Reviewerly/exHarmony huggingface dataset:
Image Classification with Deep Learning Methods for Detecting and Staging Bone Cancer from MRI

Mutations in cancer-causing genes can be passed down from parents to offspring. or because of DNA dam age from unprotected exposures to the elements. Cancer, which is brought on by unchecked cell development, can be lethal. Bone cancer begins when normally functioning bone cells undergo a malignant transformation and proliferate out of control, resulting in a tumour. When it comes to cancers of the bones, the incidence and severity rank high. To treat bone cancer effectively, early diagnosis and staging are essential. This study utilized CT and MRI with a Convolutional Neural Network to detect bone malignancy. The accuracy of the suggested technique was evaluated using 100 bone MRIs from patients who had already been validated. In this analysis, CNNs are utilised to determine if a bone tumour is benign or malignant. Classifying bone cancer using the proposed method is highly accurate (93.75 percent).
Empirical evaluation of LZW-Compressed Multiple Pattern Matching Algorithms

Data compression is used to reduce the cost of storing and transmitting increasingly large datasets in various domains ranging from bioinformatics to particle physics and general purpose computing. Most of these areas require the ability to scan the datasets for patterns like DNA sequences, malicious executable code or various other string queries, an operation that is hindered by the altered form of the data. In order to speed up the matching process, several compressed pattern matching algorithms have been previously proposed. This paper presents an overview of the state of the art in multiple pattern matching of Lempel-Ziv-Welch encoded archives, together with experimental results.
Contextual learning in Video Analytics for Human pose Detection using Bayesian Learning and LSTM

With the increase in the number of crimes in the city, we are in need of a Smart surveillance camera that detects anomalies in advance. In real-world object detection identity switching and object interactions are difficult and retain identities. Due to a lack of situational awareness real-time object detection and tracking lack semantic information. Surveillance cameras are installed everywhere, and we can’t identify peoples who might be a potential threat to security, Surveillance camera needs to be monitored all the time. Existing algorithm concentrate on feature aggregation at the pixel level. A novel method is proposed to track human different movements and positions encompassing deep and detailed features. The main goal of this paper is to propose a feature aggregation at a semantic level that will prevent threats in advance by introducing a deep learning technique with Contextual inference-based object detection using the Bayesian Rule which incorporates semantic relations between classes to recognize the location. It also integrates the relationship between the object in unseen classes which helps to identify located instances and predicts the location and extracts context features for superclass prediction.
Attribute Based Access Control for Cloud Resources using Smart Contracts

Accessing clo!ud resources requires critical security controls as there will be tracking of all the access attempts made by the users to access the resources from the system both legitimate as well as illegal. Traditional cloud-based systems are centralized and dependent on a single third party. This results in single-point failure and lacks data access control and privacy. Blockchain facilitates decentralized data management. Blockchain specifically allows construction of access control methods by using smart contract. To provide reliable and secure access control, we propose a decentralized system whose access is based on the attributes of the user with the help of a smart contract. Attribute-based access control allows subject and object attributes to be included in authorization decisions. Policies for each user are defined. The user’s attributes have to match with the policies in order to access the resources. It also authenticates end users requesting access to cloud resources. The performance evaluation of the proposed access control mechanism is carried out using Ethereum blockchain platform in terms of transaction latency, gas consumption and false positive rate.
Efficient Bounding Box Selection for Object Localization

Autonomous edge systems require a high level of accuracy with a small energy footprint for integration into real time applications. This paper proposes a novel neural network, the Bounding Box Vision Transformer (BB-ViT), designed to provide efficient localization of a single object of interest. BB-ViT is a vision transformer built for both speed and energy efficiency at only 40,092 parameters, it is capable of achieving mean intersection of union scores above 70% for five diverse classes of the Caltech101 dataset. On the Airplane class the network received a score of 92.52% mean IoU and MSE of 0.00042, equating to superior confidence of target localization.
Face Recognition Based on Cascade Classifier Using Deep Learning

The human face is significant because it is used as a tool of identification in our daily lives. One kind of biometric identification is Face Recognition (FR), which works by memorizing and using a person's unique facial features to identify that person in the future. Numerous researchers have been interested in biometric facial recognition technology due to its widespread use. Because of the non-contact nature of the process, FR technology surpasses other biometric-based authentication methods like fingerprint, palm print, & iris recognition. One such potential use for facial recognition (FR) algorithms is in remote identification when no physical touch or interaction with the target individual is required. This study proposes a method for constructing an FR system with a learning strategy depending on the DCNN algorithm. The approach of Deep Learning (DL) is presented for learning & assessing all samples & new inputs in a scheme. The ORL and YALE face datasets are used in studies that compare the findings to those produced by the Deep CNN approach. The proposed DL method uses a systematic approach to learning from and assessing new inputs and samples. The proposed model Deep CNN obtained 96.04 percentage points (or a validation accuracy of 62.2%) over the ORL dataset after 100 iterations with a loss of 17.33 percent. DCNN achieves a 99.99% training accuracy and a 99.89% validation accuracy without any data loss after 100 epochs of training on the Extended Yale B dataset. The proposed DL method analyses and rates are existing data from the system and fresh face inputs. In addition, it gathers information from the facial features & enhances the D-CNN algorithm for facial classification. Utilizing CNN (Convolutional Neural Networks), the accomplishments in several contests are advancing and becoming the focus of study.
Medical Image Denoising Expending A Hybrid Recurrent Neural Network Through Support Vector Machine

Medical imaging helps doctors make more informed treatment decisions by providing a visual representation of the anatomy that can't be gleaned from a physical exam alone. Medical diagnosis relies heavily on the visual. Over the past few years, medical imaging has established itself as a crucial tool in the diagnosis of illness. Over the past few of decades, a number of medical imaging modalities have emerged, each with its own unique set of capabilities. These methods allow for the acquisition of images of the internal anatomical structures that need to be inspected, without the need to physically open the body. In this study, a system was suggested that uses a Hybrid Recurrent Neural Networks with Support Vector Machine (HRNN-SVM) method to effectively remove white noise, salt and pepper noise, Gaussian and speckle noise from CT lung images. To enhance RNNs' discrimination skills, the SVM is implemented in the final RNN layer in the HRNN-SVM technique. Batch normalization is combined with residual learning to speed up the learning process and increase accuracy. In this study, LSTM is used to carry out batch normalization. The Firefly Algorithm is used to determine the best batch size for batch normalization (FA). When compared to the PSO algorithm, it reduces the time required for training. To do this, the RNN's hidden layers are gradually splitting apart the image's structure from the noisy observation.
IoT Enabled Trustless Trusts for Water Distribution Systems in SmartCities – An Architectural Design

The decisive policies of agile automated machines and the collective involvement of residents/authorities signal a certain shift in the development of smart city solutions such as water distribution, electricity utilization, and so forth. Residents, in modern era, might show distrust with officials, especially while sharing the scarcely available drinking water sources. This article proposes a trustless trust architectural design for smart water distribution systems; it discloses a few water distribution mechanisms and their challenges. The proposed architecture enriches the trust among residents and authorities. The notions, illustrated in the article, would spark innovations for future developments.
Semi-Supervised Pipeline for Human Sprites Neural Rendering

In this paper we are discussing the possibility of a hybrid approach in renderable scenes. The main idea of the presented experiment is to render the human actors by using existing videos of the characters. The input video is first converted to a sprite dataset. The dataset is generated with supervised techniques but human intervention is also required. After that we extract body and pose parameters. Lastly, we render novel poses using a GAN-based approach similar to pix2pix.
A Secure Mechanism for Prevention of Vishing Attack in Banking System

A vishing attack is a category of Phishing attack in which the attacker attempts to capture clandestine information through a phone call or Short Message Service (SMS). These types of attacks mostly target financial information and uneducated people are victims. In this paper, a user friendly security mechanism is proposed for preventing vishing attack in banking system under one nation. The proposed authentication mechanism uses a Central Banking Server (CBS) which act as an Authentication Server (AS) and a nationwide unique phone number. The proposed approach is simulated and analyzed by means of Scyther which is a protocol verification tool and the results show that our mechanism is more protected and harmless from vishing attacks.
Measurement based dimension descent association algorithm for OTHR multi-detection multi-target tracking

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms.
Regression Analysis-Based Predictive Model for E-Commerce Application

Online sales have been growing every year. It is necessary for the seller to learn about the market choice to make a profit. The black Friday Sales dataset from Kaggle is used to train the prediction model. We get the purchasing power of the customer from the dataset. The prediction model is done using regression algorithms like Linear regression, Ridge regression, and Lasso regression and also using regression trees like Random forest, and Decision tree regressor. The performance evaluation is done using performance metrics like Mean Square Error(MSE), Mean Absolute Error(MAE), and Root Mean Square Error(RMSE). The model which uses the Random Forest Regressor outclasses the other training models with the least MAE, and MSE scores of 47.19 and 3062.72. Also the Decision Tree Regressor performed well with the MAE, and MSE scores of 48.73 and 3363.87, with better timing of 16s, whereas Random Forest Regressor took 6 mins. The Random Forest Regressor has the better Cross-Validation score of 3053.05 followed by the Decision tree with a score of 3340.25.
Survey of Evidence Collection Methods for Internet of Things Forensics

Internet of Things (IoT) usage in different applications and the usage of open source communication band are vulnerable to different types of attacks. Determining the root cause of the attacks, isolating the nodes, and providing countermeasures to overcome such threats in the future are important aspects for the success of IoT and its usage in more areas of applications. This paper presents a survey of recent works on the evidence collection module in IoT forensics. The evidence collection is the second stage in the forensics investigation cycle. The classification of the evidence collection mechanisms based on the strategy adapted has been discussed. Additionally, the discussion on the evidence collection methodologies and the future directions for research in the evidence collection module pertaining to IoT forensics is presented.
A Low-Randomness First-Order Masked Xoodyak

Xoodyak, a finalist in the Round 3 of the Lightweight Cryptography Standardization Process, is a compact and efficient lightweight cryptographic algorithm. Resistance to side-channel attacks is an important evaluation metric of this Process. In this work, we dedicatedly analyze and realize the domainoriented masking implementation of Xoodyak. We first perform a security evaluation for the domain-oriented masking in Xoodyak’s implementation. Moreover, we propose a randomness reduction technique for domain-oriented masking implementation of Xoodyak, where existing techniques are inapplicable. Finally, our protected design is implemented on FPGA and evaluated on ASIC. Potential side-channel leakage is evaluated using Test Vector Leakage Assessment. Results show that our implementation is compact, side-channel leakage-free, and only consumes 33% of the originally required randomness.
MagHop: Magnetic Spectrum Hopping for Securing Voltage and Current Magnetic Sensors

Voltage and current magnetic sensors (VCMSs) are pervasive in safety-critical systems. They use a magnetic field as a transduction medium to sense the input signal. Therefore, if an attacker manipulates the magnetic transduction medium of this sensor by using an intentional EMI or external magnetic fields, no amount of security mechanism after the fact can help. Fortunately, our work provides a defense against this form of physical attack.The core idea of our defense is to shift the frequency spectrum of the magnetic field, which is used as the transduction medium of the sensor, to another spectrum unknown to an attacker. In addition, the frequency spectrum which carries the magnetic field in the transduction medium, is varied in a pseudo-random fashion so that the attacker will not be able to track it to inject any EMI into it. Even a sweeping attacker, who can vary the EMI’s frequency, cannot bypass our defense because of the check and select approach of our defense. As the magnetic field’s spectrum in the transduction medium of the sensor hops in a different spectrum, the defense is named as Magnetic Spectrum Hopping (MagHop). While prior works fail to prevent an EMI, which has the same frequency as the input signal, MagHop is equipped to handle this limitation of the prior works. Moreover, a low-power, real-time coherent prototype of MagHop is designed that is evaluated with a realworld application: a grid-tied inverter. Finally, we thoroughly evaluate MagHop on ten different sensors from six different manufacturers to prove its robustness against the EMI or external magnetic field injection attack on VCMSs.
Gamification of Learning using 3D Spaces in the Metaverse

The concept of a multiway system of learning refers to an educational approach that uses multiple methods and modes of learning. This creates a dynamic system where information rapidly flows horizontally and vertically, increasing the user’s knowledge in the most efficient way possible using the best and most immersive technologies. The goal is to create the most interactive and effective e-learning platform, powered by next-gen AI, metaverse, and peer-to-peer learning. The system is designed to foster healthy competition among peers and encourage collaboration, It uses an interactive learning module within a 3D metaverse where users can interact with each other, solve STEM and other challenges, and interact with virtual teachers powered by next-gen AI such as ChatGPT GPT-3.5, IBM Watson etc. The aim is to gamify the learning and assessment experience, allowing learners to compare progress with peers, learn concepts in unique and fun ways, and interact with other learners seamlessly. They can also take part in varied and interactive assessments. By creating a learning system using the metaverse, the objectives of gamifying the learning experience and enhancing the knowledge of users are successfully achieved.
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification

Dataset distillation aims at synthesizing a dataset by a small number of artificially generated data items, which, when used as training data, reproduce or approximate a machine learning (ML) model as if it were trained on the entire original dataset. Consequently, data distillation methods are usually tied to a specific ML algorithm. While recent literature deals mainly with distillation of large collections of images in the context of neural network models, tabular data distillation is much less represented and mainly focused on a theoretical perspective. The current paper explores the potential of a simple distillation technique previously proposed in the context of Less-than-one shot learning. The main goal is to push further the performance of prototype-based soft-labels distillation in terms of classification accuracy, by integrating optimization steps in the distillation process. The analysis is performed on real-world data sets with various degrees of imbalance. Experimental studies trace the capability of the method to distill the data, but also the opportunity to act as an augmentation method, i.e. to generate new data that is able to increase model accuracy when used in conjunction with - as opposed to instead of - the original data.
FLOOR - Forklift Laser OmnidirectiOnal Robot

The robot presented in this paper was developed with the main focus on participating in robotic competitions. Therefore, the subsystems here presented were developed taking into account performance criteria instead of simplicity. Nonetheless, this paper also presents background knowledge in some basic concepts regarding robot localization, navigation, color identification and control, all of which are key for a more competitive robot.
SCALE: Secure and Scalable Cache Partitioning

Dynamically partitioned last-level caches enhance performance while also introducing security vulnerabilities. We show how cache allocation policies can act as a side-channel and be exploited to launch attacks and obtain sensitive information. Our analysis reveals that information leaks due to predictable changes in cache allocation for the victim, that is caused and/or observed by the adversary, leads to exploits We propose SCALE, a secure cache allocation policy and enforcement mechanism, to protect the cache against timing-based side-channel attacks. SCALE uses randomness, in a novel way, to enable dynamic and scalable partitioning while protecting against cache allocation policy side-channel attacks Non-determinism is introduced into the allocation policy decisions by adding noise, which prevents the adversary from observing predictable changes in allocation and thereby infer secrets. We leverage differential privacy (DP), and show that SCALE can provide quantifiable and information theoretic security guarantees. SCALE outperforms state-of-the-art secure cache solutions, on a 16-core tiled chip multi-processor (CMP) with multi-programmed workloads, and improves performance up to 39%and by 14%, on average.
Securely Transmit Data Over Long Distances Using Quantum Key Distribution Based On E91 Protocol

At its core, quantum computing is a fastdeveloping technology that has the potential to process massive volumes of data at high speeds. Some factoring issues may be difficult for the classical computer to solve because of the nature of the factoring. Quantum computing data connections are carried out using optical fiber at a distance as short as possible. While using the E91 protocol for quantum key distribution in a wireless network communication channel, we may extend the distance over which keys are sent. How to design a wireless communication channel for use in quantum computing applications. Using this technology, we are now investigating the development of a broadcast channel that will enhance the data transmission range while maintaining high security and minimizing time consumption. In our technique, we employed polarization multiplexing to communicate across a broadcast channel satellite connection, which allowed us to share data between the transmitter and receiver at the same time. The transfer key is protected by the use of polarization multiplexing and the E91 protocol, both of which are implemented. In a security network, it is used to create an extended communication channel distance between two nodes. In the end, we can meet our goal in this study by comparing the existing method to the performance analysis shown in a graph.
A Deep Reinforcement Learning Solution for the Low Level Motion Control of a Robot Manipulator System

Motion planning and control is a necessary aspect of incorporating robots into the real world. There are a variety of different types of control tasks that involve collision avoidance and fine control, that are difficult to program without the use of artificial intelligence (AI), especially in an non-stationary environment. In this paper, one method for applying deep reinforcement learning (RL) to the motion planning of a manipulator robot is described. Using a soft actor-critic (SAC) network, a model is trained to direct the manipulator to various locations so as to avoid colliding either its hand or the object it carries with a game tower. This demonstrates a simple and effective method for training an agent to achieve its goal that generalizes to similar but different environments.