Miracle-dz/UniFAR-FTUs huggingface dataset:
What's in a "Name"? Impact of Use of Customer Information in E-Mail Advertisements

In this study, we examine how consumers respond to firms’ use of two types of information for personalization: product preferences and name. We collect a unique data set of over 10 million e-mail advertisements sent by a website to over 600,000 customers who could buy the advertised products from the online merchant. We estimate a two-stage hierarchical model using Bayesian analysis to account for observable and unobservable consumer heterogeneity. Our analysis suggests several interesting results regarding consumers’ responses to firms’ use of information. When firms use product-based personalization (where the use of information is not explicitly mentioned), consumers respond positively. On the other hand, consumers respond negatively when firms are explicit in their use of personally identifiable information (i.e., a personalized greeting). We also find that negative responses to personalized greetings are moderated by consumers’ familiarity with firms. The main contribution of this study is that it not only indicates the economic benefits of personalization in e-mails but also highlights consumers’ concerns over the use of information in personalization.
Insights on the development of visual tools for analysis of pollution data

Developing visual tools that support data analysis in a specific application domain requires a careful investigation in order to understand needs and expectations of people who will use such tools. The domain experts addressed in this paper are chemists specialized in environmental data analysis. Their main activity is to detect and monitor chemical compounds in the air through many devices in order to detect anomalies or prevent risks. One of the main problems that chemists face is the analysis of the huge amount of data produced by devices. They perform explorative data analysis and are willing to use software tools that can help them to get insights from data. This paper reports the experience in working with chemists to identify interactive visual tools that can be useful for their purposes. It provides insights on the difficulty of creating systems that users find really useful for their work, even when users participate in the design team. Because of the complexity of the considered problem and the fact that people are unable to make explicit all their needs and requirements, the identification of proper tools resulted very
Unmanned ground vehicle local trajectory planning algorithm

Local trajectory is the base of motion UGV and several algorithms used to calculate desired land robot path. A common approach is to use a 2-D grid based algorithm, such a well-known A*. But problems arise due to specific kinematic of ground vehicle and nonideal movement realisation. In this paper presented an approach to local trajectory planning on base 2-D passability map with considering UGV kinematic constrains and also errors in the implementation of the path, computed for UGV motion.
A SEMANTIC SIMILARITY MEASURE BETWEEN SENTENCES

The purpose of this paper is to present a mathematical model for estimating semantic similarity among sentences in texts. The similarity measure is constructed from the semantic similarity among concepts and a set of concepts. Based on this model, we develop algorithms to calculate the semantic similarity between two set of concepts and then the ones to estimate the semantic similarity between sentences. This work is considered as a continuation of our research [18] on the model of semantic similar measures among sentences.
Adaptive evolutionary optimization algorithm for problems with hybrid indices

Problems with hybrid indices are common, but the performance of previous methods in solving these problems needs to be further improved. We propose an adaptive evolutionary optimization algorithm of solving the above problems effectively in this study. First, the convergence rate of a population is calculated based on the distance between the optimal individuals with adjacent generations; then the crossover and mutation probabilities are adjusted dynamically according to the diversity, the convergence rate of a population and the number of generations. We apply the proposed algorithm to an interior layout design problem, a typical optimization problem with hybrid indices, and compare it with the algorithm with constant crossover and mutation probabilities. The experimental results confirm that the proposed algorithm has advantages in the number, quality and distribution of optimal solutions. The proposed algorithm has a good tradeoff between exploration and exploitation, and provides an efficient way to solve an optimization problem with hybrid indices.
Imaging the universe in 3D with the VLT: the next-generation field spectrometer SPIFFI

We present SPIFFI, the integral field spectrometer for the VLT. This instrument allows simultaneous observation of IR spectra in more than 1000 image points of a 2D field. With its set of four gratings and a pixel scale that can be varied by a factor of ten, SPIFFI provides high flexibility, and at the same time offers the unique possibility of diffraction limited imaging spectroscopy at an 8m-class telescope, when fed by the adaptive optics system MACAO. We outline the scientific drivers for building such an instrument, the concept of image slicing, the optical design, and the implementation of SPIFFI.
Research on associational rule mining for water environment database

Water environment databases deposit a great deal of information which may predict the future development of some water minim elements in water. These databases are so complex that some environment predicted methods, generally, can not objectively analysis the associational rules of these minim elements in water. To discover some important information from these water databases, this paper proposes the associational rule mining. Associational rule mining is one well known algorithm which can find important and interesting information from large database. Candidate itemsets will be exponentially increased during discovering frequent itemsets if the algorithm is not pretreated. This paper generalizes three lemmas on support count of frequent itemsets, by which the candidate itemsets are greatly decreased. The efficiency of these lemmas on mining rules is validated by some graphs.
Religion and right-wing populism in Italy: using ‘Judeo-Christian roots’ to kill the European Union

ABSTRACT This contribution offers new directions to explore how implicitly religious customs and norms may influence voting for right-wing populist parties. It also suggests an indirect influence of government regulation of religion on the rise of populism in the European Union. The church–state relationship is viewed as providing a stable context for religious biases to emerge, thereby enabling populist forces to capitalise on a sense of loss of national identity which in turn reinforces citizens’ negative attitude towards immigrants, particularly of different religious backgrounds. Combining the Religion and State Project Round 3 (RAS3) dataset with electoral and socio-demographic data, this contribution presents time-series analyses and linear regression in order to explore the impact of religious factors on populist voting patterns and support for the Italian Eurosceptic populist movement, the League, between 1992 and 2014. I argue that the success of this populist party lies in its ability to use an adaptive mythology that plays on pre-existing religious norms to stoke fears of a decline of cultural homogeneity and a loss of political and economic power.
Research about relation of music preference and brain-wave

In our research, classification of music preference based on brain-wave is studied. We assume that there is a clear difference between brain-wave when hearing favorite music and it when hearing disgusting music, and we collect the brain-wave of human while hearing the music. And, there are two methods of collecting: one is separation of favorite/disgusting music clips, and the other is mixing of them. In this paper, the experimental results for two cases, which are obtained with very simple classification method, are presented.
AVCS RESEARCH AT CARNEGIE MELLON UNIVERSITY

For the last 10 years, Carnegie Mellon University has been building increasingly competent systems for autonomous driving. The approach has been to develop smart vehicles, capable of driving in natural outdoor environments without intervehicle communication or infrastructure modifications. These computer-controlled vehicles now driver themselves at speeds up to 55 mph and for distances of over 90 miles on public roads without human intervention. They are capable of driving both during the day and night, on a wide variety of road types. They can sense, and avoid obstacles, and even automatically parallel park. The techniques developed are suitable both for AHS applications where the vehicle is controlled automatically, and in driver warning systems where the role of the AVCS system is to monitor the environment and suggest actions for the human driver. This paper presents some of the capabilities of these systems, and the processing techniques that underlie them. These techniques include: artificial neural networks for road following, model-based image processing for convoy following, smart obstacle maps based on sonar, radar and microwave sensor processing and integrated control systems.
Cross-Collection Topic Models : Automatically Comparing and Contrasting Text

This paper describes cross-collection latent Dirichlet allocation (ccLDA), a probabilistic topic model that captures meaningful word co-occurrences across multiple text collections. The model is applied to three different applications: discovering cultural differences in blogs and forums from different countries, discovering research topics across multiple scientific disciplines, and comparing editorial differences between multiple media sources. A variety of qualitative and quantitative evaluations of ccLDA are performed, including log-likelihood measurements and performance measurements of the model used as a generative classifier. Improvements over previous work are demonstrated. Finally, possible extensions and modifications to the model are presented with promising results.
Annexin 2 is a phosphatidylinositol (4,5)-bisphosphate binding protein recruited to actin assembly sites at cellular membranes

Annexin 2 is a Ca2+-regulated membrane protein and an F-actin-binding protein enriched at actin assembly sites both, on the plasma membrane and on endosomal vesicles. Here, we identify annexin 2 as a phosphatidylinositol (4,5)-bisphosphate (PtdIns(4,5)P2)-interacting protein, thereby explaining this specific membrane association. Using the pleckstrin-homology (PH) domain of phospholipase Cδ1 fused to yellow fluorescent protein as a marker for PtdIns(4,5)P2, we show that annexin 2 and its ligand p11 (S100A10) are targeted to sites of PtdIns(4,5)P2 enrichment where F-actin accumulates. At the plasma membrane, adhesion of pedestal-forming enteropathogenic Escherichia coli induces a recruitment of 1-phosphatidylinositol-4-phosphate 5-kinase (PtdIns4P 5-kinase) and an enrichment of PtdIns(4,5)P2 and annexin 2-p11 at sites of bacterial adhesion. Induction of PtdIns(4,5)P2-enriched ruffles and PtdIns(4,5)P2-positive, actin-coated vacuoles by Arf6-mediated activation of PtdIns4P 5-kinase also leads to a concomitant accumulation of the annexin 2-p11 complex and the PH domain. Binding studies with immobilized phosphoinositides and phosphoinositide-containing liposomes reveal that the purified annexin 2-p11 complex directly and specifically binds to PtdIns(4,5)P2 with an affinity comparable to that of the PH domain of phospholipase Cδ1. Experiments using individual subunits identify annexin 2 as the PtdIns(4,5)P2-binding entity. Thus, the direct interaction of annexin 2 with PtdIns(4,5)P2 is a means of specifically recruiting the annexin 2-p11 complex to sites of membrane-associated actin assembly.
Gender Recognition from Unconstrained and Articulated Human Body

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.
Classified Vector Quantisation and population decoding for pattern recognition

Learning Vector Quantisation (LVQ) is a method of applying the Vector Quantisation (VQ) to generate references for Nearest Neighbour (NN) classification. Though successful in many occasions, LVQ suffers from several shortcomings, especially the reference vectors are prone to diverge. In this paper, we propose a Classified Vector Quantisation (CVQ) to establish VQ for classification. By CVQ, each data category is represented by its own codebook, which can be implemented by some learning algorithms. In classification process, each codebook offers a generalised NN. The examples of handwritten digit recognition and offline signature verification are used to demonstrate the efficiency of the proposed scheme.
An Examination of the Relationship Between Prior Musical Sophistication and Language Outcomes in People With Aphasia

Title of Thesis: AN EXAMINATION OF THE RELATIONSHIP BETWEEN PRIOR MUSICAL SOPHISTICATION AND LANGUAGE OUTCOMES IN PEOPLE WITH APHASIA Sarah J. Fisher, Master of Arts, 2018 Thesis Directed By: Professor Yasmeen Faroqi-Shah Department of Hearing and Speech Sciences Research suggests there is a neural relationship between music and language, such that higher levels of musical sophistication may be positively correlated with a person’s linguistic and cognitive functioning. Though most of the research has focused on neurotypical individuals, the implication is that musical sophistication could benefit a person with a neurological impairment such as aphasia, perhaps by preserving linguistic abilities after the person has sustained a stroke. The study outlined here seeks to replicate and expand on the findings of Faroqi-Shah et al. (in prep) by looking at musical sophistication’s influence on aphasia severity as well as on specific language and cognitive domains (e.g., syntax, auditory processing, memory, and cognitive control). Knowing what specific domains of language or cognition are involved could help researchers better understand the neural location of musical and linguistic resources as well as the behavioral benefit of increased reserve in a neurologically impaired individual.
IR-98-022 / May 1998 Modeling Industrial Dynamics with Innovative Entrants

The paper analyzes some generic features of industrial dynamics whereby innovative change is carried, stochastically, by new entrants. Relying on the formal representation suggested in Winter et al. (1997), it studies both the asymptotic properties of such processes and their finite time dynamics to account for a few empirical stylized facts, including persistent entry and exit, skewed size distributions and turbulence in market shares.
“I’m Being told on Sunday Mornings that There’s Nothing Wrong with Me”: Lesbian’s Experiences in an LGBTQ-Oriented Religious Leisure Space

Abstract Research has suggested that some leisure spaces can be unwelcoming for those who are lesbian, gay, or bisexual (LGB) while other leisure spaces can provide an important oasis for LGB individuals to escape homophobia and heterosexism. Leisure research, however, has yet to explore religious spaces as a context for leisure among LGB people. The purpose of this study was to explore the experiences of a group of lesbians who attended an LGBTQ-oriented church as a source of leisure and investigate the meanings they associated with this unique space. Data collection consisted of individual interviews with seven women who were members of the church. Interviews were audio-recorded, transcribed verbatim, and analyzed thematically. The findings suggested that the church provided a space in which the women could find increased opportunities for leisure activities, build a strong community, experience emotional healing and spiritual expression, and address institutionalized and internalized homophobia.
Robust visual tracking via weighted incremental subspace learning

A method is proposed for tracking objects in face with varying viewpoints and partial occlusions. A low-dimensional subspace is built to model the appearance of the target. And each image sample is presented as a coefficient vector in the subspace. A collection of image patches are sampled as the candidates of the object image region in the current frame, and their likelihoods of being the object are evaluated resorting to the reconstructions from the corresponding coefficient vectors. The image patch with highest likelihood is selected as the object image region. Finally, the subspace is incrementally updated based on the coefficient vectors, which are assigned with the temporal weights to enhance the adaptability of the tracker. Experimental results show that our proposed method can locate the target accurately even when the target undergoes changes in viewpoints and partial occlusions.
Retransmission policies for concurrent multipath transfer using SCTP multihoming

Concurrent multipath transfer (CMT) uses the stream control transmission protocol's (SCTP's) multihoming feature to distribute data across multiple end-to-end paths in a multihomed SCTP association. We propose five retransmission policies for CMT. We demonstrate the occurrence of spurious retransmissions in CMT with all of the five policies, and propose an amendment to the timeout retransmission mechanism to avoid spurious retransmissions. We also modify the Cwnd Update for CMT (CUC) algorithm to allow better cwnd growth in CMT with the different retransmission policies. We then evaluate the retransmission policies using ns-2 simulations, and discuss the distributions of traffic that result. We operate under the strong assumptions that the receiver's advertised window does not constrain the sender, and that the bottleneck queues on the end-to-end paths used in CMT are independent.
Software synthesis of CAL actors for the MPEG reconfigurable Video Coding framework

The MPEG reconfigurable video coding (RVC) framework aims to provide a unified specification of all video technology. In this framework, a decoder is modularly built as a configuration of video coding tools taken from the MPEG toolbox library. The elements of the library are specified using the CAL actor language. CAL is a dataflow based language providing computation models that are concurrent and modular. This paper presents a synthesis tool that from a CAL specification generates C code. Indeed, code generators are fundamental supports for the deployment and success of the MPEG RVC framework. This paper focuses on the automatic translation of a CAL actor. This approach has been used to obtain a C implementation of the inverse DCT module which is part of the MPEG-4 Simple Profile decoder, chosen by MPEG experts to validate the RVC approach. The generated code is validated against the original CAL description and simulated using the Open Dataflow environment.
Hierarchical Models in Environmental Science

Environmental systems are complicated. They include very intricate spatio‐temporal processes, interacting on a wide variety of scales. There is increasingly vast amounts of data for such processes from geographical information systems, remote sensing platforms, monitoring networks, and computer models. In addition, often there is a great variety of scientific knowledge available for such systems, from partial differential equations based on first principles to panel surveys. It is argued that it is not generally adequate to consider such processes from a joint perspective. Instead, the processes often must be considered as a coherently linked system of conditional models. This paper provides a brief overview of hierarchical approaches applied to environmental processes. The key elements of such models can be considered in three general stages, the data stage, process stage, and parameter stage. In each stage, complicated dependence structure is mitigated by conditioning. For example, the data stage can incorporate measurement errors as well as multiple datasets with varying supports. The process and parameter stages can allow spatial and spatio‐temporal processes as well as the direct inclusion of scientific knowledge. The paper concludes with a discussion of some outstanding problems in hierarchical modelling of environmental systems, including the need for new collaboration approaches.
Predicting Job Completion Time in Heterogeneous MapReduce Environments

MapReduce is a popular paradigm for processing big data due to wide availability of its implementations in open source such as Apache Hadoop. This framework is primarily designed for optimal execution of an application on a commodity cluster of homogeneous nodes where all machines in the cluster have identical hardware configurations. However, most organizations have surplus unused heterogeneous infrastructure which gets accumulated over a period of time - this may have machines with varying number of CPU cores, RAM and disk speed. The question for an organization is whether it is efficient to set up a heterogeneous Hadoop cluster on available set of different types of hardware configured nodes for executing their analytic workload. This may help organizations in reducing their e-waste and cost for data analytic. In this paper, we propose a simulator based what-if engine to predict job execution time of a MapReduce based application for varying size of cluster with varying types of heterogeneity in the cluster and growing data sizes. The simulator has been validated for three open source MapReduce benchmarks and two industrial Hive based analytic workloads on three different heterogeneous clusters for data sizes up to 100 GB. The largest cluster size considered is of 484 cores, with three types of hardware nodes. We have observed the average prediction error to be within 10% of the actual job execution time.