A Survey of Deep Learning Methods for WTP Control and Monitoring

Drinking water is vital for everyday life. We are dependent on water for everything from cooking to sanitation. Without water, it is estimated that the average, healthy human won’t live more than 3–5 days. The water is therefore essential for the productivity of our community. The water treatment process (WTP) may vary slightly at different locations, depending on the technology of the plant and the water it needs to process, but the basic principles are largely the same. As the WTP is complex, traditional laboratory methods and mathematical models have limitations to optimize this type of operations. These pose challenges for water-sanitation services and research community. To overcome this matter, deep learning is used as an alternative to provide various solutions in WTP optimization. Compared to traditional machine learning methods and because of its practicability, deep learning has a strong learning ability to better use data sets for data mining and knowledge extraction. The aim of this survey is to review the existing advanced approaches of deep learning and their applications in WTP especially in coagulation control and monitoring. Besides, we also discuss the limitations and prospects of deep learning

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

DIGITAL WINE: HOW PLATFORMS AND ALGORITHMS WILL RESHAPE THE WINE INDUSTRY

La tesi si propone di analizzare come la digitalizzazione e gli approcci basati sui dati, in particolare quelli che sfruttano l'intelligenza artificiale, stiano impattando il settore vitivinicolo e facendo emergere modelli nuovi di business. Quest'ultimo aspetto sarà approfondito tramite due casi studio di piattaforme digitali che, attraverso approcci diversi, stanno contribuendo a generare un ecosistema digitale virtuoso, con potenziali benefici per tutta la catena del valore a livello di settore.The thesis aims to analyze how digitalization and data-driven approaches, in particular those that leverage artificial intelligence, are impacting the wine industry and generating new business models. The latter aspect will be explored through two case studies of digital platforms which, through different approaches, are helping to generate a virtuous digital ecosystem, with potential benefits for the entire value chain at the industry level

Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy

We explore the promises and challenges of employing sequential decision-making algorithms - such as bandits, reinforcement learning, and active learning - in law and public policy. While such algorithms have well-characterized performance in the private sector (e.g., online advertising), their potential in law and the public sector remains largely unexplored, due in part to distinct methodological challenges of the policy setting. Public law, for instance, can pose multiple objectives, necessitate batched and delayed feedback, and require systems to learn rational, causal decision-making policies, each of which presents novel questions at the research frontier. We highlight several applications of sequential decision-making algorithms in regulation and governance, and discuss areas for needed research to render such methods policy-compliant, more widely applicable, and effective in the public sector. We also note the potential risks of such deployments and describe how sequential decision systems can also facilitate the discovery of harms. We hope our work inspires more investigation of sequential decision making in law and public policy, which provide unique challenges for machine learning researchers with tremendous potential for social benefit.Comment: Version 1 presented at Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice, a NeurIPS 2021 Worksho

TOWARDS BUILDING AN INTELLIGENT INTEGRATED MULTI-MODE TIME DIARY SURVEY FRAMEWORK

Enabling true responses is an important characteristic in surveys; where the responses are free from bias and satisficing. In this thesis, we examine the current state of surveys, briefly touching upon questionnaire surveys, and then on time diary surveys (TDS). TDS are open-ended conversational surveys of a free-form nature with both, the interviewer and the respondent, playing a part in its progress and successful completion. With limited research available on how intelligent and assistive components can affect TDS respondents, we explore ways in which intelligent systems such as Computer Adaptive Testing, Intelligent Tutoring Systems, Recommender Systems, and Decision Support Systems can be leveraged for use in TDS. The motivation for this work is from realizing the opportunity that an enhanced web based instrument can offer the survey domain to unite the various facets of web based surveys to create an intelligent integrated multi-mode TDS framework. We envision the framework to provide all the advantages of web based surveys and interviewer assisted surveys. The two primary challenges are in determining what data is to be used by the system and how to interact with the user – specifically integrating the (1) Interviewer-assisted mode, and (2) Self-administered mode. Our proposed solution – the intelligent integrated multi-mode framework – is essentially the solution to a set of modeling problems and we propose two sets of overreaching mechanisms: (1) Knowledge Engineering Mechanisms (KEM), and (2) Interaction Mechanisms (IxM), where KEM serves the purpose of understanding what data can be created, used and stored while IxM deals with interacting with the user. We build and study a prototype instrument in the interviewer-assisted mode based on the framework. We are able to determine that the instrument improves the interview process as intended and increases the data quality of the response data and is able to assist the interviewer. We also observe that the framework’s mechanisms contribute towards reducing interviewers’ cognitive load, data entry times and interview time by predicting the next activity. Advisor: Leenkiat So

Smart Cities and FDI

Smart cities have emerged as a worldwide trend, progressing from the implementation of sensors and technologies to enhance infrastructures and service delivery to the development of city-wide policy through the utilization of big data analysis. The goal of a "Smart City" is to improve standard of life by acquiring knowledge from information gathered from people, technologies, and networked sensors. This research argues that smart cities may attract inflows Foreign Direct Investment FDI by influencing the investment choices of global corporate players in the new age by facilitating the flow of data, technology, innovations, and best practices while offering a livable and productive environment. When deciding where to invest, foreign investors will take new criteria into account. These factors include how sociable the environment is, how stable the economic condition is, and how digitally advanced the destination is. These variables will outweigh conventional investment considerations like inexpensive labor, abundant resources, and a large population. For developing nations and rising economies where businesses need capital and knowledge to increase their worldwide sales, foreign direct investment is crucial. To maintain high growth rates the countries should attract international investors, and, most importantly, provide its citizens with a good standard of living, and therefore, should speed up its investments in sustainable smart cities. &nbsp

Serendipitous News Discovery Increases News Consumption in News Recommender Systems

News recommender system users obtain news via incidental exposure to news and experience serendipity in the incidental news consumption. Serendipitous news discovery, the same as serendipity, refers to discovering unexpected and useful information unintentionally. Researchers suggest building serendipitous news recommender systems and increasing serendipitous news discovery to increase the diversity of the news consumption. However, the impacts of serendipitous news discovery on news consumption are uninvestigated, and rare research provides theoretical guidance to the serendipitous news recommender systems. The thesis investigated the impacts of serendipitous news discovery on news consumption with a serendipityrelated emotion, surprise, as a mediator and need for activation as a moderator. 463 participants recruited from Amazon MTurk completed the online survey-experiment. The findings suggest that surprise mediates the correlations between serendipitous news discovery and news consumption. Users who experience higher serendipitous news discovery indicate more positive attitudes on news consumption in the news recommender systems. The results also indicate the possibility that the lack of constant serendipitous news discovery may lead to the consumption of the news similar to the news that trigger serendipity. The research suggests that serendipitous news discovery increases news consumption, including news selection and reading