Reviewerly/exHarmony huggingface dataset:
EV-Action: Electromyography-Vision Multi-Modal Action Dataset

Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and skeleton). To make up this, we introduce a new, largescale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities. Compared with the conventional datasets, EV-Action dataset has two major improvements: (1) we deploy a motion capturing system to obtain high quality skeleton modality, which provides more comprehensive motion information including skeleton, trajectory, acceleration with higher accuracy, sampling frequency, and more skeleton markers. (2) we introduce an EMG modality which is usually used as an effective indicator in the biomechanics area, also it has yet to be well explored in motion related research. To the best of our knowledge, this is the first action dataset with EMG modality. The details of EVAction dataset are clarified, meanwhile, a simple yet effective framework for EMG-based action recognition is proposed. Moreover, state-of-the-art baselines are applied to evaluate the effectiveness of all the modalities. The obtained result clearly shows the validity of EMG modality in human action analysis tasks. We hope this dataset can make significant contributions to human motion analysis, computer vision, machine learning, biomechanics, and other interdisciplinary fields.
Designing Everyday Conversational Agents for Managing Health and Wellness

Conversational agents have been developed for supporting a wide array of areas, including autonomous vehicles, decision making, and health behavior change. In the last few years, conversational agents increasingly became available as everyday technologies. This phenomenon enables opportunities for finding novel ways to support health and wellness in everyday contexts. By conducting a content analysis of 433 user reviews of Amazon Alexa's Skills, the goal of this study is two-fold: (1) Extract users' perceived strengths of conversational agents in everyday health and wellness management, (2) develop design heuristics for developing conversational agents for health and wellness. We found that the role of trustworthy content providers is critical during the adoption. The Skills enabled people to overcome logistical barriers to improving daily health and wellness routines. The findings also revealed the importance of transparency in the limitations of the Skill and how to better design command dialogues. We present the design heuristics of conversational agents, building on Nielsen's Usability Heuristics, and discuss implications for designing conversational agents that support health and wellness.
Comparing U-Net Convolutional Network with Mask R-CNN in Agricultural Area Segmentation on Satellite Images

Deep learning is the fastest-growing trend in statistical analysis of remote sensing data. Deep learning models are used for information processing of spectral steps, identification statistics, segmentation and classification of the objects in satellite images, etc. Image segmentation could help to make the object statistics more accurate by separating the objects from the background. In this paper, we propose knowledge of Mask R-CNN and U-Net in satellite imagery segmentation, and we also make an experiment for these models to show the appropriateness in this field. Experimental result of the mean average precision (mAP) on dataset of Vietnam satellite images is 95.21% for Mask R-CNN and 92.69% for U-Net.
Novel Intuitionistic-Based Interval Type-2 Fuzzy Similarity Measures With Application to Clustering

Similarity measures have been widely used in applications dealing with reasoning, classification, and information retrieval. In this article, we first propose three new interval type-2 fuzzy similarity measures (IT-2 FSMs) as a dual concept of some semimetric distances between intuitionistic fuzzy sets (IFSs). We also prove that the extended IT-2 FSMs satisfy many common properties (i.e., reflexivity, transivity, symmetry, and overlapping). Experiments are carried out on a variety of datasets including UCI learning machine and real data. Comparative studies between the proposed IT-2 FSMs and the other well-known existing similarity measures (Gorzalczany, Bustince, Mitchell, Zeng, and Li as well as VSM and Jaccard) are performed. Obviously, the best results are obtained with the IT-2 FSMs being resilient to the high levels of uncertainty noise. We also prove that our IT-2 FSMs can overcome the drawbacks of some existing similarity measures based on the accuracy rate measure. In addition, the proposed IT-2 FSMs are joined with fuzzy C-means algorithm as a clustering method and the proposed system is compared against the existing clustering algorithms (type-1 fuzzy k-means, type-1, and type-2 fuzzy C-means, cluster forest, bagged clustering, evidence accumulation, and random projection). Relying on the clustering quality parameters R and C (equivalent to the standard classification accuracy), the advanced IT-2FSMs show higher classification accuracy of about 86% which outperforms nearly the other classifiers.
Social Behaviors Associated With a Positive COVID-19 Test Result

ObjectiveTo compare the social behaviors of individuals who were tested positive for COVID-19 relative to noninfected individuals. MethodsWe sent COVID positive cases and age/gender-matched controls a survey regarding their social behaviors via MyChart (online patient portal).We called cases if they did not complete the electronic survey within two days.Data were collected from May to June 2020.Survey responses for cases without close contact and controls were compared using Pearson chi-square or Fisher's exact tests as appropriate. ResultsA total of 339 participants completed the survey (113 cases, 226 controls); 45 (40%) cases had known contact with COVID-19.Cases were more likely to have recently traveled (4% vs. 0%, p = 0.01) or to work outside the home (40% vs. 25%, p = 0.02).There was no difference in the rates of attending private or public gatherings, mask/glove use, hand-washing, cleaning surfaces, and cleaning mail/groceries between cases and controls. ConclusionsSixty percent of cases had no known contact with COVID-19, indicating ongoing community transmission and underlining the importance of contact tracing.The greater percentage of cases who work outside the home provides further evidence for social distancing and remote telework when possible.
Adversarial example generation with adabelief optimizer and crop invariance

Deep neural networks are vulnerable to adversarial examples, which are crafted by applying small, human-imperceptible perturbations on the original images, so as to mislead deep neural networks to output inaccurate predictions. Adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, most existing adversarial attacks often achieve relatively low success rates on normally trained networks and adversarially trained networks. In this paper, we regard the generation process of adversarial examples as an optimization process similar to deep neural network training. From this point of view, we introduce AdaBelief optimizer and crop invariance into the generation of adversarial examples, and propose AdaBelief Iterative Fast Gradient Method (ABI-FGM) and Crop-Invariant attack Method (CIM) to improve the transferability of adversarial examples. By adopting adaptive learning rate into the iterative attacks, ABI-FGM can optimize the convergence process, resulting in more transferable adversarial examples. CIM is based on our discovery on the crop-invariant property of deep neural networks, which we thus leverage to optimize the adversarial perturbations over an ensemble of crop copies so as to avoid overfitting on the white-box model being attacked and improve the transferability of adversarial examples. ABI-FGM and CIM can be readily integrated to build a strong gradient-based attack to further boost the success rates of adversarial examples for black-box attacks. Moreover, our method can also be naturally combined with other gradient-based attack methods to build a more robust attack to generate more transferable adversarial examples against the defense models. Extensive experiments on the ImageNet dataset demonstrate the method’s effectiveness. Whether on normally trained networks or adversarially trained networks, our method has higher success rates than state-of-the-art gradient-based attack methods.
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations

The previous study has shown that universal adversarial attacks can fool deep neural networks over a large set of input images with a single human-invisible perturbation. However, current methods for universal adversarial attacks are based on additive perturbation, which cause misclassification when the perturbation is directly added to the input images. In this paper, for the first time, we show that a universal adversarial attack can also be achieved via non-additive perturbation (e.g., spatial transformation). More importantly, to unify both additive and non-additive perturbations, we propose a novel unified yet flexible framework for universal adversarial attacks, called GUAP, which is able to initiate attacks by additive perturbation, non-additive perturbation, or the combination of both. Extensive experiments are conducted on ImageNet dataset with several deep neural network models including GoogLeNet, VGG and ResNet. The empirical experiments demonstrate that GUAP can obtain up to 99.24% successful attack rate on ImageNet dataset, leading to over 19% improvements than current state-of-the-art universal adversarial attacks. The code for reproducing the experiments in this paper is available at https://github.com/TrustAI/GUAP.
From hot-spots towards experience-spots: Leveraging on users’ sociocultural experiences to enhance security in cued-recall graphical authentication

This paper suggests a novel cued-recall-based graphical authentication method, which leverages on users’ sociocultural experiences for improving the security and memorability of selected secrets. We evaluated the suggested approach in the context of three user studies (n = 139): a) an eye-tracking study (n = 42) focusing on security in terms of resistance to brute-force attacks; b) a two-week study (n = 71) focusing on memorability and login usability; and c) a controlled in-lab user study (n = 26) focusing on human attack vulnerabilities among people sharing common sociocultural experiences. Analysis of results revealed that the suggested approach influenced visual behavior strategies of end-users, which subsequently resulted in significantly stronger passwords created on images reflecting their prior experiences than on images unfamiliar to them. Simultaneously, both reference and control groups performed similarly in terms of memorability and login efficiency and effectiveness. On the downside, the suggested approach introduces password guessing vulnerabilities in terms of allowing attackers who share common experiences with the end-users to more easily identify regions of their selected secrets. Findings point towards a new direction for delivering personalized cued-recall graphical authentication schemes that depict image semantics bootstrapped to users’ real-life experiences.
Laser-based structured light technique for 3D reconstruction using Extreme Laser stripes extraction method with global information extraction

Abstract A laser based structured-light technique is proposed for use in three-dimensional reconstruction. This technique is produced from an extensive experimental evaluation for the fundamental laser stripe extraction methods used with the structured-laser-light technique. Based on this evaluation, the proposed technique uses the extreme with centroid for calculating the probable laser stripe’s locations. It then extracts the global visual information from the scanned scene and uses this information to filter out all the unreliable locations. This makes the new technique has superior extraction capability for the structures-laser-light and robust to the scanning conditions that disturb the reconstruction process. The performance of the proposed technique was evaluated through a series of experiments. The evaluation results show high 3D reconstruction performance for the proposed technique in comparison to the traditional technique when extracting laser-stripes. They also show how the global visual information is efficient for locating these stripes from the other illumination sources.
Archetype analysis: A new subspace outlier detection approach

The problem of detecting outliers in multivariate data sets with continuous numerical features is addressed by a new method. This method combines projections into relevant subspaces by archetype analysis with a nearest neighbor algorithm, through an appropriate ensemble of the results. Our method is able to detect an anomaly in a simple data set with a linear correlation of two features, while other methods fail to recognize that anomaly. Our method performs among top in an extensive comparison with 23 state-of-the-art outlier detection algorithms with several benchmark data sets. Finally, a novel industrial data set is introduced, and an outlier analysis is carried out to improve the fit of footwear, since this kind of analysis has never been fully exploited in the anthropometric field.
DGIG-Net: Dynamic Graph-in-Graph Networks for Few-Shot Human–Object Interaction

Few-shot learning (FSL) for human-object interaction (HOI) aims at recognizing various relationships between human actions and surrounding objects only from a few samples. It is a challenging vision task, in which the diversity and interactivity of human actions result in great difficulty to learn an adaptive classifier to catch ambiguous interclass information. Therefore, traditional FSL methods usually perform unsatisfactorily in complex HOI scenes. To this end, we propose dynamic graph-in-graph networks (DGIG-Net), a novel graph prototypes framework to learn a dynamic metric space by embedding a visual subgraph to a task-oriented cross-modal graph for few-shot HOI. Specifically, we first build a knowledge reconstruction graph to learn latent representations for HOI categories by reconstructing the relationship among visual features, which generates visual representations under the category distribution of every task. Then, a dynamic relation graph integrates both reconstructible visual nodes and dynamic task-oriented semantic information to explore a graph metric space for HOI class prototypes, which applies the discriminative information from the similarities among actions or objects. We validate DGIG-Net on multiple benchmark datasets, on which it largely outperforms existing FSL approaches and achieves state-of-the-art results.
A Robust Biometric Authentication System for Handheld Electronic Devices by Intelligently Combining 3D Finger Motions and Cerebral Responses

Rapid advancement in sensor technology through miniaturization of electronic components has enabled the consumer electronic (CE) research community including the manufacturers to embed various utility sensors into handheld devices. In addition to traditional sensors such as Inertial Measurement Unit (IMU), camera, fingerprint or proximity, futuristic sensors such as Electroencephalogram (EEG) or Electromyography (EMG) are also being included in the next-generation CE devices. Air or touch signature-based authentication systems are common in modern CE devices. However, cerebral activities clubbed with gestures will certainly enhance the security of such authentication systems. This can help consumers from being the victims of shoulder surfing attacks. In this article, a new method is proposed to verify air signatures by analyzing finger movements and cerebral activities together with the help of sensors in next-generation CE devices. Signatures are first spotted by analyzing 3D geometrical features of the finger movement during the signing. Concurrent EEG responses are then analyzed for the verification. Hidden Markov Model (HMM) and Random Forest (RF) classifiers have been used to train the system. Experiments reveal that EEG signals are highly correlated with the finger movements during air signatures even in the presence of motion artifacts. Therefore, false-positive rates have significantly reduced as compared to the existing tracking-based methods. Verification accuracy as high as 95.5% (HMM) and 98.5% (RF) have been recorded when tested on our dataset.
Retrieval of Family Members Using Siamese Neural Network

Retrieval of family members in the wild aims at finding family members of the given subject in the dataset, which is useful in finding the lost children and analyzing the kinship. However, due to the diversity in age, gender, pose and illumination of the collected data, this task is always challenging. To solve this problem, we propose our solution with deep Siamese neural network. Our solution can be divided into two parts: similarity computation and ranking. In training procedure, the Siamese network firstly takes two candidate images as input and produces two feature vectors. And then, the similarity between the two vectors is computed with several fully connected layers. While in inference procedure, we try another similarity computing method by dropping the followed several fully connected layers and directly computing the cosine similarity of the two feature vectors. After similarity computation, we use the ranking algorithm to merge the similarity scores with the same identity and output the ordered list according to their similarities. To gain further improvement, we try different combinations of backbones, training methods and similarity computing methods. Finally, we submit the best combination as our solution and our team(ustc-nelslip) obtains favorable result in the track3 of the RFIW2020 challenge with the first runner-up, which verifies the effectiveness of our method. Our code is available at: https://github.com/gniknoil/FG2020-kinship.
Concept Drift-Tolerant Transfer Learning in Dynamic Environments

Existing transfer learning methods that focus on problems in stationary environments are not usually applicable to dynamic environments, where concept drift may occur. To the best of our knowledge, the concept drift-tolerant transfer learning (CDTL), whose major challenge is the need to adapt the target model and knowledge of source domains to the changing environments, has yet to be well explored in the literature. This article, therefore, proposes a hybrid ensemble approach to deal with the CDTL problem provided that data in the target domain are generated in a streaming chunk-by-chunk manner from nonstationary environments. At each time step, a class-wise weighted ensemble is presented to adapt the model of target domains to new environments. It assigns a weight vector for each classifier generated from the previous data chunks to allow each class of the current data leveraging historical knowledge independently. Then, a domain-wise weighted ensemble is introduced to combine the source and target models to select useful knowledge of each domain. The source models are updated with the source instances performed by the proposed adaptive weighted CORrelation ALignment (AW-CORAL). AW-CORAL iteratively minimizes domain discrepancy meanwhile decreases the effect of unrelated source instances. In this way, positive knowledge of source domains can be potentially promoted while negative knowledge is reduced. Empirical studies on synthetic and real benchmark data sets demonstrate the effectiveness of the proposed algorithm.
Traffic Sign Classification Using Deep Neural Network

Technology has transformed the way we live. Artificial Intelligence has positively impacted every field and is making life easier for us. Technology aided traffic system is a remarkable breakthrough in the automotive industry. Automotive Industry is making giant leaps with the advancement of autonomous cars. Autonomous cars represent a certitude for the near future and it is expected to be safe and hassle-free. Traffic Sign Classification is one of the most important tasks of autonomous cars. Our research aims to do this classification efficiently to avoid accidents and to foster the credibility of autonomous cars. Otherwise, this could jeopardize traffic ecology. This research led to the implementation of a Traffic Sign (TS) CNN model for traffic sign classification, with the aid of the GTSRB dataset. An accuracy of 98.44% was achieved on test data.
A UAV Path Planning Algorithm Based on an Improved D* Lite Algorithm for Forest Firefighting

This paper proposes a new type of forest firefighting method. When one or more suspected fire spots are found in the forest, the firefighting Unmanned Aerial Vehicles (UAVs) can safely and quickly reach the desired location, conduct a thorough investigation of the suspected fire spots. If a fire occurs, it is necessary to drop firefighting materials to put out the fire. At the same time, considering the characteristics of limited endurance and low load capability have always limited the applications of UAVs, Unmanned Ground Vehicles (UGVs) are considered in this paper as an additional and comprehensive problem solution to provide energy and supplement materials for UAVs. In order to enable firefighting UAV to plan a safe and effective path in real time in the complex forest environment, a new improved D*Lite algorithm is proposed for UAVs in the mission team. Even when the 3D workspace is partially known or completely unknown, the algorithm can plan an effective path so that the firefighting UAV can move to the fire spots and UGV in real time.
Coded Machine Unlearning

There are applications that may require removing the trace of a sample from the system, e.g., a user requests their data to be deleted, or corrupted data is discovered. Simply removing a sample from storage units does not necessarily remove its entire trace since downstream machine learning models may store some information about the samples used to train them. A sample can be perfectly unlearned if we retrain all models that used it from scratch with that sample removed from their training dataset. When multiple such unlearning requests are expected to be served, unlearning by retraining becomes prohibitively expensive. Ensemble learning enables the training data to be split into smaller disjoint shards that are assigned to non-communicating weak learners. Each shard is used to produce a weak model. These models are then aggregated to produce the final central model. This setup introduces an inherent trade-off between performance and unlearning cost, as reducing the shard size reduces the unlearning cost but may cause degradation in performance. In this paper, we propose a coded learning protocol where we utilize linear encoders to encode the training data into shards prior to the learning phase. We also present the corresponding unlearning protocol and show that it satisfies the perfect unlearning criterion. Our experimental results show that the proposed coded machine unlearning provides a better performance versus unlearning cost trade-off compared to the uncoded baseline.