Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilient Service Embedding in IoT Networks

The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency.
What’s in my Room? Object Recognition on Indoor Panoramic Images

In the last few years, there has been a growing interest in taking advantage of the 360° panoramic images potential, while managing the new challenges they imply. While several tasks have been improved thanks to the contextual information these images offer, object recognition in indoor scenes still remains a challenging problem that has not been deeply investigated. This paper provides an object recognition system that performs object detection and semantic segmentation tasks by using a deep learning model adapted to match the nature of equirectangular images. From these results, instance segmentation masks are recovered, refined and transformed into 3D bounding boxes that are placed into the 3D model of the room. Quantitative and qualitative results support that our method outperforms the state of the art by a large margin and show a complete understanding of the main objects in indoor scenes.
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules

Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient's polysomnogram (PSG) from a single night. Although deep learning models have demonstrated state-of-the-art performance in automating sleep staging, interpretability which defines other desiderata, has largely remained unexplored. In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models. In particular, SLEEPER utilizes sleep scoring rules and expert defined features to derive prototypes which are embeddings of PSG data fragments via convolutional neural networks. The final models are simple interpretable models like a shallow decision tree defined over those phenotypes. We evaluated SLEEPER using two PSG datasets collected from sleep studies and demonstrated that SLEEPER could provide accurate sleep stage classification comparable to human experts and deep neural networks with about 85% ROC-AUC and . 7 kappa.
Improving Neural Chinese Word Segmentation Using Unlabeled Data

Supervised word segmentation heavily relies on large-scale and high quality labeled data. However, building such a corpus is difficult, especially with respect to domain specific data. In this paper, we propose a novel semi-supervised Chinese word segmentation (CWS) method. Specifically, we seek to select more useful sample sentences from the large-scale unlabeled sentences to extend the training data, by means of a sampling strategy that uses character-based semantic similarity. The presented similarity algorithm is used to calculate the similarity between unlabeled sentences and the training data, which can help select helpful sample sentences from unlabeled data. In addition, we integrate an attention mechanism into our word segmentation model to focus on available contextual information. Experiments on PKU, MSR and Weibo benchmark data sets show that our method outperforms the previous neural network models and state-of-the-art methods.
Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods

We are living at a time that allows the generation of mass data in almost any field of science. For instance, in pharmacogenomics, there exist a number of big data repositories, e.g., the Library of Integrated Network-based Cellular Signatures (LINCS) that provide millions of measurements on the genomics level. However, to translate these data into meaningful information, the data need to be analyzable. The first step for such an analysis is the deliberate selection of subsets of raw data for studying dedicated research questions. Unfortunately, this is a non-trivial problem when millions of individual data files are available with an intricate connection structure induced by experimental dependencies. In this paper, we argue for the need to introduce such search capabilities for big genomics data repositories with a specific discussion about LINCS. Specifically, we suggest the introduction of smart interfaces allowing the exploitation of the connections among individual raw data files, giving raise to a network structure, by graph-based searches.
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks

Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation in the Brain Tumors in Multimodal Magnetic Resonance Imaging Challenge 2019 that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set. The proposed method was ranked among the top in the task of Quantification of Uncertainty in Segmentation.
Low-Cost Low-Power Acceleration of a Microwave Imaging Algorithm for Brain Stroke Monitoring

Microwave imaging can effectively image the evolution of a hemorrhagic stroke thanks to the dielectric contrast between the blood and the surrounding brain tissues. To keep low both the form factor and the power consumption in a bedside device, we propose implementing a microwave imaging algorithm for stroke monitoring in a programmable system-on-chip, in which a simple ARM-based CPU offloads to an FPGA the heavy part of the computation. Compared to a full-software implementation in the ARM CPU, we obtain a 5× speed increase with hardware acceleration without loss in accuracy and precision.
How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time-consuming and expensive. Most of current studies on human attention and saliency modeling have used high-quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label-preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label-preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial networks (dubbed GazeGAN). A modified U-Net is utilized as the generator of the GazeGAN, which combines classic “skip connection” with a novel “center-surround connection” (CSC) module. Our proposed CSC module mitigates trivial artifacts while emphasizing semantic salient regions, and increases model nonlinearity, thus demonstrating better robustness against transformations. Extensive experiments and comparisons indicate that GazeGAN achieves state-of-the-art performance over multiple datasets. We also provide a comprehensive comparison of 22 saliency models on various transformed scenes, which contributes a new robustness benchmark to saliency community. Our code and dataset are available at: https://github.com/CZHQuality/Sal-CFS-GAN.
Pagoda: A Hybrid Approach to Enable Efficient Real-Time Provenance Based Intrusion Detection in Big Data Environments

Efficient intrusion detection and analysis of the security landscape in big data environments present challenge for today's users. Intrusion behavior can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. Existing intrusion detection methods typically analyze and identify the anomaly either in a single provenance path or the whole provenance graph, neither of which can achieve the benefit on both detection accuracy and detection time. We propose Pagoda, a hybrid approach that takes into account the anomaly degree of both a single provenance path and the whole provenance graph. It can identify intrusion quickly if a serious compromise has been found on one path, and can further improve the detection rate by considering the behavior representation in the whole provenance graph. Pagoda uses a persistent memory database to store provenance and aggregates multiple similar items into one provenance record to maximumly reduce unnecessary I/O during the detection analysis. In addition, it encodes duplicate items in the rule database and filters noise that does not contain intrusion information. The experimental results on a wide variety of real-world applications demonstrate its performance and efficiency.
Nonlinear PID actuator speed control in inverter based electro-hydraulic systems subjected to external leakage

ABSTRACT Actuators of constant speed operation in hydraulic power systems are highly required in many applications such as steel sheet rolling and pressing operations. To achieve this, a variable pump and/or control valve may be used to control the actuator speed in different operating conditions. A mathematical model of the selected hydraulic system and its components has been carried out using SIMULINK to study its static and dynamic characteristics. A direct comparison between the mathematical model results and experimental results of the same hydraulic system published by the author has been introduced. For the experimental and the mathematical models, a linear and nonlinear PID control algorithm has been applied for pump speed control and proportional directional control valve in the presence of system disturbance, herein is an external leakage, to maintain the actuator velocity constant either by controlling the pump speed or the proportional directional control valve with achieving the minimum error. The nonlinear control PID showed a better system response than the PID controller and reduced the error hence improved the system response. It has been shown that the proportional valve control is better than the pump speed control for different external leakage, 5–10%, with an error of the order 3% from the setting velocity.
Keys to successful balanced scorecard implementation and use based on published implementation attempts

In recent years many companies have evolved from being centrally located and managed to decentralized, multi-national companies consisting of many separate entities to be strategically managed. In response to this and other changes, such as the need for better measurement of performance, a strategic management tool was developed called the Balanced Scorecard (BSC). This research provides a tool to guide and evaluate BSC implementation. A meta-synthesis approach was used to examine qualitative BSC data available in the literature that suggested eleven keys to successful BSC implementation and use. These keys are then used to benchmark an implementation in a government logistics organization.
Security Model of Authentication at the Physical Layer and Performance Analysis over Fading Channels

Compared with conventional authentication schemes, which utilize cryptographic mechanisms and operate at an upper layer, authentication at the physical (PHY) layer possesses many advantages, such as, higher information-theoretic security by introducing uncertainty to an adversary, better efficiency, and better compatibility by avoiding any operations at the upper layer. An effective PHY-layer authentication scheme should consider covertness, security, and robustness together. However, in published literature, these three properties have been separately analyzed. This paper proposes a generic security model for PHY-layer authentication, and we design a new systematic metric, which is referred to as the probability of security authentication (PSA). According to the proposed generic security model, we can not only systematically analyze the effect of the parameter of a certain PHY-layer authentication scheme on the final performance, but also fairly compare the performance of different PHY-layer authentication schemes under the same channel conditions at the legitimate and adversary receivers. For performance analysis, the probability of detection (PD) and the probability of false alarm (PFA) of two PHY-layer authentication schemes over fading channels are defined, and their closed-form expressions are well derived. On the basis of the proposed generic security model, a systematic performance comparison between different PHY-layer schemes is provided.
Driver's Gaze Zone Estimation in Realistic Driving Environment by Kinect

Driver’s distraction is one of the main areas, which researchers are focusing on, in design of Advanced Drivers Assistance Systems (ADASs). Head pose and eye-gaze direction are two reliable indicators of a driver’s gaze and the current focus of attention. Compared with other methods that make use of head pose only, methods that combine eye information can achieve higher accuracy. The naturalistic driving environment always presents unique challenges (e.g., unstable illumination, jolts, etc.) to video-based gaze estimation and tracking systems. Some methods can achieve relatively high proficiency in the stationary laboratory environment, but they may not be suitable for the unstable driving environment. In addition, performing in real time or near-real time is another consideration for gaze estimation in an ADAS. Therefore, these special challenges need to be overcome to design ADASs. In this thesis, we proposed a new driver’s gaze zone estimation framework designed for the naturalistic driving environment. The framework combines head and eye information to estimate the gaze zone of the driver in both daytime and nighttime. The framework is composed of five main components: Facial Landmark Detection, Head Pose Estimation, Iris Center Detection, Upper Eyelid Information Extraction, and Gaze Zone Estimation. First, Constrained Local Neural Field (CLNF) is applied to obtain the facial landmarks in the image plane and the 3D model of the face in the object frame. In addition, extracting region of interest (ROI) is utilized as an optimization strategy for CLNF facial landmark detection. Second, head pose estimation can be regarded as a Perspective-n-Point (PnP) problem. Levenberg-Marquardt optimization method is used to solve the PnP problem based on the 2D landmark locations in the image plane and their corresponding 3D locations in the object frame. Third, a regression model-based method is employed to obtain the iris center from eye landmarks detected in the previous part. For upper eyelid information extraction, a quadratic function is utilized to model the upper eyelid, and the second-order coefficient is extracted. Finally, the head pose and the eye information are combined to form a feature vector, and Random Decision Forest classifier is utilized to estimate the current gaze zone of the driver from the feature vector extracted. The experiment is carried out in the realistic driving environment in both daytime and nighttime with three volunteers by Kinect sensor V2 for Windows that is put at the back of windshield. Weighted and unweighted accuracy are utilized as evaluation metrics in gaze zone estimation. Weighted accuracy evaluates gaze zones with different significance while unweighted accuracy treats each gaze zone equally. Experiment results show that the gaze zone estimation framework proposed in this work has better performance compared to the reference in the daytime. The weighted and unweighted accuracy of gaze zone estimation reach 96.6% and 95.0% for daytime, respectively. For nighttime, the weighted and unweighted accuracy can reach 96% and 91.4%.
Sonification of Flow Instabilities in CFD Aneurysm Models

— Recent investigations using image-based computational flow dynamics (CFD) have revealed turbulent-like blood flow instabilities (hemodynamics) within intracranial cerebral aneurysms and surrounding vasculature, which may contribute to aneurysm initiation, growth, and rupture. We describe a method derived from spectral decomposition of flow for inspecting and characterizing these “turbulent-like” hemodynamic structures in intracranial aneurysms by sonification of CFD generated data. Motivated by auscultation techniques, the current research seeks to address the limitations of conventional CFD animations by allowing the user to listen to complex flow patterns that are often difficult to discern visually. In the process, the proposed method of sonification is successfully applied to a series of datasets resulting from high-fidelity numerical simulations of intracranial aneurysms. The resulting framework is shown to be highly efficient in performing parametric sonification in real time.
Image edge detection based on Sparse Autoencoder network

Edge detection plays an important role in image pattern recognition. Because of the shortcomings of poor anti-noise and spurious edges by using traditional edge detection methods. A method of image edge detection based on Sparse Autoencoder neural work is proposed in this paper. This method uses Berkeley Segmentation data set to extract the highdimensional edge features of sample data by training the sparse autoencoder. Through the ZCA (Zero-phase Component Analysis) whitening treatment, the correlation between images is effectively reduced. The standard edge images are input into a Softmax classifier to train a classifier that can classify the edge features of each pixel. Last, the extracted features of each pixel sample are input into the trained Softmax classifier to classify the edge pixels to achieve edge detection. Experiments show that the algorithm has good noise immunity and certain application value.
3D video conversion system based on depth information extraction

3D movies have received more and more attention in recent years. However, the investment in making 3D movies is high and difficult, which restricts its development. And there are many existing 2D movie resources, and how to convert it into 3D movies is also a problem. Therefore, this paper proposes a 3D video conversion system based on depth information extraction. The system consists of four parts: segmentation of movie video frame sequences, extraction of frame image depth information, generation of virtual multi-viewpoint and synthesis of 3D video. The system can effectively extract the depth information of the movie and by it finally convert a 2D movie into
Colour Coded Emotion Classification in Mental Health Social Media

This research applies emotion detection to messages from online mental health social media. In particular, this focusses on specialised social media for users to report health or mental health problems. Automatically detecting the emotion in social media can help to rapidly identify any concerning problems which could benefit from intervention aiming to prevent self-harming or suicide. Detecting emotions enables messages to be colour coordinated according to the emotion to enhance the human-computer interaction. A supervised classification method is applied to a labelled dataset and results presented. A prototype user interface system is developed based on detecting emotion, colour coding the message to display detected emotions to users in real-time. Emotion
Control laws for pneumatic cylinder in order to emulate the Loss Of Resistance principle

Medicine requires advanced teaching methods in order to reach an efficient student training without having to train them directly on patients. In France, the Haute Autorite de la Sante (H.A.S.) has stated to "never [do] the first time with a patient" as a requirement for the training of new doctors. The goal of this work is to other a novel robotic solution to teach students how to perform an epidural anaesthesia. This medical operation can be divided intotwo different gestures: First the insertion of a needle between two vertebrae and second the application of pressure on the plunger all along the insertion of the needle. This works aims at simulating this second part as it has already been done on the first part in previous studies. We introduce a way to emulate the principle of loss of resistance felt by the anaesthetist when the needle reaches the appropriate depth.