Reviewerly/exHarmony huggingface dataset:
Mobile Skin Disease Classification using MobileNetV2 and NASNetMobile

The people of Indonesia often suffer from three diseases: tinea versicolor, ringworm, and scabies. Most Indonesians cannot distinguish the type of skin disease they suffer from because some have the same characteristics and patterns. Therefore, this study built an M-Health application to predict skin diseases. The prediction process uses a deep learning model deployed in the smartphone. The challenge of this study is the limited number of datasets because the data used is personal and requires permission from the patient or hospital. Therefore, transfer learning is used to overcome these data limitations. The transfer learning process in this study uses two pre-trained models, MobileNetV2 and NASNetMobile. To obtain a model with high accuracy, performed modifications to the architecture MobileNetV2 and NASNetMobile. The test results showed that MobileNetV2 performs best using a learning rate of 0.0005 and activation function ELU. While NASNetMobile produces the best performance using a learning rate of 0.0001 and activation function ReLU6. The test results using data from gallery smartphones show that NASNetMobile has an accuracy of 91.6%, while MobileNetV2 has an accuracy of 88.9%. They were testing using the camera in real-time, which resulted in accuracy that was not as accurate as if using data from the gallery. Accuracy using a smartphone camera shows an accuracy of 75% when using NASNetMobile and 72.2% when using MobileNetV2. Accuracy will increase when using a flashlight while capturing objects. Based on the testing results using a flashlight, the NASNetMobile accuracy value increased to 80.5%, while MobileNetV2 accuracy changed to 77.8%.
AI-Human Collaboration for in Situ Interactive Exploration of Behaviours From Immersive Environment

Experiments in immersive environments allow the collection of large amounts of data that are closely related to individual behaviour. The recording of such experiments allows for the complex study of under-constrained tasks. That is, tasks that allow for a high degree of contingency in their resolution. This contingency allows for better discrimination of individual behaviour. However, the high complexity of the tasks makes them difficult to analyse.
Video Consumption in Context: Influence of Data Plan Consumption on QoE

User expectations are one of the main factors on providing satisfactory QoE for streaming service providers. Measuring acceptability and annoyance of video content, therefore, provide a valuable insight when measured under a given context. In this ongoing work, we measure video QoE in terms of acceptability and annoyance for the remaining data in a mobile data plan context.. We show that simple logos can be used during the experiment to prompt the context to subjects and the different context levels may impact the user expectations and consequently their satisfactions. Finally, we show that objective metrics can be used to determine the acceptability and annoyance thresholds for a given context.
Owl: A Pre-and Post-processing Framework for Video Analytics in Low-light Surroundings

The low-light environment is an integral surrounding in real-world video analytic applications. Conventional wisdom claims that in order to adapt to the extensive computation requirement of the analytics model and achieve high inference accuracy, the overall pipeline should leverage a client-to-cloud framework that designs a cloud-based inference with on-demand video streaming. However, we show that due to the amplified noise, directly streaming the video in low-light scenarios can introduce significant bandwidth inefficiency.In this paper, we propose Owl, an intelligent framework to optimize the bandwidth utilization and inference accuracy for the low-light video analytic pipeline. The core idea of Owl is two-fold: on the one hand, we will deploy a light-weighted pre-processing module before transmission, through which we will get the denoised video and significantly reduce the transmitted data; on the other hand, we recover the information from the denoised video via an enhancement module in the server-side. Specifically, through well-designed training mechanism and content representation technique, Owl can dynamically select the best configuration for time-varying videos. Experiments with a variety of datasets and tasks show that Owl achieves significant bandwidth benefits, while consistently optimizing the inference accuracy.
A Positive-Negative Dual-View Model for Knowledge Tracing

Knowledge Tracing (KT) aims to accurately trace the states of evolving knowledge of students and reliably predict students’ performances on future exercises. This task has been widely studied, leading to fast promotion on the development of online education. However, KT still faces two problems. First, most of previous work directly assigned an embedding for each question, which ignores semantic information contained in the questions. Secondly, students may learn differently from correct and incorrect answers to a question. Therefore, the embedding of a question should change based on the correctness of a student’s answer. In this paper, we propose a positive-negative dual-view model named PDNV for knowledge tracing. Firstly, we leverage two Graph Convolutional Networks to learn question embeddings from both positive and negative perspectives. Secondly, an information filtering module is designed based on students’ answers to selectively enhance positive or negative information in question embeddings. Experiment results based on three widely-used datasets demonstrate that our model outperforms state-of-the-art baseline models.
Smart Contracts as a tool for small and midsized companies

&lt;p&gt;Smart Contracts are a technology that leverages blockchain to automate and secure transactions. They are commonly used to manage transaction fees and mining rewards within blockchain networks, and can be programmed to execute automated transactions based on predetermined conditions. The use of smart contracts offers numerous benefits for small and medium-sized enterprises, including reduced transaction costs, improved transparency and accountability, and enhanced security and efficiency. The deployment of smart contracts in a company can also lead to increased automation of transactions, further streamlining processes and reducing the potential for human error. &lt;/p&gt;
Pelatihan Desain Poster Menggunakan Canva Bagi Siswa SMA Negeri 1 Surabaya

The rapid development of science and technology has resulted in various applications that can help various human needs, including in terms of design. Currently, there are various online-based design applications that provide a variety of attractive designs that can be accessed for free or for a fee, including Canva. This community service aims to increase the design creativity of SMAN 1 Surabaya students, especially in making poster designs with the Canva application. The results of the training showed that SMA Negeri 1 Surabaya students agreed that the training could increase students' knowledge and skills in making poster designs using Canva, as seen from the results of the questionnaire where 81.33% of students gave very good predicates to the training they had attended. The knowledge gained during the training is expected to be useful for students in the short and long term, namely in the wider scope of student life in the future.
Perspective matters: the role of scene point of view on infants’ looking strategies

Building on decades of research on infants’ visual perception of simplified experimental stimuli, recent attention has been directed at infants’ visual perception of naturalistic scenes (e.g., Pomaranski et al., 2021; van Renswoude et al., 2019). These studies have revealed patterns consistent both with work examining adults’ scene perception and developmental changes in infants’ viewing and perception of simpler stimuli. However, this work has focused on infants’ scanning of photographs taken by and for adults. Thus, both the content and the point of view may be unfamiliar to infants. Here, we recorded eye movements of seventy-four 5- to 10-month-old infants as they viewed photographs of scenes types familiar to infants (e.g., toys on the floor of a living room, playgrounds), taken from both an adult and an infant point of view. Following previous research, we asked how physical salience as modeled by the Graph Based Visual Salience toolbox (GBVS, Harel et al., 2007) predicted infants’ eye gaze at both types of scenes. We found that the fit between GBVS salience and the distribution of infants’ fixations increased over this age range, F(1, 2086) = 9.33, p = .002. However, we also observed that the fit between GBVS salience and infant fixation was better for images from an adult point of view than for images from an infant point of view, F(1, 2021.76) = 4.48, p = .03, suggesting that physical salience may have a stronger influence on where infants look when they view less familiar and less meaningful scenes.
Gesture Recognition Model with Multi-Tracking Capture System for Human-Robot Interaction

This study develops a wireless gesture recognition system for human-robot interaction using a high-speed marker-based motion capture system that requires no hardware development and no onboard power source. A novel gesture recognition model recognizes four gestures performed while holding a rigid-body object and translates them into robot control signals. The gestures are Flick Back to Front, Flick Front to Back, Rotate Clockwise, and Rotate Counter-clockwise. The system has four main components: a host PC with Vicon Tracker software, a set of Vicon Vantage V8 infrared cameras, a client PC that receives motion capture data and translates gestures to instruct a simulated KUKA youBot robot in the Gazebo simulation environment, and a gesture input unit. The poses of the gesture input unit are used to train the model using a surrogate deep neural network and the XGBoost ensemble method in a semi-supervised setting. The algorithm’s decision-making process is explicated through the implementation of the Layer-wise Relevance Propagation methodology in PyTorch. The control approach is similar to the way trainers teach domestic pets to perform specific actions in response to different gestures. The proposed method offers an alternative to commanding the robot through typing or using joysticks. The current gesture recognition rate is around 60%, but performance will improve over time as new training samples are collected and event detection algorithms are improved to avoid misinterpreting unrelated movements as classified gestures.
Pengaruh Variasi Waktu Pemanasan Sintesis Perak Nitrat (AgNO3) Menggunakan Metode Reduksi Kimia

Synthesis of silver nitrate using the chemical reduction method with variations in heating time for 1 hour, 2 hours and 3 hours has been successfully carried out. This study aims to determine the effect of variations in heating time on the phase structure, elemental concentration, morphology, and mass percentage of elements. The synthesis process was carried out by heating a mixture of silver and nitric acid at T = 85 °C for 1 hour, 2 hours and 3 hours, then the evaporation process was carried out at T = 125 °C for 10 minutes. The results of the XRF analysis on the AgNO3 samples showed differences in the percentage of Ag concentrations, with the largest percentage of Ag concentrations produced, namely 98.839% at a 2 hour heating time variation, the presence of impurities in the XRF results of AgNO3 samples at a concentration of more than 1%, with a concentration of impurities the largest is copper (Cu), this is what causes the synthesized sample to turn blue. The results of the XRD analysis showed that the AgNO3 phase had an orthorhombic structure with the largest percentage of AgNO3 produced, namely 27.85% at 2 hours of heating time variation.
Entanglement Distribution in Quantum Repeater with Purification and Optimized Buffer Time

Quantum repeater networks that allow long-distance entanglement distribution will be the backbone of distributed quantum information processing. In this paper we explore entanglement distribution using quantum repeaters with optimized buffer time, equipped with noisy quantum memories and performing imperfect entanglement purification and swapping. We observe that increasing the number of memories on end nodes leads to a higher entanglement distribution rate per memory and higher probability of high-fidelity entanglement distribution, at least for the case with perfect operations. When imperfect operations are considered, however, we make the surprising observation that the permemory entanglement rate decreases with increasing number of memories. Our results suggest that building quantum repeaters that perform well under realistic conditions requires careful modeling and design that takes into consideration the operations and resources that are finite and imperfect.
Where are my students looking at? Using Gaze Synchronicity to Facilitate Online Learning

Giving effective lectures requires managing all students' attention. In lecture rooms, such crowd attention is typically intuitively assessed by judging students' gaze cues. However, in online teaching, such gaze cues are often missing. In a series of studies, we aimed at facilitating online teaching by visualizing eye movement synchronicity and predicting learning success: (1) We recorded students‘ gazes (fixation positions) on PowerPoint slides and visualized the aggregated gazes to the lecturers as a heat map, an ellipse map, two moving bars, and one vertical bar superimposed on instructional videos. We found that the lecturers preferred the ellipse for live teaching and the heat map for retrospective analysis because they valued the spatial proximity of the visualization to the content. (2) In addition to passively watching visualizations of students‘ attention, signaling (with a digital laser pointer) is often used to actively direct and synchronize learners‘ attention. We investigated to which extent learners follow signals on PowerPoint slides and whether this synchronization predicts post-quiz performance. We found that the average distance between the learner’s gaze and the pointer position predicts the student’s post-quiz performance. (3) So, looking at the same point as the lecturer seems to be beneficial for learning success. But does synchronicity between individuals (how synchronous one is to the average of all learners) predict performance as well? In principle, yes. However, we were not able to reproduce the findings with unreliably-sampled webcam data, highlighting the challenges lying ahead of webcam-based eye tracking in practice. Overall, we found that measuring and visualizing gaze synchronicity can be a useful tool for investigating the students‘ attention in applied settings. It is also beneficial for online education by providing direct feedback to the lecturer and provide quizless assessment of the learner. These insights can help in creating automated immediate-feedback systems for educational videos.
ScenaConnect: an original device to enhance experiences with multisensoriality

This demonstration aims at presenting ScenaConnect, a multisensory device which allows people to live various several multisensory experiences. ScenaConnect is inexpensive, compact, easy to install and allows to improve experiences in added new interactions. The demonstration will present two cases of use. The first one is an interactive math exercise and the second one is a multisensory experience that will take the visitor on a journey through history. Moreover, ScenaConnect could be used in museums for immersive and interactive experiences or by a teacher who can use it to make the learning of his students more interactive and adapted. The perspectives are to allows non-expert in computer science to quickly integrate ScenaConnect in several and various experiences thanks to the software ScenaProd, which is, like ScenaConnect, a goal of the PRIM project presented in more detail on this paper.
Recommendation Systems on the Blockchain

&lt;p&gt;A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. Constantly growing as ‘completed’ blocks (the most recent transactions) are recorded and added to it in chronological order, it allows market participants to keep track of digital currency transactions without central recordkeeping. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as banking, financing, trading, manufacturing, supply chain management, healthcare, and government. Actually, there is a gap between the need for analytics expertise and the existing skills to comprehend the transformative power of Blockchain. This research project hence calls for innovative and advanced research on Blockchain and recommendation systems. We aim at building a secure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recommendation system with acceptable performance. &lt;/p&gt;
Finding a rhythm: The mediality of researching digital skill as process

Our objective for this article is to illustrate the importance of understanding digital skill as process by taking its mediality—interweaving tools, technologies, and media—into consideration. Drawing on 12 case studies with participants performing digital tasks, we use Ingold’s four phases of skill (getting ready, setting out, carrying on, and finishing off) to research and represent the rhythm of digital skill. By using medialities of inscription, scripting, and annotation, we demonstrate how researchers can use mediality to perceive rhythms of digital skill without being physically co-located in the performance. As different medialities enable and constrain the perception and descriptions of digital skill, we develop spotlines as a method that combines different medialities particularly well suited for describing and comparing the temporal order of phases for performing digital skill by rendering each performer’s pace and intensity.
Exploring Research Landscapes: Unveiling Insights from Diverse Domains

In the ever-evolving world of academic inquiry, the pursuit of knowledge transcends boundaries and defies convention. The realm of research is a dynamic landscape, and as we delve into the latest edition of JCitation, we are presented with an eclectic collection of articles, reports, and reviews that collectively capture the diverse tapestry of contemporary scholarly exploration. From scientometric analyses to evaluative reviews of digital resources, this issue offers a comprehensive view of research endeavors that span disciplines, methodologies, and geographiesa. Read more...
Rancang Bangun Aplikasi Peramalan Jumlah Penumpang Menggunakan Long Short-Term Memory (LSTM)

Public services such as public transportation are closely related to user satisfaction. Busway DKI Jakarta is one of the first public transportation services in Southeast and South Asia. In order to maintain passenger satisfaction, the management of Busway continues to improve services such as adding buses and opening a new line. Opening a new lane or adding buses must necessarily be adjusted also with the increasing number of passengers. So to know the number of passengers in the future, it is necessary to forecast the number of passengers through existing historical data. The historical data used is time series data from January 2015 to January 2016. The method used in forecasting is Long Short-Term Memory (LSTM), one of the machine learning methods. The method is measured in accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). As a comparison of LSTM accuracy in forecasting, we also use the Exponential Smoothing method. Based on the results of forecasting, the most and least dominant method of producing RMSE and MAPE is the LSTM method. Only in corridor 3 LSTM can not provide RMSE and MAPE values below baseline values. While corridor 5 LSTM can give better results after the data transformation process by using exponential smoothing. However, overall the LSTM method provides the best accuracy based on the lowest average RMSE and MAPE values, namely RMSE of 2640.53 and MAPE of 9.14%.
An Analysis Of English For Specific Purpose (ESP) Textbook For Maritime And Fisheries Students Published By Indonesian Ministry Of Education And Culture

This research analyze and describe the quality of the English textbook in term of the activities provided by this book. The object of this research was the Bahasa Inggris Maritim dan Perikanan textbook for 10th grade students published by the Indonesian Ministry of Education and Culture. The current research was conducted to analyze the need of suitable and comprehensive learning materials for English for Specific Purpose (ESP) Class. This research used an analysis format as the instrument to guide the researcher in analyzing the English textbook. The analysis format contained of six categories which was made based on the principles of a good textbook proposed by the experts those are readiness activities, experiential activities, intake response activities, development activity 1, input response activity, and development activity 2. The result of this research showed that this English textbook for maritime students published by the Indonesian Ministry of Education and Culture can be categorized as good textbook since all of the criteria was found in this book. Meanwhile, there are two categories that were rarely found and need to be revised in this textbook; development activity 1 and development activity 2. However, the other criteria had enough number as the activities provided by this book.
The dominance of persuasion in interpretation and communication

This research is part of the doctoral thesis requirements entitled Perception domain in interpretation and communication of speech, this study is in the field of psychological language science and is an attempt to analyze the main theoretical fields of speech and how to reach Speech perception By demonstrating interact between sensory and the perception of speech, the influence of both vision and hearing canal in the process of speech perception based on acoustic cues of sound signals of speech, the acoustic signs (the place of voice expression, the way the voice is expressed and when the voice starts) play an important role in recognizing and perception and separating the sounds.The visual channel gives the listener information about where the sound is expressed and the auditory channel gives the listener information about how the sound is expressed and when the sound starts. Infants are able to use visual and auditory information to recognize and follow the listener's voice.