Reviewerly/exHarmony huggingface dataset:
Tweets Classification on the Base of Sentiments for US Airline Companies

The use of data from social networks such as Twitter has been increased during the last few years to improve political campaigns, quality of products and services, sentiment analysis, etc. Tweets classification based on user sentiments is a collaborative and important task for many organizations. This paper proposes a voting classifier (VC) to help sentiment analysis for such organizations. The VC is based on logistic regression (LR) and stochastic gradient descent classifier (SGDC) and uses a soft voting mechanism to make the final prediction. Tweets were classified into positive, negative and neutral classes based on the sentiments they contain. In addition, a variety of machine learning classifiers were evaluated using accuracy, precision, recall and F1 score as the performance metrics. The impact of feature extraction techniques, including term frequency (TF), term frequency-inverse document frequency (TF-IDF), and word2vec, on classification accuracy was investigated as well. Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset. The results show that the proposed VC performs better than that of other classifiers. The VC is able to achieve an accuracy of 0.789, and 0.791 with TF and TF-IDF feature extraction, respectively. The results demonstrate that ensemble classifiers achieve higher accuracy than non-ensemble classifiers. Experiments further proved that the performance of machine learning classifiers is better when TF-IDF is used as the feature extraction method. Word2vec feature extraction performs worse than TF and TF-IDF feature extraction. The LSTM achieves a lower accuracy than machine learning classifiers.
Progressive Reconstruction of Visual Structure for Image Inpainting

Inpainting methods aim to restore missing parts of corrupted images and play a critical role in many computer vision applications, such as object removal and image restoration. Although existing methods perform well on images with small holes, restoring large holes remains elusive. To address this issue, this paper proposes a Progressive Reconstruction of Visual Structure (PRVS) network that progressively reconstructs the structures and the associated visual feature. Specifically, we design a novel Visual Structure Reconstruction (VSR) layer to entangle reconstructions of the visual structure and visual feature, which benefits each other by sharing parameters. We repeatedly stack four VSR layers in both encoding and decoding stages of a U-Net like architecture to form the generator of a generative adversarial network (GAN) for restoring images with either small or large holes. We prove the generalization error upper bound of the PRVS network is O(1\sqrt(N)), which theoretically guarantees its performance. Extensive empirical evaluations and comparisons on Places2, Paris Street View and CelebA datasets validate the strengths of the proposed approach and demonstrate that the model outperforms current state-of-the-art methods. The source code package is available at https://github.com/jingyuanli001/PRVS-Image-Inpainting.
Learning Spatial Awareness to Improve Crowd Counting

The aim of crowd counting is to estimate the number of people in images by leveraging the annotation of center positions for pedestrians' heads. Promising progresses have been made with the prevalence of deep Convolutional Neural Networks. Existing methods widely employ the Euclidean distance (i.e., L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> loss) to optimize the model, which, however, has two main drawbacks: (1) the loss has difficulty in learning the spatial awareness (i.e., the position of head) since it struggles to retain the high-frequency variation in the density map, and (2) the loss is highly sensitive to various noises in crowd counting, such as the zeromean noise, head size changes, and occlusions. Although the Maximum Excess over SubArrays (MESA) loss has been previously proposed by [16] to address the above issues by finding the rectangular subregion whose predicted density map has the maximum difference from the ground truth, it cannot be solved by gradient descent, thus can hardly be integrated into the deep learning framework. In this paper, we present a novel architecture called SPatial Awareness Network (SPANet) to incorporate spatial context for crowd counting. The Maximum Excess over Pixels (MEP) loss is proposed to achieve this by finding the pixel-level subregion with high discrepancy to the ground truth. To this end, we devise a weakly supervised learning scheme to generate such region with a multi-branch architecture. The proposed framework can be integrated into existing deep crowd counting methods and is end-to-end trainable. Extensive experiments on four challenging benchmarks show that our method can significantly improve the performance of baselines. More remarkably, our approach outperforms the state-of-the-art methods on all benchmark datasets.
Requirements Engineering for Machine Learning: Perspectives from Data Scientists

Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.
Iterative Qubit Coupled Cluster Approach with Efficient Screening of Generators

An iterative version of the qubit coupled cluster (QCC) method [I. G. Ryabinkin et al., J. Chem. Theory Comput.2019,14, 6317] is proposed. The new method seeks to find ground electronic energies of molecules on noisy intermediate-scale quantum devices. Each iteration involves a canonical transformation of the Hamiltonian and employs constant-size quantum circuits at the expense of increasing the Hamiltonian size. We numerically studied the convergence of the method on ground-state calculations for LiH, H2O, and N2 molecules and found that the exact ground-state energies can be systematically approached only if the generators of the QCC ansatz are sampled from a specific set of operators. We report an algorithm for constructing this set that scales linearly with the size of the Hamiltonian.
E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework

Although statistical learning methods have achieved success in e-commerce platform product review sentiment classification, two problems have limited its practical application: 1) The computational efficiency to process large-scale reviews; 2) the ability to continuously learn from increasing reviews and multiple domains. This paper presents a continuous naïve Bayes learning framework for large-scale and multi-domain e-commerce platform product review sentiment classification. While keeping the high computational efficiency of the traditional naïve Bayes model, we extend the parameter estimation mechanism in naïve Bayes to a continuous learning style. We furthermore propose ways to fine-tune the learned distribution based on three kinds of assumptions to better adapt to different domains. Experimental results on the Amazon product and movie review sentiment datasets show that our model can use the knowledge learned from past domains to guide learning in new domains, and has a better capacity of dealing with reviews that are continuously updated and come from different domains.
LVBS: Lightweight Vehicular Blockchain for Secure Data Sharing in Disaster Rescue

In disaster areas, a large amount of data (e.g., rescue commands, road damage, and rescue experience) should be delivered among ground rescuing vehicles for safe driving and efficient rescue. When communication infrastructures are destroyed by disasters, unmanned aerial vehicles (UAVs) can be employed to perform immediate rescue missions in destroyed areas and assist data sharing for ground Internet of vehicles (IoV). However, in such UAV-assisted IoV under disaster situation, there exist potential security threats on data sharing among vehicles and UAVs because of the untrusted network environment, unreliable misbehavior tracing, and low-quality shared data. To address these issues, in this article, we develop a <u>l</u>ightweight <u>v</u>ehicular <u>b</u>lockchain-enabled <u>s</u>ecure (LVBS) data sharing framework in UAV-aided IoV for disaster rescue. First, we propose a novel UAV and blockchain-assisted collaborative aerial-ground network architecture in disaster areas. Second, we develop a credit-based consensus algorithm in the lightweight vehicular blockchain to securely and immutably trace misbehaviors and record data transactions for UAVs and vehicles with improved efficiency and security in reaching consensus. Third, since UAVs and vehicles have little explicit knowledge of the whole network, we develop reinforcement learning-based algorithms to optimally schedule the pricing and quality of data sharing strategies for both data contributor and data consumer via trial and error. Finally, extensive simulations are conducted, which demonstrate that LVBS can effectively improve the security of consensus phase and promote high-quality data sharing.
Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.
Hybrid CNN-SVM Classifier for Handwritten Digit Recognition

The aim of this paper is to develop a hybrid model of a powerful Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for recognition of handwritten digit from MNIST dataset. The proposed hybrid model combines the key properties of both the classifiers. In the proposed hybrid model, CNN works as an automatic feature extractor and SVM works as a binary classifier. The MNIST dataset of handwritten digits is used for training and testing the algorithm adopted in the proposed model. The MNIST dataset consists of handwritten digits images which are diverse and highly distorted. The receptive field of CNN helps in automatically extracting the most distinguishable features from these handwritten digits. The experimental results demonstrate the effectiveness of the proposed framework by achieving a recognition accuracy of 99.28% over MNIST handwritten digits dataset.
HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

We present Hybrid Voxel Network (HVNet), a novel one-stage unified network for point cloud based 3D object detection for autonomous driving. Recent studies show that 2D voxelization with per voxel PointNet style feature extractor leads to accurate and efficient detector for large 3D scenes. Since the size of the feature map determines the computation and memory cost, the size of the voxel becomes a parameter that is hard to balance. A smaller voxel size gives a better performance, especially for small objects, but a longer inference time. A larger voxel can cover the same area with a smaller feature map, but fails to capture intricate features and accurate location for smaller objects. We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder (VFE) of different scales at point-wise level and project into multiple pseudo-image feature maps. We further propose an attentive voxel feature encoding that outperforms plain VFE and a feature fusion pyramid network to aggregate multi-scale information at feature map level. Experiments on the KITTI benchmark show that a single HVNet achieves the best mAP among all existing methods with a real time inference speed of 31Hz.
Taming Pretrained Transformers for Extreme Multi-label Text Classification

We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input text could be a product description on Amazon.com and the labels could be product categories. XMC is an important yet challenging problem in the NLP community. Recently, deep pretrained transformer models have achieved state-of-the-art performance on many NLP tasks including sentence classification, albeit with small label sets. However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue. In this paper, we propose X-Transformer, the first scalable approach to fine-tuning deep transformer models for the XMC problem. The proposed method achieves new state-of-the-art results on four XMC benchmark datasets. In particular, on a Wiki dataset with around 0.5 million labels, the [email protected] of X-Transformer is 77.28%, a substantial improvement over state-of-the-art XMC approaches Parabel (linear) and AttentionXML (neural), which achieve 68.70% and 76.95% [email protected], respectively. We further apply X-Transformer to a product2query dataset from Amazon and gained 10.7% relative improvement on [email protected] over Parabel.
geemap: A Python package for interactive mapping with Google Earth Engine

geemap is a Python package for interactive mapping with Google
Efficient Intent Detection with Dual Sentence Encoders

Building conversational systems in new domains and with added functionality requires resource-efficient models that work under lowdata regimes (i.e., in few-shot setups).Motivated by these requirements, we introduce intent detection methods backed by pretrained dual sentence encoders such as USE and Con-veRT.We demonstrate the usefulness and wide applicability of the proposed intent detectors, showing that: 1) they outperform intent detectors based on fine-tuning the full BERT-Large model or using BERT as a fixed black-box encoder on three diverse intent detection data sets; 2) the gains are especially pronounced in few-shot setups (i.e., with only 10 or 30 annotated examples per intent); 3) our intent detectors can be trained in a matter of minutes on a single CPU; and 4) they are stable across different hyperparameter settings.In hope of facilitating and democratizing research focused on intention detection, we release our code, as well as a new challenging single-domain intent detection dataset comprising 13,083 annotated examples over 77 intents.
Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners

This article describes motion planning networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems.MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It takes environment information such as raw point cloud from depth sensors, as well as a robot's initial and desired goal configurations and recursively calls itself to bidirectionally generate connectable paths. In addition to finding directly connectable and near-optimal paths in a single pass, we show that worst-case theoretical guarantees can be proven if we merge this neural network strategy with classical sample-based planners in a hybrid approach while still retaining significant computational and optimality improvements. To train the MPNet models, we present an active continual learning approach that enables MPNet to learn from streaming data and actively ask for expert demonstrations when needed, drastically reducing data for training. We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from two-dimensional to seven-dimensional robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently.
An incentive-aware blockchain-based solution for internet of fake media things

The concept of Fake Media or Internet of Fake Media Things (IoFMT) has emerged in different domains of digital society such as politics, news, and social media. Due to the integrity of the media being compromised quite frequently, revolutionary changes must be taken to avoid further and more widespread IoFMT. With today’s advancements in Artificial Intelligence (AI) and Deep Learning (DL), such compromises may be profoundly limited. Providing proof of authenticity to outline the authorship and integrity for digital content has been a pressing need. Blockchain, a promising new decentralized secure platform, has been advocated to help combat the authenticity aspect of fake media in a context where resistance to the modification of data is important. Although some methods around blockchain have been proposed to take on authentication problems, most current studies are built on unrealistic assumptions with the after-the-incident type of mechanisms. In this article, we propose a preventative approach using a novel blockchain-based solution suited for IoFMT incorporated with a gamification component. More specifically, the proposed approach uses concepts of a customized Proof-of-Authority consensus algorithm, along with a weighted-ranking algorithm, serving as an incentive mechanism in the gamification component to determine the integrity of fake news. Although our approach focuses on fake news, the framework could be very well extended for other types of digital content as well. A proof of concept implementation is developed to outline the advantage of the proposed solution.
Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics

Video coding, which targets to compress and reconstruct the whole frame, and feature compression, which only preserves and transmits the most critical information, stand at two ends of the scale. That is, one is with compactness and efficiency to serve for machine vision, and the other is with full fidelity, bowing to human perception. The recent endeavors in imminent trends of video compression, e.g. deep learning based coding tools and end-to-end image/video coding, and MPEG-7 compact feature descriptor standards, i.e. Compact Descriptors for Visual Search and Compact Descriptors for Video Analysis, promote the sustainable and fast development in their own directions, respectively. In this paper, thanks to booming AI technology, e.g. prediction and generation models, we carry out exploration in the new area, Video Coding for Machines (VCM), arising from the emerging MPEG standardization efforts1. Towards collaborative compression and intelligent analytics, VCM attempts to bridge the gap between feature coding for machine vision and video coding for human vision. Aligning with the rising Analyze then Compress instance Digital Retina, the definition, formulation, and paradigm of VCM are given first. Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications. Finally, we come up with potential VCM solutions, and the preliminary results have demonstrated the performance and efficiency gains. Further direction is discussed as well.
Crow Search Algorithm: Theory, Recent Advances, and Applications

In this article, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.
Bias and Debias in Recommender System: A Survey and Future Directions

While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, and so on. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology “bias” is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at https://github.com/jiawei-chen/RecDebiasing .