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Statistical analysis and data visualization of Indonesia and Malaysia SARS Cov-2 metadata

AIP Conference Proceedings 2023年第1期2594卷

作者： D. Sudigyo A. Budiarto B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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The Design of Smart Cashless Transaction 2020

The Design of Smart Cashless Transaction

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5th International Conference on Intelligent Information Technology, ICIIT 2020

作者： Fernando, Anthony Bryan Shreevasta Desprianto, Ferry Gaol, Ford Lumban Oktavia, Tanty Mobile Application and Technology-School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department-School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450376594

Cashless Transaction describes as a state where financial transactions are not bound by physical form such as banknotes or coins, but rather trough transfer of digital information (electronic representation of money). Cashless Transaction exists because of fast majorities of human transaction evolved towards digital environment, this creates an opportunity where legal money exists, recorded, and exchanged in digital form. Thus cashless transaction will make less effort in currency operation cost, keep on track with expenses, connected anytime everywhere as long as connected with internet, and eliminates the needs of banknotes or receipt. Cashless Transaction have done over the past few years making life easier by reducing a number of objects to merge into 1 functional object that could make payments faster and simpler starting with check the first mark of cashless transaction that's still being used nowadays, but cashless transaction doesn't stop evolving in this modern era where money is demonetized, cashless transaction is moving into the digital grid resulting a breakthrough mobile wallets and online transfer that offers people flexibility because its technology advancement makes it possible to make payments anytime and anywhere since it's only depend on a secure network(internet) and a platform, therefore cashless transaction could be done as fast as possible saving precious time. Cashless transaction has some of key components such as security, UX, Functionality, Performance and Data Integrity. Five key components could make cashless transaction become reality but also poses a different threat to cashless transaction. Hacking is one of them. Hacking could be done by anyone that have the ability to compromise the transmission, the transmission needs to have more data protection thus the cashless transaction could be done safely and free of risk. © 2020 ACM.

关键词： Personal computing

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Blockchain Applied In Decentralization of Ground Stations To Educational Nanosatellites

Blockchain Applied In Decentralization of Ground Stations To...

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Latin American Test Workshop, LATW

作者： Edilson Filho Jarbas Silveira César Marcon Department of Teleinformatics Engineering (DETI) Federal University of Ceara (UFC) Fortaleza Brazil Graduate Program in Computer Science (PPGCC) Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil

Space has increasingly attracted the attention of governments, large industries, and universities. One of the most popular strategies in recent years has been the adoption of nanosatellites to fulfill different missions, which can work alone or in constellations. Universities stand out among the agents launching nanosatellites, with more than 600 launches until 2022. Given the growth of entities that control space missions, it is necessary to implement new methods for communication between control and satellite to accelerate data transmission and provide a high-security degree. Our work proposes a consortium archi-tecture between Ground Stations (GSs) so that a GS as a Service (GSaaS) works with low cost, reliability, and resource sharing. We simulated a nanosatellite mission in Low Earth Orbit (LEO) with MATLAB to obtain the parameters of average communication time, propagation loss, and at which angles the communication would be most affected by atmospheric phenomena. Then, we implement business rules for communication between GS and satellites using smart contract concepts. We set up a blockchain to provide the decentralization infrastructure and created a web service to provide a communication API between nanosatellite and blockchain. We simulated the firmware update process, showing that the nanosatellite took around 20 minutes to request all 32-byte fragments of 301 Kb firmware. Considering the time interval that the communication window between GS and nanosatellite remains active, the entire firmware transmission takes two to three communication slots. However, the transmission time is drastically reduced in a scenario with two or more GSs. Furthermore, the GSaaS decentralized infrastructure allows the consortium of GSs to communicate agnostically with the satellites, preserving firmware privacy due to the cryptography used in blockchain transactions

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Accelerating free5GC Data Plane Using programmable Hardware

Accelerating free5GC Data Plane Using Programmable Hardware

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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Muthuraman Elangovan Muhammad Shahid Iqbal Chien Chen Jyh-Cheng Chen EECS International Graduate Program National Yang Ming Chiao Tung University Taiwan Department of Computer Science National Yang Ming Chiao Tung University Taiwan Saviah Technologies Inc. Taiwan

The demands of today’s 5G mobile network, especially low latency and high bandwidth, are a big challenge for the 5G Core (5GC) provider. The most critical user data packet handler in the 5GC Network Function (NF) is the User Plane Function (UPF), which is responsible for moving data from the user equipment to the destination data network, and vice versa. Existing work mainly focuses on implementing UPF using the key technologies of high-speed data processing. In this paper, with a mobile core provider called free5GC for a stand-alone (SA) 5G network, we share our experience with the implementation of UPF by using a programmable hardware appliance, which can offer more Tbps compared to the implementation of software UPF that can offer only a few hundred Gbps. For that, we demonstrate how to build up a more flexible architecture of UPF by using the Software-Defined Networking (SDN) concept due to the opacity of protocol specification. We split the UPF control signal implementation into a software application, and user data packet processing into a programmable hardware appliance. We also show how to integrate a number of current UPF data plane free5GC implementations such as Data Plane Development Kit (DPDK), Linux kernel module, and SmartNIC. Furthermore, we analyze and make use of microservices to support the specific features of the UPF data plane that cannot be implemented in a programmable hardware appliance. We tested our free5GC mobile network and the new UPF design architecture that can run on a real programmable hardware appliance from Accton CSP-7551. The evaluation results show that our programmable user plane can reach the line rate.

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Towards Adversarial Robustness for Multi-Mode Data through Metric Learning

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Sensors 2023年第13期23卷 6173页

作者： Khan, Sarwar Chen, Jun-Cheng Liao, Wen-Hung Chen, Chu-Song Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Social Networks Human-Centered Computing Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106319 Taiwan

Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial training, which is currently one of the most effective defense methods, mainly focus on the single-mode setting and thus fail to capture the full data representation to defend against adversarial attacks. To confront this challenge, we propose a novel multi-prototype metric learning regularization for adversarial training which can effectively enhance the defense capability of adversarial training by preventing the latent representation of the adversarial example changing a lot from its clean one. With extensive experiments on CIFAR10, CIFAR100, MNIST, and Tiny ImageNet, the evaluation results show the proposed method improves the performance of different state-of-the-art adversarial training methods without additional computational cost. Furthermore, besides Tiny ImageNet, in the multi-prototype CIFAR10 and CIFAR100 where we reorganize the whole datasets of CIFAR10 and CIFAR100 into two and ten classes, respectively, the proposed method outperforms the state-of-the-art approach by 2.22% and 1.65%, respectively. Furthermore, the proposed multi-prototype method also outperforms its single-prototype version and other commonly used deep metric learning approaches as regularization for adversarial training and thus further demonstrates its effectiveness. © 2023 by the authors.

关键词： Deep neural networks

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Social Network Analysis Algorithms, Techniques and Methods

Social Network Analysis Algorithms, Techniques and Methods

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Lidia Sandra Agung Trisetyarso Arief Ramadhan Edi Abdurachnan Ford Lumbangaol Sani Muhamad Isa Psychology Department Krida Wacana Christian University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Trasportation and Logistic Trisakti University Jakarta Indonesia

ISBN: (纸本)9781665499705

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings.

关键词： Support vector machines Machine learning algorithms Systematics Mechatronics Social networking (online) Bibliographies Regression tree analysis

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Voice-Based Virtual-Controlled Intelligent Personal Assistants

Voice-Based Virtual-Controlled Intelligent Personal Assistan...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Satya Prakash Yadav Amit Gupta Caio Dos Santos Nascimento Victor Hugo C. de Albuquerque Mahaveer Singh Naruka Sansar Singh Chauhan Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) GreaterNoida U.P. India Graduate Program in Telecommunications Engineering. (PPGET) Federal Institute of Education Science and Technology of Ceará (IFCE) Fortaleza-CE Brazil Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Teleinformatics Engineering Federal University of Ceará Fortaleza Fortaleza/CE Brazil Computer Science & Engineering GL Bajaj Institute of Technology & Management Greater Noida India

Virtual assistants are improving and providing consumers with greater advantages. The comprehension and fulfilment of requests by virtual assistants will increase as voice recognition and natural language processing continue to grow. Virtual assistants are projected to be employed in more commercial activities as speech recognition technology advances. The main goal of developing personal assistant software (virtual assistant) is to use web-based semantic data sources, user-generated content, and knowledge from knowledge libraries. Basically, main objective of making this Voice-Based Virtual Assistant is to make life easier and having a personal assistant to everyone which can perform many tasks. As the end user interacts with a virtual assistant, the AI programming learns from the data provided and improves its ability to forecast the end user's needs. Virtual assistants are often used to do things like add tasks to a calendar, provide information that would normally be found in a website, and operate and monitor Smart Home devices like lighting and cameras and thermostats. Massive volumes of data are required to fuel virtual assistant technologies, which feed Artificial Intelligence (AI) platforms such as machine learning, natural language processing, and speech recognition. Speech recognition has a lengthy history and has seen several key advancements. On smartphones and wearable devices, speech recognition for dictation, search, and voice commands has become a standard feature. Design of a small, large vocabulary speech recognition system that can run quickly, accurately, and with minimum latency on mobile devices.

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Vehicle Occurrence-Based Parking Space Detection

Vehicle Occurrence-Based Parking Space Detection

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IEEE International Conference on Systems, Man and Cybernetics

作者： Paulo R. Lisboa de Almeida Jeovane Honório Alves Luiz S. Oliveira Andre Gustavo Hochuli João V. Fröhlich Rodrigo A. Krauel Department of Informatics Federal University of Paraná (UFPR) Curitiba Brazil Graduate Program in Informatics Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil

Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60% and AP50 score up to 79.90%.

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Deep and shallow data science for multi-scale optical neuroscience

arXiv

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arXiv 2024年

作者： Mishne, Gal Charles, Adam Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering the Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit, challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solutions to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination. © 2024, CC BY.

关键词： Neuroimaging

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Next Generation Firewall for Improving Security in Company and IoT Network

Next Generation Firewall for Improving Security in Company a...

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2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019

作者： Soewito, Benfano Andhika, Charlie Erwin Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia

ISBN: (纸本)9781728137490

The goal of this research is to analyze of effectiveness the Next Generation Firewall that implemented to secure IoT in smart house and company network. The method used in this research is the method of comparison with a test of DDoS attacks, phishing and SQL Injection on both network, smart house network and company network. From the results of experiment, it can be concluded that the Next Generation Firewall has significantly better performance for protecting smart house and company network, it can increase security of data communication networks against threats from the Internet. © 2019 IEEE.

关键词： Network security

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