Supervising Offline Partial Evaluation of Logic Programs using Online Techniques

A major impediment for more widespread use of offline partial evaluation is the difficulty of obtaining and maintaining annotations for larger, realistic programs. Existing automatic binding-time analyses still only have limited applicability and annotations often have to be created or improved and maintained by hand, leading to errors. We present a technique to help overcome this problem by using online control techniques which supervise the specialisation process in order to help the development and maintenance of correct annotations by identifying errors. We discuss an implementation in the Logen system and show on a series of examples that this approach is effective: very few false alarms were raised while infinite loops were detected quickly. We also present the integration of this technique into a web interface, which highlights problematic annotations directly in the source code. A method to automatically fix incorrect annotations is presented, allowing the approach to be also used as a pragmatic binding time analysis. Finally we show how our method can be used for efficiently locating built-in errors in Prolog source code

Fast Offline Partial Evaluation of Logic Programs

One of the most important challenges in partial evaluation is the design of automatic methods for ensuring the termination of the process. In this work, we introduce sufficient conditions for the strong (i.e., independent of a computation rule) termination and quasitermination of logic programs which rely on the construction of size-change graphs. We then present a fast binding-time analysis that takes the output of the termination analysis and annotates logic programs so that partial evaluation terminates. In contrast to previous approaches, the new binding-time analysis is conceptually simpler and considerably faster, scaling to medium-sized or even large examples. © 2014 Elsevier Inc. All rights reserved.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovacion under grant TIN2008-06622-C03-02 and by the Generalitat Valenciana under grant PROMETEO/2011/052.Leuschel, M.; Vidal Oriola, GF. (2014). Fast Offline Partial Evaluation of Logic Programs. Information and Computation. 235:70-97. https://doi.org/10.1016/j.ic.2014.01.005S709723

Digital Visitor Evaluations at the NHA

The goal of this project was to create technology-assisted surveys for the Nantucket Historical Association (NHA). After researching industry best practices and consulting with NHA staff, the student team used web-based software to create a 2014 NHA Programs Survey and Museum Survey. The team conducted public testing to gauge impressions of the digital technology, and analyzed response data from the implemented surveys. With the instruments developed, the NHA will be able to collect more in-depth visitor feedback that will help the NHA improve its administrative decision-making and adjust its practices to better meet its patrons’ needs. It is our hope that the NHA continues to use and create digital surveys to further enhance visitor experiences

A Partial Evaluation Framework for Order-sorted Equational Programs modulo Axioms

[EN] Partial evaluation is a powerful and general program optimization technique with many successful applications. Existing PE schemes do not apply to expressive rule-based languages like Maude, CafeOBJ, OBJ, ASF+SDF, and ELAN, which support: 1) rich type structures with sorts, subsorts, and overloading; and 2) equational rewriting modulo various combinations of axioms such as associativity, commutativity, and identity. In this paper, we develop the new foundations needed and illustrate the key concepts by showing how they apply to partial evaluation of expressive programs written in Maude. Our partial evaluation scheme is based on an automatic unfolding algorithm that computes term variants and relies on high-performance order-sorted equational least general generalization and order-sorted equational homeomorphic embedding algorithms for ensuring termination. We show that our partial evaluation technique is sound and complete for convergent rewrite theories that may contain various combinations of associativity, commutativity, and/or identity axioms for different binary operators. We demonstrate the effectiveness of Maude's automatic partial evaluator, Victoria, on several examples where it shows significant speed-ups. (C) 2019 Elsevier Inc. All rights reserved.This work has been partially supported by the EU (FEDER) and the Spanish MCIU under grant RTI2018-094403-B-C32, by Generalitat Valenciana under grant PROMETEO/2019/098, and by NRL under contract number N00173-17-1-G002. Angel Cuenca-Ortega has been supported by the SENESCYT, Ecuador (scholarship program 2013).Alpuente Frasnedo, M.; Cuenca-Ortega, AE.; Escobar Román, S.; Meseguer, J. (2020). A Partial Evaluation Framework for Order-sorted Equational Programs modulo Axioms. Journal of Logical and Algebraic Methods in Programming. 110:1-36. https://doi.org/10.1016/j.jlamp.2019.100501S13611

Automatic management tool for attribution and monitorization of projects/internships

No último ano académico, os estudantes do ISEP necessitam de realizar um projeto final para obtenção do grau académico que pretendem alcançar. O ISEP fornece uma plataforma digital onde é possível visualizar todos os projetos que os alunos se podem candidatar. Apesar das vantagens que a plataforma digital traz, esta também possui alguns problemas, nomeadamente a difícil escolha de projetos adequados ao estudante devido à excessiva oferta e falta de mecanismos de filtragem. Para além disso, existe também uma indecisão acrescida para selecionar um supervisor que seja compatível para o projeto selecionado. Tendo o aluno escolhido o projeto e o supervisor, dá-se início à fase de monitorização do mesmo, que possui também os seus problemas, como o uso de diversas ferramentas que posteriormente levam a possíveis problemas de comunicação e dificuldade em manter um histórico de versões do trabalho desenvolvido. De forma a responder aos problemas mencionados, realizou-se um estudo aprofundado dos tópicos de sistemas de recomendação aplicados a Machine Learning e Learning Management Systems. Para cada um desses grandes temas, foram analisados sistemas semelhantes capazes de solucionar o problema proposto, tais como sistemas de recomendação desenvolvidos em artigos científicos, aplicações comerciais e ferramentas como o ChatGPT. Através da análise do estado da arte, concluiu-se que a solução para os problemas propostos seria a criação de uma aplicação Web para alunos e supervisores, que juntasse as duas temáticas analisadas. O sistema de recomendação desenvolvido possui filtragem colaborativa com factorização de matrizes, e filtragem por conteúdo com semelhança de cossenos. As tecnologias utilizadas no sistema centram-se em Python no back-end (com o uso de TensorFlow e NumPy para funcionalidades de Machine Learning) e Svelte no front-end. O sistema foi inspirado numa arquitetura em microsserviços em que cada serviço é representado pelo seu próprio contentor de Docker, e disponibilizado ao público através de um domínio público. O sistema foi avaliado através de três métricas: performance, confiabilidade e usabilidade. Foi utilizada a ferramenta Quantitative Evaluation Framework para definir dimensões, fatores e requisitos(e respetivas pontuações). Os estudantes que testaram a solução avaliaram o sistema de recomendação com um valor de aproximadamente 7 numa escala de 1 a 10, e os valores de precision, recall, false positive rate e F-Measure foram avaliados em 0.51, 0.71, 0.23 e 0.59 respetivamente. Adicionalmente, ambos os grupos classificaram a aplicação como intuitiva e de fácil utilização, com resultados a rondar o 8 numa escala de 1 em 10.In the last academic year, students at ISEP need to complete a final project to obtain the academic degree they aim to achieve. ISEP provides a digital platform where all the projects that students can apply for can be viewed. Besides the advantages this platform has, it also brings some problems, such as the difficult selection of projects suited for the student due to the excessive offering and lack of filtering mechanisms. Additionally, there is also increased difficulty in selecting a supervisor compatible with their project. Once the student has chosen the project and the supervisor, the monitoring phase begins, which also has its issues, such as using various tools that may lead to potential communication problems and difficulty in maintaining a version history of the work done. To address the mentioned problems, an in-depth study of recommendation systems applied to Machine Learning and Learning Management Systems was conducted. For each of these themes, similar systems that could solve the proposed problem were analysed, such as recommendation systems developed in scientific papers, commercial applications, and tools like ChatGPT. Through the analysis of the state of the art, it was concluded that the solution to the proposed problems would be the creation of a web application for students and supervisors that combines the two analysed themes. The developed recommendation system uses collaborative filtering with matrix factorization and content-based filtering with cosine similarity. The technologies used in the system are centred around Python on the backend (with the use of TensorFlow and NumPy for Machine Learning functionalities) and Svelte on the frontend. The system was inspired by a microservices architecture, where each service is represented by its own Docker container, and it was made available online through a public domain. The system was evaluated through performance, reliability, and usability. The Quantitative Evaluation Framework tool was used to define dimensions, factors, and requirements (and their respective scores). The students who tested the solution rated the recommendation system with a value of approximately 7 on a scale of 1 to 10, and the precision, recall, false positive rate, and F-Measure values were evaluated at 0.51, 0.71, 0.23, and 0.59, respectively. Additionally, both groups rated the application as intuitive and easy to use, with ratings around 8 on a scale of 1 to 10

Sandboxed, Online Debugging of Production Bugs for SOA Systems

Short time-to-bug localization is extremely important for any 24x7 service-oriented application. To this end, we introduce a new debugging paradigm called live debugging. There are two goals that any live debugging infrastructure must meet: Firstly, it must offer real-time insight for bug diagnosis and localization, which is paramount when errors happen in user-facing applications. Secondly, live debugging should not impact user-facing performance for normal events. In large distributed applications, bugs which impact only a small percentage of users are common. In such scenarios, debugging a small part of the application should not impact the entire system. With the above-stated goals in mind, this thesis presents a framework called Parikshan, which leverages user-space containers (OpenVZ) to launch application instances for the express purpose of live debugging. Parikshan is driven by a live-cloning process, which generates a replica (called debug container) of production services, cloned from a production container which continues to provide the real output to the user. The debug container provides a sandbox environment, for safe execution of monitoring/debugging done by the users without any perturbation to the execution environment. As a part of this framework, we have designed customized-network proxies, which replicate inputs from clients to both the production and test-container, as well safely discard all outputs. Together the network duplicator, and the debug container ensure both compute and network isolation of the debugging environment. We believe that this piece of work provides the first of its kind practical real-time debugging of large multi-tier and cloud applications, without requiring any application downtime, and minimal performance impact

MIT OpenCourseWare 2005 Program Evaluation Findings

MIT OpenCourseWare (OCW) is a large-scale, web-based electronic publishing initiative, accessible on the Internet at ocw.mit.edu. Through OCW, MIT makes its core teaching materials -- lecture notes, problem sets, syllabi, reading lists, simulations, etc. -- openly available for non-commercial educational purposes. OCW publishes those materials in standards-based formats for anyone with access to the Internet. OCW has a dual mission: * To provide free access to virtually all MIT course materials for educators, students, and individual learners around the world.* To extend the reach and impact of MIT OCW and the opencourseware concept.Beginning in 2002, MIT OpenCourseWare has published 1,259 of MIT's approximately 1,800 courses to date. In addition, OCW has published 133 updated versions of previously published courses. MIT OpenCourseWare expects to have published 1,800 courses by 2008, and beyond that milestone will continue to update courses as an ongoing activity of the Institute.Major findings* Access: Online access to MIT OpenCourseWare content continues to grow dramatically on ocw.mit.edu and on translation sites, with currently more than 1 million monthly visits to OCW content, and a 56% annual increase in visits.* Use: The OCW site is being used by educators, students and self learners to successfully accomplish a wide range of educational objectives; and visitors are widely satisfied with the breadth, depth and quality of OCW content.* Impact: Individual educators and learners report high levels of current impact on their learning goals, and expectations for even higher impact in the future; institutions worldwide are both using MIT OpenCourseWare materials and publishing their own materials openly -- with more than 2,000 courses representing over 50 institutions currently available online

New Media and Privacy the Privacy Paradox in the Digital World: I Will Not Disclose My Data. Actually, I Will ... It Depends

The inconsistency of privacy attitudes and privacy behavior is often referred to as the “privacy paradox”. In this study, we analyze the privacy paradox through a methodology that allows investigating user behavior in relation to transferring personal data or not in a real context. Our intention is to investigate privacy as a negotiation by verifying whether the incongruous consumer behavior known as the privacy paradox also occurs in a real and blind context, and how this is affected by the data commoditization trend.The classical methodology in these types of studies is based on questionnaires administered to participants in an experiment and the questions relate to their intention to disclose their personal information in different hypothetical scenarios. In all these types of research, the respondents know they are participating in an experiment without any real gains or losses as a result of their actions. To understand how users behave when facing the disclosure or otherwise of personal data in a real context, we analyzed ex-post data from different digital campaigns through one of the most frequently used data vault platforms. Via this platform, a company can configure a series of user actions by rewarding them for every action with a discount on a product. Through this platform, we therefore had the opportunity to investigate how real users in real contexts manage the exchange of personal data for discounts on one or more products

Big data in education and organizational change: Evidence from private K12 schools in China

China is a time-honored civilization with a long history of private education. In China, private education has played an important role in preserving Chinese civilization. At the end of the 20th century, private education in China began to develop thanks to government support. As such, remarkable progress was made during the past decade. Due to specific conditions within the education industry, however, the administration of private edu-cation - and basic education, in particular - has remained rudimentary compared with other more mature service industries. To address the many problems in basic education, such as rig-id teaching methods, heavy teacher workloads and long, repetitive working hours, it is imper-ative in this information era to conduct innovative explorations with the help of the “internet of things” (IoT), big data and other scientific and technological means to carry out organiza-tional reform in schools and to establish contemporary organizational structures and manage-ment modes. Doing so will comprehensively improve the administration of basic education, which will in turn promote the quality of education and teaching. This thesis examines Tianli Education Group, a typical example of private, basic educa-tion in China. By adopting experimental research methods, the behavior of students and teachers in Tianli’s schools were experimentally analyzed. IoT technology was employed to collect data about student behavior at school. Likewise, after collecting and analyzing big data on the behavior of teachers at school, the content and processes of their work were analyzed. Based on these experiments, this thesis explores a new 5G era-appropriate mode of stu-dent selection and training that makes use of big data technology. It outlines the standard work scenario for teachers and improves both their work efficiency and salaries by “trimming staff and streamlining administration,” thus rekindling enthusiasm among teachers for their work. Finally, as a part of this thesis, a series of organizational changes were implemented at Tianli Education Group and its schools to boost organizational vitality, improve overall levels of education, teaching and operational efficiency, raise teachers’ salaries and enhance student happiness.A China é uma civilização muito antiga, com uma longa história de educação privada. A educação privada desempenhou um papel importante na preservação da civilização chinesa. No final do século 20, a educação privada na China começou a desenvolver-se com o apoio do governo. Nos últimos dez anos, devido ao apoio concedido temos assistido a um grande progresso. Contudo e em virtude das condições específicas da indústria da educação, a administração da educação privada – a educação básica em particular – permaneceu rudimentar quando comparada com outras indústrias de serviços. Para resolver os muitos problemas da educação básica, tais como os métodos rígidos de ensino, as cargas de trabalho pesadas e horas de trabalho repetitivas, torna-se imperativo nesta era da informação realizar pesquisas inovadoras com a ajuda da “Internet das Coisas”, do “Big Data” e meios científicos e tecnológicos que nos permitam realizar a reforma nas escolas e estabelecer estruturas organizacionais e métodos de gestão adaptados aos tempos em que vivemos. Os resultados destas pesquisas irão contribuir para melhorar de uma forma abrangente a administração da educação básica, o que por sua vez promoverá a qualidade da educação e do ensino. Esta tese estuda o Tianli Education Group, que consideramos um bom exemplo do ensino privado na educação básica na China. Adoptando métodos experimentais de pesquisa, o comportamento dos estudantes e professores das escolas Tianli foram analisados. Aplicamos a tecnologia da “Internet das Coisas” para recolher informações sobre comportamento dos alunos na escola. Da mesma forma, após a recolha e análise dos dados sobre o comportamento dos professores na escola, efetuamos a análise do conteúdo e dos processos do seu trabalho. Tendo por base estas experiências, esta tese explora na nova era 5G, um modo apropriado para seleção e formação dos alunos. Esta tese descreve o cenário padrão de trabalho para professores e melhora não somente a eficiência do trabalho como também os seus salários ao “reduzir o pessoal e simplificar a administração”, reacendendo assim o entusiasmo dos professores pelo seu trabalho. Finalmente, como parte desta tese, uma série de mudanças organizacionais foram implementadas nas escolas do grupo Tianli Education Group com a finalidade de impulsionar a vitalidade organizacional, melhorar todos os níveis gerais de educação, aumentar a eficiência operacional e de ensino, aumentar os salários dos professores e aumentar a felicidade dos alunos