Reviewerly/exHarmony huggingface dataset:
Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures are highly-efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks including imagelevel classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks. Our code will be released soon at this https URL .
Robust 3D Human Pose Estimation from Single Images or Video Sequences

We propose a method for estimating 3D human poses from single images or video sequences. The task is challenging because: (a) many 3D poses can have similar 2D pose projections which makes the lifting ambiguous, and (b) current 2D joint detectors are not accurate which can cause big errors in 3D estimates. We represent 3D poses by a sparse combination of bases which encode structural pose priors to reduce the lifting ambiguity. This prior is strengthened by adding limb length constraints. We estimate the 3D pose by minimizing an <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> norm measurement error between the 2D pose and the 3D pose because it is less sensitive to inaccurate 2D poses. We modify our algorithm to output <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> 3D pose candidates for an image, and for videos, we impose a temporal smoothness constraint to select the best sequence of 3D poses from the candidates. We demonstrate good results on 3D pose estimation from static images and improved performance by selecting the best 3D pose from the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> proposals. Our results on video sequences also show improvements (over static images) of roughly 15%.
Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing

In this article, we focus on a topic of detecting unstable periodic orbits (UPOs) only based on the time series observed from the nonlinear dynamical system whose explicit model is completely unknown a priori. We articulate a data-driven and model-free method which connects a well-known machine learning technique, the reservoir computing, with a widely-used control strategy of nonlinear dynamical systems, the adaptive delayed feedback control. We demonstrate the advantages and effectiveness of the articulated method through detecting and controlling UPOs in representative examples and also show how those configurations of the reservoir computing in our method influence the accuracy of UPOs detection. Additionally and more interestingly, from the viewpoint of synchronization, we analytically and numerically illustrate the effectiveness of the reservoir computing in dynamical systems learning and prediction.
Evidential combination of augmented multi-source of information based on domain adaptation

In the applications of domain adaptation (DA), there may exist multiple source domains, and each source domain usually provides some auxiliary information for object classification. The combination of such complementary knowledge from different source domains is helpful for improving the accuracy. We propose an evidential combination of augmented multi-source of information (ECAMI) method. The information sources are augmented at first by merging several randomly selected source domains to generate extra auxiliary information. We can obtain one piece of classification result with the assistance of each information source based on DA. Then these multiple classification results are combined by belief functions theory, which is expert at dealing with the uncertain information. Nevertheless, the classification results derived from different information sources may have different weights. The optimal weights are calculated by minimizing an given error criteria defined by the distance between the combination result and the ground truth using some training data. For each object, the augmented information sources will produce multiple classification results that will be discounted by the learnt weights under the belief functions framework. Then the combination of these discounted results is employed to make the final class decision. The effectiveness of ECAMI is evaluated with respect to some related methods based on several real data sets, and the experimental results show that ECAMI can significantly improve the classification accuracy.
Emotion Recognition from Text Stories Using an Emotion Embedding Model

In this paper, we analyze emotions in a story text using an emotion embedding model. Firstly, we collected 144,701 tweets, and each tweet is given an emotional hashtag. Using the emotion hashtag as an emotion label, we built a CNN model for emotion classification. We then extracted the embedding model created during the learning process. We then extracted word embedding layer created during the emotion classification learning process. We defined this as an `Emotion embedding model', and applied it to classify story text emotions. The story text used in emotion analysis was ROC story data, and those story sentences are classified into eight emotions based on plutchik's emotion model.
Development of global solar radiation models: A comprehensive review and statistical analysis for Indian regions

The number of developed Global Solar Radiation models is relatively high, making it challenging to choose the most appropriate solar conversion system model. However, there is no comprehensive review of global solar radiation models available for India. This study’s main objective is to develop a comprehensive review of the work done by Indian researchers in the field of solar radiation modelling such that it will provide a current state of the art for further research. The other aim is to carry out a statistical analysis of the developed solar radiation model for 522 locations across India. Which will help in the selection of the most appropriate and accurate model based on: (a) Country Best model, and (b) State Best Model. It is concluded that the sunshine duration is the most prominently used input parameter for the prediction of global solar radiation. For the region where the measured ground data is not available, the satellite-derived data can be used within a mean percentage error between −36.65% and 0.93% for all parts of India. As per the statistical analysis, the GSR model M-78 indicates the country’s best GSR model, which is the function of relative humidity and coefficient of determination of 0.7959 and MPE of −2.5771%. Also, the GSR model M-78 has the least error in the following test, i.e., RMSRE (0.1483), MAE (0.5402), U95 (1.3771), and RMSE (0.6936). For State best, the GSR model M-179 indicates most suitable for the prediction of global solar radiation for most parts of India where the statistical error varies between; MBE (−0.429 to 0.449), RMSE (0.337–0.998), t- Stat (0.005–15.491), U95 (0.226–1.956), MARE (0.160–0.531), MAE (0.283–0.846), RMSRE (0.064–0.205) and GPI (0.366–2.010). The statistical error indicated by all the state is within the acceptable value.
Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing

Photonic neural networks have significant potential for high-speed neural processing with low latency and ultralow energy consumption. However, the on-chip implementation of a large-scale neural network is still challenging owing to its low scalability. Herein, we propose the concept of a photonic neural field and implement it experimentally on a silicon chip to realize highly scalable neuro-inspired computing. In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip. In this study, we use the on-chip photonic neural field as a reservoir of information and demonstrate a high-speed chaotic time-series prediction with low errors using a training approach similar to reservoir computing. We show that the photonic neural field is potentially capable of executing more than one peta multiply–accumulate operations per second for a single input wavelength on a footprint as small as a few square millimeters. The operation of the neural field is energy efficient due to a passive scattering process, for which the required power comes only from the optical input. We also show that in addition to processing, the photonic neural field can be used for rapidly sensing the temporal variation of an optical phase, facilitated by its high sensitivity to optical inputs. The merging of optical processing with optical sensing paves the way for an end-to-end data-driven optical sensing scheme.
In Code(rs) We Trust

This chapter addresses the myth of ‘decentralized governance’ of public blockchains, arguing that certain people who create, operate, or reshape them function much like fiduciaries of those who rely on these data structures. It compares the role of leading software developers and Frankel’s conception of a ‘fiduciary’ and finds much in common, as users place extreme trust in the developers to be both competent and loyal (i.e. to be free of conflicts of interest). The chapter frames the cost–benefit analysis necessary to evaluate whether it is wise to treat these parties as fiduciaries, and outlines key questions needed to flesh out the fiduciary categorization. For example, which software developers are influential enough to resemble fiduciaries? Are all users of a blockchain ‘entrustors’ of the fiduciaries who operate the blockchain, or only a subset of those who rely on the blockchain? The chapter concludes by considering the broader implications of treating software developers as fiduciaries, given the existing accountability paradigm that largely shields them from liability for the code they create.
Self-Supervised Learning for Videos: A Survey

The remarkable success of deep learning in various domains relies on the availability of large-scale annotated datasets. However, obtaining annotations is expensive and requires great effort, which is especially challenging for videos. Moreover, the use of human-generated annotations leads to models with biased learning and poor domain generalization and robustness. As an alternative, self-supervised learning provides a way for representation learning that does not require annotations and has shown promise in both image and video domains. In contrast to the image domain, learning video representations are more challenging due to the temporal dimension, bringing in motion and other environmental dynamics. This also provides opportunities for video-exclusive ideas that advance self-supervised learning in the video and multimodal domains. In this survey, we provide a review of existing approaches on self-supervised learning focusing on the video domain. We summarize these methods into four different categories based on their learning objectives: (1) pretext tasks , (2) generative learning , (3) contrastive learning , and (4) cross-modal agreement . We further introduce the commonly used datasets, downstream evaluation tasks, insights into the limitations of existing works, and the potential future directions in this area.
Anomaly explanation: A review

Anomaly detection has been studied intensively by the data mining community for several years. As a result, many methods to detect anomalies have emerged, and others are still under development. But during the recent years, anomaly detection, just like a lot of machine learning tasks, is facing a wall. This wall, erected by the lack of trust of the final users, has slowed down the usage of these algorithms in the real-world situations for which they are designed. Having the best empirical accuracy is not enough anymore; there is a need for algorithms to explain their outputs to the users in order to increase their trust. Consequently, a new expression has emerged recently: eXplainable Artificial Intelligence (XAI). This expression, which gathers all the methods that provide explanations to the output of algorithms has gained popularity, especially with the outbreak of deep learning. A lot of work has been devoted to anomaly detection in the literature, but not as much to anomaly explanation. There is so much work on anomaly detection that several reviews can be found on the topic. In contrast, we were not able to find a survey on anomaly explanation in particular, while there are a lot of surveys on XAI in general or on XAI for neural networks for example. With this paper, we want to provide a comprehensive review of the anomaly explanation field. After a brief recall of some important anomaly detection algorithms, the anomaly explanation methods that we discovered in the literature will be classified according to a taxonomy that we define. This taxonomy stems from an analysis of what is really important when trying to explain anomalies.
Explainable Machine Learning for Data Extraction Across Computational Social System

This article addresses the explainable machine learning for data extraction on diverse datasets. In many cases, individual or specific approaches have been developed for feature selection (FS) on a certain dataset, but collecting the diversity dataset and demonstrating it through different FS methods are challenging. Thus, this article proposed multiapproaches for FS with the classification of diverse datasets. The proposed framework is developed using various methods, such as extendable particle swarm optimization (PSO), global and local searching, feature ranking, feature clustering, computational cost-based FS, and multiobjective optimization. We effectively used these methods in our proposed work in a single-setting framework. We focused on three essential computational items in our framework: classification accuracy, selected features, and computational times. Due to the diverse dataset, few methods have been considered challenging during computational evaluation for classification accuracy with test cost. We tried to manage the classification accuracy based on total cost and high accuracy with less cost. The proposed framework is experimented with the above methods and analyzed through comparative results on diversity datasets. For example, when regular parameter values are in the range of 2⁻¹³-2⁻⁶, the evaluation result affects all items, i.e., decreasing during this range; other values do not affect results. We used thresholds ranging from 0.6 to 0.9 for highly correlated feature pairs as per the support vector machine (SVM) method for recursive feature elimination.
The Convolutional Neural Networks Training With Channel-Selectivity for Human Activity Recognition Based on Sensors

Recently, the state-of-the-art performance in various sensor based human activity recognition (HAR) tasks have been acquired by deep learning, which can extract automatically features from raw data. In order to obtain the best accuracy, many static layers have been always used to train deep neural networks, and their weight connectivity in network remains unchanged. Pursuing the best accuracy in mobile platforms with a very limited computational budget at millions of FLOPs is impractical. In this paper, we make use of shallow convolutional neural networks (CNNs) with channel-selectivity for the use of HAR. As we have known, it is for the first time to adopt channel-selectivity CNN for sensor based HAR tasks. We perform extensive experiments on 5 public benchmark HAR datasets consisting of UCI-HAR dataset, OPPORTUNITY dataset, UniMib-SHAR dataset, WISDM dataset, and PAMAP2 dataset. As a result, the channel-selectivity can achieve lower test errors than static layers. The existing performance of deep HAR can be further improved by the CNN with channel-selectivity without any extra cost.
Performance comparison of solar radiation forecasting between WRF and LSTM in Gifu, Japan

Abstract Three months comparison of hourly solar radiation forecasting from 1st January to 31st March 2017 between Weather Research and Forecasting (WRF) mesoscale model and Long short-term memory (LSTM) algorithm is presented in this study. One-way grid nesting technique of the WRF model is applied for the simulation with a six-hourly input dataset downloaded from the National Oceanic and Atmospheric Administration - National Operational Model Archive and Distribution System (NOMADS) website. Three years’data of solar radiation from 1st January 2014 to 31st December 2016 are used as input data for Long Short Term Memory (LSTM) algorithm to simulate solar radiation. The results show the root mean square error of the LSTM algorithm is 310 W m −2 higher compared to 210 W m −2 from the WRF model. The MBE and the nMBE of the WRF model are obtained positive value 96 W m −2 and 9% compared to −101 W m −2 and −9% of LSTM for 2160 h prediction. Meanwhile, the performance error percentage of WRF is 19% lower compared to 28% of LSTM for the nRMSE error metric. Although this study found that the WRF model performed better and lower error compared to the LSTM algorithm, however, it also recommends the LSTM algorithm configuration can be used for long-term prediction.
Efficient Computation Offloading in Mobile Cloud Computing for Video Streaming Over 5G

In this paper, we investigate video quality enhancement using computation offloading to the mobile cloud computing (MCC) environment. Our objective is to reduce the computational complexity required to covert a low-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication costs. To do so, we propose an energy-efficient computation offloading framework for video streaming services in a MCC over the fifth generation (5G) cellular networks. In the proposed framework, the mobile client offloads the computational burden for the video enhancement to the cloud, which renders the side information needed to enhance video without requiring much computation by the client. The cloud detects edges from the upsampled ultra-high-resolution video (UHD) and then compresses and transmits them as side information with the original low-resolution video (e.g., full HD). Finally, the mobile client decodes the received content and integrates the SI and original content, which produces a high-quality video. In our extensive simulation experiments, we observed that the amount of computation needed to construct a UHD video in the client is 50%-60% lower than that required to decode UHD video compressed by legacy video encoding algorithms. Moreover, the bandwidth required to transmit a full HD video and its side information is around 70% lower than that required for a normal UHD video. The subjective quality of the enhanced UHD is similar to that of the original UHD video even though the client pays lower communication costs with reduced computing power.
SecureIIoT Environment: Federated Learning Empowered Approach for Securing IIoT From Data Breach

The growing congruence of gadgets today resulted in a numerous type of cyber attacks. A similar trend occurs with the industrial Internet of Things (IIoT), wherein increasing data created by connected equipment offers up new opportunities for enhancing service quality for new applications. But, security has become a major design priority for smart devices since the introduction of the IIoT. However, data providers have significant challenges in exchanging their data between different cyber physical systems due to privacy and security concerns (e.g., data imbalance and leakage). In this article, we developed a safe data sharing architecture for various IIoT devices using federated learning (FL). The proposed architecture incorporates FL into the edge computing consensus process, allowing the consensus computing activity to be used for federated training as well. The proposed framework achieves high efficiency, and better security, according to numerical findings generated by experimenting deep learning models. More precisely, the proposed framework named as SecureIIoT, is able to achieve 99.79% accuracy by detecting attacks as a binary classification problem.
Phishing Attacks Detection using Machine Learning and Deep Learning Models

Because of the fast expansion of internet users, phishing attacks have become a significant menace where the attacker poses as a trusted entity in order to steal sensitive data, causing reputational damage, loss of money, ransomware, or other malware infections. Intelligent techniques mainly Machine Learning (ML) and Deep Learning (D L) are increasingly applied in the field of cybersecurity due to their ability to learn from available data in order to extract useful insight and predict future events. The effectiveness of applying such intelligent approaches in detecting phishing web sites is investigated in this paper. We used two separate datasets and selected the highest correlated features which comprised of a combination of content-based, URL lexical-based, and domain-based features. A set of ML models were then applied, and a comparative performance evaluation was conducted. Results proved the importance of features selection in improving the models' performance. Furthermore, the results also aimed to identify the best features that influence the model in identifying phishing websites. For classification performance, Random Forest (RF) algorithm achieved the highest accuracy for both datasets.
Do RNNs learn human-like abstract word order preferences?

RNN language models have achieved state-of-the-art results on various tasks, but what exactly they are representing about syntax is as yet unclear. Here we investigate whether RNN language models learn humanlike word order preferences in syntactic alternations. We collect language model surprisal scores for controlled sentence stimuli exhibiting major syntactic alternations in English: heavy NP shift, particle shift, the dative alternation, and the genitive alternation. We show that RNN language models reproduce human preferences in these alternations based on NP length, animacy, and definiteness. We collect human acceptability ratings for our stimuli, in the first acceptability judgment experiment directly manipulating the predictors of syntactic alternations. We show that the RNNs' performance is similar to the human acceptability ratings and is not matched by an n-gram baseline model. Our results show that RNNs learn the abstract features of weight, animacy, and definiteness which underlie soft constraints on syntactic alternations.
An Effective Label Noise Model for

Ishan Jindal, Daniel Pressel, Brian Lester, Matthew Nokleby. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.