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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 performance of each exercise. Given these labeled data, we trained a multi-class support vector machine (SV...
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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 decentralized) settings. In this paper, an algorithm termed Byzantine-resilient distributed coordinate descent ...
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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: 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: Li, N.; Advisor: -; Participants: Zhang, J.; Wang, S.; Jiang, Y.; Ma, J.; Dong, L.; Gong, G. (2019) - Noninvasive assessment of severity of liver brosis is crucial for understanding histology and making decisions on antiviral treatment for chronic HBV in view of the associated risks of biopsy. We aimed to develop a computer-assisted assessment system for the evaluation of liver disease severity by using machine leaning classi er based on physical-layer with serum markers. The retrospective data set, including 920 patients, was used to establish Decision Tree Classi er (DTC), Random Forest Classi er (RFC), Logistic Regression Classi er (LRC), and Support Vector Classi er (SVC) for liver brosis severity assessment. Training and testing samples account for 50% of the data set, respectively. The best indicator combinations were selected in random combinations of 24 indicators includin...
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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 Anti Jamming protocol for vehicular traf c environments. It focuses on jamming vehicle's discriminate...
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Authors: Li, W.; Advisor: -; Participants: Ni, L.; Li, Z.; Duan, S.; Wu, H. (2019) - Land surface temperature (LST) is described as one of the most important environmental parameters of the land
surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs),
support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m,
and downscaling results would be validated by the r...
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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: 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: Alves, J. M.; Advisor: -; Participants: Honório, L. M.; Capretz, M. A. M. (2019) - Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to quali ed knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML work ows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of speci c ML work ows, which often lead to bottlenecks, production issues, and code management complexity and even then may not have a nal desirable outcome. This paper proposes the Machine Learning Framework for IoT data ...
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