Reviewerly/exHarmony huggingface dataset:
Emotion Based Chatbot Using Deep Learning

The use of chatbots has been evolving rapidly in numerous fields in recent years which includes Education, Marketing, Health Care, Entertainment, etc. Chatbots utilise the technology of Artificial Intelligence (AI) to mimic human conversation. It is like a virtual assistant that is able to assist in conversation, answering questions, getting directions in the area of knowledge they are designed under. Chatbot can reduce customer service cost and handle multiple users at a time which makes it become more popular. But yet to accomplish a task in detecting and identifying emotion from users the chatbot interacts with. To address this issue, this paper provides a design of a chatbot, in which the chatbot is able to identify the user’s emotional status through the text provided by the users through the communication and then provide suggestions according to the input given. The emotion detection model will be trained using Artificial Neural Networks (ANN) and supervised learning. This chatbot is proposed to be used by i-CATS University College to help in the Marketing department to automate the process of answering FAQs and inquiries. However, a full chatbot implementation that replies to human queries is suggested that this research paper should shows to indicate how effective the chatbot can be. It is also suggested that strong implementation of chatbot must be considered for the improvement of this research in the future.
Photovoltaics Performance Evaluation Using IoT Technology

The excessive utilization of non-renewable energy sources to fulfill the energy demands of the world, particularly in the electricity generation sector, has brought upon dire effects to the Earth’s ecosystems. To reduce the dependence on non-renewable energy sources, the Malaysian government has pushed for the utilization of solar photovoltaics (PV) to produce electricity for the nation. This initiative is present in both utility scale, via the Large-Scale Solar (LSS) scheme, and domestic scale via the Net Metering (NEM) scheme. Contained in this paper is the elaboration on the utilization of Internet-of-Things (IoT) technology to evaluate the performance of PV on the domestic scale. A prototype is designed to collect data, which will then feed the data into an algorithm which will provide an evaluation of the health of a PV system. Two experiments were conducted to determine the if the integration of the mathematical model, algorithm, software, and hardware would produce a prototype can provide accurate results. The experiments conducted provided conclusive results that the data collected by the prototype is accurate as it is able to detect PV performance issues, and the results were verified via manual measurements.
Construction of knowledge graph for security threat tracing in Power Distribution Internet of Things

As the application of Power Internet of Things in the distribution network, Power Distribution Internet of Things is a new type of power network produced by the deep integration of traditional power industry and Internet of Things technology. The threat tracing method of security events in Power Distribution Internet of things is much significant to the security of Power Distribution Internet of things, it can help to quickly locate the source of security events and send back the threats of security events in distribution network. This research focuses on the construction of network security knowledge graph for the threat tracing of power distribution iot security events, which is the basic work of the threat tracing method.
A Lightweight Named Entity Recognition Method for Chinese Power Equipment Defect Text

During the operation and maintenance of power equipment, a large amount of text data is accumulated, and it is of great importance to mine valuable information and evaluate the operation status of the equipment. Among them, named entity recognition technology is a key prerequisite for downstream tasks. However, with the development of natural language processing technology, while improving the accuracy of entity recognition, the existing models are gradually unable to meet the requirements of time and equipment cost for model training in practice. In this paper, we propose a low-cost ALBERT-BiLSTM-CRF-based named entity recognition model applicable to power equipment defective text. The model achieves an F1 score of 92.47% in entity recognition in the power domain, outperforming the benchmark BERT model performance in terms of time cost and effect.
Proposal for an adaptation of a Strategic communication model applicable to the Scrum framework in a GSD environment

Effective communication is a success factor when developing software projects in both a local and geographically distributed environment (Global Software Development, GSD). The communication problems that arise between the development team and the product owner, as well as in the sprint planning, daily scrum, sprint review and sprint retrospective events, result in problems of ambiguity in determining requirements. Low customer involvement, low uncertainty definition, or risk identification directly affect the scope, budget, and delivery time of a software product. Likewise, distance, differences between time zones and culture are some current challenges that present communication problems between the members of the work team, which have a great impact on project management. This paper briefly describes a proposal to adapt a strategic communication model that fits the Scrum framework and is suitable for both local and distributed environments.
Design of Distribution Network Monitoring System Based on EPICS for Accelerators

Excellent distribution network is prerequisite to ensure stable operation of accelerator with high power and high-intensity. Distribution network of new generation accelerator with high power and high-intensity may suffer natural disasters and external damage. At the same time, there are also some problems inside distribution network like high power, large inductive load, impact load, overvoltage, resonance and three-phase unbalance, etc. Because of these factors, building the new accelerator is in the face of some challenges. It is indispensable to acquire in real time the operation status of accelerator distribution network. And it is conducive to perform fault detection, anomaly analysis prediction and active defense. So based on some open source software such as EPICS, Archiver Appliance and Pheobus, a monitoring system of accelerator distribution network is designed and implemented in this paper. The system has some advantages of not only open source, low cost and short development cycle, but also good real-time performance and high reliability. It integrates data visualization, data archiving, fault alarm and other functions. And essentially, it realizes informationize monitoring of accelerator distribution network and lays a foundation for intelligentization of accelerator distribution network.
Intention Recognition Method of Power Grid Dispatching professional language Based on Hybrid Neural Network

The construction of a new power system puts forward higher requirements for dispatching human-computer interaction and professional language understanding. Aiming at the problem of lack of effective identification methods for power grid dispatching business intentions, this paper proposes a language intent recognition method for power grid dispatching based on the fusion model of bidirectional encoder representations from transformers (BERT) and text convolutional neural networks (TextCNN). First, the text features of the power grid dispatching language are calculated to generate the dispatching language word vector based on the dynamic word vector pre-trained by BERT. Then, the word vector of the power grid dispatching language is used as the input of TextCNN to implement feature encoding and classify dispatching language intent. Finally, through the verification of the dispatching professional language of a control center, the proposed power grid dispatching intention identification method has stronger identification ability and generalization ability compared with other methods.
Security Risk Identification in Power Grid Infrastructure Construction Site Based on Micro Intelligence Device

With the development of the Power Internet of Things and the digital transformation of energy, the amount of power grid endings data increases dramatically, the security risk identification of power grid infrastructure construction based on micro intelligence will effectively reduce the data transmission bandwidth and ensure the timeliness of risk warning. First, we collect and pretreat on-site operation samples; Secondly, the yolov3 network structure is compressed based on inter layer fusion, channel pruning and other technologies, and the model is completely trained; Finally, the model is quantified and deployed to the edge device. The experimental results show that this method not only ensures the accuracy of identification, but also greatly improves the speed of identification when identifying multiple types of security risks.
Application Research of Daytime and Night Road Conditions Maps Conversion Based on CycleGAN Style Conversion Technology

With the development of deep learning technology in recent years, autonomous driving technology has gradually become mature. However, the training of the network model of autonomous driving technology requires a large number of data sets. Due to the limitation of conditions, it is very difficult to collect daytime and night road conditions map at the same location and under the same road conditions. Therefore, this paper uses the CycleGAN network model to convert the daytime road conditions maps into the corresponding night road conditions maps, and changes the ResNet network structure of the generator in the CycleGAN network model to the U-Net network structure. Then uses the YOLOv 5 target detection model to verify the results. The results show that when using the CycleGAN network model for the daytime and night road map conversion task, replacing the ResNet network structure in the generator with the U-Net network structure would achieve better conversion results.
Deep Learning-Based Text Entity Recognition Method for Distribution Network Operation and Maintenance

The distribution network has accumulated a large amount of text data in long-term operation and maintenance, dispatching and other operations, and a large amount of entity data of the distribution network is contained in these text data. However, due to the large amount of text data and most of the texts are proper nouns, there is no one entity recognition algorithm suitable for this field, leading to a series of defects caused by poor entity recognition in the process of constructing knowledge graphs. To this end, this paper proposes a new deep learning-based entity recognition method (D-BERT+Bi-LSTM+GAM+CRF) for distribution networks. The method uses the distribution network operation and maintenance scheduling and other protocol documents to train the model. Text entity recognition for distribution network operation and maintenance dispatching operation work orders in real scenarios. Experiments showed that the F1_score, Precision, and recall of this scheme reached 81.8%,80.4%, and 83.2%, respectively, which improved the F1_score by 1.3%-26.6% compared with the conventional scheme. It effectively guides the construction of knowledge graphs.
A Container Task Scheduling System for Edge Computing in the Power Industry based on Server Zone

This paper presents a container task scheduling system for edge computing in the power industry based on server zone. The system uses four elements: computing power, storage space, network bandwidth and data to define the dispatching resource requirements for container tasks. The dispatching system divides the entire power edge computing network into several different types of work zone, each focusing on the execution of one type of task, which reduces the fragmentation of system resources caused by virtual container tasks when allocating resources. Experiments show that the system designed in this paper can better improve the resource fragmentation problem caused by virtualization container task when requesting resources in power edge computing network.
Design of Intelligent Wearable Device Based on Embedded System

In view of the current serious aging problem and the raise number of empty nester elderly, in order to lessen the risk of accidents in the aged, a smart wearable device based on the Internet of things was designed. The device comprehensively uses iBeacon, sensor perception, ASR, intelligent algorithm and wireless positioning technology. The device monitor a number of physiological parameters of the body in true time, and the youngster of the elderly can remotely grip the physical health status of the elderly through the WeChat public number. The device also has fall detection, electronic fence, Temperature monitoring, sleep monitoring and blood oxygen monitoring functions, multi-dimensional to provide convenience for children to monitor the physical condition of the elderly, and provide convenience for the high-quality life of the elderly. This smart wearable device is also called as a safety and hygiene for the elderly.
Trend Analysis and Fault Diagnosis of Equipment State Based on Transformer Operation Mechanism Modeling

Nowadays 3D visualization technology is widely used to model transformers and other equipment in substations in China, which solves the problem of collaborative management in traditional management methods in some extent. However with the development of open power system with non-linear and high-dimensional characteristics bring great challenges to 3D digital visualization. It is necessary to deeply study the linkage mechanism, constraint relationship and expression method of dynamic operation process among substation equipment. In this project, Resource-Oriented Petrinet (ROPN) system engineering modeling method is adopted. It carried out comprehensive and fine-grained mechanism modeling of transformer operation state process, constructed digital twin of transformer operation system, and realized information interaction feedback between transformer physical system and virtual image system. Transformer equipment status trend analysis and fault diagnosis is promoted through the functions of the model, including virtual real interaction feedback, data fusion analysis, decision iterative optimization and so on.
A Method for Measuring the Broken State of Electric Meters

In order to improve the safety and economy of the power grid and prevent the loss of uploaded data and other more serious consequences caused by the damage of installed meters, it is necessary to identify whether meters are broken through cameras before becoming completely broken meters. In this paper, based on a certain number of images of broken meters and normal meters, the image classification task of meters is implemented based on ResNet18 network. Through the ResNet18 network training, it can be obtained that the result of judging and identifying the broken meter and the normal meter, so as to realize the automatic monitoring of the meter and improve the working efficiency of instrument maintenance. The proposed model can be validated through the case in this paper.
Research on blockchain consensus algorithm for large-scale high-concurrency power transactions

At present, the application model and business model of the blockchain are still being explored. How to better integrate the blockchain technology into the power transaction business scenario to improve the quality and efficiency needs to be further explored in combination with the specific business development needs. In view of the large scale of market entities and high transaction processing capabilities in the large-scale and high-concurrency power transaction business scenario, this paper studies the parallel processing technology of blockchain transactions, realizes the parallel processing of transactions, and supports the parallel reading and writing capabilities of tens of thousands of entities. Research on the blockchain consensus algorithm suitable for power trading business can shorten the transaction consensus and execution time, and ensure that the consensus mechanism meets the concurrency requirements of business responsiveness and performance requirements.
Improving Mass-Based Anomaly Detection Using Half-Space Trees and Data Drift for Streaming Data

Anomaly detection is a key issue in wireless sensor networks. One of the most challenging aspects is to carefully specify a data region of ‘normal’ instances to identify anomalous ones. Determining this region and fine-tuning it to improve the anomaly classification, is a not a trivial task. This study is an extension of a previous work, it is based on Half-Space Tree classifier and mass estimation for anomaly detection. Our approach is divided into two phases: the training and the testing phases. It works at the level of a single sensor node and relies on the decision from neighboring sensor nodes to increase the reliability. The improvement was achieved by working on four subject matters: (1) choose split points to create the workspace trees, (2) extract a representative normal mass range and include the data drifting concept, (3) retrain the classification model which was not taken into consideration in our previous work, (4) involve neighboring sensor nodes in the decision making process to decide when to refine the normal range and when to re-build the model. The experiments results show that HS-Tree combined with our proposed work, form an effective combination in terms of time saving, memory space and f1-score.
Mathematical Model for an Autonomous Vehicle to Fulfill the Closed Area

This research presents the mathematical model to roam an autonomous vehicle in a closed region with minor coincident areas. This model is Blind Algorithm, no literature that concern on mathematical model. It is essential for agricultural technology, for example, shoveling soil for planting, spraying liquid fertilizer, harvesting agricultural products, to control the movement of the vehicle with the redundant path is as less as possible is benefited to decrease the working hours and also the burning oil and more for cost-saving without any expensive hardware. The almost wholly filled farmland depends on the shape, the size of the area, and the vehicle dimension.
Obstacle avoidance path planning algorithm for mobile robot based on improved RRT

For traditional RRT* algorithm, It has long search time, low sampling efficiency, and the path is not smooth. To solve these problems, this paper presents an improved algorithm. First, the algorithm uses the strategy of selecting the target point as the sampling point with adaptive probability to guide the random tree to grow towards the target point and accelerate the convergence of the algorithm; Then, we pruned and smoothed the path to solve the problem of redundant and tortuous paths after planning. The simulation results show that compared with the traditional RRT* algorithm, the improved RRT* algorithm has less planning time, shorter road stiffness length and higher path quality.
Assessing the effectiveness of the scrum framework and its successful implementation

This paper explores and assesses the effectiveness of a popular agile methodology in project management known as Scrum and analyses the probability of its successful implementation. From a positivism perspective, a survey was used to collect data via survey monkey and descriptive statistics were used to analyse the data collected. The results reveal numerous benefits of the Scrum methodologies as well as challenges faced in its implementation and identify the success factors that must be considered in implementing the Scrum framework. It also indicates the key characteristics that an agile team must adopt for successful scrum practices.
Image Captioning: From Encoder-Decoder to Reinforcement Learning

With the rise of artificial intelligence technology, both image feature extraction technology and text automatic generation technology have made great progress, and the image description generation technology that combines the two has also been attached burgeoning importance by academic fields. It enables computer to have the ability to catch features of a picture and generate a sentence, which is another new type of task after relaying image recognition, image segmentation, and target tracking in image processing. This paper provides a few popular datasets and methods of feature extraction. Moreover, some improving methods, based on Encoder-Decoder, Attention Mechanism, and Reinforcement Learning, are discussed. In the end, this paper provides some applications as examples accomplished by image caption.
Invisible Trigger Based Backdoor Attack and Its Evaluation

A backdoor attack is a threat to deep neural networks (DNN). In an attack on a DNN which is used for image classification, an attacker prepares poison data obtained from an image of a training data set and contaminates the inference mechanism by mixing it with a label different from the original. Conventional backdoor attacks utilize dots in inconspicuous places such as the edges of the image as a trigger in the poison data. This study propose a backdoor attack based on an invisible trigger which cannot be found out by the naked eye by using a steganography.
An Investigation into The Challenges and Solutions of E-Learning in Tertiary Institutions During The COVID-19 Pandemic

The move to online learning from traditional methods has increased recently. With the outbreak of the COVID-19 virus, this change was suddenly at the doorstep of more students. The closing of educational institutions, with particularly stringent rules on tertiary institutions, resulted in numerous challenges faced by students and lecturers alike. This systematic literature review (SLR) investigated the challenges of e-learning implementation faced during the pandemic globally. The main challenges identified included: (a) the digital divide; (b) adaptation to home studies by students; (c) governmental contributions; (d) course quality and content and lecturer competency; and (e) methods of assessment. These challenges were consistent within 36% of the articles analysed. The review discusses useful solutions and innovative teaching methods, including (a) amendment to teaching policies and curriculum design; (b) e-learning workshops to enhance teacher/learner capabilities; (c) cultivation of strong online communities, (d) blended learning methods and (e) course design with the guidance of thinking-based instruction theory. The solutions were consistent in 34% of the articles analysed, while innovations were discussed in 11% of articles. The challenges were generally observed within developing countries. The solutions and innovations were varied across different demographics. Discussion of the challenges with the known requirements can be informative to major stakeholders within the educational landscape such as parents, investors, unaware students, and educators. The solutions and innovations discussed may be tailored according to the context in which an institution exists to enhance the digital learning and teaching experience.