Reviewerly/exHarmony huggingface dataset:
Real-Time Radiance Fields for Single-Image Portrait View Synthesis

We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a neural radiance field for 3D-aware novel view synthesis via volume rendering. Our method is fast (24 fps) on consumer hardware, and produces higher quality results than strong GAN-inversion baselines that require test-time optimization. To train our triplane encoder pipeline, we use only synthetic data, showing how to distill the knowledge from a pretrained 3D GAN into a feedforward encoder. Technical contributions include a Vision Transformer-based triplane encoder, a camera data augmentation strategy, and a well-designed loss function for synthetic data training. We benchmark against the state-of-the-art methods, demonstrating significant improvements in robustness and image quality in challenging real-world settings. We showcase our results on portraits of faces (FFHQ) and cats (AFHQ), but our algorithm can also be applied in the future to other categories with a 3D-aware image generator.
Mapping Photovoltaic Panels in Coastal China Using Sentinel-1 and Sentinel-2 Images and Google Earth Engine

Photovoltaic (PV) panels convert sunlight into electricity, and play a crucial role in energy decarbonization, and in promoting urban resources and environmental sustainability. The area of PV panels in China’s coastal regions is rapidly increasing, due to the huge demand for renewable energy. However, a rapid, accurate, and robust PV panel mapping approach, and a practical PV panel classification strategy for large-scale applications have not been established. Here, we developed a new approach that uses spectral and textural features to identify and map the PV panels there were in coastal China in 2021 using multispectral instrument (MSI) and synthetic aperture radar (SAR) images, and the Google Earth Engine (GEE), to differentiate PV panels according to their underlying surface properties. Our 10-m-spatial-resolution PV panel map had an overall accuracy of 94.31% in 2021. There was 510.78 km2 of PV panels in coastal China in 2021, which included 254.47 km2 of planar photovoltaic (PPV) panels, 170.70 km2 of slope photovoltaic (SPV) panels, and 85.61 km2 of water photovoltaic (WPV) panels. Our resultant PV panel map provides a detailed dataset for renewable layouts, ecological assessments, and the energy-related Sustainable Development Goals (SDGs).
Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms

Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be seen as an optimization problem, and thus meta-heuristic techniques can be utilized to find a solution. Methodology: In this work, we propose a novel hybrid binary meta-heuristic algorithm to solve the feature selection problem by combining two algorithms: Dipper Throated Optimization (DTO) and Sine Cosine (SC) algorithm. The new algorithm is referred to as bSCWDTO. We employed the sine cosine algorithm to improve the exploration process and ensure the optimization algorithm converges quickly and accurately. Thirty datasets from the University of California Irvine (UCI) machine learning repository are used to evaluate the robustness and stability of the proposed bSCWDTO algorithm. In addition, the K-Nearest Neighbor (KNN) classifier is used to measure the selected features’ effectiveness in classification problems. Results: The achieved results demonstrate the algorithm’s superiority over ten state-of-the-art optimization methods, including the original DTO and SC, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Multiverse Optimization (MVO), Satin Bowerbird Optimizer (SBO), Genetic Algorithm (GA), the hybrid of GWO and GA, and Firefly Algorithm (FA). Moreover, Wilcoxon’s rank-sum test was performed at the 0.05 significance level to study the statistical difference between the proposed method and the alternative feature selection methods. Conclusion: These results emphasized the proposed feature selection method’s significance, superiority, and statistical difference.
A competitive learning scheme for deep neural network pattern classifier training

To reduce the computational complexity of training a deep neural network architecture using large data sets of 3D scenes, a competitive learning scheme was devised. The proposed algorithm pits a neural network learning algorithm, in this case the standard Adam optimiser, against an evolutionary algorithm that is used to select the most difficult training examples. The overall scheme is similar to a predator–prey system, where the predator (the neural network) strives to optimise its ability to capture (identify) the prey (the training patterns), and the evolutionary procedure selects the prey that so far evaded capture. As a consequence of the evolutionary process, the neural network is presented only a fraction of the training examples, and the computational complexity of the learning procedure is reduced. Experimental evidence showed that the proposed scheme allows reducing the deep neural network training time on different model sets, sometimes significantly, without affecting the recognition accuracy. The proposed predator–prey scheme is fairly independent of the ANN type and training algorithm employed, and has the potential to be beneficial to a wide range of deep learning applications, where practical implementations are often hindered by the time complexity of the training process.
Identity-Based Broadcast Proxy Re-Encryption for Flexible Data Sharing in VANETs

Data sharing is an integral part of vehicular ad hoc networks (VANETs), which provide drivers with safe and comfortable driving environments. However, when data are shared among multiple vehicles, they must be encrypted multiple times. Some solutions have used identity-based broadcast encryption to solve this problem. However, these schemes have two major limitations. First, the decryption cost is linearly related to the number of data receivers, where the identity of other receivers must be known. Second, only the data sender can forward the data. To address these important deficiencies, we propose an identity-based broadcast proxy re-encryption scheme to realize flexible and efficient data-sharing in VANETs. The data sender generates a fixed ciphertext that can be obtained by newly added vehicles through authorized vehicles. Data receivers can decrypt ciphertext directly without knowing the identities of other receivers, where the decryption overhead is constant. In addition, our scheme can achieve complete anonymous data sharing to protect vehicle privacy. A security proof shows that our scheme has sufficient security, and a performance analysis shows that our scheme performs well. Our proposed scheme is thus suitable for securing VANETs.
Autoencoding Galaxy Spectra. I. Architecture

We introduce the neural network architecture SPENDER as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional encoder, which pays attention to up to 256 spectral features and compresses them into a low-dimensional latent space, with a decoder that generates a restframe representation, whose spectral range and resolution exceeds that of the observing instrument. The decoder is followed by explicit redshift, resampling, and convolution transformations to match the observations. The architecture takes galaxy spectra at arbitrary redshifts and is robust to glitches like residuals of the skyline subtraction, so that spectra from a large survey can be ingested directly without additional preprocessing. We demonstrate the performance of SPENDER by training on the entire spectroscopic galaxy sample of SDSS-II; show its ability to create highly accurate reconstructions with substantially reduced noise; perform deconvolution and oversampling for a super-resolution model that resolves the [OII] doublet; introduce a novel method to interpret attention weights as proxies for important spectral features; and infer the main degrees of freedom represented in the latent space. We conclude with a discussion of future improvements and applications.
Continuous user identification in distance learning: a recent technology perspective

Abstract The worldwide shift to distance learning at Higher Education Institutions (HEIs) during the COVID-19 global pandemic has raised several concerns about the credibility of online academic activities, especially regarding student identity management. Traditional online frameworks cannot guarantee the authenticity of the enrolled student, which requires instructors to manually verify their identities, a time-consuming task that compromises academic quality. This article presents a comprehensive review of existing efforts around continuous user identification, focusing on intelligent proctoring systems and automatic identification methods, as well as their applicability in this domain. We conclude that there is a clear need for continuous user identification technology by HEIs, but existing systems lack agile system integration models that combine many inputs, such as face, voice and behavioural data in a practical manner, and encounter numerous barriers related to data protection during implementation.
Cross-view Graph Matching Guided Anchor Alignment for Incomplete Multi-view Clustering

Multi-view bipartite graph clustering methods select a few representative anchors and then establish a connection with original samples to generate the bipartite graphs for clustering, which maintains impressive performance while reducing time and space complexities. Despite their effectiveness in large-scale applications, few of them focus on cross-view anchor misalignment (CAM) problem. Then, misaligned anchor sets could destroy the consistent graph semantic structure of bipartite graphs across different views, thus hindering subsequent graph fusion and degrading the clustering performance. Especially when it comes to incomplete data, solving CAM problem becomes an intractable challenge. To address this challenge, we propose a novel Cross-view Graph Matching guided Anchor Alignment (CGMAA) for incomplete multi-view bipartite clustering. Specifically, we first propose a novel CGMAA framework to address CAM problem by predefining an anchor graph according to the prior anchor information. In addition, we unify CGMAA and bipartite graph tensor learning for incomplete multi-view clustering. Extensive experiments on ten complete/incomplete benchmark datasets demonstrate the effectiveness, efficiency, and superiority of the proposed CGMAA framework.
A Prediction Model for Energy Production in a Solar Concentrator Using Artificial Neural Networks

Solar energy is widely adopted today and produced by photovoltaic or concentrator solar power (CSP). Photovoltaic technology is the most prevalent, thanks to its well-established technology and low costs. CSP technology, on the other hand, has received less attention and interest, as it requires larger investments and a considerable surface. A relevant difficulty connected to the CSP is decoupling solar randomness and energy production. This paper proposes an artificial neural network (ANN) which foresees energy production using a solar parabolic dish installed at Politecnico di Torino (Energy Center Lab). The investigation was performed using a backpropagation ANN. Different learning algorithms were used: Levenberg-Marquardt, Bayesian regularization, resilient backpropagation, and scaled conjugate gradient. Seven atmospheric condition parameters were adopted (humidity, temperature, pressure, wind velocity and direction, solar radiation, and rain), to calculate the receiver temperature as an output. Bayesian regularization was found to be the optimal model for CSP energy production. The results of this investigation suggest that the ANNs are a strong, reliable, and useful tool for predicting temperature in a CSP receiver that can be of great value in the forecasting of energy production. The outcome of this investigation can simplify energy production forecasting using readily available meteorological data.
Self-Sovereign Identity for Trust and Interoperability in the Metaverse

With the advancement in computing power and speed, the Internet is being transformed from screen-based information to immersive and extremely low latency communication environments in web 3.0 and the Metaverse. With the emergence of the Metaverse technology, more stringent demands are required in terms of connectivity such as secure access and data privacy. Future technologies such as 6G, Blockchain, and Artificial Intelligence (AI) can mitigate some of these challenges. The Metaverse is now on the verge where security and privacy concerns are crucial for the successful adaptation of such disruptive technology. The Metaverse and web 3.0 are to be decentralized, anonymous, and interoperable. Metaverse is the virtual world of Digital Twins and non-fungible tokens (NFTs).The control and possession of users’ data on centralized servers are the cause of numerous security and privacy concerns. This paper proposes a solution for the security and interoperability challenges using Self-Sovereign Identity (SSI) integrated with blockchain. The philosophy of Self-Sovereign Identity, where the users are the only holders and owners of their identity, comes in handy to solve the questions of decentralization, trust, and interoperability in the Metaverse. This work also discusses the vision of a single, open standard, trustworthy, and interoperable Metaverse with initial design and implementation of SSI concepts.
Enhancing Cloud-Based Video Streaming Efficiency using Neural Networks

The growing demand for high-quality video streaming services has increased the need for efficient utilization of cloud resources. This research proposes a novel solution to this challenge by utilizing neural networks to enhance video streaming performance in the cloud. Specifically, the research introduces a neural network architecture capable of dynamically adjusting video quality based on network conditions and user preferences. By combining convolutional and recurrent neural networks, the proposed model learns patterns in video streams and predicts the optimal bit rate for each frame. The architecture also considers network conditions such as bandwidth, latency, and packet loss to adapt video quality in real-time. The proposed approach aims to improve quality of service while reducing cloud resource utilization, leading to cost-effective and scalable video streaming services. Extensive simulations and experiments were conducted to evaluate the feasibility and effectiveness of the approach. The proposed neural network architecture achieved up to 30% and 40% higher PSNR and SSIM scores, respectively, than the baseline approach. These results demonstrate the effectiveness of the proposed approach in enhancing video streaming efficiency in the cloud. Overall, this research provides valuable insights into designing efficient video streaming systems and highlights the potential benefits of neural networks in cloud-based video streaming services.
Two Algorithms for Excited-State Quantum Solvers: Theory and Application to EOM-UCCSD

Near-term quantum devices promise to revolutionize quantum chemistry, but simulations using the current noisy intermediate-scale quantum (NISQ) devices are not practical due to their high susceptibility to errors. This motivated the design of NISQ algorithms leveraging classical and quantum resources. While several developments have shown promising results for ground-state simulations, extending the algorithms to excited states remains challenging. This paper presents two cost-efficient excited-state algorithms inspired by the classical Davidson algorithm. We implemented the Davidson method into the quantum self-consistent equation-of-motion unitary coupled-cluster (q-sc-EOM-UCC) excited-state method adapted for quantum hardware. The circuit strategies for generating desired excited states are discussed, implemented, and tested. We demonstrate the performance and accuracy of the proposed algorithms (q-sc-EOM-UCC/Davidson and its variational variant) by simulations of H2, H4, LiH, and H2O molecules. Similar to the classical Davidson scheme, q-sc-EOM-UCC/Davidson algorithms are capable of targeting a small number of excited states of the desired character.
Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley Additive Explanations

Software testing represents a crucial component of software development, and it is usually making the difference between successful and failed projects. Although it is extremely important, due to the fast pace and short deadlines of contemporary projects it is often neglected or not detailed enough due to the lack of available time, leading to the potential loss of reputation, private users' data, money, and even lives in some circumstances. In such situations, it would be vital to have the option to predict what modules are error-prone according to the collection of software metrics, and to focus testing on them, and that task is a typical classification task. Machine learning models have been frequently employed within a wide range of classification problems with significant success, and this paper proposes eXtreme gradient boosting (XGBoost) model to execute the defect prediction task. A modified variant of the well-known reptile search optimization algorithm has been suggested to carry out the calibrating of the XGBoost hyperparameters. The enhanced algorithm was named HARSA and evaluated on the collection of challenging CEC2019 benchmark functions, where it exhibited excellent performance. Later, the introduced XGBoost model that uses the proposed algorithm has been evaluated on two benchmark software testing datasets, and the simulation outcomes have been compared to other powerful swarm intelligence metaheuristics that were used in the identical experimental environment, where the proposed approach attained superior classification accuracy on both datasets. Finally, Shapley Additive Explanations analysis was conducted to discover the impact of various software metrics on the classification results.
An Interval Type-3 Fuzzy–Fractal Approach for Plant Monitoring

In this article, a plant monitoring approach based on a hybrid mixture of type-3 fuzzy logic (T3FL) and the fractal dimension (FD) is presented. The main reason for combining type-3 and the fractal dimension is to take advantage of both their capabilities in solving the problem of monitoring a plant. Basically, T3FL helps in handling the uncertainty in monitoring the variables of a nonlinear system, while the FD helps to capture the signal complexity by finding key or hidden patterns in the data. The FD is utilized to estimate data complexity of the process variables being monitored. We utilize the box counting algorithm to approximate the values of the FD. A set of T3FL rules is utilized to model monitoring knowledge. The proposed approach was tested with a plant studied in previous works, which was solved with type-1 and type-2 fuzzy logic, and now type-3 is able to surpass the performance of previous approaches for this problem. The main contribution is the T3FL and FD hybrid proposal for plant monitoring, which has not been presented before in the literature. Simulation results illustrate the potential advantage of utilizing the T3FL and FD combination in this area.
MindMiner: Uncovering linguistic markers of mind perception as a new lens to understand consumer–smart object relationships

Abstract Prior research revealed a striking heterogeneity of how consumers view smart objects, from seeing them as helpful partners to merely a useful tool. We draw on mind perception theory to assess whether the attribution of mental states to smart objects reveals differences in consumer–smart object relationships and device usage. We train a language model to unobtrusively predict mind perception in smart objects from consumer‐generated text. We provide a rich set of interpretable linguistic markers for mind perception, drawing on a diverse collection of text‐mining techniques, and demonstrate that greater mind perception is associated with expressing a more communal (vs. instrumental) relationship with the device and using it more expansively. We find converging evidence for these associations using over 20,000 real‐world customer reviews and also provide causal evidence that inducing a more communal (vs. instrumental) relationship with a smart object enhances mind perception and in turn increases the number of tasks consumers engage in with the device. These findings have important implications for the role of mind perception as a novel lens to study consumer–smart object relationships. We offer an easy‐to‐use web interface to access our language model using researchers own data or to fine‐tune the model to entirely new domains.
Improving Data Processing Speed on Large Datasets in a Hadoop Multi-node Cluster using Enhanced Apriori Algorithm

For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset’s subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60–80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save up to 84–90% of that time.