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Authors: He, F.; Advisor: -; Participants: Wen, Y. (2019) - Arbitrage risk management is a very hot and challengeable topic in the commodity future market. To resist the possible risk of an arbitrage, exchanges have to withdraw margin from clients referring to the case of maximum risk. However, if this arbitrage is in the riskless state actually, the capital of clients will be inef cient. Therefore, by investigating the applications of machine learning techniques, we here propose a novel algorithm named PRAM to predict the riskless state of arbitrage, by integrating multi-scale data ranging from contract quotation to contract parameters. Unlike the traditional models, PRAM explores the arbitrage risk management from the view of minimum risk, which can form a powerful supplement with the available risk management systems. Benchmark results ba...
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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 using code smell. Six typical machine learning algorithms (Naive Bayes Classi er, Multilayer Percep...
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Authors: Yang, Y.; Advisor: -; Participants: Deng, X.; He, D.; You, Y.; Song, R. (2019) - In future 5G user-centric ultra-dense networks (UUDN), demands of high data rate and high spectrum efficiency are effectively met by dual connectivity (DC) technology. However, due to huge increase of base stations (BSs) and mobile users (MUs), it becomes difficult for BSs to quickly and precisely select the codeword and provide DC to MUs. Hence, different from some traditional methods, this correspondence paper aims to improve the network performance using the method of machine learning. First, we model the random distribution of BSs by homogeneous Poisson point processes, where each MU is served by millimeter-wave channel. Second, the probabilities that macro cell BS or small cell BS serves the MU are further derived to get the average sum rate (ASR) in UUDN. Third, inspired by ML,...
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Authors: Wang, T.; Advisor: -; Participants: Wang, S.; Zhou, Z. (2019) - During the past few decades, mobile wireless communications have experienced four generations of technological revolution, namely from 1G to 4G, and the deployment of the latest 5G networks is expected to
take place in 2019. One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system. We believe that the answer lies in the huge volumes of data produced by the network itself, and machine learning may become a key to exploit such information. In this paper, we elaborate why the conventional model-based paradigm, which has been widely proved useful in pre-5G networks, can be less efficient or even less practical in the future 5G and beyond mobile networks. Then, we explain how ...
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Authors: Tao, R.; Advisor: -; Participants: Zhang, S.; Huang, X.; Tao, M.; Ma, J.; Ma, S.; Zhang, C.; Zhang, T.; Tang, F.; Lu, J.; Shen, C.; Xie, X. (2019) - Objective: This study focused on developing a fast and accurate automatic ischemic heart disease
detection/localization methodology. Methods: Twavewas segmented from averaged Magnetocardiography (MCG)
recordings and 164 features were subsequently extracted. These features were categorized into three groups: time domain features, frequency domain features, and informa-tion theory features. Next, we compared different machine learning classifiers including: k-nearest neighbor, decision tree, support vector machine (SVM), and XGBoost. To identify ischemia heart disease (IHD) case, we selected three classifiers with best performance and applied model ensemble to average results. All 164 features were used in this stage. To localize ischemia, we classified IHD group according to stenosis ...
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Authors: Jeong, S.; Advisor: -; Participants: Hester, J. G. D.; Su, W.; Tentzeris, M. M. (2019) - This letter describes the implementation of a machine learning (ML) classification strategy for read/interrogation
enhancement in chipless radio frequency identification (RFID) applications. A novel ML-based approach for classification and of detection tag identifications (IDs) has been presented, which can perform effective transponder readings for a wide variety of ranges and contexts, while providing tag-ID detection accuracy of up to
99.3%. Four tags encoding the four 2 bit IDs were inkjet-printed onto flexible low-cost polyethylene terephtalate substrates and interrogated without crosstalk or clutter interference de-embedding at ranges up to 50 cm, with different orientations and with and without the presence of scattering objects in the vicinity of the tags and reader. A support ...
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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 deep learning algorithm, recurrent neural network, was explored to further improve the training perform...
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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 frequency-domain features and provide them to a classi cation algorithm. In this paper, we test the perfor...
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Authors: Nieto, Y.; Advisor: -; Participants: GacÃa-DÃaz, V.; Montenegro, C.; González, C. C.; Crespo, R. González (2019) - Decisions made at the strategic level of Higher Educational Institutions (HEIs) affect policies, strategies, and actions that the institutions make as a whole. Decision's structures at HEIs are depicted in this paper and their effectiveness in supporting the institutions' governance. The disengagement of the stakeholders and the lack of using ef cient computational algorithms lead to 1) the decision process takes longer; 2) the ``whole picture'' is not involved along with all data necessary; and 3) small academic impact is produced by the decision, among others. Machine learning is an emerging eld of arti cial intelligence that using various algorithms analyzes information and provides a richer understanding of the data contained in a speci c context. Based on the author's previous...
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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 subsets of explanatory variables. These events are typically related to high returns, but also higher...
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