Reviewerly/exHarmony huggingface dataset:
Reach on Visual Image Restoration Method for AUV Autonomous Operation: A Survey

AUV (Autonomous Underwater Vehicle) is playing an increasingly important role in the ocean development process. Autonomous operation capability is the development direction of AUV, which mainly focuses on underwater observation, marine resource development, sampling and inspection, and autonomous reconnaissance. People pay more attention to military, political, economic and social needs. The autonomous operation of AUV has the advantages of a wide range of activities and good concealment. AUV can obtain images through an optical vision system. The image is prone to distortion, low saturation, blur, etc. The restoration of underwater images has been attracting more and more research work. Underwater light scattering and absorption are the root reasons of image quality degradation. This paper introduces the latest development of underwater image restoration methods, analyzes and summarizes the research status of several types of methods. The latest data set is given, several image evaluation indicators are summarized, and the future development direction is pointed out.
Image processing technology based on computer vision algorithm

Aiming at the problem of intelligent interactive 3D display image distortion, this paper designs a display computer vision system based on ARM processor. The paper realizes the 3D dimensional reconstruction by means of high-speed projector to high-speed projection of transmissive directional scattering screen. Then this paper establishes a mathemational model for distorted images and proposes a deep learning method based on CNN model to correct the distorted images in real time during projection. The experiment shows that the effect of correction is obvious. The system has the advantages of small size, low cost, wide viewing angle, high resolution and strong human-computer interaction.
Template-based mapping of reversible circuits to IBM quantum computers

In order to make the most of the increasing computational power of recently developed quantum computers, it is crucial to perform an efficient mapping of a given quantum circuit that realizes the desired quantum algorithm to the targeted quantum computer (so-called technology mapping). In most cases, the limitations of the targeted quantum hardware have not been taken into account when generating these quantum circuits in the first place. Thus, the technology mapping is likely to induce a considerable overhead for such circuits in order prepare them for the execution on the actual device. In this work, we consider the realization of reversible circuits consisting of multiple-controlled Toffoli gates on IBM quantum computers. Using templates for the realization of the reversible/quantum gates allows to perform a topology-aware decomposition of MCT gates that exhibits the potential of significant reductions of the technology mapping overhead.
An Automatic Detection of Liver Tumor from CT Abdominal Images—A Comparative Approach

The liver is a vital organ in human body. Liver performs an important function including metabolism, digestion, and detoxification. Liver is a significant organ in an abdomen, and is connected to the nearby organ such as spleen, pancreas, gallbladder, abdomen, and gut through blood vessels. Specific approaches such as image gradient and region growing are not quite reliable for the segmentation of the liver tumor. A level-set approach is evaluated in this paper compared with the active contour approach of segmentation of the liver imaging from the image of the CT abdomen and Unified level set method, spatial Fuzzy C-means method for segmenting tumor from segmented liver images is appraised. The proposed approach is implemented by using the 3DIRCADB dataset available to the public as well as non-public datasets taken from Arthi Hospital, Chennai and Tirunelveli scanning centre. For validating the system based on the diverse quantitative measures, including space overlap, coefficient of similarity, Jaccard indices, using ground truth images, which are available in the public data set 3DIRCADB and the expert segmentation results which are manually identified by the clinical partner for nonpublic datasets. The analysis of the algorithm shows the better results for segmenting liver using level set system and spatial segmentation of Fuzzy C means of the tumor segmentation.
Revised direct batch evaluation of optimal orthonormal eigenvectors of the DFT-IV matrix using the notion of matrix pseudoinverse

Batch algorithms for the generation of optimal orthonormal eigenvectors of a square unitary matrix F are presented. Since the unitarity of a matrix implies the orthogonality of its eigenspaces pertaining to its distinct eigenvalues, the problem is decoupled in the sense that optimal eigenvectors are individually generated for the eigenspaces. In one algorithm, the singular value decomposition (SVD) technique is applied to a rectangular matrix whose columns are nonorthogonal eigenvectors of matrix F pertaining to a distinct eigenvalue. In another algorithm, the SVD technique is applied to a square matrix obtained by pre multiplying this rectangular matrix by its complex conjugate transpose. For the sake of generality, this rectangular matrix is allowed to be rank-deficient and the notion of matrix pseudoinverse is resorted to in order to find optimal orthonormal eigenvectors. The general results are applied to the discrete Fourier transform of type IV (DFT-IV) kernel matrix G where the objective is the generation of Hermite-Gaussian-like (HGL) orthonormal eigenvectors as an essential step towards the development of the fractional discrete Fourier transform of type IV (FDFTIV). Since the contributed algorithms necessitate knowledge of the orthogonal projection matrices on the eigenspaces of the unitary matrix G, explicit expressions are derived for those projection matrices. The extensive simulation results show the relative merits of the various algorithms especially for large values of the order N of the matrix.
A Coding Scheme for Urban Area in Remote Sensing Optical Images

Urban area has rich texture and structure information, especially from high-resolution optical images. Reducing the loss of textures and structures caused by image compression is very important for remote sensing applications. In this letter, we proposed a compression scheme for urban area based on its inherent characteristic and applications. The original image is represented by a combined dictionary. The learnt atoms are propitious to texture parts representing, and the atoms generated by Gaussian derivative functions are used to enhance the edges. Sparse coding is carried out by matching pursuit algorithm. Finally, Huffman code is applied for entropy coding. The experiments show that the proposed approach outperforms some well-known schemes in terms of texture distortion and geometric distortion.
Fuzzy Classification of the Flow of Events for Decision-Making in Smart Systems

Decision making engine based on the classification of the data stream in real time is the main challenge of the Smart City Application (SCA). Fuzzy logic system (FLS) can be used as such engine if the data stream dimension will be significantly reduced. The two-stage Computing with Words (CWW) approach overcomes the dimensionality problem by using abstraction engine at the first stage, which maps the meaning of stream data from sensors to the meaning of a few words. In this article the CWW approach is extended by a Short-Term Memory (STM) model that stores a time sequence of events in the form of fuzzy characteristics of words of a high level of abstraction. The STM footprint model is supported by the footprint blur algorithm, in which calculations are performed with blurring of information about events with time expiration. The classification is proposed to be performed by a fuzzy assessment of the proximity of the STM footprint and the prototype of the class of the flow of events. An example of the use of fuzzy classification of the flow of events model is given.
QLiG: Query Like a Graph For Subgraph Matching

A graph is a natural and flexible modeling approach to represent entities and relationships between them in the real world. A Knowledge Graph (KG) is a specialized graph with formal and structured representations of facts, relationships, annotated with semantic descriptions. Subgraph matching is one of the fundamental graph problems to identify relationships, interactions, and activities of interest within a large graph. A query specification is a collection of abstract components, operations, and constraints to express a pattern. The specification can be implemented in different ways based on the underlying data model. Various graph query specifications have been developed over the years that has led to the development of different open-sourced and vendor-specific query languages. Such specifications are modeled as an extension of relational algebra used in relational query languages such as SQL. Such approaches do not inherently support graph queries. There is a need to represent graph queries in terms of graph-based components to expedite the query construction by non-database experts. We present a graph-based query approach QLiG (pronounced cleeg), to perform subgraph matching in a Labeled Property Graph (LPG). QLiG provides required expressivity to represent a query graph in a natural way using high-level concepts such as path, structure, and constraints. We present the query specification, salient features, and a real-world use case to show functional examples.
Blockchain Usage in Theoretical Structure for Enlarging the Protection of Computerised Scholarly Data

All scholarly activities generate documents such as statements of results, testimonials, certificates, and transcripts, which are often held in manual file cabinets and relational databases. With the value of scholarly data, it has become critical to safeguard them against falsification and unintentional alteration. Despite the fact that relational databases have become the de facto norm for storing scholarly documents in recent years, the digitised contents are still under the control of a single administrator who has authoritative access to them as well as full rights and privileges. However, because of the centralised control of the system, authoritative access is likely to introduce instances of unauthorised record alteration. This paper provides a blockchain-based theoretical structure for the authentication of digitised scholarly documents with digital signatures. The storage, alteration, dissemination and verification of scholarly records would all be based on blockchain technology. Also, using this technology will notify all approved parties about the changes and they will be able to confirm or deny the status of any update on an earlier stored scholarly record by using its hash.
Privacy Attacks Based on Correlation of Dataset Identifiers: Assessing the Risk

Data de-identification refers to any process of removing data-identifying attributes and dependencies from a data set. Currently practiced techniques of de-identification of personal and confidential information either insufficiently consider or plainly ignore data dependencies, and, therefore, carry serious risks of re-identification of confidential data. This paper describes four privacy attack models which correlate data identifiers from the existing internal data set with those data identifiers which are available from external sources. The attacks described herein allow a hacker to perform de-clustering of a public data set by either disclosing values of identifiers, or by adding new values into the original dataset which will increase the probability of re-identification. These attribute disclosure attacks carry direct risks to generally accepted privacy models, such as 1-diversity, k-anonymity, and t-closeness. We develop probabilistic risk metrics for re-identification and sensitive data disclosure, and use them to calculate the risks associated with each of the attribute disclosure attacks. The attribute disclosure attacks demonstrably increase the overall re-identification and disclosure risk by an average of up to 3.3 times the original risk.
Convergence of Data Science and Blockchain

Since the terms ‘Data Science’ and ‘Blockchain’ were coined in 2008 and 1991, both these technologies have seen massive improvement and are being utilized by industries and individuals, across the globe. Since their inception, industries have been able to utilize their potential to solve real world problems. The one thing that has been neglected is to find out the areas of convergence of both these technologies to solve a common purpose, that is decentralization and to extract meaningful information and to predict future patterns and behaviors. In this paper, we investigate how Blockchain and Data Science can converge to complement each other and areas which are difficult to converge so that we are able to harness the full potential of both these technologies for the good of mankind.
Hybrid Model Optimization Algorithm for Efficient design of Digital filter

This scenery of design of digital filter is an optimized problem. Therefore, to build up the design method based on different recent universal optimization algorithms. hybrid algorithmic program is economical optimizing method within the fashionable and future technology. It has numerous improvement tools. Particle Swarm Optimization-PSO each particle within the algorithmic program acts as some extent within the N-dimensional. All particles keep the data within the answer for every iteration and therefore the best answer is-pbest. The Genetic Algorithm-GA depends regular choice by which the fittest characterization among the gathering or inhabitants is crossed with significance the health performs and thusly the progression among the imitated people helps in achieving the foreordained smoothed out result. The integral improvement tool in MATLAB software package provides, the simplest finest worth of the input variables of PSO-GA-DE technique is wont to offer correct and absolute results.
Investigating Hourly Global Horizontal Irradiance Forecasting Using Long Short-Term Memory

Solar energy is one of the cleanest renewable energy sources available. Several studies proposed models for Global Horizontal Irradiance (GHI) forecasting, which showed that deep learning models such as Long Short-Term Memory (LSTM) could be successful at this task. However, these models needed relatively large amounts of training data to reach excellent accuracy, which may be challenging for locations where only limited data are available. This experiment aims to determine the minimum amount of historical data needed by researchers to forecast GHI one hour ahead, using a deep learning LSTM model whilst maintaining an excellent accuracy range measured by nRMSE. To achieve this objective, we trained an LSTM model with different amounts of training data, starting with five years of training data then gradually reducing the amount by one year in each trial until using only one year of training data. The accuracy of the model in terms of nRMSE is reported in each trial. The experiments were conducted using historical GHI and meteorological data from three locations in Saudi Arabia. It is concluded that it is possible for an LSTM model to achieve excellent GHI forecasting accuracy (nRMSE<10%) using only two years of training data.
Digital Contact Tracing through the use of NFC on mobile applications as a future viable alternative to QR Code Scanning in the context of Fiji

Coronavirus disease 2019 continues to devastate many countries around the world including Fiji, which relies on digital contact tracing apps such as careFiji to help contain the virus. Certain audits and papers show that Quick Response (QR) codes have low rates of usage which might affect the effectiveness of contact tracing efforts, especially in Fiji. This paper is a limited review of the technologies as well as contact protocols used in contact tracing, the official contact tracing apps used in the south pacific and an overview of the careFiji app. The aim of this is to find out about the contact tracing technologies and protocols to aid in designing a solution to the problems encountered in careFiji as well as other similar contact tracing apps in the South Pacific. The authors propose a NearField Communication (NFC) Contact Tracing Solution Model to supplement the current QR scanning feature of the careFiji app to allow for increased usage of the location-coupled tracking feature of contact tracing efforts. Results of tests conducted prove the convenience and time saving measures of a sample contact tracing app employing NFC versus the careFiji app relying on QR scanning for location-coupled tracking.
Resolving Camera Position for a Practical Application of Gaze Estimation on Edge Devices

Most Gaze estimation research only works on a setup condition that a camera perfectly captures eyes gaze. They have not literarily specified how to set up a camera correctly for a given position of a person. In this paper, we carry out a study on gaze estimation with a logical camera setup position. We further bring our research in a practical application by using inexpensive edge devices with a realistic scenario. That is, we first set up a shopping environment where we want to grasp customers gazing behaviors. This setup needs an optimal camera position in order to maintain estimation accuracy from existing gaze estimation research. We then apply the state-of-the-art of few-shot learning gaze estimation to reduce training sampling in the inference phase. In the experiment, we perform our implemented research on NVIDIA Jetson TX2 and achieve a reasonable speed, 12 FPS which is faster compared with our reference work, without much degradation of gaze estimation accuracy. The source code is released at https://github.com/linh-gist/GazeEstimationTX2.
Evaluating Multi-Level Checkpointing for Distributed Deep Neural Network Training

Deep learning (DL) applications are becoming one of the most important applications for HPC and cloud systems. The massive datasets and deep neural networks (DNN) used by DL applications introduce many HPC challenges. Therefore, HPC checkpoint/restart tools are an attractive choice. However, most data-parallel DL training jobs use a naive scheme, called root checkpointing, which is subject to blocking semantics and straggling forward progress. In this work, we apply a multi-level checkpointing tool (SCR-Exa) to distributed DL applications. We examine the performance of two DNN models at scale on Lassen (a leading TOP500 system), while ensuring the DNN's accuracy is maintained after restart from simulated system failures. Our test results show that multi-level checkpointing schemes are able to achieve nearly constant overhead at scale. To the best our knowledge, this study presents the first evaluation to demonstrate strong scalability of a checkpointing scheme for distributed DL without making framework-specific changes.
Unreal Engine Blueprint: Developing Hack and Slash Using Spawn Projectile Method

Spawn projectile method is an algorithm where projectiles are created and it generates output on the basis of the collision of those projectiles. Spawn projectile methods are widely used in any shooting game in the Unreal engine blueprint framework with a ray-tracing feature or other features. Hack and Slash games are mainly close combat action games where Socket collider methods are used for core features and projectile-based weapons may be used as secondary options or other unique methods which are not widely used in a game. Firing system of first and third-person shooting games, selected assets with a skeletal mesh, secondary attack, or special abilities of a player were successfully implemented by using the spawn projectile method as a central algorithm. This research found that Hack and Slash games also can be designed based on the basic algorithm of spawn projectile method, which is mainly an enhanced domain of the current features of Unreal Engine 4.
Machine Vison-Based System for Vehicle Classification and Counting Using YOLO

This paper proposes a machine vision-based system that is used for vehicle classification and counting. Vehicle classifier and counter are a very crucial in road design to determine the road load capacity. It is also important to monitor, manage, and analyze the traffic flow. In this work, the input video is obtained from a static video camera and from drone-captured video to count the cars, determine its direction and to classify the vehicle types on the road. The data will be fed to the vision-based system that will pass the frames captured by the camera through a YOLO neural network to classify the vehicles. Then, the cars contours are used to determine the car path that determines in which direction the vehicle is moving before it is being counted. The system will display the total number of vehicles moved in each direction and while identifying each vehicle’s type. This work is possible to be used for other application such as surveillance tracking and monitoring.
An effective colour image retrieval with fusion based CNN frameworks

Content base image retrieval (CBIR) has become one of major research area in the field of digital stream. In the last decade the neural networks placed major in retrieval the colour images from huge data bases. In this paper a VGG16 network and Deepfixnet network are fused to get better retrieval qualities. This is tested on five different image sets, each has 500 images.
Dpb-Mopso: A Dynamic Pareto Bi-Level Multi-Objective Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization, but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracking the most effective solutions over time and ensuring good exploitation and exploration are the main challenges of solving DMOP. This study proposes a Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on a set of DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), and hypervolume difference (HVD) metrics are used to assess the DPb-MOPSO performances. Quantitative results are analyzed using Friedman's analysis of variance, while stability is analyzed using Lyapunov's theorem. The DPb-MOPSO is more robust than several dynamic multi-objective evolutionary algorithms in solving 21 complex problems over a range of changes in both the POS and POF. On IGD and HVD, DPb-MOPSO can solve 8/13 and 7/13 of the 13 UDF and ZJZ functions with moderate changes. DPb-MOPSO is able to resolve 4/8 FDA and DMOP benchmarks with severe changes to the MIGD, and 5/8 with moderate changes. DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD metrics when taking into account 3 kinds of environmental changes.