Reviewerly/exHarmony huggingface dataset:
NeX: Real-time View Synthesis with Neural Basis Expansion

We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects—in real time. Unlike traditional MPI that uses a set of simple RGBα planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated on benchmark forwardfacing datasets as well as our newly-introduced dataset designed to test the limit of view-dependent modeling with significantly more challenging effects such as the rainbow reflections on a CD. Our method achieves the best overall scores across all major metrics on these datasets with more than 1000× faster rendering time than the state of the art. For real-time demos, visit https://nex-mpi.github.io/
A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach

Recently, online shopping has turned into a mainstream means for users to purchase as well as consume with the upsurge development of Internet technology. User satisfaction can be improved effectively by doing Sentiment Analysis (SA) of a large quantity of user reviews on e-commerce platforms. It is still challenging to envisage the accurate sentiment polarities of the user reviews because of the changes in sequence length, textual order, along with complicated logic. This paper proposes a new optimized Machine Learning (ML) algorithm called the Local Search Improvised Bat Algorithm based Elman Neural Network (LSIBA-ENN) for the SA of online product reviews. The proposed work of SA encompasses ‘4’ major steps: i) Data Collection (DC), ii) preprocessing, iii) Features Extraction (FE) or Term Weighting (TW), Feature Selection (FS), and polarity or Sentiment Classifications (SC). Initially, the Web Scrapping Tool (WST) is utilized to extract the customer reviews of the products for which the data is gathered as of the E-commerce websites. Next, preprocessing is carried out on the web scrap extracted data. Those preprocessed data go through TW and FS for additional processing by means of Log Term Frequency-based Modified Inverse Class Frequency (LTF-MICF) and Hybrid Mutation based Earth Warm Algorithm (HMEWA). Lastly, the HM-EWA data is rendered to the LSIBA-ENN, which classifies the customer reviews’ sentiment as positive, negative, and neutral. For the performance analysis of the proposed and prevailing classifiers, ‘2’ yardstick datasets are taken. The outcomes exhibit that the LSIBA-ENN attains the best performance in SC when weighted against the existing top-notch algorithms. The observations of the reviewer are exact. The prevailing ENN proffers recall of 87.79 when utilizing the proposed LTF-MICF scheme, whereas ENN only achieve 83.55, 84.03, 85.48, and 86.04 of recall whilst utilizing W2V, TF, TF-IDF, and TF-DFS schemes respectively.
3D Object Detection for Autonomous Driving: A Survey

Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quantitative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work.
Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks

Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user's recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within individual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does not consider the cross-session information due to the anonymity of the session data, where the linkage between different sessions is prevented. In this paper, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence structure, based on which a novel Full Graph Neural Network (FGNN) is proposed to learn complicated item dependency. To exploit and incorporate cross-session information in the individual session's representation learning, we further construct a Broadly Connected Session (BCS) graph to link different sessions and a novel Mask-Readout function to improve session embedding based on the BCS graph. Extensive experiments have been conducted on two e-commerce benchmark datasets, i.e., Yoochoose and Diginetica, and the experimental results demonstrate the superiority of our proposal through comparisons with state-of-the-art session-based recommender models.
Persistent Anti-Muslim Bias in Large Language Models

It has been observed that large-scale language models capture undesirable societal biases, e.g. relating to race and gender; yet religious bias has been relatively unexplored. We demonstrate that GPT-3, a state-of-the-art contextual language model, captures persistent Muslim-violence bias. We probe GPT-3 in various ways, including prompt completion, analogical reasoning, and story generation, to understand this anti-Muslim bias, demonstrating that it appears consistently and creatively in different uses of the model and that it is severe even compared to biases about other religious groups. For instance, Muslim is analogized to terrorist in 23% of test cases, while Jewish is mapped to its most common stereotype, money, in 5% of test cases. We quantify the positive distraction needed to overcome this bias with adversarial text prompts, and find that use of the most positive 6 adjectives reduces violent completions for Muslims from 66% to 20%, but which is still higher than for other religious groups.
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language modeling problem. However, for relation extraction, determining an appropriate prompt template requires domain expertise, and it is cumbersome and time-consuming to obtain a suitable label word. Furthermore, there exists abundant semantic and prior knowledge among the relation labels that cannot be ignored. To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt). Specifically, we inject latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words. Then, we synergistically optimize their representation with structured constraints. Extensive experimental results on five datasets with standard and low-resource settings demonstrate the effectiveness of our approach. Our code and datasets are available in GitHub1 for reproducibility.
Makie.jl: Flexible high-performance data visualization for Julia

Makie.jl is a cross-platform plotting ecosystem for the Julia programming language (Bezanson et al., 2012), which enables researchers to create high-performance, GPU-powered, interactive visualizations, as well as publication-quality vector graphics with one unified interface.The infrastructure based on Observables.jlallows users to express how a visualization depends on multiple parameters and data sources, which can then be updated live, either programmatically, or through sliders, buttons and other GUI elements.A sophisticated layout system makes it easy to assemble complex figures.It is designed to avoid common difficulties when aligning nested subplots of different sizes, or placing colorbars or legends freely without spacing issues.Makie.jlleverages the Julia type system to automatically convert many kinds of input arguments which results in a very flexible API that reduces the need to manually prepare data.Finally, users can extend every step of this pipeline for their custom types through Julia's powerful multiple dispatch mechanism, making Makie a highly productive and generic visualization system.
Transfer learning for image classification using VGG19: Caltech-101 image data set

Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques. Presently, deep learning-based techniques have given stupendous results. The performance of a classification system depends on the quality of features extracted from an image. The better is the quality of extracted features, the more the accuracy will be. Although, numerous deep learning-based methods have shown enormous performance in image classification, still due to various challenges deep learning methods are not able to extract all the important information from the image. This results in a reduction in overall classification accuracy. The goal of the present research is to improve the image classification performance by combining the deep features extracted using popular deep convolutional neural network, VGG19, and various handcrafted feature extraction methods, i.e., SIFT, SURF, ORB, and Shi-Tomasi corner detector algorithm. Further, the extracted features from these methods are classified using various machine learning classification methods, i.e., Gaussian Naïve Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBClassifier) classifier. The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether shallow or deep is not enough to achieve satisfactory results. So, a combined approach using deep learning features and traditional handcrafted features is better for image classification.
Absence of Barren Plateaus in Quantum Convolutional Neural Networks

Quantum neural networks (QNNs) have generated excitement around the possibility of efficiently analyzing quantum data. But this excitement has been tempered by the existence of exponentially vanishing gradients, known as barren plateau landscapes, for many QNN architectures. Recently, quantum convolutional neural networks (QCNNs) have been proposed, involving a sequence of convolutional and pooling layers that reduce the number of qubits while preserving information about relevant data features. In this work, we rigorously analyze the gradient scaling for the parameters in the QCNN architecture. We find that the variance of the gradient vanishes no faster than polynomially, implying that QCNNs do not exhibit barren plateaus. This result provides an analytical guarantee for the trainability of randomly initialized QCNNs, which highlights QCNNs as being trainable under random initialization unlike many other QNN architectures. To derive our results, we introduce a novel graph-based method to analyze expectation values over Haar-distributed unitaries, which will likely be useful in other contexts. Finally, we perform numerical simulations to verify our analytical results.9 MoreReceived 12 March 2021Revised 13 July 2021Accepted 2 August 2021DOI:https://doi.org/10.1103/PhysRevX.11.041011Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum algorithmsQuantum computationQuantum Information
Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification

Sentiment analysis has been a well-studied research direction in computational linguistics. Deep neural network models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), yield promising results on text classification tasks. RNN-based architectures, such as, long short-term memory (LSTM) and gated recurrent unit (GRU) can process sequences of any length. However, using them in the feature extraction layer of a deep neural network architecture increases the dimensionality of the feature space. In addition, such models value different features equally. To solve these issues, we propose a bidirectional convolutional recurrent neural network architecture, which utilizes two separate bidirectional LSTM and GRU layers, to derive both past and future contexts by connecting two hidden layers of opposite directions to the same context. The group-wise enhancement mechanism has been employed on the features extracted by bidirectional layers, which divides features into multiple classes, enhancing important features in each group while weakening the less important ones. The presented scheme employs convolution and pooling layers to extract high level features and to reduce the dimensionality of the feature space. The experimental results indicate that the presented bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism can outperform the state-of-the-art results for sentiment analysis.
Deep learning, reinforcement learning, and world models

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.
Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques from Evolutionary Computation (EC) resulting in hybrid methods classified according to their goal, new focus, and their component methodologies. We denote this class of hybrid algorithmic techniques as the evolutionary computation versus reinforcement learning (ECRL) paradigm. This overview considers the entire spectrum of algorithmic aspects and proposes a novel methodology that analyses the technical resemblances and differences in ECRL. Our design analyses the motivation for each ECRL paradigm, the underlying natural models, the sub-component algorithmic techniques, as well as the properties of their ensemble.
Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication

Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.
Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries

Segmentation of anatomical structures in ultrasound images requires vast radiological knowledge and experience. Moreover, the manual segmentation often results in subjective variations, therefore, an automatic segmentation is desirable. We aim to develop a fully convolutional neural network (FCNN) with attentional deep supervision for the automatic and accurate segmentation of the ultrasound images.FCNN/CNNs are used to infer high-level context using low-level image features. In this paper, a sub-problem specific deep supervision of the FCNN is performed. The attention of fine resolution layers is steered to learn object boundary definitions using auxiliary losses, whereas coarse resolution layers are trained to discriminate object regions from the background. Furthermore, a customized scheme for downweighting the auxiliary losses and a trainable fusion layer are introduced. This produces an accurate segmentation and helps in dealing with the broken boundaries, usually found in the ultrasound images.The proposed network is first tested for blood vessel segmentation in liver images. It results in F1 score, mean intersection over union, and dice index of 0.83, 0.83, and 0.79, respectively. The best values observed among the existing approaches are produced by U-net as 0.74, 0.81, and 0.75, respectively. The proposed network also results in dice index value of 0.91 in the lumen segmentation experiments on MICCAI 2011 IVUS challenge dataset, which is near to the provided reference value of 0.93. Furthermore, the improvements similar to vessel segmentation experiments are also observed in the experiment performed to segment lesions.Deep supervision of the network based on the input-output characteristics of the layers results in improvement in overall segmentation accuracy.Sub-problem specific deep supervision for ultrasound image segmentation is the main contribution of this paper. Currently the network is trained and tested for fixed size inputs. It requires image resizing and limits the performance in small size images.
A multitask multiview clustering algorithm in heterogeneous situations based on LLE and LE

Multi-view clustering and multi-task clustering attract much attention in recent years. With the development of data mining, a new learning scenario containing the properties of multi-task and multi-view together appears, which called multi-task multi-view learning. Existing multi-task multi-view learning usually applies for classification and considers that all tasks share the same class label sets. Nevertheless, there is not much information about label sets in real world applications and it is difficult for all learning tasks to contain the same label sets. Hence, in order to overcome the two limitations, we propose a multi-task multi-view clustering algorithm in heterogeneous situations based on Locally Linear Embedding (LLE) and Laplacian Eigenmaps (LE) methods (L3E-M2VC). It maps the samples of multiple views from each task to a common view space firstly, then transforms the samples to a discriminative task space secondly, and finally exploits K-Means for clustering. Experiments on several multi-task multi-view data sets are evaluated by RI and CA and the results show that our L3E-M2VC outperforms the other 11 methods, including single-task single-view, multi-view, multi-task, multi-view multi-task algorithms and the varieties of our method.
Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval

In order to exploit the abundant potential information of the unlabeled data and contribute to analyzing the correlation among heterogeneous data, we propose the semi-supervised model named adaptive semi-supervised feature selection for cross-modal retrieval. First, we utilize the semantic regression to strengthen the neighboring relationship between the data with the same semantic. And the correlation between heterogeneous data can be optimized via keeping the pairwise closeness when learning the common latent space. Second, we adopt the graph-based constraint to predict accurate labels for unlabeled data, and it can also keep the geometric structure consistency between the label space and the feature space of heterogeneous data in the common latent space. Finally, an efficient joint optimization algorithm is proposed to update the mapping matrices and the label matrix for unlabeled data simultaneously and iteratively. It makes samples from different classes to be far apart, while the samples from same class lie as close as possible. Meanwhile, the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm constraint is used for feature selection and outlier reduction when the mapping matrices are learned. In addition, we propose learning different mapping matrices corresponding to different sub-tasks to emphasize the semantic and structural information of query data. Experiment results on three datasets demonstrate that our method performs better than the state-of-the-art methods.