Reviewerly/exHarmony huggingface dataset:
Potential of Deep Features for Opinion-Unaware, Distortion-Unaware, No-Reference Image Quality Assessment

Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted “reference” version of the input image to compare with, in order to predict this score. However, recent “No-reference” methods circumvent this requirement by modelling the distribution of clean image features, thereby making them more suitable for practical use. However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are difficult to obtain and standardise. We explore the possibility of using deep features instead, particularly, the encoded (bottleneck) feature maps of a Convolutional Autoencoder neural network architecture. Also, we do not train the network on subjective scores (unsupervised learning). The primary requirements for an IQA method are monotonic increase in predicted scores with increasing degree of input image distortion, and consistent ranking of images with the same distortion type and content, but different distortion levels. Quantitative experiments using the Pearson, Kendall and Spearman correlation scores on a diverse set of images show that our proposed method meets the above requirements better than the state-of-art method (which uses hand-crafted features) for three types of distortions: blurring, noise and compression artefacts. This demonstrates the potential for future research in this relatively unexplored sub-area within IQA.
Blockchain Applications in Power Systems: A Bibliometric Analysis

Power systems are growing rapidly, due to ever-increasing demand for electrical power. These systems require novel methodologies and modern tools and technologies, to better perform, particularly for communication among different parts. Therefore, power systems are facing new challenges such as energy trading and marketing and cyber threats. Using blockchain in power systems, as a solution, is one of the newest methods. Most studies aim to investigate innovative approaches of blockchain application in power systems. Even though, many articles published to support the research activities, there has not been any bibliometric analysis which specifies the research trends. This paper aims to present a bibliographic analysis of the blockchain application in power systems related literature, in the Web of Science (WoS) database between January 2009 and July 2019. This paper discusses the research activities and performed a detailed analysis by looking at the number of articles published, citations, institutions, research area, and authors. From the analysis, it was concluded that there are several significant impacts of research activities in China and USA, in comparison to other countries.
Intelligent evaluation of teaching based on multi-networks integration

It is of great significance to develop a scientific and reasonable evaluation system for improving the teaching quality in colleges or universities. Traditional teaching evaluation systems are always limited to subjectivity and injustice for single dimensional evaluation index, subjective weight setting, and inefficient integration. In this paper, neural networks are introduced to evaluate teaching quality in an objective way. Firstly, three sub-networks S-NET, P-NET and L-NET are trained on the evaluation sets of students, peers, and leaders. To boost the performance of a single network, we offer an integrating way to synthesize the results of three sub-networks. The integrated network I-NET is trained on the multi-dimensional data sets to acquaint comprehensive evaluations. The experimental results demonstrate the feasibility of three sub-networks with the results that the average error rate of S-NET is 1.457%, P-NET is 1.003% and L-NET is 1.528%. To be further, the prominent evaluation ability of I-NET is also verified with the accuracy of 98.59% on the verification data sets. Finally, online teaching evaluating system based on the SSM framework is designed to provide a convenient and friendly interface.
Towards Deep Network &amp; Application Integration

The collaboration between networks and applications provides opportunities to both applications to improve their performances and network service providers to increase business offering. Although many systems are proposed to support such collaborations, they are point or incremental solutions. In this paper, we propose the exploration of a more integrated architecture with huge possibilities taking a network-application integration (NAI) approach. Specifically, we explore the NAI possibilities in two concrete aspects: application-aware networking and network-aware applications. We review recent progress in these two aspects, and identify the key challenges in systematically realizing such a deep integration. To address these challenges, we present the initial design of a generic NAI possibilities exposure and discovery framework, called PED, based on satisfiability modulo theories (SMT). The key components of PED include a unified, abstract representation of network information using mathematical programming constraints, a declarative language for applications to express their intents on discovering network information, and an efficient compiler to translate application intents to constraint programming problems and discover corresponding network information. Preliminary evaluation results demonstrate the potentials of the PED framework. At the end of the paper, we also discuss a series of key future research directions toward deep NAI.
A Programmable SoC-Based Accelerator for Privacy-Enhancing Technologies and Functional Encryption

A multitude of privacy-enhancing technologies (PETs) has been presented recently to solve the privacy problems of contemporary services utilizing cloud computing. Many of them are based on additively homomorphic encryption (AHE) that allows the computation of additions on encrypted data. The main technical obstacles for adaptation of PETs in practical systems are related to performance overheads compared with current privacy-violating alternatives. In this article, we present a hardware/software (HW/SW) codesign for programmable systems-on-chip (SoCs) that is designed for accelerating applications based on the Paillier encryption. Our implementation is a microcode-based multicore architecture that is suitable for accelerating various PETs using AHE with large integer modular arithmetic. We instantiate the implementation in a Xilinx Zynq-7000 programmable SoC and provide performance evaluations in real hardware. We also investigate its efficiency in a high-end Xilinx UltraScale+ programmable SoC. We evaluate the implementation with two target use cases that have relevance in PETs: privacy-preserving computation of squared Euclidean distances over encrypted data and multi-input functional encryption (FE) for inner products. Both of them represent the first hardware acceleration results for such operations, and in particular, the latter one is among the very first published implementation results of FE on any platform.
Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring

Robotic agents should be able to learn from sub-symbolic sensor data, and at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a semantic world modeling approach based on bottom-up object anchoring using an object-centered representation of the world. Perceptual anchoring processes continuous perceptual sensor data and maintains a correspondence to a symbolic representation. We extend the definitions of anchoring to handle multi-modal probability distributions and we couple the resulting symbol anchoring system to a probabilistic logic reasoner for performing inference. The resulting framework, which combines perceptual anchoring and statistical relational learning, is able to maintain a semantic world model of all the objects that have been perceived over time, while still exploiting the expressiveness of logical rules to reason about the state of objects which are not directly observed through sensory input data. To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations. The learned logical rules are, subsequently, used to assess our proposed probabilistic anchoring procedure. We demonstrate our system in a setting involving object interactions where object occlusions arise and where probabilistic inference is needed to correctly anchor objects.
Parallel/distributed implementation of cellular training for generative adversarial neural networks

Generative adversarial networks (GANs) are widely used to learn generative models. GANs consist of two networks, a generator and a discriminator, that apply adversarial learning to optimize their parameters. This article presents a parallel/distributed implementation of a cellular competitive coevolutionary method to train two populations of GANs. A distributed memory parallel implementation is proposed for execution in high performance/supercomputing centers. Efficient results are reported on addressing the generation of handwritten digits (MNIST dataset samples). Moreover, the proposed implementation is able to reduce the training times and scale properly when considering different grid sizes for training.
Dragoon: Private Decentralized HITs Made Practical

With the rapid popularity of blockchain, decentralized human intelligence tasks (HITs) are proposed to crowdsource human knowledge without relying on vulnerable third-party platforms. However, the inherent limits of blockchain cause decentralized HITs to face a few "new" challenges. For example, the confidentiality of solicited data turns out to be the sine qua non, though it was an arguably dispensable property in the centralized setting. To ensure the "new" requirement of data privacy, existing decentralized HITs use generic zero-knowledge proof frameworks (e.g., SNARK), but scarcely perform well in practice, due to the inherently expensive cost of generality.We present a practical decentralized protocol for HITs, which also achieves the fairness between requesters and workers. At the core of our contributions, we avoid the powerful yet highlycostly generic zk-proof tools and propose a special-purpose scheme to prove the quality of encrypted data. By various nontrivial statement reformations, proving the quality of encrypted data is reduced to efficient verifiable decryption, thus making decentralized HITs practical. Along the way, we rigorously define the ideal functionality of decentralized HITs and then prove the security due to the ideal/real paradigm.We further instantiate our protocol to implement a system called Dragoon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , an instance of which is deployed atop Ethereum to facilitate an image annotation task used by ImageNet. Our evaluations demonstrate its practicality: the on-chain handling cost of Dragoon is even less than the handling fee of Amazon's Mechanical Turk for the same ImageNet HIT.
Fast incremental discovery of pointwise order dependencies

Pointwise order dependencies (PODs) are dependencies that specify ordering semantics on attributes of tuples. POD discovery refers to the process of identifying the set Σ of valid and minimal PODs on a given data set D. In practice D is typically large and keeps changing, and it is prohibitively expensive to compute Σ from scratch every time. In this paper, we make a first effort to study the incremental POD discovery problem, aiming at computing changes ΔΣ to Σ such that Σ ⊕ ΔΣ is the set of valid and minimal PODs on D with a set Δ D of tuple insertion updates. (1) We first propose a novel indexing technique for inputs Σ and D. We give algorithms to build and choose indexes for Σ and D , and to update indexes in response to Δ D. We show that POD violations w.r.t. Σ incurred by Δ D can be efficiently identified by leveraging the proposed indexes, with a cost dependent on log (| D |). (2) We then present an effective algorithm for computing ΔΣ, based on Σ and identified violations caused by Δ D. The PODs in Σ that become invalid on D + Δ D are efficiently detected with the proposed indexes, and further new valid PODs on D + Δ D are identified by refining those invalid PODs in Σ on D + Δ D. (3) Finally, using both real-life and synthetic datasets, we experimentally show that our approach outperforms the batch approach that computes from scratch, up to orders of magnitude.
Digital Ecosystem Security Issues for Organizations and Governments

The growth of the digital ecosystem has given a sense that the rise of security implementations must be considered by every organization including governments in terms of adopting the best digital ethical approaches and awareness on the importance of ensuring privacy. Increased use of the internet has also increased matters of cyber threats and unethical behaviors. Therefore, implementation of digital ethics has become crucial to prevent or minimize the impacts of cybercrime, and so, securing sensitive information from unauthorized access has become extremely important. This study analyses and describes the future trends regarding security in digital ethics and privacy within the digital ecosystem. The results point to a relative correlation between the government and business sector and the types of attacks and that digital ethics and privacy makes up the core elements of security. Implementing cautionary steps are also necessary to prevent from any form of cyber-attack.
Digital Signature Authentication for a Bank Using Asymmetric Key Cryptography Algorithm and Token Based Encryption

Nowadays security system is being with more important issues. In modern science, technology is updated day by day and we are getting insecure in our daily life. Through this project, a digital signature authentication security system has been designed to protect the bank from unauthorized access. In this paper, we are producing the most efficient and productive security system based on digital signature authentication using the asymmetric key cryptography algorithm and token-based encryption. In this project, we are using public and private keys for encryption and decryption data with the hash function and also provide digitally signed data, RSA algorithm to encrypt the data. The receiver will send a certificate and confirmation request to the sender to verify the certificate and the sender will send an OTP via phone through the Internet to authenticate the receiver. All of these works are producing a good and valuable security system in the banking sector.
Research on English score analysis system based on improved decision tree algorithm and fuzzy set

At present, the data mining technology is introduced into the analysis of English scores, the data is deeply explored and analyzed reasonably, and the analysis results are used to guide the smooth development of teaching, which is conducive to improving the quality of English teaching. The main work of this thesis is based on the background of this study: taking the academic performance of college students as the application background, this paper first introduces the basic theoretical knowledge of data mining and the application status of data mining technology in education field. Secondly, this paper establishes a student performance database and uses data mining technology to carry out in-depth mining of the established performance database. Finally, the mining results are analyzed, and the factors affecting students’ academic performance are obtained. These analysis results have important reference value for the future improvement of teaching work in colleges and universities.
Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
How do emerging market SMEs utilize resources in the face of environmental uncertainty?

Using the resource-based view of the firm, this article examines how different resources are used by small- and medium-sized enterprises (SMEs) in response to environmental uncertainty in an emerging market context. A survey was conducted involving 212 owners, CEOs, or managing directors of SMEs in the United Arab Emirates (UAE) to explore the drivers of firm growth strategies. Our empirical results show that higher environmental uncertainty, in terms of market and regulatory uncertainty, drives SMEs to pursue a more exploratory instead of an exploitative growth strategy. Our results also show that SMEs pursuing more of an exploratory growth strategy focus predominantly on intangible resources such as human capital and social capital while firms pursuing more of an exploitative growth strategy focus more on financial resources. The results from our study provide valuable insights for how managers can structure, bundle, and leverage different resources for developing their strategies based on their level of perceived environmental uncertainty. The policy implications from our study are that support for SMEs should be multi-faceted depending on the combination of uncertainty faced and type of strategy chosen by SMEs. JEL CLASSIFICATION: M10; M19; M13
A Novel Laplacian of Gaussian (LoG) and Chaotic Encryption Based Image Steganography Technique

Information sharing through internet has becoming challenge due to high-risk factor of attacks to the information being transferred. In this paper, a novel image-encryption edge based Image steganography technique is proposed. The proposed algorithm uses logistic map for encrypting the information prior to transmission. Laplacian of Gaussian (LoG) edge operator is used to find edge areas of the colored-cover-image. Simulation analysis demonstrates that the proposed algorithm has a good amount of payload along with better results of security analysis. The proposed scheme is compared with the existing-methods.
Fuzzy Clustering Based on Automated Feature Pattern-Driven Similarity Matrix Reduction

Most of the medoid-based fuzzy clustering algorithms only use one similarity matrix to organize objects into groups. The similarity matrix is often constructed by equally employing all features which may ignore the contribution differences existing among the features. In this study, we also propose a medoid-based fuzzy clustering algorithm feature pattern-driven similarity matrices-reduction based fuzzy clustering (FP-SMR-FC) which is different from the existing ones in the following two aspects. First, multiple similarity matrices are constructed to represent the similarity between objects. Additionally, a feature pattern-driven Shannon entropy which combines nondeterminacy information contained in the similarity matrices and statistical information contained in the features together is used to learn the weight of each similarity matrix. Second, during the clustering processes of FP-SMR-FC, a new schema for eliminating some of the similarity matrices with very few contributions is developed for similarity matrices reduction. The comparison studies in terms of time complexity and clustering accuracy for FP-SMR-FC with various medoid-based clustering algorithms on real-life data sets are done. In addition, FP-SMR-FC is applied to head pose estimation of human behavior analysis. Comparisons, indeed, demonstrate the promising performance of FP-SMR-FC in practice.
Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization

Game Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of population in a similar way to human social evolution. Two ideas are proposed to be incorporated into the GA. First, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Genetic Algorithm with Social Interaction</i> (GASI), a family of GAs that uses GT in selection phase to increase the diversification of the solutions. Second, the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Game-Based Crossover</i> ) GBX and GBX2 crossover operators, competition-based tournament selection methods that employ social dispute to generate more diverse offspring. Performance and robustness of the new approaches were assessed by ten continuous and constrained engineering design optimization problems and compared against variants of the canonical GA, as well as well-known heuristics from the literature. Results indicate significant performance relevance in most instances compared to other algorithms and highlight the benefits of combining GT and GA.
ID3 Decision Tree Classification: An Algorithmic Perspective based on Error rate

Classification in Data mining is a very important approach that is widely used in all the applications including medical diagnoses, agriculture, and other decision making systems. Data mining deals primarily with classification due to dynamic varieties of datasets available online today. Decision tree based classification is the foundation of all the classification algorithms and is extensively used by experts in all types of research. As ID3 decision tree algorithm has been used popularly for classification, this proposed work focuses on the implementation of ID3 algorithm with different standard UCI datasets and are also analyzed using statistical measures. The Error rate determines the misclassification of an algorithm and the splitting attribute. Hence, the ID3 algorithmic perspective was carried out in this proposed work by analyzing the error rate.