Reviewerly/exHarmony huggingface dataset:
Dual-axis schedule tracker with an adaptive algorithm for a strong scattering of sunbeam

The efficiency of photovoltaic panels is one of the main challenges of solar energy today. The sharp decline in solar cell performance in cloudy weather is the most significantlimiting factor for the transition to a green economy. As a result, today's priority task is the development of various algorithms to improve the performance of solar cells in cloudy weather. This paper compares the performance of dual-axis schedule tracker and dual-axis schedule tracker with an adaptive algorithm for a strong scattering of the sun's rays by clouds. A comparative analysis of the manufactured trackersoperation in adverse weather conditions was performedand with the use of small solar panels, an effective method was proposed for determining the minimum flow of solar energy entering the surface of photovoltaic installations with strong cloud scattering. The developed dual-axis method of tracking the sun is 41% more efficient than the dual-axis schedule tracker. The energy produced by trackers with high-power panels for three months in the previous threeyears was also estimated using availablemeteorological data in Almaty, Kazakhstan. The results obtained can be used to design solar trackers in areas with a high probability of intensesolar energy scattering on clouds.
Learning the Non-differentiable Optimization for Blind Super-Resolution

Previous convolutional neural network (CNN) based blind super-resolution (SR) methods usually adopt an iterative optimization way to approximate the ground-truth (GT) step-by-step. This solution always involves more computational costs to bring about time-consuming inference. At present, most blind SR algorithms are dedicated to obtaining high-fidelity results; their loss function generally employs L1 loss. To further improve the visual quality of SR results, perceptual metric, such as NIQE, is necessary to guide the network optimization. However, due to the non-differentiable property of NIQE, it cannot be as the loss function. Towards these issues, we propose an adaptive modulation network (AMNet) for multiple degradations SR, which is composed of the pivotal adaptive modulation layer (AMLayer). It is an efficient yet lightweight fusion layer between blur kernel and image features. Equipped with the blur kernel predictor, we naturally upgrade the AMNet to the blind SR model. Instead of considering iterative strategy, we make the blur kernel predictor trainable in the whole blind SR model, in which AMNet is well-trained. Also, we fit deep reinforcement learning into the blind SR model (AMNet-RL) to tackle the non-differentiable optimization problem. Specifically, the blur kernel predictor will be the actor to estimate the blur kernel from the input low-resolution (LR) image. The reward is designed by the pre-defined differentiable or non-differentiable metric. Extensive experiments show that our model can outperform state-of-the-art methods in both fidelity and perceptual metrics.
Pocket Diagnosis: Secure Federated Learning Against Poisoning Attack in the Cloud

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this article, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> . Concretely, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to resist DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFAP</small> as well as its capability of resisting DI-level poisoning attacks.
Learning Convex Optimization Models

A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression. We propose a heuristic for learning the parameters in a convex optimization model given a dataset of input-output pairs, using recently developed methods for differentiating the solution of a convex optimization problem with respect to its parameters. We describe three general classes of convex optimization models, maximum a posteriori (MAP) models, utility maximization models, and agent models, and present a numerical experiment for each.
Learnable Motion Coherence for Correspondence Pruning

Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing with complex motion patterns. In this paper, we introduce a network called Laplacian Motion Coherence Network (LMCNet) to learn motion coherence property for correspondence pruning. We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian. This closed-form solution enables us to design a differentiable layer in a learning framework to capture global motion coherence from putative correspondences. The global motion coherence is further combined with local coherence extracted by another local layer to robustly detect inlier correspondences. Experiments demonstrate that LMCNet has superior performances to the state of the art in relative camera pose estimation and correspondences pruning of dynamic scenes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices

Currently, 3D pose estimation methods are not compatible with a variety of low computational power devices because of efficiency and accuracy. In this paper, we revisit a pose estimation architecture from a viewpoint of both efficiency and accuracy. We propose a mobile-friendly model, MobileHumanPose, for real-time 3D human pose estimation from a single RGB image. This model consists of the modified MobileNetV2 backbone, a parametric activation function, and the skip concatenation inspired by U-Net. Especially, the skip concatenation structure improves accuracy by propagating richer features with negligible computational power. Our model achieves not only comparable performance to the state-of-the-art models but also has a seven times smaller model size compared to the ResNet-50 based model. In addition, our extra small model reduces inference time by 12.2ms on Galaxy S20 CPU, which is suitable for real-time 3D human pose estimation in mobile applications. The source code is available at: https://github.com/SangbumChoi/MobileHumanPose.
Evaluation of the impact of blockchain technology on supply chain using cognitive maps

Blockchain technology have gained importance in the supply chain with its transparency, robustness, and elimination of intermediaries. Different impacts are expected with the integration of blockchain technology in supply chain processes. Comprehensive evaluation methods are required to take important strategic decisions about blockchain technology. To examine the impact of blockchain technology, factors of cost, risks, business, and customer related benefits should be considered comprehensively. The purpose of this paper is to investigate the causal relationships among the factors to evaluate blockchain technology impact on supply chain. Cognitive maps (CM) methods which are hesitant fuzzy cognitive map (HFCM), probabilistic linguistic fuzzy cognitive map (PL-FCM) and rough set cognitive map (RS-CM) are used in this study. Also, PL-FCM that is generated based on probabilistic linguistic term sets is used for the first time in this study as a novel cognitive model. This study provides a contribution by developing cognitive map methods to measure and examine the impact of blockchain technology on supply chain for the first time. Firstly, the impacts of blockchain technology on supply chain and their relations are formed according to expert views and literature review. Then, five scenarios are considered using cognitive mapping methods, future predictions in terms of supply chain management are determined. Sensitivity analysis is performed. In this way, firms can analyze various implications and under what conditions how blockchain technology will have an impact on the supply chain.
Survey of Smart Contract Framework and Its Application

This article is a literature review on smart contract applications in various domains. The aim is to investigate technological developments and implementation of smart contracts in various domains. For this purpose, the theoretical basis of various papers that have been published in recent years is used as a source of theoretical and implementation studies. Smart contracts are the latest technology that is developing in line with the development of blockchain technology. The literature review that we have carried out explains that smart contracts work automatically, control, or document legally relevant events and actions in accordance with the agreements set forth in the contract agreement. This technology is one of the newest technologies that is expected to provide solutions for trust, security, and transparency in various domains. This literature review was conducted using an exploratory approach. This literature review focuses on reviewing frameworks, methods, and simulations of smart contract implementations in various domains.
Aker: A Design and Verification Framework for Safe and Secure SoC Access Control

Modern systems on a chip (SoCs) utilize heterogeneous architectures where multiple IP cores have concurrent access to on-chip shared resources. In security-critical applications, IP cores have different privilege levels for accessing shared resources, which must be regulated by an access control system. Aker is a design and verification framework for SoC access control. Aker builds upon the Access Control Wrapper (ACW) - a high performance and easy-to-integrate hardware module that dynamically manages access to shared resources. To build an SoC access control system, Aker distributes the ACWs throughout the SoC, wrapping controller IP cores, and configuring the ACWs to perform local access control. To ensure the access control system is functioning correctly and securely, Aker provides a property-driven security verification using MITRE common weakness enumerations. Aker verifies the SoC access control at the IP level to ensure the absence of bugs in the functionalities of the ACW module, at the firmware level to confirm the secure operation of the ACW when integrated with a hardware root-of-trust (HRoT), and at the system level to evaluate security threats due to the interactions among shared resources. The performance, resource usage, and security of access control systems implemented through Aker is experimentally evaluated on a Xilinx UltraScale+ programmable SoC, it is integrated with the OpenTitan hardware root-of-trust, and it is used to design an access control system for the OpenPULP multicore architecture.
Toward Integrated CNN-based Sentiment Analysis of Tweets for Scarce-resource Language—Hindi

Linguistic resources for commonly used languages such as English and Mandarin Chinese are available in abundance, hence the existing research in these languages. However, there are languages for which linguistic resources are scarcely available. One of these languages is the Hindi language. Hindi, being the fourth-most popular language, still lacks in richly populated linguistic resources, owing to the challenges involved in dealing with the Hindi language. This article first explores the machine learning-based approaches—Naïve Bayes, Support Vector Machine, Decision Tree, and Logistic Regression—to analyze the sentiment contained in Hindi language text derived from Twitter. Further, the article presents lexicon-based approaches (Hindi Senti-WordNet, NRC Emotion Lexicon) for sentiment analysis in Hindi while also proposing a Domain-specific Sentiment Dictionary. Finally, an integrated convolutional neural network (CNN)—Recurrent Neural Network and Long Short-term Memory—is proposed to analyze sentiment from Hindi language tweets, a total of 23,767 tweets classified into positive, negative, and neutral. The proposed CNN approach gives an accuracy of 85%.
Region-Adaptive Deformable Network for Image Quality Assessment

Image quality assessment (IQA) aims to assess the perceptual quality of images. The outputs of the IQA algorithms are expected to be consistent with human subjective perception. In image restoration and enhancement tasks, images generated by generative adversarial networks (GAN) can achieve better visual performance than traditional CNN-generated images, although they have spatial shift and texture noise. Unfortunately, the existing IQA methods have unsatisfactory performance on the GAN-based distortion partially because of their low tolerance to spatial misalignment. To this end, we propose the reference-oriented deformable convolution, which can improve the performance of an IQA network on GAN-based distortion by adaptively considering this misalignment. We further propose a patch-level attention module to enhance the interaction among different patch regions, which are processed independently in previous patch-based methods. The modified residual block is also proposed by applying modifications to the classic residual block to construct a patch- region-based baseline called WResNet. Equipping this baseline with the two proposed modules, we further propose Region-Adaptive Deformable Network (RADN). The experiment results on the NTIRE 2021 Perceptual Image Quality Assessment Challenge dataset show the superior performance of RADN, and the ensemble approach won fourth place in the final testing phase of the challenge.
Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks

Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation scheme for fooling video classifiers by introducing a flickering temporal perturbation that in some cases may be unnoticeable by human observers and is implementable in the real world. After demonstrating the manipulation of action classification of single videos, we generalize the procedure to make universal adversarial perturbation, achieving high fooling ratio. In addition, we generalize the universal perturbation and produce a temporal-invariant perturbation, which can be applied to the video without synchronizing the perturbation to the input. The attack was implemented on several target models and the transferability of the attack was demonstrated. These properties allow us to bridge the gap between simulated environment and real-world application, as will be demonstrated in this paper for the first time for an over-the-air flickering attack.
Physique-Based Human Activity Recognition Using Ensemble Learning and Smartphone Sensors

Traditional methods of Human Activity Recognition (HAR) do not take into consideration physical attributes of human subjects such as height, weight, gender etc. Thus, a particular recognition model does not perform consistently for different subjects having diverse physical attributes. In this paper, we propose a novel Physique-based HAR method that takes care of this problem and provides better accuracy in comparatively less time. Raw sensor data are acquired from inbuilt accelerometer and gyroscope sensor modules of smartphones. After pre-processing of the collected data physique-based datasets were prepared, based on the similarities of subjects' physique. Analysis is done on both of these physique-based datasets and normal traditional dataset with the help of various machine learning algorithms. The work not only identifies the suitable learning algorithms for HAR but also shows that the proposed physique-based method outperforms the traditional HAR approach for both publicly available dataset and our own generated dataset, with an accuracy of 99.88%. Individual activity-wise accuracy results are also compared with several recent benchmarks to show the efficiency of the proposed Physique-based HAR method.
Spoken Language Understanding on the Edge

We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contribution is two-fold. First, we outline the design of an embedded, private-by-design SLU system and show that it has performance on-par with cloud-based commercial solutions. Second, we release the datasets used in our experiments in the interest of reproducibility and in the hope that they can prove useful to the community.
Partisan Bias of Perceived Incivility and its Political Consequences: Evidence from Survey Experiments in Hong Kong

Abstract Exposure to presumably uncivil content is neither a necessary nor a sufficient condition for perceptions of incivility and thus could lead to differential political consequences. To examine the emergence and consequences of perceived incivility in disagreement comments, the present study reports on two population-based online survey experiments in Hong Kong (N1 = 1,207, N2 = 611). The results indicate that individuals perceive a higher degree of incivility in disagreement comments directed to in-group members than in those directed to out-group members, regardless of content features. This bias perception is greater when respondents can easily identify the incivility in a comment. Furthermore, exposure to disagreement comments can only influence willingness to participate and affective polarization indirectly via perceived incivility, and such effects are conditional on whether respondents can easily identify the incivility in a comment.
Virtual to Real-World Transfer Learning: A Systematic Review

Machine learning has become an important research area in many domains and real-world applications. The prevailing assumption in traditional machine learning techniques, that training and testing data should be of the same domain, is a challenge. In the real world, gathering enough training data to create high-performance learning models is not easy. Sometimes data are not available, very expensive, or dangerous to collect. In this scenario, the concept of machine learning does not hold up to its potential. Transfer learning has recently gained much acclaim in the field of research as it has the capability to create high performance learners through virtual environments or by using data gathered from other domains. This systematic review defines (a) transfer learning; (b) discusses the recent research conducted; (c) the current status of transfer learning and finally, (d) discusses how transfer learning can bridge the gap between the virtual and real-world.
Enhanced 3D Human Pose Estimation from Videos by Using Attention-Based Neural Network with Dilated Convolutions

The attention mechanism provides a sequential prediction framework for learning spatial models with enhanced implicit temporal consistency. In this work, we show a systematic design (from 2D to 3D) for how conventional networks and other forms of constraints can be incorporated into the attention framework for learning long-range dependencies for the task of pose estimation. The contribution of this paper is to provide a systematic approach for designing and training of attention-based models for the end-to-end pose estimation, with the flexibility and scalability of arbitrary video sequences as input. We achieve this by adapting temporal receptive field via a multi-scale structure of dilated convolutions. Besides, the proposed architecture can be easily adapted to a causal model enabling real-time performance. Any off-the-shelf 2D pose estimation systems, e.g. Our method achieves the state-of-the-art performance and outperforms existing methods by reducing the mean per joint position error to 33.4mm on Human 3.6M dataset. Our code is available at https://github.com/lrxjason/Attention3DHumanPose
MenuNER: Domain-Adapted BERT Based NER Approach for a Domain with Limited Dataset and Its Application to Food Menu Domain

Entity-based information extraction is one of the main applications of Natural Language Processing (NLP). Recently, deep transfer-learning utilizing contextualized word embedding from pre-trained language models has shown remarkable results for many NLP tasks, including Named-entity recognition (NER). BERT (Bidirectional Encoder Representations from Transformers) is gaining prominent attention among various contextualized word embedding models as a state-of-the-art pre-trained language model. It is quite expensive to train a BERT model from scratch for a new application domain since it needs a huge dataset and enormous computing time. In this paper, we focus on menu entity extraction from online user reviews for the restaurant and propose a simple but effective approach for NER task on a new domain where a large dataset is rarely available or difficult to prepare, such as food menu domain, based on domain adaptation technique for word embedding and fine-tuning the popular NER task network model ‘Bi-LSTM+CRF’ with extended feature vectors. The proposed NER approach (named as ‘MenuNER’) consists of two step-processes: (1) Domain adaptation for target domain; further pre-training of the off-the-shelf BERT language model (BERT-base) in semi-supervised fashion on a domain-specific dataset, and (2) Supervised fine-tuning the popular Bi-LSTM+CRF network for downstream task with extended feature vectors obtained by concatenating word embedding from the domain-adapted pre-trained BERT model from the first step, character embedding and POS tag feature information. Experimental results on handcrafted food menu corpus from customers’ review dataset show that our proposed approach for domain-specific NER task, that is: food menu named-entity recognition, performs significantly better than the one based on the baseline off-the-shelf BERT-base model. The proposed approach achieves 92.5% F1 score on the YELP dataset for the MenuNER task.