Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with a fixed number of sensors installed when compared to the state-of-the-art, which is also proven by our theoretical analysis.

Machine learning and deep learning have provided us with an exploration of a whole new research era. As more data and better computational power become available, they have been implemented in various fields. The demand for artificial intelligence in the field of health informatics is also increasing and we can expect to see the potential benefits of artificial intelligence applications in healthcare. Deep learning can help clinicians diagnose disease, identify cancer sites, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes, and predict infectious disease outbreaks with high accuracy. In contrast to traditional models, its approach does not require domain-specific data pre-process, and it is expected that it will ultimately change human life a lot in the future. Despite its notable advantages, there are some challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label) and model (reliability, interpretability, feasibility, security, scalability) for practical use. This article presents a comprehensive review of research applying deep learning in health informatics with a focus on the last five years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research. We highlight ongoing popular approaches' research and identify several challenges in building deep learning models.

In picker-to-parts warehouses, order picking is a cost- and labor-intensive operation that must be designed efficiently. It comprises the construction of order batches and the associated order picker routes, and the assignment and sequencing of those batches to multiple order pickers. The ever-increasing competitiveness among e-commerce companies has made the joint optimization of this order picking process inevitable. Inspired by the large number of product returns and the many but small-sized customer orders, we address a new integrated order picking process problem. We integrate the restocking of returned products into regular order picking routes and we allow for the decomposition of customer orders so that multiple batches may contain products from the same customer order. We thereby generalize the existing models on order picking processing. We provide Mixed Integer Programming (MIP) formulations and a tailored adaptive large neighborhood search heuristic that, amongst others, exploits these MIPs. We propose a new set of practically-sized benchmark instances, consisting of up to 5547 to be picked products and 2491 to be restocked products. On those large-scale instances, we show that integrating the restocking of returned products into regular order picker routes results in cost-savings of 10 to 15%. Allowing for the decomposition of the customer orders' products results in cost savings of up to 44% compared to not allowing this. Finally, we show that on average cost-savings of 17.4% can be obtained by using our ALNS instead of heuristics typically used in practice.

The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.

Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios. In the present paper, we propose to develop computational models inspired by our knowledge of human infants' social adaptive abilities. We believe this may provide solutions at an architectural level to overcome the limits of current systems. Specifically, we present the functional advantages that adaptive Theory of Mind (ToM) systems would support in robotics (i.e., mentalizing for belief understanding, proactivity and preparation, active perception and learning) and contextualize them in practical applications. We review current computational models mainly based on the simulation and teleological theories, and robotic implementations to identify the limitations of ToM functions in current robotic architectures and suggest a possible future developmental pathway. Finally, we propose future studies to create innovative computational models integrating the properties of the simulation and teleological approaches for an improved adaptive ToM ability in robots with the aim of enhancing human-robot interactions and permitting the application of robots in unexplored environments, such as disasters and construction sites. To achieve this goal, we suggest directing future research towards the modern cross-talk between the fields of robotics and developmental psychology.

A wide range of claims and news can be found online, from the technology "not being ready" to vendors declaring that AI is already within their tools. Ultimately, firms must decide when to invest in AI, or rather, which AI-enabled tool provides the best return on investment. By understanding how AI fits into their technology infrastructure and audit processes, audit managers and partners can most effectively choose the right solution. It's critical to understand what real AI and machine learning techniques mean, and the implications for the firm. So how do firms measure their readiness for AI and assess potential vendors? Before adopting AI, firms should understand how it will improve their business and bring new value to clients.

As the capabilities of deep learning algorithms have improved exponentially over the last few years, there has been increasing awareness of the ethical considerations in deploying technology that can autonomously capture the underlying patterns of data and make decisions based upon it with precision and nuance unthinkable even a few years ago. This rapid developmental pace is enabling deep learning applications that push the boundaries of computational decision-making, from today's facial recognition algorithms to tomorrow's driverless cars to future autonomous "killer robots." At the same time, the impact of algorithmic bias is becoming more visible as AI systems are being deployed into ever more influential roles. As society reacts to AI adoption in sensitive areas like military, judicial and surveillance use, governments and companies have responded by arguing that so long as their algorithms perform as intended, they are ethical, suggesting there is considerable confusion about the difference between AI correctness and AI ethics. Like all computer code, deep learning algorithms and the data-driven models that power them are designed to perform specific tasks within specific operating constraints with a guaranteed accuracy rate.

For more depth, we recommend this insightful review article by UW Prof. Pedro Domingos: A Few Useful Things to Know about Machine Learning

Automated tutoring systems offer the flexibility and scalability necessary to facilitate the provision of high quality and universally accessible programming education. In order to realise the full potential of these systems, recent work has proposed a diverse range of techniques for automatically generating hints to assist students with programming exercises. This paper integrates these apparently disparate approaches into a coherent whole. Specifically, it emphasises that all hint techniques can be understood as a series of simpler components with similar properties. Using this insight, it presents a simple framework for describing such techniques, the Hint Iteration by Narrow-down and Transformation Steps (HINTS) framework, and it surveys recent work in the context of this framework. It discusses important implications of the survey and framework, including the need to further develop evaluation methods and the importance of considering hint technique components when designing, communicating and evaluating hint systems. Ultimately, this paper is designed to facilitate future opportunities for the development, extension and comparison of automated programming hint techniques in order to maximise their educational potential.

Abstract--In the era of the Internet of information, we have gone through layering, cross-layer, and cross-system desi gn paradigms. Recently, the "curse of modeling" and "curse of d i-mensionality" of the cross-system design paradigm have res ulted in the popularity of using artificial intelligence (AI) to op timize the Internet of information. However, many significant rese arch challenges remain to be addressed for the AI approach, inclu ding the lack of high-quality training data due to privacy and resources constraints in this data-driven approach. T o add ress these challenges, we need to take a look at humans' cooperati on in a larger time scale. T o facilitate cooperation in modern h istory, we have built three major technologies: "grid of transporta tion", "grid of energy", and "the Internet of information". In this paper, we argue that the next cooperation paradigm could be the "Internet of intelligence (Intelligence-Net)", where intelligence can be easily obtained like energy and information, enabled by the recent advances in blockchain technology. We present so me recent advances in these areas, and discuss some open issues and challenges that need to be addressed in the future. The Internet has become one of the major foundations for our socioeconomic systems by enabling information exchan ge among people and machines.