Reviewerly/exHarmony huggingface dataset:
When Memory Mappings Attack: On the (Mis)use of the ARM Cortex-M FPB Unit

In recent years we have seen an explosion in the usage of low-cost, low-power microcontrollers (MCUs) in embedded devices around us due to the popularity of Internet of Things (IoT) devices. Although this is good from an economics perspective, it has also been detrimental for security as microcontroller-based systems are now a viable attack target. In response, researchers have developed various protection mechanisms dedicated to improve security in these resource-constrained embedded systems.We demonstrate in this paper these defenses fall short when we leverage benign memory mapped design-for-debug (DfD) structures added by MCU vendors in their products. In particular, we utilize the Flash Patch and Breakpoint (FPB) unit present in the ARM Cortex-M family to build new attack primitives which can be used to bypass common defenses for embedded devices. Our work serves as a warning and a call in balancing security and debug structures in modern microcontrollers.
HeisenTrojans: They Are Not There Until They Are Triggered

The hardware security community has made significant advances in detecting Hardware Trojan vulnerabilities using software fuzzing-inspired automated analysis. However, the Electronic Design Automation (EDA) code base itself remains under-examined by the same techniques. Our experiments in fuzzing EDA tools demonstrate that, indeed, they are prone to software bugs. As a consequence, this paper unveils HeisenTrojan attacks, a new hardware attack that does not generate harmful hardware, but rather, exploits software vulnerabilities in the EDA tools themselves. A key feature of HeisenTrojan attacks is that they are capable of deploying a malicious payload on the system hosting the EDA tools without triggering verification tools because HeisenTrojan attacks do not rely on superfluous or malicious hardware that would otherwise be noticeable. The aim of a HeisenTrojan attack is to execute arbitrary code on the system on which the vulnerable EDA tool is hosted, thereby establishing a permanent presence and providing a beachhead for intrusion into that system. Our analysis reveals 83% of the EDA tools analyzed have exploitable bugs. In what follows, we demonstrate an end-to-end attack and provide analysis on the existing capabilities of fuzzers to find HeisenTrojan attacks in order to emphasize their practicality and the need to secure EDA tools against them.
UAVs Path Planning Using Visual-SLAM Technique Based Hybrid Particle Swarm Optimization

Abstract Due to their excellent mobility on various robotics platforms, unmanned-aerial vehicles (UAVs) are becoming extremely popular. We trace the UAV poses while simultaneously creating an iterative and progressive map of the surrounding area using a cutting-edge VSLAM technique, termed as visual simultaneous localization and mapping. In this case, a single UAV initially creates a map of the area of interest using a monocular vision-based method. In order to determine the best pathways for several UAVs, the created map is treated as an input for the optimization method. UAVs need to execute missions effectively, and they need to access the best route quickly in a challenging environment. This necessitates solving the automatic path planning problem. In this paper, a new hybrid particle swarm optimization (HPSO) technique is suggested as a solution to this issue. The proposed algorithm enhances the optimization capability and prevents dropping into local convergence by combining the simulated annealing algorithm; each particle integrates the advantageous information of the optimization method in accordance with the dimensional learning approach, which reduces the occurrence of particles fluctuation during the transition process and improves the convergence speed. Additionally, we proposed dynamic fitness function (DFF) in order to assess the path planner’s planning approach while taking into account a variety of optimization parameters, including the calculation of flight risk, energy usage, and operation completion time. The efficiency of our proposed H-PSO-VSLAM system, as shown by the simulation results, is validated by the recommended planner’s high fitness value and safe arrival at the destination while avoiding all unanticipated dangerous events and restricted locations.
Web-based Efficiency of Distributed Systems and IoT on Functionality of Smart City Applications

Abstract In the current technological environment, the Internet of Things (IoT) has arisen as a significant subject of research because of its importance. A robust cloud computing framework is established via the collaboration of sensors and actuators. This framework has the potential to be used in the creation of autonomous object networks in real-time systems. The Internet of Things (IoT) is playing a significant role in the redefinition of other significant ideas, such as urbanism and smart cities. The Internet of Things (IoT) technology has the potential to improve the quality of life for inhabitants of smart cities, which are relatively new developments. This might be accomplished by providing people with enhanced facilities and services. The Internet of Things is seeing a fast expansion in a variety of sectors, including smart cities, everyday life, and other fields. Through the use of home automation, smart wearables, security solutions, and smart healthcare, the Internet of Things makes it possible to create intelligent environments. In the realm of the Internet of Things, some of the possible topics of study include intelligent transportation systems for cities and intelligent electrical systems. This article offers a succinct summary of the most recent advancements in the Internet of Things (IoT) and the influence those advancements have had on the lives of humans in their day-to-day activities. In addition to this, the Internet of Things (IoT) is also considered in connection to artificial intelligence, autonomous control, and cloud computing. It is important to synchronise a number of technologies, such as wireless sensors, actuators, the internet, and distributed computing, in order to successfully allow upcoming Internet of Things technologies.
Denoising Technique and Analysis of Statistical Parameters for Endoscopic Images of Gastric Cancer

Gastric cancer (GC), which is commonly referred to as stomach cancer, is a kind of cancer that develops in the stomach cells. The gastrointestinal tract is a component of the alimentary canal, which is composed of a series of spherical muscular organs linked by a long, curved tube that passes from the inside of the mouth to the anus. Endoscopic gastroduodenoscopy (EGD) using an upper endoscope is a procedure that helps identify most stomach cancers because the endoscopic pictures that using filtering algorithm to increase endoscopic image tissue that are obtained vulnerable noises. Denoising is extremely important of the endoscopic images in diagnosis and subsequent medical care. On the basis of the endoscopic imaging data that has been obtained, filtering and denoising for image management were performed in order to obtain quality data and, as a result, to protect and safeguard the dependability of important clinical information. During the course of this study, numerous algorithms like Gaussian, median, wavelet, Weiner, filtering were performed on the selected endoscopic images and the resulting data has been statistically analyzed and characterized in detail. The various filtering algorithms were used on the endoscopic images and the results have been analyzed using a descriptive parameter statistical analysis approach. The skewness and kurtosis values for all the filtered images were calculated and compared. The results obtained from the parametric method were analyzed using various tools like ANOVA, t-test, z-test, linear regression, and covariance. The data obtained were used to calculate the ranking and percentile for analyzing the performance of the filters. Based on the results obtained from the analysis the good and best filter that can be used for denoising endoscopic images of gastric cancer was identified.
Data Address focused Implementation of Low Latency Secure Blockchain for EMRs

Data privacy is always a concern when it comes to any kind of personal data. In medical domain, the medical data of a patient needs to be more protected and also be accessible. The medical industry is facing the problem of storing and sharing medical data with multiple parties while promising security at the same time. The hospitals don’t share their patient data with other hospitals or organizations because of a lack of security in the infrastructure used to transfer this sensitive data. With the introduction of a blockchain based Electronic Medical Records (EMR) may help to solve the issue to a certain extent. From literature it is found that the current blockchain EMR schemas focus on patient-owned models. In this paper, design and development of a web-based software to store patient electronic medical records in a block chain-based back end that is supplied over the internet using Amazon Web Services (AWS) Managed Blockchain is proposed. The major goal is to ensure that the model fits the Confidentiality, Availability, and Integrity (CIA) trinity and performs effectively in benchmarks. In comparison to literature, the proposed block chain demonstrated up to a 65% increase in read throughput, 260%-400% improvement in write throughput on certain work loads, in addition to being exceptionally dependable and secure. The underlying model can be upgraded using cutting-edge algorithms and approaches to lower the model’s maintenance costs and raise its efficiency. The proposed model has been compared with already proposed method in literature and tends to give significant results.
Text Post-processing on Optical Character Recognition output using Natural Language Processing Methods

The technique of turning images of printed or written text from scanned documents, images of documents, or simple photos into machine-encoded text is known as optical character recognition (OCR). OCR has proven to be very useful in terms of digitizing documents and making them easier to analyze. Despite the advancement in the technology since it was introduced, there are still areas OCR falls short. If either the written text is illegible, or the OCR software isn’t powerful enough, it results in inaccurate translations. This research work aims at addressing this shortcoming by performing post-processing on OCR outputs primarily using Transformers such as BERT in a two-step pipeline to correct these mistakes and improve the quality of the document.
Diagnosis of Liver Tumor from CT Scan Images using Deep Segmentation Network with CMBOA based CNN

Accurate segmentation and categorization of liver tumours are crucial for the diagnosis and management of carcinoma or metastases. The liver tumour presents a challenging problem for precise and automated tumour segmentation and classification because of its blurry boundaries and large variety of potential forms, sizes, and placements. New AI models have emerged as computer technology has progressed. The NLP community has had such success with the transformer paradigm that the CV community has adopted it as well. Although established methods exist for categorising the liver, especially in clinical settings, they might be refined to be more accurate. Two deep learning-based models are used to do the segmentation and classification of the liver tumour in this study. As a first step, the input photos are median filtered and their histograms are equalised to prepare them for further processing. Then, Deep Segmentation Network (DSegNet) extracts the liver from the input pictures. The tumour is then categorised using an Optimised Convolutional Neural Network (OCNN) model, with the CNN's weight chosen using a Cat and Mouse Based Optimisation Algorithm (CMBOA). Two openly accessible datasets are used for the experimental study, with a focus on certain key metrics. The results demonstration that associated to pre-existing deep learning representations, the suggested model is about 98% more accurate in its classifications.
Hybrid Optimization Based BERT Model for Drug Detection in NLP

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and Drugs.com and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model’s hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-the-art models.
Secure Image Transmission with Efficient Resource Constraints Using Multiple Chaotic Systems

Electronic medical records, including medical images, clinical observations, medical histories and prescriptions are very confidential data. They are vulnerable to many security-related issues and they are not sent as it is through the network. In general, some cryptographic novel algorithms were being used prior to transmitting the data. Due to constraint of resources in some devices, complex encryption cannot be applied. Cloud (as a Third Party) is used to get the encryption done by outsourcing. The proposed method uses the same technique to accomplish the data transmission securely. Mixed encryption is used and encryption on both sides is done on medical images, which enhances image security. The sobel edge technique is used to extract ROI in medical images. To further reduce the algorithmic complexity, the upper four planes of the ROI part undergo shuffle and diffuse operation. The reformation of binary planes is performed using a chaotic system, followed by diffusion operation. Experiments including data loss attacks, differential attacks, statistical attacks, and brute force assaults show that the suggested encryption method performs better in terms of security. These findings demonstrate that compared to numerous chaotic encryption techniques put forth recently, the suggested method’s security is far better.
An Integrated Framework for Location Based Security in Cloud Environment using Multi Objective Optimization (ILBS-MOO)

Cloud computing is a nascent paradigm in the field of data technology and computer science which is predicated on the use of the Internet, often known as the World Wide Web. One of the prominent concerns within this field is the security aspects of cloud computing. Contrarily, ensuring the preservation of access to the protection of sensitive and confidential information inside financial organizations, banks and other pertinent enterprises holds significant significance. This holds significant relevance. The efficacy of the security measures in providing assurance is not infallible and can be compromised by malevolent entities. In the current study, our objective is to examine the study about the security measures through the use of a novel methodology. The primary objective of this research is to investigate the subject of data access in the realm of cloud computing, with a particular emphasis on its ramifications for corporations and other pertinent organizations. The implementation of locationbased encryption facilitates the determination of accurate geographical coordinates. In experiment apply Integrated Location Based Security using Multi objective Optimization (ILBS-MOO) on different workflows and improve performance metrics significantly. Time delay averagely approximates improvement 6-7%, storage 10-12% and security 8-10%.
Plug-and-Play with POA based Maximum a Posteriori Denoisers for Image

Restoring damaged images has been an issue in the fields of image processing and basic computer vision for decades. Due to their improved performance, techniques based on discriminative convolutional neural networks (CNNs) have recently garnered a lot of interest. However, most of these frameworks only operate well on one type of picture restoration assignment, hence their performance on other types of image restoration is usually subpar. Research techniques leverage the fact that the Maximum a Posteriori (MAP) optimisation may be broken down into smaller sub-problems, such as a MAP denoising optimisation, to resolve this issue. We introduce the first full-stack method for MAP estimation in deep neural network image denoising. We prove that our approach will always result in a smaller MAP denoising goal, which can subsequently be employed in the general picture restoration optimisation technique known as the Puzzle Optimisation technique (POA). Our technique is theoretically analysed, and its quantitative efficacy is demonstrated through a number of tests. Our experiments validate the theoretical foundations of MAP and demonstrate that the suggested approach can reach 70x quicker presentation than the state-of-the-art.
Designing a Deep Learning Model for recommendation of Drugs on Sentiment Analysis

There has been a rise in creative automation work in recent years, and various applications have been found for machine learning. This essay's focus is on introducing a pharmaceutical recommendation system with the potential to greatly reduce the burden on experts. With the help of a deep learning algorithm, we create a drug recommendation system that utilises patient reviews to predict sentiment through the use of a number of vectorization techniques. Synthetic minority over-sampling method (SMOTE) is used to rectify the effects of unbalanced data on classification accuracy. The evaluation is sorted using a Recurrent Neural Network (RNN), with the weights chosen using an enhanced version of the firefly optimisation algorithm (IFFOA). Predicted feelings were evaluated using a battery of metrics counting precision, recall, f1score, accuracy, and specificity score. The classifier RNN-FCR has a higher accuracy rate (96%) than any other models as a direct result of TF-IDF vectorization.
Self-Dual Digital Devices with Calculations Testing by Modified Hamming Code

The authors proposed to implement self-checking devices with built-in control circuits according by two diagnostic parameters – belonging of code words to modified Hamming codes and belonging of check functions to the type of self-dual Boolean functions. The principles of constructing modified Hamming codes are given. Conditions are established under which the check bits of modified Hamming codes will be described by self-dual Boolean functions. It is proved that for each value of the number of check bits k, there is only one value of the number of data bits m for which the check functions will be self-dual. It is m=2k–2, k ∈ ℕ (k>1). For all other modified Hamming codes, the check functions are not self-dual. A condition for converting check functions of such Hamming codes into self-dual functions is formulated. The authors proposed the structure of the organization of the built-in control circuit according by the two diagnostic parameters indicated above. The results of simulating self-dual devices with calculations testing using modified Hamming codes with self-dual control functions are presented.
Method and Module on FPGA for Parallel Unification of Predicates

To solve the problem of increasing the performance of inference machines, the authors propose to use parallel methods for implementing the basic knowledge processing operations and a specialized device based on modern FPGAs. The block of parallel unification was designed in CAD Quartus II, and the prototype of the block was implemented on the Cyclone IV FPGA. The experiments carried out showed the correct operation of the unit and a significant increase in its performance.
Security Issues in the Deep and Dark Web: What to know?

Anything beyond Surface Web is defined as the Deep Web. In the literature, several researchers use the terms Deep Web and Dark Web interchangeably, but this is a misperception. The latter is a subpart of the deep web, and needs special software to access - the most popular of which is the Tor browser. None of these layers were developed for malicious objectives, however, in the same way that any tool can be utilized for completely different purposes than what it was initially intended for, their functionalities have been exploited and used illicitly. This paper aims to shed light on both oftenmisunderstood parts of the web, particularly on the security issues that surrounds them. The deep web and the dark web are defined herein, their uses and benefits outlined, and some security issues in these unindexed spaces are presented.
Research and implementation on automatic test verification technology for trust root and trust function

Trusted Computing technology is the basic key technology in cybersecurity. Trusted Computing is based on trusted root of TPCM, Equal Protection 2.0 has make clear requirements for trusted verification, but lack of testing methods and tools for the capabilities of trust root and trust functions. This paper analyzes the characteristics and test points of trusted verification technology, proposed a detected method with the fusion of scene simulation exhaustive testing, automated scripts, hardware serial port output and other test technologies, established a testing tool system of trusted function, realized to test of trusted functions such as resource isolation mechanism, trust chain construction and active measurement, supported for testing and assessment work, improved the development of Trusted Computing products.
Enhancing Cloud Network Security: A Comprehensive Review of Hybrid Cryptographic Algorithms

The need for strong data security while preserving maximum speed is becoming more and more important as cloud computing continues to change the digital world. Even if they are efficient, conventional cryptographic techniques sometimes give off security for efficiency. To overcome these difficulties, hybrid cryptographic algorithms, an emerging paradigm, combine the benefits of symmetric and asymmetric encryption methods. This article presents a thorough analysis of the design, use, and ramifications of hybrid cryptographic algorithms used in cloud networks. The first step of the research is to establish the shortcomings of current cryptographic techniques and the necessity of hybrid strategies. The work focuses on creating unique algorithms that take use of the strength of symmetric encryption for data secrecy and the security of asymmetric encryption for key exchange, drawing conclusions from the convergence of encryption paradigms. The project investigates the applicability and feasibility of these algorithms in real-world contexts by a methodical implementation within cloud networks.