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

We tested the hypothesis that underrepresented students in active-learning classrooms experience narrower achievement gaps than underrepresented students in traditional lecturing classrooms, averaged across all science, technology, engineering, and mathematics (STEM) fields and courses. We conducted a comprehensive search for both published and unpublished studies that compared the performance of underrepresented students to their overrepresented classmates in active-learning and traditional-lecturing treatments. This search resulted in data on student examination scores from 15 studies (9,238 total students) and data on student failure rates from 26 studies (44,606 total students). Bayesian regression analyses showed that on average, active learning reduced achievement gaps in examination scores by 33% and narrowed gaps in passing rates by 45%. The reported proportion of time that students spend on in-class activities was important, as only classes that implemented high-intensity active learning narrowed achievement gaps. Sensitivity analyses showed that the conclusions are robust to sampling bias and other issues. To explain the extensive variation in efficacy observed among studies, we propose the heads-and-hearts hypothesis, which holds that meaningful reductions in achievement gaps only occur when course designs combine deliberate practice with inclusive teaching. Our results support calls to replace traditional lecturing with evidence-based, active-learning course designs across the STEM disciplines and suggest that innovations in instructional strategies can increase equity in higher education.

https://www.pnas.org/content/pnas/117/12/6476.full.pdf

Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math.

Decades of research has shown that people are poor at detecting lies Two explanations for this finding have been proposed First, it has been suggested that lie detection is inaccurate because people rely on invalid cues when judging deception Second, it has been suggested that lack of valid cues to deception limits accuracy A series of 4 meta-analyses tested these hypotheses with the framework of Brunswik's (1952) lens model Meta-Analysis 1 investigated perceived cues to deception by correlating 66 behavioral cues in 153 samples with deception judgments People strongly associate deception with impressions of incompetence (r = 59) and ambivalence (r = 49) Contrary to self-reports, eye contact is only weakly correlated with deception judgments (r = -15) Cues to perceived deception were then compared with cues to actual deception The results show a substantial covariation between the 2 sets of cues (r = 59 in Meta-Analysis 2, r = 72 in Meta-Analysis 3) Finally, in Meta-Analysis 4, a lens model analysis revealed a very strong matching between behaviorally based predictions of deception and behaviorally based predictions of perceived deception In conclusion, contrary to previous assumptions, people rarely rely on the wrong cues Instead, limitations in lie detection accuracy are mainly attributable to weaknesses in behavioral cues to deception The results suggest that intuitive notions about deception are more accurate than explicit knowledge and that lie detection is more readily improved by increasing behavioral differences between liars and truth tellers than by informing lie-catchers of valid cues to deception

Why Do Lie-Catchers Fail? A Lens Model Meta-Analysis of Human Lie Judgments.

Smell and Taste Dysfunction in Patients With COVID-19: A Systematic Review and Meta-analysis.

A complex infrastructure is usually a reality in a virtual enterprise. For these enterprises to operate well they would need notions of workflows, global and local business processes, Service Level Agreements, and business transactions. Web Service Standards such as SOAP, WSDL, and BPEL4WS make virtual enterprise increasingly practical, speeding up the flow of business and reducing costs. These web services have to be interfaced with the internal business processes. The interchange of service results in the interaction of existing business processes and results in new distributed processes.

技術解説 IEEE Internet Computing

The casual user is typically overwhelmed by the formal logic of the Semantic Web. The gap between the end user and the logic-based scaffolding has to be bridged if the Semantic Web’s capabilities are to be utilized by the general public. This paper proposes that controlled natural languages offer one way to bridge the gap. We introduce GINO, a guided input natural language ontology editor that allows users to edit and query ontologies in a language akin to English. It uses a small static grammar, which it dynamically extends with elements from the loaded ontologies. The usability evaluation shows that GINO is well-suited for novice users when editing ontologies. We believe that the use of guided entry overcomes thehabitability problem, which adversely affects most natural language systems. Additionally, the approach’s dynamic grammar generation allows for easy adaptation to new ontologies.

/pdf/gino-a-guided-input-natural-language-ontology-editor-35nqq0jsjy.pdf

GINO – a guided input natural language ontology editor

Natural language interfaces offer end-users a familiar and convenient option for querying ontology-based knowledge bases. Several studies have shown that they can achieve high retrieval performance as well as domain independence. This paper focuses on usability and investigates if NLIs are useful from an end-user's point of view. To that end, we introduce four interfaces each allowing a different query language and present a usability study benchmarking these interfaces. The results of the study reveal a clear preference for full sentences as query language and confirm that NLIs are useful for querying Semantic Web data.

/pdf/how-useful-are-natural-language-interfaces-to-the-semantic-qbamulksdm.pdf

How useful are natural language interfaces to the semantic web for casual end-users?

The logic-based machine-understandable framework of the Semantic Web typically challenges casual users when they try to query ontologies. An often proposed solution to help casual users is the use of natural language interfaces. Such tools, however, suffer from one of the biggest problems of natural language: ambiguities. Furthermore, the systems are hardly adaptable to new domains. This paper addresses these issues by presenting Querix, a domain-independent natural language interface for the Semantic Web. The approach allows queries in natural language, thereby asking the user for clarification in case of ambiguities. The preliminary evaluation showed good retrieval performance.

/pdf/querix-a-natural-language-interface-to-query-ontologies-4t0kl6zwm6.pdf

Querix: A Natural Language Interface to Query Ontologies Based on Clarification Dialogs

https://www.zora.uzh.ch/id/eprint/44850/4/JWS_NLPV.pdf

Evaluating the usability of natural language query languages and interfaces to Semantic Web knowledge bases

Finding a good similarity assessment algorithm for the use in ontologies is central to the functioning of techniques such as retrieval, matchmaking, clustering, data-mining, ontology translations, automatic database schema matching, and simple object comparisons. This paper assembles a catalogue of ontology based similarity measures, which are experimentally compared with a “similarity gold standard” obtained by surveying 50 human subjects. Results show that human and algorithmic similarity predications varied substantially, but could be grouped into cohesive clusters. Addressing this variance we present a personalized similarity assessment procedure, which uses a machine learning component to predict a subject’s cluster membership, providing an excellent prediction of the gold standard. We conclude by hypothesizing ontology dependent similarity measures.

/pdf/how-similar-is-it-towards-personalized-similarity-measures-4xezxp9ah6.pdf

Esther Kaufmann

Papers

GINO – a guided input natural language ontology editor

How useful are natural language interfaces to the semantic web for casual end-users?

Querix: A Natural Language Interface to Query Ontologies Based on Clarification Dialogs

Evaluating the usability of natural language query languages and interfaces to Semantic Web knowledge bases

How Similar Is It? Towards Personalized Similarity Measures in Ontologies