Reviewerly/exHarmony huggingface dataset:
Deep Learning Based on Fourier Convolutional Neural Network Incorporating Random Kernels

In recent years, convolutional neural networks have been studied in the Fourier domain for a limited environment, where competitive results can be expected for conventional image classification tasks in the spatial domain. We present a novel efficient Fourier convolutional neural network, where a new activation function is used, the additional shift Fourier transformation process is eliminated, and the number of learnable parameters is reduced. First, the Phase Rectified Linear Unit (PhaseReLU) is proposed, which is equivalent to the Rectified Linear Unit (ReLU) in the spatial domain. Second, in the proposed Fourier network, the shift Fourier transform is removed since the process is inessential for training. Lastly, we introduce two ways of reducing the number of weight parameters in the Fourier network. The basic method is to use a three-by-three sized kernel instead of five-by-five in our proposed Fourier convolutional neural network. We use the random kernel in our efficient Fourier convolutional neural network, whose standard deviation of the Gaussian distribution is used as a weight parameter. In other words, since only two scalars for each imaginary and real component per channel are required, a very small number of parameters is applied compressively. Therefore, as a result of experimenting in shallow networks, such as LeNet-3 and LeNet-5, our method achieves competitive accuracy with conventional convolutional neural networks while dramatically reducing the number of parameters. Furthermore, our proposed Fourier network, using a basic three-by-three kernel, mostly performs with higher accuracy than traditional convolutional neural networks in shallow and deep neural networks. Our experiments represent that presented kernel methods have the potential to be applied in all architecture based on convolutional neural networks.
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility

Recent learning-based multi-view stereo (MVS) methods show excellent performance with dense cameras and small depth ranges. However, non-learning based approaches still outperform for scenes with large depth ranges and sparser wide-baseline views, in part due to their PatchMatch optimization over pixelwise estimates of depth, normals, and visibility. In this paper, we propose an end-to-end trainable PatchMatch-based MVS approach that combines advantages of trainable costs and regularizations with pixelwise estimates. To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals. We incorporate normal estimation by using dilated patch kernels and propose a recurrent cost regularization that applies beyond frontal plane-sweep algorithms to our pixelwise depth/normal estimates. We evaluate our method on widely used MVS benchmarks, ETH3D and Tanks and Temples (TnT). On ETH3D, our method outperforms other recent learning-based approaches and performs comparably on advanced TnT.
A Feature Selection Approach for Fall Detection Using Various Machine Learning Classifiers

Falls are one of the most serious dangers for elderly people who live alone at home.It has become a widespread issue all across the world.Reliable fall detection systems can help to mitigate the negative consequences of accidentally falling.Several techniques for automatically falling detection machines have been suggested in the past.The existing technologies are classified into three types of fall detectors: wearable sensor-based, ambient device-based, and computer vision-based approaches.This paper focuses on a dataset comprising signals from wearable sensors, ambient sensors, and vision devices.We propose a novel feature subset selection to reduce the number of effective input attributes based on a hybridized metaheuristic -an Adaptive Particle Swarm and Grey Wolf Optimization (APGWO).Classification results use various machine learning classifiers such as Logistic Regression (LR), K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), and Multilayer Perceptron (MLP), show that the proposed approach is highly effective.Classification accuracy and F 1 score can reach as high as 99% and 96%, respectively.
Detection of weak electromagnetic interference attacks based on fingerprint in IIoT systems

In Industrial Internet of Things (IIoT) systems, the intelligent devices are vulnerable to be attacked by weak Electromagnetic Interference (EMI), thereby threatening the security of the systems. Therefore, it is of great significance to investigate the weak EMI attack of IIoT systems. The different manufacturing processes and deployment environments make the intelligent devices carry different noises, called fingerprints, which are unchanged unless these intelligent devices are attacked. Hence, we can detect weak EMI attacks by judging whether the fingerprint of intelligent device has been changed, which is different from using professional detection equipment as in other methods. Based on the fingerprint of intelligent device, this paper proposes a highly efficient weak EMI attack detection method which is divided into three steps. First, the fingerprint is extracted by Linear Time-Invariant (LTI) model and Kalman algorithm. Second, according to the extracted fingerprint, a fusion model is designed to determine whether the device is attacked by weak EMI. In the fusion model, Feature Extraction Unit (FEU) combines with Long Short-Term Memory (LSTM) to improve the detection accuracy. Finally, an edge computing framework is proposed to enhance the efficiency of the method. The experimental results show that the detection accuracy and the efficiency of the proposed method are 5.2% and 42.2% higher than those of the state-of-the-art method, respectively.
HIAuth: A Hierarchical Implicit Authentication System for IoT Wearables Using Multiple Biometrics

Day-by-day wearable devices, such as smartwatches, are getting popular with their wide range of services, including allowing financial transactions, unlocking cars, tracking health and fitness, among many others.Most often, these services are managed based on users' personal data.Unfortunately, due to various limitations, most of the market-wearables either do not have any user authentication or have a knowledge-based authentication, such as passwords, PINs, or pattern locks, which are not only burdensome for users in the age of the internet of things (IoT) when the users are already flooded with so many passwords.Therefore, there is a need for a burden-free implicit authentication mechanism for wearables that can utilize different less-informative soft-biometric data easily obtainable from the market wearables.In this work, we present a hierarchical implicit authentication (HIAuth) system that utilizes the heart rate, gait, and breathing audio signals based on their availability to authenticate a user.From our detailed analysis, we find that binary support vector machine (SVM) classifiers with radial basis function (RBF) kernels can achieve an average accuracy of 0.98±0.04(non-sedentary) and 0.94±0.03(sedentary), F 1 score of 0.98±0.03(non-sedentary) and 0.94±0.04(sedentary), and genuine rejection rate of about 0.97±0.07(non-sedentary) and 0.93±0.04(sedentary), which shows the feasibility and promise of this work.
Social Bots and Their Coordination During Online Campaigns: A Survey

Online social networks (OSNs) are a major component of societal digitalization. OSNs alter how people communicate, make decisions, and form or change their beliefs, attitudes, and behaviors. Thus, they can now impact social groups, financial systems, and political communication at scale. As one type of OSN, social media platforms, such as Facebook, Twitter, and YouTube, serve as outlets for users to convey information to an audience as broad or targeted as the user desires. Over the years, these social media platforms have been infected with automated accounts, or bots, that are capable of hijacking conversations, influencing other users, and manipulating content dissemination. Although benign bots exist to facilitate legitimate activities, we focus on bots designed to perform malicious acts through social media platforms. Bots that mimic the social behaviors of humans are referred to as social bots. Social bots help automate sociotechnical behaviors, such as “liking” tweets, tweeting/retweeting a message, following users, and coordinating with or even competing against other bots. Some advanced social bots exhibit highly sophisticated traits of coordination and communication with complex organizational structures. This article presents a detailed survey of social bots, their types and behaviors, and how they impact social media, identification algorithms, and their coordination strategies in OSNs. The survey also discusses coordination in areas such as biological systems, interorganizational networks, and coordination games. Existing research extensively studied bot detection, but bot coordination is still emerging and requires more in-depth analysis. The survey covers existing techniques and open research issues on the analysis of social bots, their behaviors, and how social network theories can be leveraged to assess coordination during online campaigns.
Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network

In the era of Web 2.0, there is a huge amount of user-generated content, but the huge amount of unstructured data makes it difficult for merchants to provide personalized services and for users to extract information efficiently, so it is necessary to perform sentiment analysis for restaurant reviews. The significant advantage of Bi-GRU is the guaranteed symmetry of the hidden layer weight update, to take into account the context in online restaurant reviews and to obtain better results with fewer parameters, so we combined Word2vec, Bi-GRU, and Attention method to build a sentiment analysis model for online restaurant reviews. Restaurant reviews from Dianping.com were used to train and validate the model. With F1-score greater than 89%, we can conclude that the comprehensive performance of the Word2vec+Bi-GRU+Attention sentiment analysis model is better than the commonly used sentiment analysis models. We applied deep learning methods to review sentiment analysis in online food ordering platforms to improve the performance of sentiment analysis in the restaurant review domain.
On posterior contraction of parameters and interpretability in Bayesian mixture modeling

We study posterior contraction behaviors for parameters of interest in the context of Bayesian mixture modeling, where the number of mixing components is unknown while the model itself may or may not be correctly specified. Two representative types of prior specification will be considered: one requires explicitly a prior distribution on the number of mixture components, while the other places a nonparametric prior on the space of mixing distributions. The former is shown to yield an optimal rate of posterior contraction on the model parameters under minimal conditions, while the latter can be utilized to consistently recover the unknown number of mixture components, with the help of a fast probabilistic post-processing procedure. We then turn the study of these Bayesian procedures to the realistic settings of model misspecification. It will be shown that the modeling choice of kernel density functions plays perhaps the most impactful roles in determining the posterior contraction rates in the misspecified situations. Drawing on concrete posterior contraction rates established in this paper we wish to highlight some aspects about the interesting tradeoffs between model expressiveness and interpretability that a statistical modeler must negotiate in the rich world of mixture modeling.
Occlusion-robust online multi-object visual tracking using a GM-PHD filter with CNN-based re-identification

We propose a novel online multi-object visual tracker using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has a linear complexity with the number of objects and observations while estimating the states and cardinality of time-varying number of objects, however, it is susceptible to miss-detections and does not include the identity of objects. We use visual-spatio-temporal information obtained from object bounding boxes and deeply learned appearance representations to perform estimates-to-tracks data association for target labeling as well as formulate an augmented likelihood and then integrate into the update step of the GM-PHD filter. We also employ additional unassigned tracks prediction after the data association step to overcome the susceptibility of the GM-PHD filter towards miss-detections caused by occlusion. Extensive evaluations on MOT16, MOT17 and HiEve benchmark data sets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy and identification.
Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder

Deep clustering attempts to capture the feature representation that benefits the clustering issue. Although the existing deep clustering methods have achieved encouraging performance in many research fields, there still present some shortcomings, such as the lack of consideration of local structure retention and sparse characteristics of input data. To this end, we propose a deep stacked sparse embedded clustering method in this paper, which considers both the local structure preservation and sparse property of inputs. The proposed method is trained to capture the feature representation for an input data by the guidance of the clustering and reconstruction loss, where the reconstruction loss prevents the corruption of feature space and preserve the local structure. Besides, sparse constraint is added to the encoder to avoid learning of unimportant features. Through simultaneously minimizing the reconstruction and clustering loss, the proposed method is able to jointly learn the clustering oriented features and optimize the assignment of cluster labels. Then we conduct amounts of comparative experiments, which consists of seven clustering methods and six publicly available image data sets. Eventually, comprehensive experiments validate the effectiveness of introducing sparse property and preserving local structure in the proposed method.
Context-aware recommender systems and cultural heritage: a survey

Abstract In the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.
Early Prediction of Breast Cancer by using Artificial Neural Network and Machine Learning Techniques

Acoustic For a limited number of disorders, early diagnosis of any illness may be curable to mankind's commitment. Before it becomes chronic, most persons fail to detect their illness. This adds to a global rise in mortality rates. Breast cancer is one of the cancers that can be treated until it progresses to all areas of the body as the condition is diagnosed at early stages. Breast cancer primarily affects women and it is also an important factor in raising the rate of female mortality. We are both mindful that the diagnosis of breast cancer is very time-consuming. On the other hand, the availability of technology used to diagnose early-stage cancer is very limited. Different algorithms for Machine Learning and Deep Learning have been used to distinguish benign and malignant tumors. UCI Wisconsin database containing 569 samples and 31 features is included in these papers. The paper focuses on numerous models that are applied to the dataset taken, such as K Nearest Neighbor, Support Vector Machine, Random Forest, Naive Bays, Logistic Regression, Gradient Booster, and Artificial Neural Network (ANN), etc. In terms of accuracy, cross-validation, sensitivity, and specificity gained, each of these algorithms was calculated and compared. From the experiments, we come to the solution that Random Forest gives the best accuracy is 98.83% and the and the worst algorithm K-nearest Neighbors accuracy is 91.22%. Deep learning algorithms ANN have been applied to improve prediction accuracy. The overall accuracy reached in the case of ANN 99.73%, respectively. There are two types of activation functions used for forecasting namely Relu and Sigmoid.
Continual Multi-Agent Interaction Behavior Prediction With Conditional Generative Memory

Multi-agent trajectory prediction plays a crucial role in robotics and autonomous driving. The current mainstream research focuses on how to achieve accurate prediction on one large dataset. However, whether the multi-agent trajectory prediction model can be trained with a sequence of datasets, i.e., continual learning settings, remains a question. Can the current prediction methods avoid catastrophic forgetting? Can we utilize the continual learning strategy in the multi-agent trajectory prediction application? Motivated by the generative replay methods in continual learning literature, we propose a multi-agent interaction behavior prediction framework with a graph-neural-network-based conditional generative memory system to mitigate catastrophic forgetting. To the best of our knowledge, this work is the first attempt to study the continual learning problem in multi-agent interaction behavior prediction problems. We empirically show that several approaches in literature indeed suffer from catastrophic forgetting, and our approach succeeds in maintaining a low prediction error when datasets come in a sequential way. We also conduct an ablation analysis to show the effectiveness of our proposed approach.
Explainable Person Re-Identification with Attribute-guided Metric Distillation

Despite the great progress of person re-identification (ReID) with the adoption of Convolutional Neural Networks, current ReID models are opaque and only outputs a scalar distance between two persons. There are few methods providing users semantically understandable explanations for why two persons are the same one or not. In this paper, we propose a post-hoc method, named Attribute-guided Metric Distillation (AMD), to explain existing ReID models. This is the first method to explore attributes to answer: 1) what and where the attributes make two persons different, and 2) how much each attribute contributes to the difference. In AMD, we design a pluggable interpreter network for target models to generate quantitative contributions of attributes and visualize accurate attention maps of the most discriminative attributes. To achieve this goal, we propose a metric distillation loss by which the interpreter learns to decompose the distance of two persons into components of attributes with knowledge distilled from the target model. Moreover, we propose an attribute prior loss to make the interpreter generate attribute-guided attention maps and to eliminate biases caused by the imbalanced distribution of attributes. This loss can guide the interpreter to focus on the exclusive and discriminative attributes rather than the large-area but common attributes of two persons. Comprehensive experiments show that the interpreter can generate effective and intuitive explanations for varied models and generalize well under cross-domain settings. As a by-product, the accuracy of target models can be further improved with our interpreter. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Online Multi-Granularity Distillation for GAN Compression

Generative Adversarial Networks (GANs) have witnessed prevailing success in yielding outstanding images, however, they are burdensome to deploy on resource-constrained devices due to ponderous computational costs and hulking memory usage. Although recent efforts on compressing GANs have acquired remarkable results, they still exist potential model redundancies and can be further compressed. To solve this issue, we propose a novel online multi-granularity distillation (OMGD) scheme to obtain lightweight GANs, which contributes to generating highfidelity images with low computational demands. We offer the first attempt to popularize single-stage online distillation for GAN-oriented compression, where the progressively promoted teacher generator helps to refine the discriminator-free based student generator. Complementary teacher generators and network layers provide comprehensive and multi-granularity concepts to enhance visual fidelity from diverse dimensions. Experimental results on four benchmark datasets demonstrate that OMGD successes to compress 40× MACs and 82.5× parameters on Pix2Pix and CycleGAN, without loss of image quality. It reveals that OMGD provides a feasible solution for the deployment of real-time image translation on resource-constrained devices. Our code and models are made public at: https://github.com/bytedance/OMGD
Automatic Speech Recognition Using Limited Vocabulary: A Survey

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Wanderlust: Online Continual Object Detection in the Real World

Online continual learning from data streams in dynamic environments is a critical direction in the computer vision field. However, realistic benchmarks and fundamental studies in this line are still missing. To bridge the gap, we present a new online continual object detection benchmark with an egocentric video dataset, Objects Around Krishna (OAK). OAK adopts the KrishnaCAM videos, an ego-centric video stream collected over nine months by a graduate student. OAK provides exhaustive bounding box annotations of 80 video snippets (~17.5 hours) for 105 object categories in outdoor scenes. The emergence of new object categories in our benchmark follows a pattern similar to what a single person might see in their day-to-day life. The dataset also captures the natural distribution shifts as the person travels to different places. These egocentric long running videos provide a realistic playground for continual learning algorithms, especially in online embodied settings. We also introduce new evaluation metrics to evaluate the model performance and catastrophic forgetting and provide baseline studies for online continual object detection. We believe this benchmark will pose new exciting challenges for learning from non-stationary data in continual learning. The OAK dataset and the associated benchmark are released at https://oakdata.github.io/.