Identification-method research for open-source software ecosystems

In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information

Inferring transportation modes from GPS trajectories using a convolutional neural network

Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human's bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN's input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C: Emerging Technologie

Typing plasmids with distributed sequence representation

Multidrug resistant bacteria represent an increasing challenge for medicine. In bacteria, most antibiotic resistances are transmitted by plasmids. Therefore, it is important to study the spread of plasmids in detail in order to initiate possible countermeasures. The classification of plasmids can provide insights into the epidemiology and transmission of plasmid-mediated antibiotic resistance. The previous methods to classify plasmids are replicon typing and MOB typing. Both methods are time consuming and labor-intensive. Therefore, a new approach to plasmid typing was developed, which uses word embeddings and support vector machines (SVM) to simplify plasmid typing. Visualizing the word embeddings with t-distributed stochastic neighbor embedding (t-SNE) shows that the word embeddings finds distinct structure in the plasmid sequences. The SVM assigned the plasmids in the testing dataset with an average accuracy of 85.9% to the correct MOB type

Virtual Resources & Internet of Things

Internet of Things (IoT) systems mostly follow a Cloud-centric approach. These systems get the benefits of the extensive computational capabilities and flexibility of the Cloud. Although Cloud-centric systems support virtualization of components to interact with IoT networks, many of these systems introduce high latency and restrict direct access to IoT devices. Fog computing has been presented as an alternative to reduce latency when engaging IoT networks, however, new forms of virtualization are required to access physical devices in a direct manner. This research introduces a definition of Virtual Resources to enable direct access to IoT networks and to allow richer interactions between applications and IoT components. Additionally, this work proposes Virtual Resources as a mechanism to handle the multi-tenancy challenge that emerges when more than one tenant tries to access and manipulate an IoT component simultaneously. Virtual Resources are developed using Go language and CoAP protocol. This work proposes permission-based blockchain to provision Virtual Resources directly on IoT devices. Seven experiments have been done using Raspberry Pi computers and Edison Arduino boards to test the definition of Virtual Resources presented by this work. The results of the experiments demonstrate that Virtual Resources can be deployed across different IoT platforms. Also, the results show that Virtual Resources and blockchain can support multi-tenancy in the IoT space. IBM Bluemix Blockchain as a Service and Multichain blockchain have been evaluated handling the provisioning of Virtual Resources in the IoT network. The results of these experiments show that permission-based blockchain can store the configurations of Virtual Resources and provision these configurations in the IoT network

Multimedia and the Hybrid City: Geographies of Techno cultural Spaces in South Korea

The purpose of this research is to explore how multimedia technologies such as the Internet, satellite TV, cable TV and mobile phones, combined with people's everyday practices, produce the hybrid city where the boundaries between binary territories are blurred; and to offer implications for understanding our everyday lives and cities. Here, multimedia technologies are crucial triggers by which the boundaries between binary categories such as time/space, actual/virtual, human/machine and so on are blurred. And, cities, where urban locales are connected to electronic networks and human bodies are wired to electronic machines, are locations where such boundary-blurring processes occur intensively. I call such a city the 'hybrid city' where we can observe various geographies of technocultural spaces formed by multimedia technologies. In this epistemological context, I investigate cities in South Korea, a country that is one of the most 'wired' to electronic networks in the world. My argument is that the hybrid city, composed of global-local networks, actual- virtual circuits, centripetal-centrifugal vectors and human-machine hybrids, cannot be explained as a singular and consistent space, but rather as multiple and complex spaces. This is because the hybrid city itself exists in between different categories or territories. That is, the hybrid city does not exist as A or B, but instead in between A and в which are deterritorialised towards each other through a-parallel evolution or co-evolution, and thus it can be seen as fractal and fluid. In this sense, the hybrid city can be defined as not a 'being', but 'becomings' always in motion through the continuous 'dis/appearances' or 'dis/connections' of heterogeneous networks. In Latour's, Deleuze and Guattari's and Haraway's terms, the hybrid city is not only composed of a number of actor-networks, rhizomes or cyborgs, but also a kind of actor-network, rhizome or cyborg itself. That is, the hybrid city is the 'middle kingdom' in Latour's terms