Reviewerly/exHarmony huggingface dataset:
An aggregation context network for detecting small ships in remote sensing images

Small ship detection in remote sensing images is an increasingly important area in computer vision. The main greatest challenges that face it are a small size and different aspect ratios of ships. Recently, convolution neural network (CNN) achieve outstanding performance in object detection. CNN consists of several consecutive layers categorized to shallow and deep according to their positions. The shallow layers contribute in the detection of tiny targets significantly. However, the semantic features of these layers are weak to classify them correctly. In this paper, an aggregation context network is proposed to enhance the semantic features of the shallow layers for the state-of-the-art RetinaNet detector. This network is located before the feature pyramid network of the RetinaNet. It consists of aggregation and context modules. The aggregation module aggregates the different features to improve the semantic features at the shallow layers. The context module is to increase the receptive fields at each layer of the feature pyramid network. The experiments are carried on a proposed ship dataset. The dataset is carefully picked up where most of its instances relative area is less than 1% of the input image. The proposed network achieves 89.39%, which outperforms other state-of-the-art detectors.
Conclusion

This chapter summarizes the book’s contents. It explains that book reveals that generalizations obscure the fact that African states behave much like other states in the global system, and that they produce billions of dollars in capital accumulation each year that create home-grown millionaires (and a few billionaires) while serving a world economy that craves African products such as chocolate, diamonds, petroleum, caffeinated drugs and the mineral ingredients necessary for mobile phones, computers, and other products.
A Study of Chinese Pastry Naming Patterns Based on Cognitive Semantics

This paper makes quantitative statistics on the naming patterns of Peking and Guang-style pastries and analyzes the similarities and differences between the naming patterns of Peking and Guang-style pastries from the perspective of prototype and interaction in cognitive semantics.Based on the "Event-domain Cognition Model" (ECM), the naming patterns of Peking and Guang-style pastries include two core elements: action and event.Among them, the elements of event can also be divided into many sub-elements.By summing up the names of pastries, the naming mode of Peking-style cakes is " action elements + event elements" or "sub-elements of event and their mutual collocation", which can be specifically divided into six types: "raw material", "raw material + color", "raw material + taste", "production method + raw material", "color + shape" and "production method + shape".The main naming mode of Guang-style pastries is "sub-elements of event and their mutual collocation", which can be specifically divided into three types: "raw materials", "shape + raw materials" and "taste + raw materials".From the perspective of archetypal view, "bing(饼)", "gao(糕)" and "su(酥)" are the original common names, while "juan(卷)", "qiu(球)", "tuan(团)" and "quan(圈)" are the paraphrased common names, which reflects the similarity of people's cognition to the common names of these two styles of pastries.From the perspective of interaction, the special names of Peking-style pastries highlight various characteristics of things, while the special names of Guang-style pastries mainly highlight the characteristics of materials.
2pSA3 - On the Acoustic Black-Hole Effect in Waveguides

[Itinerary Planner](https://eventpilotadmin.com/web/planner.php?id=ASASPRING21) [Gather.town information](https://acousticalsociety.org/meeting-faq-gather-town-tour-yoga/) [Gather.town website (password: sound)](https://gather.town/app/lhwwaMoJFhRdAcyH/AcousticsInFocus2021) [Yoga Class, 9 June, 12:30 p.m.](https://us02web.zoom.us/meeting/register/tZwkf--srDIiH9VRaHkGAGXr3z29rov-MTBa) [Yoga Class, 10 June 12:30 p.m.](https://us02web.zoom.us/meeting/register/tZ0kf-usqTsqGNG3OYDNLOOpy64YUXjYCTR1)
Improved 3D Human Pose Real-time Estimation based on Monocular Video

Aiming at the ambiguity of real-time estimation of human posture depth of criminal behavior in the field of monocular visual surveillance, an improved 3D human posture real-time estimation method based on video is proposed. Firstly, the 2D joint point coordinates of each frame of characters in unmarked video are detected and used as the input of constrained generation countermeasure network to generate 3D human posture with human joint constraints, Combined with the one-dimensional motion information back projected onto the two-dimensional plane, the result is consistent with the corresponding input in the same frame. The information combination method used in this method can not only rely on a small amount of data to obtain a more ideal accuracy, but also correct and optimize the character motion in the three-dimensional space. At the same time, the constrained generation confrontation network is used to solve the ambiguous motion posture, The experimental results on human 3.6m public data set and self collected criminal behavior video set show the accuracy and practical applicability of this method, and the comparison results are also better than the previous benchmark algorithms.
Passive RFID-based Intelligent Gloves for Alternative and Assistive Communication - A Preliminary Study

We introduce intelligent gloves based on passive ultrahigh frequency (UHF) radio frequency identification (RFID) technology, which comprises of four antenna parts and three RFID integrated circuits (ICs). Each of the ICs (in middle finger, ring finger and small finger) have their unique IDs, which can be activated by gentle touch of thumb, and used to send a specific message, which is displayed on a computer screen. Two users tested the gloves in an office environment with M6 mercury RFID reader and a specially developed software. The achieved success rate in these preliminary tests was 100 %. We consider these results promising first steps for future wearable passive RFID-based augmentative and alternative communication (AAC) solutions.
Advanced Blockchain Security for Medical Data

This project presents an efficient data security system for Medical Reports using BlockChain Technology. The exactness of conclusion and the viability of treatment can be improved by this proposed structure of social insurance frameworks dependent on the frameworks plus computing results plus parallel execution (ACP) approach. ACP approach is utilized to show and speak to patient's conditions, analysis, and treatment process, at that point break down and assess. Likewise, the developing square chain innovation with the human services framework makes simple the connection between the specialist and cloud, Doctor and patient and Patient and cloud for thorough social insurance information sharing, medicinal records survey, and care suitability. A frontend system is created utilizing Django structure for the patient module where the patient can enter the information for examining the sickness nearness without visiting the specialist. On the server-side, the information is gathered and decoded utilizing effective square chain innovation and a report is likewise produced for specialist confirmation which he can see utilizing his login made utilizing Django structure.
CIS Society Officers

meeting of 2019 of the IEEE Computational Intelligence Society (CIS) was held on March 24, 2019 at the Jupiter Lisboa Hotel in Lisbon, a very beautiful city, the capital of Portugal.As usual, we had a packed list of agenda items.We began at 8:00 AM and finished at about 6:30 PM.To give some idea of how such a meeting is run and why it requires so much time, let me explain that activities of CIS are divided into six major heads: Finances, Publications, Memberships, Education, Technical Activities, and Conferences.Each of these areas is led by a Vice-President.These six Vice Presidents, the President-Elect/Past-President, and the President constitute the ExCom.During any ExCom meeting, every member makes a presentation giving the present status in his/her area of activities, explains the future plans, and more importantly, almost every member brings up some proposals/issues that require intense discussion.I have been serving this committee for a long time, initially as the Vice President for Publications for four years, then as the President-Elect for one year, and have been chairing this committee for about one and a half year.I must acknowledge that I am extremely fortunate to get associated with such a wonderful team.Everyone is an outstanding volunteer.We all are very good friends.We argue intensively about issues and obviously sometimes we have strong disagreement among us.But this never affects our friendship.We all are strongly united and we fight together any crisis, if and when appears.Having such a team has made my life much enjoyable.In one of my messages, I had mentioned that we try to choose the place of ExCom where there is a potential to grow computational intelligence activities further.To reinforce this idea, we try to organize a workshop in the city in which the ExCom meeting is held.To run the workshop we find a champion, a CIS volunteer Our First ExCom Meeting of 2019
Organization experience activities for developing environmental protection capacity in teaching at primary school level

Experimental activities emphasizes the subjective experience of the learners, it requires practical experience and reflects the experience of learners. For environmental education, this is an important educational orientation - education in the environment. With this approach, the environment will become a various and lively realistic laboratory for teachers and learners, with students' excitement and high learning efficiency. This article analyzes a number of theoretical and practical issues about the organization of experiential activities to develop environmental protection capacity in teaching at primary school level. From there, some recommendations are given to teachers in organizing experiential activities to develop environmental protection capacity in teaching at the primary school level.
Virtual sensors with long term estimations using multi-stage deep recurrent neural networks

Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions.
Hybrid Machine Learning Model of Extreme Learning Machine Radial basis  function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert  System

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.
Light Estimation and Environment Texturing

Once upon a time, a rock skid made a spark, and humankind learned to create fire. This is the fifth installment of our ARKit series. Today, we will walk you through how to implement light estimation in augmented reality with ARKit.
Enhanced Shadow Removal for Surveillance Systems

Shadow removal has been provedJishnu, P. very helpful in higher-levelRajathilagam, B. computer vision applications which involves object detection, object tracking as part of the process. Removal of the shadow has always been a challenge, especially for ensuring higher-quality images after the shadow removal process. In order to unveil the information occluded by shadow, it is essential to remove the shadow. This is a two-step process which involves shadow detection and shadow removal. In this paper, shadow-less image is generated using a modified conditional GAN (cGAN) model and using shadow image and the original image as the inputs. The proposed novel method uses a discriminator that judges the local patches of the images. The model not only use the residual generator to produce high-quality images but also use combined loss, which is the weighted sum of reconstruction loss and GAN loss for training stability. Proposed model evaluated on the benchmark dataset, i.e., ISTD, and achieved significant improvements in the shadow removal task compared to the state of the art models. Structural similarity index (SSIM) metric also used to evaluate the performance of the proposed model from the perspective of Human Visual System.
Vehicle Speed Estimation and Tracking Using Deep Learning and Computer Vision

This paper proposesSathyabama, B. an intelligent traffic surveillance system thatDevpura, Ashutosh detects the vehicle and its speed, colour, direction,Maroti, Mayank and type of vehicle using computer vision and deepRajput, Rishabh Singh learning. This information can be used to find the traffic violator using automatic number plate recognition. Research shows that over-speeding accounts for 60% of total accidents in India, which raises a serious concern. The proposed approach uses TensorFlow object detection API for vehicle detection, cumulative Vehicle counting, and colour detection of the vehicle using colour histogram integrated with the KNN machine learning algorithm in a real-time environment and a robust approach using deep learning and computer vision for speed estimation and direction detection. This study will effectively monitor traffic usage and help officials track, detect, and lay a floor plan to effectively stop speeding and wrong-side driving vehicles from getting into accidents. This paper proposed an efficient and robust approach for detecting moving vehicles along with their speed and other attributes. The proposed approach can be integrated with a pre-installed traffic monitoring camera system without significant adjustments.
Getting Started with Java 17

This chapter presents a handful of recipes to help programmers who are new to the Java language and those with experience in other languages become accustomed to Java 17. You learn to install Java and configure an integrated development environment (IDE) from which you develop applications and experiment with the solutions provided in this book. For security reasons, we don’t recommend installing something unless absolutely necessary. With no alternative, it is easier and safer to download a compressed version and run the relevant scripts for the respective operating system. You should be able to adapt our instructions to install in the future any latest versions and releases of all the packages mentioned in the book without any problem. You learn the basics of Java, such as creating a class and accepting keyboard input. Documentation is often overlooked, but you also quickly learn how to create great documentation for your Java code in this chapter.
BEVDS: A Blockchain Model for Multiparty Authentication of COVID-19 Vaccine Beneficiary

COVID-19 vaccinations have been approved for public immunization and are going through clinical trials. Vaccinating a billion people of India is a huge challenge. Tracing, last mile delivery, data privacy, double spend of dosage, and multiparty authorization pose serious challenges for providing COVID-19 vaccines. Policies have been framed on who gets the access to the vaccine. However, there is a need of a technological solution to create an efficient vaccine distribution or delivery system. This paper proposes a novel blockchain enabled vaccine delivery system in the context of India. A consortium blockchain has been proposed which has different participants, like the UIDAI, healthcare facilities, pharmaceutical supply chain, etc. These participants are responsible for authenticating the beneficiary of the COVID-19 vaccine. Sequence diagram of how the beneficiary is authorized to access the vaccine has been proposed.
Application and Challenges of “Blockchain Technology” in the Oil and Gas Industry

The need to enhance the petroleum and gas supply chain’s efficiency has led the industry to focus on information and digital technology. Numerous enterprises have recently adopted blockchain technology to enhance their productivity. The industry recognizes blockchain as a technology to boost organizational structure, productivity and data protection. Blockchain is an evolving technical framework intended to create pathways in economic systems that would revolutionize corporate cultures. The chapter discusses the essential theories and methods in implementing blockchain in the petroleum and gas sector. The status of protocols, the degree of awareness, and the opportunities, challenges, risks and growth patterns are also analyzed. The author concludes that the technique has tremendous potential to improve efficiency and maximize the benefits of the oil and gas industry.
Lambda Expressions

A convenient way to create anonymous functions is using the lambda expressions using an expression or series of statements. Lambda expressions are built on functional interfaces, which contain a single abstract method, and that method has no implementation.
Standalone Photovoltaic System for Boys Hostel at Fiji National University, Natabua Campus, Fiji

Fiji is located in the Sunbelt region of the globe and experiences on average more than 6 hours of bright sunshine daily. Sun’s energy is abundantly available to be harnessed. This chapter describes the design of standalone photovoltaic (PV) off-grid system for the boys’ hostel consisting of 35 rooms located at Natabua campus, Fiji National University, Lautoka, Fiji, to supply its energy requirements. The daily demand of electricity for the hostel is 157 KWh consisting of lights, fans, and computers. The daily hourly energy demand was used to determine the various components of PV standalone system. The meteorological data on irradiation and bright sunshine hours used for the designing of PV system were collected from the Fiji Meteorological Service.
Secure And Dynamic Multi Keyword Ranked Search

Nowadays, information storing in third party storage is increased. Outsourcing the data to other storage device or servers which may questioned to the secure environment. However, sensitive data like medical information should need an privacy when it is stored in cloud storage. In this paper, a secure keyword search which provide the resultant data in a encrypted form where the end user can decrypt using the key given to them. It uses the Blowfish to encrypt the data and it also supports the data owner to delete or modify the content of their document. It also ensure accurate relevance score calculation between encrypted index and query vectors.
A empirical study of perception of the end-user on the acceptance of smart government service in UAE

The end-users’ acceptance of electronic government applications is crucial for the effective delivery of public services. This study intends to investigate the factors that influence the end-users' acceptance of smart-government services.This paper identifies key determinants of the end-users’ acceptance of smart government services in the UAE to develop a theoretical model which is tested empirically, using the partial least squares structural equation modelling. This study examines the relationship among the factors that influence the adoption of smart government applications, along with the moderation effects of gender, age, and experience of the end-users on this linkage. The paper reveals that performance expectancy is the strongest factor influencing adoption of smart government, followed by trust in government, effort expectancy, and social influence. The Multi Group Analysis is used to test the moderation effects of the gender, age, and smart service use experience of the end-users on the relationship between the factors that influence smart-government service adoption.
OVERVIEW OF THE TEXT AREA DETECTION ALGORITHMS IN VIDEO STREAM FRAMES

. The widespread development of video technologies in cooperation with social networks such as YouTube and TicToc has led to a sharp increase in the volume of information. The processing and analysis of this data fall on the shoulders of a person. It happens as especially difficult to extract useful text information from the entire video stream. That issue is particularly acute in areas such as the state defense and law enforcement, where the volume of incoming video information is huge. A video stream can contain useful meta information helpful to search, classify, and link the data to specific video content. There are already systems for automatic recognition of the state registration plates, vehicles, railway car numbers, street names, and video titles. However, there may still be a lot of metainformation in the frames of the video stream, such as the marking of various objects and technical products, building numbers, ads, signs, inscriptions, text on documents, and much more. Creating a system for automatic detection of any text in video content will significantly improve the processing and increase the efficiency of information search engines. This article provides an overview of methods for detecting text regions in frames of the video stream. The methods are analyzed in terms of applicability and universality. A comparative analysis and evaluation of popular methods for detecting text areas are given.