Reviewerly/exHarmony huggingface dataset:
Tracking System Using Artificial Neural Network for FPGA Cart Follower

Abstract Wheelchair users may have difficulty carrying their luggage while travelling. A proposed solution is introduced based on the problem stated. A tracking system for cart followers introduced to improve wheelchair mobility while transporting their luggage. The cart will track and follow the wheelchair at an appropriate distance. A Camera acts as input to enable the cart to track and follow the wheelchair. Pixy CMUcam5 is used to detect the predefined colour code (CC) in this project. The visual-based sensor gathered the information of the CC, which situated behind the wheelchair and translated the collected data into relative position information, such as distance and skew angle, which helps the cart in following the wheelchair. This translation is done in the Artificial Neural Network (ANN). The type of ANN used is a Multilayer perceptron (MLP) with the Levenberg-Marquardt training algorithm. Log sigmoid (Logsig) and Pure linear (Purelin) activation functions used in the hidden and output layer, respectively. The errors for distances and angles after computed with ANN are presented accordingly in Simulink. The MSE value obtained is 0.14007. The ANN implemented in the Field Programmable Gate Array (FPGA). The implementation of the ANN on the FPGA done through software and hardware configuration. The time taken for hardware implementation is faster than software due to parallel computations. The error value for output distance is less than 0.8000, and 0.3000 for the output angle during the simulation.
Adaptive Frost Filtered Quantile Regressive Artificial Deep Structure Learning Framework for Image Quality Assessment

An image quality assessment (IQA) aims for predicting the quality of the images with or without any previous information of reference image. Assessing image quality is significant in image communication and processing. There are several techniques been proposed for quality assessment, but the accurate feature learning and complexity analysis are still challenging issues in various image processing applications. To improve the quality assessment with minimum complexity, an adaptive frost filtered quantile regression-based artificial deep structure learning (AFFQR-ADSL) framework is introduced. The AFFQR-ADSL framework performs the image quality assessment based on two methods, namely full reference and no reference. The AFFQR-ADSL framework with full reference (AFFQR-ADSL-FR) is carried out based on the test image with the reference image information. Initially, the numbers of input images and reference images are collected from the image database. The proposed AFFQR-ADSL-FR framework trained the input images and reference images with different layers to progressively learn the higher-level features from the raw input. The numbers of images are given to the input layer of the artificial feed-forward deep structure learning network. Then, the inputs are transferred into the hidden layers to repeatedly learn the features. In the first hidden layer, the input images are preprocessed to filter the noise present in the input images using an adaptive mean frost filtering technique. Followed by, the input images are divided into the number of patches and feature extraction is carried out in the next successive hidden layer. Then, the learned features are combined and fed into the output layer. Finally, the proposed technique uses linear quantile regression at the output layer for analyzing the extracted feature vectors and obtains the quality assessment results. Then, the AFFQR-ADSL framework with no reference (AFFQR-ADSL-NR) is carried out by using the test image without using the reference image information, extracts the feature vectors, and provides the assessment results at the output layer. Result evaluation is carried out with metrics such as mean square error, peak signal-to-noise ratio (PSNR), accuracy, and computational complexity (CC). The qualitative and quantitative results show that the proposed AFFQR-ADSL-FR and no reference achieve better results in terms of PSNR, error, and CC.
Remote Cloud Data Auditing Protocols: A Comprehensive Analysis and Comparative Study

Cloud storage enables users to provide public verification schemes to verify the reliability of information stored in the cloud servers. The goal of public verification scheme is to avoid attacks on remote data stored in cloud by checking the consistency of the data. Public verification schemes allow a third-party auditor to validate the data kept in the cloud representing the original client. Various scholars suggested different schemes to confirm the data truthfulness of the outsourced information. This paper discusses the major merits and demerits of the most widely used public verification schemes in cloud computing that includes id-based, attribute-based and iO-based. Also, a comparative analysis of these major auditing schemes is discussed by identifying three performance metrics including tag generation time, proof generation time and verification overhead at auditor's side. The prime apprehension of the remote auditing schemes is the computational load balancing at the server side and computational overhead at the auditor side. A future direction to the researchers is also specified.
Sentiment analysis of online product reviews using Lexical Semantic Corpus-Based technique

Large number of product reviews may help the product looks more interesting, but it also may cause difficulties for customers in deciding to buy the product. The seller will also be facing difficulties such as to improve the quality of their product due to the huge number of reviews. The aim of this project is to develop a web-based system for customers and sellers to get an analysis of the online product reviews. The customer and seller will get the visualization for the positive, neutral, and negative distributions of the product reviews. The system uses MySQL as a database and Python as a programming language for development of the system. For the future work, the system could be able to compare the analysis of product reviews from other brands.
A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM

Aiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KELM), is proposed in this paper. Firstly, the dual tree complex wavelet transform instead of the discrete wavelet transform is used to decompose the motor bearing signal; then, the features with large differentiation of motor-bearing fault are extracted; finally, the states of motor bearing are classified by using artificial fish swarm optimization-kernel extreme learning machine. In order to better prove the superiority of this method, four kinds of state data of motor bearing under the conditions of 0 HP (horsepower) load, 1 HP load, 2 HP load, and 3 HP load are used to test. The experimental results indicate that the diagnosis accuracies of DTCWT-AFSO-KELM are obviously better than those of discrete wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DWT-AFSO-KELM) or discrete wavelet transform and extreme learning machine (DWT-ELM) under different loads.
Do more data equal more truth? Toward a cybernetic approach to data

In Australia, as in other places in the world, we accumulate and curate Big Data – in the public and private spheres; as consumers and citizens; for profit, for proof and for the promises it holds. Yet, data are only ever a partial snapshot of the lives of humans, communities, environments and places. Data has its place, but in order to use data to effect change, we need to understand which questions to ask, which assumptions to suspend, which data to collect and how to interpret them, we need knowledge of the broader context. Taking a systems‐level view of data allows us to ask questions about it as a technical and sense making apparatus, as well as about the consequences of data collection, storage and use. This article advocates for a cybernetic approach to thinking about and using data. Cybernetics can be broadly understood as the study of feedback, communication and control in a system. A cybernetic approach to Big Data means de‐centring data to focus on a broader system of cultural, technological and environmental dimensions. In this approach, the system itself is the core unit of analysis, and also a vantage point or an approach through which to encounter and re/structure such systems. Employing a cybernetic approach allows us to move beyond attempts to order and reorder information, and instead see it as making implicit knowledge explicit, and then knowing that the hard work is what to do with that knowledge. Through this we are able to conceptualise more complex inputs, outputs and interactions, than simply those captured in a dataset. The insights drawn from a particular dataset or datasets, or from a particular model or algorithm (however well parameterised) never represent the whole truth, and a cybernetics framing enables practitioners to say, if that is the case, what now? A cybernetic approach suggests we should preference deeper understanding, not simply more truth.
Iterated shape-bias graph cut with application to ellipse segmentation

We present a novel graph cut method for iterated segmentation of objects with specific shape bias (SBGC). In contrast with conventional graph cut models which emphasize the regional appearance only, the proposed SBGC takes the shape preference of the interested object into account to drive the segmentation. Therefore, the SBGC can ensure a more accurate convergence to the interested object even in complicated conditions where the appearance cues are inadequate for object/background discrimination. In particular, we firstly evaluate the segmentation by simultaneously considering its global shape and local edge consistencies with the object shape priors. Then these two cues are formulated into a graph cut framework to seek the optimal segmentation that maximizing both of the global and local measurements. By iteratively implementing the optimization, the proposed SBGC can achieve joint estimation of the optimal segmentation and the most likely object shape encoded by the shape priors, and eventually converge to the candidate result with maximum consistency between these two estimations. Finally, we take the ellipse shape objects with various segmentation challenges as examples for evaluation. Competitive results compared with state-of-the-art methods validate the effectiveness of the technique.
Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.
System Level Knowledge Representation for Complexity

To develop systems capable of high level cognitive functions such as intelligence, it is necessary to formally capture different types of knowledge, so that they can be used to support complex processes, such as inference and reasoning. The design and engineering of Intelligent Systems to support large distributed socio technical processes increasingly leverages converging techniques from Artificial Intelligence, Knowledge Representation (KR) and Cognitive Architectures. This is resulting in multi layered architectures and AI technologies which one the one hand offer unprecedented capabilities, on the other hand present innumerable, often inconceivable risks. Sophisticated conceptual structures are necessary not only to support the modeling, validation and explanation of complex engineered systems, but primarily to support cognition and conceptualization of the complexities involved, for designers, developers, end users and any stakeholder. Depending on the cognitive makeup of observers, and on the knowledge available, complexity can be conceptualized and traversed following a diversity of methods and patterns. Sometimes complexity can be broken down into cognitively accessible chunks, in other cases however, it cannot be broken down without losing essential information about the system as a whole. Addressing the need to develop cognitive artifacts, methods and techniques that can capture and represent complexity, this paper proposes the outline of conceptual structure that bridges existing approaches which tend to distinguish between cognitive engineering and Knowledge Representation, with the aim to integrate technical and socio technical systems dimensions. The paper presents considerations about cognitive aspects of complex systems theory and practice. It anticipates a convergence between cognitive architectures and KR, introduces the notion of System Level Knowledge Representation and applies it to navigate socio technical complexity in systems engineering. A summary of related work where the System Level Knowledge Representation is being developed and evaluated is also provided.
Express Delivery Handling Model Based on Monte Carlo Simulation and Linear Programming in the Background of Cloud Computing

Abstract This paper addresses the purchase options for courier companies looking for the type and number of machines to handle couriers, using Monte Carlo simulation and linear programming to simulate the processing of courier companies. The Jacques-Bella test for normality is first used, and the original hypothesis that both the machine processing capacity and the number of couriers given retain the random variables obey a normal distribution. The data are integrated into hourly time units using the principle of additivity of the normal distribution. Problem 1 requires the optimal number of equipment 1 to meet the basic operation demand of the courier company. According to the daily conditions, the article sets linear programming constraints, using Monte Carlo simulation of the number of machines working per hour for 200 cycles and gets the result that 16 machines are required. Problem 2 requires a comparison of the costs of purchasing equipment 2 and equipment 1. Due to the limitations of the work cycle of equipment 2, the article simulates 50 Google cycles to obtain the result that purchasing of three equipment 2 would meet the requirements. Because equipment 1 program costs less, the courier company should choose to buy 4 equipment 1.
IntruDTS: Interactive Visual Analysis System for Intrusion Detection in Time Series

The network environment is increasingly complex, resulting in the explosive growth of network traffic logs. Discovering the patterns of these logs and detecting various cyber-attacks and anomalies have become a widespread concern. However, traditional network analysis techniques and Intrusion Detection System (IDS) have limited ability to identify and respond to the malicious activities hidden in dynamic and long-duration time series. This paper proposes a novel visual analysis system, combining visual analysis and machine learning model, to better reveal the pattern of various traffic logs, detect and classify abnormal network behaviors from enormous traffic logs. The system supports interactive exploration in data space and comparative analysis of the normal and abnormal pattern. It could help users analyze traffic logs conveniently and identify network intrusions efficiently. Besides, a supervised classifier in our system supports the prediction of a single traffic log which facilitates users' analysis of the patterns of traffic logs. A case study conducted on the CICIDS-2017 dataset demonstrates the feasibility of our system.
Bootstrapping for Batch Active Sampling

The goal of active learning is to select the best examples from an unlabeled pool of data to label to improve a model trained with the addition of these labeled examples. We discuss a real-world use case for batch active sampling that works at larger scales. The standard margin algorithm has repeatedly been shown difficult to beat in practice for the classic active sampling set-up, but for larger batches and candidate pools, we show that margin sampling may not provide enough diversity. We present a simple variant of margin sampling for the batch setting that scores candidate samples by their minimum margin to a set of bootstrapped margins, and explain how this proposal increases diversity in a supervised and efficient way, and why it differs from the usual ensemble methods for active sampling. Experiments on benchmark datasets show that the proposed min-margin sampling consistently works better than margin as the batch size grows, and better than the five other diversity-encouraging active sampling methods we tested. Two real-world case studies illustrate the practical value, and help highlight challenges of applying and deploying batch active sampling.
Sublinear-time Algorithms for Stress Minimization in Graph Drawing

We present algorithms reducing the runtime of the stress minimization iteration of stress-based layouts to sublinear in the number of vertices and edges. Specifically, we use vertex sampling to further reduce the number of vertex pairs considered in stress minimization iterations. Moreover, we use spectral sparsification to reduce the number of edges considered in stress minimization computations to sublinear in the number of edges, esp. for dense graphs.Specifically, we present new pivot selection methods using importance-based sampling. Then, we present two variations of sublinear-time stress minimization method on two popular stress-based layouts, Stress Majorization and Stochastic Gradient Descent.Experimental results demonstrate that our sublinear-time algorithms run, on average, about 35% faster than the state-of-art linear-time algorithms, while obtaining similar quality drawings based on stress and shape-based metrics.
Using combined Soft-NMS algorithm Method with Faster R-CNN model for Skin Lesion Detection

The detection of skin diseases has always been a hot topic in the medical field. With the development of deep learning, more and more neural network models have been used in medical research and have achieved good results. In this paper, based on the existing target detection model Faster R-CNN, we replace the NMS algorithm in it with Soft-NMS. The experimental results verify the effectiveness of our improvement. Compared with Faster R-CNN, our method can frame the skin disease area more accurately by reducing the misrecognized area of non-lesion areas. At the same time, our method can better deal with the situation of blurred boundaries of skin diseases. The data set we used comes from ISIC (International Skin Imaging Collaboration).
A New Algorithm to Estimate the Parameters of Nonlinear Regression

Abstract The procedures to estimate the parameters are important in many scientific fields that are required to develop mathematical models. Thus, this paper is proposed as a Gravitational Search algorithm for estimating the parameters of nonlinear regression models. Also, a simulation study is conducted to investigate the performance of the proposed methods in this paper. The results show that GSA approach provides accurate estimates and is satisfactory for the parameter estimation of the nonlinear regression models.
Paraphrasing Academic Text: A Study of Back-Translating Anatomy and Physiology with Transformers

This paper explores a general approach to paraphrase generation using a pre-trained seq2seq model fine-tuned using a back-translated anatomy and physiology textbook. Human ratings indicate that the paraphrase model generally preserved meaning and grammaticality/fluency: 70% of meaning ratings were above 75, and 40% of paraphrases were considered more grammatical/fluent than the originals. An error analysis suggests potential avenues for future work.
An image forensic technique based on JPEG ghosts

The unprecedented growth in the easy availability of photo-editing tools has endangered the power of digital images. An image was supposed to be worth more than a thousand words, but now this can be said only if it can be authenticated or the integrity of the image can be proved to be intact. In this paper, we propose a digital image forensic technique for JPEG images. It can detect any forgery in the image if the forged portion called a ghost image is having a compression quality different from that of the cover image. It is based on resaving the JPEG image at different JPEG qualities, and the detection of the forged portion is maximum when it is saved at the same JPEG quality as the cover image. Also, we can precisely predict the JPEG quality of the cover image by analyzing the similarity using Structural Similarity Index Measure (SSIM) or the energy of the images. The first maxima in SSIM or the first minima in energy correspond to the cover image JPEG quality. We created a dataset for varying JPEG compression qualities of the ghost and the cover images and validated the scalability of the experimental results. We also, experimented with varied attack scenarios, e.g. high-quality ghost image embedded in low quality of cover image, low-quality ghost image embedded in high-quality of cover image, and ghost image and cover image both at the same quality. The proposed method is able to localize the tampered portions accurately even for forgeries as small as 10 × 10 sized pixel blocks. Our technique is also robust against other attack scenarios like copy-move forgery, inserting text into image, rescaling (zoom-out/zoom-in) ghost image and then pasting on cover image.
Wavelet-Based Medical Image Compression and Optimization Using Evolutionary Algorithm

Term diagnosis is a popular word in today’s scenario. Such a diagnosis is carried out by the physicians with the help of medical images generated by the acquisition devices. Such digital images vary with a huge amount of volumetric data that needs a large storage space. An efficient compression algorithm solves the problem, which is achieved by the proposed method of using a wavelet-based transform for decomposing the image utilizing less memory. To enhance the quality of the image, we propose to utilize the optimization with evolutionary algorithms. It works on the principle evolution of species and its mechanism. Artificial bee colony and particle swarm optimization are compared here in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE) and compression ratio (CR).