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Reversing obfuscated control flow structures in android apps using ReDex optimizer 9

Reversing obfuscated control flow structures in android apps...

引用

9th International Conference on Smart Media and Applications, SMA 2020

作者： You, Geunha Kim, Gyoosik Park, Jihyeon Cho, Seong-Je Park, Minkyu Dept. of Computer Science and Engineering Dankook University Korea Republic of Dept. of Convergence Technology Korea Telecom Korea Republic of Dept. of Software Science Dankook University Korea Republic of Dept. of Software Technology Konkuk University Korea Republic of

ISBN: (纸本)9781450389259

Code obfuscation is a technique that makes programs harder to understand. Malware writers widely the obfuscation technique to evade detection from anti-malware software, or to deter reverse engineering attempts for their malicious code. If we de-obfuscate the obfuscated code and restore it to the original code before obfuscation was applied, we can analyze the obfuscated malware effectively and efficiently. In this paper, we apply ReDex optimizer for reversing the control-flow obfuscation performed by the Obfuscapk system on open-source Android applications. We then analyze the effectiveness and limitations of ReDex in terms of its deobfuscation ability to reverse the control-flow obfuscation of Android apps. The experimental results show that ReDex can recover 1089 of 1108 apps obfuscated with control-flows obfuscation techniques of Obfuscapk obfuscator. During the process of optimizing bytecode, ReDex reduces the number of methods and fields significantly while it has a limitation in removing dead codes related to both useless goto statements and random nop instructions. © 2020 ACM.

关键词： Reverse engineering

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EEG Feature Extraction with Fast Fourier Transform for Investigating different Brain regions in Cognitive and Reasoning Activity 5

EEG Feature Extraction with Fast Fourier Transform for Inves...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Amin, Hafeez Ullah Hafeez, Yasir Reza, Mohammed Faruque Adil, Syed Hasan Hasan, Rumaisa Abu Ali, Syed Saad Azhar University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Sains Malaysia Dept. of Neurosciences Kota Bharu Kubang Kerian Malaysia Iqra University Software Engineering Department Karachi Pakistan Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia King Fahd University of Petroleum and Minerals Aerospace Engineering Department Dhahran31261 Saudi Arabia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

In this study, cortical brain activity during a pattern matching task (PMT) was measured by employing electroencephalography (EEG). The EEG data were recorded from 128 scalp locations during a pattern-matching task and in rest conditions (eyes open and eyes closed). Spectral Analysis of EEG frequency bands reflected a significant (p © 2022 IEEE.

关键词： Fast Fourier transforms

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Signed Network Embedding with Dynamic Metric Learning 16

Signed Network Embedding with Dynamic Metric Learning

引用

16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Wu, Huanguang Guan, Donghai Han, Guangjie Yuan, Weiwei Guizani, Mohsen Nanjing University of Aeronautics and Astronautics Dept. of Computer Science and Technology Nanjing China Dalian University of Technology Dept. School of Software Dalian China Qatar University Dept. of Computer Science and Engineering Qatar

ISBN: (纸本)9781728131290

Network embedding is an important method to learn low-dimensional vector representations of nodes in networks, which has wide-ranging applications in network analysis such as link prediction. Most existing network embedding models focus on the unsigned networks with only positive links. However, networks should have both positive and negative links in practical applications such as the trust and distrust relationships in social networks. It is certain that there are different properties between positive links and negative links, which means the network embedding models designed for unsigned networks are not suitable for signed networks. In this paper, we propose SNE-DML, a signed network embedding model with dynamic metric learning. The model learns positive and negative distance metrics respectively in the training process. We conduct sign prediction experiments on three datasets and compare with seven baselines including three signed network embedding models and four state-of-the-art unsigned network embedding models. The experimental results show the effectiveness of our model. © 2020 IEEE.

关键词： Forecasting

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MOOC student success prediction using knowledge distillation

MOOC student success prediction using knowledge distillation

引用

2020 International Conference on computer Information and Big Data Applications, CIBDA 2020

作者： Kang, Tingting Wei, Zhengang Huang, Jianxiong Yao, Zhaoliang Dept. of Computer Science and Technology Ocean University of China Qingdao Shandong China Dept. of Software Engineering Zhejiang University Hangzhou Zhejiang China Dept. of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (纸本)9781728198378

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. Due to the low success rate of MOOCs, student success prediction has received much attention recently. Yet many utilize post-hoc prediction architectures, where model fitting requires features which are not knowable until a course completes. These works do not match the actual environment in which the prediction model is used, which may lead to unsatisfactory results. In order to build a reliable model that can be used in the actual environment, we propose a new student success prediction algorithm based on knowledge distillation. This algorithm can make the model obtain good prediction results with only a few basic features, and solve the problem of using features which are not knowable until a course completes. © 2020 IEEE.

关键词： Forecasting

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Optimized Crop Detection Using IoT and Deep Neural Networks

Optimized Crop Detection Using IoT and Deep Neural Networks

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Tanupriya Choudhury Rohini A Hussain Falih Mahdi SOCS University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhnad India Graphic Era Hill University Dehradun India Dept. of Computer Science & Engg Anil Neerukonda Institute of Technology and Sciences (ANITS) Visakhapatnam India Department of Computer and Software Engineering University of Diyala Baquba Iraq

The advancement of agriculture in food production is an increasing concern. It is one of the major challenges in contemporary farming. The development methods of soil farming have increased the fabrication of crops and food. It emphasizes the vital role of sustainability in soil. Different technologies were used to grow and flourish in soil to supply the nutrition's. It buffers and delicate the plant roots from fluctuations in temperature. Hydroponically grown fruits can produce rich nutrition's like traditional grown fruits. Proposed research has focused on the IoT in Hydroponics for Growing Tomato Plants. The Nutrient Film Technique (NFT) is needed for the present farmers for monitor and managing the crop system. For managing and monitoring the crop to execute the method scientifically and take the decision about the crops in time. The integration of IoT has given the solutions to solve the contemporary problems of calcium deficiency, lack of airflow around plants and so on. This proposal has developed a smart hydroponic system with technology enabled by IoT system.

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning

Exploiting Partial Observability and Optimized Simple State ...

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Frontiers of Information Technology (FIT)

作者： Danyaal Mahmood Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-dept. exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent’s performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent’s decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game.

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Characterization and Control of a Superconducting Homopolar Motor with Cryogenic GaN Inverter for Electric Aircrafts

Characterization and Control of a Superconducting Homopolar ...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Felix Gliese Matthew G. S. Pearce Aaron Wadsworth Mohammad Siamaki James Storey Grant Lumsden Duleepa J. Thrimawithana Rod Badcock Joerg Roth-Stielow Martin Neuburger Faculty of Mobility and Technology Esslingen University Germany Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand OpenStar Technologies New Zealand Robinson Research Institute Victoria University of Wellington New Zealand Institute for Power Electronics and Electrical Drives University of Stuttgart Germany

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

In the quest for a zero-emissions future, the aviation sector is facing the challenge of reducing its carbon footprint. This paper presents a characterization of a superconducting homopolar motor designed to meet the stringent power and efficiency requirements of all-electric aircraft. The control system, which features field-oriented control and a multi-layer architecture, ensures precise operation of the motor under various load conditions. The motor uses a stationary cryogenic superconducting DC coil to generate the rotor’s magnetic field. Utilizing superconducting materials helps produce the high power densities critical for aviation. The characterization of the key parameters of the motor, such as inductances, stator resistance and flux, are presented. A description of the cryogenic GaN power electronic inverter power stage immersed in liquid nitrogen is provided. This design enables future integration next to the motor, and improved power density and efficiency. This paper outlines the design, implementation, and initial testing of the superconducting homopolar motor and its power electronics, highlighting its potential for electric propulsion in aviation by providing a viable solution for long-range flights.

关键词： Resistance Power system measurements Electric potential Torque Density measurement Cryogenics Motors Control systems Inverters Mathematical models

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Fatima Faridoon Raja Hashim Ali Zain Ul Abideen Nazia Shahzadi Ali Zeeshan Ijaz Usama Arshad Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis.

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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Innovative Poverty Estimation through Machine Learning Approaches

Innovative Poverty Estimation through Machine Learning Appro...

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International Conference on Emerging Technologies, ICET

作者： Ahmad Mashhood Zain ul Abideen Usama Arshad Raja Hashim Ali Ammad Aslam Khan Bahram Khan Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Dept. of Biological Sciences M.A. Road Satellite Town Virtual University of Pakistan Rawalpindi Pakistan

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges.

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