International CARDS Journal
Volume 2, Issue 3 2020 Q1
Blockchain applications have applications beyond financial transactions such as science, cryptography, and data storage (Schellekens, 2019). Bartoletti and Zunino (2019) propose a standardized model for developing smart contracts based on cryptocurrencies. Choi, Kiran, Oh, and Kwon (2019) present a blockchain technology solution for delivering and publishing performance assessments in education. Nowostawski and Tøn (2019) explore how the development of the lightning network affects cryptographic capabilities of blockchain technologies. Hussein, Alzubaidi, Habash, and Jaber (2019) describe three types of blockchain technologies that may be effective in biomedical studies, private, public, and consortium blockchains.
Sgantzos and Grigg (2019) discuss measures for implementing artificial intelligence in blockchain applications for advanced computing such as cellular automation and genetic algorithms, and possibly additionally for digital signatures, accounting, financial transactions, and live data feeds. O’Donovan and O’Sullivan (2019) evaluate energy blockchain initiatives such as energy trading, grid management, asset management, and eco-friendly certificates. Fulmer (2019) explains some of the challenges for taxing virtual currencies and the risks of anonymous transactions. Schellekens (2019) describes the technological challenges with blockchain technologies such as computer processing and data storage requirements.
Bartoletti, M., & Zunino, R. (2019). Formal models of bitcoin contracts: A survey. Frontiers in Blockchain, 2(8). doi:10.3389/fbloc.2019.00008
Choi, M., Kiran, R. S., Oh, S.-C., & Kwon, O.-Y. (2019). Blockchain-based badge award with existence proof. Applied Sciences, 9(12). doi:10.3390/app9122473
Fulmer, N. (2019). Exploring the legal issues of blockchain applications. Akron Law Journal, 52(1).
Hussein, F. A., Alzubaidi, K. A., Habash, A. Q., & Jaber, M. M. (2019). An adaptive biomedical data managing scheme based on the blockchain technique. Applied Sciences, 9(12). doi:10.3390/app9122494
Nowostawski, M., & Tøn, J. (2019). Evaluating methods for the identification of off-chain transactions in the lightning network. Applied Sciences, 9(12). doi:10.3390/app9122519
O’Donovan, P., & O’Sullivan, T. J. D. (2019). A systematic analysis of real-world energy blockchain initiatives. Future Internet, 11(8). doi:10.3390/fi11080174
Schellekens, M. (2019). Does regulation of illegal content need reconsideration in light of blockchains? International Journal of Law and Information Technology, 27(3), 292-305. doi:10.1093/ijlit/eaz009
Sgantzos, K., & Grigg, I. (2019). Artificial intelligence implementations on the blockchain. Use cases and future applications. Future Internet, 11(8). doi:10.3390/fi11080170
Applications of biometrics
A variety of applications of biometrics have developed with advances in technology (Tharwat, 2018). Chordia, Chavan, Patil, Patil, and More (2013) propose implementing biometrics for a secure online voting system. Mahalle, Railkar, and Barde (2016) introduce how sensors attached to RFID tags can be implemented in health care. Tharwat (2018) explain how visualizations of classification assessment can assist in developing and evaluating biometric models.
Vladimír and Jindřich (2018) explain risks in biometric security such as securing the database of biometric samples and the possibility of changes to biometric features over time that may skew accuracy. Chen, Fang, Li, and Wang (2019) explain how identify authentication is critical for the sustainability of e-commerce transactions. Maximilian, Markl, and Mohamed (2018) describe some of the security challenges in the Internet of Things such as lack of efficient encryption, authentication, and authorization of devices and transactions.
Chen, X., Fang, S., Li, Y., & Wang, H. (2019). Does identification influence continuous e-commerce consumption? The mediating role of intrinsic motivations. Sustainability, 11(7), 1944.
Chordia, P., Chavan, P., Patil, B., Patil, R., & More, P. (2013). OnVote: Secured online voting. International Journal of Innovative Research in Computer and Communication Engineering, 1(9), 2117-2120.
Mahalle, P. N., Railkar, P. N., & Barde, S. S. (2016). Telehomecare: An ICT use case based on M2M communication. Journal of Global Research in Computer Science, 7(4), 1-5.
Maximilian, L., Markl, E., & Mohamed, A. (2018). Cybersecurity management for (Industrial) Internet of Things: Challenges and opportunities Journal of Information Technology & Software Engineering, 8(5). doi:10.4172/2165-7866.1000250
Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics. doi:https://doi.org/10.1016/j.aci.2018.08.003
Vladimír, S., & Jindřich, K. (2018). Dynamic biometric signature - an effective alternative for electronic authentication. Advances in Technology Innovation, 3(4).
Emerging trends in machine learning
Enhancements to machine learning algorithms may have the potential to improve the efficiency of algorithms on tiny devices (Gopinath, Ghanathe, Seshadri, & Sharma, 2019). Gopinath et al. (2019) propose an approximate computing machine learning framework that reduces the amount of code and memory to compile thus allowing machine learning to run on smaller devices. Krishnamurthy, Agarwal, Huang, Langford, and Daumé III (2019) develop a machine learning algorithm that implements regression functions to compute computational costs through active learning. Huang, Deng, Wu, Liu, and He (2018) propose an algorithm that enhances tensor product representations for natural language processing.
Adedokun (2019) describes deep learning as a subset of machine learning that implements data representation for advanced algorithms. Schaer, Otálora, Jimenez-del-Toro, Atzori, and Müller (2019) apply deep learning in image scanning to compare pictures of tissues in medicine. Parisi, Kemker, Part, Kanan, and Wermter (2019) propose a combination of machine learning algorithms for developing autonomous machine learning agents.
Young, Cai, and Lu (2017) describe how deep learning models are derived from artificial neural networks. Goudarzi and Stefanovic (2014) explain an echo state network as an example of a neural network capable of representing unconventional computing architectures. Ghosh et al. (2019) compare three types of neural networks, multilayer perceptron, convolutional neural networks, and deep tensor neural networks, for prediction of molecules. Tanaka et al. (2019) propose recurrent neural networks for managing resources in computing environments.
Supervised machine learning may be applied as a tool for verifying information (Shao et al., 2018). Rietzler, Stabinger, Opitz, and Engl (2019) describe the ability to transfer a supervised machine learning model from a source domain to a target domain for sentiment classification. Shao et al. (2018) describe supervised machine learning tools for detecting whether social media accounts are authentic or automatically generated. Fernandez and Alani (2018) describe supervised machine learning classification models to identify the credibility of social media messages.
Adedokun, O. G. (2019). Deep learning: An overview. J Electr Electron Syst, 8(1).
Fernandez, M., & Alani, H. (2018). Online misinformation: Challenges and future directions. Paper presented at the Companion Proceedings of the The Web Conference 2018, Lyon, France.
Ghosh, K., Stuke, A., Todorović, M., Jørgensen, P. B., Schmidt, M. N., Vehtari, A., & Rinke, P. (2019). Deep learning spectroscopy: Neural networks for molecular excitation spectra. Advanced Science, 6(9), 1801367. doi:10.1002/advs.201801367
Gopinath, S., Ghanathe, N., Seshadri, V., & Sharma, R. (2019). Compiling KB-sized machine learning models to tiny IoT devices.
Goudarzi, A., & Stefanovic, D. (2014). Towards a calculus of echo state networks. Procedia Computer Science, 41, 176-181. doi:https://doi.org/10.1016/j.procs.2014.11.101
Huang, Q., Deng, L., Wu, O., Liu, C., & He, X. (2018). Attentive tensor product learning. arXiv e-prints, 1802.07089v2
Krishnamurthy, A., Agarwal, A., Huang, T.-K., Langford, J., & Daumé III, H. (2019). Active learning for cost-sensitive classification. Journal of Machine Learning Research.
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54-71. doi:https://doi.org/10.1016/j.neunet.2019.01.012
Rietzler, A., Stabinger, S., Opitz, P., & Engl, S. (2019). Adapt or get left behind: Domain adaptation through BERT Language Model finetuning for aspect-target sentiment classification. eprint arXiv:1908.11860, arXiv:1908.11860.
Schaer, R., Otálora, S., Jimenez-del-Toro, O., Atzori, M., & Müller, H. (2019). Deep learning-based retrieval system for gigapixel histopathology cases and the open access literature. J Pathol Inform, 10(19).
Shao, C., Hui, P.-M., Wang, L., Jiang, X., Flammini, A., Menczer, F., & Ciampaglia, G. L. (2018). Anatomy of an online misinformation network. PLoS One, 13(4), e0196087-e0196087. doi:10.1371/journal.pone.0196087
Tanaka, G., Yamane, T., Héroux, J. B., Nakane, R., Kanazawa, N., Takeda, S., . . . Hirose, A. (2019). Recent advances in physical reservoir computing: A review. Neural Networks, 115, 100-123. doi:https://doi.org/10.1016/j.neunet.2019.03.005
Young, J. D., Cai, C., & Lu, X. (2017). Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma. BMC Bioinformatics, 18(11), 381. doi:10.1186/s12859-017-1798-2
Energy conservation and measurement in cloud computing
Measuring energy usage may provide advantages for conserving energy consumption in cloud computing (Backialakshmi & Hemavathi, 2015). Hindle (2018) describe the high cost of cooling servers in cloud computing, as much as 40% of cloud computing energy consumption. Backialakshmi and Hemavathi (2015) survey a number of energy efficiency strategies including green data center networks, energy-aware scheduling, energy-aware algorithms, distributed energy efficiency services, and energy efficient data centers. Shehabi, Walker, and Masanet (2014) estimate energy consumption savings and reduction in carbon dioxide emissions due to replacing DVDs with video streaming. Yavari, Ghaffarpour Rahbar, and Fathi (2019) describe how energy consumption of virtual machines in cloud computing are connected with CPU utilization and temperature and can be optimized for performance and efficiency based on memory utilization and lowest temperature.
Market based factors and government regulations have provided companies with encouragement to provide energy efficient alternatives for the development and delivery of products and services (Radu, 2017; Yang, Yang, & Xu, 2018). Ostad-Ali-Askari et al. (2017) describe how the exhaustion of non-renewable resources has created the need for environment-friendly materials and also allowed for the implementation of natural, cost effective raw materials. Xiong, Li, Jia, Hou, and Zheng (2018) implement simulation analysis to improve the energy efficiency of manufacturing.
Yang et al. (2018) explain engineering technology solutions, industrial restructuring, and management innovation for energy efficient production. Song et al. (2017) propose a model to improve the decision making of choosing environmental friendly suppliers. Long et al. (2018) suggest the possibility of an aqueous sodium-ion battery to replace lithium ion batteries.
Emerging technologies such as 5G network technology and machine learning algorithms may provide advanced capabilities in weather forecasting to support energy conservation (Moharir & Pise, 2019). Moharir and Pise (2019) propose implementing multilayer perceptron of deep learning networks for predicting rainfall with greater accuracy. Jaya and Putri (2019) describe methods for forecasting weather models with Bayesian structural time series algorithms. Goudos et al. (2019) compare implementations of 5G network technology and how they increase in power consumptions as number of connected devices are increased.
Backialakshmi, M., & Hemavathi, N. (2015). Survey on energy efficiency in cloud computing. Journal of Information Technology & Software Engineering, 6(1).
Goudos, S. K., Deruyck, M., Plets, D., Martens, L., Psannis, K. E., Sarigiannidis, P., & Joseph, W. (2019). A novel design approach for 5G massive MIMO and NB-IoT green networks using a hybrid jaya-differential evolution algorithm. IEEE Access, 7, 105687-105700. doi:10.1109/ACCESS.2019.2932042
Hindle, A. (2018). If you bill it, they will pay: Energy consumption in the cloud will be irrelevant until directly billed for. Paper presented at the 7th International Workshop on Requirements Engineering for Sustainable Systems (RE4SuSy 2018), Banff, Alberta.
Jaya, G. N. M., & Putri, V. N. (2019). Forecasting daily temperature in the city of Bandung, Indonesia by means Bayesian structural time series. International Journal of Scientific & Engineering Research, 10(8), 42-46.
Long, H., Zeng, W., Wang, H., Qian, M., Liang, Y., & Wang, Z. (2018). Self‐assembled biomolecular 1D nanostructures for aqueous sodium‐ion battery. Advanced Science, 5(3), 1700634.
Moharir, S. S., & Pise, N. (2019). Medium term rainfall forecasting in pune region using multi-layer perceptron. Paper presented at the IRAJ International Conference, Pune, India
Ostad-Ali-Askari, K., Eslamian, S., Namadi, A., Ghane, M., Gandomkar, A., Dehghan, S., . . . Dalezios, N. R. (2017). Reinforcing liquefied weak soils using eco-friendly synthetic polymers. Int J Eme Eng Rese Tech, 5, 30-42.
Radu, L.-D. (2017). Green cloud computing: A literature survey. Symmetry, 9(12), 295.
Shehabi, A., Walker, B., & Masanet, E. (2014). The energy and greenhouse-gas implications of internet video streaming in the United States. Environmental Research Letters, 9(5), 054007. doi:10.1088/1748-9326/9/5/054007
Song, W., Chen, Z., Wang, X., Wang, Q., Shi, C., & Zhao, W. (2017). Environmentally friendly supplier selection using prospect theory. Sustainability, 9(3), 377.
Xiong, T., Li, J., Jia, H., Hou, B., & Zheng, W. (2018). Simulation analysis of the performance of an environmentally friendly double-suction cutter suction dredger based on SolidWorks and ANSYS. Paper presented at the IOP Conference Series: Materials Science and Engineering.
Yang, F., Yang, M., & Xu, J. (2018). The productivity dynamics of China’s environmentally friendly production technologies in terms of wastewater treatment techniques. BioMed Research International, 2018.
Yavari, M., Ghaffarpour Rahbar, A., & Fathi, M. H. (2019). Temperature and energy-aware consolidation algorithms in cloud computing. Journal of Cloud Computing, 8(1), 13. doi:10.1186/s13677-019-0136-9
Strategies for improving software development processes
Representing software architecture in views with a common cross language approach may be an approach that can improve software development (Mendivelso, Garcés, & Casallas, 2018). de Almeida Farzat, de Oliveira Barros, and Horta Travassos (2018) explain how genetic algorithms can be implemented to improve programming languages. dos Santos and Nunes (2018) explore technical and non-technical factors that influence the efficiency of the code review process. Mendivelso et al. (2018) develop an approach for developing software architectural views that are not language specific and can represent the architecture through annotations.
Analyses of hardware and software configurations may provide software engineers tools to optimize performance of software systems (Rose & Furneaux, 2016). Harwood (2019) deploys peer to peer communication libraries on mobile devices to compare performance of hardware configurations. Rose and Furneaux (2016) combine a content analysis and a casual analysis to identify drivers for software innovation. Panda, Munjal, and Mohapatra (2016) conduct a mutant analysis for experimental programs to measure prioritization times in testing.
dos Santos, E. W., & Nunes, I. (2018). Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data. Journal of Software Engineering Research and Development, 6(1), 14. doi:10.1186/s40411-018-0058-0
Harwood, A. R. G. (2019). GPU-powered, interactive flow simulation on a peer-to-peer group of mobile devices. Advances in Engineering Software, 133, 39-51. doi:https://doi.org/10.1016/j.advengsoft.2019.04.003
Mendivelso, L. F., Garcés, K., & Casallas, R. (2018). Metric-centered and technology-independent architectural views for software comprehension. Journal of Software Engineering Research and Development, 6(1), 16. doi:10.1186/s40411-018-0060-6
Panda, S., Munjal, D., & Mohapatra, D. P. (2016). A slice-based change impact analysis for regression test case prioritization of object-oriented programs. Advances in Software Engineering, 2016, 20. doi:10.1155/2016/7132404
Rose, J., & Furneaux, B. (2016). Innovation drivers and outputs for software firms: Literature review and concept development. Advances in Software Engineering, 2016, 25. doi:10.1155/2016/5126069