Reviewerly/exHarmony huggingface dataset:
DWT, DCT and SVD Based Hexagonal Image Compression

Inspired by the human visual sys- tem(HVS), hexagonal grid computation bears numerous advantages like equidistance, good angular resolution, isoperimetry and fixed neighbours. As the digital technology is advancing day by day, it is often need to handle large volumes of data for transmission and storage purpose. Hence there is a need to focus on different compression techniques without comprising the data quality for reducing the storage space, faster computation and with less computational cost. Image compression techniques exploits the human visual imperceptibility to encode the image with lesser number of bits for the same image. This paper, presents different compression methods like SVD(Singular Value Decomposition), DCT(Discrete Cosine Transform) and discrete wavelet transform (DWT) for hexagonal image grid. Hexagonal image compression is accomplished by outsourcing hexagonal grid images. The performance comparison for different hexagonal image compression are made against rectangular grid and are analysed from MSE, size of the image, CR and PSNR. Hexagonal grid compression provides better results over all the compression methods when compared to the rectangular grid image compression. Although biological processes inspire the structure, the current state of the art of recording and output devices, are often constrained by their square structure. Therefore resampling techniques are used to outsource hexagonal grid images from the rectangular grid.
Developing Accurate Predictive Model Using Computational Intelligence for Optimal Inventory Management

People are all currently living in the world where data has changed how company think, act and plan. Data, if used correctly, might be able to become a company's sharpest weapon in fighting the competition with other companies. Inventory cost is one of the most burdening costs in the food and beverage industry with the items like degradable raw materials or fresh ingredients. If not managed correctly might become a waste causing loss to the company. Degraded ingredients also might lower the overall food quality which might result in unsatisfied customers. Managing inventory, however, is not as easy as it seems, especially with the traditional method. This paper focuses on development of accurate predictive model using computational intelligence for optimal inventory management with a case study of restaurant ingredient management. Several machine learning algorithms like linear regression, multi-layer perceptron, random tree, random forest, and model trees were utilized to build accurate predictive models from time series data of the restaurant inventory. With good prediction system using computational intelligence, the inventory cost and wasted ingredients can be significantly reduced, which this eventually maximizes the profit.
A Comparative Study on Concept Drift Detectors for Regression

Context: in the field of machine learning models are trained to learn from data, however often the context at which a model is deployed changes, degrading the performances of trained models and giving rise to a problem called Concept Drift (CD), which is a change in data distribution. Motivation: CD has attracted attention in machine learning literature, with works proposing modification to well-known algorithms' structures, ensembles, online learning and drift detection, but most of the CD literature regards classification, while regression drift is still poorly explored. Objective: The goal of this work is to perform a comparative study of CD detectors in the context of regression. Results: we found that (i) PH, KSWIN and EDDM showed higher detection averages; (ii) the base learner has a strong impact in CD detection and (iii) the rate at which CD happens also affects the detection process. Conclusion: our experiments were executed in a framework that can easily be extended to include new CD detectors and base learners, allowing future studies to use it.
Improved Otsu Multi-Threshold Image Segmentation Method based on Sailfish Optimization

Image segmentation is a key step from image processing to image analysis. The classical multi-threshold Otsu algorithm has achieved good results in image segmentation, but it is very time-consuming to use exhaustive search methods to find the optimal threshold. To solve this problem, this paper proposes an improved Otsu multi-threshold image segmentation method based on sailfish optimization(SFO). In order to reduce the time complexity of the algorithm, the heuristic search of sailfish biota is simulated to find the optimal threshold of image segmentation. The inter-class variance of multi-threshold is used as the fitness function of SFO, and the fitness value of each iteration is calculated. The final maximum fitness value is the optimal threshold of image segmentation. The experimental results show that the proposed algorithm in this paper not only improves the segmentation quality, but also shortens the optimization time, which demonstrates the correctness and efficiency of the algorithm.
Construction of Contemporary Art Teaching Platform Based on Virtual Reality Technology

Learning in the campus environment constructed by virtual reality (VR) technology often has more sense of participation and identity than traditional online teaching using words or pictures. With the advent of the era of big data and self-media, art colleges are facing the changes of contemporary media technology and the popularization and application of the Internet, and integrating digital and information teaching methods into all aspects of experimental teaching. Many concepts and basic principles involved in art teaching are difficult to understand and observe. The author puts forward a design method of art teaching platform based on VR technology, which embodies and visualizes the abstract and profound concepts, theories and working processes in art teaching; the modeling software and the realization of interactive functions are introduced in detail.
Edge-Based Detection and Classification of Malicious Contents in Tor Darknet Using Machine Learning

With the increase of data in the network, the load of servers and communication links becomes heavier and heavier. Edge computing can alleviate this problem. Due to a sea of malicious contents in Darknet, it is of high research value to combine edge computing with content detection and analysis. Therefore, this paper illustrates an intelligent classification system based on machine learning and Scrapy that can detect and judge fleetly categories of services with malicious contents. Because of the nondisclosure and short survival time of Tor Darknet domain names, obtaining uniform resource locators (URLs) and resources of the network is challenging. In this paper, we focus on a network based on the Onion Router (tor) anonymous communication system. We designed a crawler program to obtain the contents of the Tor network and label them into six classes. We also construct a dataset which contains URLs, categories, and keywords. Edge computing is used to judge the category of websites. The accuracy of the classifier based on a machine learning algorithm is as high as 89%. The classifier will be used in an operational system which can help researchers quickly obtain malicious contents and categorize hidden services.
Modified Newton Integration Algorithm With Noise Tolerance for Image Deblurring

This paper addresses image deblurring problem, which consists of recovering a rather high resolution image from its blurred and noisy observation version. Different from the existing solving methods, influence of noise perturbation is considered when modeling inverse solution process. To remedy shortcomings that the traditional and current computing methods have encountered under a noisy environment, a modified Newton integration (MNI) algorithm is presented from the control perspective. Besides, through the corresponding theoretical analyses and proofs, the proposed MNI algorithm is confirmed to handle image deblurring problem with noise-tolerance ability and fast convergence performance. Finally, multiple simulative experiments are performed to verify excellent image deblurring ability and noise-tolerance advantage of proposed MNI algorithm compared among Newton-Raphson iterative (NRI) algorithm, gradient descend dynamic (GDD) algorithm, and discrete time derivative (DTD) algorithm.
A new gap-based obstacle avoidance approach: follow the obstacle circle method

Abstract One of the most challenging tasks for autonomous robots is avoiding unexpected obstacles during their path following operation. Follow the gap method (FGM) is one of the most popular obstacle avoidance algorithms that recursively guides the robot to the goal state by considering the angle to the goal point and the distance to the closest obstacles. It selects the largest gap around the robot, where the gap angle is calculated by the vector to the midpoint of the largest gap. In this paper, a novel obstacle avoidance procedure is developed and applied to a real fully autonomous wheelchair. This proposed algorithm improves the FGM’s travel safety and brings a new solution to the obstacle avoidance task. In the proposed algorithm, the largest gap is selected based on gap width. Moreover, the avoidance angle (similar to the gap center angle of FGM) is calculated considering the locus of the equidistant points from obstacles that create obstacle circles. Monte Carlo simulations are used to test the proposed algorithm, and according to the results, the new procedure guides the robot to safer trajectories compared with classical FGM. The real experimental test results are in parallel to the simulations and show the real-time performance of the proposed approach.
Knowledge Graph Construction for Fault Diagnosis of Aircraft Environmental Control System

With the continuous improvement of the aircraft environmental control system, the content of maintenance manuals on which the maintenance work is based are constantly enriched, causing inconvenience of quick fault positioning. Maintenance engineers’ experience and knowledge are often required and the labor cost of maintenance work increases. For improving the utilization efficiency of these resources, this paper uses the deep text matching model, BERT, to extract semantic information in the maintenance record provided by China Eastern Airlines. After obtaining the entities of warnings and fault causes and the relationship between them, a knowledge graph for fault diagnosis of civil aircraft environmental control system is constructed. And a fault diagnosis support algorithm is completed, which is conducive to improving fault location and reducing aircraft maintenance costs.
Determining Enterprise Architecture Smells from Software Architecture Smells

Software Architectural Smells (SA smells) are design problems in the internal structure and behavior of an SA. These can be seen as a specific category under the umbrella concept of Technical Debt (TD). TD is a central concept in software development projects and having the means to detect and measure the smells is important to understand impairments they may cause. However, TD is only limited to the technical aspects and does not describe smells found on an enterprise level. Enterprise Architecture Debt (EAD) expands the concepts of TD beyond the technical aspects such that it covers the debts that can be found in all layers of an Enterprise Architecture (EA). EA smells give a measurement for EAD, by providing means for detecting the smell, hence enabling a method to quantify the level of debt. The goal of this paper is to find EA smells derived from existing SA smells. This has resulted in three new EA smells that could be used as measurements for the quality of an EA. They can also be used in the future as a basis for automatic EA smell detection.
NLIRE: A Natural Language Inference method for Relation Extraction

Relation extraction task aims at detecting the semantic relation between a pair of entities in a given target sentence. However, previous methods lack the description of the relation definition, thus needing to model the implication of relations during training. To tackle this issue, we propose a natural language inference method for relation extraction. Given a premise and a hypothesis, the natural language inference task refers to predicting whether the facts in the premise necessarily imply the facts in the hypothesis. Specifically, for each relation type, we construct a relation description. These relation descriptions are the definition of relation, containing prior knowledge that helps model understand the meaning of relation. The given target sentence is viewed as the premise, and these descriptions are viewed as the hypotheses. Then model infers whether these hypotheses can be concluded from the premise. Based on the inference results, our model selects the relation corresponding to the most confident hypothesis as the prediction. Substantial experiments on SemEval2010 Task8 dataset demonstrate that the proposed method achieves state-of-the-art performance.
Simulation of Continuous-Variable Quantum Systems with Tensor Network

Tensor network is known as a powerful method to simulate quantum computation on a classical computer. Recently, the random quantum circuit used in the demonstration of quantum supremacy by Sycamore quantum computer have been simulated with tensor network method. Compared to the application of tensor networks to Discrete-Variable (DV) quantum computation, there are very few examples of application of tensor network to Continuous-Variable (CV) quantum computation. Therefore, we applied a tensor network structure called Matrix Product States (MPS) to CV quantum computation. Tensor network is expected to be useful for simulating Gaussian Boson Sampling (GBS), which is a promised near-term application of photonic quantum system. We outline the performance of tensor network-based simulation of CV quantum computation in GBS.
Adaptive Learning on Fog-Cloud Collaborative Architecture for Stream Data Processing

Recently, learning from continuously evolving streaming data attracts many researchers, especially when this data is inclined to change trends regularly (i.e. concept drift). Unluckily, conventional mining techniques and algorithms are proved inadequate to solve this problem, in which the model’s performance degrades in stationary data, let alone in the case of data streams. Correspondingly, adjusting the model manually and constantly is ineffective, and with the current growth of the data size, it becomes impractical as well. However, automatic adaptive learning methods and algorithms could be a good solution, but they are seldom exploited in IoT business applications to address this issue. To that end, we aim to tackle the problem of concept drift occurs in time-series streaming data on IoT applications, in general, meteorology prediction, in specific. And taking into account the rapidly growing structure of fog-cloud computing, we attempt to leverage the powerful computation of the cloud layer as well as the closeness to IoT devices of the fog layer to design and implement a cooperative fog-cloud architecture in order to produce a faster and more accurate model. Our main objective is to obtain high performance and low latency, as these are the key requirements for real-time or nearly real-time data processing on IoT applications. The experimental findings have confirmed the study’s hypothesis, where the adaptive model on the suggested cooperative fog-cloud architecture reduces both the error of prediction by 20% and the overall time for training the model by almost 41% compared to the baseline model.
A Novel Natural Language Processing approach to automatically Visualize Entity-Relationship Model from Initial Software Requirements

An Entity Relationship Model is a crucial design artifact used in data modelling. An additional time and numerous resources are involved in generating the ER model from preliminary software requirements manually. In contrast, Natural Language Processing is a knowledge acquisition methodology to generate several design artifacts from preliminary software requirements automatically. However, some manual pre-processing tasks are required to perform on these textual requirements before going towards the application of Natural Language Processing techniques due to which the whole process remains semi-automatic. This research study presents a novel and fully automated Natural Language Processing approach to generate the Entity Relationship Diagram model from preliminary software requirements. Entity Relationship Diagram generator tool (ERD-Gen) is developed as a part of this research study to generate the ERD model automatically from the preliminary set of requirements. Validation of this research study is performed through two benchmark case studies. The experimental outcomes proved that our proposed NLP methodology is fully automated and does not requires any manual processing step on the textual requirements.
An Improved Automatic Shape Feature Extraction Method Based on Template Matching

Abstract Image shape extraction is an important step in the image analysis, AI electronic industry and automation, as well as a significant part of content-based image retrieval(CBIR), which cannot be separated from contour extraction. However, traditional approach of the border following algorithm is susceptible to noise interference, thus the shape extracted is always complex in real images and cannot express feature of the target image well. Therefore, an improved shape feature extraction method is proposed, which converts color space into HSV model when preprocessing, filters contour by area size, merges adjacent contours by drawing convex hull and filters with template shapes. Lastly, this method is tested on UAV123 and YCB_Video dataset, which showed that the proportion of valid contour improved from less than 10% to 87.7% based on border following algorithm. In the experiment of OPenCV open source library in Visual Studio environment, we hope to improve the extraction efficiency of shape features.
An Algorithmic Approach to Machine Learning Techniques for Fraud detection: A Comparative Analysis

Fraud using credit cards is still rife today, and the modes are increasingly varied. To avoid scams with various ways of credit cards, we must identify and find out what methods are often used by fraudsters. The comparative analysis depicts that the parameters, i.e., Precision/Recall and F1-Score the K-Nearest Neighbor, are better for detecting fraudulent transactions than the Logistic Regression and Na&#x00EF;ve Bayes. However, the accuracy is marginal high of Logistic Regression, but the False Positive parameters cannot identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K--Nearest Neighbor deems fit for such cases. Kaggle Dataset for fraud detection has been used to experiment. Therefore, under the scheme, we used various models of machine learning models based on classification and Regression. The results show that the K--Nearest Neighbor is the better approach for detecting fraudulent transactions than the Logistic Regression and Na&#x00EF;ve Bayes.
Multi-label Prediction for Visual Sentiment Analysis using Eight Different Emotions based on Psychology

In visual sentiment analysis, sentiment estimation from images is a challenging research problem. Previous studies focused on a few specific sentiments and their intensities and have not captured abundant psychological human feelings. In addition, multi-label sentiment estimation from images has not been sufficiently investigated. The purpose of this research is to build a visual sentiment dataset, accurately estimate the sentiments as a multi-label multi-class problem from images that simultaneously evoke multiple emotions. We built a visual sentiment dataset based on Plutchik's wheel of emotions. We describe this 'Senti8PW' dataset, then perform multi-label sentiment analysis using the dataset, where we propose a combined deep neural network model that enables inputs from both hand-crafted features and CNN features. We also introduce a threshold-based multi-label prediction algorithm, in which we assume that each emotion has a probability distribution. In other words, after training our deep neural network, we predict evoked emotions for an image if the intensity of the emotion is larger than the threshold of the corresponding emotion. Extensive experiments were conducted on our dataset. Our model achieves superior results compared to the state-of-the-art algorithms in terms of subsets.
The Use of Decentralized and Semantic Web Technologies for Personal Data Protection and Interoperability

The enactment of the General Data Protection Regulation (GDPR) has been the response of the European Union to the growing data-driven economy backed up by the largest companies in the world. It provides the data protection and portability needed by individuals that “unconsciously” generate personal data for “free” services offered by providers that lack transparency on their use. Meanwhile, the rise of Distributed Ledger Technologies (DLTs) offers new possibilities for the management of general purpose data, hence being suitable for handling personal data in a trustless scenario. These decentralized technologies bring a new concept of contract called smart because of its ability to be self-executable. DLTs and smart contracts, together with the use of Semantic Web standards, allows the creation of a decentralized digital space controlled entirely by an individual, where his personal data can be stored and transacted.
A reward response game in the blockchain-powered federated learning system

This paper focuses on a mobile-crowd federated learning system that includes a central server and a set of mobile devices. The server, acting as a model requester, motivates all devices to train an accurate model by paying them based on their individual contributions. Each participating device needs to balance between the training rewards and costs for profit maximization. A Stackelberg game is proposed to model interactions between the server and devices. To match with reality, our model takes the training deadline and the device-side upload time into consideration. Two reward policies, i.e. the size-based policy and accuracy-based policy, are compared. The existence and uniqueness of Stackelberg equilibrium (SE) under both policies are analyzed. We show that there is a lower bound of 0.5 on the price of anarchy in the proposed game. We extend our model by considering the uncertainty in the upload time. We also utilize the blockchain technique to ensure a truthful, trust-free, and fair system. This paper also analyzes how devices maximize their utilities when making profits via training and blockchain mining in the fixed-upload-time setting. A blockchain-powered testbed is implemented, and experiments are conducted to validate our analysis.