Reviewerly/exHarmony huggingface dataset:
AI Based Self – Learning Intelligent Information Leak Protection System for Insurance Companies

Preventing the leak of sensitive information, also popularly known as data leak or data loss to an unauthorized recipient, is the primary goal of an organization's information security system. Like all other financial institutions, Insurance companies collect sensitive personal information of their customers for business purposes. This information is often categorized into three primary types; Nonpublic Personal Information (NPI), Personally Identifiable Information (PII), and Personal Information (PI) are the designated types in the descending order of sensitivity. While it may not always be possible to prevent it entirely, measures can be taken to minimize the possibility of the occurrence. An intelligent and robust system is required where the content is analyzed by state-of-the-art data mining, statistical and machine learning techniques from various data dimensions. An Artificial Intelligence (AI) based self-learning Intelligent Information Leak Protection System (IILPS) using Long Short Term Memory (LSTM) is proposed in the project that mines and extracts information and categorizes the document images, to Secured Documents (SD) or Non-secured documents(NSD), based on the presence of NPI and PII semantic signatures without any explicit rule configuration. The system is designed to be used proactively as an early warning system to tag the SD images while resting in the data store. It can also act as a real-time checkpoint for the information loss by the documents in transit or use. The proposed model prescribes an information loss protection mechanism using a binary classifier based on the state-of-the-art LSTM technique within the paradigm of Artificial Intelligence.
Text Detection from Scene and Born Images: How Good is Tesseract?

Detection of texts from scene images has been an active research area from last couple of decades. The problem of the research becomes challenging due to several environmental clutters such as background complexities, poor resolution, arbitrary orientation of texts, and appearance of texts in multi-lingual scenario. Tesseract is a well-known OCR engine for document-level image analysis. However, to the best of our knowledge, implementation of Tesseract in text detection has not been reported yet. Therefore, this paper presents a fair assessment of the performance of Tesseract in text detection. Reported work is evaluated on multiple benchmark datasets, viz. ICDAR 2013 (born images), ICDAR 2013 (focused scene text), and ICDAR 2019-MLT to validate its performance and efficiency.
Quantum States of Light

It is evident from the previous chapter that certain quantities that appear continuous at the macroscopic scale does actually exist in a discrete form at the microscopic scale. So, quantization of these entities makes sense and reveals a plethora of information at such length scales. In this book, our key concern lies with the properties of light, and rightfully so, we will explore different states of the optical radiation field. We will learn that light exists in many states that makes it so unique. Experimental evidence shows that light behaves as a particle at the microscopic scale (subject to specific conditions), although it behaves like a wave at the macroscopic scale. In this chapter, we will dwell deep and discuss many characteristics related to the particle nature of light. Specifically, we will consider two important states of light, the Fock and coherent states. These states can be regarded as the most fundamental states of light that enable complex interpretation of quantum features in the radiation field.
Atom–Field Interaction

The interaction of electromagnetic fields with atomic/molecular systems is the genesis of most optical phenomena observed to date. To explain these effects, we need two broad classifications based on both classical and quantum theories. This strategy involves treating (1) fields as classical systems and atoms as quantum systems having discrete energy, (2) fields as quantum systems (particles rather than waves) and atomic systems as classical, or (3) both as quantum systems. Of course, schemes 1 and 2 are easy to solve and visualize, while scheme 3 is the most accurate description but complex in nature. In general, quantum approaches do not lead to exact solutions and require assumptions and simplifications to achieve a reasonably approximate solution. Thus, it is important to choose an appropriate strategy for solving a particular problem. The problem associated with atom–field interactions will also be subjected to different strategies to understand some of the experimentally observed phenomena such as Rabi oscillations. In this chapter, we will derive a master equation that serves as the starting point for solving a large number of problems associated with atom–field interactions.
Model of Finding Associative Rules in Inhomogeneous Data of Semantic Networks

The article considers the process of generating candidates from a structured database and discusses the basic concepts and metrics (support, confidence, lift and conviction).The principle of operation of the FP-Growth algorithm is described in detail, both its mathematical part and the software part on the example of the Python language.Two primary levels of scientific knowledge are considered for research: empirical and theoretical.The advantages and disadvantages of the Apriori algorithm are presented.These shortcomings can be overcome with the FP-Growth algorithm.This algorithm is an improved method of Apriori.Frequent patterns are formed without the need to generate candidates.The study considers the main stages of the alternative algorithm FP-Growth search for associative rules.The process of transaction arrears in the database is considered in detail.The method of transforming multimodal data into a tree structure is thoroughly discussed.The example assumes a detailed iteration process for each transaction.The procedure for generating candidates is analyzed in such a way as to reduce the number of passes for a given data set.The paper examines the process of compressing transactions into a simple structure.The efficient and complete output of partial data sets is provided.
HTTPX

Many systems expose a web-based API. The httpx library is useful for automating web-based APIs. It is designed to be easy to use while still exposing many powerful features.
Invertible Underwater Image Enhancement Network

An underwater image enhancement (UIE) network can improve the diverse degradation caused by light scattering and attenuation in the water scenarios. The recent focus of UIE technologies is an interest in developing lightweight deep networks, which can directly to practical applicability. However, while existing deep learning UIE methods achieve an impressive performance, those models face a dilemma of trade-off between performance and efficiency. To address this issue, we propose an invertible UIE network, called InvUIE, to enhance the degraded images. InvUIE has two core components: Flow-Based Generator networks (FGB) and Invertible DownScale blocks (IDS). Our key insight is that FGB transforms the input images into latent space to improve the expressive, and IDS disentangles noise and reconstruct the clean image in reversion to reduce the parameters. Compared with state-of-the-art models, our model provides 1.07/0.51dB gains in enhancement performance on synthetic and real underwater datasets. Moreover, the ablation experiments denote the core components and loss functions are superiority performance in UIE tasks.
Cctseg: A Cascade Composite Transformer Semantic Segmentation Network for Uav Visual Perception

Semantic segmentation could obtain the pixel level classification of surrounding environments which is an essential task for autonomous vehicles and mobile robots visual perception. Most existing semantic segmentation networks were focused on the visual perception of autonomous vehicles. Little attention is paid to the semantic segmentation for UAV (Unmanned Aerial Vehicle) visual perception, which is crucial to UAV autonomous flight and landing spot searching. Compared with views from autonomous vehicles, the UAV-based views were more challenging for the semantic segmentation task due to images captured by UAV containing large-scale variation of objects size caused by different altitude and angle. Â The existing semantic segmentation networks for the visual perception of autonomous vehicles are generally inadequate to effectively extract the representative features of UAV images which required contain context information and local information simultaneously. A cascade composite transformer-based semantic segmentation network is proposed in this study for UAV visual perception. A cascade composite encoder is designed which consists of three transformer-based feature extraction backbones and cascade fused low-level features, middle-level features and high-level features to achieve better feature representation capacity. The spatial enhanced transformer block is implemented as the basic feature extraction block of each backbone to make the extracted features contain context information of environments and local information of objects. A symmetric rhombus decoder is proposed to integrate multi-stage features and make fully utilise of middle stage features which contained abundance of useful information, thus accurately pixel level prediction could be obtained in this way.
To Make Yourself Invisible with Adversarial Semantic Contours

Modern object detectors are vulnerable to adversarial examples, which may bring risks to real-world applications. The sparse attack is an important task which, compared with the popular adversarial perturbation on the whole image, needs to select the potential pixels that is generally regularized by an l0-norm constraint, and simultaneously optimize the corresponding texture. The non-differentiability of l0 norm brings challenges and many works on attacking object detection adopted manually-designed patterns to address them, which are meaningless and independent of objects, and therefore lead to relatively poor attack performance. In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour. The object contour prior effectively reduces the search space of pixel selection and improves the attack by introducing more semantic bias. Extensive experiments demonstrate that ASC can corrupt the prediction of 9 modern detectors with different architectures (\eg, one-stage, two-stage and Transformer) by modifying fewer than 5% of the pixels of the object area in COCO in white-box scenario and around 10% of those in black-box scenario. We further extend the attack to datasets for autonomous driving systems to verify the effectiveness. We conclude with cautions about contour being the common weakness of object detectors with various architecture and the care needed in applying them in safety-sensitive scenarios.
An RBF Neural Network Clustering Algorithm Based on K-Nearest Neighbor

Neural network is a supervised classification algorithm which can deal with high complexity and nonlinear data analysis. Supervised algorithm needs some known labels in the training process, and then corrects parameters through backpropagation method. However, due to the lack of marked labels, existing literature mostly uses Auto-Encoder to reduce the dimension of data when facing of clustering problems. This paper proposes an RBF (Radial Basis Function) neural network clustering algorithm based on K-nearest neighbors theory, which first uses K-means algorithm for preclassification, and then constructs self-supervised labels based on K-nearest neighbors theory for backpropagation. The algorithm in this paper belongs to a self-supervised neural network clustering algorithm, and it also makes the neural network truly have the ability of self-decision-making and self-optimization. From the experimental results of the artificial data sets and the UCI data sets, it can be proved that the proposed algorithm has excellent adaptability and robustness.
Effectiveness of Using Paired Storytelling Technique Towards the Students’ Reading Comprehension

This research aimed to find out whether: (1) the paired storytelling technique is more effective than the conventional technique to teach reading; (2) the average score of students’ reading comprehension who learn reading through the paired storytelling technique is higher than those who learn reading through the conventional technique. The population was the second semester students of SMA Muhammadiyah Prambanan which consist of 77 students. Purposive sampling was used to determine the experimental class and control class. Each class consists of 26 students. The experimental class was treated by using paired storytelling technique, while control class was treated by using conventional technique. Then, the technique which was used to collect the data were multiple choice test for getting the data of students’ reading skill. The instrument of reading test was tried out to get the validity and reliability items. The data from post test were tested their normality and homogeneity. It was found out that the data were normal distribution and homogeneous. After that the data were analyzed to get the hyphothesis test by using ANCOVA.
Pengaruh Persepsi pada Kreativitas Guru dalam Penggunaan Media Belajar dan Iklim Sekolah terhadap Prestasi Belajar Peserta Didik Mata Pelajaran Ekonomi di SMK Swasta Jakarta Selatan

The purpose of this study is to obtain answers empirically the effect of (1) teacher creativity in the use of learning media and school climate on student learning achievement simultaneously, (2) teacher creativity in using learning media on student learning achievement partially, (3) school climate on student learning achievement partially. This research was conducted at South Jakarta Private Vocational High School with 100 students as respondents. The method used is a survey with multiple linear regression analysis approach. Based on the research and calculation of SPSS, the following research results were obtained: (1) there was an influence of teacher creativity in the use of learning media and school climate on student learning achievement simultaneously. (2) There was a partial effect of teacher creativity in the use of learning media on student learning achievement (this indicates that the more creative and varied the media used by teachers in learning was proven to increase students' enthusiasm and learning achievement). (3) There was a partial influence of school climate on learning achievement (this indicates that the more comfortable and conducive the classroom atmosphere is proven to increase the learning achievement of students). Teachers' expertise in managing learning media will make students enthusiastic and interest in them to be active in the learning process so that it will affect student learning achievements.
Aplikasi Pengukuran Kinerja Layanan Perpustakaan berdasarkan ISO 11620:2008

&lt;p&gt;&lt;em&gt;Libraries as institutions that manage documents and information services need to be handled professionally by following professional methods and standards. The performance of a library is reflected in its service to users. Therefore, the library needs to always improve the services provided to meet user expectations. To make it easier to measure performance, it is necessary to have an application that processes data and produces reports on the performance of library services. The purpose of this research is to produce an application to measure the performance of library services based on ISO 11620:2008. Applications are made using the Java programming language. From the results of performance measurement, the highest performance value on the user satisfaction indicator is 0.768 and the lowest is on the per capita library staff indicator. The total value of library service performance is 2,142 in the low category.&lt;/em&gt;&lt;/p&gt;
Implementing the Sustainable Development Goals in Nigeria (Routledge, 2022).

No abstract.
Literasi Digital sebagai Faktor Penentu Prestasi Akademik

One indicator of teaching and learning success is the academic achievement of the students. There have been many efforts made by previous researchers and policymakers to help improve students' academic abilities, often with the development of digital technology today. The study aims at examining the influence of digital literacy and control variables on academic achievement. The sample size used as many as 134 students of SMA Negeri 5 Tangerang Regency and simple random sampling as a sampling technique. As for determining the sample size utilized the Krecjie &amp; Morgan table (sampling error 10%). We distributed questionnaires using google forms through student WhatsApp groups and have been tested for validity and reliability. Analysis of the data used was descriptive causality. The author utilized a multiple linear regression model for his testing. The results showed that digital literacy contributes significantly to improving academic achievement. The higher the digital literacy of students, the more the average trend of academic achievement increases. The findings of this study contribute to science, particularly the field of education, and help students to have good attitudes, behaviors, and morals in utilizing digital developments both inside the school and outside the school.
Klasifikasi Down Syndrome Menggunakan Tekstur LBP dengan Tiga Variasi Distance Classifiers

Down syndrome is the most easily identified and most common genetic disorder. Down syndrome variation of trisomy 21 has characteristics that are common to people with Down syndrome. But, children with this Down syndrome variation have slight differences in the mouth and appearance because they tend to have similar oral features with their parents and siblings so that distinguishing them is quite complex. Therefore, it is important to know in more details the special characteristics of children with Down syndrome. This study aims to create an innovative image processing-based system so that it is more practical to classify people with down syndrome and normal people. The method that can be used is the Local Binary Pattern (LBP) method. This study used 2400 mouth frames of children with Down syndrome and normal children for data training. Then, it also uses 3600 mouth frames for children with Down syndrome and normal children for testing data. The results obtained are the threshold value which gives a good classification of 0.1-0.2 for the three variations of the distance calculation method. The Euclidean and Chebychev methods have an accuracy quality of 100% while the City block method at the threshold of 0.1-0.2 has an accuracy of 91.6. So, it can be said that the most accurate method in this research is the Chebychev, Euclidean, then City Block method.
Addressing the Dynamic Information Technology Landscape: A Course Reflection

The field of Information systems (IS) is dynamic and rapidly changing due to technology innovation and emerging technologies. The changing nature of this field poses a significant challenge for educators trying to stay up to date in their preparation of future technology leaders. Despite this challenge, the IS2020 model curriculum, as well as organizational leaders, are calling on IS educators to find ways to develop and deliver student competencies related to innovation and emerging technologies to meet the needs of the IS workforce. Therefore, the goal of this paper is to present a plan for the teaching and learning of these topics within an IS program. This paper outlines a framework for course delivery based on foundational topics that can be utilized for mastering the technology innovation challenge in the classroom and be adapted in response to the changing nature of the discipline. The course plan focuses on theoretical foundations and connects with practice for exploring the application of concepts so students can be introduced to what is really happening in relation to technology in business and the discipline of IS. The course model was tested in-person and online and can be adapted depending on university schedules and teaching modalities.
The Impact of Technology Threat Avoidance Theory Constructs on Cybersecurity Avoidance Behaviour

In the field of cybersecurity, human behaviour is considered as the weakest link. We applied gamification techniques to the development of an Augmented Reality game, CybAR, which was designed to educate users about cybersecurity in an effective and entertaining way. Technology Threat Avoidance Theory (TTAT) provided the theoretical model for understanding cybersecurity avoidance behaviour. Its constructs of perceived severity, perceived susceptibility, fear, safeguard effectiveness, self-efficacy and safeguard costs were considered as direct antecedents of cybersecurity avoidance motivation and indirect predictors of cybersecurity avoidance behaviour. The purpose of the research was to examine the role of these TTAT constructs in explaining individuals' cybersecurity avoidance behaviour. Structural equation modeling was used to analyse the relationships and test the hypotheses. A cross-sectional survey of 128 students at Macquarie University was conducted to assess the effect of the TTAT on motivation and behaviour. The results showed positive support for most of the proposed relationships, with the exception of safeguard cost on cybersecurity avoidance motivation as well as perceived susceptibility on fear construct of the threat, but safeguard cost factor positively contributed to students' cybersecurity avoidance behaviour. Coping appraisal variables (perceived effectiveness, self-efficacy) were the strongest predictors of cybersecurity avoidance behaviour, especially safeguard effectiveness. Threat severity was also a significant predictor of the fear factor.
Applications of Mixed Reality with Unity and Arduino

: in a fast-developing world of augmented reality and virtual reality, where advancement in both AR and VR has been unprecedent. there is a case to be made with mixed reality also. Basically, mixed reality a form of augmented reality where physical interactions can be made with computer generated objects placed in the user’s environment using physical input methods. This can have a profound impact on the way augmented reality application are developed and can open a world of endless possibilities for AR applications.&#x0D; In this paper, a study of mixed reality application development is carried out using Unity as a development environment for mixed reality applications, along with Arduino for the development of different input/output methods used within the experience.&#x0D; Unity is a game development engine that can be utilized to develop AR/VR/MR applications. The extensive toolkit in Unity enables developers to create applications with 3d models that can be experienced though AR glasses or devices capable of AR. Arduino is an open-source electronics platform based on easy-to-use hardware and software. Using Arduino microcontrollers and a variety of sensors, input and output methods, AR experiences can be made interactable physically.