Reviewerly/exHarmony huggingface dataset:
Automated Verification of Correctness for Masked Arithmetic Programs

Abstract Masking is a widely-used effective countermeasure against power side-channel attacks for implementing cryptographic algorithms. Surprisingly, few formal verification techniques have addressed a fundamental question, i.e., whether the masked program and the original (unmasked) cryptographic algorithm are functional equivalent. In this paper, we study this problem for masked arithmetic programs over Galois fields of characteristic 2. We propose an automated approach based on term rewriting, aided by random testing and SMT solving. The overall approach is sound, and complete under certain conditions which do meet in practice. We implement the approach as a new tool and carry out extensive experiments on various benchmarks. The results confirm the effectiveness, efficiency and scalability of our approach. Almost all the benchmarks can be proved for the first time by the term rewriting system solely. In particular, detects a new flaw in a masked implementation published in EUROCRYPT 2017.
Formally Verified EVM Block-Optimizations

Abstract The efficiency and the security of smart contracts are their two fundamental properties, but might come at odds: the use of optimizers to enhance efficiency may introduce bugs and compromise security. Our focus is on (Ethereum Virtual Machine) block-optimizations , which enhance the efficiency of jump-free blocks of opcodes by eliminating, reordering and even changing the original opcodes. We reconcile efficiency and security by providing the verification technology to formally prove the correctness of block-optimizations on smart contracts using the Coq proof assistant. This amounts to the challenging problem of proving semantic equivalence of two blocks of instructions, which is realized by means of three novel Coq components: a symbolic execution engine which can execute an block and produce a symbolic state; a number of simplification lemmas which transform a symbolic state into an equivalent one; and a checker of symbolic states to compare the symbolic states produced for the two blocks under comparison. Artifact: https://doi.org/10.5281/zenodo.7863483
Commutativity for Concurrent Program Termination Proofs

Abstract This paper explores how using commutativity can improve the efficiency and efficacy of algorithmic termination checking for concurrent programs. If a program run is terminating, one can conclude that all other runs equivalent to it up-to-commutativity are also terminating. Since reasoning about termination involves reasoning about infinite behaviours of the program, the equivalence class for a program run may include infinite words with lengths strictly larger than $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ω</mml:mi> </mml:math> that capture the intuitive notion that some actions may soundly be postponed indefinitely. We propose a sound proof rule which exploits these as well as classic bounded commutativity in reasoning about termination, and devise a way of algorithmically implementing this sound proof rule. We present experimental results that demonstrate the effectiveness of this method in improving automated termination checking for concurrent programs.
Advanced framework for enhancing ultrasound images through an optimized hybrid search algorithm and a novel motion compounding processing chain

Ultrasound imaging is a fast, widespread, and essential diagnostic technique for examining the body’s internal anatomy to find abnormal tissues or diseases. However, speckle noise in ultrasound imaging corrupts fine details and edges and degrades the image’s resolution and contrast, making diagnosing more difficult. In this work, a speckle reduction method using motion compounding is proposed. The objective of this work is ultrasound image enhancement keeping all the diagnostics details and edges. The proposed method uses the pre-locations frames that the sonographer generates before locating the required diagnostic frame. These frames are applied to the proposed optimized Modified Three-Step Search (O-MTSS) algorithm to enhance the final processed frame. The O-MTSS algorithm is a hybrid between the Three Step Search algorithm (TSS) and the New Diamond Search Algorithm (NDS). The method requires a scoring layer for storing additional frames to select optimal frames that will be used for enhancement. The proposed methodology is tested on synthetic and real ultrasound images. The outcomes produce considerable speckle reduction while maintaining the image edges with good computational time for real-time scanning. The result is evaluated by subjective physicians, radiologists, and image evaluation metrics. According to the percentages of the subjective analysis, there is a remarkable improvement in the processed image. The proposed algorithm result is compared with existing algorithms; It is observed that the improvement in terms of signal to noise ratio, peak signal to noise ratio, mean square error, root mean square error and structural similarity index values of the proposed method are 4.6%, 3.32%, 12.02%, 6.2% and 1.57% respectively over Non-Local Low-Rank (NLLR) method. According to qualitative and quantitative analysis, the suggested method outperforms existing speckle reduction techniques regarding edge and fine detail preservation.
Sustainability production in natural leather art: Evidence-based studio practices and techniques for leather phone cases

A growing demand for sustainable goods has led to the development of environmentally friendly methods for producing leather goods. This article explored evidence-based studio practices and techniques for the sustainable production of natural leather phone case art. As part of an aesthetico-action research design, a cyclic model for observing, reflecting, planning, and acting followed the studio investigation of the tools and materials as well as the systematic procedures for making leather phone cases. Studio-based data were analysed using the visual analysis tool under the qualitative research approach. It was possible to create natural leather phone cases with different finishing techniques that were not only functional and aesthetically pleasing, but also environmentally friendly and socially responsible. By incorporating these sustainability techniques into the production of natural leather phone case art, artists and industry manufacturers should create beautiful and functional products that are also environmentally and socially responsible.
On remote electronic voting with both coercion resistance and cast-as-intended verifiability

In this work, we study two essential but apparently contradictory properties of electronic voting systems: coercion resistance (CR) and cast-as-intended verifiability (CAI). Informally, the CR property ensures that a voter cannot prove to anybody else the vote content, which prevents vote selling and voting under duress. The CAI property ensures that a malicious voting device cannot cheat the voter and send to the ballot box an encryption of a voting option different from the one chosen by the voter. In this work, we formalize security definitions capturing both coercion resistance and cast-as-intended verification in settings without secure delivery channels between the election authority and voters. After that, we consider some previously proposed solutions aimed at providing these two properties. For some of them (that we call unsatisfactory solutions) we show why they fail to achieve some of the two properties. We then concentrate on one of the two generic solutions that we call satisfactory: we prove that it satisfies the two proposed definitions and we detail how it can be instantiated in both classical cryptographic (e.g., ElGamal ciphertexts) and quantum-resistant (e.g., using lattice-based cryptosystems) settings.
Forest Wildfire Detection from Satellite Images using Deep Learning

Forest wildfires pose a significant threat to ecosystems, human lives, and infrastructure, and this project presents a deep learning-based approach for the early detection of forest wildfires from satellite images, employing data preprocessing, model training, and inference steps, where satellite images are pre-processed to enhance features and reduce noise, a deep learning model is trained using convolutional neural networks on a large dataset of labelled images, and the trained model is applied to new images in a sliding window manner to detect potential wildfire regions and generate a heatmap for visualization, resulting in a high accuracy detection system that surpasses traditional methods and aids in early warning and decision-making for fire management authorities
A GAN-Based Defense Framework Against Model Inversion Attacks

With the development of deep learning, deep neural network (DNN)-based application have become an indispensable aspect of daily life. However, recent studies have shown that these well-trained DNN models are vulnerable to model inversion attacks (MIAs), where attackers can recover their training data with high fidelity. Although several defensive strategies have been proposed to mitigate the impact of such attacks, existing defenses will inevitably compromise the model performance and are ineffective against more sophisticated attacks, such as Mirror [5]. In this paper, we introduce a novel GAN-based defense approach against model inversion attacks. Unlike previous works that perturb the prediction vector of the model, we manipulate the training procedure of the victim model by incorporating carefully-designed GAN-based fake samples. We also adjust the loss of the inversed samples to inject misleading features into the protected label of the victim model. Additionally, we adopt the concept of continual learning to improve the utility of the model. Extensive experiments conducted on the CelebA, VGG-Face, and VGG-Face2 datasets demonstrate that our proposed method outperforms existing defenses against state-of-the-art model inversion attacks, including DMI [9], Mirror [5], Privacy [13], and AMI [42]. It is shown that our proposed method can also retain a high defense performance in black-box scenarios.
Design of control system for intelligent sweeping robot

As the pace of life continues to accelerate, sweeping robot in people's life is more and more important. Sweeping robot in the execution of cleaning task obstacle avoidance ability is particularly important, this paper mainly through the mechanical device and MCU design of an intelligent sweeping robot. The design of STC89C52 microcontroller as the control center, with infrared sensor, through the sensor module to achieve the judgment of obstacles in front, and with walking system and cleaning system to judge the position of the robot, to achieve the sweeping robot obstacle avoidance function and automatic sweeping function. The design has the advantages of low cost, simple structure, simple design and operation, which can meet the needs of most people. Therefore, it has a good application prospect in the market.
A Data Analytics Strategy to the Underground Economy of Cybercrime

People, organisations, and governments have tried to discover strategies to fight against the danger of large-scale cyberattacks (such as ransomware and distributed denial of service (DDoS) assaults) and criminality. In 2017, the WannaCry ransomware was to blame for approximately 45,000 strikes across nearly 100 nations. Governments are under pressure to enhance their cybersecurity spending due to the increasing effect of cybercrime. As part of his 2017 budget, U.S. President Barack Obama suggested allocating more than $19 billion on cybersecurity, a more than 35% increase over 2016. As a result, the cybercriminal underground has changed into a brand-new kind of group that runs underground marketplaces and fosters the growth of cybercriminal conspiracies. Cybercrime networks, in contrast, are lateral, diffuse, fluid, and dynamic. Governments, organisations, and people are typically unaware of the threat posed by the development of highly professional network-based cybercrime business models, such as crime ware-as-a-service (CaaS). Despite the quick rise in cyberthreats, little is known about the basics of the field or the approaches that can help practitioners and researchers in information systems who are interested in cybersecurity. Additionally, little is known about Crime-as-a-Service (CaaS), the illegal business model that supports the shadowy world of cybercrime
Software Reuse

Program advancement has ended up a significant viewpoint of numerous businesses within the advanced world, with businesses depending on computer program applications to oversee their operations and give administrations to their clients
Forensic Investigations in Cloud Environments

Cloud computing has revolutionized the way organizations store, process, and share data. However, this technological shift has also introduced new challenges for forensic investigations. As more businesses migrate their critical operations to the cloud, it becomes crucial to understand the unique aspects of conducting forensic investigations in cloud environments. This research paper explores the key considerations, methodologies, and tools involved in conducting effective forensic investigations in cloud environments. The paper highlights the potential challenges and provides recommendations to address them, emphasizing the importance of proactive measures and collaboration between cloud service providers and investigators. The insights gained from this research aim to contribute to the development of best practices for forensic investigations in the cloud.
Privileged Access Management: Ensuring Security and Accountability

An essential security strategy known as Privileged Access Management (PAM) focuses on monitoring and controlling access to critical internal systems, networks, and data. This research paper provides a comprehensive overview of Privileged Access Management, examines its relevance for modern cybersecurity, and lists the key principles and components needed to develop an effective PAM program. The research also discusses the benefits and drawbacks of PAM and provides deployment best practices. Through a detailed investigation of the body of prior research, industry reports, and case studies, the study offers insights into the current state of PAM and predicted changes
Model-Agnostic Meta-Learning Techniques: A State-of-The-Art Short Review

In the last few years, novel meta-learning techniques have established a new field of research. Great emphasis is given to few-shot learning approaches, where the model is trained by using only a few training examples. In this work, a review of several model-agnostic meta-learning methodologies (MAML) is presented. Firstly, we identify and discuss the typical characteristics of the first proposed MAML algorithm. Next, we classify the model-agnostic approaches into three main categories: Regular gradient descent MAML, Hessian-free MAML, and Bayesian MALM, by presenting their advantages and limitations. Finally, we conclude this work with further discussion and highlight the future research directions.
RA-GAN data enhancement method to improve the performance of lung parenchyma segmentation

Poor generalization and low accuracy of the neural network for segmentation is often lead by a small medical images dataset during model training for segmentation tasks. That is the mainly reason that data enhancement methods are usually needed to augment the dataset. Currently, data enhancement methods based on generative adversarial networks generate unstable images with local blurring and unclear edge distinction, which lead to the insignificant improvement of segmentation performance after data enhanced medical images. The data enhancement method is proposed which based on improved conditional generative adversarial network for augmenting lung CT images in this paper, in order to effectively solve this problem. The network depth is deepened by introducing residual blocks, and the CBAM attention module is added in the jump connection stage to obtain richer global features; the ViT-based (Vision Transformer) discriminator is used to guide the generator to generate CT images with higher quality and more distinct edge differentiation. A segmentation experiments are conducted by data enhancement to verify the effectiveness of the method. The experimental results show that the method performs better in the small sample segmentation task compared to other segmentation methods.
An Effective Approach for Violence Detection using Deep Learning and Natural Language Processing

An effective tool for violence detection is highly demanded to examine the rise in crime rate in today's era. Artificial Intelligence can play a significant role in violence detection and monitoring to tackle various problems of security and safety concerns. This research proposes strategies to incorporate Deep Learning and Natural Language Processing (NLP) to simultaneously detect anomalous objects and scenarios from videos using TensorFlow and aggressive, offensive, and hate speech from an audio channel of surveillance cameras. This research aims to automatically detect violence in real-time from surveillance footage by using TensorFlow custom object detection upon identification of firearms, robbery, fistfights, sexual harassment, and fire in successive images from the video feed. In addition, the audio channel of such surveillance cameras can also be significantly fruitful in detecting hate speech, verbal sexual abuse, and profanity. The proposed system includes an alert mechanism that detects any type of violence and automatically notifies the security administrator, enabling timely intervention to prevent potential damage to society. The developed models can be deployed on any existing surveillance system with next to negligible additional hardware and software resource requirements, thereby making it an efficient, fast, accurate, and economical solution. To train the model, custom datasets were designed for 6 categories in images and 2 categories in speech. The accuracy of the developed system was found to be 84%, with adequate performance under various luminance conditions, including night vision images.
Implementation of Agricultural Path Planning with Unmanned Ground Vehicles (UGV) based on Enhanced A* Algorithm

The A* algorithm is well-known for its use in numerous applications, including robots and GPS systems, for the purpose of route planning. The algorithm, despite its usefulness, has several restrictions regarding its operational efficiency and the length of its paths. This article makes a suggestion for improving the standard A* algorithm in order to overcome these constraints. Compared to the A* algorithm's standard performance, the findings showed that the improved algorithm reduce the amount of time needed for route planning by 9.11% on average, while also cutting the path length by 9.29% on average. The A* algorithm's performance can be significantly improved with the help of the method that we propose, both in terms of its operational efficiency and the length of its paths.
Analysis of High-Order Aliasing in Bandpass Sub-Nyquist Sampling

One of the directions of digital signal processing in a wide frequency band allows operation with a sampling frequency lower than that required by the Shannon-Nyquist sampling theorem, with subsequent compensation for distortions caused by aliasing. Publications devoted to bandpass sampling mainly consider the transformation of the spectrum of a sampled signal in the absence of aliasing. The structure of the spectrum during aliasing is poorly studied. At the same time, distortion compensation algorithms depend on their nature. Algorithms based on multichannel sampling are among such algorithms. This paper discusses the features of aliasing, including high-order aliasing.
Myocardial D-Spect Image Segmentation Using RSF Model and Kernal Difference Operator

One of the top causes of death worldwide is now ischemic heart disease. The modern diagnostic technology known as dynamic single-photon emission computed tomography (D-SPECT) is frequently utilized to confirm the myocardial function in patients with a variety of heart disorders. To build a 3-D Myocardial model and let doctors conduct assessments of cardiac function, accurate automatic localization and segmentation of myocardial areas is beneficial. Image segmentation is a crucial method in preclinical cardiac research because of this. One of the frequent problems with image segmentation is intensity inhomogeneity, which is brought on by imperfect instrumentation and image blemishes. This research presents a unique region-based active contour model that can precisely segment the myocardial D-SPECT image. As RSF Model detects the intensity inhomogeneity of the object and KD operator makes the weak boundaries of the object strong. By integrating the RSF (region-scalable fitting) energy model and the KD (kernel difference) operator, the suggested method ensured accuracy, computational efficiency where it showed results in less iterations and can address the problem of high sensitivity for Myocardial D-SPECT segmentation.
Efficient ASIC Implementation for Satellite-IoT Security Co-processor

ASIC implementation of a security co-processor for Satellite-IoT devices is introduced. It provides a hardware acceleration for AES-GCM, the CCSDS-compliant Authenticated Encryption algorithm. The Key size is programmable, supporting both 128 and 256-bit modes, to comply with baseline implementation of CCSDS SDLS protocol, and Extended Procedures. Implementation targets 65nm CMOS technology using low-power standard cell library, resulting in a synthesis cell area of 52.2kGE. Achieving max. clock frequency of 617.3MHz, data throughput is 7.18Gbps for 128-bit mode, and 5.27Gbps for 256-bit mode. Output Tag throughput is 418 and 333Mbps, for 128-bit and 256bit modes, respectively. Implementation is primarily optimized for power to cater for battery-operated IoT devices. Average post-synthesis energy consumption is 1.25pJ/bit at 128-bit mode, which is almost the same as the most efficient design in literature with >91% area saving. Energy consumption is 1.54pJ/bit for the 256-bit mode. Post-PnR’s GDSII area is 125.2µm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and energy consumption is 2.82 and 3.55pJ/bit, for 128 and 256-bit modes, respectively.
Surface-Based Computation of the Euler Characteristic in the BCC Grid

Abstract As opposed to the 3D cubic grid, the body-centered cubic (BCC) grid has some favorable topological properties: each set of voxels in the grid is a 3-manifold, with 2-manifold boundary. Thus, the Euler characteristic of an object O in this grid can be computed as half of the Euler characteristic of its boundary $$\partial O$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>∂</mml:mi> <mml:mi>O</mml:mi> </mml:mrow> </mml:math> . We propose three new algorithms to compute the Euler characteristic in the BCC grid with this surface-based approach: one based on (critical point) Morse theory and two based on the discrete Gauss–Bonnet theorem. We provide a comparison between the three new algorithms and the classic approach based on counting the number of cells, either of the 3D object or of its 2D boundary surface.