Reviewerly/exHarmony huggingface dataset:
Leveraging LightGBM for Categorical Big Data

LightGBM is a popular Gradient Boosted Decision Tree implementation for classification and regression tasks. Our contribution is to answer a research question regarding LightGBM. We would like to know which alternative yields better performance for classifying highly imbalanced Big Data with high-cardinality categorical features: relying entirely on LightGBM’s Exclusive Feature Bundling as a way to encode categorical features, or using LightGBM’s built-in encoding for categorical features? Our study of LightGBM revealed two alternatives for a Big Data classification task to do anomaly detection. We may one-hot encode the data into a sparse representation, and then rely entirely on LightGBM’s Exclusive Feature Bundling to complete encoding of the categorical features. Exclusive Feature Bundling is LightGBM’s optimization technique that exploits sparsity in features for reducing the dimensionality of a dataset. On the other hand, also because our data has categorical features, it is a candidate for LightGBM’s built-in encoding technique for categorical features. Since we did not find a clear indication in a survey of related work for which direction to take – using Exclusive Feature Bundling or using LightGBM’s built-in encoding for categorical features – we experiment with these options to determine the best one for highly imbalanced Big Data. Furthermore, we show LightGBM’s built-in encoding is best in a statistically significant sense. Our work is important because it fills a gap in LightGBM-related literature on how to best handle categorical features in imbalanced Big Data with high-cardinality categorical features.
A photovoltaic power output dataset: Multi-source photovoltaic power output dataset with Python toolkit

The power output of photovoltaic (PV) systems is chiefly affected by climate and weather conditions. In that, PV farm requires accurate weather data, particularly, solar irradiance, in order to predict its power output as a means to improve solar energy utilization. Nevertheless, publicly available datasets which consist both power and weather data are exceptionally few. This may be a combined effect of data propriety and cumbersome collection procedure. And the rarity of such data greatly hinders the progress of solar PV research. Indeed, most solar energy meteorology applications, such as solar forecasting or PV performance evaluation, can benefit from multi-source high-quality datasets. In view of that, we release a PV power output dataset (PVOD), which contains metadata, numerical weather prediction data, and local measurements data from 10 PV systems located in China. In PVOD, a Python toolkit with basic functions for data access and preprocessing is provided. Additionally, a case study on PV power output estimation is depicted to demonstrate the potential usage of the dataset.
Short Communication: A Quantum Algorithm for Linear PDEs Arising in Finance

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 9 February 2021Accepted: 30 August 2021Published online: 12 October 2021Keywordsquantum algorithms, option pricing, PDE, Schrödinger equationAMS Subject Headings35Q40, 91G20, 91G80Publication DataISSN (online): 1945-497XPublisher: Society for Industrial and Applied MathematicsCODEN: sjfmbj
Hello Alexa! Exploring effects of motivational factors and social presence on satisfaction with <scp>artificial intelligence</scp> ‐enabled gadgets

From requesting Alexa to set a reminder to asking Google Assistant to make a call, artificial intelligence (AI)-enabled voice assistants are quickly melding into our lives. This study aims to understand why users interact with a voice assistant system. Results from an online survey identified four types of motivations underlying the use of voice assistants: entertainment, companionship, dynamic control, and functional utility. Results showed that functional utility and dynamic control were positively related to users' satisfaction, while companionship and entertainment were not. The effect of social presence on users' satisfaction was also explored. The moderation analyses showed that social presence not only had a main effect but also played a significant role in increasing satisfaction among the users who perceived low levels of functional utility and dynamic control. This study advances a growing body of human–AI interaction literature by demonstrating the underlying mechanism behind voice assistants' use. Practical and theoretical implications are also discussed.
Few-Shot Object Detection of Remote Sensing Images via Two-Stage Fine-Tuning

In recent years, there have been many developments in object detection on remote sensing images. However, those deep convolutional neural network (CNN) models always demand a great number of labeled samples, which leads to a significant decrease in performance on rare categories. Recently, the fine-tuning-based method of few-shot learning has drawn attention in the field of computer vision. In this letter, we proposed a multiscale few-shot object detection approach for remote sensing images, which is built upon faster region-CNN (R-CNN) architecture. First, we build the whole backbone of the detector with the involution operator, which enhances the classification ability of the features extractor. Then, our proposed detector learns multiscale features with the assistance of a path-aggregation module, which shortens the information transmission path by a bottom-up flow and uses semantic information of low-level features for localization. Eventually, we increase the shape bias in the detector in the training phase, which further improves the robustness and performance of the model. Experiments on two optical remote sensing datasets demonstrate that the proposed method outperforms the current few-shot detection models in the remote sensing field.
A Novel Fast Feedforward Neural Networks Training Algorithm

Abstract In this paper 1 a new neural networks training algorithm is presented. The algorithm originates from the Recursive Least Squares (RLS) method commonly used in adaptive filtering. It uses the QR decomposition in conjunction with the Givens rotations for solving a normal equation - resulting from minimization of the loss function. An important parameter in neural networks is training time. Many commonly used algorithms require a big number of iterations in order to achieve a satisfactory outcome while other algorithms are effective only for small neural networks. The proposed solution is characterized by a very short convergence time compared to the well-known backpropagation method and its variants. The paper contains a complete mathematical derivation of the proposed algorithm. There are presented extensive simulation results using various benchmarks including function approximation, classification, encoder, and parity problems. Obtained results show the advantages of the featured algorithm which outperforms commonly used recent state-of-the-art neural networks training algorithms, including the Adam optimizer and the Nesterov’s accelerated gradient.
Hybrid attention-based transformer block model for distant supervision relation extraction

With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As one basic task for natural language processing (NLP), relation extraction (RE) aims to extract semantic relations between entity pairs based on the given text. To avoid manual labeling of datasets, distant supervision relation extraction (DSRE) has been widely used, aiming to utilize knowledge base to automatically annotate datasets. Unfortunately, this method heavily suffers from wrong labelling due to its underlying strong assumptions. To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning for DSRE. More specifically, the Transformer block is, for the first time, used as a sentence encoder, which mainly utilizes multi-head self-attention to capture syntactic information at the word level. Then, a novel sentence-level attention mechanism is proposed to calculate the bag representation, aiming to exploit all useful information in each sentence. Experimental results on the public dataset New York Times (NYT) demonstrate that the proposed approach can outperform the state-of-the-art algorithms on the adopted dataset, which verifies the effectiveness of our model on the DSRE task.
A robust visual image encryption scheme based on controlled quantum walks

Considering that traditional cryptographic systems are under threat of quantum computing, a visual image encryption algorithm based on controlled quantum is proposed. First, the plaintext image is encrypted by compressed sensing, and the compressed image is scrambled using Arnold to obtain the secret image. Then the segmented secret image is embedded into the carrier image by singular value decomposition (SVD) in accordance with different intensities to generate images with visual significance. In order to make the algorithm more secure, the controlled quantum walk is used to generate pseudorandom sequence to construct the measurement matrix of compressed sensing and the position of the secret image embedded into the carrier image. In addition, the use of SVD embedding can make the plaintext image flexible to choose the carrier image.
A novel matrix block algorithm based on cubature transformation fusing variational Bayesian scheme for position estimation applied to MEMS navigation system

It is great significant for autonomous underwater vehicle (AUV) to obtain its position in real time. Great developments in low-cost inertial navigation system (INS) have been made due to outstanding merits in micro-electro-mechanical system (MEMS) technologies. The navigation errors of the MEMS grade inertial measurement unit (IMU) increase greatly over time because of the complex and changeable marine environment. The measurement noise plays an important role in state estimation with high accuracy. However, the accuracy of measurement noise will be degraded due to larger MEMS sensors’ errors. To solve the problem above, a novel algorithm which fuses variational Bayesians into nonlinear filtering is proposed. Firstly, the position information is augmented to the measurement vector. The measurement functions are divided into linear and nonlinear. Secondly, the variational Bayesian (VB) method is used to estimate the probability density function of the state vector, predicted error covariance matrix and measurement noise matrix. In the case of calculating the second order moment estimation, the cubature transformation is used to determine nonlinear integral equations, and the linear integral equations are derived by the Kalman filter. Finally, the accurate state vector and error covariance matrix are obtained. The real underwater experiments are performed and experiment results show that the proposed algorithm has better performance in aspect of positioning accuracy of AUV and robustness than the traditional algorithms.
From differential equation solvers to accelerated first-order methods for convex optimization

Convergence analysis of accelerated first-order methods for convex optimization problems are presented from the point of view of ordinary differential equation solvers. A new dynamical system, called Nesterov accelerated gradient flow, has been derived from the connection between acceleration mechanism and $A$-stability of ODE solvers, and the exponential decay of a tailored Lyapunov function along with the solution trajectory is proved. Numerical discretizations are then considered and convergence rates are established via a unified discrete Lyapunov function. The proposed differential equation solver approach can not only cover existing accelerated methods, such as FISTA, G\"{u}ler's proximal algorithm and Nesterov's accelerated gradient method, but also produce new algorithms for composite convex optimization that possess accelerated convergence rates.
Reading the road: challenges and opportunities on the path to responsible innovation in quantum computing

Novel technologies such as quantum computing present new opportunities to support societal needs, but societal engagement is vital to secure public trust. Quantum computing technologies are at a pivotal point in their journey from foundational research to deployment, creating a moment for society to investigate, reflect, and consult on their implications. Responsible Innovation (RI) is one method for considering impacts, engaging with societal needs, reflecting on any concerns, and influencing the trajectory of the innovation in response. This paper draws on the empirical work of the RI team embedded in the Networked Quantum Information Technologies Hub. The team investigated researchers’ perceptions of RI and their understanding of societal impacts of quantum technologies, and sought to gauge the challenges of embedding RI across a multi-disciplinary, large-scale enterprise such as the UK quantum programme. The work demonstrated some of the difficulties involved in embedding RI approaches, and in creating a dialogue between innovators and societies. Finally, the authors offer recommendations to policymakers, researchers, and industrial organisations, for better practice in responsible quantum computing, and to ensure that societal considerations are discussed alongside commercial motivations. Applying RI to quantum computing at this pivotal point has implications for RI in other emerging technologies.
When Blockchain Meets Edge Intelligence: Trusted and Security Solutions for Consumers

Nowadays, mission-critical applications need delay-free responses, which can be achieved by computation at the edge devices known as edge computing. But edge computing needs cloud computing services to perform massive intelligent tasks like AI-based prediction and analysis, which still possesses high latency in making intelligent decisions. This can be resolved by bringing intelligence at the edge device or edge server, which introduces complex problems either at the proximity of edge devices, that is, consumer electronic devices (CED), or at the CED by bringing intelligence at the edge. The process of bringing intelligence to the edge is called edge intelligence (EI). The computation at edge servers is susceptible to various security and privacy issues and possesses high latency due to data propagation from the device to the dedicated edge server. To overcome the aforementioned issues, this study presents a blockchain-based edge intelligence system to ensure the CEDs' data security, privacy, latency, and efficiency. The proposed system uses public and private blockchains to fulfil the gaps mentioned above of the traditional systems. The public blockchain ensures CEDs' data communication privacy security, whereas private block-chain ensures secure communication among the EI servers. Then, we present the use case scenario of blockchain and edge intelligence (EI) in the COVID-19 pandemic and evaluate its performance over computation cost by comparing the intelligence at CED with the intelligence at the edge server and centralized cloud server (CCS).
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network

Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applications. Despite such progress, trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, this paper proposes a novel adversarial scoring network (ASNet) to gradually bridge the gap across domains from coarse to fine granularity. In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning. The distributions between two domains can thus be aligned roughly. At the fine-grained stage, we explore the transferability of source characteristics by scoring how similar the source samples are to target ones from multiple levels based on generative probability derived from coarse stage. Guided by these hierarchical scores, the transferable source features are properly selected to enhance the knowledge transfer during the adaptation process. With the coarse-to-fine design, the generalization bottleneck induced from the domain discrepancy can be effectively alleviated. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance compared with major unsupervised methods.
The Pandemic Shift to Remote Learning under Resource Constraints

The COVID-19 pandemic has forced the transition of workflows across sectors to digital platforms. In education settings, stakeholders previously reluctant to integrate computing technology in the classroom now find themselves with little choice but to embrace it. This move to the digital brings additional challenges in underserved contexts with limited, intermittent, and shared access to mobile or computing devices and the internet. In this rapidly evolving digital landscape, we investigate how educational institutions (schools and non-profit organizations) working with underserved populations in India are managing the transition to online or remote learning. We conducted twenty remote interviews with students, teachers, and administrators from underserved contexts across India. We found that online learning efforts in this setting relied on a resilient human infrastructure comprised of students, teachers, parents, administrators, and non-profit organizations to help navigate and overcome the limitations of available technical infrastructure. Our research aims to articulate lessons for educational technology design in the post-COVID period, outlining areas for improvement in the design of online learning platforms in resource-constrained settings, and identifying elements of online learning that could be retained to strengthen the education system overall.
APPLI: Adaptive Planner Parameter Learning From Interventions

While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptimal behavior in certain scenarios. The current practice in such scenarios is to manually re-tune the system’s parameters, e.g. max speed, sampling rate, inflation radius, to optimize performance. This practice requires expert knowledge and may jeopardize performance in the originally good scenarios. Meanwhile, it is relatively easy for a human to identify those failure or suboptimal cases and provide a teleoperated intervention to correct the failure or suboptimal behavior. In this work, we seek to learn from those human interventions to improve navigation performance. In particular, we propose Adaptive Planner Parameter Learning from Interventions (APPLI), in which multiple sets of navigation parameters are learned during training and applied based on a confidence measure to the underlying navigation system during deployment. In our physical experiments, the robot achieves better performance compared to the planner with static default parameters, and even dynamic parameters learned from a full human demonstration. We also show APPLI’s generalizability in another unseen physical test course, and a suite of 300 simulated navigation environments.
Universal Paralinguistic Speech Representations Using self-Supervised Conformers

Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishing real from synthetic speech. In this work, we introduce a new state-of-the-art paralinguistic representation derived from large-scale, fully self-supervised training of a 600M+ parameter Conformer-based architecture. We benchmark on a diverse set of speech tasks and demonstrate that simple linear classifiers trained on top of our time-averaged representation outperform nearly all previous results, in some cases by large margins. Our analyses of context-window size demonstrate that, surprisingly, 2 second context-windows achieve 96\% the performance of the Conformers that use the full long-term context on 7 out of 9 tasks. Furthermore, while the best per-task representations are extracted internally in the network, stable performance across several layers allows a single universal representation to reach near optimal performance on all tasks.
Meta Self-Paced Learning for Cross-Modal Matching

Cross-modal matching has attracted growing attention due to the rapid emergence of the multimedia data on the web and social applications. Recently, many re-weighting methods have been proposed for accelerating model training by designing a mapping function from similarity scores to weights. However, these re-weighting methods are difficult to be universally applied in practice since manually pre-set weighting functions inevitably involve hyper-parameters. In this paper, we propose a Meta Self-Paced Network (Meta-SPN) that automatically learns a weighting scheme from data for cross-modal matching. Specifically, a meta self-paced network composed of a fully connected neural network is designed to fit the weight function, which takes the similarity score of the sample pairs as input and outputs the corresponding weight value. Our meta self-paced network considers not only the self-similarity scores, but also their potential interactions (e.g., relative-similarity) when learning the weights. Motivated by the success of meta-learning, we use the validation set to update the meta self-paced network during the training of the matching network. Experiments on two image-text matching benchmarks and two video-text matching benchmarks demonstrate the generalization and effectiveness of our method.
Data-driven Holistic Framework for Automated Laparoscope Optimal View Control with Learning-based Depth Perception

Laparoscopic Field of View (FOV) control is one of the most fundamental and important components in Minimally Invasive Surgery (MIS), nevertheless the traditional manual holding paradigm may easily bring fatigue to surgical assistants, and misunderstanding between surgeons also hinders assistants to provide a high-quality FOV. Targeting this problem, we here present a data-driven framework to realize an automated laparoscopic optimal FOV control. To achieve this goal, we offline learn a motion strategy of laparoscope relative to the surgeon&#x2019;s hand-held surgical tool from our in-house surgical videos, developing our control domain knowledge and an optimal view generator. To adjust the laparoscope online, we first adopt a learning-based method to segment the two-dimensional (2D) position of the surgical tool, and further leverage this outcome to obtain its scale-aware depth from dense depth estimation results calculated by our novel unsupervised RoboDepth model only with the monocular camera feedback, hence in return fusing the above real-time 3D position into our control loop. To eliminate the misorientation of FOV caused by Remote Center of Motion (RCM) constraints when moving the laparoscope, we propose a novel rotation constraint using an affine map to minimize the visual warping problem, and a null-space controller is also embedded into the framework to optimize all types of errors in a unified and decoupled manner. Experiments are conducted using Universal Robot (UR) and Karl Storz Laparoscope/Instruments, which prove the feasibility of our domain knowledge and learning enabled framework for automated camera control.