Reviewerly/exHarmony huggingface dataset:
An Improved Blur Circle Detection Method for Geometric Calibration of Multi-Focus Light Field Cameras

Multi-focus light field cameras are increasingly gaining attention in the field of computational imaging and machine vision due to their capability to capture both spatial and angular information of light rays. Accurate calibration of the geometric parameters of multi-focus light field cameras is the prerequisite for implementing computational imaging techniques such as digital refocusing, depth reconstruction and 3D reconstruction. In this paper, an improved blur circle detection method is proposed for the calibration of multi-focus light field cameras. The Circular Hough Transform based circle detection method (CHTCDM) is used to obtain the sub-image centers accurately in white raw images. On this basis, the sub-images in the checkerboard images are classified, and the image corners are detected to determine the clusters of image corners corresponding to checkerboard corners. Then, the centers of plenoptic disc features (PDFs) are used as projection points of checkerboard corners on the light field image to calculate the camera pose. Levenberg-Marquardt method is further used to solve the calibration model. Finally, the calibration experiments of multi-focus light field cameras are carried out to evaluate the improved blur circle detection method. Experimental results indicated that the improved blur circle detection method has higher calibration accuracy of multi-focus light field camera than the blur circle detection method, while the mean reprojection error of the micro-lens centers and the image corners are 0.12 pixels and 0.33 pixels, and the mean relative distance error between adjacent checkerboards is 3.5%.
Fuzzy Logic Control for Multi-Robot Navigation and Arrival-Time Control in a Cluttered Environment

Coordination in multi-robot systems has attracted much attention in recent years. This study presents a hierarchical fuzzy controller (HiFC) to navigate a robot team and coordinate their moving speed and path for arrival time control. The proposed HiFC consists of multilayered architecture to reduce the number of free parameters and improve the scalability. The multilayered architecture includes a lower-level rule-embedded controller serving as behaviour selector and a higher-level fuzzy-logic-based coordinator to adjust each robot's moving speed and direction in the team. The HiFC is trained with a multi-objective multi-swarm evolutionary PSO (MMEPSO). Simulation results demonstrate the effectiveness of this technique in reliably solving the proposed navigation problem.
Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series

Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features, and can be simulated by nonlinear dynamic time series outputs of chaotic systems. This article presents the research work of chaotic system generator design based on artificial neural network (ANN), for studying the chaotic features of human brain dynamics. The ANN training performances of Nonlinear Auto-Regressive (NAR) model are evaluated for the generation and prediction of chaotic system time series outputs, based on varying the ANN architecture and the precision of the generated training data. The NAR model is trained in open loop form with 1,000 training samples generated using Lorenz system equations and the forward Euler method. The close loop NAR model is used for the generation and prediction of Lorenz chaotic time series outputs. The training results show that better training performance can be achieved by increasing the number of feedback delays and the number of hidden neurons, at the cost of increasing the computational load.
Influencers, Inefficiency and Fraud – The Bitcoin Price Discovery Network Under the Microscope

We present a TriSNAR modeling framework for understanding the dynamic interactions of multiple markets for Bitcoin trading, including market efficiency, and for identifying influential exchanges in the global trading network. We consider two types of influential exchanges from the perspectives of investors, regulators, and policymakers: exchanges that are market leaders and exchanges potentially used for market manipulation. Among 194 Bitcoin exchanges, we find that exchange Kraken was the leading exchange prior to the market frenzy of 2017. We also find a fraud-related exchange (Bitfinex) where some other exchanges display a similar role in the price discovery network than this exchange, raising questions about whether they may also be used for fraudulent activities. In addition, price discovery shows that the Bitcoin exchange network has been decreasing in efficiency from 2015 to 2017, and it has been increasingly efficient since 2018. We investigate the finite sample and asymptotic properties of TriSNAR. Compared to alternative methods, TriSNAR outperforms in terms of accuracy, runtime, and ability to discover multi-market network structures.
Task Clustering and Scheduling in Fault Tolerant Cloud Using Dense Neural Network

The scheduling in cloud is considered as a major task in cloud computing environment as it is associated with severe constraints while deploying or scheduling a task. It is hence necessary to model a schedule that effectively deploys the schedule the task based on the constraints. In this paper, we develop a DenseNet model to improve the rate of scheduling in cloud computing environment. This algorithm helps in the task assignment and execution in the cloud. The simulation is conducted in cloudsim simulator, where the communication costs is lesser than existing methods. Similarly, the execution of task takes lesser time in proposed than in other methods.
Blockchain Technology in Forensic Science. A Bibliometric Review

Blockchain technology in forensic science can have meaningful changes and improvements in digital forensics, forensic investigation, evidence management, and chain of evidence. The consolidation of research on the application of blockchain technology on forensic management, basing Scopus database was conducted and the research trends of bibliometric analysis were visualized. The prominent funding agencies, leading research organizations, authors, countries, journals, and keywords were mentioned in this research. The future scope of research on this research domain can be on enhancing the privacy, authenticity, reliability, and evidence handling in forensic science by the implementation of blockchain technology.
Development of preprocessing and statistical analysis tools in the integrated biomechanics informatics system (IBIS)

The field of biomechanics integrates a variety of data types to provide meaningful feedback to athletes, coaches, and healthcare professionals to reduce injury and improve performance. These different sources of data include waveform, video, discrete, and performance metrics. An integrated biomechanics informatics system (IBIS)<sup>1,2</sup> has been developed to apply imaging informatics principles to the field of biomechanics. The IBIS system helps create a space for stakeholders to make informed decisions using multimodal biomechanics data. This current work details the development of two new decision support tools within the IBIS system that enhance the ability of users to effectively analyze and interpret data. These two tools include an R Shiny statistical analysis tool and an algorithm for detecting foot contact with the ground. Organizing data into SQL databases on the IBIS system provides the backend for the R Shiny analysis tools. This connection provides statistical analysis tools to review data over time and across individuals. Its integration also incorporates interactive data visualization techniques to put biomechanical data in context. A semi-automated foot contact detection algorithm was created to reduce the need for researchers to manually search through thousands of video frames to synchronize video data accurately with other information such as force data. Overall, these two improvements to the system will support researchers working to provide meaningful insight about human performance and injury prevention. These additions to the IBIS system provide quicker access to data, develop standardized pipelines, and create additional time for biomechanists to focus on interpreting results rather than data management.
Mas-Cloud+: A Multiagent Architecture for Optimized Cloud Resource Management

Optimized cloud resource management is a challenging issue since cloud resources involve different characteristics, technologies and financial costs. Multiagent technology can offer noticeable improvements, with intelligent agents deciding how much resources are needed for virtual machines (VMs) to perform a given task in the shortest time, lowest cost, and/or least waste. This article presents MAS-Cloud+, an architecture with autonomous agents for monitoring, predicting, and provisioning cloud computing resources. Our agents use a hybrid rationality model based on heuristics, combinatorial optimization, and metaheuristics. MAS-Cloud+ instantiates VMs considering SLA and QoS on computing cloud platforms (e.g., AWS EC2), prioritizing user needs considering time and cost to perform tasks. To evaluate MAS-Cloud+ we used a DNA sequence comparison application subjected to different workload sizes for performance evaluations. The choice and provisioning of VMs were done using the three rationality models allowing to compare and validate the agents’ decisions. Our optimized model presents 23.50% better performance and 21.04% less monetary cost when compared to the heuristic one, and 19.27% better performance and 6.53% less monetary cost when compared to the metaheuristic one.
Calculation of Action Times of Arrester with Clearance Based on GA-BP Neural Network Technology

The operation times of arrester counter are one of the main bases for judging the operation status of arrester, but it is not only time-consuming and laborious, but also difficult to implement the operation times of arrester read by artificial and unmanned aerial photographs. This paper presents a new method for calculating the action times of lightning arresters with gap based on the combination of lightning location system, transmission line data and GA-BP neural network technology. Firstly, the maximum lightning current in the lightning-induced area is determined. Then, the structure of GA-BP neural network is determined and trained according to the selected samples. The trained network is used to calculate whether the arrester acts after lightning strike, so as to realize the online prediction of the number of arrester actions. Examples show that the accuracy rate of this method is 90% for action recognition of lightning-induced arresters, 86.25% for non-action samples of lightning-induced arresters, and 80% for action recognition of lightning-induced arresters. The accumulative action times of the arrester calculated by the network basically coincide with the actual action times, and the overall accuracy rate is better.
A smart access control mechanism based on user preference in online social networks

Summary Access control can effectively protect users' data and privacy in social networks. Different access control models address different data privacy requirements, and the corresponding implementation technologies and performance are also quite different. The existing access control mechanisms of social networks rarely take into account the individual preferences of users, and cannot provide personalized services for users. Therefore, we have studied an analysis mechanism of user preference to quantify user preferences, and then use user preference values and some key information of users' social resources as decision vectors, and proposed an access control mechanism based on user preference (ACMBUP). ACMBUP turns the problem of access decision‐making into a problem of classification, that is, whether to allow access or not to allow access. At the same time, we add an encryption and decryption mechanism in ACMBUP to prevent data leakage and protect data when users interact with access mechanisms. Experiments show that this mechanism can automatically generate appropriate access control policies to meet the potential privacy needs of different users, so as to better protect the privacy of social networks data.
A Neuro-Fuzzy Approach to MOPP Open Time Prediction for Chemical Threats in Low-Intensity Conflicts

Chemical, Biological, Radiological and Nuclear (CBRN) weapon systems hold the potential to cause great damage to a fighting unit. Chemical warfare agents are cheaper and easier to produce and deploy. Mission oriented protective posture (MOPP) equipment is used to isolate soldiers from the toxic environment . Sustained wearing of MOPP overgarments can put soldiers at the risk of dehydration and heat stress. A hierarchal adaptive neuro-fuzzy inference system (HANFIS) model is proposed to predict worst case MOPP open times for surfaces covered with chemical agent resistant coating (CARC) and sand. The model can provide decision support for commanders when determining MOPP open or unmasking time. From HANFIS simulation results it was concluded that the model can accurately predict MOPP open times according to data present in military field manuals, in a manner which is computationally efficient. and easy to interpret based on linguistic rules and most importantly, easy to customize.
Architecture for Real-time Intelligent Storage Expansion System in Distribution Substation

With the increasing requirements of real-time data acquisition in large scale distribution network, current data storage capacity is inadequate to meet such requirements. This paper proposes a design of real-time intelligent storage expansion system for monitoring data in distribution substation. By upgrading the hardware module and data acquisition interface, the proposed method can realize real-time intelligent storage expansion from both hardware and software aspect for monitoring data. The simulation results show that the proposed method can enhance the capacity expansion of real-time monitoring data storage while ensuring the stability of the expansion.
An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.
Film Shot Type Classification Based on Camera Movement Styles

Visual information contains the most important characteri-stics of a movie regarding the related content and filming techniques. Especially the way the camera moves to capture the scene is vital to define the director's aesthetics. However, most of the machine learning tasks existing in the literature treat the movie as shallow content, rather than as an artistic work, and therefore focus on detecting objects and faces, recognizing activities and extracting plot-related topics. On the other hand, cinematography is closely connected to the choice of different ways to handle the camera, and thus camera movements include information that is useful in order to analyse the artistic style of a movie. In this work we present an original, publicly available ( https://github.com/magcil/movie_shot_classification_dataset ) dataset for film shot type classification that is associated with the distinction across 10 types of camera movements that cover the vast majority of types of shots in real movies. In addition, two different methods are evaluated on the new dataset, one static that is based on feature statistics across frames, and one sequential that tries to predict the target class based on the input frame sequence using LSTMs. Based on the evaluation process it is inferred that the sequential method is more suited for modeling the camera movements.
About statistical analysis of environmental pollution monitoring data obtained in the vicinity of a copper quarry

The article is devoted to the issue of the effectiveness of using statistical methods for processing of local environmental monitoring data with a small number of sampling points, on the example of research in the area of the quarry for the extraction of copper pyrite ores. Cluster analysis was used as a method for classifying 12 sampling points according to the level of soil and vegetation contamination with 6 metals. The results of the work showed that the statistical analysis of data on vegetation pollution (in contrast to data on soil pollution) makes it possible to quite clearly classify observation points subject to the direct influence of polluted industrial waters, background points with a natural level of the metals content, and also the points subject to the influence of another powerful and long working source of dust emissions - a metallurgical enterprise on processing of nickel ores.
Smartphones as Alternatives to Computers for Learning and Collaboration in a Multinational Disability-Inclusive Community of Practice

For decades, computers have facilitated many complex tasks, but not everyone has access, especially in low-income countries. However, the current affordability and increasing use of smartphones make them a good alternative for education and research collaboration. This Partnerships for Inclusive Research and Learning (PIRL) study explores smartphones' role in learning and research collaboration in a multinational community of practice (CoP) involving participants in the Global South and the Global North. The PIRL study used a survey and interviews to realize the optimal use of information and communication technologies (ICTs) in a CoP for knowledge provision, research, and professional development in a disability-inclusive development (DID) context. The CoP included some academic and community researchers with disabilities. Survey results showed that 50% of the PIRL CoP participants from African countries cannot use computers as much as wanted because they are unaffordable or lack reliable or affordable internet. All respondents from the Global North could use the internet and computers as much as they wanted, results that reflect the digital divide. Since PIRL CoP participants in the Global South are disadvantaged in computer use, they turn to more affordable smartphones for collaboration and learning. However, small devices challenge the performance of complex tasks like collaborative writing, coding interview transcripts, and contributing effectively to teamwork. This study provides some recommendations to improve collaboration in such situations.
Significant Support (SISU): A New Interest Measure in Association Rule Mining

In machine learning, association rule mining is a field with immense opportunity to explore relationships among various attributes and item-sets. However, in Association rule mining, statistically it is the interest measure which play the crucial role to decide these relationships. There exist various types of interest measures based upon the business needs and problem statements. In this paper, a novel interest measure has been proposed to decide the overall importance of an association rule. Statistical comparisons and experimental results have also been embedded to support its potential.
Digital Image Conspicuous Features Classification Using TLCNN Model with SVM Classifier

We present a transfer learning convolutional neural network (TLCNN) model in this study that permits classification of noticeable noise characteristics from degraded images using dispositional criteria. Various digitally degraded images comprising additive, impulsive, as well as multiplicative noise are used to test the suggested approach for noise type detection techniques. We further show that our representations sum up effectively when applied to additional datasets, achieving best-in-class results. In order to use the TL approach, we selected three CNN models: VGG19, Inception V3, and ResNet50, which are profound for visual recognition in computer vision to detect correct noise distribution. In this perplexing setting, the system's capacity to deal with the degraded image has outperformed human vision in noise type recognition. By getting noise classification performance of 99.54, 95.91, and 99.36%, while observing the nine classes of noises, the author's testing confirmed the constant quality of the recommended noise type's categorization.
Learning with Differential Privacy

Considering the internal representations in machine learning models may potentially imply some of training data, an adversary may launch attacks to extract parts of training data from a trained model (e.g., the “black-box” model-inversion attack Fredrikson et al. (2015)). Thus, the privacy guarantees if datasets contain correlated inputs is also important.
Cryptography and Digital Transformation

Abstract Cryptography is implemented using discrete mathematics with security defined in complexity theory. In this article, we review some cryptographic primitives for encryption, signing messages and interactive proofs. By combining cryptographic primitives, we can design and digitally implement various services with desired features in security, privacy and fairness. We will discuss some examples such as electronic voting and cryptocurrencies.
Secret Key Optimization for Secure Speech Communications

This paper answers three essential questions for audio speech cryptosystems in time and discrete transform domains.The first question is, what are the best values of sub-keys that must be used to get the best quality and security for the audio cryptosystem in time and discrete transform domains.The second question is the relation between the number of sub-keys, the number of secret keys used, and the audio speech signal block's size.Finally, how many possible secret keys can be used to get the best quality and security results for the audio speech cryptosystem in time and discrete transform domains.An audio cryptosystem discussed before in recent research is applied to answer the three previous inquiries.Accurate simulation results and analysis answer all three questions; first, there is no specific, well-defined format or rule for sub-key values that must be used to get a better cryptosystem.For the second question, yes, there is a direct relationship between the number of applicable keys, number of available sub-keys, and block size of speech audio signal and formulated into a first-order equation.For the third question, each discrete transform domain has a specific acceptable range of sub-keys that imply a particular number of keys that can be used to get a better cryptosystem.