Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Neural Network for visual Emotion Recognition based on ResNet50 using Song-Speech characteristics

Visual emotion recognition is a very large field. It plays a very important role in different domains such as security, robotics, and medical tasks. The visual tasks could be either image or video. Unlike the image processing, the difficulty of video processing is always a challenge due to changes in information over time variation. Significant performance improvements when applying deep learning algorithms to video processing. This paper presents a deep neural network based on ResNet50 model. The latter is conducted on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) due to the variance of the nature of the data exists which is speech and song. The choice of ResNet model is based on the ability of facing different problems such as of vanishing gradients, the performing stability offered by this model, the ability of CNN for feature extraction which is considered to be the base architecture for ResNet, and the ability of improving the accuracy results and minimizing the loss. The achieved results are 57.73% for song and 55.52% for speech. Results shows that the Resnet50 model is suitable for both speech and song while maintaining performance stability.
Facilitating Decentralized and Opportunistic Learning in Pervasive Computing

We explore algorithms and develop tools to support the collaboration between devices to build personalized deep learning models for pervasive computing applications. In our work, we first investigate whether individual devices belonging to different users can learn robust models that are personalized to their user’s own experiences while preserving the privacy of the data that resides on the devices. We term this approach Opportunistic Collaborative Learning (OppCL), and we build a resilient learning algorithm that ameliorates the negative effects when coping with unpredictable mobility patterns. Moreover, we extend OppCL from networks of homogeneous devices to heterogeneous ones by employing lossy compression techniques for deep learning models. Furthermore, we develop a programming environment for developers to test decentralized learning algorithms in scale.
TinyFedTL: Federated Transfer Learning on Ubiquitous Tiny IoT Devices

TinyML refers to the intersection of machine learning (ML), mathematical optimization, and tiny IoT embedded systems. In the current era of ubiquitous connectivity and pervasive data, TinyML has emerged as an effective method to continuously analyze real-world data without the resource overhead of traditional ML hardware. However, as valuable data around us is subjected to strict privacy and security guarantees, there is a distinct lack of ML solutions that operate under both constrained computing environments and privacy concerns.In light of these challenges, we present TinyFedTL1, the first open-sourced implementation of federated learning (FL) at the most resource-constrained level in IoT containing microcontroller unit (MCU) and small CPU based devices. Using transfer learning (TL) as a representative task, we demonstrate that privacy-centric FL on devices with a tiny memory footprint (less than 1MB) is not only possible but also effective. Researchers and engineers can use TinyFedTL to open up data across various fields that can be used to gain insights for improving quality of life or user experience without sacrificing privacy.
Contribution on the Combination of Artificial Neural Network and Incremental Conductance Method to MPPT Control Approach

This paper proposes a robust approach to design a maximum power point tracking (MPPT) controller based on artificial neural networks (ANN). This approach is developed by combining the incremental conductance (IC) method and ANN. The first step presents the MPPT control system using the conventional IC method (MPPT_IC). Then, a second step proposes an algorithm exploiting the IC method to generate the appropriate training data for the ANN. The results show the ability of the ANN controller (MPPT_ANN) to provide the best control performance under various solar irradiance and temperature values. Its effectiveness of tracking has been compared to the traditional MPPT_IC controller. The test results presented show the performance of the proposed MPPT_ANN controller. The study results are simulated and validated by MATLAB/Simulink software.
Encrypted Data Aggregation in Mobile CrowdSensing based on Differential Privacy

The increasing sensing capabilities of mobile devices enable the collection of sensing-based data sets, by exploiting the active participation of the crowd. Often, it is not required to disclose the identity of the owners of the data, as the sensing information are analyzed only on an aggregated form. In this work we propose a privacy-preserving schema based on differential privacy which offers data integrity and fault tolerance properties. In our schema, data providers firstly add a noise component to the sensed data and, secondly, they encrypt and send the cryptogram to the aggregator. The data aggregator is in charge of only decrypting the cryptograms, by preserving the identify of the data owners. We extend such schema by enabling data providers to submit multiple cryptograms in a time window, by using time-varying encryption keys. We evaluate the impact of the noise component to the generated cryptograms so that to evaluate the data loss during the encryption process.
Demo: Distracted Driving Detection

Rear-end collisions, which account for over 33% of crashes, are the most frequent type of accidents in the USA. According to reports on road safety, almost all rear-end collisions are due to the distracted driving behavior of other nearby drivers. Ego vehicles should detect such deviated driving behaviors on other cars and warn their drivers to avoid rear-end crashes. In this paper, we demonstrate such a distracted driving detection system through field trials with multiple test vehicles. The ego vehicle observes the distance to preceding and following vehicles and detects distracted driving behavior on follower vehicles. A warning is generated whenever the follower vehicle exhibits distracted driving behavior.
Active Disturbance Rejection Control of a Magnetic Screw Motor for High Tracking Performance

A magnetic screw motor integrated by a magnetic screw and a permanent magnet synchronous motor offers high thrust density without friction and loss. However, its internal structure is complicated, resulting in difficulty to control its linear displacement to track the given signal in time and even getting out of control due to interference. In this article, to improve the tracking performance and anti-interference performance of the system, an active disturbance rejection controller (ADRC) is adopted to improve the current loop and makes up for nonlinear regional control in the closed-loop control system. To ensure the stability of the proposed scheme in the entire operation range, the design and the stability analysis are carried out in the continuous-time domain. The novel control algorithm enhances the flexibility of the magnetic screw motor by optimizing the nonlinear function in ADRC. In addition, to highlight the high-performance control of the ADRC for the magnetic screw motor, sliding mode control and PI control are added to compare with it. Finally, the reciprocating motion control is added to show the diversity of the functions of the magnetic screw motor, which verifies the effectiveness and feasibility of the control algorithm.
Why Slammed the Brakes On? Auto-annotating Driving Behaviors from Adaptive Causal Modeling

Annotating on-road driving behavior is essential for various applications like app-cabs, behavior-based insurance, etc. However, existing platforms for driving behavior analysis mainly focus on the impacts of different on-road maneuvers taken by a driver. This paper establishes that analyzing the causes behind the driving maneuvers is essential to characterize the driving behavior correctly. Accordingly, we develop DriveIn, a smartphone-based pervasive sensing system that explores both causes and impacts of driving maneuvers from video, GPS, and inertial sensor data to score a driver based on a thorough understanding of their on-road driving behavior. We evaluate DriveIn with datasets for three countries and observe that a score obtained by considering such causality factors provides a better insight into the driving behavior compared to the naive approaches.
Singer Diarization for Polyphonic Music With Unison Singing

This paper introduces a new framework for singer diarization, which is a technique to reveal who sings when in songs with multiple singers. Although various techniques have been developed to analyze and extract features of singing voices in musical audio signals, most of them assume that a song is sung by a single singer, and singer diarization for multiple singers has not been well studied in the field of singing information processing. To deal with multiple speakers in speech analysis, speaker diarization has been explored to handle overlapped speech voices, but cannot handle singing voices well because of acoustic differences between singing and speech voices. This paper therefore proposes a new diarization framework specialized in singing voices. To achieve high accuracy in overlap detection, this paper proposes a novel acoustic feature named Cosacorr score, which is helpful in estimating whether a song is sung by more than one singer. After extracting singing voices from polyphonic music by using a singing voice separation technique, the framework adopts an existing ArcFace technique to extract discriminative singer representations from short segments of the separated singing voices. The framework is evaluated by using a new private dataset of unison singing voices, which is constructed using commercially available compact discs (CDs). The experimental results show that the proposed framework outperformed the baseline method for speaker diarization in terms of diarization error rate (DER).
Dynamic Event-Triggered Hᵢ/H∞ Optimization Approach to Fault Detection for Unmanned Aerial Vehicles

This article dealt with the problem of dynamic-event-triggered fault detection (FD) for unmanned aerial vehicles (UAVs) in the <inline-formula> <tex-math notation="LaTeX">$H _{i}/H _{\infty }$ </tex-math></inline-formula> optimization criterion. To make use of limited communication resources, a dynamic-event-triggered mechanism (ETM) was considered to determine whether system information will be transmitted to the ground station. Note that the transmitted data failing to satisfy a predefined triggered condition were discarded directly. As such, FD performance was affected by not only disturbances and faults but also dynamic-event-triggered transmission errors, namely, the errors between the data of non-event time and the current actual system. To solve this problem, a new dynamic-event-triggered <inline-formula> <tex-math notation="LaTeX">$H _{i}/H _{\infty }$ </tex-math></inline-formula> optimization approach was presented, whose contribution lied in the complete decoupling of residuals and event transmission errors under dynamic ETM, which can avoid continuous communication and Zeno phenomenon. First, a dynamic ETM was presented to determine communication time. Then, a variable sampling period model of UAV was established based on the dynamic ETM. On this basis, a dynamic-event-triggered residual generator was constructed. In the <inline-formula> <tex-math notation="LaTeX">$H _{i}/H _{\infty }$ </tex-math></inline-formula> optimization criterion, Riccati recursion was used to compute the optimum solution of a dynamic-event-triggered FD filter. Moreover, a new residual evaluation method was proposed, whose effectiveness and feasibility were finally verified by the altitude control system of a UAV.
Sleep Quality through Vocal Analysis: a Telemedicine Application

Voice is a reservoir of valuable health data. Recent studies highlighted its efficacy in predicting sleep quality, and its potential as biomarker of neurodegeneration. This study assesses the feasibility of a Telemedicine system for the evaluation of sleep quality through brief vocal recordings. Machine Learning models were employed in the binary classification between good and poor sleepers, with great performance in scoring poor sleep quality – 88% and 85% F-1 score on a 5-fold Cross Validation (CV) for females and males, respectively. Moreover, the correlation between perceived sleep quality and a validated global score was studied, as well as the influence of external factors and sleep-wake schedule.
Transparency for Bystanders in IoT regarding audiovisual Recordings

Recent developments increasingly merge visions of ubiquitous computing and the Internet of Things (IoT) with artificial intelligence. Despite the wealth of benefits, this technological evolution also opens up a number of new privacy issues. In particular, the establishment of nearly invisible audiovisual sensors, such as those in smart speakers or smart cars, affect not only device owners, but everyone who enters the recording radius of them. Currently, it is nearly impossible for such a bystander to identify surrounding smart devices and corresponding data handling, which leads to foreign control of the bystander’s recorded data. Therefore, we introduce our transparency app to enable bystanders insights into surrounding, audiovisual smart devices in everyday life. Further, we conducted a semi-structured interview (N = 8) to analyze the desire for transparency as well as the acceptance and usability of our solution. We verified that there is a high demand for transparency, which, however, is very individual and context-dependent. Moreover our solution helped to satisfy the bystanders’ desire for transparency.
A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices based Applications in Mobile Cloud Computing

The number of smartphone users and mobile devices has been increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloud-based frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games task by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.
Glioma segmentation based on deep CNN

Brain tumor segmentation represents a hard job for radiologists as the brain is the most complicated and complex organ. Among the several brain tumors that existed, gliomas are the most aggressive and common. They lead to a short life in their highest grade especially. It is usually the most found tumors, which have various shapes, sizes, and brightness. It can appear anywhere in the brain. These causes make the automatic brain tumor segmentation a challenging problem. In this area, different Deep Learning (DL) models are suggested to help doctors. In this work, a new deep Convolutional Neural Network (CNN) architecture is presented to surpass these drawbacks. Our contributions incorporate three aspects. First, we exploited a pre-processing step based on intensity normalization with the goal to enhance the quality of the images. Second, we suggested an automatic segmentation model using CNN. The new scheme contains various convolutional layers, all exploiting 3 × 3 kernels, and one fully connected layer. Finally, we exploit a post-processing approach with the goal to ameliorate the segmentation results of the suggested model. We have evaluated the proposed technique based on the Multimodal Brain Tumor Image Segmentation Challenge datasets (BRATS 2017). The gained results provide the effectiveness of the suggested model compared with several techniques.
SDN for Public Safety Networks

Commercial users of modern communications networks have benefited from a huge progress of the related technologies. However, Public Safety Networks (PSNs) and devices did not follow the same trend. Very often, they still rely on voice or low speed data communications, tempting first responders to use their own private devices when they need to exchange real-time video or geolocation information. Under this consideration, national authorities and specialized organizations have recently initiated the integration of more recent technologies, such as cellular Long Term Evolution (LTE), even though they need further developments to cope with the harsh usages that safety personnel may face. This report proposes to move the evolution of these networks towards the recent evolution of networking technologies started with Software Defined networking (SDN) and Network Functions Virtualization (NFV). Based on the requirements derived from a standardized earthquake scenario and a study of the main improvements brought by this network softwarization, it analyzes how SDN and NFV can solve part of the issues raised with commercial LTE and enhance PSN communications. The capabilities of these new technologies are applied to a list of characteristics required by mission-critical networks, e.g., rapid deployment, reliability, security or resilience, taking advantage of features such as the separation between control and data planes or the simplified dynamic resources management. The resulting enhancements are then illustrated using example frameworks published in the literature for Cloud Radio Access Networks, resilient backhaul solution , isolated base stations, SDN-based architecture or Service Function Chaining.
An efficient Design of 1:2 Demultiplexer Based on QCA technology

Quantum-dot cellular automata (QCA) offers significant area reduction due to nano-size, high operational speed with significantly low power levels. It is a wireless technology and does not use current or voltage to carry information. Instead, it utilizes the positioning of the electrons. In this paper, an optimal 1:2 demultiplexer based on QCA technology has been investigated. QCAPro software has been used to estimated the energy dissipation of the suggested design. Simulation is achieved for the suggested design using QCADesigner software. The proposed design has less number of cells, area and energy dissipation as compared to the previous structures.
FAirWrite - Movement Reconstruction and Recognition Using a Low-cost IMU

We present a system for detecting air-writing motions conducted with a finger worn IMU. The vision is that a person could casually leave virtual notes in the environment by air-writing short notes when passing by a location. The challenge that we address is that such casual finger writing, in particular without a visual reference feedback (the user doing the air writing does not see the result), tends to produce extremely noisy trajectories which are inherently challenging to decode. The main contribution of this work is the development and systematic evaluation of a system for reconstructing such noisy trajectories and mapping them onto digits and characters. We describe the FAirWrite system’s architecture, a Graphical User Interface (GUI) to interact with it, and the recognition and classification process to achieve the state-of-the-art results.
Design, Modeling, And Control Of Three-port Converters For Solar Power Applications

This paper describes the results of research into multi-port converter design and control, specifically a pair of three-port topologies based on the half-bridge and full-bridge topologies. These converters are capable of simultaneous and independent regulation of two out of their three ports, while the third port provides the power balance in the system. A dynamic model was developed for each topology to aid in testing and for designing the control loops. The models were then used to design the control structures, and the results were tested in Simulink. In addition, a basic outline of a system level architecture to control multiple converters working in parallel is presented. To improve the reliability of this system, output current sharing controls were also developed. Finally, one of the topologies is analyzed in detail in order to obtain a set of design equations that can be used to improve the efficiency, weight, and cost of the converter for a specific application.
Bridging the gap between source code and high-level concepts in static code analysis: student research abstract

Static code analysis has proven beneficial for many domains, including security assessment, error detection, and more. As systems have grown more reliant on conceptual components without a one-to-one mapping in the source code, however, traditional static code analysis tools have become less ideal for the task. This is greatly because current static code analysis libraries focus on individual code constructs, forcing the library's user to manually combine these into high-level components before performing any meaningful analysis. To bridge this gap, we put forth a novel approach for extracting components from programs using sets of parsers called Relative Static Structure Analyzers (ReSSA). This approach builds a grammar to match structures within an abstract syntax tree, abstracting away the boilerplate of manually traversing, matching, and extracting data. This empowers the user to focus on the structure of components in their chosen language and framework, and their final output. Our approach holds potential to greatly simplify static code analysis, allowing for fast and accurate identification of important components without needing to utilize arcane parsing libraries.
The Use of Data-Driven Personas to Facilitate Organizational Adoption – A Case Study

Abstract Personas have gained popularity within design practice. A persona represents a group of target users using a vivid, fictional character. While the benefits of utilizing personas have been documented, one of the common challenges is identifying the personas using a rigorous approach that leads to organizational adoption. This paper illustrates an approach to developing personas that combines quantitative and qualitative research. An application of the approach led to the resulting personas gaining organizational credibility and adoption. This credibility allowed the personas to be centrally integrated into the product development process and adopted by the broader corporate culture.