Reviewerly/exHarmony huggingface dataset:
Suspicious Activity Detection in Surveillance Footage

Suspicious activities are of a problem when it comes to the potential risk it brings to humans. With the increase in criminal activities in urban and suburban areas, it is necessary to detect them to be able to minimize such events. Early days surveillance was done manually by humans and were a tiring task as suspicious activities were uncommon compared to the usual activities. With the arrival of intelligent surveillance systems, various approaches were introduced in surveillance. We focus on analyzing two cases, those if ignored could lead to high risk of human lives, which are detecting potential gun-based crimes and detecting abandoned luggage on frames of surveillance footage. We present a deep neural network model that can detect handguns in images and a machine learning and computer vision pipeline that detects abandoned luggage so that we could identify potential gun-based crime and abandoned luggage situations in surveillance footage.
Detection of quantum non-Markovianity close to the Born-Markov approximation

We calculate in an exact way the conditional past-future correlation for the decay dynamics of a two-level system in a bosonic bath. Different measurement processes are considered. In contrast to quantum memory measures based solely on system propagator properties, here memory effects are related to a convolution structure involving two system propagators and the environment correlation. This structure allows one to detect memory effects even close to the validity of the Born-Markov approximation. An alternative operational-based definition of environment-to-system backflow of information follows from this result. We provide experimental support to our results by implementing the dynamics and measurements in a photonic experiment.
Do Game Data Generalize Well for Remote Sensing Image Segmentation?

Despite the recent progress in deep learning and remote sensing image interpretation, the adaption of a deep learning model between different sources of remote sensing data still remains a challenge. This paper investigates an interesting question: do synthetic data generalize well for remote sensing image applications? To answer this question, we take the building segmentation as an example by training a deep learning model on the city map of a well-known video game “Grand Theft Auto V” and then adapting the model to real-world remote sensing images. We propose a generative adversarial training based segmentation framework to improve the adaptability of the segmentation model. Our model consists of a CycleGAN model and a ResNet based segmentation network, where the former one is a well-known image-to-image translation framework which learns a mapping of the image from the game domain to the remote sensing domain; and the latter one learns to predict pixel-wise building masks based on the transformed data. All models in our method can be trained in an end-to-end fashion. The segmentation model can be trained without using any additional ground truth reference of the real-world images. Experimental results on a public building segmentation dataset suggest the effectiveness of our adaptation method. Our method shows superiority over other state-of-the-art semantic segmentation methods, for example, Deeplab-v3 and UNet. Another advantage of our method is that by introducing semantic information to the image-to-image translation framework, the image style conversion can be further improved.
Contraction properties of shrinkage priors in logistic regression

Bayesian shrinkage priors have received a lot of attention recently because of their efficiency in computation and accuracy in estimation and variable selection. In this paper, we study the contraction properties of shrinkage priors in a logistic regression model where the number of covariates is high. For a shrinkage prior distribution that is heavy-tailed and concentrated around zero with high probability such as the horseshoe prior, the Dirichlet–Laplace prior, and the normal-gamma prior with appropriate choices of hyper-parameters, estimates of the logistic regression coefficient are shown to asymptotically concentrate around the true sparse vector in the L2-sense. It is shown that the proposed contraction rate is comparable with the point mass prior that is studied in Atchadé (2017). The simulation study under the logistic regression model verifies the theoretical results by showing that the horseshoe prior and the Dirichlet–Laplace prior perform like the point mass prior for the estimation, variable selection and prediction, and yield much better results than Bayesian lasso and the non-informative normal prior.
Low-complexity deep learning and RBFN architectures for modulation classification of space-time block-code (STBC)-MIMO systems

Due to the constraint of spectrum availability in the recent era, technologies such as cognitive radio and spectrum sensing have found a central stage in the communications arena. Automatic modulation classification (AMC) plays a pivotal role in most of these technologies. AMC for Multiple-Input Multiple-Output (MIMO) systems is a domain that still lacks the due attention. Keeping in view the recent advancements in the field of artificial intelligence, we propose two relatively simple neural network architectures, i.e., Sparse Autoencoders(SAE) based Deep Neural Network (DNN) and Radial Basis Function Network (RBFN) for AMC of Space-Time-Block-Codes(STBC)-MIMO systems. An assorted set of features is proposed on the basis of distribution analysis to train these classifiers. Limited-memory Broyden–Fletcher–Goldfarb–Shanno(L-BFGS) algorithm and the least square method is used for weight optimization of DNN and RBFN, respectively. The impact of channel estimation error on the classification outcome is thoroughly investigated for blind classification scenarios. Performance comparison of the proposed methods with one of the most recent works in this context validates the effectiveness of proposed features and classifiers. Moreover, comparison with optimal Maximum-Likelihood(ML) based classifier and state of the art machine learning algorithms including, Adaptive Boosting (AdaBoost) and Convolutional Neural Networks (CNN) is also carried out. Simulation results substantiate the viability of proposed architectures for three different configurations of the STBC-MIMO system for perfect channel state information (CSI) and blind scenarios amidst channel estimation error.
A Comparison of Quantized Convolutional and LSTM Recurrent Neural Network Models Using MNIST

In this paper, a software comparative analysis of two neural network models is presented, namely, Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM) neural network. The evaluation is performed using the famous deep learning database the “MNIST” to check the accuracy, model size, speed and complexity of the two models for future digital realization on reconfigurable hardware. In addition to that, we optimize the size of the two models by quantizing the weights width to 8-bits instead of 32-bits. The results show an extensive reduction in the size of each model (by 10X) with a slight drop in the accuracy. The results also show that the CNN is more accurate and much faster than LSTMs making it the best model to be implemented on reconfigurable hardware.
CodeKernel: A Graph Kernel Based Approach to the Selection of API Usage Examples

Developers often want to find out how to use a certain API (e.g., FileReader.read in JDK library). API usage examples are very helpful in this regard. Over the years, many automated methods have been proposed to generate code examples by clustering and summarizing relevant code snippets extracted from a code corpus. These approaches simplify source code as method invocation sequences or feature vectors. Such simplifications only model partial aspects of the code and tend to yield inaccurate examples. We propose CodeKernel, a graph kernel based approach to the selection of API usage examples. Instead of approximating source code as method invocation sequences or feature vectors, CodeKernel represents source code as object usage graphs. Then, it clusters graphs by embedding them into a continuous space using a graph kernel. Finally, it outputs code examples by selecting a representative graph from each cluster using designed ranking metrics. Our empirical evaluation shows that CodeKernel selects more accurate code examples than the related work (MUSE and eXoaDocs). A user study involving 25 developers in a multinational company also confirms the usefulness of CodeKernel in selecting API usage examples.
Joint Transaction-Image Steganography for High Capacity Covert Communication

Conventional secret communications suffer from communication channel insecurity and lack of secrecy. Blockchain-based steganography comes up by proposing secure communication channel with the expense of low message capacity. In this paper, we propose joint steganography by utilizing blockchain-based transaction steganography and image steganography to achieve both advantages: a secure and secret communication medium that also possess a high capacity. We use image steganography to store the real hidden message while transaction steganography stores only the instruction manual to retrieve the stegano-image. We proposed group encoding and multi-field embedding to improve the capacity and robustness of transaction steganography. As a result, the joint method can achieve a high message capacity of ≈ 759 KB. The method also retaining the natural transaction pattern without generating anomalous transactions number.
AdaBoost neural network and cyclopean view for no-reference stereoscopic image quality assessment

Stereoscopic imaging is widely used in many fields. In many scenarios, stereo images quality could be affected by various degradations, such as asymmetric distortion. Accordingly, to guarantee the best quality of experience, robust and accurate reference-less metrics are required for quality assessment of stereoscopic content. Most existing stereo no-reference Image Quality Assessment (IQA) models are not consistent with asymmetrical distortions. This paper presents a new no-reference stereoscopic image quality assessment metric using a human visual system (HVS) modeling and an advanced machine-learning algorithm. The proposed approach consists of two stages. In the first stage, cyclopean image is constructed considering the presence of binocular rivalry in order to cover the asymmetrically distorted part. In the second stage, gradient magnitude, relative gradient magnitude, and gradient orientation are extracted. These are used as a predictive source of information for the quality. In order to obtain the best overall performance against different databases, Adaptive Boosting (AdaBoost) idea of machine learning combined with artificial neural network model has been adopted. The benchmark LIVE 3D phase-I, phase-II, and IRCCyN/IVC 3D databases have been used to evaluate the performance of the proposed approach. Experimental results have demonstrated that the proposed metric performance achieves high consistency with subjective assessment and outperforms the blind stereo IQA over various types of distortion.
HS-TCN: A Semi-supervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT

The rapid development of Internet of Things (IoT) accumulates lots of communication data. Not being processed in time, these massive data increase the difficulty of anomaly detection for smart service. Furthermore, labeling all communication data is unpractical. Therefore, it is necessary to accomplish some feasible ways which can incorporate unlabeled data effectively. In recent years, Temporal Convolutional Network (TCN) has been proposed to solve sequence problems and got better performance compared to Recurrent Neural Network (RNN) in most cases. This paper proposes a semi-supervised hierarchical stacking TCN for the first time, which concentrates on the anomaly detection of the communication in smart home. The ideology of hierarchical model fully considers the features of streaming data in smart home scenario, and the stacking method is used to weed out the outliers. In this way, the detection accuracy can be highly improved. Finally, experimental results demonstrate that the proposed model can promote the security of the communication in smart home with a large extent as well as get much better performance.
A Bitcoin payment network with reduced transaction fees and confirmation times

The high transaction fees and confirmation times made Bitcoin unfeasible for many applications when the payments are in small amounts and require instant approval. As a result, many other cryptocurrencies were introduced for addressing these issues, but the Bitcoin network is still the most widely used payment system. Without doubt, to benefit from its network of users, there is a need for novel solutions that can successfully address the problems about high transaction fees and transaction verification times. Recently, payment network ideas have been introduced including the Lightning Network (LN) which exploits off-chain bidirectional payment channels between parties. As off-chain links can be configured to perform aggregated transactions at certain intervals without writing to blockchain, this would not only reduce the transaction fees but also decrease the verification times significantly. Nevertheless, LN still deploys relay nodes which charge fees and its growth leads certain nodes to become monopolies which defeat the very purpose of decentralization. Despite the thought that LN will provide a "scale-free network mechanism" along with high decentralization, how to form such a network among multiple parties is never studied before. Therefore, in this paper, by exploiting the LN, we offer to form a purely decentralized payment network which will allow Bitcoin to handle a lot of transactions with enough capacity. The idea is to form a network of retailers (i.e., nodes) which do business with Bitcoin by connecting them through off-chain links (i.e., payment channels) based on the business needs. The problem is initially modeled as a network optimization but since scalability would be a concern the next steps follow a heuristic approach where certain links are pruned to force evenly distributed payment flows while minimizing the total investments made to create initial off-chain links. The evaluations demonstrate the extent of scalability of the network along with the trade-offs between the distribution of flows and the initial flow capacities of the nodes.
A Systematic Literature Review of Machine Learning Estimation Approaches in Scrum Projects

It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx. 43% of the projects are often delivered late and entered crises because of overbudget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g., Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon for strengthening the collaboration between developer and customer. Estimation has always been challenging in Agile as requirements are volatile. This encourages researchers to work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paper reviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation.
A Comparative Study of Blockchain Consensus Algorithms

Abstract A blockchain is a decentralized distributed public database. It does not have a central authority to maintain this public database by running a cryptographic protocol with distributed nodes. Bitcoin is currently the hottest item in the blockchain, and the Bitcoin node can verify the transaction content and package it into the block. The blockchain guarantees the consistency of the books through the underlying consensus agreement. These consensus algorithms are different because the algorithm security assumptions are different from the actual requirements. This paper sorts and compares various blockchain consensus algorithms, sorts out the development based on blockchain consensus, and points out the advantages and disadvantages of various algorithms, and proposes the development direction of blockchain consensus algorithm. CCS Concepts •Anonymity and privacy issues and measures to enhance them➝Consensus protocols for blockchains
Trajectory optimization on manifolds with applications to quadrotor systems

Manifolds are used in almost all robotics applications even if they are not modeled explicitly. We propose a differential geometric approach for optimizing trajectories on a Riemannian manifold with obstacles. The optimization problem depends on a metric and collision function specific to a manifold. We then propose our safe corridor on manifolds (SCM) method of computationally optimizing trajectories for robotics applications via a constrained optimization problem. Our method does not need equality constraints, which eliminates the need to project back to a feasible manifold during optimization. We then demonstrate how this algorithm works on an example problem on [Formula: see text] and a perception-aware planning example for visual–inertially guided robots navigating in three dimensions. Formulating field of view constraints naturally results in modeling with the manifold [Formula: see text], which cannot be modeled as a Lie group. We also demonstrate the example of planning trajectories on [Formula: see text] for a formation of quadrotors within an obstacle filled environment.
Block image encryption based on modified playfair and chaotic system

A new image encryption was done through using chaotic theory in the diffusion and confusion processes. Diffusion process was performed by using the chaotic cross map to permutate the matrix of each image color channel. Confusion process was implemented by a modified Playfair algorithm to generalize the classical one. Playfair algorithm divides the original text of characters into blocks, where each block usually contains two-character. The main limitations of Playfair cipher are that: it encrypts only 25 characters and one cannot use special characters and numbers. In addition, keyword can only take 25 letters alone without duplicates. The new method uses two cross chaotic maps to generate 16×16 playfair matrix as a key. The image is partitioned into a definite number of blocks such that each block has 16×16 bytes. Then, each block will be encrypted by different playfair matrix. All input data such as capital letter, small letter, digits and special characters, etc. can be encrypted by this new method. Experimental results showed that the proposed playfair cipher is more secure than the classical one and very sensitive to a slight change in the secret keys. The actual results of the encryption process showed that the proposed algorithm can defend against different attacks.
Segmentation based rotated bounding boxes prediction and image synthesizing for object detection of high resolution aerial images

Object detection for aerial images is becoming an active topic in computer vision with many real-world applications. It is a very challenging task due to many factors such as highly complex background, arbitrary object orientations, high input resolution, etc. In this paper, we develop a new training and inference mechanism, which is shown to significantly improve the detection accuracy for high resolution aerial images. Instead of estimating the orientations of objects using direct regressions like in previous methods, we propose to predict the rotated bounding boxes by leveraging a segmentation task, which is easier to train and yields more accurate detection results. In addition, an image synthesizing based data augmentation strategy is presented to address the data imbalance issues in aerial object detection. Extensive experiments have been conducted to verify our contribution. The proposed method sets new state-of-the-art performance on the challenging DOTA dataset. The source codes will be available at http://ice.dlut.edu.cn/lu/publications.html.
Optimizing Hardware Accelerated General Matrix-Matrix Multiplication for CNNs on FPGAs

Convolution is inarguably the most complex operation utilized in Convolutional Neural Networks (convnets). Owing to the billions of independent multiply-adds involved, convolution is being massively parallelized by the simultaneous utilization of many cores of Graphical Processing Units (GPUs). Although GPUs have shown significant performance improvements in both training and inference stages, they are not well-suited for mobile vision applications where both energy and real-time constraints need to be satisfied. In contrast, Field Programmable Gate Arrays (FPGAs) have demonstrated massive parallelization capabilities, with fast DSPs and on-chip memory, at a lower energy cost than GPUs. Hence, they are being utilized to design convnet accelerators for embedded applications. In this brief, we design an FPGA-based accelerator for general matrix-matrix multiplication (GeMM) to improve the efficiency of convolutional layers of Shufflenet, an efficient convnet architecture. Experimental results show significant performance improvements against the state-of-the-art FPGA-based implementations of both efficient convnets that are tailored towards mobile vision applications, and complex convnets that are used in traditional applications.
MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging. To solve these issues we present MT-EA4Cloud, a formal approach to check the correctness – from an energy-aware point of view – of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical, MT-EA4Cloud combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically. MT-EA4Cloud uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulators.