Reviewerly/exHarmony huggingface dataset:
An adaptively reversed diffusion dual-drive evolutionary algorithm in dynamic environments for intelligence prediction

Prediction problems are difficult to be carried out in a dynamic environment, for two key questions: one is how to monitor environmental changes, the other is how to make respond timely after the environment has changed. Multi-population strategy is often adopted to address both two key problems. However, there are two stubborn questions that limit and affect the effectiveness of the strategy, i.e., (1) Search overlaps are easy to occur which lead to lose the ability of local exploit, (2) In the course of evolution, the search range of subpopulations tends to assimilate, so the subpopulation will gradually lose the ability to explore whole search space. Therefore, this paper adopts multi-population strategy, and presents a novel intelligence algorithm based on particle swarm optimization to solve the above problem, called adaptively reversed diffusion dual-drive evolutionary algorithm (ARDDEA). (1) Firstly, ARDDEA monitors the environmental changes by setting the global dynamic sentry in each subgroup. (2) Secondly, in order to avoid searching overlapping of the sub-population, a new exclusion strategy is proposed in this paper. A new distance determination method, i.e., between-swarms average Mahalanobis​ distance, is devised in the exclusion strategy to decide the inter-population distance. If the distance is too small between two sub-populations, furtherly, a Hill–valley decision function is used to determine whether they tracked the same peak or not. If so, the inferior subpopulations will be reinitialized by a reverse diffusion operation (RD) proposed in this paper. Besides, (3) a new dual-drive kinetic updating equation is proposed to enhance the search capability of the population. The new algorithm compared with several state-of-art dynamic optimization algorithms on the moving peak problem. The results show that the ARDDEA algorithm can track the optimal solution more effectively in the dynamic environment, and shows strong robustness and adaptability. It is a hope algorithm applied to prediction problems.
Computationally sound Bitcoin tokens

We propose a secure and efficient implementation of fungible tokens on Bitcoin. Our technique is based on a small extension of the Bitcoin script language, which allows the spending conditions in a transaction to depend on the neighbour transactions. We show that our implementation is computationally sound: that is, adversaries can make tokens diverge from their ideal functionality only with negligible probability.
Regularized neuro-fuzzy AI model to aid score management in Online distance learning forums

This paper proposes to use an artificial intelligence (AI) model based on neuro-fuzzy techniques to aid in the automatic evaluation of notes originated from the student's interaction with their performed activities in online distance learning (ODL) forums. The evolution of non-classroom teaching allows new business opportunities and studies to emerge for the population. Some people who do not have enough time to attend traditional teaching choose distance learning to boost their tasks and money. The increasing demand for ODL courses creates challenges that are mainly aimed at automating the tasks commonly required for students' evaluation routines during their activities. For distance learning to maintain an acceptable level of costs, routine activities must be automated to reduce values related to more straightforward operations execution. In this paper, we will use a real dataset on evaluations of frequent interactions of academic in forums, allowing the obtained data to be submitted to a fuzzy neural network able to estimate the value of the student's score value according to the activities carried out by them, beyond the extraction of knowledge through fuzzy rules. Model outputs confirm that the approach may be feasible to automate the presence and participation process in ODL forums through a specialist system based on fuzzy rules. The tests performed with a resulting low RMSE of 1.37 suggest that our neuro-fuzzy-based AI approach performs better than traditional state-of-the-art regressive models.
OTF Gym: A Set of Reinforcement Learning Environment of Layered Optical Thin Film Inverse Design

OTF gym is a python library for optical thin film inverse design. This paper focuses on creating a collection of the reinforcement learning environment to achieve a multi-layer optical thin film inverse design. We develop environments based on TMM simulation to solve various op- timization targets(material, thickness, structural) with a single-agent and multi-agent reinforcement learning algorithm. This code is available in Github.
Appearance difference makes relationship: A new visual relationships inferance mechanism

To understand visual information better, the machine needs to go to a higher space based on the object recognition, which is to understand the relationship between objects. Despite recent advances in this field through deep learning technology, the detection and grounding for visual relationships is still a difficult task. In this work, we propose a relational attention model that considers appearance differences to try to get rid of the long tail distribution problem in data-driven methods. Appearance difference is used to highlight the difference between the entity in the image and the entity of the same category which determines the relationship. This is a customized structure based on a transformer, which considers the impact from subject, object and their Co-occurrence on the relationship. Our representation generation method based on multi-head attention can effectively model the relationship and solve the multi-label problem of the visual relationship. In comparison to other state of the art approaches, we achieve an absolute mean improvement in performance on the Visual Genome dataset.
The Role of Artificial Intelligence in The Decision-Making Process at The Ministry of Interior in The United Arab Emirates

This research paper is part of an extensive study conducted by the researcher on the UAE strategy for artificial intelligence, and its role in changing the mechanism of government services, leading to a complete data analysis by 2031, according to the latest techniques and tools of artificial intelligence and their application in various fields of work efficiently. The problems of smart devices lie in the bubble filter phenomenon, which causes a lack of elasticity, increased systemic fragility, which can have serious and unexpected systemic effects. The paper objectives were; to discuss the decision-making process and the employment of artificial intelligence in the United Arab Emirates. Through the Descriptive Analytical method, the topic was presented in two sections. The discussions showed that the UAE's strategy for artificial intelligence targets several vital sectors in the country, including the transportation sector, by reducing accidents and operational costs. The health sector by reducing the proportion of chronic and dangerous diseases. The space sector conducts accurate experiments and reduces costly errors. Energy sector through utility management and smart consumption. The water sector by conducting accurate analysis and studies to save resources. The technology sector by raising the rate of production and helping in public spending, the education sector by reducing costs and increasing the desire to learn. The environment sector by increasing the proportion of afforestation and planting suitable plants. Traffic Sector Develop preventive mechanisms such as predicting accidents and traffic congestion, and setting more effective traffic policies. With the presence of the infrastructure and knowledge in the successful recruitment of artificial intelligence in the Ministry of Interior in the United Arab Emirates.
Comparison of classifiers for different data in application of classification

Abstract The classification task is very important in many application fields, such as image recognition, speech recognition, and text classification. Machine learning and deep learning methods are used as the classifiers in their specific classification tasks. Classical machine learning classifiers, including Random Forest, XGBoost, GMM, and SVM, and deep learning classifiers including CNN and LSTM are compared in this paper to show the different computing characteristics in their specific classification tasks. The comparison results show that the CNN-based classifier performs the best in its own classification, especially the image classification. The results illustrate that the complexity of the classification task may heavily influence the performance of the classifiers. The research in this paper has a reference significance for choosing the right classifier in applying the classification task.
Analysis on Machine Translation of Data News Based on Translation Typology in Functionalism

With the advent of the data age, data news appeared as a new form of reporting. Driven by big data technology, data news has made a qualitative and quantitative leap. Some machine translation tools are frequently used to translate the data news to get newest data information, especially some online translation websites. Whether machine translation can replace real translators? Based on translation typology in functionalism, this essay mainly compares different machine translation versions of data news, and the essay mainly focuses on the translation versions from two online translation websites, namely fanyi.baidu.com, translate.google.cn. And the essay finds out that there are some mistakes, namely mistranslation, and omitted translation in these machine translation versions which needs further correction and polishing by the translators and optimization of these websites.
Producer Competition and Cooperation Within the Infrastructure Sector of the Economy

The present work is devoted to mathematical modeling of infrastructural branch of economy built on the scheme of vertical integration with competition. The paper develops a mathematical model, which is a normal-form game that is suitable for determining the optimal values of the most important parameters of the industry, such as the degree of its vertical integration, the output volume and price level of the natural monopoly core of the industry, which is a network enterprise.To study various options for building the infrastructure industry, the article develops a methodology for the integrated application of game-theoretic capabilities of mathematical modeling and optimization capabilities of structural modeling of the industry based on the optimization of the integrated characteristics of the manufacturing and network sectors of the industry.The paper shows that the main structural feature of the industries under consideration are fundamentally different characteristics of production costs of the producing and network sector. Therefore, the proposed game model of the industry is based on the use of the functions of production costs of both components of the industry. The game nature of the model makes it possible to investigate different variants of interaction between public and third-party private producers, to identify possible options for building stable agreements between them, which makes it possible to find the best option for meeting the requirements of the European Union’s Third Energy Package in the study of export opportunities for Russian infrastructure industries. The construction of the joint characteristics of the manufacturing and network sectors of the industry as payoff functions in the proposed game model allowed us to explore various options for integrating sectors.As a result, an economically sound level of industry integration was found.As a result of researches carried out according to the model proposed in the work, it was established that in the market of infrastructure industry there is a necessity of joint development of strategies of public and private producers, limits of possible variants of their cooperation are established.
Distributed Edge Learning with Inter-Type and Intra-Type Over-the-Air Collaboration

Federated learning (FL) has been widely utilized to leverage the distributed dataset and processing capability of local sensors while preserving data privacy. Considering the utilization of multiple groups of sensors with diverse sensing functions in IoT wireless networks, we focus on a new hybrid data partitioning scenario, where each sensor can only obtain partial data samples on type-specific feature space. This results in the combined sample parallelism among same-type sensors and feature parallelism among different types of sensors, which brings challenges to designing scalable and communication-efficient training algorithms. Different from the conventional FL settings, we transform the training problem to the primal-dual domain and propose a novel hierarchical FL framework where both intra-type and inter-type over-the-air collaboration between local sensors are utilized to exploit the sample and feature diversity. Simulation results illustrate the importance of such collaborative training and the efficiency of the proposed transmission scheme.
Multiple pulse-mode Bell states heralded via entanglement swapping

A novel scheme is presented for simultaneous heralding of multiple distinct pulse-mode entangled states via frequency-resolved Bell-state measurements. The heralded states are verified using spectral measurements and two-photon interference.
Content Driven and Reinforcement Learning Based Resource Allocation Scheme in Vehicular Network

Lots of videos are transmitted for content understanding tasks in vehicular networks, which puts pressure on limited communication resources. Traditional resource allocation schemes do not consider video content, so the performance of content understanding tasks performed on the transmitted videos is not optimal. To solve this problem, in this paper, we proposed a video frames priority driven and reinforcement Q-learning based resource allocation scheme. First, we propose a video frames content priority evaluation method from the perspective of video content, and the evaluation results are the basis of resource allocation. Then, we propose a Q-learning based resource allocation scheme in vehicular network scenario, which provides more reliable resources for video frames with higher priority. Finally, experiments on real datasets validate that the proposed scheme can help to improve the performance of video content understanding tasks, such as traffic accident detection tasks.
Gaussian process regression and classification using International Classification of Disease codes as covariates

Abstract In electronic health records (EHRs) data analysis, nonparametric regression and classification using International Classification of Disease (ICD) codes as covariates remain understudied. Automated methods have been developed over the years for predicting biomedical responses using EHRs, but relatively less attention has been paid to developing patient similarity measures that use ICD codes and chronic conditions, where a chronic condition is defined as a set of ICD codes. We address this problem by first developing a string kernel function for measuring the similarity between a pair of primary chronic conditions, represented as subsets of ICD codes. Second, we extend this similarity measure to a family of covariance functions on subsets of chronic conditions. This family is used in developing Gaussian process (GP) priors for Bayesian nonparametric regression and classification using diagnoses and other demographic information as covariates. Markov chain Monte Carlo (MCMC) algorithms are used for posterior inference and predictions. The proposed methods are tuning free, so they are ideal for automated prediction of biomedical responses depending on chronic conditions. We evaluate the practical performance of our method on EHR data collected from 1660 patients at the University of Iowa Hospitals and Clinics (UIHC) with six different primary cancer sites. Our method provides better sensitivity and specificity than its competitors in classifying different primary cancer sites and estimates the marginal associations between chronic conditions and primary cancer sites.
Abnormal Driving Detection Based on Human Pose Estimation and Facial Key Points Detection

In recent years, there have been an increasing number of traffic accidents which are caused by the abnormal driving behaviors, including dangerous driving and fatigue driving. This research aims to identify these abnormal driving behaviors immediately and warn the drivers by proposing a hybrid approach based on human pose estimation and facial key points detection. Initially, we utilize OpenPose neural network model to detect the key points of body, face and hand in real time. These data are employed to judge the dangerous driving behaviors such as smoking, phoning, etc. Furthermore, Dlib model are selected to detect facial key points, which are passed to fatigue detection algorithm PERCLOS. Facial features are extracted as a supplement to human pose data. Finally, our proposed hybrid method for abnormal driving detection can effectively reduce the risk of traffic accidents. Hence, it has a great social significance and application value in the transportation field.
AI Regulated E-Commerce Portal

An online shopping system is the one that allows a customer to place online orders for items and / or services from a store that caters to online customers. The online shopping system offers an online display of the goods and utilities and a corresponding delivery window for customer - selected items. The system accepts delivery to the customer for the purchase of ordered item in response to the delivery time. When ordering goods, we offer a convenient shopping cart to hold selected items for purchase. All the items selected to be purchased are placed in a the shopping cart until the customer completes their virtual shopping trip.Virtual shopping carts can be checked at any time, and the content can be edited or removed at the customer's choice. With the help of Artificial Intelligence, traders understand a customer's requirements better and suggests related products at the right time An online shopping plan does not remain with the customer credit provider until the item selected by the customer is taken from the inventory to the final shopping cart and then finally the order is confirmed by customer after providing the necessary information like billing address, mode of payment etc.Thus, amendments to the order can be made anytime by the customer. In addition, features like wish-list, various filters to sort the selected items, customers-care window, exclusive offer window etc. is also provided to make customer’s shopping experience more pleasant and memorable. Once a customer has decided to submit a purchase order, the customer can print the contents of the original shopping basket for a record with a copy of the transaction.
Developing the COVID-19 Malay Corpus Using WordPress Content Management System (CMS)

This paper presents the development of COVID-19 Malay Corpus using the WordPress content management system (CMS). The COVID-19 Malay Corpus is an information retrieval system that collects articles related to COVID-19 from Malay online news media by using a crawler or web content extractor. This proposed system used a search engine that takes search queries to retrieves the news articles in the Malay language over the COVID-19 and related. The system can be used by Malay-speaking people to search and read documents about COVID-19 in the Malay language since most of the documents available are in English. WordPress has been used for developing this system as it has more plugins, themes, and other customizations available than the other CMS. Besides, it provides many plugins that enhance website functionality, including the Octolooks Scrapes plugin and Relevanssi plugin that are used for crawler and indexing, and the Yoast SEO plugin used for search engine optimization (SEO). This system’s collection has been tested and evaluated by COVID-19 Malay Corpus documents, stopwords list, Malay root words, terms weighting, indexing result, Malay natural language query list, relevant judgment list, relevant queries feedback, and retrieval evaluation by using precision and recall.
A Matter of Life and Death: How the Covid-19 Pandemic Threw the Spotlight on Digital Financial Exclusion in the UK

Since the world's first ATM was unveiled in London in 1967, the deployment of digital financial services has exploded. However, the issue of access for vulnerable groups such as elderly people, people with disabilities, financially less well off people and the unbanked, has never been fully addressed. Then, in 2020, the COVID-19 lockdown threw a glaring spotlight on accessibility. For millions of vulnerable people, a trip outdoors to make a financial transaction could have life-threatening consequences. Digital financial services were vital lifelines for millions in the lockdown, but those unable to access them were severely disadvantaged. Financial services providers, deluged with requests for help from customers unable to access digital services, were brought face to face with the problem of poor accessibility. That makes this an excellent time to ask: "What problems do vulnerable people face in accessing digital financial services and what can UK financial services providers do about them?" This paper addresses that issue, drawing on in-depth qualitative and quantitative research carried out in 2018–2019 and selectively updated in June 2020. Data, interviews with organisations representing vulnerable people, and comments from vulnerable people themselves, provide a picture of the access problems that they face. The main finding is that financial services institutions still have insufficient knowledge about accessible systems and processes, and about user-demand for them. More work is expeditiously needed. This paper aims to help inform that work, so that financial services providers can offer services that turn a problem into an opportunity for both vulnerable people and service providers.
Application of Image Processing for Reconstruction of PATHA Art, A Cultural Heritage of Eastern India: An Approach Using Particle Swarm Optimization Technique

India is culturally very rich and developed from the ancient time. There are lots of different cultures and heritages are prevailing throughout the country from prehistoric time. PATAH art is a very important and old form of performing art of eastern India especially at the Santhal Parganas’ region. This form of art is very ethnic but dyeing because of proper preservation and restoration. Proper image processing technique is required for reconstruction and preservation of these form art works. There are lots of techniques available for this purpose. However instead of using a pure graphical algorithm, a computationally intelligent natural agent based approach called Swarm Intelligence Metaphor or Particle Swarm Optimization (PSO) technique is used, for the restoration of the distorted image quality and reported in this paper. Result shows significant quality improvement of the distorted images.
Identification of Facial Image Based Detection Using Image Transformation Approach

This paper describes how security is provided through the use of facial recognition. Discrete Cosine Transform is a technique used for identifying images based on the images that are already available in the database. Each image is split into parts, resulting in a spatial domain. It is then transformed to a frequency domain representation. The features present in each segment are extracted to obtain the detection of face based on the features extracted from the images present in database. Face recognition technique is extensively used for computing JPEG (joint photography expert group) image files and MPEG (moving photography expert group). Video files are familiar even to those who are not aware of the technology. When compared to DFT, the Discrete Cosine Transform is far more efficient.