Reviewerly/exHarmony huggingface dataset:
Segtransvae: Hybrid Cnn - Transformer with Regularization for Medical Image Segmentation

Current research on deep learning for medical image segmentation exposes their limitations in learning either global semantic information or local contextual information. To tackle these issues, a novel network named SegTransVAE is proposed in this paper. SegTransVAE is built upon encoder-decoder architecture, exploiting transformer with the variational autoencoder (VAE) branch to the network to reconstruct the input images jointly with segmentation. To the best of our knowledge, this is the first method combining the success of CNN, transformer, and VAE. Evaluation on various recently introduced datasets shows that SegTransVAE outperforms previous methods in Dice Score and 95%-Haudorff Distance while having comparable inference time to a simple CNN-based architecture network. The source code is available at: https://github.com/itruonghai/SegTransVAE.
NAG: neural feature aggregation framework for credit card fraud detection

Abstract The state-of-the-art feature-engineering method for fraud classification of electronic payments uses manually engineered feature aggregates, i.e., descriptive statistics of the transaction history. However, this approach has limitations, primarily that of being dependent on expensive human expert knowledge. There have been attempts to replace manual aggregation through automatic feature extraction approaches. They, however, do not consider the specific structure of the manual aggregates. In this paper, we define the novel Neural Aggregate Generator (NAG), a neural network-based feature extraction module that learns feature aggregates end-to-end on the fraud classification task. In contrast to other automatic feature extraction approaches, the network architecture of the NAG closely mimics the structure of feature aggregates. Furthermore, the NAG extends learnable aggregates over traditional ones through soft feature value matching and relative weighting of the importance of different feature constraints. We provide a proof to show the modeling capabilities of the NAG. We compare the performance of the NAG to the state-of-the-art approaches on a real-world dataset with millions of transactions. More precisely, we show that features generated with the NAG lead to improved results over manual aggregates for fraud classification, thus demonstrating its viability to replace them. Moreover, we compare the NAG to other end-to-end approaches such as the LSTM or a generic CNN. Here we also observe improved results. We perform a robust evaluation of the NAG through a parameter budget study, an analysis of the impact of different sequence lengths and also the predictions across days. Unlike the LSTM or the CNN, our approach also provides further interpretability through the inspection of its parameters.
Open Set RF Fingerprinting using Generative Outlier Augmentation

RF devices can be identified by unique imperfections embedded in the signals they transmit called RF fingerprints. The closed set classification of such devices, where the identification must be made among an authorized set of transmitters, has been well explored. However, the much more difficult problem of open set classification, where the classifier needs to reject unauthorized transmitters while recognizing authorized transmitters, has only been recently visited. So far, efforts at open set classification have largely relied on the utilization of signal samples captured from a known set of unauthorized transmitters to aid the classifier learn unauthorized transmitter fingerprints. Since acquiring new transmitters to use as known transmitters is highly expensive, we propose to use generative deep learning methods to emulate unauthorized signal samples for the augmentation of training datasets. We develop two different data augmentation techniques, one that exploits a limited number of known unauthorized transmitters and the other that does not require any unauthorized transmitters. Experiments conducted on a dataset captured from a WiFi testbed indicate that data augmentation allows for significant increases in open set classification accuracy, especially when the authorized set is small.
A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation

In the last years, deep learning has dramatically improved the performances in a variety of medical image analysis applications. Among different types of deep learning models, convolutional neural networks have been among the most successful and they have been used in many applications in medical imaging. Training deep convolutional neural networks often requires large amounts of image data to generalize well to new unseen images. It is often time-consuming and expensive to collect large amounts of data in the medical image domain due to expensive imaging systems, and the need for experts to manually make ground truth annotations. A potential problem arises if new structures are added when a decision support system is already deployed and in use. Since the field of radiation therapy is constantly developing, the new structures would also have to be covered by the decision support system. In the present work, we propose a novel loss function to solve multiple problems: imbalanced datasets, partially-labeled data, and incremental learning. The proposed loss function adapts to the available data in order to utilize all available data, even when some have missing annotations. We demonstrate that the proposed loss function also works well in an incremental learning setting, where an existing model is easily adapted to semi-automatically incorporate delineations of new organs when they appear. Experiments on a large in-house dataset show that the proposed method performs on par with baseline models, while greatly reducing the training time and eliminating the hassle of maintaining multiple models in practice.
A Universal Reversible Data Hiding Method in Encrypted Image Based on MSB Prediction and Error Embedding

Image encryption is used for privacy protection in cloud computing. Nowadays, reversible data hiding in encrypted image (RDHEI) has achieved great success with the demand of embedding additional information into encrypted image. The existing algorithms cannot implement large embedding capacity and good reconstructed image quality simultaneously. Besides, the universality of some methods is limited when they are used in images with different textural characteristics. In this work, an RDHEI method based on most significant bit (MSB) prediction and error embedding is proposed. On one hand, all types of prediction errors are considered in the proposed method, therefore all the pixels with prediction errors can be recovered correctly. On the other hand, error blocks are utilized to mark the locations of prediction errors and message blocks are utilized to embed data. Moreover, flag blocks are utilized to distinguish error and message blocks. To solve the problem of misjudgement for flag blocks in the decoding phase, special operations are conducted on error blocks and message blocks. Experimental results demonstrate that, compared with the state-of-the-art RDHEI methods, the proposed method has good universality on well-known databases.
On the Performance of Blockchain-enabled RAN-as-a-service in Beyond 5G Networks

Blockchain (BC) technology can revolutionize the future of communications by enabling decentralized and open sharing networks. In this paper, we propose the application of BC to facilitate Mobile Network Operators (MNOs) and other players such as Verticals or Over-The-Top (OTT) service providers to exchange Radio Access Network (RAN) resources (e.g., infras-tructure, spectrum) in a secure, flexible and autonomous manner. In particular, we propose a BC-enabled reverse auction mecha-nism for RAN sharing and dynamic users&#x0027; service provision in Beyond 5G networks, and we analyze its potential advantages with respect to current service provisioning and RAN sharing schemes. Moreover, we study the delay and overheads incurred by the BC in the whole process, when running over both wireless and wired interfaces.
Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems

Deep learning (DL)-based recommendation system (RS) has drawn extensive attention during the past years. Its performance heavily relies on hyperparameter tuning. However, the most common approach of hyperparameters tuning is still Grid Search—a tedious task that consumes immerse computational resources and human efforts. To aid this issue, this paper proposes a general hyperparameter optimization framework for existing DL-based RSs based on differential evolution (DE), named DE-Opt. Its main idea is to incorporate DE into a DL-based RS model's training process to auto-learn its hyperparameters λ (regularization coefficient) and η (learning rate) simultaneously at layer-granularity. Empirical studies on three benchmark datasets verify that: 1) DE-Opt is compatible with and can automate the training of the most recent DL-based RSs by making their λ and η adaptively learned, and 2) DE-Opt significantly outperforms the state-of-the-art hyperparameter searching competitors in terms of both higher learning performance and lower runtime.
Image lossless encoding and encryption method of SPECK based on 1D chaotic map

Abstract In order to satisfy the requirements of high quality and security during image transmission and storage, this paper proposes an image lossless compression encryption algorithm based on 1D chaotic map and Set Partitioned Embedded block encoder (SPECK). Initially, this paper proposes a new 1D chaotic map, and applies the chaotic sequences generated by it to each stage of the compression encryption algorithm. In addition, according to the feature that the degree of energy concentration in the wavelet coefficient matrix gradually decreases from low frequency to high frequency, this paper proposes a wavelet coefficient encryption algorithm, which can balance security and compression performance. Furthermore, multiple encryption points are introduced in the SPECK encoding process, and a secure SPECK encoding algorithm is proposed. Finally, theoretical analysis and simulation results show that the proposed algorithm is close to the theoretical value in terms of information entropy, correlation coefficient, mean square error of reconstructed image and other related indicators. Therefore, the algorithm has high security and lossless compression performance.
Motion Planning for Autonomous Vehicles Based on Sequential Optimization

This study presents the development and analysis of a technique for planning the autonomous vehicle (AV) motion references using sequential optimization. The method determines the trajectory plan, speed and acceleration distributions, and other AV kinematic parameters. The approach combines the basics of the finite element method (FEM) and nonlinear optimization with nonlinear constraints. First, we briefly described the generalization of representing an arbitrary function by finite elements (FE) within a road segment. We chose a one-dimensional FE with two nodes and three degrees of freedom (DOF) in a node corresponding to the 5th-degree polynomial. Next, we presented a method for defining the motion trajectory. The following are considered: the formation of a restricted space for the AV’s allowable maneuvering, the motion trajectory geometry and its relation with vehicle steerability parameters, cost functions and their influences on the desirable trajectory’s nature, and the compliance of nonlinear restrictions of the node parameters with the motion area boundaries. In the second stage, we derived a technique for optimizing the AV’s speed and acceleration redistributions. The model considers possible combinations of objective functions, limiting the kinematic parameters by the tire slip critical speed, maximum speed level, maximum longitudinal acceleration, and critical lateral acceleration. In the simulation section, we compared several variants of trajectories and versions of distributing the longitudinal speed and acceleration curves. The advantages, drawbacks, and conclusions regarding the proposed technique are presented.
Intelligent Blockchain-Enabled Adaptive Collaborative Resource Scheduling in Large-Scale Industrial Internet of Things

With the explosive growth of devices and tasks deployed in the industrial Internet of Things (IIoT), the lack of interconnection and collaboration between devices leads to poor timeliness and security in IIoT resource scheduling. This article focuses on the issue of adaptive scheduling of resources in large-scale IIoT. First, a collaborative terminal-edge IIoT architecture is designed, which introduces blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments. Then, a smart contract-based multidimensional resource transaction model is developed to improve the efficiency and security of resource scheduling by establishing a credit-based consensus mechanism. Distributed transaction learning resource scheduling algorithm is further proposed to implement resource-adaptive scheduling between devices in IIoT. Extensive simulation experiments are conducted to evaluate the proposed method with respect to several performance aspects covering the scheduling decision delay, transaction generation ratio, and security. The obtained results demonstrate that the comprehensive scheduling performance of the proposed method outperforms other existing algorithms.
Neural Disparity Refinement for Arbitrary Resolution Stereo

We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile phones. Our approach relies on a continuous formulation that enables to estimate a refined disparity map at any arbitrary output resolution. Thereby, it can handle effectively the unbalanced camera setup typical of nowadays mobile phones, which feature both high and low resolution RGB sensors within the same device. Moreover, our neural network can process seamlessly the output of a variety of stereo methods and, by refining the disparity maps computed by a traditional matching algorithm like SGM, it can achieve unpaired zero-shot generalization performance compared to state-of-the-art end-to-end stereo models.
Ecuadorian university English teachers' reflections on emergency remote teaching during the COVID-19 pandemic

COVID-19 appeared at the beginning of 2020, affecting, among others, the education industry. As a result, a lock-down quarantine was declared, and on-campus classes were suspended. Accordingly, emergency remote teaching (ERT) was set into motion to solve the education issue. This investigation surveyed 20 Ecuadorian polytechnic university English teachers and obtained their reflections on their experience with ERT. This paper bases on mixed-methods research that used a Likert-scaled survey and interviews to respond to the established research questions. The results show that, in a general sense, teachers were not ready for the sudden shift to ERT, which generated feelings of anxiety. The most significant disadvantage reported was the extra workload caused by adapting materials and giving feedback to students. COVID-19 struck at the beginning of 2020, affecting, among others, the education industry. As a result, a lockdown quarantine was declared, and on-campus classes were suspended. Accordingly, emergency remote teaching (ERT) was set into motion to solve the education issue. This research aimed to obtain the reflections of 20 Ecuadorian polytechnic university English teachers on their experiences using ERT during two semesters. This paper is based on an explanatory sequential mixed-methods research design that used a Likert-scale survey and interviews to respond to the established research questions. In a general sense, the findings show that teachers were not ready for the sudden shift to ERT at the beginning of the pandemic, which generated feelings of anxiety. The most significant disadvantage reported was the extra workload caused by adapting materials and giving feedback to students. The study suggests that changing classes from on-campus to emergency remote teaching was not easy to carry out at the beginning of the pandemic. There are practical implications for language department managers as it gives them light to prepare for the continuing pandemic and any other crisis that might require ERT to be in practice again.
Understanding of Human Factors in Cybersecurity: A Systematic Literature Review

Cybersecurity is paramount for all public and private sectors for protecting their information systems, data, and digital assets from cyber-attacks; thus, relying on technology-based protections alone will not achieve this goal. This work examines the role of human factors in cybersecurity by looking at the top-tier conference on Human Factors in Cybersecurity over the past 6 years. A total of 24 articles were selected for the final analysis. Findings show that most of the authors used a quantitative method, where survey was the most used tool for collecting the data, and less attention has been paid to the theoretical research. Besides, three types of users were identified: university-level users, organizational-level users, and unspecified users. Culture is another less investigated aspect, and the samples were biased towards the western community. Moreover, 17 human factors are identified; human awareness, privacy perception, trust perception, behavior, and capability are the top five among them. Also, new insights and recommendations are presented.
KDE-OCSVM model using Kullback-Leibler divergence to detect anomalies in medical claims

Detecting and preventing unusual claims behavior in medical insurance can reduce the drawdown of a group medical insurance pool. Taking the case of the urban employee basic medical insurance (UEBMI) in China, this paper develops a method to detect unusual medical claim patterns in the UEBMI. We collect public domain records involving gastric malignancy as the experimental sample. A method to preprocess the experimental sample for data learning is provided. We present a feature selection method, involving variance analysis and similarity analysis, to determine the core features. Next, we establish an extended one-class support vector machine (OCSVM) model, the kernel density estimation (KDE)-OCSVM, which exploits the Kullback-Leibler divergence and the KDE method to estimate the parameter v of the OCSVM model, to improve model performance. An experiment and two analyses are performed to validate the proposed method.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network

Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated either by the same genre or by the same director/actor. If we use a single graph to elaborate all these relations, the graph can be too complex to process. To address this issue, we bring the idea of pre-training to process the complex graph step by step. Based on the idea of divide-and-conquer, we separate the large graph into three sub-graphs: user graph, item graph, and user-item interaction graph. Then the user and item embeddings are pre-trained from user and item graphs, respectively. To conduct pre-training, we construct the multi-relational user graph and item graph, respectively, based on their attributes.In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to pre-train user and item embeddings on the user and item graph prior to the recommendation step. Specifically, we design a relation-level attention layer to learn the importance of different relations. Next, a Reinforced Neighbor Sampler (RNS) is applied to search the optimal filtering threshold for sampling top-k similar neighbors in the graph, which avoids the over-smoothing issue. We initialize the recommendation model with the pre-trained user/item embeddings. Finally, an aggregation-based GNN model is utilized to learn from the collaborative relations in the user-item interaction graph and provide recommendations. Our experiments demonstrate that RAM-GNN outperforms other state-of-the-art graph-based recommendation models and multi-relational graph neural networks.
Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review

Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population.The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes.Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers.This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia.The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.