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arXiv 2024年

作者： Andreeva, Daniela Savova, Guergana London Metropolitan University London United Kingdom Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School BostonMA United States

The Artificial Intelligence (AI) field experienced a renaissance in the last few years across various fields such as law, medicine, and finance. Firms have been devoting significant resources to developing and evaluating AI applications including the creation of internal AI Innovation Departments. While there are studies outlining the landscape of AI in the legal field as well as surveys of the current AI efforts of law firms, to our knowledge there has not been an investigation of the intersection of law students and AI. Such research is critical to help ensure current law students are positioned to fully exploit this technology as they embark on their legal careers but to also assist existing legal firms to better leverage their AI skillset both operationally and in helping to formulate future legal frameworks for regulating this technology across many industries. The study presented in this paper addresses this gap – the intersection of law students and AI. Through a survey of 27 questions conducted over the period of July 22 – Aug 19, 2024, the study covers the law students’ background, AI usage, AI applications in the legal field, AI regulations and open-ended comments to share opinions. The results from this study show the uniqueness of law students as a distinct cohort. The results differ from the ones of established law firms especially in AI engagement - established legal professionals are more engaged than law students. The law firm participants show much higher enthusiasm about AI than this student cohort - somewhat surprising as one would expect the younger generation to be more open to new technologies. Collaborations with Computer Science departments would further enhance the AI knowledge and experience of law students in AI technologies such as prompt engineering, chain-of-thought prompting, zero- and few-shot prompting, and language model hallucination management. As future work, we would like to expand the study to include more variables and a large

关键词： Students

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An Investigation of Ultrashort Pulses Propagation in Atmospheric Turbulence of FSO Communications 9

An Investigation of Ultrashort Pulses Propagation in Atmosph...

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9th International Conference on Electrical Engineering, Computer Science and informatics, EECSI 2022

作者： Darusalam, Ucuk Priambodo, Purnomo Sidi Universitas Nasional Informatics Study Program Universitas Siber Asia of IEEE Photonics Society Informatics Faculty of Information and Communications Technology Jakarta Indonesia Universitas Indonesia IEEE Photonics Society Faculty Engineering Department of Electrical Engineering Depok Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

The atmospheric turbulence as the natural media of optical propagation for the free-space optical (FSO) communications is the great advantage to reach flexible connection and low-cost deployment but also is the major problem as well. Since, the phenomena of optical propagation in the atmosphere are random process, an optical signal is induced that produces severe effects to the performance. Thus, a method to reach best performance of FSO must be developed intensively to reduce the severe effects of atmospheric turbulence. One of the solutions to reduce those aforementioned effects is an ultrashort pulse implemented in FSO to modulate frequency carrier as the signal of optical propagation. In this paper, an investigation of an ultrashort pulse was performed through simulation. An ultrashort pulse has a great chance to be implemented in FSO since it has shorter width of pulse duration than a short pulse. An ultrashort pulse is generated using optical second harmonic pulse generator that produce a pulse at 50 fs of width. Based on the results of simulation, the ultrashort pulses showing better performance of FSO than short pulses have. © 2022 Institute of Advanced Engineering and Science (IAES).

关键词： atmospheric turbulence free-space optical communications optical propagation optical scintillation temporal broadening turbulence effects ultrashort pulses

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Detection and Sub-Classification of Acute Lymphoblastic Leukemia Cell Types from the Microscopic Images Based on The Object Detection Model YOLOV5

Detection and Sub-Classification of Acute Lymphoblastic Leuk...

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Electrical Engineering (ICEENG), 12th International Conference on

作者： Donia M. Mustafa Perryhan E. Mohamed Maha E. Saed Gehad A. Elsady Shehab R. Abudaif Eman F. Sawires Faculty of Computers and Artificial Intelligence Medical Informatics Program Helwan University Cairo Egypt The Electronics and Communications Engineering Department Faculty of Engineering Helwan University Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

Acute lymphoblastic leukemia (ALL), a disease that is quite common, necessitates invasive, costly, and time consuming diagnostic procedures for a final diagnosis. The early classification of cancer cases from noncancerous samples plays a crucial role in ALL detection utilizing peripheral blood smear (PBS) images. This research highlights the successful application of the YOLOv5 object detection model in detecting ALL cells and classify it into four distinct types, achieving 97.8%,96%,96.7%, accuracy, precision, and recall respectively using an ALL image dataset after adding the bounding boxes manually. Moreover, a comparison is conducted among three popular convolution neural network (CNN) models, namely GoogleNet, ResNet, and AlexNet, alongside YOLOv5 using the ALL dataset. The results clearly demonstrate that YOLOv5 model outperforms the other CNN models, further confirming its superiority in ALL detection.

关键词： YOLO Electrical engineering Accuracy Convolution Microscopy Neural networks Benchmark testing

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Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach 7

Minimizing the Number of Stunting Prevalence Using the Eucli...

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7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023

作者： Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin Bina Nusantara University Information System Management Department BINUS Graduate Program - Master of Information System Management Jakarta11480 Indonesia STMIK Kaputama Informatics Engineering Binjai20714 Indonesia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan26600 Malaysia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350360752

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. © 2023 IEEE.

关键词： Classification (of information)

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Systematic Mapping Review on Log Preparation for Process Mining

Systematic Mapping Review on Log Preparation for Process Min...

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International Conference on Computer Supported Cooperative Work in Design

作者： Luiz Fernando Puttow Southier Sheila Cristiana de Freitas Adriano Pizzini Eduardo Alves Portela Santos Edson Emilio Scalabrin Graduate Program in Informatics (PPGIa) Pontifícia Universidade Catôlica do Paraná Curitiba Brazil CEPPAD - UFPR Curitiba Brazil

Process Mining (PM) is a research discipline that helps organizations track and optimize processes to support their business. Further, it focuses on providing process analysis techniques and tools, and several of its applications have been described in the literature. The start point for PM is using event logs generated by information systems to analyze processes. These event logs need to be extracted from databases and prepared for use because the quality of the event logs used as input is critical to the success of any PM effort. In this article, we present a systematic mapping review to provide the reader with highlights of the state-of-the-art techniques for event log preparation. Based on the retrieved studies, we identified six main categories of log preparation techniques: extraction, cleaning, repair, non-adequate granularity, quality evaluation, and privacy. The results are explored quantitatively and qualitatively. All results are made available through spreadsheets and charts. We believe this paper is a starting point for researchers to identify the studies that would help them prepare event logs for PM.

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Cultivating the Garden of Eden

arXiv

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arXiv 2022年

作者： Beer, Randall D. Cognitive Science Program Informatics Dept Luddy School of Informatics Computing and Engineering Indiana University United States Cognitive Science Program Indiana University 3052 Geology Building BloomingtonIN47405 United States

Garden of Eden (GOE) states in cellular automata are grid configurations which have no precursors, that is, they can only occur as initial conditions. Finding individual configurations that minimize or maximize some criterion of interest (e.g., grid size, density, etc.) has been a popular sport in recreational mathematics, but systematic studies of the set of GOEs for a cellular automaton have been rare. This paper presents the current results of an ongoing computational study of GOE configurations in Conway’s Game of Life (GoL) cellular automaton. Specifically, we describe the current status of a map of the layout of GOEs and nonGOEs in density/size space, characterize how the density-dependent structure of the number of precursors varies with increasing grid size as we approach the point where GOEs begin to occur, provide a catalog of all known GOE configurations up to a grid size of 11 × 11, and initiate a study of the structure of the network of constraints that characterize GOE vs. nonGOE configurations. © 2022, CC BY.

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Hybrid Encryption Based on Fernet and Rivest Shamir Adleman (RSA)

Hybrid Encryption Based on Fernet and Rivest Shamir Adleman ...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Muhammad Dafi Hisbullah Christy Atika Sari Eko Hari Rachmawanto Lekso Budi Handoko Chaerul Umam Folasade Isinkaye Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Ekiti State University Ekiti Nigeria

RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. This study uses Fernet and RSA which is the combination of symmetric and asymmetric encryption called hybrid encryption. In addition, the cipher text will be hidden inside an image using Stepic. Hybrid Encryption uses asymmetric encryption to encrypt the symmetric encryption secret key, it will secure the symmetric encryption. The result of this study is the lowest error that we got as the MSE is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche (AVA) with 79.00% and 42.34% in order. Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 46.48% for UACI and 99.86% for the NPCR.

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Optimizing Advantage Actor-Critic with Policy Gradient and Deep Q-learning to Maximize Profit in Forex Trading Prediction

Optimizing Advantage Actor-Critic with Policy Gradient and D...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Abdillah Baradja Rahmat Gernowo Adi Wibowo Doktoral Program of Information System Universitas Diponegoro Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia

Financial markets, such as forex markets, are inherently complex, with high volatility, noise, trends, and market shocks. Therefore, designing effective trading strategies is crucial for maximizing investment returns. The implementation of deep reinforcement learning (DRL) in financial trading has become an attractive research topic in recent years. This study proposes Advantage Actor-Critic-based algorithms (A2C) algorithms to learn forex trading strategies using deep reinforcement schemes. The ensemble trading strategy was obtained by combining the best features of the two algorithms' Policy Gradient as Actor and Q-learning as Critic, resulting in a robust strategy that adapts to different market situations. Our experiment on the EURUSD currency on the 4 hour candlestick timeframe using five years of data history was divided into three parts for training, validating, and testing. The A2C agent learns to buy, sell, and hold a trade with the goals of maximizing profits and reducing losses. Our proposed method provides an effective and robust solution to various financial trading problems using deep reinforcement learning. Our proposed methods provide effective and robust solutions to various financial trading problems using deep reinforcement learning. The evaluation results improved from each training, where the Cumulative Return evaluation increased from 29.65 to 112.74, the Maximum Drawdown reduced from 36.18% to 4.48%, the Sharp Ratio increased from 0.94 to 2.47%, and the AHPR increased from 0.19% to 3.56%. This indicates that longer training data result in higher cumulative rewards and an increase in total profit.

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Automatic quantitative and morphometric analysis of muscle fibers

Automatic quantitative and morphometric analysis of muscle f...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Sergio A. Da Silva Eduardo O. Santos Mateus F. T. Carvalho Franklin C. Flores Fernanda L. A. Almeida Cecília E. M. Costa Yandre M. G. Costa Department of Informatics State University of Maringá Maringá Brazil Graduate Program in Physiological Sciences State Univerity of Maringá Maringá Brazil Graduate Program in Biological Sciences State University of Maringá Maringá Brazil

In this work, we propose an automated solution to support researchers of histological sciences labs on the analysis of muscle fibers microscopic images. For this purpose, we developed a dataset carefully curated composed of semi-seriated muscle samples. The manual annotation of these images is laborious and tiring, and the results may become compromised as the human gets tired. Thus, we propose a framework to address this problem which starts with a preprocessing step, aiming to mitigate discrepancies regarding the color variation frequently found in this type of image, followed by the use of Mask R-CNN, to automatically perform muscle fibers segmentation and also to extract the smallest diameter of these fibers, which is an important measure for further analysis. Experiments showed that there is no statistically significant difference between the results obtained manually and those obtained using our proposal. The dataset used in the experiments is also a contribution of this work.

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Multi-Speaker Voice Cloning in Indonesian Using Sequence to Sequence Speech Synthesis

SSRN

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SSRN 2022年

作者： Awangga, Rolly Maulana Majesty, Dinda Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung40151 Indonesia

Multi-Speaker Voice Cloning is a system that can produce various voices based on voice samples and text input. Voice cloning can be an alternative to get audio data quickly without recording, which requires a lot of time, cost, and effort. Sequence to Sequence Speech Synthesis produces sounds that are naturally similar to speech equivalent to the human voice. Sequence to Sequence Speech Synthesis is a technology for generating speech directly from the text, consisting of attention-based predictive network features. Multi-Speaker Voice Clone consists of speaker encoder, encoder-decoder, and vocoder models. Tacotron-2 as model encoder-decoder and WaveNet as a vocoder. The speaker encoder will produce a speaker embedding to recognize the sound from the speaker. Tacotron-2 will embed text into the mel scale spectrogram. WaveNet will synthesize the time domain waveform from the mel scale spectrogram and generate sound predictions based on the samples. This model uses a multi-speaker voice dataset in Indonesian. Combining the three models produces a value of 3.28 for speech naturalness and 1.94 for speech similarity. © 2022, The Authors. All rights reserved.

关键词： Cloning

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