Reviewerly/exHarmony huggingface dataset:
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Zero-shot text classification (0SHOT-TC) is a challenging NLU problem to which little attention has been paid by the research community.0SHOT-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.g., topic, emotion, event, etc.) described by the label.And there are only a few articles studying 0SHOT-TC, all focusing only on topical categorization which, we argue, is just the tip of the iceberg in 0SHOT-TC.In addition, the chaotic experiments in literature make no uniform comparison, which blurs the progress.This work benchmarks the 0SHOT-TC problem by providing unified datasets, standardized evaluations, and state-of-the-art baselines.Our contributions include: i) The datasets we provide facilitate studying 0SHOT-TC relative to conceptually different and diverse aspects: the "topic" aspect includes "sports" and "politics" as labels; the "emotion" aspect includes "joy" and "anger"; the "situation" aspect includes "medical assistance" and "water shortage".ii) We extend the existing evaluation setup (labelpartially-unseen) -given a dataset, train on some labels, test on all labels -to include a more challenging yet realistic evaluation label-fully-unseen 0SHOT-TC (Chang et al., 2008), aiming at classifying text snippets without seeing task specific training data at all.iii) We unify the 0SHOT-TC of diverse aspects within a textual entailment formulation and study it this way. 1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC.We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition.Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods.We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.
Accelerating 3D deep learning with PyTorch3D

1. Accelerating 3D Deep Learning with PyTorch3D, arXiv 2007.08501
To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression

We have witnessed the tremendous growth of videos over the Internet, where most of these videos are typically paired with abundant sentence descriptions, such as video titles, captions and comments. Therefore, it has been increasingly crucial to associate specific video segments with the corresponding informative text descriptions, for a deeper understanding of video content. This motivates us to explore an overlooked problem in the research community — temporal sentence localization in video, which aims to automatically determine the start and end points of a given sentence within a paired video. For solving this problem, we face three critical challenges: (1) preserving the intrinsic temporal structure and global context of video to locate accurate positions over the entire video sequence; (2) fully exploring the sentence semantics to give clear guidance for localization; (3) ensuring the efficiency of the localization method to adapt to long videos. To address these issues, we propose a novel Attention Based Location Regression (ABLR) approach to localize sentence descriptions in videos in an efficient end-to-end manner. Specifically, to preserve the context information, ABLR first encodes both video and sentence via Bi-directional LSTM networks. Then, a multi-modal co-attention mechanism is presented to generate both video and sentence attentions. The former reflects the global video structure, while the latter highlights the sentence details for temporal localization. Finally, a novel attention based location prediction network is designed to regress the temporal coordinates of sentence from the previous attentions. We evaluate the proposed ABLR approach on two public datasets ActivityNet Captions and TACoS. Experimental results show that ABLR significantly outperforms the existing approaches in both effectiveness and efficiency.
Nature-inspired optimization algorithms: Challenges and open problems

Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

The main goal behind state-of-the-art pretrained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot crosslingual transfer.However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pretraining.We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations.In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pretrained multilingual model to a new language.MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering.Our code and adapters are available at AdapterHub.ml.
Adaptive Attention Span in Transformers

We propose a novel self-attention mechanism that can learn its optimal attention span.This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time.We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters.
Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.
Virtual and augmented reality: Advancing research in consumer marketing

Virtual reality (VR) and augmented reality (AR) technologies are having a profound impact on a variety of marketing practices and are attracting increasing attention from marketing researchers. In this article, we review developments in VR/AR applications and research in the area of consumer marketing. We propose a conceptual framework for VR/AR research in consumer marketing that centers around consumer experiences provided by VR/AR applications along the customer journey and the effectiveness of such applications, and delve into the key concepts and components of the framework. Next, we provide a comprehensive overview of VR/AR applications in current practices and extant research on VR/AR in consumer marketing. Finally, based on this framework, we offer an outlook for future developments of VR/AR technologies and applications, discuss managerial implications, and prescribe directions for research on consumer marketing.
Adam Optimization Algorithm for Wide and Deep Neural Network

The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.
Chaotic Local Search-Based Differential Evolution Algorithms for Optimization

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to lean a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further proposed upon the adversarial training to strengthen the correlations between inputs and corresponding outputs. Our approach is generative to learn hash functions such that the learned hash codes can maximally correlate each input-output correspondence, meanwhile can also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method achieves better retrieval results than the state-of-the-arts.
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization

Localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object localization from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet is a single, unified network composed of four task-specific subnetworks, responsible for 2D object detection, instance depth estimation (IDE), 3D localization and local corner regression. Unlike the pixel-level depth estimation that needs per-pixel annotations, we propose a novel IDE method that directly predicts the depth of the targeting 3D bounding box’s center using sparse supervision. The 3D localization is further achieved by estimating the position in the horizontal and vertical dimensions. Finally, MonoGRNet is jointly learned by optimizing the locations and poses of the 3D bounding boxes in the global context. We demonstrate that MonoGRNet achieves state-of-the-art performance on challenging datasets.
Measuring Bias in Contextualized Word Representations

Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks.Since they are optimized to capture the statistical properties of training data, they tend to pick up on and amplify social stereotypes present in the data as well.In this study, we (1) propose a template-based method to quantify bias in BERT; (2) show that this method obtains more consistent results in capturing social biases than the traditional cosine based method; and (3) conduct a case study, evaluating gender bias in a downstream task of Gender Pronoun Resolution.Although our case study focuses on gender bias, the proposed technique is generalizable to unveiling other biases, including in multiclass settings, such as racial and religious biases.
Qubit-ADAPT-VQE: An Adaptive Algorithm for Constructing Hardware-Efficient Ansätze on a Quantum Processor

Quantum simulation, one of the most promising applications of a quantum computer, is currently being explored intensely using the variational quantum eigensolver. The feasibility and performance of this algorithm depend critically on the form of the wavefunction ansatz. Recently in Nat. Commun. 10, 3007 (2019), an algorithm termed ADAPT-VQE was introduced to build system-adapted ans\"atze with substantially fewer variational parameters compared to other approaches. This algorithm relies heavily on a predefined operator pool with which it builds the ansatz. However, Nat. Commun. 10, 3007 (2019) did not provide a prescription for how to select the pool, how many operators it must contain, or whether the resulting ansatz will succeed in converging to the ground state. In addition, the pool used in that work leads to state preparation circuits that are too deep for a practical application on near-term devices. Here, we address all these key outstanding issues of the algorithm. We present a hardware-efficient variant of ADAPT-VQE that drastically reduces circuit depths using an operator pool that is guaranteed to contain the operators necessary to construct exact ans\"atze. Moreover, we show that the minimal pool size that achieves this scales linearly with the number of qubits. Through numerical simulations on $\text{H}_4$, LiH and $\text{H}_6$, we show that our algorithm ("qubit-ADAPT") reduces the circuit depth by an order of magnitude while maintaining the same accuracy as the original ADAPT-VQE. A central result of our approach is that the additional measurement overhead of qubit-ADAPT compared to fixed-ansatz variational algorithms scales only linearly with the number of qubits. Our work provides a crucial step forward in running algorithms on near-term quantum devices.
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real-world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally.
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research

We present a new large-scale multilingual video description dataset, VATEX <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which contains over 41,250 videos and 825, 000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSRVTT dataset [64], VATEX is multilingual, larger, linguistically complex, and more diverse in terms of both video and natural language descriptions. We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context. Extensive experiments on the VATEX dataset show that, first, the unified multilingual model can not only produce both English and Chinese descriptions for a video more efficiently, but also offer improved performance over the monolingual models. Furthermore, we demonstrate that the spatiotemporal video context can be effectively utilized to align source and target languages and thus assist machine translation. In the end, we discuss the potentials of using VATEXfor other video-and-language research.
Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition

In 2018, a landmark challenge in artificial intelligence (AI) took place, namely, the Explainable Machine Learning Challenge.The goal of the competition was to create a complicated black box model for the dataset and explain how it worked.One team did not follow the rules.Instead of sending in a black box, they created a model that was fully interpretable.This leads to the question of whether the real world of machine learning is similar to the Explainable Machine Learning Challenge, where black box models are used even when they are not needed.We discuss this team's thought processes during the competition and their implications, which reach far beyond the competition itself.
Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision

The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a large training set of in-the-wild images, which by construction, lack ground truth 3D shape. To train a network without any 2D-to-3D supervision, we present RingNet, which learns to compute 3D face shape from a single image. Our key observation is that an individual’s face shape is constant across images, regardless of expression, pose, lighting, etc. RingNet leverages multiple images of a person and automatically detected 2D face features. It uses a novel loss that encourages the face shape to be similar when the identity is the same and different for different people. We achieve invariance to expression by representing the face using the FLAME model. Once trained, our method takes a single image and outputs the parameters of FLAME, which can be readily animated. Additionally we create a new database of faces “not quite in-the-wild” (NoW) with 3D head scans and high-resolution images of the subjects in a wide variety of conditions. We evaluate publicly available methods and find that RingNet is more accurate than methods that use 3D supervision. The dataset, model, and results are available for research purposes at http://ringnet.is.tuebingen.mpg.de.
BEST: Blockchain-based secure energy trading in SDN-enabled intelligent transportation system

Tactile Internet is a fairly recent technological trend associated with the Internet-of-Things (IoT) era, with potential applications in a broad range of industrial, societal and business use cases. The real-time machine-to-machine and human-to-machine interactions (e.g., in unmanned vehicles and the underpinning infrastructure within the smart city ecosystem) in the intelligent transportation sector, for example, contribute to the potential utility of Tactile Internet in this particular sector (and the broader smart city). In the context of unmanned vehicles, such as unmanned aerial vehicles and electric (ground) vehicles, one of several key challenges to its broader utility is how to design a secure energy trading ecosystem that can be used for purposes such as charging and discharging from the supporting smart grids. Most existing approaches in the literature focused on conventional and centralized security mechanisms, which may not be applicable for energy trading in a smart city environment. Moreover, the need for real-time processing for energy trading computation is one of the essential requirements of Tactile Internet. Therefore, to address these challenges, BEST: a Blockchain-based secure energy trading scheme for electric vehicles (EVs) is proposed in this paper. Specifically, in BEST, blockchain is used to validate EVs’ requests in a distributed manner; thus, ensuring resilience against the single point of failure. The miner nodes are selected to validate the requests on the basis of energy requirements, time of stay, dynamic pricing, and connectivity record, as well as other factors that are crucial for the operator at the time of operation. Moreover, to provide low latency and real-time services, software-defined networking is used as the network’s backbone to transfer EVs’ requests to a global software defined network controller. Finally, BEST is evaluated on the basis of the communication and computation costs incurred during various transactions between the EVs and the smart grid. A case study is also provided to demonstrate the potential deployment of BEST in energy trading.