Reviewerly/exHarmony huggingface dataset:
Mulsemedia data consolidation method

The synchronization of multimodal data is one of the essential tasks related to mulse-media data processing. The concept of mulsemedia (MULtiple SEnsorial MEDIA) involves the registration, storage, processing, transmission and reproduction by computer-based tools of multimodal information about a physical object that humans can perceive through their senses. Such information includes audiovisual information (object's appearance, acoustic properties, etc.), tactile information (surface texture, temperature), kinesthetic information (weight, object's centre of gravity), information about its taste, smell, etc. The perception of mulsemedia information by a person is the process that exists over time. Because of this, the registration of mulsemedia data should be carried out with the fixation of the moments of time when the relevant mulsemedia information existed or its perception made sense for a human who supervises the object as mulsemedia data is temporal. This paper presents a method that enables the consolidation and synchronization of mulsemedia data using the principles of multithreading. The universal method was designed to support combining data of different modalities in parallel threads. The application of the proposed method solves problems associated with integrating data of different modalities and formats in the same time interval. The effectiveness of applying this method increases by us-ing multithreaded distributed computing. This method is designed for use in the development of mulsemedia software systems. The modified JSON format (TJSON – Timeline JSON) was proposed in the paper, as well. TJSON-object is a complex data structure for representing the synchronized mulsemedia data and their further processing. The proposed method can be further extended with other approaches and technologies. For example, artificial intelligence methods can be applied to assess the correlation between data from different modalities. This can help improve the method's accuracy and the output files' quality.
An Adaptive Denoising Recommendation Algorithm for Causal Separation Bias

In recommender systems, user selection bias often influences user-item interactions, e.g., users are more likely to rate their previously preferred or popular items. Existing methods can leverage the impact of selection bias in user ratings on the evaluation and optimization of recommendation system. However, these methods either inevitably contain a large amount of noise in the sampling process or suffer from the confound between users’ conformity and interests. Inspired by the recent success of causal inference, in this work we propose a novel method to separate popularity biases for recommendation, named adaptive denoising and causal inference algorithm (ADA). We first compute the average rating of all feedback items of each user as the basis in converting explicit feedback to implicit feedback, and then obtain the true positive implicit data through adaptive denoising method. In addition, we separate the confounding of users’ conformity and interest in the selection bias by causal inference. Specifically, we construct a multi-task learning model with regularization loss functions. Experimental results on the two datasets demonstrate the superiority of our ADA model over state-of-the-art methods in recommendation accuracy.
Item Recommendation on Shared Accounts Through User Identification

Nowadays, people often share their subscription accounts, e.g. online content subscription accounts, among family members and friends. It is important to identify different users under one single account and then recommend specific items to decoupled individuals. In this paper, we propose the Projected Discriminant Attentive Embedding (PDAE) model and the Shared Account-aware Bayesian Personalized Ranking (SA-BPR) model for user identification and item recommendation, respectively. PDAE separates item consumption actions of each individual from mixed account history by learning the item representation that has both the user preference and user demographic information integrated; SA-BPR is a robust recommendation model based on a hierarchical ranking strategy where items from different sets are recommended with different levels of priorities. The experiments show that the proposed models generally outperform state-of-the-art approaches in both user identification and item recommendation.
Upaya Guru Dalam Meningkatkan Budaya Literasi Siswa Di MI Ma'arif NU Manbaur Rohmah Gresik

This study aims to determine literacy activities, obstacles, and efforts made by schools in increasing elementary school students' interest in reading. This study used a descriptive qualitative method with interview and observation data collection techniques. The results of the study show that 2 factors influence literacy culture, namely inhibiting factors and supporting factors. Inhibiting factors include students' lack of interest in reading, the influence of gadgets due to increasingly sophisticated technological developments, lack of awareness from students, and lack of parental involvement in literacy activities. Supporting factors from parents, teachers, and facilities and infrastructure. Efforts made by the teacher in improving literacy culture are making class libraries or reading corners, moving class activities used for literacy once a week, muhadhoro activities which are carried out once a month on the last Friday of the week where children display creations such as speeches and sholawat, Providing tiered pin rewards starting from beetle pins to lions, Providing a representative library and providing 70% fiction books (story books) and 30% non-fiction books.
MCDM Based Evaluation of Software Defect Prediction Models

Software defect prediction is an essential activity for software quality assurance. Various researchers have developed a large number of models for software defect prediction based on machine learning. Different models may have the varying capability for software defect prediction with respect to different conflicting accuracy measures. This paper aims to propose a Multi-Criteria Decision Making (MCDM) based approach for selecting the most appropriate machine learning technique for software defect prediction with respect to more than one accuracy measure. Because of the involvement of various accuracy measures, the problem of selecting an appropriate machine learning technique for software defect prediction can be modeled as a Multi-Criteria Decision Making (MCDM) problem. An experimental study was conducted using nine machine learning techniques, four performance measures and one MCDM method over three datasets to validate the proposed method. Experimental results of this study show that the proposed method can be applied as an efficient decision-making tool in software industries for choosing the most suitable machine learning technique for software defect prediction.
Speech Recognition for Kannada Using LSTM

Speech recognition is defined as the ability of a program or machine to recognize words and phrases in a spoken language and transform it in a way that is machine understandable. The idea behind automatic speech recognition (ASR) is to identify speech and transcribe it to text without taking speaking style, accent, region, language, etc. into consideration. A major problem is the absence of a free program or a system, which effectively provides transcripts to a video or audio file in Kannada Language. There is a need to develop a system that automatically provides subtitles to a video/audio file. This can have several use cases. In this paper, speech recognition is implemented using LSTM-based networks and is compared with other methods such as monophone-based HMMS, triphone-based HMMs and DNNs. Pre-processing of data involves conversion of raw data into ILSL format and removal of non-ASCII characters. Kaldi toolkit was used for running the experiments. Word accuracy of 97.92% was achieved using LSTM-based networks. The results obtained are better than those achieved by other people in other works due to a different method of transliteration and using LSTM-based networks. Hence, a speech recognition model for Kannada language was implemented successfully.
A Computer Vision-Based Human–Computer Interaction System for LIS Patients

The advancements in computer technology have brought massive changes around the world. It has redefined the mundane tasks performed in our day-to-day lives into simpler solutions. One such field that has contributed massively and is growing rapidly in the modern world is Computer Vision (CV). Our work is based on the interaction and communication of computers with humans. One such application that could be used in the medical field is that people suffering from paralysis or locked-in syndrome (LIS) find it very difficult to communicate with the world. The degradation of motor neurons in human being results in their muscles weakening, which eventually results in paralysis that restricts people's ability to perform voluntary actions. To communicate with such people, techniques such as brain waves, Electro Oculography (EOG), ElectroEncephaloGram (EEG), etc. were used, which were highly uncomfortable and less efficient. We have presented a Video-Oculography (VOG)-based model that automates the task of communicating with paralyzed patients by reading their facial landmarks and eye movements and transforming them into textual communication. We have used the Haar cascade classifier for face and eye detection and then we have distinguished between voluntary and involuntary blinks through which the user can communicate. We have also tested this model’s performance based on the distance between the user and the system, which came out to be typing at 1.96 words per minute on average. The aim is not only to provide accessible technology but also to help such people to achieve independence. This could be morally uplifting as well.
Recent Advancement in Pancreatic Cancer Diagnosis Using Machine Learning-Based Methods: A Systematic Review

Pancreatic cancer is a leading cause of mortality worldwide, which usually occurs due to atypical growth of some cells in pancreas or its nearby organ. Moreover, due to its location in abdomen and smaller size, it is quite difficult to detect and diagnose this cancer till tumor reached to certain size. Computer assistive methods like machine learning techniques may help in early diagnosis of pancreatic cancer due to its high computation efficiency and accuracy. Various machine learning-based techniques have been employed in different cancer type diagnosis, i.e., brain, breast, lung, liver cancer, etc. in past studies by researchers, however very limited work has been observed, dedicated to pancreas cancer detection. Therefore, it’s a need of hour to deliver a viable framework with established cancer avoidance, assessment and provision for initial stage detection of pancreas cancer for upkeeping of patients against mortality. The aim of this study is to explore, analyze, classify and discuss the current technological advancement in diagnosis of pancreas cancer using various machine learning methods. Several utilized machine learning techniques are grouped into alike groups and results are scrutinized using key performance metrics, i.e., accuracy, sensitivity, dice score and area under the curve and evaluated on benchmark datasets utilized by previous studies and concluded with witnessed challenges.
PEMETAAN PRASARANA DAN KONTUR KAMPUS UNIVERSITAS WIRARAJA BERDASARKAN CITRA PENGINDRAAN JAUH

Wiraraja University campus from its establishment in 1986 to 2023 has a period of 37 years which has experienced many developments, especially learning infrastructure. The development of infrastructure needs to be mapped to determine the distribution of learning infrastructure on the Wiraraja University campus. The utilization of geographic information system (GIS) technology and the development of infrastructure on the Wiraraja University campus, it is necessary to map the infrastructure and contours on the Wiraraja University campus which will later obtain an overview of the infrastructure. The purpose of the study was to determine the infrastructure map and the percentage of infrastructure to the overall land and contour map of Wiraraja University campus. The research used Google Earth and ArcMap. The research data collection was carried out by field survey and data collection of infrastructure on the campus of Wiraraja University and the research data was carried out by frequency analysis.&#x0D; Land use on the campus of Wiraraja University is divided into the categories of buildings, sports fields, covered and open parking, parks, roads and vacant land. The land area of Wiraraja University campus based on Google Earth images with the digitization method is 22,711.86 m2. The largest land use is used as a building at 37.54%, while the smallest land use is used as a covered parking lot at 1.78%. The elevation of the Wiraraja University campus is between 3.67 - 5.94 m elevation and the highest elevation is 5.94 meters and the lowest elevation is 3.67 meters above mean sea level.
PEMANFAATAN MEDIA PEMBELAJARAN ONLINE PADA MATA PELAJARAN PAI DENGAN MODEL HYBRID LEARNING

Due to the large spread of Covid-19 in Indonesia, the learning process is only limited to face-to-face interactions in locations with a high incidence of infection spread. To accommodate students who attend face-to-face in class or who attend online, teachers can take advantage of online learning media using hybrid learning. The purpose of this study is to explain how to use online learning resources in PAI subjects using a hybrid learning approach. This research is a literature study with a qualitative descriptive approach with data sourced from the library as a source of information, then the data is analyzed using content analysis. The results of this study indicate that PAI teachers can take advantage of various online learning media platforms, including WhatsApp, Google Classroom, and Zoom Meeting. Teachers can use accessible capabilities to deliver information or materials, monitor attendance, and evaluate students participating in online learning.
Face Recognition Using Deep Neural Network with MobileNetV3-Large

With the advancement of computer technology, multiple advanced techniques rely on machine vision, especially biometric systems, to play an essential part. The recording shows an image or video data with a face in it, then recognizes and analyzes the face region. Detection and recognition consist of a group of Artificial Intelligence (AI) techniques. It has a broad scope of uses and has become a thriving topic of study. In our proposed model, we employed AveragePooling2D with MobileNetV3Large. The algorithm is trained on the collected dataset following a few preprocessing processes. According to the findings, MobileNetV3 with AveragePooling2D has an accuracy rate of 91.12 percent on the training dataset and 99.79 percent on the verification dataset for 50 epochs.
Radon Transformation-Based Mammogram Image Classification

The common challenges faced by mammogram image classification techniques include the classification accuracy, underfitting/overfitting of data, removal of artifacts, identification of correct region of interest (ROI), insignificant size of data, extraction of quality features etc. In this direction, the authors proposed an approach which overcomes these problems and classifies the input mammograms into benign, malignant or normal classes. The Mammographic Image Analysis Society (MIAS) dataset’s mammogram images are initially pre-processed for artifact removal and segmentation of ROI. All ROI images are further augmented to generate 71 additional copies using flip and rotation transformations. To balance the classifier's input data, 3000 ROI images from each of the three classes have been randomly selected and their radon transform has been computed using projection lines for each of 180 directions (0° to 179°). Projection wise minimum, maximum and mean values are merged to form a feature vector (FV) whose performance has been evaluated on 18 classifiers using k-fold (k = 5) cross-validation approach. In addition to good sensitivity, specificity, precision and f-1 score results, 90.84 and 98.44% are the average and maximum classification accuracy achieved for computed FV.
Abusive Content Detection on Social Networks Using Machine Learning

For online social media, automatically identifying abusive language is a challenging but essential task. In recent years, finding abusive language in user-generated internet content has gained substantial attention. Our analysis focuses on human rights, including the development of fresh methods for identifying and defending the freedom of speech. In this paper, we consider strategies for distinguishing defamatory text from common profanity while detecting it. We have used the benchmark dataset of the Hate Speech Twitter Corpus, which contains 136,052 total tweets. Furthermore, this research uses the Long Short-Term Memory (LSTM) model for the method of classifying hate speech and abusive language. The final accuracy is ~82%. The technique revolves around including an extension that will accept the website's content (mostly text) as input, assess the contents for abusive purposes using specially developed neural networks, and hide the unethical content from the user. In contrast to the traditional way of users reporting abusive content, our solution does not require any human involvement and thereby limits offensive language by automatically recognizing and blocking it.
Solar Power Generation Forecasting Using Deep Learning

This paper presents a deep learning based solar power generation forecasting model. Open-source data from Neural Designer has been used to collect the data. The data points used by authors is 4213 and the number of parameters chosen are 20 from the database. This makes 20 columns in the dataset and the number of data points (rows) equal to 4213. In the implemented model four layers, namely, 1 input layer, 2 hidden layers and 1 output layer have been considered. For the first layer, 20 neurons were considered as input because of the 20 input parameters, for the second layer i.e., the first hidden layer, 32 neurons were selected. The third layer which is the second hidden layer has 64 neurons. For these 3 layers, ‘relu’ function has been used as an activation function. For the predicted and generated power on training data as well as on test data a clear agreement was found. Also, the R squared values on the training set have been found 0.9286 and for the test set as 0.7795. In the future the model can be improved by using recurrent neural networks, time stamp data, and larger dataset. Further it can be applied on a power plant with larger capacity models.
Training and Assistance in the Implementation of Interactive Learning Media and Utilization of Technology by Using the Wordwall Application

One of the impacts of the Covid-19 Pandemic is the change of learning process in elementary schools from offline learning to online learning. The results of the observations at Al-Khoiriyyah islamic elementary school show that partners still had difficulties in utilizing information technology facilities besides there was also a decrease in the enthusiasm of teachers and students in the learning process. The community service team offers solutions by providing training related to interactive learning media and the utilization of technology using the wordwall application. The wordwall application can attract the enthusiasm of teachers and students in the learning process by utilizing various features. This training was attended by 8 participants located at UHW Perbanas on June 24, 2022. The methods used were discussion, independent practice, and evaluation obtained from filling out the pre-test and post-test. The results of the training and mentoring show that 100% of the training participants are able to use wordwalls. The learning media using this wordwall had a significant effect and this was shown by the practical results of each participant who successfully completed the tasks given by the community service team which is 8 participants successfully made a quiz using the wordwall application.
Designing an E-Recruitment Information System Using Simple Additive Weighting Method for Employee Recruitment in Banking Industry

This research aims to find out how to design and build an employee e-recruitment information system using the simple additive weighting method. There are three types of data collection methods in this research: observation, interviews, and literature study. The system development method used in this research is the OOAD (Object-Oriented Analysis and Design) method with the rapid application development model, while the tools used in object modeling are UML (Unified Modeling Language). From the discussion that has been described, the following conclusions can be drawn: This e-recruitment information system was designed and built using the RAD system development method and the SAW method for calculations in decision-making, using the PHP and MySQL programming languages as the database, and tested with black box testing. By using the e-recruitment information system to recruit employees, the HR and general divisions have a reference for selecting which prospective employees most closely match the predetermined criteria. This e-recruitment information system can reduce the possibility of human error occurring during the selection process, and with the value of each applicant calculated in the system, it will become a reference for HR staff in selecting employees more objectively. The administrative selection process, which takes one month, can be reduced to a maximum of seven working days. With the e-recruitment information system, selection results that were previously reported via message can now be seen directly in each applicant's account at a predetermined time.
USING SHARDING TO IMPROVE BLOCKCHAIN NETWORK SCALABILITY

Blockchain is a distributed and decentralized database for recording transactions. It is shared and maintained by network nodes, which ensures its operations using cryptography and consensus rules that allow all nodes to agree on a unique structure of the blockchain. However, modern blockchain solutions face network scalability issues due to different protocol design decisions. In this paper, we discuss sharding as a possible solution to overcome the technical limitations of existing blockchain systems and different forms of its practical realization presented in recent research spurred by blockchain popularity.
Management of data flows in modern industry using blockchain

Recent research and publications. "Industry 4.0" is a concept of the industrial revolu-tion, which is based on the use of modern technologies and digital innovations in production and distribution processes. The introduction of the concept of "Industry 4.0" was designed to improve the competitiveness of European industry and increase productivity and product quality. A blockchain is a distributed data structure that is replicated and distributed among network members. The purpose of the study is to improve automation processes, increase efficiency, re-duce delays and errors in information systems of industry and supply chains by using block-chain technologies in the construction of information systems. Main material of the study. The paper makes an analysis of approaches and algorithms to data management in "Industry 4.0" information systems. Blockchain algorithms are com-pared to classical approach with other databases in the client-server architecture. Conclusions. By implementing algorithms based on blockchain technology, namely by using the Merkle Tree, digital signature technology, and by using consensus algorithms in the framework of decentralized data storage in Distributed Ledger Technology, the processes of automation and efficiency in data flow management are improved, providing a secure and transparent way to store and share data that reduces delays and errors in industry informa-tion systems and supply chains.