Reviewerly/exHarmony huggingface dataset:
Construction of Malaria Disease Prediction System using Deep Learning

Malarial fever is an infectious sickness that influences a large number of lives each year. Malarial fever is as yet a significant worldwide wellbeing trouble, with more than 3.2 billion individuals in 91 nations staying in danger of the infection. Precisely recognizing intestinal sickness from different illnesses, particularly simple malarial fever from non-malarial contaminations, stays a test. Customary analysis of malarial fever in lab requires an accomplished individual and cautious assessment to segregate solid and contaminated red platelets. Profound learning-based models have had extraordinary accomplishment in object recognition; however, the cutting-edge models have not yet been generally applied to natural picture informationIn this article, item location model recently utilized on normal pictures to distinguish cells is appliedand perceive their stages in brightfield microscopy pictures of malarial fever tainted blood This paper, is incorporated with Spotting of Malarial fever from the output images of tiny malign by employing CNN (Convolutional Neural Network) model. The datasets are collected from the Kaggle and used for testing and training. In this model, malarial parasites are discerned from infinitesimal output images with 97.76% of accuracy. In this paper, the CNN model is evaluated using Accuracy and Loss.
Toward a movie recommender system based on association rules and LDA approach

With the growing amount of data on the web, it becomes very difficult for users to find the resources they are looking for. Recommender systems are used to solve the problem of information overload in order to offer the user more relevant resources.This work falls within the context of film recommender systems. We chose the area of movies because with the sky-rocketing rise of streaming platforms, more and more research is being conducted around recommending movies. There are several recommendation approaches, each with advantages and disadvantages. In our case, we realized a web platform named Movie Discovery which integrates two different recommendation techniques based on association rules and content-based filtering.
Music Composition Patterns: A Pattern Language for Touching Music

This paper introduces some patterns of Music Composition Patterns: A Pattern Language for Touching Music. This pattern language is the verbalized and systematized rules of music design hidden in the music of Takashi Watanabe, a famous Japanese film music composer (and one of the authors of this paper). It consists of 27 patterns, each of which describes a design feature or rule that appears repeatedly in his music. This paper covers 7 patterns from this pattern language: Regular Repetition, Small Alteration, Basic Configuration, Discernible Change, Guide to Finish, Enrich Inner Parts, Weight Balance of the Sound. In addition, this paper describes the analysis of two musical pieces using patterns: “Memory Switch” by Takashi Watanabe and “Sonata for Horn, Trumpet and Trombone” by Francis Poulenc. As a result, it became clear that the design rules that were found in the music by Mr. Watanabe could also be seen in the music created by other composers. It is expected that the results of this research can be used in the education of music creation and as a reference for beginners in composition.
Machine Learning Based Text Classification Technology

The Internet is now the largest order of magnitude data center in human history. With the access of the industry and the continuous increase of existing database data, the scale is also increasing. The main purpose of this paper is to analyze and research the progress of existing text classification technology based on the related algorithms of machine learning. This paper focuses on improving the classification ability of text classification algorithms, proposes concept indexing and principal component analysis, and conducts related experiments. The research results show that in the process of increasing the dimension of the concept indexing method, when the dimension increases to 5, the accuracy of the text frequency method will be greatly improved.
Integrity Assured Digital Voting System by using Blockchain as the Technology

In order to reduce limitations and as a practical substitute for voting on paper, electronic voting has developed gradually as a feasible alternative. The last decade's worth of related studies implies that, building a secure e-voting system using web 2.0 technology that assures the integrity of the votes while also giving transparency has been a long-standing difficulty. In a network of centralized systems, a single party has the right to administer data sources. Since the centralized voting systems are more vulnerable to data manipulation issues, the trust has faded in the voters on behalf of the entire process. In this paper authors discuss the solution with blockchain which is secured. The proposed implementation is ideal for small or medium-scale elections, and this includes several basic components of our methodology in three fictitious modules: the administrative module, the Voters module, and the Blockchain module. Node JS, Ganache, and Web3 JS are used as the main technologies. In conclusion, it is expected to implement a secure system for conducting web-based digital voting that ensures the integrity of the votes registered. Furthermore, it intends to enhance the system in the future to accommodate large-scale elections with a viable solution to the blockchain trilemma issues.
Computer Vision- Hybrid Learning based on Multi Scale Dilated Convolution Module Mechanism Implemented for Object Detection

For smart traffic management and highway regulation, vehicle detection and statistics in highway surveillance video scenes are crucial. The massive installation of traffic surveillance cameras resulted in the collection of a sizable database of traffic video footage for analysis. In order to achieve the best detection from prediction, YOLOV3 (You Only Look Once) can be achieved in this study by including a multi-scale augmentation convolutional module technique. The first camera reads the licence plates of approaching vehicles before blockchain technology is used to store predictions in the cloud. It can display available parking spaces and be embedded in parking spaces. If there aren't any open spots, they are shown similarly. To make sure the automobile stays in the parking spot after it leaves the lot, a second camera scans the licence plate once again and matches it to the time of arrival. The size of vehicle objects varies substantially when viewed from this angle, and it is harder to spot small items that are off the road. It is crucial to successfully address the aforementioned issues and further apply them while dealing with complicated camera scenarios. This article concentrates on the aforementioned issue and offers a workable method so that the outcomes of vehicle detection can be used for tracking numerous objects and vehicle counts. The convolutional network’s takes an input must be a fixed size, and its more complex structure necessitates lengthy training sessions and substantial resources. It extracts the feature points that need to be identified, compares them with the ORB algorithm, establishes road detection lines, and counts the number of moving vehicles and the volume of traffic.
Queue Orchestration Using an In-Memory Broker

Existing solutions for a task queue have overheads that slow down the task queueing process. The system proposed is built with the help of Redis as the message broker. The Redis Zset data structure reduces the time taken for queuing significantly relative to existing solutions such as Celery which has a high overhead due to the various other tasks that it must perform. Celery will be used as the benchmark for comparison against the proposed system’s performance. The time taken for the task in the queue will be calculated and compared for both Celery and the proposed system for 1, 2, 4, and 8 workers. From the results, it can be observed that the tasks in the proposed system spent less time in the queue for all four cases when compared with the relative performance of Celery’s, which has increasing queue time with an increase in the number of workers.
An overview of blockchain technology in STAMINA project

The future of the healthcare sector and its systems are beginning to take shape by combining technological innovations with traditional techniques to generate new and efficient solutions for patient care and data storage. Most conventional medical information management and storage systems are centralized, which means there is a risk of data loss in the event of malicious attacks or even natural disasters because of the single point of failure that centralized systems have. Blockchain technology is decentralized and emerging, offering the potential to significantly revolutionize how data is stored and managed in the healthcare industry. This paper presents the advantages of using blockchain in the healthcare sector, as this technology has a strategic role in both the management of stored data and the creation of predictive systems that could prevent a pandemic like the one generated by the SARS-Cov-2 virus. We present different existing studies and the method addressed in the ongoing STAMINA project.
Managing the Fall Risk in Smart Homes Using Monocular Cameras, Ambient Sensors and an Interactive Social Robot

One of the biggest challenges faced by older adults is the risk of falling. Falls and the fear of falling cause serious physical and mental health issues. They are also a very impacting factor on the independence of the individual. This paper approaches the problem through the view of a population afflicted by the self-neglect syndrome. Therefore, we propose the NAOREA system, using monocular cameras, various ambient sensors and a humanoid social robot, to manage the risk of falling through a step-by-step process. This system achieved up to 100% detection rates of obstacles for proactive fall control in a real-home setting, as well as a 87% detection rates of falls after they happened.
Design and Implementation of a Teaching Verbal Behavior Analysis Aid in Instructional Videos

With the rapid development of artificial intelligence technology and its deep integration with education, the use of intelligent means to analyze and study teaching behaviors has become a research hotspot. Teaching behavior can be divided into verbal behavior and non-verbal behavior, among which verbal behavior is the main way of classroom teacher-student interaction, accounting for about 80% of all teaching behaviors. At present, teaching researchers mainly use manual annotation to code and analyze teaching verbal behaviors, which has the problems of low efficiency and individual subjectivity, and it is difficult to collect a large amount of teaching verbal behavior data to analyze teaching patterns. This paper designs and implements a teaching verbal behavior analysis tool using teaching verbal behavior recognition algorithm. It provides the functions of speech transcription of teaching videos, automatic recognition and analysis of teaching verbal behaviors, and analysis of teaching verbal contents, with a view to providing assistance to teaching researchers in analyzing teaching verbal behaviors in instructional videos by using intelligent technology.
A New Uncertain Reasoning Approach For Example Selection in the Example-Based Problem Solving Tutoring

This paper proposes EBT, an example-based tutoring tool which helps learners to work out their own solutions to specific problems. It provides adaptive suggestions on problem solving in the sense that it guides the user through the entire problem solving path based on their individual problem solving ability and performance along the problem solving path steps. On request of assistance on solving a problem, the EBT tool sifts out students with “similar” ability to the user in solving this problem and presents to him/her solutions of these students as examples in an order dependent on their similarity values. Example solutions are cut into slices. Appropriate slices are presented dependent on the stage of the problem solving path. At any stage of the path the user may choose to accept assistance or try to give an answer on his/her own. The tool is built within a first-order reasoning system with an uncertain reasoning function. The problem solving abilities of students are carefully analyzed on the basis of facts and rules in database. A detailed description of the EBT tool, including its model and the deduction process, as well as its implementation using the Haskell language, a leading functional programming language, is given.
Instruction Ability Intelligent Evaluation System(ES) on Account of Neural Network Algorithm (NNA)

For instruction, the teacher team is the most fundamental component. These resources are the main part of innovation and a sign of the excellence of the school. Over the years, many schools have reformed their appointment platform, phasing out the buildment and replacing it with the appointment platform. This, to a huge extent, broke the original "big pot" platform. In view of this situation, the appraisal of tutors can occasionally do fair, reasonable has become an important subject. It is very necessary to build a logical and efficient harmony evaluation system(ES) of instruction ability or teacher appraisal model. This paper studies the instruction ability harmony ES on account of NNA, introduces the function and tenet of the instruction ability harmony ES, and explains the concept and tenet of the NNA. The test shows that the harmony instruction ability ES on account of NNA has a more efficient mark in the harmony instruction ability appraisal.
Research on the Training Mode of Collaborative Innovation Talents under the Cloud Platform Environment

This research discusses the innovative talent training mode in which teachers, students and resources cooperate with each other in the “cloud platform” teaching environment. The construction mode of curriculum resources under the cloud platform has been constructed, the construction of “fragmentation” and “micro curriculum” of specific curriculum resources has been studied, various types of teaching resources suitable for mobile learning have been explored in depth, and a large number of “micro resources” have been created and released to the cloud platform, facilitating teachers and students to share learning resources; Through the cloud platform, real-time and non real-time teacher student interaction and student student communication are realized before, during and after class, forming a smart classroom teaching mode based on the cloud platform, realizing online micro class viewing before class, face-to-face discussion between teachers and students in class, online discussion, question answering and homework after class, and timely feedback, formative evaluation and peer mutual evaluation; At the same time, the mutual evaluation mechanism of teaching effect between students and teachers and the evaluation mechanism of resource utilization effect have been established, forming a scientific evaluation system of teaching effect.
Online Voice based Smart Security and Automation System for Real Time Application using Artificial Intelligence

Nowadays, security is a major issue everywhere, and as a result, there are numerous assets available to resolve this controversy. Most security systems have several vulnerabilities that can be resolved to access the desired location, home and environment. Furthermore, theft and unauthorized access to places have become severe problems. To this end, the design and prototype of an online digital password lock and automation system were awarded in this work. This research work will propose the design and physics of a basic online security and digital surveillance system. The system employs an integrated nodeMCU wireless protocol for remote security testing via an Android smartphone running the IoT app. This framework is applied with the standard local voice command transmitted to the Google Assistant through IFTTT and BLYNK platforms. The Voice secret word orders are then sent to the NodeMCU and control the transfers to kill on or the gadget associated with the individual hand-off in line with the client to the Google Assistant. Wi-Fi is used for communication between the ESP32 and the cloud and applications. The digitally intelligent safety system is a special amalgamation that offers an unquestionable remedy to the safety issues of the various safety functions mentioned above from that point on. The device was efficiently implemented and the purpose was performed except for deviations. This study has much future space because of its security capabilities.
Dynamic Pagerank Frequent Subgraph Mining by GraphX in the Distributed System

Graph Mining has been the most demanding research area for the last few decades in different fields, such as biological networks, the world wide web, mobile applications, sensors, online, social networks, etc. Frequent Subgraph Mining (FSM) plays a vital role in Graph Mining to exercise, study and generate interesting patterns from graph data. Basically, FSM techniques are classified into two types such as an apriori-based method, and a pattern growth-based method. This technique faces the problems such as the generation of the duplicate frequent subgraph, having no proper technique to rank during candidate generation, and how to map the threshold values. In this proposed system, a Dynamic PageRank GraphX- based Frequent Subgraph Mining (DPRGFSM) model that is able to extract interesting patterns from the distributed system by eliminating duplicates by ranking them to the proper level. In addition, we also use load balancing, pre-punning, and optimization techniques to improve its performance in both memory requirements and time complexity. The potency of methods defined in this paper is evaluated rigorously with different threshold values and comparative studies with different parameters with existing Spark- based Single Graph Mining (SSIGRAM) and A Ranked Frequent pattern Growth Framework (A- RAFF) and found drastic improvement with all four datasets. The proposed methodology is 1.6 times faster than the Spark-based Single Graph Mining (SSIGRAM) model and 50 times faster than the A Ranked Frequent pattern Growth Framework (A- RAFF) for recurrent subgraph extraction.
Analysis of Crowd Features based on Deep Learning

Everyday, crowds assemble for temples, concerts, marathons, anniversaries, memorials, holidays, political events, demonstrations, and shopping malls. In recent years, the automated assessment of crowd counting, gender, and age from face pictures has gained much interest due to its wide range of applications. Estimating people’s ages and gender in a crowd is difficult, but it has several applications in surveillance, management, and planning. A new approach to gender re-colonization, age estimation, and crowd counting based on gender with the location of individuals in visual frames is proposed in this study. Extended Mask R-CNN has been instructed to deal with all four issues simultaneously. Because localization necessitates the annotation of high-quality frames, created the SGSITS institute dataset, which overcomes the constraints of previous datasets. In addition, the study proposes evaluation methods and a full comparison with other approaches. With the training dataset of SGSITS, the proposed technique has outperformed state-of-the-art algorithms on a large dataset with exact annotations.
Fast Estimation of NTT/INTT Accelerator Costs for RNS-Based Homomorphic Encryption

This paper proposes a hardware estimator for accelerating number theoretic transform (NTT) for fast polynomial operations in the ring learning with error (RLWE) based homomorphic encryption (HE). By modifying the number of unit processing elements, modulus bit-width, and residue number system (RNS) parameters, we present a systematic way for rapidly calculating the hardware complexity without realizing the target accelerator. Compared with the actual synthesis results in 28nm CMOS technology, experimental results show that the proposed work successfully estimates the NTT accelerator area even under the large-valued parameters with acceptable errors, greatly relaxing the overheads for the design space exploration. Homomorphic encryption, residue number system, hardware accelerator
Analysis of a Network Forensic Investigation Technique

This paper discusses the topic of Network Forensics, the issues of Network Forensic techniques, and dives deeper into a specific Network Forensic Investigation model. The impact that Network Forensics has on current Digital Forensic trends and the challenges faced is also covered. Every step of the investigation model is thoroughly analyzed and based on the analysis a new investigation model is proposed that could also be used in its stead. The framework under analysis can work alongside other Network Forensic tools and techniques, making it quite versatile. Adaptability is crucial for digital forensic investigation models to fit into several different scenarios and instances and this framework has that part covered. There are no major requirements to utilize either of the investigative frameworks however since the proposed framework was derived from the investigative framework discussed earlier on in this paper, there is still room for improvements in the future for both investigative models.
Production Planning in Process Industries using CEC 2021 Winning Algorithms

In recent years, the researchers have been studying the potential of Evolutionary Computation (EC) techniques on solving various combinatorial problems. The complex nature of combinatorial problems provides the opportunity for metaheuristic techniques. This work analyzes the efficacy of two winner algorithms of "CEC 2021 special session", a "competition on real parameter bound-constrained numerical optimization" to solve the combinatorial production planning of the petrochemical industry. The optimal production plan specifying the production quantity of each process has to be determined by maximizing the profit while satisfying all the associated constraints. The performance of the winner algorithms, namely Adaptive Gaining-Sharing Knowledge with Improved Multi-Operative Differential Evolution Algorithm (APGSK-IMODE) and Multiple Adaptation Differential Evolution (MadDE), are compared with other popular metaheuristic techniques such as Sanitized Teaching-Learning based Optimization (sTLBO), Dynamic Neighborhood Learning based Particle Swarm Optimizer (DNLPSO), and Artificial Bee Colony (ABC). MadDE algorithm has been employed with a modification that enhanced the algorithm’s performance. It was observed that modified MadDE outperformed all other algorithms whereas APGSK-IMODE determined feasible solutions for all different cases in the problem.
Data augmentation method for simulating lung lesion evolution

In the medical image field, deep learning, as a method of computer-aided diagnosis, is widespread and performs well, but deep learning relies on a large amount of data for training, yet it is often difficult to collect enough data for training in the medical image field. To overcome this problem, data augmentation has become a popular method to increase the size of training datasets, using data augmentation techniques to process existing samples can significantly alleviate the problem of lack of training data. At the same time, because medical images contain information about physiological structures, data augmentation needs to consider the lesion region, so that not all data augmentation methods can be applied to medical images. In this paper, we propose a data augmentation method based on the evolution phase of lung lesions for medical lung images and generate data on different evolution phases of lung lesions for training purposes while ensuring that the edge information and texture information are consistent with the lesion characteristics. Also we used our own lung dataset to experiment with our proposed method, experiment show that the data generated can ensure physiological structures.