Reviewerly/exHarmony huggingface dataset:
Learning to Capture the Query Distribution for Few-Shot Learning

In the Few-Shot Learning (FSL), much of the related efforts only rely on the few available labeled samples (support set) building approach. However, the challenge is that the support set is easy-to-be-biased, so that they cannot be competent prototypes and are hard to represent the class distribution, leading to performance bottlenecks. In this paper, we propose to solve this obstacle by capturing the distribution of the unlabeled samples (query set). We propose two sampling methods: DeepSearch ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cal DS$ </tex-math></inline-formula> ) and WideSearch ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cal WS$ </tex-math></inline-formula> ). Both approaches are simple to implement and have no trainable parameters. They search the query samples near to the support set in different manners. Afterward, the statistic information is calculated, and we generate the latent samples according to it. The generated latent set is promising. First, it brings the query set distribution information to the classifier, which significantly improves the performance of the cross-entropy-based classifier. Second, it helps the support set become the better prototypes, which boosts the performance of the prototype-based classifier. Third, we find few latent samples are enough to boost the performance. Abundant experiments prove the proposed method achieves state-of-the-art performance on the few-shot tasks. Finally, rich ablation studies explain the compelling details of our approach.
George

Online cloud services are widely deployed as Long-Running Applications (LRAs) hosted in containers. Placing LRA containers turns out to be particularly challenging due to the complex interference between co-located containers and the operation constraints in production clusters such as fault tolerance, disaster avoidance and incremental deployment. Existing schedulers typically provide APIs for operators to manually specify the container scheduling requirements and offer only qualitative scheduling guidelines for container placement. Such schedulers, do not perform well in terms of both performance and scale, while also requiring manual intervention.
Synthesizing Decentralized Controllers With Graph Neural Networks and Imitation Learning

Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controllers are readily available, they face limitations in terms of scalability and implementation, as they do not respect the distributed information structure imposed by the network system of agents. Given the difficulties in finding optimal decentralized controllers, we propose a novel framework using graph neural networks (GNNs) to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn</i> these controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. We show that GNNs learn appropriate decentralized controllers by means of imitation learning, leverage their permutation invariance properties to successfully scale to larger teams and transfer to unseen scenarios at deployment time. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
A YouTube Spam Comments Detection Scheme Using Cascaded Ensemble Machine Learning Model

This paper proposes a technique to detect spam comments on YouTube, which have recently seen tremendous growth.YouTube is running its own spam blocking system but continues to fail to block them properly.Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naïve Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and two ensemble models (Ensemble with hard voting, Ensemble with soft voting) combining these techniques in the comment data from popular music videos -Psy, Katy Perry, LMFAO, Eminem and Shakira.
Intra-Inter View Interaction Network for Light Field Image Super-Resolution

Light field (LF) cameras, which can record real-word scenes from multiple viewpoints in a single shot, are widely used in 3D reconstruction, re-focusing, and virtual reality <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . However, the inherent trade-off between spatial resolution and angular resolution of LF images hinders their applications for scenarios requiring high resolutions. In this paper, we propose a novel intra-inter view interaction network for LF image super-resolution, termed as LF-IINet, to exploit the correlations among all views and simultaneously preserve the parallax structure of LF views. The proposed LF-IINet consists of two parallel branches. Specifically, the top branch extracts global inter-view information, and the bottom branch first independently maps each view to deep representations and then models the correlations among all intra-view features via proposed multi-view context block (MCB). The two branches interact with each other by proposed inter-assist-intra feature updating module (IntraFUM, where the intra feature are updated with the assistance of the inter feature) and intra-assist-inter feature updating module (InterFUM, where the inter feature are updated with the assistance of the intra feature). In this way, our LF-IINet incorporates rich angular and spatial information for LF image super-resolution. Extensive comparison with state-of-the-art methods demonstrates that our method achieves superior performance visually and quantitatively. Furthermore, quantitative results also show that our method is effective for LF images with either small or large disparities. Our code is shared in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GaoshengLiu/LF-IINet</uri> .
Deep Learning Methods for Sign Language Translation

Many sign languages are bona fide natural languages with grammatical rules and lexicons hence can benefit from machine translation methods. Similarly, since sign language is a visual-spatial language, it can also benefit from computer vision methods for encoding it. With the advent of deep learning methods in recent years, significant advances have been made in natural language processing (specifically neural machine translation) and in computer vision methods (specifically image and video captioning). Researchers have therefore begun expanding these learning methods to sign language understanding. Sign language interpretation is especially challenging, because it involves a continuous visual-spatial modality where meaning is often derived based on context. The focus of this article, therefore, is to examine various deep learning–based methods for encoding sign language as inputs, and to analyze the efficacy of several machine translation methods, over three different sign language datasets. The goal is to determine which combinations are sufficiently robust for sign language translation without any gloss-based information. To understand the role of the different input features, we perform ablation studies over the model architectures (input features + neural translation models) for improved continuous sign language translation. These input features include body and finger joints, facial points, as well as vector representations/embeddings from convolutional neural networks. The machine translation models explored include several baseline sequence-to-sequence approaches, more complex and challenging networks using attention, reinforcement learning, and the transformer model. We implement the translation methods over multiple sign languages—German (GSL), American (ASL), and Chinese sign languages (CSL). From our analysis, the transformer model combined with input embeddings from ResNet50 or pose-based landmark features outperformed all the other sequence-to-sequence models by achieving higher BLEU2-BLEU4 scores when applied to the controlled and constrained GSL benchmark dataset. These combinations also showed significant promise on the other less controlled ASL and CSL datasets.
Disentangling Preference Representations for Recommendation Critiquing with ß-VAE

Modern recommender systems usually embed users and items into a learned vector space representation. Similarity in this space is used to generate recommendations, and recommendation methods are agnostic to the structure of the embedding space. Motivated by the need for recommendation systems to be more transparent and controllable, we postulate that it is beneficial to assign meaning to some of the dimensions of user and item representations. Disentanglement is one technique commonly used for this purpose. We presenta novel supervised disentangling approach for recommendation tasks. Our model learns embeddings where attributes of interest are disentangled, while requiring only a very small number of labeled items at training time. The model can then generate interactive and critiquable recommendations for all users, without requiring any labels at recommendation time, and without sacrificing any recommendation performance. Our approach thus provides users with levers to manipulate, critique and fine-tune recommendations, and gives insight into why particular recommendations are made. Given only user-item interactions at recommendation time, we show that it identifies user tastes with respect to the attributes that have been disentangled, allowing for users to manipulate recommendations across these attributes.
Deep ReLU Networks Overcome the Curse of Dimensionality for Generalized Bandlimited Functions

We prove a theorem concerning the approximation of generalized bandlimited multivariate functions by deep ReLU networks for which the curse of the dimensionality is overcome.Our theorem is based on a result by Maurey and on the ability of deep ReLU networks to approximate Chebyshev polynomials and analytic functions efficiently.
Massively parallel algorithms for personalized pagerank

Personalized PageRank (PPR) has wide applications in search engines, social recommendations, community detection, and so on. Nowadays, graphs are becoming massive and many IT companies need to deal with large graphs that cannot be fitted into the memory of most commodity servers. However, most existing state-of-the-art solutions for PPR computation only work for single-machines and are inefficient for the distributed framework since such solutions either (i) result in an excessively large number of communication rounds, or (ii) incur high communication costs in each round. Motivated by this, we present Delta-Push , an efficient framework for single-source and top- k PPR queries in distributed settings. Our goal is to reduce the number of rounds while guaranteeing that the load, i.e., the maximum number of messages an executor sends or receives in a round, can be bounded by the capacity of each executor. We first present a non-trivial combination of a redesigned parallel push algorithm and the Monte-Carlo method to answer single-source PPR queries. The solution uses pre-sampled random walks to reduce the number of rounds for the push al6gorithm. Theoretical analysis under the Massively Parallel Computing (MPC) model shows that our proposed solution bounds the communication rounds to [EQUATION] under a load of O ( m/p ), where m is the number of edges of the input graph, p is the number of executors, and ϵ is a user-defined error parameter. In the meantime, as the number of executors increases to p' = γ · p , the load constraint can be relaxed since each executor can hold O (γ · m/p' ) messages with invariant local memory. In such scenarios, multiple queries can be processed in batches simultaneously. We show that with a load of O (γ · m/p' ), our Delta-Push can process γ queries in a batch with [EQUATION] rounds, while other baseline solutions still keep the same round cost for each batch. We further present a new top- k algorithm that is friendly to the distributed framework and reduces the number of rounds required in practice. Extensive experiments show that our proposed solution is more efficient than alternatives.
Exploration of Techniques for Rapid Activation of Glanceable Information in Head-Worn Augmented Reality

Future augmented reality (AR) glasses may provide pervasive and continuous access to everyday information. However, it remains unclear how to address the issue of virtual information overlaying and occluding real-world objects and information that are of interest to users. One approach is to keep virtual information sources inactive until they are explicitly requested, so that the real world remains visible. In this research, we explored the design of interaction techniques with which users can activate virtual information sources in AR. We studied this issue in the context of Glanceable AR, in which virtual information resides at the periphery of the user's view. We proposed five techniques and evaluated them in both sitting and walking scenarios. Our results demonstrate the usability, user preference, and social acceptance of each technique, as well as design recommendations to achieve optimal performance. Our findings can inform the design of lightweight techniques to activate virtual information displays in future everyday AR interfaces.
Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis

Although Deep Learning models have achieved incredible results in medical image classification tasks, their lack of interpretability hinders their deployment in the clinical context. Case-based interpretability provides intuitive explanations, as it is a much more human-like approach than saliency-map-based interpretability. Nonetheless, since one is dealing with sensitive visual data, there is a high risk of exposing personal identity, threatening the individuals’ privacy. In this work, we propose a privacy-preserving generative adversarial network for the privatization of case-based explanations. We address the weaknesses of current privacy-preserving methods for visual data from three perspectives: realism, privacy, and explanatory value. We also introduce a counterfactual module in our Generative Adversarial Network that provides counterfactual case-based explanations in addition to standard factual explanations. Experiments were performed in a biometric and medical dataset, demonstrating the network’s potential to preserve the privacy of all subjects and keep its explanatory evidence while also maintaining a decent level of intelligibility.
The measurement, intensity and determinants of fear of cybercrime: A systematic review

It has only been recently that fear of crime scholars have shifted their attention to online contexts. The current systematic review provides an overview of available studies on measurement, intensity and determinants of fear of cybercrime. While matters of measurement and conceptualisation has sprung a sizeable and vivid debate in the general fear of crime literature, we aim to scope this debate for the online context by scrutinizing the available knowledge base. At the same time, and by providing an overview of correlates of fear of cybercrime, we aim to provide a fertile ground for theory building specific to the online context. A comprehensive literature search was conducted, yielding a total of 28 relevant studies from a range of (sub)disciplines, using a variety of measures in terms of the particular emotion measured (fear, worry, anxiety) and the type of cybercrime it related to. We find considerable agreement between studies on classic fear of crime indicators such as gender, victimization and risk perception. At the same time, various studies report a relationship between fear of cybercrime and what is termed 'constrained behavior', as outcome measure. Implications of these findings, and future directions for fear of cybercrime research are discussed.
Gaussian Dropout Based Stacked Ensemble CNN for Classification of Breast Tumor in Ultrasound Images

• This research aims to address the research gaps in the classification of breast tumors from ultrasound images. • The study proposes a novel stacking ensemble CNN architecture to detect breast tumors. • The use of gaussian dropout layer and a customized alternative pooling scheme have been explored in this study. Breast cancer and breast tumors have been considered to be the most pervasive form of cancer in medical practice. Breast tumors are life-threatening to women, and their early detection could save lives with the proper treatment. Physical methods for detection of Breast Cancer are time-consuming and often prone to a misdiagnosis at classifying tumors. Recent trends in radiological imaging have significantly improved the efficiency and veracity of breast tumor classification. Artificial intelligence techniques could be used as an automated detection and classification system. In this research, we propose a novel configuration of a Stacking Ensemble with custom Convolutional Neural Network architectures to classify breast tumors from ultrasound images into ‘Normal’, ‘Benign’, and ‘Malignant’ categories. After thorough experimentation, our ensemble has performed with an accuracy, f1-score, precision, and recall of 92.15%, 92.21%, 92.26%, 92.17% respectively. The presented ensemble leverages three Stacked Feature Extractors coupled with a characteristic meta-learner to provide an overall balanced classification performance, with better accuracy and lower false positives. The architecture works in association with gaussian dropout layers to improve the computation and an alternative pooling scheme to retain essential features.
Short Minimal Codes and Covering Codes via Strong Blocking Sets in Projective Spaces

Minimal linear codes are in one-to-one correspondence with special types of blocking sets of projective spaces over a finite field, which are called strong or cutting blocking sets. Minimal linear codes have been studied since decades but their tight connection with cutting blocking sets of finite projective spaces was unfolded only in the past few years, and it has not been fully exploited yet. In this paper we apply finite geometric and probabilistic arguments to contribute to the field of minimal codes. We prove an upper bound on the minimal length of minimal codes of dimension <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> over the <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-element Galois field which is linear in both <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, hence improve the previous superlinear bounds. This result determines the minimal length up to a small constant factor. We also improve the lower and upper bounds on the size of so called higgledy-piggledy line sets in projective spaces and apply these results to present improved bounds on the size of covering codes and saturating sets in projective spaces as well.
Towards Cross-Blockchain Smart Contracts

In recent years, manifold blockchain protocols have been proposed by researchers and industrial companies alike. This has led to a very heterogeneous blockchain landscape. Accordingly, it would be desirable if blockchains could interact with each other. However, current protocols offer only limited support for interoperability, thus preventing tokens or smart contracts from leaving the scope of a particular blockchain. As a first step towards cross-chain smart contract interactions, we introduce a framework which allows to invoke a smart contract from another blockchain. We offer support for continuing a smart contract after receiving a result from a different blockchain, and for calling smart contracts recursively across blockchains. We provide a reference implementation for Ethereum-based blockchains using Solidity and evaluate the performance regarding time and cost overheads.
Distantly Supervised Relation Extraction using Global Hierarchy Embeddings and Local Probability Constraints

To find relational facts of interest from plain texts, distantly supervised relation extraction (DSRE) has drawn significant attention. Recent works exploit relation hierarchies to mine more clues for long-tail relations and achieve good performance. However, they ignore or underutilize the correlation of relations in the hierarchical structure. Empirically, the correlation facilitates knowledge transfer between different relations to further handle long-tail relations and improves inter-relational discrimination. In this paper, we devise an end-to-end network to model the correlation of relations from two perspectives. Globally, we construct an undirected connected graph according to the relation hierarchies, and employ Graph Attention Networks (GATs) to aggregate node information and generate correlation-aware Global Hierarchy Embeddings (GHE). Locally, we assume that along the relation hierarchies, the classification results of adjacent levels should be highly interdependent, and introduce a constraint called Local Probability Constraints (LPC) to take it into account. LPC is then combined with a branch network for both sentence-level and bag-level classification. Experimental results on the popular New York Times (NYT) dataset show that, our model GHE-LPC outperforms other state-of-the-art baselines in terms of AUC, Top-N precision, accuracy of [email protected], etc.
Emerging trends: A gentle introduction to fine-tuning

Abstract The previous Emerging Trends article (Church et al ., 2021. Natural Language Engineering 27 (5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.
AdaSwarm: Augmenting Gradient-Based Optimizers in Deep Learning With Swarm Intelligence

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Particle Swarm Optimizer (EMPSO), is proposed. The ability of AdaSwarm to tackle optimization problems is attributed to its capability to perform good gradient approximations. We show that, the gradient of any function, differentiable or not, can be approximated by using the parameters of EMPSO. This is a novel technique to simulate GD which lies at the boundary between numerical methods and swarm intelligence. Mathematical proofs of the gradient approximation produced are also provided. AdaSwarm competes closely with several state-of-the-art (SOTA) optimizers. We also show that AdaSwarm is able to handle a variety of loss functions during backpropagation, including the maximum absolute error (MAE).