Reviewerly/exHarmony huggingface dataset:
A Crypto-Stego Distributed Data Hiding Model for Data Protection in a Single Cloud Environment

With the ever-increasing use of cloud services and the security issues in cloud computing, cloud users are faced with a huge threat of data breaches, consequently, improved techniques are being sought to ensure that data resources in the cloud are protected from violations. In this study, we examined the different modifications to steganographic techniques used in the cloud over the last decade. A popular and recent technique is the distributed data hiding model in single cloud storage. This technique improved the security of distributed steganography by hiding messages without cover media modification. However, we observed that hiding secret messages directly without an additional layer of security makes secret messages vulnerable to detection and recovery. Consequently, we propose a crypto-stego distributed data hiding model for data protection in single cloud storage. The proposed model incorporates the RSA algorithm into the existing model, and this added an extra layer of security and altered the stego key using two stego keys instead of one stego key used in the existing models. More so, the recovered message in the proposed model is in form of a cipher text which will need to be deciphered. The results from comparative analysis showed that the proposed model is more resistant to message detection if there is a spy in the cloud environment and also more resistant to message recovery if the spy gained access to the stego key. Our proposed model improved the imperceptibility and security of distributed steganography.
Implementation of Blockchain Technology for Secure Image Sharing Using Double Layer Steganography

Healthcare information management has received an enormous deal of attention in recent times due to enormous potential for delivering more precise and cost-effective patient care. The Blockchain network could be used in the healthcare to exchange user data among hospitals, diagnostic laboratories, and pharmaceutical enterprises. Nowadays securing images is a big provocation to maintain confidentiality and integrity. The developed technology in the health industry might be misused by the public network and give chance to unauthorized access. The Blockchain network could be used in the healthcare to exchange user data among hospitals, diagnostic laboratories, and pharmaceutical enterprises. To put it another way, blockchain provides a public record of peer-to-peer transactions so that everyone can view them. This technology helps medical organizations in obtaining insight and enhancing the analysis of medical records. Blockchain technology provides a robust and secure framework for storing and sharing data throughout the healthcare business. In the health sector, the image-based diagnostic is an essential process. This proposed research in blockchain technology allows sharing of patient records in a secured way for telemedicine applications. These images will be shared geographically because these medical images will be passed through public networks, so the security issues like integrity and authentication may occur. These images will be encrypted using cover image and final steganography image is created. Steganography is used as major tool to improve the security of one's data. This proposed system will have two layers in medical security by using LSB (Least Significant Bit) method with encryption. The medical image should be inserted into a cover image by LSB this is also known as the Stego image. Encryption will be provided in integrity which is a piece of cryptography. The medical image will be secured in the steganography process. The entire process can be executed by using MATLAB 2021 version. The simulation results show that the medical images are secured from various attacks. The extracted image shows minimum mean square error of 0.5.
Bar-Code Recognition Based on Machine Vision

In order to enhance the speed, accuracy and robustness of identifying static barcodes, this paper provides an efficient and high-precision barcode recognition technology based on machine vision. This paper analyzes the principle of barcode recognition technology and coding rules, saves the complicated traditional barcode recognition process, and focuses on enhancing barcode recognition. First, grayscale the image, and then use Halcon operators emphasize operator to enhance the image. After debugging the enhancement operator, finally set the MaskWidth of the emphasize operator to 100, MaskHeight to 3, and Factor to 2, the recognition degree is higher under the same conditions, and the system is more robust to static pictures. This system can be applied to book management and warehouse management in various libraries, and provides a faster, more accurate and more robust identification technology for barcode detection.
Combination Probability in Finite State Machine Model for Intelligent Agent of Educational Game “I LOve Maratua”

The Edu-game “I love Maratua” was built through multimedia stages to introduce Bajau language learning to children. Matching type game, where players have to match pictures and words in the Bajau language. This study applies a randomization technique to the arrangement of pictures and words in each round to make the game no less challenging. We also apply randomization and probability to intelligent agents who accompany children to play in the form of little characters in Dayak traditional clothes. Intelligent agents applying the Finite State Machine (FSM) model will give happy, sad and so on expressions according to the child are playing style. The existence of randomness and probability in the FSM model makes the agent sometimes also give random expressions. This makes the agent more natural.
An optimized device‐to‐device (D2D) blockchain network for the insurance industry

Summary Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two‐stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step‐by‐step calculation method that is not feasible under real‐time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system.
Guarding the First Order: The Rise of AES Maskings

We provide three first-order hardware maskings of the AES, each allowing for a different trade-off between the number of shares and the number of register stages. All maskings use a generalization of the changing of the guards method enabling the re-use of randomness between masked S-boxes. As a result, the maskings do not require fresh randomness while still allowing for a minimal number of shares and providing provable security in the glitch-extended probing model. The low-area variant has five cycles of latency and a serialized area cost of 8.13 kGE. The low-latency variant reduces the latency to three cycles while increasing the serialized area by $$67.89\%$$ compared to the low-area variant. The maskings of the AES encryption are implemented on FPGA and evaluated with Test Vector Leakage Assessment (TVLA).
Cycle-Accurate Power Side-Channel Analysis Using the ChipWhisperer: A Case Study on Gaussian Sampling

This paper presents an approach to uncover and analyze power side-channel leakages on a processor cycle level precision. By carefully designing and evaluating the measurement setup, accurate trace timing is enabled, which is used to overlay the trace with the corresponding assembly code. This methodology allows to expose the sources of leakage on a processor cycle scale, which allows for evaluating new implementations. It also exposes that the default ChipWhisperer configuration for STM32F4 targets used in prior work includes wait cycles that are rarely used in real-world applications, but affect power side-channel leakage. As an application for our setup, we target the widely used $$\text {Sign-Flip}$$ function of Gaussian sampling code used in multiple Post-Quantum Key-Exchange Mechanisms and Signature schemes. We propose new implementations for the $$\text {Sign-Flip}$$ function based on our analysis on the original implementation and further evaluate their leakage. Our findings allow the conclusion that unmasked cryptographic implementations of schemes based on Gaussian random numbers for STM32F4 cannot be secure against power side-channel, and that masking just the Gaussian sampler is not a viable option.
Reveal the Invisible Secret: Chosen-Ciphertext Side-Channel Attacks on NTRU

NTRU is a well-known lattice-based cryptosystem that has been selected as one of the four key encapsulation mechanism finalists in Round 3 of NIST’s post-quantum cryptography standardization. This paper presents two succinct and efficient chosen-ciphertext side-channel attacks on the latest variants of NTRU, i.e., NTRU-HPS and NTRU-HRSS as in Round 3 submissions. Both methods utilize the leakage from the polynomial modular reduction to recover the long-term secret key. For the first attack, although the side-channel leakage does not directly reveal the secret polynomial $$\textbf{f}$$ , we recover differences between adjacent coefficients using appropriately chosen ciphertexts, and finally reconstruct $$\textbf{f}$$ through linear algebra. The second attack is based on the inherent relation between the secret key and the public key in NTRU-HPS: we first reveal the “invisible” secret polynomial $$\textbf{g}$$ with chosen ciphertexts and then use $$\textbf{g}$$ and the public polynomial $$\textbf{h}$$ to compute $$\textbf{f}$$ . In theory, these attacks only need 4 and 2 ciphertexts, respectively. We then practically apply those attacks on all reference implementations of four instances in the PQClean library and show that the accuracy of secret-key recovery can reach 100% with only few traces (4 to 24 and 2 to 6, respectively). We also observe similar leakage in optimized implementations in the pqm4 library and propose an according analysis scheme.
Reversible Data Hiding via Arranging Blocks of Bit-Planes in Encrypted Images

Reversible data hiding in encrypted images (RDHEI) is a useful technique for protecting data security, but most RDHEI methods do not make a satisfied embedding performance yet. To address this, we propose a novel RDHEI method via arranging blocks of bit-planes to vacate more room. In the proposed RDHEI method, the eight bit-planes of prediction error image are divided into non-overlapping blocks, which are classified into two kinds: uniform block (UB) and non-uniform block (NUB). To improve the embedding performance, we not only use the UBs but also exploit the NUBs to vacate room. Specifically, the NUB is further divided into compressible block or non-compressible block according to its detailed bits, where the compressible block is used to vacate room. As more blocks are used for data embedding, the proposed RDHEI method reaches a high embedding performance. Many experiments are done to validate performances of the proposed RDHEI method. The results show that the embedding rates of the proposed RDHEI method on the BOSSbase, BOWS-2, and UCID databases are 3.9773, 3.8800, and 3.1527 bpp, respectively. Comparison illustrates that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in terms of embedding rate.
Voice Conversion Using Learnable Similarity-Guided Masked Autoencoder

Voice conversion (VC) is an important voice forgery method that poses a serious threat to personal privacy protection, especially with remarkable achievements in timbre modification. To support forensic research on converted speech and further enrich the sources of fake speech, it is imperative to investigate new robust VC methods. VC is also considered a typical style transfer task, where style refers to speaker identity, suggesting that achieving sufficient feature decoupling is the key to obtaining robust performance. However, mainstream decoupling methods based on information-constrained bottlenecks still fail to obtain robust content-style trade-offs. In this paper, we propose a learnable similarity-guided mask (LSGM) algorithm to address the robustness problem. First, to make feature decoupling independent of specific language constructs and more applicable to diverse content, LSGM performs inter-frame feature compression only relying on the similarity of adjacent frames instead of complex inter-frame content correlation. Second, we implement feature compression by masking instead of dimensionality reduction, so no additional modules are needed to convey the speech frame length information. Moreover, we propose MAE-VC by using LSGM, which is an end-to-end masked autoencoder (MAE) with self-supervised representation learning. Experimental results indicate that MAE-VC performs comparable to state-of-the-art methods on speaker similarity and significantly improves the performance on content consistency.
SE-ResNet56: Robust Network Model for Deepfake Detection

In recent years, high quality deepfake face images generated by Generative Adversarial Networks (GAN) technology have caused serious negative impacts in many fields. Traditional image forensics methods are unable to deal with deepfake that relies on powerful artificial intelligence technology. Most of the emerging deep learning-based deepfake detection methods have the problems of complex models and weak robustness. In this study, to reduce the number of network parameters, improve the detection accuracy and solve the problem of weak robustness of the detection algorithm, we propose a new lightweight network model SE-ResNet56 to detect fake face images generated by GAN. The proposed algorithm has high detection accuracy, strong robustness to content-preserving operations and geometric distortions, and strong generalization ability to different types of deepfake images generated by the same GAN.
cnpy++: A C++17 library for reading and writing .npy/.npz files

cnpy++ is an easy-to-use C++17 library to read and write data in the NumPy file format (.npy and .npz files), offering more features than other implementations. Besides writing standard arrays of data, it also supports writing data that are exposed via C++ iterators, allowing to serialize data structures that do not reside in contiguous memory. Furthermore, support for structured arrays is provided, which facilitates reading and writing tabular data with labeled columns conveniently.
Alteration and structural controllers of gold and copper ores in Ahar region, Arasbaran zone, northwest of Iran

Alteration and structural controllers of gold and copper ores in Ahar region, Arasbaran zone, northwest of Iran
Magnetite geochemistry an approach to determining the physicochemical conditions of Alam- Kandy iron skarn formation, West Mahneshan, Zanjan province

Magnetite geochemistry an approach to determining the physicochemical conditions of Alam- Kandy iron skarn formation, West Mahneshan, Zanjan province
The Scientific Method(s)

In Chapter 2 we have seen how scientific inquiry can be characterised, distinguished from other ways of knowledge construction, and that scientifically acquired knowledge has a high chance of being viable, reliable, and of withstanding critical scrutiny. It examines aspects of our natural world and enables evidence-based factual statements and judgments. But how can we make sure that a statement is scientific in a sense that it fulfils certain requirements of scientific knowledge generation? What is the method with which scientists arrive at insights that deserve the label scientific? This chapter and the next aim at describing all the features that make scientific research such a powerful way of gaining viable insights. After identifying the basic steps of a scientific investigation and their characteristics in terms of typical activities of scientists and researchers, a systematic step-by-step guide through the elements of a chemical research project is presented in the form of Lee's scientific knowledge acquisition web. Key issues are the formulation of hypotheses, the analysis and interpretation of experimental results and data, appropriate strategies in cases of errors and encountered difficulties, record keeping, and reporting and publishing considerations. While this chapter discusses conceptual and methodological issues, it defines the arena of good scientific conduct and helps to enlighten the standard of what counts as good. Thus, it lays a foundation for Chapters 5 to 8.
Joyful elements implementation on online learning: How it should be?

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Bertha Wikara, Sutarno Sutarno, Suranto Suranto, Sajidan Sajidan; Joyful elements implementation on online learning: How it should be?. AIP Conference Proceedings 27 January 2023; 2540 (1): 110005. https://doi.org/10.1063/5.0111920 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Comparison of TOSLS-B instrument analysis result using Iteman and Rasch model

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Risky Agustina Maria Sibarani, Afandi, Andi Besse Tenriawaru, Eka Bilanti; Comparison of TOSLS-B instrument analysis result using Iteman and Rasch model. AIP Conference Proceedings 27 January 2023; 2540 (1): 020006. https://doi.org/10.1063/5.0105736 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Analysis of potential development of preserved media with bioplastic techniques as a biology learning media in Indonesia

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Iin Musannadah, Bernadetta Octavia, Eka Sulistiyowati; Analysis of potential development of preserved media with bioplastic techniques as a biology learning media in Indonesia. AIP Conference Proceedings 27 January 2023; 2540 (1): 020018. https://doi.org/10.1063/5.0105765 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The effectiveness of human nervous system comic as an alternative for online learning during Covid-19

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Insar Damopolii, Jan H. Nunaki, Wiranto Wiranto, Fridolin F. Paiki; The effectiveness of human nervous system comic as an alternative for online learning during Covid-19. AIP Conference Proceedings 27 January 2023; 2540 (1): 020001. https://doi.org/10.1063/5.0105777 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Android-based e-module of excretory system to improve high school students’ engagement

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ahmad Agus Saputra, Ixora Sartika Mercuriani, Dewi Puspo Rini; Android-based e-module of excretory system to improve high school students’ engagement. AIP Conference Proceedings 27 January 2023; 2540 (1): 020022. https://doi.org/10.1063/5.0106505 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Adaptive Robust Watermarking Method Based on Deep Neural Networks

Aiming at the problem of digital multimedia piracy and infringement, an adaptive robust watermarking algorithm based on Deep Neural Networks (DNNs) is proposed. In our method, the watermark sequence to be embedded is mapped to a noise pattern first, which has the same dimension as the carrier image. Specifically, the noise pattern is generated adaptively according to the statistical properties of the carrier image, in which the noise intensity corresponding to the texture area of the carrier image is large, and that corresponding to the smooth area is small. Thus, after adding the generated noise pattern to the carrier image, good visual quality can be easily obtained. Furthermore, considering a series of attacks such as adding noise and JPEG compression, the watermark encoder and decoder in our scheme are jointly trained to resist the potential attacks in the physical world. Experimental results demonstrate that better visual quality and higher robustness can be obtained compared with those state-of-the-art algorithms based on DNNs. This means that we have better solved the problem of mutual restriction between visual quality and robustness.
Hate Speech Research: Algorithmic and Qualitative Evaluations. A Case Study of Anti-Gypsy Hate on Twitter

Abstract Hate speech may be the research focus of the interdisciplinary field of hate studies, but it is also a difficult phenomenon to define. Internationally, there are several detection studies on automatically detecting hate speech. They can be grouped according to two approaches: the first includes searching using only machine learning methods, while the second includes studies that combine automatic searching with human classification. The case study on anti-Gypsy hate in Italian on Twitter in the second half of 2020 falls into the second category, and its methods are outlined here. Based on the results (annotation as ‘hate’/‘non-hate’, identification of forms of rhetoric and anti-Gypsyism), the researchers propose classifying online content according to seven indicators called the ‘spectrum of online hate’.