Reviewerly/exHarmony huggingface dataset:
Point‐Based Neural Rendering with Per‐View Optimization

Abstract There has recently been great interest in neural rendering methods. Some approaches use 3D geometry reconstructed with Multi‐View Stereo (MVS) but cannot recover from the errors of this process, while others directly learn a volumetric neural representation, but suffer from expensive training and inference. We introduce a general approach that is initialized with MVS, but allows further optimization of scene properties in the space of input views, including depth and reprojected features, resulting in improved novel‐view synthesis. A key element of our approach is our new differentiable point‐based pipeline, based on bi‐directional Elliptical Weighted Average splatting, a probabilistic depth test and effective camera selection. We use these elements together in our neural renderer, that outperforms all previous methods both in quality and speed in almost all scenes we tested. Our pipeline can be applied to multi‐view harmonization and stylization in addition to novel‐view synthesis.
Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation

In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be specific, our framework consists of a speaker-independent stage and a speaker-specific stage. In the speaker-independent stage, we design three parallel networks to generate animation parameters of the mouth, upper face, and head from texts, separately. In the speaker-specific stage, we present a 3D face model guided attention network to synthesize videos tailored for different individuals. It takes the animation parameters as input and exploits an attention mask to manipulate facial expression changes for the input individuals. Furthermore, to better establish authentic correspondences between visual motions (i.e., facial expression changes and head movements) and audios, we leverage a high-accuracy motion capture dataset instead of relying on long videos of specific individuals. After attaining the visual and audio correspondences, we can effectively train our network in an end-to-end fashion. Extensive experiments on qualitative and quantitative results demonstrate that our algorithm achieves high-quality photo-realistic talking-head videos including various facial expressions and head motions according to speech rhythms and outperforms the state-of-the-art.
Question Answering Over Temporal Knowledge Graphs

Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (e.g., start and end times) on each edge in the KG.While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area.Lack of broadcoverage datasets has been another factor limiting progress in this area.We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity.CRONQUESTIONS expands the only known previous dataset by a factor of 340×.We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset.In response, we also propose CRONKGQA, a transformerbased solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method.Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible.In addition to the dataset, we have released our code as well.
ELSA: Hardware-Software Co-design for Efficient, Lightweight Self-Attention Mechanism in Neural Networks

The self-attention mechanism is rapidly emerging as one of the most important key primitives in neural networks (NNs) for its ability to identify the relations within input entities. The self-attention-oriented NN models such as Google Transformer and its variants have established the state-of-the-art on a very wide range of natural language processing tasks, and many other self-attention-oriented models are achieving competitive results in computer vision and recommender systems as well. Unfortunately, despite its great benefits, the self-attention mechanism is an expensive operation whose cost increases quadratically with the number of input entities that it processes, and thus accounts for a significant portion of the inference runtime. Thus, this paper presents ELSA (Efficient, Lightweight Self-Attention), a hardware-software co-designed solution to substantially reduce the runtime as well as energy spent on the self-attention mechanism. Specifically, based on the intuition that not all relations are equal, we devise a novel approximation scheme that significantly reduces the amount of computation by efficiently filtering out relations that are unlikely to affect the final output. With the specialized hardware for this approximate self-attention mechanism, ELSA achieves a geomean speedup of 58.1× as well as over three orders of magnitude improvements in energy efficiency compared to GPU on self-attention computation in modern NN models while maintaining less than 1% loss in the accuracy metric.
Orbital angular momentum mode logical operation using optical diffractive neural network

Optical logical operations demonstrate the key role of optical digital computing, which can perform general-purpose calculations and possess fast processing speed, low crosstalk, and high throughput. The logic states usually refer to linear momentums that are distinguished by intensity distributions, which blur the discrimination boundary and limit its sustainable applications. Here, we introduce orbital angular momentum (OAM) mode logical operations performed by optical diffractive neural networks (ODNNs). Using the OAM mode as a logic state not only can improve the parallel processing ability but also enhance the logic distinction and robustness of logical gates owing to the mode infinity and orthogonality. ODNN combining scalar diffraction theory and deep learning technology is designed to independently manipulate the mode and spatial position of multiple OAM modes, which allows for complex multilight modulation functions to respond to logic inputs. We show that few-layer ODNNs successfully implement the logical operations of AND, OR, NOT, NAND, and NOR in simulations. The logic units of XNOR and XOR are obtained by cascading the basic logical gates of AND, OR, and NOT, which can further constitute logical half-adder gates. Our demonstrations may provide a new avenue for optical logical operations and are expected to promote the practical application of optical digital computing.
Teaching and Learning Modalities in Higher Education During the Pandemic: Responses to Coronavirus Disease 2019 From Spain

The effects of the pandemic have affected and continue to affect education methods every day. The education methods are not immune to the pandemic periods we are facing, so teachers must know how to adapt their methods in such a way that teaching, and its quality, is not negatively affected. This study provides an overview of different types of teaching methodology before, during, and after the coronavirus disease 2019 (COVID-19) pandemic. This study describes the different types of teaching (e.g., presence learning, blended learning, and distance education) used in two Spanish Universities (i.e., one private and one public) during the pandemic. A new teaching methodology is proposed. The purpose of this study report is to share what we learned about the response to COVID-19. Results provide a basis for reflection about the pros and cons of teaching and learning modalities in higher education. The current situation demands that we continue to rethink what is the best methodology for teaching so that the education of students is not affected in any way. This study is useful for learning about different teaching methods that exist and which ones may suit us best depending on the context, situation, and needs of our students.
High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks

The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have exhibited high efficiency in the financial transaction systems, their payment transactions are vulnerable to several ransomware attacks. For that reason, investigators have been working on developing ransomware payment identification techniques for bitcoin transactions’ networks to prevent such harmful cyberattacks. In this paper, we propose a high performance Bitcoin transaction predictive system that investigates the Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks. Specifically, our system makes use of two supervised machine learning methods to learn the distinguishing patterns in Bitcoin payment transactions, namely, shallow neural networks (SNN) and optimizable decision trees (ODT). To validate the effectiveness of our solution approach, we evaluate our machine learning based predictive models on a recent Bitcoin transactions dataset in terms of classification accuracy as a key performance indicator and other key evaluation metrics such as the confusion matrix, positive predictive value, true positive rate, and the corresponding prediction errors. As a result, our superlative experimental result was registered to the model-based decision trees scoring 99.9% and 99.4% classification detection (two-class classifier) and accuracy (multiclass classifier), respectively. Hence, the obtained model accuracy results are superior as they surpassed many state-of-the-art models developed to identify ransomware payments in bitcoin transactions.
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models.It is commonly argued that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language.In this study, we experiment with over 30 prompt templates manually written for natural language inference (NLI).We find that models can learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively "good" prompts.Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on hundreds of prompts (Sanh et al., 2021).That is, instruction-tuned models often produce good predictions with irrelevant and misleading prompts even at zero shots.In sum, notwithstanding prompt-based models' impressive improvement, we find evidence of serious limitations that question the degree to which such improvement is derived from models understanding task instructions in ways analogous to humans' use of task instructions.* Unabridged version available on arXiv.Code, interactive figures, and statistical test results available at https://github.com/awebson/prompt_semantics arbitrary dimensions of a one-hot vector.In contrast, suppose a human is given a prompt such as: Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that " Given that "no weapons of mass destruction found in Iraq yet.", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that " ", is it definitely correct that "weapons of mass destruction found in Iraq."? "? "? "? "? "? "? "? "? "? "? "? "? "? "? "? "? 1 Then it would be no surprise that they are able to perform the task more accurately and without needing many examples to figure out what the task is.
Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images

Most deep-learning based target detection methods have high computational complexity and memory consumption, and they are difficult to be deployed on edge devices with limited computing resources and memory.To tackle this problem, this paper proposes to learn a lightweight detector named Light-YOLOv4, and it is obtained from YOLOv4 through model compression.To this end, firstly, we perform sparsity training by applying L1 regularization to the channel scaling factors, so the less important channels and layers can be found.Secondly, channel pruning and layer pruning are enforced on the network to prune the less important parts, which could significantly reduce network's width and depth.Thirdly, the pruned model is retrained with knowledge distillation method to improve the detection accuracy.Fourthly, the model is quantized from FP32 to FP16, and it could further accelerate the model with almost no loss of detection accuracy.Finally, to evaluate Light-YOLOv4's performance on edge devices, Light-YOLOv4 is deployed on NVIDIA Jetson TX2.Experiments on SAR ship detection dataset (SSDD) demonstrate that the model size, parameter size and FLOPs of Light-YOLOv4 have been reduce by 98.63%, 98.66% and 91.30% compared with YOLOv4, and the detection speed has been increased to 4.2 times.While the detection accuracy of Light-YOLOv4 is only slightly reduced, for example, the mAP has only reduced by 0.013.Besides, experiments on Gaofen Airplane dataset also prove the feasibility of Light-YOLOv4.Moreover, compared with other state-of-the-art methods, such as SSD and FPN, Light-YOLOv4 is more suitable for edge devices.
Adiabatic quantum linear regression

A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to [Formula: see text] speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric. The quantum approach used the D-Wave 2000Q adiabatic quantum computer, whereas the classical approach used a desktop workstation with an 8-core Intel i9 processor. As such, the results obtained in this work must be interpreted within the context of the specific hardware and software implementations of these machines.
DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

Cyberbullying (CB) has become increasingly prevalent in social media platforms.With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying.This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network.The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for finetuning the Elman RNN's parameters and reducing training time.We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF).The experimental results show that DEA-RNN was found to be superior in all the scenarios.It outperformed the considered existing approaches in detecting CB on Twitter platform.DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.
Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network

Recent work has shown that deep convolutional neural network is capable of solving inverse problems in computational imaging, and recovering the stress field of the loaded object from the photoelastic fringe pattern can also be regarded as an inverse problem solving process. However, the formation of the fringe pattern is affected by the geometry of the specimen and experimental configuration. When the loaded object produces complex fringe distribution, the traditional stress analysis methods still face difficulty in unwrapping. In this study, a deep convolutional neural network based on the encoder-decoder structure is proposed, which can accurately decode stress distribution information from complex photoelastic fringe images generated under different experimental configurations. The proposed method is validated on a synthetic dataset, and the quality of stress distribution images generated by the network model is evaluated using mean squared error (MSE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and other evaluation indexes. The results show that the proposed stress recovery network can achieve an average performance of more than 0.99 on the SSIM.
Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms

This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction problems, based on deep learning approaches. Predictive models are useful ploys in solar energy industries to optimize the performance of photovoltaic power systems. Specifically, in this work we develop a new 4-phase hybrid CXGBRFR framework, which includes a deep learning Convolutional Neural Network, an Extreme Gradient Boosting with Random Forest Regression, and a Harris Hawks Optimization for initial feature selection. The proposed system has been tested in a problem of daily GSR prediction at six solar farms in Australia. Data from three global climate models (GCM) (CSIRO-BOM ACCESS1-0, MOHC Hadley-GEM2-CC and MRI MRI-CGCM3) have been considered as predictive (input) variables for the proposed approach. The variables from these GCMs contain enough information to obtain an accurate prediction of the GSR at each solar farm. The performance of the proposed approach is compared against different deep and shallows learning approaches: Deep Belief Network, Deep Neural Network, Artificial Neural Network, Extreme Learning Machine and Multivariate Auto-Regressive Spline models. We show that the proposed approach exhibits an excellent performance in GSR prediction, against all alternative approaches in all solar farms considered.
Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learning-based approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.
Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction

Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring solar power systems. However, its stochastic behaviour is a significant challenge in achieving satisfactory prediction results. This study aims to design an innovative hybrid prediction model that integrates a feature selection mechanism using a Slime-Mould algorithm, a Convolutional-Neural-Network (CNN), a Long–Short-Term-Memory Neural Network (LSTM) and a final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed model was applied to six solar farms in Queensland (Australia) at daily temporal horizons in six different time steps. The comprehensive benchmarking of the obtained results with those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) and three Machine-Learning (Artificial-Neural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted a higher performance of the proposed prediction model in all the six selected solar farms. From the results obtained, this work establishes that the designed SCLC algorithm could have a practical utility for applications in renewable and sustainable energy resource management.