Reviewerly/exHarmony huggingface dataset:
A Bibliometric Analysis of Publications on Customer Service Chatbots

Abstract This study aims to understand the use of chatbots in Customer Service and their potential benefits for companies. The research includes a literature review combined with a bibliometric analysis using the Scopus database as raw data for the usage of the VOSviewer application. The article identifies the important topics on which scholars have focused, in order to serve as a reference for the future research and discussions of chatbots. The first goal was to understand what bots are, how they can be classified and how they work. The second objective was to analyse the specific case of chatbots, know their characteristics in-depth, their operation and some platforms for their creation function without programming. The last objective was based on identifying the development opportunities for bots in the company and the benefits they bring. The results indicated that the main advantages associated with bots in the field of business management are related to the areas of marketing, e-commerce and human resources. The research found that chatbots can offer 24-hour customer service and improve customer satisfaction through natural and efficient interactions. The study also suggests that there are opportunities for chatbots in digital marketing strategies, such as providing personalized recommendations and automating customer service tasks.
Deep convolutional auto-encoders for emotion recognition from facial expressions

One of the unsolved problems in computer vision is recognizing or understanding other people's emotions and feelings. Deep Convolutional Neural Networks (CNNs) have attempted to be effective in addressing emotion recognition issues. The significant level of performance achieved by these classifiers can be attributed to their ability to self-learn a downsampled feature vector that retains abstraction information through filter kernels in convolutional layers. Despite these advancements, challenges persist in capturing subtle and context-dependent emotional nuances, hindering the development of more nuanced emotion recognition systems. Ongoing research explores multimodal approaches, integrating various sensory inputs, to enhance the accuracy and reliability of emotion detection in real-world scenarios. In this paper, we explore the impact of training the initial weights in an unsupervised manner. We study the results of pretraining a Deep CNN as a Convolutional Auto-Multiplexer (CAM) in a greedy layer-wise unsupervised fashion for emotion recognition using facial feature images. When trained with randomly initialized weights, our CNN emotion recognition model achieves a performance rate of 92.16% on the Karolinska Directed Emotional Faces (KDEF) dataset. In contrast, by using this pre-trained model, the performance increases to 93.52%. Pre-training our CNN as a CAM also marginally reduces training time.
Design and Build a Web-Based Guestbook Case Study Gresik Regency Education Office

This research was conducted at the Gresik Regency Education Office, namely how to design a Web-Based Guestbook and Guest Complaints Information System at the Gresik Regency Education Office. The management of guest data and guest complaints is still very conventional, so delays often occur in visiting services and in conducting guest complaint consultations. The purpose of this research is to make it easier for employees to process guest data and consult guest complaints and to make it easier for guests to register guest data and guest complaints wherever they are. This study uses the Waterfall method. The stages of the waterfall are analysis, system, coding design and testing. Methods of data collection through interviews and observation, literature study. The results of this study are to make it easier to register guests and submit/consult guest complaints and manage guest data, guest complaints.
THE AGILE TRANSITION PROCESS OF ORGANIZATIONS IN SOFTWARE DEVELOPMENT PROJECT MANAGEMENT

Organizations which use Traditional Project Management Models face the great challenge to transform their processes and their culture. In this context, this research aimed to develop a set of recommendations to be adopted by the organizations to facilitate the transition from the traditional to the agile model in software development project management. Exploratory qualitative research was carried out in two steps: a Systematic Literature Review and semi-structured interviews. The conclusion was that the ATP causes great impact on people and the main practices should aim to reduce team resistance. In addition, it should be an initiative of the whole organization.
A Literature Survey on Completely Blind Image Quality Evaluator Feature-Enriched

Existing blind image quality assessment (BIQA) technique in which are principally opinion-aware. They learn deterioration models from training photos with associated human subjective scores to predict the sensory activity quality of take a look at pictures. Such opinion-aware methods, however, need a bulky amount of training example with associated human prejudiced achieve moreover of a variety of deformation category. The BIQA representation find out through opinion-aware strategies typically have weak generalization capability, herewith limiting their usability in observe. By comparison, opinion-unaware strategies don't require human prejudiced achieve for training, and then have superior possible designed for good quality simplification ability. Unluckily, to the present point no opinion-unaware BIQA technique has shown systematically better quality prediction accuracy than the opinion-aware strategies. Here, we tend to aim to develop an opinionunaware BIQA technique that may compete with, and maybe exceed, the present opinion-aware methods. By integration the options of ordinary picture information consequent as of multiple cues, we have an affinity to discover a multivariate Gaussian structure of representation patches from a set of perfect natural pictures. using the learned multivariate Gaussian form, a Bhattacharyya-like distance is employed to measure the standard of every image patch, and then a generally value score is find by average pooling. The projected BIQA technique doesn't want any distorted sample images or subjective class achieve for training, yet extensive experiment show its better quality-prediction presentation to the situation of the art opinion-aware BIQA strategies.
Successful chatbot design for polytechnic students in Singapore

In Temasek Polytechnic, Singapore, several AI chatbots acting as digital teaching assistants were trialed between July 2021 and August 2022. As these were developed for different purposes, it is important to learn if these AI chatbots help achieve the desired learning outcomes. This paper focuses on lessons learnt from three chatbots designed for different learning experiences: the independent learning of EXCEL, the correction of errors in the application of APA citation style, and the provision of supplementary theoretical support for software usage for Social Media Analytics. The research team studied the students' reaction to these educational chatbots, their motivation to use them, as well as the efficacy of the chatbot features to support specific purposes. This paper offers a design model that describes the three-stage approach for learning with an AI chatbot, with examples elucidating them. These sequential stages are foundational in the chatbot design to provide focused, personalised, student-controlled online learning environment. The application of this model is detailed in a fourth AI chatbot designed for the subject, Airline Management.
Design, simulation and evaluation of two coding programming languages for an eye-tracking controlling system for a three degrees of freedom robot useful for paralyzed people

In this study, the use of an eye tracking system is tested and discussed by using the tracked gaze point as a means to control a device. In this project, a two-dimensional simulated robot for paralyzed people was used to move the arm in the plane where the wrist is in a plane parallel to the frontal plane of the eye. Both MATLAB and Python programs are proposed for programming the eye tracking system (ET) and their results are compared. Moreover, the tests show how can this system works with robot and sampling rate which is important in robotic system is better than previous studies. At the end of the study, after mathematical modeling, a simulation of the three joint movements of the robotic arm was performed. This robot is designed in a way to move the arm of the paralyzed people. Several tests were performed and a multi-body simulation was connected to the eye tracker to verify the resulting interaction concept.
Implementation of Basic Literacy Models for Elementary School Students

One of the efforts made by the government to implement an independent curriculum is to create a School Literacy Movement. This program is implemented to foster students' interest in reading and can improve reading skills to master knowledge better. Various obstacles arose regarding the implementation of the Basic Literacy Movement program in elementary schools. This study aims to analyze the coverage of the implementation of the basic literacy model in elementary schools. The method in this study uses a qualitative approach with an exploratory descriptive design. The source of data in this study is an elementary school. The data collection techniques are carried out by observation, and documentation. The data analysis techniques include data reduction, data display, and data verification. The test of validity data using technical triangulation. The results showed that the coverage of the implementation of the literacy model was 100%, the numeracy literacy model was 100%, the scientific literacy model was 50%, the digital literacy model was 64.10%, the financial literacy model was 41.02%, and the civics cultural literacy model was 100%. The conclusion in this study is that the basic literacy movement in elementary schools is carried out with several activities including literacy, numeracy, science, finance, digital, and culture. It is hoped that future research can pay more attention to literacy models that have not yet achieved basic literacy implementation, so that the embodiment of the school literacy movement can be achieved more proportionally and comprehensively.
The RADEC Learning Model Improves Explanatory Text Writing Skill in Elementary Schools

The skill of writing explanatory text is an essential skill to be mastered by elementary school students. However, most students still have low explanatory text writing skills. The purpose of this study was to analyze the Read, Answer, Discuss, Explain, and Create (RADEC) model in improving the writing skills of explanation texts of fifth grade elementary school students. This study used a quantitative approach with a Quasi Experimental Design type of Nonequivalent Control Group Design. The population in this study were fifth grade elementary school students consisting of two groups. The technique of determining the sample is purposive sampling. The data collection method in this study was a test using an explanatory text writing assessment rubric. Data analysis techniques in this study are descriptive statistical analysis and inferential statistical analysis. The results showed that there were significant differences in the skills of writing explanatory texts between the group that applied the RADEC model and the group that applied the conventional method. Thus, the RADEC model can improve the skills of writing explanatory texts of fifth grade elementary school students.
Augmented Reality-Oriented Problem-Based Learning in Natural Science Materials

Problems that arise from students' difficulties in participating in the learning process and working on media for a long time make students' abilities low. Student learning outcomes are common because the lack of student knowledge is the main problem in learning. To help improve student learning outcomes can be assisted by using learning media, this study uses augmented reality learning media. This study aims to design problem-based learning based on augmented reality on science material for class V. This research uses a quantitative method. The subjects in this study consisted of 1 media expert, one design expert, and one content expert; three students as individual trials, nine as small group trials, and 23 as field trials. Data collection techniques include qualitative description, quantitative description, and statistical inferential t-test. The results of the study were (1) the design of augmented reality with the ADDIE model stages, (2) the media expert scored 94.4% very good qualification, the design expert test scored 92% very good qualification, the content expert test scored 98.6% qualification very good, individual trials obtained a score of 96.47% very good qualification, group trials obtained a score of 95.57% very good qualifications, (3) based on the effectiveness test it was declared effective, based on the average pretest results with a score of 49.2 and the average posttest results with a score of 84.78. It can be concluded that problem-based learning-oriented augmented reality in science material is feasible.
Digital Literacy and its Impact on Reading Interest in Prospective Elementary School Teachers

The rapid development of technology in the 21st century requires that every individual is skilled in using digital devices, including in the world of education. The ease of accessing information through digital media should increase students' interest in reading. This study aims to analyze the effect of digital literacy on students' reading interest. The research method used is quantitative with survey research methods. The population in this study were all 6th semester students. Sampling in this study used a purposive sampling technique that found 44 students in total. The instrument uses a Likert scale type interval questionnaire. Data analysis techniques, namely descriptive test, normality test, linearity test, and research hypothesis testing using a simple linear regression test. The results of this study indicate that digital literacy has a significant influence on students' reading interest. This means that digital literacy has a strong influence on students' reading interest. Digital literacy also has a positive influence on reading interest, which means that the better the student's digital literacy, the better the student's reading interest. This is indicated by the results of the coefficient table which has a significant value of 0.000 &lt; 0.05. So it can be concluded that there is an influence of digital literacy on students' reading interest.
Enhancing Value Co-Creation in Wenzhou's Elderly Care Networks:Mathematical Fusion Algorithms for Multi-Sensor Integration in Social Robotics

The study has two objectives: foremost, it is imperative to establish likely roles for social robots in facilitating collaboration and interaction among stakeholders such as the senior citizens, caregivers, health care professionals and traditional Chinese medicine (TCM) practitioners. Secondly, mathematical fusion algorithms discussion capable of integrating data from multiple sensors is meant to enhance perception capacity of social robots in select care settings while promoting value co-creation in existing networks for elderly care. In that regard, therefore, it is about exploring how quality care services can be improved through the use of social robots coupled with mathematical fusion algorithms; thus looking at effective communication processes as well as creating a collaborative environment among stakeholders involved. Meanwhile, applying social robotics together with multisensor integration within elder care networks located in Wenzhou city may have feasibility, effectiveness and potential benefits hence leading improved overall wellbeing satisfaction levels among its aging residents.
Complex Skill Learning using Modified Transformers with Automatic Chunking and Forgetting

Transformers are increasingly becoming mainstream in the field of reinforcement learning due to their ability to learn complex behaviour sequences over long periods of time. Human learning and decision-making works on recollection which is based on only parts of memory that are important to the current context. The past contains information that might not be useful for making decisions in the present. The human brain reduces its need to store every past event by forgetting less important events and only associating importance to the events that lead to the desired outcome, giving a broader picture of the past and helping with faster decision making. Current transformer models lack the ability to scale to large memory sizes and learn long horizon tasks efficiently as the computational requirements needed to run these models scale non-linearly with memory length. We propose Automatic Chunking + ForgetSpan, an architecture that reduces the computational performance needed to train and run transformer models by leveraging automatic chunking and forgetting. Automatic chunking helps with chunking the memory into parts which might be relevant in the present context while forgetting removes events within the memory that do not contribute to learning, helping the model learn multiple sequences of events with ease and reducing the computational cost of larger memory sizes. We demonstrate that Automatic Chunking + ForgetSpan can help models to memorize important information and achieve improved performance with reduced computational needs on various memory, visual navigation, robotic locomotion and multimodal tasks. We also test SimilarityWeight, a method which weighs a new memory element based on its similarity with already existing elements in the buffer. Comparative analysis with existing memory architectures was performed to understand the effects of relevant memory selection and forgetting. Gating connections which make use of gated recurrent units and memory mean pooling based on masking are also used for further improvement in performance. Effects of using multiple memory-based transformers with automatic chunking in series operating on data from consecutive timesteps were also studied.
Serverless-DFS: Serverless Federated Learning with Dynamic Forest Strategy

Training large-scale machine learning models typically requires support from High-performance Computers (HPCs) or cloud servers. One strategy to reduce the computational burden and alleviate the communication bottleneck of servers or HPCs is to move a subset of training computation to the edge or end-user devices. Federated learning is a distributed training approach that aims to protect the privacy of end-user data. However, most federated learning systems utilize centralized server architecture, which slows down the model training and limits the system's scalability. Many machine learning trainings require high performance while maintaining end-user data security. The state-of-the-art serverless approaches have performance limitations and can be further improved. This paper proposes Serverless-DFS, a serverless federated learning with a dynamic forest strategy, to improve the performance and scalability of federated learning while keeping its privacy protection capability. From the experimental results, our Serverless-DFS approach is observed to have a 63.9X speedup compared to the default server-client method in a large-scale system with 256 clients.
Computation Offloading in Mobile Edge Computing-Enabled Blockchain using Matching and Contract Theory

The application of blockchain technology on mobile devices requires significant computing resources. However, the limited capacity of mobile devices cannot meet the high computing power requirements of blockchain technology, which limits the development of blockchain technology. Mobile Edge Computing (MEC) is capable of alleviate the limited computing capacity of IoT terminals. This paper introduces an edge computing-enabled blockchain system consisting of an edge computing allocation center (ECAC), multiple edge service providers (ESPs), and multiple miners. To efficiently allocate resources between these miners and ESPs, we propose a hierarchical computation offloading strategy based on contract theory and matching game. During contract design phase, ECAC designs a contribution-reward contract to attract ESPs to join the trading market, and provide services to miners. By analyzing the attributes and conditions of feasible contracts of feasible contract, the optimal contract is devised using Lagrange multiplication. During matching phase, an iterative matching algorithm (IMA) is proposed to achieve the matching between ESPs and miners by constructing preference sets for miners and ESPs. Both the stability and convergence have been proved by theoretical analysis. Finally, we conduct experiments to validate the feasibility and effectiveness of the design contract. Experimental results also demonstrate the stability of IMA matching algorithm.
A Study of Summarization and Keyword Extraction Function in Meeting Note Generation System from Voice Records

Currently, we are studying a meeting note generation system to automatically make a note from voice records. Previously, we implemented the first function of the system to convert voice records to texts using the Whisper model. However, the current implementation suffers from a lack of coherency due to the vast text from audio. Therefore summary and keywords should be derived from the text to improve comprehension. In this paper, we propose the second function of the system to make summaries and extract keywords from texts. The BART model is used to generate human-like summaries from the semantic idea of texts. The keyword extraction method ranks the importance of each word or phrase in texts. For evaluations, we apply the proposal to three voice records with varied durations and compare the generated summary and keywords with manually made ones. The results show that for the summary, the average ROUGE-1 is 50.02%, ROUGE-2 is 24.04%, and ROUGE-L is 43.14%, and for keywords, the average cosine similarity is 54.09%. Thus, the validity of the proposal is confirmed. In future works, we will focus on incorporating additional methods to extract the memo from audio and text from the documents.