Miracle-dz/UniFAR-FTUs huggingface dataset:
Developments in global value chains for bananas : trade policies , multinationals , shipping modes and product differentiation . The case of Cameroon exports

Global value chains for bananas have been changing significantly over recent years. Several factors are responsible for these changes. First, the continuous rapid concentration of the retail sector determined a parallel increase in its capacity to impose quality standards and acquisition prices upon its suppliers. Second, innovation in sea shipment technologies induced a rapid growth in the use of refrigerated containers and, conversely, a decline of the share of bananas being transported using traditional reefers. Third, the increased demand by more educated and higher income consumers in developed countries for environment‐friendly grown and Fair Trade bananas, which created opportunities for product differentiation. Fourth, several subsequent changes in the EU import regime for bananas (the EU is the single largest importer of bananas, with 1/3 of the world market) had significant trade creation and trade diversion effects, as well as implication for the distribution of market power along the chains for bananas. One of the consequences of these changes has been the decline of the share of the international market held by the largest multinationals, with new trade operators appearing on the scene and a number of transactions occurring directly between retailers and small exporters and producer cooperatives.
C Values of C 4 Types in Grasses

S13C values were determined for leaves of C4 grasses (Poaceae) of different photosynthetic types. For plants grown in the same environment at the same time, mean 613C values for the three C4 types were: NADP-ME-type, 11,35%,k0.13 s.e. (11 spp.); PCK-type, 11.95%,k0.19 s.e. (11 spp.); and NAD-ME-type, -12.7%,+0.21 s.e. (9 spp.). Although there is some overlap between the values for individual species of the three groups, the difference between any two means is highly significant [P(t) c 0.011 and is not due to taxonomic sampling bias at the subfamily level. The differences in means may suggest that C4 types differ in rates of leakage of COz and HCO; from PCR tissue ('photosynthetic carbon reduction' tissue: equivalent to 'Kranz' tissue), and/or, using Farquhar's (Appendix) expression for plant 6 values, that C4 types differ in their average intercellular CO, concentrations (c,) . It is also possible that differences between C4 types exist in some other, unknown, leaf fractionation process. Apoplastic C 0 2 leakage from PCR tissue in NAD-ME-type C4 grasses, which do not possess a PCR 'suberized lamella' as found in NADP-MEtype and PCK-type C4 grasses, may give these species the most negative 613C values. Expressions for C4 plant 613C values, and a model for the 613C values of CO, and HCO; in various pools and fluxes in C4 plant leaves, are given.
1 Control and Change Analysing the Process of Institutionalisation Management Accounting Research

This paper studies the process by which a change in the institutional logic of an organisational field diffuses through the management control system of a firm. The theoretical framework proposed by Hasselbladh and Kallinikos (2000) enables us to describe the institutionalisation process of management control systems in more detail by observing how ideals are translated into discourses and control techniques. We argue that both the process by which institutional changes are implemented inside organisations and the process of decoupling are two aspects of the same issue. Revisiting core notions of new-institutional theory such as internalisation and decoupling, our findings question the systematic as well as the linear nature of the institutionalisation process. Empirical findings, based on a field study conducted in the French subsidiary of a pharmaceutical laboratory, highlight how the discourses of organisational actors contradict new ideals and control techniques. More particularly, it appears that, when a discourse cannot be heard, it can be partly bypassed using techniques. It is also argued that intra-organisational change builds on stable discourses and ambivalent technology, which foster insidious dissemination of the new institutional logics when " what can be done cannot be said " .
Facial Cues for Deception Detection in Virtual Reality Based Communication

Deception and deception detection is a part of human communication and needs to be accurately represented in computer mediated communication. In this study deception facial micro-expressions are translated from live human footage onto virtual reality based avatars in order to assess how well these cues can be represented and communicated in a virtual world. The results indicate that human observers are no better at detecting deception in this scenario than in previous experiments. Classification methods identify limitations in accurately representing the expression space. The experimental design does suggest strategies for using virtual reality for more accurately investigating deception practices for future studies.
Will They Use It or Not? Investigating Software Developers’ Intention to Follow Privacy Engineering Methodologies

With the increasing concerns over privacy in software systems, there is a growing enthusiasm to develop methods to support the development of privacy aware software systems. Inadequate privacy in software system designs could result in users losing their sensitive data, such as health information and financial information, which may cause financial and reputation loss. Privacy Engineering Methodologies (PEMs) are introduced into the software development processes with the goal of guiding software developers to embed privacy into the systems they design. However, for PEMs to be successful it is imperative that software developers have a positive intention to use PEMs. Otherwise, developers may attempt to bypass the privacy methodologies or use them partially and hence develop software systems that may not protect user privacy appropriately. To investigate the factors that affect software developers’ behavioural intention to follow PEMs, in this article, we conducted a study with 149 software developers. Findings of the study show that the usefulness of the PEM to the developers’ existing work to be the strongest determinant that affects software developers’ intention to follow PEMs. Moreover, the compatibility of the PEM with their way of work and how the PEM demonstrates its results when used were also found to be significant. These findings provide important insights in understanding the behaviour of software developers and how they perceive PEMs. The findings could be used to assist organisations and researchers to deploy PEMs and design PEMs that are positively accepted by software developers.
BTMonitor

With the rapid growth of connectivity and autonomy for today’s automobiles, their security vulnerabilities are becoming one of the most urgent concerns in the automotive industry. The lack of message authentication in Controller Area Network (CAN), which is the most popular in-vehicle communication protocol, makes it susceptible to cyber attack. It has been demonstrated that the remote attackers can take over the maneuver of vehicles after getting access to CAN, which poses serious safety threats to the public. To mitigate this issue, we propose a novel intrusion detection system (IDS), called BTMonitor (Bit-time-based CAN Bus Monitor). It utilizes the small but measurable discrepancy of bit time in CAN frames to fingerprint their sender Electronic Control Units (ECUs). To reduce the requirement for high sampling rate, we calculate the bit time of recessive bits and dominant bits, respectively, and extract their statistical features as fingerprint. The generated fingerprint is then used to detect intrusion and pinpoint the attacker. BTMonitor can detect new types of masquerade attack that the state-of-the-art clock-skew-based IDS is unable to identify. We implement a prototype system for BTMonitor using Xilinx Spartan 6 FPGA for data collection. We evaluate our method on both a CAN bus prototype and a real vehicle. The results show that BTMonitor can correctly identify the sender with an average probability of 99.76% on the real vehicle.
CT LIS

How to quantify influences between users, seeing that social network users influence each other in their temporal behaviors? Previous work has directly defined an independent model parameter to capture the interpersonal influence between each pair of users. To do so, these models need a parameter for each pair of users, which results in high-dimensional models becoming easily trapped into the overfitting problem. However, such models do not consider how influences depend on each other if influences are sent from the same user or if influences are received by the same user. Therefore, we propose a model that defines parameters for every user with a latent influence vector and a susceptibility vector, opposite to define influences on user pairs. Such low-dimensional representations naturally cause the interpersonal influences involving the same user to be coupled with each other, thus reducing the model’s complexity. Additionally, the model can easily consider the temporal information and sentimental polarities of users’ messages. Finally, we conduct extensive experiments on two real-world Microblog datasets, showing that our model with such representations achieves best performance on three prediction tasks, compared to the state-of-the-art and pair-wise baselines.
Comparative Study of Forecasting Schemes for IoT Device Traffic in Machine-to-Machine Communication

We present a comparative study of Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1-D CNN), and Long-Short Term Memory (LSTM) models on the problem of forecasting the traffic generation patterns of individual Internet of Things (IoT) devices in Machine-to-Machine (M2M) communication. We classify IoT traffic into four classes: Fixed-Bit Periodic (FBP), Variable-Bit Periodic (VBP), Fixed-Bit Aperiodic (FBA), and Variable-Bit Aperiodic (VBA). We show that LSTM outperforms all of the other models significantly in the symmetric Mean Absolute Percentage Error (sMAPE) measure for devices in the VBP class in our simulations. Furthermore, we show that LSTM has almost the same performance in this metric for the FBA class as MLP and 1-D CNN. While the training time per IoT device is the highest for LSTM, all of the forecasting models have reasonable training times for practical implementation. Our results suggest an architecture in which an IoT Gateway predicts the future traffic of IoT devices in the FBP, VBP and FBA classes and pre-allocates the uplink wireless channel for these classes in advance in order to alleviate the Massive Access Problem of M2M communication.
Determinants of gonadal function after childhood cancer

markdownabstractEach year, 600–700 children are diagnosed with cancer in the Netherlands. Over the last decades, the overall long-term survival of childhood cancer has increased up to ~75%. Due to the expansion of the childhood cancer survivor population, chronic health sequelae resulting from cancer and its treatment have become more prevalent and require our attention. Approximately 75% of the childhood cancer survivors have developed at least one long-term complication of cancer treatment. Gonadal dysfunction is one of the important late effects. The impact of cancer treatment on gonadal function has extensively been studied. However, the extent of loss of gonadal function differs between equally treated childhood cancer survivors. Information on determinants of ovarian function in childhood cancer survivors other than previous cancer treatment is largely lacking. Therefore, in this thesis we aimed to identify determinants of gonadal function and its potential recovery after childhood cancer, and, where possible, to evaluate alternative fertility options for survivors of childhood cancer.
Extended Reality Experiences Prediction using Collaborative Filtering

New immersive technologies are providing added value to many areas of our lives. A wide spectrum of hardware and software, including sensory interfaces, applications, and infrastructures are some of the technical drivers of these new opportunities. In particular, Extended Reality applications and simulators are increasingly becoming popular in business, but are often limited by their predictability and moreover, they lack personalization in the selection of the scenarios. The author proposes the usage of recommendation systems in Extended Reality simulators as a contribution to solve this problem, proposing a system in which Extended Reality experiences (or scenarios) are suggested to the user at the beginning of the experience. An investigation on the usage of item-based collaborative filtering has been performed, resulting in a developed system that suggests an experience based on item feature similarity (in terms of the genre of the experiences) and the actions of similar users. This system uses KNN to find clusters of similar items based on a user's ratings, and proposes ten similar items with a precision of 71%. In the context of Extended Reality simulators, this platform lead to a new generation of smart simulators that can be potentially used for entertainment as well as customized professional training. The developed simulator can potentially recommend personalized Extended Reality training experiences to the user, facilitating the learning of new skills. Also, this research applies Recommender Systems to the new field of Extended Reality.
Profiling Dynamic Data Access Patterns with Controlled Overhead and Quality

Modern workloads tend to have huge working sets and low locality. Despite this trend, the capacity of DRAM has not been increased enough to accommodate such huge working sets. Therefore, memory management mechanisms optimized for such modern workloads are widely required today. For such optimizations, knowing the data access pattern of given workloads is essential. However, manually extracting such patterns from huge and complex workloads is exhaustive. Worse yet, existing memory access analysis tools incur unacceptably high overheads for unnecessarily detailed analysis results. To mitigate this situation, we introduce a tool that is designed for data access pattern tracing. Two core mechanisms in this tool, a region-based sampling and an adaptive region adjustment, allow users to limit the tracing overhead in a bounded range regardless of the size and complexity of target workloads, while preserving the quality of results. Our empirical evaluations that conducted with 20 realistic workloads show the high quality, low overhead, and a potential use case of this tool.
Semileptonic decays of 
D
 and 
Ds
 mesons in the relativistic quark model

The form factors parameterizing the weak $D$ and $D_s$ transitions to light pseudoscalar and vector mesons are calculated in the framework of the relativistic quark model based on the quasipotential approach. The special attention is paid to the systematic account of the relativistic effects including transformation of the meson wave function from the rest to moving reference frame and contributions of the intermediate negative-energy states. The form factors are expressed through the overlap integrals of the meson wave functions, which are taken from previous studies of meson spectroscopy. They are calculated in the whole range of the transferred momentum $q^2$. Convenient parameterization of the form factors which accurately reproduces numerical results is given. The obtained values of the form factors and their ratios at $q^2=0$ agree well with the ones extracted form the experimental data. On the basis of these form factors and helicity formalism, differential and total semileptonic decay rates of $D$ and $D_s$ mesons as well as different asymmetries and polarization parameters are calculated. The detailed comparison of the obtained results with other theoretical calculations and experimental data is given.
Indistinguishability and collection efficiency of transition metal dichalcogenides single photon emitters embedded in silicon nitride photonic integrated circuits.

This work explores the impact of waveguide design in photon collection efficiency and indistinguishability of single photon emitters embedded in photonic integrated circuits. Transition Metal Dichalcogenides (TMDC) materials have been selected as single photon emitters because their prominent properties for single photon emission: their giant oscillator strength promotes a stronger Purcell effect and their short exciton lifetimes enhances the indistinguishability of photons. We have calculated the photon extraction efficiency and the indistinguishability of a TMDC point-source of photons with arbitrary orientation embedded at an arbitrary location within a SiN waveguide. For the calculation we propose an analytical model based in the Green dyadic of the Helmholtz equation for different geometries of the waveguide, position of the source and orientation. Calculations have been numerically evaluated through finite-difference time-domain (FDTD) simulations showing consistent results. We have found a maximum coupling up to 81% to the fundamental mode when the quantum emitter is placed in the centre of the waveguide and a maximum indistinguishability of 81% when the emitter is placed 10 nm away from the edge of the waveguide. The results help for a better understanding of the coupling of quantum emitters to nanophotonic devices and photonic integrated circuits (PICs).
Dynamics of non-Abelian strings in the theory interpolating from 
N=2
 to 
N=1
 supersymmetric QCD

We study the dynamics of non-Abelian vortex strings supported in ${\mathcal N}=2$ supersymmetric QCD with the U$(N)$ gauge group and $N_f=N$ quark flavors deformed by the mass $\mu$ of the adjoint matter. In the limit of large $\mu$ the bulk four-dimensional theory flows to ${\mathcal N}=1$ supersymmetric QCD. The dynamics of orientational zero modes of the non-Abelian string is described by the world sheet CP$(N-1)$ model. At $\mu=0$ this model has ${\mathcal N}= \left(2,2\right) $ supersymmetry while at large $\mu$ it flows to the non-supersymmetric CP$(N-1)$ model. We solve the world sheet model in the large $N$ approximation and find a rich phase structure with respect to the deformation parameter $\mu$ and quark mass differences. The phases include two strong coupling phases and two Higgs phases. In particular, the Higgs phase at small $\mu$ supports CP$(N-1)$ model kinks representing confined monopoles of the bulk QCD, while in the large-$\mu$ Higgs phase monopoles disappear.
Unveiling Sizes of Compact AGN Hosts with ALMA

We present rest-frame far-infrared (FIR) and optical size measurements of active galactic nucleus (AGN) hosts and star-forming galaxies (SFGs) in the COSMOS field, enabled by high-resolution Atacama Large Millimeter/submillimeter Array (ALMA)/1 mm (0.″1–0.″4) and Hubble Space Telescope (HST)/F814W imaging (∼0.″1). Our sample includes 27 galaxies at z < 2.5, classified as infrared-selected AGN (three sources), X-ray selected AGN (four sources), and non-AGN SFGs (20 sources), for which high-resolution Band 6/7 ALMA images are available at 1 mm from our own observing program as well as archival observations. The sizes and star formation rate surface densities measured from both ALMA/1 mm and HST/F814W images show that obscured AGN host galaxies are more compact than non-AGN SFGs at similar redshift and stellar mass. This result suggests that the obscured accretion phase may be related to galaxies experiencing a compaction of their gaseous component, which could be associated with enhanced central star formation before a subsequent quenching driving the formation of compact passive galaxies. Moreover, most of the detected and stacked rest-frame FIR sizes of AGNs in our sample are similar or more compact than their rest-frame optical sizes, which is consistent with recent results of ALMA-detected sources. This might be explained by the fact that the dusty starbursts take place in the compact regions, and suggests that the star formation mechanisms in the compact regions of AGN hosts are similar to those observed in SFGs observed with ALMA.
Towards Non-Toxic Landscapes: Automatic Toxic Comment Detection Using DNN

The spectacular expansion of the Internet has led to the development of a new research problem in the field of natural language processing: automatic toxic comment detection, since many countries prohibit hate speech in public media. There is no clear and formal definition of hate, offensive, toxic and abusive speeches. In this article, we put all these terms under the umbrella of “toxic speech”. The contribution of this paper is the design of binary classification and regression-based approaches aiming to predict whether a comment is toxic or not. We compare different unsupervised word representations and different DNN based classifiers. Moreover, we study the robustness of the proposed approaches to adversarial attacks by adding one (healthy or toxic) word. We evaluate the proposed methodology on the English Wikipedia Detox corpus. Our experiments show that using BERT fine-tuning outperforms feature-based BERT, Mikolov’s and fastText representations with different DNN classifiers.
New dual representation for staggered lattice QCD

We propose a new strategy to evaluate the partition function of lattice QCD with Wilson gauge action coupled to staggered fermions, based on a strong coupling expansion in the inverse bare gauge coupling $\beta= 2N/g^{2}$. Our method makes use of the recently developed formalism to evaluate the ${\rm SU}(N)$ $1-$link integrals and consists in an exact rewriting of the partition function in terms of a set of additional dual degrees of freedom which we call "Decoupling Operator Indices" (DOI). The method is not limited to any particular number of dimensions or gauge group ${\rm U}(N)$, ${\rm SU}(N)$. In terms of the DOI the system takes the form of a Tensor Network which can be simulated using Worm-like algorithms. Higher order $\beta$-corrections to strong coupling lattice QCD can be, in principle, systematically evaluated, helping to answer the question whether the finite density sign problem remains mild when plaquette contributions are included. Issues related to the complexity of the description and strategies for the stochastic evaluation of the partition function are discussed.
Visualization approach to assess the robustness of neural networks for medical image classification

The use of neural networks for diagnosis classification is becoming more and more prevalent in the medical imaging community. However, deep learning method outputs remain hard to explain. Another difficulty is to choose among the large number of techniques developed to analyze how networks learn, as all present different limitations. In this paper, we extended the framework of Fong and Vedaldi [IEEE International Conference on Computer Vision (ICCV), 2017] to visualize the training of convolutional neural networks (CNNs) on 3D quantitative neuroimaging data. Our application focuses on the detection of Alzheimer's disease with gray matter probability maps extracted from structural MRI. We first assessed the robustness of the visualization method by studying the coherence of the longitudinal patterns and regions identified by the network. We then studied the stability of the CNN training by computing visualization-based similarity indexes between different re-runs of the CNN. We demonstrated that the areas identified by the CNN were consistent with what is known of Alzheimer's disease and that the visualization approach extract coherent longitudinal patterns. We also showed that the CNN training is not stable and that the areas identified mainly depend on the initialization and the training process. This issue may exist in many other medical studies using deep learning methods on datasets in which the number of samples is too small and the data dimension is high. This means that it may not be possible to rely on deep learning to detect stable regions of interest in this field yet.