Reviewerly/exHarmony huggingface dataset:
Trajectory planning based on non-convex global optimization for serial manipulators

To perform specific tasks in dynamic environments, robots are required to rapidly update trajectories according to changing factors. A continuous trajectory planning methodology for serial manipulators based on non-convex global optimization is presented in this paper. First, a kinematic trajectory planning model based on non-convex optimization is constructed to balance motion rapidity and safety. Then, a model transformation method for the non-convex optimization model is presented. In this way, the accurate global solution can be obtained with an iterative solver starting from arbitrary initializations, which can greatly improve the computational accuracy and efficiency. Furthermore, an efficient initialization method for the iterative solver based on multivariable-multiple regression is presented, which further speeds up the solution process. The results show that trajectory planning efficiency is significantly enhanced by model transformation and initialization improvement for the iterative solver. Consequently, real-time continuous trajectory planning for serial manipulators with many degrees of freedom can be achieved, which lays a basis for performing dynamic tasks in complex environments.
Violence Detection in Automated Video Surveillance: Recent Trends and Comparative Studies

There is an increasing demand for automated video surveillance with a wide range of threats in the society and less manpower to monitor them. Especially, detecting violence in crowded scenes is challenging because of the rapid movement, overlapping features due to occlusion, and cluttered backgrounds. In this paper, we review the recent trends in violence detection and perform a comparative study of different state-of-the-art shallow and deep models using the Histogram of Oriented Optical Flow feature descriptor on the Crowd Violence and Hockey Fight data sets.
Multiswarm Artificial Bee Colony algorithm based on spark cloud computing platform for medical image registration

Over the years, medical image registration has been widely used in various fields. However, different application characteristics, such as scale, computational complexity, and optimization goals, can cause problems. Therefore, developing an optimization algorithm based on clustering calculation is crucial. To solve the aforementioned problem, a multiswarm artificial bee colony (MS-ABC) multi-objective optimization algorithm based on clustering calculation is proposed. This algorithm can accelerate the resolution of complex problems on the Spark platform. Experiments show that the algorithm can optimize certain conventional complex problems and perform medical image registration tests. Results show that the MS-ABC algorithm demonstrates excellent performance in medical image registration tests. The optimization results of the MS-ABC algorithm for conventional problems are similar to those of existing algorithms; however, its performance is more time efficient for complex problems, especially when additional goals are needed. The MS-ABC algorithm is applied to the Spark platform to accelerate the resolution of complex application problems. It can solve the problem of traditional algorithms regarding long calculation time, especially in the case of highly complex and large amounts of data, which can substantially improve data-processing efficiency.
Using Floating-Point Intervals for Non-Modular Computations in Residue Number System

The residue number system (RNS) provides parallel, carry-free, and high-speed arithmetic and is therefore a good tool for high-performance computing.However, operations such as magnitude comparison, sign computation, overflow detection, scaling, and division are difficult to perform in RNS, since it is problematic to determine the magnitude of an RNS number.In order to resolve this problem, we propose to compute the interval evaluation of the fractional representation of an RNS number in floating-point arithmetic of limited precision.No matter what the size n of the moduli set and dynamic range, only small arithmetic operations are required, and most of the computations are performed in parallel with n threads, which allows for efficient implementation of our method on many general-purpose computing platforms.Using this method, we propose new algorithms for magnitude comparison and general division in RNS and implement them for GPUs using the CUDA platform.We evaluate the performance of our algorithms on an NVIDIA GTX 1080 GPU using sets of 4 to 256 RNS moduli that provide dynamic ranges from 64 to 4096 bits.Experimental results show that the proposed new algorithms are efficient for large moduli sets and clearly outperform the existing RNS magnitude comparison and division algorithms in terms of execution time.
Optimizing Nested Virtualization Performance Using Direct Virtual Hardware

Nested virtualization, running virtual machines and hypervisors on top of other virtual machines and hypervisors, is increasingly important because of the need to deploy virtual machines running software stacks on top of virtualized cloud infrastructure. However, performance remains a key impediment to further adoption as application workloads can perform many times worse than native execution. To address this problem, we introduce DVH (Direct Virtual Hardware), a new approach that enables a host hypervisor, the hypervisor that runs directly on the hardware, to directly provide virtual hardware to nested virtual machines without the intervention of multiple levels of hypervisors. We introduce four DVH mechanisms, virtual-passthrough, virtual timers, virtual inter-processor interrupts, and virtual idle. DVH provides virtual hardware for these mechanisms that mimics the underlying hardware and in some cases adds new enhancements that leverage the flexibility of software without the need for matching physical hardware support. We have implemented DVH in the Linux KVM hypervisor. Our experimental results show that DVH can provide near native execution speeds and improve KVM performance by more than an order of magnitude on real application workloads.
Teaching an Undergraduate Software Engineering Course using Active Learning and Open Source Projects

This work in progress presents a model for first undergraduate software engineering course as a core course of the computer science curriculum. The course is designed to be offered in the fourth or fifth semesters (i.e., end of sophomore or beginning of the junior year) for students who have completed the introductory programming and data structures courses. In addition, they may have some basic knowledge of databases and web technologies. Moreover, in our curriculum, students at this level have not been exposed to any codebase of real-world application and particularly of large size (>10,000 lines) codebases yet. Based on such situations, the major focus of this course is on teaching the fundamentals of software engineering as a methodology of developing real-world software with an emphasis on: 1) software systems in the enterprise level, 2) basic modeling using functional, flow, and behavioral diagrams, and 3) team-based agile project development. The paper discusses our novel course configuration of the three emphasized elements above, where students work with open source software in this class as part of course activities and assignments to simulate working on an enterprise project and learn agile development Our observation as well as our industry partners indicated that students enjoy the open source challenges and demonstrate professional competency after this course. Our initial findings include a positive impact of open source and team work on our students.
Data Leakage Prevention for Data in Transit using Artificial Intelligence and Encryption Techniques

Data leakage in an organization is a very important concern that leads to the ex-filtration of data, The work in this paper addresses a novel concept for prevention of data leakage for data in transit. The text under consideration is classified to confidential or non-confidential category based on the content and context using Machine Learning technique. Subsequent action for encryption is performed on the confidential data and then transmitted from the Intranet domain to Internet domain ensuring that the data is not compromised to unauthorized users. In addition, normal transactional data which is nonconfidential in nature is not prevented from transmitting and is easily accessible by a third party. Encryption is applied only to selected data and not the entire data in transit, ensuring that the hardware resources are efficiently utilized. An adversary can simply compose an e-mail with the organization's confidential information as the body of the mail, in such scenarios our method will classify the e-mail content and will encrypt the data so that the data is not revealed to an outsider.
Learning Safe Policies via Primal-Dual Methods

In this paper, we study the learning of safe policies in the setting of reinforcement learning problems. This is, we aim to control a Markov Decision Process (MDP) of which we do not know the transition probabilities, but we have access to sample trajectories through experiments. We define safety as the agent remaining in a desired safe set with high probability for every time instance. We therefore consider a constrained MDP where the constraints are probabilistic. Due to the difficulty of addressing these constraints in a reinforcement learning framework, we propose an ergodic relaxation of the problem. Nonetheless, this relaxation is such that we are able to provide safety guarantees on the resulting policies. To compute these policies, we resource to a stochastic primal-dual method. We test the proposed approach in a navigation task in a grid world. The numerical results show that our algorithm is capable of dynamically adapting the policy to the environment and the required safety levels.
Asian Female Facial Beauty Prediction Using Deep Neural Networks via Transfer Learning and Multi-Channel Feature Fusion

Facial beauty plays an important role in many fields today, such as digital entertainment, facial beautification surgery and etc. However, the facial beauty prediction task has the challenges of insufficient training datasets, low performance of traditional methods, and rarely takes advantage of the feature learning of Convolutional Neural Networks. In this paper, a transfer learning based CNN method that integrates multiple channel features is utilized for Asian female facial beauty prediction tasks. Firstly, a Large-Scale Asian Female Beauty Dataset (LSAFBD) with a more reasonable distribution has been established. Secondly, in order to improve CNN's self-learning ability of facial beauty prediction task, an effective CNN using a novel Softmax-MSE loss function and a double activation layer has been proposed. Then, a data augmentation method and transfer learning strategy were also utilized to mitigate the impact of insufficient data on proposed CNN performance. Finally, a multi-channel feature fusion method was explored to further optimize the proposed CNN model. Experimental results show that the proposed method is superior to traditional learning method combating the Asian female FBP task. Compared with other state-of-the-art CNN models, the proposed CNN model can improve the rank-1 recognition rate from 60.40% to 64.85%, and the pearson correlation coefficient from 0.8594 to 0.8829 on the LSAFBD and obtained 0.9200 regression prediction results on the SCUT dataset.
DeepTrust

Opinion spamming has recently gained attention as more and more online platforms rely on users' opinions to help potential customers make informed decisions on products and services. Yet, while work on opinion spamming abounds, most efforts have focused on detecting an individual reviewer as spammer or fraudulent. We argue that this is no longer sufficient, as reviewers may contribute to an opinion-based system in various ways, and their input could range from highly informative to noisy or even malicious. In an effort to improve the detection of trustworthy individuals within opinion-based systems, in this paper, we develop a supervised approach to differentiate among different types of reviewers. Particularly, we model the problem of detecting trustworthy reviewers as a multi-class classification problem, wherein users may be fraudulent, unreliable or uninformative, or trustworthy. We note that expanding from the classic binary classification of trustworthy/untrustworthy (or malicious) reviewers is an interesting and challenging problem. Some untrustworthy reviewers may behave similarly to reliable reviewers, and yet be rooted by dark motives. On the contrary, other untrustworthy reviewers may not be malicious but rather lazy or unable to contribute to the common knowledge of the reviewed item. Our proposed method, DeepTrust, relies on a deep recurrent neural network that provides embeddings aggregating temporal information: we consider users' behavior over time, as they review multiple products. We model the interactions of reviewers and the products they review using a temporal bipartite graph and consider the context of each rating by including other reviewers' ratings of the same items. We carry out extensive experiments on a real-world dataset of Amazon reviewers, with known ground truth about spammers and fraudulent reviews. Our results show that DeepTrust can detect trustworthy, uninformative, and fraudulent users with an F1-measure of 0.93. Also, we drastically improve on detecting fraudulent reviewers (AUROC of 0.97 and average precision of 0.99 when combining DeepTrust with the F&G algorithm) as compared to REV2 state-of-the-art methods (AUROC of 0.79 and average precision of 0.48). Further, DeepTrust is robust to cold start users and overperforms all existing baselines.
Multi-Scale Neural Network With Dilated Convolutions for Image Deblurring

In image deblurring, information from the regions where the blur was propagated is needed for effective deblurring.For example, large blurs, such as those caused by fast-moving objects leaving a trail of afterimages, need spatial context from a large region, while small blurs, such as those caused by slight camera shake, need spatial context from a smaller scope.In this paper, we used multi-scale features to provide the spatial dependencies needed to deblur non-uniform blurs.Compared to previous works, we efficiently extract multi-scale features using two approaches: (1) coarse-to-fine scheme that can extract multi-scale features by applying the network to different scales of the images, and (2) dilated convolutions that can extract multi-scale features by using different dilation rates.Combining both methods has a multiplicative effect since multi-scale features from dilated convolutions are extracted from the input images at different scales (i.e.coarse-to-fine scheme).Furthermore, we optimized our network architecture by using parallel convolutions to decrease the execution time of the deblurring process.We show that our proposed method has better results than state-of-the-art methods in terms of image quality and execution time.
Cloudlet-Based Intelligent Auctioning Agents for Truthful Autonomous Electric Vehicles Energy Crowdsourcing

This paper proposes cloudlet-based intelligent agents for energy crowdsourcing from autonomous electric vehicles (AEVs). Existing energy crowdsourcing mechanisms focus on load shedding and cost savings, but lack incentive models for strategically behaving agents. In the proposed model, crowdsourcing agents residing on the edge network communicate with AEVs to stimulate them to participate in providing energy to the grid during peak time periods. The focus of this paper is on the development of incentive mechanisms that allow the agents to engage in an auctioning process according to their energy contribution. Through theoretical analysis and simulations, we evaluate the performance of the proposed model with respect to individual rationality, computational efficiency, and truthfulness.
A pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism

To solve low convergence precision and slow convergence speed, a pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism (PACON) is proposed. Firstly, the algorithm introduces an angle in the pheromone transfer rule. Through the rule for calculating the angle, multiple cities with smaller angles are also included in the next candidate city list. It affects the probability of city selection and enhances the algorithm’ performance to avoid local optimization. Secondly, the algorithm updates the pheromone concentrations on the worst and optimal path simultaneously, and enhances the weights of the pheromone concentrations on the optimal path. It improves the convergence speed of the algorithm. Based on experiments adopting TSPLIB data sets, the results demonstrate the improved algorithm improves the convergence accuracy by at least 1.26% and increases the convergence speed by at least 9.5%, both on large-scale and small-scale urban data. The novel algorithm will improve convergence precision and speed better.
Convolutional Neural Networks Using Skip Connections with Layer Groups for Super-Resolution Image Reconstruction Based on Deep Learning

In this paper, we propose a deep learning method with convolutional neural networks (CNNs) using skip connections with layer groups for super-resolution image reconstruction. In the proposed method, entire CNN layers for residual data processing are divided into several layer groups, and skip connections with different multiplication factors are applied from input data to these layer groups. With the proposed method, the processed data in hidden layer units tend to be distributed in a wider range. Consequently, the feature information from input data is transmitted to the output more robustly. Experimental results show that the proposed method yields a higher peak signal-to-noise ratio and better subjective quality than existing methods for super-resolution image reconstruction.
Learning Parameters of Stochastic Radio Channel Models From Summaries

Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other on deep learning, for fitting stochastic channel models to data directly. The proposed methods make use of easy-to-compute summary statistics of measured data instead of relying on extracted multipath components. Moreover, the need for post-processing of the extracted multipath components is omitted. Taking the polarimetric propagation graph model as an example stochastic model, we present relevant summaries and evaluate the performance of the proposed methods on simulated and measured data. We find that the methods are able to learn the parameters of the model accurately in simulations. Applying the methods on 60 GHz indoor measurement data yields parameter estimates that generate averaged power delay profile from the model that fits the data.
Classification of ECG signals by dot Residual LSTM Network with data augmentation for anomaly detection

Classification of ECG signals is of great importance for the detection of cardiac dysfunction. Recurrent Neural Network family has been greatly successful for time series related problems. In this paper, we compare different RNN variants and propose dot Residual LSTM network for ECG classification. Here, we use extracted features both from time and frequency domain with the network to improve the classification performance. A data generation scheme was developed with Conditional variational autoencoder (CVAE) and LSTM to increase training samples. A comparative analysis was studied to assess the performance of the model. The proposed dot Res LSTM achieved maximum accuracy of 80.00% and F1 score of 0.85. Furthermore, the model achieved maximum F1 score of 0.87 with augmented data. The study is expected to be useful in automatic cardiac diagnosis research.
Sentiment Analysis using Recurrent Neural Network

Abstract This study aims to measure the accuracy of the sentiment analysis classification model using deep learning and neural networks. This study used the algorithm Recurrent Neural Network (RNN) and Word2vec. No previous research has used this model to analyze sentiments written using Indonesian language so that the level of accuracy is unknown. The research began by making a classification model of sentiment analysis. Then, the model was tested through experiments. In this study, They used two classifications (positive and negative). Experiments are carried out using training data sets and the test used data sets sourced from Traveloka theybsite. The result shows that the model presents outstanding results and reaches about 91.9%.
Bitcoin price prediction using Deep Learning Algorithm

The world has more than 5000 digital-currencies, bitcoin is one of it, which has more than 5.8 million dynamic client and approximately more than 111 exchanges throughout the world. So, the aim for this paper is to do the near prediction of the price of Bitcoin in USD. Precious details are taken from the price index of Bitcoin. A Bayesian recurrent hierarchical (RNN) neural network and a long-term memory (LSTM) network can accomplish this function. The total identification accuracy of 52% and an 8% RMSE is obtained by the LSTM. In contrast to the profound training systems, the common ARIMA method for the prediction of time series. This model have not much efficient as deep learning model can be performed. The deep learning methods were predicted to outperform the poorly performing ARIMA prediction. So here we used Gated Recurrent Network model (GRU) to forecasting Bitcoin price Eventually, all deep learning models have a GPU and CPU that beat the GPU implemented by 94.70 percent for their GPU training time.
Evaluating the Effectiveness of Cloud Services

This article presents technologies for evaluating cloud services based on the feedback test method. The main parameters of the formation of cloud architecture are determined, taking into account the requirements for security and reliability of resources. For the practical implementation of the technology for evaluating the efficiency of modern cloud services, based on the previously identified criteria, a software product was developed that determines the speed of downloading data for the cloud resource used. Analysis of the simulation results allows to qualitatively determine the effectiveness of cloud technologies for the user in multitasking, the necessary reliability and performance.
Color and white balancing in low-light image enhancement

Images acquired during night either appear too dark or either seem biased towards the color of lighting source. This paper proposes a low-light image enhancement technique that simultaneously preserves contrast and removes color saturation from an input image. Initially low and high frequencies are extracted from the low light image. Color balancing is performed on low frequencies using a sigmoid function. This is followed by chromatic balancing which is done using constrained linear least square minimization in YCbCr color space. The high frequencies are added back with respect to the ratio of enhanced and original low frequencies. The proposed method improves input contrast and object prominence while reducing artifacts as compared to state-of-art low light image enhancement methods.