Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Predictability in Multi-Sensor Monitoring of Indoor Wireless Communication Environment

A multi-sensor monitoring system has been developed for real-time monitoring and analysis of indoor wireless communication environments. The system uses commodity hardware and open-source software platforms, which are low cost and suitable for open and cooperative development. It is shown that analysis of time series data of Wi-Fi signal strength obtained from multiple sensors in different rooms can be used to detect predictability relations between signals at different positions.
Exploring Different Techniques for Emotion Detection Through Face Recognition

A vital part of affective computing, artificial intelligence, and human-computer interaction is emotion recognition from visual material. The creation of an emotion identification system utilizing open-source tools like OpenCv, TensorFlo w, and several Python libraries is explored in this research article. With the use of facial expressions in pictures and videos, the suggested framework seeks to precisely classify and detect various emotions. Additionally, in order to improve the robustness and context awareness of the emotion detection system, this research discusses the significance of labeled datasets for training and validation as well as the incorporation of multi-modal datasources, such as audio and text sentiment analysis. Convolutional neural networks (CNNs) have become a game-changing force in computer vision, with uses ranging from object identification to picture categorization. In order to further our knowledge and practical use of these technologies, we investigate the synergistic link between CNNs, Facial Recognition Systems (FRS) which is to recognize the basic features of the Humanface using Deep Learning concepts, and Facial Emotion Recognition (FER) which will detect the emotion of the face after the full recognition of the face using CNN and the datasets in this study.
Clustering as a Catalyst for Big Data Classification (CC-BC)

In supervised learning, data classification is the method of categorizing data to facilitate data mining processes for informed decision-making. The central aim of a classification model is to accurately predict the categorical data for both familiar and unfamiliar instances. The classification models in machine learning are usually trained with datasets where instances are labeled. This paper explores an alternative way of constructing classification models based on the similarities of the instances rather than labels annotated by experts. The process of labeling data is a resource-intensive and time consuming process incredibly challenging when dealing with large datasets known as big data. In light of the proposed methodology clusters, the big data developing classifiers based on these clusters while bypassing the predefined class labels. This approach enhanced the performance of the classifier. Moreover, the generated clusters can be associated with the relevant class labels introducing a link between the unsupervised clustering and the supervised classification task. To validate our proposed approach, we gathered a diverse collection of data from Kaggle. For experimental analysis, we applied three widely recognized decision tree induction ID3 (Iterative Dichotomiser 3), C4.5 (extension of ID3 algorithm), CART (Classification & Regression Tree), NavieBayes classifier and Ensemble classifier(RandomForest, Bagging, Boosting). The outcomes of our investigation shed light on the potential of leveraging instance clustering for classification tasks, potentially revolutionizing the conventional paradigms of supervised learning in the domain of data mining and decision support.
Revisiting Collapsed-Forwarding Caching for Delay-Sensitive Applications

Caches are instrumental in reducing network and server loads that are typically deployed at the network edge to shorten the response time. Cache optimization for modern delay-sensitive applications is not only concerned with maximizing the cache hit ratio but is also related to minimizing the cache response time. In this paper, we revisit collapsed-forwarding caches and propose COLFT, which is a cache-friendly transport equipped with a cache admission filter that takes into account the download time between the cache and content server. To analyze the performance of COLFT, we developed an in-depth analysis of the cache system that characterizes its behavior, either with or without collapsed forwarding-enhanced cache admission, capturing the effect of the bandwidth share in the transport layer. A numerical evaluation agrees well with the analytical results and shows a significant improvement of COLFT in both the hit rate and response time in realistic scenarios when the transport-dependent download time is considered.
Adaptive Augmentation of Imbalanced Class Distribution in Road Segmentation

Deep learning has achieved significant improvements in various tasks in Computer Vision. However, acquiring a large number of the dataset is a challenge in real-world applications, especially if they are new class objects for Deep Learning. Furthermore, the distribution of classes in the dataset is often imbalanced - a bottleneck of the neural network’s performance in classification. One possible real-world application is road segmentation, which is crucial for autonomous driving and sophisticated driver assistance systems to comprehend the driving environment. Recent years have seen significant advancements in road segmentation with the help of Deep Learning. Inaccurate road boundaries and lighting fluctuations such as shadows and overexposed zones are still challenging issues. Prediction performance is also impacted by an improper class distribution, which arises because most image pixels belong to the background (negative class), while the goal is to identify road pixels (positive class). In this paper, we focus on the topic of "visual road classification," where the target is to label each pixel as containing either a road or a background. We tackle this task by implementing a novel Adaptive Augmentation algorithm by integrating with some recently suggested encoder-decoder based convolutional neural network architecture and compare the qualitative and quantitative experimental results with traditional augmentation algorithm. The proposed method uses an adaptive augmentation module to improve performance under improper class distribution conditions. Experimental results show that the suggested method achieves higher segmentation accuracy than state-of-the-art methods on the KITTI road detection benchmark datasets.
Attention-Based Interpretable Semi-Supervised Federated Learning for Intrusion Detection in IoT Wireless Networks

Intrusion detection is a crucial task to ensure the security of the Internet of Things (IoT) wireless networks. While different Machine Learning (ML) methods have been leveraged to detect network intrusion, they often require data from multiple devices to be collected and stored on a central server to train underlying ML models, raising privacy concerns. Federated Learning (FL) can preserve data privacy where local devices can iteratively update the model parameters trained by using their own local datasets and send them to a server for model ag-gregation. Moreover, most existing ML-based intrusion detection designs are based on supervised ML using labeled data, which may not be available or time-consuming to build. Therefore, semi-supervised learning methods that effectively utilize both labeled and unlabeled data would be very desirable and necessary. This paper proposes a semi-supervised FL method for intrusion detection in IoT networks. Specifically, our proposed framework leverages the attention-based architecture called TabNet to selec-tively focus on important features of network flow and we propose a crucial data preparation procedure before training the ML model using the FL approach. We conduct extensive numerical studies to demonstrate the effectiveness of our approach and compare its performance to other baselines. We also present empirical evidence to support the interpretability of our method. We also show that the proposed data pre-processing procedure indeed greatly enhances the intrusion detection performance. 1
Prediction of Medical Insurance Costs: A SHAP-Enhanced Predictive Analysis for Transparency and Interpretability

The escalation of healthcare expenses has been markedly pronounced in the aftermath of the COVID-19 pandemic’s onset. Consequently, health insurance has ascended to prominence as a critical domain of investigation, with a series of ongoing endeavors underway to navigate this complex issue. The task of forecasting medical insurance expenditures is laden with challenges, attributed to the dynamic nature of healthcare, the intricacies and occasional disarray of data, unpredictable human behaviors, economic oscillations, unforeseen events, and prevailing privacy apprehensions. In the quest to surmount these obstacles, a predictive model has been unveiled, engineered to impart insights into the nuances of healthcare pricing structures. The examination of SHAP values facilitates the discernment of pivotal elements that steer cost prognostications, a process instrumental in augmenting transparency and enabling judicious choices for both insurance entities and the insured. This inquiry underscores the cardinal role of amalgamating predictive precision with an element of transparency within the realm of healthcare insurance.
An Accurate FPGA-Based ORB Feature Extractor for SLAM with Row-Wise Keypoint Selection

In oriented FAST and rotated BRIEF (ORB) based SLAM, keypoint detection and feature description are among the computationally expensive parts. We propose an accurate hardware accelerator for these processes. Our method combines row-wise keypoint selection and multi-cycle feature description, which enables a more accurate feature description than previous works while hardware resource requirements are as small as them.
Multi-Frequency Band Physical-Layer Authentication in Indoor Environment

In this paper, we propose a physical-layer authentication (PLA) scheme based on wireless channel responses of multiple frequency bands. Channel-based PLA has been regarded as a desirable scheme as it simplifies the authentication process by comparing received and previously acquired channel state information (CSI). However, it may fail authentication due to potential disagreements in CSI among legitimate users. Moreover, in an indoor environment, spatial correlation occasionally arises between legitimate and illegitimate channels, which can degrade the authentication reliability. To mitigate undesired CSI disagreements and spatial correlations, we propose a joint utilization of channel responses in multiple frequency bands. As spatial correlation is unlikely to arise simultaneously in different frequency bands, any correlation with illegitimate channels in one band can be compensated by the other band, providing reliable authentication even in indoor environments. Similarly, any disagreement among legitimate channels in one band can be compensated by the other band that has sufficient channel coherence time. To demonstrate the performance of our proposed scheme, we constructed a test-bed based on universal software radio peripherals. The experimental analysis shows that the proposed PLA scheme can significantly improve the authentication performance as much as orders of error reduction in failures by doubling the frequency bands in channel-based PLA.
A Comparison of Serialization Formats for Point Cloud Live Video Streaming over WebRTC

In this paper, we consider a system for WebRTC live streaming of real-time captured point cloud data. In particular, to this end, we consider various serialization formats that need to be performed in advance. We also propose a new serialization format in this paper and show its effectiveness by comparing it with the conventional formats.
Shaping AI Behavior: A Q-Learning Driven Approach to Automatic Behavior Tree Creation

In this paper we propose a novel solution to generate Behavior Trees automatically and autonomously from reinforcement learned autonomous agents. We developed a Capture The Flag-style game in which two teams compete to win. The Behavior Trees are generated from the knowledge of Q-Learned autonomous agents competing in this game by extracting their knowledge and parsing it into a Behavior Tree format. The proposed algorithm can generate these trees with comparable performance to the autonomous agents as well as compete with handmade state machine like solutions.
Application Research of Digital Virtual Human Models Based on Sensor Data

This research aims to create a virtual space with distinct Wuhan characteristics using a modular design approach, enhancing users’ sense of identification and emotional resonance with the virtual world in the metaverse. By focusing on interaction process design, user body scans data, and visual information collection for user modeling, we employ Kinect, temperature sensors, and pulse sensors for interactive design to improve the user experience. We adopt an interdisciplinary research framework centered around the user to explore effective ways for individuals to maximize their experience of diverse life within their finite lifespans. Through data collection, we decompose real user data and integrate Wuhan’s urban characteristics, creating versatile and relatively independent modules that expand the diversity and multiplicity of the virtual space.
PLCoroner: A Novel JTAG-based PLC Memory Forensics Method

Programmable Logic Controller (PLC) is one of the most widely used industrial equipment in Industrial Control Systems (ICS) which the PLC security has received more and more attention in recent years. PLC memory forensics is an effective PLC security protection method. However, due to the special hardware architecture, how to avoid hang and crash in the PLC memory forensics process is a challenge. In this paper, we present PLCoroner, a novel PLC memory forensics method based on the Joint Test Action Group (JTAG). With hardware evaluation, JTAG pin identification, memory mapping and result analysis, the PLCoroner greatly improved the reliability and stability of PLC memory forensics. Compared with traditional methods, PLCoroner effectively avoiding the existing problems such as easy crash and limited memory address. Experimental results also show that for the current mainstream PLC security threats, PLCoroner can complete attack forensics and threat discovery in a few seconds, which provides more theoretical and practical references for PLC forensic analysis and security protection.
A Productive HLS Simulation Approach for Multi-FPGA Systems

When implementing an application on an FPGA cluster with multiple FPGAs connected, the existing high-level synthesis design environment assumes functional simulation with a single module, and it requires a great deal of effort by the designer to create verification code for processes that reproduce communication among multiple modules. Therefore, we propose a method to easily simulate the function of multi-FPGA systems by executing each process of multiple FPGAs in parallel as a task through a system-of-tasks feature.
NOMA or Puncturing for Uplink eMBB-URLLC Coexistence from an AoI Perspective?

Through the lens of the age-of-information (AoI) metric, this paper takes a fresh look into the performance of coexisting enhanced mobile broadband (eMBB) and ultra-reliable low-latency (URLLC) services in the uplink scenario. To reduce AoI, a URLLC user with stochastic packet arrivals has two options: orthogonal multiple access (OMA) with the preemption of the eMBB user (labeled as puncturing) or non-orthogonal multiple access (NOMA) with the ongoing eMBB transmission. Puncturing leads to lower average AoI at the expense of the decrease in the eMBB user's rate, as well as in signaling complexity. On the other hand, NOMA can provide a higher eMBB rate at the expense of URLLC packet loss due to interference and, thus, the degradation in AoI performance. We study under which conditions NOMA could provide an average AoI performance that is close to the one of the puncturing, while maintaining the gain in the data rate. To this end, we derive a closed-form expression for the average AoI and investigate conditions on the eMBB and URLLC distances from the base station at which the difference between the average AoI in NOMA and in puncturing is within some small gap $\beta$. Our results show that with $\beta$ as small as 0.1 minislot, the eMBB rate in NOMA can be roughly 5 times higher than that of puncturing. Thus, by choosing an appropriate access scheme, both the favorable average AoI for URLLC users and the high data rate for eMBB users can be achieved.
On the Design of Efficient Lattice-Code based Multiple Access

This paper studies a lattice-code based multiple-access (LCMA) system. In the uplink, $K$ users encode their messages with the same 2m-ary ring code mapped to 2m- PAM, belonging to the ensemble of lattice codes. All users transmit simultaneously. The receiver attempts to compute $K$ independent streams of integer-combinations (ICBs) of the users' messages. For this, 1) we establish and solve a new “bounded independent vectors problem” (BIVP) which identifies a near-optimal set of coefficient vectors w.r. t. the ICBs, outperforming existing LLL and HKZ lattice reduction methods; 2) we put forth new LCMA soft detection algorithms, which calculate the a posteriori probability w.r. t. the ICB over the lattice. The per-user complexity is of order less than O(K), suitable for massive access of $K$ being large. The soft detection outputs are forwarded to $K$ ring-code decoders to recover the messages. With our developed techniques, LCMA is shown to support a significantly higher load of users and exhibits an improved frame error rate over state-of-the-art IDMA and SCMA schemes. Such advanced functionality are achieved with just parallel processing and $K$ single-user decoding operations.
Traffic Fatality Prediction using Machine Learning Algorithms: Performance Analysis and Comparison Study

Many nations are concerned about the high number of traffic fatalities, which has led to a search for efficient prevention methods. This study addresses road fatalities brought on by drunk driving incidents. The study uses machine learning algorithms to detect and comprehend patterns related to fatalities brought on by drunk driving. The study builds prediction models that can help with early detection and policy development using several variables, including crash features, demographic information, and historical trends. Datasets on traffic fatalities are analyzed using a variety of supervised machine learning algorithms, including Random Forests (RF), Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). According to the results of this study, the RF model has demonstrated great potential as a reliable predictor of fatalities caused by drunk driving. RF algorithm achieves better accuracy than other methods.
Partial SRv6 Deployment and Routing Optimization: A Deep Reinforcement Learning Approach

Segment Routing over IPv6 (SRv6) is a promising efficient technology for traffic engineering (TE). As transitioning from a traditional distributed network to a full SRv6 network faces technical and economic challenges, partially deploying SRv6 has attracted much attention from academic communities. Many TE research attempts have been made on SRv6 deployment and routing optimization, among which Deep Reinforcement Learning (DRL) based algorithms have shown their advantages over traditional algorithms. However, with the incremental deployment of SRv6 nodes, training costs for DRL as well as solution space of routing optimization increase significantly, which obstructs the application of DRL-based algorithms in practice. To address this issue, we propose a DRL-based SRv6 deployment and routing optimization (SDRO) algorithm, with the TE objective of minimizing the maximum link utilization. In SDRO, the DRL agent is only trained once on a full SRv6 network and then used for different SRv6 deployment ratios. Hence, training costs can be obviously reduced. To reduce the solution space of routing optimization, the DRL agent performs routing pre-optimization on a portion of the traffic before routing is finally optimized by the Linear Programming method. By doing so, the execution time for routing optimization can be greatly reduced. Besides, for the issue of frequent link failures in the network, SDRO leverages the generalization of Graph Neural Networks to improve its robustness. Simulation results demonstrate that SDRO outperforms existing algorithms under different SRv6 deployment ratios and link failures, and completes routing optimization in a few seconds.
Generating Realistic X-ray Images Using GANs

Modern developments in the medical industry have been significantly accelerated by the promising advancements of Generative Adversarial Networks (GANs). This study introduces an intriguing exploration into the utilization of Deep Convolutional Generative Adversarial Networks (DCGANs). Using GANs’ inherent capabilities, our objective is to build high-fidelity pictures with detailed anatomical features and qualities similar to actual X-ray images. The proposed methodology of this work proves effective in addressing the challenges posed by limited medical imaging data. It generates diverse and expansive datasets, which can enhance machine learning models in medical research and diagnostics. The study presents a novel approach to create synthetic X-ray images that closely resemble original CXR images, with the potential to significantly contribute to medical imaging. Consequently, our research not only advances the application of GANs in medical imaging but also holds promise in shaping the future landscape of healthcare technology.
Mobile Data Security with Attribute-based Encryption and Confidential Computing

Mobile devices have a significant value in individuals’ lives by offering various applications and functions within a single device. Considering their widespread social use, mobile devices are alluring targets for amassing personal data, including photos, videos, identities, financial details, and passwords. Nonetheless, challenges such as mobility, connectivity, and device security restrictions can give rise to issues like data breaches, financial scams, privacy breaches, and unauthorized data utilization. This paper proposes a private data management system that applies confidential computing to enhance the user’s personal data security and reliability. The proposal involves the employment of Attribute-based Encryption within a trusted execution environment (e.g., provided by the mobile device’s TrustZone technology). This setup ensures encrypted data storage and manipulation, seamlessly integrated and fortified alongside other applications on the device. By implementing this private data management system, we aim to mitigate potential security risks and bolster user confidence in their transactions and interactions.