Reviewerly/exHarmony huggingface dataset:
An Underwater Single Target Tracking Method Using SiamRPN++ Based on Inverted Residual Bottleneck Block

Traditional tracking algorithms reply on manually extracted features while deep learning algorithms can automatically extract features, which is of great importance for single target tracking in the complex underwater environment that is characterized by poor visibility, low contrasts, and occlusion. The convolutional neural network SiamRPN++ is an advanced deep learning algorithm whose backbone network structure is. However, this algorithm is difficult to be implemented in an underwater platform due to its large amount of computation and high requirement for the computing power of the hardware. To solve this problem, a new backbone network structure NewNet-62 characterized by inverted residual bottleneck block and the SiamRPN++ algorithm are proposed. The depth-wise separable convolution in NewNet-62 simplifies the computation so that the SiamRPN++ algorithm can realize fast and accurate tracking of underwater single targets. The video set in VOT2016 was used to test the algorithm. The results show that the SiamRPN++ algorithm using NewNet-62 as the main network has the great performance. Compared with the algorithms using ResNet-50, the accuracy of the SiamRPN++ algorithm increased to 0.524 and its EAO increased to 0.303. And its tracking speed increased to 73.74. The network complexity reduced to 3.013 billion and the network parameters reduced to 12.538 million, which significantly reduces network complexity and number of network parameters.
Image-based Pose Representation for Action Recognition and Hand Gesture Recognition

In this paper, we propose an effective and compact image-based pose representation named Poseimage Pyramid, which encodes the spatial and temporal information of human pose or hand pose as an image pyramid. Poseimage is constructed by the normalized distance between pairwise joints, and it has the advantage of its invariant to similarity transformations. With our Poseimage representation we can design the pose based action recognition or hand gesture recognition model using existing image or video classification models. In order to adapt to different actions with a variety of movement speed, we design Poseimage Pyramid to encode the multi-scale temporal information of human pose or hand pose. Experiments demonstrate that our pose representation is effective, and we achieve state-of-the-art performance on the action recognition datasets and the hand gesture recognition datasets. Our pose presentation is also complementary to video and optical flow streams in the seminal action recognition network I3D, and we achieve the state-of-the-art performance on the JHMDB, HMDB and UCF101 datasets by integrating our pose representation with I3D.
Deep Weakly Supervised Domain Adaptation for Pain Localization in Videos

Automatic pain assessment has an important potential diagnostic value for populations that are incapable of articulating their pain experiences. As one of the dominating nonverbal channels for eliciting pain expression events, facial expressions has been widely investigated for estimating the pain intensity of individual. However, using state-of-the-art deep learning (DL) models in real-world pain estimation applications poses several challenges related to the subjective variations of facial expressions, operational capture conditions, and lack of representative training videos with labels. Given the cost of annotating intensity levels for every video frame, we propose a weakly-supervised domain adaptation (WSDA) technique that allows for training 3D CNNs for spatiotemporal pain intensity estimation using weakly labeled videos, where labels are provided on a periodic basis. In particular, WSDA integrates multiple instance learning into an adversarial deep domain adaptation framework to train an Inflated 3D-CNN (I3D) model such that it can accurately estimate pain intensities in the target operational domain. The training process relies on weak target loss, along with domain loss and source loss for domain adaptation of the I3D model. Experimental results obtained using labeled source domain RECOLA videos and weakly-labeled target domain UNBC-McMaster videos indicate that the proposed deep WSDA approach can achieve significantly higher level of sequence (bag)-level and frame (instance)-level pain localization accuracy than related state-of-the-art approaches.
Dark Knowledge and Platform Governance: A Case of an Illicit E-Commerce Community in Reddit

This study examines the dark information commons in Reddit, a pseudonymous social news site, in the context of illicit e-commerce communities. Unlike market forums that are hosted in the dark web, Reddit is open to users of both the clear and dark web, unwittingly serving as a gateway for potential newcomers to learn and prepare their entrance into darknet market systems. To illustrate how information commons in Reddit bolster the resilience of illicit e-commerce communication ecology, this study presents information and knowledge flows in a subreddit community dedicated to a now-defunct market called AlphaBay. This study expands the discussion of the challenges that Reddit, as information commons, encounters in governing the flow of dark knowledge.
Asymptotically Normal Estimators for the Parameters of the Gamma-Exponential Distribution

Currently, much research attention has focused on generalizations of known mathematical objects in order to obtain adequate models describing real phenomena. An important role in the applied theory of probability and mathematical statistics is the gamma class of distributions, which has proven to be a convenient and effective tool for modeling many real processes. The gamma class is quite wide and includes distributions that have useful properties such as, for example, infinite divisibility and stability, which makes it possible to use distributions from this class as asymptotic approximations in various limit theorems. One of the most important tasks of applied statistics is to obtain estimates of the parameters of the model distribution from the available real data. In this paper, we consider the gamma-exponential distribution, which is a generalization of the distributions from the gamma class. Estimators for some parameters of this distribution are given, and the asymptotic normality of these estimators is proven. When obtaining the estimates, a modified method of moments was used, based on logarithmic moments calculated on the basis of the Mellin transform for the generalized gamma distribution. On the basis of the results obtained, asymptotic confidence intervals for the estimated parameters are constructed. The results of this work can be used in the study of probabilistic models based on continuous distributions with an unbounded non-negative support.
List Decodable Mean Estimation in Nearly Linear Time

Learning from data in the presence of outliers is a fundamental problem in statistics. Until recently, no computationally efficient algorithms were known to compute the mean of a high dimensional distribution under natural assumptions in the presence of even a small fraction of outliers. In this paper, we consider robust statistics in the presence of overwhelming outliers where the majority of the dataset is introduced adversarially. With only an fraction of “in-liers” (clean data) the mean of a distribution is unidentifiable. However, in their influential work, [1] introduces a polynomial time algorithm recovering the mean of distributions with bounded covariance by outputting a succinct list of O(1/α) candidate solutions, one of which is guaranteed to be close to the true distributional mean; a direct analog of `List Decoding' in the theory of error correcting codes. In this work, we develop an algorithm for list decodable mean estimation in the same setting achieving up to constants the information theoretically optimal recovery, optimal sample complexity, and in nearly linear time up to polylogarithmic factors in dimension. Our conceptual innovation is to design a descent style algorithm on a nonconvex landscape, iteratively removing minima to generate a succinct list of solutions. Our runtime bottleneck is a saddle-point optimization for which we design custom primal dual solvers for generalized packing and covering SDP's under Ky-Fan norms, which may be of independent interest. We refer the reader to [2] for the full version of this paper.
High Level Synthesis of Chaos based Text Encryption Using Modified Hill Cipher Algorithm

In this 21st Century, data has emerged as a vital resource, and hence data security in communication is of primary concern. In this paper, we present a modified three stages hill cipher-based text encryption and decryption scheme that utilizes Nose Hoover chaotic generator to provide bitwise XOR operation for improved robustness and security. The aim of the proposed model is to allow the recipient of the message to receive the message and curb the eavesdropping of the original message. Further, we propose a text encryption and decryption model for FPGAs using Xilinx Vivado HLS tool-suite with a C++ oriented design and conversion to Hardware Description Language (HDL). The proffered algorithm is highly propitious to provide us with enhanced security. High-Level Synthesis (HLS) is not yet widely used but might provide better options in the upcoming era.
Online Virtual Machine Evacuation for Disaster Resilience in Inter-Data Center Networks

With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi-objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.
Deep ancient Roman Republican coin classification via feature fusion and attention

We perform the classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the classification task non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than 98% and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability.
BlockChain/ABE-based Fusion Solution for E-government Data Sharing and Privacy protection

the instinct characteristics of blockchain technology to write transactions on distributed ledgers offers new opportunities for government to improve transparency, prevent fraud, and establish trust in the public sector. However, there still exits the challenge to protect data confidentiality, authenticity and ownership when sharing and exchanging e-document in decentralized blockchain network. In order to address this concern, we propose a fusion scheme of CP-ABE and Blockchain called GovChain. In GovChain, a blockchain framework is used to implement trusted identity authentication environment and improve scalability of CP-ABE policy. Moreover, encrypted e-government documents are stored on off-chain InterPlanetary File System(IPFS) and the data users can be authorized to obtain the index of documents on the chain. Finally, the security analysis is given in GovChain to prove the security of proposed fusion scheme. Meanwhile, experimental results also show that our system is feasible and effective.
iviz: A ROS visualization app for mobile devices

In this work, we introduce iviz, a mobile application for visualizing data in the Robot Operating System (ROS). In the last few years, the popularity of ROS has grown enormously, making it the standard platform for robotic programming. However, the availability of this environment is generally restricted to PCs with the Linux operating system. Thus, users wanting to see what is happening in the system with a smartphone or a tablet are stuck with solutions such as screen mirroring or web browser versions of rviz, making newer visualization modalities such as Augmented Reality impossible. Our application iviz, based on the Unity engine, addresses these issues by providing a visualization platform designed from scratch to be usable in mobile platforms such as iOS, Android, and UWP, and including native support for Augmented Reality for all three platforms. If desired, it can also be used in a PC with Linux, Windows, or macOS without any changes.
Granular Classification for Imbalanced Datasets: A Minkowski Distance-Based Method

The problem of classification for imbalanced datasets is frequently encountered in practical applications. The data to be classified in this problem are skewed, i.e., the samples of one class (the minority class) are much less than those of other classes (the majority class). When dealing with imbalanced datasets, most classifiers encounter a common limitation, that is, they often obtain better classification performances on the majority classes than those on the minority class. To alleviate the limitation, in this study, a fuzzy rule-based modeling approach using information granules is proposed. Information granules, as some entities derived and abstracted from data, can be used to describe and capture the characteristics (distribution and structure) of data from both majority and minority classes. Since the geometric characteristics of information granules depend on the distance measures used in the granulation process, the main idea of this study is to construct information granules on each class of imbalanced data using Minkowski distance measures and then to establish the classification models by using “If-Then” rules. The experimental results involving synthetic and publicly available datasets reflect that the proposed Minkowski distance-based method can produce information granules with a series of geometric shapes and construct granular models with satisfying classification performance for imbalanced datasets.
Single image super-resolution using multi-scale feature enhancement attention residual network

Aptness of convolutional neural networks (CNNs) in the field of single image super-resolution (SISR) have strengthen over the years due to their noteworthy performance. Mostly, CNNs are based on end-to-end learning through non-linear feature extraction mechanism. However, most of the methods try to attain better feature extraction through deeper architecture, which results in higher computations and complex models. Moreover, utilizing different types of features present in the image, simultaneously, is still a challenging issue. In addition, bi-cubic interpolation is mostly used for up-scaling the LR image before feeding it to CNNs, which generally has unpropitious effects on feature extraction process. In this paper, to alleviate the aforementioned issues, an ensemble of a shallow and a deep CNN is implemented, and a multi-scale feature extraction and attention block (MSFEAAB) is proposed for better extraction of low and high frequency of information, simultaneously, from the LR image. To suppress the checkerboard problem due to deconvolutional layer, here, pixel deconvolution is used for up-scaling of the feature map. Extensive experiments and evaluations are performed on public benchmark datasets to show efficacy of the proposed method.
Color Image Restoration by Saturation-Value Total Variation Regularization on Vector Bundles

Color image restoration is one of the important tasks in color image processing. Covariant differentiation has been applied to handle vector bundles arising from color images in the red, green, blue (RGB) color space. However, there are strong correlations among these three channels, and color image regularization in RGB color space may not be effective enough. The main aim of this paper is to study vector bundles of color images in saturation-value color space and to develop color image regularization models based on vector bundles in saturation-value color space. We develop the saturation-value metric of a vector bundle of $\mathbb{R}^5$-valued functions, and we generalize the vectorial total variation and the vector bundle-valued total variation in saturation-value color space based on the saturation-value metric via the transformation between RGB color space and saturation-value color space. We then develop a saturation-value total variation regularization on vector bundles. We study color image restoration models by using such total variation, and show numerical examples that the proposed color image restoration model outperforms existing methods in terms of visual quality, peak signal-to-noise ratio, and structural similarity.
Testing of Highly Configurable Cyber-Physical Systems – A Multiple Case Study

Cyber-physical systems, i.e., systems that seamlessly integrate computation and physical components, are typically highly-configurable systems. Testing such systems is particularly challenging because they comprise a large number of heterogeneous components that can be configured and combined in different ways. Despite a plethora of work investigating software testing in general and software product line testing in particular, variability in tests and how industry does actually manage testing highly configurable cyber-physical systems is not well understood. In this paper, we report the results of a multiple case study we conducted with three companies developing and maintaining highly-configurable cyber-physical systems focusing on their testing practices, with a particular focus on how they manage variability in tests. We conclude that experienced-based selection of configurations for testing is currently predominant. Variability modeling techniques are not utilized and the dependencies between configuration options are only partially modeled at best. However, the companies are aware of the situation and have the need and desire to cover more configuration combinations by automated tests. This in turn raises many questions, which might also be of interest to the scientific community and motivate future research.
Short Text Intent Classification for Conversational Agents

Intent classification is an important and relevant area of research in artificial intelligence and machine learning, with applications ranging from marketing and product design to intelligent communication. This paper explores the performance of various models and techniques for short text intent classification in the context of chatbots. The problem was explored for use within the mental wellness and therapy chatbot application, Wysa, to give improved responses to free-text user input. The authors looked at classifying text samples in-to 4 categories - assertions, refutations, clarifiers and transitions. For this, the suitability of the following techniques was evaluated: count vectors, TF-IDF, sentence embeddings and n-grams, as well as modifications of the same. Each technique was used to train a number of state-of-the-art classifiers, and the results have been compiled and presented. This is the first documented implementation of Arora's modification to sentence embeddings for real world use. It also introduces a technique to generate custom stop words that gave a significant gain in performance (10 percentage points). The best pipeline, using these techniques together, gave an accuracy of 95 percent.
Exploring the Use of Wireless Emergency Alerts for Notifications Regarding COVID-19: Initial Lines of Inquiry

US and international officials have sought ways to effectively use mobile technology to communicate health information to help thwart the spread of coronavirus disease 2019 (COVID-19). This study offers a preliminary exploration into the use of the state-level (N=6) and local-level (N=53) Wireless Emergency Alert (WEA) for notifications regarding COVID-19 in the US. The study compares changes in reported rates of infections and deaths between states and localities that issued WEA messages in March and April of 2020 with states that did not. Small sample sizes and differences in the rates of COVID-19 spread prohibit robust statistical analysis and detection of clear effect sizes, but estimated effects are generally in the right direction. Combining statistical analysis with preliminary categorization of both WEA message content and social media themes suggests that WEA messages might play an important role in instructing people to take protective in response to COVID-19. These initial lines of inquiry will be further developed in subsequent research.
An Efficient Binarized Neural Network for Recognizing Two Hands Indian Sign Language Gestures in Real-time Environment

This paper proposes an efficient architecture based on Binarized Neural Network (BNN) for recognizing two-hand Indian Sign Language (ISL). Some of the existing works apply deep Convolutional Neural Networks (CNN) and machine learning (ML) models to solve the challenging task of ISL recognition in a real-time environment. Therefore, the proposed BNN architecture having binary weights and activations, with bitwise operations used to reduce computational complexity during training and also, the method based on skin color segmentation and Otsu thresholding is applied to extract hand region during sign recognition. Extensive experiments are carried out on the ISL database having English alphabets and words static gestures show that the proposed work achieves a higher recognition rate of 98.8% in real-time as compared to other works. Moreover, a comparative analysis of the proposed BNN is performed over CNN in terms of memory consumption, speed, and training accuracy.