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

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions The book is intended for a professional audience composed of researchers and practitioners in industry This book is also appropriate for advanced-level students in computer science

Data Streams: Models and Algorithms

With rising importance of knowledge interchange, many industrial and academic applications have adopted ontologies as their conceptual backbone. However, industrial and academic environments are very dynamic, thus inducing changes to application requirements. To fulfill these changes, often the underlying ontology must be evolved as well. As ontologies grow in size, the complexity of change management increases, thus requiring a well-structured ontology evolution process. In this paper we identify a possible six-phase evolution process and focus on providing the user with capabilities to control and customize it. We introduce the concept of an evolution strategy encapsulating policy for evolution with respect to user?s requirements.

/pdf/user-driven-ontology-evolution-management-1dcex5h6ej.pdf

User-Driven Ontology Evolution Management

Translation is a repetitive activity. The attempt to automate such a difficult task has been a long-term scientific dream; in the past years research in this field has acquired a growing interest, making some forms of Machine Translation (MT) a reality. Among the several types of approaches in MT, one of the most promising paradigms is MAHT and, in particular, example-Based Machine Translation (EBMT). An EBMT system translates by analogy, using past translations to translate other, similar sourcelanguage sentences into the target language. The basic premise is that, if a previously translated sentence occurs again, the same translation is likely to be correct. In this paper, we propose a solution based on a purely syntactic approach for searching similar sentences and parts of them in an EBMT system; the underlying similarity measure is based on the similarity between sequence of terms such that the sentences most close to a given one are those who maintain most of the original form and contents. The system efficiently retrieves and ranks the most similar sentences available and, when no useful suggestion exists, it proceeds with the retrieval of similar parts. We opted for a design that would require minimal changes to existing databases and whose similarity measure and search algorithms are completely independent from the involved languages. This work has been developed as a joint work with LOGOS S.p.A., a worldwide leader in multilingual document translation.

Searching Similar (Sub)Sentences for Example-Based Machine Translation

In several application fields including legal and medical domains, XML documents are “versioned” along.different.dimensions.of.interest,.whose.nature.depends.on.the.application.needs.such.as.time,. space and security. Specifically, temporal and Semantic versioning is particularly demanding in a broad range.of.application.domains.where.temporal.versioning.can.be.used.to.maintain.histories.of.the.underlying.resources.along.various.time.dimensions,.and.Semantic.versioning.can.then.be.used.to.model. limited.applicability.of.resources.to.individual.cases.or.contexts..The.selection.and.reconstruction.of. the.version(s).of.interest.for.a.user.means.the.retrieval.of.those.fragments.of.documents.that.match.both. the.implicit.and.explicit.user.needs,.which.can.be.formalized.as.what.we.call.personalization queries.. In this chapter, the authors focus on the design and implementation issues of a personalization query processor..They.consider.different.design.options.and,.among.them,.they.introduce.an.in-depth.study. of.a.native.solution.by.showing,.also.through.experimental.evaluation,.how.some.of.the.best.performing. technological.solutions.available.today.for.XML.data.management.can.be.successfully.extended.and. optimally combined in order to support personalization queries.

Issues in Personalized Access to Multi-Version XML Documents

In this paper, we present the results of an ongoing research involving the design and implementation of systems supporting personalized access to multi-version resources in an eGovernment scenario. Personalization is supported by means of Semantic Web techniques and is based on an ontology-based profiling of users (citizens). Resources we consider are collections of norm documents in XML format but can also be generic Web pages and portals or eGovernment services. We introduce a reference infrastructure, describe the organization and present performance figures of a prototype system we have developed.

/pdf/semantic-web-techniques-for-personalization-of-egovernment-3ta42bfhck.pdf

Semantic web techniques for personalization of egovernment services

Hashtags are increasingly used to promote, foster and group conversations around specific topics. For example, the entertainment industry widely uses hashtags to increase interest around their products. In this paper, we analyze whether hashtags are effective in a niche scenario like the art exhibitions. The obtained results show very different behaviors and confused strategies: from museums that do not consider hashtags at all, to museums that create official hastags, but hardly mention them; from museums that create multiple hashtags for the same exhibition, to those that are very confused about hashtag usage. Furthermore, we discovered an interesting case, where a smart usage of hashtags stimulated the interest around art. Finally, we highlight few practical guidelines with behaviors to follow and to avoid; the guidelines might help promoting art exhibitions.

The Use of Hashtags in the Promotion of Art Exhibitions

In the first part of this chapter we will introduce a general temporally enhanced conceptual data model able to represent time varying data in the spirit of a temporally enhanced entity-relationship data model. In the second part, we will introduce an object-oriented conceptual data model enriched with schema change operators, which can represent the explicit temporal evolution of the schema while maintaining a consistent view of (static) instantiated data. We will introduce a provably correct encoding of both conceptual data models and their inference problems in description logics. In this way, we study the properties of both the temporal conceptual data model and the object-oriented data model with schema change facilities.

Federica Mandreoli

Papers

Searching Similar (Sub)Sentences for Example-Based Machine Translation

Issues in Personalized Access to Multi-Version XML Documents

Semantic web techniques for personalization of egovernment services

The Use of Hashtags in the Promotion of Art Exhibitions

Description Logics for Modeling Dynamic Information