output device, and rendered to that device. Psychophysical experiments validate the superior performance of the proposed algorithm, which reduces many of the artifacts arising from standard pointwise techniques.
Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability.

To assess whether talking or reading (silently or aloud) could affect heart rate variability (HRV) and to what extent these changes require a simultaneous recording of respiratory activity to be correctly interpreted.



Sympathetic predominance in the power spectrum obtained from short- and long-term HRV recordings predicts a poor prognosis in a number of cardiac diseases. Heart rate variability is often recorded without measuring respiration; slow breaths might artefactually increase low frequency power in RR interval (RR) and falsely mimic sympathetic activation.



In 12 healthy volunteers we evaluated the effect of free talking and reading, silently and aloud, on respiration, RR and blood pressure (BP). We also compared spontaneous breathing to controlled breathing and mental arithmetic, silent or aloud. The power in the so called low- (LF) and high-frequency (HF) bands in RR and BP was obtained from autoregressive power spectrum analysis.



Compared with spontaneous breathing, reading silently increased the speed of breathing (p < 0.05), decreased mean RR and RR variability and increased BP. Reading aloud, free talking and mental arithmetic aloud shifted the respiratory frequency into the LF band, thus increasing LF% and decreasing HF% to a similar degree in both RR and respiration, with decrease in mean RR but with minor differences in crude RR variability.



Simple mental and verbal activities markedly affect HRV through changes in respiratory frequency. This possibility should be taken into account when analyzing HRV without simultaneous acquisition and analysis of respiration.
Group Buying on the Web: A Comparison of Price-Discovery Mechanisms

Web-based Group-Buying mechanisms, a refinement of quantity discounting, are being used for both Business-to-Business (B2B) and Business-toConsumer (B2C) transactions. In this paper, we survey currently operational online Group-Buying markets, and then study this phenomenon using analytical models. We surveyed over fifty active Group-Buying sites, and provide a comprehensive review of Group-Buying practices in the B2B, B2C and non-profit sectors, across three continents. On the modeling side, we build on the coordination literature in Information Economics and the quantity-discounts literature in Operations to develop an analytical model of a monopolist who uses web-based Group-Buying mechanisms under different kinds of demand uncertainty. We derive the monopolist's optimal Group-Buying schedule, and compare his profits with those that obtain under the more conventional posted-price mechanism. We also study the effect of heterogeneity in the demand regimes, in combination with uncertainty, on the relative performance of the two mechanisms. We further study the impact of the timing of the pricing decision (vis-à-vis the production decision) by modeling it as a two-stage game between the monopolist and buyers. Finally, we investigate how Group-Buying schemes compare with posted price markets when buyers can revise their prior valuation of products based on information received from third parties (infomediaries). In all cases, we characterize the conditions under which one mechanism outperforms the other, and those under which the posted price and Group-Buy mechanisms lead to identical seller revenues. Our results have implications for firms' choice of price discovery mechanisms in electronic markets and scheduling of production and pricing decisions in the presence (and absence) of scale economies of production.  Tel:(215) 573 5677.
Sequential Mining: Patterns and Algorithms Analysis

This paper presents and analysis the common existing sequential pattern mining algorithms. It presents a classifying study of sequential pattern-mining algorithms into five extensive classes. First, on the basis of Apriori-based algorithm, second on Breadth First Search-based strategy, third on Depth First Search strategy, fourth on sequential closed-pattern algorithm and five on the basis of incremental pattern mining algorithms. At the end, a comparative analysis is done on the basis of important key features supported by various algorithms. This study gives an enhancement in the understanding of the approaches of sequential
LOCI: Fast Outlier Detection Using the Local Correlation Integral

Outlier detection is an integral part of data mining and has attracted much attention recently [BKNS00, JTH01, KNT00]. In this paper, we propose a new method for evaluating outlier-ness, which we call theLocal Correlation Integral(LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers ( a.k.a.micro-clusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, data-dictated cut-off to determine whether a point is an outlier—in contrast, previous methods force users to pick cut-offs, without any hints as to what cut-off value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, micro-clusters, their diameters and their inter-cluster distances. None of the existing outlier-detection methods can match this feature, because they output only a single number for each point: its outlierness score.(c) Our LOCI method can be computed as quickly as the best previous methods. (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI ), which provides fast highly-accurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection. Experiments on synthetic and real world data sets show that LOCI and aLOCI can automatically detect outliers and micro-clusters, without user-required cut-offs, and that they quickly spot both expected and unexpected outliers.
Cross-Language Entity Linking

There has been substantial recent interest in aligning mentions of named entities in unstructured texts to knowledge base descriptors, a task commonly called entity linking. This technology is crucial for applications in knowledge discovery and text data mining. This paper presents experiments in the new problem of crosslanguage entity linking, where documents and named entities are in a different language than that used for the content of the reference knowledge base. We have created a new test collection to evaluate cross-language entity linking performance in twenty-one languages. We present experiments that examine issues such as: the importance of transliteration; the utility of cross-language information retrieval; and, the potential benefit of multilingual named entity recognition. Our best model achieves performance which is 94% of a strong monolingual baseline.
A New Derivation and Dataset for Fitts ’ Law of Human Motion

Human motion models for reaching tasks facilitate the design of many systems such as computer and cellphone interfaces, cockpits, and assembly lines. Fitts’ Law specifies a logarithmic two-parameter relationship between motion duration and the ratio of target distance over target size, and more recent models consider square root and modified logarithmic relationships. This paper contributes new theory and new experiments. For the former, we provide a succinct derivation of the square-root model based on optimal control theory. Our derivation is intuitive, exact, makes fewer assumptions, and requires fewer steps than prior derivations. We present data from two experimental user studies, one a controlled (in-lab) study and the second an uncontrolled (online) study with a total of 94,580 timing measurements. We consider three two-parameter models that relate motion duration to the ratio of target distance over target size: LOG (the classic logarithmic function), SQR (squareroot), and LOG’ (logarithmic plus 1.0). We find that: (1) the data from the controlled and uncontrolled studies are remarkably consistent; (2) for homogeneous targets, the SQR model yields a significantly better fit than LOG or LOG’, except with the most difficult targets (i.e., the ratio of target distance over target size is large) where the models are not significantly different; (3) for heterogenous targets, SQR yields a significantly better fit than LOG for easier targets and LOG yields a significantly better fit for more difficult targets, while the the LOG’ model yields a significantly better fit than both LOG and SQR on more difficult targets.
A Minimal Span-Based Neural Constituency Parser

In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top-down inference algorithm based on recursive partitioning of the input. We demonstrate empirically that both prediction schemes are competitive with recent work, and when combined with basic extensions to the scoring model are capable of achieving state-of-the-art single-model performance on the Penn Treebank (91.79 F1) and strong performance on the French Treebank (82.23 F1).
Discriminative Batch Mode Active Learning

Active learning sequentially selects unlabeled instances to label with the goal of reducing the effort needed to learn a good classifier. Most previous studies in active learning have focused on selecting one unlabeled instance at one time while retraining in each iteration. However, single instance selection systems are unable to exploit a parallelized labeler when one is available. Recently a few batch mode active learning approaches have been proposed that select a set of most informative unlabeled instances in each iteration under the guidance of some heuristic scores. In this paper, we propose a discriminative batch mode active learning approach that formulates the instance selection task as a continuous optimization problem over auxiliary instance selection variables. The optimization is formuated to maximize the discriminative classification performance of the target classifier, while also taking the unlabeled data into account. Although the objective is not convex, we can manipulate a quasi-Newton method to obtain a good local solution. Our empirical studies on UCI datasets show that the proposed active learning is more effective than current state-of-the art batch mode active learning algorithms.
ActiveStereoNet: End-to-End Self-supervised Learning for Active Stereo Systems

In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of 1/30th of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-toend without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.
Personal Recommendation as Link Prediction using a Bipartite Graph Kernel

We implement a personal recommendation system on the Yelp academic dataset using a novel algorithm proposed by [Li and Chen., 2012]. By constructing a bipartite userbusiness graph where positive reviews are edges between nodes, we pose the recommendation problem as link prediction. More specifically, we construct a kernel which combines the information from both the node features and local graph structure. This kernel is then put into a one-class support vector machine, which generates an ordering of likely user-business links. We downsample the graph and tune the parameters of our algorithm (via cross validation). In what follows we provide a summary of the Yelp data, describe the recommendation algorithm in detail, and present the results of our implementation.
Bayesian Intermittent Demand Forecasting for Large Inventories

We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items.
Using WordNet to Disambiguate Word Senses for Text Retrieval

This paper describes an automatic indexing procedure that uses the “IS-A” relations contained within WordNet and the set of nouns contained in a text to select a sense for each plysemous noun in the text. The result of the indexing procedure is a vector in which some of the terms represent word senses instead of word stems. Retrieval experiments comparing the effectivenss of these sense-based vectors vs. stem-based vectors show the stem-based vectors to be superior overall, although the sense-based vectors do improve the performance of some queries. The overall degradation is due in large part to the difficulty of disambiguating senses in short query statements. An analysis of these results suggests two conclusions: the IS-A links define a generalization/specialization hierarchy that is not sufficient to reliably select the correct sense of a noun from the set of fine sense distinctions in WordNet; and missing correct matches because of incorrect sense resolution has a much more deleterious effect on retrieval performance than does making spurious matches.
Object Constraint Language (OCL): A Definitive Guide

The Object Constraint Language (OCL) started as a complement of the UML notation with the goal to overcome the limitations of UML (and in general, any graphical notation) in terms of precisely specifying detailed aspects of a system design. Since then, OCL has become a key component of any model-driven engineering (MDE) technique as the default language for expressing all kinds of (meta)model query, manipulation and specification requirements. Among many other applications, OCL is frequently used to express model transformations (as part of the source and target patterns of transformation rules), well-formedness rules (as part of the definition of new domain-specific languages), or code-generation templates (as a way to express the generation patterns and rules). This chapter pretends to provide a comprehensive view of this language, its many applications and available tool support as well as the latest research developments and open challenges around it.
An Intelligent Software defined Network Controller for preventing Distributed Denial of Service Attack

Software Defined Network (SDN) architecture is a new and novel way of network management mechanism. In SDN, switches do not process the incoming packets like conventional network computing environment. They match for the incoming packets in the forwarding tables and if there is none it will be sent to the controller for processing which is the operating system of the SDN. A Distributed Denial of Service (DDoS) attack is a biggest threat to cyber security in SDN network. The attack will occur at the network layer or the application layer of the compromised systems that are connected to the network. In this paper a machine learning based intelligent method is proposed which can detect the incoming packets as infected or not. The different machine learning algorithms adopted for accomplishing the task are Naive Bayes, K-Nearest neighbor (KNN) and Support vector machine (SVM) to detect the anomalous behavior of the data traffic. These three algorithms are compared according to their performances and KNN is found to be the suitable one over other two. The performance measure is taken here is the detection rate of infected packets.
Analysis of the MED Oscillation Problem in BGP

The Multi Exit Discriminator (MED) attribute of the Border Gateway Protocol (BGP) is widely used to implement “cold potato routing” between autonomous systems. However, the use of MED in practice has led to BGP persistent oscillation. The MED oscillation problem has been described with example configurations and complicated, step-by-step evaluation of dynamic route computations performed at multiple routers. Our work presents the first rigorous analysis of the MED oscillation problem. We employ the Stable Paths Problem (SPP) formalism that allows a static analysis of the interaction of routing policies. We give a formal definition of MED Induced Routing Anomalies (MIRA) and show that, in general, they can span multiple autonomous systems. However, if we assume that the BGP configurations between ASes follows a common model based on customer/provider and peer/peer relationships, then we show that the scope of any MIRA is always contained within a single autonomous system. Contrary to widely held assumptions, we show that a MIRA can occur even in a fully meshed IBGP configuration. We also show that a stable BGP routing may actually violate the stated semantics of the MED attribute.
Vision-Based Fallen Person Detection for the Elderly

Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20% of the falls. The time it takes to respond and treat a fallen person is crucial. With this paper we present a new, non-invasive system for fallen people detection. Our approach uses only stereo camera data for passively sensing the environment. The key novelty is a human fall detector which uses a CNN based human pose estimator in combination with stereo data to reconstruct the human pose in 3D and estimate the ground plane in 3D. Furthermore, our system consists of a reasoning module which formulates a number of measures to reason whether a person is fallen. We have tested our approach in different scenarios covering most activities elderly people might encounter living at home. Based on our extensive evaluations, our systems shows high accuracy and almost no miss-classification. To reproduce our results, the implementation is publicly available to the scientific community.