Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud-Mobile Video Super-Resolution Method Based on Deep Learning

With the rapid popularity of short video applications and the rise of streaming media platforms, video images have become an important medium for people's daily communication, and users' requirements for image quality are getting higher and higher. In order to improve the image quality of mobile devices or web applications, the Image Super-Resolution (SR) algorithm is often used to help improve the user experience. In order to effectively solve the problems of image deterioration caused by limited cloud-mobile transmission bandwidth, difficult model deployment, high energy consumption, and slow inference speed, this paper proposes a lightweight image super-resolution model based on attention mechanism and multi-scale residual network. First of all, the multi-scale characterization ability of the algorithm model is enhanced by combining dense connection and channel attention mechanism to improve the performance of image super-score reconstruction. Secondly, the loss function of mean variance and total variation is used in the training process to eliminate the impact of noise in the reconstruction process. Finally, through experimental analysis and comparison of results, it is concluded that the proposed cloud-mobile video super-resolution method has better image reconstruction performance and faster processing speed.
MDEmoNet: A Multimodal Driver Emotion Recognition Network for Smart Cockpit

The automotive smart cockpit is an intelligent and connected in-vehicle consumer electronics product. It can provide a safe, efficient, comfortable, and enjoyable human-machine interaction experience. Emotion recognition technology can help the smart cockpit better understand the driver’s needs and state, improve the driving experience, and enhance safety. Currently, driver emotion recognition faces some challenges, such as low accuracy and high latency. In this paper, we propose a multimodal driver emotion recognition model. To our best knowledge, it is the first time to improve the accuracy of driver emotion recognition by using facial video and driving behavior (including brake pedal force, vehicle Y-axis position and Z-axis position) as inputs and employing a multi-task training approach. For verification, the proposed scheme is compared with some mainstream state-of-the-art methods on the publicly available multimodal driver emotion dataset PPB-Emo.
Alchemist: A Component-Oriented Development Tool of FPGA based on Publish/Subscribe Model

High-level synthesis (HLS) technologies, which facilitate the synthesis of HDL from software language descriptions, have become integral in FPGA (Field Programmable Gate Array) development. Despite these advancements, crafting high-performance digital circuits and integrating circuit modules continues to demand expertise in both software and circuit design techniques. This complexity restricts the efficient reuse of FPGA circuit components, a crucial step towards streamlined development processes. To address this, we introduce a novel approach grounded in the principles of model-driven development (MDD) that leverages component-oriented design methods to enhance the reusability of FPGA circuit components. Central to our method is the utilization of a publish/subscribe model orchestrated through the Data Distribution Service (DDS). This paper delineates our pioneering component-oriented design method and introduces a supportive tool developed to operationalize this method, thus facilitating more efficient FPGA development cycles. Through a detailed case study, we illustrate the effective application of our tool in real-world settings, thereby highlighting its potential to significantly streamline FPGA development processes and encourage the reuse of circuit components.
Learning Bayesian classiﬁers from positive and unlabeled examples

The positive unlabeled learning term refers to the binary classiﬁcation problem in the absence of negative examples. When only positive and unlabeled instances are available, semi-supervised classiﬁcation algorithms cannot be directly applied, and thus new algorithms are required. One of these positive unlabeled learning algorithms is the positive naive Bayes (PNB), which is an adaptation of the naive Bayes induction algorithm that does not require negative instances. In this work we propose two ways of enhancing this algorithm. On one hand, we have taken the concept behind PNB one step further, proposing a procedure to build more complex Bayesian classiﬁers in the absence of negative instances. We present a new algorithm (named positive tree augmented naive Bayes, PTAN) to obtain tree augmented naive Bayes models in the positive unlabeled domain. On the other hand, we propose a new Bayesian approach to deal with the a priori probability of the positive class that models the uncertainty over this parameter by means of a Beta distribution. This approach is applied to both PNB and PTAN, resulting in two new algorithms. The four algorithms are empirically compared in positive unlabeled learning problems based on real and synthetic databases. The results obtained in these comparisons suggest that, when the predicting variables are not conditionally independent given the class, the extension of PNB to more complex networks increases the classiﬁcation performance. They also show that our Bayesian approach to the a priori probability of the positive class can improve the results obtained by PNB and PTAN.
VRcabulary: A VR Environment for Reinforced Language Learning via Multi-Modular Design

We demonstrate VRcabulary, a hierarchical modular virtual reality (VR) environment for language learning (LL). Current VR LL apps lack the benefit of reinforcement presented in typical classroom environments. Apps either introduce content in the second language and lack retention testing, or provide gamification without an in-environment instructional component. To acquire reinforcement of knowledge, the learner needs to visit the app multiple times, increasing the potential for monotony. In VRcabulary, we introduce a multi-modular hierarchical design with 3 modules—an instructional module providing AI-generated audio playbacks of object names, a practice module enabling interaction based reinforcement of object names in response to audio playback, and an exam module enabling retention testing through interaction. To incentivize engagement by reducing monotony, we keep the designs of each modules distinct. We provide sequential object presentations in the instructional module and multiple object assortments in the practice and exam modules. We provide feedback and multiple trials in the practice module, but eliminate them from the exam module. We expect cross-module diversity of interaction in VRcabulary to enhance engagement in VR LL.
Bitcoin Price Prediction Based on Sentiment Analysis

Bitcoin is a decentralized digital currency that has received a lot of interest in recent years because of its unique qualities and possibilities as an alternative investment. Trading in Bitcoin might be difficult owing to the extreme volatility of its price, which is impacted by a variety of variables such as market sentiment and regulatory changes. To solve this issue, researchers and traders have been investigating the use of social media data, namely Twitter data, to forecast Bitcoin price changes using sentiment analysis. The work focuses on leveraging real-time Twitter data and bitcoin prices from the Twitter and Coin Market API respectively to predict short-term Bitcoin price movements. Real-time tweets are collected, sentiment is extracted using sentiment analysis methods, and then combined with relevant pricing data. A predictive framework is developed to forecast the next hour's Bitcoin price by employing univariate and multivariate time series forecasting and generating deep learning models such as LSTM, BIGRU, RNN and LSTM+GRU. Multivariate time series forecasting model based on BIGRU has performed well among the deep learning models used, attaining a 130.529 RMSE score. The primary aim of this study is to provide valuable insights to traders, as short-term market sentiment plays a crucial role in their trading strategies.
Speaker Identification System To Enhance Real-Time Security Using Convolutional Neural Network

The speaker authentication is important for many domains such as safe keying systems, voice aids, or even criminal investigations and others. The shortcomings of conventional approaches for speaker extraction derive from subjectively determined parameters and empirical models employed. This paper responds to these constraints by constructing the proposed deep learning-speaker recognition system using CNN model. A dataset of speech recordings in 16 KHz sample rate PCM format will be used for training that system. To make the system more robust, we will mimic real-life scenarios by adding noise backgrounds. This study intends to design a deep learning model able to identify various voice features and discriminate voices from noise for speaker recognition. By tuning the CNN model, it will be possible to identify specific traits in each speaker’s voice and differentiate among individuals using their unique voices. The emphasis would be placed on building a practical lightweight model suitable for mobile based implementations in real-time speaker ID scenarios.
A GAN-Based Approach for ECG Reconstruction from Doppler Sensor Signals

An Electrocardiograms (ECG) is a recording of the heart's electrical activity. It can help detect problems with one's heart rate or heart rhythm. Traditional methods to measure and collect ECG are not practical for daily use due to their invasive nature and inaccessibility. Thus, a less intrusive and more accessible method is needed. In this paper, we propose a method that uses attention-based Wasserstein Generative Adversarial Networks-Gradient Penalty (aWGAN-GP) to reconstruct ECG signals from ones collected using a Doppler sensor. GANs can capture pertinent features in the Doppler signals to enable such reconstruction. Our approach uses the integrated spectrum of the Doppler signal to identify R-peaks, which are then employed to train the aWGAN-GP to reconstruct the ECG signal. We evaluated this method on 19 healthy subjects and obtained a correlation coefficient of 0.88 between the reconstructed and actual ECG signals, outperforming the conventional CNN-based method which reached only 0.35. Our results demonstrate that it is possible to reconstruct an ECG signal from a heartbeat signal collected via a Doppler sensor using aWGAN-GP.
Coffee Plant Disease Identification using Enhanced Short Learning EfficientNetV2

Plant diseases are prime and major root causes that directly impact agriculture production and quality. Reliable Classification and Identification of infection appearance at the initial stages could increase the yield and benefit the farmer economically. Hence an automotive system that can provide more reliable results in real-time is most needed for cash crops like coffee. In recent works, the Deep learning (DL) model and Machine Learning (ML) techniques have proven remarkable agricultural enhancements and improvements. Hence, significant work has been carried out by employing Deep Learning models, specifically with the help of the Transfer Learning (TL) design technique and Convolutional Neural Networks (CNN) for various crop species. This work addresses four different categories of coffee plant diseases. The proposed system avails the benefits of the EfficientNetV2-S TL model with slight modifications according to the dataset, namely Enhanced EfficientNetV2-S. The main idea of using the TL model is to enhance the training speed with fewer model parameters. Applying this technique with diverse model configurations at various training phases and varying image sizes, Enhanced EfficientNetV2-S outperformed with an accuracy of 98.1% compared to DenseNet-121 with 92.9% accuracy for 5000 images.
ScholarFace: Scanning Faces, Discovering Minds

In today’s data-driven world, quick access to scholarly info is vital. However, current academic search engines face challenges such as restricted text-based searching, uncertainties related to researcher names, absence of contact details, and lack of profile summaries. To mitigate these issues, we introduce ScholarFace, an innovative concept that could transform how we search for academic knowledge. It uses face recognition and language generation technology to spot scholars in photos effortlessly, giving us rich profiles and summaries of their work. It also offers an interactive chat element for users to get more insights about a scholar, reducing online search efforts. We take privacy and ethics issues seriously and ensure ScholarFace complies with rules and regulations. With ScholarFace, we hope to create a smarter, effortless, more connected scholarly world.
Efficient COO to CSR Conversion for Accelerating Sparse Matrix Processing on FPGA

Sparse matrix processing is an important computational kernel widely applied in various fields such as graph processing, and big data analysis. In many applications, sparse matrix processing is often a bottleneck, so various accelerators for it have been proposed. These accelerators often process sparse matrices in compressed formats like Coordinate (COO) or Compressed Sparse Row (CSR), which store only nonzero elements, to optimize memory usage. Some accelerators perform the conversion of output matrices calculated in COO format to CSR format, which enables a higher compression ratio, in order to reduce external memory traffic. Given that external memory bandwidth is the performance bottleneck in most cases, designing an efficient COO to CSR (COO2CSR) converter is an important issue that needs to be addressed. In this paper, we propose a COO2CSR conversion method that overcomes the challenge of performing the conversion in a highly parallel manner without prior knowledge of the target matrix. Based on this method, we develop a high-performance COO2CSR converter. We simulated our converter and found that it achieves near-optimal throughput. In addition, logic synthesis results on an Alveo U55C FPGA board showed that the converter consumes only 1.07% of LUTs, 0.65% of FFs, and 1.24% of BRAMs.
Performance Analysis of Ternary NAND Gate Based on FinFET Models

The rising complexity of digital circuits based on binary logic demands the use of multivalue logic. This article outlines the detailed device level behavior of LVT FinFET model and a ternary NAND gate is designed and compared using three distinct FinFET models: LVT, SVT, and HVT. The simulation results show the power consumption as 10.7, 7.5 and $5.01\ \mu\mathrm{W}$ for LVT, SVT and HVT models, respectively. Further, the obtained power delay product (x 10−15J) is 379.26, 265.52 and 175.65 for LVT, SVT and HVT models, respectively. The anticipated ternary NAND gate design is convenient for optical computing, error detection and cryptographic algorithms.
BcIIS: Blockchain-Based Intelligent Identification Scheme of Massive IoT Devices

With the rapid development of loT technology, various loT devices enter our daily life. The continuously increasing scale of the massive loT devices in both of numbers and types further bring heavy pressure on loT network management and security. Therefore, how to accurately identify and efficiently manage so massive loT devices has become a challenge. In this paper, we propose BcllS, Blockchain-based Intelligent Identification Scheme of Massive loT Devices. It applies a decentralized identification architecture and realizes learning sustainably as well as efficiently identifying by updating the identification model constantly according to the ledger which is maintained by all gateways collaboratively. Experiments show that the identification accuracy can achieve up to 99.5 %.
CP-FL: Practical Gradient Leakage Defense in Federated Learning with Compressive Privacy

Federated learning (FL) requires clients to train constituted models based on their local datasets. Clients usually directly train local models using their entire datasets without distinguishing which information of data is task-relevant or irrelevant. Task-irrelevant information does not contribute to the learning task but exposes additional privacy information to adversaries. Studies have shown that unintended information leakage from gradients during FL iterations threatens clients' privacy. Researchers applied differential privacy (DP) to protect clients' gradients, but it does not help to reduce task-irrelevant information from the gradients. In this paper, we propose a compressive privacy federated learning (CP-FL) scheme to protect the task-irrelevant information from gradient leakage attacks. In CP-FL, clients train a local compressive model according to the global task. The local compressive model constructs a new representation, which extracts task-relevant and removes task-irrelevant information from clients' data. Since the global model is updated based on the compressed representation that eliminates the task-irrelevant information, it can effectively prevent adversaries from inferring those property values from the uploaded gradients. Moreover, with the help of a powerful local compressive model that sanitizes the challenging data into a low-dimension space representation, CP-FL can use a small global model instead of a sizeable one, significantly reducing communication. Both theoretical analysis and extensive experimental results demonstrate that CP-FL can effectively defend against gradient leakage attacks while maintaining practical utility.
Water level prediction using long short-term memory network with residual structure based on ensemble empirical mode decomposition

To solve the problem about low accuracy of water level prediction caused by its nonlinearity and high noise, a water level prediction model based on EEMD-RESNET-LSTM is proposed. First, EEMD (Ensemble Empirical Modal Decomposition) is used for data noise reduction. Then, the RESNET-LSTM model is employed to predict the multiple components and residual term obtained after EEMD decomposition. Finally, the individual prediction results are aggregated to obtain the actual water level predictions. Compared to the three models, LSTM, RESNET-LSTM and EEMD-LSTM, the performance of this model is improved. Its RMSE is 0.127 m, MAE is 0.102 m, and R2 reaches 94.5% on water level prediction of Hongze Lake.
Privacy-Preserving EEG Signal Analysis with Electrode Attention for Depression Diagnosis: Joint FHE and CNN Approach

Artificial intelligence has been utilized to analyze patients' electroencephalograms (EEG) to diagnose depression. However, attackers can deduce patients' privacy after analyzing patients' EEG time series. Therefore, researchers propose to operate ciphertext calculation in depression diagnosis models based on homomorphic encryption. Nevertheless, homomorphic encryption requires consistent private keys during training, which could result in other participants decrypting the ci-phertexts. Additionally, existing EEG-based models neglect the relationship among electrode positions during EEG acquisition. To address these issues, we propose a novel training strategy for the depression diagnosis model based on fully homomorphic en-cryption (FHE) and electrode topology. Specifically, we establish a training strategy that prioritizes the privacy of patients' EEG data without compromising the cost-effectiveness of the diagnosis model. Furthermore, we incorporate the attention mechanism of electrode topology into our model to improve its performance and verify the relationship among topology locations. Our proposed model outperforms the original convolution neural network model, achieving higher accuracy in depression diagnosis and identifying virtual electrode channels for the first time.
Adaptive Fuzzy Neural Network Modelling and Simulation of Switch Reluctance Motor

Due to the highly nonlinear relationship between the magnetic flux linkage, phase current, and rotor position of a switch reluctance motor (SRM), the torque generated by the SRM exhibits significant fluctuations, seriously affecting the stability of motor operation, especially at low speeds and high torques. In order to reduce torque ripple, based on the measured characteristics of each phase of the motor, an adaptive fuzzy neural network (ANFIS) is employed to identify the direct torque model, inverse torque model, and inverse flux linkage model of the SRM, thus establishing a model for the SRM. The motor model is then simulated and analyzed. The simulation results indicate that compared to conventional control methods, the proposed method of using adaptive fuzzy neural network modeling can reliably obtain operational parameters such as motor current and torque, ensuring smooth motor operation.
Improved Multi-Step, Multi-Variate, and Spatiotemporal 5G Data Usage Forecasting Without Deploying Data Imputation Techniques

Accurately predicting 5G network usage is essential for efficient network resource allocation and high-quality service delivery. However, dynamic usage patterns, nonstationary usage distribution, and the presence of a high amount of missing data make forecasting future network usage statistics challenging. To address these challenges, this paper proposes a deep learning based time series forecasting approach that handles multi-step, multi-variate, and spatiotemporal time series forecasting problems. The proposed approach scans the data for continuity of time-steps and identifies an optimal sequence length in order to address the issue of missing records in supervisory data. It uses separate BiLSTM layers to capture the impact of temporal 5G data usage changes in adjacent networks. Next, it utilizes the adjacent cell's impact captured in permutationally invariant ordering for the central/targeted cell. The deep learning techniques used to capture the impact of changes in neighboring cells on the target cell and for multi-step and multivariate forecasting are: Self-attention, a RepeatVector, a BiLSTM network, and a Time distributed dense layer. The proposed approach outperforms existing state-of-the-art methods on the Milan dataset and has the potential to improve forecast accuracy in various applications, including network traffic forecasting.
Security Analysis of OAuth 2.0 Implementation

Many users all over the world routinely use open authentication and authorization providers based on OAuth 2.0 framework such as Google, Facebook etc. to sign in to third-party websites (relying party). The authentication (OpenID connect) and authorization (OAuth) protocol based on OAuth 2.0 framework enables the relying party application to authenticate its users and gain access to their protected resources without storing any of the sensitive user credentials such as usernames and passwords. Hence, the implementation of these protocols by the relying parties and identity providers play an important role in security of the user information and their privacy. The risk of attacks on improper implementation of these protocols can cause unauthorized access to the user accounts. In this work, we focused on testing the OAuth implementations done by the relying parties. For the secure implementation of OAuth 2.0 we found that following parameters must be present while generating OAuth request: client_id, state, response_type, redirect_uri and scope. We have analyzed our work with around 75 websites that were using Google as an identity provider to provide access to its users. Out of 75 relying parties, 30% were found vulnerable to CSRF attack due to missing state parameter, 13% used implicit flow which reveals id_token or access_token in the OAuth URL and 2% had secret parameters in OAuth URL. We developed a browser extension which successfully identify and alert its user on these issues.