Reviewerly/exHarmony huggingface dataset:
Comment se fait-il qu’une pensée qui émerge ici et maintenant soit attribuée au passé ?

How is a Thought that Emerges Here and Now Attributed to the Past? During the last twenty years, many works have questioned the still dominant conception of memory, which holds that memories are the product of the activation of stored knowledge. On the contrary, memories are constructions that emerge in the situation of recollection. Moreover, the act of remembering must be considered as a process of attribution (SCAPE model) resulting from an inference that has its origin in the subjective sense of fluency associated with the process currently at work. This is why we suggest linking the SCAPE model to the PP (Predictive Process) model, because on one hand these two models are inferential models, and on the other hand they attribute an essential role to the evaluation of the processes in progress. To conclude, we propose that memory should no longer be considered exclusively as a mean of recalling the past, but as a mean of predicting the information that will be useful in reducing prediction errors. In other words, memory is made for the future (i. e., action to be performed and its consequences) considering the present.
Web-Based Population Data Management Application

The RW17 Citaliktik Soreang office is one of the Residents' Associations, having its address at Kp. Citaliktik Soreang Islamic Boarding School has various kinds of resident activities. Currently, the population data collection at the Citaliktik RW17 Office is to collect citizen data conventionally according to its category. With this, it is necessary to implement an information system, the Population Data Collection Application, which is implemented through the website. The system will be developed using object-oriented software development and programming (OOAD) methods using a computer or laptop device. To assist the RW17 Citaliktik Soreang Office in improving the existing system with the hope that the processing and presentation of population data, which is still conventional, can make it easier to make this population data collection application. The result of making the application is a Population Data Collection Application at the RW17 Citaliktik Soreang Office.
An EEG Study of Virtual Reality Motion Sickness Based on MVMD Combined with Entropy Asymmetry

The existence of virtual reality motion sickness is a key factor limiting the further development of the VR industry, the prerequisite for solving this problem is to accurately and effectively detect its occurrence. Therefore, in this paper, the EEG signals of resting states before and after the evocation of motion sickness in subjects were acquired and used to realize the detection. First, the EEG signals from the four selected pairs of electrodes in the bilateral regions were decomposed by multivariate variational modal decomposition (MVMD), and the fuzzy entropies were calculated from the selected components; Then, the absolute differences of features in the pair of electrodes were computed, and selected according to the t-test. In final, the selected features were fed into SVM for classification. The results showed that the accuracy, sensitivity and specificity of this method reached 99%, 99.2% and 98.8% respectively, and AUC curve reached 1, which proved that this method could be an effective indicator for detecting the occurrence of virtual reality motion sickness.
A Real Time Design Pattern Using MapReduce Strategy Visit for Distributed Service Oriented Strategy

In Recent developments towards programming models such as MapReduce have introduced many techniques to evolve and utilize the use of design patterns. MapReduce is a computing methodology for processing various data which resides on thousands of computers that are popularized by google, Hadoop and others. Design patterns have become ease for developers, since they are the tools for developers to solve complicated problems in a reusable and general way with less time. Using these techniques in the domain of distributed Service Oriented Architecture (SOA) is simple, faster, accurate, flexible and adaptive than the old systems. Due to this flexibility and power of design patterns in composition with, it is easy to realize and understand the effects of the loose coupling between the components interactions in the designed architecture. Hence, this paper aims in proposing a real time design pattern using MapReduce based strategy visit and factory based design patterns for Distributed Service Oriented Architecture (DSOA). In addition, along with the merge of component based programming model to introduce dynamic service injection of SOA and to develop adaptive scalable distributed systems. By this proposed model, the performance and interactions between the end users are most highly appreciable and sophisticated. Moreover, this system achieves an expected real time distributed environment for Service Oriented Architecture.
Study on Copy-Move Forgery Detection in Medical Images using Handcrafted Features

Abstract: Medical image analysis and diagnosis are significantly hampered by digital picture alteration and fabrication. Copymove forgery, which involves copying a section of a picture and pasting it somewhere else in the same image to generate a fake copy, can result in incorrect diagnoses and even damage patients. Using handmade characteristics, we suggest a reliable technique in this study for identifying copy-move forgery in medical photos. Our method uses sophisticated feature extraction methods and image processing techniques to find duplicated portions within a picture. The integrity of medical image analysis is improved by experimental findings showing the efficacy of our suggested strategy in spotting copy-move forgery in medical pictures.
Mere Data Makes a Man: Artificial Intelligences in Blade Runner 2049

This chapter explores how the representation of artificial intelligences in Denis Villeneuve’s <italic>Blade Runner 2049</italic> (2017) proposes a wider discourse around notions of identity, memory, and the reformulation of the human self and subjectivity. The author traces the links between Villeneuve’s deployment of AI as a biocapitalist venture and our own consumerist, technocratic world. The film’s representation of the Replicant protagonist’s subjectivity and consciousness encourages the spectator to question the roles of technology, capitalism and neoliberalism in contemporary society.
Plastics: What Are They Good For?

In order to understand the relationships individuals have with plastics today, it is useful to reckon how people related to those materials in the past. Thus, the author traces the history of consumption to the 1950s, when the post–World War II society was encouraged to consume products to facilitate economic growth and to improve their lives more generally. Disposability, the notion of using something once and then throwing it away, grew to become a sign of wealth and cleanliness as consumers were encouraged to use disposable products. This has led to certain new meanings of plastic: the popular understanding that plastics are low value and therefore disposable, Dennis argues, has been built up over a history of misuse of long-lived materials as short-lived products. The author examines the value placed on plastics through an exploration of their uses and misuses, their consumption and, significantly, their conspicuous non-consumption, and how consumers relate to products made from them at the end of their useful life.
Bidirectional Transformers as a Means of Efficient Building of Knowledge Bases: A Case Study with XLM-RoBERTa

Knowledge bases are an essential component of various natural language processing tasks, providing structured information and facilitating better comprehension of unstructured data. Manually constructing knowledge bases is a time-consuming and labor-intensive process. This paper explores the use of bidirectional transformer models, specifically XLM-RoBERTa, to efficiently build knowledge bases. We focus on the extraction of concepts and relations to construct ontologies. Our approach demonstrates significant advantages over traditional methods, as well as some limitations. The study is carried out using Russian language books as a source for building the knowledge base.
OPERATIONAL INFORMATION SYSTEM DESIGN AT SIGAR JAYA FOTOCOPY

UMKM Sigar Jaya Fotocopy is one of the services in the form of photocopying which was founded in 2020 until now located in the Medan Sunggal sub-district area. Sigar Jaya Fotocopy not only serves photocopies but can print documents and photos and sell other office stationery. In the sales process, the owner needs help handling sales data recap, and in the purchasing process, the owner needs help checking the selling price of goods in large quantities. In the inventory process, the owner still uses manual checking where there can be a discrepancy in the available inventory. The System Development Life Cycle (SDLC) is the Information System Design method. The input design uses Visual Studio 2015
DEVELOPMENT OF STREAMLIT-BASED HIGHER EDUCATION RANKING INSTRUMENT BOARDS

The dashboard is a tool used to measure and evaluate comparisons of tertiary institutions based on six main aspects, namely teaching, research, industry income, international outlook, and citation scores. (citation score). This research aims to develop an interactive and easy-to-use college ranking dashboard using the Python language. The data obtained will be taken from a website using the scrapping method, and then the data will be combined annually. The method applied in this study uses data life cycle management with essential stages, including collection, processing, analysis, visualization, and data presentation. The results of this study are expected to be able to create a website that can be used by various parties, including prospective students, parents, and researchers who plan to conduct further research. This dashboard lets users obtain relevant information and compare multiple tertiary institutions effectively and efficiently.
ANALYSIS OF LINEAR REGRESSION AND TREND MOMENT METHODS IN PREDICTING SALES USING MAPE

ABSTRACK- Sales transaction data stored in the database stores a large number of transaction records, causing the amount of data to continue to increase every day. To explore sales transaction data, data mining techniques are used. One of the goals of data mining is prediction. Prediction is basically an assumption or estimate about the occurrence of an event or event in the future. Through prediction, it is expected to minimize the influence of uncertainty from the future, so that getting results that have the least prediction error is the goal of prediction. This shows that prediction is a very important tool in planning effectively and efficiently. The discussion method used to predict sales is the time series method by using a comparison of two types of prediction methods, namely the Linear Regression method and the Trend Moment method. The use of these two methods will be a better basis for making decisions to determine which method is suitable for predicting future sales. The result of a prediction cannot always be verified in absolute 100%. Therefore, the parameters used to determine the better method are based on the smallest error accuracy rate calculated using MAPE. Based on the results of the comparative prediction analysis of the Linear Regression method and the Trend Moment method, the recommended prediction result is to use the Trend Momnet method because the resulting MAPE error value is smaller, namely 0.439845%. Meanwhile, the MAPE error value with the Linear Regression method is 1.511509%..
Comparative Review of Object Detection Algorithms in Small Single-Board Computers

Object detection is a crucial task in computer vision with a wide range of applications. However, deploying object detection algorithms on small single-board computers (SBCs) poses unique challenges. In this review article, we present an in-depth comparative analysis of object detection algorithms tailored for small SBCs. We have conducted an extensive literature review on existing research in object detection algorithms and evaluated the performance of different approaches on benchmark datasets. Our review encompasses cutting-edge deep learning methods, which are YOLO, SSD, and Faster R-CNN. We delve into the challenges and limitations of implementing these algorithms on small SBCs and offer recommendations for optimizing their performance in such environments. Our analysis aims to shed light on the strengths and weaknesses of various object detection algorithms for small SBCs, ultimately guiding practitioners in making informed decisions and identifying potential avenues for future research in this domain.
Survey on Hinglish to English Translation and Classification Techniques

Code-mixing is the process of using many languages in one sentence and has a widespread occurrence in multilingual communities. It is particularly prevalent in texts on social media. Due to the widespread usage of social networking sites, a substantial amount of unstructured text is produced. Hinglish, i.e. code-mixed Hindi and English, is a frequent occurrence in everyday language use in India. Hence, a translation process is required to help monolingual users and to aid in the comprehension of language processing models. In this paper, we study the effective techniques for classification and translation tasks and also find gaps and challenges in the current research domain. After comparing a few existing methodologies for machine translation, a framework which showed an improvement in task of translation over the previous methods is proposed.
PROTOTYPE OF AIR PURIFIER TO REDUCE AIR CONTAMINANTS AND ELIMINATE INSECTS BASED ON ESP32 AT UNIVERSITAS HARAPAN MEDAN

Lifestyles have undergone significant changes; people spend more time indoors, and poor air quality occurs not only outdoors but also potentially indoors. According to the EPA, indoor pollution is in the third position of environmental factors affecting human health. in order to meet the need for cleaner air than before, the existing air filtration system is an important thing to pay attention to. Handling the filtration method to deal with pollutants will be a long-term requirement. The problem studied in this study is how to build an air filtration system in a room that can work efficiently without having to replace the filter media such as a HEPA filter, the design of this tool does not use a HEPA filter in general but uses dew dispersion from a mist maker, this tool is designed to be able to capture air particles and combine them with particles of air. This tool is designed to be able to display the value of CO2 conductivity, temperature, and humidity through an application on an Android device and an LCD on an air purifier device.
A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance

In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for concurrently detecting and classifying several faults using a multilabel classification approach based on binary relevance classifiers. The proposed binary relevance multilabel detection and classification models’ performances are examined. Under both ideal and problematic circumstances, faults in the dataset are collected. A variety of multilabel fault types detection and classification determines the suggested method’s effectiveness.
Laptop Price Prediction with Machine Learning Using Regression Algorithm

Since the COVID-19 pandemic, many activities are now carried out in a Work From Home (WFH) manner. According to data from the Central Statistics Agency (BPS) of East Java, in 2021, large and medium-sized enterprises (UMB) who choose to work WFH partially are 32.37%, and overall WFH is 2.24% (BPS East Java, 2021 ). With this percentage of 32.37%, many people need a work device (in this case, a laptop) that can boost their productivity during WFH. WFH players must have laptops with specifications that match their needs to encourage productivity. To prevent buying laptops at overpriced prices, a way to predict laptop prices is needed based on the specified specifications. This study presents a Machine Learning model from data acquisition (Data Acquisition), Data Cleaning, and Feature Engineering for the Pre-Processing, Exploratory Data Analysis stages to modeling based on regression algorithms. After the model is made, the highest accuracy result is 92.77%, namely the XGBoost algorithm. With this high accuracy value, the model created can predict laptop prices with a minimum accuracy above 80%.
Comparative Analysis of Indonesian Text Mining News Online Classification Using the K-Nearest Neighbor and Random Forest Algorithm

The rapid development of internet technology today makes many news media grow pretty rapidly. Newspaper companies have utilized internet technology to spread the latest news online through online mass media. Hundreds of thousands of stories are written and published daily on online-based Indonesian news portals, making it difficult for readers to find the news topics they want to read. In making it easier for readers to find the news they are looking for, news needs to be classified according to its respective categories, such as education, current news, finance, and sports. So to classify categories, a text classification method is needed or often called Text Mining. Text mining is a data mining classification technique for processing text using a computer to produce helpful text analysis. In this study, a comparison of 2 methods for developing texts was carried out to get accuracy above 80%.
Risk Mitigation and Monitoring Challenges in Software Organizations: A Morphological Analysis

Context: The present body of research on risk mitigation focuses mostly on sparse and dispersed research using various approaches during project development.&#x0D; Objectives: This paper's goals are to conceptualize and construct a morphological analysis (MA) framework, examine the literature that has already been published on these risks and how to mitigate them in software development, and identify research gaps that suggest areas for further study.&#x0D; Methods: Based on a survey of 102 works, we provide an MA framework. The MA framework, which is composed of five dimensions and 17 variants, identifies 33 distinct research gaps as the maximum number of potential areas for future study. The implications for future research are covered in the paper's conclusion. Risk reduction during the project development process is extremely beneficial for project managers at an organization in delivering high-quality goods on time and within budget.&#x0D; Results: Based on a review of the literature, it was discovered that the development of software projects used a very low percentage of official risk mitigation and elimination methods. Research also revealed the significance of people-related risks in the risk mitigation process for the benefit of the organization. Some key research demonstrated experimentation, case studies, and empirical approaches for evaluation. Future work may include examining project managers' perceptions and knowledge of risk management. Managers of software development teams require greater risk-mitigation strategies.&#x0D; Conclusion: Scholars and researchers need to put more effort into developing quantitative and intelligent risk models. It is crucial to identify major risks so that they won't compromise the project's future success. In our future work, we can employ more models that are helpful for combining quantitative and intelligent risk models using various AI strategies to reduce hazards.
A Study of Techniques and Challenges in Text Recognition Systems

The core system for Natural Language Processing (NLP) and digitalization is Text Recognition. These systems are critical in bridging the gaps in digitization produced by non-editable documents, as well as contributing to finance, health care, machine translation, digital libraries, and a variety of other fields. In addition, as a result of the pandemic, the amount of digital information in the education sector has increased, necessitating the deployment of text recognition systems to deal with it. Text Recognition systems worked on three different categories of text: (a) Machine Printed, (b) Offline Handwritten, and (c) Online Handwritten Texts. The major goal of this research is to examine the process of typewritten text recognition systems. The availability of historical documents and other traditional materials in many types of texts is another major challenge for convergence. Despite the fact that this research examines a variety of languages, the Gurmukhi language receives the most focus. This paper shows an analysis of all prior text recognition algorithms for the Gurmukhi language. In addition, work on degraded texts in various languages is evaluated based on accuracy and F-measure.
Harnessing Deep Learning Techniques for Text Clustering and Document Categorization

This research paper delves into the realm of deep text clustering algorithms with the aim of enhancing the accuracy of document classification. In recent years, the fusion of deep learning techniques and text clustering has shown promise in extracting meaningful patterns and representations from textual data. This paper provides an in-depth exploration of various deep text clustering methodologies, assessing their efficacy in improving document classification accuracy. Delving into the core of deep text clustering, the paper investigates various feature representation techniques, ranging from conventional word embeddings to contextual embeddings furnished by BERT and GPT models.By critically reviewing and comparing these algorithms, we shed light on their strengths, limitations, and potential applications. Through this comprehensive study, we offer insights into the evolving landscape of document analysis and classification, driven by the power of deep text clustering algorithms.Through an original synthesis of existing literature, this research serves as a beacon for researchers and practitioners in harnessing the prowess of deep learning to enhance the accuracy of document classification endeavors.