Reviewerly/exHarmony huggingface dataset:
Lightweight image denoising network with four-channel interaction transform

Image denoising has always been a fundamental task in computer vision. In recent years, deep learning methods have emerged as the dominant approach for image denoising and have significantly improved denoising performance. However, these deep denoising methods typically require large model sizes, making network training prohibitively expensive and limiting their applicability in realistic scenarios. To address this issue, we propose a Lightweight Image Denoising Network (LWNet) with a four-channel interaction transform that effectively reduces the model size. The proposed four-channel interaction transform first constructs the LWNet using four channels within the input and output dimensions. Specifically, an additional empty channel with all zeros is attached to the input image, and the output dimension has four channels. This additional channel significantly enhances the robustness of network training, as the expansion of features in the channel dimension provides richer information. Compared to three-channel networks, LWNet exhibits greater fault tolerance. Furthermore, the proposed LWNet uses a dual-branch structure to achieve the four-channel interaction transform in the feature space. One branch focuses on the feature learning of the additional channel within the input dimension, while the other branch handles the original three channels. This mechanism enables the network to retrieve abundant denoising features and adaptively inject them into the denoised images, significantly enhancing the denoising performance. Thanks to the powerful feature retrieval ability of the four-channel transform, the proposed LWNet can significantly decrease the required number of parameters. Extensive experimental results show that LWNet achieves the best denoising results on synthetic datasets using much fewer parameters. Even when extrapolating to real datasets for validation, it maintains better denoising performance with effective model size. Overall, the proposed LWNet offers an effective solution to reduce model size without compromising denoising performance and has potential practical applications in various image denoising scenarios.
Quantum computing architectures with signaling and control mimicking biological processes

Earlier reports have described a quantum computing architecture, in which key elements are derived from control functions in biology. In this further continuing research, focus is on the signaling and control of a flow of qubits in that architecture, mimicking synapse signals and neurological controls. After a short description of that architecture, and of quantum sensing elements, it is first shown how the coloring of quantum particle flows, implemented as in mathematical colored algebras, can reduce decoherence and enhance the decidability of quantum processing elements. Next, after reviewing specific human biology functions, and exploiting experimental results on excitation modes in live animals, it is shown how to achieve separation of the quantum control & signaling signals. Technologies and designs from particle physics are discussed as well as open research issues towards a realization of a quantum computing architecture with decidable signaling.
Object Detection Based Approaches in Image Classification: A Brief Overview

Several methods have been put out in the past few years in the field of computer vision for the goal of obj ect detection. The offered methods carry out the task of classifying and locating an object inside an image or a series of photos. Modern object detection frameworks and techniques are referred to as evolutionary approaches. The challenges of obj ect detection in numerous photos and movies are addressed through evolutionary techniques. Using the spatial information of the object in the image frames, the evolutionary approach based approaches may detect objects. By sliding a window across the object instances in the given image sequence, these algorithms can extract the features of the objects. In this paper, we have reviewed the object detection field's evolutionary strategy. The backend CNN architectures, CNN-based object identification techniques, and common object detection databases are discussed in this paper. This paper primarily focus on dataset detection and finding suitable object detection method. We have also provided a list of open source frameworks for academics and students who may be interested in using them to create object identification methods and algorithms.
Distributed Twins in Edge Computing: Blockchain and IOTA

Blockchain (BC) and Information for Operational and Tactical Analysis (IOTA) are distributed ledgers that record a huge number of transactions in multiple places at the same time using decentralized databases. Both BC and IOTA facilitate Internet-of-Things (IoT) by overcoming the issues related to traditional centralized systems, such as privacy, security, resources cost, performance, and transparency. Still, IoT faces the potential challenges of real-time processing, resource management, and storage services. Edge computing (EC) has been introduced to tackle the underlying challenges of IoT by providing real-time processing, resource management, and storage services nearer to IoT devices on the network’s edge. To make EC more efficient and effective, solutions using BC and IOTA have been devoted to this area. However, BC and IOTA came with their pitfalls. This survey outlines the pitfalls of BC and IOTA in EC and provides research directions to be investigated further.
I4AS-Cloud: Identification, Authentication and Authorization As A Service Cloud Computing

The data storage service offered by cloud service providers is one of the most important markets today. For this reason, cloud computing has become indispensable for all information systems in the new era. However, there are several security risks associated with virtual data storage. Unauthorised access to data remains one of the most significant security risks. Reliable identification, robust authentication and fine-grained access for each client and service are the cornerstones of data security. This paper presents the importance of these models in data security. It also focuses on the proposal of an identification, authentication and access control (I4AS-cloud) module. It presents and discusses a taxonomy of authentication methods and access control models.
New secure access control model for cloud computing based on Chaotic systems

Cloud computing is a cutting-edge technology that integrates a variety of networked devices to provide required services. Individuals as well as organizations can use cloud computing to gain on-demand access to handled and scalable IT resources such as servers, storage, and apps via a shared network.. However, in such systems, a large user base from many fields, each with their own set of policies, can lead to a variety of issues, such as the loss of access control information, theft, or unauthorized use of information. To overcome these issues, researchers and specialists have developed a number of approaches for ensuring secure access to cloud services. This essay compares and summarizes access control models.To overcome the drawbacks of existing models, we propose a more effective and sophisticated extended access control model based on chaotic systems.
Enhancing the Efficiency of UAV Swarms Communication in 5G Networks through a Hybrid Split and Federated Learning Approach

The integration of unmanned aerial vehicles (UAVs) with 5G networks presents a promising opportunity to revolutionize wireless communication and provide high-speed internet access to remote areas. Nevertheless, the vast quantity of data generated by UAVs requires the implementation of efficient distributed learning techniques. In this study, we present a novel hybrid approach that merges Federated Learning (FL) and Split Learning (SL) to optimize the performance of UAV swarms in 5G networks. While FL is capable of reducing communication overhead and preserving privacy, SL can enhance the accuracy of the model through the utilization of the local computational resources of each device. To realize the hybrid approach, we first locally train the model on each UAV using split learning. Subsequently, the encrypted model parameters are transmitted to a central server for federated averaging. Finally, the updated model is dispatched back to each UAV for local fine-tuning, and this cycle is repeated until convergence is achieved. The hybrid approach capitalizes on the strengths of both FL and SL to minimize communication overhead and increase accuracy. To tackle the challenge of selecting the most suitable UAVs for participation in the learning process, we propose a multiagent algorithm that considers factors such as communication latency and training time. Our experimental results indicate that the proposed approach leads to substantial improvements in communication overhead and accuracy compared to conventional methods.
Influence of the Degree of Interactivity of Lecture Courses ON Improving the Quality of Learning Using the Electronic Educational Environment

A comparison of the results of testing students before and after the use of interactive technologies in lecture courses. The conducted testing using the electronic educational environment (EEE) confirmed that the level of residual knowledge has become higher. It is shown that the assessment of students’ knowledge using traditional grades “excellent”, “good”, etc. is less informative than the average assessment by the value of the average percentage (%).
Towards a Lightweight CRYSTALS-Kyber in FPGAs: an Ultra-lightweight BRAM-free NTT Core

CRYSTALS-Kyber is the first quantum-resilient, lattice-based Public Key Encryption (PKE)/Key Encapsulation Mechanism (KEM) cryptosystem that is chosen by the ongoing National Institute of Standards and Technology post-quantum cryptography standardization (NIST PQC) for standardization. This work presents a lightweight and efficient, FPGA-based hardware implementation for polynomial multiplication unit (NTT), which is the major bottleneck in the Kyber scheme. As a first step, an optimzed modular multiplication architecture combining KRED and lookup table-based algorithms is presented, which reduces the resources of slices by 16.7%. It is used in a pipelined NTT/INTT architecture that is completely BRAM free and instead uses 3 FIFOs for coefficients storage. We hereby present the most compact FPGA based design for NTT architecture in Kyber till date. Experimental results bench marked on comparable FPGA devices show that our proposed design is 36-75% better than the state-of-the-art implementations in terms of hardware efficiency for NTT/INTT calculations and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.4-4.4\times$</tex> better for the Point-wise Multiplication (PWM) operation.
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression

Steered-Mixtures-of-Experts (SMoE) models provide sparse, edge-aware representations, applicable to many use-cases in image processing. This includes denoising, super-resolution and compression of 2D- and higher dimensional pixel data. Recent works for image compression indicate that compression of images based on SMoE models can provide competitive performance to the state-of-the-art. Unfortunately, the iterative model-building process at the encoder comes with excessive computational demands. In this paper we introduce a novel edge-aware Autoencoder (AE) strategy designed to avoid the time-consuming iterative optimization of SMoE models. This is done by directly mapping pixel blocks to model parameters for compression, in spirit similar to recent works on “unfolding” of algorithms, while maintaining full compatibility to the established SMoE framework. With our plug-in AE encoder, we achieve a quantum-leap in performance with encoder run-time savings by a factor of 500 to 1000 with even improved image reconstruction quality. For image compression the plug-in AE encoder has real-time properties and improves RD-performance compared to our previous works.
Practical Program Modularization with Type-Based Dependence Analysis

Today's software programs are bloating and have become extremely complex. As there is typically no internal isolation among modules in a program, a vulnerability can be exploited to corrupt the memory and take control of the whole program. Program modularization is thus a promising security mechanism that splits a complex program into smaller modules, so that memory-access instructions can be constrained from corrupting irrelevant modules. A general approach to realizing program modularization is dependence analysis which determines if an instruction is independent of specific code or data; and if so, it can be modularized. Unfortunately, dependence analysis in complex programs is generally considered infeasible, due to problems in data-flow analysis, such as unknown indirect-call targets, pointer aliasing, and path explosion. As a result, we have not seen practical automated program modularization built on dependence analysis.This paper presents a breakthrough—Type-based dependence analysis for Program Modularization (TyPM). Its goal is to determine which modules in a program can never pass a type of object (including references) to a memory-access instruction; therefore, objects of this type that are created by these modules can never be valid targets of the instruction. The idea is to employ a type-based analysis to first determine which types of data flows can take place between two modules, and then transitively resolve all dependent modules of a memory-access instruction, with respect to the specific type. Such an approach avoids the data-flow analysis and can be practical. We develop two important security applications based on TyPM: refining indirect-call targets and protecting critical data structures. We extensively evaluate TyPM with various system software, including an OS kernel, a hypervisor, UEFI firmware, and a browser. Results show that on average TyPM additionally refines indirect-call targets produced by the state of the art by 31%-91%. TyPM can also remove 99.9% of modules for memory-write instructions to prevent them from corrupting critical data structures in the Linux kernel.
Vectorized Batch Private Information Retrieval

This paper studies Batch Private Information Retrieval (BatchPIR), a variant of private information retrieval (PIR) where the client wants to retrieve multiple entries from the server in one batch. BatchPIR matches the use case of many practical applications and holds the potential for substantial efficiency improvements over PIR in terms of amortized cost per query. Existing BatchPIR schemes have achieved decent computation efficiency but have not been able to improve communication efficiency at all. Using vectorized homomorphic encryption, we present the first BatchPIR protocol that is efficient in both computation and communication for a variety of database configurations. Specifically, to retrieve a batch of 256 entries from a database with one million entries of 256 bytes each, the communication cost of our scheme is 7.5x to 98.5x better than state-of-the-art solutions.
Hide and Seek with Spectres: Efficient discovery of speculative information leaks with random testing

Attacks like Spectre abuse speculative execution, one of the key performance optimizations of modern CPUs. Recently, several testing tools have emerged to automatically detect speculative leaks in commercial (black-box) CPUs. However, the testing process is still slow, which has hindered in-depth testing campaigns, and so far prevented the discovery of new classes of leakage.In this paper, we identify the root causes of the performance limitations in existing approaches, and propose techniques to overcome these limitations. With these techniques, we improve the testing speed over the state-of-the-art by up to two orders of magnitude.These improvements enable us to run a testing campaign of unprecedented depth on Intel and AMD CPUs. As a highlight, we discover two types of previously unknown speculative leaks (affecting string comparison and division) that have escaped previous manual and automatic analyses.
Private Collaborative Data Cleaning via Non-Equi PSI

We introduce and investigate the privacy-preserving version of collaborative data cleaning. With collaborative data cleaning, two parties want to reconcile their data sets to filter out badly classified, misclassified data items. In the privacy-preserving (private) version of data cleaning, the additional security goal is that parties should only learn their misclassified data items, but nothing else about the other party’s data set. The problem of private data cleaning is essentially a variation of private set intersection (PSI), and one could employ recent circuit-PSI techniques to compute misclassifications with privacy. However, we design, analyze, and implement three new protocols tailored to the specifics of private data cleaning that outperform a circuit-PSI-based approach. With the first protocol, we exploit the idea that a small additional leakage (the differentially private size of the intersection of data items) allows for a reduction in complexity over circuit-PSI. The other two protocols convert the problem of finding a mismatch in data classifications into finding a match, and then follow the standard technique of using oblivious pseudorandom functions (OPRF) for computing PSI. Depending on the number of data classes, this leads to a concrete runtime improvement over circuit-PSI.
Blue Is the New Black (Market): Privacy Leaks and Re-Victimization from Police-Auctioned Cellphones

In the United States, items in police possession are often sold at auction if they are not claimed. This includes cellphones that the police obtained through civil asset forfeiture, that were stolen, or that were turned in to lost-and-found. Thousands of US police departments partner with a website, PropertyRoom, to auction their items. Over the course of several months, we purchased 228 cellphones from PropertyRoom to ascertain whether they contained personal information. Our results show that a shocking amount of sensitive, personal information is easily accessible, even to a "low-effort" adversary with no forensics expertise: 21.5% of the phones we purchased were not locked at all, another 4.8% used top-40 most common PINs and patterns, and one phone had a sticky-note from the police with the PIN on it. We analyze the content on the 61 phones we could access, finding sensitive information about not only the phones’ previous owners, but also about their personal contacts, and in some cases, about victims of those persons’ crimes. Additionally, we analyze approximately two years of PropertyRoom cellphone auctions, finding multiple instances of identifying information in photos of the items being auctioned, including sticky-notes with PINs, owners’ names and phone numbers, and evidence stickers that reveal how the phones were obtained and the names of the officers who obtained them. Our work shows that police procedures and phone auctions can be a significant source of personal information leakage and re-victimization. We hope that our work is a call to arms to enforce new policies that either prohibit the selling of computing devices containing user information, or at the very least impose requirements to wipe phones in a manner that the US federal government already employs.
Towards a Rigorous Statistical Analysis of Empirical Password Datasets

A central challenge in password security is to characterize the attacker's guessing curve i.e., what is the probability that the attacker will crack a random user's password within the first G guesses. A key challenge is that the guessing curve depends on the attacker's guessing strategy and the distribution of user passwords both of which are unknown to us. In this work we aim to follow Kerckhoffs's principal and analyze the performance of an optimal attacker who knows the password distribution. Let λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</inf> denote the probability that such an attacker can crack a random user's password within G guesses. We develop several statistically rigorous techniques to upper and lower bound λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</inf> given N independent samples from the unknown password distribution ${\mathcal{P}}$. We show that our upper/lower bounds on λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</inf> hold with high confidence and we apply our techniques to analyze eight large password datasets. Our empirical analysis shows that even state-of-the-art password cracking models are often significantly less guess efficient than an attacker who can optimize its attack based on its (partial) knowledge of the password distribution. We also apply our statistical tools to re-examine different models of the password distribution i.e., the empirical password distribution and Zipf's Law. We find that the empirical distribution closely matches our upper/lower bounds on λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</inf> when the guessing number G is not too large i.e., G ≪ N. However, for larger values of G our empirical analysis rigorously demonstrates that the empirical distribution (resp. Zipf's Law) overestimates the attacker's success rate. We apply our statistical techniques to upper/lower bound the effectiveness of password throttling mechanisms (key-stretching) which are used to reduce the number of attacker guesses G. Finally, if we are willing to make an additional assumption about the way users respond to password restrictions, we can use our statistical techniques to evaluate the effectiveness of various password composition policies which restrict the passwords that users may select.
SoK: Anti-Facial Recognition Technology

The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition. The set of AFR tools proposed in the last few years is wide-ranging and rapidly evolving, necessitating a step back to consider the broader design space of AFR systems and long-term challenges. This paper aims to fill that gap and provides the first comprehensive analysis of the AFR research landscape. Using the operational stages of FR systems as a starting point, we create a systematic framework for analyzing the benefits and tradeoffs of different AFR approaches. We then consider both technical and social challenges facing AFR tools and propose directions for future research in this field.