Reviewerly/exHarmony huggingface dataset:
Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.
The effects of appearance personification of service robots on customer decision-making in the product recommendation context

Purpose The COVID-19 pandemic has made contactless services such as those provided by robots increasingly pervasive. Some stores are gradually adopting service robots to sell products, which has not been explored in previous research. This study aims to explore how appearance personification of service robots affects customer decision-making in the product recommendation context. Design/methodology/approach Based on authentic in-store product recommendation service interactions, an experiment for three simulated scenarios was conducted and data was collected from 338 valid samples. Findings The results show appearance personification has a positive impact on customer purchase behavior while it has negative impacts on customer decision time and degree of hesitation. Originality/value This study not only enriches the literature on application scenarios of service robots but also supplements the literature on various customer decision-making variables in the field of service robots. It provides important practical guidance for designing robots to optimize their impact on customer decision-making.
Characterization of fracture match associations with automated image processing

During the examination of trace evidence, often a realignment along the edges of known and questioned items are made to determine if a physical fit is present and if those objects were once one continuous piece or object. Duct tape is an evidence type in which the evaluation of physical fits is often conducted and is regarded as conclusive evidence of an association between the items. The examination and conclusion of a physical fit between edges relies heavily on examiner discretion to identify distinctive features across the edges since there are no statistical approaches or objective methodologies for the comparison. This study developed an automated image processing and comparison method to quantify tape end matches using cross-correlation scores and an empirical approach to the assessment. Characterization of 150 hand torn duct tape end pair physical fits were also conducted where matching and non-matching sample distributions were created. This study also evaluated partial duct tape edges and the influence this has on a comparison. Given the strength associated with a physical fit and the presence of stretching or deformation along the fractured edge, an understanding of the value these samples have is paramount. Furthermore, random match probabilities were calculated based on the correlation scores from the inter-comparisons to model the weight of evidence or strength of association between the edges. Finally, the study demonstrated that not every true match holds the same association strength through score distributions, but the approach is able to distinguish matching and non-matching samples at edge widths greater than 27 %.
A LITERATURE SURVEY OF SECURITY AND PRIVACY ISSUES IN INTERNET OF MEDICAL THINGS

A technology answer to the world's health concerns, ubiquitous healthcare is being considered. A combination of rising healthcare expenses and a growing demand for high-quality medical care has led to this. The development of the Internet of Things (IoT) has a greater impact on IoMT. Improved health care and safety are being provided to millions of people worldwide as a result of the Internet of Things (IoMT). Remote monitoring and transfer of data can provide medical data centres, such as those in the cloud, with real-time access to patient health characteristics. As a result, healthcare is more accessible, more effective, and less expensive. It's a problem, however, because of the proliferation of Internet of Things devices. This poses a problem because IoMT devices are compact and have a limited number of schemes and computing power. It is challenging to administer and safeguard IoMT systems because of their widespread use. This is a major problem that prevents the therapeutic application of IoMT. Internet of Things (IoT) security issues, threats, requirements, and potential future research are all covered in this report. Existing solutions and unresolved issues in the realms of security and privacy are also receiving considerable attention. This paper provides a general overview of the various art techniques by using a recognised solution.
Generative Adversarial Networks With AdaBoost Ensemble Learning for Anomaly Detection in High-Speed Train Automatic Doors

Due to the scarcity of abnormal condition data in components of transportation systems, only normal condition data are typically used to train models for anomaly detection.One of the main challenges is the difficulty of properly representing the data distribution which is typically non-smooth, highdimensional and on a manifold.This work develops an anomaly detection model based on an Auto-Encoder (AE) formed by the generator of a Generative Adversarial Network (GAN) and an auxiliary encoder to capture the sophisticated data structure.The reconstruction error of the AE is, then, used as anomaly score to detect anomalies.Additionally, an adaptive noise is added to the data to make easier the GAN optimization, an AdaBoostbased ensemble learning scheme is used to improve detection performance and a new approach for setting the hyperparameters of the AE-GAN model based on the derivation of a lower bound of the Jensen-Shannon divergence between generator and normal condition data distributions is developed.The method has been applied to synthetic and real data collected from automatic doors of high-speed trains.
Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN

We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to the conventional Cycle GAN architecture to exert adaptive control on image translation. The performance of Ad CycleGAN is compared with the Cycle GAN without the external criterion. The quality of the synthetic images is evaluated by quantitative metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Universal Image Quality Index (UIQI), visual information fidelity (VIF), Frechet Inception Distance (FID), and translation accuracy. The experimental results indicate that the synthetic images generated either by the Cycle GAN or by the Ad CycleGAN have lower MSE and RMSE, and higher scores in PSNR, UIQI, and VIF in homogenous image translation (i.e., Y → Y) compared to the heterogenous image translation process (i.e., X → Y). The synthetic images by Ad CycleGAN through the heterogeneous image translation have significantly higher FID score compared to Cycle GAN (p < 0.01). The image translation accuracy of Ad CycleGAN is higher than that of Cycle GAN when normal images are converted to COVID-19 positive images (p < 0.01). Therefore, we conclude that the Ad CycleGAN with the independent criterion can improve the accuracy of GAN image translation. The new architecture has more control on image synthesis and can help address the common class imbalance issue in machine learning methods and artificial intelligence applications with medical images.
A Privacy-Preserving Authentic Healthcare Monitoring System Using Blockchain

Integrating the internet of things (IoT) and healthcare monitoring systems is one of the most dynamic innovations in the research area. Since the tremendous number of IoT devices in smart healthcare systems is increasing exponentially, privacy and security issues related to the patient's data are significant concerns. The authors propose an access control for a healthcare monitoring system using blockchain-based smart contracts. They created four smart contract forms for user registration, authentication, access control including misbehavior detection and access revocation. The sensor automatically measures the patient's health data and filters the data before determining whether to write the data into the blockchain or not. The sensor detects abnormal data and alerts doctors and hospitals for immediate treatment. The efficiency of the proposed framework is verified by performance evaluation based on the Ethereum test environment. The proposed system outperforms existing approaches by reducing deployment and execution latency and average response latency in the real-time smart healthcare system.
Masked Face Inpainting Through Residual Attention UNet

Realistic image restoration with high texture areas such as removing face masks is challenging. The state-of-the-art deep learning-based methods fail to guarantee high-fidelity, cause training instability due to vanishing gradient problems (e.g., weights are updated slightly in initial layers) and spatial information loss. They also depend on intermediary stage such as segmentation meaning require external mask. This paper proposes a blind mask face inpainting method using residual attention UNet to remove the face mask and restore the face with fine details while minimizing the gap with the ground truth face structure. A residual block feeds info to the next layer and directly into the layers about two hops away to solve the gradient vanishing problem. Besides, the attention unit helps the model focus on the relevant mask region, reducing resources and making the model faster. Extensive experiments on the publicly available CelebA dataset show the feasibility and robustness of our proposed model.
CoupleFace: Relation Matters for Face Recognition Distillation

Knowledge distillation is an effective method to improve the performance of a lightweight neural network (i.e., student model) by transferring the knowledge of a well-performed neural network (i.e., teacher model), which has been widely applied in many computer vision tasks, including face recognition (FR). Nevertheless, the current FR distillation methods usually utilize the Feature Consistency Distillation (FCD) (e.g., $$L_2$$ distance) on the learned embeddings extracted by the teacher and student models for each sample, which is not able to fully transfer the knowledge from the teacher to the student for FR. In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective FR distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework. Specifically, in MRD, we first propose to mine the informative mutual relations, and then introduce the Relation-Aware Distillation (RAD) loss to transfer the mutual relation knowledge of the teacher model to the student model. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed CoupleFace for FR. Moreover, based on our proposed CoupleFace, we have won the first place in the ICCV21 Masked Face Recognition Challenge (MS1M track).
Design of Nonlinear Component of Block Cipher Using Gravesian Octonion Integers

Being the only nonlinear component in many cryptosystems, an S-box is an integral part of modern symmetric ciphering techniques that creates randomness and increases confidentiality at the substitution stage of the encryption.The ability to construct a cryptographically strong S-box solely depends on its construction scheme.The primary purpose of an S-box in encryption standards is to establish confusion between the m-bit input into the n-bit output (both m, n >= 2).This article proposed a robust way to construct S-boxes based on the Gravesian octonion integers.We chunk the paper into threefold: firstly, a comprehensive technique for constructing S-box using affine mapping is described.The presented work is developed in such a way that for every valid input, it generates two S-boxes.Secondly, the strength of the newly generated S-box is evaluated by passing through a rigorous security analysis.Finally, a thorough comparison of the newly developed method with some well-known existing schemes is conducted.We mainly targeted some elliptic curve-based S-boxes in comparison by taking the same parameters in our scheme.The computational results and performance analysis reveal that the propose algorithm can construct a large number of distinct S-boxes that are cryptographically secured and create high resistance against various cryptanalysis attacks.
Clustering-based Curriculum Construction for Sample-Balanced Federated Learning

Federated learning is a distributed machine learning scheme that provides data privacy-preserving solution. A key challenge is data distribution heterogeneity of on different parties in federated learning. Existing methods only focus on the training rule of local model rather than data itself. In this paper, we reveal an fact that improving the performance of the local model can bring performance gain to the global model. Motivated by this finding, this paper proposes a Clustering-based curriculum construction method to rank the complexity of instances, and develops a Federation curriculum learning algorithm (FedAC). Specifically, FedAC assigns different weights to training samples of different complexity, which is able to take full advantage of the valuable learning knowledge from a noisy and uneven-quality data. Experiments were conducted on two datasets in terms of performance comparison, ablation studies, and case studies, and the results verified that FedAC can improve the performance of the state-of-the-art Federated learning methods.
Implementation and Analysis of Centroid Displacement-Based k-Nearest Neighbors

k-NN is a widely used supervised machine learning method in different domains. Despite its simplicity, effectiveness, and robustness, k-NN is limited to the use of the Euclidean distance as the similarity metric, the arbitrarily selected neighborhood size k, the computational challenge from high dimensional data, and the use of the simple majority voting rule. Among different variants of k-NN in classification, we sought to address the last issue and proposed the Centroid Displacement-based k-NN (CDNN), where centroid displacement is used for class determination. In this study, we present an implementation of CDNN for scikit-learn, a well-known machine learning library for the Python programming language, and a comprehensive comparative performance analysis of CDNN with different variants of k-NN in scikit-learn. We open-source our algorithm to benefit the users, and to the best of our knowledge, no similar studies on performance analysis of k-NN and its variants in scikit-learn have been done. We also examine the effectiveness of different distance metrics on the performance of CDNN on different datasets. Extensive experiments on real-world and synthetic datasets verify the effectiveness of CDNN compared to the standard k-NN and other state-of-the-art k-NN-based algorithms. The results from the distance metrics comparison study also show that other distance metrics can further improve the classification performance of CDNN.
A Broad Study of Pre-training for Domain Generalization and Adaptation

Deep models must learn robust and transferable representations in order to perform well on new domains. While domain transfer methods (e.g., domain adaptation, domain generalization) have been proposed to learn transferable representations across domains, they are typically applied to ResNet backbones pre-trained on ImageNet. Thus, existing works pay little attention to the effects of pre-training on domain transfer tasks. In this paper, we provide a broad study and in-depth analysis of pre-training for domain adaptation and generalization, namely: network architectures, size, pre-training loss, and datasets. We observe that simply using a state-of-the-art backbone outperforms existing state-of-the-art domain adaptation baselines and set new baselines on Office-Home and DomainNet improving by 10.7% and 5.5%. We hope that this work can provide more insights for future domain transfer research.
Design of a Channel Robust Radio Frequency Fingerprint Identification Scheme

Radio frequency fingerprint (RFF) identification is an emerging device authentication technique that exploits the hardware imperfections resulting from the manufacturing process. Due to the varying impact of the wireless channel during RFF training and test stages, it is challenging to design channel-independent RFF techniques. This article designs a channel robust RFF identification scheme by leveraging the different spectrum of adjacent signal symbols, named the Difference of the Logarithm of the Spectrum (DoLoS), which does not rely on a single RFF feature or requires additional manipulation of the devices under test. Specifically, DoLoS exploits the fact that two different symbols in a packet exhibit different RFF features but have a similar channel response during the channel coherence time. We implemented the DoLoS with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system as a case study. We carried out extensive experiments using seven Wi-Fi devices of the same model in different wireless channel environments, including 12 data collection positions in two completely different environments. Compared with conventional RFF identification schemes that do not eliminate channel effects, our scheme is robust to channel variations and the highest identification accuracy is 99.02% in the single-environment evaluation and 97.05% in the cross-environment evaluation.
PPAD is as Hard as LWE and Iterated Squaring

One of the most fundamental results in game theory is that every finite strategic game has a Nash equilibrium, an assignment of (randomized) strategies to players with the stability property that no individual player can benefit from deviating from the assigned strategy. It is not known how to efficiently compute such a Nash equilibrium—the computational complexity of this task is characterized by the class PPAD, but the relation of PPAD to other problems and well-known complexity classes is not precisely understood. In recent years there has been mounting evidence, based on cryptographic tools and techniques, showing the hardness of PPAD. We continue this line of research by showing that PPAD is as hard as learning with errors (LWE) and the iterated squaring (IS) problem, two standard problems in cryptography. Our work improves over prior hardness results that relied either on (1) sub-exponential assumptions, or (2) relied on “obfustopia,” which can currently be based on a particular combination of three assumptions. Our work additionally establishes public-coin hardness for PPAD (computational hardness for a publicly sampleable distribution of instances) that seems out of reach of the obfustopia approach. Following the work of Choudhuri et al. (STOC 2019) and subsequent works, our hardness result is obtained by constructing an unambiguous and incrementally-updateable succinct non-interactive argument for IS, whose soundness relies on polynomial hardness of LWE. The result also implies a verifiable delay function with unique proofs, which may be of independent interest.
Semi-supervised Temporal Action Detection with Proposal-Free Masking

Existing temporal action detection (TAD) methods rely on a large number of training data with segment-level annotations. Collecting and annotating such a training set is thus highly expensive and unscalable. Semi-supervised TAD (SS-TAD) alleviates this problem by leveraging unlabeled videos freely available at scale. However, SS-TAD is also a much more challenging problem than supervised TAD, and consequently much under-studied. Prior SS-TAD methods directly combine an existing proposal-based TAD method and a SSL method. Due to their sequential localization (e.g., proposal generation) and classification design, they are prone to proposal error propagation. To overcome this limitation, in this work we propose a novel $${\underline{S}emi-supervised~Temporal~action~detection~model~based~on}$$ $${\underline{P}rop\underline{O}sal-free~\underline{T}emporal~mask}$$ (SPOT) with a parallel localization (mask generation) and classification architecture. Such a novel design effectively eliminates the dependence between localization and classification by cutting off the route for error propagation in-between. We further introduce an interaction mechanism between classification and localization for prediction refinement, and a new pretext task for self-supervised model pre-training. Extensive experiments on two standard benchmarks show that our SPOT outperforms state-of-the-art alternatives, often by a large margin. The PyTorch implementation of SPOT is available at https://github.com/sauradip/SPOT
DiffuStereo: High Quality Human Reconstruction via Diffusion-Based Stereo Using Sparse Cameras

We propose DiffuStereo, a novel system using only sparse cameras (8 in this work) for high-quality 3D human reconstruction. At its core is a novel diffusion-based stereo module, which introduces diffusion models, a type of powerful generative models, into the iterative stereo matching network. To this end, we design a new diffusion kernel and additional stereo constraints to facilitate stereo matching and depth estimation in the network. We further present a multi-level stereo network architecture to handle high-resolution (up to 4k) inputs without requiring unaffordable memory footprint. Given a set of sparse-view color images of a human, the proposed multi-level diffusion-based stereo network can produce highly accurate depth maps, which are then converted into a high-quality 3D human model through an efficient multi-view fusion strategy. Overall, our method enables automatic reconstruction of human models with quality on par to high-end dense-view camera rigs, and this is achieved using a much more light-weight hardware setup. Experiments show that our method outperforms state-of-the-art methods by a large margin both qualitatively and quantitatively.