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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Pangestu, John Suryajaya Kho, Kelvin Suryani, Dewi Andika, Leo Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Many teachers still utilize exams to gauge their pupils' knowledge. However, as the teacher must comprehend each answer provided by each student individually, analyzing the exam response can take a long time. Additionally, different handwriting styles may lead to errors in scoring the response, resulting in an erroneous mark. This study suggests a web-based program that can read students' handwriting to assist teachers in assessing students' responses. The objective of this study was to develop a web-based application that can accept student responses as input and output correctness. © 2023 The Authors. Published by Elsevier B.V.

关键词： Exam Handwriting Recognition Recurrent Neural Network

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Image Pre-Processing Effect on OCR's Performance for Image Conversion to Braille Unicode 8

Image Pre-Processing Effect on OCR's Performance for Image C...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Ignasius, Andrean Chandra, Joerio Christo Oscadinata, Roberto Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The number of cases of visual impairment has been rising, particularly in Indonesia. Additionally, most disabilities in Indonesia are visual impairments, according to data from the Population Census. Blindness is one aspect of this condition that accounts for a sizable percentage of it. Considering this, we are making an effort to assist them by translating literature, which entails converting text included in images into braille Unicode. A braille embosser, a device that can print braille sheets for the blind to read, will then be used to print this translation into braille. In this study, we discuss the consequences of pre-processing while converting a text-containing image to braille Unicode. Here, we employ the pre-processing techniques of binarization, denoising, and skew correction. These techniques are used before using the OCR, and the results are documented in this study. Additionally, the outcomes of pre-processed and raw photos are contrasted to determine the efficacy of applying pre-processing to an image. © 2023 The Authors. Published by Elsevier B.V.

关键词： Binarization Denoising Optical Character Recognition (OCR) Skew Correction' Text to Braille conversion Visual Impairment

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Chess Piece Image Recognition Using Transfer Learning, Simple Neural Network, and Convolutional Neural Network 4

Chess Piece Image Recognition Using Transfer Learning, Simpl...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Yohanes, Gabriel Nursalim, Mario Nicholas Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Online chess has opened up a way for players to sharpen their skills through move analytics. Based on this feature, a support system called chess advisor can be utilized to assist players in a real-time match. However such system doesn't exist within the website itself, rather an involvement of 3rd party software is required. These kinds of software usually need assistance for input. This paper aims to create a system that is able to recognize chessboard state in real-time. We have proposed a chess piece image recognition system using transfer learning using ResNet50, a simple neural network, and a convolutional neural network (CNN). We collected our own 693 2D chess piece images dataset and performed data preprocessing to standardize the image dimensions. The proposed system detects and converts individual positions of each piece into a computer-readable output. This output can be used to facilitate the chess advisor in determining the best move. The transfer learning approach utilizing the ResNet50 model exhibited superior performance with an accuracy of 95.65%. This outperformed alternative methods such as the simple neural network and the CNN. The ResNet50 model's advantage stemmed from its pre-training on the ImageNet dataset, enabling effective pattern generalization. In contrast, the simple neural network and CNN achieved lower accuracies of 73.4% and 85%. These models encountered difficulties due to limited dataset quantity and quality, as well as the absence of regularization techniques. Nevertheless, the CNN displayed better consistency in generalizing patterns compared to the simple neural network. © 2023 IEEE.

关键词： Image recognition

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Building Serious Game Design to Prepare University Students for The World of Work 9

Building Serious Game Design to Prepare University Students ...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Steven Malik, Victor C. Harefa, Jeklin Alexander Chowanda, Andry School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350304084

Pandemic hits the world catastrophically, leading to the disruption on our everyday life including how students learn in the pandemic era. Most of the students nowadays have difficulties preparing the way to face the world of work. This research proposes a serious game design to prepare university students for the world of work by combining the Six Facets of Serious Game Design and Adam's Ernest Game Design. Six Facets of Serious Game Design is the design pattern for serious games (e.g., pedagogical games) [1]. Two systems (i.e. the serious game and the baseline system) were designed, deployed and evaluated to evaluate the effectiveness of the proposed serious game design. The serious game design was built on an Android mobile phone. Moreover, a simple simulation that has identical scenarios with the serious game was also built as the baseline in the evaluation process. Thirty-seven respondents who will be or have been entering the workplace for their first time participated in the evaluation process. A Wilcoxon rank test was applied to the data as the data does not come from a normal distribution. The results show that the proposed serious game design provides more effective results than the baseline. The user experiences' score provided by the serious game design system is statistically higher than the baseline system. Moreover, the serious game system increases the learners' motivation compared to the baseline system. © 2023 IEEE.

关键词： Game design

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Lower Envelopes of Surface Patches in 3-Space 32

Lower Envelopes of Surface Patches in 3-Space

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Agarwal, Pankaj K. Ezra, Esther Sharir, Micha Department of Computer Science Duke University DurhamNC United States Department of Computer Science Bar-Ilan University Ramat-Gan Israel School of Computer Science Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9783959773386

Let Σ be a collection of n surface patches, each being the graph of a partially defined semi-algebraic function of constant description complexity, and assume that any triple of them intersect in at most s = 2 points. We show that the complexity of the lower envelope of the surfaces in Σ is O(n2 log6+Ε n), for any Ε > 0. This almost settles a long-standing open problem posed by Halperin and Sharir [26], thirty years ago, who showed the nearly-optimal albeit weaker bound of O(n2 · 2c√log n) on the complexity of the lower envelope, where c > 0 is some constant. Our approach is fairly simple and is based on hierarchical cuttings and gradations, as well as a simple charging scheme. We extend our analysis to the case s > 2, under a "favorable cross section" assumption, in which case we show that the bound on the complexity of the lower envelope is O(n2 log11+Ε n), for any Ε > 0. Incorporating these bounds with the randomized incremental construction algorithms of Boissonnat and Dobrindt [16], we obtain efficient constructions of lower envelopes of surface patches with the above properties, whose overall expected running time is O(n2polylogn), as well as efficient data structures that support point location queries in their minimization diagrams in O(log2 n) expected time. © Pankaj K. Agarwal, Esther Ezra, and Micha Sharir;licensed under Creative Commons License CC-BY 4.0.

关键词： Describing functions

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

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Emotion Detection in Textual data using Deep Learning 8

Emotion Detection in Textual Data using Deep Learning

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Yohanes, Daniel Putra, Jessen Surya Filbert, Kenneth Suryaningrum, Kristien Margi Saputri, Hanis Amalia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Technological advancement is something we can no longer ignore, deep learning being one of those advancements. With deep learning, emotions can be extracted and analyzed in textual data to be utilized in various sectors, involving human behavior analysis. The main objective of this paper is to investigate and discover basic emotional categories that are typically exhibited by humans, such as sadness, joy, anger, fear, and disgust. Sectors focused on business can utilize emotion detection for various applications, such as to create personalized services, and in medical scenarios to help develop specialized mental treatments. This paper enables other researchers to enrich their knowledge and develop a more accurate and efficient deep learning model. To create an emotion detection model, analysis regarding the best performing model has to be done. This paper discusses the difference in performance across three different RNN models, namely LSTM, BiLSTM, and GRU. The RNN based models, in theory, share the same architecture and will be evaluated using the ISEAR dataset to create an emotion detection model. The result of this paper concluded that the simplest RNN model, GRU, achieved the highest score across four scoring metrics: accuracy, recall, precision, and F1 score. The GRU model achieved an accuracy of 60.26%, BiLSTM with 59.3%, and LSTM with 57.65%. Hence, based on the results, the GRU-based models are a strong choice for emotion detection when tested on the ISEAR dataset. © 2023 The Authors. Published by Elsevier B.V.

关键词： Deep Learning Emotion Detection Natural Language Processing Recurrent Neural Network Textual data

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Classification of Customer Satisfaction in Marketplace

Classification of Customer Satisfaction in Marketplace

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Emmanuela, Ester Pricilia Tjendra, Felicia Katarina Kezia, Stephanie Suryani, Dewi Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Customer satisfaction is one of the most important things in the marketplace to evaluate the customer's impression of the service in the marketplace. Customer satisfaction is measured in five aspects, i.e., the application user interface, application features, discounts and promotions, ease of access to the applications, and customer service. This paper used three classification algorithms in data mining: KNN, Decision Tree, and Naïve Bayes to classify customer satisfaction. Calculation of the algorithms performed in this paper using the Rapid Miner. There are two experiments on each algorithm using the RapidMiner application, but the method used to calculate data is undoubtedly different. There are 549 data taken from marketplace user surveys, namely Shopee, Tokopedia, and Lazada. The highest accuracy result obtained was 96.36%, with a precision of 96.97% and a recall of 98.96% of the classification using the Decision Tree algorithm through the second experiment. From these results, it can be inferred that Decision Tree is the most accurate algorithm in this paper because it has the highest accuracy when performing experiments. According to the results of this research, we desire to be an accurate reference source that can be used by marketplaces to obtain information on customer satisfaction and improve their service. © 2023 IEEE.

关键词： Decision trees

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Low-Overhead Packet Loss Diagnosis for Virtual Private Clouds using P4-programmable NICs 36

Low-Overhead Packet Loss Diagnosis for Virtual Private Cloud...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Aalibagi, Soroush Dolati, Mahdi Sadrhaghighi, Sogand Ghaderi, Majid University of Calgary Department of Computer Science Calgary Canada School of Computer Science Tehran Iran

ISBN: (纸本)9781665477161

Virtual private clouds have become a huge trend because of their cost-efficiency. However, the complex and virtualized nature of clouds limits the ability of cloud tenants to pinpoint and amend performance degradation problems, such as packet drops. Existing monitoring systems are either designed for the physical network or insufficient to present the concrete reason for packet loss with low overhead. In this paper, we present a Packet Loss Diagnosis (PLD) system, a specific monitoring service designed to detect packet drops on cloud networks and report diagnosis information to tenants. PLD is based on the modern capabilities of P4 data plane programmable NICs and has a limited footprint in the network. It provides detailed information that enables tenants to locate and resolve their issues with respect to the abstraction of the services. It also meets the requirements of a monitoring system designed for large-scale multi-tenant clouds. We implemented the proposed scheme in P4 to demonstrate its viability and investigate its performance and overhead through extensive experiments and Mininet simulation. Our results show that PLD ensures full packet drop detection coverage and can notify tenants in real-time while imposing low overhead. © 2023 IEEE.

关键词： Packet loss

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