Miracle-dz/UniFAR-FTUs huggingface dataset:
The Teaching of Translation at the University Level: Constraints and Aspirations

This paper is an attempt to address the situation of translation learning and teaching at the university level with special reference to its teaching in the universities of the Arab World/ countries. It is worth pointing that the paper is a kind of reflection touching upon certain aspects of translation learning and teaching with specific focus on the three dimensions/ aspects being the learner, the teacher and the teaching material(s).
DAMPING: A SENSITIVE STRUCTURAL PROPERTY FOR DAMAGE DETECTION

Data from vibration measurements opens a way to damage assessment by correlating changes in system dynamic parameters with damage indicators. Most methods proposed in the literature are based on the measurement of natural frequencies or modal shapes and modelling damage as local reduction in structural stiffness. In some particular applications these methods, however, have several practical limitations on account of their low sensitivity to damage. This paper shows that damping can be used as a damage-sensitive system property and presents a procedure for instantaneous natural frequencies and damping identification from free vibration response using the wavelet transform. Experimental and simulated examples show that in the structures analyzed damage causes important variations of damping parameters.
Service action research: review and guidelines

Purpose Conducting research that is both practice- and theory-relevant is important for the service research community. Action research can be a fruitful approach for service researchers studying the transformative role of service research and wanting to make contributions to both the research community and to practical development. By exploring the current use of action research in service research, this study aims to make suggestions for enhancing the contribution to theory and practice development and to propose criteria for research quality for action research in service research. Design/methodology/approach This study builds on a systematic literature review of the use of action research approaches in service research. Findings The study makes three main contributions. First, it posits that any action research project needs to consider the four elements of problem identification, theorization, creating guiding concepts and intervention. Second, based on these elements mirrored in service action research, it outlines and analyzes three approaches to action research (i.e. theory-enhancing, concept developing and practice-enhancing). Third, it suggests a move from instrumental to a more conceptual relevance of the research and elaborates on the criteria for research quality. Originality/value This study contributes to the understanding of how action research may be applied for conducting high-quality collaborative research in services and proposes measures to enhance research quality in action research projects focusing services.
M2 internship at LIGM, Paris Est

The intership would be located at the LIGM laboratory (ligm.u-pem.fr/). The expected outcome is a research paper, ideally ready for the EUROGRAPHICS 2017 submission. This project is part of a bigger project PAPYRUS (jointly with other institutes, including UbiSoft). PhD funding is available to continue the work started in this internship (funded by PAPYRUS). The student is expected to have solid programming skills in C++, as well as a good background in probability and data structures.
VERIFICATION METHODOLOGY FOR DISCRETE EVENT SIMULATION MODELS OF PERSONNEL IN THE CANADIAN ARMED FORCES

The Canadian Armed Forces (CAF) uses a variety of tools to model, forecast and investigate workforce behavior; one such tool is a custom Discrete Event Simulation (DES) framework designed to address military Human Resources (HR) and personnel questions called the Force Flow Model (FFM). In this work we propose a methodology to verify workforce simulations and we discuss its use for verification of the FFM. We demonstrate precise agreement with an analytic model when the FFM parameters are matched to exactly solvable analytic scenarios. The methodology is simple to understand and apply, is generally applicable to workforce simulations, and helps to meet the need to verify simulations by providing known analytic results to benchmark against.
Continuous Time Models of Computations and Computations Over the Ordinals

Classical computability theory deals with computation over a finite time. From Church-Turing thesis, all sufficiently powerfull models of computations are basically equivalent, and hence equivalent to Turing machines. Computability theory is motivated by understanding what is computable, but also by understanding what is provable or not in mathematics. Recent years and months have seen a clear renewal of interest for models of computation over the ordinals. That is to say, for models where the time is not necessarily finite, and can be transfinite. This is a very old idea, already discussed in [10, 9], but the model of Infinite Time Turing Machines, popularized by [6] in 2000, brought clearly new lights on this field. In particular, some very surprising results have been proved for this model. This includes the fact that there exists some (ordinal) time that do not correspond to a halting time, and there are even interval of such times: there are called gaps in this contexts. Actually, the model has been developed meanwhile in many directions, and have demonstrated to be very useful to revisit fundamentals questions of foundations of mathematics with a new computability light. In particular, constructions from set theory [1]. The approach is also alternative to classical higher order recursion theory [11] in several important aspects, in particular by the fact that it is closer to aspects related to machines. The purpose of this work is in a long term to contribute to understand the related computability theory, in relation with classical approaches developed in reference books such as [1, 11].
PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors

We address the problem of algorithmic fairness: ensuring that sensitive information does not unfairly inﬂuence the outcome of a classiﬁer. We face this issue in the PAC-Bayes framework and we present an approach which trades oﬀ and bounds the risk and the fairness of the Gibbs Classiﬁer measured with respect to diﬀerent state-of-the-art fairness measures. For this purpose, we further develop the idea that the PAC-Bayes prior can be deﬁned based on the data-generating distribution without actually needing to know it. In particular, we deﬁne a prior and a posterior which gives more weight to functions which exhibit good generalization and fairness properties.
Towards Sustainable Diets in Europe

INRA-ALISS France --Universita di Bologna Italy --Università Cattolica del Sacro Cuore Italy --CREDA-UPC-IRTA Barcelona Spain --LUKE Natural Resources Institute Finland --Norwegian University of Life Sciences Norway --Swedish University of Agricultural Sciences Sweden --UMR NORT, INRA, INSERM, Aix-Marseille University France --UMR GREMAQ-INRA, Toulouse France --INRA UMR Economie Publique Paris France --INRA UMR Gael Grenoble France --Thünen Institute Germany --SRUC United Kingdom --University of Oxford United Kingdom --National Institute for Agrarian and Veterinary Research, (INIAV) Portugal
WHO EMRO | Descriptive epidemiology of physical activity among Omani adults: the Oman World Health Survey, 2008 PDF version

There is an increasing burden of obesity and obesity-related noncommunicable diseases in Gulf Cooperation Council countries, including Oman. This descriptive, epidemiological study assessed physical activity among 2977 Omani adults using a population-based household survey in 2008. Overall, 54.2% of men and 41.6% of women were physically active; the rate was higher in younger cohorts and varied significantly by region of residence. Physical activity related to the transportation (walking and cycling) domain was higher than in the leisure or work domains. Unmarried men aged 30–39 years were twice as likely to be physically active (OR 2.25) and unmarried women aged 40+ years were half as likely to be active (OR 0.58) than their married counterparts. Young women not working were less active (OR 0.18) than working women. Higher education was significantly associated with leisure activity for men aged 30+ years and women aged 40+ years. Further research to understand regional variations and to identify culturally appropriate strategies to promote physical activity is required.
UrbanRhythm: Revealing Daily Urban Dynamics Hidden in Mobility Data

Understanding the regularity of urban residents’ behaviors, or urban dynamics, is of urgent demand for building an efficient and livable city. Nonetheless, this is challenging due to the expanding urban population and city size. In this paper, we propose a novel sys-tem UrbanRhythm to reveal the urban dynamics hidden in human mobility data from the view of the city, which is a new perspective. To obtain UrbanRhythm, we first divide the city into different time slots. For each urban region in each time slot, we define its mobility feature as the number of people staying in, leaving from and arriving at this region. Then we utilize an image processing method Saak transform to capture the mobility spatial distribution pattern in the city for each time slot and classify time slots into hierarchical city states. Finally, we characterize the urban dynamics as the transform of city states along time axis. We evaluate our proposed system on two real-life datesets. Several city states are identified and interpreted. Interestingly, we not only discover general states which correspond to residents’ daily behaviors like sleeping, working and relaxing, but also distinguish sub-states such as deep-sleeping and light-sleeping. We find that the urban temporal dynamics are highly daily repeated except that the regularity are different in working day and non-working day. Besides, we implement an App analysis to further validate the detected city states. This study sheds light on urban dynamics hidden in human mobility and can further pave the way for more complicated mobility behavior modeling and deeper urban understanding.
Incentives and Efficiency in Constrained Allocation Mechanisms

We study private-good allocation mechanisms where an arbitrary constraint delimits the set of feasible joint allocations. This generality provides a unified perspective over several prominent examples that can be parameterized as constraints in this model, including house allocation, roommate assignment, and social choice. We first characterize the set of two-agent strategy-proof and Pareto efficient mechanisms, showing that every mechanism is a "local dictatorship. " For more than two agents, we leverage this result to provide a new characterization of group strategy-proofness. In particular, an N-agent mechanism is group strategy-proof if and only if all its two-agent marginal mechanisms (defined by holding fixed all but two agents' preferences) are individually strategy-proof and Pareto efficient. To illustrate their usefulness, we apply these results to the roommates problem to discover the novel finding that all group strategy-proof and Pareto efficient mechanisms are generalized serial dictatorships, a new class of mechanisms. Our results also yield a simple new proof of the Gibbard-Satterthwaite Theorem.
Commodity Market Interest , Inflation , and Asset Prices ∗

Open interest in commodity futures, which signals commodity market activity, contains information about future commodity and bond prices. High open-interest growth predicts high commodity returns, a rising short rate, and low bond returns. Open-interest growth contains information beyond contemporaneous commodity returns, with which it is positively correlated. Open interest is a more powerful and robust predictor of commodity prices than other known predictors such as the short rate, the yield spread, the basis, and hedging pressure. Overall, our findings are consistent with gradual information diffusion, that is commodity market activity gets priced into commodity and bond markets with delay. JEL classification: G12; G13; E31; E37
Widespread Hydrothermal Alteration Minerals in the Fine-Grained Black Matrix of the Tieschitz Unequilibrated Ordinary Chondrite

Introduction: Tieschitz is an unequilibrated ordinary chondrite (UOC) classified as a type H/L3.6 [1]. This meteorite contains two types of matrix, termed white and black matrix [2]. The white matrix is a distinctive matrix material enriched in Na, K and Al, the most abundant phases being nepheline and albite [3-6]. This white matrix has been interpreted as representing a component of the chondrules, i.e. mesostasis glass which had been leached out and redeposited by a fluid phase. This conclusion is consistent with previous studies that have provided significant evidence that Tieschitz has experienced metasomatic alteration on the ordinary chondrite parent body [3, 7-9]. In this study, we have focused on determining the effects of metasomatism on the fine-grained material in the black matrix of this meteorite. The texture and mineralogy of Tieschitz matrix were characterized using Scanning Electron Microscopy (SEM), electron microprobe (EMPA) and Focused Ion Beam/Transmission Electron Microscopy (TEM). Methods: Fragments of Tieschitz and one polished thin section were initially studied on a FEI Quanta 3D FEGSEM/FIB operating at 30 kV, using backscattered electron (BSE) imaging. Elemental distribution maps were made by energy dispersive X-ray spectroscopy (EDS) for initial identification of different mineral phases. Quantitative mineral analyses were determined using a JEOL 8200 Superprobe electron microprobe (EMPA), operating at an acceleration voltage of 15 kV. Two TEM sections of selected regions of matrix were prepared using the focused ion beam technique, with a FEI Quanta 3D FEGSEM/FIB instrument. Bright field and scanning transmission electron microscopy images, electron diffraction and EDS X-ray analyses were carried out at 200 kV on the JEOL 2010 high resolution TEM (HRTEM) and JEOL 2010F FEG TEM/Scanning TEM (STEM). Results: The fragment of Tieschitz that was analyzed in this study contains one large dark clast (4.7 x 5 mm), several chondrules and regions of both black and white matrix. In the clast and the black matrix we observed micron-sized, irregularly-shaped voids containing a fibrous mineral extending inwards from the void walls (Figure 1a). These geode-like voids are widespread at the surface of the Tieschitz polished section. The bulk compositions of the adjacent areas are similar to the black matrix described by [10] and are characterized by their low abundances of Na, K and Al (Table 1). Sometimes the voids contain elongated (up to 20 μm in length) iron-rich olivines (Figure 1, b) with morphologies similar to those in the matrix of the Allende CV3 carbonaceous chondrite.
An Adaptive Model of Pulse in Jazz Percussion: Rhythmic Generation in Quasi-Periodic Musical Contexts using Sequence-to-Sequence Learning

This project attempts to formulate a novel approach to musical rhythm generation by incorporating pulse estimation in a jazz performance context. In light of the success of sequence-to-sequence language models [17], this project oversees a "beat-to-beat" translation model to characterize the musical behavior of a particular jazz drummer. This "beat-to-beat" encoder-decoder recurrent neural network (RNN) is trained on data associated with local and global beat features extracted from audio in order to reproduce rhythmic sequences conditioned on prior pulse estimates. After training, I predict and generate sequences of musical rhythms that have been conditioned on the past rhythmic material of the performer. The model was shown to be able to infer future rhythmic sequences with a maximum accuracy of 30% on the test set. A small survey was conducted to gauge the qualitative performance of the generated rhythmic sequences in comparison to a ground-truth baseline.
Darkroom The Use of ARM TrustZone for Secure Data Processing on the Cloud

—Nowadays, ofﬂoading storage and processing capacity to cloud servers is a growing trend among web services. This happens because high storage capacity and powerful processors are expensive, whilst cloud services provide a cheaper, ongoing, and reliable solution. The problem with cloud-based solutions is that servers are highly accessible through the Internet and exposed to attacks. By exploring subtle vulnerabilities present in applications, OSs and hypervisors, an attacker may compromise the system and disclose sensitive user data hosted in it. This data can then be used for malicious purposes such as espionage, blackmail, identity theft and harassment. A solution to this problem is processing data without exposing it to untrusted components, such as an OS. This paper describes the design and implementation of Darkroom, a secure data processing service for the cloud leveraging ARM TrustZone technology. Our system enables users to securely process data in a secure environment that prevents the data from being exposed to untrusted software. As a use case for this secure processing approach, the current version of the system focuses on image processing, which can be used to support popular web services such as Instagram or Facebook. Through experimental evaluation we observe that our solution adds a small overhead to data processing when compared to computer platforms that require the entire OS to be trusted.
Transfer Learning for transferring machine-learning based models among hyperspectral sensors

Using previously generated machine learning models under changing sensor hardware with nearly the same performance is a desirable goal. This constitutes a model transfer problem. We compare a Radial Basis Function Network adapted for transfer learning to a classical data alignment approach. This approach to transfer machine-learning models is tested on a task of material classification using hyperspectral imaging recorded with different camera systems and the aim to make camera systems interchangeable. The results show that a machine-learning based algorithm outperforms a state-of-the-art hyperspectral data alignment algorithm.
How Do U.S. Visa Policies Affect Unauthorized Immigration?

Abstract How would increasing the number of visas available to potential migrants affect unauthorized immigration from Mexico to the U.S.? Current U.S. policy bans people who are deported from receiving legal status for a period of time. This policy aims to serve as an additional deterrent to unauthorized immigration, but may be ineffective given that most potential Mexican migrants have an extremely low probability of ever being able to legally move to the U.S. We develop a dynamic discrete location choice model, which we estimate using data from the Mexican Migration Project, and consider various counterfactual policies that vary the intensity of enforcement and access to work visas. We find that legal entry bans for deported individuals are ineffective at current rates of legal immigration, but that increased legalization rates would amplify the deterrent effects of deportation. We also show that a temporary work visa program would yield similar deterrent effects as an increase in permanent legalization without resulting in very large increases in the total stock of migrants residing in the U.S. These findings have important implications for structuring future immigration reforms.
AN INTEGRATIVE MODEL FOR MEASURING GLOBAL PERFORMANCE IN PUBLIC HEALTH SERVICE

Financial management of modern healthcare organization controls all the assets and liabilities, and makes contributions to the strategic planning process, performance information and cost control. Its function is to achieve performance improvement, guarding assets and resources against waste or loss, to manage human resources and all other kinds of resources. The organization is supported by a governing board that must perform well in order to achieve excellence and accomplish all governance needs. Financial management is a forward-looking activity with long term time horizon so the aim is to balance costs and benefits, improving quality care programs. The health system has to develop strategies for costeffectiveness and rationalization of all the resources involved according to the organizational needs. It is important to create and support an efficient and effective structure. This paper presents a multi-criteria decision analysis by the application of the ANP Analytic Network Process and the BSC Balanced Scorecard. The research suggests a new methodological approach that can improve the efficiency of a well managed health organization characterized by multidisciplinary teams that have to interact together in order to gain continuous performance improvement and implement all functions efficiently and effectively.
MODELLING ENERGY CONSUMPTION IN SIMULATION MODELS BY THE USAGE OF DEEP LEARNING

Traditionally, simulation has used data about the real system for input data analysis or within data-driven model generation. Automatically extracting behavioral descriptions from the data and representing it in a simulation model is a weak point of these approaches. Machine learning, on the other hand, has proven successful in extracting knowledge from large data sets and transform it into more useful representations. Combining simulation approaches with methods from machine learning seems therefore promising. By representing some aspects of a real system by a traditional simulation model and others by a model generated from machine learning, a hybrid system model is generated. This extended abstract suggests a specific hybrid system model that incorporates a deep learning method for predicting high-resolution time series of power usage of machining jobs according to the control code the machine is operated on.
BDSM as Sexual Orientation: A Comparison to Lesbian, Gay, and Bisexual Sexuality

One less common and more stigmatized form of sexuality is BDSM, which is an umbrella term for consensual practices that involve, but are not limited to, bondage and discipline (B&D), dominance and submission (D&s), and sadomasochism (S&M). Focusing on one of many different conceptualizations, BDSM is comprised of a power dynamic between partners enacted through various activities (for an inclusive list, see Sandnabba et al., 1999; Weinberg et al., 1984). However, beyond being recently accepted as nonpathological, research has yet to reach
Hyperledger-based Data Provenance in Distributed Co m puting Environ m ents ∗

A new approach to m anaging provenance m etadata and data access rights in a distributed environ m ent is proposed. It is based on the integration of blockchain technology, s m art contracts and provenance m etadata driven data m anage m ent. We also suggest a new m ethod for delegation of rights fro m a user or service to another service within distributed co m puting syste m s. T he i m ple m entation of the proposed approach, entitled ProvHL, is based on the per m issioned blockchains and on the Hyperledger Fabric blockchain platfor m in conjunction with Hyperledger Co m poser.
Search for Jet Extinction in the Inclusive Jet pT spectrum at sqrt(s)=7 TeV

A search for the extinction of QCD jet production is presented using data collected with the CMS detector corresponding to 5.0 fb−1 of proton-proton collisions at √ s = 7 TeV. The extinction model studied in this analysis is motivated by the search for signatures of terascale gravity at the LHC and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts suppression of all high transverse momentum standard model processes, including jet production, beyond a certain energy scale. A shape comparison between the measured transverse momentum spectrum and the theoretical prediction is conducted. No significant deficit of events is found at high transverse momentum. A 95% CL lower limit of 3.2 TeV is set on the extinction energy scale.