Invisible Data Research Articles

Ganet: graph attention based Terracotta Warriors point cloud completion network

Point cloud completion technology is used to address incomplete three-dimensional point cloud data, predicting and reconstructing the original shape and details to achieve virtual restoration. While existing learning-based methods have made significant progress in point cloud completion, they still face challenges when dealing with noise and invisible data. To address these issues, this paper proposes a multi-layer upsampling network based on a graph attention mechanism, called GANet. GANet consists of three main components: (1) feature extraction; (2) seed point generation; (3) State Space Model-based Point Cloud Upsampling Layer. GANet demonstrates exceptional robustness in handling noise and invisible data. To validate the effectiveness of GANet, we applied it to Terracotta Warrior data. The Terracotta Warriors, as important cultural heritage, present a challenging test case due to damage and missing parts caused by prolonged burial and environmental factors. We trained and tested GANet on both the PCN dataset and Terracotta Warrior data, comparing it with several recent learning-based methods. Experimental results show that GANet can effectively reconstruct missing or damaged parts of 3D point clouds, providing more detailed and structurally accurate completion results. These completion models not only validate GANet’s effectiveness but also offer valuable references for cultural heritage restoration work.

A robust medical image zero-watermarking algorithm using Collatz and Fresnelet Transforms

Zero-watermarking in medical images is an emerging field that focuses on calculating the invisible data (key) using medical imagery to ensure data integrity and authenticity without compromising diagnostic accuracy. This paper introduces a robust zero-watermarking technique leveraging the Collatz and Fresnelet Transforms. The Forward Collatz Transform (FCT) is initially applied to create a secure and encrypted embedding pattern for medical images. Subsequently, the Fresnelet Transform (FT) is employed, offering superior localization and frequency selectivity. From the fresnelet values, we extract two strongest Oriented FAST and Rotated BRIEF (ORB) points to enhance watermark robustness, resulting in a 64-bit perceptual image hash. Our approach adopts a dual-layer security strategy by combining FCT and Cyclic-Shift-Transformation (CST) methods, significantly fortifying the protection of watermark image data. The watermark can be efficiently extracted using the Inverse Collatz Transform (ICT). A comprehensive performance analysis evaluates our system under single, double, and multiple attacks on medical images. Our experiments clearly show that our system outperforms existing methods in medical image watermarking, demonstrating its resilience against various manipulations. This approach can significantly improve data security and reliability in medical imaging applications.

Framework for smart contract blockchain in halal traceability, integrity, and transparency

Currently, the halalness of a product may only be determined by the halal certificate or halal label displayed on the product packaging. Simply put, several halal-related issues, like cross-contamination, logistical issues, halal counterfeiting, global halal acceptance, and so forth, keep coming up. To address the numerous issues raised above, it is believed that a framework that can support worldwide halal provisions is necessary. The objective of this research is to create a blockchain smart contract framework for halal transparency, integrity, and traceability. Blockchain technology along with smart contracts can solve classic tracing problems including data leakage, manipulation, and invisible data. Organizational consistency across the halal production process and the availability of raw materials that adhere to sharia laws are two more basic challenges with halal certification that blockchain and smart contracts can resolve. The usual traceability problems, such as data explosion, material cross-contamination between halal and haram, and revelation of private information, can all be resolved by smart contracts that make use of on-chain, off-chain, and electronic product code information services (EPCIS).

Collecting, assembling, ordering: Border politics and the invisible data work of asylum

This article proposes to understand the ‘invisible data work’ that asylum seekers must do to put together a ‘credible’ asylum application. While the intersections between asylum and work have typically been analysed in relation to access to employment and labour conditions, we attend to the work of collecting, assembling, and ordering different forms of analogue and digital data inherent to the asylum process. Building on feminist interdisciplinary debates on work and drawing on a selection of asylum appeals from Italy and the UK, we argue that seeking asylum entails extensive and continual invisible work that requires significant resources, effort, skills and time. Attending to these forms of invisible work is crucial to understanding the challenges of seeking asylum beyond the migration journey and the implications of performing ‘invisible data work’ unaided and unequipped. It also counters problematic depictions of asylum seekers as passive subjects who are ‘just waiting’ for a decision to be made. Finally, rendering the collection and assemblage of data as ‘invisible work’ rather than just ‘doings’ has political implications for understanding the resources, responsibilities and resistance to the border politics of making precarious subjects.

Who Controls the Code, Controls the System: Algorithmically Amplified Bullshit, Social Inequality, and the Ubiquitous Surveillance of Everyday Life

In an era of digital surveillance, data profiteering companies have created a surveillance‐based economy that uses algorithms to target messages and content that increase social divisions and inequality. This economic model poses an existential threat to democracy and social stability not only because of the amplification but because these algorithms are embedded in social cultural context. The emergence of cheap computing and massive—largely invisible—data collection in every aspect of daily life has enabled the creation of massive amounts of data on individuals that can be merged and enriched and aggregated. These data can determine outcomes such as what homes buyers are shown or whether they are approved to purchase a home, whether travelers are added to the no‐fly list or patients receive lifesaving medical treatment, or what price one pays for a service or is even allowed to access a service—all based on commercial data collection. Given existing and entrenched systems of social inequality, the questions of what gets encoded into data, who controls it, and who has access to it, are key existential questions facing sociology in the coming century and hold the potential to dramatically remake every aspect of society.

A Novel Short-Memory Sequence-Based Model for Variable-Length Reading Recognition of Multi-Type Digital Instruments in Industrial Scenarios

As a practical application of Optical Character Recognition (OCR) for the digital situation, the digital instrument recognition is significant to achieve automatic information management in real-industrial scenarios. However, different from the normal digital recognition task such as license plate recognition, CAPTCHA recognition and handwritten digit recognition, the recognition task of multi-type digital instruments faces greater challenges due to the reading strings are variable-length with different fonts, different spacing and aspect ratios. In order to overcome this shortcoming, we propose a novel short-memory sequence-based model for variable-length reading recognition. First, we involve shortcut connection strategy into traditional convolutional structure to form a feature extractor for capturing effective features from characters with different fonts of multi-type digital instruments images. Then, we apply an RNN-based sequence module, which strengthens short-distance dependencies while reducing the long-distance trending memory of the reading string, to greatly improve the robustness and generalization of the model for invisible data. Finally, a novel short-memory sequence-based model consisting of a feature extractor, an RNN-based sequence module and the CTC, is proposed for variable-length reading recognition of multi-type digital instruments. Experimental results show that this method is effective on variable-length instrument reading recognition task, especially for invisible data, which proves that our method has outstanding generalization and robustness in real-industrial applications.

The practical ethics of repurposing health data: how to acknowledge invisible data work and the need for prioritization.

Throughout the Global North, policymakers invest in large-scale integration of health-data infrastructures to facilitate the reuse of clinical data for administration, research, and innovation. Debates about the ethical implications of data repurposing have focused extensively on issues of patient autonomy and privacy. We suggest that it is time to scrutinize also how the everyday work of healthcare staff is affected by political ambitions of data reuse for an increasing number of purposes, and how different purposes are prioritized. Our analysis builds on ethnographic studies within the Danish healthcare system, which is internationally known for its high degree of digitalization and well-connected data infrastructures. Although data repurposing ought to be relatively seamless in this context, we demonstrate how it involves costs and trade-offs for those who produce and use health data. Even when IT systems and automation strategies are introduced to enhance efficiency and reduce data work, they can end up generating new forms of data work and fragmentation of clinically relevant information. We identify five types of data work related to the production, completion, validation, sorting, and recontextualization of health data. Each of these requires medical expertise and clinical resources. We propose that the implications for these forms of data work should be considered early in the planning stages of initiatives for large-scale data sharing and reuse, such as the European Health Data Space. We believe that political awareness of clinical costs and trade-offs related to such data work can provide better and more informed decisions about data repurposing.

A Lightweight Hybrid Scheme for Hiding Text Messages in Colour Images Using LSB, Lah Transform and Chaotic Techniques

Data security can involve embedding hidden images, text, audio, or video files within other media to prevent hackers from stealing encrypted data. Existing mechanisms suffer from a high risk of security breaches or large computational costs, however. The method proposed in this work incorporates low-complexity encryption and steganography mechanisms to enhance security during transmission while lowering computational complexity. In message encryption, it is recommended that text file data slicing in binary representation, to achieve different lengths of string, be conducted before text file data masking based on the lightweight Lucas series and mod function to ensure the retrieval of text messages is impossible. The steganography algorithm starts by generating a random key stream using a hybrid of two low-complexity chaotic maps, the Tent map and the Ikeda map. By finding a position vector parallel to the input image vector, these keys are used based on the previously generated position vector to randomly select input image data and create four vectors that can be later used as input for the Lah transform. In this paper, we present an approach for hiding encrypted text files using LSB colour image steganography by applying a low-complexity XOR operation to the most significant bits in 24-bit colour cover images. It is necessary to perform inverse Lah transformation to recover the image pixels and ensure that invisible data cannot be retrieved in a particular sequence. Evaluation of the quality of the resulting stego-images and comparison with other ways of performing encryption and message concealment shows that the stego-image has a higher PSNR, a lower MSE, and an SSIM value close to one, illustrating the suitability of the proposed method. It is also considered lightweight in terms of having lower computational overhead.

Diabetes Prediction System

Abstract: This model of retrieving useful information and models from the data is also called database knowledge discovery, which involves certain phases such as data selection, classification and transformation evaluation. Machine learning algorithms are primarily classified as supervised and unsupervised. A supervised learning algorithm uses past experience to make predictions about new or invisible data, whereas unsupervised algorithms can draw interferences from data sets. Supervised learning is also described as classification. This study uses classification technique to produce a more accurate belong to class. The classification algorithms have been applied to the Indian Diabetes Dataset of the PIMA of the National Institute of Diabetes, Digestive and Kidney Disease which contains data on diabetic women.

An Examination of Academic Library Privacy Policy Compliance with Professional Guidelines

Objective – The tension between upholding privacy as a professional value and the ubiquity of collecting patrons’ data to provide online services is now common in libraries. Privacy policies that explain how the library collects and uses patron records are one way libraries can provide transparency around this issue. This study examines 78 policies collected from the public websites of U.S. Association of Research Libraries’ (ARL) members and examines these policies for compliance with American Library Association (ALA) guidelines on privacy policy content. This overview can provide library policy makers with a sense of trends in the privacy policies of research-intensive academic libraries, and a sense of the gaps where current policies (and guidelines) may not adequately address current privacy concerns. Methods – Content analysis was applied to analyze all privacy policies. A deductive codebook based on ALA privacy policy guidelines was first used to code all policies. The authors used consensus coding to arrive at agreement about where codes were present. An inductive codebook was then developed to address themes present in the text that remained uncoded after initial deductive coding. Results – Deductive coding indicated low policy compliance with ALA guidelines. None of the 78 policies contained all 20 codes derived from the guidelines, and only 6% contained more than half. No individual policy contained more than 75% of the content recommended by ALA. Inductive coding revealed themes that expanded on the ALA guidelines or addressed emerging privacy concerns such as library-initiated data collection and sharing patron data with institutional partners. No single inductive code appeared in more than 63% of policies. Conclusion – Academic library privacy policies appear to be evolving to address emerging concerns such as library-initiated data collection, invisible data collection via vendor platforms, and data sharing with institutional partners. However, this study indicates that most libraries do not provide patrons with a policy that comprehensively addresses how patrons’ data are obtained, used, and shared by the library.

Code index modulation method with multidimensional invisible data mapping

With the continuous improvement of the application requirements of Direct Sequence Spread Spectrum (DSSS), especially the high requirements of data transmission rate, the advantages of traditional DSSS system are limited, so the index modulation DSSS technology appears. However, at present, there are still some key problems such as high-order data rate, high bit error rate and high synchronisation complexity. Therefore, in view of the above bottleneck problems, the code index modulation method with multidimensional invisible data (MID-CIM) is proposed. On the basis of deeply mining the hidden features of DSSS communication mechanism, the index order and shift order are established, and the two-dimensional invisible data index transmission rules are established using a bit reversed and pseudo code (PN) set mapping mechanism, and then the three-dimensional invisible data index transmission rules are established by using the cyclic displacement and pseudo code offset mechanism. The MID-CIM method not only ensures the effective transmission of one-dimensional data, but also uses the transmission of low-speed one-dimensional data to index transmit high-speed two-dimensional and three-dimensional invisible data. This research can provide the technical basis for the efficient application of DSSS system, and also provide theoretical support and technical support for the new development and application of a communication system based on the spread spectrum system.

Comprehensive Review on Video Watermarking Security Threats, Challenges, and Its Applications

Data is a crucial resource for every business, and it must be protected both during storage and transmission. One efficient way of securing data and transferring it is through digital watermarking, where data is hidden inside a medium like text, audio, or video. Video watermarking is visible or invisible embedded data on a video in a logo, text, or video copyright disclaimer. In this proposed paper, the goal is to analyze the characteristics of video watermarking algorithms and the different metrics used for them. It deals with the extent to which the different requirements can be fulfilled, taking into consideration the conflicts between them and the practical challenges of video watermarking in terms of attacks like geometric attacks and non-geometric attacks. It also focuses on the process of watermarking a video. Recent advances in data security indicate that employing a video watermarking technology to transmit private data will be an effective method of transmitting sensitive data.

Making Visible the Invisible

This article documents how I came to combine autoethnographic accounting with visual arts practice. I developed this mixed methods approach for my PhD study which explores the interdisciplinary possibilities offered by combining visual arts practice with STEM (science, technology, engineering, and mathematics). Visual arts practices as narrative forms tend toward the non-linear (Anae, 2014), whilst autoethnography offers self-reflection. Writing an autoethnographic account for an artwork has the potential to generate a wealth of data, some of which are visible, some of which are not. The invisible data become available only when the artist speaks to/writes about the artwork. If some content/context of a visual artwork is only visible through background information provided by the art maker, this discovery troubles another issue concerning our notions of what a good visual artwork is. Finally, I test this article’s autoethnographic authenticity against Adam’s four characteristics of autoethnography.

Reversible Secured Data Hiding using Binary Encryption and Digital Bit Modification Scheme

The information is getting precious day by day because of the digital revolution and more reach exposure of citizens to technology. Information security is in demand and the topmost priority among researchers to develop data security systems. Image steganography is an invisible data hiding scheme, although it uses visual image media to hide information. Steganography is not only limited to images. There are other media like text, audio, or video that can hide information. In this research work, a novel data hiding is being developed using an image as cover media. During the development of the technique, researchers face maintaining the payload capacity and higher Peak Signal to Noise Ratio (PSNR) values while maintaining other parameters. This work uses color images as cover and digital bit modification methods to hide secure information. Only hiding is not enough to secure information. An additional layer of security is added by encrypting a secure message using an encryption method. The digital bit modification method is unconventionally spread the sequential bitstream over channels. This would make this approach unique and better as it keeps the PSNR level higher along with NCC and lowers MSE. All this is done by keeping the payload capacity as the maximum available. According to the proposed algorithm results, the average value of PSNR is 60.295dB, NCC is 1.00, and MSE is 0.1567.

TRANSITION RS: the design component for the communication of medical research


 Over the last few years, design has increasingly entered the realm of health, thanks to the connection with other disciplines, such as science and medicine.
 
 
 Information design, due to its ability to translate complex and invisible data into visible and understandable ones, can put the interpretative filter of contemporary scientific research over product, services and communicative artifacts, useful to transmit to people knowledge related to psycho-physical well-being.
 
 
 In addition to being associated with more conscious choices and activities, information design and, as well, design, through the connection with medicine, can help in the communication of diseases, both for treatment and preservation.
 
 
 The aim of the paper is to introduce the methodology and, as well, the firsts results of an ongoing research which focus is the divulgation of medical-scientific knowledge on Neurofibromatosis Type 1 (NF1) and epigenetic factors, which technical data have been translated into different king of devices able to relate people everyday life with the managing and prevention of this pathology.

Artificial Intelligence in Biomedical Applications of Zirconia

Artificial intelligence (AI) is rapidly developed based on computer technology, which can perform tasks that customarily require human intelligence by building intelligent software or machines. As a subfield of AI, machine learning (ML) can learn from the intrinsic statistical patterns and structures in data through algorithms to predict invisible data. With the increasing interest in aesthetics in dentistry, zirconia has drawn lots of attention due to its superior biocompatibility, aesthetically pleasing, high corrosion resistance, good mechanical properties, and absence of reported allergic reactions. The evolution of AI and ML led to the development of novel approaches for the biomedical applications of zirconia in dental devices. AI techniques in zirconia-related research and clinical applications have attracted much attention due to their ability to analyze data and reveal correlations between complex phenomena. The AI applications in the field of zirconia science change according to the application direction of zirconia. Therefore, in this article, we focused on AI in biomedical applications of zirconia in dental devices and AI in zirconia-related applications in dentistry.

GIFMarking: The robust watermarking for animated GIF based deep learning

Animated GIF has become a key communication tool in contemporary social platforms thanks to highly compatible with affective performance, and it is gradually adopted in commercial applications. Therefore, the copyright protection of the animated GIF requires more attention. Digital watermarking is an effective method to embed invisible data into a digital medium that can identify the creator or authorized users. However, few works have been devoted to robust watermarking for the animated GIF. One of the main challenges is that the animated image also contains time frame dimension information compare with still images. This paper proposes a robust blind watermarking framework based 3D convolutional neural networks for the animated GIF image, which achieves watermark image embedding and extraction for the animated GIF. Also, noise simulation is developed in frame-level to ensure robustness for the attack of the temporal dimension in this framework. Furthermore, the invisibility of the watermarked animated image is optimized by adversarial learning. Experimental results provide the effectiveness of the proposed framework and show advantages over existing works.

Focus on lasers, imaging, microscopy, and nanoscience

Focus on lasers, imaging, microscopy, and nanoscience

Invisible Data: A Framework for Understanding Visibility Processes in Social Media Data

Social media data are increasingly used to study a variety of social phenomena. This development is based on the assumption that digital traces left on social media can provide insights into the nature of human interaction. In this research, we turn our attention to what remains invisible in research based on social media data. Using Andrea Brighenti’s work on “social visibility” as a point of departure, we unpack data invisibilities, as they are created within four dimensions: people and intentionality, technologies and tools, accessibility and form, and meaning and imaginaries. We introduce the notion of quasi-visible data as an intermediary between visible and invisible data highlighting the processual character of data invisibilities. With this conceptual framework, we contribute to developing a more reflective and ethical field of research into the study of social phenomena based on social media data. We conclude by arguing that distancing ourselves from the assumption that all social media data are visible and focusing on the invisible will enhance our understanding of digital data.

An Unconventional Approach for Analyzing the Mechanical Properties of Natural Fiber Composite Using Convolutional Neural Network

Over the past few years, natural fiber composites have been a strategy of rapid growth. The computational methods have become a significant tool for many researchers to design and analyze the mechanical properties of these composites. The mechanical properties such as rigidity, effects, bending, and tensile testing are carried out on natural fiber composites. The natural fiber composites were modeled by using some of the computation techniques. The developed convolutional neural network (CNN) is used to accurately predict the mechanical properties of these composites. The ground‐truth information is used for the training process attained from the finite element analyses below the plane stress statement. After completion of the training process, the developed design is authorized using the invisible data through the training. The optimum microstructural model is identified by a developed model embedded with a genetic algorithm (GA) optimizer. The optimizer converges to conformations with highly enhanced properties. The GA optimizer is used to improve the mechanical properties to have the soft elements in the area adjacent to the tip of the crack.