Reviewerly/exHarmony huggingface dataset:
Taylor Horse Herd Optimized Deep Fuzzy clustering and Laplace based K-nearest neighbor for web page recommendation

: Web-page recommendation serves a significant part in intelligent web frameworks and discovery of meaningful information from web and sufficient knowledge illustration for efficient web-page recommendations are quintessential and highly demanding issue. The conventional key matching and statistical models for web page recommendation are insufficient as they recommend web pages to the user mostly irrelevant to the query. It is essential to satisfy individual user separately according to the available preference data. In contrast, graph framework can deliver suitable illustration of user-item preference data and by employing this graph structure-based web page recommendation system is highly desirable and efficient. : This research proposes a web page recommendation model using Taylor Horse Herd Optimization (THHO)-based Deep Fuzzy Clustering (DFC). Here, the interesting sub graphs from web log database are retrieved using Weighted-Gaston (W-Gaston) algorithm. DFC is exploited to cluster the sub graphs and DFC is effectively trained using THHO algorithm. Moreover, THHO is devised by the incorporation of Taylor series with Horse Herd Optimization Algorithm (HOA). Finally, suitable web pages are recommended to the user based on their query using Laplace correction-based K-Nearest neighbor (LKNN) model. : The proposed THHO_DFC approach has obtained maximum precision of 0.950, recall of 0.897, F-measure of 0.922, and accuracy of 0.926 while analyzing the system based on 90% data using MSNBC dataset. : The proposed model has delivered better insights in terms of efficiency and provides high performance in recommending suitable web pages.
Cyberspace attack and defense game based on reward randomization reinforcement learning

The existing cyberspace attack and defense method can be regarded as game, but most of the game only involves network information, not include cyberspace's states, attacker's and defender's actions. To address this challenge, this paper proposed a cyberspace attack and defense game model based on reinforcement learning. By establishing two agents, representing the attacker and the defender respectively, defender will select the actions in the cyberspace to obtain defender's optimal reward by reinforcement learning. In order to improve the defense ability of defender, a game model based on reward randomization reinforcement learning is proposed. When the defender takes the defense action, the reward is randomly given and subject to linear distribution, so as to find the better defense policy and improve defense successfully rate. The experimental results show that the game model can effectively simulate the attack and defense state of cyberspace, and the reward randomization reinforcement learning method has a higher defense success rate than existing methods. © 2001 Elsevier Science. All rights reserved.
Imputation Strategies for Clustering Mixed-Type Data with Missing Values

Abstract Incomplete data sets with different data types are difficult to handle, but regularly to be found in practical clustering tasks. Therefore in this paper, two procedures for clustering mixed-type data with missing values are derived and analyzed in a simulation study with respect to the factors of partition, prototypes, imputed values, and cluster assignment. Both approaches are based on the k-prototypes algorithm (an extension of k-means), which is one of the most common clustering methods for mixed-type data (i.e., numerical and categorical variables). For k-means clustering of incomplete data, the k-POD algorithm recently has been proposed, which imputes the missings with values of the associated cluster center. We derive an adaptation of the latter and additionally present a cluster aggregation strategy after multiple imputation. It turns out that even a simplified and time-saving variant of the presented method can compete with multiple imputation and subsequent pooling.
Training Hard Skills in Virtual Reality: Developing a Theoretical Framework for AI-Based Immersive Learning

Abstract Advances in virtual reality (VR) technology afford creation of immersive virtual learning environments that simulate real-life learning contexts with increasing fidelity. When supported by sufficiently advanced artificial intelligence (AI)-based tutoring software, such environments may facilitate asynchronous, embodied learning approaches for learning hard, procedural skills in industrial settings – addressing timeliness, accuracy, and scalability issues common in the industry. This chapter reflects on the pedagogical setting of immersive virtual reality-based hard skills training guided by an AI tutor software agent. We examine the interfacing of traditional intelligent tutoring system (ITS) software with an immersive virtual environment. Further, we suggest the philosophies of embodied, embedded, enacted, and extended (4E) cognition as a way to fully consider learner epistemology in a virtual world and to account for and make full use of the unique opportunities afforded by the synthetic nature of the immersive virtual learning environment. To explore possibilities for improved pedagogical approaches, we project the 4E cognition approach into the abovementioned learning context and outline a theoretical framework for a VR-native AI tutor. We then propose VR-native pedagogical principles for such as framework that could inform follow-on research.
SyReC Synthesizer: An MQT tool for synthesis of reversible circuits

Reversible circuits form the backbone for many promising emerging technologies such as quantum computing, low power/adiabatic design, encoder/decoder devices, and several other applications. In the recent years, the scalable synthesis of such circuits has gained significant attention. In this work, we present the SyReC Synthesizer, a synthesis tool for reversible circuits based on the hardware description language SyReC. SyReC allows to describe reversible functionality at a high level of abstraction. The provided SyReC Synthesizer then realizes this functionality in a push-button fashion. Corresponding options allow for a trade-off between the number of needed circuit signals/lines (relevant, e.g., for quantum computing in which every circuit line corresponds to a qubit) and the respectively needed gates (corresponding to the circuit's costs). Furthermore, the tool allows to simulate the resulting circuit as well as to determine the gate costs of it. The SyReC Synthesizer is available as an open-source software package at https://github.com/cda-tum/syrec as part of the Munich Quantum Toolkit (MQT).
Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning

Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as "DeepSurv") and a multivariate Cox proportional hazard model in view of discrimination (C-index: 0.839 vs. 0.755 vs. 0.762, respectively), calibration (better fit with a 45-degree line), and ability of risk stratification, especially identifying patients with high risk of mortality. The proposed RNNSurv demonstrated an improved prediction performance in consideration of temporal information from time-varying covariates that could assist clinical decision-making. Additionally, this study found that significant risk and protective factors of mortality were specific to risk levels, highlighting the demand for an individual-specific clinical strategy instead of a uniform one for all patients.
Two-Stage UA-GAN for Precipitation Nowcasting

Short-term rainfall prediction by radar echo map extrapolation has been a very hot area of research in recent years, which is also an area worth studying owing to its importance for precipitation disaster prevention. Existing methods have some shortcomings. In terms of image indicators, the predicted images are not clear enough and lack small-scale details, while in terms of precipitation accuracy indicators, the prediction is not accurate enough. In this paper, we proposed a two-stage model (two-stage UA-GAN) to achieve more accurate prediction echo images with more details. For the first stage, we used the Trajectory Gated Recurrent Unit (TrajGRU) model to carry out a pre-prediction, which proved to have a good ability to capture spatiotemporal movement of rain field. In the second stage, we proposed a spatiotemporal attention enhanced Generative Adversarial Networks (GAN) model with a U-Net structure and a new deep residual attention module in order to carry out the refinement and improvement of the first-stage prediction. Experimental results showed that our model outperforms the optical-flow based method Real-Time Optical Flow by Variational Methods for Echoes of Radar (ROVER), and some well-known Recurrent Neural Network (RNN)-based models (TrajGRU, PredRNN++, ConvGRU, Convolutional Long Short-Term Memory (ConvLSTM)) in terms of both image detail indexes and precipitation accuracy indexes, and is visible to the naked eye to have better accuracy and more details.
E-guerrilla 3D participation: Approach, implementation, and usability study

Typical urban participatory approaches engage citizens through lengthy sessions far from the area under transformation by an urban project. Several issues result from these settings of involvement including the mobilization of similar individuals, overwhelming participatory codes, or a gap between the affected urban landscape and the location of the session. This study introduces a modern approach that leverages the use of 3D web applications to address some of the critical challenges of popular participatory sessions. The developed approach, named e-guerrilla 3D participation, is based on five dimensions: immediate participation, ease of use, flexibility, place-based engagement, and immersivity. A prototype complying with these five dimensions was implemented in this study. The prototype promotes an in situ engagement where all the users (without distinction) of a public area can explore a future urban project and get involved within minutes. A usability study conducted with 26 expert and non-expert participants investigated the prototype through a fictive scenario. The findings demonstrate a positive outcome in terms of participatory results that are identifiable with the prototype (highlighting the controversial elements of the projects) and encourage feedback collected during a survey and interview. The usability study suggests key aspects that should be considered to improve the design of participatory sessions and their interactive mediums (or tools), such as realism, affordance, incentive, and purpose. The promising participatory approach (and prototype), which was unpacked step-by-step in this study, does not replace typical practices but could help to complement them by reaching a non-selected and broader public; hence leading to the design of more inclusive participatory approaches.
Anti-Collusion Multiparty Smart Contracts for Distributed Watchtowers in Payment Channel Networks

Leveraging watchtowers to monitor payment channel networks (PCNs) is regarded to be a promising option to ensure off-chain transaction security and boost cryptocurrency scalability. However, existing solutions have two major limitations: First, since the watchtower’s inaction or collusion with counterparties, the deposits in off-chain transactions will be threatened; Second, due to occasional false positives, the efficiency of the single watchtower in monitoring the payment channels for fraud is questionable. To solve this, we present anti-collusion multiparty smart contracts for distributed watchtowers in PCNs. Specifically, we first design the distributed watchtower mechanism to solve the false positive problem in regulating PCNs. In addition, we utilize smart contracts to constrain and force counterparties to relinquish collusion in the distributed watchtower mechanism, thus making collusion impossible for rational parties. We further offer a mathematical proof and contract implementation in Solidity. Finally, extensive experiments and contracts executed on Ethereum under various benchmarks with baseline comparison demonstrate the validity of our proposals. Specifically, our scheme can both improve the throughput and accuracy by up to 20-25% and 10-15%, respectively, and reduce the false positive rate by up to 10% compared with existing single watchtower mechanism.
Performance Evaluation of Data Enhancement Methods in SAR Ship Detection

In recent years, researchers have started to apply neural networks to ship detection of synthetic aperture radar (SAR). However, SAR images are difficult to acquire and interpret manually. Sufficient training samples cannot be obtained, which limits the performance of ship detection. Therefore, data enhancement has become an active means to handle the issue of insufficient samples. To evaluate the performance of diverse data enhancement methods, a variety of data enhancement methods is used to expand the ship samples in the SAR ship detection dataset, including rotation, shift, mirror, brightening, etc. Moreover, we used the combination of diverse data enhancement methods for experiments. Experiments were performed using the SSDD dataset. Based on the experimental results, we analyze the characteristics of diverse data enhancement methods.
Scheme to implement moving target detection of coastal defense radar in complicated sea conditions

Although the data on a particular complicated sea conditions are available, it is difficult to collect the data on various complicated sea conditions simultaneously. When the sea conditions change, the test results of the detection network based on the data of simple sea conditions may be unacceptable. It is proposed that the VGG16, pix2pixHD, and cycleGAN methods should be applied to establish related datasets of different complicated sea conditions. After the unified image quality assessment indicators are determined, it is found that the images generated by the VGG16 network are most in line with the subsequent target detection standards. The real images of complicated sea conditions have the smallest root-mean-square error, the largest peak signal-to-noise ratio, and the best regression coefficient (R2). Meanwhile, the improved Faster R-CNN is introduced for target detection of small sample datasets. First, the ResNet50_FPN module, as the backbone feature extractor, is used to improve the detection performance of small-sized objects. Moreover, because there are many small target objects to be detected, the ROI Align module is preferred, for it is more accurate. Finally, the Softer-NMS algorithm is selected to significantly improve the positioning accuracy through confidence estimation. Compared with some previous Faster R-CNN methods, the accuracy and missed detection rate of the proposed method outperform other networks, with a mean average precision of 85.11%.
Photonic Bayesian Neural Network Using Programmed Optical Noises

The Bayesian neural network (BNN) combines the strengths of neural networks and statistical modeling in that it simultaneously performs posterior predictions and quantifies the uncertainty of the predictions. Integrated photonics has emerged as a promising hardware platform of neural network accelerators capable of energy-efficient, low latency, and parallel computing. However, photonic neural networks demonstrated to date are mostly deterministic network models. Here, we extend the photonic neural network to a statistical model and proposed a photonic Bayesian neural network (P-BNN) architecture based on the integrated photonic platform and harnessing the inherent optical noises. The Bayesian neuron is realized by controlling the probability distribution of the signal-amplified spontaneous emission (signal-ASE) beat noise. We show the P-BNN's advantages in making predictions using the posterior distribution by simulating a p-BNN to perform handwritten number classification tasks. The simulation results show that the proposed P-BNN not only makes successful predictions on the expected images from the test dataset but also detects and rejects the unexpected images outside the training datasets. The P-BNN architecture is compatible with on-chip optical amplifiers and can be scaled up using current and emerging integrated photonics technologies, thus is promising for practical neural network applications.
Detecting Arbitrage on Ethereum Through Feature Fusion and Positive-Unlabeled Learning

Due to the lack of supervision in the decentralized exchanges (DEXs), arbitrageurs can utilize information and take advantage of price gap to make profits over such platforms such as Ethereum blockchain. DEX arbitrage poses possibilities and opportunities for defrauding and can seriously impair the operation of the Ethereum ecosystem. It motivates this work to explore and characterize the unique features of arbitrage which differ from other frauds such as money laundering and Ponzi games for better detection. This work makes the first attempt for detecting arbitrage on Ethereum through feature fusion and positive-unlabeled learning (PU learning). We first conduct an in-depth analysis and exploit two-fold arbitrage features by fusion including: 1) statistical features that explicitly represent the node activity levels according to expert knowledge; and 2) structural features that implicitly encode the transactions information by graph machine learning. We then apply PU learning to generate negative instances for compensating the imbalanced arbitrage datasets. We evaluate our proposed method through extensive experiments over a real-world dataset and demonstrate that it can achieve 90% accuracy in detecting arbitrage activities on Ethereum.
Unsharp masking image enhancement the parallel algorithm based on cross-platform

In view of the low computational efficiency and the limitations of the platform of the unsharp masking image enhancement algorithm, an unsharp masking image enhancement parallel algorithm based on Open Computing Language (OpenCL) is proposed. Based on the analysis of the parallel characteristics of the algorithm, the problem of unsharp masking processing is implemented in parallel. Making use of the characteristics of data reuse in the algorithm, the effective allocation and optimization of global memory and constant memory are realized according to the access attributes of the data and the characteristics of the OpenCL storage model, and the use efficiency of off-chip memory is improved. Through the data storage access mode, the fast computing local memory access mode is discovered, and the logical data space transformation is used to convert the storage access mode, so as to improve the bandwidth utilization of the on-chip memory. The experimental results show that, compared with the CPU serial algorithm, the OpenCL accelerated unsharp masking image enhancement parallel algorithm greatly reduces the execution time of the algorithm while maintaining the same image quality, and achieves a maximum speedup of 16.71 times. The high performance and platform transplantation of the algorithm on different hardware platforms are realized. It provides a reference method for real-time processing of a large amount of data of high-resolution images for image enhancement.
Compact representation of interval graphs and circular-arc graphs of bounded degree and chromatic number

In this paper we initiate the study of designing parameterized compact data structures for an interval graph G with n vertices. First, we show that when the maximum degree of G is bounded by Δ, we show that the space requirement of our data structure is at least (16nlog2⁡Δ−O(n))-bit, which is one of the main contributions of this work. Next, by straightforward modifications of the result of Acan et al. (2021) [5], we obtain an (nlog2⁡Δ+O(n))-bit data structure supporting the standard navigational queries i.e., degree, adjacency, and neighborhood optimally. Hence, we provide a compact representation of interval graphs with bounded degree for the first time in literature. Note that this upper bound result takes less space than their (nlog2⁡n+O(n))-bit upper bound when Δ=O(nϵ), for any 0<ϵ<1. Next, we consider the interval graphs with bounded chromatic number , and design a -bit data structure with efficient query times. Unlike the previous upper bound, this data structure is completely new and doesn't follow from the result of Acan et al. (2021) [5]. Moreover, this takes less space than their data structure when . Finally, we provide parameterized compact data structures for circular-arc graphs as well with bounded degree or bounded chromatic number condition.
Robust monocular 3D face reconstruction under challenging viewing conditions

Despite extensive research, 3D face reconstruction from a single image remains an open research problem due to the high degree of variability in pose, occlusions and complex lighting conditions. While deep learning-based methods have achieved great success, they are usually limited to near frontal images and images that are free of occlusions. Also, the lack of diverse training data with 3D annotations considerably limits the performance of such methods. As such, existing methods fail to recover, with high fidelity, the facial details especially when dealing with images captured under extreme conditions. To address this issue, we propose an unsupervised coarse-to-fine framework for the reconstruction of 3D faces with detailed textures. Our core idea is that multiple images of the same person but captured under different viewing conditions should provide the same 3D face. We thus propose to leverage a self-augmentation learning technique to train a model that is robust to diverse variations. In addition, instead of directly employing image pixels, we use a set of discriminative features describing the identity and attributes of the face as input to the refinement module, making the model invariant to viewing conditions. This combination of self-augmentation learning with rich face-related features allows the reconstruction of plausible facial details even under challenging viewing conditions. We train the model end-to-end and in a self-supervised manner, without any 3D annotations, landmarks or identity labels, using a combination of an image-level photometric loss and a perception-level loss that is identity and attribute-aware. We evaluate the proposed approach on CelebA and AFLW2000 datasets, and demonstrate its robustness to appearance variations despite learning from unlabeled images. The qualitative comparisons indicate that our method produces detailed 3D faces even under extreme occlusions, out of plane rotations and noise perturbations where existing state-of-the-art methods often fail. We also quantitatively show that our method outperforms SOTA with more than 30.14%, 9.87% and 11.3% in terms of PSNR, SSIM and IDentity similarity, respectively.