Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Social Network Analysis

The authors examine the implications of electronic shopping for consumers, retailers, and manufacturers. They assume that near-term technological developments will offer consumers unparalleled opportunities to locate and compare product offerings. They examine these advantages as a function of typical consumer goals and the types of products and services being sought and offer conclusions regarding consumer incentives and disincentives to purchase through interactive home shopping vis-à-vis traditional retail formats. The authors discuss implications for industry structure as they pertain to competition among retailers, competition among manufacturers, and retailer-manufacturer relationships.

Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in

Process mining is a growing and promising study area focused on understanding processes and to help capture the more significant findings during real execution rather than, those methods that, only observed idealized process model. The objective of this article is to map the active research topics of process mining and their main publishers by country, periodicals, and conferences. We also extract the reported application studies and classify these by exploration domains or industry segments that are taking advantage of this technique. The applied research method was systematic mapping, which began with 3713 articles. After applying the exclusion criteria, 1278 articles were selected for review. In this article, an overview regarding process mining is presented, the main research topics are identified, followed by identification of the most applied process mining algorithms, and finally application domains among different business segments are reported on. It is possible to observe that the most active research topics are associated with the process discovery algorithms, followed by conformance checking, and architecture and tools improvements. In application domains, the segments with major case studies are healthcare followed by information and communication technology, manufacturing, education, finance, and logistics.

Process mining techniques and applications – A systematic mapping study

This study defines MOOC success as the extent of student satisfaction with the course. Having more satisfied MOOC students can extend the reach of an institution to more people, build the brand name of the institution, and even help the institution use MOOCs as a source of revenue. Traditionally, student completion rate is frequently used to define MOOC success, which however, is often inaccurate because many students have no intention of finishing a MOOC. Informed by Moore's theory of transactional distance, this study adopted supervised machine learning algorithm, sentiment analysis and hierarchical linear modelling to analyze the course features of 249 randomly sampled MOOCs and 6393 students' perceptions of these MOOCs. The results showed that course instructor, content, assessment, and schedule play significant roles in explaining student satisfaction, while course structure, major, duration, video, interaction, perceived course workload and perceived difficulty play no significant roles. This study adds to the extant literature by examining specific learner-level and course-level factors that can predict MOOC learner satisfaction and estimating their relative effects. Implications for MOOC instructors and practitioners are also provided.

What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach

Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884 students, who participated in one or more of 18 highly rated MOOCs. Highly rated MOOCs were sampled because they exemplify good practices or teaching strategies. We selected highly rated MOOCs from Coursetalk, an open user-driven aggregator and discovery website that allows students to search and review various MOOCs. We defined a highly rated MOOC as a free online course that received an overall five-star course quality rating, and received at least 50 reviews from different learners within a specific subject area. We described six specific themes found across the entire data corpus: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment. The findings of this study provide valuable insight into factors that students find engaging in large-scale open online courses.

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Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student's Reflections in 18 Highly Rated MOOCs.

Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing, however, their focus was more on semantics than on graph structure. This study, by contrast, takes a special interest in exploring the structural patterns of knowledge graphs. Inspired by human cognitive processes, we propose novel methods of feature extraction for capturing the local and global structural information of knowledge graphs. These features not only exhibit good indicative power, but can also facilitate text analysis with explainable meanings. Moreover, aiming to better combine structural and semantic evidence for prediction, we propose a Neural Knowledge Graph Evaluator (NKGE) which showed superior performance over existing methods. Our contributions include a novel set of interpretable structural features and the effective NKGE for compatibility evaluation between knowledge graphs. The methods of feature extraction and the structural patterns indicated by the features may also provide insights for related studies in computational modeling and processing of knowledge.

A neural knowledge graph evaluator: Combining structural and semantic evidence of knowledge graphs for predicting supportive knowledge in scientific QA

AO_SCPLOWBSTRACTC_SCPLOWRNA velocity is a promising technique to reveal transient cellular dynamics among a heterogeneous cell population and quantify their transitions from single-cell transcriptome experiments. However, the cell transitions estimated from high dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present VeloAE, a tailored representation learning method to learn a low-dimensional representation of RNA velocity on which cell transitions can be robustly estimated. From various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture the expected cellular differentiation in different biological systems. VeloAE therefore enhances the usefulness of RNA velocity for studying a wide range of biological processes.

https://www.biorxiv.org/content/biorxiv/early/2021/03/20/2021.03.19.436127.full.pdf

Representation learning of RNA velocity reveals robust cell transitions

Chinese Acad Sci, Shenyang Inst Appl Ecol, Key Lab Terr Ecol Proc, Shenyang 110016, Liaoning, Peoples R China; Chinese Acad Sci, Inst Zool, Beijing 100080, Peoples R China; Chinese Acad Sci, S China Sea Inst Oceanog, Key Lab Trop Marine Environm Dynam, Guangzhou 510301, Peoples R China

Chen Qiao

Papers

What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach

Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student's Reflections in 18 Highly Rated MOOCs.

A neural knowledge graph evaluator: Combining structural and semantic evidence of knowledge graphs for predicting supportive knowledge in scientific QA

Representation learning of RNA velocity reveals robust cell transitions

Biodegradation of the herbicides atrazine, cyanazine, and dicamba by methanogenic enrichment cultures from selective soils of China