Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio and Video-based Emotion Recognition using Multimodal Transformers

Emotion recognition, an important research problem in human-robot interactions, is primarily achieved by extracting human emotions from audio and visual data. State-of-the-art performance is reported by audio-visual sensor fusion algorithms using deep learning models such as CNN, RNN, and LSTM. However, the RNN and LSTM are shown to be limited in handling the long-term dependencies over the entire input sequence. In this work, we propose to improve the performance of audio-visual emotion recognition using a novel transformer-based model, containing three transformer branches, named multimodal transformers. The three transformer branches, in our work, compute the audio self-attention, the video self-attention, and the audio-video cross attention. The self-attention branches identify the most relevant information in the audio and video input, while the cross-attention branch identifies the most relevant audio-video interactive information. The relevant information from these three branches report the best performance in our ablation study. We also propose a novel temporal embedding scheme, termed block embedding, to add the temporal information to the visual feature, derived from the multiple frames in the video. The proposed architecture is validated using the RAVDESS, CREMA-D, and SAVEE audio-visual public datasets. A detailed ablation study and comparative analysis with baseline models is performed. The results show that the proposed multi-modal transformer framework is better than the baseline methods.
Anomaly Detection on Real-time Security Log using Stream Processing

Many critical tasks such as document approval and banking services, which are now hosted on cloud infrastructure. This transformation introduces stress on cloud security from the physical layer of the data center to the application layer of web application. All data access and service access need to be monitored and responded to in real-time. In this paper, we study methods to detect anomaly incidents such as spikes from network volume, malicious incidents from API scanning, error messages from internal systems and timeout from Slowloris attack[l]. We select machine learning based anomaly detection algorithms, such as LOF, Isolation Forest and Elliptic Envelope, to find suitable methods to detect incidents in real-time using stream processing tools including Kafka and message ingression. The result shows that LOF is fast and robust in most of the cases. However, when log messages have unseen words, which normally need to be hashed to preprocess, the Isolation Forest shows better results. This study shows the possibility of applying stream processing with machine learning to detect anomaly behavior for cloud services.
Performance Comparison of Transverse and Axially Laminated Synchronous Reluctance Machines

Transverse and axially laminated synchronous reluctance machines were designed out of a 6-pole and a 2-pole 0.75kW induction machines and compared. Two-pole synchronous reluctance machines are not common and are interesting to study, while six poles might be the best and most feasible upper limit for number of poles for this type of machine. Results show differences between the performance of axial and transverse laminated machines. The 6-pole transverse laminated machine appeared to have superior performance in several respects than the axial version, while the opposite held for the 2-pole machines
A Near Memory Computing FPGA Architecture for Neural Network Acceleration

The Deep Learning Accelerators (DLAs) are gaining attention in recent years due to their advantages in effi-ciency, privacy, and bandwidth usage efficiency to operate deep neural networks. Field Programmable Gate Arrays (FPGAs) can offer low-power computation capacity, which is profound for the deployment of DLAs in AI edge computing devices. However, there are two major problems in deploying DLAsin FPGA: Firstly, processor and memory units in FPGA are separated, and the data transfer between them requires a large energy overhead. Secondly, the complexity of Deep Neural Network (DNN) models and the variety of FPGA platforms make it hard to design hardware accelerators when taking into account both performance and energy efficiency simultaneously. Near-Memory-Computing (NMC) has been a promising candidate to accelerate neural network computing. Therefore, this paper proposes an NMC FPGA architecture, which can be adapted to various neural network models by using Verilog-to-Routing (VTR) tool.
How the First Year of a Work-based Degree in Software Engineering Prepares Students for Industry

This Practice Work-in-Progress paper presents a work-based undergraduate degree program in Software Engineering, designed in consultation with industry. Work-based learning is often seen as a means of ensuring university graduates are equipped with the skills and knowledge required to succeed in the workplace. The purpose of this study was to determine the extent to which the program has prepared students for working in industry. To this end, the students were surveyed after they had completed their first semester, and returned to their workplace for two months. Qualitative analysis of survey data revealed the aspects of their first year courses that apprentices valued most in the workplace. These aspects included exposure to multiple programming languages and existing codebases, as well as knowledge of Software Engineering tools and practices. Underpinning many of the students’ responses, however, was the increased confidence that their university instruction afforded them in the workplace.
Control design of high-speed train against uncertain parameters and external disturbances

Considering the uncertainty of the internal parameters of high-speed train and the influence of external disturbances, based on the generalized internal-mode control framework(GIMC), H2 control and $\mu -$synthesis methods are used to design a nominal controller that satisfies tracking performance and a robust controller with rejection to disturbances, respectively. A dynamic model of a high-speed train with internal uncertain parameters coupled by multiple carriages via train couplers and subject to air resistance, mechanical resistance and sudden gust disturbances is established. The corresponding controller is designed by the above method, and the dynamic model of CRH2 high-speed train is simulated to verify the tracking performance and anti-disturbance performance of the control design.
PRNU-Net: a Deep Learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone

Recent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and Machine Learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. The experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.
Stealthy Clustering of Ethereum Transactions Using Time Leakage from Fixed Nodes

In recent years, data mining with users as entities has become a fundamental tool for digital currency investigations. In Ethereum, researchers can perform transaction clustering based on users' behavioral habits and usage patterns in specific scenarios. However, the major shortcoming of these technologies is that they cover only a small fraction compared to the huge number of transactions. In this paper, we propose a stealthy clustering of Ethereum transactions using time leakage from fixed nodes (CIFO), which is applied to all transactions, while also being stealthy and difficult to detect. Leveraging timestamps relayed from $N$ fixed nodes, CIFO can better describe the network properties of transactions, in compared with the first $N$ timestamps. The experimental results show that CIFO can determine whether two transactions enter the network from the same source with the precision of 87%.
On Gender Disparity in Engineering Academia

This Research, Full Paper presents a study that sheds some light on the gender related disparities that women face in engineering academia. STEM has always been a male dominated field with, for example, less than ten percent women in electrical engineering jobs, a figure that continues in academia, but dwindles when it comes to senior academic positions at universities. Though studies have uncovered that such disparities exist in academia, little is known about difficulties that female engineering university teachers face in low- and middle-income countries in South Asia. In this study, twenty-five female (N=25) and eighteen male (N=18) university teachers from the electrical engineering and computer science departments of three different universities in Pakistan were interviewed, using a semi-structured interview methodology. The interviewees had between one and twenty five years of teaching experience, holding junior teaching positions, all the way to Professor. Interview questions were mainly related to job responsibilities, tasks assigned, authorities given to them and comparisons to male colleagues. Data collected was analyzed using phenomenological research techniques, with different meaning units listed and then clustered into seven common themes. Challenges related to gender disparity in academia were mainly reported by female participants. Only five of the women in this study had persevered despite challenges and had either improved or were in the process of improving their qualification. These were also the women that seemed more driven and claimed to have had more support from their family, home and from the workplace. The rest of the women were somewhat uncertain about their professional progress and preferred to remain in their ‘comfort zone’ i.e. at a lower rank and lesser salary. The importance of this study is in the fact that it highlights the problems that female academics face in a region of the world where such studies are few and far between. Though women university teachers in Asia and the Middle East could relate more to these problems, the insights gained from this study could benefit women in this profession in any part of the world.
Mathematical Derivation of Current Reference for Radial-Force Sum Flattening in Switched Reluctance Motors

Switched reluctance motors generate a variation of radial forces upon the stator poles, causing considerable vibration and acoustic noise. This paper introduces a novel mathematical derivation of the current waveform to flatten the radial-force sum. The proposed mathematical method takes into consideration the phase shifts of three current components, hence providing a comprehensive flattening solution. The proposed method was implemented and validated through 3D finite element analysis, on an 18/12 switched reluctance motor. The results illustrated an effective correspondence between the proposed mathematical method and the simulation model, as well as reducing the acoustic noise components, especially the third and sixth components.
Predictive Control and Modulation Scheme for Matrix Converters Featuring Constant Switching Frequency

Matrix Converter (MC) is a compact and power efficient replacement of the traditional two-stage ACIDC/AC power conversion. This paper proposes a novel control and modulation scheme that combines the advantages of Model Predictive Control (MPC) with constant switching frequency modulation. The proposed control is based on inverse system models. The proposed modulation scheme utilizes cost functions. The reference voltage and current vectors are reproduced with high accuracy. The paper demonstrates advantages of the proposed modulation scheme in comparison to the traditional control with SVM, conventional MPC and Modulated MPC, with respect to both tracking errors and harmonic distortions. The results are validated by simulations.
Comparison of Performance of EEG-Based Depression classification

In recent years, the incidence of depression is increasing year by year, and depression lasts too long after the onset of the disease, which seriously hinders people's normal working life. In this study, based on the characteristics of scalp EEG signals, we compared the classification performance of Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM) classification models for depression by using 16 channels of clean EEG data, and the accuracy of the model using the combination of CNN and LSTM was improved about 9.21%, which confirms that the use of LSTM to help process EEG signals and improve classification is real and effective, and the effect of model parameters on model performance is discussed at the end of the paper to adjust model parameters and algorithms to improve the performance of classification.
Experimental Investigation on Vibro-Acoustic Characteristics of Stiffened Plate Structures with Different Welding Parameters

Varied welding process parameters result in different welding energy inputs and welding residual stresses, significantly impacting the vibro-acoustic characteristics. This work investigated the influence of different welding energy inputs on the vibro-acoustic characteristics of the stiffened plate structure. Several experiments on the stiffened plate structure with different welding energy inputs were conducted regarding modal, underwater vibration, and acoustic radiation. The results revealed that welding energy input had the most significant impact on the first-order natural frequency, and the impact first becomes higher and subsequently decreases as welding energy input increases. The welding energy input had relatively little effect on the peak point distribution of vibration and acoustic radiation curves but could affect the peak point amplitude. With the increase in welding energy input, the overall vibration acceleration level and sound radiation level in each frequency band decreased and then increased. The best result was obtained when the welding energy input was 167 J/cm with a welding current of 200 A, a welding voltage of 25 V, and a welding speed of 3.02–3.06 mm/s. Based on construction technology, this research can provide some instructive insights for enhancing the acoustic stealth performance of ships and marine structures.
Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders

The susceptibility of Variational Autoencoders (VAEs) to adversarial attacks indicates the necessity to evaluate the robustness of the learned representations along with the generation performance. The vulnerability of VAEs has been attributed to the limitations associated with their variational formulation. Deterministic au-toencoders could overcome the practical limitations associated with VAEs and offer a promising alternative for image generation applications. In this work, we propose an adversarially robust deterministic autoencoder with superior performance in terms of both generation and robustness of the learned representations. We introduce a regularization scheme to incorporate adversarially perturbed data points to the training pipeline without increasing the computational complexity or compromising the generation fidelity when compared to the robust VAEs by leveraging a loss based on the two-point Kolmogorov–Smirnov test between representations. We conduct extensive experimental studies on popular image benchmark datasets to quantify the robustness of the proposed approach based on the adversarial attacks targeted at VAEs. Our empirical findings show that the proposed method achieves significant performance in both robustness and fidelity when compared to the robust VAE models. An implementation is available at https://github.com/boschresearch/Robust_GMM_DAE .
Velocity Zero Crossing Detection-Based Variablestep Perturbation and Observation for Wave Power Generation System

Based on the mathematical model of the direct-drive wave generator system, while combining the potential energy theory of hydrodynamics, the relationship among the velocity, electromagnetic force, electromagnetic power, and damping coefficient of the permanent magnet linear generator is analyzed in the frequency domain. As a result, the condition that the electromagnetic power reaches the maximum point is obtained, and the curves of the maximum power point and instantaneous power with respect to the damping coefficient are performed. According to the characteristics of wave period variation, aiming at the phenomenon that the perturbation period of traditional perturbation observation method is difficult to select and the perturbation step size error caused by instantaneous power ripple error, a variable-step size velocity zero-crossing detection-based perturbation observation method is proposed to realize maximum power point tracking. Simulation results demonstrate that this method is a fast convergence and high accuracy rectifier side control strategy for direct-drive power generation system.
Design Optimization and Comparison of PM-Assisted Synchronous Reluctance Machine using Different Magnet Combinations

The aim of this paper is to introduce motor topologies that combine different types of Permanent Magnets (PM) in order to reduce the expensive rare-earth PM content while maintaining the torque capability equivalent to the Chevy-Bolt traction motor, taken here as the baseline design, at a minimum risk of permanent demagnetization of magnets. For this purpose, five different magnet combinations are proposed, namely - (i) NdFeB and Ferrite, (ii) NdFeB and Iron-Nitride, (iii) Dysprosium (Dy) Free NdFeB and Ferrite, (iv) DyFree NdFeB and Iron-Nitride, and (v) Iron-Nitride and Ferrite. A Permanent Magnet assisted Synchronous Reluctance Machine (PMASynRM) utilizing each magnet combination is subjected to a multi-objective design optimization process to achieve the goal without pushing the magnets towards permanent demagnetization. The optimization results are obtained at 4 different speed operating points in order to cover the required torque-speed range. A detailed analysis of various performance characteristics of the best designs obtained from the optimization process of each magnet combination, in addition to their quantitative comparison is presented to highlight the various tradeoffs. All designs share the same stator and number of poles and the focus is on rotor designs with three layers of permanent magnets. A mechanical stress analysis is also conducted to optimize the rotor stress and ensure its structural integrity.
SA-CenterNet: Scale Adaptive CenterNet for X-RAY Luggage Image Detection

Using deep-learing based object detection technology to detect contraband is to locate and classify it in X-RAY luggage images. It can circumvent the problem of missed and false detection caused by traditional manual security inspection. This paper proposes a novel anchor-free detector named Scale Adaptive CenterNet (SA-CenterNet), which models the contraband as its center point at multiple scales. Our method uses keypoint estimation to find center points and regresses to object’s sizes to obtain bounding boxes. SA-CenterNet introduces a scale adaptive module which uses multi-level feature maps to predict occluded contraband with the different sizes and the same center point. In additIon, we propose a feature enhancement module (FEM) to better extract the feature of small and obscure contraband. We use lightweight backbone and group convolution in detection head to reduce the amount of calculation and speed up the inference speed. Experiments demonstrate that our method achieves 95.2%AP50 at 105 FPS on our dataset named UNICOMP, which surpasses the state-of-the-art methods. Our method can robustly detect contraband passing the security inspection machine in real time, saving manpower and material resources.
A Simplified Microgrid Architecture with Reduced Number of Measurement Units

This paper proposes a simplified central-controller based microgrid architecture that eliminates the need for local measurements traditionally required for inverter synchronization and control. This architecture reduces overall number of required measurements in microgrids and consequently the cost of the system. The architecture relies upon state-estimation (performed at the central-controller) and GPS time-synchronization (at the local controller). Local controllers receive voltage/current reference from the central-controller, thus eliminating the need of sensors, analog to digital cards and processors at the inverter level. The proposed architecture is validated through the real time simulations using Typhoon HIL. The results show that the inverters, which operate without the local measurements according to the proposed architecture, have comparatively sluggish response. This represents a trade-off between the cost saving due to eliminated measurements and overall dynamic performance of the microgrid. Further, this indicates that smaller inverters which do not impact the microgrid stability are good candidates to be operated without local measurements.
Evaluating Tumour Bounding Options for Deep Learning-based Axillary Lymph Node Metastasis Prediction in Breast Cancer

The involvement of axillary lymph node metastasis in breast cancer is one of the most important independent prognostic factors. While the metastasis of lymph node depends on primary tumour intrinsic behaviour, morphology and angioinvasivity, the involvement of the peritumoral tissue by the neoplastic cells also provides useful information for the potential tumour aggressiveness. The lymph node status is currently evaluated by histological invasive procedures with possible complications, asking for introducing safer approaches. Among different imaging techniques, the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) highlights physiological and morphological characteristics, reflecting breast lesions behaviour and aggressiveness. In the recent years, deep learning (DL) approaches, such as Convolutional Neural Networks, gained increasing popularity for biomedical image processing. Thanks to their ability to autonomously learn from images the set of features for the specific task to solve, they allow finding non-invasive alternatives to the standard procedures used up to now. This paper aims to evaluate the applicability of DL approaches for the axillary lymph node metastasis prediction, considering primary tumour DCE-MRI sequence. Differently from other work in the literature, we include a detailed analysis of healthy tissue influence in lymph node tumour spread through the evaluation of different tumour bounding options. Promising results are reported on a dataset of 153 patients with 155 malignant lesions.