Reviewerly/exHarmony huggingface dataset:
Survey on Image Compression using Machine Learning and Deep Learning

Machine learning and deep learning techniques are few of the important data analysis methods having interesting property of being able to learn complex feature representation from data. In recent years, machine learning and deep learning techniques have lead to new approaches for large number of applications in a variety of domains. Further, due to large increase in image data, the research activities in image compression continue to preserve bandwidth or storage resources. Various review articles on image compression or data compression is found but this review focus on the research work having combination of traditional compression algorithms and machine learning or deep learning techniques. This help to understand the current trends and future scope in image compression using machine learning or deep learning techniques.
Aspect Extraction for Tourist Spot Review in Indonesian Language using BERT

Review is an essential aspect to support decision making in various fields and industries, with tourism being one of them. With the emergence of Internet, especially the Web 2.0, feedbacks and reviews can now be found easily from forums, blogs, and social websites. However, the sheer amount of data making it a tedious task for user to analyze and extract useful information from all those reviews. Aspect-based sentiment analysis (ABSA) is one of solutions to solve the aforementioned problem with aspect extraction phase as one of the essential component of it. In this paper, we adapt Bidirectional Encoder Representations from Transformers (BERT) to process aspect extraction by adding the Indonesian language specific preprocessing phase and further pretrain the multilingual base model with 4220 review sentences in Indonesian language. For the experiment, we collect 501 review sentences from TripAdvisor and do the performance evaluation using the 10-fold cross validation method. The collected reviews are about tourism spots, specifically amusement park, in Indonesian language. We found our adaptation succeed in improving the performance of BERT for aspect extraction with the new further pretrained base model produced the best accuracy and F1-measure value with 0.799 and 0.738 respectively, while adding preprocessing phase with the new further pretrained base model produced the best accuracy with 0.824. The impact of preprocessing phase can also be seen with the best improvement for each labels ROC-AUC value happens on scenarios that contain preprocessing phase.
Fine-Grained Action Recognition in Assembly Work Scenes by Drawing Attention to the Hands

The analysis of assembly working scenes is an important tool for improving work efficiency and detecting worker error. Many factories choose to analyze videos of employees working by human observation. It is more efficient, however, to approach this as an action segmentation task, but this is difficult due to the fine-grained actions. In this paper, we focus on featuring the workers hands in action segmentation to describe the detailed moves in assembly work scenes and utilize the tool or product information that the worker is using. We propose two methods to draw attention to the hand from each image in the video: the first is by cutting out the workers hand image and extracting features, and the second is by using an attention module specialized to extract hand features by the training of a regression network. For both methods we combine different types of features - image features and pose features - which are both important information in fine-grained actions. Due to the lack of action datasets that focus on assembly work scenes, we create a new assembly work dataset for our experiments, and the results of those experiments show that our method is effective for analyzing fine-grained action segmentation tasks.
Federating Cloud Systems for Collaborative Construction and Engineering

The construction industry has undergone a transformation in the use of data to drive its processes and outcomes, especially with the use of Building Information Modelling (BIM). In particular, project collaboration in the construction industry can involve multiple stakeholders (architects, engineers, consultants) that exchange data at different project stages. Therefore, the use of Cloud computing in construction projects has continued to increase, primarily due to the ease of access, availability and scalability in data storage and analysis available through such platforms. Federation of cloud systems can provide greater flexibility in choosing a Cloud provider, enabling different members of the construction project to select a provider based on their cost to benefit requirements. When multiple construction disciplines collaborate online, the risk associated with project failure increases as the capability of a provider to deliver on the project cannot be assessed apriori. In such uncontrolled industrial environments, “trust” can be an efficacious mechanism for more informed decision making adaptive to the evolving nature of such multi-organisation dynamic collaborations in construction. This paper presents a trust based Cooperation Value Estimation (CoVE) approach to enable and sustain collaboration among disciplines in construction projects mainly focusing on data privacy, security and performance. The proposed approach is demonstrated with data and processes from a real highway bridge construction project describing the entire selection process of a cloud provider. The selection process uses the audit and assessment process of the Cloud Security Alliance (CSA) and real world performance data from the construction industry workloads. Other application domains can also make use of this proposed approach by adapting it to their respective specifications. Experimental evaluation has shown that the proposed approach ensures on-time completion of projects and enhanced collaboration performance.
A Proposed Framework for Improving Analysis of Big Unstructured Data in Social Media

With the rapid development of Big Data and the necessity for analyzing their huge volumes, the issue of Unstructured Data analysis in social media was appeared. The Data analysis process is very important in all fields as to make decisions at the right time and over certain facts. The usage of social media has become the latest trend in today's world in which users send, read posts known as `message' and communicate with various groups. Users are sharing their regular life, posting their views on everything like products and locations. This data is extremely unstructured, making it hard to analyze. Machine learning technology offers important data preparation techniques for processing large-scale data to extract knowledge, e.g., classifying data. Extract useful information from social media data is essential to success in the big data age. Therefore, fresh strategies are needed for handling huge quantities of unstructured data and finding the hidden information in these data and achieving better data analysis outcomes, In this paper, the proposed framework recommends the construction of a machine-learning model capable of analyzing unstructured text data with highly accuracy compared to other machine learning algorithms.
Lenet-5 Convolution Neural Network with Mish Activation Function and Fixed Memory Step Gradient Descent Method

Convolutional neural network is the most important algorithm in the field of deep learning. The traditional convolution neural network usually uses Sigmoid or Relu as the activation function, but the two sides of Sigmoid are saturated, and Relu has a dead zone, which is very easy to cause gradient disappearance and gradient explosion. In this paper, Mish activation function is introduced into LENET-5 convolutional neural network, which overcomes the shortcomings of traditional activation function. At the same time, the fixed memory step gradient descent method is used to replace the gradient descent method of the optimization part, which improves the global convergence of the algorithm.
Approximate Acceleration for CNN-based Applications on IoT Edge Devices

Machine learning based sub-systems are increasingly becoming part of IoT edge devices, thereby requiring resource-efficient architectures and implementations, especially when subjected to battery-constrained scenarios. The non-exact nature of Convolutional Neural Networks (CNNs) opens the possibility to use approximate computations to reduce their required runtime and energy consumption on resource-constrained IoT edge devices without significantly compromising their classification output. In this paper, we propose a resilience exploration method and a novel approximate accelerator to speed up the execution of the convolutional layer, which is the most time consuming component of CNNs, for IoT edge devices. Trained CNNs with Caffe framework are executed on a System-on-Chip with reconfigurable hardware available, where the approximate accelerator is deployed. CNN applications developed with Caffe can take advantage of our proposed approximate acceleration to execute them on IoT edge devices.
Learning stochastic closures using ensemble Kalman inversion

Abstract Although the governing equations of many systems, when derived from first principles, may be viewed as known, it is often too expensive to numerically simulate all the interactions they describe. Therefore, researchers often seek simpler descriptions that describe complex phenomena without numerically resolving all the interacting components. Stochastic differential equations (SDEs) arise naturally as models in this context. The growth in data acquisition, both through experiment and through simulations, provides an opportunity for the systematic derivation of SDE models in many disciplines. However, inconsistencies between SDEs and real data at short time scales often cause problems, when standard statistical methodology is applied to parameter estimation. The incompatibility between SDEs and real data can be addressed by deriving sufficient statistics from the time-series data and learning parameters of SDEs based on these. Here, we study sufficient statistics computed from time averages, an approach that we demonstrate to lead to sufficient statistics on a variety of problems and that has the secondary benefit of obviating the need to match trajectories. Following this approach, we formulate the fitting of SDEs to sufficient statistics from real data as an inverse problem and demonstrate that this inverse problem can be solved by using ensemble Kalman inversion. Furthermore, we create a framework for non-parametric learning of drift and diffusion terms by introducing hierarchical, refinable parameterizations of unknown functions, using Gaussian process regression. We demonstrate the proposed methodology for the fitting of SDE models, first in a simulation study with a noisy Lorenz ’63 model, and then in other applications, including dimension reduction in deterministic chaotic systems arising in the atmospheric sciences, large-scale pattern modeling in climate dynamics and simplified models for key observables arising in molecular dynamics. The results confirm that the proposed methodology provides a robust and systematic approach to fitting SDE models to real data.
A hybrid classical-quantum workflow for natural language processing

Natural language processing (NLP) problems are ubiquitous in classical computing, where they often require significant computational resources to infer sentence meanings. With the appearance of quantum computing hardware and simulators, it is worth developing methods to examine such problems on these platforms. In this manuscript we demonstrate the use of quantum computing models to perform NLP tasks, where we represent corpus meanings, and perform comparisons between sentences of a given structure. We develop a hybrid workflow for representing small and large scale corpus data sets to be encoded, processed, and decoded using a quantum circuit model. In addition, we provide our results showing the efficacy of the method, and release our developed toolkit as an open software suite.
A novel Approach of Retinal Image Enhancement using PSO System and Measure of Fuzziness

Retinal image analysis has become a promising research field in the diagnosis of ophthalmological diseases since optic disc, fovea, and blood vessels are essential features based on which the analysis is performed. This paper introduces a novel retinal image enhancement scheme based on particle swarm optimization (PSO) under fuzzy framework as it overcomes the problem of upgrading the imprecise nature of retinal image in traditional PSO system in the field of image enhancement. Firstly, two sub-images such as lower and upper in fuzzy regions of the image are determined by type-2 fuzzy system. Then,an S-shape membership function is utilized on lower and upper sub-images. An objective function such as index of fuzziness is optimized in terms of particle swarm optimization (PSO) system and it also defines the adaptive parameters which are incorporated with the proposed enhancement technique. The suggested model applies low contrast color retinal images to treat different diseases of eye such as diabetic retinopathy, glaucoma, optic disc etc. The proposed algorithm demonstrates excellent result in terms of both subjective and objective evaluation than traditional PSO systems and other baseline methods.
A UAV-Based Traffic Monitoring System for Smart Cities

The new development of the generation and a modern procedure called Industry 4.0 and its advances in enormous information investigation and digital physical frameworks, regardless of others have an obscure potential effect on maintainability and the earth. Traffic congestion is a major problem which is increasing day by day in developing countries. So traffic monitoring is becoming a more important issue especially in urban areas for a developing country as well as for the Industry. For the development of smart cities, traffic management is one of the most important features. To reduce traffic congestion for smart cities, this paper proposes a traffic monitoring system based on the information captured by Unmanned Aerial Vehicles (UAVs). To capture image and record traffic video, Unmanned Aerial Vehicles and on-board cameras are used in our proposed system model. Then, these captured or recorder information are sent and processed in the cloud server and the cloud server sends data to the main server. After providing some background knowledge for implementing UAV, this paper describes the proposed system model elaborately. The user can get update for selecting the best path for avoiding traffic congestion via a web application. Finally, some challenges and future directions have discussed about the UAV traffic monitoring system.
Passive Underwater Target Tracking: Conditionally Minimax Nonlinear Filtering with Bearing-Doppler Observations

The paper presents an application of the Conditionally-Minimax Nonlinear Filtering (CMNF) algorithm to the online estimation of underwater vehicle movement given a combination of sonar and Doppler discrete-time noisy sensor observations. The proposed filter postulates recurrent "prediction-correction" form with some predefined basic prediction and correction terms, and then they are optimally fused. The CMNF estimates have the following advantageous features. First, the obtained estimates are unbiased. Second, the theoretical covariance matrix of CMNF errors meets the real values. Third, the CMNF algorithm gives a possibility to choose the preliminary observation transform, basic prediction, and correction functions in any specific case of the observation system to improve the estimate accuracy significantly. All the features of conditionally-minimax estimates are demonstrated by the regression example of random position estimate given the noisy bearing observations. The contribution of the paper is the numerical study of the CMNF algorithm applied to the underwater target tracking given bearing-only and bearing-Doppler observations.
Image Quality Assessment Parameters for Despeckling Filters

Images Quality assessment shows vital role in image processing applications and is an active field of research. In medical imaging field various imaging modalities i.e. Ultrasound (US), X-ray, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) etc. are used to diagnose the disease by the doctors. In US imaging modality coherent sources are used for imaging purpose. Due to coherent sources, speckle noise inserts in US images and causes loss of information and blurring which leads to misdiagnose of the patients by the doctors. To avoid this situation, various despeckling filters are used to remove the speckle noise and their performance is analysed on the basis of subjective and objective assessment parameters. This paper summarizes the various performance evaluation parameters i.e MSE, RMSE, SNR, PSNR, AD, SI, NK, MD, LMSE, NAE, IQI, SSIM, and BETA, their significance & relevance with the example of despeckling filters i.e. LEE Sigma, Detail Preserving Anisotropic Diffusion (DPAD), Fourier Butterworth Filter (FBF), ROFTV (Rudin, Osher and Fatem) and Hybridized Mean and Wiener Filter.
A Pipeline-Based Scheduler for Optimizing Latency of Convolution Neural Network Inference over Heterogeneous Multicore Systems

Parallelization is a common design practice for throughput improvement on multicore systems. However, the existing operating systems' schedulers for CNN inference essentially divide the computational tasks of each convolution layer onto different CPU cores and cause significant inter-core feature-map data movement. Therefore, the overall performance is often degraded. In this paper, we propose a pipeline-based scheduler for convolution neural network inference parallelization with minimal feature-map data movement requirements. The experimental results show that our approach can achieve 73% performance improvement on throughput compared to the existing multi-thread scheduler.
Everyday curation? Attending to data, records and record keeping in the practices of self-monitoring

This paper is concerned with everyday data practices, considering how people record data produced through self-monitoring. The analysis unpacks the relationships between taking a measure, and making and reviewing records. The paper is based on an interview study with people who monitor their blood pressure and/or body mass index/weight. Animated by discussions of ‘data power’ which are, in part, predicated on the flow and aggregation of data, we aim to extend important work concerning the everyday constitution of digital data. In the paper, we adopt and develop the idea of curation as a theory of attention. We introduce the idea of discerning work to characterise the skilful judgements people make about which readings they record, how readings are presented, and about the records they retain and those they discard. We suggest self-monitoring produces partial data, both in the sense that it embodies these judgements, and also because monitoring might be conducted intermittently. We also extend previous analyses by exploring the broad set of materials, digital and analogue, networked and not networked, involved in record keeping to consider the different ways these contributed to regulating attention to self-monitoring. By paying attention to which data is recorded and the occasions when data is not recorded, as well as the ways data is recorded, the research provides specificity to the different ways in which self-monitoring data may or may not flow or contribute to big data sets. We argue that ultimately our analysis contributes to nuancing our understanding of ‘data power’.
Research on vehicle detection algorithm based on convolutional neural network and combining color and depth images

In order to ensure the safety of driving, this paper studies the vehicle detection algorithm based on convolutional neural network which integrates color and depth images. This paper mainly discusses the subject through convolutional network multi-scale forward looking depth imaging positioning model, forward looking variable scale vehicle detection pre-positioning algorithm, and typical model identification algorithm based on transfer learning, and verifies the algorithm performance and model identification algorithm accuracy by combining with simulation test. The results show that the accuracy of the algorithm is 96.89% under different weather conditions, indicating its effectiveness in application. Compared with previous vehicle detection algorithms, the detection accuracy of this algorithm is effectively improved by 3.96%, indicating that this algorithm has innovative significance.
Detection and Counting of Red Blood Cells in Human Urine using Canny Edge Detection and Circle Hough Transform Algorithms

Integration of image processing in order to detect red blood cells (RBCs) in human urine enables technology to reduce medical technician's manual counting time and error factor. The general objective of this study was to detect and count the red blood cells in human urine using Canny Edge Detection (CED) and Circle Hough Transform (CHT) algorithms. CED is an edge detection algorithm used in order to identify a great variety of edges in an image. CHT is one of the features of the Hough Transform. Specifically, CHT is used in order to detect circular objects. The basis of the CHT operation will be dependent on the circular edges detected by the CED. In order to identify a specific circle size, the minimum and maximum radius must be set. Particularly, to differentiate the RBCs from other cells such as white blood cells. For this study, the minimum radius was 4 pixels while the maximum was 6. Compared to the manual counting of a medical technician, the automated counting of the device produced a percent error of 9.561% and an average counting time of 0.4561 seconds per sample.
Image Captioning with Generative Adversarial Network

Automatic image annotation, automatic image tagging, and image linguistic indexing functions use methodologies that significantly overlap. In this paper, we use the general term, image captioning, to refer to all forms of such functions. Image captioning is the process of automatically generating metadata in the form of captioning (i.e., generating sentences that describe the content of the image). Image captioning is used in image retrieval systems to locate images of interest from a database, web, or personal devices. In recent years, investigators have been using Deep Learning to caption images with some success. However, the reported results do suffer from a number of deficiencies; namely, accuracy, lack of diversity and emotions in resultant captions. In order to address some of these deficiencies, we propose to use Generative Adversarial models to produce new and combinatorial samples. More specifically, we propose to explore various autoencoders to generate more accurate and meaningful captions for images. Autoencoders are neural networks that learn data codings in an unsupervised manner. The research outlined in this paper is an ongoing investigative research project.