Reviewerly/exHarmony huggingface dataset:
Performance Analysis of TensorFlow2 Object Detection API Models for Engineering Site Surveillance Applications

Object detection in camera images is a powerful tool that can be utilized for a variety of applications. The COCO 2017 dataset assembled by Microsoft is one of the most extensive datasets publicly available for training and testing object detection models. The TensorFlow2 Object Detection API developed by Google contains a collection of pre-trained models using the COCO dataset and provides the tools for testing and deploying object detection models. Six of these models were used to test detections for motion capture images from SitePro Inc.
Camera with Artificial Intelligence of Things (AIoT) Technology for Wildlife Camera Trap System

With regards to wildlife research, field observation and investigation are generally required for researchers to find out the species and behaviours of the inhabitants around a certain area. Since the activity time for different species varies, the researchers usually spend a consciously large amount of time in the field recording the information. To save manpower and time, camera traps are thus created to carry out this time-consuming task in an effective way. Researchers can obtain information such as the inhabitants' species, their show-up time and the on-the-spot temperature which could be analyzed preliminarily so that an advanced and productive study can follow. In fact, owing to the various constraints and inconvenience upon data access to the camera traps, those devices are now designed with IoT technology as its development becomes more successful, which allows users to access information in real time and more conveniently. Sadly, it also causes some other problems in the meantime. Consequently, throughout this research, we hope those associated problems can be resolved in use of AIoT technology and hence figure out a dedicated architecture for the wild environment thereafter.
ALAE-TAE-CutMix+: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors

Human Activity Recognition (HAR) through wear-able sensors greatly improves the quality of human life through its multiple applications in health monitoring, assisted living, and fitness tracking. For HAR, multi-sensor channel information is vital to performance. Current work states that applying an attention neural network to prioritize discriminatory sensor channels helps the model classify activity more precisely. However, getting discriminatory information from multisensory channels is not always trivial. For example, when collecting data from elderly hospitalized patients. In this context, existing HAR methods struggle to classify activities, particularly activities with similar natures. Moreover, HAR deep models predominantly suffer from overfitting due to small datasets, which leads to poor performance. Data augmentation is a viable solution to this problem. However, currently available data augmentation methods to HAR have various drawbacks, including the pos-sibility of being domain-dependent, and resulting in distorted models for test sequences. To address the aforementioned HAR problems, we propose a novel framework that primarily focuses on two aspects. First, enhancing the latent information across each sensor channel and learning to exploit the relation among multiple latent features and the ongoing activity. Consequently, this enriches the discriminatory feature representations of each activity. Second, a new augmentation strategy is introduced to address the shortcomings of existing multi-sensor channel data augmentation to generalize our HAR model. Our model outperforms existing state-of-the-art approaches on the four most commonly used HAR datasets from diverse domains. We exten-sively demonstrate the effectiveness of the proposed framework through detailed quantitative analysis of experimental results and ablation studies.
Template Attack Against AES in Counter Mode With Unknown Initial Counter

Despite long-contested viability, numerous applications still rely upon Advance Encryption Standard (AES) in Counter mode (AES-CTR). Research supports that the vulnerabilities associated with CTR from a mathematical perspective, mainly forgery attempts, stem from misusing the nonce. When paired with cryptographic algorithms, assuming no nonce misuse increases the complexity of unraveling CTR. This paper examines the pairing of CTR with AES-128 (AES-CTR). It includes (1) full key recovery for a software implementation of AES-CTR utilizing a template attack (TA) and (2) enhancing the TA analysis's point of interest (POI) using first-order analysis and known key to identify leaky samples.
Blockchain Development in Colab: An Ethereum-Based Bicycle Registry System

Blockchain is a technology that has taken the business world by storm. The worldwide spending on blockchain solutions is expected to grow from 1.5 billion in 2018 to an estimated 15.9 billion by 2023. According to a recent study by LinkedIn, it is found that Blockchain tops the list of most in-demand hard skills for 2020. Colaboratory, or “Colab” for short, is a product from Google Research, that allows anybody to write and execute arbitrary python code through the browser. This paper will begin with the concept and discussion of Blockchain and Colab followed by developing an Ethereum-based project that creates smart contracts within Colab for bicycle registry that will communicate with a Database system.
Recent Advances in Artificial Intelligence Enabled Tutoring Systems: A Survey

Applications of Artificial Intelligence (AI) have been increasing day by day. Moreover, with the effect of the pandemic, it is observed that there is no sector left from education to transportation, to entertainment to the health sector, that has not seen an increase in the usage of AI. Recently, even though the increase in AI interactive tutoring studies can be understood as a side effect of the pandemic, the fact that many companies invest and work in AI systems indicates that many researchers are eager to design and develop new applications. This paper presents an overview and survey of how AI techniques are used in tutorial systems and how such applications are developed for tutoring applications and systems.
Using Reinforcement Learning to Train Generative Adversarial Networks for Image Generation

Image generation using machine learning techniques is not a new concept and the most popular class of models is the Generative Adversarial Networks, known as GANs. This paper will investigate a novel method of using deep reinforcement learning in conjunction with the GANs framework to train neural networks to produce images. Two reinforcement learning algorithms - the Advantage Actor-Critic and the Twin Delayed Deep Deterministic Policy Gradient algorithms, are applied in this preliminary work. Several models were experimentally trained using varied hyperparameters and the quality of the resulting generated images shows promise and presents a baseline for further study on this problem.
IoT Security: AI Blockchaining Solutions and Practices

The IoT market is continuously expanding, and security measures parallel to the growing industrialization has drawn the research community's attention. While several works have focused on the different technologies used to protect IoT devices, this paper aims to narrow the focus to Blockchain and Artificial Intelligence (AI) solutions for security and privacy issues faced by these devices. These technologies address two major vulnerabilities with edge devices on IoT networks: centralization and weak edge nodes. Blockchain provides a distributed system that can help avoid a single-point failure and can be used to optimize battery life on low-level devices by load balancing. On the other hand, AI's power to learn and adapt is critical to automate systems and make them intelligent enough to analyze collected data. The combination of these two technologies is capable of mitigating multiple challenges IoT architectures face currently. The paper discusses the latest major architectures that integrate Blockchain and AI to enhance security within the. This paper also presents the comprehensive take on the device security and drawbacks to these mechanisms. This work also addresses the future of security in IoT and suggests different areas that need further work to better comprehend how the combination of these technologies can assist in enhancing security.
The Impact of Dynamic Adjustment of Swarm Behavior on Particle Swarm Optimization Performance using Benchmark Functions

Solving Optimization problems require finding the best solution while considering a large set of possible feasible answers. One of the important factors in tuning the search optimization methods that can significantly impact their performances is to determine what parameter settings to use. Real world problems are often consist of search spaces that are quite dynamic with varying degrees of complexities. In such search spaces, the conventional search optimization methods in which the parameters and behaviors of the optimization methods are pre-set and unchanging in response to the changes in the complexity of search space from one iteration to the next are known to be less adequate and unsuitable. Particle Swarm Optimization (PSO) is known as one of the most well-known meta-heuristics with known search characteristics in response to benchmark functions and is considered as methodological basis for multiple dynamic search behavior innovations this study proposes to adapt to search dynamics of a problem in each iteration. This study introduces three dynamic behavior-based PSO methods (DPPSO, DACPSO, and C-AEPSO). These methods are inspired from heterogeneous and sub-swarm principles. Thirteen benchmark functions are considered to evaluate the performance of the meth-ods proposed. DPPSO and C-AEPSO are found to outperform a collection of 8 well-known evolutionary optimization methods across the benchmark functions considered. The results indicated that DACPSO is ranked as one of the top four best performing algorithms in this study, further indicating the superiority of the methods proposed.
Multi-Source Pandemic Data Visualization and Synchronization for Information Extraction

Visualization is integral to investigating information hidden in data and providing users with intuitive feedback for decision-making. No matter the field a data set describes, inspecting the data visually will yield fruitful insights into the trends and statistics. Over the past calendar year, COVID-19 vaccines have become increasingly available for much of the population. However, the CDC (Centers for Disease Control and Prevention) fails to consider multiple sets of pandemic data in a side-by-side view and synchronize multiple key factors in one web page, limiting medical professionals and individuals to seeing, comparing, and interacting with complete data visualization. To analyze the coronavirus and vaccination data collected from multiple sources, effectively displaying them is critically important for interpreting the pandemic transmission pattern and vaccine efficiency. This paper presents new algorithms for innovative data visualizations that provide users with intuitive feedback and enable them to see a complete story of where the data is concerned. The information derived from our developed web-based data visualization will aid healthcare professionals and everyday citizens in moving forward as the pandemic progresses.
File Allocation Chronology and its Impact on Digital Forensics

Event construction and sequencing are integral to the digital investigation process to build a sound case and admissible evidence. When dealing with deleting files, forged files, or file fragments, an investigator might not be able to consider many key artifacts because of forged or missing timestamps. In this work, we are investigating the applicability of using neighboring files to infer a timestamp of a key artifact using a real data set of over a thousand hard drives focusing on FAT drives. We performed an empirical study using the Real Data Set to understand the adjacent files' chronology and present our findings in this research.
Federated Learning vs Edge Learning for Hot Water Demand Forecasting in Distributed Electric Water Heaters for Demand Side Flexibility Aggregation

Hot water demand forecasting is crucial for estimating aggregated flexibility from distributed electric water heaters (EWHs). High-performance deep learning models for forecasting require large amounts of training data that may not be available to aggregators. Privacy preserving edge learning methods for individual EWHs cannot learn from other EWHs. To address these challenges, this paper proposes a federated learning (FL) framework for hot water demand class forecasting across multiple distributed EWHs with limited demand data. Feed-forward neural network (FNN) based global model is collaboratively trained by multiple edge devices without sharing data to predict hot water demand. The global model is specifically further fine-tuned to individual EWHs using their own data. The proposed approach is tested with synthetic hot water demand data from 40 households and the results indicate that the proposed FL framework can perform better than edge learning and has numerous other advantages.
QR Based Virtual Shopping System

In day to day life everyone uses smartphone and people don’t have time to go to shop for shopping they always prefer online website that is very easy to use and its more convenient for all the user and it also save lots of time. As we already know lots of things about online shopping we use them to save our money as it have lots of features like huge discount buy this two items you will get this much amount as a cashback etc. Based on current need and increasing use of network we are developing QR based online shopping website that help people to purchase any product as per their need.
Automation of Krishi Market System and Real Time Price Display System

A krishi market system is automated by utilising technology to speed up the buying and selling of agricultural goods. To gain a complete picture of the agricultural market, the system gathers information from a variety of sources, including farmers, dealers, and government organisations. In order to find trends, patterns, and insights that can aid in decision-making, the data is then processed and analysed. The system displays the data in a comprehensible way using visualisation techniques like charts, graphs, and maps to make it simpler for users to understand and make defensible judgements. Additionally, the system automatically creates invoices for transactions based on the gathered and processed data. This guarantees correctness and gets rid of mistakes that could happen with manual bill production. The krishi market system with visualization of data and an automated bill generation system is implemented as a website application that provides a user-friendly interface for farmers, traders, and government agencies to access the data and bills. The website application also integrates with payment gateways to enable online payment for transactions, making the process faster, more secure, and more convenient for all parties involved. Furthermore, the website application provides alerts and notifications to users based on their preferences, including alerts for price changes, new crops in the market, and payment reminders. The system also ensures the security and privacy of the data collected and stored, using encryption and secure data storage methods to prevent unauthorized access. Overall, the automation of a krishi market system with visualization of data and an automated bill generation system for a website application streamlines the process of buying and selling agricultural products, making it more efficient and convenient for all parties involved. The website application provides a user-friendly interface that makes it easy to access the data and bills, and the integration with payment gateways makes transactions more secure and convenient.
Informational Query Detection on Social Media Posts in Bengali Language Using Machine Learning And Transfer Learning Techniques

Informational Queries are those queries or questions where a user's goal is to get an answer or fact to a specific question or to find a relevant piece of information. In recent years, social media has become an important platform for people to seek knowledge, data, and information from others. However, the vast amount of information on social media makes it difficult to identify and prioritize posts that are specifically informational queries. This paper presents machine learning and transfer learning methods for detecting informational queries on social media in the Bengali language using natural language processing and machine learning techniques. The approach involves analyzing the text of posts to identify keywords and phrases commonly used in informational queries and using this information to train machine learning classifiers, including transfer learning models. The method was evaluated on a dataset of real-world Bengali social media posts and was effective in accurately identifying posts that are informational queries with high precision and recall. This work aims to assist organizations and individuals in providing support to individuals seeking information, data, and knowledge on social media, particularly in low-resource languages.
A Coding Translation to Increase the Efficiency of Programmatic Data Analyses

Programmatic data analysis (PDA) is the design of software that is capable of performing a range of data analyses. When developed well, the computer programming code can enhance the generalizability and efficiency of data analyses, along with facilitating dynamic calculations in realtime applications. Programmatic designs may introduce some trade-offs, such as making the data analysis software more flexible at the expense of increased running time in calculations. This study identifies a class of programmatic designs in the R language that significantly degrade the run-time performance of data analyses relative to more direct coding syntax. To address this issue, this study introduces a translation algorithm of programmatic coding statements for use in the R language. These translations are based upon natural language processing of the coding statement and metaprogramming techniques for constructing new statements. The study's experiments demonstrate that utilizing the translated coding statement improves the running time performance. Reductions in the running time up to 60% are observed, with additional gains for increasing sample sizes of data. The proposed translation algorithm resolves the observed trade-offs of using programmatic data analyses for this class of problems, showing comparable performance with direct coding statements that lack programmatic designs. These promising results suggest that coding translations can be a valuable tool for improving the running time performance of programmatic data analyses.
Study on Modified Public Key Cryptosystem Based on ElGamal and Cramer-Shoup Cryptosystems

The current encryption scheme depends on the fact that there is a shared secret whose value is only computable. Cramer and Shoup suggested a strategy for improving an existing ElGamal cryptosystem, cryptosystem by creating a numerical verification step using a function. One major feature of ElGamal's scheme that is used in our cryptosystem is the incorporation of the modular inverse of the shared secret. In this paper, a modified variant of the public key cryptosystem that relies on the difficulty of solving the Diffie-Hellman decision problem and the collision-resistant hash function was proposed. By employing the Diffie-Hellman(DDH) assumption. The two public key cryptosystems such as the ElGamal cryptosystem and the Cramer-Shoup cryptosystem are considered to suggest the modified algorithm. For the secure cryptosystem against both chosen-plaintext attacks and chosen-ciphertext attacks, the security and efficiency were analyzed using numerical analysis. An extended form of the current encryption scheme can be applied to determine the correlation between the hardness of the Diffie-Hellman decision problem and the security of our system against adaptive chosen-ciphertext attacks. The presented strategy incorporated many strengths of different cryptosystems to create a modified cryptosystem, and we believe that there are still other areas that remain to be explored in another way.
CHAR: Composite Head-body Activities Recognition with A Single Earable Device

The increasing popularity of earable devices stimulates great academic interest to design novel head gesture-based interaction technologies. But existing works simply consider it as a singular activity recognition problem. This is not in line with practice since users may have different body movements such as walking and jogging along with head gestures. It is also beneficial to recognize body movements during human-device interaction since it provides useful context information. As a result, it is significant to recognize such composite activities in which actions of different body parts happen simultaneously. In this paper, we propose a system called CHAR to recognize composite head-body activities with a single IMU sensor. The key idea of our solution is to make use of the inter-correlation of different activities and design a multi-task learning network to extract shared and specific representations. We implement a real-time prototype and conduct extensive experiments to evaluate it. The results show that CHAR can recognize 60 kinds of composite activities (12 head gestures and 5 body movements) with high accuracies of 97.0% and 89.7% in user- dependent and independent cases, respectively.
Cable-Driven Parallel Robot for Warehouse Monitoring Tasks

Present manufacturing processes demand innovative methods for inventory management. Unmanned aerial and ground vehicles (UAVs and UGVs) have been shown to be potential solutions as heightened mobility and wide areas of operation are provided. However, they struggle with battery life, physical restrictions, and highly trained operators. A feasible alternative is offered through cable driven parallel robots. They provide low inertia, high payload-to-weight ratios, and a large operation volume. In order to design any given CDPR system, the static behavior must be understood and is presented. This paper also integrates actuation, camera stabilization mechanisms, and computer vision into a singular CDPR system. Being modular and reconfigurable, this system may quickly and accurately scan storage racks within a warehouse.
DS-HPE: Deep Set for Head Pose Estimation

Head pose estimation is a critical task that is fundamental to a variety of real-world applications, such as virtual and augmented reality, as well as human behavior analysis. In the past, facial landmark-based methods were the dominant approach to head pose estimation. However, recent research has demonstrated the effectiveness of landmark-free methods, which have achieved state-of-the-art (SOTA) results. In this study, we utilize the Deep Set architecture for the first time in the domain of head pose estimation. Deep Set is a specialized architecture that works on a “set” of data as a result of the “permutation-invariance” operator being utilized in the model. As a result, the model is a simple yet powerful and edge-computation-friendly method for estimating head pose. We evaluate our proposed method on two benchmark data sets, and we compare our method against SOTA methods on a challenging video-based data set. Our results indicate that our proposed method not only achieves comparable accuracy to these SOTA methods but also requires less computational time. Furthermore, the simplicity of our proposed method allows for its deployment in resource-constrained environments without the need for expensive hardware such as Graphics Processing Units (GPUs). This work underscores the importance of accurate and resource-efficient head pose estimation in the fields of computer vision and human-computer interaction, and the Deep Set architecture presents a promising approach to achieving this goal.