Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity based virtual fields for identifying hyperelastic constitutive parameters

: In this study, the Virtual Fields Method (VFM) is applied to identify constitutive parameters of hyperelastic models from a heterogeneous test. Digital image correlation (DIC) was used to estimate both displacement and strain ﬁ elds required by the identi ﬁ cation procedure. Two different hyperelastic models were considered: the Mooney model and the Ogden model. Applying the VFM to the Mooney model leads to a linear system which involves the hyperelastic parameters due to the linearity of the stress with respect to these parameters. In the case of the Ogden model, the stress is a nonlinear function of the hyperelastic param- eters and a suitable procedure shall be used to determine virtual ﬁ elds leading to the best identi ﬁ cation. This complicates the identi ﬁ cation procedure and affects its robustness. This is the reason why the sensitivity based virtual ﬁ eld approach recently proposed in case of anisotropic plasticity by Marek et al. (2017) has been successfully implemented to be applied in case of hyperelasticity. Results obtained clearly highlight the bene ﬁ ts of such an inverse identi ﬁ cation approach in case of non-linear systems.
Physical Layer Design And Implementation Of A Biometric Authentication System Using Galvanic Coupling Intra-body Communication

of the Thesis Physical Layer Design and Implementation of a Biometric Authentication System using Galvanic Coupling Intra-body Communication by William J. Tomlinson, Jr. Doctor of Philosophy in Computer Engineering Northeastern University, December 2018 Dr. Kaushik Chowdhury, Advisor The emerging field of Intra-Body Communication (IBC) promises innovation in both the commercial and medical domain, by initiating data transfer from within and across the body surface to specific target devices. Traditional forms of RF-based communication face drawbacks in terms of high signal absorption within tissues that can potentially result in security and privacy issues, owing to its omnidirectional radiation. Instead, we propose the use of Galvanic Coupling (GC), wherein a weak electrical current ( 0.5mA) is used, resulting in a significant increase in energy efficiency compared to classical RF. In this thesis, we illustrate an example use case of intra-body communication in the form of a radically different biometric authentication system. Instead of using an over-the-air channel, we demonstrate an alternate method of transmitting data securely using GC that bypasses the vulnerable RF-channel. The GC-method injects weak electrical current into human tissue, primarily propagating through the skin. The proposed design provides impermeability to malicious attacks when sending biometric data (e.g. side-channel sniffing) as the body behaves as a natural waveguide. This dissertation makes the following contributions leading to a proof-of-concept implementation for the aforementioned application: (i) The use of analytical and empirical channel modeling techniques via a multilayered tissue equivalent circuit representing the human arm-wrist-palm propagation path and an electrically equivalent synthetic tissue phantom, respectively. (ii) Utilization of empirically verified and stored channel models for system design and testing with various narrowband and ultra-wideband modulation schemes. (iii) Demonstrating the electrocardiogram (ECG) signal, as one example biometric signatures to identify an individual. (iv) Implementation of a low-power GC transceiver with optimized communication parameters with an end-to-end validation with the transmission of biological data and (v) Demonstration of the eavesdropping susceptibility of GCsignals, and similar body communication techniques, over-the-air and while in direct contact with
Towards 5G Cloud Radio Access Network: An Energy and Latency Perspective

Future generation networks will entirely deploy virtualisation paradigms to enhance performance and capabilities of current cellular networks. In order to achieve the vision of ﬁfth generation networks, software-deﬁned networking and network function virtualisation will be applied not only at the core network but also at the radio access network. That will help to achieve signiﬁcant reduction in power consumption while increasing energy efﬁciency, ﬂexibility and scalability. This article proposes a general mathematical model that can correctly and accurately describe spatial/topological characteristics, power consumption and latency of Cloud radio access network in future generation networks. Thanks to the development of this novel model based on stochastic geometry, tessellation theory and random multilayer hypergraphs, we can numerically estimate the overall energy efﬁciency (in bit per Joule) of Cloud radio access network in 5G (considering either edge or cloud computing), and we can compare that to energy efﬁciency of legacy radio access network of current 4G cellular networks. Moreover, the analysis includes a preliminary discussion about latency: that shows edge computing to be the best paradigm for 5G radio access network, that can concurrently satisfy energy efﬁciency and latency requirements.
Understanding Multi-Agent Capability in Solving Complex Problem like Traffic Management

- For resolving traffic management problems it requires a coordinative control solutions where controls like traffic signals, vehicles progressing direction, roads capacity and many other parameters needs to be carefully analyzed and decisions to be made individually and collectively. Multi-agents systems have the potential and framework to define several agents to monitor various parameters across various locations and help co-ordinate agents to resolve individual decision for every single problem and collectively decisions could be made from the available individual decisions. This paper focuses on the various capabilities available in Multi-agents framework to resolve complex issues like traffic management system.
Human Input about Linguistic Summaries in Time Series Forecasting

Finding an appropriate predictive model for time series and formulating its assumptions may become very challenging task. We propose to represent time series in a human-consistent way using linguistic summaries. Such summaries describe general trends in time series and are easily interpretable for decision makers. The aim of this contribution is to show that the linguistic summaries may be successfully applied to support the analysis and forecasting of time series. Information about trends is first retrieved from experts, and then, processed with soft computing tools. The performance of the approach is verified on the real-world datasets from the M3-Competition. Users are asked to evaluate linguistic summaries that are intuitive and easy for interpretation. This paper shows that human-consistent summaries deliver new knowledge for forecasting. Keywords-information retrieval; human-computer interaction; time series and sequence models; Bayesian methods; supervised learning.
Dynamic Bayesian Networks for Speaker Verification

This thesis is concerned with the statistical modeling of speech signal applied to Speaker Verification (SV) using Bayesian Networks (BNs). The main idea of this work is to use BNs as a mathematical tool to model pertinent speech features keeping its relations. It combines theoretical and experimental work. The difference between systems and humans performance in SV is the quantity of information and the relationships between the sources of information used to make decisions. A single statistical framework that keeps the conditional dependence and independence relations between those variables is difficult to attain. Therefore, the use of BNs as a tool for modeling the available information and their independence and dependence relationships is proposed. The first part of this work reviews the main modules of a SV system, the possible sources of information as well as the basic concepts of graphical models. The second part deals with Modeling. A new approach to the problems associated with the SV systems is proposed. The problem of inference and learning (parameters and structure) in BNs are presented. In order to obtain an adapted structure the relations of conditional independence among the variables are learned directly from the data. These relations are then used in order to build an adapted BN. In particular, a new model adaptation technique for BN has been proposed. This adaptation is based on a measure between Conditional Probability Distributions for discrete variables and on Regression Matrix for continuous variables used to model the relationships. In a large database for the SV task, the results have confirmed the potential of use the BNs approach.
Research and Implement of Cache Fault Tolerance Technique

For the space application, cache rams are easy to be affected by single event upset (SEU). To protect against SEU error, it is necessary to design fault tolerance cache. Multiple bit-flips faults are increasing as integrated circuits continue to scale into the deep sub-micron regime, the error detection ability need to be improved, so an interleaving grouping parity error detection code scheme is proposed in this paper, each 32-bit data and each 19-bit tag adding 4 valid bits adopt 4 parity bits respectively, the adjacent 4 bits data are assigned to different groups, so all less than 8 bits burst faults can be detected. The hit structure and updating structure of the cache memory are redesigned. No timing penalty occurs since parity checking is performed in parallel with tag comparison. The experimental results show that, by using the proposed technique, there is a small performance loss, but the advantage is the higher reliability valuable for safety system. The failure rate of the cache memory is lower by 6 orders of magnitude.
SpaceGRID: the next generation internet as a new platform for the Earth Observation user's community

The aim of this paper is to present the results of the SpaceGRID project, funded by the ESA, at the end of 18 months of activities, focusing on the major outcomes of the analysis and the prototyping activities based on the next generation Grid Services architectures, also as a proof of concept for a new idea of Earth Observation enlarged Ground Segment.
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis

Latent factor models (LFMs) such as matrix factorization have achieved the state-of-the-art performance among various collaborative filtering approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is their lack of interpretability. Extensive efforts have been devoted to interpreting the prediction results of LFMs. However, they either rely on auxiliary information which may not be available in practice, or sacrifice recommendation accuracy for interpretability. Influence functions, stemming from robust statistics, have been developed to understand the effect of training points on the predictions of black-box models. Inspired by this, we propose a novel explanation method named FIA (Fast Influence Analysis) to understand the prediction of trained LFMs by tracing back to the training data with influence functions. We present how to employ influence functions to measure the impact of historical user-item interactions on the prediction results of LFMs and provide intuitive neighbor-style explanations based on the most influential interactions. Our proposed FIA exploits the characteristics of two important LFMs, matrix factorization and neural collaborative filtering, and is capable of accelerating the overall influence analysis process. We provide a detailed complexity analysis for FIA over LFMs and conduct extensive experiments to evaluate its performance using real-world datasets. The results demonstrate the effectiveness and efficiency of FIA, and the usefulness of the generated explanations for the recommendation results.
Using Wavelet Transforms for ECG Characterization

he rapid and objective measurement of timing intervals of the electrocarT diogram (ECG) by automated systems is superior to the subjective assessment of ECG morphology. The timing interval measurements are usually made from the onset to the termination of any component of the ECG, after accurate detection of the QRS complex. This article describes a real-time system that uses wavelet transforms to overcome the limitations of other methods of detecting QRS and the onsets and offsets of P- and T-waves. Wavelet transformation is briefly discussed, and detection methods and hardware and software aspects of the system are presented, as well as experimental results.
SCADA based nonparametric models for condition monitoring of a wind turbine

High operation and maintenance costs for offshore wind turbines push up the LCOE of offshore wind energy. Unscheduled maintenance due to unanticipated failures is the most prominent driver of the maintenance cost which reinforces the drive towards condition-based maintenance. SCADA based condition monitoring is a cost-effective approach where power curve used to assess the performance of a wind turbine. Such power curves are useful in identification of wind turbine abnormal behaviour. IEC standard 61400-12-1 outlines the guidelines for power curve modelling based on binning. However, establishing such a power curve takes considerable time and is far too slow to reflect changes in performance to be used directly for condition monitoring. To address this, data-driven, nonparametric models being used instead. Gaussian Process models and regression trees are commonly used nonlinear, nonparametric models useful in forecasting and prediction applications. In this paper, two nonparametric methods are proposed for power curve modelling. The Gaussian Process treated as the benchmark model, and a comparative analysis was undertaken using a Regression tree model; the advantages and limitations of each model will be outlined. The performance of these regression models is validated using readily available SCADA datasets from a healthy wind turbine operating under normal conditions.
Object Properties Influence Visual Guidance of Motor Actions

The dynamic nature of the real world poses challenges for predicting where best to allocate gaze during object interactions. The same object may require different visual guidance depending on its current or upcoming state. Here, we explore how object properties (the material and shape of objects) and object state (whether it is full of liquid, or to be set down in a crowded location) influence visual supervision while setting objects down, which is an element of object interaction that has been relatively neglected in the literature. In a liquid pouring task, we asked participants to move empty glasses to a filling station; to leave them empty, half fill, or completely fill them with water; and then move them again to a tray. During the first putdown (when the glasses were all empty), visual guidance was determined only by the type of glass being set down—with more unwieldy champagne flutes being more likely to be guided than other types of glasses. However, when the glasses were then filled, glass type no longer mattered, with the material and fill level predicting whether the glasses were set down with visual supervision: full, glass material containers were more likely to be guided than empty, plastic ones. The key finding from this research is that the visual system responds flexibly to dynamic changes in object properties, likely based on predictions of risk associated with setting-down the object unsupervised by vision. The factors that govern these mechanisms can vary within the same object as it changes state.
Transfer Learning for Cross-domain Visual Recognition

Machine learning algorithms usually require a huge amount of training examples to learn a new model from scratch and often fail to apply the learned model to test data acquired from the scenarios different from those of the training data mainly due to domain divergence and task divergence. Transfer learning tries to use previously available data, models or knowledge effectively for a new domain or task with scarce data. This thesis focuses on addressing the cross-domain visual recognition using transfer learning. First, a comprehensive literature review of transfer learning methods for cross-dataset visual recognition is presented by taking a problem-oriented perspective. Second, though there has been extensive research on unsupervised domain adaptation, the performance on the target domain is still far from comparable to that without distribution shift. Hence, a novel feature transformation-based method on unsupervised domain adaptation is proposed by taking both geometrical and statistical shift into consideration, and the performance is improved compared to previous methods. Third, a novel classifier-based unsupervised domain adaptation method is proposed by presenting a new perspective that the unsupervised domain adaptation can be formulated as a multi-task learning problem. This formulation removes the commonly used shared classifier assumption in previous methods and proposes unshared classifiers for the source and target domains for exploiting more domain specific features. Fourth, an important weighted adversarial nets-based method for partial domain adaptation is proposed, where the target domain has less number of classes compared to the source domain. Different from previous domain adaptation methods that generally assume the identical label spaces, a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes is considered. Last, a new research topic called multi-source domain expansion (MSDE) is introduced, which expands the source domains to include the new domain, such that the learned model is capable to perform well on both the new target domain and the old source domains. The MSDE is different from traditional domain adaptation whose target domain is defined only as the new domain excluding the source domains. MSDE is also different from multi-task learning, lifelong Learning, and incremental learning all of which assume no domain shift among different tasks. Specifically, a novel method is proposed for unsupervised MSDE without source data.
Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning

Image Captioning aims at generating a short description for an image. Existing research usually employs the architecture of CNN-RNN that views the generation as a sequential decision-making process and the entire dataset vocabulary is used as decoding space. They suffer from generating high frequent n-gram with irrelevant words. To tackle this problem, we propose to construct an image-grounded vocabulary, based on which, captions are generated with limitation and guidance. In specific, a novel hierarchical structure is proposed to construct the vocabulary incorporating both visual information and relations among words. For generation, we propose a word-aware RNN cell incorporating vocabulary information into the decoding process directly. Reinforce algorithm is employed to train the generator using constraint vocabulary as action space. Experimental results on MS COCO and Flickr30k show the effectiveness of our framework compared to some state-of-the-art models.
A Survey of Accelerometer-Based Techniques for Road Anomalies Detection and Characterization

In this paper, we present a decade survey of accelerometer-based sensor approaches proposed in literature for road surface conditions monitoring and anomalies detection. The main objective is to evaluate the performance of different documented accelerometer-based road anomaly detection techniques proposed, towards identifying their various strengths and weaknesses. We observed that a major challenge associated with these approaches, is in detection of the road anomaly and characterising it into potholes or speed-bumps directly from the irregularly fluctuating measured signals by the accelerometer. This drawback limits the efficacy of the proposed variants of accelerometer sensor-based approaches when used for road condition monitoring. Thus, future investigation will factor these in the design process towards aiding the choice of favourable techniques for future vehicle deployments by vehicle technology developers, as well as improvement for future unmanned vehicles. Furthermore, several open research issues that need to be addressed in designing and developing a robust accelerometer-based road monitoring system are highlighted in the conclusion.
Exploring the Augmentation of Fuzzing Techniques with Interface Awareness

Author(s): Corina, Jacob Mathew | Advisor(s): Vigna, Giovanni; Kruegel, Christopher | Abstract: Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse.In my thesis, I present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping of device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.
Mitigating Illusory Results Through Pre-Registration in Education

Like performance enhancing drugs inflating apparent athletic achievements, several common social science practices contribute to the production of illusory results. In this article, we examine the processes that lead to illusory findings and describe their consequences. Borrowing from an approach used increasingly by other disciplines—the norm of pre-registering studies—we examine how this practice could improve scientific integrity within education. As an initial attempt to develop guidelines that facilitate pre-registrations in education, we discuss the types of studies that ought to be pre-registered and the logistics of how educational researchers might execute pre-registrations. We conclude with ideas for how researchers, reviewers, and the field of education more broadly might speed the adoption of this new norm.
A Database of Dutch–English Cognates, Interlingual Homographs and Translation Equivalents

To investigate the structure of the bilingual mental lexicon, researchers in the field of bilingualism often use words that exist in multiple languages: cognates (which have the same meaning) and interlingual homographs (which have a different meaning). A high proportion of these studies have investigated language processing in Dutch–English bilinguals. Despite the abundance of research using such materials, few studies exist that have validated such materials. We conducted two rating experiments in which Dutch–English bilinguals rated the meaning, spelling and pronunciation similarity of pairs of Dutch and English words. On the basis of these results, we present a new database of Dutch–English identical cognates (e.g. “wolf”–“wolf”; n = 58), non-identical cognates (e.g. “kat”–“cat”; n = 74), interlingual homographs (e.g. “angel”–“angel”; n = 72) and translation equivalents (e.g. “wortel”–“carrot”; n = 78). The database can be accessed at http://osf.io/tcdxb/.