Reviewerly/exHarmony huggingface dataset:
Spatial evolutionary generative adversarial networks

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems. We hybridize two of these approaches that promote training diversity. One, E-GAN, at each batch, injects mutation diversity by training the (replicated) generator with three independent objective functions then selecting the resulting best performing generator for the next batch. The other, Lipizzaner, injects population diversity by training a two-dimensional grid of GANs with a distributed evolutionary algorithm that includes neighbor exchanges of additional training adversaries, performance based selection and population-based hyper-parameter tuning. We propose to combine mutation and population approaches to diversity improvement. We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner's grid. Instead, each training round, a loss function is selected with equal probability, from among the three E-GAN uses. Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate that Mustangs provides a statistically faster training method resulting in more accurate networks.
Blockchain and contract theory: modeling smart contracts using insurance markets

Purpose The purpose of this paper is to model blockchain-based smart contracts specifically for the insurance industry. The authors introduce the concept of smart contracts and further discuss the implementation of a decentralized insurance marketplace, namely Etherisc, using smart contracts on the Ethereum blockchain platform. Design/methodology/approach The authors employ three methods in this paper. The first one is a design illustration of a live application, namely, Etherisc. The second one is an economic model using demand–supply and equilibrium economics. The third one is an illustration using principal–agent modeling using constrained optimization. Findings The findings illustrate the following: in the design discussion, the authors demonstrate the architecture of a live Ethereum-based smart contract system. In the economic model, the authors illustrate how decentralized smart contract systems can increase social welfare by shifting demand and supply by reducing transactional costs. In the principal–agent model, the authors show how both the principal and agent are positively benefited by various mechanisms. Originality/value The paper is an original contribution and can be used as a reference model to study insurance or other similar marketplaces and the underlying economic transformations happening therein.
Learning Structure-And-Motion-Aware Rolling Shutter Correction

An exact method of correcting the rolling shutter (RS) effect requires recovering the underlying geometry, i.e. the scene structures and the camera motions between scanlines or between views. However, the multiple-view geometry for RS cameras is much more complicated than its global shutter (GS) counterpart, with various degeneracies. In this paper, we first make a theoretical contribution by showing that RS two-view geometry is degenerate in the case of pure translational camera motion. In view of the complex RS geometry, we then propose a Convolutional Neural Network (CNN)-based method which learns the underlying geometry (camera motion and scene structure) from just a single RS image and perform RS image correction. We call our method structure-and-motion-aware RS correction because it reasons about the concealed motions between the scanlines as well as the scene structure. Our method learns from a large-scale dataset synthesized in a geometrically meaningful way where the RS effect is generated in a manner consistent with the camera motion and scene structure. In extensive experiments, our method achieves superior performance compared to other state-of-the-art methods for single image RS correction and subsequent Structure from Motion (SfM) applications.
Using Twitter to Predict When Vulnerabilities will be Exploited

When a new cyber-vulnerability is detected, a Common Vulnerability and Exposure (CVE) number is attached to it. Malicious "exploits'' may use these vulnerabilities to carry out attacks. Unlike works which study if a CVE will be used in an exploit, we study the problem of predicting when an exploit is first seen. This is an important question for system administrators as they need to devote scarce resources to take corrective action when a new vulnerability emerges. Moreover, past works assume that CVSS scores (released by NIST) are available for predictions, but we show on average that 49% of real world exploits occur before CVSS scores are published. This means that past works, which use CVSS scores, miss almost half of the exploits. In this paper, we propose a novel framework to predict when a vulnerability will be exploited via Twitter discussion, without using CVSS score information. We introduce the unique concept of a family of CVE-Author-Tweet (CAT) graphs and build a novel set of features based on such graphs. We define recurrence relations capturing "hotness" of tweets, "expertise" of Twitter users on CVEs, and "availability" of information about CVEs, and prove that we can solve these recurrences via a fix point algorithm. Our second innovation adopts Hawkes processes to estimate the number of tweets/retweets related to the CVEs. Using the above two sets of novel features, we propose two ensemble forecast models FEEU (for classification) and FRET (for regression) to predict when a CVE will be exploited. Compared with natural adaptations of past works (which predict if an exploit will be used), FEEU increases F1 score by 25.1%, while FRET decreases MAE by 37.2%.
PIR-DSP: An FPGA DSP Block Architecture for Multi-precision Deep Neural Networks

Quantisation is a key optimisation strategy to improve the performance of floating-point deep neural network (DNN) accelerators. Digital signal processing (DSP) blocks on field-programmable gate arrays are not efficiently utilised when the accelerator precision is much lower than the DSP precision. Through three modifications to Xilinx DSP48E2 DSP blocks, we address this issue for important computations in embedded DNN accelerators, namely the standard, depth-wise, and pointwise convolutional layers. First, we propose a flexible precision, run-time decomposable multiplier architecture for CNN implementations. Second, we propose a significant upgrade to DSPDSP interconnect, providing a semi-2D low precision chaining capability which supports our low-precision multiplier. Finally, we improve data reuse via a register file which can also be configured as FIFO. Compared with the 27 × 18-bit mode in the Xilinx DSP48E2, our Precision, Interconnect, and Reuseoptimised DSP (PIR-DSP) offers a 6× improvement in multiplyaccumulate operations per DSP in the 9 × 9-bit case, 12× for 4 × 4 bits, and 24× for 2 × 2 bits. We estimate that PIR-DSP decreases the run time energy to 31/19/13% of the original value in a 9/4/2-bit MobileNet-v2 DNN implementation.
Convolutional Multitimescale Echo State Network

As efficient recurrent neural network (RNN) models, echo state networks (ESNs) have attracted widespread attention and been applied in many application domains in the last decade. Although they have achieved great success in modeling time series, a single ESN may have difficulty in capturing the multitimescale structures that naturally exist in temporal data. In this paper, we propose the convolutional multitimescale ESN (ConvMESN), which is a novel training-efficient model for capturing multitimescale structures and multiscale temporal dependencies of temporal data. In particular, a multitimescale memory encoder is constructed with a multireservoir structure, in which different reservoirs have recurrent connections with different skip lengths (or time spans). By collecting all past echo states in each reservoir, this multireservoir structure encodes the history of a time series as nonlinear multitimescale echo state representations (MESRs). Our visualization analysis verifies that the MESRs provide better discriminative features for time series. Finally, multiscale temporal dependencies of MESRs are learned by a convolutional layer. By leveraging the multitimescale reservoirs followed by a convolutional learner, the ConvMESN has not only efficient memory encoding ability for temporal data with multitimescale structures but also strong learning ability for complex temporal dependencies. Furthermore, the training-free reservoirs and the single convolutional layer provide high-computational efficiency for the ConvMESN to model complex temporal data. Extensive experiments on 18 multivariate time series (MTS) benchmark datasets and 3 skeleton-based action recognition datasets demonstrate that the ConvMESN captures multitimescale dynamics and outperforms existing methods.
A Workload-Aware VM Consolidation Method Based on Coalitional Game for Energy-Saving in Cloud

Virtual Machine (VM) consolidation technique plays an important role in energy management and load-balancing of cloud computing systems. Dynamic VM consolidation is a promising consolidation approach in this direction, which aims at using least active physical machines (PMs) through appropriately migrating VMs to reduce resource consumption. The resulting optimization problem is well-acknowledged to be NP-hard optimization problems. In this paper, we propose a novel merge-and-split-based coalitional game-theoretic approach for VM consolidation in heterogeneous clouds. The proposed approach first partitions PMs into different groups based on their workload levels, then employs a coalitional-game-based VM consolidation algorithm (CGMS) in choosing members from such groups to form effective coalitions, performs VM migrations among the coalition members to maximize the payoff of every coalition, and finally keeps PMs running in a high energy-efficiency state. The simulation results based on three scenarios clearly suggest that our proposed approach outperforms traditional ones in terms of energy-saving, and also achieve a fair level of load balance.
Efficient and Flexible Low-Power NTT for Lattice-Based Cryptography

Secure communication is being threatened by the foreseeable breakthrough of quantum computers. When a larger quantum computer is developed, traditional public key cryptography will be broken. Lattice-based cryptography appears as an alternative to protect the communications in the era of quantum computers. However, empowering current electronic devices with these new algorithms poses a challenging problem due to tight performance requirements as well as area and power constraints. Polynomial multiplication is the basic and most computationally intensive operation in lattice-based cryptosystems. The Number Theoretic Transform (NTT) is an attractive technique to perform polynomial multiplication efficiently. So far, previous works have focused on developing fast and compact forward and inverse NTT implementations. However, efficient and low-power NTT design has not been considered before although a low power consumption is crucial for many systems, such as battery-powered Internet of Things (IoT) devices. In this paper, we present the first low-power, fast and secure NTT ASIC design for lattice-based cryptography able to support different NTT parameters. The contribution of this work is three-fold. First, the implementation of a fast NTT through three optimization techniques. Second, utilization of methods for ASIC power minimization in the NTT design. Third, review of previously proposed side-channel attacks and discussion about countermeasures for our design. Our proposed architecture requires only n log(n) clock cycles for the forward and inverse NTT and can be implemented using a cheap single port RAM. The results of our work show that it is possible to decrease the power dissipation by more than 30% at nearly no cost.
Capsule Neural Networks and Visualization for Segregation of Plastic and Non-Plastic Wastes

Building an image processing model for prediction or classification application has to overcome quite a lot of challenges. Convolutional neural network (CNN) is the pillar of image processing in deep learning perspective. In order to bring down the disadvantages and for improving the performance compared to the CNN, a new architecture of CNN had been devised which is known as Capsule neural network (Capsule-Net). By this paper we analyze Capsule-Net for solid waste management which is separation of plastic and non-plastic. This task is viewed as of at most significance in today's world due to volumes of waste generated and nonavailability of human labor for this work. The capsule-Net is evaluated using 2 different datasets. Dataset 1 represents materials collected from public places and Dataset 2 represents materials collected from private environment. The proposed architecture with capsule-Net gives an accuracy of 96.3% for Dataset 1 and 95.7% for Dataset 2. The necessary hardware setup has been developed and tested. This will be a grace to the society which faces unexplainable difficulty in disposing wastes. It is inexpensive labor free and harmless to health.
Integration of water pollution indices and DRASTIC model for assessment of groundwater quality in El Fayoum depression, western desert, Egypt

Groundwater is an important water resource in Egypt for many purposes. Heavy metal pollution index (HPI), heavy metal evaluation index (HEI), pollution index (PI) and DRASTIC model were used to assess the groundwater quality and the aquifer vulnerability in El Fayoum depression, Western desert, Egypt. Depths to groundwater surface were measured in 27 observation wells. Groundwater samples were collected from 38 wells of depths less than 60 m. Physiochemical parameters; pH, TDS, EC, Al, Ba, Cd, Cr, Cu, Fe, Pb, Mn, Ni, Sb and Se, of the collected samples were measured using standard analytical methods. Multivariate statistical analysis and GIS techniques were used for processing the obtained results. The HPI results indicated that 74% of groundwater points have HPI values exceed the critical pollution level. The HEI values indicated that the majority of the groundwater samples are affect by heavy metal pollution. The PI values revealed that the groundwater samples are slightly affected by Pb and Sb, and are moderately affected by Se. The vulnerability index (VI) results, which were calculated by DRASTIC model, revealed that the groundwater aquifer vulnerability increases radially from the depression center to the peripheries of the study area. An agreement is revealed from comparison of spatial distribution maps of HPI, HEI and VI results. The integration between water pollution indices and DRASTIC model is a valuable and applicable approach for assessment of groundwater quality using physiochemical parameters of the groundwater and the aquifer characteristics.
A method of 3D path planning for solar-powered UAV with fixed target and solar tracking

To harvesting the more net energy, a method of 3D path planning for solar-powered UAV with fixed target and solar tracking has been presented in this paper. However, how to deal with the coupling between UAV motion, mission constraints, energy production, and the energy consumption is the key to 3D path planning for solar-powered UAVs to continuously monitor fixed targets. Hence, in this study, the flight paths of SUAV will be planned on a virtual cylinder surface in 3D space, with the fixed target center as the origin. In order to realize the UAV path planning based on the virtual cylinder surface, firstly, the UAV motion is re-modeled, and the UAV's motion characteristics and force are analyzed based on this model. Then, based on the motion trajectory characteristics in relation to the force balance, the state variables at each waypoint of the UAV are parameterized in the form of the variables to be optimized and their first and second derivatives, and a spline interpolation function is introduced three times to obtain the first and second derivatives of the solution variables. Finally, with reference to the UAV's minimum power flight strategy, the horizontal plane component of the UAV flight speed is set to the minimum power level flight speed, while the yaw rate is solidified and the sun position is assumed to be unchanged for a short period of time to simplify the optimization process and thus obtain the final optimal solution. The simulation experiment shows that unlike the UAV that flies at a fixed height, the UAV herein will climb or descend to seek a more favorable attitude to maximize energy production.
Deep learning models and datasets for aspect term sentiment classification: Implementing holistic recurrent attention on target-dependent memories

An essential challenge in aspect term sentiment classification using deep learning is modeling a tailor-made sentence representation towards given aspect terms to enhance the classification performance. To seek a solution to this, we have two main research questions: (1) Which factors are vital for a sentiment classifier? (2) How will these factors interact with dataset characteristics? Regarding the first question, harmonious combination of location attention and content attention may be crucial to alleviate semantic mismatch problem between aspect terms and opinion words. However, location attention does not reflect the fact that critical opinion words usually come left or right of corresponding aspect terms, as implied in the target-dependent method although not well elucidated before. Besides, content attention needs to be sophisticated to combine multiple attention outcomes nonlinearly and consider the entire context to address complicated sentences. We merge all these significant factors for the first time, and design two models differing a little in the implementation of a few factors. Concerning the second question, we suggest a new multifaceted view on the dataset beyond the current tendency to be somewhat indifferent to the dataset in pursuit of a universal best performer. We then observe the interaction between factors of model architecture and dimensions of dataset characteristics. Experimental results show that our models achieve state-of-the-art or comparable performances and that there exist some useful relationships such as superior performance of bi-directional LSTM over one-directional LSTM for sentences containing multiple aspects and vice versa for sentences containing only one aspect.
QoS-aware secure transaction framework for internet of things using blockchain mechanism

The Internet of Things (IoT) paradigm enables an enormous network with millions of connected smart things to control almost all aspect of services. In an IoT scenario, self-configured things are dynamically connected in a universal network. The small things are broadly distributed in a real world model with some essential processing capacity and finite storage. Security, privacy and reliability are the most important pillars of the IoT infrastructure. It is challenging to share personal data, highly confidential professional data over a centralized system. The infrastructure itself is capable to provide the guarantee to transmit data safely. However, a special attention for the data along with data consistency is required. Cyber attackers and hackers mostly target the centralized systems. To triumph over these types of situations we propose a security based blockchain service along with some encryption approaches for a secure communication. The encryption and decryption is performed by a hash based secret key. Risk modelling and relative risk reduction also done to prevent attacks under a virtual private network in IoT domain. Before and after attack users also wish to know quality of service (QoS). We have developed a mobile application framework which provides desire output of a user from different aspect of QoS for better understanding. The MQTT broker performs as a negotiator to convey data between cloud consumer and cloud provider. In QoS monitoring section user get chance to reconsider availability, reliability and responsibility like QoS factors while recovering resources and actual resources before attacking system based on CPU utilization, cache status and storage. As per experiment result we able to recover 100% RAM, 97.64% CPU utilization and 78.7% storage using proposed encryption and algorithm on blockchain infrastructure.
Real-time Sentiment Analysis On E-Commerce Application

Opinion mining is one of the most important tasks of natural language processing, which is also known as sentiment analysis, used to identify about what people have an impression about their services and products in social media platforms. To improve marketing strategies using product reviews, an effective method should be used for predicting the sentiment polarity. In this research article, a Machine learning technique called Support Vector Machine (SVM) is used to design a model and this model has been implemented on an E-commerce application. The data used in this study are online product reviews which are collected from Amazon.com. The experiments of sentiment analysis are performed for two levels of categorization: review level and sentence level. The main focus of this paper is to present a real-time sentimental analysis on the product reviews of e-commerce application so that the user experience can be enhanced.
Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.
Collaborative filtering embeddings for memory-based recommender systems

Word embeddings techniques have attracted a lot of attention recently due to their effectiveness in different tasks. Inspired by the continuous bag-of-words model, we present prefs2vec, a novel embedding representation of users and items for memory-based recommender systems that rely solely on user–item preferences such as ratings. To improve the performance and prevent overfitting, we use a variant of dropout as regularization, which can leverage existent word2vec implementations. Additionally, we propose a procedure for incremental learning of embeddings that boosts the applicability of our proposal to production scenarios. The experiments show that prefs2vec with a standard memory-based recommender system outperforms all the state-of-the-art baselines in terms of ranking accuracy, diversity, and novelty.