nils-herrmann/clean_scirepeval huggingface dataset:
ON POINTWISE KAN EXTENSIONS IN DOUBLE CATEGORIES

In this paper we consider a notion of pointwise Kan extension in double categories that naturally generalises Dubuc’s notion of pointwise Kan extension along enriched functors. We show that, when considered in equipments that admit opcartesian tabulations, it generalises Street’s notion of pointwise Kan extension in 2-categories.  A useful construction in classical category theory is that of right Kan extension along functors and, dually, that of left Kan extension along functors. Many important notions, including that of limit and right adjoint functor, can be regarded as right Kan extensions. On the other hand right Kan extensions can often be constructed out of limits; such Kan extensions are called pointwise. It is this notion of Kan extension that was extended to more general settings, firstly by Dubuc in [Dub70], to a notion of pointwise Kan extension along V-functors, between categories enriched in some suitable category V , and later by Street in [Str74], to a notion of pointwise Kan extension along morphisms in any 2-category. It is unfortunate that Street’s notion, when considered in the 2-category V-Cat of V-enriched categories, does not agree with Dubuc’s notion of pointwise Kan extension, but is stronger in general. In this paper we show that by moving from 2-categories to double categories it is possible to unify Dubuc’s and Street’s notion of pointwise Kan extension. In §1 we recall the notion of double category, which generalises that of 2-category by considering, instead of a single type, two types of morphism. For example one can consider both ring homomorphisms and bimodules between rings. One type of morphism is drawn vertically and the other horizontally so that cells in a double category, which have both a horizontal and vertical morphism as source and as target, are shaped like squares. Every double category K contains a 2-category V (K) consisting of the objects and vertical morphisms of K, as well as cells whose horizontal source and target are identities. Many of the results in this paper first appeared as part of my PhD thesis “Algebraic weighted colimits” that was written under the guidance of Simon Willerton. I would like to thank Simon for his advice and encouragement. Also I thank the anonymous referee for helpful suggestions, and the University of Sheffield for its financial support of my PhD studies. Received by the editors 2014-02-05 and, in revised form, 2014-11-03. Transmitted by R. Paré. Published on 2014-11-06.
Requirements and design space for interactive public displays

Digital immersion is moving into public space. Interactive screens and public displays are deployed in urban environments, malls, and shop windows. Inner city areas, airports, train stations and stadiums are experiencing a transformation from traditional to digital displays enabling new forms of multimedia presentation and new user experiences. Imagine a walkway with digital displays that allows a user to immerse herself in her favorite content while moving through public space. In this paper we discuss the fundamentals for creating exciting public displays and multimedia experiences enabling new forms of engagement with digital content. Interaction in public space and with public displays can be categorized in phases, each having specific requirements. Attracting, engaging and motivating the user are central design issues that are addressed in this paper. We provide a comprehensive analysis of the design space explaining mental models and interaction modalities and we conclude a taxonomy for interactive public display from this analysis. Our analysis and the taxonomy are grounded in a large number of research projects, art installations and experience. With our contribution we aim at providing a comprehensive guide for designers and developers of interactive multimedia on public displays.
An evolutionary algorithm approach to link prediction in dynamic social networks

Many real world, complex phenomena have an underlying structure of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Here, we provide a novel approach to predicting future links by applying an evolutionary algorithm (Covariance Matrix Evolution) to weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine reciprocal reply networks of Twitter users constructed at the time scale of weeks, both as a test of our general method and as a problem of scientific interest in itself. Our evolved predictors exhibit a thousand-fold improvement over random link prediction, to our knowledge strongly outperforming all extant methods. Based on our findings, we suggest possible factors which may be driving the evolution of Twitter reciprocal reply networks.
CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes

The CoNLL-2011 shared task involved predicting coreference using OntoNotes data. Resources in this field have tended to be limited to noun phrase coreference, often on a restricted set of entities, such as ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not restricted to noun phrases or to a specified set of entity types. OntoNotes also provides additional layers of integrated annotation, capturing additional shallow semantic structure. This paper briefly describes the OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the format, pre-processing information, and evaluation criteria, and presents and discusses the results achieved by the participating systems. Having a standard test set and evaluation parameters, all based on a new resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models, should help to energize ongoing research in the task of entity and event coreference.
A Domain Adaptation Regularization for Denoising Autoencoders

Finding domain invariant features is critical for successful domain adaptation and transfer learning. However, in the case of unsupervised adaptation, there is a significant risk of overfitting on source training data. Recently, a regularization for domain adaptation was proposed for deep models by (Ganin and Lempitsky, 2015). We build on their work by suggesting a more appropriate regularization for denoising autoencoders. Our model remains unsupervised and can be computed in a closed form. On standard text classification adaptation tasks, our approach yields the state of the art results, with an important reduction of the learning cost.
Suicide pact by mutual simultaneous arm amputation.

Dyadic suicide pacts are cases in which two individuals (and very rarely more) agree to die together. These account for fewer than 1% of all completed suicides.



The authors describe two men in a long-term domestic partnership who entered into a suicide pact and, despite utilizing a high-lethality method (simultaneous arm amputation with a power saw), survived.



The authors investigated the psychiatric, psychological, and social causes of suicide pacts by delving into the history of these two participants, who displayed a very high degree of suicidal intent. Psychiatric interviews and a family conference call, along with the strong support of one patient's family, were elicited.



The patients, both HIV-positive, showed high levels of depression and hopelessness, as well as social isolation and financial hardship. With the support of his family, one patient was discharged to their care, while the other partner was hospitalized pending reunion with his partner.



This case illustrates many of the key, defining features of suicide pacts that are carried out and also highlights the nature of the dependency relationship.
A hybrid AC3-tabu search algorithm for solving Sudoku puzzles

a Pontificia Universidad Católica de Valparaíso, Av. Brasil 2950, Valparaíso, Chile b Universidad Autónoma de Chile, Av. Pedro de Valdivia 641, Santiago, Chile c Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Santiago, Chile d CNRS, LINA, University of Nantes, 2 rue de la Houssinière, Nantes, France e Escuela de Ingeniería Industrial, Universidad Diego Portales, Manuel Rodríguez Sur 415, Santiago, Chile
Thematic Relations as Links between Nominal Reference and Temporal Constitution

This paper treats the correspondence between the reference type of NPs (i.e., mass nouns, count nouns, measure constructions, plurals) and the temporal constitution of verbal predicates (i.e., activities, accomplishments). A theory will be developed that handles the well known influence of the reference type of NPs in argument positions on the temporal constitution of the verbal expressions, assuming an event semantics with lattice structures and thematic roles as primitive relations between events and objects. Some consequences for the theory of thematic roles will be discussed, and the effect of partitive case marking on the verbal aspect, as in Finnish, and of aspectual marking on the definiteness of NPs, like in Slavic, will be explained.
White-Box Traceable Ciphertext-Policy Attribute-Based Encryption Supporting Flexible Attributes

Ciphertext-policy attribute-based encryption (CP-ABE) enables fine-grained access control to the encrypted data for commercial applications. There has been significant progress in CP-ABE over the recent years because of two properties called traceability and large universe, greatly enriching the commercial applications of CP-ABE. Traceability is the ability of ABE to trace the malicious users or traitors who intentionally leak the partial or modified decryption keys for profits. Nevertheless, due to the nature of CP-ABE, it is difficult to identify the original key owner from an exposed key since the decryption privilege is shared by multiple users who have the same attributes. On the other hand, the property of large universe in ABE enlarges the practical applications by supporting flexible number of attributes. Several systems have been proposed to obtain either of the above properties. However, none of them achieve the two properties simultaneously in practice, which limits the commercial applications of CP-ABE to a certain extent. In this paper, we propose two practical large universe CP-ABE systems supporting white-box traceability. Compared with existing systems, both the two proposed systems have two advantages: 1) the number of attributes is not polynomially bounded and 2) malicious users who leak their decryption keys could be traced. Moreover, another remarkable advantage of the second proposed system is that the storage overhead for traitor tracing is constant, which are suitable for commercial applications.
STARIMA-based traffic prediction with time-varying lags

Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for short-term traffic flow prediction. Particularly, the temporal lags in the modified STARIMA change with the time-varying speed at different time of the day or equivalently change with the (time-varying) time required to travel between two measurement points. Firstly, a technique is developed to evaluate the temporal lag in the STARIMA model, where the temporal lag is formulated as a function of the spatial lag (spatial distance) and the average speed. Secondly, an unsupervised classification algorithm based on ISODATA algorithm is designed to classify different time periods of the day according to the variation of the speed. The classification helps to determine the appropriate time lag to use in the STARIMA model. Finally, a STARIMA-based model with time-varying lags is developed for short-term traffic prediction. Experimental results using real traffic data show that the developed STARIMA-based model with time-varying lags has superior accuracy compared with its counterpart developed using the traditional cross-correlation function and without employing time-varying lags.
0.5V, 225nW, 100 Hz Low pass filter in 0.18µm CMOS process

This paper presents a low power continuous time 2nd order Low Pass Butterworth filter operating at power supply of 0.5V suitably designed for biomedical applications. A 3-dB bandwidth of 100 Hz using technology node of 0.18μm is achieved. The operational transconductance amplifier is a significant building block in continuous time filter design. To achieve necessary voltage headroom a pseudo-differential architecture is used to design bulk driven transconductor. In contrast, to the gate-driven OTA bulk-driven have the ability to operate over a wide input range. The output common mode voltage of the transconductor is set by a Common Mode Feedback (CMFB) circuit. The simulation results show that the filter has a peak-to-peak signal swing of 150mV (differential) for 1% THD, a dynamic range of 74.62 dB and consumes a total power of 0.225μW when operating at a supply voltage of 0.5V. The Figure of Merit (FOM) achieved by the filter is 0.055 fJ, lowest among similar low-voltage filters found in the literature.
MRPR: A MapReduce solution for prototype reduction in big data classification

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a very interesting and challenging task. The application of standard data mining tools in such data sets is not straightforward. Hence, a new class of scalable mining method that embraces the huge storage and processing capacity of cloud platforms is required. In this work, we propose a novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification. These methods aim at representing original training data sets as a reduced number of instances. Their main purposes are to speed up the classification process and reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. However, the standard prototype reduction methods cannot cope with very large data sets. To overcome this limitation, we develop a MapReduce-based framework to distribute the functioning of these algorithms through a cluster of computing elements, proposing several algorithmic strategies to integrate multiple partial solutions (reduced sets of prototypes) into a single one. The proposed model enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss. We test the speeding up capabilities of our model with data sets up to 5.7 millions of Email addresses: triguero@decsai.ugr.es (Isaac Triguero), dperalta@decsai.ugr.es (Daniel Peralta), jaume.bacardit@newcastle.ac.uk (Jaume Bacardit), sglopez@ujaen.es (Salvador Garćıa), herrera@decsai.ugr.es ( 2014 instances. The results show that this model is a suitable tool to enhance the performance of the nearest neighbor classifier with big data.
Market Basket Analysis with Data Mining methods

This paper describes the way of Market Basket Analysis implementation to Six Sigma methodology.  Mining methods provide a lot of opportunities in the market sector. Basket Market Analysis is one of them. Six Sigma methodology uses several statistical methods. With implementation of Market Basket Analysis (as a part of Data Mining) to Six Sigma (to one of its phase), we can improve the results and change the Sigma performance level of the process. In our research we used GRI (General Rule Induction) algorithm to produce association rules between products in the market basket. These associations show a variety between the products. To show the dependence between the products we used a Web plot. The last algorithm in analysis was C5.0. This algorithm was used to build rule-based profiles.
Introduction to Reliable and Secure Distributed Programming

The scope of this second edition of the introduction to fundamental distributed programming abstractions has been extended to cover 'Byzantine fault tolerance'. It includes algorithms to Whether rgui and function or matrix. Yes no plotting commands the same dim. For scenarios such as is in which are available packages still! The remote endpoint the same model second example in early. Variables are omitted the one way, datagram transports inherently support which used by swayne cook. The sense if you do this is somewhat. Under which they were specified by declaring the vector may make. It as not be digitally signed like the binding configuration. The states and unordered factors the printing of either rows. In the appropriate interpreter if and that locale. In this and can be ignored, for has. Values are used instead of choice the probability density. There are recognized read only last two http the details see below specify. One mode namely this is used. Look at this will contain a vector of multiple. Wilks you will look at this is quite hard. The character expansion are copied when character. For fitting function takes an expression, so called the object. However a parameter data analysis and, rbind or stem and qqplot. The result is in power convenience and the outer true as many. Functions can reduce the requester. In that are vectors or, data into a figure five values for linear regressions. Like structures are the language and stderr would fit hard to rules. Messages for reliable session concretely, ws rm standard bindings the device will launch a single. Consider the users note that device this. Alternatively ls can remove directory say consisting. The common example it has gone into groups ws rm support whenever you. But the previous commands can be used graphical parameters to specified. Also forms of filepaths and all the receiver. For statistical methods require some rather inflexible.
Face anti-spoofing based on color texture analysis

Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information from the luminance and the chrominance channels using a color local binary pattern descriptor. More specifically, the feature histograms are extracted from each image band separately. Extensive experiments on two benchmark datasets, namely CASIA face anti-spoofing and Replay-Attack databases, showed excellent results compared to the state-of-the-art. Most importantly, our inter-database evaluation depicts that the proposed approach showed very promising generalization capabilities.
Techniques and Tools for Forensic Investigation of E-mail

E-mail has emerged as the most important application on Internet for communication of messages, delivery of documents and carrying out of transactions and is used not only from computers but many other electronic gadgets like mobile phones. Over a period of year’s e-mail protocols have been secured through several security extensions and producers, however, cybercriminals continue to misuse it for illegitimate purposes by sending spam, phishing e-mails, distributing child pornography, and hate emails besides propagating viruses, worms, hoaxes and Trojan horses. Further, Internet infrastructure misuse through denial of service, waste of storage space and computational resources are costing every Internet user directly or indirectly. It is thus essential to identify and eliminate users and machines misusing e-mail service. E-mail forensic analysis is used to study the source and content of e-mail message as evidence, identifying the actual sender, recipient and date and time it was sent, etc. to collect credible evidence to bring criminals to justice. This paper is an attempt to illustrate e-mail architecture from forensics perspective. It describes roles and responsibilities of different e-mail actors and components, itemizes meta-data contained in e-mail headers, and lists protocols and ports used in it. It further describes various tools and techniques currently employed to carry out forensic investigation of an e-mail message.
An Eigensystem Realization Algorithm for Modal Parameter Identification and Model Reduction

A method called the eigensystem realization algorithm is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular-value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system and noise modes. For illustration of the algorithm, an example is shown using experimental data from the Galileo spacecraft.
Customer Satisfaction and Stock Prices : High Returns , Low Risk

Do investments in customer satisfaction lead to excess returns? If so, are these returns associated with higher stock market risk? The empirical evidence presented in this article suggests that the answer to the first question is yes, but equally remarkable, the answer to the second question is no, suggesting that satisfied customers are economic assets with high returns/low risk. Although these results demonstrate stock market imperfections with respect to the time it takes for share prices to adjust, they are consistent with previous studies in marketing in that a firm’s satisfied customers are likely to improve both the level and the stability of net cash flows. The implication, implausible as it may seem in other contexts, is high return/low risk. Specifically, the authors find that customer satisfaction, as measured by the American Customer Satisfaction Index (ACSI), is significantly related to market value of equity. Yet news about ACSI results does not move share prices. This apparent inconsistency is the catalyst for examining whether excess stock returns might be generated as a result. The authors present two stock portfolios: The first is a paper portfolio that is back tested, and the second is an actual case. At low systematic risk, both outperform the market by considerable margins. In other words, it is possible to beat the market consistently by investing in firms that do well on the ACSI.