The Diamond, October 13, 2005

Front Page: Dordt Prepares for Families; Fast Forward; Football Fans and Foes Gather to Understand Status of the Sport at Dordt News: Students Required to Pay for Larger Course Loads; Volkers Delivers Filmmaking Experience; October Mondays Focus on a Safer Dordt College; Allen Brings Experience and Expertise to Teach Physics at Dordt College Opinion: Guest Editorial: A Righteous Mission or Dangerous Theology?; Is There a Difference in Gender Roles or is There a Difference in Being?; Letter to the Editor Features: A Closer Look-The Humble Bean; Gen-100 Group Leads Fundraiser for Student; KDCR Comes to Life; A Divided House Manifested; Out and About; Morning Merry Maid Arts: Dinner Party Cones to Dordt College; Ode to Bugs Leisure: Wheels of the Week; Free Movies Evaluate Effects of Domestic Violence Sports: Cross Country Takes Off; New Athletic Trainer to Fix Dordt Injuries; Playoffs: Stage for October Magichttps://digitalcollections.dordt.edu/dordt_diamond/1098/thumbnail.jp

More security or less insecurity (transcript of discussion)

The purpose of this talk is to explore the possibility of an exploitable analogy between approaches to secure system design and theories of jurisprudence. The prevailing theory of jurisprudence in the West at the moment goes back to Hobbes. It was developed by Immanuel Kant and later by Rousseau, and is sometimes called the contractarian model after Rousseau’s idea of the social contract. It’s not the sort of contract that you look at and think, oh gosh, that might be nice, I might think about opting in to that, it’s more like a pop up licence agreement that says, do you want to comply with this contract, or would you rather be an outlaw. So you don’t get a lot of choice about it. Sometimes the same theory, flying the flag of Immanuel Kant, is called transcendental institutionalism, because the basic approach says, you identify the legal institutions that in a perfect world would govern society, and then you look at the processes and procedures, the protocols that everyone should follow in order to enable those institutions to work, and then you say, right, that can’t be transcended, so therefore there’s a moral imperative for everyone to do it. So this model doesn’t pay any attention to the actual society that emerges, or to the incentives that these processes actually place on various people to act in a particular way. It doesn’t look at any interaction effects, it simply says, well you have to behave in this particular way because that’s what the law says you have to do, and the law is the law, and anybody who doesn’t behave in that way is a criminal, or (in our terms) is an attackerFinal Accepted Versio

OPAL Community Environment Report

The Open Air Laboratories network, or OPAL, as it quickly became known, was launched in 2007 following a successful application to the Big Lottery Fund It was the first time that Big Lottery funding on this scale had been awarded to academic institutions. The University of Central Lancashire led by Dr Mark Toogood was responsible for understanding public engagement with OPAL. The Open Air Laboratories (OPAL)network is a nationwide partnership comprising of ten universities and five organisations with grants awarded totalling £14.4 million. • Over half a million people have participated in the OPAL programme. OPAL activities are carried out by people of all ages, backgrounds and abilities, including 10,000 people in ‘hard to reach’ communities. • OPAL opens people’s eyes to the natural world. Nearly half (44%) of OPAL survey participants said that this was the first time that they had carried out a nature survey. 90% of participants have learnt something new. • OPAL has the ability to change people’s behaviour. Almost half (43%) of respondents said OPAL had changed the way they thought about the environment and more than a third (37%) said they will change their behaviour towards it. • In addition to raising environmental awareness, OPAL also improves personal well-being by motivating people to spend time outdoors doing something positive, while connecting with people and nature

Disparity between the Programmatic Views and the User Perceptions of Mobile Apps

User perception in any mobile-app ecosystem, is represented as user ratings of apps. Unfortunately, the user ratings are often biased and do not reflect the actual usability of an app. To address the challenges associated with selection and ranking of apps, we need to use a comprehensive and holistic view about the behavior of an app. In this paper, we present and evaluate Trust based Rating and Ranking (TRR) approach. It relies solely on an apps' internal view that uses programmatic artifacts. We compute a trust tuple (Belief, Disbelief, Uncertainty - B, D, U) for each app based on the internal view and use it to rank the order apps offering similar functionality. Apps used for empirically evaluating the TRR approach are collected from the Google Play Store. Our experiments compare the TRR ranking with the user review-based ranking present in the Google Play Store. Although, there are disparities between the two rankings, a slightly deeper investigation indicates an underlying similarity between the two alternatives

Vol. 10, No. 10, Jun. 21, 2004: Illinois Fruit and Vegetable News

published or submitted for publicationnot peer reviewe

Going Stupid with EcoLab

In 2005, Railsback et al. proposed a very simple model ({\em Stupid Model}) that could be implemented within a couple of hours, and later extended to demonstrate the use of common ABM platform functionality. They provided implementations of the model in several agent based modelling platforms, and compared the platforms for ease of implementation of this simple model, and performance. In this paper, I implement Railsback et al's Stupid Model in the EcoLab simulation platform, a C++ based modelling platform, demonstrating that it is a feasible platform for these sorts of models, and compare the performance of the implementation with Repast, Mason and Swarm versions

Digging up History

Students hone archaeological skills as they search for remnants of a camp used by Chinese immigrants in the late 19th and early 20th centuries

Locating bugs without looking back

Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We compare our approach to eight others, using their own five metrics on their own six open source projects. Out of 30 performance indicators, we improve 27 and equal 2. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. These results show the applicability of our approach to software projects without history

Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs