Reviewerly/exHarmony huggingface dataset:
A parallel computing and mathematical method optimization of CNN network convolution

Training convolutional neural network (CNN) is a compute-intensive task in parallel tolerance has become a complete training is very important. There are two obstacles in the distributed memory computing environment to develop a scalable parallel CNN. It depends on a small volume across the two model parameters shown adjacent to the height data. The other presents the maximum overlapping parallel computing inter-process communication, large amounts of data over a communication channel to go. They will be transferred to the calculation. Replication by using threads on each compute node to initiate communication available after gradient is achieved. Reverse spread output data is generated in each stage of the model layer, and data communication may be performed in parallel with the calculation of other layers. To impact the replication study's efficiency and scalability, evaluated the model structure and optimization of various mathematical methods. When using the image VGG- net model training dataset, use 256 and 512, respectively, small batch size to achieve speedup computing nodes.
Second-order multi-object filtering with target interaction using determinantal point processes

The probability hypothesis density (PHD) filter, which is used for multi-target tracking based on sensor measurements, relies on the propagation of the first-order moment, or intensity function, of a point process. This algorithm assumes that targets behave independently, an hypothesis which may not hold in practice due to potential target interactions. In this paper, we construct a second-order PHD filter based on determinantal point processes which are able to model repulsion between targets. Such processes are characterized by their first- and second-order moments, which allows the algorithm to propagate variance and covariance information in addition to first-order target count estimates. Our approach relies on posterior moment formulas for the estimation of a general hidden point process after a thinning operation and a superposition with a Poisson point process, and on suitable approximation formulas in the determinantal point process setting. The repulsive properties of determinantal point processes apply to the modeling of negative correlation between distinct measurement domains. Monte Carlo simulations with correlation estimates are provided.
Remote-sensing image super-resolution using classifier-based generative adversarial networks

The rapid development of the aerospace industry has significantly increased the demand for remote-sensing images with high resolution and quality. Generating images with expected resolution from the samples obtained by common acquisition devices is a challenging task as the trade-off between cost and efficiency must be considered. We propose a super-resolution (SR) algorithm especially for remote-sensing images that is based on generative adversarial networks optimized by a classifier, which is called classifier-based super-resolution generative adversarial network (CSRGAN). We hypothesize that the confidence scores of classification can be a critical factor for representing the features in target remote-sensing images. To sufficiently take this factor into account during training, we add the class-score as an error into the loss function in addition to mean square error and high-dimensional features extracted from deep neural networks. Then, the classifier is utilized for both better SR performance and more precise classification. The classifier-testing branch of our system can also be flexibly combined with other network architectures to optimize SR performance on remote-sensing images. We validate the model on the NWPU-RESISC45 dataset considering both SR and classification performance. The final analysis is also provided and shows that the proposed CSRGAN outperforms existing algorithms.
Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection

Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture.
Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3

Deep learning algorithms have demonstrated remarkable performance in many sectors and have become one of the main foundations of modern computer-vision solutions. However, these algorithms often impose prohibitive levels of memory and computational overhead, especially in resource-constrained environments. In this study, we combine the state-of-the-art object-detection model YOLOv3 with depthwise separable convolutions and variational dropout in an attempt to bridge the gap between the superior accuracy of convolutional neural networks and the limited access to computational resources. We propose three lightweight variants of YOLOv3 by replacing the original network’s standard convolutions with depthwise separable convolutions at different strategic locations within the network, and we evaluate their impacts on YOLOv3’s size, speed, and accuracy. We also explore variational dropout: a technique that finds individual and unbounded dropout rates for each neural network weight. Experiments on the PASCAL VOC benchmark dataset show promising results where variational dropout combined with the most efficient YOLOv3 variant lead to an extremely sparse solution that reduces 95% of the baseline network’s parameters at a relatively small drop of 3% in accuracy.
An Optimization-based Motion Planning Method for Autonomous Driving Vehicle

With the progress of autonomous driving technology, obtaining a safe and smooth trajectory in complex environments is the focus of motion planning in recent years. This paper presents a post-optimization method based on the gradient descent and Bèzier curve, which can obtain a safer and more comfortable trajectory in the static obstacle scene. The optimized path is farther away from obstacles locally and can be tracked by the low-controller. Our post-optimization method has two main steps which consider the factors of collision avoidance and smoothing separately. The first step uses the gradient descent method to optimize so that the path is farther away from obstacles. The cost function of the gradient descent method mainly considers obstacle punishment. The second step uses the variable order Bèzier curve to smooth segments which have sharp inflection points caused by the gradient descent. The simulation experiments have been performed to evaluate the feasibility and efficiency of the proposed method. The simulation results show that our algorithm can achieve the purpose of staying away from obstacles locally and refine the vehicle smoothness as well.
Vector Operations for Accelerating Expensive Bayesian Computations – A Tutorial Guide

Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, however, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs and is the basis of GPGPU computing. In this work, we practically demonstrate, using standard programming libraries, the utility of the SIMD approach for several topical Bayesian applications. We show that SIMD can improve the floating point arithmetic performance resulting in up to $6\times$ improvement in serial algorithm performance. Importantly, these improvements are multiplicative to any gains achieved through multi-core processing. We illustrate the potential of SIMD for accelerating Bayesian computations and provide the reader with techniques for exploiting modern massively parallel processing environments using standard tools.
A Parallel Retrodiction Algorithm for Large-Scale Multitarget Tracking

Kalman filter-based retrodiction plays an indispensable role in modern multitarget tracking and retrodiction (MTTR) algorithms. To this end, the Rauch-Tung-Striebel (RTS) smoother is a widely used Kalman filter-based target state smoother. With a large number of targets, MTTR algorithms, particularly with large window sizes, become very computationally intensive. If not addressed, these algorithms will not meet the requirements for tracking a large number of targets in real time. A natural approach is to parallelize these algorithms to render them useful, particularly in the context of emerging multicore platforms. However, this is nontrivial, as the governing mathematical framework of the RTS smoother, namely the dependencies between complex computations, prevents any form of parallelization. Although the MTTR component can naively be parallelized ignoring the smoothing component, the overall benefit, as we demonstrate in this article, is a fraction of the best possible benefits. In this article, by carefully reformulating the underlying mathematical framework that is necessary for retrodiction, we propose a novel, easily parallelizable RTS smoother. The proposed parallelized RTS smoother we outline in this article has best data reuse and enables the overall MTTR problem to be parallelized more efficiently. We demonstrate this on a state-of-the-art multicore processor platform using the shared-memory parallelism. Our results show that the parallel MTTR solution, which includes gating, assignment, tracking, and retrodiction, can offer nearly 150 times speed up against a fully sequential version. With excellent computational performance, our proposed RTS smoother enables very large window sizes with little or no impact on the overall performance.
Photonic analog-to-digital converter powered by a generalized and robust convolutional recurrent autoencoder

We propose a convolutional recurrent autoencoder (CRAE) to compensate for time mismatches in a photonic analog-to-digital converter (PADC). In contrast of other neural networks, the proposed CRAE is generalized to untrained mismatches and untrained category of signals while remaining robust to system states. We train the CRAE using mismatched linear frequency modulated (LFM) signals with mismatches of 35 ps and 57 ps under one system state. It can effectively compensate for mismatches of both LFM and Costas frequency modulated signals with mismatches ranging from 35 ps to 137 ps under another system state. When the spur-free dynamic range (SFDR) of the unpowered PADC decreases from 10.2 dBc to -3.0 dBc, the SFDR of the CRAE-powered PADC is over 31.6 dBc.
Image deblurring using adaptive fractional‐order shock filter

In this paper, a novel image enhancement model based on shock filter for image deblurring is proposed in three cases. For the weight of shock filter, the fractional‐order derivative of initial blurry image is used. This fractional‐order weight can be adjusted adaptively according to the gradient of blurred image. Compared with the traditional integer‐order shock filter, the proposed model creates less staircase effect, whereas the enhancement and deblurring of the resulted images are efficiently good. Stability properties of the proposed method are studied theoretically, and also to show the validity of stability studies results, some numerical experiments are presented. The three cases of proposed method are compared with together and with other methods. Different test images are used to show the validity of these cases. Numerical experiments confirm the efficiency of these cases.
Social Media Analysis through Big Data Analytics: A Survey

This paper discusses the social media analysis through big data analytics. It includes the various techniques such as NLP, news analysis, opinion mining, scraping, and sentiments analysis for unstructured textual data sets. This paper discusses the various text formats such as HTML, XML, JSON, and CSV. Through the detailed survey, we identify the social media analytics techniques like sentiment analysis & computational science technique. Sentiment analysis is related to attitude, feelings & emotions for specific purpose (e.g. product prediction).This paper also discusses the platform and tools for social media analysis such as business toolkit, scientific programming tools, monitoring tools and feature analysis tools.
Efficient computation of the waiting time and fidelity in quantum repeater chains

We provide two algorithms for computing the probability distribution of waiting time and fidelity in quantum repeater chains constructed from probabilistic components. Their polynomial runtimes improve upon existing algorithms’ exponential scaling.
Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm

In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm.
Privacy-Preserving Logistic Regression as a Cloud Service Based on Residue Number System

The security of data storage, transmission, and processing is emerging as an important consideration in many data analytics techniques and technologies. For instance, in machine learning, the datasets could contain sensitive information that cannot be protected by traditional encryption approaches. Homomorphic encryption schemes and secure multi-party computation are considered as a solution for privacy protection. In this paper, we propose a homomorphic Logistic Regression based on Residue Number System (LR-RNS) that provides security, parallel processing, scalability, error detection, and correction. We verify it using six known datasets from medicine (diabetes, cancer, drugs, etc.) and genomics. We provide experimental analysis with 30 configurations for each dataset to compare the performance and quality of our solution with the state of the art algorithms. For a fair comparison, we use the same 5-fold cross-validation technique. The results show that LR-RNS demonstrates similar accuracy and performance of the classification algorithm at various thresholds settings but with the reduction of training time from 85.9% to 96.1%.
Automatic Acquisition of Failure Mode and Effect Analysis Ontology for Sustainable Risk Management

In this piece of research, we have presented an approach to populate Failure Mode and Effect Analysis (FMEA) ontology from existing worksheets prepared by experts. FMEA is a commonly used method for risk assessment in any organization. This method is initiated by domain experts who analyze all the associated risks to a product or process, their causes, severity, effects and mitigation actions. Besides domain experts, time and cost are the other two factors involved in successful completion of FMEA. Reusability of the knowledge produced at the end of this method can bring numerous benefits to an organization. Some ontologies are available for semantic content management of FMEA knowledge but in order to avail their full benefits, it is must that they can acquire the existing knowledge automatically. Major objective of this article is to develop an algorithm, which can populate FMEA ontology from existing worksheets. Major contribution of this work is to identify an existing FMEA ontology and its evaluation for schema and relationship richness, then its automatic population using proposed algorithm without human intervention, and finally making it a part of complete knowledge management system. Our proposed algorithm correctly mapped 1357 instances to FMEA ontology from manually prepared FMEA spreadsheets. This FMEA ontology has been queried by domain experts and it was proved to be very helpful in experts like decision-making.
Technologies of Artificial Intelligence in Educational Management

This research paper presents the analysis of artificial intelligence techniques employed in the field of educational management. The author reviews intelligent learning systems. Particular attention is drawn to the expert systems that are integrated into the design of platforms for on-line learning. Highlighted are the features of intelligent systems allowing for organization of the optimized digital learning process. By way of illustration, suggested is the creation of an interactive training project aiming at the successful acquisition of knowledge by students taking courses in philosophy, as well as at improving the quality of this knowledge. The methods of the project implementation include design and development of the software necessary to create and operate the on-line platforms; systematic approach including the databank formation and classification of the data; approach to the intelligent data processing to activate the interactive learning models.
Model Checking Bitcoin and other Proof-of-Work Consensus Protocols

The Bitcoin Backbone Protocol [4] is an abstraction of the bitcoin proof-of-work consensus protocol. We use a model-checking tool (UPPAAL-SMC) to examine the security of proof-of-work consensus by varying protocol parameters, using an adversary that leverages the selfish mining strategy introduced in [4]. We provide insights into modeling proof-of-work protocols and demonstrate trade-offs between operating parameters. Applying this methodology to protocol design, we show that the uniform tie-breaking rule from [11], an attempt to mitigate selfish mining, improves chain quality but decreases the common prefix probability. This trade-off illustrates how design decisions affect desirable protocol properties, within a range of concrete operating conditions, in a manner that is not evident from prior asymptotic analysis.
Verified Transformations and Hoare Logic: Beautiful Proofs for Ugly Assembly Language

Hand-optimized assembly language code is often difficult to formally verify. This paper combines Hoare logic with verified code transformations to make it easier to verify such code. This approach greatly simplifies existing proofs of highly optimized OpenSSL-based AES-GCM cryptographic code. Furthermore, applying various verified transformations to the AES-GCM code enables additional platform-specific performance improvements.
Probability hypothesis density filter with uncertainty in the probability of detection

The space around the earth is becoming increasingly populated. Efficient tracking algorithms are hence integral to protect active space assets from collisions. Ground-based measurements are the primary source of information for any tracking algorithm. Multi-Target Tracking (MTT) algorithms use the measurements to jointly estimate the number of objects and their states in a surveillance scene. MTT has mainly been developed by two approaches: the track-based approach and the population-based approach. Population-based approaches are formulated using Finite Set Statistics (FISST) and have been widely researched in the past decade. A critical component of the population-based algorithms is the probability of detection (pD), which determines how likely it is that a measurement can be obtained from an object. pD is usually modeled as a constant quantity, when it is exactly known. This assumption is not valid in the space environment, where pD depends on the state of the object, the object's attitude and the materials used to make it. Such information is often vague or unknown for space objects. This research focuses on designing a pD model that takes into account the state dependency of pD and, most importantly, the uncertainty in pD. The MTT algorithm considered in this work is the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. A variation of GM-PHD filter that incorporates the uncertainty and the state dependency of pD is developed and validated in this work. Results are validated via simulations of an orbital tracking scenario.
Social Network Analysis using Python Data Mining

Analyzing public information from social networking sites could produce exciting results and insights on the public opinion of almost any product, service, or behaviour. One of the most effective and accurate public sentiment indicators is through social networks data mining, as many users tend to express their opinions online. The internet's advanced technology has managed to increase activity in blogging, tagging, posting, and online social networking. As a result, people are starting to grow interested in mining these vast data resources to analyze opinions. Sentiment analysis is one of the computational techniques of opinion, sentiments, and the variety of texts subjectivity. In this paper, the methodology of determining these public opinions are discussed. The development of a program for sentiment analysis is done to create a platform for social network analysis. This paper also discusses the sentiment analysis design, gathering data, training the data, and visualizing the data using the Python library. Finally, a platform is designed in order for other users to search the sentiment results of particular topics of interest. A total of 3000 Reddit data and 3000 Twitter data has been gathered, cleaned, analyzed, and visualized in this research. The analysis has produced an excellent percentage result of 83% and 77% for Twitter and Reddit data, respectively. Moreover, the GUI platform has been built using the Tkinter library.
BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images

Real visual-world datasets tend to have few classes with large numbers of samples (i.e., head classes) and many others with smaller numbers of samples (i.e., tail classes). Unfortunately, this imbalance enables a visual recognition system to perform well on head classes but poorly on tail classes. To alleviate this imbalance, we present BLT, a novel data augmentation technique that generates extra training samples for tail classes to improve the generalization performance of a classifier. Unlike prior long-tail approaches that rely on generative models (e.g., GANs or VQ-VAEs) to augment a dataset, BLT uses a gradient-ascent-based image generation algorithm that requires significantly less training time and computational resources. BLT avoids the use of dedicated generative networks, which adds significant computational overhead and require elaborate training procedures. Our experiments on natural and synthetic long-tailed datasets and across different network architectures demonstrate that BLT consistently improves the average classification performance of tail classes by \(11\%\) w.r.t. the common approach that balances the dataset by oversampling tail-class images. BLT maintains the accuracy on head classes while improving the performance on tail classes.