Image Files Research Articles

Asymmetric CycleGANs for inverse design of photonic metastructures

Using deep learning to develop nanophotonic structures has been an active field of research in recent years to reduce the time intensive iterative solutions found in finite-difference time-domain simulations. Existing work has primarily used a specific type of generative network: conditional deep convolutional generative adversarial networks. However, these networks have issues with producing clear optical structures in image files; for example, a large number of images show speckled noise, which often results in non-manufacturable structures. Here, we report the first use of cycle-consistent generative adversarial networks to design nanophotonic structures. This approach significantly reduces the amount of speckled noise present in generated geometric structures and allows shapes to have clear edges. We demonstrate that for a given input reflectance spectra, the system generates designs in the form of images, and a complementary network generates reflectance spectra if an image containing a shape is provided as an input. The results show a higher Frechet Inception Distance score than previous approaches, which indicates that the generated structures are of higher quality and are able to learn nonlinear relationships between both datasets. This method of designing nanophotonics provides alternative avenues for development that are more noise robust while still adhering to desired optical properties.

Implementasi Algoritma Yamamoto’s Recursive Code Dalam Mengkompresi File Gambar

The size of the data stored whether it is stored on a computer or in cloud storage is one of the problems in the computer world. One of the best solutions to overcome this problem is to use compression techniques. In this study, the author uses Yamamoto's Recursive Code Algorithm to compress image files. Image files with bitmap (bmp) extension have a smaller size than those in jpeg format, this causes problems if the recipient's download speed is slow or the internet connection is unstable, information, information cannot be processed immediately because the data transfer time is too long. For that we need a technique to change the size of the data to be smaller . This technique is known as data compression. Data compression is a process of converting a set of data into a form of code to save the need for data storage. On this basis, the application of Yamamoto's Recursive Code Algorithm in Compressing Image Files is made in order to help computer users compress image files that are initially large into their smallest size.

Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models

Rapid advancements in artificial intelligence, machine learning, deep learning, coupled with easy access to high-capacity processing hardware, expansive organized datasets, and the evolution of artificial intelligence algorithms, have extensively influenced numerous fields. Digital Forensics is one such discipline where the application of artificial intelligence has been significantly amplified in recent years. The analysis of extensive image and video files derived from forensic evidence presents challenges in terms of time efficiency and accuracy. To surmount these challenges, artificial intelligence models can be employed to perform identification and classification processes on these data, thus expediting the resolution of forensic cases with enhanced precision. In the current study, state-of-the-art pre-trained YOLOv8 object recognition models - nano, small, medium, large, and extra-large - were utilized. These models were trained on the Wider-Face dataset with the objective of identifying suspects from images and videos sourced from digital materials in the field of digital forensics. The models achieved mean Average Precision (mAP) values of 97.513%, 98.569%, 98.763%, 98.775%, and 99.032% respectively. The YOLOv8 architecture demonstrated superior performance, outperforming the YOLOv5 architecture by a margin of 7.1% to 8.8%. To aid digital forensic experts in the detection and identification of suspicious individuals, a desktop application capable of real-time image analysis was developed.

Converting Image Files to LaTeX Format Using Computer Vision, Natural Language Processing, and Machine Learning

Converting Image Files to LaTeX Format Using Computer Vision, Natural Language Processing, and Machine Learning

Deep learning pest detection on Indonesian red chili pepper plant based on fine-tuned YOLOv5

.This research developed a pest detection model for Indonesian red chili pepper based on fine-tuned YOLOv5. Indonesian red chili pepper is the third largest vegetable commodity produced in Indonesia. Pest attacks disrupt the quantity and quality of crop yields. To control pests effectively, it is necessary to detect the type of pest correctly. A viable solution is to leverage computer vision and deep learning technologies. However, no previous studies have developed a pest detection model for Indonesian red chili pepper based on this technology. YOLOv5 is a variant of the YOLO object detection algorithm, which has major advantages in terms of computation cost and execution speed. The dataset comprises 4,994 image files collected from a chili plantation in Bengkulu province, Indonesia, covering 4 different classes and a total of 10,683 pests. The image is 1216 x1216 px with the smallest, largest, and average object dimensions of 2%, 35%, and 4% of the image dimensions. The training model used is fine-tuning YOLOv5s with variations of patience as an early stop parameter of 100, 200, and 300. The evaluation of the trained model is based on train loss, validation loss, and mAP@0.5:0.95, the best-trained model is the 445th epoch on patience 100 with the best confidence value of 0.321 and the highest TF1 of 0.74. From the best-trained model testing on the test dataset, the mAP@0.5 performance for all classes is 81.3%. The model not only detected large pests but was also able to detect objects that were small in size compared to the image size. The best-trained model's best mAP@0.5 performance and speed are 82.6% and 20 ms/image, or 50 fps on NVIDIA P100 GPU.

PGR - 2 Demons In The Mirrors: A Childhood Case Report of 22q11.2 Deletion Syndrome First Addressed By Tele-Neuropsychological Evaluation During The COVID-19 Lockdown.

22q11.2 Deletion Syndrome (22q11DS) results in physical, cognitive, and behavioral issues. Although it is the most common microdeletion syndrome, there remains a high prevalence of delayed diagnosis. Its heterogeneous phenotypic presentation lead to differing considerations for early diagnostic identification. If initial symptomatology includes behavioral and cognitive disturbance, it is likely for such individuals to be referred first for neuropsychological testing before genetic testing. This case will expand upon diagnostic identification of 22q11DS. A 12-year-old, 5-years educated, white female was seen for tele-neuropsychological evaluation due to COVID-19 restrictions in December 2020. Patient's mother reported concern regarding sudden and worsening behavioral changes including hallucinations, unusual affects, odd beliefs, academic decline, illegible handwriting, attention and memory issues, and behavioral problems in school to the point of removal. Overall performance on the Meyers Neuropsychological Battery was severely impaired. See uploaded image file for specific test findings. Speech, affect, and behavior were notable during examination. Diagnoses included schizophrenia (provisional) and specific learning disorder with reading impairment (historical). Immediate neurology consultation was recommended. Later EEG, brain MRI, and CSF were unremarkable. Acute metabolic etiology was ruled out. Subsequent genetic testing revealed 22q11DS. Diagnostic identification of 22q11DS and resulting access to care management heavily depends on initial symptom presentation. In this specific case, the COVID-19 lockdown created barrier to sufficient services. This case expands upon knowledge of the cognitive profile of 22q11DS and calls upon increased awareness. Targeted improvement of neurocognition potentially protects against cognitive decline related to psychotic symptoms, and thus neuropsychologists play a vital role in referral to such resources.

SimpliPyTEM: An open-source Python library and app to simplify transmission electron microscopy and in situ-TEM image analysis.

Introducing SimpliPyTEM, a Python library and accompanying GUI that simplifies the post-acquisition evaluation of transmission electron microscopy (TEM) images, helping streamline the workflow. After an imaging session, a folder of image and/or video files, typically containing low contrast and large file size 32-bit images, can be quickly processed via SimpliPyTEM into high-quality, high-contrast.jpg images with suitably sized scale bars. The app can also generate HTML or PDF files containing the processed images for easy viewing and sharing. Additionally, SimpliPyTEM specifically focuses on in situ TEM videos, an emerging field of EM involving the study of dynamic processes whilst imaging. The package allows fast data processing into preview movies, averages, image series, or motion-corrected averages. The accompanying Python library offers many standard image processing methods, all simplified to a single command, plus a module to analyse particle morphology and population. This latter application is particularly useful for life sciences investigations. User-friendly tutorials and clear documentation are included to help guide users through the processing and analysis. We invite the EM community to contribute to and further develop this open-source package.

Tiffany Pollard GIFs and nostalgia for the negative

ABSTRACT This essay asks how the nostalgic attachments to particular Black women’s performances on reality television inform current uses of those images through internet GIFs and memes. Examining the usage and exchange of files depicting the mid-2000s reality television star Tiffany Pollard on Twitter, I compare appropriation of black women’s humour within digital spaces alongside appreciation of ‘bad television’ [McCoy, C.A., and Scarborough, R.C., 2014. Watching “bad” television: ironic consumption, camp, and guilty pleasures. Poetics, 47, 41–59] and the useful potentials of ‘negative’ representation [Gates, R.J., 2018. Double negative: The black image and popular culture. Duke University Press]. Debates about representational politics are complicated when circulating digital images and files that invoke nostalgic response through racialized gendered performances. What does circulation of these GIFs and memes say about nostalgia for a unique moment in reality television where ‘ratchet’ behaviour [Brock, A., 2020. Distributed blackness. New York University Press] was encouraged and the limits of respectability politics were experimented with? Examining how some have labelled exchange of these GIFs as perpetuating ‘digital blackface’ [Jackson, L.M., 2017. We need to talk about digital blackface in reaction GIFs. Teen vogue, 2], I theorize how contemporary accusations of exploiting stereotypical depictions of Black people in order to gain online social capital mirrors the history of American minstrelsy’s purported nostalgia for ‘authentic’ Black performance. As the majority of popular GIFs of Pollard present ‘ugly feelings’ [Ngai, S., 2005. Ugly Feelings. Harvard University Press] such as irritation, resentment and disgust, the relationship between nostalgia for so-called better times, and desire for a recent moment in cable television’s past in which the boundaries of ‘good representation’ were pushed and disregarded become entangled. By looking to animation of disembodied digital images in the use of GIFs as moving image files that are looped, refigured and circulated, I draw attention to new sites of digital racialization, popular culture criticism and examination of digital affect as it corresponds with Black diasporic cultural production.

Optimal Planning Target Margin for Prostate Radiotherapy Based on Interfractional and Intrafractional Variability Assessment during 1.5T MR-Guided Radiotherapy.

MR-guided radiotherapy (MRgRT) provides superior soft-tissue contrast over CT-based image guidance. We collected and analyzed daily pre-treatment (PRE) and real-time motion-monitoring (MM) MR images of patients receiving prostate radiotherapy to assess interfractional and intrafractional variability of prostate using two localization methods: pelvic bony anatomy (bone) and prostate during online adaptive radiotherapy (ART). PRE and MM MRIs for the first five fractions of twenty prostate cancer patients who received definitive MRgRT with 1.5T MRI were collected. Using MIM software, rigid registration between PRE MRI and planning CT images based on pelvic bony anatomy and prostate reproduced bone localization and online ART, respectively. To determine interfractional setup margin (SM), prostate was delineated on all PRE MRIs registered after bone and prostate localizations by a radiation oncologist, and centroid values of prostate contours between planning CT and PRE MRIs were compared. To determine interobserver variability, another radiation oncologist, a medical physicist, and a radiotherapist contoured prostate for both localization methods. For internal margin (IM) assessment, we used MM MRIs of the five patients who had all three sets of coronal, sagittal, and axial cine images and determined the maximum contour displacement using in-house MATLAB-based software converting binary image files to 2D cine images with a superimposed grid of 1 mm spacing. A total of 100 PRE and 25 MM MRIs were analyzed. Four hundred prostate contours were delineated on MR images registered with planning CT based on both bony anatomy and prostate. After bone localization, SM was 0.57±0.42 mm in left-right (LR), 2.45±1.98 mm in anterior-posterior (AP), and 2.28±2.08 mm in superior-inferior (SI) directions, and IO was 1.06±0.58 mm in LR, 2.32±1.08 mm in AP, and 3.30±1.85 mm in SI directions. After prostate localization, SM was 0.76±0.57 mm in LR, 1.89±1.60 mm in AP, and 2.2±1.79 mm in SI directions, and IO was 1.11±0.55 mm in LR, 2.13±1.07 mm in AP, and 3.53±1.65 mm in SI directions. Average IM was 2.12±0.86 mm, 2.24±1.07 mm, and 2.84±0.88 mm in LR, AP, and SI directions, respectively. Using daily MRIs from MRgRT, we showed that movements in the SI direction were the largest source of variability in prostate definitive RT. In addition, interobserver variability was a non-negligible source of margin. Optimal PTV margin should also consider internal margin, especially in the SI direction.

ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ.

ModularImageAnalysis (MIA) is an ImageJ plugin providing a code-free graphical environment in which complex automated analysis workflows can be constructed and distributed. The broad range of included modules cover all stages of a typical analysis workflow, from image loading through image processing, object detection, extraction of measurements, measurement-based filtering, visualisation, and data exporting. MIA provides out-of-the-box compatibility with many advanced image processing plugins for ImageJ including Bio-Formats, DeepImageJ, MorphoLibJ and TrackMate, allowing these tools and their outputs to be directly incorporated into analysis workflows. By default, modules support spatially calibrated 5D images, meaning measurements can be acquired in both pixel and calibrated units. A hierarchical object relationship model allows for both parent-child (one-to-many) and partner (many-to-many) relationships to be established. These relationships underpin MIA's ability to track objects through time, represent complex spatial relationships (e.g. topological skeletons) and measure object distributions (e.g. count puncta per cell). MIA features dual graphical interfaces: the "editing view" offers access to the full list of modules and parameters in the workflow, while the simplified "processing view" can be configured to display only a focused subset of controls. All workflows are batch-enabled by default, with image files within a specified folder being processed automatically and exported to a single spreadsheet. Beyond the included modules, functionality can be extended both internally, through integration with the ImageJ scripting interface, and externally, by developing third-party Java modules that extend the core MIA framework. Here we describe the design and functionality of MIA in the context of a series of real-world example analyses. This article is protected by copyright. All rights reserved.

Foci-Xpress: Automated and Fast Nuclear Foci Counting Tool.

In the nucleus, distinct, discrete spots or regions called "foci" have been identified, each harboring a specific molecular function. Accurate and efficient quantification of these foci is essential for understanding cellular dynamics and signaling pathways. In this study, we present an innovative automated image analysis method designed to precisely quantify subcellular foci within the cell nucleus. Manual foci counting methods can be tedious and time-consuming. To address these challenges, we developed an open-source software that automatically counts the number of foci from the indicated image files. We compared the foci counting efficiency, velocity, accuracy, and convenience of Foci-Xpress with those of other conventional methods in foci-induced models. We can adjust the brightness of foci to establish a threshold. The Foci-Xpress method was significantly faster than other conventional methods. Its accuracy was similar to that of conventional methods. The most significant strength of Foci-Xpress is automation, which eliminates the need for analyzing equipment while counting. This enhanced throughput facilitates comprehensive statistical analyses and supports robust conclusions from experiments. Furthermore, automation completely rules out biases caused by researchers, such as manual errors or daily variations. Thus, Foci-Xpress is a convincing, convenient, and easily accessible focus-counting tool for cell biologists.

Analysis of the Influence of Number of Segments on Similarity Level in Wound Image Segmentation Using K-Means Clustering Algorithm

This study underscores the importance of wound image segmentation in the medical world to speed up first aid for victims and increase the efficiency of medical personnel in providing appropriate treatment. Although the body has a protective function from external threats, the skin can be easily damaged and cause injuries that require rapid detection and treatment. This study used the K-Means clustering algorithm to segment the external wound image dataset consisting of three types of wounds, namely abrasion, puncture, and laceration. The results showed that K-Means clustering is an effective method for segmenting wound images. The greater the number of segments used, the better the quality of the resulting segmentation. However, it is necessary to take into account the specific characteristics of each type of wound and the number of segments used in order to choose the most suitable segmentation method. Evaluation using various metrics, such as VOI, GCE, MSE, and PSNR, provides an objective assessment of the quality of segmentation. The results showed that abrasion wounds were easier to segment compared to puncture wounds and lacerations. In addition, the size of the image file also affects the speed of program execution, although it is not always absolute depending on the characteristics of the image.

An AI-based Wild Animal Detection System and Its Application

An AI-based Wild Animal Detection System and Its Application

What is the educational value and clinical utility of artificial intelligence for intraoperative and postoperative video analysis? A survey of surgeons and trainees.

Surgical complications often occur due to lapses in judgment and decision-making. Advances in artificial intelligence (AI) have made it possible to train algorithms that identify anatomy and interpret the surgical field. These algorithms can potentially be used for intraoperative decision-support and postoperative video analysis and feedback. Despite the very early success of proof-of-concept algorithms, it remains unknown whether this innovation meets the needs of end-users or how best to deploy it. This study explores users' opinion on the value, usability and design for adapting AI in operating rooms. A device-agnostic web-accessible software was developed to provide AI inference either (1) intraoperatively on a live video stream (synchronous mode), or (2) on an uploaded video or image file (asynchronous mode) postoperatively for feedback. A validated AI model (GoNoGoNet), which identifies safe and dangerous zones of dissection during laparoscopic cholecystectomy, was used as the use case. Surgeons and trainees performing laparoscopic cholecystectomy interacted with the AI platform and completed a 5-point Likert scale survey to evaluate the educational value, usability and design of the platform. Twenty participants (11 surgeons and 9 trainees) evaluated the platform intraoperatively (n = 10) and postoperatively (n = 11). The majority agreed or strongly agreed that AI is an effective adjunct to surgical training (81%; neutral = 10%), effective for providing real-time feedback (70%; neutral = 20%), postoperative feedback (73%; neutral = 27%), and capable of improving surgeon confidence (67%; neutral = 29%). Only 40% (neutral = 50%) and 57% (neutral = 43%) believe that the tool is effective in improving intraoperative decisions and performance, or beneficial for patient care, respectively. Overall, 38% (neutral = 43%) reported they would use this platform consistently if available. The majority agreed or strongly agreed that the platform was easy to use (81%; neutral = 14%) and has acceptable resolution (62%; neutral = 24%), while 30% (neutral = 20%) reported that it disrupted the OR workflow, and 20% (neutral = 0%) reported significant time lag. All respondents reported that such a system should be available "on-demand" to turn on/off at their discretion. Most found AI to be a useful tool for providing support and feedback to surgeons, despite several implementation obstacles. The study findings will inform the future design and usability of this technology in order to optimize its clinical impact and adoption by end-users.

High Throughput Information Extraction of Printed Specimen Labels from Large-Scale Digitization of Entomological Collections using a Semi-Automated Pipeline

Insects account for half of the total described living organisms on Earth, with a vast number of species awaiting description. Insects play a major role in ecosystems but are yet threatened by habitat destruction, intensive farming, and climate change. Museum collections around the world house millions of insect specimens and large-scale digitization initiatives, such as the digitization street digitize! at the Museum für Naturkunde, have been undertaken recently to unlock this data. Accurate and efficient extraction of insect specimen label information is vital for building comprehensive databases and facilitating scientific investigations, sustainability of the collected data, and efficient knowledge transfer. Despite the advancements in high-throughput imaging techniques for specimens and their labels, the process of transcribing label information remains mostly manual and lags behind the pace of digitization efforts. In order to address this issue, we propose a three step semi-automated pipeline that focuses on extracting and processing information from individual insect labels. Our solution is primarily designed for printed insect labels, as the OCR (optical character recognition) technology performs well for printed text while handwritten texts still yield mixed results. The pipeline incorporates computer vision (CV) techniques, OCR, and a clustering algorithm. The initial stage of our pipeline involves image analysis using a convolutional neural network (CNN) model. The model was trained using 2100 images from three distinct insect label datasets, namely AntWeb (ant specimen labels from various collections), Bees & Bytes (bee specimen labels from the Museum für Naturkunde), and LEP_PHIL (Lepidoptera specimen labels from the Museum für Naturkunde). The first model enables the identification and isolation of single labels within an image, effectively segmenting the label region from the rest of the image, and crops them into multiple new, single-label image files. It also assigns the labels to different classes, i.e., printed text or handwritten, with handwritten labels sorted out from the printed ones. In the second step, labels classified as “printed” are then parsed by an OCR engine to extract the text information from the labels. Tesseract and Google Vision OCRs were both tested to assess their performance. While Google Vision OCR is a cloud-based service with limited configurability, Tesseract provides the flexibility to fine-tune settings and enhance its performance for our specific use cases. In the third step, the OCR outputs are aggregated by similarity using a clustering algorithm. This step allows for the identification and formation of clusters that consist of labels sharing identical or highly similar content. Ultimately, these clusters are compared against a curated database of labels and are assigned to a known label or highlighted as new and manually added to the database. In order to assess the efficiency of our pipeline, we performed benchmarking experiments using a set of images similar to those the models were trained on, as well as additional image sets obtained from various museum collections. Our pipeline offers several advantages, streamlining the data entry process, and reducing manual extraction time and effort, while also minimizing potential human errors and inconsistencies in label transcription. The pipeline holds the promise of accelerating metadata extraction from insect specimens, promoting scientific research and enabling large-scale analyses to achieve a more profound understanding of the collections.

High-Throughput Deconvolution of Native Protein Mass Spectrometry Imaging Data Sets for Mass Domain Analysis.

Protein mass spectrometry imaging (MSI) with electrospray-based ambient ionization techniques, such as nanospray desorption electrospray ionization (nano-DESI), generates data sets in which each pixel corresponds to a mass spectrum populated by peaks corresponding to multiply charged protein ions. Importantly, the signal associated with each protein is split among multiple charge states. These peaks can be transformed into the mass domain by spectral deconvolution. When proteins are imaged under native/non-denaturing conditions to retain non-covalent interactions, deconvolution is particularly valuable in helping interpret the data. To improve the acquisition speed, signal-to-noise ratio, and sensitivity, native MSI is usually performed using mass resolving powers that do not provide isotopic resolution, and conventional algorithms for deconvolution of lower-resolution data are not suitable for these large data sets. UniDec was originally developed to enable rapid deconvolution of complex protein mass spectra. Here, we developed an updated feature set harnessing the high-throughput module, MetaUniDec, to deconvolve each pixel of native MSI data sets and transform m/z-domain image files to the mass domain. New tools enable the reading, processing, and output of open format .imzML files for downstream analysis. Transformation of data into the mass domain also provides greater accessibility, with mass information readily interpretable by users of established protein biology tools such as sodium dodecyl sulfate polyacrylamide gel electrophoresis.

A Development and Analysis of Color Image New Blind Watermarking Based on DWT-SVD Swapping and 3-Dimensional Cryptography Technique

Several acceptable watermarking methods are designed to minimize this problem depending on the desired applications. A watermarking system that is stable and healthy at the same time, however, is hard to achieve. This paper provides information about the standard method for watermarking by using different methodologies including an arrangement of Singular Value Decomposition (SVD) and Discrete wavelet transforms (DWT) technique, ensuring a fully blind and robust watermarking technique. New swapping techniques and a nonlinear three-dimensional Chaos-based simple technique of Cryptography are implemented to improve the protection of the proposed method. Three-dimensional chaos is the first time used technique for permutated positions and value transformation techniques. In addition, this work compares the efficiency and safety analysis provided for the various fractal cover combinations by some metrics, which are deliberated in the result section. The proposed project will also be compared with some metrics such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The outcomes show that image degradation is much less in the dimension of the hidden image file than in the cover image file, which is double in size. Therefore, the proposed method saves the bandwidth and storage requirements.

Assessing the reliability of CBCT-based AI-generated STL files in diagnosing osseous changes of the mandibular condyle: a comparative study with ground truth diagnosis.

This study aims to evaluate the reliability of AI-generated STL files in diagnosing osseous changes of the mandibular condyle and compare them to a ground truth (GT) diagnosis made by six radiologists. A total of 432 retrospective CBCT images from four universities were evaluated by six dentomaxillofacial radiologists who identified osseous changes such as flattening, erosion, osteophyte formation, bifid condyle formation, and osteosclerosis. All images were evaluated by each radiologist blindly and recorded on a spreadsheet. All evaluations were compared and for the disagreements, a consensus meeting was held online to create a uniform GT diagnosis spreadsheet. A web-based dental AI software was used to generate STL files of the CBCT images, which were then evaluated by two dentomaxillofacial radiologists. The new observer, GT, was compared to this new STL file evaluation, and the interclass correlation (ICC) value was calculated for each pathology. Out of the 864 condyles assessed, the ground truth diagnosis identified 372 cases of flattening, 185 cases of erosion, 70 cases of osteophyte formation, 117 cases of osteosclerosis, and 15 cases of bifid condyle formation. The ICC values for flattening, erosion, osteophyte formation, osteosclerosis, and bifid condyle formation were 1.000, 0.782, 1.000, 0.000, and 1.000, respectively, when comparing diagnoses made using STL files with the ground truth. AI-generated STL files are reliable in diagnosing bifid condyle formation, osteophyte formation, and flattening of the condyle. However, the diagnosis of osteosclerosis using AI-generated STL files is not reliable, and the accuracy of diagnosis is affected by the erosion grade.

Reducing cost in DNA-based data storage by sequence analysis-aided soft information decoding of variable-length reads.

DNA-based data storage is one of the most attractive research areas for future archival storage. However, it faces the problems of high writing and reading costs for practical use. There have been many efforts to resolve this problem, but existing schemes are not fully suitable for DNA-based data storage, and more cost reduction is needed. We propose whole encoding and decoding procedures for DNA storage. The encoding procedure consists of a carefully designed single low-density parity-check code as an inter-oligo code, which corrects errors and dropouts efficiently. We apply new clustering and alignment methods that operate on variable-length reads to aid the decoding performance. We use edit distance and quality scores during the sequence analysis-aided decoding procedure, which can discard abnormal reads and utilize high-quality soft information. We store 548.83 KB of an image file in DNA oligos and achieve a writing cost reduction of 7.46% and a significant reading cost reduction of 26.57% and 19.41% compared with the two previous works. Data and codes for all the algorithms proposed in this study are available at: https://github.com/sjpark0905/DNA-LDPC-codes.

Deepfake Technology: A Criminological Literature Review

Cybercrimes are rapidly evolving with the advancement of technology and reaching new dimensions. Deepfake technology, which is the subject of this article, has also become a part of cybercrime. Deepfake technology, which has similar features to the CGI technology we have encountered in the cinema, is a technology that allows the image or voice of a person - or both - to be transferred to another image or video file in a hyper-realistic way and first came to the agenda in 2017. Although this technology has many benevolent uses, its malicious use is more prominent. This study aims to compile the criminological studies and current events in the literature on Deepfake for researchers who will study this field in the future. Within the scope of this purpose, this study examines the nature of Deepfake technology, the methods that can be used to detect Deepfake videos, the criminal dimensions of Deepfake technology and the violations it causes, the characteristics of victims and criminals, and the measures/senate resolutions taken by countries regarding the negative use of Deepfake technology.