THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Data Mining Practical Machine Learning Tools and Techniques

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).

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SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

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SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Microservices are a new and rapidly growing architectural model aimed at developing highly scalable software solutions based on independently deployable and evolvable components. Due to its novelty, microservice-related discussions are increasing in Q&A websites, such as StackOverflow (SO). In order to understand what is being discussed by the microservice community, this work has applied mining techniques and topic modelling to a manually-curated dataset of 1,043 microservice-related posts from StackOverflow. As a result, we found that 13.68% of microservice technical posts on SO discuss a single technology: Netflix Eureka. Moreover, buzzwords in the microservice ecosystem, e.g., blue/green deployment, were not identified as relevant subjects of discussion on SO. Finally, we show how a high discussion rate on SO may not reflect the popularity of a certain subject within the microservice community.

We need to talk about microservices: an analysis from the discussions on StackOverflow

Architectural decisions have a big influence on basic properties of a software system, and are difficult to change once implemented. Technology decisions deserve special attention because they are one of the most frequently occurring types of architectural decisions. Architectural knowledge is crucial for good decisions. Current architecture knowledge management approaches try to support architects by offering a rich base of architectural solutions and design decision rules. However, they mostly depend on manually capturing and maintaining the architectural knowledge. In this paper, we utilize the most popular online software development community (StackOverflow) as a source of knowledge for technology decisions to support architecture knowledge management approaches with a more efficient methods for knowledge capturing. We conducted an exploratory study, and followed a qualitative and quantitative content analysis approach. We analysed the posts in this community to identify architecture-relevant and technology-related knowledge, and to classify the posts into different types for the purpose of knowledge structuring. In addition, we evaluated our findings through feedback from practitioners.

Architectural Knowledge for Technology Decisions in Developer Communities: An Exploratory Study with StackOverflow

To make good architecture design decisions, developers need to know suitable architecture solution alternatives. However, with the rapid and continuous increase of solution alternatives (e.g. technologies, patterns, tactics), it is challenging to acquire architecture knowledge and to ensure that this knowledge is up to date. Our goal is to improve how architects search for architecturally relevant information in online developer communities. We developed a new search approach for architecturally relevant information using Stack Overflow as an example of an online developer community. Our search approach differs from a conventional keyword-based search in that it considers semantic information of architecturally relevant concepts in Stack Overflow. We also implemented the search approach as a web-based search engine. To show the effectiveness of the search approach compared to a conventional keyword-based search, we conducted an experiment with 16 practitioners. To ensure realism of the experiment, tasks given to practitioners are based on real scenarios identified in interviews with a different set of practitioners. The experiment showed that the new search approach outperforms a conventional keyword based search.

Improving the Search for Architecture Knowledge in Online Developer Communities

Eye4Ref is a rich multimodal dataset of eye-movement recordings collected from referentially complex situated settings where the linguistic utterances and their visual referential world were available to the listener. It consists of not only fixation parameters but also saccadic movement parameters that are time-locked to accompanying German utterances (with English translations). Additionally, it also contains symbolic knowledge (contextual) representations of the images to map the referring expressions onto the objects in corresponding images. Overall, the data was collected from 62 participants in three different experimental setups (86 systematically controlled sentence–image pairs and 1844 eye-movement recordings). Referential complexity was controlled by visual manipulations (e.g. number of objects, visibility of the target items, etc.), and by linguistic manipulations (e.g., the position of the disambiguating word in a sentence). This multimodal dataset, in which the three different sources of information namely eye-tracking, language, and visual environment are aligned, offers a test of various research questions not from only language perspective but also computer vision.

Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations

This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, and supervised approach, which combines dependency graph similarity and coverage features with lexical similarity measures using regression methods. We also present a way on ensembling both models. Out of 84 submitted runs, our team best multi-lingual run has been ranked 12th in overall performance with correlation of 0.61, 7th among 31 participating teams.

STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble.

Dependency parsing is a popular approach for syntactic analysis of natural language utterances. It concerns building a dependency tree of the linguistic input relying only on a model of syntactic regularities. The cognitive process of human language processing, however, has also access to other sources of knowledge, like visual clues that can be used to improve language understanding.

Amr Rekaby Salama

Papers

Architectural Knowledge for Technology Decisions in Developer Communities: An Exploratory Study with StackOverflow

Improving the Search for Architecture Knowledge in Online Developer Communities

Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations

STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble.

Multimodal Graph-Based Dependency Parsing of Natural Language