Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronological Age Estimation Under the Guidance of Age-Related Facial Attributes

Although the researches of facial attributes’ analysis have been launched for decades, the estimation of chronological age attribute remains a big challenge. Previous researchers have found that some facial attributes (e.g., gender and race attributes) have close connections with the age attribute and make age estimation under a specific condition decided by various combinations of those age-related attributes which should be more reasonable. In this paper, we propose a generic framework based on a convolutional neural network, which can consider different conditions for age estimation and jointly output age and age-related facial attributes in the end. Compared with conventional methods, it is more efficient and universal. Besides, we view age estimation as a special multi-class ordinal classification problem and use a losses combination function to optimize the predicted probability distribution of individual age classes. These operations further improve the performance of age estimation. Finally, the proposed method achieves state-of-the-art results on both controlled and wild face datasets.
Design and Development of the I.T.U. Biped Robot

This paper presents the design and construction of a 12-DoF biped walking robot. The kinematics of electrically actuated 6-DoF legs is similar to that of human legs with three DoF at the hip, one at the knee and two at the ankle joints. The mechanical design of the robot was based on dynamical simulations realized in a modular PC environment. Two communicating software were used in order to solve the forward dynamics of the system and to design walking controllers. Forward, backward and lateral walking as well as stair climbing behaviors with up to 6 km/h forward walking speeds have been simulated in order to determine the nominal power rates required at joints. Hip joints are actuated by DC motors coupled to harmonic reducers situated both in joint axes. The actuators driving the knee and ankle joints are situated higher than the respective joint axes and the rotational output motion of DC motors are transmitted to the joints through linear ball-screw mechanisms. Spherical joints are used within the transmission of spatial motion required for the 2-DoF at ankle joints. All joints consist of absolute encoders and 6-axes force/torque transducers are mounted at the ankle joints. The robot is controlled through an embedded industrial PC running real-time operating system. All electronic control hardware including the motor drivers and sensors communicate through CAN bus. The robot’s mass without batteries is 55 kg and its height is 142 cm.
IoT Platforms for Building Automation with Energy Efficiency and Comfort Concerns

It is increasingly common to work and live in buildings controlled by some system, the so-called Building Automation Systems, where to keep the levels of comfort and reduce energy consumption are very important requirements. These systems control from heating, ventilation, air conditioning, to lights intensity, with the goal of reducing energy costs and make the building occupants satisfied. However, these systems are usually proprietary and have high costs associated, due to the required equipment to deal with all the devices and the distinct communications. Therefore, our goal is to reduce reduce these costs, which is quite difficult due to the vast devices heterogeneity. In this dissertation, we implement a Building Automation System taking advantage of existing Internet of Things (IoT) solutions. Thus, this thesis explores how IoT solutions can fit adequately into the scenario of building automation. To validate our technological choices and evaluate the adequacy of the chosen middleware, we made use of an existing case study of a room with multiple components and an aquarium as a subsystem. We have compared different IoT approaches and their impact on the energy consumption and occupants comfort. The results obtained helped us to realise that in fact there are several aspects that can be enhanced in order to reduce energy consumption and maintain occupants’ comfort. An initial investment in the implementation of these systems may involve different types of equipment and development effort to achieve the desired solution. However in long term, it is worth the effort and initial investment on these systems since they can actually reduce the energy consumption and guarantee good conditions for the room occupants.
Ethical Framework for Designing Autonomous Intelligent Systems

To gain the potential benefit of autonomous intelligent systems, their design and development need to be aligned with fundamental values and ethical principles. We need new design approaches, methodologies and processes to deploy ethical thought and action in the contexts of autonomous intelligent systems. To open this discussion, this article presents a review of ethical principles in the context of artificial intelligence design, and introduces an ethical framework for designing autonomous intelligent systems. The framework is based on an iterative, multidisciplinary perspective yet a systematic discussion during an Autonomous Intelligent Systems (AIS) design process, and on relevant ethical principles for the concept design of autonomous systems. We propose using scenarios as a tool to capture the essential user’s or stakeholder’s specific qualitative information, which is needed for a systematic analysis of ethical issues in the specific design case.
A formal and automated approach to exploiting multi-stage attacks of web applications

The complexity of modern web applications, due to the implementation of new services, has rapidly increased the need of new automatic security analysis methods and tools. Today, the leading methodology for the security analysis of web applications is a combination of vulnerability assessment and penetration testing. Vulnerability assessment has received much attention and several tools have been proposed to identify vulnerabilities. On the other hand, penetration testing has been left to the experience of the security analyst. In this thesis, I address this problem by proposing a formal, model-based testing approach for the security analysis of web applications that can support the penetration testing phase. The approach I propose is based on the formal definition of web applications and their vulnerabilities which allow one to (i) reason about vulnerabilities of web applications and (ii) combine multiple vulnerabilities for the identification of complex, multi-stage attacks. I have developed WAFEx, an automated tool that implements my approach and I show its efficiency by applying it to real-world case studies. WAFEx was able to find previously unknown attacks, which are witness to the fact that WAFEx can generate, and exploit, attacks that, to the best of my knowledge, no other tool for the security analysis of web applications can find.
A Hybrid Genetic Algorithm and Parallel Variable Neighborhood Search for Jobshop Scheduling With an Assembly Stage

Neighborhood Search. In this research, a job shop scheduling problem with an assembly stage is studied. The objective function is to find a schedule that minimizes the completion time of all products. At first, a linear model is introduced to express the problem. Then, in order to confirm the accuracy of the model and to explore the efficiency of the algorithms, the model is solved by GAMS. Since the job shop scheduling problem with an assembly stage is considered as an NP-hard problem, a hybrid algorithm is used to solve the problem in medium to large sizes in a reasonable amount of time. This algorithm is based on genetic algorithm and parallel variable neighborhood search. The results of the proposed algorithms are compared with those of genetic algorithm. Computational results showed that, for small problems, both HGAPVNS and GA have approximately the same performance. In addition, in medium to large problems, HGAPVNS outperforms GA.
SmartCell: An Energy Efficient Reconfigurable Architecture for Stream Processing

Data streaming applications, such as signal processing, multimedia applications, often require high computing capacity, yet also have stringent power constraints, especially in portable devices. General purpose processors can no longer meet these requirements due to their sequential software execution. Although fixed logic ASICs are usually able to achieve the best performance and energy efficiency, ASIC solutions are expensive to design and their lack of flexibility makes them unable to accommodate functional changes or new system requirements. Reconfigurable systems have long been proposed to bridge the gap between the flexibility of software processors and performance of hardware circuits. Unfortunately, mainstream reconfigurable FPGA designs suffer from high cost of area, power consumption and speed due to the routing area overhead and timing penalty of their bit-level fine granularity. In this dissertation, we present an architecture design, application mapping and performance evaluation of a novel coarse-grained reconfigurable architecture, named SmartCell, for data streaming applications. The system tiles a large number of computing cell units in a 2D mesh structure, with four coarse-grained processing elements developed inside each cell to form a quad structure. Based on this structure, a hierarchical reconfigurable network is developed to provide flexible on-chip communication among computing resources: including fully connected crossbar, nearest neighbor connection and clustered mesh network. SmartCell can be configured to operate in various computing modes, including SIMD, MIMD and systolic array styles to fit for different application requirements. The coarse-grained SmartCell has the potential to improve the power and energy efficiency compared with fine-grained FPGAs. It is also able to provide high performance comparable to the fixed function ASICs through deep pipelining and large amount of computing parallelism. Dynamic reconfiguration is also addressed in this dissertation. To evaluate its performance, a set of benchmark applications has been successfully mapped onto the SmartCell system, ranging from signal processing, multimedia applications to scientific computing and data encryption. A 4 by 4 SmartCell prototype system was initially designed in CMOS standard cell ASIC with 0.13 μm process. The chip occupies 8.2 mm2 and dissipates 1.6 mW/MHz under fully operation. The results show that
STaDA: Style Transfer as Data Augmentation

The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer algorithms can apply the artistic style of one image to another image without changing the latter's high-level semantic content, which makes it feasible to employ neural style transfer as a data augmentation method to add more variation to the training dataset. The contribution of this paper is a thorough evaluation of the effectiveness of the neural style transfer as a data augmentation method for image classification tasks. We explore the state-of-the-art neural style transfer algorithms and apply them as a data augmentation method on Caltech 101 and Caltech 256 dataset, where we found around 2% improvement from 83% to 85% of the image classification accuracy with VGG16, compared with traditional data augmentation strategies. We also combine this new method with conventional data augmentation approaches to further improve the performance of image classification. This work shows the potential of neural style transfer in computer vision field, such as helping us to reduce the difficulty of collecting sufficient labelled data and improve the performance of generic image-based deep learning algorithms.
Three Filters and Their Applications: A Comparison Case Study

Filtering has been shown successful in prediction from dynamically changing data. In this thesis, we perform case studies and comparison among three filters: Kalman filter, unscented Kalman filter and particle flow filter. We consider Kalman filter in the first chapter where we focus on studying the S&P model in a time-discrete dynamics with time-discrete observations for dividend yield and S&P returns. For this filtering problem, Kalman filter performs well only in the first few time steps. Since the S&P model we consider is nonlinear, we are motivated to apply nonlinear filters and use unscented Kalman filter. The key technique is to approximate non-Gaussian processes (non-linear models) by assigning the so-called sigma points (nonrandom) around the priori mean. We implement it on the S&P model in Chapter 2. We also implement unscented Kalman filter for a two-dimensional tumor growth model. Unscented Kalman filter works reasonably well for both models with capturing the trend and predicting the values. We consider the recently-developed particle flow filter in Chapter 3. Particle flow filter is a method of moving the particles by partial differential equations generated from proper chosen likelihood functions via the Bayes rule. By solving partial differential equations, one can construct an explicit dynamic model on how to move particles. In this chapter, we implement two models as in Chapter 2. One is the S&P model and the other is perturbed tumor growth model. We compare performance of particle flow filter and unscented Kalman filter for these two models.
HyFAT: Affordable Hybrid Fast Address Translating Device Driver for Multichannel-Based Flash Devices

Artificial intelligence (AI)-based consumer electronic (CE) devices generate massive amounts of accumulated hot data. Persisting this data into such devices’ storage media like the universal flash storage (USF) becomes an issue for flash storage system to manage hot data optimally. Consequently, efforts have been made to support many CE applications and achieve high performance and scalability storage in CE devices. However, it is challenging to find a compatible flash translation layer (FTL) for such flash devices, which can efficiently handle frequent updates imposed by the incoming hot data. Moreover, FTL should be able to handle the hot multimedia data that induces cache-miss penalties which in-turn degrades our flash storage performance. This paper proposes a hybrid flash device for fast address translations called HyFAT which is workload-compatible. HyFAT improves device performance and efficiently manages the combined QuadLevel cell (QLC) for data storage and scalability with SingleLevel cell (SLC) for fast mapping entry storage. The experimental results with realistic workloads show that our approach can improve device performance throughput by 18% on average, compared to traditional DFTL and RFTL.
Bio-inspired Event-based Motion Analysis with Spiking Neural Networks

This paper presents an original approach to analyze the motion of a moving pattern with a Spiking Neural Network, using visual data encoded in the Address-Event Representation. Our objective is to identify a minimal network structure able to recognize the motion direction of a simple binary pattern. For this purpose, we generated synthetic data of 3 different patterns moving in 4 directions, and we designed several variants of a one-layer fully-connected feed-forward spiking neural network with varying number of neurons in the output layer. The networks are trained in an unsupervised manner by presenting the synthetic temporal data to the network for a few seconds. The experimental results show that such networks quickly converged to a state where input classes can be successfully distinguished for 2 of the considered patterns, no network configuration did converge for the third pattern. In the convergence cases, the network proved a remarkable stability for several output layer sizes. We also show that the sequential order of presentation of classes impacts the ability of the network to learn the input.
Fusional Recognition for Depressive Tendency With Multi-Modal Feature

In recent years, people with depression tend to be younger and normalized. Although current findings can identify the depressive tendency, the effects of recognition are unsatisfactory. Therefore, this paper aims to integrate multi-modal features and build a more objective and accurate model to identify the depressive tendency effectively. First, we combine mental health self-examination data and eye tracking data to extract multi-modal features. Explicitly, we integrate a variety of functions, including eye movements, memory features, cognitive style features, and network behavior. Second, we innovatively propose a scanning stacking model to capture complex nonlinear relationships between features. Finally, the effectiveness of our proposed method is verified by experiments. The experimental results show that our multi-modal based scanning stacking method is superior to the state-of-the-art detection methods.
Power System Optimization of Static VAR Compensator Using Novel Global Harmony Search Method

The increasing number of electrical loads and their far away location from the power plant sites are becoming the main causes of the degradation of voltage quality and the increase of power system losses. The problems may be difficult to be handled if the reactive power supply is limited. One effort to overcome can be done through the use of reactive power compensation using bank capacitors and Static VAR Compensator (SVC). In this paper, the optimal location and rating of capacitor bank and SVC are determined by exploring the novel global harmony search (NGHS) method. The performance of the NGHS method is tested by using the real data of JAMALI500 kV system. Two considered methods in controlling the SVC covered the Voltage Control (VC) and the reactive power control (QC). The optimization using the NGHS method resulted in the optimal location of the SVC on buses 15, 20 and 25 with a total rating of 1443.14 MVAR for the VC method and on the 15, 19, 20 buses with a total rating of 1773.84 MVAR for the QC method. The achieved power losses improvements using Bank Capacitors, SVC with VC, and SVC with QC were 9.27%, 13.19%, and 15.63% respectively, being compared to a given base-case. The simulation results showed that the SVC with reactive power control is better than the use of the other compensation approach considered. 
Mathematical Analysis of a Mathematical Model of Chemovirotherapy: Effect of Drug Infusion Method

A mathematical model for the treatment of cancer using chemovirotherapy is developed with the aim of determining the efficacy of three drug infusion methods: constant, single bolus, and periodic treatments. The model is in the form of ODEs and is further extended into DDEs to account for delays as a result of the infection of tumor cells by the virus and chemotherapeutic drug responses. Analysis of the model is carried out for each of the three drug infusion methods. Analytic solutions are determined where possible and stability analysis of both steady state solutions for the ODEs and DDEs is presented. The results indicate that constant and periodic drug infusion methods are more efficient compared to a single bolus injection. Numerical simulations show that with a large virus burst size, irrespective of the drug infusion method, chemovirotherapy is highly effective compared to either treatments. The simulations further show that both delays increase the period within which a tumor can be cleared from body tissue.
3D Cylinder Pose Estimation by Maximization of Binary Masks Similarity: A simulation Study for Multispectral Endoscopy Image Registration

In this paper we address the problem of simultaneous pose estimation for multi-modal registration of images captured using a fiberscope (multispectral) inserted through the instrument channel of a commercial endoscope (RGB). We developed a virtual frame using the homography-derived extrinsics parameters using a chessboard pattern to estimate the initial pose of both cameras and simulate two types of fiberscope movements (i.e, insertion and precession). The fiberscope pose is calculated by the maximization of similarity measures between the 2D projection of the simulated fiberscope and the fiberscope tip segmentation from the endoscopic images. We used the virtual frame to generate sets of synthetic fiberscope data at two different poses and compared them after the maximization of similarity. The performance was assessed by measuring the reprojection error of the control points for each pair of images and the pose absolute error in a sequential movement mimicking scenario. The mean reprojection error was 0.38 ± 0.5 pixels and absolute error in the tracking scenario was 0.05 ± 0.07 mm.
Design of experiments and goodness-of-fit in comparison of velocity fields in curved open channel flow simulation

In this paper, a methodology is suggested to evaluate the goodness-of-fit between observed and numerically predicted variables in curved open channel flows. The mathematical model uses the continuity and the Reynolds averaged Navier–Stokes (RANS) equations with the RNG κ-ɛ turbulence model, numerically solved with the commercial software Ansys Inc. The concept of design of experiments (DOE) together with some criteria of goodness-of-fit was used in a new coherent suggested methodology for the calibration and validation of the numerical model. The goodness-of-fit criterion was applied to secondary flow variables like helicity, intensity of the secondary current and the vortex index instead of the primary variables like the velocity field and the free surface. This methodology guarantees that the numerical model reproduces values of the response variables very near to the observed values and it is valid for the mesh independence analysis and the setup of other numerical parameters, including those related to the boundary conditions.
Semantic Segmentation via Global Convolutional Network and Concatenated Feature Maps

Most of the segmentation CNNs (convolutional neural network) based on the ResNet. Recently, Huang et al. introduced a new classification CNN called DenseNet. Then Jégou et al. used a sequence of building blocks for DenseNet to build their semantic segmentation CNN, called FC-DenseNet, and achieved state-ofthe-art results on CamVid dataset. In this paper, we implement the design concept of DenseNet into a ResNet-based semantic segmentation CNN called Global Convolutional Network (GCN) and build our own network by switching every identity mapping operation of the decoder network in GCN to a concatenation operation. Our network uses less computational resources than FC-DenseNet to obtain a mean IoU score of 69.34% on CamVid dataset, and surpass the 66.9% obtained in the paper of FC-DenseNet.
A Reinforcement Learning Based Smart Cache Strategy for Cache-Aided Ultra-Dense Network

The integration of caching and ultra-dense network (UDN) can not only improve the efficiency of content retrieval by reducing duplicate content transmissions but also improve the network throughput and system energy efficiency (EE) of the UDN. In this paper, we focus on energy consumption aspects of cache-aided UDN (CUDN) and develop a novel caching strategy to improve the system EE. Different from the existing researches, we consider a more realistic scenario where the popularity of the cache content is dynamic and unknown. The proposed caching strategy is a deep reinforcement learning (DRL)-based approach that uses a deep Q-network to approximate the Q action-value function. We optimize the structure and corresponding parameters of the deep Q-network according to the latest findings in the field of DRL and deep learning (DL) to improve the performance of this caching strategy. The simulation results show that the performance in terms of EE of the CUDN can be significantly improved by using our proposed caching strategy.