Reviewerly/exHarmony huggingface dataset:
Graph Learning Based Sentiment Analysis System for Chinese Course Evaluation

Natural language processing (NLP) is an important research direction of artificial intelligence. Text sentiment classification in NLP is a compromising method to exploit the constructive feedback to improve teaching quality. This paper captures the course reviews from online learning platform China University MOOC as the dataset, and uses an aspect-level sentiment classification method to analyze the course evaluation, via a graph convolution network (GCN) to characterize the syntactic dependency between context words and various aspects of sentences, and decide the emotions described by multiple non-adjacent Chinese words. As for the 1837 comments of online courses, there is obvious aggregation in the aspect of extraction. Most of the comments mainly focus on the two aspects of course and teacher, and a few comments describe other aspects related to the course. The results demonstrate that the accuracy of the model is more than 80%. Additionally, a visual interface is designed to provide the sentiment analysis results no matter what data set of course reviews is given, and make the graph learning based sentiment analysis tool user-friendly.
Convolutional Neural Network Architecture for Detecting Facemask and Social Distancing: A Preventive Measure for COVID19

COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up of SOPs, this paper proposes a system to detect whether the people are wearing face masks and maintaining social distance or not in order to break the chain of COVID 19. The proposed system uses Deep Learning (DL) model based on Convolutional Neural Network (CNN) architecture for training the facemask detector and OpenPose 2D skeleton extraction technique for detecting social distance. A DL model based on a 7-layered CNN architecture was proposed in this research to detect masked and unmasked faces. Based on the proposed technique, 99.98% validation and 99.98% testing accuracies were achieved. In addition to that, the maintenance of social distance which is the new normal nowadays was also detected using the images obtained from the internet as currently, there is no such database available for detecting social distancing.
Comparison Between the Polarity Inference Approach and the Sentiment Fine Analysis Approach Using Tweets

Social networks have become a rich source of information, as smartphone users in 2014 reached 1.5 billion, and in 2019 2.5 billion, an increase of 36%. And most of the smartphone users use social networking platforms to express their opinions about specific policies or services. Major companies analyze this data to find out the feelings of their customers. In order to improve services and ensure that customers do not go to competitors. In this work we will try to show the difference between the methods used of sentiment analysis in social networks, such as the Lexicon-Based Approach and the machine-learning approach. And a comparison between the polarity inference approach and the sentiment fine analysis approach to find out the degree of customer satisfaction (very positive, positive, neutral, negative, very negative).
Optimal Min-Communication and Migration Cost Algorithm based Approach for Efficient Task Migration in Cloud Computing

As more endeavors and organizations use cloud conditions and administrations to lessen IT costs, cloud specialist co-ops need to limit their activity cost of server farms to acquire benefits while contending with one another. In this review, a proficient virtual machine provisioning system for cloud server farm is presented. In light of cloudsim 3.0 test system, our test results have shown that the proposed provisioning component is extremely effective as far as CPU use and delivers less number of virtual machine relocations in correlation with existing provisioning instruments like LrMmt(Local Regression and Minimum Migration Time Approach). The test results show that our Task relocations into virtual machine plainly improve the correspondence cost and movement cost. This implies that these three calculations further develop the organization execution and decrease the organization utilization in cloud-edge combination figuring conditions. They likewise beat existing calculations as far as activity time by 70% overall.
RIRL: A Recurrent Imitation and Reinforcement Learning Method for Long-Horizon Robotic Tasks

The developments in reinforcement learning provide a powerful and efficient learning framework for autonomous robotic systems. However, prior works rarely embed historical observations due to the exponentially increasing complexity, which may not perform well for large-scale long-horizon tasks that might require hundreds and thousands of steps to complete. In this paper, we propose Recurrent Imitation and Reinforcement Learning (RIRL) to address the challenges and enable robots for such tasks. The proposed RIRL incorporates a long short-term memory (LSTM) network to retain long-term memories, which could be an effective and efficient method to tackle the long dependency problem raised in long-horizon robotic tasks. To assess the performance of the RIRL, we test it with an optimized path planning problem for a robot to perform a Radiofrequency identification (RFID) inventory in dynamic and previously unknown environments. We experimentally validate RIRL’s feasibility and effectiveness in a visual game-based simulation platform, where the proposed RIRL model outperforms three baseline schemes with considerable gains.
A Specific Public Sentiment Clustering Problem Based on Hybrid Bat Algorithm

With the release of the “double reduction” policy, more public sentiments about this policy have been produced on the internet. In this paper, public sentiments about “double reduction” policy is crawled from weibo and studied. After data preprocessing, a hybrid bat algorithm with K-means method is proposed to cluster this type of public sentiment problem. With the crawled data, the performance of the proposed algorithm is verified by the traditional K-means clustering method and proposed hybrid bat algorithm.
RelNet: Relevance-Based Punctuation Prediction Network in Speech

In the automatic speech recognition system, the text content of the voice transcription is usually different in different application scenarios. Punctuation prediction networks cannot adapt well to many application scenarios at the same time. When the testing set comes from different scenarios, the accuracy of the punctuation prediction system will drop sharply. Therefore, to alleviate the above problem, this paper proposes a relevance calculation module. The experimental results show that adding a relevance calculation module can indeed enhance the generalization of the model. The relevance calculation module enables the model to learn more general and effective characteristics for punctuation prediction to alleviate the problem of model accuracy deterioration. Experiments are conducted on Tsinghua news data and Sohu News data. The final model can get up to 3.59% absolute improvement of the F1 value than the baseline on the testing set.
A Selective Ensemble Classifier Using Multiobjective Optimization Based Extreme Learning Machine Algorithm

In a single hidden layer feedforward neural network (SLFN), acquiring optimal values for the number of hidden neurons and connection parameters simultaneously is regarded as one of challenges, which has attracted extensive attention. This is because changing the number of hidden neurons and connection parameters greatly affect overall performance of the SLFN and increase the training complexity. In this article, the training error, validation error, and network complexity are treated as three conflicting objectives of multiobjective model for getting a compact network with good generalization ability. For solving the multiobjective model, a hybrid coding scheme is designed for network structure and connection parameters of a SLFN, and then a multiobjective optimization based extreme learning machine (MOELM) is proposed for structure learning and parameter optimization simultaneously. To improve recognition accuracy, a selective ensemble classifier with three base classifiers according to the selection strategy is utilized to make final decision. Experimental results and comparison with other classifiers on several benchmark classification problems indicate the effectiveness and superiority of the proposed MOELM.
Enhancement of Camera Blur Image Using Optimization Techniques

Image enhancement is the technique which improves the quality of a blurred or noisy image and indirectly assists in extracting the significant information from the image. A number of methods have been suggested in the past for carrying out this task. The methods to enhance an image are generally based on pixel intensity transformation. The outcome of these methods indicates that these methods are ineffective for extracting all relevant information from the images. This research work proposes a hybrid image fusion model for extracting significant information from the image. The ACO (Ant Colony Optimization) is integrated with PSO (Particle Swarm Optimization) in the projected model. The proposed method was implemented and tested on a general set of images. The results demonstrated that the projected model provided higher PSNR (Peak signal-to-noise ratio), lesser MSE (Mean Square Error) and SSIM than the conventional methods while enhancing the image.
App controlled Robotic Arm with Ultrasonic Sensor and Solar Panels

Usually movement of goods from one location to another requires human input. This is a tiring job in industries where goods are picked and placed in bulk and the weight of goods to be carried is beyond human potential. Automation industry can play a large role in reducing the human labour and making the process more efficient. In this paper, we put forward a solution to this problem using environment friendly automation technology. We use NodeMCU as the connection between the system and the device through the internet. Our goal is to reduce human interference in hazardous work environment and place with oxygen deficiency through an app controlled 3D printed robotic arm. This arm can be controlled through an android application from anywhere around the globe and can be programmed to perform basic tasks. Furthermore, we are using 3D printing technology so as to eliminate the requirements of production moulds thereby reducing the cost of the arm and making it more feasible. Another objective of this Project was to make the Arm as environmentally safe as possible. Hence, a solar panel will be connected to the Chassis. This ensures that the primary source of power for the Arm is solar energy which is a renewable resource. In addition to this, the plastic which will be used to 3D print the individual parts will be biodegradable and thereby reducing the carbon footprint.
A Novel Method to Develop MiniDB Using DBMS and Information Digitization

The existing commercial DBMS is too complex, which make it difficult for students to study DBMS. This paper proposes an novel method to build education oriented mini-DBMS with the support of MiniDB. MiniDB is a software architecture that supports user customized development. Based on MiniDB, some novel modules can be customized and aggregated to realize specific software instances. This paper presents a practical case of MiniDB to construct a education oriented DBMS, and shows the possibility and method of constructing a mini-DBMS with the cooperation of teachers and students. Our practice shows that it is feasible to develop the mini-DBMS which can reduce students&#x0027; investment in the preliminary work of practice, and help students understand the principle of database through practice.
Deep Learning Based Face Mask Detection System for COVID-19 Control

COVID-19 pandemic is spreading continuously causing serious health problems. Wearing face mask is one of the prominent precautions people can easily follow. In this paper, we have built a model for face-mask detection system using deep learning technique that uses Histogram of Oriented Gradients (HOG) based features for face detection and Convolutional Neural Network (CNN) for detecting whether the person is wearing face mask or not. The model has also the capability of detecting whether the wearer is wearing the face mask properly or not. This model has been trained with 3650 images using python script in Google Colab environment applying Keras and TensorFlow. After a number of trials we have found that our model gives best result with 50 epochs. We have found training and validation accuracy 94.59% and 98.51% respectively. The model has been tested with real time inputs. From the experimental results it has been found that the proposed model is capable of detection faces with-mask and without-mask with 97% accuracy.
Enhanced the Face Recognition Accuracy by Using Histogram of Oriented Gradients (HOG) in Pre-processing Approach

Face recognition has received significant attention because of its numerous applications in access control, security, and surveillance system. In real-world scenarios, uncontrollable lighting conditions, a variety of posture and facial expressions, and noisy facial images can degrade the face recognition accuracy. Hence, the research based on the HOG algorithm and pre-processing implementation framework processing framework to improve face recognition accuracy is proposed. This proposal consists of four stages where the first stage is to build a dataset of 15 subjects and has five series of multi-poses of facial images. The second stage is focused on enhancing the pre-processing framework that consists of denoising colored, illumination normalization, and facial alignment algorithms. For the third stage, the HOG algorithm is utilized as a feature descriptor to detect the face. The fourth stage is implementing the deep convolution neural network to evaluate the accuracy of face recognition. From the observation, the improvement in the accuracy rate is up by 4.37% after the enhancement of the pre-processing framework.
Contactless EVM with Enhanced Security Mechanism

In this paper we propose the use of secure contactless Electronic Voting Machine(EVM). We used a Radio Frequency Identification(RFID) reader to authenticate the voter&#x0027;s identity, and Infrared(IR) sensors to implement contact-less voting to curb the spread of Covid-19. Significant components used are Arduino Uno, FC-51 Infrared Module, EM-18 RFID reader, keypad, and I2C PCF5874T for Liquid Crystal Display(LCD). The software used to program these components is Arduino IDE. This EVM ensures security in two ways - authorizing the identity of the voters before they are allowed to vote, as well as providing a contact-less experience for the voter. An extra layer of security is added, so that when the candidates and Election Commission are present and enter their respective passwords, only then can the results be accessed.
Gradually channel pruning algorithm via joint training

Convolutional neural network pruning is commonly adopted for alleviating the high computational cost of resource-limited devices. However, most pruning algorithms separate the pruning process from the fine-tuning process, so the whole pruning algorithm will take a long time. In this paper, we propose a joint training channel pruning algorithm (JTPA) that automatically prunes the network in channel level based on the stage stability in model training. The main idea is two-fold: a) through a priori experiments, it is proved that the training of convolutional neural network has the characteristics of phased fitting, and b) the importance of model channel is judged according to the stability of feature maps similarity and gradient value of stage. More specifically, we can judge whether this stage is well trained from the shape similarity stability of the stage feature map, so as to judge which channels are more important with the gradient value. In the experiments, ResNet series networks is selected to evaluate the pruning performance on CIFAR-10, CIFAR-100. The results demonstrate that compared with state-of-the-art of neural network pruning algorithms, the proposed JTPA model achieves not only has high pruning efficiency but also a better accuracy.
An Efficient and Reliable Multi-Robot Navigation Platform with Collision Avoidance

With the ever-increasing demand for consumer goods, companies are pivoting towards semi or fully autonomous robotic warehouses to efficiently fulfill orders. Large companies have become increasingly reliant on autonomous robotic warehouse solutions, however, the demand from small to medium-sized companies has increased dramatically [8]. These companies often rely on manual solutions, such as forklifts, to traverse their warehouses. In 2019, forklifts accounted for 79 work related deaths and 8,140 nonfatal injuries [9]. Warehouse accidents are the direct result of their fast paced, ever-changing nature. With warehouses containing a multitude of static and dynamic variables, an open-source robotics platform is needed that can safely traverse a warehouse, preventing unnecessary death and injury. The proposed solution allows for the safe and effective navigation of these environments utilizing a path-planning algorithm in tandem with a dynamic obstacle avoidance algorithm. The dynamic obstacle avoidance algorithm uses LiDAR sensor data to detect and avoid obstacles along a path. The path-planning algorithm uses a map of a warehouse environment that accounts for static objects, such as shelves, and generates an efficient path from a starting point to a goal point. These algorithms are simulated in Gazebo allowing for a detailed visual of the platform&#x0027;s performance. These algorithms, together, create a platform that allows for robots to autonomously navigate to desired destinations while safely avoiding a myriad of obstacles.
Hybrid Flower Pollination Algorithm for Optimization Problems

In this article, an evolutionary approach with different objectives based on the improvement of the hybrid GSA (golden section search algorithm) using the flower pollination algorithm (FPA) with fitness distance balance selection (FDS) is proposed. In GSFPAFDS algorithm, the optimization problem is improved in several sub-problems. The pollination of flowers in the population is enhanced in Levy flight with fitness distance to enhance convergence. Therefore, the GSA can be used as a local search in FPA. This simulation is part of the traditional GSA, FPA, GSFPA, and personnel processing to solve the consequences of sequential global optimization problems. A local update strategy is used to increase optimization capacity. Its main objectives are to minimize the overall execution time and the allocation of the balance between local and global optimal zone. It allows global parallelization with the simultaneous performance of the processor to the functions, and determine the ratio of the communication to the functions. The behavior of the GSFPAFDS with few methods such as GSA, FPA, and GSFPA understandability has been showcased to solve high-dimensional technical problems.
Cyber Supply Chain Security: A Cost Benefit Analysis Using Net Present Value

Cyber supply chain (CSC) security cost effectiveness should be the first and foremost decision to consider when integrating various networks in supplier inbound and outbound chains. CSC systems integrate different organizational network systems nodes such as SMEs and third-party vendors for business processes, information flows, and delivery channels. Adversaries are deploying various attacks such as RAT and Island-hopping attacks to penetrate, infiltrate, manipulate and change delivery channels. However, most businesses fail to invest adequately in security and do not consider analyzing the long term benefits of that to monitor and audit third party networks. Thus, making cost benefit analysis the most overriding factor. The paper explores the cost-benefit analysis of investing in cyber supply chain security to improve security. The contribution of the paper is threefold. First, we consider the various existing cybersecurity investments and the supply chain environment to determine their impact. Secondly, we use the NPV method to appraise the return on investment over a period of time. The approach considers other methods such as the Payback Period and Internal Rate of Return to analyze the investment appraisal decisions. Finally, we propose investment options that ensure CSC security performance investment appraisal, ROI, and business continuity. Our results show that NVP can be used for cost-benefit analysis and to appraise CSC system security to ensure business continuity planning and impact assessment.
Online Alarm Recognition of Power Grid Dispatching Based on PRADO

The alarm recognition of power grid is an important link to realize smart grid dispatching. An online alarm recognition method of power grid dispatching based PRADO and knowledge base is proposed in this paper. Firstly, a natural language processing-deep learning text data mining architecture is introduced, and the PRADO model is used to vectorize the alarm information of grid regulation. Then the vectorized information is input into the CNN deep learning model for training, and the Attention mechanism is introduced to improve the CNN model according to the characteristics of grid alarm information. The accuracy and validity of the model are verified by example. Finally, the online alarm recognition of power grid dispatching integrating PRADO model and traditional knowledge base is proposed, which provides an idea for engineering application.
Ranking Authors in Citation Networks with Automated Keyword Extraction using Word Embeddings

The main objective of this present article is to propose an efficient way to evaluate and rank the researchers and academic authors by using citation network dataset. To rank the authors in the citation network to predict the prominent authors in the research domain for making ease for authors for identifying the predominant authors in their area of research work. Popular independent metrics like h-index, number of citations, and so on is not very reliable when it comes to ranking authors. Author come up with a new method called Citation enhanced Ranking of Authors (CeRA) to rank authors. The proposed approach CeRA approach utilizes content-related similarity between abstracts that extract the keywords by using the word2vec model. K-means clustering algorithm is applied to extract authors clusters based on domains. Finally, specific authors rank is retrieved using the page rank of the author&#x0027;s node in a cluster.
Menggagas Kampung Inggris Padang menjadi Kampung Inggris Masyarakat

The aim of this social service is to initiate an English camp in Padang to become an English Camp Village like other famous English camps. This English camp is still in the form of an English course where many students come here to study English for some purposes like to prepare to continue to under graduate degree; to get a job or to pass TOEFL test. As its name suggest, an English camp is not merely Englishmen who live there, but it is only a collection of English courses located in one area. Since there are students coming here to study so that the researcher names this course Kampung Inggris Padang. Based on the initiation carried out for this social service, it was found that there is improvement of students’ ability in speaking English like doing corversation or dialogue and reading aloud English textbook. If it is viewed from the participation of society, there are participations of people around the course like sharing ideas, skill, energy, social participation for better future of Padang English Camp. The prominece is that the realizing and caring of people about their children’s education.