Reviewerly/exHarmony huggingface dataset:
Efficient Hardware/Software Implementation for GoogLeNet Using Xilinx SDSoC

Convolution Neural Networks (CNNs) are recently deployed in many applications. The huge number of network parameters and the intensive operations in CNN models make it challenging to achieve desired performance levels using general-purpose processors. Therefore, different hardware accelerators for deep CNNs have recently been developed to improve throughput. Field Programmable Gate Array (FPGA)-based accelerators are mostly used. Different approaches such as Register Transfer Level (RTL) or High-Level Synthesis (HLS) are used for implementing FPGA-based accelerators. In this work, the Hardware/Software (HW/SW) Co-design Partitioning methodology is introduced as a solution to speed up the design time and shorten the time to market for CNN implementations. This work focuses on implementing the GoogLeNet CNN network. Xilinx Software-Defined System on Chip (SDSoC) tool is used to achieve the HW/SW Co-design by moving the most computationally intensive components to run on FPGA while keeping the rest of the network running on an embedded Central Processing Unit (CPU). Experiments are evaluated on Xilinx Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit. Experimental results show a speedup of 48x for 32-bit float data precision, with 3.8 watts for total on-chip power consumption. Also, the proposed accelerator utilizes 40% fewer hardware resources compared with the corresponding RTL accelerator.
Work-in-Progress: Utilizing latency and accuracy predictors for efficient hardware-aware NAS

With the increased size and complexity of state-of-the-art language models such as BERT, deploying them on resource-constrained devices has become challenging. Latency-aware Neural Architecture Search (NAS) is an effective solution for finding an efficient implementation of complex models that satisfy hardware limitations. However, collecting on-device accuracy and latency feedback would significantly slow down the search process, making NAS impractical. To address this, we propose a low-cost method that models both accuracy and latency of BERT-based models on the target device, NVIDIA Jetson TX2, and removes the hardware-related delays from the search loop. Using a Random Forest regressor, our predictors outperform the state-of-the-art and achieve up to 57x speedup while finding a set of near-optimal models.
A Wearable Body Fat Distribution Feature Low Error Detection Algorithm Based on Kernel Density Estimation

In order to improve the accuracy of wearable body fat distribution feature detection, a wearable body fat distribution feature detection algorithm based on kernel density estimation is designed. This paper collects human body data and classifies the data features. In this paper, the kernel density estimation method is used to extract the data features of body fat distribution. It judges the similarity of the data and realizes the wearable body fat distribution feature detection based on the kernel density estimation. The experimental results show that the proposed detection algorithm can accurately detect the wearable body fat distribution characteristics and meet the design requirements of the method.
Suggestion new monitoring system by depending on the human activity recognition videos

Modern home monitoring system techniques, such as motion detection technology and home camera system intrusion warning, are said to be insufficient, especially to meet the needs of whole automation with flaws such as needing human interaction. We suggest a substitute system, a human activity recognition (HAR) method based on the video, and a combination of long short-term memory (LSTM) and convolution neural networks (CNN) algorithm, to address the flaws that have been found. Our suggestion doesn't need to be changed. is simply deployed utilizing just low-cost modifications to the current home security protocols, and commercially available hardware The conventional security camera may be used with ease for computer vision applications. Utilizing information on actual activity gathered by video-based sensors, we assess our strategy. By drawing Loss and Accuracy curves, we demonstrate how successful it is. Show Results demonstrate that the video-approved human activity recognition method can deliver complete home automation. The monitoring system has higher accuracy as compared to traditional camera motion detectors. The precision of the system may be improved further, and we can attain for best results. (Long-term Recurrent Cnvlution Network) implementation yields result better.
Applying Perlin Noise on 3D Hexagonal Tiled Maps

Procedural Content Generation gained importance together with the rapid increase in video computer game industry. Procedural terrain generation, which is a sub-category of procedural content generation, is used to generate terrains for games. However, it is inadequate to use terrain generation in strategy games, as it does not support tiling. This study aims to generate 3d hexagonal tiled terrains using procedural terrain generation methods using Perlin noise. This method can also be used as a filter algorithm to tile terrains that have already been generated.
Applied Single Image Super-Resolution for Aerial Imagery Enhancement

One of the greatest challenges while dealing with UAV datasets for computer vision applications is the poor resolution of captured images. Super-resolution, the class of techniques that focus on improving the resolution of imaging systems, can be applied to UAV images to improve the performance across object detection, segmentation and surveillance applications. This paper attempts to apply a deep learning based method of single-image super resolution with the goal of enhancing the resolution of drone captured images. The super resolution model accepts the low resolution image as input and outputs the high resolution one through mapping that is represented as a deep convolutional neural network. The evaluation of the applied methodology was carried out using eight image quality metrics, and the results show promise for the future of deep super resolution enhanced aerial images.
Stable Integration Method of English Multimedia Teaching Resources Based on Particle Swarm Optimization Algorithm

In order to improve the integration effect of English teaching resources, a method of Integrating English multimedia teaching resources based on particle swarm optimization is proposed. The integration platform of teaching resources is constructed, and particle swarm optimization algorithm is used to identify the characteristics of teaching resources. The classification management of teaching resources is realized according to the feature recognition method to ensure the effect of teaching information integration. The experimental results show that the proposed teaching resource integration effect has high adaptability and can fully meet the requirements of resource integration.
Work-in-Progress: What to Expect of Early Training Statistics? An Investigation on Hardware-Aware Neural Architecture Search

Neural architecture search (NAS) is an emerging paradigm to automate the design of top-performing deep neural networks (DNNs). Specifically, the increasing success of NAS is attributed to the reliable performance estimation of different architectures. Despite significant progress to date, previous relevant methods suffer from prohibitive computational overheads. To avoid this, we propose an effective yet computationally eﬃcient proxy, namely Trained Batchwise Estimation (TBE), to reliably estimate the performance of different architectures using the early batchwise training statistics. We then integrate TBE into the hardware-aware NAS scenario to search for hardware-eﬃcient architecture solutions. Experimental results clearly show the superiority of TBE over previous relevant state-of-the-art approaches.
Integration of Federated Machine Learning in Smart Metering Systems

The applications of Federated Learning are many, and they can be used to predict electricity consumption and, at the same time, enable smart meters to collaboratively learn a shared model while keeping all their data locally in their own private database. With this approach, the central model will see more data and will work better to predict electricity consumption more accurately than the models trained on only one local Dataset. The planning of infrastructure, grid operation, and budgeting all depend on accurate load forecasting. As a result, this paper suggests federated learning for load forecasting using smart meter data. Using this method, all participants' smart meters may train a single model without exchanging local data. The FedAVg technique, which executes numerous processes before the merger, is investigated. Remarkably, the diversity of residential customers makes it difficult to train a single model since consumer load profiles differ. A neural network was implemented to predict energy consumption. The proposed approach outperformed less mean square error than papers in the literature. Finally, FedAVG delivers equivalent or less error than separate models for every meter and a single central model for all meters.
Online Exam Data Security Encryption Algorithm Based on ASP.NET

In order to improve the security of online examination data and the fairness in the examination process, this paper describes a secure encryption algorithm of online examination data based on ASP.NET. The overall structure and functional modules of the system are proposed based on the actual situation Net technology and SQL back-end database, with the following functions: test question database management, online examination, computer automatic marking, score analysis, test paper analysis and user management. The ADO.NET component of ASP. NET is used to realize the connection between the web page and the database. The matching of fields is used to realize the function of student login and super user management login. The statement realizes the function of analyzing the test taker's detailed results and analyzing the test paper. The characteristics, advantages and disadvantages of the system are summarized, and the future development and application prospects are prospected.
Image Filtering of Impulsive Noise Using Biologically Inspired Algorithms

A digital image is a two-dimensional representation of an image in the form of a numerical matrix. In grayscale images, pixels are represented by an integer numeric value that is between 0 and 255. One of the main problems encountered today is the appearance of noise in digital images. The main sources of noise appear during the image acquisition and transmission phases. There are many types of noise and among the best known are some such as Gaussian or impulsive. The main goals of applying filters are to smooth the image, remove noise, enhancement, and edge detection, in this paper we propose the combining of the powerful set of Fuzzy rules with the genetic algorithm pattering recognition features to filter the image from impulsive noise yet retaining the image features unfiltered thus performing a powerful noise filtering on the noise without and destruction to the image itself.
Leveraging Experience Telemetry: Use Cases

Experience Telemetry is a branch of Telemetry that collects non-operational telemetry data from IT infrastructure in order to facilitate the work of roles such as architects, product managers, customer success and user experience planners, in any organization. These roles have interests geared more towards the business rather than operational utility of any product or service. Such data is consumed by various management applications to provide analytics and insights for the above mentioned users. The novelty in this paper lies in the demonstration and qualitative evaluation of how Experience Telemetry concepts can be applied in solving two specific lifecycle management and operations problems, thus advancing the current state-of-practice.
Virtual Intelligent Assistant: A Support for Elderly People

The objective of a virtual assistant is to use web-based data sources, user-generated content, and knowledge from knowledge libraries. An Intelligent virtual assistant's main purpose is to deliver solutions to users' questions. It is possible to do it in a business setting, like on a company website with a chat interface. Virtual assistant is very helpful especially for elder as well as for people with cognitive disability such as Alzheimer's disease. For someone with physical limitations, a virtual assistant can be very useful. Seniors with poor vision or limited mobility, for example, one can operate the device simply by speaking into it. New techniques of caring for the elderly that include advanced information and communication technology are desperately required. As a percentage of the total population, the world's senior population is rapidly growing, but the fraction of the working population who will create the resources to assist the aged is declining. Voice searches have a number of advantages for elders as well as for cognitive disability such as speed. In fact, voice searches are much faster than written searches. A survey has been carried out in this work in which some questions have been asked from elderly people and people with abnormality or disability. The questions were based on Virtual Assistant. The aim of this task was to evaluate how many people use these assistants in their everyday life. Based on this survey it has been concluded that the prevalence of Virtual Assistants is growing in our day to day lives.
Developing a Watermarking Algorithm Using Hide Information Techniques

Digital information (images, texts, audio) has become widely available on the Internet, stored and transmitted in digital form. Effective, and we can easily reproduce, retransmit and even manipulate them, This is due to the emergence of a wide range of unreliable copies and infringement of the property rights of the true owner by piracy (person/organization). The main objective of this research Watermarking techniques are applied in many tools and applications to take advantage of their properties and the solutions that provided by these techniques, and this research covers some simple techniques and algorithms and measures the effectiveness of each of them according to the basic characteristics of the watermark concept in addition to clarifying the weaknesses of these algorithms and developing a solution to reduce the weaknesses by improving the basic watermark algorithms or applying advanced techniques and concepts in this field to improve this technology by designing a system that uses the knowledge and information gained from the cover image and this process is called Side Information Techniques. This project will demonstrate theoretically and practically the benefits and effectiveness of applying Side Information technologies within the field of branding, The first important thing in this research covers the term Steganographic watermark (cover letter depends on the message in addition to concealing the presence of this message). The second important thing is to know the difference between the two types of reagents (informed) and the other (blind) And we use some techniques such as (LSB, DCT, SEW, AEW) and compare the results to choose the best technology from them.
Design and Implementation of An Intelligent Health Management System for Nursing Homes

The ageing population has led to a dramatic increase in the demand for analysis and assessment of the health of older persons in public health services. Due to medical conditions and other reasons, most of the elderly in some urban nursing homes will only detect and analyze their own physiological indicators when they are sick. From the perspective of health management, we should continuously monitor the physiological indicators of each elderly individual, and through the analysis and evaluation of their daily physiological indicators data, and then predict and timely intervene in their health. This can not only effectively improve the health of the elderly, but also effectively reduce the pressure on public health services. In order to allow more elderly people in nursing homes to enjoy effective health monitoring and early warning and timely intervention, we have designed an intelligent health management system based on technologies such as cloud computing, Internet of Things, knowledge graph, and deep learning. The system consists of three parts: the Internet of Things platform, the intelligent analysis platform, and the SAAS management platform. The IoT platform is mainly responsible for collecting data such as daily physiological indicators, sleep data, air indicators, and service demands of elderly people in nursing homes. The intelligent analysis platform is mainly responsible for analyzing and evaluating the data collected by the IoT platform based on the disease knowledge map and related deep learning frameworks. The SAAS management platform is mainly responsible for background management and health data visualization on the nursing terminal, service terminal, and health monitoring terminal. The system realizes continuous monitoring, analysis, assessment, prediction and early intervention of the health of each elderly person in the nursing home, which effectively improves the health of the elderly and effectively reduces the pressure on public health services.
Research on Intelligent Operation and Maintenance System of Urban Rail Transit Vehicles Based on PHM Technology

Based on the current situation of rail operation and maintenance of urban rail transit, combined with the technical system of fault diagnosis and health management (PHM), the operation and maintenance requirements of urban rail transit vehicles are investigated in depth. Due to the feasibility and superiority of PHM technology in rail operation and maintenance management, an intelligent operation and maintenance system for urban rail transit vehicles based on PHM technology is designed. Through PHM technology framework, the key hardware of the system is designed. Based on the hardware system, the vehicle information management module, operation day plan management module, data preprocessing module, health status identification module, operation and maintenance decision module and construction operation management module are established. The results show that the effectiveness of the designed system is proved through functional and performance tests, which provides solutions for the intelligent operation and maintenance of urban rail transit vehicles, and improves the safety and economy of operation and maintenance.
Personalized Accurate Recommendation Algorithm for Distance Education Courses Based on Apriori Classification

Personalized recommendation of distance education courses is a technology that actively recommends resource information to meet the learning needs of learners according to their personalized characteristics. It supports learners' individualized learning and facilitates their individual development. In order to grasp the domestic research status and development trend in this field, a personalized recommendation algorithm for distance education courses based on Apriori classification is proposed, a distance education course interest recognition model is constructed, and the personalized recommendation algorithm for distance education courses is standardized. The personalized recommendation algorithm of distance education courses classified by Apriori has high practicability and fully meets the design requirements.
The process of creating teacher's personal page structure model for an integrated education system

In the article “The process of creating a model of the structure of a teacher's personal page”, a model of the structure of a teacher's personal page was developed. The special system serves to effectively work with the information base developed for all users, to obtain available information and to have information. Working with information consists of the process of processing “big data” and software is created accordingly. The model of the structure of the teacher's personal page has as its goal the task of working with all information resources. The content is divided into two parts (educational content and scientific content) and the principle of working with them is presented.
Survey on Handwritten Recognition

Character recognition is one of the most rapidly developing areas of computer technology. People have the greatest ability to identify any image or item. People can quickly understand the hand transcription. The vast wide variety writer’ handwriting makes selecting suitable component sets much more difficult, and this has been extensively researched in the context of handwriting recognition. While encouraging, the current findings have many disadvantages, including computation time, dependency on the used algorithms, and uncertainty assessing function interfaces. Many areas, such as medical imaging, science, and archaeology, rely heavily on handwriting. Forensic medicine, for instance, may deduce details from handwriting, such as age bracket and hand used in certain instances. Because of the rapid growth of handheld devices, virtual textbooks, and specialized communication devices, it has gotten a lot of attention recently. Human-machine interaction has typically concentrated on keyboards and targeting tools. For the deep learning network's training process, we construct a dataset of our own handwriting, which contains 2200 illustrations of each alphabet and is combined with another readily viewable database. Multidimensional Long Short-Term Memory networks are heavily used in emerging state-of-the-art strategies to fine Handwritten Text Classification. However, these frameworks have a significant limitation, and we find that they remove feature vectors that are close to those extracted by convolution layer, which are computational complexity less costly.
Aeroengine Robust Component Model Design Based on 3D Virtual Design

Aero engine is the focus of aero power research and development. Due to its complex structure, long development cycle and high scientific research investment, the cost and risk of engine test are high, and the advantages of 3D virtual design are reflected. For this reason, the aeroengine component model based on 3D virtual design is designed. This paper deeply analyzes the aeroengine exterior components, applies the 3D virtual technology to realize the 3D virtual visualization of aeroengine exterior components, and then establishes the exterior component model base through Virtual reality modeling language (VRML). Finally, the 3D virtual model of shape components is simplified based on Level of detail (LOD) algorithm. The experimental results show that compared with the given maximum limit, the design delay of the model in this paper is small, and the deviation between the model and the entity is small, indicating that the application performance of the model is good.
Predicting Satisfaction of Airline Passengers Using Classification

This research paper is exploring the application of classification data mining algorithms on specific data. The data set used in this paper is open and available to everyone, it contains several attributes that are measuring the overall quality of the flight. The main aim of the paper is to explore the effect of different flight attributes on the overall satisfaction of the passenger.