Reviewerly/exHarmony huggingface dataset:
Construction of Uyghur Scene Text Image Database

In recent years, the International Conference on Document Analysis and Recognition (ICDAR) has released a data set of multilingual text in natural scene images. The data set contains natural scene images in nine different languages, including Chinese, Arabic, Korean, etc. At present, it is rare to see a comprehensive data set of Uyghur scene text images in natural scene images. Therefore, the main purpose of this article is to create a new Uyghur text data set in natural scene images and provide it to the research community to develop and evaluate the latest Uyghur text recognition and detection algorithms for text retrieval and recognition. Images of natural scenes, segmented characters and composite images of scenes, and Uyghur words in the images are manually marked at the word level from the captured images. The data set includes 2381 Uyghur advertisement pictures, 17,638 video interceptions, 394 complex natural scenes that can be used for Uyghur text detection in complex scenes, and 200067 Uyghur text pictures that can be used for Uyghur text recognition. These pictures come from web crawlers, Uighur animated films and natural street scenes in Xinjiang, China. They include flat text, multi-angle text, raised 3D text, artistic text, distant text, low-light text, partially occluded text, etc. Due to the diversity of the collection environment and the complexity of the image background, the database can be used as a benchmark for Uyghur text detection and recognition in natural scene images, which poses great challenges.
Effect of Absorptivity Oceanic Turbulence on the Concurrence of Spatial Two-qubit States

Wootters&#x2019;s concurrence is a physical quantity that quantitatively describes the degree of entanglement of two qubit states. The concurrence of spatial entangled two-qubit states produced by spontaneous parametric down transform in turbulent adsorbent seawater is presented. The Kolmogorov ocean spectrum and structural constants of the salinity-temperature fluctuation are presented, which can simplify the complexity of the study of the turbulence effects of light wave transport in weakly turbulent oceans. The optimization parameters of the information source system corresponding to the transmission rate of high concurrence are also explored. The results show that the parameters of the spontaneous parametric down transform will make a great impact on the concurrence. The smaller interval between the two signal apertures and the interval between the signal and the idle apertures will make the communication system have higher transmission fidelity of the concurrence. The concurrence can maintain higher in the oceanic turbulence with small the rate of dissipation of mean-square temperature and large rate of dissipation of kinetic energy per unit mass of fluid. These results are of great significance for the design of an underwater optical quantum communication system with optimal concurrence transmission.
Deep Learning to Mosquitoes Species Pattern Recognition

The epidemic from mosquito-borne pathogens is difficult to control and monitor and is considered as a silent threat that cannot be realized. Importantly, therapeutic vaccines are developed to increase immunization to avoid the danger of life. We found these problems in daily life. Mosquitoes live with people in society and the most common species are Aedes Aegypti, Aedes Albopictus, Anopheles minimus, Culex Quinquefasciatus and Armigeres subalbatus. In general, they carry germs to people. In this paper, we are interested in classifying 5 types of mosquitoes. So, we apply LeNet 5. The results show that training network for is each species has the accuracy of 96–99%. Testing network for is each species has the accuracy of 96.25% The results are based on mosquitoes image dataset of 1,200 images, divided as 840 training images and 120 validating images, and 240 testing images.
Two-Step Image-in-Image Steganography via GAN

Recently, convolutional neural network has been introduced to information hiding and deep net- work has shown great potential in steganography. However, one drawback of deep network is that it’s sensitive to small fluctuations. In previous works, the encoder-decoder structure is trained end-to-end, but in practice, encoder and decoder should be used separately. Therefore, end-to-end trained steganography networks are vulnerable to fluctuations and the secret decoded from those networks suffers from unpleasant noise. In this work, we present an image-in-image steganog- raphy method called TISGAN to achieve better results, both in image quality and security. In particular, we divide the training process into two parts. Moreover, perceptual loss is applied to encoder, to improve security in our work. We also append a denoising structure to the end of de- coder to achieve better image quality. Finally, the adversarial structure with useful techniques employed is also used in secret revealed process.
Develop a smart phone's base machine vision system

The machine vision system is widely used in manufacturing factories. It is used to inspect the quality of products. This system reduces the inspection time and human error. Recently, there are various brand of machine vision systems. However, the machine vision systems are high-cost systems. There are two types of machine vision systems: smart cameras and PC bases. Generally, the cost of camera for smart camera system is higher than PC base. Especially, the high-resolution vision system is more expensive than the low-resolution. Therefore, this study proposes to develop the machine vision system from the smart phone. Since the smart phone is continuously improving, easy to reinstall (in the hardware damaged case), high resolution and lower cost than the machine vision system in the market. Thus, it is challenging to use the smart phone as an industrial machine vision system. In this study, the smart phone base machine vision system was used to detect the missing part application. The broken foil on the top of the milk carton was used to simulate the inspection process. The results found the smart phone&#x0027;s base machine vision system can detect the broken foil with an accuracy = 95% and an average processing time = 0.9 sec
Development of a Robot for Building a Map of the Area

The active development of the field of robotics makes it necessary to study the possibilities of new approaches. The purpose of this study is to identify the operability of the Kalman filter as a tool for calculating the SLAM (Simultaneous Localization And Mapping) method for the orientation of a two-wheeled mobile robot. The robot itself is a moving platform on which a servo drive with an ultra-sonic sensor is installed to scan the front hemisphere. Data about the surrounding area is transmitted from the robot to the computer as the robot moves. For data transmission, a wireless Wi-Fi network is used for autonomous operation of the robot or an RS-232 interface for debugging. The program filters out the received data using the Kalman filter and builds a map. The result of the experiment is to obtain an adequate map of the room, which confirms the efficiency of the pro-posed models.
Face Recognition Using Fisherface and Support Vector Machine Method

COVID-19 is declared as a pandemic by WHO and until now COVID-19 pandemic remains a problem in 2021. Many efforts have been made to reduce the spreading virus, one way to reduce its spread is by wearing a mask but most people often ignore it. Monitoring large groups of people becomes difficult by the government or the authorities. Face recognition, a biometric technology, is based on the identification of a face features of a person. This paper describes a face recognition using Fisherface and Support Vector Machine method to classify face mask dataset. Face recognition using Fisherface method is based on Principal Component Analysis (PCA) and Fisher's Linear Discriminant (FLD) method or also known as Linear Discriminant Analysis (LDA). The algorithm used in the process for feature extraction is Fisherface algorithm while classification using Support Vector Machine method. The results show that for face recognition on face mask dataset using cross validation with 10 fold, the average percentage accuracy is 99.76%.
Research on Mixed Transaction Analytical Data Management Oriented to Data Middle Platform

To solve the problem of non-synchronization between enterprise application development and data development, this paper puts forward the concept of data middle platform, which combines the two data processing mechanisms of online analytical processing (OLAP) and online transaction processing (OLTP), so that faster and better data services can be provided to the foreground business. On this basis, this paper summarizes the research status of the related technologies of the data middle platform, including the architecture of the data middle platform and the key technologies of constructing the data middle platform. In-depth analysis of the business scale and business characteristics of OLTP and OLAP in various application scenarios, focusing on the technical difficulties in the application process of OLTP and OLAP in the application scenario. Finally, it summarizes the challenges faced by the basic research from three aspects: the construction of data middle platform, data quality assurance, and the application of mixed-thing analytical data management.
eAR: An Edge-Assisted and Energy-Efficient Mobile Augmented Reality Framework

Mobile Augmented Reality (MAR) apps may cause short battery life due to high-quality virtual objects rendered in the augmented environment. State-of-the-art solutions propose to balance energy consumption and user-experience using a static set of decimated object versions within the app. However, they do not consider that each object has unique characteristics, which highly influence how the user-perceived quality changes according to user-object distance and triangle count. As a result, they may lead to limited energy savings, a high storage overhead, and a high burden on the MAR app developer. In this paper, we propose eAR, an edge-assisted autonomous and energy-efficient framework for MAR apps designed to solve the limitations of state-of-the-art solutions. eAR features an offline software running on an edge server that leverages Image Quality Assessment (IQA) to model user-perceived quality for each virtual object in terms of triangle count and user-object distance. In addition, eAR features a runtime lightweight optimization algorithm that dynamically decides the most energy-efficient virtual object triangle count to request from the edge server based on (i) the per-object models of user-perceived quality, (ii) energy consumption models for mobile GPU and network interface, and (iii) a user path prediction system that estimates near-future user-object distances. eAR is completely autonomous and can be easily integrated into most MAR apps as an open-source library. Our results show that eAR can help reduce energy consumption by up to 16.5% while reducing storage overhead by almost 60% compared to existing schemes, with minimal MAR app developer effort and minimal impact on user-perceived quality.
Overview of the Causality-driven Adhoc Information Retrieval (CAIR) task at FIRE-2021

The CAusality-based Information Retrieval (CAIR) track at FIRE 2021 focuses on the task of retrieving potentially relevant documents in response to a query indicating one or more events, where the notion of relevance is determined by whether a document indicates potential causes that might have led to the specified events in the query. In 2020, we released a dataset comprised of a benchmark set of 25 queries along with the relevance judgments. The target document collection is the English monolingual FIRE ad-hoc document collection. This second iteration of the track acted as a continuation of the same task with the same dataset as in the last year. The objective was to encourage the participants to try out more involved approaches (e.g. supervised ones) for improving on the retrieval effectiveness.
Hypothesis Tests

The previous chapter considered the following problem: given a distribution, deduce the characteristics of samples drawn from that distribution. This chapter goes in the opposite direction: given a random sample, infer the distribution from which the sample was drawn. It is impossible to infer the distribution exactly from a finite sample. Our strategy is more limited: we propose a hypothesis about the distribution, then decide whether or not to accept the hypothesis based on the sample. Such procedures are called hypothesis tests. In each test, a decision rule for deciding whether to accept or reject the hypothesis is formulated. The probability that the rule gives the wrong decision when the hypothesis is true leads to the concept of a significance level. In climate studies, the most common questions addressed by hypothesis test are whether two random variables (1) have the same mean, (2) have the same variance, or (3) are independent. This chapter discusses the corresponding tests for normal distributions, called the (1) t-test (or difference-in-means test), (2) F-test (or difference-in-variance test), and (3) correlation test.
Exposing Functions Using Rcpp

In the previous chapter, we built an R package using Rcpp. Moreover, using CodeBlocks, we established the infrastructure for developing and building our ABI-compliant library of statistical functions (libStatsLib.a), which we linked into our R package (StatsR.dll). For the moment, we have only used a single function, library_version (defined in StatsR.cpp). We used this to illustrate the build process and to test the communication between R and C++.
Big data and AI: a potential solution to end the conundrum of too big to fail financial institutions?

Abstract The “too big to fail” institutions are a widespread concern, especially in the financial world. Their failure can create severe economic downturns and social turmoil. In past bank failures, governments intervened with public funds to save such institutions from collapse to avoid economic downturns. Since, measures have been put in place to prevent bank failures and limit the utilisation of public funds. However, failures cannot be prevented and risks of affecting the economy are always present in the case of too big to fail institutions. This article explores the possibilities offered by recent advancements in the fields of Big Data and Artificial Intelligence, widely implemented by the financial institutions themselves, as tools to be used by authorities in ending the too big to fail conundrum. The adequate implementation of these technological capabilities will contribute to the areas already targeted by governments – reducing the probability of failure and providing tools to limit negative externalities and spillover effects – and will also introduce a new capability that could address the too big to fail matter. Since financial institutions are, in their essence, data hubs, now in a digitalised format, the possibilities to automate tasks and provide insight for decisions should address the issue. The actual transfer of assets and liabilities to institutions that can carry on the activity, currently need years to be handle:. Big Data and Artificial Intelligence technologies could make such operations a matter of hours or days.
Comparison of Adolescent Vaccination Data Accuracy by Urban Village in DKI Jakarta Province in July 2021 Using Several Data Mining Methods

Since 2020, the outbreak of the Coronavirus disease has begun to enter the territory of Indonesia. For a year and a half, various efforts have been made to reduce the number of deaths caused by this pandemic. One of the efforts made by the government is the provision of vaccinations for the community, especially for adolescents. This is one way to attract people's interest to vaccinate and also make it easier for the government and the system to process vaccination data, especially for youth vaccination. The purpose of this study is to determine the accuracy of the data on adolescents who have been vaccinated in the DKI Jakarta province in July 2021 by using several methods of data mining. Of the three data mining methods used in this study, the JRip method produces the highest percentage of accuracy, which is 100%.
A New Watermarking Approach for Ensuring Patient Data Authentication over a Low-quality Communication Environment

Healthcare data management has improved in the last decade with increasing data volume due to the advancement of information technologies. While the digital patient data are accessed through a bad network from a remote place, data could be altered. Any modification of transferring data over the networks may create security issues like data integrity and authentication. The solution to these security problems is significant because they may cause sensitive data loss can hamper medical diagnoses. Watermarking is an effective method to provide such data security, which has attracted the researcher’s attention with a cheaper computational cost. Different watermarking techniques had applied to secure medical data, especially for images, in the healthcare system. Watermarking text data is a task that ensures authenticity and integrity on text patient data. It has become difficult for the lack of proper techniques. This paper proposes a new watermarking approach for real-time text patient data to provide their authentication and integrity. Both robust and fragile watermarks are used to detect reliable source information and any data alternation. Experiment results show that our proposed algorithm can identify the source information correctly and provide expected accuracy against possible attacks. The proposed technique could help to serve authentic data to the recipient from the geographically trustworthy distant source without any data manipulation.
The Behavior of Information – Searching for Normal

The definition for social norm varies by field of study, and the causal mechanisms by which norms are established and maintained remain substantially open questions across the behavioral sciences. Here we show that norms and normative phenomena are not independent social mechanisms, but instead represent stages of information processing within a collective of interacting social agents. We have found that the emergence of norms and normativity can be explained as a distributed network for communication, filtering, and preservation of experiential information within social networks. Norms represent coalescence of prior expectations for observed outcomes, whereas normative expectations reflect the convergence of those experiences towards a stable posterior expectation. In combination, this recursive “emergence-convergence” process forms an efficient heuristic for aggregating and preserving experiential information. We show that social norms and institutions are not merely conventions that facilitate the coordination of social behavior. They are the objective of that coordination and, ultimately, of the evolutionary adaptation of sociality itself. This theoretical framework resolves open questions across several fields of social and behavioral sciences relating to the origins of social norms and institutions, the adaptive mechanisms of cultural evolution, and the origins and clear definition of what defines culture.
Visual AI Applications on Smart Delivery Units

As actors in an IoT production environment, smart delivery units are tasked with identifying loaded components and acquiring shopfloor events such as consumption of material. Conventional identification procedures rely heavily on tags and markers that are applied on components. For processes that require marker-less identification procedures, AI-based object identification can be incorporated. In this paper, we present a novel integration of such visual applications on smart delivery units. We address the main challenges of this approach, namely the need for computational resources and integration with low-cost components. Additionally, we propose a scalable IoT concept for the distribution of the AI functionalities on those delivery units by utilizing containerized applications. We demonstrate the validity of this AI integration with a real-world implementation on delivery units, tested in an application near environment.
Adaptive System for Object Detection and Picking Based on Efficient Convolutional Neural Networks

Thanks to the rapid development of technology and urbanization, the generation of a vast amount of waste has created an environmental problem that demands an urgent solution. The conventional approach to dealing with the problem is to hire more human workers. However, humans are vulnerable to musculoskeletal injuries from repetitive motions. In recent years, many experts and scholars have committed to the development of robots to replace or complement human beings. As part of this effort, this work aims to develop a mobile robot, which can be combined with cellular phones and the Internet of Things to realize long-distance control, query, and monitoring. A camera is affixed to the robot to return environmental information. A sophisticated yet effective machine learning-based image recognition method is used to detect several common types of garbage in the surrounding area. Test data shows a precision of 0.42 for bottles and 0.99 for cans, and an F1_score of 0.57 for bottles and 0.94 for trash. After the garbage is identified, the robot will move toward it and pick it up. In the design and implementation of the system, great care has been taken to enhance its efficiency and dependability. Wireless communication and lightweight messaging protocol have also been used to facilitate agile mobility and fast information exchanges.
Learning Enabled Optical Encryption in Complex Scattering Media

In this paper, we apply deep learning for optical encryption in complex scattering media. Ciphertexts are optically obtained by using an interferometric setup through complex scattering media. After the recording of ciphertext, a designed machine learning model is trained by usage of ciphertexts and their corresponding plaintexts. Then, security keys consist of the optimized parameters and the well-trained machine learning model. Supposing that proper security keys are used in the decryption process, plaintexts can be recovered in real time. One point to be highlighted is that the decryption process in the proposed method is not a directly reverse process of encryption. Moreover, security keys in the proposed method can be updated during the training when there is information leakage. In addition to the optimized parameters and the well-trained learning model, extra parameters can be applied to enhance the security. Robustness of the proposed method has been investigated by the eavesdropping attacks. In optical experiments, it is verified that the proposed method by using machine learning to implement optical encryption in complex scattering media is valid and robust. It is expected that this research work can contribute to optical cryptography schemes.