Reviewerly/exHarmony huggingface dataset:
Decentralized Web Application for Real Estate Property Transaction using Blockchain and Smart Contract Technology

Real Estate is a wide area consisting of a physical piece of land and the developments on it such as buildings, roads, and other infrastructures. Real Estate can be classified based on the purpose of usage of the property i.e. Residential, Commercial, or Industrial. Since longtime real estate is considered the most secure investment for good returns. But several issues arise in the field of property transactions which are based on the trust that the buyer or seller has in a third-party middleman/broker as well as frauds, inaccurate market statistics, time-consuming processes as well as high processing fees charged by the middlemen/broker. To provide a solution problem a decentralized web application for real estate transactions is proposed in the project. To attain the solution, in this project we propose to design and develop a decentralized web application using blockchain and smart contract technology that avails property buyers and sellers to interact with the help of a private Ethereum network and deploy smart contracts which create a precise and transparent digital property transaction process. This results in the advantages over traditional real estate systems by neglecting the requirement of non-trusted third-party entities, reducing transaction processes as well as transaction costs.
Resource Allocation for Multi-Task Federated Learning Algorithm over Wireless Communication Networks

The multi-task federated learning (FL) problem in the wireless communication system is investigated in this paper. The base station (BS) and wireless users cooperatively perform a two-task FL algorithm in the established model. Users use their local datasets to train two local models of two different tasks. The trained local model of only one task is transmitted to the BS at each time and the BS aggregates the obtained models to calculate a global model, which will be sent back to all users. Since the resources for wireless transmission, such as transmit power and number of subcarriers are limited, the BS have to allocate resources reasonably to minimize the time consumption of the FL procedure while meeting the required learning performance. On the other hand, users are dynamically arranged to participate in different tasks in each iteration. This resource allocation and users arrangement problem is formulated as an optimization problem which aims to minimize time consumption of the two-task FL procedure. To address this nonconvex problem, we first decompose it into two convex sub-problems. Then we propose an iterative algorithm to solve this problem via iteratively obtaining the optimal solution of the joint power control and communication round optimization subproblem, and user arrangement subproblem. Simulation results of this multi-task FL system show that the proposed algorithm can reduce 7.02% and 9.67% completion time compared to the uniform and random user selection schemes respectively.
A Comparative Study of State-of-the-art Image Splicing Localization Methods Using Fully Convolutional Networks

Image splicing forgery is one of the significant image manipulations. It is very difficult to find the location of forged portion in a spliced image. Nowadays, deep neural networks have been used to detect and localize image forgeries. In this paper, a brief survey and comparison on splicing localization of images using Fully Convolutional Network (FCN), a deep neural network is presented.
Real-time Detection and Classification of Porous Bone Structures Using Image Segmentation and Opening Operation Techniques

Porous bone structures with different lattices have great potential application in medical tissue engineering as they exhibit excellent mechanical properties.In this study, we utilize the optical microscope system as the optical sensor and imager to achieve real-time detection and categorization of pores bone materials based on machine learning techniques.The initial bone images are pictured using an industrial camera, and the image processes are compiled for defining the superficial shapes of the bone configuration.The image segmentation approaches contain Canny edge detection, k-means clustering, and binarization.The initial bone surface images are transformed into the gray-scale mode, and k-means clustering is utilized to normalize the gray-scale mode for enhancing binarization precision.The erosion and dilation of the opening operation are used to extract image noises and improve the pores characteristics.The profiles and the dimensions of the pores characteristics are precisely obtained by using Canny edge detection.The Gaussian blur method is performed to acquire obvious surface profiles of the pores configurations without background noise.The experimental results show that the geometric sizes of artificial pores implants can be clearly examined by this optical microscope system after metal additive manufacturing.
Speaking Competence of STMIK STIKOM Indonesia Students Through Video Presentation Project

English at higher education level requires the development of language competence, especially in the development of speaking competence which will be important when it must be applied to communication needs in the industry. To achieve the goal of joining these needs, the use of technology in learning and its evaluation can be one of the determinants of increasing competence. The form of implementation is the assignment of English video presentation which is considered effective in developing competence in the digital and global era as it is today. The stage of creating videos in learning activities can increase student engagement in the lecturing process, increase the effectiveness of the English learning process, stimulate students' creativity and independence, and most importantly foster students' courage to appear and communicate in English. Assessment of the linguistic aspect will help students identify grammatical errors that need to be corrected and improve their verbal skills in English itself. The object of this research is the seventh semester students majoring in Informatics Engineering, STMIK STIKOM Indonesia. The technique of collecting data applied in this research is observation. The data is qualitative data and in the form of English video presentations uploaded by students in the Google Classroom application in the online English class which will be analyzed by using descriptive qualitative methods. The analysis of the linguistic aspect includes pronunciation, grammar, vocabulary, fluency, content, and comprehension. The data obtained will be classified so that students' English competence, especially speaking, can be identified and can be used as the basis for implementing appropriate teaching methods.
Teachers' Perception towards the Establishment and Effectiveness of Early Childhood Education

Education is a systemic framework for building the cornerstone of civilization. It is animportant factor in any country's success worldwide. Early Childhood Education (ECE) is a branch ofeducational philosophy that refers to education for young children up to the age of eight years. The mainobjective of this study was to find out teachers’ perceptions of this ECE setup. A teacher is a part andparcel of our education system and any policy can’t succeed without satisfaction. The population of thisstudy was teachers involved in early childhood education set up in the district Nankana Sahib. Whereas,308 teachers were selected as a sample using a stratified sampling technique. The data was collectedthrough instruments by survey method. On the whole, it was found that most of the statements on thequestionnaire had a mean score higher than 3 which shows teachers’ positive perception of the ECEsetup. There was a significant difference in the perception of teachers based on gender. This study isbeneficial for the development of early childhood education.
Research on memory-enhanced mobile video object detection

In recent years, deep learning for mobile object detection has received increasing attention, and at the same time,handdesigned neural networks for mobile object detection have made remarkable progress, there is still a wide scope for exploring how to ensure accuracy and speed while pursuing the design of networks with fewer parameters and less computation, i.e., small and fast. Inspired by biological science, we know that humans only need to gaze at a glance, their visual system can have a rich perception of the complex environment and achieve a rough understanding of target detection, and the realization of this result relies on the corresponding prior knowledge, we propose a strategy of feature extractor crossover to adjust the results of the detection obtained by NAS with hyperparameters to achieve improve the detection performance on mobile devices. In this paper, we propose an efficient method that combines the MobileNetV3 high-speed and the improved LSTM with temporal memory, and this network can effectively capture the key parts of the scene. Experiments show that we achieve an improvement in accuracy while ensuring the most important speedup in mobile. We reduction in the number of parameters from 4.9million to 3.96million, while reaching 90+ FPS in the Samsung Note10+ phone.
Time Attendance Using FELE Face Identification Algorithms

Time attendance system has been used to check-in and check-out of employees in organizations for many years. Biometric technique such as the fingerprint scanner is among the popular and widely used in the past and present. Nowadays, face recognition techniques have been employed to identify the faces such as eigen face, fisher face and local binary patterns histograms (LBPH). These techniques provide acceptable results but may not accurate enough due to a number of false positive cases. Therefore, this paper proposes a better face identification technique to reduce the false positive cases. The method presents the combination of the above three existing algorithms to produce the more accurate result for time attendance by means of the actual experiment. The experiment is carried out using the 30 employees face each with 50 images with a total of 1,500 images as a face database. The testing process contains real faces of 20 employees and 5 non-employees. These faces are tested against the Eigenface, Fisherface, LBPH and the proposed method Fisherface, Eigenface, LBPH Extension (FELE) algorithms. We present and compare the results among these four techniques in term of the false positive, accuracy and precision. It has been shown that the FELE has an accuracy of 100% and outperforms other methods in all categories.
Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network

Breast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women's health. The appearance of clustered calcifications is one of the important signs of breast cancer, and thus how to classify clustered calcifications comes to be a key breakthrough in controlling breast cancer. In this study, the discriminant model based on image convolution is used to learn the image features related to the classification of clustered microcalcifications, and the graph convolutional network (GCN) based on topological graph is used to learn the spatial distribution characteristics of clustered microcalcifications. These two models are fused to obtain a complementary model of image information and spatial information. The results show that the performance of the fusion model proposed in this paper is obviously superior to that of the two classification models in the classification of clustered microcalcification.
Search based Software Modularization Using Evolution Algorithm

To comprehend a Software system, Software modularization strategies are used. The goal of modularization is to break down a software system into meaningful and intelligible sub-systems from its source-code (modules). Because software classification modularization is an NP-hard task, evolutionary methods produce better modularization quality rather than avaricious algorithms. All available transformative techniques for software modularization only take into account structural aspects reliant on programming language syntax. Because most computer languages lack a mechanism for extracting structural characteristics, they cannot be modularized. A novel heuristic is proposed in this work. with several objectives that accomplishes both in order to lead optimization algorithms towards a proper decomposition of software systems automatically, structural (e.g.; calling dependence and in-heritance dependency) and non- structural (e.g.; semantics in code comments and identifier names) aspects are used. It is analyzed using 3 optimization plans, viz; global-based-search, combining global and local search, and Estimation of Distribution (EoD) to upgrade it. According to outcomes on Mozilla Firefox, suggested optimization algorithm based on EoD and the newly developed MOF function exceed those so it use structural-based objective functions in finding more understandable modules, as well as guiding the optimization procedure. In the lack of a unique concept, structure, the original design can be identified by using the source code of the disturbed software. Effective software maintenance depends on the concept of software system. One of most powerful techniques in software clustering is the ability to divide enormous Software systems into workable subsystems with modules of identical characteristics, thereby reducing the complexity of the system. A metaheuristic optimization imperialist competitive system has emerged algorithm, genetic algorithm, and their combination is examined for software clustering in this paper. When it comes to value, of clustering, the number of epochs required for convergence, and the standard unconventionality found at the end of repeated application of these algorithms, it appears that recursive application is the most effective for achieving the best performance.
Performance of Quantum Computers in Machine Learning and Big Data Analysis

Quantum computing is a cutting-edge computing technique based on quantum physics. Quantum Computers have low energy consumption and escalated speed over traditional computers. This paper will help you understand how information is implemented and actions are taken in quantum systems. We wanted to focus on how these computers are made and processed differently from the PC on your desk. Each of the topics can have a seminar of its own, so only the basics are covered.
An Approach of Damped Electrical and Mechanical Resonators

The Dinesh Verma Transform analyzes the damped mechanical and electrical oscillators in this paper. This paper introduces the Dinesh Verma Transform as a new mathematical approach for analyzing the damped mechanical and electrical oscillators. The Dinesh Verma Transform provides a new mathematical tool for obtaining the responses of damped mechanical and electrical oscillators and reveals that it is also effective and simple, like other integral transforms and approaches.
Three-dimensional low-altitude flight trajectory planning technology based on digital map

Abstract Aiming at the intricate terrain of Yunnan, a three-dimensional low-altitude flight trajectory planning technology based on digital maps is proposed. First, divide the map of Yunnan into 36 digital maps, use VC++ to write a program to read the longitude and latitude coordinates of each point in the digital map and the height of each point into the memory, generate a grid coordinate system based on the start and end points, and find the grid from the memory The height data corresponding to each point in the coordinate system; then, the dynamic planning algorithm and the simple method are used to perform terrain avoidance and terrain following in the grid coordinate system, and finally, use MATLAB to draw the planning path map in the geographic coordinate system.
Automated Resume Builder for Fresh - Graduates Using Html and JavaScript

This project is an online resume builder. It is an application that simplifies the task of creating a resume for individuals. The program is based on web development using Html,CSS,JS and BootStrap. The system is developed to provide an easy means for creating a professional looking resume. The system is flexible to be used and reduces the need of thinking and designing an appropriate resume according to qualifications. Individuals just have to fill up a form that specifies questions from all required fields such as personal questions, educational, qualities, interest, skills and so on. The answers provided by the users are stored and the system automatically generates a well-structured resume. Creating a resume is a bit of a tedious task for any working professional from any industry. One has to keep it short, simple, and with the latest work experience, and constantly update it over a while. Individuals just have to fill up a form that specifies questions from all required fields such as personal questions, educational, qualities, interest, skills and so on. The answers provided by the users are stored and the system automatically generates a well-structured resume.
Experimental analysis of position embedding in graph transformer networks

The Transformer structures have achieved excellent performances in an increasing number of fields. In the field of natural language processing, the original Transformer structure was used to work on a full connectivity graph, which depicts all connections between words in a phrase. It has been subsequently applied in areas such as computer vision. Recently, many studies have attempted to apply Transformer structures to graph data and achieved good performance. However, it is still a challenge to embedding the structural information of the graph data. To understand the representation of graph data, we employed three distinct ways of embedding the positional and structural information of graph data based on the Transformer structure in this work. To learn the feature representation of the graph data, the encoded structural information and original feature information of the graph data were fed into the Transformer structure. For the learned feature representations, we used them for node classification and graph regression, and then compared and analyzed the three different encoding methods.
Online Public Shamming using Machine Learning

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platforms in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonoring comments, tweets towards people are categorized into 9 types. The tweets are further classifying into one of these types or non- shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share is probably going to modify the person in question. Moreover, it is not the non-shaming devotee who checks the increment quicker but of shaming in twitter.
Web-Based Shoes Service Application

Shoes are one type of footwear that serves to protect the entire foot. Shoes are a type of footwear that usually consists of parts of the sole, heel, hood, laces, and tongue. Shoes for some people are not an ordinary item, as a favorite. There needs to be special treatment to make shoes look clean, beautiful, and well-maintained. In fact, not everyone is able to take care of their own shoes, this is due to skill and time. Then people choose to submit their shoes to experts or shoe care services, so we need an application that can help in the process of these problems. The method used in making this system is the waterfall method, where the stages of the method consist of analysis, design, testing and maintenance. The application that was built has several facilities, namely ordering shoe service, shoe pick-up and delivery services, transaction services, customer service so that customers can check whether shoe services have been serviced or not, and also transaction report services. The design for this shoe service application uses UML (Unified Modeling Language) while for system testing using the black box method The black box test results show that the system is running according to its functionality. With this application, it can make it easier for customers to order shoe serviceservices online and help increase revenue for shoe serviceservices .
Reinforcement Learning based Efficient Mapping of DNN Models onto Accelerators

The input tensors in each layer of Deep Neural Network (DNN) models are often partitioned/tiled to get accommodated in the limited on-chip memory of accelerators. Studies show that efficient tiling schedules (commonly referred to as mapping) for a given accelerator and DNN model reduce the data movement between the accelerator and different levels of the memory hierarchy improving the performance. However, finding layer-wise optimum mapping for a target architecture with a given energy and latency envelope is an open problem due to the huge search space in the mappings. In this paper, we propose a Reinforcement Learning (RL) based automated mapping approach to find optimum schedules of DNN layers for a given architecture model without violating the specified energy and latency constraints. The learned policies easily adapt to a wide range of DNN models with different hardware configurations, facilitating transfer learning improving the training time. Experiments show that the proposed work improves latency and energy consumption by an average of 21.5% and 15.6% respectively compared to the state-of-the-art genetic algorithm-based GAMMA approach for a wide range of DNN models running on NVIDIA Deep Learning Accelerator (NVDLA). The training time of RL-based transfer learning is 15× faster than that of GAMMA.
Developing agricultural and forestry college students’ capacity for innovation, practice, and comprehensive quality in off-campus class-industry-practice

In terms of the engineering students in agricultural and forestry colleges, the off-campus class-industry-practice were discussed in this paper based on the existing problems in higher education innovation. It is meaningful to develop students’ capacity for innovation, practice, and comprehensive quality.
A multilateral fair negotiation model solved by Hungarian solution

Multi-agent was introduced to study the issue of multilateral and multi-attribute fair negotiation in e-commerce. In this paper, a mathematical model is established through the Hungarian solution to solve the problem. Taking into account the multi-attribute and multilateral conditions and fairness in the negotiation, the real negotiation information is simulated in the form of randomly generated data, and the evaluation profit system and bilateral negotiation model are established to solve the problem and then the profit matrix can be obtained. Analogy 0-1 assignment problem is solved by Hungarian solution. Through numerical experiments and analysis, it indicates that this algorithm can obtain the overall maximum profit value that the system can achieve, and make the number of matching people as large as possible to achieve better negotiation results.