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Authors: Ma, Z.; Advisor: -; Participants: Ge, H.; Liu, Y.; Zhao, M.; Ma, J. (2019) - Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control ow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed....
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Authors: Kasongo, S. M.; Advisor: -; Participants: Sun, Y. (2019) - In recent years, the increased use of wireless networks for the transmission of large volumes of information has generated a myriad of security threats and privacy concerns; consequently, there has been the development of a number of preventive and protective measures including intrusion detection systems (IDS). Intrusion detection mechanisms play a pivotal role in securing computer and network systems; however, for various IDS, the performance remains a major issue. Moreover, the accuracy of existing methodologies for IDS using machine learning is heavily affected when the feature space grows. In this paper, we propose a IDS based on deep learning using feed forward deep neural netwo...
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Authors: Chelli, A.; Advisor: -; Participants: Pätzold, M. (2019) - The number of older people in western countries is constantly increasing. Most of them prefer to live independently and are susceptible to fall incidents. Falls often lead to serious or even fatal injuries which are the leading cause of death for elderlies. To address this problem, it is essential to develop robust fall detection systems. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. We use acceleration and angular velocity data from two public databases to recognize seven different activities, including falls and activities of daily living. From the acceleration and angular velocity data, we extract time- and freque...
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Authors: Amari, A.; Advisor: -; Participants: Lin, X.; Dobre, O. A.; Venkatesan, R.; Alvarado, A. (2019) - We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital
back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal–noise interactions, which are not taken into ac...
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Authors: Liu, H.; Advisor: -; Participants: Liu, Z.; Liu, S.; Liu, Y.; Bin, J.; Shi, F.; Dong, H. (2019) - The integrity of geomagnetic data is a critical factor in understanding the evolutionary process of Earth’s
magnetic field, as it provides useful information for near-surface exploration, unexploded explosive ordnance detection, and so on. Aimed to reconstruct undersampled geomagnetic data, this paper presents a geomagnetic data reconstruction approach based on machine learning techniques. The traditional linear interpolation approaches are prone to time inefficiency and high labor cost, while the proposed approach has a significant
improvement. In this paper, three classic machine learning models, support vector machine, random forests, and gradient boosting were built. Besides, a dee...
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Authors: Xie, J.; Advisor: -; Participants: Yu, F. R.; Huang, T.; Xie, R.; Liu, J.; Wang, C.; Liu, Y. (2019) - In recent years, with the rapid development of current Internet and mobile communication technologies, the
infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN)
brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the n...
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Authors: Yang, P.; Advisor: -; Participants: Xiao, Y.; Xiao, M.; Guan, Y.L.; Li, S.; Xiang, W. (2019) - In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multipleinput
multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the
problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K-nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO...
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Authors: Lai, D.; Advisor: -; Participants: Zhang, Y.; Zhang, X.; Su, Y.; Heyat, M. B. Bin (2019) - ABSTRACT Early risk identi cation of an unexpected sudden cardiac death (SCD) in a person who is
suffering malignant ventricular arrhythmias is highly signi cant for timely intervention and increasing
the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD
with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as TpTe/QT, JTp/JTe, and T and two conduction-repolarization markers, such as TpTe/QRS and TpT/(QT QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. ...
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Authors: Hassan, A.; Advisor: -; Participants: Hamza, R.; Yan, H.; Li, P. (2019) - Cloud computing has been widely applied in numerous applications for storage and data analytics tasks. However, cloud servers engaged through a third party cannot be fully trusted by multiple data users. Thus, security and privacy concerns become the main obstructions to use machine learning services, especially with multiple data providers. Additionally, some recent outsourcing machine learning schemes have been proposed in order to preserve the privacy of data providers. Yet, these schemes cannot satisfy the property of public veri ability. In this paper, we present an ef cient privacy-preserving machine learning scheme for multiple data providers. The proposed scheme allows all par...
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Authors: Lee, W.; Advisor: -; Participants: Ham, Y.; Ban, T.; Jo, O. (2019) - Estimating the growth performance in pigs is important in order to achieve a high productivity of pig farming. We herein analyze and verify the machine learning based estimations for the growth performance in swine which includes the daily gain of body weight (DG), feed intake (FI), required growth period for growing/ nishing phase (GP), and marketed-pigs per sow per year (MSY), based on the farm speci c data and climate, i.e., temperature, humidity, initial age (IA), initial body weight (IBW), number of pigs (NU) and stocking density (SD). The growth data used in our work is collected from 55 pig farms which are located across South Korea for the period between October 2017 and Septe...
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Authors: Pritam, N.; Advisor: -; Participants: Khari. M.; Hoang Son, L.; Kumar, R.; Jha, S.; Priyadarshini, I.; Abdel-Basset, M.; Viet Long, H. (2019) - Assessment of code smell for predicting software change proneness is essential to ensure its signi cance in the area of software quality. While multiple studies have been conducted in this regard, the number of systems studied and the methods used in this paper are quite different, thus, causing confusion for understanding the best methodology. The objective of this paper is to approve the effect of code smell on the change inclination of a speci c class in a product framework. This is the novelty and surplus of this work against the others. Furthermore, this paper aims to validate code smell for predicting class change proneness to nd an error in the prediction of change proneness ...
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Authors: Arroyo, J.; Advisor: -; Participants: Corea, F.; Jimenez-Diaz, G.; A. Recio-Garcia, J. (2019) - The venture capital (VC) industry offers opportunities for investment in early-stage companies where uncertainty is very high. Unfortunately, the tools investors currently have available are not robust enough to reduce risk and help them managing uncertainty better. Machine learning data-driven approaches can bridge this gap, as they already do in the hedge fund industry. These approaches are now possible because data from thousands of companies over the world is available through platforms such as Crunchbase. Previous academic efforts have focused only on predicting two classes of exits, i.e., being acquired by other company or offering shares to the public, using only one or a few s...
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Authors: Bao, T.; Advisor: -; Participants: Klatt, B. N.; Whitney, S. L.; Sienko, K. H.; Wiens, J. (2019) - Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to
the lack of feedback from physical therapists (PTs). In this paper, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments outside of the clinic. We recruited sixteen participants to perform standing balance exercises. For each exercise, we recorded trunk sway data and had a PT rate balance performance on a scale of 1–5. The rating scale was adapted from the Functional Independence Measure. From the trunk sway data, we extracted a 61-dimensional feature vector representing the perform...
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Authors: Yang, Z.; Advisor: -; Participants: Bajwa, W. U. (2019) - Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest
to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional learning in fully distributed (also known as decentraliz...
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Authors: Sun, P.; Advisor: -; Participants: Wang, D.; Mok, V. C.; Shi, L. (2019) - Radiomics-based researches have shown predictive abilities with machine-learning approaches. However, it is still unknown whether different radiomics strategies affect the prediction performance. The aim of this study was to compare the prediction performance of frequently utilized radiomics feature selection and classi cation methods in glioma grading. Quantitative radiomics features were extracted from tumor regions in 210 Glioblastoma (GBM) and 75 low-grade glioma (LGG) MRI subjects. Then, the diagnostic performance of sixteen feature selection and fteen classi cation methods were evaluated by using two different test modes: ten-fold cross-validation and percentage split. Balanced...
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Authors: Kumar, S.; Advisor: -; Participants: Singh, K.; Kaiwartya, O.; Cao, Y.; Zhou, H. (2019) - Recently, Internet of vehicles (IoV) has witnessed signi cant research and development attention in both academia and industries due to the potential towards addressing traf c incidences and supporting green mobility. With the growing vehicular network density, jamming signal centric security issues have become challenging task for IoV network designers and traf c applications developers. Global positioning system (GPS) and roadside unit (RSU) centric related literature on location-based security approaches lacks signal characteristics consideration for identifying vehicular network intruders or jammers. In this context, this paper proposes a machine learning oriented as Delimitated A...
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Authors: Karn, R. R.; Advisor: -; Participants: Kudva, P.; Elfadel, I. A. M. (2019) - Cloud network monitoring data is dynamic and distributed. Signals to monitor the cloud can appear, disappear or change their importance and clarity over time. Machine learning (ML) models tuned to a given data set can therefore quickly become inadequate. A model might be highly accurate at one point in time but may lose its accuracy at a later time due to changes in input data and their features. Distributed learning with dynamic model selection is therefore often required. Under such selection, poorly performing models (although aggressively tuned for the prior data) are retired or put on standby while newor standby models are brought in. The well-known method of Ensemble ML (EML) ma...
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Authors: Kim, H.; Advisor: -; Participants: Kim, S.; Hwang, J. Y.; Seo, C. (2019) - A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many applications such as in banking, nance, insurance, healthcare and business. Recently, many communities in blockchain networks want to deploy machine learning models to get meaningful knowledge from geographically distributed large-scale data owned by each participant. To run a learning model without data centralization, distributed machine learning (DML) for blockchain networks has been studied. While several works have been proposed, privacy and security have not been suf ciently addressed, and as we show later, there are vulnerabilities in the architecture and limitations in ter...
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Authors: Xu,X.; Advisor: -; Participants: Zhang, Y.; Tang, M.; Gu, H.; Yan, S.; Yang, J. (2019) - Corresponding to the continual development of human-computer interaction technology, the use of emotional computing (EC) is gradually emerging in the Internet of Things (IoT). Emotion recognition is considered a highly valuable aspect of EC. Numerous studies have examined emotion recognition based on electroencephalogram (EEG) signals, but the recognition rate is unreliable. In this paper, a feature extraction method is proposed that is based on double tree complex wavelet transform (DTCWT) and machine learning. The emotions of 16 subjects are induced under video stimulation, and the original signal is acquired using a Neuroscan device. Both EEG and electromyography (EMG) signal are t...
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Authors: Strodthoff, N.; Advisor: -; Participants: Göktepe,B.; Schierl, T.; Hellge, C.; Samek, W. (2019) - We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine
learning techniques as a path towards ultra-reliable and lowlatency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below 10 at small latency overheads. We provide
realistic performance estimates in a system model in...
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