Reviewerly/exHarmony huggingface dataset:
Heterogeneous Graph Neural Networks

As a powerful graph representation technique based on deep learning, graph neural networks (GNNs) have shown superior performance and attracted considerable research interest. Recently, some works attempt to generalize them to heterogeneous graphs which contain different types of nodes and relations. In this chapter, we introduce three heterogeneous graph neural networks (HGNNs), including heterogeneous graph propagation network (hpn), distance encoding-based heterogeneous graph neural network (DHN), and self-supervised heterogeneous graph neural network with co-contrastive learning (HeCo).
LAMBO: Landmarks Augmentation With Manifold-Barycentric Oversampling

The training of Generative Adversarial Networks (GANs) requires a large amount of data, stimulating the development of new augmentation methods to alleviate the challenge. Oftentimes, these methods either fail to produce enough new data or expand the dataset beyond the original manifold. In this paper, we propose a new augmentation method that guarantees to keep the new data within the original data manifold thanks to the optimal transport theory. The proposed algorithm finds cliques in the nearest-neighbors graph and, at each sampling iteration, randomly draws one clique to compute the Wasserstein barycenter with random uniform weights. These barycenters then become the new natural-looking elements that one could add to the dataset. We apply this approach to the problem of landmarks detection and augment the available annotation in both unpaired and in semi-supervised scenarios. Additionally, the idea is validated on cardiac data for the task of medical segmentation. Our approach reduces the overfitting and improves the quality metrics beyond the original data outcome and beyond the result obtained with popular modern augmentation methods.
QR Codes

By Angela D. Jones, DNP, RN, DeWitt School of Nursing, Stephen F. Austin State University, Nacogdoches, Texas, [email protected]. The author declares no conflicts of interest.
Risk in Digital Assets

The digital transformation of modern economies continues apace. The digital economy engenders many new interactions, intensifies information flows, and presents one with unique digital assets. All of these serve to greatly increase the risk exposure of organizations and individuals by both modifying probability of occurrence, and amplifying impact. On the analytic front, this puts new challenges and requires new approaches and tools for modeling cyber risks. This chapter reviews the key types of existing and newly arising risks in the digital arena and presents a novel way of looking at risk management for emerging digital assets such as cryptocurrencies.
Live Demonstration: A Portable Four-Lenses Panoramic-Stereo Endoscope System Development

A four-lenses endoscopic put on a Trocar side-by-side and the wide-angle stitching image can produce any viewing angle between the two sets of lenses. This work proposes a combination of two techniques of image stitching and view synthesis to a three-dimensional view.
On Solving Cold Start Problem in Recommender Systems Using Web of Data

Data on the web has grown insanely large at a point it becomes unmanageable and very difficult to deal with using traditional tools. Hence the need for adequate tools to filter such enormous size of information and extract only the useful part has risen. Recommender systems are one of the de facto tools for such purpose. Varying from collaborative filtering to content-based filtering; their primary goal is to suggest the suitable items for the suitable users. However, due to the lack of information about both entities, especially the new ones, these systems may suffer from what is known as the cold start problem that prevents delivering appropriate recommendations. In this work, we propose a solution to overcome the two issues related the cold start problem, namely user and item cold start. The main idea is to use the web of data, a publicly available set of interlinked data and documents, to extract supplementary and useful information about new users and items which allows feeding the recommender systems with more relevant data. The proposed solution can be used an extension i.e. plug-in to an existing recommender system offering additional features to that system. The results of the experiments performed on the Movielens dataset are very promising and show the effectiveness of our proposal.
E-Learning - The New Face of Education: Pros and Challenges

Today is the era of technology. After the recent COVID-19 outbreak, one of the most frequently used terms is “new normal.” In the field of education, this can be related to the rapidly increasing use of online learning tools. All over the globe, educational institutions are adapting online learning platforms to pick up the threads of the process of educating students. The new normal now is a modified concept of education with online learning being at the core of this transformation. In the present scenario, digital learning has emerged as a unavoidable resource for students and institutions all over the world. As is the case with most of the traditional teaching methods, online learning also has its own set of positives and challenges. The process of decoding and understanding these two will aid the institutions in creating strategies for more efficient delivery of the lessons, eventually, creating an uninterrupted learning environment for the students. So, this paper attempts to reveal these pros and challenges of E-learning faced by the institutions as well as the learners.
Lessons Learned from Two Earthquakes That Afflicted Mexico City—After a Pause of 32 Years

As history would have it, the recent Mw7.1 Puebla-Morelos intraplate event occurred on September 19, 2017, coincidental to the 32nd anniversary of the 1985 Mw8.0 Michoacan interplate event. The former had less impact both in affected areas and in severity than the latter which had profound impacts on the nation. Factors affecting the respective impacts include: ground motion input, site setting of Mexico City with its thick lake-sediment deposits, and structural types of affected building stocks. The interplay of these factors at the two events shared general similarities with specific nuances, and the author participated in the Canadian reconnaissance teams after both events. This paper discusses the vulnerability of Mexico City to the two events that are representative of two types of source mechanisms: the recent event being moderate yet closer, while the earlier one being major but further away. In addition to the difference in earthquake magnitude and epicentral distance, they also generate ground motions of different frequency contents. Thus, site amplification differs in the two events depending on thickness of lake sediments in various seismic zones within the city, and the paper first presents an overview of the physical and seismo-tectonic setting of Mexico City. The earthquake and damage data for this pair of events are summarized in a table. Four aspects of the general earthquake impacts are then discussed: ground failure, building performance, building retrofit, and lifelines performance. Moreover, the earthquake early warning system, having its origin because of the 1985 event, has been fully operative since 1993. Up to the 2017 event, the system had recorded 6,896 earthquakes and issued 158 early warnings. Finally, a succinct summary highlights key lessons learned from this pair of seismic events.
Decentralized P2P Federated Learning on Ad-hoc Like Networks with Non-IID Dataset

In the last few decades, Federated Learning (FL) is proposed in order to perform Machine Learning (ML) tasks in a distributed manner while protecting users' privacy and data. However, most of the traditional FL methods rely on centralized entities while in many real-life situations, there isn't any central server which can orchestrate the training procedure. Moreover, it is also easy to be ignored by researchers that the network topology is likely to be changing all the time in some scenarios such as Ad-hoc networks. Besides, how to deal with the unbalanced data which are not independently identically distributed (IID) collected by devices is also an important open problem. As a consequence, in this paper, we propose a peer-to-peer federated learning algorithm with ad-hoc network, along with five model aggregation strategies. We tested our algorithm on self-made Non-IID datasets. After 5000 epochs, the average accuracy of devices reaches 69% under the best strategies on unbalanced CIFAR10 dataset, improving 28% from the self-training cases without P2P communication. The results indicate that our strategies can reduce the negative effect caused by Non- IID datasets even with ad-hoc networks.
Image Encryption Based on 5-Neighbor Programmable Cellular Automata

With the rapid development and spread of non-face-to-face digital technologies and services due to the spread of COVID-19, real-time non-face-to-face online meetings, education, telemedicine, online collaboration, telecommuting, various non-face-to-face tasks in the financial sector, and the sharing economy are frequent. As a result, network traffic increases and the demand for real-time security of multimedia is increasing. Security of information, including image content, is an essential part of today's communication technology and is very important for safe transmission. In this paper, we design a 5-neighbor programmable cellular automata (FNPCA) based ROI (region of interest) image encryption system that can effectively reduce computational cost and maintain an appropriate level of security.
High-Resolution with Global Context Network for Human Pose Estimation

Despite significant improvement in human pose estimation research, most top-performance methods are challenging to deploy in practical applications because of their complex architecture and high computational costs. Although the lightweight human pose estimation approach requires less processing and may be deployed on devices with low resources, such as mobile phones or robots, its network model performance is not exceptional. In this paper, we design the structure based on High-Resolution Network (HRNet), and propose a High-Resolution and Global Context Network (HRGCNet) based on the attention mechanism. Our approach redesigns the bottleneck block according to the attention mechanism of the Global Context Network (GCNet). By combining lightweight and high-performance GC blocks with bottleneck blocks, HRGCNet adds global context features at each location in the high-resolution subnet. The resulting high-resolution representation contains richer feature information. Our experiments on the COCO train2017 dataset show the efficiency of our method. Compared to HRNet with state-of-the-art performance, HRGCNet achieves higher accuracy, and the AP score improves by 2.0 percentage points with similar model size (#Params) and computational complexity (FLOPs). On the COCO test-dev set, HRGCNet has an AP score of 78.3, which is better than most current methods with good performance.
Deep Learning-based Transceiver Design for Pilotless Communication over Fading Channel with one-bit ADC and Oversampling

With the aim of addressing power consumption issues for terahertz band wireless communication, this work presents a deep learning-based solution for transceiver design with 1-bit quantization and oversampling at the receiver, and Faster-than-Nyquist transmission over fading channel. Specifically, by implementing the transceiver using a convolutional autoencoder, our work allows higher-order modulation transmission over one-bit fading channel without pilots. Transfer learning from previously trained blocks over simple noisy channel is used to minimize the probability of bit error and outperforms the convolutional autoencoder at low oversampling rates. The biterror-rate gain offered at 20dB SNR by the transfer learning is seen to be as high as half of one order at low oversampling rates and to saturate as oversampling rate increases. Furthermore, by allowing explicit phase synchronization, the autoencoder-based transceiver with partial channel matching is able to approach unquantized performance with 4dB gap in Rayleigh fading environment.
FFD: A Full-Stack Federated Distillation method for Heterogeneous Massive IoT Networks

Data imbalance and complexity are the key challenges of applying federated learning (FL) techniques for wireless networks. In this paper, we propose a novel framework inspired by a divide-and-conquer algorithm. We aim to develop a full-stack federated distillation (FFD) method for federated learning over a massive Internet of Things network. We first divide the network into sub-regions that can be represented by a neural network model. After performing local training, these models are then aggregated into a global model by using a novel knowledge-distillation method. This FFD method allows each local model to be efficiently updated by learning the features of the other models. Furthermore, this method can be easily deployed in new and large-scaled environments without requiring the models to be re-trained from scratch. Finally, we conduct extensive simulations to evaluate the performance of the proposed FFD method. The results show that our solution outperforms many contemporary FL techniques with non-IID (i.e., not independent and identically distributed) and imbalanced data.
Adapting without forgetting: KnowBert-UMLS

Domain adaptation in pretrained language models usually comes at some cost, most notably out-of-domain performance. This type of specialization typically relies on pre-training over a large in-domain corpus, which has the side effect of causing catastrophic forgetting on general text. We seek to specialize a language model by incorporating information from a knowledge base into its contextualized representations, thus reducing its reliance on specialized text. We achieve this by following the KnowBert method, applied to the UMLS biomedical knowledge base. We evaluate our model on in-domain and out-of-domain tasks, comparing against BERT and other specialized models. We find that our performance on biomedical tasks is competitive with the state-of-the-art with virtually no loss of generality. Our results demonstrate the applicability of this knowledge integration technique to the biomedical domain as well as its shortcomings. The reduced risk of catastrophic forgetting displayed by this approach to domain adaptation broadens the scope of applicability of specialized language models.
Improved Solution of Topology Concealment Problem in Computing Network Reliability

This paper proposes an improved method to solve the topology concealment problem in the secret computation to be used in computing network reliability. The key idea is to add dummy links with reliabilities of zeros and ones, while the existing method adds only dummy links with reliabilities of ones. Our proposal realizes stronger security than the existing method without increasing the number of dummy links. A numerical test shows the effectiveness of our proposal.
Study on data center server management system for using server power estimation technology

The increasing power consumption in servers is a problem in data centers. One approach to address this is to optimize physical resource allocation for virtualized functions. For this approach, power consumption needs to be estimated, and many papers have proposed models to estimate server power consumption. However, applying such estimation techniques to data center servers presents an operational challenge. Specifically, it is difficult to reduce the cost of model building and data collection while maintaining estimation accuracy. To address this problem, we propose a management system with a modeling function and a data selection function. The modeling function builds accurate models in accordance with application requirements, and the data selection function selects data related to server power consumption and then instructs data collection to each processing server. Our main effort in this paper was to address the monitoring function issue to reduce server power consumption during data collection. We analyze the relationship between server performance data and server power consumption and reduce the type of data. Experimental results showed that our selected data are sufficient to construct a server power modeling. Furthermore, by limiting the amount of data collected, we were able to reduce server power consumption by about 3 W and monitoring overhead by about 30%.
Pure Voting (PV): An Offline Voting Algorithm

This work proposes the use of blockchain for offline voting. Using the Smart contract feature of the Ethereum blockchain network. The voter’s registration is made. An offline token is generated which is used for offline voting. The offline token and the voting information are sent to the vote counters Smart contact. This is where the votes are counted and results extracted. However if a voter can share the cast vote to another voter who will send the new token to the vote counters. In a situation a vote is submitted by multiple voters, the counter has the ability to identify and count such votes only once.
Action Recognition of Traffic Police by Attentive for Self-Driving Vehicles

This work develops a Deep Neural Network (DNN) with the spatiotemporal skeleton-based attentions to effectively perceive traffic officers for self-driving vehicles. The DNN framework includes two Convolutional Neural Networks (CNNs), Attention-Jointed Appearance (AJA) and Attention-Based Motion (ABM) layers, Recurrent Neural Networks (A_RNN), and Feed-Forward Networks (FFNs) where RGB and optical-flow streams are inputs accompanied with pose joint maps of two-dimensional subject skeletons. The AJA, and ABM layers pay attention to poses, and motions of subjects, respectively. The A_RNNs generate the attention weights over time steps to highlight rich temporal context. In FFN s, one takes the outputs of A_RNNs to determine the action type, and the other processes the outputs of the AJA layer together with the majority voting to enhance subject identification. Based on transfer learning, the initial parameters of two CNN s are from the converged network of Google Inception V3 trained by ImageN et and Kinetics. The experimental results reveal that the proposed DNN achieves the average accuracies around 100.0%, and 97.6% for subject, and action recognitions, respectively, at the traffic police dataset. Comparing to the conventional work, our DNN with superior performance can be a great context-aware system for self-driving vehicles.
A Study on Adversarial Attacks and Defense Method on Binarized Neural Network

Binarized Neural Networks (BNNs) are relatively hardware-efficient neural network models which are seriously considered for edge-AI applications. However, BNNs are like other neural networks and exhibit certain linear properties and are vulnerable to adversarial attacks. This work evaluates the robustness of BNNs under Projected Gradient Descent (PGD) - one of the most powerful iterative adversarial attacks, on BNN models and analyzes the effectiveness of corresponding defense methods. Our extensive simulation shows that the network almost malfunction when performing recognition tasks when tested with PGD samples without adversarial training. On the other hand, adversarial training could significantly improve robustness for both BNNs and Deep learning neural networks (DNNs), though strong PGD attacks could still be challenging. Therefore, adversarial attacks are a real threat, and more effective adversarial defense methods and innovative network architectures may be required for practical applications.
A case study for evaluating learners' behaviors from online cybersecurity training platform on digital forensics subject

Virtual cybersecurity training platforms play an important role in developing the knowledge and practice skills of students in educational institutions and universities. It helps learners can access virtual laboratories through web interfaces without any geolocation restriction, especially in the Covid-19 pandemic. Furthermore, instructors can monitor and understand learners' behaviors in practice sessions by analyzing actions and logs from the virtual platform. But, to realize this feature, such a platform must gather data during cybersecurity training for data mining tasks. In this paper, we introduce a virtual laboratory platform to facilitate cybersecurity training courses, namely vLab. In addition, we apply clustering analysis to the actions of learners to better understand the capabilities of trainees in resolving given challenges in digital forensics subject. With the built-in behavior analyzer in vLab, instructors can find out the common mistakes, and the reasons for learners' failure results, or identify whether they actually conduct experiments to get answers for digital forensics challenges or not.
A Security-Centric Deep Learning Enabled Camera Solution for Real-Time Human Fall Detection

Automatic human real-time fall detection is a challenging task in remote healthcare, demanding a non-intrusive, secure and affordable solution. In this paper, we present a real-time hardware system that uses a deep learning model for fall detection embedded in a color camera. To reduce the startup delay and achieve real-time performance for the inference phase, we optimized our model using TensorRT. In addition, we addressed the board memory limitation using virtual memory and linear memory allocation and garbage collection. Moreover, GStreamer was used to perform most of the video processing using Jetson's GPU. Our live evaluation shows that our system achieved the accuracy of 84.44% and real-time performance.
A lightweight Machine-learning based Wireless Link Estimation for IoT devices

Robust wireless communication between devices is crucial to ensuring the reliability of IoT systems. However, it is strictly relied on estimating the link quality of these devices, which is usually interfered with by environmental factors. In such a scenario, intelligent algorithms based on machine learning to select resistant communication links are promising solutions, but they demand sophisticated computation, limiting their deployment on resource-constrained IoT devices. Therefore, this paper introduces a lightweight link quality estimation algorithm, namely LLQE, built from the gradient boosting decision tree. The superiority of our proposal not only precisely assesses several levels of link quality but also is lightweight enough for resource-constraint devices. The evaluation results on publicly available datasets show that LLQE accurately estimates various link quality indicators with 97% accuracy.
Radar Communication System Recalibration using DNN-based Unscented Kalman Filter Modeling

In typical air traffic control (ATC) scenario where it is apparently challenging to deploy aircraft for special missions as reconnaissance and surveillance, a proposed ACD-UKF model becomes suitable especially where operational flexibility and without manual aircraft-turning are prioritized objectives. In this paper, novel deep neural network model (DNN) enhanced accurate continuous-discrete unscented Kalman filtering (ACD-UKF) model for a radar’s ordinary differential equation (ODE) solver system navigation tool is presented. The ODE solver system essentially works to control radar navigation parameters with-respect-to (w.r.t) global error control, monitoring metrics and tracking capabilities. With the proposed DNN scheme, limitations resulting from matrix factorization are addressed. A seven-dimensional (7-D) radar tracking drawback in con-strained condition is mirrored, allowing the deployed aircraft to conduct supervised turns using the proposed ACD-UKF model. Performance evaluation was then conducted where real-time factors such as the system’s outage thresholds, network sum-rate and yaw differences for the global navigation satellite system (GNSS) propelled aircraft radar tracker data-set, in stationary and in accelerating positions were trained and validated using the proposed DNN model.