In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

/pdf/machine-learning-algorithms-real-world-applications-and-3ah2ghoulk.pdf

Machine Learning: Algorithms, Real-World Applications and Research Directions

In the current age of the Fourth Industrial Revolution (4IR or Industry 40), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more We also highlight the challenges and potential research directions based on our study Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view

/pdf/machine-learning-algorithms-real-world-applications-and-383hp9fib7.pdf

Deep learning for smart manufacturing: Methods and applications

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

Blockchain for Internet of Things: A Survey

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive, and anticipatory communications. Such innovative interaction paradigms make AmI technology a suitable candidate for developing various real life solutions, including in the healthcare domain. This survey will discuss the emergence of AmI techniques in the healthcare domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of AmI, such as smart environments and wearable medical devices. We will summarize the state-of-the-art artificial intelligence (AI) methodologies used for developing AmI system in the healthcare domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions), and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

/pdf/a-survey-on-ambient-intelligence-in-healthcare-wkvwfodws9.pdf

A Survey on Ambient Intelligence in Healthcare

Over the last two decades, manufacturing across the globe has evolved to be more intel-ligent and data driven. In the age of industrial Internet of Things, a smart production unit can be perceived as a large connected industrial system of materials, parts, machines, tools, inventory, and logistics that can relay data and communicate with each other. While, traditionally, the focus has been on machine health and predictive maintenance, the manufacturing industry has also started focusing on analyzing data from the entire production line. These applications bring a new set of analytics challenges. Unlike tradi-tional data mining analysis, which consists of lean datasets (that is, datasets with few fea-tures), manufacturing has fat datasets. In addition, previous approaches to manufacturing analytics restricted themselves to small time periods of data. The latest advances in big data analytics allows researchers to do a deep dive into years of data. Bosch collects and utilizes all available information about its products to increase its understanding of complex linear and nonlinear relationships between parts, machines, and assembly lines. This helps in use cases such as the discovery of the root cause of internal defects. This article presents a case study and provides detail about challenges and approaches in data extraction, modeling, and visualization.

Manufacturing Analytics and Industrial Internet of Things

Gesture spotting is the task of detecting and recognizing gestures defined in a vocabulary. The difficulty of gesture spotting stems from the fact that valid gestures appear sporadically in a continuous gesture stream, interspersed with invalid gestures (movements that do not correspond to any gesture contained in the vocabulary). In this paper, a novel method for designing threshold models from valid gesture models learnt through Adaptive Boosting is proposed. This threshold model is adaptive in nature and discriminates between valid and invalid gestures. Furthermore, a gesture spotting network consisting of the individual gesture models and the threshold model is proposed to perform the task of spotting and recognition simultaneously. This technique is evaluated in the context of spotting and recognizing activity gestures (hand gestures) from continuous accelerometer data streams. The proposed technique results in a precision of 0.78 and a recall of 0.93 out performing the HMM based threshold model which resulted in 0.4 and 0.81 precision and recall values.

Activity gesture spotting using a threshold model based on Adaptive Boosting

Linus' Law reflects on a key characteristic of open source software development: developers' tendency to closely work together in the bug resolution process. In this paper we empirically examine Linus' Law using a data-set of 1,000+ Android bugs, owned by 70+ developers. Our results indicate that encouraging developers to work closely with one another has nuanced implications; while one form of contact may help reduce bug resolution time, another form can have quite the opposite effect. We present statistically significant evidence in support of our results and discuss their relevance at the individual and organizational levels.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6583&context=sis_research

How many eyeballs does a bug need? an empirical validation of Linus' Law

Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data. We present a Support Vector Machine (SVM) based supervised DA technique, where the similarity between source and target domains is modeled as the similarity between their SVM decision boundaries. We couple the source and target SVMs and reduce the model to a standard single SVM. We test the Coupled-SVM on multiple datasets and compare our results with other popular SVM based DA approaches.

Coupled Support Vector Machines for Supervised Domain Adaptation

Cooking activities are complex activities consisting of multiple steps or tasks. These tasks can be associated with one another based on two properties - the temporal structure that defines the sequence of occurrence of tasks and the objects that are used in the activity. This paper develops cooking activity models for the purpose of task prediction based on these two properties. The temporal structure of the sequence of tasks is captured by the novel hierarchical state space markov chain (HMC) and the object usage is represented using the object based task group (OTG) models. A probabilistic task prediction algorithm that fuses the HMC and OTG models has been developed to predict the next most probable task, given that a sequence of tasks has been completed. The proposed models and algorithms have been evaluated on two complex cooking activities - making brownies and making eggs, achieving a subject independent accuracy of 68.5% for predicting tasks, which is an improvement by an average of 6% in comparison to a Markov chain. The work done in the paper is first of its kind as it focuses on task prediction rather than task recognition. The task prediction framework described in the paper can be easily adapted to any complex activity supporting various annotation schemes and activity models.

Task Prediction in Cooking Activities Using Hierarchical State Space Markov Chain and Object Based Task Grouping

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