Reviewerly/exHarmony huggingface dataset:
Real-Time Gesture Recognition System using CNN

Hand gesture is the initial method for communication that was used by cave men. The verbal communication was developed by the more developed civilization. Hand gesture has been the oldest form of non-verbal communication. In current time, hand gesture is not only a way of communication between people who may or may not be physically challenged but also between humans and machines in diversified fields such as aviation, surveys, military simulation, medical training etc. It is the best way of interaction between man and machine as no peripheral devices are required and a wide range of gesture can be used for communication with high accuracy. For such human and machine interaction a real-time gesture recognition system is proposed. Detection of motion and skin is used for capturing region-of-interest. The region of interest is further processed by convolutional neural network in order to get a proper result.
Developing of Palembang-Local-Culture Narrative Reading Materials for the Eleventh Grade of Senior High School

The objectives of this study were to find out the validity, practicality, and potential effect of the developed Palembang-local-culture narrative reading materials about Palembang food. This study was designed as research and development involving three phases including analysis, design, and evaluation and revision phases. The formative evaluation was done through one-to-one evaluation, small group evaluation, and field trial. The subjects of this study were eleventh grade of senior high school in Palembang. The validity was done in expert review. The validity score of the developed product was 3,20 which was categorized as high validity level. The practicality was conducted in one-to-one and small group evaluation. The practicality of one-to-one evaluation was 3,6 which was at very high practical level and the practicality in small group evaluation was 3,7 which was at very high practical level. The result in field trial was categorized as effective. It showed that there were 18 students (78,26%) out of 23 students in field trial reached the MMC (Minimum Mastery Criterion) which was 75.
The Story of Loved Ones as Description Learning Media in the Elementary School

Writing lessons are generally a scourge for learners because the management of learning is less attractive, effective, and efficient. The purpose of this research is to improve the ability to write the description text of students’grade V at SD Pertiwi Makassar by using the story of loved ones’media. The research method used wasa one-group pretest-posttest design, namely a research design that was given a pre-test before being given treatment and a post-test after being given action or treatment. The thing done in the implementation of the experiment was to assign to a sample that had not been given treatment or called a pre-test to measure the initial ability to write the descriptivetext. The conclusion of this research was the result of writing a descriptivetext of students’grade V at SD Pertiwi Makassar using the story of loved ones’ media increased by looking at the improvement of the average score of acquisition from 35.62 on the pre-test to 67.04. This scorewas less than 0.98 to achieve minimum completeness, which was 68.
Profile of Students’ Character in the Reading to Learn (R2L) Model in Improving Scientific Literacy Skills

This study aims to determine the description of the character of students during learning with the Reading to Learn (R2L) Model in order to improve their scientific literacy skills. This learning was carried out for 3 meetings on dynamic electricity material. During the study, observations were made by two observers using an observation sheet containing the student's character. Aspects of student character observed in this study were curiosity, adaptability, social behavior, and initiative. The students observed consisted of four classes. The number of students involved was 64 students (n=64). Based on observations, the highest character that emerges from students during learning is the character of socio-cultural behavior (52.95%), while the lowest character is curiosity (44.44%).
Student Perceptions in Using Sisfo E-Learning Applications at PGRI Palembang University

Access to learning that is not limited by space and time must continue even though during the COVID-19 emergency, lecturers and students are required to prepare devices for learning from home and PGRI Palembang University has facilitated by using an application called E-Learning Sisfo. Although the use of e-learning as a learning medium in higher education is not yet optimal, it has become one of the interesting things to be researched so that a study was conducted to determine the perceptions of students of the Accounting Education Study Program in using SISFO. E-learning network application at FKIP PGRI Palembang University. The sample in this study is a student majoring in Accounting who uses e-learning in the lecture process where data is collected by means of a questionnaire or questionnaire and analyzed using the regression method. The results of the regression test show that the perception of students of the Accounting Education Study Program. Based on the results of the study, it can be concluded that the perception of students of the Accounting Education Study Program in using the e-learning application SISFO at PGRI Palembang University is an average of seventy three point fifty percent in the high category. and have good motivation in using e-learning as a learning medium.
Development of Interactive Digital Modules Based on Augmented Reality

Interactive digital modules have now become a new commodity in the world of learning that must be a major concern in order to facilitate an efficient and flexible learning process. The purpose of this development is to create augmented reality to be used as a learning resource by all academics. This study uses the Lee Owens development model which consists of 4 stages, namely assessment/analysis, design, development and implementation, and evaluation. This development activity involved a learning media expert, a material expert, and 8 small group pilots to assess the feasibility of the digital module being developed. Based on the presentation of the validator's assessment of the augmented reality, it can be concluded that augmented reality is very valid criteria which means it is very good for use in learning with a percentage of 90.25% of media experts, 88.75% from material experts, and 86.5% from small group trials. Researchers recommend using this material as a learning resource for Information Technology courses that can be accessed on the Halu Oleo University website. Researchers also suggest making augmented reality as part of the learning materials developed.
Deep Learning Algorithms for Classification and Prediction of Acute Lymphoblastic Leukemia

The death due to cancer increases daily due to late or even no diagnosis. It is necessary for early detection and treatment of such fatal cancers, and avoiding carcinogenic agents is an important step for the prevention of cancers. The majority of the cancers are detected by computed tomography (CT), magnetic resonance imaging (MRI), and even X-ray images. There are different techniques used by the doctors, like pattern recognition; seven-point detection; and Asymmetry, Border, Color, and Diameter (ABCD) method. However, these methods may not be efficient always. Hence, currently, there is a huge scope for automation in the medical field for better performance and early diagnosis of fatal diseases to prevent further complexities and also for the betterment of mankind. There are several automation techniques—among which, deep learning techniques are considered much efficient for the diagnosis of diseases. The automation of the classification of acute lymphoblastic leukemia is demonstrated by using Kaggle datasets in this chapter. To achieve this purpose, the variants of CNN along with different activation functions are experimented and achieved the highest accuracy of 81.04% with ResNet50, which are better in diagnosis compared to the efficiency of manual diagnosis, reducing the risk of cancer in mankind.
Empirical Software Engineering and Its Challenges

In the current years, “artificial intelligence (AI)” systems have shown encouraging results. Numerous of these milestones have remained accomplished in academic settings or by significant expertise firms through extremely trained study assemblies and specialized substructure funding. The development of superior yield set systems with AI mechanisms has proved stimulating aimed at corporations deprived of significant exploration collections or progressive substructure. There is a substantial shortage of healthy operative resources and the most exemplary practice for designing AI schemes. It aims to classify the critical tasks by smearing an explanatory study method closely associated with corporations of variable sizes and types. Related to additional parts like “software engineering (SE)” or “database technologies,” it is flawless that AI remains motionless relatively juvenile and that extra effort is required to promote high-quality 34systems. Problems described in this chapter can be used to direct upcoming studies in SE and AI societies. Together, we will encourage an incredible amount of corporations to start captivating the benefit of the high latent of AI tools.
Fuzzy Logic Based Computational Technique for Analyzing Software Bug Repository

Software development is a collaborative process in which programmers build software by integrating all the stages of the software development life cycle (SDLC). A software repository is a central file storage location where various software packages are stored, and these packages are retrieved and shared between all the software development team members at various locations. The software repositories are divided into various categories based on cooperation, coordination, and communication among the stakeholders as well as evolutionary changes to various software arti-facts such as source code repositories, software bug repositories, historical repositories, run-time repositories and requirement documents, and other documentation. The software bug repository is an essential repository among the entire repositories since the completion of the software is entirely dependent on the bug fixing mechanism associated with this repository in software development. Today’s software systems are larger and more complex as they go through various stages from the requirement 98analysis phase to the maintenance phase. A variety of tasks and activities are carried out in each stage of software development, and these are expensive and vulnerable to errors. During software development, a large number of software bugs are continuously generated, and that has become the main reason for the delay in software completion. Hence, there is a vast demand for computational intelligent techniques to accomplish various tasks of software development. In recent years, fuzzy logic techniques emerged and played an important role in various fields of data mining and text mining. Since most of the content related to software bug repositories is text in nature, it is possible to effectively use fuzzy logic techniques to analyze these software bugs.
Literature Study on the Effect of Blockchain Usage on Cryptocurrencies Looks at Comparisons of Previous Research

With the advancement of information and communication technology (ICT) and technological innovation, the learning ecosystem has changed greatly facilitating open and distance learning systems (ODL) in providing education anywhere at any time in meeting the changing learning needs in modern society. Blockchain is currently attracting attention as an innovative technology to change the future, has emerged as a new paradigm of financial markets. A blockchain is a distributed ledger created by blocks containing details of transactions connected in chronological order to form a series of chains. Blockchain is designed to store and use a cryptocurrency called bitcoin securely. Cryptocurrency is a virtual currency that operates in a blockchain-based decentralized system. Cryptocurrency transactions are conducted peer to peer (P2P) without the intervention of financial intermediaries. The increasing interest in cryptocurrencies and the underlying technology of blockchain is also reflected in the entry of regular businesses into the crypto world by using token hype prices (HYPE) or token inflated prices to profit. Therefore, the focus in this study is to compare the influence of blockchain usage on cryptocurrencies looking at previous research.
PROFILING PRE-SERVICE TEACHERS’ PERCEPTION ON COURSEBOOK USED IN SENIOR HIGH SCHOOL

This study focused on investigating six criteria of a coursebook, these are: content, vocabulary, methodology, visual aids, practice and testing, and objectives.This study employ mixed method research design in which quantitative and qualitative descriptive methods were mixed. The data were obtained through questionnaires and administered to 15 pre-service teachers of English Language Education Program of Unika Santu Paulus Ruteng as the participants of this study. The questionnaire were distributed to pre-service teachers through geogle form. Percentage and mean were calculated by using SPSS software program to describe the overall picture of how the pre-service seachers rated the textbook in terms of six criteria. The result of this study revealed that the pre-service teachers showed a positive response for each criteria that being analyzed in this study. They perceive that Buku Bahasa Inggris Kelas XI had been categorize as a good textbook in terms of content, vocabulary, methodology, visual aids, practice and testing, and objectives.The mean score of each item in this six criteria were over 3,00 which classified as good response. However, there were two items that showed a bad response from pre-service teachers such the presentation of new lexical item which covered only 2,87 and developmental of objectives should need more attention which covered only 2,93. Based on the result of this study, the writer concludedthat Buku Bahasa Inggris kelas xi is a good textbook for senior high school students especially for xi class because it categorize as a good textbook in terms of content, vocabulary, methodology, visual aids, practice and testing, and objectives.
Inflected Forms Are Redundant in Question Generation Models

Neural models with an encoder-decoder framework provide a feasible solution to Question Generation (QG). However, after analyzing the model vocabulary we find that current models (both RNN-based and pre-training based) have more than 23\% inflected forms. As a result, the encoder will generate separate embeddings for the inflected forms, leading to a waste of training data and parameters. Even worse, in decoding these models are vulnerable to irrelevant noise and they suffer from high computational costs. In this paper, we propose an approach to enhance the performance of QG by fusing word transformation. Firstly, we identify the inflected forms of words from the input of encoder, and replace them with the root words, letting the encoder pay more attention to the repetitive root words. Secondly, we propose to adapt QG as a combination of the following actions in the encode-decoder framework: generating a question word, copying a word from the source sequence or generating a word transformation type. Such extension can greatly decrease the size of predicted words in the decoder as well as noise. We apply our approach to a typical RNN-based model and \textsc{UniLM} to get the improved versions. We conduct extensive experiments on SQuAD and MS MARCO datasets. The experimental results show that the improved versions can significantly outperform the corresponding baselines in terms of BLEU, ROUGE-L and METEOR as well as time cost.
Automatic classification of Customer Complaints in a Chilean Company Using DialogFlow

Customer service is currently of great importance because this process supports the management of customer queries, requests and disagreements. In customer service, noncompliance with resolution times as well as closing times appearsas a recurring problem, which has a direct relationship with the customer experience. An alternative to improve this process isthrough the use of natural language processing algorithms to facilitate the complaint of customers considering platforms and solutions already applied in the market. In this paper, a customer complaint classifier is proposed using Google's Dialog Flow system in a Chilean company considering the implementation of a prototype composed of a text classification model and a system that manages customer interactions. This system allows an automatic categorization of customer interactions, allowing the tasks of a customer service representative to be reduced, minimizing the risks of misclassified interactions and avoiding re-categorizations that increase resolutiontimes. The proposed model allows improvements over 50% accuracy over the classification given by the pre-existing system based on user feedback. As future work we propose to create a chatbot system that allows improving user feedback.
[Exploration and practice on the teaching reform of "Bioremediation of Environmental Pollution"].

The course Bioremediation of Environmental Pollution, which plays a vital role in the professional training system, is a professional elective course for college students majored in environmental science, environmental engineering and agricultural resources and environment. In view of the problems identified in previous teaching experiences, the teachers carried out teaching reform to meet the demand for high-quality personnel training. The teaching reform included optimization of course objectives, reconstruction of course content and knowledge integration, reform and innovation of teaching methods. The practices indicate that a reformed curriculum teaching significantly improves the achievement of the teaching objectives. Moreover, it effectively enhances the students' independent learning, thinking and comprehensive knowledge application ability, achieving sound teaching effects.
Algorithmic Recourse based on User’s Feature-order Preference

The state-of-the-art recourse generation methods solely rely on the user’s profile (feature vector). However, two users having the same profile may still have different preferences. Consequently, the recourse generated from a single profile may not have the same appeal to both the users. For example, one rejected loan applicant may prefer changes in Savings Amount, whereas, another - being a financial expert - may prefer changes in Investment Amount. Taking into account these preferences in feature-change can be very helpful in generating more user-satisfying recourses. To this end, we propose a simple user-preference representation and design a method to generate a recourse that adheres to the user preference. We empirically demonstrate the effectiveness and ease of the proposed method at generating recourses satisfying user preferences.
Emergent best practices in trauma-informed design from Chayn's interventions with and for survivors of technology abuse

Chayn is a global non-profit that uses technological interventions with and for survivors of technology abuse. This chapter discusses the design principles that have emerged from Chayn’s work, and how they can be applied to create trauma-informed technology to counter and tackle technology-facilitated gender-based violence.
Weaponising technology in intimate relationships

Domestic and family violence (DFV) is widely recognised as an important social issue, impacting the safety and wellbeing of victim/survivors and their children, and also on a broader scale, threatening risk and security on global levels. The uptake of digital technology and digital media is increasing and so is perpetrator weaponising of these channels.
A Coyote Optimization Algorithm-trained Dendritic Neuron Model for Prediction Tasks

Time series prediction is always a hot topic. Many artificial neural network-based models are continuously proposed to improve the predicting performance. However, in practical situations, the large scale of training data and the complex structure of network often lead to slow training speed and unsatisfactory result. Thus, it is reasonable to use a suitable algorithm to train a well-structured network for such an issue. In this paper, we use the coyote optimization algorithm to train a dendritic neuron and propose the COADNM to tackle time series prediction problems. Three datasets are used to verify the performance of the proposed model, meanwhile, four other neural network-based models are employed to compare with COADNM. The experiment result shows that our model outperforms all its competitors via different evaluation criteria.
Trust in Adoption of Internet of Things: Role of Perceived Ease of Use and Security

This study was designed to examine the effect of perceived ease of use, security, integrity, and trustworthiness on the adoption of the Internet of things. The mediating role of trust was also examined in the context of the UAE hospitality sector. The data were collected using a survey approach including the tourists of UAE and using convenient sampling. The questionnaire was distributed to 400 respondents and the usable questionnaires which were received back were 269. The data were examined using Smart-PLS 3.3.9. The results revealed that trust is directly affected by security and perceived ease of use. Besides, trust has a direct effect on the adoption of IOT. This study confirms the mediating role of trust as well. This study addresses the research gap of limited studies conducted on IoT adoption in the context of the hospitality industry of UAE.
Cell-expanded Long Short-term Memory

Long short-term memory (LSTM) is a recurrent neural network (RNN) that is widely used for historical time series prediction. LSTM has a memory architecture called a memory cell, which enables long-term time-series prediction. Many historical time series have complex changes; thus, a single memory cell cannot fully capture the features. In this paper, we propose a method that exhibits good predictive performance against complex time-series changes by incorporating additional memory cells.
A Study on Cases applying to the DMT Program in the Process of creating Dance

The purpose of this study is to research and analyze cases applying to the DMT(Dance Movement Therapy) program in the process of creating dance. Each session of the program that consisted of a total of 8 sessions was carried out in 60 minutes per week. The qualitative case research method was conducted based on accumulated date such as research participants(dancers)’ self-reports on each session, video records, in-depth interviews, and group discussions. The research results showed that the DMT program applying to Whitehouse’s active imagination was divided into 3 main areas and 12 sub-areas. Research participants’ physical perception(physical discomfort, physical transformation), emotional perception(trust, stability, responsibility, vividness, happiness, inspirational motivation, positive feeling, sorriness), and thinking perception(expansion of self-perception, expansion of creative· autonomous thinking perception) were drawn from the research. Through these research results, this study got developmental outcomes in expanding dancers’ physical, emotional, and thinking perception, in being specific about sending work images through integration, and in conveying physical meaning in the creation process. It is expected that follow-up studies continue as being interested in applicability of the DMT program in the process of creating dance.