Reviewerly/exHarmony huggingface dataset:
Explainable extreme boosting model for breast cancer diagnosis

&lt;span lang="EN-US"&gt;This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.&lt;/span&gt;
WHY DO THEMES MATTER? THE TEACHERS’ VOICES ABOUT THEMATIC UNITS FOR TEACHING ENGLISH TO YOUNG LEARNERS

Teaching English to Young Learners (TEYL) in Indonesia has been an intriguing field that there never been ending studies about it. As a locally-tailored school subject that can be local content or elective subject without curriculum guideline, English for Young Learners (EYL) program is carried out in various ways by the primary schools in Indonesia. This present study aims to discover the essential role of implementing thematic model in TEYL. This paper discusses a phenomenological investigation of five EYL teachers who employ thematic units for teaching the pupils in different cities of Indonesia. Graduated from English education department, all participants have been teaching young learners English more than five years and planning their teaching based on their students’ needs by utilizing themes. Using semi-structured interview questions, the teachers were guided to explain their experience in valuing the themes for teaching English thematically, how to incorporating themes into TEYL, and challenges in implementing EYL thematic units. The data were analyzed thematically to get the emerging themes which correspond to this research topic. The results revealed that teaching English thematically is critical for young learners as their English learning scaffolding to achieve language skills and ability to communicate in particular discourse. All participants agreed that their students enjoyed the EYL thematic activities for gaining new knowledge using English despites challenges faced by the teachers in preparing the lessons.
IMPROVING THE LISTENING ABILITY OF ELEMENTARY SCHOOL STUDENTS THROUGH THE USE OF AUGMENTED REALITY-BASED LEARNING MEDIA

This research is motivated by the low listening ability of elementary school students. The purpose of this study is to determine the improvement of listening skills of fifth-grade elementary school students by using augmented reality-based learning media. This study uses an experimental model with a pretest-posttest control group design with a sample of 60 students. The instrument used in this study is to use a listening ability test, besides that the test results are analyzed by looking at the differences in students' listening abilities between students who learn by using augmented reality-based learning media and students who learn by using conventional learning media. The results showed that (1) there was a very significant difference in listening ability between the experimental class and the control class, and (2) the listening ability of students who studied using augmented reality-based learning media was higher than students who studied using conventional learning media. Through the use of augmented reality media, learning activities can be more fun and meaningful so that students do not feel bored studying learning materials.
The Importance Of English Vowel In English Linguistics For Literacy Study

English is a native-speaking language hundreds of millions of the world's population consisting of Americans, Australia, New Zealand, Canada and of course·only the British themselves. This fact allows the emergence of several English variants or varieties; Among another emergence of British English and American English. In the world of education, English as a language foreigners in Indonesia, the second variant has not received attention. Most English teaching institutions without be the choice of certain English variants. On Basically, the official provisions regarding this matter are not yet there is. However, there are variants of British English and American English can bring teacher doubts in dealing with abnormalities, vowel, vocabulary, or pronunciation the language. This is one of the considerations for the need to know English variants for prospective teachers and students English teacher, especially when the American influence is growing.
The Influence of Game Media on Children's Knowledge and Attitudes in Diarrhea Prevention at SDN Kembang Tanjong Sub-District

Diarrhea is an environment-based disease and occurs in almost all geographical areas in the world. Sustainable Development Goals (SDGs) are improvements to the Millennial Development Goals (MDGs). SDGs target number three, namely healthy and prosperous life, is to ensure a healthy life and improve the welfare of all people of all ages. The purpose of this study is to analyze the influence of snakes and ladders game media on children's knowledge in diarrhea prevention at SDN Kembang Tanjong District. This research is quantitative research with a quasi-experimental research design (pseudo-experiment) using a non-equivalent control group design. Gender. In this study, the results of frequency distribution based on the characteristics of respondents totaling 80 people were as many as 40 people (50%) were male and as many as 40 people (50%) were female. Based on the results of the distribution of efficiency based on class characteristics, as many as 16 people (20%) are in class IV (four), as many as 28 people (35%) are in class V (five) and as many as 36 people (45%) are in class VI (six). The results of the analysis using the Wilcoxon test with a confidence level of 95% using snake and ladder game media with a p-value of knowledge value of 0.000 ? 0.05 so that it can be concluded that there is an influence of health education with snakes and ladders game media on children's knowledge in preventing diarrhea in children at SDN Kembang Tanjong District.
Towards Dynamic Evolutionary Analysis of ProgrammableWeb for API-Mashup Ecosystem

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline.
Iterative Greedy Selection Hyper-heuristic with Linear Population Size Reduction

Selecting appropriate algorithms for specific problems has become a significant challenge with the remarkable growth of heuristics and meta-heuristics. To address this challenge, an iterative greedy selection hyper-heuristic algorithm with linear population size reduction (LIGSHH) was proposed in this paper. Using an iterative greedy strategy to choose the high level of exploration, this heuristic selects the Low-Level Heuristics (LLHs) that best suit the current problem. Nine LLHs are specifically designed for continuous optimization problems. Additionally, the exploration and exploitation capabilities of the LIGSHH are balanced by reducing the population size linearly at different stages of the problem. The proposed LIGSHH algorithm and comparison algorithms are tested on the CEC2017 benchmark test suite, and the experimental results show that the LIGSHH algorithm outperforms other comparison algorithms.
ACG: Attack Classification on Encrypted Network Traffic using Graph Convolution Attention Networks

Attack classification of network traffic is valuable for many security solutions as it points out a clear direction for attack responses. Nowadays, most network traffic is encrypted, which protects user privacy but hides attack traces, further hindering identifying attacks to inspect traffic packages. Machine Learning(ML) methods are widely applied to attack classification on encrypted traffic owing to no need for manual analysis. However, existing studies only concentrate on basic statistical features, which are easily modified, and cannot obtain the crucial attack behaviors hiding in the encrypted traffic. In this paper, we propose an attack classification method, ACG. We create attack graphs to depict interaction behaviors of attack-victim hosts from network traffic containing crucial attack behaviors. Besides, we divide a specific duration for each attack to precisely elaborate attack graphs, where temporal, statistical, and aggregate features are extracted to portray attack behaviors. Finally, we utilize Graph Neural Networks (GNNs) to mine and grasp the crucial behavior patterns from attack graphs to generate fingerprints and classify attacks. Extensive experiments are conducted on three datasets to verify our method. It achieves a precision of 99% in attack classification on encrypted traffic, an average higher than other ML methods of 50%.
USE OF DIGITAL TECHNOLOGY IN INFORMAL ENGLISH LANGUAGE LEARNING: HOW THE PRACTICES SUPPORT LEARNING OUTCOMES?

Informal digital learning of English (IDLE) is Benson’s conceptual framework that refer to the context where English language learners learn English, in which they can learn the language beyond classroom. The framework can be applied with any digital media. The present study was conducted by means of surveys and open-ended questions to investigate how students in an Islamic university in Jakarta used digital technology and how it supported their English learning outcome. The survey of the use of digital devices for English learning was adapted, and the open- ended questions were developed to find out more information about the way how they use the digital tools beyond classroom English learning activities. To analyze the data, descriptive quantitative and thematic analysis were employed. And the results show that several digital applications were used by the students of teacher training education faculty to help them learn English vocabulary and the way how they used them were discussed.
BiRD 2023: Second International Workshop on Behavior analysis and Recognition for knowledge Discovery - Welcome and Committees

We would like to welcome you to the International Workshop on Behavior analysis and Recognition for knowledge Discovery (BiRD 2023) in conjunction with the IEEE International Conference on Pervasive Computing and Communications (PerCom 2023).BiRD workshop aims to provide a forum for presenting and discussing research on the recognition and understanding of behavior data collected from various sources, such as humans, animals, and automobiles, towards scientific discovery (e.g., ecology and medical science) and/or actual business applications (e.g., industry, sports, healthcare, and stockbreeding).The BiRD Program Committee and the Chairs selected a total of 3 oral papers from a pool of 8 submissions.In addition, 7 poster papers are presented including reports by winners of an activity recognition competition (OpenPack Challenge) hosted by BiRD.The oral and poster presentation sessions comprises presentations related to conventional pervasive computing studies regarding human behaviors and behavior analysis methods for biology.Based on these presentations, BiRD provides a forum for discussion, debate, and collaboration focused on behavior data analysis.
Keynote: Computational Behavior Analysis - Pushing the Boundaries Towards Usable Digital Health

We live in an era in which the number of smart devices is now greater than the number of humans living on Earth. Such smart devices include smartphones and wearables but also the myriad of IoT devices that are integrated into the very built environment we live in. As such, the field of mobile and ubiquitous computing is transforming many-if not all-areas of our lives. This overall transformation has great potential for many application areas. Most prominently, it is now possible to continuously and unobtrusively record rich behavior data that can inform objective health assessments thereby serving as basis for improved care and treatment, and thus well-being. The basis for effective health assessments are robust and reliable methods for human activity recognition - more generally referred to as sensor-based Computational Behavior Analysis (CBA). From a technical perspective, the analysis task translates into a time-series assessment problem, yet with a number of domain-specific constraints and requirements. In response to challenges such as noisy sensor data, ambiguous ground truth annotation, and typically limited size sample datasets CBA researchers have developed and validated sensor data analysis and machine learning methods that focus on these domain specifics and thus enable effective operation. In this talk, I will give an overview of where the broader field of Computational Behavior Analysis is today before I focus on current and next frontiers specifically related to smart sensor data analysis. I will illustrate how the constraints and requirements of real-world application scenarios force pushing boundaries of core sensor data analysis research as well as real-world deployments.
Hydrological research &amp; open access publications

Water is the basic needs of living being on the earth.Life on earth is impossible without water, but increasing materialism day by day has given rise to various types of serious problems related to water, whom study and search for solutions is the basic responsibility of hydrologists and researchers.Researchers usually publish their research output in research journals or present it in scientific seminars.From the point of view of use, the information sources that publish research output can be of two types, closed access or open access.In this article, an attempt has been made to assess open access research articles from various aspects in ten years from 2012 to 2021, for which data has been taken from the database of Web of Science.A broad searchable term 'Water Research' has been used for data mining.The search results have been presented through tables and figures.
Blockchain of Medical Things: Security Challenges and Applications

Blockchain of Medical Things is the fusion of blockchain technology and Internet of Medical Things that introduces smart contracts in the network enabling medical devices to function anonymously in a secured way by leveraging cryptographic techniques. Internet of Medical Things integrated with the cloud environment has a centralized architecture that suffers with single point of failure. The privacy and authentication of data are also the major threats to the Internet of Medical Things. It is prone to various security attacks categorized as physical attacks, network attacks and software attacks. Blockchain design, its architecture, consensus methods and cryptographic techniques are supplemented with the Internet of Medical Things to deal with security issues in healthcare system. This integration is a favourable and distributed approach to provide transparency, privacy, immutability and failure resilience in remote healthcare monitoring. This paper presents a systematic review of Internet of Medical Things, security requirements and security attacks. An integrated architecture of blockchain and Internet of Medical Things, named as blockchain of Medical Things, is presented after a detailed analysis of blockchain technology for the purpose. The consensus algorithms, its compatibility with the Internet of Medical Things and its applicability for different security attacks has been illustrated. The paper also contains the applications of Blockchain of Medical Things in electronic health records, pharmaceutical supply chain and vaccine management for better administration of healthcare data.
An Improved Security Schematic based on Coordinate Transformation

An earlier research project that dealt with converting ASCII codes into 2D Cartesian coordinates and then applying translation and rotation transformations to construct an encryption system, is improved by this study.Here, we present a variation of the Cantor Pairing Function to convert ASCII values into distinctive 2D Coordinates.Then, we apply some novel methods to jumble the ciphertext generated as a result of the transformations.We suggest numerous improvements to the earlier research via simple tweaks in the existing code and by introducing a novel key generation protocol that generates an infinite integral key space with no decryption failures.The only way to break this protocol with no prior information would be brute force attack.With the help of elementary combinatorics and probability topics, we prove that this encryption protocol is seemingly infeasible to overcome by an unwelcome adversary.
Smart Anti-Burglary Surveillance System using Image AI

In today's world, with an increasing number of robberies, looting and shoplifting, it is indispensable to ensure the safety of our belongings and valuables. When it comes to certain places like bank vaults, uninhabited houses, business establishments like retail shops, jewellery stores, watch stores, etc., surveillance for observing the premise is very essential and has become the unavoidable need of the hour. At such places, it is unlikely to continuously monitor the venue using conventional cameras or a security guard person. To address such type of issues which involve uncertainty of safety, our project proposes a system which will tirelessly monitor the venue at required hours, intimate the intended person (administrator) via mobile communication (Short Message Service), initiate an alert alarm at the premise in order to aware the surroundings and also save the intrusion detected frames in the specified choice of storage. Also, it is developed in such a manner that the system recognizes and does not initiate alert/notification for persons those who are reliable and trustworthy (such as the shopkeeper, habitant of the house, etc.). This system is implemented with specific considerations using Image AI and Tensor flow built upon Convolutional Neural Networks, remotely operatable by users, without compromising the importance of security and protection.
Penerapan Algoritma Convolutional Neural Network dan Haar Cascade Untuk Presensi Dengan Video Rekaman Zoom

The COVID-19 pandemic that is engulfing the world is one of the situations that urges the use of and even human dependence on technology to become higher. This pandemic that can cause death by spreading through droplets or water droplets, so many countries have implemented social restrictions by prohibiting their citizens from doing many activities outside. The use of technology such as the Google Meet and Zoom applications has become a new habit for the community, especially in learning and teaching. Attendance is important to know and control the presence of students in the teaching and learning process. Currently, attendance at online lectures is still mostly done by calling students one by one or asking students to fill out certain forms. It has weaknesses and is less effective. Therefore, this study proposes presence with face identification using a web-based application system. The method used in this research are Haar Cascade face detection for facial image extraction and Convolutional Neural Network (CNN) algorithm for face identification process. The contribution given from this research in the form of identification can be done using video recordings of zoom and images or screenshots from zoom and displaying student attendance. In this study, the dataset was sourced from taking photos directly on individuals. There are 20 labels used in this study. The identification results of 1119 facial images (test data) on the CNN custom model, obtained 88% accuracy, 88% precision, and 83% recall.
GIS-BASED INTELLIGENT DECISION-MAKING SUPPORT SYSTEM FOR THE DISASTER RESPONSE OF INFECTIOUS DISEASES

Abstract. The study aims to develop GIS technology for managing pathogen contamination disaster situations to cope with the occurrence of disaster situations caused by the climate crisis. This study was conducted in a situation where the budgets of institutions with many emergency rescue workers are being invested worldwide, and there are various risks such as water pollution due to climate change, so it is necessary to develop technologies to respond to them.In this study, methods and technical means for emergency response to pathogen contamination were prepared through professional analysis results based on spatial information, and rapid and accurate pathogen detection, situation recognition, event control and response methods, risk assessment and epidemiological investigation methods, and technologies were developed.Technological means include collecting and analyzing water quality data using satellites and drones, and evaluating the severity and degree of water pollution using social media data and photos. Through these technologies, a GIS platform that can effectively manage and provide data sources, data collection, interoperability, data harmonization, data management, and data processing is established, and commercialization is aimed at real users and field-oriented. These technology developments enable rapid response and response in disaster situations, and can reduce threats to life and safety.
SEMIOTIC ANALYSIS OF COVID-19 MEME IN SOCIAL MEDIA

The presence of social media makes the process of social interaction experience very significant changes. Communication through social media is a very important social interaction in human life. Along with the increasing use of social media, it has spawned new phenomena among social media users, one of which is creativity in making memes. Memes can be in the form of text or images that contain issues and events in a style that intrigues and are widely discussed because they spread and comment so quickly on social media. The purpose of this study was to describe the signifier and signified in the Covid-19 meme and explain the meaning contained in the Covid-19 meme. The Covid-19 meme itself is one of the memes found on social media. The approach used in this research was a qualitative approach which produces descriptive analysis. The researcher seeked the facts behind the creation of the Covid-19 meme circulating on social media. The signifiers found in the Covid-19 meme include snippets of famous movie and game scenes. The insinuations in the sentences are also reality. The sentences provide a statement that contains information and expression of the feelings of the creator as a fighter in the pandemic season.