Reviewerly/exHarmony huggingface dataset:
Multidimensional Cybersecurity Framework for Strategic Foresight

Cybersecurity is now at the forefront of most organisations’ digital transformative agendas and National economic, social and political programmes. Hence its impact to society can no longer be seen to be one dimensional. The rise in National cybersecurity laws and regulations is a good indicator of its perceived importance to nations. And the recent awakening for social and ethical transparency in society and coupled with sustainability issues demonstrate the need for a paradigm shift in how cybersecurity discourses can now happen. In response to this shift, a multidimensional cybersecurity framework for strategic foresight underpinned on situational awareness is proposed. The conceptual cybersecurity framework comprising six domains – Physical, Cultural, Economic, Social, Political and Cyber – is discussed. The guiding principles underpinning the framework are outlined, followed by in-depth reflection on the Business, Operational, Technological and Human (BOTH) factors and their implications for strategic foresight for cybersecurity.
Quantum Oscillations: A Promising Field for Secure Communication

Quantum communication has been identified as the most secured technique of future communication systems. Its security is directly tied up to the fundamental laws of quantum mechanics that never fail. The success of quantum communication hinges on the notion of oscillations and synchronization in the quantum regime. In this chapter, we discuss self-sustained oscillations and their synchronizations in the quantum regime. Unlike classical oscillations, quantum oscillations are bounded by several constraints of quantum mechanics. For example, although a prominent phase trajectory is possible in classical systems, quantum phase trajectories are broad due to the Heisenberg uncertainty principle. Here we systematically introduce the concept of quantum limit cycle using the open quantum formulation. Solutions of the quantum master equation and visualizations through the Wigner function are used to demonstrate the quantum limit cycle. Next, we discuss the possibility of synchronization in the quantum regime. Finally, we discuss some possible applications of quantum oscillations and synchronization in quantum key distribution and cybersecurity.
Effect of initial squeezing on tripartite continuous-variable entanglement of dynamical processes

In this paper, we investigate the effect of initial squeezing on tripartite continuous-variable (CV) Hamiltonians based on real physical systems. The Hamiltonians are based on the three-mode magnons excited by an external field, the one-dimensional interconnected tripartite spring-like system, and two spin ensembles coupled to a cavity resonator in the nondispersive regime in the CV limit. We find that for the three-mode magnons and the spring-like system, implementation of initial squeezing invariably enhances the two-way and three-way entanglement, while for the spin-resonator system, it depends on the alignment of the spin ensembles relative to the resonator field.
Edge Computing System applying Integrated Object Recognition based on Deep Learning

With the development of personal broadcasting such as YouTube, the demand for editing the video he filmed himself is steadily increasing. Traditional media video editing solutions help edit the results of the filming. However, there is a problem that requires a lot of editing time, as people usually edit videos that are from tens to hundreds of times longer than the final result of a personal broadcasting after filming. To overcome this problem, this paper automatically classifies images with specific scenes in the whole media image editing process, and secondly proposes automatic media editing solution technology in which people intervene. In particular, personal broadcasting focuses on the use of images that include characters, specific objects, and cue sign gestures among the entire. While the existing deep learning techniques such as faces, objects and gestures are advanced, integrated recognition technologies that simultaneously deal with special requirements for editing videos are still in the early stages of research. In this paper, the automatic composite recognition technology for editing video based on deep learning is proposed. The proposed technology was implemented with python and tensorflow software based on edge computing equipment. Using actual youtube videos, it took 0.1 second to process five-person recognition, 63-food recognition, or cue sign recognition using clapping or V poses at the same time. The recognized results are divided into timestamps of the entire movie, recognition results, and locations of objects on the screen, and are output to the json file. In addition, this solution was developed on an edge computing in order to increase real-time reliability. We expect to provide automatic video editing based on perceived json results as well as shorter editing times based on this implementation.
Adversarially Training MCMC with Non-Volume-Preserving Flows

Recently, flow models parameterized by neural networks have been used to design efficient Markov chain Monte Carlo (MCMC) transition kernels. However, inefficient utilization of gradient information of the target distribution or the use of volume-preserving flows limits their performance in sampling from multi-modal target distributions. In this paper, we treat the training procedure of the parameterized transition kernels in a different manner and exploit a novel scheme to train MCMC transition kernels. We divide the training process of transition kernels into the exploration stage and training stage, which can make full use of the gradient information of the target distribution and the expressive power of deep neural networks. The transition kernels are constructed with non-volume-preserving flows and trained in an adversarial form. The proposed method achieves significant improvement in effective sample size and mixes quickly to the target distribution. Empirical results validate that the proposed method is able to achieve low autocorrelation of samples and fast convergence rates, and outperforms other state-of-the-art parameterized transition kernels in varieties of challenging analytically described distributions and real world datasets.
An Image Compression-Encryption Algorithm Based on Compressed Sensing and Chaotic Oscillator

In this chapter, a chaotic oscillator is presented. Various dynamical behaviors of the oscillator are analyzed. The complex dynamics of the proposed oscillator are applied in a compression-encryption algorithm. Here, we propose an image compression-encryption method using compressed sensing and a chaotic oscillator. At first, the original image is represented in the wavelet domain to obtain sparse coefficients. The sparse representation is scrambled with a chaotic sequence. The scrambling operation increases the security level and improves the performance of sparse recovery in the decryption process. The sparse scrambled representation is then compressed using the chaotic dynamics. The compressed matrix is also scrambled to reduce the elements’ correlation. To obtain an unrecognizable encrypted image, the XOR operation is used. In the decryption process, the smoothed $${l}_{0}$$ norm (SL0) algorithm decreases the complexity of calculations for image reconstruction. Wiener filter is used in sparse recovery based on SL0 to improve image reconstruction. The results of the presented method are satisfying in various compression ratios. Security analysis illustrates the effectiveness of our method.
An Experimental Approach in Colour Correction and Contrast Improvement for Underwater Images

Underwater captured images often face low visibility and low colour cast because of the light absorbed and scattered when traveling in water. In the marine environment, it is difficult to view an image clearly and with proper details as compared to other environments. This is primarily due to the factors such as pollution, light, depth etc. It becomes an arduous task to produce a clear image in such situations. However, with the help of advanced technology, it has become easier to solve such issues using the appropriate algorithm or methodology. In this paper, a Bi-interval contrast enhancement and color correction is recommended to improve the image quality underwater. The technology or the process adopted here is such that it makes sure that the quality of the image is being restored and an enhanced image is produced as a result. Here the first step involves subinterval linear transformation with effective and simple color correction methodology to address color distortion. Color correcting strategy ensures the quality of the image restoration and hence a single image is produced by information from multiple images. This process is followed by the application of a Gaussian low-pass filter in the L-Channel in order to segregate the high and low frequency components. The last step of the process involves use of Bi-interval histogram using S-shaped function and optimal equalization threshold strategy for high and low frequency components. A comparison of the proposed work with other traditional methodologies indicates that this work shows high-quality underwater images with quantitative and qualitative evaluation.
RPViT: Vision Transformer Based on Region Proposal

Vision Transformers constantly absorb the characteristics of convolutional neural networks to solve its shortcomings in translational invariance and scale invariance. However, dividing the image by a simple grid often destroys the position and scale features in the image at the beginning of the network. In this paper, we propose a vision transformer based on region proposal, which obtains the inductive bias in a simple way. Specifically, RPViT achieves locality and scale-invariance by extracting regions with locality using a traditional region proposal algorithm and deflating objects of different scales to the same scale by a bilinear interpolation algorithm. In addition, to enable the network to fully utilize and encode diverse candidate objects, a multi-class token approach based on orthogonalization is proposed and applied. Experiments on ImageNet demonstrate that RPViT outperforms baseline converters and related work.
Improvement of constructing real numbers by Dedekind cuts

In current teaching materials, when using Dedekind cuts to construct real numbers, the definition of a Dedekind cut is always involved in defining addition and multiplication. In this paper, as it is done in many current textbooks, Dedekind cuts are used to construct the set of real numbers. Then the order in it is defined, and the least-upper-bound property, as well as the density properties of rational cuts in the set of real numbers, is established. Later, when defining addition and multiplication and establishing relative properties, the definition of cut will not be used again. This makes the process simpler and easier to understand.
Analyzing The Impact of Different Meteorological Variables on Large-Scale Solar generation: A Case Study of Spain

Scarcity and adverse environmental impacts of fossil fuels for electricity generation along with international emission reduction goals necessitate large-scale integration of renewable energies into power grids. These resources have intermittent nature which leads to a tradeoff between the inclusion of large shares of such sources of energy into the electricity network and their economical and environmental advantages. Therefore, it is important to study the impact of different meteorological variables on solar generation. To that end, correlation and interdependence among different meteorological variables were analyzed, and multiple linear regression was utilized to determine the significant variables that impact solar generation. Four years of hourly cumulative total solar generation of Spain has been used as a case study along with its meteorological variables. Twelve different multiple linear regression models were developed for each month and the significant variables were reported. The dependent variable was the cumulative solar generation in MW and the independent variables were the temperature, humidity, wind speed, pressure, and cloudiness. The variables with the highest influence were recorded for different months.
Effects of the Organizational Climate of Firefighters on the Job Performance

This study aimed to contribute to the preparation of an action plan for improving job performance at an individual level for firefighters by examining how the organizational climate perceived by firefighters affects job performance. For the foregoing, this study conducted a survey with a purposive sampling method targeting 187 firefighting officers in Seoul and Incheon. Survey results showed that autonomy (β = .202, p &lt; .05) and supervisors' consideration (β = .370, p &lt; .001) of the sub-variables of the organizational climate of firefighters have a significant influence on job performance. Based on the results, this study suggests various action plans that can support the organizational climate and improve the job performance of firefighters.
The Method of Learning Personal Preference with Reinforcement Learning

We propose a method of learning personal preference using Reinforcement Learning (RL). Existing research on personal preference is a method using machine learning, and the learning results are simply related to input data. In order to better learn personal preference, evaluation of learning result is also important. Therefore, we use RL to calculate reward for learners’ choice. For this, we design a model that calculates and evaluates reward. The results show learned policy and reward using proposed method in environment.
Vision: An empirical framework for examiners to accessing password-protected resources for on-the-scene digital investigations

It is increasingly becoming difficult to acquire meaningful information in the field of digital forensics through the traditional approach owing to advances in information security and anti-forensics techniques. To counteract anonymous services such as data in remote areas without authentication information, data encryption, device locks, and cryptocurrencies, it is important to acquire key information through live forensics at search and seizure sites. Thus, it is necessary to establish a response system that explores and processes credential information on site and extracts meaningful information based on the processed information. To this end, this study proposes a new digital forensics framework for application at search and seizure sites. The proposed framework is designed to allow expansion in the form of additional functions on account of a module type development for the system even when new services and digital devices appear in the future. We then explain its applicability through case studies of actual digital investigations.
Research and Implementation of Automatic Indexing Method of PDF for Digital Publishing

With the rapid development of mobile Internet technology and artificial intelligence technology, the digital publishing industry is in urgent need of using intelligent technology to change the current way of content production and service. Most of the e-book resources owned by publishing enterprises are in PDF format, which is not suitable for reading on mobile devices, and it is not convenient to directly extract key information and construct knowledge graph. With this in mind, this article designs a PDF automatic indexing scheme that can identify all the element information in PDF and output structured data automatically and then extract all the key information in it to generate a keyword library with tag weights. The scheme mainly involves two key technical points: parsing PDF based on text features and grammar rules and extracting keywords based on tag weights. The former visualizes the text block in PDF into a rectangular area, divides the elements by clustering algorithm, and, finally, outputs structured data containing all the information. The latter combines the tags and their weights in the structured data and extracts the keywords in it by the inter-word relation algorithm. The structured data and keywords database produced by this scheme can be used to produce intelligent e-book and build knowledge graph, thus helping publishing enterprises to transform from a content service provider to an intelligent knowledge service provider. This transformation can deeply excavate the core value of the content held by the publishing industry and promote the digitization and intelligentization process of the whole industry.
Sentiment Analysis on Smartphone Using Support Vector Machine

In a world made of emotion and sentiments in every action of living things contains emotion. It is positive, negative, and neutral. Nowadays mobile phones play an important role in every man&#x0027;s life. Having the mobile in the hand is like having the world in the hand. The goal of this research is to provide a review of the smartphone. The Naive Bayes algorithm is used to classify the accuracy of the result in the current system. The reviews are collected through the amazon.com website. The existing paper produced a result of 75&#x0025; accuracy. In the proposed research a more number of datasets is used to produce better accuracy with the SVM algorithm.
High-resolution white light scanning stitching measurement of full-surface microstructure

With the development of ultra-precision machining technology, parts with microstructures on the surface are playing an increasingly important role in engineering practice. The scanning interference microscope measurement method is widely used in the measurement of microscopic surface topography. This paper proposes a method of interference microscopy combined with subaperture stitching. The first step is to divide and measure the sub-aperture. There is a partial overlap area between the divided adjacent subapertures. The white light interferometer measures each sub-aperture in turn. The second step is edge feature detection. There are highly steep edge features between the bottom of the groove and the surface of the surface microgroove. The stitching algorithm detects edge features by detecting the slope threshold of the sub-aperture. The third step is to correct the abscissa. Project the detected edge feature data onto the OXY plane to generate a 2D data set. The multi-dimensional motion scanning platform inevitably has motion errors in the measurement process, so the edge characteristic curves of adjacent sub-apertures have obvious lateral displacement in the overlapping area. The lateral displacement and angle of the lateral coordinate of each sub-aperture are corrected by the least square method. The fourth step is the height coordinate correction. The stitching algorithm minimizes the height difference between the corresponding points in the overlapping area, and solves it with a linear least squares problem model. We have completed the measurement of the fringe pattern on the computer-generated hologram (CGH) substrate and the complete topography of the long groove on the 10mm hemisphere. After the sub-aperture global iterative stitching algorithm, there is no stitching trace in the simulation result. For the long groove on the hemisphere, we designed a four-dimensional scanning motion platform and a special fixture to ensure that the long groove on the hemisphere rotates around an axis perpendicular to the geodesic line. The center of the white light interference fringe is always at the center of the long slot aperture.
Design and Implementation of a Test Automation Framework for Configurable Devices

Test automation supports organizations in developing better products as they gain quick feedback and thus lower the risk of bottlenecks and service disruptions that hinder customer experience. Nevertheless, software testing has always been a time-consuming task. Furthermore, the feedback gained from test execution results has to be accurate to be beneficial. Testing software product lines is particularly time-consuming, as different software variants are needed to reach good coverage. Maintaining separate test suites for different variants is hard. In this paper, we present an approach, that matches the capabilities of available test environments with the requirements of test cases and executes only those test cases that are relevant for a given variant. The approach is meant to reduce execution times by skipping those tests that will never be able to be executed in a given environment and avoid false-negative test execution results. The framework was developed in the context of systems for public transportation, where different hard- and software setups are used depending on the use case (e.g., SIM-Card, GPS-Antenna, Voice-Radio).
A Simulation-Based Optimization for the Deployment of Multiple Urban Road Rescue Vehicles

In the rescue process of urban road traffic accidents, the decision-making support system is of great importance since it will affect the rescue response time, and response time is used as an evaluation index for the performance of the system. A shorter response time can not only guarantee the rescue of injuries, but also help to restore the road as soon as possible. This paper provides a decision tool for traffic accidents rescue vehiclesâ€™ integrated deployment problem in urban area. In order to better mimic the stochastic rescue requests, the time and spatial distribute won of traffic, and the dynamic rescue process, a simulation optimization model is established to determine the optimal deployment plan for three kinds of road rescue vehicles, including ambulances, wreckers, and sweepers, in several time intervals of a day. To find a better solution, we incorporate a particle swarm optimization (PSO) method into the simulation and optimization procedures. This strategy is employed in a case study in Shanghai. Additionally, sensitivity analysis is included to study the effects of various parameters and to confirm the suggested method's effectiveness and efficiency. The numerical results indicate that just increasing the number of road rescue resources does not always result in an increase in road rescue efficiency.
IoT-edge analytics for BACON-assisted multivariate health data anomalies

Anomaly detection in Internet of Things (IoT)-enabled systems can significantly improve the quality of the deployed systems. Though existing techniques can detect anomalies from a dataset, more efficient algorithms can be used to minimize the burden of excessive computational overhead on the resource-constrained IoT-edge device pool. In this chapter, we implement the blocked adaptive computationally efficient outlier nominators (BACON) algorithm along with the estimated-expectation/maximization (EEM) method to improve the anomaly nomination for IoT-based health dataset. We deploy the weighted variant of the BACON algorithm package—“wbacon” from the R repository to validate the utilization of anomaly nomination for an IoT-edge-enabled health dataset. The deployed scheme advocates the importance of distance wise Mahalanobis statistical tool for efficient nomination of potential outliers from the IoT dataset.
Identifying the changing faces of international fraudsters

Businesses are exploring new frontiers in e-commerce, with lucrative growth opportunities opening all over the globe. The acceleration of online buying, fuelled by the pandemic, means that retailers are scrambling to serve a rapidly expanding global customer base. It's clear that consumers everywhere have quickly adapted to e-commerce but accompanying this huge evolution in consumer demand are emerging fraud threats, designed to exploit vulnerabilities in the digital ecosystem.