Reviewerly/exHarmony huggingface dataset:
Universal lymph node detection in T2 MRI using neural networks

Identification of lymph nodes (LNs) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) is critical for assessment of lymphadenopathy. Prior work on LN detection has been limited to specific anatomical regions of the body (pelvis, rectum). Therefore, an approach to universally detect both benign and metastatic nodes in T2 MRI studies of the body is highly desirable.We developed a Computer Aided Detection (CAD) pipeline to universally identify LN in T2 MRI. First, we trained various neural networks for detecting LN: Faster RCNN with and without Hard Negative Example Mining (HNEM), FCOS, FoveaBox, VFNet, and Detection Transformer (DETR). Next, we show that VFNet with Adaptive Training Sample Selection (ATSS) outperformed Faster RCNN with HNEM. Finally, we ensembled models that surpassed a 45% mAP threshold.Experiments on 122 test studies revealed that VFNet achieved a 51.1% mAP and 78.7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52.3% and sensitivity of 78.7% at 4FP. We found that VFNet and the one-stage model ensemble can be interchangeably used in the CAD pipeline.Our CAD pipeline universally detected both benign and metastatic nodes in T2 MRI studies, resulting in a sensitivity improvement of [Formula: see text]14% over the current LN detection approaches (sensitivity of 78.7% at 4 FP vs. 64.6% at 5 FP per volume).
Richards’s curve induced Banach space valued ordinary and fractional neural network approximation

Here we perform the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivatives. Our operators are defined by using a density function generated by the Richards curve, which is generalized logistic function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer.
Conditional Variational Autoencoder-Based Sampling

Imbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias in machine learning models. The present paper proposes an autoencoder-based sampling approach to balance the data. Concretely, the proposed method utilizes a conditional variational autoencoder (VAE) to learn the latent variables underpinning the distribution of minority labels. Then, the trained encoder is employed to produce new minority samples to equalize the sample distribution. The results of numerical experiments reveal the potency of the suggested technique on several datasets.
Visibility and Meteorological Parameter Model Based on Rashomon Regression Analysis

Atmospheric visibility is one of the critical indicators for meteorological characterization and environmental quality evaluation. This paper studies the influence of different meteorological parameters on atmospheric visibility, including seven main factors: temperature, humidity, wind speed, and atmospheric pressure. To establish a regression model of visibility calculation under the influence of multiple factors, this paper proposes a method named Rashomon principal component optimization regression. This paper specifically introduces the modeling and implementation of this method. The key is to solve the Rashomon coefficient, the uncertainty influence coefficient, and the regression dimension coefficient. This method employs principal component analysis to establish a loop algorithm that effectively selects different feature spaces. The main purpose is to reflect the multi-scale characteristics of the sample data, and not only consider the overall or local characteristics to deviate from the actual situation. In addition, the interaction between different factors is considered, and the analytic network process (ANP) model is used to reflect the uncertainty in the modeling. The proposed method benefits the future analysis and prediction of visibility based on meteorological data. Meanwhile, it provides theoretical support for big data problems under multiple factors.
Improving the Energy Efficiency of Real-time DNN Object Detection via Compression, Transfer Learning, and Scale Prediction

In recent years, computational accessibility has enabled the use of Deep Neural Network (DNN) for computer vision applications on devices with limited computational resources. We focus on the real-time object detection algorithms deployed on UAV -friendly devices. The hardware deployed on UAV must be lightweight and thus limited in processing power, memory, and storage capacity. Lightweight modeling architecture does not suffice for high-recall reconnaissance applications. In this paper, we propose to reduce power consumption of YOLOv5 DNN architecture. We decided to use compressed convolutional technique, transfer learning, backbone shrinkage, and scale prediction to reduce the number of learnable parameters from the YOLOv5model. Our approach reduced the size of the model significantly and lowered the power consumption in turn. GPU memory and the Billion Floating-Point Operations Per Second (GFLOPS) for the YOLOv5model will keep the performance measure of the model as the baseline state-of-the-art. The best resulting model has a 63.86% mean average precision (mAP) and a GFLOPS of 97.7 on “DIOR”, an overhead imagery data set. The proposed approach has lowered GPU memory consumption of the model by 34% and lowered the energy consumption by 10 Watts compared to the baseline model.
Specific Emitter Identification Model Based on Improved BYOL Self-Supervised Learning

Specific emitter identification (SEI) is extracting the features of the received radio signals and determining the emitter individuals that generate the signals. Although deep learning-based methods have been effectively applied for SEI, their performance declines dramatically with the smaller number of labeled training samples and in the presence of significant noise. To address this issue, we propose an improved Bootstrap Your Own Late (BYOL) self-supervised learning scheme to fully exploit the unlabeled samples, which comprises the pretext task adopting contrastive learning conception and the downstream task. We designed three optimized data augmentation methods for communication signals in the former task to serve the contrastive concept. We built two neural networks, online and target networks, which interact and learn from each other. The proposed scheme demonstrates the generality of handling the small and sufficient sample cases across a wide range from 10 to 400, being labeled in each group. The experiment also shows promising accuracy and robustness where the recognition results increase at 3-8% from 3 to 7 signal-to-noise ratio (SNR). Our scheme can accurately identify the individual emitter in a complicated electromagnetic environment.
Living Beyond Data with Feature Concepts

The inconsistencies between the requirement for, the collection of, and the use of data, and the inconsistencies among analysis models for dealing with different datasets have been solved via communication and thoughts using data jackets to solve problems in real life and solutions to problems using data. In these cases, essential information for the data-federativeInnovation process has been externalized and used/reused. Feature concept,Feature concept a model of the concept to be acquired from data that cannot be represented by a simple feature, such as a single variable but can be by a conceptual illustration, turned out to be a core essence here. Decision trees, clusters, and even deep neural networks can be regarded as examples of feature concepts. Useful feature concepts for satisfying the requirements of a data user have been elicited so far among data providers, data users, and other stakeholders in the market of data. In this chapter, the data-jacket-based design of creative communication is reviewed with some cases of application—marketing, detection of earthquake precursors, suppression of COVID-19, cases, and highlight the elicited feature concepts.
Self-organizing pipelined recurrent wavelet neural network for time series prediction

The pipelined recurrent wavelet neural network (PRWNN) with nested structure has been widely used for time series prediction. However, selecting the appropriate network structure for different problems is still an unsolved problem. To solve this issue, the self-organizing PRWNN (SPRWNN) is proposed. Different from the PRWNN with fixed structure, the number of modules and hidden neurons of SPRWNN can be automatically determined during the training process. Firstly, the number of hidden neurons in each module of the SPRWNN is adjusted by the spiking strength (SS) of nodes. Then, the module growth mechanism is designed by measuring the performance of the SPRWNN. Furthermore, the convergence of SPRWNN is proved theoretically. The experimental results show that the SPRWNN can not only automatically adjust the network structure, but also improves the prediction accuracy by about 40% as compared with the PRWNN with fixed structure.
Bilateral anisotropic Gabor wavelet transformation based deep stacked auto encoding for lossesless image compression

Summary A highly challenging aspect of the data compression technique is maintaining the quality of data that reconstructs in high compression rates. To overcome these limitations, a bilateral anisotropic Gabor wavelet transformation with deep stacked auto encoding (BAGWT‐DSAE) technique based lossesless image compression is proposed in this article to save the storage space and processing time during transferring the images. The proposed method contains three main processes namely preprocessing, compression and decompression. Initially input aerial image and digital image are taken and these images are given bilateral filter based preprocessing for eliminates the different types of noises and also multiple artifacts. Then the preprocessed images are given to anisotropic Gabor wavelet transformation based deep stacked auto encoding to compress and decompress the wavelet transform's sensitive sub‐bands effectually. In DSAE, the decoder of the auto encoder achieves a better quality decompressed image. The proposed method is implemented in MATLAB simulations run in PC through Intel Core, 8 GB of RAM, 2.50 GHz CPU and Windows 8. Then, the simulation performance of proposed BAGWT‐DSAE‐LIC method provides 20.23%, 24.85%, and 38.56% low compression ratio and 26.48%, 21.23%, and 12.53% lower computational time, 4.56%, 7.68%, and 8.34% high space saving than the existing methods.
Local feature-based mutual complexity for pixel-value-ordering reversible data hiding

In reversible data hiding, pixel-value-ordering (PVO) has become a widely used framework benefiting from its high-fidelity under low-capacity requirements. As an essential element in PVO-based methods, complexity could effectively avoid embedded images from unnecessary embedding distortions. There are two main context-pixel-selection strategies in existing complexity methods: inside-block and outside-block pixel selection strategy, which show strong complementarity. To make full use of this characteristic and further mitigate the insufficient feature representation problem in complexity, we propose a multi-complexity mechanism: Mutual Complexity. First, we analyse the relationship between complexity and Capacity-Distortion performance and innovatively regard the complexity problem as a binary classification problem. Then, precision and recall are taken as the optimization objectives and mutual pixels with the best performances could be selected. As a result, our proposed method can leverage different local features represented by various complexities and obtain the best classification result. Furthermore, to solve the block-dependent embedding problem in existing complexities, a simple but effective complexity, named Neighbor Complexity, is designed according to pixel location information. Experimental results show that Mutual Complexity could be easily generalized to different PVO-based methods and the embedding distortions are all effectively controlled.
Boosting source code suggestion with self-supervised Transformer Gated Highway

Attention-based transformer language models have shown significant performance gains in various natural language tasks. In this work, we explore the impact of transformer language models on the task of source code suggestion. The core intention of this work is to boost the modeling performance for the source code suggestion task and to explore how the training procedures and model architectures impact modeling performance. Additionally, we propose a transformer-based self-supervised learning technique called Transformer Gated Highway that outperforms recurrent and transformer language models of comparable size. The proposed approach combines the Transformer language model with Gated Highway introducing a notion of recurrence. We compare the performance of the proposed approach with transformer-based BERT (CodeTran), RoBERTa (RoBERTaCode), GPT2 (TravTrans), CodeGen and recurrent neural language-based LSTM (CodeLSTM) models. Moreover, we have experimented with various architectural settings for the transformer models to evaluate their impact on modeling performance. The extensive evaluation of the presented approach exhibits better performance on two programming language datasets; Java and C#. Additionally, we have adopted the presented approach for the syntax error correction task to predict the correct syntax token to render its possible implications for other source code modeling tasks.
Semantic Attribute-Based Encryption: A framework for combining ABE schemes with semantic technologies

Attribute-Based Encryption (ABE) is a cryptographic solution to protect resources in a fine-grained manner based on a set of public attributes. This is similar to attribute-based access control schemes in the sense that both rely on public attributes and access control policies to grant access to resources. However, ABE schemes do not consider the semantics of attributes provided by users or required by access structures. Such semantics not only improve the functionality by making proper access decisions but also enable cross-domain interoperability by making users from one domain able to access and use resources of other domains. This paper proposes a Semantic ABE (SABE) framework by augmenting a classical Ciphertext-Policy ABE (CP-ABE) scheme with semantic technologies using a generic procedure by which any CP-ABE scheme can be extended to an SABE. The proposed SABE framework is implemented in Java and the source code is publicly available. The experiment results confirm that the performance of the proposed framework is promising.
W2WNet: A two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality

Ideally, Convolutional Neural Networks (CNNs) should be trained with high quality images with minimum noise and correct ground truth labels. Nonetheless, in many real-world scenarios, such high quality is very hard to obtain, and datasets may be affected by any sort of image degradation and mislabelling issues. This negatively impacts the performance of standard CNNs, both during the training and the inference phase. To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training and a Wiped module takes care of the final classification, while broadcasting information on the prediction confidence at inference time. The goodness of our solution is demonstrated on a number of public benchmarks addressing different image classification tasks, as well as on a real-world case study on histological image analysis. Overall, our experiments demonstrate that W2WNet is able to identify image degradation and mislabelling issues both at training and at inference time, with positive impact on the final classification accuracy.
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

In this article we propose a heuristic algorithm to explore search space trees associated with instances of combinatorial optimization problems. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees and represents the state-of-the-art algorithm for a number of games. Several enhancements to Monte Carlo tree search are proposed that make the algorithm more suitable in a combinatorial optimization context. These enhancements exploit the combinatorial structure of the problem and aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. The algorithm was implemented with its components specifically tailored to two combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. For the first problem our algorithm surpasses the state-of-the-art results and several new best solutions are found for a benchmark set of instances. For the second problem our algorithm typically produces near-optimal solutions that are slightly worse than the state-of-the-art results, but it needs only a small fraction of the time to do so. These results indicate that the algorithm is competitive with the state-of-the-art for two entirely different combinatorial optimization problems.
The Presentation of Self in Virtual Reality: A Cognitive Load Study

The use of virtual reality (VR) in interior design has increased dramatically. Its interactive and visualization benefits are undeniable. Designers, clients, developers, and stakeholders can immerse themselves in future or existing design projects without the need to be physically there. Thanks to more immersive and realistic experiences, the boundaries that separate the physical and the virtual world are becoming nonexistent. Nonetheless, research has focused on the visual characteristics of the virtual space, undermining the consequences for individuals engaged within it. In this study, we assessed the effects on mental workload caused by how individuals visualize themselves in VR using a virtual body (VB). The VB is typically represented by the use of avatars. An experimental setup was carried out with a convenience sample of 72 individuals. Participants interacted in an immersive VR interface with three different conditions of the VB. They were randomly assigned to one of the three conditions and engaged for a period of approximately 20 minutes in tackling a design-like activity. Data were collected through self-report questionnaires in addition to a psychophysiological device accounting for the cognitive load (CL) and task difficulty. The statistical analysis supported differences in CL between conditions. A more detailed visual representation of the VB increased the sensation of being there but contributed extraneous CL that can hinder the task at hand. The findings of this study can guide interior designers in selecting the type of VB they should use for their immersive VR experiences.
A secure and efficient access control scheme with attribute revocation and merging capabilities for fog-enabled IoT

The current era’s technological innovation has accelerated the expansion of the Internet of things (IoT), leading to an exponential growth of the number of connected devices and the produced data. This inspired the emergence of fog computing paradigm that enables data processing and analysis near end-users. The cloud co-exists with fog to offer services like processing resources, huge storage, etc. But sending the data through multiple levels for analysis and storage poses a severe threat to data security in cloud-fog-IoT framework. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a well-known cryptographic mechanism for providing fine-grained access control. However, the existing CP-ABE schemes are not suitable as they fail to address the issues of attribute merging, attribute revocation, outsourcing operations, and facilitating privileged access altogether. Hence, this paper proposes a CP-ABE scheme addressing all the aforementioned issues. Further, security and performance analyses show that the proposed scheme is secure and suitable for IoT devices. • Addresses issues: attribute merging, revocation, outsourcing, and facilitates privileged access. • The resource-constrained data user needs to store only a constant size secret key. • The security proof shows that it is secure against CPA Attack under DBDH assumption.
Application of choice models in tourism recommender systems

Abstract Choice models (CM) are proposed in the field of tourism recommender systems (TRS) with the aim of providing algorithms with both a theoretical understanding of tourist's motivations and a certain degree of transparency. The goal of this work is to overcome some of the limitations of current state‐of‐art algorithms used in TRSs by providing: (1) accurate preferences, which are learnt from user choices rather than from ratings, and (2) interpretable coefficients, which are achieved by means of the set of estimated parameters of CM. The study was carried out with a gastronomic data set generated in an ecological experiment in the tourism domain. The performance of CM has been compared with a set of baseline algorithms (rating‐based and ensembles) by using two evaluation metrics: precision and DCG. The CM outperformed the baseline algorithms when the size of the choice set was limited. The findings suggest that CM may provide an optimal trade‐off between theoretical soundness, interpretability and performance in the field of TRS.
Efficient Perturbation Inference and Expandable Network for continual learning

Although humans are capable of learning new tasks without forgetting previous ones, most neural networks fail to do so because learning new tasks could override the knowledge acquired from previous data. In this work, we alleviate this issue by proposing a novel Efficient Perturbation Inference and Expandable Network (EPIE-Net), which dynamically expands lightweight task-specific decoders for new classes and utilizes a mixed-label uncertainty strategy to improve the robustness. Moreover, we calculate the average probability of perturbed samples at inference, which can generally improve the performance of the model. Experimental results show that our method consistently outperforms other methods with fewer parameters in class incremental learning benchmarks. For example, on the CIFAR-100 10 steps setup, our method achieves an average accuracy of 76.33% and the last accuracy of 65.93% within only 3.46M average parameters.