Reviewerly/exHarmony huggingface dataset:
Capacities of Western Balkan Economies (and Their Public Sectors) to Respond to Ransomware Attacks

Ransomware became a global cybersecurity threat affecting not only individuals and private companies, but governments and other public bodies. The paper will firstly introduce the concept of ransomware and emphasize the implications of the global rise of ransomware. Secondly, the paper will provide an overview of the regulatory framework in relation to Ransomware in Western Balkan (WB) economies (Albania, Bosnia and Herzegovina, North Macedonia, Montenegro, and Serbia), while an ethical and practical guidance will be proposed. Then, the paper will briefly present Western Balkan Case Studies, including recent key ransomware attacks occurred in the region. Lastly, the paper will provide recommendations regarding prevention and recovery from the ransom attack focusing on WB region.
Copyright in Generative AI training: Balancing Fair Use through Standardization and Transparency

The rapid evolution of Generative Artificial Intelligence (GAI) has brought about transformative changes across industries, often raising challenging questions surrounding data rights, especially within the context of copyrighted content. This paper delves into the nuances of the relationship between GAI and the fair use doctrine, highlighting the complexities that emerge when copyrighted data serves as the backbone for the development of large-scale AI models. By combining Benjamin Sobel’s training data taxonomy with the distinct stages of the Generative AI cycle, a hybrid framework is presented, offering a more granulated perspective on the applicability of fair use in GAI contexts. Recognizing the inherent limitations of the current legal paradigms, the paper introduces actionable proposals, emphasizing the need for enhanced transparency, data provenance measures, and the implementation of Standardized Data Licensing Agreements (SDLAs). Such measures aim to bridge the gap between AI developers and copyright holders, facilitating smoother negotiations and fostering trust. While the core discussion revolves around the interplay of GAI and fair use, the paper acknowledges broader policy challenges in the AI domain, urging for continuous exploration. Overall, this work underscores the necessity of adaptive, collaborative, and transparent strategies in harmonizing the objectives of innovation with the imperatives of intellectual property rights in the GAI landscape.
Recognition of Static American Sign Language Hand Gestures Using Convolutional Neural Networks

"It's not what happens to us, but it's how we respond to it that matters the most. "That is what motivated us to undertake this project. People with disabilities are not considered to be impaired. They simply communicate in a way that normal humans like us are unable to understand. The primary form of communication for those who face difficulty in hearing or who cannot speak is sign language. Without a translation, it can be difficult for people with hearing and speech impairments to interact with other average people. This paper would like to present a Convolutional Neural Network (CNN) model for predicting American Sign Language. We utilize a Modified National Institute of Standards and Technology dataset, as well as the Grassknoted ASL datasets in order to apply transfer learning to this task. The proposed model significantly raises the ASL recognition accuracy reported by various existing methods that are already studied. The successful development of this model can be used as the foundation for the creation of a complex sign language translator.
Using Perceptual Hashing for Targeted Content Scanning

The Internet is increasingly used to disseminate unethical and illegal content. A grave concern is child sexual abuse material that is often disseminated via end-to-end-encrypted channels. Such encryption defeats network- and server-based scanning measures used by law enforcement. A trade-off is to enable confidential communications channels for users and scanning opportunities for law enforcement by employing perceptual-hashing-based targeted content scanning on user devices. This has generated intense discussions between policymakers, privacy advocates and child protection organizations. This chapter summarizes the current state of reserch in perceptual-hashing-based targeted content scanning with a focus on classical metrics such as false positives, false negatives and privacy aspects. Insights are provided into the most relevant perceptual hashing methods and an attack taxonomy for perceptual-hashing-based targeted content scanning is presented. The complexity in generating false negatives is evaluated and the feasibility of evading perceptual-hashing-based targeted content scanning is demonstrated.
Verification and Its Role in Design of Modern Computers

Computer-aided verification is an integral and crucial step in the design process of modern computing systems. This section sheds light on different computer-aided verification techniques that have been designed and optimized to solve specific verification challenges that were encountered by designers of modern computers. Computer-aided verification is an active area of research and not all verification topics are discussed in this section; instead, the section highlights the verification topics that are at the center stage in the application world – either because of their use in the day-to-day work of verification engineers engaged in verifying various design fragments or because of their impact of some emerging aspect of computer architecture. The chapters under this section should help a computer architect understand the challenges and opportunities in verifying complex system, and in turn, this could influence his or her design decisions.
Forensic Analysis of the iOS Apple Pay Mobile Payment System

Mobile payment systems enable users to complete financial transactions with their smartphones, including contactless payments at retail stores. Because the financial transactions of individuals are indicators of their lifestyles, they are potential sources of data in criminal investigations. The items purchased and even the locations of transactions can constitute valuable evidence. However, mobile payment system data is intended to be interpreted by computer software not human investigators. As a result, a barrier exists between raw application data and their clear interpretations for evidentiary purposes. This chapter describes research focused on Apple Pay, the leading mobile payment system for mobile debit wallet transactions in the United States. The research has sought to examine the Apple Pay mobile payment system and identify specific sources of forensic artifacts. This includes determining and interpreting the payment card and transaction data residing in the Apple Pay application and syncing across user devices, as well as the implications of deleting Apple Pay application data.
Deepfake Detection Using Multiple Facial Features

Deepfake digital forgery techniques leverage deep learning to replace faces and modify facial expressions in images and videos. The techniques have been used to produce fake pornography, spread fake news and rumors, influence public opinion and even elections. However, deepfake detection techniques are well behind deepfake generation technology. This chapter describes a deepfake video detection method that leverages aspect ratios to express multiple facial features. The aspect ratios of facial features are computed for every frame in a video and a time window is used to segment processed frame sequences into multiple short segments, following which pattern matching is employed to identify abnormal expressions that are indicative of deepfakes. Experiments with the FaceForensics++ and Celeb-DF datasets reveal that the proposed method detects deepfake videos effectively. Moreover, the aspect ratio computations improve the ability to detect compressed deepfake videos.
DeCloakFace

With edge computing and public networks on the rise, safeguarding personal data is paramount. This article presents a comprehensive survey of Privacy-Enhancing Technologies (PETs) in emerging edge envisioned networks, focusing on privacy-preserving image processing and data analysis. The survey highlights the prevailing trend of using differential privacy techniques for image de-identification, offering insights into the state-of-the-art literature in this area. Additionally, it introduces DeCloakFace, an advanced technique for privacy-preserving image recognition on edge devices, showcasing its applicability and advantages. By identifying research gaps and exploring future directions, the article aims to advance PETs in addressing privacy concerns within emerging edge envisioned public networks. Differential privacy for image de-identification receives special attention, emphasizing its significance in preserving privacy while enabling effective data analysis.
Deep Learning-Based Eye Gaze Estimation for Automotive Applications Using Knowledge Distillation

Deep neural networks are currently applied in multiple domains, especially in the automotive industry. The main reason for this is related to the more complex challenges found in the field of signal processing, especially when the tasks involve image and video data types. Using conventional/statistical algorithms to deal with these high-complexity challenges is no longer a viable approach. Therefore, the involvement of artificial intelligence solutions like deep neural networks has significantly increased. In recent years, numerous architectures have been developed with the aim of maximizing performance. However, their size and computation requirements have increased at the same time. For this reason, special attention is currently being paid to the optimization of deep neural networks while trying to maintain (almost) the same performance. In this work, we aim to tackle the problem of eye gaze estimation considered within the automotive framework. Our proposal uses a knowledge distillation concept applied to a custom CNN architecture, called the teacher model. Based on this, several CNN student models are derived using layerwise and widthwise compression techniques. Furthermore, they are evaluated with respect to certain performance metrics, e.g. neural network size and inference time. In the experimental results, we propose certain compression methods which can address specific user requirements like model size, accuracy, and inference time. Finally, the student models are evaluated using an EdgeAI embedded device (STM32H747I-DISCO) in terms of accuracy, memory utilization, MACC complexity, and inference time. The combination of layerwise and widthwise compression results as the optimal method to derive student models with a good trade-off between the above-mentioned metrics. Using knowledge distillation, the accuracy can be improved by up to 9.5% over the conventional training procedure.
Multi-State Merkle Patricia Trie (MSMPT): High-Performance Data Structures for Multi-Query Processing Based on Lightweight Blockchain

Blockchain technology has emerged as a promising solution to secure and decentralized platforms. However, blockchain technology has high computational requirements, latency, and low throughput, particularly for single or multi-query processing. Lightweight blockchain has emerged as a solution to overcome these problems. It addresses performance and efficiency issues and can provide convenience in the query process. This paper proposed a novel high-performance data structure for multi-query processing based on a lightweight blockchain, namely Multi-State Merkle Patricia Trie (MSMPT). MSMPT combines Merkle Patricia Trie (MPT) based indexing and linked-list storage to achieve high performance. MPT has been used on the Ethereum network with a Key-Value database approach. The key field in this proposal is used as crucial user data. The value field is changed to the head of the linked list, and the following data elements will store a summary of the data based on the specified category. In this paper, a blockchain simulator was built to discover the performance of the proposed systems. This simulator will simulate creating blocks in a blockchain network using existing and modified blockchain data structures. The blocks created will be compared using the query process from the conventional and proposed systems. The experimental findings demonstrate that MSMPT outperforms existing blockchain-based data structures by requiring only about one millisecond in query processing performance and less than 500 bytes of additional storage. The MSMPT provides a promising solution for efficient and scalable data management in lightweight blockchain, particularly for multi-query processing.
A Complete Deep Support Vector Data Description for One Class Learning

In recent years, Deep Support Vector Data Description (Deep SVDD) has emerged as a leading method in the field of anomaly detection. However, inaccuracies in parameter solving have been identified as a limitation of this approach, which negatively affects its accuracy and efficiency. To address this issue, we propose a new method, called Complete Deep Support Vector Data Description (CD-SVDD). Our CD-SVDD is constructed with a traditional deep neural network and utilizes a modified SVDD as its last layer. Its parameters are solved by an alternate iteration algorithm that ensures both high precision and fast convergence of solutions. By keeping the network weights fixed, we solve the center and radius of the modified SVDD based on its convex dual optimization problem. We then update the parameters of the neural network by backpropagation. This approach enables us to maintain the ν-property found in shallow SVDD, which is beneficial for parameter selection and model interpretability. To evaluate the performance of CD-SVDD, we conducted extensive numerical experiments with five existing methods on two image datasets, CIFAR-10 and CIFAR-100, as well as five recorded benchmark datasets. Our results demonstrate that CD-SVDD achieves superior accuracy and efficiency in the detection of anomalies.
Hilbert Convex Similarity for Highly Secure Random Distribution of Patient Privacy Steganography

Based on Hilbert Random Secure Distribution, a novel data-hiding method for embedding secret information about the patient in a cover image MRI sample has been proposed. Least significant bit (LSB) and most significant bit (MSB) techniques are applied for the physical hiding. Medical images confidentiality suffers from potential attacks and tracing by an unauthorized access. Technically, distributing the secret text in a random way on the cover image is the core security function of the proposed model. In order to evaluate the performance of the proposed solution, three quality metrics: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), percentage residual difference (PRD) and Structural Similarity Index measure (SSIM) were computed and compared on ten MRI images. Experimental results showed significant results in comparison with other models and reached average PSNR up to 61 db. Furthermore, the security analysis in case of 512×512 image samples show complex probability of distribution based on the Hilbert space model.
Robust Perception Under Adverse Conditions for Autonomous Driving Based on Data Augmentation

Many existing advanced deep learning-based autonomous systems have recently been used for autonomous vehicles. In general, a deep learning-based visual perception system heavily relies on visual perception to recognize and localize dynamic interest objects (e.g., pedestrians and cars) and indicative traffic signs and lights to assist autonomous vehicles in maneuvering safely. However, the performance of existing object recognition algorithms could degrade significantly under some adverse and challenging scenarios including rainy, foggy, and rainy night conditions. The raindrops, light reflection, and low illumination pose a great challenge to robust object recognition. Thus, A robust and accurate autonomous driving system has attracted growing attention from the computer vision community. To achieve robust and accurate visual perception, we target to build effective and efficient augmentation and fusion techniques based on visual perception under various adverse conditions. The unpaired image-to-image (I2I) synthesis is integrated for visual perception enhancement and effective synthesis-based augmentation. Besides, we design a two-branch architecture to utilize the information from both the original image and the enhanced image synthesized by I2I. We comprehensively and hierarchically investigate the performance improvement and limitation of the proposed system based on visual recognition tasks and network backbones. An extensive experimental analysis of various adverse weather conditions is also included. The experimental results have demonstrated the proposed system could promote the ability of autonomous vehicles for robust and accurate perception under adverse weather conditions.
Background: Propositional Tableaus

The semantics for classical propositional logic is the simplest of any active logic—models are specified using truth tables, or equivalently boolean valuations. In Chap. 2 an axiomatization was developed. Semantics and axiomatic proofs fit tightly together—we have soundness and completeness theorems. But axiom systems are hard to use—discovering a proof can be difficult.
FL-ODP: An Optimized Differential Privacy Enabled Privacy Preserving Federated Learning

Privacy-preserving methods and techniques aim to safeguard the privacy of individuals and groups while facilitating data sharing for specific purposes. Federated Learning (FL) is a machine learning approach that allows multiple devices or systems to collaboratively train a model without directly sharing their data with each other or a central server. This paper proposes an Optimized Differential Privacy (ODP) approach to ensure the privacy of individual data points while enabling the extraction of valuable information. The proposed model is validated using the MNIST dataset and is analyzed with the FedAvg aggregator. Differential Privacy (DP) with FL is optimized by varying the noise and delta values. The analysis of data privacy is conducted in three phases: the first phase evaluates simple FL, and the second and third phases utilize different DP parameters to achieve optimized results. These optimized results show improved accuracy and privacy, confirming the efficacy of the proposed FL-ODP approach.
Complexity-Aware Layer-Wise Mixed-Precision Schemes With SQNR-Based Fast Analysis

Recently, deep neural network (DNN) acceleration has been critical for hardware systems from mobile/edge devices to high-performance data centers. Especially, for on-device AI, there have been many studies on hardware numerical precision reduction considering the limited hardware resources of mobile/edge devices. Although layer-wise mixed-precision leads to computational complexity reduction, it is not straightforward to find a well-balanced layer-wise precision scheme since it takes a long time to determine the optimal precision for each layer due to the repetitive experiments and the model accuracy, the fundamental measure of deep learning quality, should be considered as well. In this paper, we propose the layer-wise mixed precision scheme which can significantly reduce the time required to determine the optimal hardware numerical precision with Signal-to-Quantization Noise Ratio (SQNR)-based analysis. In addition, the proposed scheme can take the hardware complexity into consideration in terms of the number of operations (OPs) or weight memory requirement of each layer. The proposed method can be directly applied to inference, meaning that users can utilize well-trained neural network models without the need for additional training or hardware units. With the proposed SQNR-based analysis, for SSDlite and YOLOv2 networks, the analysis time required for layer-wise precision determination is reduced by more than 95% compared to conventional mean Average Precision(mAP)-based analysis. Also, with the proposed complexity-aware schemes, the number of OPs and weight memory requirement can be reduced by up to 20.94% and 37.67%, respectively, for SSDlite, and by up to 96.68% and 88.53%, respectively, for YOLOv2, with negligible model accuracy degradation.
Feature Fusion for Dual-Stream Cooperative Action Recognition

Currently, the primary methods for action recognition involve RGB-based approaches, pose-based approaches (e.g., skeleton coordinates), and multi-stream fusion methods. In this paper, we propose a novel action recognition framework based on both RGB images and motion pose images to enhance the accuracy of action recognition in videos. As a single feature representation fail to effectively capture motion trends and image variation information, it cannot accurately reflect expected action judgments in real-world scenarios. Therefore, we utilize the appearance features of video frames and the motion variation features of the subject, aiming to cooperate the action itself with appearance information for precise action recognition. We construct video representations based on local spatiotemporal features and global features, and utilize the ResNet backbone network and Temporal Shift Module (TSM) to extract action representations from multi-stream information. Driven by the motion features, the fusion of multi-stream information achieves effective expression of motion features. Experimental results on public datasets demonstrate the effectiveness of our proposed method. It achieves competitive performance compared to state-of-the-art techniques while maintaining a less complex and more interpretable model. Overall, our approach demonstrates superior effectiveness.
Predicate Abstraction, Formally

Predicate abstracts help sort out complications brought by non-rigid terms. In this chapter we present the formal machinery for predicate abstracts, and explore the application of these abstracts to non-rigid constant symbols, and also to non-rigid function symbols. And for both constant and function symbols, they might always designate, or there may be situations in which they do not. The main division is not between constants and functions—after all, a constant can be thought of as a function that takes no inputs. Rather, the division is between always designating and sometimes not doing so. We treat the always case first since it is simpler. Then we modify it to situations where non-designation could happen. We discuss constant symbols before bringing in the more complex function symbols.
First-Order Modal Axiomatics

Through all our discussions about classical and modal logics, we have examined both tableau and axiom systems. Propositionally, modal axiomatics is quite general, much more so than tableau systems. One can specify any number of modal logics axiomatically, each with its corresponding semantics, but for which no tableau systems are known. Of course, what it means to be a tableau system is somewhat open. When moving to a modal setting we added prefix machinery, in Chap. 7 . In Sect. 7.7 other kinds of machinery were discussed. How much extra machinery can we add and still have something that can be considered a tableau system? But since our primary interests in this book are philosophical, relatively simple modal logics are of primary concern to us, and for these we have both axiomatics and tableaus at the propositional level.