Reviewerly/exHarmony huggingface dataset:
Selective Spatial Regularization by Reinforcement Learned Decision Making for Object Tracking

Spatial regularization (SR) is known as an effective tool to alleviate the boundary effect of correlation filter (CF), a successful visual object tracking scheme, from which a number of state-of-the-art visual object trackers can be stemmed. Nevertheless, SR highly increases the optimization complexity of CF and its target-driven nature makes spatially-regularized CF trackers may easily lose the occluded targets or the targets surrounded by other similar objects. In this paper, we propose selective spatial regularization (SSR) for CF-tracking scheme. It can achieve not only higher accuracy and robustness, but also higher speed compared with spatially-regularized CF trackers. Specifically, rather than simply relying on foreground information, we extend the objective function of CF tracking scheme to learn the target-context-regularized filters using target-context-driven weight maps. We then formulate the online selection of these weight maps as a decision making problem by a Markov Decision Process (MDP), where the learning of weight map selection is equivalent to policy learning of the MDP that is solved by a reinforcement learning strategy. Moreover, by adding a special state, representing not-updating filters, in the MDP, we can learn when to skip unnecessary or erroneous filter updating, thus accelerating the online tracking. Finally, the proposed SSR is used to equip three popular spatially-regularized CF trackers to significantly boost their tracking accuracy, while achieving much faster online tracking speed. Besides, extensive experiments on five benchmarks validate the effectiveness of SSR.
Towards Cyberbullying-free social media in smart cities: a unified multi-modal approach

Smart cities are shifting the presence of people from physical world to cyber world (cyberspace). Along with the facilities for societies, the troubles of physical world, such as bullying, aggression and hate speech, are also taking their presence emphatically in cyberspace. This paper aims to dig the posts of social media to identify the bullying comments containing text as well as image. In this paper, we have proposed a unified representation of text and image together to eliminate the need for separate learning modules for image and text. A single-layer Convolutional Neural Network model is used with a unified representation. The major findings of this research are that the text represented as image is a better model to encode the information. We also found that single-layer Convolutional Neural Network is giving better results with two-dimensional representation. In the current scenario, we have used three layers of text and three layers of a colour image to represent the input that gives a recall of 74% of the bullying class with one layer of Convolutional Neural Network.
Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. Most of the datasets are confined to indoor scenes or object tracking and many outdoor datasets do not have sufficient human body details to apply state-of-the-art machine learning techniques. To fill this gap and enable research in wider application areas, we present an action recognition dataset recorded in an outdoor setting. A free flying drone was used to record 13 dynamic human actions. The dataset contains 240 high-definition video clips consisting of 66,919 frames. All of the videos were recorded from low-altitude and at low speed to capture the maximum human pose details with relatively high resolution. This dataset should be useful to many research areas, including action recognition, surveillance, situational awareness, and gait analysis. To test the dataset, we evaluated the dataset with a pose-based convolutional neural network (P-CNN) and high-level pose feature (HLPF) descriptors. The overall baseline action recognition accuracy calculated using P-CNN was 75.92%.
ViSiL: Fine-Grained Spatio-Temporal Video Similarity Learning

In this paper we introduce ViSiL, a Video Similarity Learning architecture that considers fine-grained Spatio-Temporal relations between pairs of videos -- such relations are typically lost in previous video retrieval approaches that embed the whole frame or even the whole video into a vector descriptor before the similarity estimation. By contrast, our Convolutional Neural Network (CNN)-based approach is trained to calculate video-to-video similarity from refined frame-to-frame similarity matrices, so as to consider both intra- and inter-frame relations. In the proposed method, pairwise frame similarity is estimated by applying Tensor Dot (TD) followed by Chamfer Similarity (CS) on regional CNN frame features - this avoids feature aggregation before the similarity calculation between frames. Subsequently, the similarity matrix between all video frames is fed to a four-layer CNN, and then summarized using Chamfer Similarity (CS) into a video-to-video similarity score - this avoids feature aggregation before the similarity calculation between videos and captures the temporal similarity patterns between matching frame sequences. We train the proposed network using a triplet loss scheme and evaluate it on five public benchmark datasets on four different video retrieval problems where we demonstrate large improvements in comparison to the state of the art. The implementation of ViSiL is publicly available.
Sampling Approaches for Imbalanced Data Classification Problem in Machine Learning

Real-world datasets in many domains like medical, intrusion detection, fraud transactions and bioinformatics are highly imbalanced. In classification problems, imbalanced datasets negatively affect the accuracy of class predictions. This skewness can be handled either by oversampling minority class examples or by undersampling majority class. In this work, popular methods of both categories have been evaluated for their capability of improving the imbalanced ratio of five highly imbalanced datasets from different application domains. Effect of balancing on classification results has been also investigated. It has been observed that adaptive synthetic oversampling approach can best improve the imbalance ratio as well as classification results. However, undersampling approaches gave better overall performance on all datasets.
Completeness theorems for non-cryptographic fault-tolerant distributed computation

research-article Share on Completeness theorems for non-cryptographic fault-tolerant distributed computation Authors: Michael Ben-Or Hebrew University Hebrew UniversityView Profile , Shafi Goldwasser MIT MITView Profile , Avi Wigderson Hebrew University Hebrew UniversityView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 351–371https://doi.org/10.1145/3335741.3335756Published:04 October 2019Publication History 26citation343DownloadsMetricsTotal Citations26Total Downloads343Last 12 Months51Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Permissioned Blockchain Based Access Control System for IOT

IoT devices produce a lot of valuable and sensitive data that is often shared with external parties to provide different kinds of useful services. Traditional IoT access control systems are centralized and do not include all the stakeholders in the access control decision making process. To fill this gap, we propose a permissioned blockchain based access control system for IoT where a different phase of access control like creating access policy and making the access control decision happens based on the consensus of all the stakeholders. To be more specific, we design and implement Attribute Based Access Control (ABAC) in a permissioned blockchain called Hyperledger Fabric and leverage its smartcontract and distributed consensus to enable a distributed access control for IoT. The effectiveness of our proposed system is demonstrated by the performance evaluation result in an IoT testbed.
A Case for Human Values in Software Engineering

This article argues that human values -such as responsibility, transparency, creativity, and equality -are heavily under-represented in software engineering methods.Based on experiences with real-world projects with not-for-profits, we explore how human values can be integrated into existing participatory agile practices.We propose new ways of considering human values in software practice, including: the use of the Schwartz taxonomy of human values and values portraits to contextualise values definitions; the use of values as a way to capture the rationale for requirements to ensure a culture of values throughout the software lifecycle; and a simple adaptation of agile methods to include a role for a 'critical friend' who can champion values during decision making.
C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) that only needs image-level annotations has obtained much attention recently. By combining convolutional neural network with multiple instance learning method, Multiple Instance Detection Network (MIDN) has become the most popular method to address the WSOD problem and been adopted as the initial model in many works. We argue that MIDN inclines to converge to the most discriminative object parts, which limits the performance of methods based on it. In this paper, we propose a novel Coupled Multiple Instance Detection Network (C-MIDN) to address this problem. Specifically, we use a pair of MIDNs, which work in a complementary manner with proposal removal. The localization information of the MIDNs is further coupled to obtain tighter bounding boxes and localize multiple objects. We also introduce a Segmentation Guided Proposal Removal (SGPR) algorithm to guarantee the MIL constraint after the removal and ensure the robustness of C-MIDN. Through a simple implementation of the C-MIDN with online detector refinement, we obtain 53.6% and 50.3% mAP on the challenging PASCAL VOC 2007 and 2012 benchmarks respectively, which significantly outperform the previous state-of-the-arts.
Cross-task extreme learning machine for breast cancer image classification with deep convolutional features

Automatic classification of breast histopathology images plays a key role in computer-aided breast cancer diagnosis. However, feature-based classification methods rely on the accurate cell segmentation and feature extraction. Due to overlapping cells, dust, impurities and uneven irradiation the accurate segmentation and efficient feature extraction are still challenging. In order to overcome the above difficulties and limited breast histopathology images, in this paper, a hybrid structure which includes a double deep transfer learning (D2TL) and interactive cross-task extreme learning machine (ICELM) is proposed based on feature extraction and representation ability of CNN and classification robustness of ELM. First, high level features are extracted using deep transfer learning and double-step deep transfer learning. Then, the high level feature sets are jointly used as regularization terms to further improve classification performance in interactive cross task extreme learning machine. The proposed method was tested on 134 breast cancer histopathology images. Results show that our method has achieved remarkable performance in classification accuracy (96.67%, 96.96%, 98.18%). From the experiment result, the proposed method is promising for providing an efficient tool for breast cancer classification in clinical settings.
Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data

SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning problem.The merits of SMOTE reflect at that in comparison with the random oversampling technique, it can alleviate the problem of overfitting to a large extent.However, two drawbacks of SMOTE have also been observed as follows, 1) it tends to propagate the noisy information in the procedure of oversampling; 2) it always assigns a global neighborhood parameter K but neglects the local distribution characteristics.To synchronously deal with these two problems, a grouped SMOTE algorithm with noise filtering mechanism (GSMOTE-NFM) is presented in this article.The algorithm firstly adopts Gaussian-Mixture Model (GMM) to explore the real distributions of the majority and minority classes, respectively.Then, most noisy instances can be removed by comparing the probability densities of the same instance in two different classes.Next, two new GMMs are constructed on the rest majority and minority class instances, respectively.Furthermore, all minority class instances can be divided into three different groups: safety, boundary and outlier, based on the corresponding probability density information.Finally, we assign an individual parameter K to the instances belonging to each specific group to generate new instances.We tested GSMOTE-NFM algorithm on 24 benchmark binary-class data sets with three popular classification models, and compared it with several state-of-the-art oversampling algorithms.The results indicate that our algorithm is significantly superior than the original SMOTE algorithm and several SMOTE-based modified methods.
Tight Bounds on the Simultaneous Estimation of Incompatible Parameters

The estimation of multiple parameters in quantum metrology is important for a vast array of applications in quantum information processing. However, the unattainability of fundamental precision bounds for incompatible observables greatly diminishes the applicability of estimation theory in many practical implementations. The Holevo Cramér-Rao bound (HCRB) provides the most fundamental, simultaneously attainable bound for multiparameter estimation problems. A general closed form for the HCRB is not known given that it requires a complex optimization over multiple variables. In this work, we develop an analytic approach to solving the HCRB for two parameters. Our analysis reveals the role of the HCRB and its interplay with alternative bounds in estimation theory. For more parameters, we generate a lower bound to the HCRB. Our work greatly reduces the complexity of determining the HCRB to solving a set of linear equations that even numerically permits a quadratic speedup over previous state-of-the-art approaches. We apply our results to compare the performance of different probe states in magnetic field sensing and characterize the performance of state tomography on the code space of noisy bosonic error-correcting codes. The sensitivity of state tomography on noisy binomial code states can be improved by tuning two coding parameters that relate to the number of correctable phase and amplitude damping errors. Our work provides fundamental insights and makes significant progress toward the estimation of multiple incompatible observables.1 MoreReceived 4 February 2020Revised 26 November 2020Accepted 24 December 2020DOI:https://doi.org/10.1103/PhysRevX.11.011028Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptimization problemsQuantum metrologyQuantum sensingTechniquesCombinatoricsQuantum InformationStatistical Physics
Gesture recognition based on multilevel multimodal feature fusion

With the development of human-computer interaction, gesture recognition has gradually become one of the research hotspots. The cost reduction and the richer information of RGB-D images make the research of gesture recognition based on RGB-D images more and more. However, the current gesture process ing methods for RGB-D images still can not fully utilize the information contained. Aiming at the above problems, this paper studies the feature extraction method of RGB-D image, and proposes a multimodal and multilevel feature extraction method. By extracting multimodal and multilevel image features for mapping and splicing, the utilization of RGB-D image information and the accuracy in recognition are improved effectively. Finally, the experiments verified the effectiveness and robustness of the proposed method based on the self-built gesture database. Compared and analyzed with several other RGB-D processing methods, the processing method of this paper is more advanced and effective, and can achieve better results in gesture recognition.
Load balance based workflow job scheduling algorithm in distributed cloud

As the scale of the geo-distributed cloud increases and the workflow applications become more complex, the system operation is more likely to cause the waste of resources and excessive energy consumption. In this paper, a workflow job scheduling algorithm based on load balancing is proposed to efficiently utilize cloud resources. Firstly, the execution time of the jobs on the cloud is estimated based on the state of the cloud. Then, a queuing model is established for each cloud to minimize the total response time of the system. Finally, the job scheduling problem in a geo-distributed cloud can be transformed into the minimum system response time problem. Moreover, a workflow task scheduling algorithm based on the shortest path algorithm is proposed to minimize all task completion time and energy consumption. Firstly, the directed acyclic graph (DAG) of the tasks can be converted into the hypergraph according to the execution order of the tasks. Then, the k-path hypergraph partition is performed with the balance of the hypergraph. Finally, the Dijkstra shortest path algorithm is used to find the optimal task scheduling strategy which is performed on each hypergraph partition. The experimental results indicate that our proposed workflow scheduling method can effectively utilize cloud resources and reduce system energy consumption. Moreover, the applicability of the proposed effective scheduling strategy is shown in the scenarios of new media live video application.
Shallow convolutional neural network for image classification

Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST.
Deep Face Recognition for Biometric Authentication

Face is one of the most widely used biometrics for human identity authentication. Facial recognition has remained an interesting and active research area in the past several decades due to its ever growing applications in biometric authentication, content based data retrieval, video surveillance, access control and social media. Unlike other biometric systems, facial recognition based systems work independently without involving the individual, due to which it does not add unnecessary delay. Its ability of recognizing multiple persons at a time further adds to its speed. There are many face recognition methods based on traditional machine learning that are available in the literature. Improvements are being made with the constant developments in computer vision and machine learning. However, most of the traditional methods lack robustness against varying illumination, facial expression, scale, occlusions and pose. With the advent of big data and graphical computing, deep learning has impressively advanced the traditional computer vision systems over the past decade. In this paper, we present a convolutional neural network based face recognition system which detects faces in an input image using Viola Jones face detector and automatically extracts facial features from detected faces using a pre-trained CNN for recognition. A large database of facial images of subjects is created, which is augmented in order to increase the number of images per subject and to incorporate different illumination and noise conditions for optimal training of the convolutional neural network. Moreover, an optimal pretrained CNN model along with a set of hyperparameters is experimentally selected for deep face recognition. Promising experimental results, with an overall accuracy of 98.76%, are obtained which depict the effectiveness of deep face recognition in automated biometric authentication systems.
A New Design of Mamdani Complex Fuzzy Inference System for Multiattribute Decision Making Problems

This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations.