Miracle-dz/UniFAR-FTUs huggingface dataset:
ecoxight

The term ecosystem is used pervasively in industry, government, and academia to describe the complex, dynamic, hyperconnected nature of many social, economic, and technical systems that exist today. Ecosystems are characterized by a large, dynamic, and heterogeneous set of geospatially distributed entities that are interconnected through various types of relationships. This study describes the design and development of ecoxight, a Web-based visualization platform that provides multiple coordinated views of multipartite, multiattribute, dynamic, and geospatial ecosystem data with novel and rich interaction capabilities to augment decision makers ecosystem intelligence. The design of ecoxight was informed by an extensive multiphase field study of executives. The ecoxight platform not only provides capabilities to interactively explore and make sense of ecosystems but also provides rich visual construction capabilities to help decision makers align their mental model. We demonstrate the usability, utility, and value of our system using multiple evaluation studies with practitioners using socially curated data on the emerging application programming interface ecosystem. We report on our findings and conclude with research implications. Collectively, our study contributes to design science research at the intersection of information systems and strategy and the rapidly emerging field of visual enterprise analytics.
Minimax estimation via wavelet shrinkage

We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets, we develop a nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coefficients. The shrinkage can be tuned to be nearly minimax over any member of a wide range of Triebel- and Besov-type smoothness constraints and asymptotically minimax over Besov bodies with p < q. Linear estimates cannot achieve even the minimax rates over Triebel and Besov classes with p < 2, so the method can significantly outperform every linear method (e.g., kernel, smoothing spline, sieve) in a minimax sense. Variants of our method based on simple threshold nonlinear estimators are nearly minimax. Our method possesses the interpretation of spatial adaptivity; it reconstructs using a kernel which may vary in shape and bandwidth from point to point, depending on the data. Least favorable distributions for certain of the Triebel and Besov scales generate objects with sparse wavelet transforms. Many real objects have similarly sparse transforms, which suggests that these minimax results are relevant for practical problems. Sequels to this paper, which was first drafted in November 1990, discuss practical implementation, spatial adaptation properties, universal near minimaxity and applications to inverse problems.
A leakage-aware cache sharing technique for low-power chip multi-processors (CMPs) with private L2 caches

Power dissipation becomes an important issue in modern microprocessors such as chip multiprocessors (CMPs). Especially as the process technology advances below 90nm, the leakage power consumption becomes dominant in the total power dissipation, thus reducing the leakage power consumption is an important design goal for low-power CMPs. In particular, since most CMPs employ a large L2 cache, reducing the leakage power consumption of the L2 cache is critical in realizing low-power CMPs. In this paper, we propose a leakage-aware on-chip L2 cache organization called LACS. The proposed LACS, like the existing RACS organization, is based on a private L2 cache organization with an inter-L2 cache sharing support. However, unlike the RACS organization, which determines a peer L2 cache block for an inter-L2 cache sharing based on the reusability of the evicted L2 block and performance implications of peer L2 cache blocks, the LACS organization considers both the performance and leakage. The LACS organization reduces the leakage power consumption significantly over the leakage-oblivious RACS organization while achieving a similar performance gain over a private L2 cache organization. Experimental results show that the proposed LACS technique reduces the energy consumption by 23.6% and improves the energy delay product by 18.6% on average over the existing RACS scheme.
Maximum-likelihood affine cepstral filtering (MLACF) technique for speaker normalization

We present a novel technique of minimizing the acoustic variability of speakers by transforming the features extracted from the speaker’s data to better ﬁt the recognition model. The concept of maximum-likelihood afﬁne cepstral ﬁltering (MLACF) will be introduced for feature transformation, along with solutions for the transformation parameters that maximize the likelihood of the test data with respect to a given recognition model. It is shown that for log-concave distributions, the solution of the MLACF problem can be obtained using convex programming. HMM-based digit recognition on the TIDIGITS database is presented to demonstrate the ﬂexibility of the transformation in compensating for large acoustic mismatches between the speakers in the training and test database. In addition, it will be shown that the technique requires estimation of far fewer transformation parameters compared to existing techniques, thus allowing fast, real-time compensation.
Indexing Methods for XML Documents

has been much research about XML storage and information retrieval. Traditionally XML documents are mapped onto relational databases. Then the data can be queried by relational queries. Nowadays many digital libraries store XML documents in a native XML database. Native means that the documents are stored and retrieved in their original format. An important task of such a database is indexing the documents. Indexing the XML data will improve the retrieval speed when querying. There are several methods for indexing XML documents that will be discussed in this paper. Keywordsindex, semi-structured, updating, sequence, path
The Curve-Fitting Problem, Akaike-type Model Selection, and the Error Statistical Approach

The curve-fitting problem is often viewed as an exemplar which encapsulates many of the problems associated with inductive inference, including underdetermination and the reliability of inference. The current view is that the ‘fittest’ curve is one which provides the optimal trade-off between goodness-offit and simplicity, with the Akaike Information Criterion (AIC) the preferred method. The primary objective of this paper is twofold. First ,t o argue that the AIC-type procedures do not provide an adequate solution to the curve fitting problem because they have no criterion to assess when a curve captures the regularities in the data inadequately. As a result, these procedures cannot ensure the reliability of inductive inference. Second, to argue that for more satisfactory answers one needs to view the curve fitting problem in the context of error-statistical approach where statistical adequacy provides such a criterion and the associated error probabilities can be used to calibrate the trustworthiness of inductive inference.
Data Dependant Learners Ensemble Pruning

Ensemble learning aims at combining several slightly different learners to construct stronger learner. Ensemble of a well selected subset of learners would outperform than ensemble of all. However, the well studied accuracy / diversity ensemble pruning framework would lead to over fit of training data, which results a target learner of relatively low generalization ability. We propose to ensemble with base learners trained by both labeled and unlabeled data, by adopting data dependant kernel mapping, which has been proved successful in semi-supervised learning, to get more generalized base learners. We bootstrap both training data and unlabeled data, namely point cloud, to build slight different data set, then construct data dependant kernel. With such kernels data point can be mapped to different feature space which results effective ensemble. We also proof that ensemble of learners trained by both labeled and unlabeled data is of better generalization ability in the meaning of graph Laplacian. Experiments on UCI data repository show the effectiveness of the proposed method.
Bioenergetic Approach to Describing Gulf Sturgeon (Acipenser oxyrinchus) Growth in Two Florida Rivers~!2009-02-17~!2010-02-04~!2010-06-03~!

Bioenergetics models are commonly used by scientist and managers to describe energy uptake and metabolism of fish species. Much data is needed to inform these models and often species specific data is unavailable or difficult to acquire, such as in the case with Gulf of Mexico sturgeon. This study applied a new method, developed by Walters and Essington (this issue), to estimate bioenergetics parameters using field observations for Gulf sturgeon populations in the Apalachicola and Suwannee Rivers, Florida, U.S.A. Bioenergetics derived growth curves were compared to growth curves developed using traditional direct aging methods. We obtained bioenergetic parameter estimates for both populations and the bioenergetic method was able to accurately simulate annual variability in Gulf sturgeon growth rates. Further, the bioenergetics growth estimates, which incorporate length-at-age and length-increment data estimated very different growth trajectories than traditional von Bertalanffy curves that used only length-at-age. This indicates that length-atage data alone can lead to errors in growth estimates, resulting in erroneous management advice. Using field data to inform bioenergetic models should be a useful approach for fisheries researchers and managers to describe the growth and energetic characteristics of fish populations.
JSONiq: The History of a Query Language

NoSQL data stores support hierarchical, heterogeneous data such as XML, JavaScript Object Notation (JSON), and Binary JSON (BSON). A need exists for a tailor-made language for querying such hierarchical, heterogeneous datasets. JSONiq was designed from the ground up to support JSON data and run against all JSONor BSON-based data stores, turning them into full-fledged databases.
A Watermarking Algorithm for Embedding of MIDI Information in the Digital Music Score

A blind watermarking algorithm for the embedding of MIDI (Musical Instrument Digital Interface) information into the binary music score image based on the OMR (Optical Music Recognition) technology is presented in this paper, and the information of digital music score and MIDI can be reconstructed. The experiment results show that the algorithm has no effect to the precision of music score recognition, and will be available to the integrity auditing and the synchronized transmit of digital music score and its MIDI information.
Medium-specific properties of urban screens: towards an ontological framework for digital public displays

The purpose of the present theoretical exploration is to lay the foundations of a platform-specific ontology of urban screens, which we define as an architectural scale media environment comprising two or more digital displays that can support interactive and/or artificial intelligence features. Still in its budding stages, this framework is intended to assist artists and HCI practitioners in the conception and evaluation of public space installations that heavily rely on digital displays. Using an architectural approach that analyzes urban screens in terms of medium specificity, this paper asks: "What are some of the key ontological attributes of urban screens as a computational medium? " We propose a taxonomy of five medium-specific properties articulated in relation to sensory modalities and modes of interaction. In providing an aesthetic, poetic, cognitive and experiential basis for understanding urban screens, this paper seeks to help researchers broadly consider their design parameters and generate new ideas.
Giambelli, Pieri, and tableau formulas via raising operators

Abstract We give a direct proof of the equivalence between the Giambelli and Pieri type formulas for Hall–Littlewood functions using Young's raising operators, parallel to joint work with Buch and Kresch for the Schubert classes on isotropic Grassmannians. We prove several closely related mirror identities enjoyed by the Giambelli polynomials, which lead to new recursions for Schubert classes. The raising operator approach is applied to obtain tableau formulas for the Hall–Littlewood functions, the theta polynomials of [Buch, Kresch and Tamvakis, 2008], and related Stanley symmetric functions. Finally, we introduce the notion of a skew element w of the hyperoctahedral group and identify the set of reduced words for w with the set of standard k-tableaux on a skew shape λ/μ.
PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Vagal Activity Is Quadratically Related to Prosocial Traits , Prosocial Emotions , and Observer Perceptions of Prosociality

In the present article, we introduce the quadratic vagal activity–prosociality hypothesis, a theoretical framework for understanding the vagus nerve’s involvement in prosociality. We argue that vagus nerve activity supports prosocial behavior by regulating physiological systems that enable emotional expression, empathy for others’ mental and emotional states, the regulation of one’s own distress, and the experience of positive emotions. However, we contend that extremely high levels of vagal activity can be detrimental to prosociality. We present 3 studies providing support for our model, finding consistent evidence of a quadratic relationship between respiratory sinus arrhythmia—the degree to which the vagus nerve modulates the heart rate—and prosociality. Individual differences in vagal activity were quadratically related to prosocial traits (Study 1), prosocial emotions (Study 2), and outside ratings of prosociality by complete strangers (Study 3). Thus, too much or too little vagal activity appears to be detrimental to prosociality. The present article provides the 1st theoretical and empirical account of the nonlinear relationship between vagal activity and prosociality.
Object Tracking with Multiple Instance Learning and Gaussian Mixture Model

Recently, Multiple Instance Learning (MIL) technique has been introduced for object tracking\linebreak applications, which has shown its good performance to handle drifting problem. While some instances in positive bags not only contain objects, but also contain the background, it is not reliable to simply assume that each feature of instances in positive bags obeys a single Gaussian distribution. In this paper, a tracker based on online multiple instance boosting has been developed, which employs Gaussian Mixture Model (GMM) and single Gaussian distribution respectively to model features of instances in positive and negative bags. The differences between samples and the model are integrated into the process of updating the parameters for GMM. With the Haar-like features extracted from the bags, a set of weak classifiers are trained to construct a strong classifier, which is used to track the object location at a new frame. And the classifier can be updated online frame by frame. Experimental results have shown that our tracker is more stable and efficient when dealing with the illumination, rotation, pose and appearance changes.
Modeling online auctions with proxy-bidding: Ascending versus Sealed Model

This paper presents several empirical tests of the hypothesis that eBay bidders bid “as if” they are in a sealed bid auction. The tests rely on observations of the magnitudes and timing of top two proxy bids. We apply the tests to data from three eBay markets - MP3 players, DVDs and Chevrolet Corvettes. All tests reject the sealed-bid model in all three datasets, and a significant portion of auctions in each dataset is better described by an ascending-bid model. A detailed test assuming independent private values rejects the sealed-bid model even in an otherwise “plausibly sealed” subset of the data which does not conform to the ascending-bid model. Given these findings, demand-estimation using eBay data is more difficult than previously thought. In particular, the results suggest that the empirical strategies based on multiple order-statistics of the bidding distribution as in Song (2005) will not work.
Development of the 2003 CU-HTK conversational telephone speech transcription system

The paper describes the development of the 2003 CU-HTK large vocabulary speech recognition system for conversational telephone speech (CTS). The system was designed based on a multipass, multibranch structure where the output of all branches is combined using system combination. A number of advanced modelling techniques, such as speaker adaptive training, heteroscedastic linear discriminant analysis, minimum phone error estimation and specially constructed single pronunciation dictionaries, were employed. The effectiveness of each of these techniques and their potential contribution to the result of system combination was evaluated in the framework of a state-of-the-art LVCSR system with sophisticated adaptation. The final 2003 CU-HTK CTS system constructed from some of these models is described and its performance on the DARPA/NIST 2003 rich transcription (RT-03) evaluation test set is discussed.
Large-Scale Longitudinal Analysis of SOAP-Based and RESTful Web Services

While the usage of web services has increased explosively in recent years, very few studies examined the characteristics of web services using large-scale real data for a long period of time. In this paper, we present one such a large scale longitudinal analysis of publicly available web services of SOAP-based and RESTful types. For the period of roughly one year and from five different world-wide locations, we closely monitor the ups and downs of various basic properties of web services and their QoS values using a total of 825,132 real web services.
"Dude, where's my Peer?"

Peer to Peer (P2P) flows constitute a large portion of Internet traffic meandering through different ISP domains. Hence, it is of prime concern for ISPs to try and gauge the number of active P2P users and where they are located, both inside and outside their respective domains. An analysis of where P2P users are located in the Internet provides an insight into understanding which ISPs harbor a majority of P2P peers, which ones afford most transit to P2P flows, and possibly which ISPs should focus on anti-P2P policies the most. We observe an extremely skewed distribution, approximately 92 to 98% of P2P flows ending in tier 1 and tier 4 ISPs, and just 2 to 8% ending in tier 2 and 3 ISPs. We quantify the role of ISPs in allowing P2P flows to traverse through their domains and observe a similar skewed distribution wherein tier 1 and tier 4 ISPs contribute 92 to 95% of all hops on most P2P flows. Moreover, we compare P2P traffic with http and Internet radio traces to uncover potential parameters to differentiate between these types of flows. We present detailed results based on active measurements taken over a 30 day period, spanning over half a million P2P flows, collected from measurements employing a number of popular P2P clients hosted inside two popular ISPs.
Compositional Model Checking and Compositional Refinement Checking of Concurrent Reactive Systems

Model checking and refinement checking are two approaches to formal verification, whose difficulties are due to the state explosion problem. As one of the proposed solutions to the problem, it is suggested to introduce compositionality in model checking and refinement checking based on the idea of divide-and-conquer, by which the verification task of the whole system is decomposed to several smaller subtasks on the subsystems. In a uniform framework, this paper surveys the approaches of compositional model checking and compositional refinement checking in a systematic way. From the perspective of module checking, the principle and verification strategies of the two compositional verification approaches are introduced. In addition, the complexities of various kinds of related problems are summarized and a comparison is made between compositional model checking and compositional refinement checking, which exposes the intrinsic relation between them. Finally, some trends are given for the future research.
Coordinating Vertical Elasticity of both Containers and Virtual Machines

Elasticity is a key feature in cloud computing as it enables the automatic and timely provisioning and depro- visioning of computing resources. To achieve elasticity, clouds rely on virtualization techniques including Virtual Machines (VMs) and containers. While many studies address the vertical elasticity of VMs and other few works handle vertical elasticity of containers, no work manages the coordination between these two ver- tical elasticities. In this paper, we present the first approach to coordinate vertical elasticity of both VMs and containers. We propose an auto-scaling technique that allows containerized applications to adjust their resources at both container and VM levels. This work has been evaluated and validated using the RUBiS benchmark application. The results show that our approach reacts quickly and improves application perfor- mance. Our coordinated elastic controller outperforms container vertical elasticity controller by 18.34% and VM vertical elasticity controller by 70%. It also outperforms container horizontal elasticity by 39.6%.
Robust Indoor Positioning Based on Received Signal Strength

A positioning algorithm based on the relative order of the received signal strengths is discussed. This algorithm in conjunction with the ray-tracing propagation model can have promising performance for indoor environments without any needs for extensive set of a priori training. Enhancements to the positioning algorithm will be proposed and investigated. Two sets of experimental results with 802.11-based infrastructure and MICA2 motes are presented to demonstrate the system capability and performance in practice.
A design for an FPGA-based implementation of Rijndael cipher

In this paper we propose a modified implementation of Rijndael AES encryption standard based on the fact that FPGAs include built-in memory blocks, therefore we store all the results of the fixed operations within the memory modules instead of using a dedicated hardware to implement them. An implementation proposed in http://islab.oregonstate. edu was implemented in FPGA but it didn't use the built-in memory resources effectively. Therefore, based on that design we made our modifications. Our modified design is compared with the original design and gives 11% reduction in area and 22% increase in speed (throughput). The memory used increases by 180% but this increase is not severe as it utilizes the already manufactured memory resources inside the FPGA efficiently. Then we compared our design with many published designs based on ALTERA FPGAs (P. Mroezkowski, 2000) and XILINIX FPGAs (D. Ka Yau Tong, et al., 2002). Our design gives the highest throughput and area utilization over all the iterative looping based FPGA implementations of the AES standard (D. Ka Yau Tong, et al., 2002). We implemented the decryption system of the standard and we compared this standard with the design based on ALTERA FPGAs (P. Mroezkowski, 2000) and our design gives 48% reduction in area and 79% increase in speed (throughput) but the memory usage is increased by 315%, but this increase is not severe as it utilizes the already manufactured memory resources inside the FPGA efficiently