Reviewerly/exHarmony huggingface dataset:
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms

Earthquake is one of the natural disasters that have a big impact on society. Currently, there are many studies on earthquake detection. However, the vibrations that were detected by sensors were not only vibrations caused by the earthquake, but also other vibrations. Therefore, this study proposed an earthquake multi-classification detection with machine learning algorithms that can distinguish earthquake and non-earthquake, and vandalism vibration using acceleration seismic waves. In addition, velocity and displacement as integration products of acceleration have been considered additional features to improve the performances of machine learning algorithms. Several machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) have been used to develop the best algorithm for earthquake multi-classification detection. The results of this study indicate that the ANN algorithm is the best algorithm to distinguish between earthquake and non-earthquake, and vandalism vibrations. Moreover, it's also more resistant to various input features. Furthermore, using velocity and displacement as additional features has been proven to increase the performance of every model.
Controllable and Guided Face Synthesis for Unconstrained Face Recognition

Although significant advances have been made in face recognition (FR), FR in unconstrained environments remains challenging due to the domain gap between the semi-constrained training datasets and unconstrained testing scenarios. To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space. CFSM learns a linear subspace with orthogonal bases in the style latent space with precise control over the diversity and degree of synthesis. Furthermore, the pre-trained synthesis model can be guided by the FR model, making the resulting images more beneficial for FR model training. Besides, target dataset distributions are characterized by the learned orthogonal bases, which can be utilized to measure the distributional similarity among face datasets. Our approach yields significant performance gains on unconstrained benchmarks, such as IJB-B, IJB-C, TinyFace and IJB-S ( $${+}5.76\%$$ Rank1). Code is available at http://cvlab.cse.msu.edu/project-cfsm.html .
3D Siamese Transformer Network for Single Object Tracking on Point Clouds

Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area. Due to the large appearance variation between the template and search area during tracking, how to learn the robust cross correlation between them for identifying the potential target in the search area is still a challenging problem. In this paper, we explicitly use Transformer to form a 3D Siamese Transformer network for learning robust cross correlation between the template and the search area of point clouds. Specifically, we develop a Siamese point Transformer network to learn shape context information of the target. Its encoder uses self-attention to capture non-local information of point clouds to characterize the shape information of the object, and the decoder utilizes cross-attention to upsample discriminative point features. After that, we develop an iterative coarse-to-fine correlation network to learn the robust cross correlation between the template and the search area. It formulates the cross-feature augmentation to associate the template with the potential target in the search area via cross attention. To further enhance the potential target, it employs the ego-feature augmentation that applies self-attention to the local k-NN graph of the feature space to aggregate target features. Experiments on the KITTI, nuScenes, and Waymo datasets show that our method achieves state-of-the-art performance on the 3D single object tracking task. Source code is available at https://github.com/fpthink/STNet .
An AI-Based Medical Chatbot Model for Infectious Disease Prediction

The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.
Multi-Goal Multi-Agent Pickup and Delivery

In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where agents constantly engage with new tasks and need to plan collision-free paths to execute them. To execute a task, an agent needs to visit a pair of goal locations, consisting of a pickup location and a delivery location. We propose two variants of an algorithm that assigns a sequence of tasks to each agent using the anytime algorithm Large Neighborhood Search (LNS) and plans paths using the Multi-Agent Path Finding (MAPF) algorithm Priority-Based Search (PBS). LNS-PBS is complete for well-formed MAPD instances, a realistic subclass of MAPD instances, and empirically more effective than the existing complete MAPD algorithm CENTRAL. LNS-wPBS provides no completeness guarantee but is empirically more efficient and stable than LNS-PBS. It scales to thousands of agents and thousands of tasks in a large warehouse and is empirically more effective than the existing scalable MAPD algorithm HBH+MLA*. LNS-PBS and LNS-wPBS also apply to a more general variant of MAPD, namely the Multi-Goal MAPD (MG-MAPD) problem, where tasks can have different numbers of goal locations.
Region-of-interest based video coding strategy for rate/energy-constrained smart surveillance systems using WMSNs

Video surveillance systems that involve embedded visual sensor nodes have significant constraints due to their limited energy sources. Reducing the power consumption of the in-node processing and the required bandwidth while maintaining a high QoS is still a challenging task. The difficulty increases when a smart task must be performed on the received video in the destination. In this context, this paper proposes an energy-efficient video coding strategy based on a new and fast region-of-interest (ROI) detection method. The lightweight ROI detection method segments the frame into four regions. And the coding strategy aims to extract two different classes of the ROI for coding and transmission using variable quality levels based on their relevance. Furthermore, the strategy aims to exclude the regions of lower importance and any non-ROI that has insignificant movement. We assess the strategy’s ability to perform object recognition tasks at the destination under quality degradation. The performance results using different datasets demonstrate a better trade-off between awareness of ROI quality, energy consumption and bandwidth savings for the proposed strategy compared to other methods. This results in 96% reduction of bandwidth and 93% reduction in energy for some sequences at the expense of a 1-4 dB decrease in PSNR when compared to MJPEG standard. While the recognition accuracy of the YOLOv3 model at the destination outperforms the other techniques by about 4% to 22%.
ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images

Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies.
The Metaverse and Higher Education Institutions

The metaverse is becoming an important part of the education technology market. Immersive learning of the metaverse is especially likely to attract young learners. This article discusses metaverse's benefits to higher education institutions and the barriers these institutions are likely to face in implementing this innovation.
Witness Encryption and Null-IO from Evasive LWE

Witness encryption (WE) allows us to use an arbitrary NP statement x as a public key to encrypt a message, and the witness w serves as a decryption key. Security ensures that, when the statement x is false, the encrypted message remains computationally hidden. WE appears to be significantly weaker than indistinguishability obfuscation (iO). Indeed, WE is closely related to a highly restricted form of iO that only guarantees security for null circuits (null iO). However, all current approaches towards constructing WE under nice assumptions go through iO. Such constructions are quite complex and are unlikely to lead to practically instantiable schemes. In this work, we revisit a very simple WE and null iO candidate of Chen, Vaikuntanathan and Wee (CRYPTO 2018). We show how to prove its security under a nice and easy-to-state assumption that we refer to as evasive LWE following Wee (EUROCRYPT 2022). Roughly speaking, the evasive LWE assumption says the following: assume we have some joint distributions over matrices $$\textbf{P}$$ , $$\textbf{S}$$ and auxiliary information $$\textsf{aux}$$ such that $$({\textbf{S}\textbf{B}+ \textbf{E}},{\textbf{S}\textbf{P}+ \textbf{E}'}, \textsf{aux}) \approx _c ({\textbf{U}},{\textbf{U}'}, \textsf{aux}),$$ for a uniformly random (and secret) matrix $$\textbf{B}$$ , where $$\textbf{U}, \textbf{U}'$$ are uniformly random matrices, and $$\textbf{E},\textbf{E}'$$ are chosen from the LWE error distribution with appropriate parameters. Then it must also be the case that: $$({\textbf{S}\textbf{B}+ \textbf{E}}, \textbf{B}^{-1}(\textbf{P}),\textsf{aux}) \approx _c (\textbf{U}, \textbf{B}^{-1}(\textbf{P}),\textsf{aux}).$$ Essentially the above says that given $${\textbf{S}\textbf{B}+ \textbf{E}}$$ , getting the additional component $$\textbf{B}^{-1}(\textbf{P})$$ is no more useful than just getting the product $$({\textbf{S}\textbf{B}+ \textbf{E}})\cdot \textbf{B}^{-1}(\textbf{P}) \approx \textbf{S}\textbf{P}+ \textbf{E}'$$ .
3D Human Pose Estimation with Spatio-Temporal Criss-Cross Attention

Recent transformer-based solutions have shown great success in 3D human pose estimation. Nevertheless, to calculate the joint-to-joint affinity matrix, the computational cost has a quadratic growth with the increasing number of joints. Such drawback becomes even worse especially for pose estimation in a video sequence, which necessitates spatio-temporal correlation spanning over the entire video. In this paper, we facilitate the issue by decomposing correlation learning into space and time, and present a novel Spatio-Temporal Criss-cross attention (STC) block. Technically, STC first slices its input feature into two partitions evenly along the channel dimension, followed by performing spatial and temporal attention respectively on each partition. STC then models the interactions between joints in an identical frame and joints in an identical trajectory simultaneously by concatenating the outputs from attention layers. On this basis, we devise STCFormer by stacking multiple STC blocks and further integrate a new Structure-enhanced Positional Embedding (SPE) into STCFormer to take the structure of human body into consideration. The embedding function consists of two components: spatio-temporal convolution around neighboring joints to capture local structure, and part-aware embedding to indicate which part each joint belongs to. Extensive experiments are conducted on Human3.6M and MPI-INF-3DHP benchmarks, and superior results are reported when comparing to the state-of-the-art approaches. More remarkably, STCFormer achieves to-date the best published performance: 40.5mm P1 error on the challenging Human3.6M dataset.
The Parameterized Complexity of Abduction

Abduction belongs to the most fundamental reasoning methods. It is a method for reverse inference, this means one is interested in explaining observed behavior by finding appropriate causes. We study logic-based abduction, where knowledge is represented by propositional formulas. The computational complexity of this problem is highly intractable in many interesting settings. In this work we therefore present an extensive parameterized complexity analysis of abduction within various fragments of propositional logic together with (combinations of) natural parameters.
Context-Aware Query Selection for Active Learning in Event Recognition

Activity recognition is a challenging problem with many practical applications. In addition to the visual features, recent approaches have benefited from the use of context, e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled, entirely available beforehand, and not designed to be updated continuously, which make them unsuitable for surveillance applications. In contrast, we propose a continuous-learning framework for context-aware activity recognition from unlabeled video, which has two distinct advantages over existing methods. First, it employs a novel active-learning technique that not only exploits the informativeness of the individual activities but also utilizes their contextual information during query selection; this leads to significant reduction in expensive manual annotation effort. Second, the learned models can be adapted online as more data is available. We formulate a conditional random field model that encodes the context and devise an information-theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative queries, which are labeled by a human. These labels are combined with graphical inference techniques for incremental updates. We provide a theoretical formulation of the active learning framework with an analytic solution. Experiments on six challenging datasets demonstrate that our framework achieves superior performance with significantly less manual labeling.
Depth-Based Human Detection Considering Postural Diversity and Depth Missing in Office Environment

To realize robust human detection in an actual office work scenario, this paper proposes two ideas using top-view depth cameras. To deal with the changing geometric human shapes caused by body posture (e.g., sitting, standing, and crouching), we propose two features to describe the human upper-back shape, i.e., roundness and size of a height-continuous region. For alleviating the influences of partial loss of depth information caused by occlusions and by the absorption of infrared light, we propose an adaptive feature adjustment algorithm, which utilizes implicitly included information in the missing region. We implemented the proposed algorithm on a system with 13 depth cameras. Application to 100-hours (10 workdays) of actual office data demonstrated that the upper-back features complement the existing head-shoulder features. It also demonstrated that both of the proposals contributed to a more robust human detection and attained 97.7 % accuracy.
Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

&lt;p&gt;In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.&lt;/p&gt;
Single sample face recognition via BoF using multistage KNN collaborative coding

In this paper, we propose a multistage KNN collaborative coding based Bag-of-Feature (MKCC-BoF) method to address SSPP problem, which tries to weaken the semantic gap between facial features and facial identification. First, local descriptors are extracted from the single training face images and a visual dictionary is obtained offline by clustering a large set of descriptors with K-means. Then, we design a multistage KNN collaborative coding scheme to project local features into the semantic space, which is much more efficient than the most commonly used non-negative sparse coding algorithm in face recognition. To describe the spatial information as well as reduce the feature dimension, the encoded features are then pooled on spatial pyramid cells by max-pooling, which generates a histogram of visual words to represent a face image. Finally, a SVM classifier based on linear kernel is trained with the concatenated features from pooling results. Experimental results on three public face databases show that the proposed MKCC-BoF is much superior to those specially designed methods for SSPP problem. Moreover, it also has great robustness to expression, illumination, occlusion and, time variation.
Digital Image Correlation Method in Monitoring Deformation During Geogrid Testing

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Extreme Classification (Dagstuhl Seminar 18291)

Extreme classification is a rapidly growing research area within machine learning focusing on multi-class and multi-label problems involving an extremely large number of labels (even more than a million). Many applications of extreme classification have been found in diverse areas ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for key industrial applications such as ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry.Extreme classification has raised many new research challenges beyond the pale of traditional machine learning including developing log-time and log-space algorithms, deriving theoretical bounds that scale logarithmically with the number of labels, learning from biased training data, developing performance metrics, etc. The seminar aimed at bringing together experts in machine learning, NLP, computer vision, web search and recommendation from academia and industry to make progress on these problems. We believe that this seminar has encouraged the inter-disciplinary collaborations in the area of extreme classification, started discussion on identification of thrust areas and important research problems, motivated to improve the algorithms upon the state-of-the-art, as well to work on the theoretical foundations of extreme classification.
Investigating 3D holoscopic visual content upsampling using super-resolution for cultural heritage digitization

Through this paper, we aim at investigating the impact of using deep learning-based technologies such as super-resolution on Holoscopic 3D (H3D) images. Holoscopic 3D imaging is a technology that aims at providing cost-effective alternatives for 3D content viewing and consumption without requiring a special headgear or posture. The technique is using a special lens array fitted to standard DSLR or mirrorless cameras to generate or capture 3D content. The output is a Holoscopic 3D image that can be displayed in lightfield displays or Multiview displays following a post-processing procedure. The main advantage of this technique is its cost-effectiveness in viewing and interacting with 3D content. However, one of its drawbacks is the low spatial density of the commercial cameras CMOS sensors and the lens induced imperfections. The latter can be fixed in software using some distortion correction techniques. However, the former is still challenging in terms of techniques that result in naturally looking output. Mitigating such issues with hardware will lead to higher costs and the technique loses its main advantage. Our approach consists of designing a framework that leverages software tools in order to upscale the output of H3D cameras whilst solving the low spatial density problem of H3D images. We also investigate the impact of deep learning-based video motion interpolation on the output quality of the cultural H3D imaging framework.
Lyapunov Theory-Based Fusion Neural Networks for the Identification of Dynamic Nonlinear Systems

This paper introduces a novel fusion neural architecture and the use of a novel Lyapunov theory-based algorithm, for the online approximation of the dynamics of nonlinear systems. The proposed neural system, in combination with the proposed update rule of the neural weights, achieves fast convergence of the identification process, ensuring at the same time stability of the error system in the sense of Lyapunov theory. The fusion neural system combines the features that are extracted from two-independent neural streams, a feedforward and a diagonal recurrent one, satisfying different design criteria of the identification task. Simulation results for five cases reveal the approximation strength of both proposed fusion neural architecture and proposed learning algorithm. Also, additional experiments demonstrate the effectiveness in cases of parameter variations and additive noise.