Miracle-dz/UniFAR-FTUs huggingface dataset:
A Methodology for Evaluating Interaction Strategies of Task-Oriented Conversational Agents

In task-oriented conversational agents, more attention has been usually devoted to assessing task effectiveness, rather than to how the task is achieved. However, conversational agents are moving towards more complex and human-like interaction capabilities (e.g. the ability to use a formal/informal register, to show an empathetic behavior), for which standard evaluation methodologies may not suffice. In this paper, we provide a novel methodology to assess - in a completely controlled way - the impact on the quality of experience of agent’s interaction strategies. The methodology is based on a within subject design, where two slightly different transcripts of the same interaction with a conversational agent are presented to the user. Through a series of pilot experiments we prove that this methodology allows fast and cheap experimentation/evaluation, focusing on aspects that are overlooked by current methods.
Sheffield Submissions for WMT18 Multimodal Translation Shared Task

This paper describes the University of Sheffield’s submissions to the WMT18 Multimodal Machine Translation shared task. We participated in both tasks 1 and 1b. For task 1, we build on a standard sequence to sequence attention-based neural machine translation system (NMT) and investigate the utility of multimodal re-ranking approaches. More specifically, n-best translation candidates from this system are re-ranked using novel multimodal cross-lingual word sense disambiguation models. For task 1b, we explore three approaches: (i) re-ranking based on cross-lingual word sense disambiguation (as for task 1), (ii) re-ranking based on consensus of NMT n-best lists from German-Czech, French-Czech and English-Czech systems, and (iii) data augmentation by generating English source data through machine translation from French to English and from German to English followed by hypothesis selection using a multimodal-reranker.
Indirect pathway to pectoral fin motor neurons from nucleus ruber in the Nile tilapia Oreochromis niloticus

Supraspinal motor control systems of pectoral fins remain unclear in teleosts. Nucleus ruber of Goldstein (1905; NRg), which has been identified as the probable homologue of nucleus ruber of tetrapods, is a candidate structure serving for such functions. In the present study, we investigated possible involvement of the NRg in the control of pectoral fin movement by tract‐tracing experiments in the Nile tilapia Oreochromis niloticus. Tracer injections into the NRg revealed the fiber course of rubrospinal tract. Rubrospinal fibers crossed the midline at the level of midbrain, descended through the tegmentum, and terminated in a region ventrally adjacent to the dorsal horn at the spinomedullary junction, without reaching the ventral horn where pectoral fin motor neurons are present. Tracer injection experiments into the dorsal horn region resulted in labeled terminals in proximities of presumed pectoral fin motor neurons in the ventral horn. Tracer injection experiments into the ventral horn resulted in retrogradely labeled neurons ventrally adjacent to the dorsal horn, where labeled terminals were detected following rubral injections. These anatomical analyses suggest that the NRg of actinopterygians is involved in the control of pectoral fin motor neurons through an indirect pathway via interneurons in the dorsal horn.
An Inverse Thermal Modeling Approach for Thermal Parameter and Loss Identification in an Axial Flux Permanent Magnet Machine

Despite the use of state-of-the-art thermal modeling tools in the design stage, the measured thermal behavior of prototype electrical machines can differ significantly from the modeled ones. This paper shows how a thermal model, based on the finite-element method, of an electric machine can be improved using inverse modeling techniques. In a thorough study, a forward high fidelity finite-element thermal model of a 4-kW axial flux permanent magnet (PM) machine is introduced and improved using inverse modeling techniques via noncollocated thermal sensors. Parametric model order reduction of the high fidelity finite-element thermal model based on the moment matching method is performed to make the recovery of the actual thermal parameters characterizing the thermal behavior of the axial flux PM machine tractable. Furthermore, the same reduced order model is used to identify the different power loss components in the machine. Experimental results confirm that the presented two-stage approach is capable of identifying the thermal parameters and losses with high accuracy.
Exploring the effect of human and animal population growth on vector-borne disease transmission with an agent-based model of Rhodesian human African trypanosomiasis in eastern province, Zambia

This paper presents the development of an agent-based model (ABM) to investigate Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) disease transmission. The ABM model, fitted at a fine spatial scale, was used to explore the impact of a growing host population on the spread of disease along a 75 km transect in the Luangwa Valley, Zambia. The model was used to gain a greater understanding of how increases in human and domestic animal population could impact the contact network between vector and host, the subsequent transmission patterns, and disease incidence outcomes in the region. Modelled incidence rates showed increases in rHAT transmission in both humans and cattle. The primary demographic attribution of infection switched dramatically from young children of both sexes attending school, to adult women performing activities with shorter but more frequent trips, such as water and firewood collection, with men more protected due to the presence of cattle in their routines. The interpretation of model output provides a plausible insight into both population development and disease transmission in the near future in the region and such techniques could aid well-targeted mitigation strategies in the future.
Reducing the Cost of Android Mutation Testing

Due to the high market share of Android mobile devices, Android apps dominate the global market in terms of users, developers, and app releases. However, the quality of Android apps is a significant problem. Previously, we developed a mutation analysis-based approach to testing Android apps and showed it to be very effective. However, the computational cost of Android mutation testing is very high, possibly limiting its practical use. This paper presents a cost-reduction approach based on identifying redundancy among mutation operators used in Android mutation analysis. Excluding them can reduce cost without affecting the test quality. We consider a mutation operator to be redundant if tests designed to kill other types of mutants can also kill all or most of the mutants of this operator. We conducted an empirical study with selected open source Android apps. The results of our study show that three operators are redundant and can be excluded from Android mutation analysis. We also suggest updating one operator’s implementation to stop generating trivial mutants. Additionally, we identity subsumption relationships among operators so that the operators subsumed by others can be skipped in Android mutation analysis.
Illuminating the Dark or how to recover what should not be seen in FE-based classifiers

Abstract Classification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption. These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear. In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.
Strain-Aware Performance Evaluation and Correction for OTFT-Based Flexible Displays

Organic thin-film transistors (OTFTs) are widely used in flexible circuits, such as flexible displays, sensor arrays, and radio frequency identification cards (RFIDs), because these technologies offer features such as better flexibility, lower cost, and easy manufacturability using low-temperature fabrication process. This paper develops a procedure that evaluates the performance of flexible displays. Due to their very nature, flexible displays experience significant mechanical strain/stress in the field due to the deformation caused during daily use. These deformations can impact device and circuit performance, potentially causing a loss in functionality. This paper first models the effects of extrinsic strain due to two fundamental deformations modes, bending and twisting. Next, this strain is translated to variations in device mobility, after which analytical models for error analysis in the flexible display are derived based on the rendered image values in each pixel of the display. Finally, two error correction approaches for flexible displays are proposed, based on voltage compensation and flexible clocking.
Machine Learning for Design Space Exploration and Optimization of Manycore Systems

In the emerging data-driven science paradigm, computing syStems ranging from IoT and mobile to manycores and datacenters play distinct roles. These systems need to be optimized for the objectives and constraints dictated by the needs of the application. In this paper, we describe how machine learning techniques can be leveraged to improve the computational-efficiency of hardware design optimization. This includes generic methodologies that are applicable for any hardware design space. As an example, we discuss a guided design space exploration framework to accelerate application-specific manycore systems design and advanced imitation learning techniques to improve on-chip resource management. We present some experimental results for application-specific manycore system design optimization and dynamic power management to demonstrate the efficacy of these methods over traditional EDA approaches.
Accompanying Observation Modes and Software Architecture for Autonomous Robot Software

To support robust task execution in open environment, autonomous robots (AR) should include reactive capabilities to cope with the dynamics and uncertainties from real-world environment. The uncertainties pose great challenges for robots being sensitive to the environmental changes and flexible to adjust self-behaviors. To this end, this paper aims to improve the sensing and acting capabilities of autonomous robots by novel behavioral theories, observation modes and software architectures. Specifically, this paper has three main contribution: (1) presents an accompanying model that specifies a novel accompanying pattern for interacting robot behaviors; (2) proposes four types of accompanying observation modes that coordinate multiple robot sensing behaviors; (3) proposes a concrete multi-agent software architecture that implements aforementioned accompanying model and accompanying observation modes. To demonstrate the applicability and validity of our accompanying modes and MAS-based software architecture, this paper conducts a case study to implement a domestic service example, which requires the robot to run in a highly dynamic environment and can adapt its behaviors to unexpected situations. ∗ Keywords-Autonomous robot software; accompanying model; accompanying observation mode; multi-agent system
Understanding and designing animations in the user interfaces. (Comprendre et concevoir les animations dans les interfaces graphiques)

Despite their increasing popularity and omnipresence in modern graphical interfaces, anima- tions are still largely under-comprehended. While prior research and practice provide useful insights about the merits and downsides of animation, it is still unclear what makes a good and effective animation that improves the usability and expressivity of graphical interfaces. The disparity of opinions about the value of animation is mainly due to the fact that most of previous studies have investigated the benefit of adding a particular animation to a particular interface, leaving a notable gap in the deep understanding of the many design aspects that influence the performance of animation. Prior research have also predominantly assessed the value of animation through a narrow empirical angle, which had left several facets of animation unveiled. This thesis contributes a first constructive step toward better understanding the vast design space of animation and mapping out the various merits of animation that can enrich user interfaces from different perspectives. We first provide a structured view of the roles and drawbacks of animation in user interfaces. We then present the theoretical fundamentals for animation in information visualization. We discuss the main challenges for designing and evaluating animation in dynamic visualizations. Through an empirical study, we investigate the meaning of the Common Fate Law, applied on animation trajectories, in dynamic visual- izations. We then introduce a design space that allows a holistic characterization of staged animation and propose an authoring tool to support the prototyping and exploration of staging in visualizations.
The Fronthaul Infrastructure of 5G Mobile Networks

The next wave of innovation will certainly generate numerous new opportunities for emerging technology solutions based on networking services and applications with stringent key performance indicators (KPIs) such as ultra-low 1 ms latency, a 1000 fold bandwidth increase, 99.99 % reliability and availability, which are immensely above those supported by current mobile networks. A new architecture of mobile networking called cloud radio access network (C-RAN) has been introduced over the last few years not only to supporting these indicators, but also to increasing scalability, manageability, and flexibility of mobile systems. In this context, this paper addresses the principal technology enablers and their features for C-RAN fronthaul architectures of the $5^{th}$ generation (5G) mobile networks, namely space-division multiplexing (SDM), massive multiple-input multiple-output (MIMO) signaling, analog radio-over-fiver (A-RoF), and millimeter wave (mmWave) frequency technology. These technologies pave the way towards a truly viable and efficient fronthaul infrastructure for 5G mobile communications with connectivity for thousands of users and strict latency control. In this fashion, we perceive a networkinfrastructure scenario with seamless starting and ending interfaces by exploiting space diversity in both radio frequency and optical domains with efficient integrated photonics technology-all combined with adaptive softwaredefined network programming, so as to satisfy the 5G KPIs. Furthermore, we address the most relevant features of these technologies as a potential guideline for potential fronthaul infrastructure deployment of next generation mobile networks.
Joint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification

A hyperspectral image (HSI) contains fine and rich spectral information and spatial information of ground objects, which has great potential in applications. It is also widely used in precision agriculture, marine monitoring, military reconnaissance and many other fields. In recent years, a convolutional neural network (CNN) has been successfully used in HSI classification and has provided it with outstanding capacity for improving classification effects. To get rid of the bondage of strong correlation among bands for HSI classification, an effective CNN architecture is proposed for HSI classification in this work. The proposed CNN architecture has several distinct advantages. First, each 1D spectral vector that corresponds to a pixel in an HSI is transformed into a 2D spectral feature matrix, thereby emphasizing the difference among samples. In addition, this architecture can not only weaken the influence of strong correlation among bands on classification, but can also fully utilize the spectral information of hyperspectral data. Furthermore, a 1 × 1 convolutional layer is adopted to better deal with HSI information. All the convolutional layers in the proposed CNN architecture are composed of small convolutional kernels. Moreover, cascaded composite layers of the architecture consist of 1 × 1 and 3 × 3 convolutional layers. The inputs and outputs of each composite layer are stitched as the inputs of the next composite layer, thereby accomplishing feature reuse. This special module with joint alternate small convolution and feature reuse can extract high-level features from hyperspectral data meticulously and comprehensively solve the overfitting problem to an extent, in order to obtain a considerable classification effect. Finally, global average pooling is used to replace the traditional fully connected layer to reduce the model parameters and extract high-dimensional features from the hyperspectral data at the end of the architecture. Experimental results on three benchmark HSI datasets show the high classification accuracy and effectiveness of the proposed method.
The Experiment Factory: Reproducible Experiment Containers

The Experiment Factory (V. Sochat 2017) is Open Source software that makes it easy to generate reproducible behavioral experiments. It offers a browsable, and tested library of experiments, games, and surveys, support for multiple kinds of databases, and robust documentation for the provided tools. A user interested in deploying a behavioral assessment can simply select a grouping of paradigms from the web interface, and build a container to serve them.
A Non-Functional Requirements Recommendation System for Scrum-based Projects

Felipe Ramos§, Alexandre Costa§, Mirko Perkusich¶, Hyggo Almeida§ and Angelo Perkusich§ §Intelligent Software Engineering (ISE) Group, Federal University of Campina Grande, Campina Grande, Paraı́ba, Brazil, 58429-140 {feliperamos, antonioalexandre}@copin.ufcg. edu.br, hyggo@dsc.ufcg. edu.br, perkusic@dee.ufcg. edu.br ¶Federal Institute of Paraı́ba, Monteiro, Paraı́ba, Brazil, 58500-000 mirko.perkusich@ifpb.edu.br
Efficient L1-based nonlocal total variational model of Retinex for image restoration

Abstract. Characteristics of an image, such as smoothness, edge, and texture, can be better preserved using the nonlocal differential operator in image processing. We establish an L1-based nonlocal total variational (NLTVL1) model based on Retinex theory that can be solved by a fast computational algorithm via the alternating direction method of multipliers. Experiential results demonstrate that our NLTVL1 method has a good performance on enhancing contrast, eliminating the influence of nonuniform illumination, and suppressing noise. Furthermore, compared with previous works, including traditional Retinex methods and variational Retinex methods, our proposed approach achieves superior performance on edge and texture preservation and needs fewer iterations on recovering the reflectance image, which is illustrated by examples and statistics.
Improving Span-based Question Answering Systems with Coarsely Labeled Data

We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely annotated data can bring significant performance gains. Experiments demonstrate that the standard multi-task learning approach of sharing representations is not the most effective way to leverage coarse-grained annotations. Instead, we can explicitly model the latent fine-grained short answer variables and optimize the marginal log-likelihood directly or use a newly proposed \emph{posterior distillation} learning objective. Since these latent-variable methods have explicit access to the relationship between the fine and coarse tasks, they result in significantly larger improvements from coarse supervision.
New tools for automated high-resolution cryo-EM structure determination in RELION-3

Here, we describe the third major release of RELION. CPU-based vector acceleration has been added in addition to GPU support, which provides flexibility in use of resources and avoids memory limitations. Reference-free autopicking with Laplacian-of-Gaussian filtering and execution of jobs from python allows non-interactive processing during acquisition, including 2D-classification, de novo model generation and 3D-classification. Per-particle refinement of CTF parameters and correction of estimated beam tilt provides higher resolution reconstructions when particles are at different heights in the ice, and/or coma-free alignment has not been optimal. Ewald sphere curvature correction improves resolution for large particles. We illustrate these developments with publicly available data sets: together with a Bayesian approach to beam-induced motion correction it leads to resolution improvements of 0.2–0.7 Å compared to previous RELION versions.
Regularization Parameter-Free Convolutional Sparse Coding via Projections Onto The ℓ1-Ball and The Discrepancy Principle

Given a set of dictionary filters, the most widely used formulation of the convolutional sparse coding (CSC) problem is convolutional basis pursuit denoising (CBPDN), in which an image is represented as a sum over a set of convolutions of coefficient maps. When the input image is noisy, CBPDN’s regularization parameter greatly influences the quality of the reconstructed image. Results for an automatic and sensible selection of this parameter are very limited for the CSC / CBPDN case.In this paper we propose a regularization parameter-free method to solve the CSC problem via its projection onto the ℓ1-Ball formulation coupled with a warm-start like strategy, which, driven by the Morozov’s discrepancy principle, adaptively increases/decreases its constrain at each major iteration. While the time performance of our proposed method is slower than that measured when solving CSC for a fixed regularization parameter, our computational results also show that our method’s reconstruction quality is, in average, very close (within 0.16 SNR, 0.16 PSNR, 0.003 SSIM) to that obtained when the regularization parameter for CBPDN is selected to produce the best (SNR) quality result.