Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.
ePubWU Institutional Repository

In the last decade, the Resource Description Framework (RDF) has become the de-facto standard for publishing semantic data on the Web. This steady adoption has led to a significant increase in the number and volume of available RDF datasets, exceeding the capabilities of traditional RDF stores. This scenario has introduced severe big semantic data challenges when it comes to managing and querying RDF data at Web scale. Despite the existence of various off-the-shelf Big Data platforms, processing RDF in a distributed environment remains a significant challenge. In this position paper, based on an indepth analysis of the state of the art, we propose to manage large RDF datasets in Flink, a well-known scalable distributed Big Data processing framework. Our approach, which we refer to as FLINKer extends the native graph abstraction of Flink, called Gelly, with RDF graph and SPARQL query processing capabilities.
New Approaches in Synthetic Biology: Abiotic Organelles and Artificial Cells Powered and Controlled by Light

One of the major goals of nanobionics and synthetic biology is the development of artificial cell organelles for the creation of cell-like structures operating similar to biological systems with a minimalistic set of building blocks. To achieve this ambitious goal, two major design strategies are followed in the field of synthetic biology. The top-down approach tries to generate a radically trimmed but still intact artificial cell by eliminating all non-essential components from the much more complex native systems. In contrast, bottom-up synthetic biology aims at constructing a functioning minimal cell by combining together all essential building blocks step-by-step starting from scratch. In the present contribution, the author ́s ongoing activities to develop artificial reaction centers for novel types of photoautotrophic processes and to provide fully biocompatible synthetic enzyme counterparts and artificial organelles as abiotic building blocks for bottom-up synthetic biology are summarized. This unique approach has the potential to create unprecedented minimal artificial celllike systems that can be powered and readily controlled by light as an external stimulus.
Generative Models for Effective ML on Private, Decentralized Datasets

To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of raw data - of representative samples, of outliers, of misclassifications - is an essential tool in a) identifying and fixing problems in the data, b) generating new modeling hypotheses, and c) assigning or refining human-provided labels. However, manual data inspection is problematic for privacy sensitive datasets, such as those representing the behavior of real-world individuals. Furthermore, manual data inspection is impossible in the increasingly important setting of federated learning, where raw examples are stored at the edge and the modeler may only access aggregated outputs such as metrics or model parameters. This paper demonstrates that generative models - trained using federated methods and with formal differential privacy guarantees - can be used effectively to debug many commonly occurring data issues even when the data cannot be directly inspected. We explore these methods in applications to text with differentially private federated RNNs and to images using a novel algorithm for differentially private federated GANs.
Putting Privacy into Perspective - Comparing Technical, Legal, and Users' View of Data Sensitivity

Web 2.0, social media, cloud computing, and IoT easily connect people around the globe, overcoming time and space barriers, and offering manifold benefits. However, the technological advances and increased user participation generate novel challenges for protecting users' privacy. From the user perspective, data disclosure depends, in part, on the perceived sensitivity of that data, and thus on a risk assessment of data disclosure. But in light of the new technological opportunities to process and combine data, it is questionable whether users are able to adequately evaluate the risks of data disclosures. As mediating authority, data protection laws try to protect user data, granting enhanced protection to 'special categories' of data. In this publication, the legal, technological, and user perspectives on data sensitivity are presented and compared. From a technological perspective, all data can be referred to as 'potentially sensitive. ' The legal and user perspective on data sensitivity deviate as some data types are granted special protection by the law but are not perceived as very sensitive by the users, and vice versa. Merging the three perspectives, the implications for informational self-determination are discussed.
Retinal Layers Changes in Diabetic Patients without Diabetic Retinopathy

Purpose To evaluate retinal layers thickness in diabetic patients without diabetic retinopathy (DR) using spectral-domain optical coherence tomography (SD-OCT). To correlate retinal layers thickness in diabetic patients with disease duration, systemic blood pressure (BP), glycemia, glycosylate haemoglobin (HbA1c), intraocular pressure (IOP) and ocular pulse amplitude (OPA). Methods Prospective, observational case–control study. A total of 175 eyes from 175 patients (125 diabetic patients without DR; 50 healthy controls) were recruited from the outpatient clinic in a tertiary center. A complete ophthalmological examination was performed (visual acuity, refraction, Goldmann applanation and dynamic contour tonometries, fundoscopy, axial length and SD-OCT-Spectralis®). After automated retinal segmentation the thickness of each layer was calculated in the 9 ETDRS areas. Results A significant increase of retinal nerve fiber layer (RNFL) thickness in inner and outer rings was found in diabetic eyes vs. controls (p between <0.0001 and 0.02). Thickness was also increased in specific sites of ganglion cell layer (GCL), inner nuclear layer (INL) and retinal pigment epithelium (RPE) in diabetic eyes (p between 0.001 and 0.03). A significant decrease of outer plexiform layer (OPL) was detected in diabetic eyes at two sites of the inner ring (p = 0.04 and p < 0.0001). The different retinal layers weren't correlated with systemic BP, glycemia, HbA1c, IOP and OPA, but the photoreceptor layer was negatively correlated with disease duration at 3 sites (r between −0.18 and −0.21, p < 0.05). Conclusions In diabetic patients without DR thickness of the inner retina seems to be increased. This suggests that metabolic and morphological changes of the cells of these inner retinal layers occur early and before the apoptosis and neural degeneration.
Batch correction of high-dimensional data

Biomedical research often produces high-dimensional data confounded by batch effects such as systematic experimental variations, different protocols and subject identifiers. Without proper correction, low-dimensional representation of high-dimensional data might encode and reproduce the same systematic variations observed in the original data, and compromise the interpretation of the results. In this article, we propose a novel procedure to remove batch effects from low-dimensional embeddings obtained with t-SNE dimensionality reduction. The proposed methods are based on linear algebra and constrained optimization, leading to efficient algorithms and fast computation in many high-dimensional settings. Results on artificial single-cell transcription profiling data show that the proposed procedure successfully removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on cell types. When applied to single-cell gene expression data to investigate mouse medulloblastoma, the proposed method successfully removes batches related with mice identifiers and the date of the experiment, while preserving clusters of oligodendrocytes, astrocytes, and endothelial cells and microglia, which are expected to lie in the stroma within or adjacent to the tumors.
Stagewise Knowledge Distillation

The deployment of modern Deep Learning models requires high computational power. However, many applications are targeted for embedded devices like smartphones and wearables which lack such computational abilities. This necessitates compact networks that reduce computations while preserving the performance. Knowledge Distillation is one of the methods used to achieve this. Traditional Knowledge Distillation methods transfer knowledge from teacher to student in a single stage. We propose progressive stagewise training to improve the transfer of knowledge. We also show that this method works even with a fraction of the data used for training the teacher model, without compromising on the metric. This method can complement other model compression methods and also can be viewed as a generalized model compression technique.
Increasing energy efficiency in WSNs using wakeup signal length optimization combined with payload aggregation and FEC

Energy efficiency in wireless sensor networks is a vital question. There are several possibilities to achieve longer battery life in such devices. We investigated delay-tolerant wireless sensor networks with battery-operated nodes and use dataaggregation to lower the size of transmitted data overhead caused by packet headers. In this paper a mathematical formula is presented to calculate the optimal wakeup signal (a special radio signal) length, which minimizes the energy consumed for waking up nodes in sleep mode. The demonstrated results and graphs are based on the investigation of an existing system. The contribution of this paper is a general method to improve the energy efficiency of wireless sensor networks by using the optimal length of the wakeup signal in case of different amounts of aggregated packet payloads and Forward Error Correction (FEC) schemes. The results presented can be applied to arbitrary packet-based wireless protocols and radio modules supporting wakeup signal listening. Keywords—energy efficiency, wireless sensor networks, aggregation, sleep-wake cycle, wakeup signal length, WSN, FEC, DTN
Social Internet of Things (SIoT) Enabled System Model for Smart Integration of Building‟s Energy, Water and Safety Management: Dhaka City, Bangladesh Perspective

Dhaka, being the largest township of Bangladesh City Buildings is excreted by in-migration, a rapid growth of population, withering of living and infrastructure standard, which eventually is threatening overall sustainability and well beings. Modernization and digitalization of building infrastructure is not only an important step towards resolving the problems but also it will be a facilitator for smart, efficient and optimized urbanization. On May 2018, the authors conducted a survey among 51 Residential Building’s owner/building managers, 25 Non-residential Building’s owner/building managers and 25 corporate building’s owner/building managers to find market adoptable IoT solutions for building’s smart efficient energy, water and safety managements. The features requested in this survey are optimized and implemented by the authors and finally, here the system model with simulation results is presented. This system shows promising energy, water resource management optimization and some intriguing factors that validate its objectives, social characteristics, market usability.
STRENGTH REQUIREMENTS AND ENERGY EFFICIENCY OF DIFFERENT STAIR-STEPPING STRATEGIES IN PERSONS WITH CHRONIC STROKE AND HEALTHY ADULTS

ii Acknowledgements iv List of Figures viii List of Tables x List of Abbreviations xi Chapter 1 General Introduction 1 1.1 Thesis outline 3 1.2 References 5 Chapter 2 Literature Review 8 2.1 Physical requirements of stair ambulation 9 2.1.1 Kinetic and kinematic requirements of stair ambulation 9 2.1.2 Energy requirements of stair ambulation 12 2.2 Age-related changes in stair ambulation 14 2.3 Changes in stair ambulation in people with stroke 17 2.4 References 22 Chapter 3 Strength and aerobic demands of different stair-stepping strategies in healthy aging and stroke 31 3.
Rapid voluntary clench induces bite force reflex in human

Objective: The purpose of this study was to test the occlusal analysis system T-Scan II for the detection of a bite force reflex and to determine the net jaw reflex modulation during a rapid voluntary clench. Materials and Methods: Eleven subjects were asked to bite as fast as possible on the pressure-sensitive sheet using occlusal scanning system T-Scan II . The bite force reflex parameters were determined from the occlusal force curve. Results :The inhibitory bite force reflex occurred in nine subjects with a latency of 34.0 ± 7.8 ms, duration of 17.0 ± 2.8 ms and strength of 1.6% ± 0.3%. A strong correlation was detected between the latency of the reflex and the time to reach 80% of the maximal relative bite force (r = 0.833; p < 0.01). A moderate correlation was found between the relative bite force at the moment of the first turning point of the reflex and the change of this force during the reflex (r = 0.677; p < 0.05). Conclusion: The reflex modulation confirms the protective role of the jaw reflex: during faster bites the latency of the bite force reflex was shorter and with a higher bite force the strength of the reflex was greater. Keywords: Bite force reflex, Human, Occlusal scanning, Rapid voluntary clench, Relative bite force
Simple iterative method for generating targeted universal adversarial perturbations

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, a single perturbation known as the universal adversarial perturbation (UAP) can foil most classification tasks conducted by DNNs. Thus, different methods for generating UAPs are required to fully evaluate the vulnerability of DNNs. A realistic evaluation would be with cases that consider targeted attacks; wherein the generated UAP causes the DNN to classify an input into a specific class. However, the development of UAPs for targeted attacks has largely fallen behind that of UAPs for non-targeted attacks. Therefore, we propose a simple iterative method to generate UAPs for targeted attacks. Our method combines the simple iterative method for generating non-targeted UAPs and the fast gradient sign method for generating a targeted adversarial perturbation for an input. We applied the proposed method to state-of-the-art DNN models for image classification and proved the existence of almost imperceptible UAPs for targeted attacks; further, we demonstrated that such UAPs can be easily generated.
An Empirical Analysis on the Efficiency of Cost-of-Carry Model for Pricing NS EStock Exchange Futures Contracts with special reference to Nifty Midcap 50 Index Futures

The study attempts to empirically test the pricing efficiency of the COC model especially adopted as a standard for calculating the price of the index futures. To achieve the objectives closing prices of the Nifty Midcap 50 index future contracts from NSE were collected for six year from January 2010 to December 2016. The theoretical prices being computed by the Cost of carry model were then compared with the actual prices of the index futures contracts which were collected from the NSE’s website. The results validated the existence of mispricing in the calculation of future prices. Further, the extent of error was calculated with statistical forecasting models namely Mean Error (ME), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The outcomes have shown significant deviation from zero. Thus help us conclude that the prices being computed by the by the COC pricing model exhibit mispricing in index future derivatives.
To be Announced: Understanding and model checking Group Announcement Logic

In this master’s thesis we present a graphical model checking tool for group announcement logic called GALMC, capable of visualizing the process of checking formulas in a step-by-step fashion. We also define how to enumerate the set of ways a given coalition can restrict a model as well as present pseudocode algorithms describing how we translated these definitions in our model checker.
Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior

In this paper we address the problem of solving ill-posed inverse problems in imaging where the prior is a neural generative model. Specifically we consider the decoupled case where the prior is trained once and can be reused for many different log-concave degradation models without retraining. Whereas previous MAP-based approaches to this problem lead to highly non-convex optimization algorithms, our approach computes the joint (space-latent) MAP that naturally leads to alternate optimization algorithms and to the use of a stochastic encoder to accelerate computations. The resulting technique is called JPMAP because it performs Joint Posterior Maximization using an Autoencoding Prior. We show theoretical and experimental evidence that the proposed objective function is quite close to bi-convex. Indeed it satisfies a weak bi-convexity property which is sufficient to guarantee that our optimization scheme converges to a stationary point. Experimental results also show the higher quality of the solutions obtained by our JPMAP approach with respect to other non-convex MAP approaches which more often get stuck in spurious local optima.
Independent and automatic evaluation of acoustic-to-articulatory inversion models

Reconstruction of articulatory trajectories from the acoustic speech signal has been proposed for improving speech recognition and text-to-speech synthesis. However, to be useful in these settings, articulatory reconstruction must be speaker independent. Furthermore, as most research focuses on single, small datasets with few speakers, robust articulatory reconstrucion could profit from combining datasets. Standard evaluation measures such as root mean square error and Pearson correlation are inappropriate for evaluating the speaker-independence of models or the usefulness of combining datasets. We present a new evaluation for articulatory reconstruction which is independent of the articulatory data set used for training: the phone discrimination ABX task. We use the ABX measure to evaluate a Bi-LSTM based model trained on 3 datasets (14 speakers), and show that it gives information complementary to the standard measures, and enables us to evaluate the effects of dataset merging, as well as the speaker independence of the model.
A crossover code for high-dimensional composition

We present a novel way to encode compositional information in high-dimensional (HD) vectors. Inspired by chromosomal crossover, random HD vectors are recursively interwoven, with a fraction of one vector's components masked out and replaced by those from another using a context-dependent mask. Unlike many HD computing schemes, "crossover" codes highly overlap with their base elements' and sub-structures' codes without sacrificing relational information, allowing fast element readout and decoding by greedy reconstruction. Crossover is mathematically tractable and has several properties desirable for robust, flexible representation.
On Model Robustness Against Adversarial Examples

We study the model robustness against adversarial examples, referred to as small perturbed input data that may however fool many state-of-the-art deep learning models. Unlike previous research, we establish a novel theory addressing the robustness issue from the perspective of stability of the loss function in the small neighborhood of natural examples. We propose to exploit an energy function to describe the stability and prove that reducing such energy guarantees the robustness against adversarial examples. We also show that the traditional training methods including adversarial training with the $l_2$ norm constraint (AT) and Virtual Adversarial Training (VAT) tend to minimize the lower bound of our proposed energy function. We make an analysis showing that minimization of such lower bound can however lead to insufficient robustness within the neighborhood around the input sample. Furthermore, we design a more rational method with the energy regularization which proves to achieve better robustness than previous methods. Through a series of experiments, we demonstrate the superiority of our model on both supervised tasks and semi-supervised tasks. In particular, our proposed adversarial framework achieves the best performance compared with previous adversarial training methods on benchmark datasets MNIST, CIFAR-10, and SVHN. Importantly, they demonstrate much better robustness against adversarial examples than all the other comparison methods.
d’Albis, Hippolyte and Boubtane, Ekrame and Coulibaly, Dramane

This paper proposes a quantitative assessment of the interactions between the GDP per capita and the unemployment rate, and permanent immigration in France over the period 1994-2008. It uses a new database where immigration is measured by the flow of issuing long-term residence permits. The flow is distinguished by nationality of the migrant and depends on the reason for issuing the permit. Estimations of VAR models indicate that immigration increases GDP per capita, in particular, for family immigration. In addition, family immigration from developing countries reduces unemployment. Moreover, GDP per capita increases immigration while the unemployment rate reduces labor migration. JEL classification: E20, F22, J61