Reviewerly/exHarmony huggingface dataset:
Quality assessment for super resolution image via feature optimization

Objectives: Most of the existing no-referenced (NR) image quality assessment (IQA) methods need to extract lots of image features and then predict the image quality. However, only a small part of these features plays a role in IQA models while the other makes the model inefficient. Therefore, a NR IQA framework based on feature optimization is proposed and applied it to the field of super-resolution (SR) IQA task. Methods: The features associated with the SR images are first collected and aggregated. Secondly, a variety of advanced feature selection algorithms are used to sort the feature sets and get the importance matrix of features. Then the linear correlation between the number of features and Pearson linear correlation coefficient (PLCC) is studied according to the importance matrix of features to determine the optimal number of features and the optimal feature selection algorithm. Results: In this paper, the image quality scores predicted by the model based on the aggregation feature are highly consistent with the human subjective scores. Using the proposed feature optimization framework, we can effectively reduce the number of features and make the model performance better. Conclusion: In this paper, feature engineering is used to optimize the performance and efficiency of existing NR IQA algorithms. Experimental results show that the image quality can be accurately predicted by only a small part of image features.
Data Architecture for Data-Driven Service Platform: Royal Project Foundation Case Study

In the data era, many organizations aim to gather and maintain data to drive their organization, Royal Project Foundation is one of them. The foundation has been working on social development, which includes population structure, drug problems, educational development, and community organization. The data of the foundation works have been collected from various sources and forms. Thus, to evaluate the Royal Project Foundation data, we proposed a hybrid big data architecture containing data storage and data processing pipelines to operate data services. Furthermore, the data model and data report system of social and community data are presented along with the business intelligence (BI) dashboard.
A Performance Analysis of Hyperledger Fabric: A Perspective of the ISO/IEC 25010 Product Quality Model

The emergence of blockchain technology, initially just a cryptocurrency with Bitcoin, brought with it a range of solution development possibilities in the most diverse sectors of society. The characteristics of blockchain raise interest in the corporate environment, which even provide complete development environments such as Hyperledger Fabric. Developing systems with blockchain is similar in many ways to developing traditional software. ISO/IEC 25010 brings models that allow measuring the quality of the software. In this work, quality measurement metrics are defined for a blockchain implemented on Hyperledger Fabric. For this, the Hyperledger Caliper was used to observe the metrics from the perspective of ISO/IEC 25010. Through the experiments it was possible to observe, based on the increase in throughput, that the number of transactions directly impacts the performance of the network, and that when analyzing the average latency and CPU utilization, the implemented network presented scalability.
Toward Secure Cyber Infrastructure for Smart Cities: Learning-Based Intelligent Solutions

Smart cities composed of multiple different sectors such as smart transportation, smart grid, eco-friendly manufacturing, among others. Data-driven decision-making advances these sectors toward the goal of turning our traditional societies into smarter ones. However, there exists concerns in handling such large amount of data, which contains personal identifiable information (PII), if proper privacy measures are not guaranteed. Moreover, the edge cyber infrastructure that promises to provide timely processing of data can also fall prey to cyber attacks in the absence of appropriate security policies. In this chapter, we discuss the cyber-security challenges as well as learning-based intelligent solution directions for those challenges in the context of smart cities. This chapter is organized in top-down approach in which we will first provide high-level overview of the topics and later dig into few sector-specific cyber-security challenges with solution directions.
Domain Adaptation for 6-DoF Pose Estimation: Deep Learning with Uncertainty

There is considerable research on debris pose estimation using neural networks. However, most researchers have not considered a domain shift, which is a change in the data domain between the training and operational environments. Therefore, we propose a method to estimate the 6-DoF pose in operational environments. The method offers high accuracy even on images obtained during the operation phase by using domain adaptation which reduces domain shift. We have generated simulation and real images that mimic in-orbit images with hardware. These data are labeled. The error rate for tasks in which no domain shift occurs, trained, and estimated on the simulation image, was 5.6&#x0025;. The error rate for tasks where domain shift occurs, trained on simulation images and estimated real images, was 42.3&#x0025;. We performed domain adaptation on various dataset with various neural network combinations. Our results in the best combinations showed that unsupervised domain adaptation reduced the error rate to 35.5&#x0025;, while supervised domain adaptation reduced the error rate to 13.0&#x0025;. Supervised domain adaptation was able to consider the domain shifts that could not be removed by unsupervised domain adaptation and could estimate pose with high accuracy. Regression problems differs from classification problems in that the effect of small domain shifts directly affects the final output, so even the slight domain shifts cannot be tolerated. For this reason, we found that supervised domain adaptation that can consider domain shift is better suited to pose estimation of the regression problem. In addition, there was no correlation between the degree of uncertainty and estimation error in the unsupervised domain adaptation. However, there was a correlation in supervised domain adaptation.
Multimedia stream hashing: A forensic method for content verification

Forensic examination of digital audio, video, and images frequently requires transforming multimedia data from one format to another. The transformative activity may cause changes to the administrative elements of the file but leave the multimedia streams unchanged and intact. However, the forensic science community has a method knowledge gap in accurately determining if the multimedia streams changed or remained the same in the transformative processes. This paper illustrates the practical use of multimedia stream hashing as a forensic method for verifying multimedia content. A universal stream hashing tool decodes the multimedia stream data at rest in a file container. Subsequently, it calculates the data stream hash using reference video, audio, and image codecs. This paper illustrates that the multimedia stream hashing method can accurately confirm the integrity of digital images, videos, and audio following transmission, transcoding, or re-containerization. Our findings confirmed that stream hashing could accurately detect changes in multimedia streams during transcoding. Furthermore, the stream hashing method can also accurately detect matching multimedia streams. In addition, this paper verified the forensic use of the multimedia stream hash method while establishing the error rate for its use. The hash algorithms used in stream hashing have zero false negative rates by design. However, the false positive (error rate) is extremely low and depends on hashing algorithm. Finally, we recommend the forensic science community adopt the multimedia stream hashing method as an initial testing method. The method can verify a multimedia stream's conversion (transcoding) from one codec to another using FFmpeg.
Missile motion parameter estimation with a passive sensor from a high speed aircraft

The 3D trajectory estimation and observability problems of a target have been solved by using angle-only measurements. In previous works, the measurements were obtained in the thrusting/ballistic phase from a single fixed passive sensor. The present work solves the motion parameter estimation of a ballistic target in the reentry phase from a moving passive sensor on a fast aircraft. This is done with a 7-dimension motion parameter vector (velocity azimuth angle, velocity elevation angle, drag coefficient, target speed and 3D position). The maximum likelihood (ML) estimator is used for the motion parameter estimation at the end of the observation interval. Then we can predict the future position at an arbitrary time and the impact point of the target. The observability of the system is verified numerically via the invertibility of the Fisher information matrix. The Cramer–Rao lower bound for the estimated parameter vector is evaluated, and it shows that the estimates are statistically efficient. Simulation results show complete observability from the scenario considered, which illustrates that a single fast moving sensor platform for a target can estimate the motion parameter in the reentry phase.
A Multi-Objective Approach for Security Hardening and Probabilistic Vulnerability Assessment on Attack Graphs

Assessing vulnerabilities of a network and mitigating them is a challenging task owing to the complexity and scale of the problem. Various probabilistic security metrics on attack graphs are presented in the literature to handle realistic attack scenarios involving large attack graphs. In our work, we propose a generalized path-enumeration based technique for computing attack probabilities on attack graphs that can handle repeated vulnerabilities as well as cyclic graphs. A multiplicative idempo-tency based approach is used for computation while aggregating the path probabilities. We employ the inclusion-exclusion principle to prove the soundness of our proposed technique. Also, in practice, we found that attackers retain experience and face reduced difficulty in exploiting a vulnerability in repeated attacks. In this paper, we extend the proposed probabilistic measure to incorporate such conditions leveraging possible decay functions. Our proposed metric is helpful in service management for network hardening. Network hardening is formulated as a multi-objective optimization problem that generates pareto-optimal solutions which trade off utility with vulnerability. Case studies are presented for complex attack graphs having interesting pareto-optimal solutions for service management.
A Study on the Development of ODA Cooperation in Vocational Education and Training in Korea - Focusing on Korea-German Cooperation Project -

The cooperation between the two countries, which began in 1961 when the German government established the Korea-German Industrial School in Incheon, maintained a close cooperative relationship through mutual exchanges even after the support project ended in 1993. From the 1960s to the early 1990s, the German government unilaterally supported Korea, and the two countries maintained mutual exchanges from the 1990s. It has grown into a partnership to build a cooperative relationship by jointly carrying out ODA projects. Meanwhile, Korea, as a donor country, has been promoting vocational education and training support projects in various developing countries for over 40 years, starting with the Indonesian Vocational Training Center support project in 1986, but there is no case of long-term cooperation such as that between Korea and Germany. Through the study on the development of the Korea-Germany long-term cooperative relationship, we are able to understand the need to build a sustainable growth model based on the demand in the field of vocational education and training in the recipient countries. And we are able to understand that the experience of cooperation between Korea and Germany can be a practical model for Korea's TVET cooperation projects in the future.
Deterministic generation of building-block photonic cluster states from a single quantum emitter

I will discuss our recent proposal on deterministic generation of photonic repeater graph states using only a single quantum emitter, our plans for its experimental implementation, and its applications in quantum repeaters and networks.
Optical transparency induced by a largely Purcell-enhanced single photon emitter in a low-Q cavity

Quantum dots coupled to high-Q cavities can induce optical transparency that provides photon switching capabilities. However, the optical access to such cavities can be inefficient due to their restrictive optical modes. Here, we observe optical transparency of ∼ 80% induced by a quantum dot coupled to a cavity with an efficient optical access, the low-Q bullseye cavity, due to the destructive interference of reflected light. Together with optical lifetimes of quantum dots as short as 80 ps, and the coherent manipulation capabilities of their spin, the transparency induced by coupling these dots to bullseye cavities makes them promising for quantum technologies.
A provably secure, shortest, and efficient identity based signcryption technique in the standard model

Abstract Singcryption is a very important concept to bind confidentiality and authenticity. This is a logical step to reduce both computation and communication costs. The proposed an identity based signcryption that removes the overhead of certificates. The proposed design is provably secure in the standard model (a widely accepted model). Furthermore, the IBSC design uses modified bilinear Diffie‐Hellman inversion and modified strong bilinear Diffie‐Hellman assumption as building blocks of the security. The proposed design is secure, efficient, and shortest to the best of our knowledge. In this design, a user does pairing free computation, and he sends only a triplet on the public channel, that makes it efficient in terms of computation and communication costs.
Short term wind power prediction based on improved quantum particle swarm optimization algorithm and kernel extreme learning machine

In order to improve the prediction accuracy of wind power prediction model, a short-term wind power prediction model based on improved quantum particle swarm optimization kernel extreme learning machine is proposed. The differential evolution algorithm is introduced into the standard quantum particle swarm optimization algorithm to enrich the diversity of the search population. In addition, the Gaussian perturbation strategy is used to increase the local optimization ability of the algorithm. The improved quantum particle swarm optimization algorithm is used to optimize the parameters of the kernel extreme learning machine. Finally, the short-term wind power prediction model is established by using the optimized KELM and the actual wind farm data. The results show that the proposed model has high prediction accuracy.
State Estimation of Initiating Explosive Device Based on Extended Kalman Filter

The initiating explosive warhead system in this paper is a nonlinear system, and the ordinary filter is difficult to estimate the rotation state of the warhead. Therefore, the extended Kalman filter is used to estimate the system state in this paper. The third-order state space equation of the initiating explosive drive system is established. The warhead rotation angle is taken as the measured value, and the signal-to-noise ratio before and after filtering is taken as the measurement standard of the filtering estimation effect. The experimental results show that the extended Kalman filter can effectively estimate the rotation state of the initiating explosive drive system.
A Blockchain Solution for Crowdfunding and Cost Analysis

Crowdfunding services help many people to carry out projects for the most diverse purposes and are easily found on the internet. There are several ways to make donations or payments, such as bank transfers and credit cards. However, it is not known exactly whether the investment was in fact for the proper purpose. In this scenario, blockchain stands out with cryptocurrency applications and smart contracts, adding credibility to the transactions carried out even if in a decentralized manner, and providing traceability. The objective of this work is to propose a solution to the problem of traceability of money invested by financiers, in addition to an analysis of financial costs and transaction times, helping investors to understand where the funds raised go. To meet the objective, an architecture was designed and the Ethereum blockchain was used in the experiments, concluding that it is necessary to evaluate the cost benefit of the solution and the size of the data to be stored in the blockchain, as well as the variations in the ether values at the time of conversion to the local currency.
MUWS’22: 1st International Workshop on Multimodal Understanding for the Web and Social Media

The 1st International Workshop on Multimodal Understanding for the Web and Social Media (MUWS 2022) is co-located with The Web Conference (WWW) and held on the 26th of April, 20221.
Harris-Lovassy N-Dimensional Rule of Combination

This paper presents a novel implementation of the principle contained in the Dubois- Prade rule of combination (DP- RoC) in order to overcome the non-associative property that exists in its original implementation. Various data fusion rules of combination have been presented in the last 30 years to deal with the uncertainty distribution associated with combining different and conflicting data sources (e.g., multi-sensor measurements), such that when combined should not have equal uncertainties assigned to the probabilistic beliefs. This new implementation is called the Harris- Lovassy N-Dimensional Rule of Combination (HL-NRoC), and like DP-RoC, it assumes the principle that given conflict between two (or more) sources, at least one of the sources is correct, however this implementation overcomes the non-associative property by achieving permutation symmetry via an n-ary operator. Implementing this principle in a manner that achieves permutation symmetry is key to overcoming the counter-intuitive results that may result from fusing conflicting overconfident sources. Testing was conducted and presented with conflicting and overconfident sources which demonstrates the value of HL-NRoC by achieving similar performance to other commonly used methods, but with better calibrated probability values - by as much as 90&#x0025;.
Towards SLA-Driven Autoscaling of Cloud Distributed Services for Mobile Communications

In recent years cloud computing has established itself as the computing paradigm that supports most distributed systems, which are essential in mobile communications, such as publish-subscribe (pub/sub) systems or complex event processing (CEP). The cornerstone of cloud computing is elasticity, and today’s autoscaling systems leverage that property by making scaling decisions based on estimates of future workload to satisfy service level agreements (SLAs). However, these autoscaling systems are not generic enough, as the workload definition is application-based. On the other hand, the workload prediction needs to be mapped in terms of SLA parameters, which introduces a double prediction problem. This work presents an empirical study on the relationship between different types of workloads in the literature and their relationship in terms of SLA parameters in the context of mobile communications. In addition, more than 30 prediction models have been trained using different techniques (time series analysis, regression, random forests) to test which ones offer better prediction results of the SLA parameters based on the type of workload and the prediction horizon. Finally, a series of conclusions on the predictive models to be used as a first step towards an autonomous decision system are presented.
Cloud enabled Blockchain-based secured communication in mutual intelligent transportation using neural synchronization

The Internet of Vehicles (IoV) will be essential in building a smart city in the forthcoming Internet of Things (IoT) age. In reality, the IoV can successfully address several traffic issues. It is essential for maximizing the use of roads, cutting down on energy use and pollution, and promoting traffic safety. However, creating safe and immediate payments and communications is the main challenge with the IoV, especially with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). To examine the application of Blockchain as middle-ware amongst diverse stakeholders of intelligent transportation systems, this paper provides an approach for establishing secure payment and communication in response to this problem. Neural synchronization is used to enhance communication security. The architecture under consideration makes used to create a system for allowing Vehicle-to-Everything (V2X) interactions and transactions. Additionally, the efficiency and resistance of the suggested systems against typical security assaults are qualitatively tested in this paper. The key issues of V2X connections, such as safety and centralization, were addressed by the suggested method, according to computational testing. In this study, we also assessed the proposed solution's most crucial characteristics, including memory and power use, modifiability, secrecy, consistency, authenticity, processing time, and affordability. The purpose of this test is to determine if the technology can provide efficient and reliable IoV communication and parking payment. The results show that the suggested methods are quicker and much more than the state-of-the-art communication technologies.
A Comprehensive Review Analysis on PSO and GA Techniques for Mathematical Programming Problems

Mathematical programming problems (MPPs) having unique mathematical format usually treated as hard problems are important optimization problems. On the other hand, particle swarm optimization (PSO) and genetic algorithm (GA) are important optimization approaches used in solving complex optimization problems. PSO and GA are nature-inspired techniques based on the social behavior of species and operations of human chromosomes, respectively. During past decades, PSO, GA, and their hybrid techniques were extensively used in solving different types of mathematical programming problems. But there is a lack of comprehensive review analysis on PSO, GA, and associated hybrid techniques for solving various types of mathematical programming problems. In this article, we present a systematic detailed review on these techniques in context of solving mathematical programming problems along with comprehensive review analysis. A systematic review procedure has been used for analysis of sixty-eight articles of reputed databases like Thomson Reuters, Web of Science, Scopus, and IEEE. The research gaps and future research scope for the researcher who inclines to solve different types of mathematical programming problems with these techniques are also identified. Relevance of the work: Mathematical programming problems (e.g., linear programming problem (LPP), NLPP, MOPP, etc.) are important optimization problems in which many types of real-world problems are formulated. On other hand, PSO and GA are prominent natured-inspired techniques used to solve complex optimization problems. This article presents comprehensive review analysis on PSO and GA in context of solving different types of mathematical programming problems. This review work also narrates the unique linkage between GA–PSO techniques and mathematical programming problems. This systematic and in-depth review analysis will be useful to the researchers and scientists working in the related areas of mathematical programming and natured-inspired optimization techniques to pursue carrying out further research in these areas.