3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network

State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53 which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process

Faculty Scholarship Celebration 2021

Program and bibliography for Western Carolina University's annual Faculty Scholarship Celebration

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

[EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607S12386Rahem, A. A. T., Ismail, M., Najm, I. A., & Balfaqih, M. (2017). Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET. Telecommunication Systems, 65(4), 739-754. doi:10.1007/s11235-016-0242-7Aalsalem, M. Y., Khan, W. Z., Gharibi, W., Khan, M. K., & Arshad, Q. (2018). Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges. Journal of Network and Computer Applications, 113, 87-97. doi:10.1016/j.jnca.2018.04.004Sunny, A., Panchal, S., Vidhani, N., Krishnasamy, S., Anand, S. V. R., Hegde, M., … Kumar, A. (2017). A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs. Journal of Network and Computer Applications, 93, 13-26. doi:10.1016/j.jnca.2017.05.006Jain, R. (1990). Congestion control in computer networks: issues and trends. IEEE Network, 4(3), 24-30. doi:10.1109/65.56532Kafi, M. A., Djenouri, D., Ben-Othman, J., & Badache, N. (2014). Congestion Control Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 16(3), 1369-1390. doi:10.1109/surv.2014.021714.00123Floyd, S. (2000). Congestion Control Principles. doi:10.17487/rfc2914Qazi, I. A., & Znati, T. (2011). On the design of load factor based congestion control protocols for next-generation networks. Computer Networks, 55(1), 45-60. doi:10.1016/j.comnet.2010.07.010Katabi, D., Handley, M., & Rohrs, C. (2002). Congestion control for high bandwidth-delay product networks. ACM SIGCOMM Computer Communication Review, 32(4), 89-102. doi:10.1145/964725.633035Wang, Y., Rozhnova, N., Narayanan, A., Oran, D., & Rhee, I. (2013). An improved hop-by-hop interest shaper for congestion control in named data networking. ACM SIGCOMM Computer Communication Review, 43(4), 55-60. doi:10.1145/2534169.2491233Mirza, M., Sommers, J., Barford, P., & Zhu, X. (2010). A Machine Learning Approach to TCP Throughput Prediction. IEEE/ACM Transactions on Networking, 18(4), 1026-1039. doi:10.1109/tnet.2009.2037812Taherkhani, N., & Pierre, S. (2016). Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3275-3285. doi:10.1109/tits.2016.2546555Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. doi:10.1109/comst.2017.2707140Gonzalez-Landero, F., Garcia-Magarino, I., Lacuesta, R., & Lloret, J. (2018). PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks. IEEE Access, 6, 14141-14150. doi:10.1109/access.2018.2811900Lloret, J., Parra, L., Taha, M., & Tomás, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks, 129, 340-351. doi:10.1016/j.comnet.2017.05.018Khan, I., Zafar, M., Jan, M., Lloret, J., Basheri, M., & Singh, D. (2018). Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems. Entropy, 20(2), 92. doi:10.3390/e20020092Elappila, M., Chinara, S., & Parhi, D. R. (2018). Survivable Path Routing in WSN for IoT applications. Pervasive and Mobile Computing, 43, 49-63. doi:10.1016/j.pmcj.2017.11.004Singh, K., Singh, K., Son, L. H., & Aziz, A. (2018). Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks, 138, 90-107. doi:10.1016/j.comnet.2018.03.023Shelke, M., Malhotra, A., & Mahalle, P. N. (2017). Congestion-Aware Opportunistic Routing Protocol in Wireless Sensor Networks. Smart Innovation, Systems and Technologies, 63-72. doi:10.1007/978-981-10-5544-7_7Godoy, P. D., Cayssials, R. L., & García Garino, C. G. (2018). Communication channel occupation and congestion in wireless sensor networks. Computers & Electrical Engineering, 72, 846-858. doi:10.1016/j.compeleceng.2017.12.049Najm, I. A., Ismail, M., Lloret, J., Ghafoor, K. Z., Zaidan, B. B., & Rahem, A. A. T. (2015). Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications, 58, 119-129. doi:10.1016/j.jnca.2015.09.003Najm, I. A., Ismail, M., & Abed, G. A. (2014). High-Performance Mobile Technology LTE-A using the Stream Control Transmission Protocol: A Systematic Review and Hands-on Analysis. Journal of Applied Sciences, 14(19), 2194-2218. doi:10.3923/jas.2014.2194.2218Katuwal, R., Suganthan, P. N., & Zhang, L. (2018). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing, 70, 1146-1153. doi:10.1016/j.asoc.2017.09.020Gómez, S. E., Martínez, B. C., Sánchez-Esguevillas, A. J., & Hernández Callejo, L. (2017). Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal. Computer Networks, 127, 68-80. doi:10.1016/j.comnet.2017.07.018Hasan, M., Hossain, E., & Niyato, D. (2013). Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches. IEEE Communications Magazine, 51(6), 86-93. doi:10.1109/mcom.2013.6525600Liang, D., Zhang, Z., & Peng, M. (2015). Access Point Reselection and Adaptive Cluster Splitting-Based Indoor Localization in Wireless Local Area Networks. IEEE Internet of Things Journal, 2(6), 573-585. doi:10.1109/jiot.2015.2453419Park, H., Haghani, A., Samuel, S., & Knodler, M. A. (2018). Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accident Analysis & Prevention, 112, 39-49. doi:10.1016/j.aap.2017.11.025Shu, J., Liu, S., Liu, L., Zhan, L., & Hu, G. (2017). Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine. Chinese Journal of Electronics, 26(2), 377-384. doi:10.1049/cje.2017.01.013Riekstin, A. C., Januário, G. C., Rodrigues, B. B., Nascimento, V. T., Carvalho, T. C. M. B., & Meirosu, C. (2016). Orchestration of energy efficiency capabilities in networks. Journal of Network and Computer Applications, 59, 74-87. doi:10.1016/j.jnca.2015.06.015Adi, E., Baig, Z., & Hingston, P. (2017). Stealthy Denial of Service (DoS) attack modelling and detection for HTTP/2 services. Journal of Network and Computer Applications, 91, 1-13. doi:10.1016/j.jnca.2017.04.015Stimpfling, T., Bélanger, N., Cherkaoui, O., Béliveau, A., Béliveau, L., & Savaria, Y. (2017). Extensions to decision-tree based packet classification algorithms to address new classification paradigms. Computer Networks, 122, 83-95. doi:10.1016/j.comnet.2017.04.021Singh, D., Nigam, S. P., Agrawal, V. P., & Kumar, M. (2016). Vehicular traffic noise prediction using soft computing approach. Journal of Environmental Management, 183, 59-66. doi:10.1016/j.jenvman.2016.08.053Xia, Y., Chen, W., Liu, X., Zhang, L., Li, X., & Xiang, Y. (2017). Adaptive Multimedia Data Forwarding for Privacy Preservation in Vehicular Ad-Hoc Networks. IEEE Transactions on Intelligent Transportation Systems, 18(10), 2629-2641. doi:10.1109/tits.2017.2653103Tariq, F., & Baig, S. (2017). Machine Learning Based Botnet Detection in Software Defined Networks. International Journal of Security and Its Applications, 11(11), 1-12. doi:10.14257/ijsia.2017.11.11.01Wu, T., Petrangeli, S., Huysegems, R., Bostoen, T., & De Turck, F. (2017). Network-based video freeze detection and prediction in HTTP adaptive streaming. Computer Communications, 99, 37-47. doi:10.1016/j.comcom.2016.08.005Pham, T. N. D., & Yeo, C. K. (2018). Adaptive trust and privacy management framework for vehicular networks. Vehicular Communications, 13, 1-12. doi:10.1016/j.vehcom.2018.04.006Mohamed, M. F., Shabayek, A. E.-R., El-Gayyar, M., & Nassar, H. (2019). An adaptive framework for real-time data reduction in AMI. Journal of King Saud University - Computer and Information Sciences, 31(3), 392-402. doi:10.1016/j.jksuci.2018.02.012Louvieris, P., Clewley, N., & Liu, X. (2013). Effects-based feature identification for network intrusion detection. Neurocomputing, 121, 265-273. doi:10.1016/j.neucom.2013.04.038Verma, P. K., Verma, R., Prakash, A., Agrawal, A., Naik, K., Tripathi, R., … Abogharaf, A. (2016). Machine-to-Machine (M2M) communications: A survey. Journal of Network and Computer Applications, 66, 83-105. doi:10.1016/j.jnca.2016.02.016Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26. doi:10.9781/ijimai.2018.02.004Lavanya, D. (2012). Ensemble Decision Tree Classifier For Breast Cancer Data. International Journal of Information Technology Convergence and Services, 2(1), 17-24. doi:10.5121/ijitcs.2012.2103Polat, K., & Güneş, S. (2007). Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Applied Mathematics and Computation, 187(2), 1017-1026. doi:10.1016/j.amc.2006.09.022Cayirci, E., Tezcan, H., Dogan, Y., & Coskun, V. (2006). Wireless sensor networks for underwater survelliance systems. Ad Hoc Networks, 4(4), 431-446. doi:10.1016/j.adhoc.2004.10.008Mezzavilla, M., Zhang, M., Polese, M., Ford, R., Dutta, S., Rangan, S., & Zorzi, M. (2018). End-to-End Simulation of 5G mmWave Networks. IEEE Communications Surveys & Tutorials, 20(3), 2237-2263. doi:10.1109/comst.2018.282888

Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004)

The invasion of freshwater ecosystems is a particularly alarming phenomenon in the Iberian Peninsula. Habitat suitability modelling is a proficient approach to extract knowledge about species ecology and to guide adequate management actions. Decision-trees are an interpretable modelling technique widely used in ecology, able to handle strongly nonlinear relationships with high order interactions and diverse variable types. Decision-trees recursively split the input space into two parts maximising child node homogeneity. This recursive partitioning is typically performed with axis-parallel splits in a top-down fashion. However, recent developments of the R packages oblique.tree, which allows the development of oblique split-based decision-trees, and evtree, which performs globally optimal searches with evolutionary algorithms to do so, seem to outperform the standard axis-parallel top-down algorithms; CART and C5.0. To evaluate their possible use in ecology, the two new partitioning algorithms were compared with the two well-known, standard axis-parallel algorithms. The entire process was performed in R by simultaneously tuning the decision-tree parameters and the variables subset with a genetic algorithm and modelling the presence-absence of the Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004), an invasive fish species that has spread across the Iberian Peninsula. The accuracy and complexity of the trees, the modelled patterns of mesohabitat selection and the variables importance were compared. None of the new R packages, namely oblique.tree and evtree, outperformed the C5.0 algorithm. They rendered almost the same decision-trees as the CART algorithm, although they were completely interpretable they performed from four to eight partitions in comparison with C5.0, which resulted in a more complex structure with 17 partitions. Oblique.tree proved to be affected by prevalence and it does not include the possibility of weighting the observations, which potentially discourage its actual use. Although the use of evtree did not suggest a major improvement compared with the remaining packages, it allowed the development of regression trees which may be informative for additional modelling tasks such as abundance estimation. Looking at the resulting decision-trees, the optimal habitats for the Iberian gudgeon were large pools in lowland river segments with depositional areas and aquatic vegetation present, which typically appeared in the form of scattered macrophytes clumps. Furthermore, Iberian gudgeon seem to avoid habitats characterised by scouring phenomena and limited vegetated cover availability. Accordingly, we can assume that river regulation and artificial impoundment would have favoured the spread of the Iberian gudgeon across the entire peninsula.The study has been partially funded by the national Research project IMPADAPT (CGL2013-48424-C2-1-R) with MINECO (Spanish Ministry of Economy) and Feder funds and by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment). This study was also supported in part by the University Research Administration Center of the Tokyo University of Agriculture and Technology. Finally, we are grateful to the colleagues who worked in the field data collection, especially Juan Diego Alcaraz-Henandez, Rui M. S. Costa and Aina Hernandez.Muñoz Mas, R.; Fukuda, S.; Vezza, P.; Martinez-Capel, F. (2016). Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004). Ecological Informatics. 34:22-34. https://doi.org/10.1016/j.ecoinf.2016.04.011S22343

Current Trends in Game-Based Learning

A myriad of technological options can be used to support digital game-based learning. One popular technology in this context is the mobile device, considering its high penetration rate in our societies, even among young people. These can be combined with other technologies, such as Augmented Reality (AR) or Virtual Reality (VR), to increase students’ motivation and engagement in learning processes.Due to this, there is an emergent need to know and promote good practices in the development and implementation of game-based learning approaches in educational settings. This was the motto for the proposal of the Education Sciences (ISSN: 2227-7102) Special Issue “Current Trends in Game-Based Learning”. This book is a reprint of this Special Issue, collecting a set of five papers that illustrate the contribution of innovative approaches to education, specifically the ones exploring the motivational factors associated with playing games and the technology that may support them

DREQUS: an approach for the Discovery of REQuirements Using Scenarios

ABSTRACT: Requirements engineering is recognized as a complex cognitive problem-solving process that takes place in an unstructured and poorly-understood problem context. Requirements elicitation is the activity generally regarded as the most crucial step in the requirements engineering process. The term “elicitation” is preferred to “capture”, to avoid the suggestion that requirements are out there to be collected. Information gathered during requirements elicitation often has to be interpreted, analyzed, modeled, and validated before the requirements engineer can feel confident that a complete set of requirements of a system have been obtained. Requirements elicitation comprises the set of activities that enable discovering, understanding, and documenting the goals and motives for building a proposed software system. It also involves identifying the requirements that the resulting system must satisfy in to achieve these goals. The requirements to be elicited may range from modifications to well-understood problems and systems (i.e. software upgrades), to hazy understandings of new problems being automated, to relatively unconstrained requirements that are open to innovation (e.g. mass-market software). Requirements elicitation remains problematic; missing or mistaken requirements still delay projects and cause cost overruns. No firm definition has matured for requirements elicitation in comparison to other areas of requirements engineering. This research is aimed to improve the results of the requirements elicitation process directly impacting the quality of the software products derived from them

Flood Risk and Resilience

Flooding is widely recognized as a global threat, due to the extent and magnitude of damage it causes around the world each year. Reducing flood risk and improving flood resilience are two closely related aspects of flood management. This book presents the latest advances in flood risk and resilience management on the following themes: hazard and risk analysis, flood behaviour analysis, assessment frameworks and metrics and intervention strategies. It can help the reader to understand the current challenges in flood management and the development of sustainable flood management interventions to reduce the social, economic and environmental consequences from flooding

Internet of Things and Sensors Networks in 5G Wireless Communications

The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic