Reviewerly/exHarmony huggingface dataset:
Blink To Speak an assistive device for Paralyzed people

Today, 1 in 5 persons over the age of 20 will experience a stroke at some point in their lifetime. If a stroke affects one side of the brain, such as the left, the opposing side of the body, such as the right, will become paralyzed. Hemiplegia is the name for this particular form of unilateral paralysis. These people who are paralyzed can see and hear what is going on around them, but they are unable to converse with them since one side of their body will be totally paralyzed. Their capacity to talk will be diminished if the paralysis affects their facial muscles. These patients must be able to communicate their wants and thoughts because they are unable to talk and perform any bodily tasks on the side of their bodies that is injured. Since “Eye-Blink” is an automatic reflex (though it can sometimes be voluntary), the affected individual can effectively blink the eye on the unaffected side. Therefore, in this research, we developed a system that recognizes a wink and speaks out to convey the person's thoughts and desires. This system's advantages include low cost and ease of transportation due to its compact size. It is also taken into account that it causes some discomfort to the patient because they have to blink their eyes constantly when it is necessary to communicate. Flickering is detected by an infrared sensor and each blink counts a word or phrase describing regular activities like eating, sleeping, drinking, etc. will be given as voice over speaker and can also perform an activity (automation).
An Extensive Study of Cervical Cancer Detection Methods Using Machine and Deep Learning Approaches

In this work, various computer-aided methods for cervical cancer screening are examined in-depth with examples. The cervical cancer can be detected using either cervical images or Pap smear cells images. Both are important to detect the cervical cancers in women patients. The several machine learning and deep learning models for detecting cervical cancer in Pap smear cell images as well as cervical images are covered in detail. The simulation study is used to evaluate the benefits and drawbacks of each cervical cancer screening method.
An Analysis Method of 5E Online Learning Emotional Experience based on Cloud Model

5E Online learning is the main form of integrated teaching, and the emotional experience of 5E online learning is an important embodiment of learners’ learning input. Based on the Ekman theoretical model, this paper designs and develops a cloud model of 5E online learning emotional experience with the six dimensions of “happy, relaxed, anxious, surprised, sad, and boredom which takes the area of the cloud as the main parameter. It determines the characteristic vectors and observation functions that characterize the learner’s emotional experience and evaluates the learner’s 5E online learning emotional experience. Otherwise, it promotes the learner’s 5E online learning ability and optimizes the evaluation of the online teaching process. Taking MOOCS as an example, this paper applies the cloud model to evaluate students’ emotions and verifies its effectiveness through specific data. This method enriches the methods of emotion calculation and characterization for online learners and provides a reference for the current multi-modal sentiment analysis research.
Currency Detection App for Blind People Using MIT App Inventor

This paper helps the visually impaired people to identify the currency notes using an app built using MIT app inventor. The user has to press the shutter button in order to identify the amount of the currency being scanned. The output of the app is a voice command that will help the blind people to identify the amount. This paper makes use of the artificial intelligence tool to classify the image and gives the output. The artificial intelligence uses the image classifier to classify the image.Normally, the image classifier takes the input given the user and classifies the given input and gives the output.Here, the input given is the currency notes of different values so that the image classifier classifies the input and gives the output. This paper uses personal image classifier model to classify or detect the captured image.Here, the captured image in indian rupee note. This personal image classifier consists of four steps.
Real-Time Communication System for Specially Abled with DL-CNN Based Approach Using Image Processing

People get to know one another by sharing their ideas, thoughts, and experiences with those in their immediate surroundings. There are numerous methods for accomplishing this, the most effective of which is the gift of “Speech.” Speech enables all people to communicate their ideas effectively and to comprehend one another. It will be unfair if we fail to take into consideration those who are denied this priceless gift: the dumb and deaf. The preferred method of communication in these situations has continued to be human hand contact. Things that have been first challenging or unattainable for people with disabilities are now regularly available to them and can be accessed by them with ease. Artificial intelligence made it possible for people with disabilities to live in a society where their challenges are acknowledged and taken into account (AI). Technological advancements have made it possible for technology to adjust and transform the world into a more open community. There is a certain sense of being human as AI directly correlates individuals, including people with and without impairments. hearing voice in the preferred language so that a message can be delivered it to normal people to build a model that is trained on various hand motions we are using a convolution neural network and deep learning on the basis of this model an app is created with the help of this app person who is deaf or dumb can communicate using postures that are translated into speech and human-understandable words
Hand Skeleton Graph Feature for Indonesian Sign Language (BISINDO) Recognition Based on Computer Vision

Sign language is a means of communication for The Deaf. Indonesian Sign language or BISINDO is one of the sign languages that is used in Indonesia. For The Deaf with The Deaf sign language is a means of communicating effectively, but not for The Deaf with the hearing. This is partially due to insufficient basic knowledge of The Hearing about how to communicate with The Deaf. A sign language translator needed to help The Deaf communicate with The Hearing. Limited of sign language translator is the reason for this research to develop sign language recognition methods. This research is about the development of methods for recognizing basic sign language alphabet and numbers based on computer vision. Basic sign language alphabet and numbers are demonstrated by arms, so they can be the basis to recognize alphabet and number from them. In this research skeletons graphs are extracted. Features are obtained from angle as direction for each chosen vertex. These features are known as skeletal based. To calculate similarity of the alphabet and numbers based on features, this research uses K-Nearest Neighbor (KNN). The best result of recognize sign language alphabet is 99.70% and to recognize sign language numbers the accuracy is 99.81%.
Black-box Adversarial Examples against Intelligent Beamforming in 5G Networks

Due to the shared and open nature of wireless medium, multiple essential components of the next-generation communication systems that rely on artificial intelligence are vulnerable to adversarial example generation attacks. The goal of these attacks is to manipulate the inputs to the machine learning models during the inference stage over the air in such a way that when the perturbed samples are fed to the target model it returns wrong outputs. This may compromise the integrity of the target model and negatively affect the functionality of the corresponding network framework under attack. In this study, we focus on adversarial example generation attacks against machine learning models which can be deployed in future 5G networks for intelligent beamforming. First, we discuss applications of machine learning for beam selection as well as vulnerability of the resulting intelligent frameworks to adversarial examples. After that, we evaluate various input-dependent black-box adversarial example attacks for multiple beamforming use cases based on deterioration of the network throughput. Finally, we implement and test several algorithms for crafting universal input-agnostic adversarial perturbations which can be employed by adversaries in realistic attack scenarios.
Design and Implementation of a Lawful Interception Architecture for B5G Systems Based on Key Escrow

Law Enforcement Authorities use Lawful Interception to prevent and investigate criminal offenses. But, the adoption of end-to-end encryption makes quite ineffective conventional approaches. To bridge this gap, this work (i) proposes a preliminary and standard-compliant Lawful Interception Architecture for Beyond 5G systems, based on a Key Escrow scheme, (ii) verifies its effectiveness through proof-of-concept tests, and (iii) proposes future research activities in this direction.
SSTI: Semantic Similarity to detect Novelty of Thesis Ideas

The act of plagiarism in a thesis can decrease the quality of a student’s thesis. The plagiarism is perhaps unintentional. One form of this plagiarism in the thesis is the similarity of ideas in terms of topics, methods and cases used. Detecting the similarity of ideas has its difficulties because it must be able to understand the context of a document. This research implements a similar idea detection framework with a semantic similarity approach. Calculating the similarity value between the thesis proposal and the crawled data is carried out by considering the cluster distance and the hierarchical structure in the knowledge base. Comparative data were obtained from open portal publications. The evaluation results return accuracy is over 100 data testing.
Designing Earthquake Monitoring System Using Earthquake Catcher Network

The earthquake monitoring system is a system that provides the latest earthquake information detected by seismic sensors ECN based on an accelerometer which has been developed independently in previous studies. This study uses ECN sensors to process and analyze peak ground acceleration (PGA), earthquake intensity, and visualization acceleration graphs in three axes (x, y, z) through the visualization on the website. This research includes sending ECN sensor data to the message broker server and then taking data (consume) to the database server and processing and analyzing data from the database into helpful information for users. The system design results are a prototype of a web-based application that displays the latest news on the state of the earthquake in the form of peak ground acceleration and earthquake intensity, as well as visualization through maps from the sensor location.
Automatic Migration From Imperfect Relational Database To Resource Description Framework

The existing data on the web is growing increasingly diverse and extensive. Unfortunately, most of the current data is not semantic web friendly. The relational database (RDB) is considered one of the most popular structured data models. This work aims to automatically migrate from an imperfect RDB to a resource description framework (RDF). This issue has been ignored by previous work. Imperfectness means the lack of constraints in RDB. We attempt to construct an algorithm to migrate the RDB (including imperfect ones) to RDF. Results and evaluation showed that the proposed algorithm has succeeded in finding the relationship in RDB and migrating the RDB to RDF.
Active-to-Passive Arabic Word Conversion and MSD Identification using RNN

Identifiying part of speech of word is critical for Arabic language morphology aspects. Existing approaches either 1) predict morphological description from active voice Arabic words with neural based; or 2) predict morphological description from active and passive voice Arabic words with rule based. Both kinds of approaches have shortcomings. Therefore, we propose on adding some other Arabic type of word, which is passive voice word. Specifically, we convert the active voice to passive voice Arabic with computation and morphological description identification from that result. Experiments show that our system sucessfully to change active to passive voice automatically and achieves good performance on morphological description identification using neural based method.
A scientific expertise classification model based on experts’ self-claims using the semantic and the TF-IDF approach

It is difficult to understand a scientific domain’s structure and extract specific information from it. A lot of human work is needed to achieve this goal. Based on previous studies, most of the data sets used in identifying the scientific expertise of academia are obtained through the information in the metadata and the contents of the papers written by academia. Therefore, machine learning tools should be utilized to accurately represent how knowledge has been arranged and presented up to this point. In this research, we compare semantic analysis approaches (Latent Dirichlet Allocation/ LDA and knowledge graph / KG) and non-explainable variables (TF-IDF) in identifying categories of scientific expertise. Dataset used based on scientific expertise self-claims written organically by academia which has not been widely studied in previous studies. The TF-IDF approach can provide better classification model accuracy results because its character only looks at the level of word importance (word relevance). However, this approach does not give meaning to the independent variable. It is also supported by the dataset with single part of speech condition. Meanwhile, the semantic analysis approach can provide meaning and relation to form the topic or cluster graph, even with a lower accuracy value.
Auto Code Comment Assessment for Online Judge using Word Embedding and Word Mover's Distance

Comments in source code are a form of inline documentation created by programmers to help others understand the function of the program. The students of the basic programming subject need how to learn to write better code comments which can be difficulties for the lecturer assessing. Therefore, the author proposes an automatic source code comment assessment method for the online judge system with a corpus-based text similarity approach. Word2vec, GloVe, and fastText models will be used to train word vectors with the Indonesian Wikipedia Dump. The Similarities will be measured using Word Mover's Distance (WMD). Experiments were carried out using epoch variations during the training process. Spearman's rho correlation coefficient, mean average error (MAE), and performance measurements of each model will be compared. The methods with the proposed word embedding approach still provide not good results.
The Effect Of Information Quality On Product Reviews In The Short Video Platform For Viewers Purchase Intention

Product review video is one of the genres created by many people on short video platforms such as TikTok, Instagram Reels, and YouTube Shorts. Especially for the business owner to promote their products to influencers so they can sell products better. Product reviews not only give an advantage to influencers and help business owners but also advise consumers. Because with product review videos, consumers can understand more about the product conditions, quality, and testimonials about the products from previous buyers. Researchers raised the topic to discuss and determine the effect of the information quality on product review videos in the short video platform for customer purchase intention. Research questions to be answered are: How can the information quality of product reviews on a short video platform affect customer purchase intention? Researchers use a quantitative method by distributing a questionnaire survey to people who use short video platforms such as TikTok, Instagram Reels, and YouTube Shorts. The survey gained 479 responses and obtained 454 valid data where the respondents have seen product review videos on short video platforms. There are 7 variables that researchers will analyze in this study, namely Information Content Quality (ICQ), Information Utility (IU), Information Expression Quality (IEQ), Perceived Diagnosticity (PD), Information Credibility (IC), Information Adoption (IA), and Purchase Intention (PI). Through this method, we can understand the importance of information quality as the root of information adoption and purchase intentions.
Comparison of Machine Learning and Deep Learning model for Medical Subject Headings Indexation

MeSH stands for Medical Subject Heading. MeSH is a thorough mastery for cataloging books and articles in the biomedical literature. MeSH works as a dictionary that facilitates searching and retrieving information in the biomedical realm. Currently, a human indexer manually indexes articles and books in biomedical literature using the MeSH vocabulary. The problem with using a human indexer is that the indexation process takes a long time and is expensive. Therefore, the indexation developed is automated, which is developed in this study using MeSH vocabulary with predictors based on machine learning and supervised deep learning. The study found that the F1-Score of the deep learning indexation model was superior compared to the machine learning used as the baseline model in predicting the indexation.
Detection of Driver Drowsiness Based on Eye and Mouth Movements Using Convolutional Neural Networks

The Increasing of road mobility triggers the increasing of the number of traffic accidents. One of the main factors of the accidents is human errors which are heavily influenced by the driver conditions. Fatigue, drowsiness, and loss of concentration are among the common driver conditions that could cause traffic accident in addition to high-speed driving behavior. This could be minimized if early warning systems of driver conditions existed. This research aims to develop an early detection system for driver conditions using Convolutional Neural Network (CNN) method. Here, we investigate the effect of the depth of CNN and other hyper-parameters and observe their performance. We used eye movements and mouth conditions to be an indicator driver conditions. We evaluate the method using public dataset that contains image data of drivers on the highway in a state of yawning, not yawning, eyes open, and eyes closed. The experiment showed the best parameters with a learning rate of 0.001 and an epoch of 100. The resulting accuracy reached 99.31%.
Sarcasm Detection in Indonesian Tweets Using Hyperbole Features

Since sarcasm has inverse meaning from what is said or written, it is very hard to detect sarcasm. Therefore, detecting sarcasm is an important task in Natural Language Processing (NLP) field. In this study, we use interjection, intensifier, capital letters, elongated words, and punctuation marks as hyperbole features to detect sarcasm in Indonesian tweets. Particularly, these hyperbole features are utilized by Support Vector Machine (SVM), Random Forest (RF), and RF+Bagging to classify Indonesian tweets in our testing data as sarcasm or not-sarcasm. English tweets obtained from Kaggle and SemEval are employed as our training data, while Indonesian tweets obtained from Drone Emprit are used as the testing data. Our experimental results show that our model with hyperbole features classifies more the tweets in the testing data as sarcasm than that without hyperbole ones. Our observation indicates that using hyperbole features could contribute well to detecting sarcasm.
The Recommendation Augmented Reality

This paper discusses the understanding of augmented reality (AR), types of AR, its current AR use, and reviews the used AR specifically for ship safety navigation. The paper will make recommendations on what type of AR products are possible to develop. The AR can be used to support maritime navigation applications in Indonesia. The method used is primary literature review analysis through Systematic Literature Review (SLR) from thirty articles. Results of the literature review show that four types of AR based on the projection techniques. The types are a marker-based AR (image recognition), markerless AR, projection-based AR (hologram), and super impositing-based AR (object recognition). Currently, in various countries, AR products have begun to be developed for maritime navigation applications such as AR social to determine point of interest (POI), AR navigation, AR safety, and an immersive underwater world. From the literature review analysis results, this paper recommends that the right AR products based on the projection technique are markerless AR and projection-based AR types. However, in the future, it is necessary to make an in-depth study of the need for AR products for maritime navigation in Indonesia. The products include traditional navigation tools, navigational aid, and simulations for marine navigation training and then be implemented so that the AR products for maritime navigation that are produced are truly in accordance with the needs of ship crew and policymakers in Indonesia.
Impact of Air Pollution on Solar Radiation in Megacity Jakarta

Air pollution can intrude on the process of solar radiation reaching the earth’s surface, disrupting the earth’s heat balance. Global warming is one of its consequences. This study aims to analyze the impact of air pollution on solar radiation using Random Forest (RF) and Support Vector Regression (SVR) models. We use six pollutant types to predict the diffuse solar radiation, i.e., PM2.5, PM10, NO2, SO2, CO, and O3. Besides, near-surface temperature and sunshine duration are also expected to influence solar radiation or vice versa. The models are applied in two locations in Jakarta, Kemayoran and Jagakarsa, from January-August 2019. Based on the model performance, RF outperformed compared to the SVR model. RF model found that all variables, pollutants, temperature, and sunshine duration, impact the solar radiation in both locations. While the SVR model showed that the solar radiation in Kemayoran is affected by all variables, excluding O3. Meanwhile, PM2.5, PM10, NO2, temperature, and sunshine duration affect the solar radiation in Jagakarsa. Overall, PM2.5 is one of the top three most influential pollutants.
Medical Assistant App

Apps for medical assistance can be quite important in the provision of emergency healthcare. Anyone may require the contact information for the closest hospital immediately following an incident due to the increase in traffic accidents. Because of this, we have suggested a location-based emergency medical aid system utilizing OpenStreetMap (OSM), where the medical facilities are mapped using their waypoints. Our suggested system consists of an android device with an application installed on it, the user, and a database serving as a central server of comprehensive information about healthcare facilities. During any emergency, a user of the application receives text information about the closest medical facilities from their present location. This mobile application improves the patient medical experience, the relationship between doctors and patients, and the doctor's self-price value and contains a huge commercial value. This app is also built with technology in mind with the potential to reduce efforts to healthcare costs and improve access to medical services and knowledge. We built a chat session in the app that engages patients in the conversation about their medical queries and problems to provide an individualized diagnosis based on their diagnosis and profile.