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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Pedro Horchulhack Eduardo K. Viegas Altair O. Santin João A. Simioni Graduate Program in Computer Science Pontifical Catholic University of Parana (PUCPR) Brazil

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Machine learning (ML) techniques for network intrusion detection is still limited in production environments despite promising results reported in the literature. Network traffic behavior exhibits considerable variability and evolves over time, requiring periodic model updates. This paper proposes a new approach to intrusion detection modeling based on CNN and transfer learning to reduce updating overhead. Its implementation is twofold. First, CNN is implemented using flow-based feature expansion derived from neural flattened hyperdimensional space. This expanded space representation contributes to a longer model lifetime and maintains system accuracy over time. Second, the required training data and computational cost are significantly reduced by performing periodic model updates based on a transfer learning approach. Experiments on a novel dataset with over 2.6 TB of data and one year of real-world network traffic demonstrate the feasibility of the proposal. Our proposal improves the average F1 score by up to 0.19 when no model updates are performed. While improving the system’s accuracy, model updates impose only 42.8% of the computational cost.

关键词： Wireless communication Accuracy Computational modeling Transfer learning Training data Network intrusion detection Telecommunication traffic

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Localization and Perception for Control of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment

arXiv

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arXiv 2024年

作者： Wen, Bowen Graduate Program in Electrical and Computer Engineering The Ohio State University United States

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. Accurate localization is essential for self driving vehicle navigation in GPS inaccessible environments. This thesis concentrates on low speed autonomous shuttles that are mainly utilized for university campus intelligent transportation systems and presents initial results of ongoing work on developing solutions to the localization and perception challenges of a university planned pilot deployment orientated application. The paper treats autonomous driving with real time kinematics GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with three-dimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost and hence lower accuracy GPS is desirable. The in-house automated low speed electric vehicle from the Automated Driving Lab is used in experimental evaluation and verification. An improved version of Hector SLAM was implemented on ROS and compared with high resolution GPS aided localization framework in the same hardware architecture. The overall configuration that combines ROS with DSpace controller can be easily transplantable prototype in other hardware architectures for future similar research. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low speed autonomous shuttle localization and perception algorithms for control and decision making. © 2024, CC BY.

关键词： Global positioning system

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Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review 6

Exploring Recurrent Neural Network Models for Depression Det...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Rumahorbo, Brilyan Nathanael Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350345162

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD. © 2023 IEEE.

关键词： deep learning facial expression GRU LSTM major depressive disorder

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Dagger Linear Logic and Categorical Quantum Mechanics

arXiv

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arXiv 2023年

作者： Srinivasan, Priyaa Varshinee Graduate Program in Computer Science CalgaryAB Canada

This thesis develops the categorical proof theory for the non-compact multiplicative dagger (†) linear logic, and investigates its applications to Categorical Quantum Mechanics (CQM). The existing frameworks of CQM are categorical proof theories of compact †-linear logic, and are motivated by the interpretation of quantum systems in the category of finite dimensional Hilbert spaces. This thesis describes a new non-compact framework called Mixed Unitary Categories which can accommodate infinite dimensional systems, and develops models for the framework. To this end, it builds on linearly distributive categories, and ∗-autonomous categories which are categorical proof theories of (non-compact) multiplicative linear logic. The proof theory of non-compact †-linear logic is obtained from the basic setting of an LDC by adding a dagger functor satisfying appropriate coherences to give a †-LDC. From every (isomix) †-LDC one can extract a canonical "unitary core" which up to equivalence is the traditional CQM framework of †-monoidal categories. This leads to the framework of Mixed Unitary Categories (MUCs): every MUC contains a (compact) unitary core which is extended by a (non-compact) isomix †-LDC. Various models of MUCs based on Finiteness Spaces, Chu spaces, Hopf modules, etc., are developed in this thesis. This thesis also generalizes the key algebraic structures of CQM, such as observables, measurement, and complementarity, to MUC framework. Furthermore, using the MUC framework, this thesis establishes a connection between the complementary observables of quantum mechanics and the exponential modalities of linear logic. © 2023, CC BY.

关键词： Linear algebra

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Development of Machine Learning Models to Predict Strip Breakage During the Aluminium Cold Rolling Process

Development of Machine Learning Models to Predict Strip Brea...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tiago Ramos Abreu Alexandre Magno Andrade Maciel Graduate Program in Computer Engineering (PPGEC) University of Pernambuco (UPE) Recife Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The aluminium processing industry relies on cold rolling as a crucial method for producing materials essential to everyday life. As a consequence of thickness reduction: Strip Breakage, which is one of the main degraders in the aluminum rolling process, arises. In more recent related studies, data were mainly chosen by experts, which can sometimes hide crucial causal factors. Therefore, this work develops Machine Learning models to predict strip breakage, by investigating the relationship between features and the breakage event, using attribute selection techniques to reduce the dimensionality of the problem, comparing classification methods based on trees, such as Decision Trees (DT), Random Forest (RF) and Extra Trees (ET). This work uses the following methodology: first, the models were evaluated for the complete set of data with default initial parameters, then a new evaluation of the models was carried out with the optimized parameters still with high data dimensions, and finally, an evaluation was carried out of the models with the best-selected attributes. The model that performed best was the DT with 21 attributes, with a recall or TPR (True Positive Rate) of 0.863 and an AUC (Compute Area Under of ROC Curve) of 0.926. The models also indicated that there is no unique cause that characterizes the breaks in the aluminium cold mill, but several others, such as the effect of the rolling mill, coil history domain, oil mill and lamination cylinders.

关键词： Industries Radio frequency Performance evaluation Strips Computational modeling Oils Aluminum Predictive models Data models Random forests

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Accelerating free5GC Data Plane Using programmable Hardware 24

Accelerating free5GC Data Plane Using Programmable Hardware

引用

24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

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

ISBN: (纸本)9788995004395

– 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 standalone (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. Copyright 2023 KICS.

关键词： Wireless networks

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Developer-Centric Task Allocation: An Intelligent Approach for Enhanced Software Productivity 15

Developer-Centric Task Allocation: An Intelligent Approach f...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Fensterseifer Filho, Evandro L. R. Assuncao, Joaquim V. C. Fontoura, Lisandra M. Federal University of Santa Maria - Ufsm Graduate Program in Computer Science RS Santa Maria Brazil Federal University of Santa Maria - Ufsm Applied Computing Department RS Santa Maria Brazil

ISBN: (纸本)9798350368833

Software projects are affected by technical knowledge as well as the personality of the team. Such factors can reduce or increase the software quality and development speed. For successful task allocation, it is essential to consider the skills and profile of each developer, thus maximizing their productivity. In projects with large teams, task allocation can be challenging, and the help of tools can facilitate its execution. In this work, we propose an intelligent approach for allocating software development tasks suitable to the profile of developers. From the literature, we define the appropriate skills and technical profiles for a development team, and the assessment is based on the developer completing a questionnaire. We developed a recommendation system to suggest tasks to be allocated to developers, employing text processing techniques. For validation, 495 tasks were used from an actual project developing simulator software for military training. The recommended allocations were evaluated by the project developers and used to improve the system. Validations showed that the developed approach makes consistent and coherent task recommendations to developers according to the developer profile, as participants agree with the recommendation for 76% of tasks. © 2024 IEEE.

关键词： Project management

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Dynamic Terrain Data Exchange in a Collaborative Terrain Editor

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Informatica (Slovenia) 2022年第4期46卷 575-582页

作者： Tarigan, Jos Timanta Sitompul, Opim Salim Zarlis, Muhammad Nababan, Erna Budhiarti Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Information Systems Management Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

In a computer supported cooperative work (CSCW), data consistency between collaborating users is a crucial issue. Based on the type of the application, ensuring data consistency can be a lengthy process that takes time and affects the system’s performance. In most 3D application, terrain data are massive due to its size. Exchanging this data may be expensive and may cause significant delay. In a real-time collaborative terrain editor, this issue becomes more significant due to terrain data exchange is consistently occurred between collaborating users. We present a solution to perform a conflict-free dynamic terrain data exchange in a real-time collaborative terrain editor. Our objective is to develop a method that able to ensure data consistency amongst collaborating peers in real-time manner. The main idea of our method is to split the terrain into smaller patches and synchronize the changes efficiently by only exchanging the modified patches. We applied our solution to a collaborative terrain editor application to test its performance in a real-time collaborative editing session. The tests were done in multiple scenarios, using different patch model, brush size (in the terrain editor), and connection setup between server and collaborating clients. The result shows that our protocol is capable to maintain data consistency between collaborating clients in a real-time terrain edition session. The delay is varied and highly depends on the data size and client-server environment setup. The overall test shows that it is possible to perform a collaborative terrain editing with an acceptable response time delay. In this paper, we present our proposed method, the implementation, and the result data from the test. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Electronic data interchange

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Automation testing using silk test workbench for website

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Procedia computer Science 2023年 216卷 128-135页

作者： Feliks Abedyoga Chandra Parapat Gede Putra Kusuma Muhammad Rizki Nur Majiid Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Automation testing is essential to carry out functional testing quickly and precisely. Software testing is beneficial for testers doing many testing processes according to the existing scenarios. So, there is an urgency to explore a more advanced solution to replace the traditional approach. Silk Test Workbench is a tool for automation testing that can accelerate the functional testing of complex applications. For this research, we proposed automation testing on website applications with various testing scenarios that have been made. The visual test recording method is used for automation testing with Silk Test Workbench. Silk Test Workbench can speed up testing time, make a good test asset testing scenario, make it easier for users to do testing, and speed up the process to get the result in the form of a screen capture of the testing process. A comparison of testing time has been performed. The automatic testing time with the scenarios is 2 hours 46 minutes. The automatic testing time will be 2 hours 36 minutes using the all-in-one scenario. This study also used questionnaires from colleagues in the Quality Assurance field to get maximum User Acceptance Test results.

关键词： Automation Testing Silk Test Workbench Testing Strategy Testing Environment Web Application

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Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Pardamean, Bens Suparyanto, Teddy Sinaga, Gokma Sahat Tua Elwirehardja, Gregorius Natanael Firmansyah, Erick Ginting, Candra Mawandha, Hangger Gahara Putra, Dian Pratama Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta55282 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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