Reviewerly/exHarmony huggingface dataset:
Trusted fingerprint localization for multimedia devices based on blockchain

The localization process is necessary for intelligent multimedia systems to provide appropriate service, but it is still a significant challenge to provide localization services for devices without active localization ability. The vulnerability of existing localization frameworks is that the incredibility of the information provided can not be verified. This remains the localization process vulnerable to forgery attacks, which can cause false localization results and impact the performance of intelligent multimedia systems. To address this problem, a blockchain-based fingerprint localization scheme is proposed and evaluated in this article. Firstly, A real-time database of verified and temper-proof electromagnetic fingerprints is established by collecting the information from contributors through blockchain. Secondly, a distributed algorithm is designed to obtain a trusted localization based on shared electromagnetic fingerprint information. Lastly, to save on-chain computing resources and defend against spoofing attacks and collusion attacks, an on-chain/off-chain scheme is proposed. The simulations show the practicability and robustness of the proposed scheme. The results are analyzed based on localization accuracy and resistance to malicious activities. It is shown that devices can be located without increasing location errors in malicious environments where 20% of data is uploaded by malicious nodes, and the localization results become more accurate as the decentralized database spontaneously established is expanded.
AAIN: Attentional aggregative interaction network for deep learning based recommender systems

Feature engineering is a classical problem in recommender systems, and feature interactions is one of the most important parts of feature engineering. Factorization based models are widely used for explicit feature interactions. However, most current works utilize separate features to model cross features. Such a pattern limits the significance of cross features, since realistic recommendation scenarios are rich in associations between features. In this paper, we classify the basic feature interactions into sum-interaction and product-interaction, and improve the current general strategy of explicit feature interactions. Based on these theoretical studies, we propose a novel explicit feature interactions model Attentional Aggregative Interaction Network (AAIN), which models higher-order features using a cyclic explicit module. Specifically, we introduce attention mechanism for the reorganization of separate features, followed by product-interaction and higher-order features’ compression and output. The model is efficient since: 1) AAIN automatically learns high-order feature interactions and filters them with different weights. 2) AAIN optimizes the interaction between features into the interaction between feature groups, which allows for other relevant information to be considered when performing interactions. Furthermore, we integrate AAIN model with the classical deep neural network (DNN) model into a new model Deep Attentional Aggregative Interaction Network (DAAIN). Experiments on real-world datasets show that our models achieve state-of-the-art results.
Instance Segmentation in the Dark

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed method is motivated by the observation that noise in low-light images introduces high-frequency disturbances to the feature maps of neural networks, thereby significantly degrading performance. To suppress this "feature noise", we propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. These components effectively reduce feature noise during downsampling and convolution operations, enabling the model to learn disturbance-invariant features. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our analysis indicates that high bit-depth can be critical for low-light instance segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a low-light RAW synthetic pipeline to generate realistic low-light data. In addition, to facilitate further research in this direction, we capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations. Remarkably, without any image preprocessing, we achieve satisfactory performance on instance segmentation in very low light (4% AP higher than state-of-the-art competitors), meanwhile opening new opportunities for future research. Our code and dataset are publicly available to the community ( https://github.com/Linwei-Chen/LIS ).
The Steganography Based On Chaotic System for Random LSB Positions

The objective of hiding text in an image is hiding text without raising suspicions that the image contains a hidden message or text, which leads to protecting and maintaining text confidentiality. The previous hiding methods have problems in capacity, randomization, and imperceptibility. This paper will be solved some of these problems; we suggested a new method for hiding text in an image. Firstly, encrypting the text by the AES-192 bit algorithm for obtaining a secret message. When the initial key of the AES-192 (bit) algorithm is generated by a chaotic system for randomness purposes, secondly, hiding the secret message is into a gray image for obtaining a stego-image. The hiding step is based on a proposed map that chooses from the last round of key expansion in the AES-192 algorithm. This map represented random positions of LSB in each byte of the gray image. The experimental result of this method proved a successful method based on metric criteria. Also, this method is the very speed for hiding ciphertext in the gray image as well as extracting ciphertext from the gray image. Also, it is very safe because it is difficult for attackers to distinguish between the original image and the stego image therefore the correlation between the original image and the stego- image is very close to 1.
Blockchain and Machine Learning as Deep Reinforcement

Due to its capacity to make wise decisions, deep learning has become extremely popular in recent years. The current generation of deep learning, which heavily rely centralized servers, are unable to offer attributes like operational transparency, stability, security, and reliable data provenance. Additionally, Single point of failure is a problem that deep learning designs are susceptible since they need centralized data to train them. We review the body of research on the application of deep learning to blockchain. We categorize and arrange the literature for developing topic taxonomy based their criteria: Application domain, deep learning-specific consensus mechanisms, goals for deployment and blockchain type. To facilitate meaningful discussions, we list the benefits and drawbacks of the most cutting-edge blockchain-based deep learning frameworks.
Leveraging Generative Models for Combating Adversarial Attacks on Tabular Datasets

Artificial Neural Networks (ANN) models – a form of discriminative models – are the workhorse of deep learning research, and have resulted in a remarkable performance on a range of applications on a large variety of datasets. On tabular datasets, ANN models are preferable when learning from large quantities of data as non-parametric models such as Random Forest and XGBoost cannot be easily used due to their inherent in-core data processing (i.e., they require loading all the data in memory). The applicability and effectiveness of ANN models, however, come with a price. They have been shown to be susceptible to adversarial attacks, which can greatly compromise their performance and trust in their utilization. There has been a surge in research in developing effective defence strategies for adversarial attacks on ANN models, e.g., Madry, D2A3, etc. Recently, it has been shown that generative models are more robust to adversarial attacks than discriminative models. A natural question is – can generative models be used as a defence for discriminative models against adversarial attacks? This work addresses this question, where we study the power of generative models in warding off adversarial attacks for discriminative models. In this work, we propose an effective defence model – gD2A3 – that exploits the generative-discriminative equivalence of some ANN models. It uses the learned probabilities from a generative model to initialize the input layer parameters of a standard ANN model, and utilizes $$L_2$$ regularization of the input layer parameters as a defence mechanism. We show that our proposed model leads to better results than the state-of-the-art method D2A3 by conducting a thorough empirical study on a variety of datasets with two major adversarial attacks.
Decryption of Deterministic Phase-Encoded Digital Holography Using Convolutional Neural Networks

Digital holographic encryption is an important information security technology. Traditional encryption techniques require the use of keys to encrypt information. If the key is lost, it is difficult to recover information, so new technologies that allow legitimate authorized users to access information are necessary. This study encrypts fingerprints and other data using a deterministic phase-encoded encryption system that uses digital holography (DPDH) and determines whether decryption is possible using a convolutional neural network (CNN) using the U-net model. The U-net is trained using a series of ciphertext-plaintext pairs. The results show that the U-net model decrypts and reconstructs images and that the proposed CNN defeats the encryption system. The corresponding plaintext (fingerprint) is retrieved from the ciphertext without using the key so that the proposed method performs well in terms of decryption. The proposed scheme simplifies the decryption process and can be used for information security risk assessment.
Recognition of Ellipsoid-like Herbaceous Tibetan Medicinal Materials Using DenseNet with Attention and ILBP-Encoded Gabor Features

Tibetan medicinal materials play a significant role in Tibetan culture. However, some types of Tibetan medicinal materials share similar shapes and colors, but possess different medicinal properties and functions. The incorrect use of such medicinal materials may lead to poisoning, delayed treatment, and potentially severe consequences for patients. Historically, the identification of ellipsoid-like herbaceous Tibetan medicinal materials has relied on manual identification methods, including observation, touching, tasting, and nasal smell, which heavily rely on the technicians' accumulated experience and are prone to errors. In this paper, we propose an image-recognition method for ellipsoid-like herbaceous Tibetan medicinal materials that combines texture feature extraction and a deep-learning network. We created an image dataset consisting of 3200 images of 18 types of ellipsoid-like Tibetan medicinal materials. Due to the complex background and high similarity in the shape and color of the ellipsoid-like herbaceous Tibetan medicinal materials in the images, we conducted a multi-feature fusion experiment on the shape, color, and texture features of these materials. To leverage the importance of texture features, we utilized an improved LBP (local binary pattern) algorithm to encode the texture features extracted by the Gabor algorithm. We inputted the final features into the DenseNet network to recognize the images of the ellipsoid-like herbaceous Tibetan medicinal materials. Our approach focuses on extracting important texture information while ignoring irrelevant information such as background clutter to eliminate interference and improve recognition performance. The experimental results show that our proposed method achieved a recognition accuracy of 93.67% on the original dataset and 95.11% on the augmented dataset. In conclusion, our proposed method could aid in the identification and authentication of ellipsoid-like herbaceous Tibetan medicinal materials, reducing errors and ensuring the safe use of Tibetan medicinal materials in healthcare.
“A Patchwork of Data Systems”: Quilting as an Analytic Lens and Stabilizing Practice for Knowledge Infrastructures

Museums and archives rely on databases and similar technologies to manage their collections, but even when tailor-made for memory institutions, databases require considerable adaptation to remain usable over long periods of time. To better understand how collection staff maintain and migrate databases over multiple years and decades, we talked to archivists from the US-based Archon User Collaborative and collection managers from the University of Michigan Research Museums. We found that the collection staff uses terms taken from quilting for database curation: they “tie” and “weave” a “patchwork of data systems” together. We extend their quilting metaphor as an analytical lens and show what can be gained through a shift in framing database work as a craft. We describe database curation as a process of creating a quilted infrastructure: a long-lived knowledge system that is sustained by the use of multiple “digital surfaces,” a reliance on a community of practice, intergenerational transfer of “quilts,” and by leveraging invisibility to conduct work. We argue that this nonnormative mode of computing needs better support from both software developers and administrators. We also show that although the invisibility of craft practices offers practitioners independence, it also can increase their precarity.
AN OVERVIEW OF FAST VARIANTS OF THE RSA CRYPTOSYSTEM FOR MODERN CRYPTOGRAPHY APPLICATIONS

RSA is a robust and widely utilized public-key scheme to ensure the confidentiality and authenticity of digital communication and data transmission over the Internet, including online banking, e-commerce, e-mail, and blockchain-based apps. The RSA decryption and signature generation algorithms are computationally expensive, particularly for resource-constrained devices. RSA algorithms have been the subject of numerous research studies to improve their speed over the past few decades. This research has focused on developing various optimizations and alternative algorithms to reduce the computational complexity of RSA processes while ensuring an acceptable security level. This paper surveys four RSA variants created to speed up RSA decryption and signing generation. Using the window exponentiation method, the implementation outcomes show that the RSA scheme gets fast decryption and signing generation processes when using a secure multi-prime modulus and CRT to decrypt and sign.
DPA‐UNet rectal cancer image segmentation based on visual attention

Summary Low segmentation accuracy and background noise exisit in rectal cancer lesion segmentation. To segment colorectal tumors in CT images accurately, we propose an improved U‐Net network for colorectal tumor image segmentation dilated‐pyramid‐attention U‐Net (DPA‐UNet). In this paper, we use the U‐Net network and combine techniques such as dilated convolution, weighted feature pyramid structure (W‐FPN), and convolutional block attention module (CBAM) mechanism. Firstly, CBAM and W‐FPN are combined to extract dense pixel‐level features for pixel labeling. Secondly, after the third network output layer, three serially dilated depth‐separable dilated convolutional layers with dilation rates of 1, 2, and 4, are added respectively to expand the feature map receptive field. Finally, the DPA‐UNet model is compared and analyzed with other new network structures. The experimental results show that DPA‐UNet achieves automatic segmentation of the colorectal cancer image region of interest (ROI).
Classified Mixed Model Projections

In many practical problems, there is interest in the estimation of mixed effect projections for new data that are outside the range of the training data. Examples include predicting extreme small area means for rare populations or making treatment decisions for patients who do not fit typical risk profiles. Standard methods have long been known to struggle with such problems since the training data may not provide enough information about potential model changes for these new data values (extrapolation bias). We propose a new framework called Prediction Using Random-effect Extrapolation (PURE) which involves constructing a generalized independent variable hull (gIVH) to isolate a minority training set which is “close” to the prediction space, followed by a regrouping of the minority data according to the response variable which results in a new (but misspecified) random effect distribution. This misspecification reflects “extrapolated random effects” which prove vital to capture information that is needed for accurate model projections. Projections are then made using classified mixed model prediction (CMMP) (?) with the regrouped minority data. Comprehensive simulation studies and analysis of data from the National Longitudinal Mortality Study (NLMS) demonstrate superior predictive performance in these very challenging paradigms. An asymptotic analysis reveals why PURE results in more accurate projections. Supplementary materials for this article are available online.
Applying Machine Learning Algorithms for Opinion Mining on a Digital Internet Service Provider

By.U is the first digital internet service provider in Indonesia that provides complete digital services for all telecommunication requirements. The By.U application on the Google Play Store has garnered many ratings and comments from users. However, the unstructured nature of these reviews makes it challenging to extract and classify valuable information, which is why opinion mining is preferred. This research compares three machine learning algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes Classifier (NBC), to conduct opinion mining. The research findings suggest that the KNN outperforms the NBC and SVM methods in terms of accuracy, precision, and recall.
Corruption and corporate innovation in Tunisia during an economic downturn

This article studies the innovation behavior of the Tunisian firms using the database collected by the World Bank Enterprise Surveys in 2020. The modeling strategy used allowed us to assess the adequacy of the main regression models and the robustness of the results. Besides the firms’ characteristics, we highlight the role of institutions by considering the infrastructure and corruption that affect the firms’ incentives to innovate. We show that the firm innovation is negatively related to the gifts or informal payments to public officials to gain advantages in the drafting of laws, decrees, regulations or other binding government decisions. Petty corruption, by officials at lower levels, can grease the wheels of innovation activities, speed up the procedures for obtaining the required administrative permits and “get things done”, since the Tunisian economy is characterized by a relatively mediocre quality of governance.
A formal model for blockchain-based consent management in data sharing

Consent is one of six legal bases for personal data processing mentioned in the General Data Protection Regulation (GDPR). The GDPR is a privacy law giving European Union (EU) citizens authority over personal data. It enforces software systems to collect, analyze, and share only necessary information (‘data minimization’) following the specific purpose (‘consent’). The GDPR defines consent as permission of individuals (‘data subjects’) to give organizations (‘data controllers’) processing their personal data. Without a data subject's consent, the data controller processes personal data unlawfully. Therefore, consent management is an essential component of a software system to build data subjects' trust and engagement. However, sharing data can lead to a potential loss of control over personal data, as data are across boundaries between software services. One of the significant risks is caused by a lack of developers' experience in data protection practices. Hence, in this paper, we propose to use blockchain technology to manage data subjects' informed consent for data sharing to build trust, transparency, and traceability to share data across software services. We formalized the semantics of smart contracts to extend the blockchain features to validate the consent authorization and manage the request-response interaction between the services. Furthermore, we used the Event-B method to describe the dynamic behavior of the proposed model and prove its correctness. Finally, we provided a mapping from the formal model to a smart contract class diagram and a prototype called SmartDataTrust implemented with solidity and Python REST API that developers can easily utilize.
Generalization capacity of multi-class SVM based on Markovian resampling

The generalization performance of "All-in-one" Multi-class SVM (AIO-MSVM) based on uniformly ergodic Markovian chain (u.e.M.c.) samples is considered. We establish the fast learning rate of AIO-MSVM algorithm with u.e.M.c. samples and prove that AIO-MSVM algorithm with u.e.M.c. samples is consistent. We also propose a novel AIO-MSVM algorithm based on q-times Markovian resampling (AIO-MSVM-MR), and show the numerical investigation on the learning performance of AIO-MSVM-MR based on public datasets. The experimental studies indicate that compared to the classical AIO-MSVM algorithm and other MSVM algorithms, the proposed AIO-MSVM-MR algorithm has not only smaller misclassification rate, but also less sampling and training total time. We present some discussions on the case of unbalanced training samples, the choices of q and two technical parameters, and present some explanations on the learning performance of the proposed algorithm.
A New Method of Construction of Permutation Trinomials with Coefficients 1

While big data technology is developing rapidly, it also brings numerous security and privacy issues, such as the security of communication between devices in Internet of Things and the confidentiality of stored data. The application of cryptography technology is the key to solving these problems. Permutation polynomials are widely utilized in both symmetric cryptography and public key cryptography. In recent years, the study of permutation polynomials with simple algebraic forms has attracted the interest of scholars. In this work, a novel approach for constructions of permutation trinomials with coefficients 1 is proposed by characterizing permutation polynomials with Niho-type exponents using the reverse method of Tu. In addition, for a three-term PP under a special case, this paper calculates its compositional inverse with explicit form.