Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Recognition in Video Streams and its Application in Freedom Fighters Discovery - A Machine Learning Approach

Face Recognition is an automated process implemented by using various tools and techniques, which is capable of detecting and identifying human faces from any image or video stream. Machine learning is an approach, which involves in training a machine for prediction and decision making. It simulates the function of a brain through proper training. In this paper, a machine learning approach is used to predict the faces of Indian freedom fighters from any video stream given as an input. To accomplish this task, the system must be trained before with an appropriate and adequate numbers of facial dataset. The training dataset is also created from a video stream, which is named as training video. Provided, that particular video stream must contain only one specific freedom fighter’s images of different stages and different styles. From the training video stream, image frames consist of their face were identified, extracted and stored for each person. Then these frames of faces were used to train the machine. After proper training, a model was created and this model was used for prediction. In this work, Indian freedom fighters’ images were used for training and prediction. The same approach could be utilized for the prediction of any person or list of persons from a video stream after procuring proper training.
FSDM: Fast Recovery Saturation Attack Detection and Mitigation Framework in SDN

The whole Software-Defined Networking (SDN) system might be out of service when the control plane is overloaded by control plane saturation attacks. In this attack, a malicious host can manipulate massive table-miss packets to exhaust the control plane resources. Even though many studies have focused on this problem, systems still suffer from more influenced switches because of centralized mitigation policies, and long recovery delay because of the remaining attack flows. To solve these problems, we propose FSDM, a Fast recovery Saturation attack Detection and Mitigation framework. For detection, FSDM extracts the distribution of Control Channel Occupation Rate (CCOR) to detect the attack and locates the port that attackers come from. For mitigation, with the attacker’s location and distributed Mitigation Agents, FSDM adopts different policies to migrate or block attack flows, which influences fewer switches and protects the control plane from resource exhaustion. Besides, to reduce the system recovery delay, FSDM equips a novel functional module called Force_Checking, which enables the whole system to quickly clean up the remaining attack flows and recovery faster. Finally, we conducted extensive experiments, which show that, with the increasing of attack PPS (Packets Per Second), FSDM only suffers a minor recovery delay increase. Compared with traditional methods without cleaning up remaining flows, FSDM saves more than 81% of ping RTT under attack rate ranged from 1000 to 4000 PPS, and successfully reduced the delay of 87% of HTTP requests time under large attack rate ranged from 5000 to 30000 PPS.
Photoinduced Anisotropic Supramolecular Assembly and Enhanced Charge Transport of Poly(3‐hexylthiophene) Thin Films

The self‐organization of organic polymer semiconductors into ordered supramolecular assemblies commensurate with efficient charge transport is achieved by tuning a range of process parameters (e.g., film deposition method (spin vs drop cast), solvent boiling point (low vs high boiling point), polymer‐dielectric interface treatment, and post‐deposition processing (solvent vapor or thermal annealing)). However, these strategies present limitations for large‐scale high‐throughput processing due to associated pre‐ and/or post semiconductor deposition steps. Here, photoinduced anisotropic supramolecular assembly of P3HT chains in solution is demonstrated. UV irradiation provides for enhanced intramolecular ordering of solubilized polymer chains, and thereby effects formation of anisotropic supramolecular polymer assemblies via favorable π–π stacking (intermolecular interaction). Molecular ordering is thus dramatically enhanced with concomitant, enhanced charge transport characteristics of corresponding films. Additional pre‐ and/or post treatments are avoided.
Reinforcement Learning-Based Physical-Layer Authentication for Controller Area Networks

In controller area networks (CANs), electronic control units (ECUs) such as telematics ECUs and on-board diagnostic ports must protect the message exchange from spoofing attacks. In this paper, we propose a CAN bus authentication framework that exploits physical layer features of the messages, including message arrival intervals and signal voltages, and applies reinforcement learning to choose the authentication mode and parameter. By applying the Dyna architecture and using a double estimator, this scheme improves the utility in terms of authentication accuracy without changing the CAN bus protocol or the ECU components and requiring knowledge of the spoofing model. We also propose a deep learning version to further improve the authentication efficiency for the CAN bus. The learning scheme applies a hierarchical structure to reduce the exploration time, and uses two deep neural networks to compress the high-dimensional state space and to fully exploit the physical authentication experiences. We provide the computational complexity and the performance analysis. Experimental results verify the theoretical analysis and show that our proposed schemes significantly improve the authentication accuracy as compared with benchmark schemes.
Latency, Capacity, and Distributed Minimum Spanning Tree†

We study the cost of distributed MST construction in the setting where each edge has a latency and a capacity, along with the weight. Edge latencies capture the delay on the links of the communication network, while capacity captures their throughput (the rate at which messages can be sent). Depending on how the edge latencies relate to the edge weights, we provide several tight bounds on the time and messages required to construct an MST.When edge weights exactly correspond with the latencies, we show that, perhaps interestingly, the bottleneck parameter in determining the running time of an algorithm is the total weight W of the MST (rather than the total number of nodes n, as in the standard CONGEST model). That is, we show a tight bound of $\tilde \Theta $ (D + $\sqrt {W/c} $) rounds, where D refers to the latency diameter of the graph, W refers to the total weight of the constructed MST and edges have capacity c. The proposed algorithm sends Õ (m + W) messages, where m, the total number of edges in the network graph under consideration, is a known lower bound on message complexity for MST construction. We also show that Ω(W) is a lower bound for fast MST constructions.When the edge latencies and the corresponding edge weights are unrelated, and either can take arbitrary values, we show that (unlike the sub-linear time algorithms in the standard CONGEST model, on small diameter graphs), the best time complexity that can be achieved is Θ(D + n/c). However, if we restrict all edges to have equal latency ℓ and capacity c while having possibly different weights (weights could deviate arbitrarily from ℓ), we give an algorithm that constructs an MST in Õ (D + $\sqrt {n\ell /c} $) time. In each case, we provide nearly matching upper and lower bounds.
Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined.
PDAS: A Digital-Signature-Based Authorization Platform for Digital Personal Data

Smart Disclosure, a new concept proposed by the U.S. government, has been widely accepted by the advanced countries globally. Previously, our team has built a personal data exchange system based on the Green Button open-source project to assist in achieving Smart Disclosure by realizing two core requirements, DMD (Download My Data) and CMD (Connect My Data). However, several issues still need to be addressed: (1) legality of data authorization, (2) security of data authorization records, and (3) multi-user authorization. To address the above issues, this research utilizes the technology of Citizen Digital Certificate, digital signature, and blockchain to establish an authorization system for digital personal data, referred to as PDAS (Personal Data Authorization System). By using PDAS, user data can be conveniently authorized by creating safe, undeniable, and tamper-proof data authorization contracts. Data requesters can acquire authorizations from multiple users as well as obtain integrated and authorized datasets.
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.
Interpreting Attention Models: LSTM vs. CNN : A case study on customer activation

The service sector seeks effective ways to improve customer interaction by means of data driven approaches. This challenge implies personalizing the types and combinations of interactions to engage and activate a customer or a specific group of customers. Prior research on predicting customer activation proposed to use recurrent neural networks powered with attention models. This contribution expands upon this prior research by suggesting a convolutional model with attention and exploring its explainability concerning customer activation in comparison to recurrent models. The results show that recurrent and convolutional models perform similarly in terms of precision and recall. However, the attention models of the recurrent and convolutional architectures focus on different aspects of the customer data, providing different perspectives on customer-based interaction.
An Improved Multi-Task Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Alzheimer’s Disease

Imaging genetics research based on Sparse Canonical Correlation Analysis (SCCA) helps to discover the correlation between pathological features reflected by neuroimaging and genotypic variation. Multi-Task SCCA (MTSCCA) method is to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. However, the MTSCCA method is unsupervised and cannot identify diagnosis-guided genotype-phenotype associations. In order to improve the performance and interpretability of MTSCCA, we propose an improved MTSCCA algorithm, which is a supervised sparse bivariate learning model fused with a linear regression model, in which the regression part plays a guiding role in imaging QT selection. To jointly understand the relationship between genotypes and phenotypes of multiple tasks, in this study, gene expression data and single nucleotide polymorphisms (SNPs) as genetic data are considered in the algorithm. The focus of each task is to determine the genotype-phenotype pattern guided by the diagnostic team to discover the association with SNP/gene and brain region changes. Besides, the Laplacian matrices of three kinds of data are added as prior knowledge to the algorithm penalty item so that the algorithm can analyze the correlation between different features. Compared with other SCCA methods, our algorithm has improved noise resistance and stability, and found some diagnostic-specific SNP/gene-ROI specific to the two diagnostic groups of MCI and AD. Significance: This method provides a way to further study the association of multi-modal biological data and identify the complex association patterns of diseases.
Truly Distributed Multicell Multi-Band Multiuser MIMO by Synergizing Game Theory and Deep Learning

Dynamic frequency allocation (DFA) with massive multiple-input multiple-output (MIMO) is a promising candidate for multicell communications where massive MIMO is adopted to maximize the per-cell capacity whereas the inter-cell interference (ICI) is tackled by DFA. Realizing this approach in a distributed fashion is however very difficult due to the lack of global channel state available at the base stations (BSs) in the cell level. We utilize a forward-looking game to automate reconciliation for DFA in a distributed manner between cells while zero-forcing (ZF) is used at each cell to maximize the multiplexing gain. To maximize the network capacity, multi-agent deep reinforcement learning (DRL) using offline centralized training is leveraged to train the BSs to master their game-theoretic reconciliation strategies. The result is a trained neural network for each BS, empowering it with rich experience of reconciliation with other BSs for converging to a network-efficient equilibrium. The online algorithm is distributed with the BSs competing as expert players to start the negotiation process using their trained actions. Simulation results show that the proposed synergized deep-learning game-theoretic algorithm outperforms significantly the DRL-only and game-theoretic only methods, and other benchmarks for multicell MIMO.
Fast Light Field Angular Resolution Enhancement Using Convolutional Neural Network

Current light field angular super-resolution algorithm based on deep learning has excessive computation cost and low operational efficiency, for sequentially up-sampling on each lenslet region of the light field image. In this paper, we propose a novel convolutional neural network to fastly enhance the angular resolution, via wholesale up-sampling lenslet regions. Firstly, the network simultaneously extracts the angular information of all lenslet regions on the input light field image. Then, from the extracted angular information, four feature maps are predicted. Especially, the angular resolution of each feature map is the same as that of the input light field image. Finally, to enhance the angular resolution, we integrate four feature maps into one image, by referring to angular information arrangement in lenslet regions. The experimental results verify the effectiveness of our proposed method. We only need 11.95s to enhance(actually double) the angular resolution of one light field image with $2562\times 3724$ pixels, which surpasses 20 times faster than the state-of-the-art method. Meanwhile, our method also achieves average PSNR gains of 0.39 dB.
Digital/Analog Performance Optimization of Vertical Nanowire FETs Using Machine Learning

Vertical nanowire field-effect transistors (NWFETs) have been optimized to maximize digital and analog performances using fully-calibrated TCAD and machine learning (ML) technique. Digital performance is quantified by RC delay (<inline-formula> <tex-math notation="LaTeX">$C_{gg}V_{dd}/I_{on}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$C_{gg}$ </tex-math></inline-formula> is gate capacitance, <inline-formula> <tex-math notation="LaTeX">$V_{dd}$ </tex-math></inline-formula> is operation voltage, and <inline-formula> <tex-math notation="LaTeX">$I_{on}$ </tex-math></inline-formula> is on-state current) at the fixed off-state currents, and analog performance is quantified by the product of cut-off frequency (<inline-formula> <tex-math notation="LaTeX">$F_{t}$ </tex-math></inline-formula>) and transconductance efficiency (<inline-formula> <tex-math notation="LaTeX">$G_{m}/I_{ds})$ </tex-math></inline-formula>. ML accurately predicted the geometry and doping parameters suggesting the best device performances. All the optimized NWFETs have larger drain diameters but smaller source diameters at the minimum of gate lengths, gate oxide thicknesses, drain junction gradients, and source/drain spacer lengths. Small source diameters are needed to tightly control the energy barrier to reduce the short-channel effects, whereas large drain diameters increase current drivability than <inline-formula> <tex-math notation="LaTeX">$C_{gg}$ </tex-math></inline-formula>. Small drain junction gradients increase the lateral electric field from source to drain, which increases the carrier velocity. Longer spacer lengths decrease both <inline-formula> <tex-math notation="LaTeX">$I_{on}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$C_{gg}$ </tex-math></inline-formula>, but the <inline-formula> <tex-math notation="LaTeX">$I_{on}$ </tex-math></inline-formula> degradation is critical. These device characteristics validate the optimization results from ML, and ML-based optimization is fast and effective to maximize both digital and analog performances.
Efficient Learning of Big ECG Data for Ventricular Fibrillation Warning

Ventricular fibrillation is the most lethal arrhythmia. At present, the treatment of ventricular fibrillation is commonly received after the onset of the disease, which mainly depends on external defibrillation and drug-assisted therapy. Although activity of heartbeats can be described and analyzed using the most popular technique ECG (electrocardiogram), there is still no widely recognized prediction methods for ventricular fibrillation. Therefore, in this paper, in order to realize warning of ventricular fibrillation, we focus on the detection of atrial fibrillation and ventricular flutter, which are the arrhythmias often occurring before ventricular fibrillation. We propose a frequency-domain LSTM (Long Short-Term Memory), which uses heartbeat waves transformed from the original time domain into the frequency domain as input. Furthermore, to address the problem of big ECG data training efficiency and scalability, we also provide an implementation of our method under the distributed computing framework MapReduce in the Spark cluster. Experimental results indicate that our method achieves excellent classification performance compared with rival methods.
A Template-Based 3D Reconstruction of Colon Structures and Textures From Stereo Colonoscopic Images

This article presents a framework for 3D reconstruction of colonic surface using stereo colonoscopic images. Due to the limited overlaps between consecutive frames and the nonexistence of large loop closures during a normal screening colonoscopy, the state-of-art simultaneous localization and mapping (SLAM) algorithms cannot be directly applied to this scenario, thus a colon model segmented from CT scans is used together with the colonosocopic images to achieve the colon 3D reconstruction with high accuracy. The proposed framework includes 3D scan (point cloud with RGB information) reconstruction from stereo images, a visual odometry (VO) based camera pose initialization module, a 3D registration scheme for matching texture scans to the segmented colon model, and a barycentric-based texture rendering module for mapping textures from colonoscopic images to the reconstructed colonic surface. A realistic simulator is developed using Unity to simulate the procedures of colonoscopy and used to provide experimental datasets in different scenarios. Experimental results demonstrate the good performance of the proposed 3D colonic surface reconstruction method in terms of accuracy and robustness. Currently, the framework requires a pre-operative colon model as the template for colon reconstruction and can reconstruct 3D colon maps when the colon has no large deformation and the colon structure is clearly visible. The datasets used in this article and the developed simulator are made publicly available for other researchers to use (https://github.com/zsustc/colon_reconstruction_dataset).