Miracle-dz/UniFAR-FTUs huggingface dataset:
Recording of brain activity using multichannel, biocompatible organic electrochemical transistors in vivo

In vivo recordings of brain activity are vital for diagnostic purposes and brain science research. Organic electrochemical transistors (OECTs) are one of the most promising candidates due to their excellent signal-to-noise ratio, mechanical flexibility, and biocompatibility. Here, we propose the engineering of a multichannel, biocompatible OECT array, that is capable of laminating onto soft tissues seamlessly, monitoring neuron firing with high spatiotemporal resolution. The successful demonstrations of this device to map micro-electrocorticography in the rat model in vivo, demonstrate the great potential of this technology for clinical applications, human-brain interfaces, metaverse, etc.
Bike Skid Detection and Smart Alert System with Route Condition Assistance

The world is in the era of mobility. There are over a million vehicles passing on a daily basis. With increasing traffic, light motor vehicles are preferred to motor vehicles like sedans and SUVs. Bikes and Scooters help commuters to travel quickly and with more agility. As a result, rise of sharing apps are seen. Even the delivery of meals, groceries, and daily foodstuffs is carried out by bikes and scooters. So there is an increase in the accident rate by two-wheelers. There is a need to safeguard the commuters by proposing an improved bike safety system. In this study with the help of IOT, skid detection system is implemented and alarm system for quick action after an unfortunate incident. Furthermore, accidents by bikes are varied. Collison, Skidding, and Rollover. But reports convey that the majority of accidents on bikes happen due to skidding and the percentage is alarming at 40.9%.
Comparative Thermometery in Paediatric Age Group: Is the Non-Touch Infrared Thermometer (NTIT) Reading Comparable to Regular Mercury-in-Glass Thermometer (MIGT) Reading?

Background: Accurate temperature measurement is a critical step in evaluating health or disease especially in children and immmunocompromised subjects; inaccurate measurement may lead to improper diagnosis, wrong treatment or inappropriate intervention. Several methods of temperature measurements exist and comparing these gives room for choosing a near ideal method in terms of speed, safety and accuracy. The study aimed to compare the forehead non touch infra-red thermometer with the axilllary mercury-in-glass method of temperature measurement in the Paediatric age-group. Methods: Study was given ethical approval as part of a larger study. Four hundred and thirty seven children aged 1 to 24 months were studied at the well-baby/immunizationclinic of the University of Ilorin Teaching Hospital over a 6-months period. Both non-touch infrared and theregular mercury-in-glass thermometers were used to take the body temperatures. Data were analysed with SPSS version 21. Pearson correlation was used to determine the relationship between the two methods of temperature measurements, while Bland-Altman method was used to test for level of agreement between them. Results: The mean age and SD was 5.81 ± 4.04 months. Pearson correlation showed a positive correlation between the axillary mercury-in-glass and forehead non-touch infra-red thermometry readings (r = 0.281, p < 0.001). Also, Bland-Altman method revealed a good agreement between both methods of thermometry as 96% of the readings were within the limits of agreement. Mean difference was 0.09°C (95% confidence interval 0.05 - 0.13). Conclusion: Axillary mercury-in-glass thermometer and forehead non touch infra-red thermometers have a good agreement and can be used interchangeably.
Characterizing neural activity in cognitively inspired RL agents during an evidence accumulation task

Evidence accumulation is thought to be fundamental for decision-making in humans and other mammals. It has been extensively studied in neuroscience and cognitive science with the goal of explaining how sensory information is sequentially sampled until sufficient evidence has accumulated to favor one decision over others. Neuroscience studies suggest that the hippocampus encodes a low-dimensional ordered representation of evidence through sequential neural activity. Cognitive modelers have proposed a mechanism by which such sequential activity could emerge through the modulation of recurrent weights with a change in the amount of evidence. This gives rise to neurons tuned to a specific magnitude of evidence which resemble neurons recorded in the hippocampus. Here we integrated a cognitive science model inside a Reinforcement Learning (RL) agent and trained the agent to perform a simple evidence accumulation task inspired by the behavioral experiments on animals. We compared the agent's performance with the performance of agents equipped with GRUs and RNNs. We found that the agent based on a cognitive model was able to learn faster and generalize better while having significantly fewer parameters. We also compared the emergent neural activity across agents and found that in some cases, GRU-based agents developed similar neural representations to agents based on a cognitive model. This study illustrates how integrating cognitive models and artificial neural networks can lead to brain-like neural representations that can improve learning.
Arithmetical Evaluation System based on improved-YOLOv5 and CRNN networks

For primary school teachers, revising arithmetical exercises is labor-intensive and time-consuming task. To reduce their burdens, we propose a lightweight arithmetic evaluation system that can automatically assess arithmetic exercises. The designed system divides into two branches: detection and recognition. In the detection branch, a coordinate attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi-scale target recognition ability. Then, the complete IoU (CIoU) loss function leads to faster convergence and better performance on the detection results. The experimental results show that the detection accuracy of the proposed method improves mAP@. 5-95(%) by 5.4% compared to the original algorithm. Moreover, the number of parameters and FLOPS decrease to 1.23M and 3.8G. In the recognition branch, we employ the improved-CRNN network to recognize arithmetical exercises, achieving an accuracy being up to 97.1 %.
Improving Human Positioning Control of Oscillatory Systems

Flexible systems are difficult to control because they deflect in response to any applied force and they tend to oscillate around the desired path or set point. Human operators driving such systems are challenged by the deflection and vibration that makes the system difficult to move and accurately position. Such systems can be augmented with an intelligent control scheme that aids the human operator. Numerous types of controllers can be used for such applications; however, it is challenging to balance the control authority of the human operator and the augmenting controller. Input shaping is a control technique that reduces unwanted flexible system responses by modifying the human-operator command in real-time. This paper investigates the use of input shaping as an augmenting controller to aid in the accurate positioning of highly-oscillatory systems. Results from operator testing verify some of the key advantages of this controller.
A Strategy for Aided Diagnosis of Obstructive Sleep Apnea in Children Based on Graph Neural Network

With the rapid development of artificial intelligence, especially deep learning technology, various new technologies and applications based on face images have emerged. Obstructive sleep apnea (OSA) is the disease with the highest morbidity and the most serious long-term harm among childhood sleep breathing disorders, and it is increasingly receiving common attention from families and society. Children with the disease have a special facial appearance and require early identification and treatment to prevent it. However, the current diagnostic methods have problems such as being invasive, time-consuming, and expensive. The purpose of this article is to use graph neural network technology based on face images to establish an OSA auxiliary diagnosis strategy for children to achieve OSA screening and analysis. Therefore, this article first takes the facial landmarks as the analysis object, divides the face into six key areas, and selects important landmarks in these regions. On this basis, to better consider the relationship between important landmarks, a global collaborative recognition strategy is proposed. By extracting the implicit relationship between landmarks, face graph structure data is established. Finally, the OSA-GNN model is established to achieve OSA screening and auxiliary analysis in children. Compared with other related studies, this strategy not only has a stronger representation and generalisation ability but can also carry out clinical applications better, providing doctors with diagnostic suggestions.
Deep Learning-Assisted Iris Liveness Detection Mechanisms

Iris recognition systems are attaining wide consideration for securing critical computing infrastructure. Though, these systems are exposed to a various kind of attacks or threat that may degrade the performance of the overall system in real-time scenario. Among all, spoofing attacks are the most attempted once, where an adversary tries to break the security by offering a fake artifact of a real biometrical trait. In this paper, we explore most recent development in the area of iris vitality detection mechanisms that are inspired by deep learning-based models. Besides, we illustrate an analysis of iris vitality detection (IVD) anti-spoofing datasets and performance evaluation protocols. Our overall instigation indicates that majority of the recent methods perform well in known attack scenario, however exhibit limited performance in unseen attacks set-up. One of the major challenges is to develop a robust IVD method with smaller datasets that has demonstrates superior performance in cross-scenario settings.
Effect of Cerebral Artery Stent on the Brain Neurons Activation under the Wireless Charging

This study aims to investigate the impact of cerebral artery stent on the brain neuron activation under a wireless charging system. A simplified wireless charger model is developed and positioned in close proximity tothe head region. A cerebral artery stent is modeled and placed inside the brain vessels, and two distinct models are developed, one with the stent and one without it. To determine the electrical potential along a set of nerve fiber, Finite Element Method (FEM) based on the electromagnetic simulation software is employed. A high-frequency waveform is generated and scaled by the stimulus amplitude. Based on the potential distributions and stimulus waveform, the activation thresholds are obtained by setting stimulus amplitudes for the MRG (McIntyre-Richardson-Grill) myelinated fiber models. Results indicate thatthe presence of a cerebral artery stent can cause significant variations in the activation thresholds of brain neurons, up to 22%. As a result, it is imperative to consider the impact of cerebral artery stents on the activation thresholds of brain neurons under a wireless charging.
Generative Adversarial Networks for Anomaly Detection in Cyber Security: A Review

The CIA model is the basis for cybersecurity in its broadest sense; this model prioritizes maintaining the confidentiality, integrity, and availability of data. The majority of attacks are launched by the attackers by exploiting flaws in the communication protocols. Because these assaults put the reputations of service providers in jeopardy, more effective mitigation strategies are essential for mitigating attacks. Therefore, there is major concern for service providers. Generative adversarial networks (GANs) are emerging techniques that are gaining importance in AI-based security defense systems. GANs are being used by the security industry to great effect in areas like intrusion monitoring or detection, steganography, cryptography, password cracking, and anomaly detection, to name a few. This study presents a comprehensive literature overview of the use of GANs in cybersecurity, including an in-depth examination of the most popular stable cybersecurity datasets in use today and the specific extended GAN frameworks behind their creation.
A Multi-task Network with Centerline Supervision for 3D Pelvis Artery Segmentation on CECT images

Accurate pelvis artery segmentation on computerized tomography images is crucial for many diagnoses and surgery related to abdominal diseases, like colorectal cancer metastasis analysis. However, seldom researches considered this, and many minor branch vessels are always broken or even disappear in existing segmentation methods, since they are thin and long, with low contrast, and many variants may exist. To address this problem, we provide a multi-task network with centerline supervision. The centerline extracting task is introduced to offer other domain-specific information and help the network learn the branch vessels. This method mainly includes an early-branch network with an inter-task feature fusion module. In the proposed network, the hard-sharing encoder extracts the shared spatial and global information. The dual-path decoder separates the learning of the tasks and has some fusion blocks to combine and exchange inter-task features. Then, a multi-task learning loss with a consistency correction penalty is designed to keep the training balance through punishing the gap between the two tasks. To evaluate the performance of our method, we conduct ablation and comprehensive experiments on a local pelvis artery dataset. Experimental results in metrics and visualization show that the proposed method achieves superior performance.
Optimal Order of Hippocampal Place Cell Models Constructed Using Expansions of Zernike Polynomials and Power Series

Hippocampus is a brain region that is important for the encoding and retrieval of episodic memories. The spiking activity of hippocampal place cells depends strongly on spatial location in an environment. Their position-dependent firing rate is usually modeled as a parametric function of 2-D or 3-D space. Yet, no study to date has optimized such functions using a rigorous statistical model selection procedure. Here, we model the position-dependent firing rate of hippocampal place cells using two different series expansion models and determine the optimal model type and order. Our results indicate that the optimal order is much higher than those used in earlier studies. We have observed that the models of some cells are reminiscent of the firing patterns of grid cells. These findings are important for elucidating the origins of place cell activity, for accurate assessments of the amount of position information encoded in this activity, and for the inference of position using neural decoding algorithms.
A Self-attention Agent of Reinforcement Learning in Continuous Integration Testing

Test case prioritization based on reinforcement learning has been seen as a promising way to achieve continuous integration testing. Agent and reward function are two crucial components of reinforcement learning. During the process of reinforcement learning in continuous integration test case prioritization, the agent decides on the execution order of test cases (actions) for the next integration testing (environment) based on the corresponding test case features (states), aiming to detect errors early by maximizing the reward. Furthermore, having more test case features allows the agent to perceive the environment better, but it also increases computation consumption and brings convergence problems to learning. In this paper, we first propose a multi-feature environment perception for continuous integration test case prioritization. It introduces multiple features based on test case history execution information to solve the agent’s limitation in obtaining environmental information. Additionally, we propose a self-attention agent network structure, which captures relationships between multiple features to prevent the convergence problem of reinforcement learning. An extensive experimental and analytical study was conducted with 15 existing reward functions on 14 industrial data sets. The results show that (1) the proposed multiple features can help the agent to perceive environmental information more comprehensively, and (2) the proposed self-attention agent can process environmental information better to achieve more effective test case prioritization in continuous integration testing.
Locating Procedural Steps in Source Code

Some documents, such as use case descriptions, describe features consisting of multiple concepts with following a procedural flow. Because existing feature location techniques lack a relation between concepts in such features, it is difficult to identify the concepts in the source code with high accuracy. This paper presents a technique to locate concepts in a feature described in a structured document consisting of multiple procedural steps, such as a use case description, using dependency between the concepts. We apply an existing concept location technique to descriptions of concepts and obtain a list of modules. Modules failing to match the dependency between concepts are filtered out. Then, we can obtain a more precise list of modules. The conducted experiment underscores the effectiveness of our technique.
Sample Size Calculations for Testing Equivalence of Two Exponential Distributions With Right Censoring: Allocation With Costs

The present study considered two independent exponential distributions with the hypothesis for testing equivalence of lifetime means or failure rates, and aimed to determine the required uncensored sample size based on power, sampling cost, and censoring proportion simultaneously in the case of right censoring. Approximate sample size formulas with an iterative procedure were proposed and an uncensored sample size allocation ratio was derived to minimize the total cost for a designated power or maximize statistical power for a limit cost. R codes are provided for easy application. The proposed methods are validated in terms of Type I errors and statistical power in a simulation study, and are recommended for the future use.
Polarization state evolution in spun birefringent optical fibers

The evolution of the polarization state (PS) of radiation propagating in spun or twisted birefringent optical fibers is considered for a wide range of fiber parameters, and presented in a descriptive way using the Poincare sphere. The effects of the bandwidth and the polarization state of input radiation, of the external magnetic field, and of the fiber bending on the polarization state evolution have been described for the most practical cases. An explanation of how the helical structure of the built-in linear birefringence in spun fibers provides the existence of the helical elliptically-polarized eigenmodes has been given. In spun fibers with a relatively large spin rate, the PS evolution is shown to change its azimuthal direction.
Robust Principal Component Analysis via Truncated $L_{1-2}$ Minimization

Robust principal component analysis (RPCA) has gained popularity for handling high-dimensional data. The nuclear norm minimization (NNM) in RPCA is a classical method and has been widely investigated, which can recover low-rank and sparse matrices with high probability under certain conditions. However, NNM shrinks all singular values by the same threshold and over-penalizes larger singular values, resulting in this model being biased. Therefore, we propose a new method based on the truncated $l_{1-2}$ norm to solve this problem in this paper, which is unbiased and flexible to capture the low-rank structure of the data matrix more accurately while separating the sparse noise. We also develop a robust and efficient algorithm to solve the proposed nonconvex optimization model, with the computational complexity and convergence discussed. Then the proposed scheme is applied to synthetic data as well as real-world data, including video background subtraction, facial shadow removal, and anomaly detection, for testing. These experimental results demonstrate that our proposed method is effect and superior in accuracy and robustness compared to other state-of-the-art methods.
Systematic Improvement of Access-Stratum Security in Mobile Networks

In mobile networks, the User Equipment (UE) secures some of the communication with its serving Radio Access Network (RAN) node ("base station") via a set of keys known as Access Stratum (AS) keys. Unfortunately, the level of secrecy of these keys varies with the mobile procedures re-establishing them. To improve the secrecy of the AS keys, we propose minimal changes to 5G & 4G handovers, i.e., the main AS-key establishment procedures. We show the minimality of our changes also via an implementation of one of our protocols in the 3GPP-compliant Open5GCore 5G testbed. We also systematically cross-compare standard handovers with our amended handovers using MobTrustCom: a framework to quantify especially trust but also communication complexity in mobile networks. Moreover, we use Tamarin, a formal security-protocol verification tool, to prove no loss of "classical" security yet an increase in AS-keys' secrecy brought by our improvements to handovers.
Detection of Flashy Segment in YouTube Videos using Computer Vision Technique to Prevent Seizure in Epilepsy Patients

Epilepsy is a medical illness that affects roughly 50 million people worldwide, with 3-5% of these people experiencing seizures caused by flashing or flickering lights or specific patterns with areas of light and dark contrast. This condition is referred to as photosensitive epilepsy (PSE). YouTube is a leading video-sharing website that contains video content that often has rapid changes in brightness or contains strong contrasts in spatial patterns that can trigger seizures. It is important to detect such content before it is distributed or displayed to prevent adverse effects on vulnerable individuals. The proposed solution is a browser extension that provides real-time scanning and processing of YouTube videos to detect and disarm potential seizure-inducing segments. The extension is compatible with Chromium-related browsers, including Brave, and is designed to display a warning if epileptic content is present in a YouTube video. The system utilizes computer vision techniques such as OpenCV to detect flashing video content that complies with the CIE 1931 color space, enabling it to skip seizure-triggering segments from YouTube videos.
A Multiscale Dynamic Temporal Convolution Network For Continuous Dimensional Emotion Recognition

Video-based dimensional emotion recognition, especially long sequence modeling, is a challenging task. This paper proposes a Multiscale Dynamic Temporal Convolution Network(MDTCN) for long sequence modeling of dimensional emotion recognition. Although Temporal Convolution Network(TCN) has performed well in sequence modeling, there are still some limitations that the feature scale of each layer is single and the receptive field of the upper layer is too large to capture the short-term dependence. Therefore, Dynamic Dilated Block(DDB) is constructed to better capture the temporal dependence by using dilated convolutions of different kernel size. In order to obtain both long-term and short-term temporal features, Multiscale TCN Structure is proposed. Extensive experiments are conducted on the AFFWILD2 and SEWA Dataset and the competitive result is shown compared to state-of-the-art methods.