Motionless Monotony: New Nowheres in Irish Photography

‘But my mind was too confused … so with a kind of madness growing upon me, I flung myself into futurity … What strange developments of humanity, what wonderful advances upon our rudimentary civilization, I thought, might not appear when I came to look into the dim elusive world that raced and fluctuated before my eyes. I saw great and splendid architecture rising about me, more massive than any buildings of our own time, and yet, it seemed, built of glimmer and mist … the earth seemed very fair. And so my mind came round to the business of stopping.’ So The Time Traveller in H.G. Wells’s novella The Time Machine recounts his experience of temporal speed — as the fluctuation of landscape. In Wells’s often relentless fascination with the possibilities of ‘progress’, future time is made real as architecture, and as the eradication of landscape and its replacement by the ever larger and more complex reshaping of the material environment

The effect of timing and frequency of push notifications on usage of a smartphone-based stress management intervention: An exploratory trial

Push notifications offer a promising strategy for enhancing engagement with smartphone-based health interventions. Intelligent sensor-driven machine learning models may improve the timeliness of notifications by adapting delivery to a user's current context (e.g. location). This exploratory mixed-methods study examined the potential impact of timing and frequency on notification response and usage of Healthy Mind, a smartphone-based stress management intervention. 77 participants were randomised to use one of three versions of Healthy Mind that provided: intelligent notifications; daily notifications within pre-defined time frames; or occasional notifications within pre-defined time frames. Notification response and Healthy Mind usage were automatically recorded. Telephone interviews explored participants' experiences of using Healthy Mind. Participants in the intelligent and daily conditions viewed (d = .47, .44 respectively) and actioned (d = .50, .43 respectively) more notifications compared to the occasional group. Notification group had no meaningful effects on percentage of notifications viewed or usage of Healthy Mind. No meaningful differences were indicated between the intelligent and non-intelligent groups. Our findings suggest that frequent notifications may encourage greater exposure to intervention content without deterring engagement, but adaptive tailoring of notification timing does not always enhance their use. Hypotheses generated from this study require testing in future work. Trial registration number: ISRCTN67177737 © 2017 Morrison et al

Wang's B machines are efficiently universal, as is Hasenjaeger's small universal electromechanical toy

In the 1960's Gisbert Hasenjaeger built Turing Machines from electromechanical relays and uniselectors. Recently, Glaschick reverse engineered the program of one of these machines and found that it is a universal Turing machine. In fact, its program uses only four states and two symbols, making it a very small universal Turing machine. (The machine has three tapes and a number of other features that are important to keep in mind when comparing it to other small universal machines.) Hasenjaeger's machine simulates Hao Wang's B machines, which were proved universal by Wang. Unfortunately, Wang's original simulation algorithm suffers from an exponential slowdown when simulating Turing machines. Hence, via this simulation, Hasenjaeger's machine also has an exponential slowdown when simulating Turing machines. In this work, we give a new efficient simulation algorithm for Wang's B machines by showing that they simulate Turing machines with only a polynomial slowdown. As a second result, we find that Hasenjaeger's machine also efficiently simulates Turing machines in polynomial time. Thus, Hasenjaeger's machine is both small and fast. In another application of our result, we show that Hooper's small universal Turing machine simulates Turing machines in polynomial time, an exponential improvement.Comment: 18 pages, 1 figure, 1 table, Conference: Turing in context II - History and Philosophy of Computing, 201

Putting the horse before the cart: formulating and exploring methods for studying cognitive technology

The First International Conference on Cognitive Technology (CT'95, Hong Kong, 1995) explored a radically new way of thinking about the impact computer technology has on humans, especially on the human mind. Our main aim at that time was a consideration of these effects with respect to rendering the interface between people and computers more humane. And we exemplified our approach by pointing to existing trends and tendencies in the vast new loosely organized field of research often referred to as `HCI' (`human computer interaction'; the replacement for the politically and factually `incorrect' MMI, `man machine interface')published_or_final_versio

Ms. Pacrat: A feeling, thinking machine

Since before the time of the first digital computers, the workings of the mind have been compared to that of a machine. With the onset of the discipline of Artificial Intelligence a truly organized attempt has been made to build intelligent machines that model the mind. Many interesting programs have been built, but the legitimacy of their success is a matter of great controversy. None of the AI programs developed so far have come close to the true power and intelligence of the brain. Expert systems, for example, are the most success ful commercial AI programs, and even they have shown to be brittle, and only able to deal with knowledge in very narrow domains. I suggest that those interested in modeling the mind should explore the emotions. I propose that intelligence and the emotions have a dependent and critical relationship. This relationship suggests that attempts to model human intelligence should consider how the emotions effect our thinking, reasoning, problem-solving, and learning and incorporate this information into computer models. This thesis will review what has been done in the field of AI to build intelligent machines and will examine the relationship between emotions and intelligence. A computer model of emotions will be presented: MS. PACRAT - A Feeling Thinking Machine

That Useless Time Machine

It is not our practice to raise complaints against a negative review report. We believe in peer refereeing and we respect it, whatever its content and consequences. However, in the case of our latest grant application (project named ‘The Time Machine’) we find it necessary to express our astonishment at the motivations with which our request for funding was turned down. Your main objection appears to be that our project is ‘philosophically interesting’ but ‘practically useless’, by which you mean that the project ‘has no potential for applications.’ We do not quite think that the main criterion for judging the scientific value of a project should be its practical usefulness, but never mind that. Let us agree that usefulness is a relevant criterion, especially when large amounts of money are involved. Why should that be a reason to turn down our project? Quite frankly, we cannot think of a project with better application potential than ours. Certainly you have noticed that our suggestions for practical applications of the time machine did not include any uses that could result in an alteration of the natural course of history. As a matter of fact, we believe that no such alteration is logically possible. According to our project, it is logically possible to visit the past but not to modify the past. No time traveler can undo what has been done or do what has not been done. So the logic is safe

A human-centric approach for adopting bug inducing commit detection using machine learning models

When developing new software, testing can take up half of the resources. Although a considerable amount of work has been done to automate software testing, fixing bugs after adding them to the source repository is still a costly task from both management and financial perspectives. In recent times, the research community has proposed various methodologies to detect bugs just-in-time at the commit level. Unfortunately, this work, including state-of-the-art techniques, do not provide real-time solutions for the problem. Such a limitation restricts developers from utilizing them in their day-to-day programming tasks. Our study focuses on providing solutions that deliver real-time support to the developers by warning them about potential bug-inducing commits. Such support can help developers by preventing them from adding a bug-inducing commit to the source repository. Keeping this goal in mind, we conducted a developer survey to understand the expectations of developers for bug-inducing commit detection tools. Motivated by their responses, we built a GUI-based plug-in that warns the developers when they attempt to perform a potential buggy commit. We accomplished this by training machine learning models on relevant features. We also built a command-line tool for the developers who prefer to use a command-line interface. Our proposed solution has been designed to work with various machine learning models (e.g. random forest, decision tree, and logistic regression) and IDEs (e.g. Visual Studio, PyCharm, and WebStorm). It enables developers to work with a familiar interface without leaving the IDE. As a proof of concept, we implemented a VSCode plug-in and an accompanying command-line tool. Developers can customize these tools by choosing among various machine learning models and features. Such customizability empowers the developers to understand the toolchain better and lets them fit it into their specific use cases. Our user study shows that the toolchain offers satisfactory performance in detecting bug-inducing commits and provides a sound user experience. The decision tree model achieved the best performance with a 79% accuracy and an f1-score of 0.70 among the tested models. In addition, we performed a user study with developers working in the software industries to validate the usability of our toolchain. We found that the users can detect whether a commit is bug-inducing or not within a short period of time. Furthermore, they prefer our tool over the state-of-the-art to detect potential bugs before the commit operation. Alongside contributing a new multi-UI toolchain, our work enriches the research community’s knowledge regarding developer usability of real-time bug detection tools