Reviewerly/exHarmony huggingface dataset:
Interactive Object Segmentation With Inside-Outside Guidance

This paper explores how to harvest precise object segmentation masks while minimizing the human interaction cost. To achieve this, we propose an Inside-Outside Guidance (IOG) approach in this work. Concretely, we leverage an inside point that is clicked near the object center and two outside points at the symmetrical corner locations (top-left and bottom-right or top-right and bottom-left) of a tight bounding box that encloses the target object. This results in a total of one foreground click and four background clicks for segmentation. The advantages of our IOG is four-fold: 1) the two outside points can help to remove distractions from other objects or background; 2) the inside point can help to eliminate the unrelated regions inside the bounding box; 3) the inside and outside points are easily identified, reducing the confusion raised by the state-of-the-art DEXTR in labeling some extreme samples; 4) our approach naturally supports additional clicks annotations for further correction. Despite its simplicity, our IOG not only achieves state-of-the-art performance on several popular benchmarks, but also demonstrates strong generalization capability across different domains such as street scenes, aerial imagery and medical images, without fine-tuning. In addition, we also propose a simple two-stage solution that enables our IOG to produce high quality instance segmentation masks from existing datasets with off-the-shelf bounding boxes such as ImageNet and Open Images, demonstrating the superiority of our IOG as an annotation tool.
SESS: Self-Ensembling Semi-Supervised 3D Object Detection

The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D annotations. However, such annotations are often tedious and expensive to collect. Semi-supervised learning is a good alternative to mitigate the data annotation issue, but has remained largely unexplored in 3D object detection. Inspired by the recent success of self-ensembling technique in semi-supervised image classification task, we propose SESS, a self-ensembling semi-supervised 3D object detection framework. Specifically, we design a thorough perturbation scheme to enhance generalization of the network on unlabeled and new unseen data. Furthermore, we propose three consistency losses to enforce the consistency between two sets of predicted 3D object proposals, to facilitate the learning of structure and semantic invariances of objects. Extensive experiments conducted on SUN RGB-D and ScanNet datasets demonstrate the effectiveness of SESS in both inductive and transductive semi-supervised 3D object detection. Our SESS achieves competitive performance compared to the state-of-the-art fully-supervised method by using only 50% labeled data. Our code is available at https://github.com/Na-Z/sess.
Cooperative Specific Emitter Identification via Multiple Distorted Receivers

Specific emitter identification (SEI) is a technique that identifies the unique emitter from its received signal by using the specific characteristics of an emitter. In this paper, we consider an SEI problem with unknown receiver distortion. Two groups of SEI schemes based on signal decomposition are proposed. In the proposed schemes, the received signal is pre-processed by either of the following decomposition, i.e., empirical mode decomposition (EMD), intrinsic time-scale decomposition (ITD), or variational mode decomposition (VMD). In the first group of the proposed schemes, the skewness and the kurtosis are extracted from the decomposed signal, which characterize the non-Gaussian features of the signal. The support vector machine (SVM) or the back-propagation (BP) neural network is applied to fuse the features extracted from the multiple distorted receivers respectively and then determine the unknown emitter. In the second group of the proposed schemes, an approach based on the long short term memory (LSTM) is proposed. The LSTM model learns the deep features rather than the specific non-Gaussian features from the pre-processed signal. In contrast to the first group, the features used to identify the unknown emitter are extracted directly from the pre-processed signal by the trained LSTM model. Simulation results show that the proposed multi-receiver cooperative schemes can achieve the diversity gain in the identification performance. Moreover, we evaluate the identification performance of the proposed schemes in various channels, including the Gaussian channel and the fading channel. Compared to the existing methods based on different time-frequency representations, the proposed schemes possess the merits of high identification accuracy and low complexity. The significance of this paper is that the receive diversity can be achieved by the proposed schemes by using multiple distorted receivers even without compensating the receiver distortion prior to the identification.
Machine learning and artificial intelligence in haematology

Summary Digitalization of the medical record and integration of genomic methods into clinical practice have resulted in an unprecedented wealth of data. Machine learning is a subdomain of artificial intelligence that attempts to computationally extract meaningful insights from complex data structures. Applications of machine learning in haematological scenarios are steadily increasing. However, basic concepts are often unfamiliar to clinicians and investigators. The purpose of this review is to provide readers with tools to interpret and critically appraise machine learning literature. We begin with the elucidation of standard terminology and then review examples in haematology. Guidelines for designing and evaluating machine‐learning studies are provided. Finally, we discuss limitations of the machine‐learning approach.
Stacked auto-encoder based tagging with deep features for content-based medical image retrieval

Content-based medical image retrieval (CBMIR) is one of the most challenging and ambiguous tasks used to minimize the semantic gap between images and human queries in datasets with rich information content. Similar to the human visual saliency mechanism, CBMIR systems also use the visual features in the images for searching purposes. As a result of this search process, automatically accessing the images is very convenient in large and balanced datasets. Still, it is generally not possible to find such datasets in the medical domain. In this study, a four-step and effective hash code generation technique is presented to reduce the semantic gap between low-level features and high-level semantics for unbalanced medical image datasets. In the first stage, the convolutional neural network (CNN) architecture, the most effective feature representation method available today, is employed to extract discriminative features from images automatically. The features obtained in the last fully connected layer (FCL) at the output of the CNN architecture are used for hash code generation. In the second stage, using the Synthetic Minority Over-sampling Technique (SMOTE), the imbalance between the classes in the dataset is reduced. The solution to the unbalanced problem increases performance by almost 3%. In the third stage, balanced features are converted to a code of 13 symbols by using deep stacked auto-encoder. Finally, this code is translated to the standard 13-character labeling and retrieval code used by the 'Image retrieval in the medical application' (IRMA) dataset, since this is the database with which experiments have been done. IRMA error parameter, classification performance, and retrieval performance of the proposed method are more successful than other state-of-the-art methods.
Blockchain for the Internet of Vehicles: A Decentralized IoT Solution for Vehicles Communication Using Ethereum

The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every "thing" to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities. The IoV has the potential to solve various traffic and road safety problems effectively in order to prevent fatal crashes. However, a particular challenge in the IoV, especially in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, is to ensure fast, secure transmission and accurate recording of the data. In order to overcome these challenges, this work is adapting Blockchain technology for real time application (RTA) to solve Vehicle-to-Everything (V2X) communications problems. Therefore, the main novelty of this paper is to develop a Blockchain-based IoT system in order to establish secure communication and create an entirely decentralized cloud computing platform. Moreover, the authors qualitatively tested the performance and resilience of the proposed system against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications such as security, centralization, and lack of privacy. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.
Semantic scene segmentation in unstructured environment with modified DeepLabV3+

Semantic scene segmentation has become a key application in computer vision and is an essential part of intelligent transportation systems for complete scene understanding of the surrounding environment. While several methods based on deep fully Convolutional Neural Network (CNN) have been emerging, there are two main challenges: (i) They mainly focus on improvement of the accuracy than efficiency. (ii) They assume structured driving environment like in USA and Europe. While most of the current works focus on the well structured driving environment, we focus our research on India Driving Dataset (IDD) which contains data from unstructured traffic scenario. In this paper, we propose modifications in the DeepLabV3+ framework by using lower atrous rates in Atrous Spatial Pyramid Pooling (ASPP) module for dense traffic prediction. We propose to use dilated Xception network as the backbone for feature extraction. A lightweight segmentation framework is also presented by exploring the effectiveness of MobileNetV2 architecture, which achieves competitively high accuracy and is much smaller than other state-of-art architectures. The performance is evaluated in terms of mean Intersection over Union (mIoU) on 26 fine grained classes of IDD. Our proposed model with 24 M parameters achieves 68.41 mIoU on test set and efficient mobile model achieves mIoU of 61.6 by reducing the parameters to 2.2 M only.
Scaling-Basis Chirplet Transform

In this study, a novel time-frequency (TF) analysis method, referred to as the scaling-basis chirplet transform (SBCT), is developed by extending the conventional chirplet transform. This method includes a replacement kernel function that can vary the chirp rate with frequency and time by scaling the TF basis at and around the corresponding time center. This enables the corresponding chirplets to accurately match the targeted slopes for every trajectory of a multicomponent signal and within any window length. Therefore, the TF representation obtained via the SBCT can achieve significantly higher energy concentrations even for multicomponent signals with close-spaced frequencies and high levels of background noise. The effectiveness of the proposed SBCT approach was demonstrated by analyzing a numerical multicomponent signal and a vibration signal obtained from a gearbox test rig. Both numerical and experimental results showed that the SBCT can satisfactorily handle multicomponent signals with nonlinear frequency trajectories, close-spaced frequencies, and noisy backgrounds, demonstrating its superiority.
Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering

Text analysis in the field of text mining requires complex techniques for handling several text documents. Text clustering is among the most effective tactics in the field of text mining, machine recruitment and pattern recognition. Computers can start organizing a corpus document in certain organizational structure of conceptual clusters using reasonable text-clustering method. Informative and un-informational functionalities of the text documents contain noisy, inconsequential and superfluous features. The main method of finding a new subset of informative feats for each document is the unsupervised selection of text features. The functional selection technique has two aims: (1) maximize text clustering algorithm reliability, (2) minimize the number of uninformative traits. The proposed technique is that it produces a mature convergence rate and requires minimal computational time and is trapped in local minima in a low dimensional space. The text data is fed as the input and pre-processing steps are performed in the document. Next, the text feature selection is processed by selecting the local optima from the text document and then selecting the best global optima from local optimum using hybrid GWO–GOA.​ Furthermore, the selected optima are clustered using the Fuzzy c-means (FCM) clustering algorithm. This algorithm improves the reliability and minimizes the computational time cost. Eight datasets are used in the proposed algorithm and the performance is envisaged efficaciously. The evaluation metrics used for performing text feature selection and text clustering are accuracy, precision, recall, F-measure, sensitivity, specificity and show better quality when comparing with various other algorithms. When comparing with GWO, GOA and the proposed hybrid GWO–GOA algorithm, the proposed methodology reveals 87.6% of efficiency.
Efficient densely connected convolutional neural networks

Recent works have shown that convolutional neural networks (CNNs) are parameter redundant, which limits the application of CNNs in Mobile devices with limited memory and computational resources. In this paper, two novel and efficient lightweight CNNs architectures are proposed, which are called DenseDsc and Dense2Net. Two proposed CNNs are densely connected and the dense connectivity facilitates feature re-use in the networks. Dense2Net adopts efficient group convolution and DenseDsc adopts more efficient depthwise separable convolution. The novel dense blocks of DenseDsc and Dense2Net improve the parameter efficiency. The proposed DenseDsc and Dense2Net are evaluated on highly competitive classification benchmark datasets (CIFAR and ImageNet). The experimental results show that DenseDsc and Dense2Net have higher accuracy than DenseNet with similar parameters or FLOPs. Compared with other efficient CNNs with less than 0.5 M parameters for CIFAR, Dense2Net and DenseDsc achieve state-of-the-art results on CIFAR-10 and CIFAR-100, respectively. DenseDsc and Dense2Net are very competitive in efficient CNNs with less than 1.0 M parameters on CIFAR. Furthermore, Dense2Net achieves state-of-the-art results on ImageNet in manual CNNs with less than 10 M parameters.
Fuzzy Integral-Based CNN Classifier Fusion for 3D Skeleton Action Recognition

Action recognition based on skeleton key joints has gained popularity due to its cost effectiveness and low complexity. Existing Convolutional Neural Network (CNN) based models mostly fail to capture various aspects of the skeleton sequence. To this end, four feature representations, which capture complementary characteristics of the sequence of key joints, are extracted with novel contribution of features estimated from angular information, and kinematics of the human actions. Single channel grayscale images are used to encode these features for classification using four CNNs, with the complementary nature verified through Kullback-Leibler (KL) and Jensen-Shannon (JS) divergences. As opposed to straightforward classifier combination generally used in existing literature, fuzzy fusion through the Choquet integral leverages the degree of uncertainty of decision scores obtained from four CNNs. Experimental results support the efficacy of fuzzy combination of CNNs to adaptively generate final decision score based upon confidence of each information source. Impressive results on the challenging UTD-MHAD, HDM05, G3D, and NTU RGB+D 60 and 120 datasets demonstrate the effectiveness of the proposed method. The source code for our method is available at https://github.com/theavicaster/fuzzy-integral-cnn-fusion-3d-har
Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks

Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as nonindependent and identically distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art federated averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the tradeoff between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced data sets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth-constrained learning wireless IoT networks.
A detailed behavioral analysis on consumer and customer changing behavior with respect to social networking sites

The main purpose of the study is to analyze the changing behavior of the customers on various products by conducting a customer review. The analysis is performed in three major steps such as, calculating customer review quality, calculating behavior quality of customer and review comparison. Behavioral analysis is a kind of science which helps to study and understand the behavior of human beings. It studies the factors that influence the behavior of living beings and non-living beings on a larger aspect. A special emphasis is placed on understanding, describing, predicting and changing behavior. Understanding the behavior of individuals is a tedious job to be performed and hence to evaluate the final result, the study used Chi-Square Method. As a tool, the paper employs customer reviews to gather primary and secondary data. These reviews inform, educate and explain the benefits of using customer reviews in gathering data. It is one of the best and easiest methods to formulate the score of comparison between customer's profile and their review. Findings of the study proved that depending on the customer's profile, the quality of the product varied. Furthermore, high rates of efficiency and accuracy is observed in gathering information on the products via customer's profile and review. Study authenticates an inclusive approach to explain factors manipulating consumer awareness, observation and sensitivity on product value. A depth analysis was carried out to identify the purchase decision, brand behavior, price influence the buying behavior and opinion of the customers. However, the factors such as high quality, social media networking sites and customer's profiles had a greater impact on buying behavior of the customers.
A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots

The use of chatbots to manage online interactions with consumers poses additional ethical challenges linked to the use of artificial intelligence (AI) applications and opens up new ethical avenues for investigation. A literature analysis identifies a research gap regarding the ethical challenges related to chatbots as non-moral and non-independent agents managing non-real conversations with consumers. It raises concerns about the ethical implications related to the progressive automation of online conversational processes and their integration with AI. The conversational approach has been explored in the organisational and management literature, which has analysed the features and roles of conversations in managing interactions ethically. This study aims to discuss conceptually the ethical challenges related to chatbots within the marketplace by integrating the current chatbot-based literature with that on conversation management studies. A new conceptual model is proposed which embraces ethical considerations in the future development of chatbots.
Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods

In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent: convex quadratic problems. We prove global non-asymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates, and dual function values. We also show that the primal iterates converge at an accelerated linear rate in a somewhat weaker sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesàro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems.