Reviewerly/exHarmony huggingface dataset:
Incomplete multi-view clustering network via nonlinear manifold embedding and probability-induced loss

Incomplete multi-view clustering, which included missing data in different views, is more challenging than multi-view clustering. For the purpose of eliminating the negative influence of incomplete data, researchers have proposed a series of solutions. However, the present incomplete multi-view clustering methods still confront three major issues: (1) The interference of redundant features hinders these methods to learn the most discriminative features. (2) The importance role of local structure is not considered during clustering. (3) These methods fail to utilize data distribution information to guide models update to decrease the effects of outliers and noise. To address above issues, a novel deep clustering network which exerted on incomplete multi-view data was proposed in this paper. We combine multi-view autoencoders with nonlinear manifold embedding method UMAP to extract latent consistent features of incomplete multi-view data. In the clustering method, we introduce Gaussian Mixture Model (GMM) to fit the complex distribution of data and deal with the interference of outliers. In addition, we reasonably utilize the probability distribution information generated by GMM, using probability-induced loss function to integrate feature learning and clustering as a joint framework. In experiments conducted on multiple benchmark datasets, our method captures incomplete multi-view data features effectively and perform excellent.
Teaching the basics of computer graphics in virtual reality

New technology such as virtual reality can help computer graphics education, for example, by providing the opportunity to illustrate challenging 3D procedures. RePiX VR is a virtual reality tool for computer graphics education that focuses on teaching the core ideas of the rendering pipeline. This paper describes the development and two initial evaluations, which aimed to strengthen the usability, review requirements for different stakeholders, and build infrastructure for learning analytics and research. The integration of learning analytics raises the question of appropriate indicators to be approached through exploratory data analysis. In addition to learning analytics, the evaluation includes quantitative techniques to get insights about usability, and didactical feedback. This paper discusses advanced aspects of learning in VR and looks specifically at movement behavior. According to the evaluations, even learners without prior experience can utilize the VR tool to pick up the fundamentals of computer graphics.
An efficient model for sentiment analysis using artificial rabbits optimized vector functional link network

Sentiment analysis using artificial intelligence is a great tool for analyzing human linguistic capabilities. The deep learning techniques combined with Natural Language Processing (NLP) has said to offer higher performance in analyzing the sentiments in implicit aspects present in high-quality training datasets. However, the different complexities associated with deep learning techniques such as high computational and space complexity, sensitivity to learning rate, high training time, etc make them hard to implement in real-time applications. However, very few researchers have used the randomization-based neural network for sentimental analysis which offers different benefits such as increased generalization capacity, improved accuracy, low training time, and universal approximator for non-linear functions. Motivated by this problem, this paper presents an Artificial Rabbits optimized Robust Random Vector Functional Link Network (RRVFLN) for improving sentiment analysis accuracy. Initially, the data is preprocessed and the word vectors are efficiently analyzed by the Fast text method by considering each token as an n-gram character. Artificial Rabbits Optimization (ARO) is utilized to RRVFLN model’s input weights and to optimize the hidden layer biases. The ARO-optimized RRVFLN model classifies the outcomes obtained in the three datasets namely the News Headline Sentiment dataset, Sentiment140 dataset, and 4000 Short Story dataset into positive and negative. The simulation results obtained by comparing the proposed model with the various existing techniques display the effectiveness in terms of F1 score, precision, classification accuracy, error rate prediction, and kappa statistics.
STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison

Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools/packages (e.g. scikit-learn) have been developed to make the various elements of data handling, processing, modeling, and interpretation accessible. However, it is not trivial for most investigators to assemble these elements into a rigorous, replicatable, unbiased, and effective data analysis pipeline. Automated machine learning (AutoML) seeks to address these issues by simplifying the process of ML analysis for all. Here, we introduce STREAMLINE, a simple, transparent, end-to-end AutoML pipeline designed as a framework to easily conduct rigorous ML modeling and analysis (limited initially to binary classification). STREAMLINE is specifically designed to compare performance between datasets, ML algorithms, and other AutoML tools. It is unique among other autoML tools by offering a fully transparent and consistent baseline of comparison using a carefully designed series of pipeline elements including (1) exploratory analysis, (2) basic data cleaning, (3) cross validation partitioning, (4) data scaling and imputation, (5) filter-based feature importance estimation, (6) collective feature selection, (7) ML modeling with 'Optuna' hyperparameter optimization across 15 established algorithms (including less well-known Genetic Programming and rule-based ML), (8) evaluation across 16 classification metrics, (9) model feature importance estimation, (10) statistical significance comparisons, and (11) automatically exporting all results, plots, a PDF summary report, and models that can be easily applied to replication data.
Human Resources Management After Industry 4.0

Artificial intelligence (AI) continues to be a puzzling hypothesis, and it's probable that philosophical zeal might have influenced service standards of AI far beyond reality. This chapter contributes to the domain of AI in HRM, gives in depth theoretical analysis of the field, and opens up new avenues for researchers to examine. While analysis found that AI technologies like machine learning (ML), natural language processing (NLP), machine vision and recommendation systems are being used in HRM functions for distinctive uses. Analysis of the literature also revealed that automation, cost optimisation, and technological development acts as enablers for AI incorporation in HRM domain. Lack of skills, unrealistic standards, privacy and security, and job obsolescence are the factors that act as hindrances during adoption of AI-systems in HRM. Study also revealed that the major applications of AI in HRM are in screening and hiring process; automated induction process; skill development and training; decision making; queries and feedback ; performance appraisal; and employee involvement.
PLDP-FL: Federated Learning with Personalized Local Differential Privacy

As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning schemes based on local differential privacy (LDP) ignore an important issue: they do not consider each client's differentiated privacy requirements. This paper introduces a perturbation algorithm (PDPM) that satisfies personalized local differential privacy (PLDP), resolving the issue of inadequate or excessive privacy protection for some participants due to the same privacy budget set for all clients. The algorithm enables clients to adjust the privacy parameters according to the sensitivity of their data, thus allowing the scheme to provide personalized privacy protection. To ensure the privacy of the scheme, we have conducted a strict privacy proof and simulated the scheme on both synthetic and real data sets. Experiments have demonstrated that our scheme is successful in producing high-quality models and fulfilling the demands of personalized privacy protection.
Practically Enhanced Hyperentanglement Concentration for Polarization-Spatial Hyperentangled Bell States with Linear Optics and Common Single-Photon Detectors

Hyperentanglement, defined as the simultaneous entanglement in several independent degrees of freedom (DOFs) of a quantum system, is a fascinating resource in quantum information processing with its outstanding merits. Here we propose heralded hyperentanglement concentration protocols (hyper-ECPs) to concentrate an unknown partially less polarization-spatial hyperentangled Bell state with available linear optics and common single-photon detectors. By introducing time-delay DOFs, the schemes are highly efficient in that the success of the scheme can be accurately heralded by the detection signatures, and postselection techniques or photon-number-resolving detectors, necessary for previous experiments, are not required. Additionally, our linear optical architectures allow certain states, where concentration fails, to be recyclable, and a trick makes the success probabilities of our schemes higher than those of previous linear optical hyper-ECPs.
A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem

We develop a novel method to improve biogeography-based optimization (BBO) for solving the traveling salesman problem (TSP). The improved method is comprised of a greedy randomized adaptive search procedure, the 2-opt algorithm, and G2BBO. The G2BBO formulation is derived and the process flowchart is shown in this article. For solving TSP, G2BBO effectively avoids the local minimum problem and accelerates convergence by optimizing the initial values. To demonstrate, we adopt three public datasets (eil51, eil76, and kroa100) from TSPLIB and compare them with various well-known algorithms. The results of G2BBO as well as the other algorithms perform close enough to the optimal solutions in eil51 and eil76 where simple TSP coordinates are considered. In the case of kroa100, with more complicated coordinates, G2BBO shows greater performance over other methods.
PMPF: Point-Cloud Multiple-Pixel Fusion-Based 3D Object Detection for Autonomous Driving

Today, multi-sensor fusion detection frameworks in autonomous driving, especially sequence-based data-level fusion frameworks, face high latency and coupling issues and generally perform worse than LiDAR-only detectors. On this basis, we propose PMPF, point-cloud multiple-pixel fusion, for 3D object detection. PMPF projects the point cloud data onto the image plane, where the region pixels are processed to correspond with the points and decorated to the point cloud data, such that the fused point cloud data can be applied to LiDAR-only detectors with autoencoders. PMPF is a plug-and-play, decoupled multi-sensor fusion detection framework with low latency. Extensive experiments on the KITTI 3D object detection benchmark show that PMPF vastly improves upon most of the LiDAR-only detectors, e.g., PointPillars, SECOND, CIA-SSD, SE-SSD four state-of-the-art one-stage detectors, and PointRCNN, PV-RCNN, Part-A2 three two-stage detectors.
Scheduling In-Band Network Telemetry With Convergence-Preserving Federated Learning

Conducting federated learning across distributed sites with In-Band Network Telemetry (INT) based data collection faces critical challenges, including control decisions of different frequencies, convergence of the models being trained, and resource provisioning coupled over time. To study this problem, we formulate a non-linear mixed-integer program to optimize the long-term INT overhead, resource cost, and federated learning cost. We then design polynomial-time online algorithms to solve this problem with only observable inputs on the fly, featuring laziness-aware resource adaption, online-learning-based INT flow selection and model aggregation control, as well as expectation-preserving randomized dependent rounding. We rigorously prove the parameterized-constant competitive ratio of our approach against the offline optimum, and the time-averaged constraint violation that vanishes in the long run. With extensive trace-driven evaluations, we confirm the superiority of our approach over other alternative approaches for reducing total cost and the efficacy of our trained models for solving real machine learning problems, reducing the real-time cost by 34% on average.
Uncertainty-Aware Panoptic Segmentation

Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metrics that only consider the quality of the segmentation. However, for the safe operation of a system in the real world it is crucial to consider the uncertainty in the prediction as well. In this work, we introduce the novel task of uncertainty-aware panoptic segmentation, which aims to predict per-pixel semantic and instance segmentations, together with per-pixel uncertainty estimates. We define two novel metrics to facilitate its quantitative analysis, the uncertainty-aware Panoptic Quality (uPQ) and the panoptic Expected Calibration Error (pECE). We further propose the novel top-down Evidential Panoptic Segmentation Network (EvPSNet) to solve this task. Our architecture employs a simple yet effective panoptic fusion module that leverages the predicted uncertainties. Furthermore, we provide several strong baselines combining state-of-the-art panoptic segmentation networks with sampling-free uncertainty estimation techniques. Extensive evaluations show that our EvPSNet achieves the new state-of-the-art for the standard Panoptic Quality (PQ), as well as for our uncertainty-aware panoptic metrics.
Deep Gaussian mixture adaptive network for robust soft sensor modeling with a closed-loop calibration mechanism

Process drift leads to an out-of-distribution problem between historical training data and online deployment data, which deteriorates the performance of soft sensors. Unfortunately, few existing deep learning soft sensors have calibration mechanisms to deal with the process drift. To this end, this article proposes a robust Deep Gaussian Mixture Adaptive Network (DGMAN)-based soft sensor with a closed-loop calibration mechanism. First, a Gaussian Mixture Conditional Variational Autoencoder (GMCVAE) is presented, with the ability to capture the multimodality in industrial data. Afterwards, we propose a novel semi-supervised Gaussian mixture domain adaptation to perform conditional and marginal probability distribution alignment in the Gaussian mixture probabilistic latent space created by GMCVAE, using both labeled and unlabeled target samples. Theoretically, two kinds of process drift, including concept drift and virtual drift, could be alleviated by conditional distribution adaptation and marginal distribution adaptation, respectively. In addition, a new closed-loop calibration mechanism is deployed in a pre-training and fine-tuning fashion. Finally, robust analysis in theory and experimental results on the gas turbine dataset demonstrate that the proposed DGMAN-based soft sensor with robustness against process drift can maintain long-term validity in real industrial processes. Calibrated by only 100 labeled samples per year, the RMSE of DGMAN is 5.55 ± 0.32 after three years of deployment in the power plant, outperforming all comparison methods.
Deakin microgrid digital twin and analysis of AI models for power generation prediction

To achieve carbon neutral by 2025, Deakin University launched a AUD 23 million Renewable Energy Microgrid in 2020 with a 7-megawatt solar farm, the largest at an Australian University. A web-based digital twin (DT) is developed to provide operators with intelligence and insights through several AI-driven capabilities. Accurate and computationally efficient power generation prediction is one of the critical elements in this DT. To this end, we researched the literature and identified the commonly used Machine Learning-based prediction models and compared them computationally using power generation and weather sensor data obtained from the solar farm. From the computational experiments, we find that, overall, Artificial Neural Network (ANN) has achieved the highest R2-score (0.944) and the lowest RMSE (14.848). To obtain further insights, we compared the methods using our two novel metrics, the x-percentile Closeness scores and the x-percentile Absolute error scores. The new metrics provide us with a spectrum to measure the consistency and robustness of the prediction methods instead of just a single value. Further, power generation can fluctuate substantially, and a prediction model should be accurate regardless of the magnitude of the output, hence measuring the relative error has its merits. By our two new metrics, using the data from our Deakin Microgrid, Random Forrest (RF) outperformed the other methods tested, with the smallest absolute relative error across the whole spectrum (from 0.011 to 0.457). RF is also the fastest in model training time at 4.894 s and XGBoost came second at 5.115 s–a big contrast to ANN at 144.102 s. All prediction times are under 1 s. RF is therefore used as a power prediction algorithm in our Deakin Microgrid Digital Twin.
Self-sovereign identity empowered non-fungible patient tokenization for health information exchange using blockchain technology

Patient tokenization is a novel approach that allows anonymous patient-level linkage across healthcare facilities, minimizing the risk of breaching protected health information in health information exchange (HIE). Most patient tokenization is the centralized approach that is unable to address data security concerns fundamentally. Non-Fungible Tokens (NFT), which are non-transferable cryptographic assets on the blockchain, have the potential to provide secure, decentralized, and trustworthy patient tokenization. Self-Sovereign Identity (SSI) is a user-centric approach to verify the ownership of NFTs in a decentralized manner.We have developed a blockchain architecture that contains four modules: (1) Creation module for NFTs creation, (2) Linkage module to link the local patients' accounts to their NFTs, (3) Authentication module that allows patients to permit healthcare providers to access their token, and (4) Exchange module, which involves the HIE process and the validation of the legitimacy of the token through SSI.A case study has been conducted on the proposed architecture. Over 3 million transactions have been completed successfully with a blockchain validation and written time of 1.17 s on average. A stability test has also been conducted with a higher throughput of 200 transactions per second running for an hour with an average transaction processing time of 1.42 s.This study proposed a blockchain architecture that achieves SSI-enabled NFT-based patient tokenization. Our architecture design, implementation, and case studies have demonstrated the feasibility and potential of NFT with SSI to establish a secure, transparent, and patient-centric identity management and HIE.
Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction

The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies.
Session-based recommendation with hypergraph convolutional networks and sequential information embeddings

Session-based recommendation focuses on predicting the next item that an anonymous user is most likely to click. Due to its privacy-protecting ability, it is receiving increasing attention from researchers in recent years. The existing studies typically focus on sequence or graph structure learning. However, they ignore the consistency and sequential dependence relationships that are widely existed between items in real-world scenarios. To address this problem, we present a novel method named HyperS2Rec. Specifically, we propose to learn two kinds of item embeddings with hypergraph convolutional network and gated recurrent unit, respectively, to account for both consistency-awareness and sequential dependence-awareness. Then the attention mechanism is designed to flexibly combine the above both embeddings. Finally, the reversed position and the soft attention mechanism are utilized to obtain session representations. To verify the effectiveness of the proposed HyperS2Rec, we conduct experiments on three real-world datasets. The results prove that the proposed HyperS2Rec significantly outperforms state-of-the-art methods. The source code of our proposed model is available at https://github.com/ZZY-GraphMiningLab/HyperS2Rec.
Performance and Security Analysis in Cloud Using Drops and T-Coloring Methods

Data outsourcing to a 3rd party organize, as is done with cloud computing, raises security concerns. Data outsourcing may result from attacks just on cloud by the other users and nodes. Therefore, to protect data on the cloud, strict security measures are required. The rate at which data could be retrieved must be optimized by the implemented security solution. This work suggests the splitting and replication the data in the cloud with optimal system efficiency (DROPS), which adopts a thorough strategy to the security and efficiency concerns. In accordance with the DROPS method, split a file into fragments, then replicate the information among some of the cloud server. In order to guarantee that in the event of a successful assault, the adversary would not gain any useful information, each node merely retains a single fragment of a particular data file. In order to prevent an adversary from determining out the pieces, the nodes which store them are also spaced separated by a specific amount utilizing graph T-coloring.