Reviewerly/exHarmony huggingface dataset:
Embedding with Bounding Box Contracting for Multi-object Tracking

The development of 5G/6G network can achieve high data transmission speed, which promotes the wide application of remote video monitoring. Multi-object tracking (MOT) aims at detecting and tracking all the objects of interesting categories in videos. Appearance and motion information of each object are significant clues utilized for finding associations between detections and tracks. Many approaches model each object appearance through bounding box region, which is vulnerable to background noise and motion deformation. In this paper, we alleviate this problem, via embedding with object bounding box contracting. We also integrate an online tracking by detection model, comprehensive use of appearance and motion information for data association. Object bounding box contracting is introduced to relieve the impact of interference and obtain high-quality re-ID embeddings. Experimental results based on the MOT17 benchmark show that the integrated tracker with bounding box contracting for embedding achieves 80.6 MOTA, 79.4 IDF1 and 64.4 HOTA.
Prevention of recurrence and relapse of tinea incognito W.S.R. to Dadru

Export Introduction: Fungal infections are the most common infection of skin in India. Recurrence and relapse are very common. Incomplete treatment and use of steroid is responsible for resistant Tinea incognito. Main Clinical Finding: A female patient of 23-year-old had erythematous papulo-vesicular lesions over the right underarm and inguinal region from the last 1 year. History of temporary relief due to modern medicine with relapse was observed. Striae marks were evident over thighs indicating skin atrophy. Diagnosis: Patient was diagnosed with Tinea incognito (Ayurvedic diagnosis Dadru with Dooshivisha as a causative factor). Interventions: Patient was successfully managed with Dooshivishahara and RaktaPrasadakChikitsa. Outcome: Improvement was evident after 3 weeks of therapy. No recurrence was reported till date. Conclusions: Ayurvedic approach to Tinea incognito not only helps to reduce relapse and recurrence but also physical discomfort and financial burden on patient.
Design and Implementation of Dual Channel Speech Signal Transceiver System Based on FPGA

Real-time speech information has always been the main way of information transmission, but there are still problems in transmission. Firstly, while speech signals are easy to transmit information, they also bring information leakage and malicious tampering. Secondly, speech signals are more or less affected by noise, interference and transmission effects in the transmission process, which will make the recovery of speech signals more complicated. Based on the above, the wireless transmission of speech signals is taken as the background to realize the merging and separation of dual-channel speech signals. The problems of information leakage and distortion during the transmission of speech signals are studied. The encryption and decryption of speech signals are taken as the key points. Realize dual speech signal receiving and receiving based on Field Programmable Gate Array (FPGA). The frequency division multi-plexing method is used to transmit dual speech signals simultaneously, realize speech signal convolution on FPGA and extract two speech signals from the convolution signal. AES algorithm is realized the encryption and decryption of on FPGA. 2FSK modulation and demodulation in digital modulation is selected to complete the design and implementation of the whole system, and the research is completed through the final overall modulation and analysis.
6G Network Traffic Intrusion Detection Using Multiresolution Auto-encoder and Feature Matching Discriminator

With the development of 6G technology, security and privacy have become extremely important in the face of larger network traffic bandwidth. An effective intrusion detection system can deal with the network attacks. Deep learning has been developed in the field of intrusion detection, which can identify normal and abnormal traffic. However, existing methods cannot guarantee good performance in accuracy and efficiency. In this paper, based on the autoencoder and generative adversarial network, the multiresolution autoencoder is adopted in the network traffic feature extraction, which can obtain different encoding lengths and guarantee better data reconstruction. In addition, we add an extra feature matching loss to encourage the discriminator to get more discriminative information from the reconstructed samples. Our experimental results on the CIC-IDS2018 dataset indicates that compared with autoencoder and generative adversarial network, our model can effectively improve the detection accuracy and can be applied to 6G network traffic security detection.
Design and Implementation of Garbage Classification System Based on Convolutional Neural Network

With the development of society, intelligent garbage classification was totally indispensable in life, and it was the key to intelligent garbage classification for the more superior image recognition technology. In order to solve the key problem of garbage identification technology, this paper designed a garbage image classification system based on transfer learning network model to recognize and classify a variety of common garbage images. By comparing the performance of the pretrained models of Alexnet, VGG, Res-Net and Mobile-Net, the optimal recognition accuracy reached more than 93%, and the most suitable network model for deployment and Mobile Terminal was selected, which provided technical support for intelligent garbage identification.
Trust-region optimization

As discussed in the previous chapters, high-order functions are useful but, unfortunately, not amenable to powerful discrete optimizers such as graph cuts. This chapter discusses iterative trust-region optimization, which splits a difficult problem into a sequence of easier subproblems, each corresponding to a local approximation of the high-order terms in the objective function. The approximation is “trusted” only within a region in the vicinity of the current solution. At each iteration, the global optimum of the approximation is evaluated, and the size of the trust region is updated, using some measure of the quality of the current approximation. We discuss how to use the trust-region framework in image segmentation, as introduced in the work by Gorelick et al. (2013) [1], and detail a specific medical-imaging example based on a high-order term enforcing a shape prior. We also provide details of how to derive approximations of high-order functions using Gateâux derivatives.
Online sequential nontuned neural network—coding and implementation

This chapter introduces a new online sequential learning approach that is known as the online sequential extreme learning machine (OSELM). The OSELM consists of the same main learning framework as the original extreme learning machine (ELM) with the bias of hidden neurons and input weights being assigned randomly while the output weights are calculated using an analytical process. The main difference between the OSELM and the original ELM algorithm is the approach taken in calculating the model output weights. The OSELM considers online learning (where the training observations are received training observations sequentially, either one by one or chunk by chunk (i.e., a block of data) when determining the output weights while the classical ELM employs batch learning algorithms in the resolution of the output weights.
Regularized mutual information

This chapter presents information-theoretic perspectives for several popular segmentation algorithms based on regularized likelihood model fitting. Image segmentation can be viewed as maximizing the mutual information between the input's features and a latent labeling, subject to spatial regularity constraints. We will examine the link between maximum likelihood (ML) estimation and entropy minimization via a generative view of the mutual information. Then, we will examine a different, discriminative view of the mutual information that reveals interesting connections to discriminative clustering models. These mutual-information insights explain some biases that are observed in practice for popular ML-based segmentation algorithms, for instance, biases towards balanced partitions. This prescribes principled methodologies for dealing with such artifacts via optimizing some high-order functionals. The discussion is supported by several experimental illustrations, including real and synthetic data.
Markov random fields

This introductory chapter discusses basic concepts in Markov Random Fields (MRFs), which are a mainstay in computer vision. MRFs represent images as graphs, whose nodes are associated with hidden variables, e.g., label assignments describing a segmentation of the image. Such graph representations are very convenient because they connect explicitly the hidden variables. We will discuss the most basic form of connections, which involve pairs of nodes and can be viewed as weighted edges on the graph. Such pairwise connections encode statistical dependences between the hidden variables, typically embedding some prior knowledge that we have about the problem. We will take pairwise regularization priors, which are very popular in computer vision, as examples. Also, we will examine these graphical models from Bayesian-estimation and energy-minimization perspectives, and discuss how inferences with respect to the hidden variables can benefit from powerful and scalable optimization techniques.
Regularized model fitting

This chapter discusses standard segmentation formulations based on likelihood model fitting and regularization. Generally, this class of techniques optimizes high-order functionals that integrate two types of terms, one data term penalizing the deviation of image features within each region from parametric statistical models and one regularizer evaluating segmentation boundary length. Reviewed are very popular algorithms such as GrabCut (Rother et al., 2004 [1]) and the Chan–Vese model (Chan and Vese, 2001 [2]), among others. We will examine the link between these segmentation formulations and generative data-clustering objectives such as K-means and its probabilistic (Kearns et al., 1997 [3]) or kernel-induced (Carreira-Perpiñán, 2016 [4]) generalizations.
Examples of high-order functionals

This chapter discusses several types of high-order functionals and their practical usefulness in image segmentation. This includes priors on the sizes and shapes of the target regions, balanced graph-clustering objectives such as the popular normalized cut, and distribution-matching priors. The chapter is supported by several experimental examples.
Capped norm linear discriminant analysis and its applications

Classical linear discriminant analysis (LDA) is based on squared Frobenious norm and hence is sensitive to outliers and noise. To improve the robustness of LDA, this paper introduces a capped l2,1-norm of a matrix, which employs non-squared l2-norm and “capped” operation, and further proposes a novel capped l2,1-norm linear discriminant analysis, called CLDA. Due to the use of capped l2,1-norm, CLDA can effectively remove extreme outliers and suppress the effect of noise data. In fact, CLDA can also be viewed as a weighted LDA and is solved through a series of generalized eigenvalue problems. The experimental results on an artificial data set, some UCI data sets and two image data sets demonstrate the effectiveness of CLDA.
Mean-field inference

This chapter discusses variational mean-field inference, which is widely used in computer vision for tackling efficiently conditional random fields (CRFs). We will focus on the parallel message-passing algorithm of Krähenbühl and Koltun (2011) [1], which has become the de facto choice in recent semantic segmentation models (Arnab et al., 2018 [2]). After develop a bound-optimization perspective of this message-passing technique, we will examine a generalization that enables parallel and convergent updates for a general form of pairwise CRFs. We will discuss the aspects related to computational scalability for computer vision problems with large numbers (millions) of variables as is the case of semantic segmentation.
Constrained deep networks

Imposing priors on deep networks can mitigate the lack/uncertainty of annotations in weakly supervised learning, leveraging unlabeled samples with domain-specific knowledge. The previous chapter discussed conditional random field (CRF) regularization losses for weakly supervised CNN segmentation. In this chapter, we focus on the setting in which prior knowledge takes the form of inequality constraints on the outputs of deep networks. For instance, in medical imaging, it is common to have some prior knowledge about the size of the target organ. Such knowledge does not have to be precise and may take the imprecise form of lower and upper bounds on segmentation-region size. We will discuss how to enforce this type of priors using inequality constraints on functions of the network outputs, and the usefulness of such constrained formulations in the context of weakly supervised CNN segmentation. Furthermore, we will give detailed descriptions of various optimization strategies for these constrained-CNN models, including Lagrangian-dual optimization and penalty methods for handling inequality constraints.
Non-tuned single-layer feed-forward neural network learning machine—concept

In this chapter, the extreme learning machine (ELM) as a fast and nontuned powerful ML approach will be presented and discussed in detail. The fact that the ELM does not require fine-tuning of parameters is an important advantage when compared to classical ML-based approaches. In the following subsection, the background of the ELM is provided as well as a discussion on the advantages of this modeling technique. Following this, the fundamental mathematical equations that form the basis of the ELM are presented. The mathematical foundation of the ELM is a requirement for advanced users seeking to develop novel ELM codes for specific applications. For beginner users, such a deep understanding of the ELM mathematical background is not a requirement for the successful employment of the algorithm in solving practical problems. To facilitate the application of the ELM for beginner users, a novel calculator is proposed and described in detail.
Self-adaptive evolutionary of non-tuned neural network—concept

In the extreme learning machine (ELM), the input weight and bias of hidden layer matrices are randomly assigned, leaving only the output weight (OutW) matrix to be determined analytically during the training phase. Indeed, the ELM converts what is originally a nonlinear problem to a linear one, resulting in a rapid training process with significantly reduced training times compared to traditional approaches (back-propagation). While the random assignment of these two matrices dramatically reduces the required training effort, it could significantly impact the ELM results. Therefore, this chapter introduces an improved learning algorithm for SLFFNN known as the self-adaptive evolutionary extreme learning machine (SaE-ELM). Similar to the ELM, the OutW in the SaE-ELM is calculated using the generalized inverse of Moore–Penrose, while a self-adaptive differential evolution algorithm optimizes the hidden node parameters of the single-layer feed-forward neural network.
Random field losses for deep networks

Deep convolutional neural networks (CNNs) are currently dominating semantic segmentation problems, yielding ground-breaking results when full-supervision is available, in a breadth of computer vision and medical-imaging applications. The main limitation of such fully supervised models is that they require a large amount of training data, which, in the case of semantic segmentation, needs prohibitively time-consuming human-expert annotations of all the pixels in the image. Such dense annotations may not be practical in a breadth of applications, particularly when dealing with volumetric data, as in medical imaging, or with massive amounts of high-resolution video data. Weakly supervised learning methods, which do not require full annotations and scale up to large problems and data sets, are currently attracting substantial research interest. This chapter discusses CNN segmentation in the weakly supervised setting, in which case some of the pairwise or high-order regularization models discussed in this book are shown to be very helpful, approaching full-supervision performance levels. More specifically, we will discuss several random field loss functions that impose regularization and edge-alignment priors on the probability outputs of a deep network. Such priors leverage unlabeled samples, mitigating the lack/uncertainty of annotations. For instance, adding unsupervised loss terms, such as pairwise conditional random fields (CRFs) or high-order balanced graph clustering, can achieve performance levels close to full-supervision results, but using only fractions of the ground-truth labels. We will discuss in great detail various optimization strategies for random field losses in the context of CNN segmentation. In particular, we discuss a general alternating direction method (ADM) strategy for training neural networks that makes it possible to directly take advantage of powerful discrete graph-cut solvers for random field losses, e.g., α-expansion, providing an effective optimization alternative to standard gradient descent.
Outlier-based models of the non-tuned neural network—coding and implementation

As a type of single-layer feed-forward neural network, the extreme learning machine (ELM) has recently garnered substantial interest from academics and scholars due to its exceptional capacity for rapid training and development, as well as its respectable ability to generalize. When dealing with real-world data sets, ELM models can be unreliable and have weak predictive capacities due to the often present heteroscedasticity within the data. Numerous ELM-based techniques capable of handling such volatility in data sets are illustrated in this chapter to address this shortcoming of the ELM model. To begin the chapter, a brief review of the background of the ELM technique is presented, following this, the detailed mathematical formulations of four ELM-based models are presented, including the classical ELM, regularized ELM (RELM), weighted regularized ELM (WRELM), and outlier-robust ELM (ORELM). Although an in-depth understanding of the formulation of these methods requires basic knowledge of ELM and coding in MATLAB, beginner users can easily apply the models developed in this chapter without having to venture into the details presented in the following sections. Following the presentation of the three developed ELM-based models, a sensitivity analysis is conducted for all user-defined parameters by considering five example cases. The user-defined parameters to be investigated include the regularization parameters, the weight function in the WRELM, and the maximum number of iterations in the ORELM. In addition to presenting an in-depth mathematical development and model description for advanced users, this chapter employs a calculator for beginner users, which allows for simple application of the methodologies without deep knowledge of MATLAB and the structure of the ELM and its developments. Indeed, the information presented in the current chapter is presented for both beginners and advanced users.
Graph cuts

This chapter discusses combinatorial graph cuts, which have made a substantial impact in computer vision for optimizing effectively an important class of conditional random field (CRF) objectives, as are commonly used in image segmentation. These powerful discrete optimizers can provide exact global optima for binary-labeling problems and competitive approximate solutions with optimality bounds for multi-label problems. We will discuss the basics of finding the minimum-cut/maximum-flow of an S-T graph for binary-labeling problems, along with standard max-flow algorithms, including the very popular Boykov–Kolmogorov (BK) algorithm (Boykov and Kolmogorov, 2004 [1]). Furthermore, we will detail well-known move-making algorithms for tackling multi-label problems (Boykov et al., 2001 [2]). We will discuss optimality guarantees, the conditions under which these powerful solvers could be used, as well as computational-efficiency aspects for computer vision problems.
Non-tuned single-layer feed-forward neural network learning machine—coding and implementation

One of the most critical features of machine learning (ML) algorithms is the fact that the model carries out the learning process without any human intervention. It has been demonstrated that, with the advancement of computing technologies, it is possible to develop ML algorithms that are not only capable of independently accessing information, but also of self-learning. In this chapter, a detailed discussion of the extreme learning machine (ELM) and its associated coding is provided. Compared to classical ML-based approaches, a critical advantage of the ELM is that it is a nontuned ML approach. Prior to starting the modeling process, traditional ML-based approaches (such as neural networks) require numerous parameters to be fine-tuned, with the model outcome being highly sensitive to the value of these various parameters. Unlike classical ML-based approaches, however, the ELM only requires the adjustment of the activation function type and the number of hidden neurons.
Outlier-based models of the non-tuned neural network—concept

The extreme learning machine (ELM) is a form of single-layer feed-forward neural network. It has recently garnered much attention from scholars and academics due to its ability for rapid model training and development as well as its respectable generalization potential. However, irrespective of these advantages, there remains a great difficulty in modeling real-world applications with the ELM model. Real data sets encountered in practice often contain a heteroscedastic nature, which can result in an unreliable ELM model with a poor predictive capacity. In order to address this shortfall in the application of ELM models, this chapter will seek to present several ELM-based methodologies which are capable of handling such variation in the data sets. To begin the chapter, a brief review of the ELM technique, as well as its advantages and disadvantages (the reader is directed to Chapter 4 for a more detailed discussion of advantages and disadvantages), is presented. Following this, the mathematical formulation of MATLAB programming is presented in complete detail for four ELM-based models, including (1) classical ELM, (2) regularized ELM, (3) weighted regularized ELM, (4) outlier-robust ELM.