Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Models for Skin Cancer Detection: A review

Skin cancer is one of those most common cancers that affect people. Due to various factors, DNA mutations grow out of control and eventually lead to skin cancer. Early cancer detection can improve the likelihood of successful treatment. Applications for computer-aided diagnosis are now used in almost every industry, especially the health sector. The data of ill people is saved in computers to create biomedical datasets. It is crucial to find a reliable method for early skin cancer diagnosis and type classification. Computer- aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. This paper presents a thorough and methodical analysis of deep learning methods for skin cancer early detection. On this subject, a great deal of research has been done. Thus, it's imperative to assess, classify, and integrate the results of previous research. In order to facilitate comprehension of research findings, they are presented using tables, methodologies, and frameworks.
MT-LGSGCN: EEG-Based Emotion Recognition Using Multi-scale Temporal and Local-Global Spatial Graph Convolution Network

Recent research has shown that different cognitive tasks and processes involve coordinated activity among multiple brain regions. The diverse topological structures of the brain network describe the connectivity patterns and interaction modes between different brain regions. In order to study the brain activity associated with different interaction types in brain networks, we propose a novel graph neural network called MT-LGSGCN to learn the local-global graph representation of electroencephalogram (EEG) signals under different brain network topologies. The input layer of MT-LGSGCN consists of multi-scale one-dimensional convolutional kernels, which are used to capture the temporal-frequency dynamics of EEG signals and serve as inputs for local and global graph convolutions. MT-LGSGCN employs purely learnable sets of additional adjacency matrices to model the inter-region correlations, enabling the aggregation of high-level features with multiple topological structures. Through comparative experiments with other state-of-the-art methods, MT-LGSGCN achieves superior performance on the DEAP dataset. The results demonstrate that incorporating different brain network topologies into the design of graph neural networks can improve the final classification performance.
Gender Differences in Competitiveness Friends matter

We run an experiment with Danish school children (7-16 years old) to shed new light on gender differences in competitive behavior. Danish girls are not significantly less likely than boys to choose a competitive scheme when we control for individual performance, risk preferences, confidence, stereotypes, and interactions with the opposite gender. However, for the children who perform above average we find a gender gap of 11.8 percentage points. Our elicitation of the network of friends allows us to study the association between a child ’ s and their friends ’ competitiveness: for each (extra) friend that is competitive, girls choose to compete more often ( + 9.6 percentage points). The same is not true for boys. Finally, boys become better at making the correct decision with age, but girls avoid competition when they should choose it.
Robust Finite-Time Stabilization of Stochastic Parabolic PDE Systems via Non-Fragile Spatial Sampled-Data Control

This paper addresses the robust finite-time stabi-lization (FTS) issue for stochastic parabolic PDE systems via non-fragile spatial sampled-data control scheme. First, a class of distributed parameter systems characterized by the delayed stochastic parabolic partial differential equation is developed for analyzing the effects of stochastic disturbance, structural uncertainty, and discrete delay on the system performance. Then, a non-fragile spatial sampled-data control scheme is established by setting sampling points in the spatial domain, which effectively saves communication resources and ensures that the closed-loop system maintains good performance when the controller is perturbed. Moreover, based on the partial differential equation theory, stochastic analysis approach, and the extended Wirtinger's inequality technique, several criteria are provided to ensure the robust FTS of stochastic parabolic PDE systems in the mean square sense. Lastly, a numerical example is provided to verify the feasibility of the suggested stabilization criteria and control scheme.
Next generation pan-european ground-motion prediction equations for engineering parameters

This study firstly presents the recent pan-European strong-motion databank that is updated and extended version of previous pan-European databases. The pertaining metadata is carefully compiled and reappraised. The database meets high standards for being resource of pan-European earthquake engineering community. Then, an empirical nonlinear site amplification model, function of time-based average of uppermost 30m shear wave velocity profile and peak ground acceleration on rock, is developed. The primary aim of deriving such a model is to use it in ground motion prediction equations (GMPEs). Besides, the evaluation of site factors in the seismic design codes shows that it is also applicable in computing site factors. To this end, an alternative methodology that considers the results of probabilistic seismic hazard analysis and deterministic site models is proposed. Finally, this study generates GMPEs for horizontal and vertical elastic response spectral ordinates for different damping values between 1% to 50%. Rather than direct equations for vertical motion, to obtain consistent horizontal and vertical hazard spectrum, compatible vertical-to-horizontal ratio GMPE is preferred. Additional damping scaling models to modify horizontal and vertical spectra at other damping ratios are proposed
LI-FPN: Depression and Anxiety Detection from Learning and Imitation

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://github.com/muzixingyun/LI-FPN
Enabling Ultra-Low Power Ultrasound Imaging with Compute-in-Memory Sparse Reconstruction Accelerator

Medical ultrasound has played an integral role in the diagnosis of diseases over the past decades. Low power consumption of portable ultrasound is critical for its application in remote and point-of-care clinical scenarios. Among power reduction techniques, subsampling the ultrasound analog frontend channels is effective for reducing power, and sparse reconstruction is needed to guarantee final image quality. Although many successful reconstruction algorithms exist, including deep learning methods, we observe that locally executing these workloads by conventional hardware platforms cannot bring power savings for portable ultrasound. In this work, we aim to enable ultra-low power ultrasound imaging by approaching from both the algorithm and hardware fronts. For sparse reconstruction, we design a compact neural network that allows at most 8× channel subsampling while only using 11.7% operations of the baseline. We also design a digital compute-in-memory (DCIM) accelerator to process the model in portable ultrasound. The proposed row-centric inter-layer pipelining addresses the challenge of accelerating U-Net models, reducing the amount of required buffers by 4.6×. With an energy efficiency of 27.8 TOPS/W at 28 nm, the DCIM accelerator provides up to 57.8% power savings for portable ultrasound.
A smart chicken farming platform for chicken behavior identification and feed residual estimation

It is very potential to develop digital villages for promoting smart agriculture. As one of the important research fields of smart agriculture, smart chicken farms encounter management problems such as difficulties in quickly and accurately warning of sick and dead chickens and estimating feed residuals. Therefore, this study not only respectively proposed CKTrack and FRCM to detect sick and dead chickens and estimate feed residuals, but also developed a smart chicken farming platform for automagical management. Our main results include (1) the proposed CKTrack method can effectively identify sick and dead chickens under the condition of limited data volume and computing capacity; (2) the proposed FRCM method can accurately estimate the feed residuals; and (3) the smart chicken farming platform developed can provide farmers with functions such as early warning of sick and dead chickens, visualization of the chicken quantity inventory, and feed residual estimation.
A Receding Horizon Scheme for EV Charging Stations in Demand Response Programs

Demand response is expected to play a fundamental role in providing flexibility for balancing operations to the grid. On the other hand, the fast electrification of the transportation sector calls for new solutions to enforce safe and reliable grid operation. Here we consider an electric vehicle charging station that participates in demand response programs. The demand response program asks for a change of the charging station load profile in exchange for a monetary reward. A stochastic receding horizon scheme that exploits the charging flexibility is then designed to optimally coordinate vehicle charging. Numerical simulations show that the proposed approach ensures substantial cost reduction compared to simpler benchmarks while maintaining the computation time feasible for real-world applications.
Strictly Positive Realness-Based Feedback Gain Design Under Imperfect Input-Output Feedback Linearization in Prioritized Control Problem

The prioritized control problem is a process to find a control strategy for a dynamical system with prioritized multiple outputs, so that it can operate outside its nonsingular domain. Singularity typically leads to imperfect inversion in the prioritized control problem, which in turn results in imperfect input-output feedback linearization. In this paper, we propose a method based on the Kalman-Yakubovich-Popov lemma that compensates nonlinear feedback terms caused by the imperfect inversion of the prioritized control problem. In order to realize this idea, we prove existence of a feedback gain matrix that gives a strictly positive real transfer function whose output matrix is identical to the feedback gain matrix. Our proof is constructive so that a set of such matrices can be found. Also, we provide a numerical approach that gives a larger set of feedback gain matrices and validate the result with numerical examples.
Air Quality Prediction in Smart Cities Using Cloud Machine Learning

In the last couple of years, significant advances have been made in implementing Internet of Things frameworks in the architecture of smart cities to enhance people’s living standards. However, poor, smart city planning might pose specific concerns regarding energy usage, infrastructure, pollution, security, and privacy. In this paper, we will address one such challenge: air pollution, which can impact the environment, humans, and other living species. The air quality index is calculated from the pollutants particulate matter, ozone, nitric oxide, nitric x-oxide, carbon monoxide, benzene, toluene, sulfur dioxide, xylene, ammonia, and nitrogen dioxide to address air pollution. We utilize machine learning techniques to forecast the air quality index. To predict air quality, we implement regression models such as decision tree regression and random forest regression. Additionally, we employ cloud computing to minimize execution time. For this, we execute the same machine-learning models on Amazon SageMaker’s Jupyter Notebook and record their execution times. For the Indian smart cities dataset, these models are compared using evaluation metrics and execution time. The results show that cloud computing reduces execution time for all models while maintaining accuracy. The random forest algorithm achieves better accuracy than the other two models.
EEG-Based Depression Recognition Using Convolutional Neural Network with FFT and EMD

Deep learning methods have been widely adopted in the field of computer-aided EEG diagnosis, one of the major topics to be investigated is the input format of EEG data. Related researches have reported the application of Fast Fourier Transform (FFT) to topology-preserving multi-spectral images generation on three frequency bands (i.e. theta, alpha and beta) jointed to preserve the spatial information. In our work, we proposed a new approach of using Empirical Mode Decomposition (EMD) instead of FFT algorithm to generate topology-preserving multi-spectral images on intrinsic mode functions (IMFs) jointed and only on the single IMF. Meanwhile, images generated on three frequency bands jointed and the single frequency band were also used for comparison. We then applied two convolutional neural network (CNN) structures to distinguish depression patients and normal subjects from the topology-preserving multi-spectral images. As a result, both convolutional networks obtain better accuracies on three IMFs jointed (about 5% better, ≈ 75% vs. ≈ 70%) than three frequency bands jointed. Analysis based on the single frequency band indicates that alpha band performs the best, with an accuracy of more than 78%, and among all classification results, the best classification accuracy obtained is 80.23% on IMF2. The results are encouraging, despite the limited size of our cohort, the use of EMD and our findings cast a new light on application of deep learning method to EEG-based depression recognition.
Retinal Fundus Diseases Detection and Identification Using CNN

This paper addresses the efficacy of conducting early fundus screenings to mitigate the risk of vision loss from ophthalmic diseases and presents a solution utilizing deep learning for the ocular disease’s diagnosis, which often involve multiple diseases in a single eye. The research utilizes the RFMiD dataset, focusing on 25 out of its 46 classes while grouping the less frequent classes into a single category termed "others. " Preprocessing steps, including background cropping and histogram equalization are employed to improve the focus on the fundus and improve feature clarity. The multi-label classification model is based on the InceptionV3 architecture and is rigorously validated on 20% of the dataset. Performance evaluation employs five key measurements: accuracy, precision, recall, F1-score, and area under the curve resulting in impressive outcomes of 97.04%, 86.62%, 68.04%, 76.21%, and 0.936 respectively. These findings highlight how well the suggested system works for diagnosing eye diseases.
Convex Q-Learning in Continuous Time with Application to Dispatch of Distributed Energy Resources

Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure. This paper explores algorithm design in the continuous time domain, with a finite-horizon optimal control objective. The main contributions are (i) The new Q-ODE: a model-free characterization of the Hamilton-Jacobi-Bellman equation. (ii) A formulation of Convex Q-learning that avoids approximations appearing in prior work. The Bellman error used in the algorithm is defined by filtered measurements, which is necessary in the presence of measurement noise. (iii) Convex Q-learning with linear function approximation is a convex program. It is shown that the constraint region is bounded, subject to an exploration condition on the training input. (iv) The theory is illustrated in application to resource allocation for distributed energy resources, for which the theory is ideally suited.
The Effect of Virtual Reality-based on Intervention in Stroke Patients: Systematic Review

In the elderly, the most common cause of disability is stroke. The current development of digital technology has brought advancements in stroke therapy exercises. Nowadays, many researchers and healthcare professionals are starting to use Virtual Reality (VR) as a new technology to implement innovative therapy exercises in training stroke patients. This review evaluates the effectiveness of using VR in the therapy process to enhance the motoric abilities of stroke patients. This review was conducted on research articles published between 2010 and 2023, meeting the inclusion criteria from PubMed, Scopus, IEEE, and Science Direct. Stroke patients showed significant improvements across various cognitive domains. 20 studies were found to meet the inclusion and exclusion criteria. The selected studies examined the impact of VR on the stroke therapy process or compared VR therapy with conventional therapy. The results show that VR has the potential to help stroke patients when undergoing rehabilitation, where patients experience significant improvements in various domains of motor function. VR therapy with its methodology is effective in improving upper/lower extremity movement, quality of life, postural balance, and reducing stress and depression levels in stroke patients. VR can be implemented both as a standalone therapy or as an addition to conventional therapy methods.
Abstraction-Based Synthesis of Controllers for Approximate Opacity

Opacity is an important information-flow security property which characterizes the plausible deniability of certain “secret behaviors” in dynamical systems. In this paper, we study the problem of synthesizing controllers enforcing a notion of opacity over discrete-time control systems with continuous state sets. In this paper, we develop an abstraction-based approach to tackle the controller synthesis problem. Specifically, we adopt a notion of approximate opacity which is suitable for continuous-space control systems. We propose a notion of approximate initial-state opacity preserving alternating simulation relation which characterizes the closeness between two systems in terms of opacity preservation. We show that, based on this new notion of system relation, one can synthesize an opacity-enforcing controller for the abstract system which is finite and then refine it back to enforce opacity over the original control system. Finally, we present a method for constructing opacity-preserving finite abstractions for discrete-time control systems under some stability properties. Our results are illustrated on a two-room temperature control problem.
A Comprehensive Feature Aggregation Network for Sleep Apnea Detection using Respiratory Signals

Sleep apnea is a prevalent sleep disorder that poses a significant public health concern. Polysomnography, the gold standard for detecting sleep apnea, is costly and time-consuming, making widespread adoption challenging. To address this issue, in this paper, we propose a novel Comprehensive Feature Aggregation Network(CFAN) model comprising an embedding layer, an attention module, and a temporal module. The proposed CFAN leverages one of the respiratory signals as its input. Grouped dilated convolutional neural networks with various dilation rates, multi-head self-attention mechanism, and bidirectional Gated Recurrent Unit effectively integrate global and local spatial-temporal information from the input signal, enabling the characterization of intricate internal details and facilitate the detection of sleep apnea syndrome. Experimental results unequivocally demonstrate the superior performance of our proposed model, surpassing both benchmark models and the current state-of-the-art in terms of its comprehensive ability and robustness. Furthermore, ablation experiments confirm the indispensability of each component within the model. Our proposed method offers a convenient and innovative approach to automatically detect sleep apnea syndrome using respiratory signals, presenting a promising avenue for further application.
Constructing a Virtual Leader for Sign Consensus of Heterogeneous Multi-Agent Systems

This paper studies output sign consensus problem for leaderless heterogeneous linear multi-agent systems (MASs) over switching signed graphs. Established on the assumption that the communication graph is jointly eventually positive, a distributed ‘sign observer’ is proposed to estimate a virtual leader. This virtual leader is not pre-determined, but induced from the topology of the graph, the initial conditions of the agents and the structure of the ‘observer’. Based on the distributed ‘sign observer’ and output regulation method, a state feedback controller is designed to drive the output signals of the MAS to have the same sign.
Development of a Smart Edge Device for Home Security

Home security remains an essential consideration in this decade, as property crime continues to be a significant concern in Malaysia. Amidst the COVID-19 pandemic, where preventing virus transmission is of utmost importance, integrating home security with the Internet of Things (IoT) technology has become increasingly relevant. This study aims to develop a smart edge device for enhancing the security of houses in residential areas. A real-time facial recognition security system is constructed using You Only Look Once (YOLO) version 7, in conjunction with the Facenet models. These models have been trained, evaluated, and integrated with a Raspberry Pi to establish communication with the user through Node-RED and the Telegram User Interface (UI), utilizing the Message Queuing Telemetry Transport (MQTT) protocol. The performance of the developed facial recognition security system is compared with other state-of-the-art systems in terms of facial recognition accuracy and inference speed on edge devices. The proposed algorithm achieves a remarkable 94.14% accuracy at a speed of 0.8 FPS.