Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Weapons Detection Using Various Deep Learning Techniques

In crime scene analysis, object detection can be used to identify and track objects and people, which can help investigators to recreate the events and understand the sequence of actions that took place during a crime. The images are manually annotated, which is a process where a human expert goes through each image and marks the location and class of objects within the image. This process is important for training object detection algorithms as it provides the necessary ground truth data for the algorithm to learn from. In the case of crime scene analysis, high accuracy is crucial, as it can help ensure that no evidence is missed, but speed is also important, as time is often a critical factor in investigations. The proposed system in this study attempts to balance this trade-off by using algorithms like YOLOv5, SSD, and RCNN, which are known for their real-time performance while maintaining a high accuracy level.
Comparative Study and Analysis of DWT-SPIHT with DWT-EZW Method for Image Compression

The use of digital photographs has increased along with the development of digital technologies. Due to the vast amounts of information it contains, digital photographs need a lot of storage space, as well as bigger transmission bandwidths and longer transmission times. Therefore, on compressing the images all the redundant bits of information present in the image under test are removed while keeping only the essential information needed to reconstruct the image later on. In this study, DWT-SPIHT technique is introduced, which may be used to compress and reconstruct images at various degrees of wavelet decomposition across wavelet families that were initially a subdivision of the MATLAB wavelet family. Simulations have been conducted on Cameraman Image during this work of different resolution at different levels of decomposition and for different types of thresholding techniques to prove that this algorithm works well and provide us with the good reconstruction quality of the image. The simulation results demonstrate that, when compared to the DWT-EZW algorithm, the proposed DWT-SPIHT algorithm performs significantly better in terms of evaluation parameters like peak signal to noise ratio (PSNR), mean square error (MSE), and visual perception at higher compression ratios (CR) and low bit per pixel values (BPP).
Smartphone based Human Activity Recognition using CNNs and Autoencoder Features

Recognition of human activities is essential for many applications, and the widespread availability of low-cost sensors on smartphones and wearables has enabled the development of mobile apps capable of tracking user activities “in the wild.” However, dealing with heterogeneous data from different devices and real-time scenarios presents significant challenges. In this study, a novel learning framework is proposed for Human Activity Recognition (HAR) that combines a Convolutional Neural Network (CNN) with an autoencoder for feature extraction. The study also investigates the importance of preprocessing techniques, including orientation-independent transformation, to mitigate heterogeneity when dealing with multiple types of smartphones. The results show that the proposed approach outperforms state-of-the-art methods in HAR, with an accuracy of 95.74% on the heterogeneous dataset used in this study. Furthermore, the study demonstrates that proposed framework can be effectively deployed on smartphones with limited computational resources, making it suitable for real-world applications.
Comparative Analysis on Text Detection for Scenic Images using EAST and CTPN

The stated goal of this study is to conduct a thorough analysis of a text detection model built with deep learning. Text detection and localisation methods can pinpoint the precise location of a picture's text region. The primary goal of text detection is to locate instances of text within images of beautiful landscapes. Text reading in scene images is a rapidly growing field of study due to its numerous practical applications in areas such as image/video interpretation, visual search, automated driving, and blind auxiliary. The primary goal of this study is to compare and contrast two text detectors, the East text detector and the CTPN Text detector, based on various criteria to determine their relative merits and shortcomings. The goal here is to improve the efficiency of detecting landscapes in photographs. Although deep learning is present in both approaches, their capabilities are different. This comparison aims to find a better method for larger datasets to develop a more sophisticated, accurate, and time-efficient detection method. Both techniques have been reviewed and implemented, illuminating the potential for enhanced and precise detection with the EAST detector. The accuracy and loss were measured against a bounding box drawn around the text in the test image, and both models performed satisfactorily. Comparison. The publicly available ICDAR 2015 dataset is used in this study. It features test images (a total of 500) and training images (1,000).
Envisioning a Hybrid Recommendation System for Filtering the Content from Short Videos

Short videos have become increasingly popular in recent years due to the rise of social media platforms. Many creators have moved away from making long form content and towards creating 6 to 15 second clips that feature humorous, viral, or relatable content. As well, people are more likely to watch short videos due to the limited amount of time for which they can devote their attention. This has become especially visible during the pandemic, as the amount of video consumption on social platforms has skyrocketed. In general, short videos offer an entertainment that requires little effort to view and consume. For a perfect short video, perfect content is purely necessary, in an analogous way as food is to humans. Platforms like YouTube, Netflix, Amazon, etc. use their own video recommendation systems for filtering out the contents and recommending the best accurate video based upon the user's choice. Filtering techniques such as content filtering, collaborative filtering, and deep learning-based filtering play important roles in the development of a good recommendation system. Most of the modern recommendation systems are also working upon the principle of ‘Hybrid Filtering’, wherein both content as well as collaborative filtering techniques are simultaneously used. In this study, use case of one of the popular online streaming platforms: YouTube is considered, which uses neural network-based collaborative filtering. As proposed research is upon the analysis of short videos, YouTube Shorts service are considered. Using the experimentation via machine learning algorithms, a comparative study between the three types of recommendation systems is developed. This research is concluded by jotting down the advantages of content filtering-based system over the collaborative one, and therefore suggest a hybrid recommendation system model which can perfectly filter out the contents of short videos being uploaded onto the YouTube platform.
Study of Image Data Security with Cloud

The on-demand availableness of computer systems resources, especially storage of data (cloud storage) and rectifying power, with no direct ongoing administration by the user is what they call cloud computing. Large cloud functions are usually spread among several locations, each of which constitutes a data center. Customers may save money on capital expenses by using cloud computing, which frequently takes a “pay-as-you-go” approach. Coherence in cloud computing is achieved by sharing resources. In today's world of the Internet, demand for cloud services is increasing drastically leading to the production of new services day by day. As the services increase, the data gets primarily targeted by spiteful users who attempt to steal the data for their own atrocious and unethical activities. Users and trustworthy applications are considering more security and privacy and services get more in demand. Moreover, this study has reviewed several algorithms such as CPE-ABE (Ciphertext policy attribute-based encryption), ABE, KP-ABE (Key policy attribute-based encryption) CSP (Constraint satisfaction problem), PKG, AES (Advanced Encryption Standards), SHA-1 (Secure Hash Algorithm), Photo encryption, Photo decryption, PRE, IDEA (International Data Encryption algorithm) and LSBG (Least Significant Bit Grouping) for image data security. As already discussed, the increasing threats and frauds around the world, safe and secure applications and services should be created to resolve this problem so that people can store data on a platform that can be relied on. This research study has discussed about the concept of what are the paradigms required for securing and protecting the data and securing the image data at an encrypted level. This study has reviewed several existing research works, studied different algorithms which have been used in different research articles, and compared their strengths and drawbacks accordingly. Different research works have been summarized in a form of table, which includes multiple algorithms that are used to secure data, which are then distinguished them based on the strengths and drawbacks.
Application of Bilateral Filters for Denoising Rician Noise in MRI Images

Medical imaging reveals the internal structures of the human body. One such imaging modality is Magnetic Resonance imaging, which reveals the soft tissue characteristics of human body. MRI can also detect tumors, hemorrhages and infections in human brain. MRI images are more prone to Rician noise. Thus, preprocessing of MRI brain images must be done to diagnose the brain diseases. The MRI brain images without Rician noise and the images with Rician noise of various noise levels were used in this experiment. The main aim of this work is to eliminate the Rician noise present in MRI brain images and to protect the edges as well as the important details in the brain image. Bilateral filter eliminates the Rician noise by taking weighted average on nearby pixels. In this research work, a comparative study of three such bilateral filters called Standard Bilateral Filter (SBF), Robust Bilateral Filter (RBF) and Weighted Bilateral Filter (WBF) was carried out. The effectiveness of the Bilateral filters was analyzed by means of Peak Signal to Noise Ratio (PSNR) and various other performance metrics. The experimental results show that the Weighted Bilateral Filter excels the other two Bilateral filters for many values of standard deviation. However, for certain other values of standard deviation, the Robust Bilateral Filter also marks a good performance.
Improved Accuracy in Image Tampering Detection using MobileNet

Recent developments in the technology and the exponential increase in accessing the digital networks have increased over the past few years. With this expansion, the data that is being accessed by the remote devices have also increased. This scenario is more pertinent over the images or image-based data which are used by unauthenticated users. It is relatively simple for unscrupulous intruders to subtly alter these digital photographs for illegal transactions. Therefore, preventing the modification of images have become a significant concern. This study mainly concentrates on the development of novel technique for the protection of medical images from tampering. It is proposed to use MobileNet for classification. Like the inception towers, MobileNets use depth-wise separable convolutions. These cut down the latency by decreasing the number of a parameters. Moreover, MobileNets have convenient model-shrinking parameters that can be called before training to achieve the desired precise size. Results show that the accuracy of the proposed technique has increased twice when compared to the existing ResNet based architecture.
Split-Slope Chaotic Map Providing High Entropy Across Wide Range

This paper presents a novel one-dimensional discrete-time chaotic map. A significantly improved chaotic behavior, compared to already published one-dimensional maps, is achieved in the proposed design by virtue of this non-linear map’s stiffer transfer characteristics. The novelty of the work comes from the proposed methodology of splitting upward and downward slopping mechanisms to gain a stiffer slope in the uni-modal nonlinear circuit. The design methodology is presented with the help of the stability analysis of fixed points, which is generally applicable to a wide variety of nonlinear circuits. The chaotic complexity of the proposed circuit is analyzed with the bifurcation plot, correlation coefficient, and Lyapunov Exponent. The results are compared with reported works to demonstrate a significant improvement. Along with high chaotic complexity, this split-slope chaotic map provides a wide chaotic range covering 100% of the overall region of operation. The high chaotic complexity across a wide chaotic range is achieved with a remarkably low transistor-count circuit which is suitable in many hardware-security applications including, random number generation, chaotic logic circuits, and so on, for resource-constrained devices.
A Comprehensive and Secure Trustless Blockchain Framework for Autonomous Vehicles

As Autonomous Vehicles (AVs) become increasingly prevalent, there is a growing need for secure and efficient data transfer between vehicles and other stakeholders in the transportation ecosystem. Blockchain technology has emerged as a promising solution to this challenge, offering a decentralized and tamper-resistant platform for storing and sharing data. This research study proposes a blockchain-based solution for addressing the challenges related to data security, ownership, trust, transparency, and auditability in AVs. It covers the use of blockchain for vehicle identity verification, secure data sharing, and real-time communication between AVs and other entities such as infrastructure and insurance providers. The proposed solution incorporates smart contracts, Merkle trees, and cloud storage to provide a trustless environment for data transfer and ensure that data is tamper-proof and auditable. The paper also provides a detailed implementation of the proposed solution. The findings of this research study provides a comprehensive overview of the state-of-the-art AVs and blockchain technology, highlighting the potential of this emerging field to transform the transportation industry.
SVM Based Fake Account Sign-In Detection

In today's society, Fake social profiles are multiplying quickly and disseminating false information and news through this expanding channel. The misuse of personal data and illegal social offenses are the primary causes of false accounts. In this research work, fake accounts are identified and prevented in the browser. The technique uses two algorithms: Neural Network (NN) and Support Vector Machine (SVM), to classify the fake accounts and block them in the browser. In modern days, everyone's life is strongly connected to online social networks. It is simpler to add new friends and stay up to speed with their changes. A variety of fields, including business, research, education, activism, and the workplace are impacted by Online Social Networks (OSN).
A<sup>2</sup>OP: an A* Algorithm OPtimizer with the Heuristic Function for PCB Automatic Routing

The A* algorithm has been widely employed in Printed Circuit Board (PCB) routing planning. As the routing complexity grows, there are two critical problems with the common A* algorithm: too large searching space and unacceptable routing results. To address these issues, this paper proposes an A* Algorithm-based OPtimizer strategy for PCB automatic routing(A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> OP). The proposed A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> OP strategy introduces two key factors to enhance the heuristic function of the A* algorithm: the parent node and dynamic weights. In such way, the initial experiments validate that a faster and more favorable routing planning can be achieved.
Automatic Subnetwork Search Through Dynamic Differentiable Neuron Pruning

Locating and pruning redundant neurons from deep neural networks (DNNs) is the focal point of DNN subnetwork search. Recent advance mainly targets at pruning neuron through heuristic "hard" constraints or through penalizing neurons. However, these two methods heavily rely on expert knowledge in designing model-and-task-specific constraints and penalization, which prohibits easily applying pruning to general models. In this paper, we propose an automatic non-expert-friendly differentiable subnetwork search algorithm which dynamically adjusts the layer-wise neuron-pruning penalty based on sensitivity of Lagrangian multipliers. The idea is to introduce "soft" neuron-cardinality layer-wise constraints and then relax them through Lagrangian multipliers. The sensitivity nature of the multipliers is then exploited to iteratively determine the appropriate pruning penalization hyper-parameters during the differentiable neuron pruning procedure. In this way, the model weight, model subnetwork and layer-wise penalty hyper-parameters are simultaneously learned, relieving the prior knowledge requirements and reducing the time for trail-and-error. Results show that our method can select the state-of-the-art slim subnetwork architecture. For VGG-like on CIFAR10, more than 6× neuron compression rate is achieved without accuracy drop and without retraining. Accuracy rates of 66.3% and 57.8% are achieved for 150M and 50M FLOPs for MobileNetV1, and accuracy rates of 73.46% and 66.94% are achieved for 200M and 100M FLOPs for MobileNetV2, respectively.
PriML: An Electro-Optical Accelerator for Private Machine Learning on Encrypted Data

The widespread use of machine learning is changing our daily lives. Unfortunately, clients are often concerned about the privacy of their data when using machine learning-based applications. To address these concerns, the development of privacy-preserving machine learning (PPML) is essential. One promising approach is the use of fully homomorphic encryption (FHE) based PPML, which enables services to be performed on encrypted data without decryption. Although the speed of computationally expensive FHE operations can be significantly boosted by prior ASIC-based FHE accelerators, the performance of key-switching, the dominate primitive in various FHE operations, is seriously limited by their small bit-width datapaths and frequent matrix transpositions. In this paper, we present an electro-optical (EO) PPML accelerator, PriML, to accelerate FHE operations. Its 512-bit datapath supporting 510-bit residues greatly reduces the key-switching cost. We also create an in-scratchpad-memory transpose unit to fast transpose matrices. Compared to prior PPML accelerators, on average, PriML reduces the latency of various machine learning applications by > 94.4% and the energy consumption by > 95%.
SQRTLIB : Library of Hardware Square Root Designs

Square-root is an elementary arithmetic function that is utilized not only for image and signal processing applications but also to extract vector functionalities. The square-root module demands high energy and hardware resources, apart from being a complex design to implement. In the past, many techniques, including Iterative, New Non-Restoring (New-NR), CORDIC, Piece-wise-linear (PWL) approximation, Look-Up-Tables (LUTs), Digit-by-digit based integer (Digit-Int) format and fixed-point (Digit-FP) format implementations were reported to realize square-root function. Cartesian genetic programming (CGP) is a type of evolutionary algorithm that is suggested to evolve circuits by exploring a large solution space. This paper attempts to develop a library of square-root circuits ranging from 2-bits to 8-bits and also benchmark the proposed CGP evolved square-root circuits with the other hardware implementations. All designs were analyzed using both FPGA and ASIC (130 nm Skywater node) flow to characterize hardware parameters and evaluated using various error metrics. Among all the implementations, CGP-derived square-root designs of fixed-point format offered the best trade-off between hardware and error characteristics. All novel designs of this work are made freely available in [1] for further research and development usage.
CMP-SiL: Confidential Multi Party Software-in-the-Loop Simulation Frameworks

Increasing complexity of systems and software in the automotive industry, coupled with distributed development environments has intensified adoption of Software in the Loop (SIL) systems, i.e. setup where traditional hardware components are designed and tested in pure virtual PC/IT environment consisting of virtualized hardware and networks. Cloud-based SiL simulation systems involving multiple contributors and orchestrators create huge risks for organizations due to potential for leakage of confidential model-IP to adversaries within the distributed infrastructure. This can create a bottleneck for wide-scale adoption of SiL-systems. We propose a data-flow architecture using trusted-computing technologies (e.g. Intel-SGX) to protect models and IP in cloud-based SiL environments. We illustrate that these protections can be designed to be compatible with existing SiL tools and workflows with minimal modifications. Further, we highlight the need for future standardization efforts of such security architectures in the SiL domain.
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning

Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enough to be compatible with NISQ devices and the method of automatic differentiation for optimizing circuit parameters is familiar to machine learning (ML). While the results bare interesting promise for an era when quantum machines are more readily accessible, if one can achieve similar results through non-quantum methods then there may be a more near-term advantage available to practitioners. To this end, the nature of this work is to investigate the utilization of stochastic methods inspired by a variational quantum version of the long short-term memory (LSTM) model in attempt to approach the reported successes in performance and rapid convergence. By analyzing the performance of classical, stochastic, and quantum methods, this work aims to elucidate if it is possible to achieve some of QML’s major reported benefits on classical machines by incorporating aspects of its stochasticity.
A SPICE-based Framework to Emulate Quantum Circuits with classical LC Resonators

A circuit system consisting of LC-oscillators is mathematically shown to emulate quantum circuits. Here we develop a SPICE-based framework for emulating universal quantum gates (the phase-shift, the Hadamard, and the CNOT gates). To reconstruct each gate behavior, the inductors and capacitors are chosen and tuned precisely. Each quantum state is perfectly described by the phase and amplitude of each oscillator. In principle, our framework can simulate a quantum system with any arbitrary number of qubits since each gate process is perfectly achieved. Finally, we have simulated a quantum circuit with 3 qubits as input that consists of all the universal quantum gates. Our simulation result shows that our framework can classically emulate the result of any quantum circuit with an arbitrary number of qubits and quantum logic components.