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1st International Conference on Innovation and Application of Science and Technology, ICIASTECH 2019

作者： Lia Hananto, April Priyatna, Bayu Fauzi, Ahmad Yuniar Rahman, Aviv Pangestika, Yulia Tukino Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Faculty of Technology and Computer Science Universitas Buana Perjuangan Karawang Indonesia Department of Informatics Engineering Universitas Widyagama Malang Indonesia Faculty of Postgraduate Management Computer Indonesia of University Indonesia

The award is given by the company to the best employees always to provide the best performance for companies that carry out duties and obligations in the company. How to give rewards to employees not only for how long they have been working at the company but about the results achieved. The gift can be in the form of salary or inventory from the company. So employees have great loyalty to the company. Decision making to determine the company can do the best employee by assessing the performance that has been done by employees at a specific time. Performance evaluation of Hon Chuan Company is influenced by several criteria, namely discipline, innovation, and responsibility. The support system for the best employee selection method uses the Analytical Hierarchy Process (AHP) method. The decision-making system is carried out to assess choices based on predetermined criteria. AHP method is applied to choose the best prospective employees. The calculation of the AHP method gives the results of the order of importance of criteria, namely discipline level (0.5584), responsibility (0.3196), and innovation (0.1220) and the best employee selection results obtained by Hendra (0.189), Vienna (0.189), Yin (0.145), Paul (0.109), Kandi (0.108), Kusworo (0.101), Bayu (0.088), and Ade Muhidin (0.072). © Published under licence by IOP Publishing Ltd.

关键词： Decision making

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Development of sandbox components with microservices architecture and design patterns in games

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Procedia computer Science 2022年 197卷 354-361页

作者： Andhik Ampuh Yunanto Fadilah Fahrul Hardiansyah Adhiemas Andira Anantha Putra Maulidan Bagus Afridian Rasyid Siska Arifiani Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Keputih Sukolilo Surabaya East Java 60111 Indonesia PT Maulidan Teknologi Kreatif Klampis Ngasem Sukolilo Surabaya 60117 Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Keputih Sukolilo Surabaya East Java 60111 Indonesia

Nowadays, making games is more complicated than typical applications. Based on the survey, most of the architecture applied by the development industry still does not have a pattern. Therefore the game becomes difficult to develop. This study aims to create a sandbox by applying a microservice and design patterns in the game. The development of this research also involves the gaming industry to increase credibility. The results show that the platform developed has run smoothly and received good feedback from users. Therefore, the output of this research can be useful for developers in making games easy and efficient.

关键词： Sandbox components microservices architecture design pattern unity game

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Improving Smart Healthcare Safety and Security Using Kinect 1st

Improving Smart Healthcare Safety and Security Using Kinect

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1st International Conference on Cybersecurity in Emerging Digital Era, ICCEDE 2020

作者： Singh, Vijai Saxena, Neetesh Pathak, Drashti Saini, Garima Bhatnagar, Divya Department of CSE G.L. Bajaj Institute of Technology and Management Gr Noida India School of Computer Science and Informatics Cardiff University Wales United Kingdom Department of CSE IMS Engineering College Ghaziabad India

ISBN: (纸本)9783030848415

Modern healthcare operating rooms are cumbersome, especially in the current pandemic of Coronavirus (COVID-19) to manage the safety and security of the patients and the staff. Conditions in a modern operating room are like, a patient’s medical diagnosis or x-ray or the MRI images are the vital resources to be considered. In general, the doctors are impelled to scrub out their hands every time they scroll through or examine the images in mid-operation. To avoid departing the operating table, several doctors put their faith in assistants or nurses and ask to examine the system that can be clumsy and disappointing and can rise the gathering in the room itself. To overcome this problem and emphasizing the use of the touchless smart healthcare system in medical diagnosis we have developed an application for the same. We propose and demonstrate a gesture and speech-based system to help staff to deal with such critical conditions. The proposed system is composed of two main parts: the healthcare operating software and sensor Microsoft Kinect. Test results conclude the efficiency of the viewing system in terms of time complexity, and the value and the safety of the recording system have increased. © 2021, Springer Nature Switzerland AG.

关键词： Diagnosis

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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False Data Injection Attacks Against High Voltage Transmission Systems

False Data Injection Attacks Against High Voltage Transmissi...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Panagiotis Radoglou-Grammatikis Magda Zafeiropoulou Maria Atanasova Pencho Zlatev Sofia Giannakidou Thomas Lagkas Vasileios Argyriou Evangelos K. Markakis Ioannis Moscholios Panagiotis Sarigiannidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Innovative Energy and Information Technologies LTD (IEIT BIC-IZOT Sofia Bulgaria K3Y Ltd Sofia Bulgaria Department of Computer Science International Hellenic University Kavala Campus Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Electrical and Computer Engineering Hellenic Mediterranean University Crete Greece Department of Informatics & Telecommunications University of Peloponnese Tripolis Greece

The digitisation of the smart electrical grid provides several advantages and valuable services, such as self-monitoring, pervasive control and smart healing. However, despite the benefits of this progression, critical cybersecurity and privacy issues are raised due to the vulnerabilities of legacy Electrical Power and Energy Systems (EPES) and the evolution of stealthy cyberthreats and malware. In this paper, we give emphasis to False Data Injection Attacks (FDIAs) that can affect the EPES State Estimation (SE). In particular, we investigate two FDIA categories, namely: (a) Global Positioning System (GPS) Spoofing Attacks and (b) IEEE C37.118 FDIAs against an actual testbed emulating a high-voltage IEEE 9-Bus transmission grid. Finally, we provide a relevant Intrusion Detection System (IDS) capable of detecting the aforementioned FDIAs. The evaluation analysis demonstrates the impact of the above FDIAs and the efficiency of the proposed IDS.

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Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

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Precision Targeting of Non-Small Cell Lung Cancer: Identifying Optimal Drug Targets and FDA-Approved Combinations for Enhanced Therapeutic Efficacy

Precision Targeting of Non-Small Cell Lung Cancer: Identifyi...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Pranabesh Bhattacharjee Aditya Lahiri Norman Peter Reeves Aniruddha Datta Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Biomedical and Health Informatics The Children's Hospital of Philadelphia Philadelphia PA USA Sumaq Life LLC East Lansing MI USA

This study proposes a Boolean network model to identify optimal drug targets and select the most effective FDA-approved drug combinations for Non-Small Cell Lung Cancer (NSCLC). The Boolean network models the signaling pathways in NSCLC to capture the intricate molecular interactions driving tumor progression. We evaluate the model by employing the size difference (SD) score, which reflects the degree of cell dysregulation due to gene mutations and allows us to identify optimal drug targets in NSCLC cells to address this dysregulation. Specifically, leveraging the FDA-approved drug database, we identified the robust drug or drug combination for 1, 2, and 3 mutations that maximize tumor cell death and minimize cell proliferation for NSCLC-associated gene mutations. Our findings provide a strong foundation for personalized therapeutic strategies and hold promise for advancing precision oncology to effectively combat NSCLC.

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Comb-calibrated Raman Spectroscopy of Molecular Hydrogen

Comb-calibrated Raman Spectroscopy of Molecular Hydrogen

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Lamperti, Marco Rutkowski, Lucile Ronchetti, Daniele Gatti, Davide Gotti, Riccardo Cerullo, Giulio Thibault, Franck Jóźwiak, Hubert Wójtewicz, Szymon Maslowski, Piotr Wcislo, Piotr Polli, Dario Marangoni, Marco Dipartimento di Fisica Politecnico di Milano IFN-CNR Via Gaetano Previati 1/C Lecco23900 Italy Department of Science and High Technology University of Insubria Como22100 Italy UMR 6251 RennesF-35000 France Department of Electrical Computer and Biomedical Engineering Università degli studi di Pavia Pavia27100 Italy Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Torun87-100 Poland

ISBN: (纸本)9798350345995

Molecular hydrogen and its isotopologues are key systems to test quantum electrodynamics (QED) at molecular length scales, as their energy levels can be calculated with high accuracy[1]. Comparison of the theoretical values with highly accurate experimental determinations of transition frequencies allows one to test QED and theories beyond the standard model of particle physics[2]. Recent accurate measurements of rovibrational frequencies of HD and D2, covering mostly the second overtone band, have shown a systematic deviation between measured and calculated transition frequencies. Yet, available measurements on H2 are characterized by uncertainties one order of magnitude larger than theory. © 2023 IEEE.

关键词： Uncertainty analysis

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Radiation-constrained algorithms for wireless energy transfer in ad hoc networks

arXiv

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arXiv 2021年

作者： Nikoletseas, Sotiris Raptis, Theofanis P. Raptopoulos, Christoforos Department of Computer Engineering and Informatics University of Patras Greece Computer Technology Institute and Press "Diophantus" Greece

We study the problem of efficiently charging a set of rechargeable nodes using a set of wireless chargers, under safety constraints on the electromagnetic radiation incurred. In particular, we define a new charging model that greatly differs from existing models in that it takes into account real technology restrictions of the chargers and nodes of the network, mainly regarding energy limitations. Our model also introduces non-linear constraints (in the time domain), that radically change the nature of the computational problems we consider. In this charging model, we present and study the Low Radiation Efficient Charging Problem (LREC), in which we wish to optimize the amount of "useful" energy transferred from chargers to nodes (under constraints on the maximum level of imposed radiation). We present several fundamental properties of this problem and provide indications of its hardness. Finally, we propose an iterative local improvement heuristic for LREC, which runs in polynomial time and we evaluate its performance via simulation. Our algorithm decouples the computation of the objective function from the computation of the maximum radiation and also does not depend on the exact formula used for the computation of the electromagnetic radiation in each point of the network, achieving good trade-offs between charging efficiency and radiation control;it also exhibits good energy balance properties. We provide extensive simulation results supporting our claims and theoretical results. © 2021, CC BY-NC-ND.

关键词： Ad hoc networks

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Password-Sniffing Acoustic Keylogger Using Machine Learning

SSRN

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SSRN 2023年

作者： Akinbi, Alex Deniz, Erkan Ismael, Aras M. Rashid, Zryan N. Sengur, Abdulkadir School of Computer Science and Mathematics Liverpool John Moores University United Kingdom Firat University Technology Faculty Electrical-Electronics Engineering Department Elazig Turkey Sulaimani Polytechnic University College of Informatics Information Technology Department Sulaymaniyah Iraq

An acoustic keylogger is a form of side-channel attack where an attacker can extract and log what a victim typed on a keyboard by capturing the keystroke emanations and audio signals. Using a compromised smartphone microphone to capture keystroke audio recordings, this attack can be used to recognise confidential information such as passwords typed by a victim. In this paper, we demonstrate the training of a machine-learning model and develop a proof-of-concept password-sniffing acoustic keylogger. To this end, the input audio signals are initially converted to audio images by using the spectrogram representation of the Mel-Frequency Cepstral Coefficients (MFCC) method. MFCC applies the Fourier transform to yield the frequency content of the input signal. Then, a novel and less complex vision transformer-based approach namely ConvMixer is applied to the spectrogram images to detect the password according to its audio. In ConvMixer model group convolution and point convolution layers are located sequence for giving depth to the architecture. A novel key logger dataset is constructed by us and used in the experimental works. Various experiments are carried out and classification accuracy is applied for performance measurement tools. In the experimental works, an average 92.44% accuracy score is obtained with the proposed method. In addition, two pre-trained convolutional neural networks (CNN) models namely ResNet18 and VGG16 are used for comparison purposes. Comparisons show that the proposed approach has the potential for a compact acoustic keylogger system. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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