Reviewerly/exHarmony huggingface dataset:
A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined over the benchmark functions designed for evolutionary algorithms competition in the special session of 2005 IEEE Congress on Evolutionary Computation. The used benchmark functions include the unimodal, multimodal, and hybrid benchmark functions with high dimensionality. Furthermore, the impact analysis with respect to different values of temperatures is conducted by executing the proposed algorithm twenty-five times independently by using each benchmark function with different random seeds.
Attribute-based proxy re-encryption from standard lattices

Attribute-based proxy re-encryption (ABPRE), which combines the notions of proxy re-encryption (PRE) and attribute-based encryption (ABE), allows a semi-trusted proxy to transform a ciphertext under a particular access-policy into a ciphertext under another access policy, without revealing any information about the underlying plaintext. This primitive is very useful in some applications, where encrypted data needs to be stored in untrusted environments, such as cloud storage. In its key-policy flavor, the secret key is associated with an access policy that specifies which type of ciphertexts can be decrypted by that key, where ciphertexts are marked with different sets of attributes. However, all existing key-policy attribute-based proxy re-encryption (KP-ABPRE) schemes are based on classical number-theoretic assumptions, which are vulnerable to quantum attacks. This paper proposes the first KP-ABPRE scheme based on the learning with errors (LWE) problem, which is widely believed to be quantum-resistant. Our scheme is multi-hop, supports polynomial-depth policy circuits and has short private keys, where the size of the keys is dependent only on the depth of the supported policy circuits. In addition, we prove that our scheme is CPA secure in the selective security model, based on the LWE assumption.
Analysis of centrality measures under differential privacy models

This paper provides the first analysis of the differentially private computation of three centrality measures, namely eigenvector, Laplacian and closeness centralities, on arbitrary weighted graphs, using the smooth sensitivity approach. We do so by finding lower bounds on the amounts of noise that a randomised algorithm needs to add in order to make the output of each measure differentially private. Our results indicate that these computations are either infeasible, in the sense that there are large families of graphs for which smooth sensitivity is unbounded; or impractical, in the sense that even for the cases where smooth sensitivity is bounded, the required amounts of noise result in unacceptably large utility losses.
Privacy-Breaching Patterns in NoSQL Databases

NoSQL databases disrupted the database market when first introduced. Their contemporary relevance has increased further in the era of big data due to the demands placed on (real-time) analytics. NoSQL databases are well placed to meet these demands due to their performance, availability, scalability, and storage solutions. Unfortunately, to achieve these features, compromises have been made with respect to security and privacy. Growing community awareness and unease combined with increased legislative requirements around data privacy have made such compromises less palatable, risky, or downright unacceptable. And though there is a growing body of knowledge related to data privacy in NoSQL databases, it is diverse and fragmented, and does not adequately address the challenges arising from the current environment. This paper aims to systematically examine various privacy weaknesses of NoSQL databases in the form of patterns. The patterns are shown to manifest themselves in well-known NoSQL databases and this evaluation can be used for benchmarking purposes. Through a survey it is demonstrated that the patterns have been observed in practice and are perceived as relevant. The pattern collection forms a repository of knowledge that can serve as a starting point for future privacy-related research for NoSQL databases through its identification of key problems, trade-offs, existing solution mechanisms, and its provision of terminology.
From Conventional Voting to Blockchain Voting: Categorization of Different Voting Mechanisms

For the development of society and the enhancement of citizen's civic knowledge, voting plays an incredibly important role in many forms of practice as a guiding factor for the true growth of democracy. The world's electoral mechanism has been marked by several structural problems, leading in an election being held by a dishonest candidate. Scientists were mentally, psychologically, socially and critically worried about the mismanagement of electoral processes documented at various stages of selecting a leader. Deal with the question about how to protect dishonest participants in elections from unethical practices such as bribery and spoofing are a great concern. The results of making a dishonest participant a winner are bad administration, uncertainty and diversification for the financial benefit of public funds. Approaches ought to be created to fix the issues of the electoral processes. Thus, this paper concentrated on a systematic analysis of multiple forms of voting with blockchain and without blockchain by numerous researchers as a forum to find vulnerabilities or weaknesses and propose a perfect voting mechanism to fulfill all the properties requires in voting system.
Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.
Federated Learning Systems: Architecture Alternatives

Machine Learning (ML) and Artificial Intelligence (AI) have increasingly gained attention in research and industry. Federated Learning, as an approach to distributed learning, shows its potential with the increasing number of devices on the edge and the development of computing power. However, most of the current Federated Learning systems apply a single-server centralized architecture, which may cause several critical problems, such as the single-point of failure as well as scaling and performance problems. In this paper, we propose and compare four architecture alternatives for a Federated Learning system, i.e. centralized, hierarchical, regional and decentralized architectures. We conduct the study by using two well-known data sets and measuring several system performance metrics for all four alternatives. Our results suggest scenarios and use cases which are suitable for each alternative. In addition, we investigate the trade-off between communication latency, model evolution time and the model classification performance, which is crucial to applying the results into real-world industrial systems.
A geometric-based data reduction approach for large low dimensional datasets: Delaunay triangulation in SVM algorithms

Training a support vector machine (SVM) on large datasets is a slow daunting process. Further, SVM becomes slow in the testing phase, due to its large number of support vectors (SVs). This paper proposes an effective geometric algorithm based on construction of Delaunay triangulation (DT) algorithm using Quickhull algorithm with a novel strategy to exactly identify and extract the boundary data points laid between the two classes of a dataset, and later uses these most informative data points as a reduced dataset to solve various SVM algorithms and proposes new DT-SVM algorithms Two synthetic datasets with the size of 1K incrementally up to 500K datasets are generated to extensively verify the effectiveness of the proposed DT-SVM algorithms over various data sizes and for further assessment, the most efficient version of proposed DT-SVM is applied on well-known benchmark datasets from UCI Machine Learning Repository. Two variant of sequential minimization optimization (SMO) decomposition methods, in addition to Least Square form of SVM are implemented to present the scalability of new DT-SVM algorithms in linear/nonlinear separable/non-separable large low dimensional datasets. Moreover, the most efficient version of the proposed algorithm is compared to RCH-SK as a known geometric approach in the SVM literature. The results demonstrate that while the proposed approach improves the scalability of DT-SVM in large low dimensional datasets, it leads SVM algorithms to maintain the accuracy in an acceptable range with considerably lower time in both training and testing phases with using a noticeably fewer number of SVs.
Frequency division denoising algorithm based on VIF adaptive 2D-VMD ultrasound image

Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis. To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. First, the ultrasound image was decomposed into a series of sub-modal images using 2D variational mode decomposition (2D-VMD), and adaptively determined 2D-VMD parameter K value based on visual information fidelity (VIF) criterion. Then, an anisotropic diffusion filter was used to denoise low-frequency sub-modal images, and a 3D block matching algorithm (BM3D) was used to reduce noise for high-frequency images with high noise. Finally, each sub-modal image was reconstructed after processing to obtain the denoised ultrasound image. In the comparative experiments of synthetic, simulation, and real images, the performance of this method was quantitatively evaluated. Various results show that the ability of this algorithm in denoising and maintaining structural details is significantly better than that of other algorithms.
Twitter Spam Detection Using Naïve Bayes Classifier

Twitter is the well liked social media platform that has over 300 million monthly users which post 500 million tweets per day. This is the main reason why spammers use Twitter for their spiteful doings such as spreading malignant software that steals the user personal information and tweets containing fake or faulty URLs, assertively follow or un-follow users and trending fake tweets to get users attention, spread pornography advertisements. In recent years twitter has reportedly collected the data of active users and analyzed their actions, the report clearly shows that over 32 million users have interacted with server for casual information in daily basis. Hence, identifying and filtering the malicious tweets or trends that are harmful or unwanted for users is very important in current social world. This paper discusses about the ways to analyze the tweets and classify them into spam and ham based on the words involved in tweets. Although there are various machine learning and deep learning methods to classify and detect spam tweets like SVM, clustering methods and binary detection models that are used Naïve Bayes classifier. Recently, twitter users are experiencing data stealing malware by accessing or visiting unnecessary spam messages or tweets. It has to be considered seriously since many people are losing money or personal information. Besides data stealing malware, fake trends also been a threat. It has to be controlled. Spammers are likely to interact with more people because of the auto-follow option.
New approach of estimating edge detection threshold and application of adaptive detector depending on image complexity

This paper presents a new approach to edge detection based on the application of an adequate adaptive algorithm for edge detection depending on the image complexity criteria. The complexity of the image was determined based on spatial information. Also, a new approach of estimating threshold for edge detection has been developed, which is based on the principles of machine learning, by applying grid search and random search. For analysis and testing, images with real scenes were used. This dataset was used to improve threshold estimation using the grid and random search methods. Based on the obtained results, it can be seen that the complexity significantly affects the edge detection, so with a low and medium number of details, the Roberts operator achieved good results. For images with a high number of details, the Canny operator has proven to be the best solution. New approach for estimate the threshold based on the grid search improves edge detection, especially with the Canny operator. Grid search-based method gives best results but it takes a lot of computational time. If computational time and the improvements obtained are taken into account, more effective optimization of the parameters for estimating threshold values is random search method.
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout

Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A successful approach to tackle this problem is to compare the similarity between examples in a learned metric space based on convolutional neural networks. However, existing methods typically suffer from meta-level overfitting due to the limited amount of training tasks and do not normally consider the importance of the convolutional features of different examples within the same channel. To address these limitations, we make the following two contributions: (a) We propose a novel meta-learning approach for aggregating useful convolutional features and suppressing noisy ones based on a channel-wise attention mechanism to improve class representations. The proposed model does not require fine-tuning and can be trained in an end-to-end manner. The main novelty lies in incorporating a shared weight generation module that learns to assign different weights to the feature maps of different examples within the same channel. (b) We also introduce a simple meta-level dropout technique that reduces meta-level overfitting in several few-shot learning approaches. In our experiments, we find that this simple technique significantly improves the performance of the proposed method as well as various state-of-the-art meta-learning algorithms. Applying our method to few-shot image recognition using Omniglot and miniImageNet datasets shows that it is capable of delivering a state-of-the-art classification performance.
AnaSearch: Extract, Retrieve and Visualize Structured Results from Unstructured Text for Analytical Queries

Modern search engines retrieve results mainly based on the keyword matching techniques, and thus fail to answer analytical queries like "apps with more than 1 billion monthly active users" or "population growth of the US from 2015 to 2019", which requires numerical reasoning or aggregating results from multiple web pages. Such analytical queries are very common in the data analysis area, the expected results would be structured tables or charts. In most cases, these structured results are not available or accessible, they scatter in various text sources. In this work, we build AnaSearch, a search system to support analytical queries, and return structured results that can be visualized in the form of tables or charts. We collect and build structured quantitative data from the unstructured text on the web automatically. With AnaSearch, data analysts could easily derive insights for decision making with keyword or natural language queries. Specifically, we build AnaSearch under the COVID-19 news data, which makes it easy to compare with manually collected structured data.
Cyber Resilience Meta-Modelling: The Railway Communication Case Study

Recent times have demonstrated how much the modern critical infrastructures (e.g., energy, essential services, people and goods transportation) depend from the global communication networks. However, in the current Cyber-Physical World convergence, sophisticated attacks to the cyber layer can provoke severe damages to both physical structures and the operations of infrastructure affecting not only its functionality and safety, but also triggering cascade effects in other systems because of the tight interdependence of the systems that characterises the modern society. Hence, critical infrastructure must integrate the current cyber-security approach based on risk avoidance with a broader perspective provided by the emerging cyber-resilience paradigm. Cyber resilience is aimed as a way absorb the consequences of these attacks and to recover the functionality quickly and safely through adaptation. Several high-level frameworks and conceptualisations have been proposed but a formal definition capable of translating cyber resilience into an operational tool for decision makers considering all aspects of such a multifaceted concept is still missing. To this end, the present paper aims at providing an operational formalisation for cyber resilience starting from the Cyber Resilience Ontology presented in a previous work using model-driven principles. A domain model is defined to cope with the different aspects and “resilience-assurance” processes that it can be valid in various application domains. In this respect, an application case based on critical transportation communications systems, namely the railway communication system, is provided to prove the feasibility of the proposed approach and to identify future improvements.
Successes and challenges of factor analysis/target transformation application to visible-to-near-infrared hyperspectral data

We designed a laboratory visible-to-near-infrared (VNIR) hyperspectral experiment to test the effectiveness of factor analysis/target transformation for detecting minerals mixed with Mars Global Simulant-1 (MGS-1). The purpose of this experiment is to test for true positive, true negative, false positive, and false negative results from application of factor analysis/target transformation methods and determine the parameters that dictate good versus bad algorithm performance. Gypsum, calcite, montmorillonite, nontronite, and kaolinite were each mixed with MGS-1 at abundances of 1%, 2.5%, 5%, 10%, 20%, and 50%. The mixtures were placed in 2.5 × 2.5 × 1 cm sample trays and imaged using a Headwall Imaging Spectrometer with a spectral range of 0.9–2.6 μm, 8.98 nm spectral sampling, and 0.34 mm/pixel spatial resolution. These images include thousands to tens of thousands of hyperspectral pixels covering each individual mixture tray. Full-image factor analysis/target transformation (FA/TT) and Dynamic Aperture Factor Analysis/Target Transformation (DAFA/TT) were applied to these data to detect the minerals mixed with MGS-1. The results demonstrate that factor analysis/target transformation is prone to both false positive and false negative detections, but in certain applications—including DAFA/TT—it can be useful for highlighting spectrally interesting areas in hyperspectral images for follow-up investigation. The results presented here demonstrate that applications of factor analysis/target transformation to VNIR hyperspectral datasets should be used to highlight small outcrops and/or weak spectral signals in pixels for follow-up investigation. This emphasizes the need for supporting evidence to be obtained—in addition to factor analysis/target transformation—before interpretations of planetary surface processes should be made.
Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Aimed at the two problems of color deviation and poor visibility of the underwater image, this paper proposes an underwater image enhancement method based on the multi-scale fusion and global stretching of dual-model (MFGS), which does not rely on the underwater optical imaging model. The proposed method consists of three stages: Compared with other color correction algorithms, white-balancing can effectively eliminate the undesirable color deviation caused by medium attenuation, so it is selected to correct the color deviation in the first stage. Then, aimed at the problem of the poor performance of the saliency weight map in the traditional fusion processing, this paper proposed an updated strategy of saliency weight coefficient combining contrast and spatial cues to achieve high-quality fusion. Finally, by analyzing the characteristics of the results of the above steps, it is found that the brightness and clarity need to be further improved. The global stretching of the full channel in the red, green, blue (RGB) model is applied to enhance the color contrast, and the selective stretching of the L channel in the Commission International Eclairage-Lab (CIE-Lab) model is implemented to achieve a better de-hazing effect. Quantitative and qualitative assessments on the underwater image enhancement benchmark dataset (UIEBD) show that the enhanced images of the proposed approach achieve significant and sufficient improvements in color and visibility.