Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on High-Quality Style Transfer Using Edge-Enhanced Gaussian Smoothing Technique(EEGST) for Minimizing Perceptual Loss

Recent advancements in image generation technologies, particularly those involving style transfer, have garnered significant attention. Style transfer involves merging the characteristics of one stylistic image with the content of another to produce a novel image. Most image generation techniques, including style transfer, heavily rely on neural networks. However, the use of neural networks, particularly during processes such as up-sampling, can introduce artifacts. This paper proposes a method to mitigate artifacts in models utilizing neural networks, specifically focusing on the style transfer model proposed in "Perceptual Losses for Real-Time Style Transfer and SuperResolution" [5]. The mentioned model exhibits advantages in terms of speed but suffers from checkerboard artifacts or similar issues. The proposed approach effectively addresses these concerns, reducing artifacts while preserving the overall form of the image. The method involves combining Gaussian smoothing to handle artifacts introduced by neural networks while ensuring the preservation of edges. The simplicity and versatility of the proposed method make it applicable to various models. This paper presents a method to reduce artifacts in neural networkbased models, with a particular focus on the style transfer model benchmarked against "Perceptual Losses for Real-Time Style Transfer and Super-Resolution. " The proposed approach proves effective in maintaining image structure while mitigating artifacts, showcasing its potential applicability across different models.
Reliability Assessment of Bypass Diodes in Photovoltaic Systems Based on Accumulated Damage Calculation Models

During the operation time, the practical lifetime of bypass diodes (BD) in photovoltaic (PV) systems is mostly less than the expected lifetime given by manufacturers. A rapid reduction in the diode lifetime can be because of electrostatic discharge, reverse bias thermal runaway, forward bias overheating, and transition operation from the forward-biased to reverse-biased states of the diode. Indeed, the aforementioned reasons for the failure of BDs are normally caused by dynamic operations, such as partial shading, fluctuation in solar irradiance and temperature, and short-circuit conditions in the PV system. Specifically, dynamic and short-circuit operations of the PV system can cause a sudden increase in junction temperature, the forward current, and reverse voltage which can influence the degradation of bypass diodes. Bypass diodes can undergo thermal runaway and consequently, these bypass diodes of the PV system can be damaged after a certain amount of the used time. To practically evaluate the reliability of bypass diodes in PV systems, this paper proposes to use accumulated damage (AD) calculation models based on the junction temperature, forward current, and reverse voltage. A grid-connected 8kW PV-system testbed is used for collecting dynamic-and-short-circuit experiment data to serve for the reliability analysis of the bypass diodes. As a result, based on the accumulated damage calculation models, the failure rate (FR) and reliability index (RI) of bypass diodes in the PV-generating system can be properly determined, hence, the periods of maintenance, repairment, or replacement of the diodes in the system can be given more practically and logically.
Software Susceptibility Detection Using Deep Learning Techniques

Software susceptibilities are flaws in the source code that could be used to do havoc or collect information. A variety of automated machine learning methods have been suggested in prior research to find them. The majority of these methods operate at the release level, which means they try to foresee which files might be vulnerable in a future version. Manually detecting insecure code is both challenging and costly in both time and money. Therefore, to reduce costs, developers must use automatic vulnerability prediction (AVP) techniques. Recent AVP research has started to employ deep learning (DL) methods. All of the suggested methods are built on feature extraction techniques that were influenced by earlier DL uses such as autonomous language processing. When developing AVP models based on traditional machine learning, code metrics were frequently employed as features. The following piece provides a method centered on deep neural networks over leveraging code metrics to detect vulnerabilities in code, bridging the difference between machine learning parameters and deep learning. Results obtained indicate that while code metrics are excellent, they weren't the best to use as DL-based AVP characteristics.
An Overview of Electrically Coupled Multi-Input DC-DC Converters for Hybrid Electric Vehicles

The modern evolution of electrically coupled multi-input dc-dc converters is pointed out and scrutinized in this paper. Initially, a few dc-dc converters are connected in parallel to form a multi-input converter. But currently, a few modern structures have been suggested for this purpose. So, they are used in hybrid electric vehicles and renewable energy systems. Three groups of multi-input converters are electrically coupled, magnetically coupled, and electro-magnetically coupled converters. This paper deals with electrically coupled dc-dc converters because of their advantages, like no need for a transformer, not requiring physical isolation between input and output, smaller and lighter, etc.
CLIP-LSTM: Fused Model for Dynamic Hand Gesture Recognition

The computer vision field continues to find dynamic hand gesture detection to be an intriguing subject. The algorithm is unable to determine accurately whether a gesture begins or ends in a video feed therefore, recognizing dynamic hand movements in real time is challenging. Real-time dynamic hand gesture detection has several applications, and numerous researchers are working on it. In this paper, we have used the CLIP model to extract the features of hand gestures. Then the extracted features passed into the BLSTM model to classify the dynamic hand gestures. Using the CLIP model for feature extraction overcomes the problem of hand detection and tracking. The various illumination makes hand detection and tracking challenging and CLIP is used to extract the features of each video. We conduct an experiment with fewer parameters on a challenging dataset. Experimental results on the CHG and LISA dataset with 97% accuracy on CHG and 86% accuracy on LISA datasets shows that our proposed model outperforms the state-of-the-art methods (SOTA).
Dry Food Classification using Hybrid Deep Transfer Learning

The proper categorization of dry foods plays a crucial role in maintaining quality control and ensuring food safety in the food industry. Efficient and precise classification methods are vital for distinguishing and organizing different types of dry foods, which range from grains, cereals, and snacks to packaged goods. To achieve this, we gathered a dataset of 12,500 samples from Kaggle and proceeded to employ various transfer learning models, including VGG16 and ResNet50. Additionally, we introduced a novel hybrid model, VGG16-ResNet, which combines the strengths of both architectures. Transfer learning involves utilizing knowledge acquired from one task or domain to enhance learning and performance in another. By fusing multiple deep learning techniques and transfer learning strategies, such as VGG16-ResNet50, we developed a robust model capable of accurately classifying a wide array of dry foods. The integration of deep learning and transfer learning techniques in the context of dry food classification signifies a drive towards automation and increased efficiency within the food industry. Notably, our approach achieved remarkable results, achieving a classification accuracy of 99.78% for various dry food images, even when dealing with limited training data for VGG16-ResNet50.
Tempered Image Detection Using ELA and Convolutional Neural Networks

Images are often manipulated to benefit one party, serving as crucial evidence. This manipulation, often used in fake news or misleading information, frequently involves image falsification. Detecting such falsification requires a robust model capable of processing vast image data efficiently. In today’s data-rich era, Deep Learning, especially the use of Convolutional Neural Networks with Error Level Analysis, has achieved an impressive 87.75% accuracy and convergence in 10 epochs for detecting forged images.
Bangla Malicious Comment Detection in Social Media Posts Using Ensemble Learning

Information exchange and the growing usage of social media have greatly benefited human beings. However, social media often has a negative effect on freedom of speech and helps promote hatred, crimes, racial profiling, misogyny, and bullying. While reflecting public opinion and allowing for constructive criticism or admiration, the comment box on public websites can be seen by some as a platform to use obscene and abusive language without any consequences. It is considerably more difficult to identify malicious text in Bengali. In the age of technological automation, manually reviewing and eliminating offensive texts from publicly accessible websites may become tiresome. In this study, we used several machine learning algorithms and ensemble methods for identifying Bangla malicious text. We created a dataset by taking Bangla comments from Facebook and analyzed the effectiveness of different machine learning algorithms and ensemble methods in detecting malicious text accurately. According to the results of our tests, XGBoost, an ensemble technique, performed the best, with 70.23% accuracy in detecting malicious text.
SelfBOT: An Automated Wheel-Chair Control Using Facial Gestures Only

Facial gestures serve as a promising means of non-verbal communication, as effectively demonstrated in applications like cursor movement, eye tracking, and eye interaction. Our study introduces a novel facial-interaction-based wheelchair system designed to enhance the mobility and independence of physically disabled individuals who rely on assistance for navigation. As a proof-of-concept, this application detects facial, nose, and eye movements to monitor user actions and execute corresponding operations. A novel algorithm accurately detects facial movements up, down, left, right, blinks, and eye flickers, enabling users to control the wheelchair’s movement in any direction, including clockwise and counterclockwise. Real-time feedback indicates whether facial gestures successfully control wheelchair movement. Our system maintains accurate gesture recognition under varying environmental conditions and provides smooth, intuitive cursor control, a user-friendly interface, and a cost-effective, energy-efficient implementation that sets our system superior apart from existing solutions. It achieves a detection accuracy of approximately 96% in well-lit environments, surpassing existing systems. While accuracy drops slightly to 83% in dark environments, it still exceeds existing capabilities. This system empowers physically disabled individuals, enabling those with quadriplegia, amyotrophic lateral sclerosis(ALS), cerebral palsy, or spinal cord injuries to control their wheelchairs using facial gestures characterized by its unique ability to move in any direction as opposed to just four or eight directions.
Optimization Method of DRL-based Route Planning under Limited Resources

The performance of the route planning module is affected by the on-board computing and storage capabilities, which are growing rapidly but are still limited by the enormous scale of sensed data that need to be stored and processed. So, aiming at the problems of resource-limited deep reinforcement learning-based route planning methods, this paper proposes an optimization method to improve computational effectiveness by using Attention Mechanism and reduce data storage by using improved Double-DQN. The results show that the proposed method enables the planning model to focus on valid sensed information and ignore irrelevant information, as well as effectively reduce the amount of vehicle agent’s experience data storage during environment exploration.
Controls on the Cadmium‐Phosphate Relationship in the Tropical South Pacific

The relationship between dissolved cadmium (Cd) and phosphate (PO4−3) can elucidate a biological role for Cd in the ocean and help to evaluate the usefulness of Cd as a tracer of past ocean circulation and nutrient distributions. Here we determine and analyze this relationship in the poorly studied region of the tropical South Pacific. The dissolved Cd distribution is generally similar to PO43−, but a plot of Cd versus PO43− shows a clear concavity resulting from distinct Cd:PO43− ratios in waters local to our transect and in waters formed distally in higher latitudes. To determine the factors affecting the subsurface Cd:PO43− ratio along our transect, we used an ocean circulation model and a multilinear regression model to determine the preformed and regenerated components of dissolved Cd and PO43−. We found that both the preformed and regenerated Cd:PO43− ratios are low in the shallow, locally formed water masses along the transect and significantly higher in the deeper and older water masses. Overall, the regenerated:preformed Cd:PO43− ratio in the deep waters (>1,000 m) along our transect is ~1.8:1, reflecting the basin‐wide average Cd:PO43− “fractionation factor” during biological uptake. However, we find a lower fractionation factor in local waters of 1.1 (± 0.6). We suggest that this locally lower biological fractionation factor is due to either the chemical speciation of Cd or to a lower efficiency of Cd assimilation by the picoplankton and nanoplankton species found in our study region.
GAN-Based Two Stage Network for De-occlusion Face Image

In deep learning applications, facial recognition systems such as facial recognition, facial emotion recognition, facial-based criminal investigation, and facial-based driver monitoring have been researched. These applications show very high performance in recognition and analysis, but there is a problem that the performance is degraded when the face is covered. The purpose of this paper is to restore facial occlusion covered by obstruction. An image generation network is used to restore the face from object such as glasses, makeup, masks, and intentional obstruction. The image generation network recognizes facial occlusion in the input image and fills the area with as natural a look as possible. The proposed model is a three-stage model, and in the first step, the occluded part is recognized and then the occluded object is separated. In the second step, two generators are connected to generate an image from which the occluded object is removed. We used two discriminators to determine the overall appearance and the hidden part, and the resulting results affect the generator’s learning compared to the correct answer image. The model’s learning uses the original image and the occulded image dataset using CelebA and FFHQ. For the performance analysis of the model, we compared the performance of the proposed model with the comparative models using Fréchet inception distance (FID), SSIM and Peak Signal to Noise ratio (PSNR) and the proposed model show 0.8 lower at FID and 0.012 higher at SSIM compared to other models.
A CEEMDAN-QPSO-BiLSTM Based Hybrid Model for Water Quality Parameter Forecasting

Water contamination is a major concern worldwide due to its impact on the global ecology. Accurate forecasting of water quality parameters is crucial for effective water contamination control. However, existing models face challenges in accurately predicting water quality series due to their nonlinearity and non-stationarity. Additionally, further investigation is needed to find out the performance of the Bidirectional Long Short-Term Memory network (BiLSTM) when combined with optimization of signal processing and adjustment of hyperparameters. In this article, we introduce a novel hybrid approach named CEEMDAN-QPSO-BiLSTM, which combines three techniques - Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Quantum behaved Particle Swarm optimization (QPSO), and BiLSTM - for predicting water quality parameters. To evaluate the effectiveness of this hybrid model, we developed four benchmark models, namely LSTM, BiLSTM, PSO-BiLSTM, and QPSO-BiLSTM, and compared their predictive accuracy with that of our hybrid approach. The training and testing of these models utilized actual water quality data from the Chinese National Marine Environmental Monitoring Center’s official system, specifically focusing on Dissolved Oxygen (DO), pH value, and Chemical Oxygen Demand (COD) parameters. The experimental results show that the CEEMDAN-QPSO-BiLSTM surpasses the baseline models, providing better error performance and prediction accuracy.
A CSRR Glucose Concentration Sensor with Increased Resolution Using the Q-value Enhancement Circuit Technique

This study proposes a new sensor that detects the glucose concentration of an aqueous solution with increased resolution. It is aimed at the realization of a wearable non-invasive glucose sensor for diabetes patients in future. A modified complementary split-ring resonator (CSRR) is used to determine the concentration by exploiting the change in frequency when an aqueous solution is applied. The Q-value enhancement circuit technique at several GHz is applied to the CSRR to cancel out the loss of a lossy aqueous solution. A prototype of the sensor is developed. The Q-value increases from 30 to over 500 at 5.2 GHz for a glucose solution with a concentration of 100 mg/dl. The measured sensitivity is 69 MHz/% for aqueous glucose solutions with concentrations ranging from 0-10 wt%.
Making Xcast a Reality and Operate at the 100 Gbps Line Rate in P4 Hardware Switches

Multicast is an approach by which a packet sent by a sending host can be received by multiple receiving hosts. Traditional methods of performing multicast is via storing multicast routing information in the multicast group table of every switch (or router) on the multicast tree. When receiving a multicast packet, a switch/router will replicate the packet and forward the replicated packets out of the switch/router based on the information stored in its multicast group table. In RFC 5058, the authors proposed the concepts of Explicit Multicast (Xcast) in which the unicast addresses of the receiving hosts of a multicast group are carried in the header of the multicast packet. The concepts of Xcast were proposed in 2007 without design and implementation details for real hardware switches/routers. Recently, due to the emergence of programmable switches, designing and implementing Xcast in hardware switches becomes feasible. In this work, we design and implement an Xcast-like scheme in P4 hardware switches and evaluate its performance. Experimental results show that our scheme can multicast packets at the 100 Gbps line rate without losing any packet inside the switch due to pipeline processing backlogs.
Lung Disease Classification using X-Ray Imaging with Ensemble Learning

Lung diseases are regarded as serious health issues in both middle-aged adults and youngsters. They cover a variety of ailments, including asthma, COPD, and other respiratory diseases. For effective treatment and positive patient outcomes, lung illnesses must be detected early and accurately. However, due to the intricacy of the circumstances, physical inspection of X-ray images of chest for lung illness diagnosis can be mistake-prone. Automated classification methods can be proposed using Machine Learning and Deep Learning. We can utilise the advantages of various independent models to increase accuracy and dependability by using ensemble learning. In this study, we utilised the benefits of two popular deep learning models, ResNet and VGG16 to classify x-ray images. The proposed ensemble model helps to multi classify the lung x-ray images and it outperforms the existing available ensemble model with an accuracy of 97.62%.
A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices.
A Predictive Approach for Compensating Transmission Latency in Remote Robot Control for Improving Teleoperation Efficiency

Transmission latency presents a significant challenge when operating remote equipment, such as a robotic arm. To address this, we developed a platform and conducted experiments to reduce transmission latency to near-zero levels. These experiments employed Long Short-Term Memory (LSTM) to anticipate future motion trends, leveraging both the controller's movement variables and Electromyography (EMG) data from the operator's arm muscles. Our findings indicate the potential to decrease transmission latency by approximately 500ms. Additionally, our research confirms a direct correlation between prediction accuracy and the brevity of prediction time, suggesting that shorter prediction times yield more accurate results when using EMG. In the context of video transmission for a remotely located robotic arm, we applied video prediction techniques using the Predictive Coding Network (PredNet) to counter network latency. Our results suggest that these predictive methods can effectively compensate for a latency period of 300ms, thereby highlighting their potential for reducing transmission latency in remote robotic operations.
Analysis and Optimization of Multi-RIS-Assisted Dual-Functional Radar-Communication Systems via Free Probability Theory

Dual-functional radar-communication (DFRC) has been widely concerned in future communication systems. By aggregating communication and detection resources, DFRC enables improved communication data rate as well as continual real-time sensing capability. However, the spectral efficiency of the communication channel will be inevitably compromised since the transceiver design has to take into account both of the communication and radar oriented beampatterns. In this paper, the reconfigurable intelligent surface (RIS) panels are deployed to assist the design of multi-antenna DFRC transceivers, where the RIS panels introduce additional degree-of-freedom to accommodate the dual-functional beampattern. First, applying the operator-valued free probability theory, we derive the closed-form expression of the asymptotic achievable rate of the multi-RIS-assisted MIMO DFRC systems in presence of general Rician fading. Then, we propose an alternating optimization (AO) algorithm to jointly optimize the transmit signals of the DFRC transmitter and the phase shifts of the reflecting elements of the RIS panels, which achieves an optimal tradeoff between the achievable rate and the desired radar beampattern. Simulation results verify the accuracy of the asymptotic expression of the achievable rate. In addition, the deployment of RISs and the proposed AO algorithm are proven to improve both the detection and communication performance.