Miracle-dz/UniFAR-FTUs huggingface dataset:
Canonical Riskless Choice Over Bundles: Aint No Reference Point Here

Prospect Theory (Kahneman and Tversky 1979), one of the most prominent models for valuation of goods and money, presumes that people have convex utility over gains and concave utility over losses; a discontinuity at something like the current wealth level or reference point. This reflects a behavioral pattern confirmed in hundreds of experimental studies where in lottery tasks people show decreasing marginal utility over gains (risk aversion) and increasing marginal utility (risk seeking) over losses relative to this ? reference? Although it is widely assumed that a reference point is also required to describe riskless choices made over bundles of goods, there is less empirical evidence for this claim. In this paper, using incentive-compatible experimental methods, we challenge the generality of this assumption. We find that in riskless choice over bundles of goods in a canonical budget set experiment, gain-loss asymmetries are not observed even while in interleaved lottery tasks the reference point is observed, in the same subjects. Our results suggest a discontinuity between the value functions inferred from choices over standard lotteries and the utility functions inferred from indifference curves in riskless choice.
The Structure of Voluntary Disclosure Narratives: Evidence from Tone Dispersion

We examine tone dispersion, or the degree to which tone words are spread evenly within a narrative, to evaluate whether narrative structure provides insight into managers’ voluntary disclosures and users’ responses to those disclosures. We find that tone dispersion is associated with current aggregate and disaggregated performance and future performance, managers’ financial reporting decisions, and managers’ incentives and actions to manage perceptions. Furthermore, we find that tone dispersion is associated with analysts’ and investors’ responses to conference call narratives. Our results suggest that tone dispersion both reflects and affects the information that managers convey through their narratives.
A Scientific Theme Emergence Detection Approach Based on Citation Graph Analysis

Citation graph analysis has been used to evaluate the significance of documents and authors, or to estimate the impact of publication venues. In this paper, we investigate its new application in topic identification. We first model the communities in the citation graph as related documents on a specific topic. And then, a scientific theme detection algorithm is proposed based on community partition, attempting to identify the emergency of a new theme by tracking the change of the community where the top cited nodes lie in. Experimental results on real dataset show that the proposed method can detect new topic timely with only a subset of data.
KrakeN: N-ary Facts in Open Information Extraction

Current techniques for Open Information Extraction (OIE) focus on the extraction of binary facts and suffer significant quality loss for the task of extracting higher order N-ary facts. This quality loss may not only affect the correctness, but also the completeness of an extracted fact. We present KrakeN, an OIE system specifically designed to capture N-ary facts, as well as the results of an experimental study on extracting facts from Web text in which we examine the issue of fact completeness. Our preliminary experiments indicate that KrakeN is a high precision OIE approach that captures more facts per sentence at greater completeness than existing OIE approaches, but is vulnerable to noisy and ungrammatical text.
A Novel Feature Clustering Algorithm for Evaluation of Descriptive Type Examination

Objective type of Examination evaluation is easy in Computer world, but the descriptive type of question evaluation is more complex and there is no significant research has been taken place. So many descriptive type examinations like University Exams, GRE etc., have been conducting from long time which is being evaluated manually by sending these types of questions and answers to the experts. This kind of exams needs automatic evaluation instead of manual correction to bring accuracy and reduce the evaluation time. In this paper, authors propose CosInfo algorithm a new solution to the above problem which can evaluate the papers automatically. This algorithm implemented the feature clustering for evaluation purpose that calculate the similarity between two documents and cluster the relevant documents in to different groups. Proposed algorithm uses the expected information function and parts of speech in English grammar as parameters to cluster the data, and also builds a model to classify the testing documents using SVM classification to assess the degree of similarity which will help to award the marks automatically. Experimental results show that the proposed method obtains better and accurate results to allocate marks compared with manual evaluation. General Terms Feature Clustering, cosine similarity, Split distribution, fuzzy clustering.
Analysis and realization of saliency map based on visual attention mechanism

In the field of digital image processing, the introduction of visual attention mechanism is of great significance to the efficiency improving in image analysis. This paper at first briefly introduces the concept of visual attention mechanism. After that, it analyzes and implements an algorithm and demonstrates its reliability and effectiveness by a large number of experiments. At last, the paper focuses on discussing the improvement methods of the algorithm, and then proposing application prospects of saliency maps such as image compression, website construction and evaluation of image quality.
From Programming to Modeling: Evolving the Contents of a Distributed Software Engineering Course

Distributed Software Engineering (DSE) concepts in Computer Science (or Engineering) Degrees are commonly introduced using a hands-on approach mainly consisting in teaching a distributed and component-based technology platform (as Java Enterprise Edition or Microsoft .NET) and proposing the students to develop a small distributed software application with it. Though this approach provides some relevant practical knowledge to the students, we believe that it is not the most appropriate one to teach all the specificities of DSE. Thus, in this paper we report on our experience with the redesign of the contents of an initial DSE course following an MDAbased approach. By raising the level of abstraction we gained modularity, separation of concerns and technology independence, while making the course evolve according to the latest trends in software development methods. Our experience was not free from problems but in general the initiative has been positively evaluated and welcomed by the students.
An Arctic CCN-limited cloud-aerosol regime

On average, airborne aerosol particles cool the Earth's surface directly by absorbing and scattering sunlight and indirectly by influencing cloud reflectivity, life time, thickness or extent. Here we show that over the central Arctic Ocean, where there is frequently a lack of aerosol particles upon which clouds may form, a small increase in aerosol loading may enhance cloudiness thereby likely causing a climatologically significant warming at the ice-covered Arctic surface. Under these low concentration conditions cloud droplets grow to drizzle sizes and fall, even in the absence of collisions and coalescence, thereby diminishing cloud water. Evidence from a case study suggests that interactions between aerosol, clouds and precipitation could be responsible for attaining the observed low aerosol concentrations.
Generating Reflected Directions from BRDF Data

Monte‐Carlo path tracing algorithms for computer graphics require that given an incident light ray at a surface an outgoing direction can be computed with a distribution given by the magnitude of the bidirectional reflectance distribution function (BRDF). For analytic reflectance functions this can be done using various techniques including inverting the function, or tabulating some representation of the inverse. However, measured BRDF data sets are too large for this to be practical.
Interoperability for Provenance-aware Databases using PROV and JSON

Since its inception, the PROV standard has been widely adopted as a standardized exchange format for provenance information. Surprisingly, this standard is currently not supported by provenanceaware database systems limiting their interoperability with other provenance-aware systems. In this work we introduce techniques for exporting database provenance as PROV documents, importing PROV graphs alongside data, and linking outputs of an SQL operation to the imported provenance for its inputs. Our implementation in the GProM system offloads generation of PROV documents to the backend database. This implementation enables provenance tracking for applications that use a relational database for managing (part of) their data, but also execute some non-database operations.
CaSMoS: A Framework for Learning Candidate Selection Models over Structured Queries and Documents

User experience at social media and web platforms such as LinkedIn is heavily dependent on the performance and scalability of its products. Applications such as personalized search and recommendations require real-time scoring of millions of structured candidate documents associated with each query, with strict latency constraints. In such applications, the query incorporates the context of the user (in addition to search keywords if present), and hence can become very large, comprising of thousands of Boolean clauses over hundreds of document attributes. Consequently, candidate selection techniques need to be applied since it is infeasible to retrieve and score all matching documents from the underlying inverted index. We propose CaSMoS, a machine learned candidate selection framework that makes use of Weighted AND (WAND) query. Our framework is designed to prune irrelevant documents and retrieve documents that are likely to be part of the top-k results for the query. We apply a constrained feature selection algorithm to learn positive weights for feature combinations that are used as part of the weighted candidate selection query. We have implemented and deployed this system to be executed in real time using LinkedIn's Galene search platform. We perform extensive evaluation with different training data approaches and parameter settings, and investigate the scalability of the proposed candidate selection model. Our deployment of this system as part of LinkedIn's job recommendation engine has resulted in significant reduction in latency (up to 25%) without sacrificing the quality of the retrieved results, thereby paving the way for more sophisticated scoring models.
Sampling Strategies for Active Learning in Personal Photo Retrieval

With the advent and proliferation of digital cameras and computers, the number of digital photos created and stored by consumers has grown extremely large. This created increasing demand for image retrieval systems to ease interaction between consumers and personal media content. Active learning is a widely used user interaction model for retrieval systems, which learns the query concept by asking users to label a number of images at each iteration. In this paper, we study sampling strategies for active learning in personal photo retrieval. In order to reduce human annotation efforts in a content-based image retrieval setting, we propose using multiple sampling criteria for active learning: informativeness, diversity and representativeness. Our experimental results show that by combining multiple sampling criteria in active learning, the performance of personal photo retrieval system can be significantly improved
Epidemiological study of klebsiella infection in the special care baby unit of a London hospital

Of the babies admitted to the Special Care Baby Unit of the Royal Free Hospital over 20 months, 10·2% were infected or colonised by klebsiella. The fluorescent antibody technique was used to identify epidemics caused by three strains: capsular type 8 K. aerogenes, type 68 K. oxytoca, or type 13 K. aerogenes, each of which was predominant at a different time, exhibited a difference in virulence, and showed a predilection for different sites of infection. Intestinal colonisation was frequently followed by the presence of sepsis in other sites by the same capsular type. Antibiotic administration led to a higher incidence of klebsiella infection, while the widespread use of compounds containing hexachlorophane could have contributed to skin colonisation and infection by klebsiella. An environmental survey indicated that 1% Hycolin failed to disinfect the incubators, that the babies were the reservoirs of the organisms, and that transmission was due to inadequate hand-washing of nurses and mothers. The mothers were found to have been uninformed of hygienic techniques. They were observed in various practices which could have contributed to the spread of the organism, including contaminating communal areas and handling babies other than their own. It has been recommended that the mothers of premature infants be instructed in the hygienic measures required in dealing with this susceptible population and that the nursing and medical staff be more strict in their own observance of these procedures.
Performance Prediction for Reconfigurable Processor

As the Integrated Circuit (IC) process improves, the microprocessors become more and more complicated. Most microprocessors allow part of their important parameters to be reconfigured, such as the frequency, cache prefetch mechanism, and so on. Predicting the performance of reconfigurable processor is still an open question since the performance model needs to consider not only program characteristic, but also processor architecture parameters. In this paper, we propose a new performance prediction model for reconfigurable processor based on machine learning. We employ the M5P as base learners to gain better performance and custom an ensemble learner to get better accuracy. The experiment results show that the ensemble learner reduce the prediction error to 3% from 8%, which is the prediction of a single M5P learner. Furthermore, benefited from considering both program architecture independent characteristics and the architecture parameters, our model can not only predict the performance of the program under specific architecture, but also help to optimize processor's architecture and scheduling processes.
Experimental methods for information retrieval

Experimental evaluation plays a critical role in driving progress in information retrieval (IR) today. There are few alternative ways of exploring, in depth, the empirical merits (or lack thereof) of newly devised search techniques. Careful evaluation is necessary for advancing the state-of-theart; yet, many published papers present work that was illevaluated. Indeed, this phenomenon has garnered attention from the community recently, after the publication of a controversial, but eye-opening, study by Armstrong et al. that suggested ad hoc search quality has not meaningfully advanced since 1984 [1]. The authors noted that the root of the problem was generally lax evaluation methodologies (e.g., weak baselines, etc.). Furthermore, many submissions to top IR research venues (e.g., SIGIR, CIKM, ECIR, WSDM, WWW, etc.) are rejected primarily due to insufficient or inappropriate evaluation. There is therefore a strong need to educate students, researchers, and practitioners about the proper way to carry out IR experiments. This is unfortunately not something that is taught in IR courses or covered in IR textbooks. Indeed, to the best of our knowledge, there is very little written work that lays down some principles for running an IR experiment [3]. More specifically, there have not been any recent tutorials or written works that have specifically, and comprehensively, addressed the question of “how to run an IR experiment” in terms of effectiveness evaluation. This has potentially yielded a number of detrimental effects, as described above. The goal of the tutorial is to provide an initial set of training material for researchers interested in rigorous evaluations of information retrieval systems. Although the primary focus is on ad hoc retrieval experiments, the principles and concepts described in the tutorial are general and can easily be applied to a wide range of experimental scenarios both within, and beyond, the field of information retrieval. The tutorial is primarily inteded for graduate students, re-
An overview on overlapping community detection

Community detection in complex network becomes increasing popular in recent years. Most existing community detection algorithms are limited to deal with non-overlapping communities, which dose not work well on overlapping community detection To address this issue, this paper gives an overview of overlapping community detection, involves community definition, detection algorithms, as well as the evaluation metric, especially focuses on analysis and comparison of the existing overlapping community detection algorithms including the basic ideas of the algorithms, and its performance. Summarization has been drawn to show the advantages and the drawbacks of the algorithm. And finally we give a forecast of the development of overlapping community detection, which would be helpful to researchers dedicate to this field.
Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering

Projected and subspace clustering algorithms search for clusters of points in subsets of attributes. Projected clustering computes several disjoint clusters, plus outliers, so that each cluster exists in its own subset of attributes. Subspace clustering enumerates clusters of points in all subsets of attributes, typically producing many overlapping clusters. One problem of existing approaches is that their objectives are stated in a way that is not independent of the particular algorithm proposed to detect such clusters. A second problem is the definition of cluster density based on user-defined parameters, which makes it hard to assess whether the reported clusters are an artifact of the algorithm or whether they actually stand out in the data in a statistical sense. We propose a novel problem formulation that aims at extracting axis-parallel regions that stand out in the data in a statistical sense. The set of axis-parallel, statistically significant regions that exist in a given data set is typically highly redundant. Therefore, we formulate the problem of representing this set through a reduced, non-redundant set of axis-parallel, statistically significant regions as an optimization problem. Exhaustive search is not a viable solution due to computational infeasibility, and we propose the approximation algorithm STATPC. Our comprehensive experimental evaluation shows that STATPC significantly outperforms existing projected and subspace clustering algorithms in terms of accuracy.
Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems Qrd-based Optimal Filtering for Multichannel Speech Enhancement Using Time Varying Smoothing Factor

In this paper, we introduce a time varying smoothing factor for the noise update in the optimal filtering based on QR decomposition (QRD). Conventionally, the noise statistics are updated during noise-only periods and kept fixed during speech present periods in QRD-based multichannel noise reduction [1]. Instead ofusing this conventional scheme that needs an explicit voice activity detector (VAD), we adopt a time varying smoothing factor which is param-eterized from the normalized cross correlation (NCC). In a multi-microphone based system, the NCCs between two microphones can be used for finding the direction of signals [2]. Provided that we know the direction of the speech signal and the time delays between microphones are compensated, the NCCs can be considered as a measure for the speech presence probability. The NCCs are estimated for a given time interval, and they are averaged over the microphones and smoothed in time. Then the averaged value is mapped to a smoothing factor through a nonlinear function. The simulation results show that the proposed algorithm yields better performance compared to the conventional QRD-based noise reduction .
Common Correlation and Calibrating the Lognormal Forward Rate Model

1997 three papers that introduced very similar lognormal diffusion processes for interest rates appeared virtuously simultaneously. These models, now commonly called the 'LIBOR models' are based on either lognormal diffusions of forward rates as in Brace, Gatarek & Musiela (1997) and Miltersen, Sandermann & Sondermann (1997) or lognormal diffusions of swap rates, as in Jamshidian (1997). The consequent research interest in the calibration of the LIBOR models has engendered a growing empirical literature, including many papers by Brigo and Mercurio, and Riccardo Rebonato (www.fabiomercurio.it and www.damianobrigo.it and www.rebonato.com). The art of model calibration requires a reasonable knowledge of option pricing and a thorough background in statistics - techniques that are quite different to those required to design no-arbitrage pricing models. Researchers will find the book by Brigo and Mercurio (2001) and the forthcoming book by Rebonato (2002) invaluable aids to their understanding.
N′-[(E)-4-(Dimethylamino)benzylidene]-2-(5-methoxy-2-methyl-1H-indol-3-yl)acetohydrazide

In the title compound, C21H24N4O2, inversion-related molecules are linked into dimers through pairs of N—H⋯O hydrogen bonds, which generate R 2 2(8) motifs. As well as dimer formation, an additional N—H⋯O hydrogen bond and two C—H⋯π contacts, involving H atoms from the phenyl ring and the pyrrole and benzene rings of the indole system, generate a three-dimensional network.
ZEIN: An indoor Zigbee planning tool

With the advent of new wireless technologies, such as ZigBee and Bluetooth Low Energy (BLE), smart home systems have become more popular and affordable. One of the main challenges of wireless networks, especially mesh networks, is optimizing these networks such that they provide strong and reliable communication channels with minimum resource consumption. In this paper, we design and build a wireless network planning tool which achieves successful node-to-node communication through proper node placement. This tool aids ZigBee network designers in optimizing their wireless networks. Zein tool is made to simulate the zigbee mesh network in any structure and determine the exact locations of additional nodes to be placed to insure proper operation of the mesh network. The structure can vary from different designs and applications in terms of closed spaces example: Smart homes, companies and large industries or even university campuses. The algorithm of this tool is based upon Tarjans algorithm and connecting the nodes with each other and the server node by finding the nodes wall intersections that will insure reaching the average of all the initially placed nodes one step at a time till eventually creating Zigbee node hopes and achieving both way communications.