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

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ACM Transactions on Database Systems

https://link.springer.com/content/pdf/10.1057%2Fjors.1990.171.pdf

Computer Intensive Methods for Testing Hypotheses: An Introduction

In 2008, Cathy O’Neil, a data scientist at the hedge fund D.E. Shaw, watched as the entire financial sector around her imploded.1 The financial crisis gave O’Neil, a self-proclaimed math nerd with a Ph.D., a front row seat to the weaponization of mathematics. During the financial crisis, complex mathematical models designed and marketed to be superior to human ability and beyond the confines of human subjectivity turned out to be farces—layers of false assumptions coated in difficult-to-understand layers of mathematics. In order to dupe both the public and themselves, financial analysts at banks used models and algorithms as a justification for their subjective and flawed inputs. Ten years after the financial crisis, O’Neil sees these nefarious models infiltrating every aspect of modern life. These “Weapons of Math Destruction”—as O’Neil calls them—are (1) “opaque,” (2) “beyond dispute or appeal,” and (3) disproportionally impact the underprivileged.2 These Weapons of Math Destruction, or WMDs, replace the role of traditional subjective decision makers and cause those affected by their decisionmaking to adjust their ways of life to the models. In Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, O’Neil highlights different WMDs that have infiltrated various areas of American life.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Distributional semantics is a usage-based model of meaning, based on the assumption that the statistical distribution of linguistic items in context plays a key role in characterizing their semantic behavior. Distributional models build semantic representations by extracting co-occurrences from corpora and have become a mainstream research paradigm in computational linguistics. In this review, I present the state of the art in distributional semantics, focusing on its assets and limits as a model of meaning and as a method for semantic analysis.

Distributional Models of Word Meaning

Modeling competences is an integral part of many Human Resource (HR) and e-Learning related activities. HR departments use competence descriptions to define requirements needed for performing specific tasks or jobs. The same competences are acquired by employees and applicants by e.g. experience or certifications. Typically, HR departments need to match such required and acquired competences in order to find suitable candidates. In e-Learning a similar situation arises. Curricula or training programmes need to describe prerequisites that must be fulfilled before joining and the competences that will be acquired after successful completion. This paper analyses the limitations and extends existing approaches for modeling competences in order to allow (semi-)automatic competence matching.

/pdf/a-model-for-competence-gap-analysis-1axabv5l8o.pdf

A model for competence gap analysis

Skyline queries are a well-established technique for database query personalization and are widely acclaimed for their intuitive query formulation mechanisms. However, when operating on incomplete datasets, skylines queries are severely hampered and often have to resort to highly error-prone heuristics. Unfortunately, incomplete datasets are a frequent phenomenon, especially when datasets are generated automatically using various information extraction or information integration approaches. Here, the recent trend of crowd-enabled databases promises a powerful solution: during query execution, some database operators can be dynamically outsourced to human workers in exchange for monetary compensation, therefore enabling the elicitation of missing values during runtime. Unfortunately, this powerful feature heavily impacts query response times and (monetary) execution costs. In this paper, we present an innovative hybrid approach combining dynamic crowd-sourcing with heuristic techniques in order to overcome current limitations. We will show that by assessing the individual risk a tuple poses with respect to the overall result quality, crowd-sourcing efforts for eliciting missing values can be narrowly focused on only those tuples that may degenerate the expected quality most strongly. This leads to an algorithm for computing skyline sets on incomplete data with maximum result quality, while optimizing crowd-sourcing costs.

/pdf/skyline-queries-in-crowd-enabled-databases-9p6jqntfsi.pdf

Skyline queries in crowd-enabled databases

Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use

By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowd-sourcing to obtain all values individually, we leverage the user-generated data found in the Social Web: By exploiting user ratings we build perceptual spaces, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.

Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion

By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowd-sourcing to obtain all values individually, we leverage the usergenerated data found in the Social Web: By exploiting user ratings we build perceptual spaces, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.

/pdf/pushing-the-boundaries-of-crowd-enabled-databases-with-query-nxybph6r2b.pdf

Christoph Lofi

Papers

A model for competence gap analysis

Skyline queries in crowd-enabled databases

Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use

Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion

Pushing the boundaries of crowd-enabled databases with query-driven schema expansion