Using keystroke logging to understand writers’ processes on a reading-into-writing test

Background Integrated reading-into-writing tasks are increasingly used in large-scale language proficiency tests. Such tasks are said to possess higher authenticity as they reflect real-life writing conditions better than independent, writing-only tasks. However, to effectively define the reading-into-writing construct, more empirical evidence regarding how writers compose from sources both in real-life and under test conditions is urgently needed. Most previous process studies used think aloud or questionnaire to collect evidence. These methods rely on participants’ perceptions of their processes, as well as their ability to report them. Findings This paper reports on a small-scale experimental study to explore writers’ processes on a reading-into-writing test by employing keystroke logging. Two L2 postgraduates completed an argumentative essay on computer. Their text production processes were captured by a keystroke logging programme. Students were also interviewed to provide additional information. Keystroke logging like most computing tools provides a range of measures. The study examined the students’ reading-into-writing processes by analysing a selection of the keystroke logging measures in conjunction with students’ final texts and interview protocols. Conclusions The results suggest that the nature of the writers’ reading-into-writing processes might have a major influence on the writer’s final performance. Recommendations for future process studies are provided

Keystroke dynamics as signal for shallow syntactic parsing

Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn better natural language processing models? We postulate that keystroke dynamics contain information about syntactic structure that can inform shallow syntactic parsing. To test this hypothesis, we explore labels derived from keystroke logs as auxiliary task in a multi-task bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising results on two shallow syntactic parsing tasks, chunking and CCG supertagging. Our model is simple, has the advantage that data can come from distinct sources, and produces models that are significantly better than models trained on the text annotations alone.Comment: In COLING 201

PILOT: Password and PIN Information Leakage from Obfuscated Typing Videos

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work [4]. In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper

Identifying the machine translation error types with the greatest impact on post-editing effort

Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices' translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected

Translation methods and experience : a comparative analysis of human translation and post-editing with students and professional translators

While the benefits of using post-editing for technical texts have been more or less acknowledged, it remains unclear whether post-editing is a viable alternative to human translation for more general text types. In addition, we need a better understanding of both translation methods and how they are performed by students as well as professionals, so that pitfalls can be determined and translator training can be adapted accordingly. In this article, we aim to get a better understanding of the differences between human translation and post-editing for newspaper articles. Processes were registered by means of eye tracking and keystroke logging, which allows us to study translation speed, cognitive load, and the usage of external resources. We also look at the final quality of the product as well as translators' attitude towards both methods of translation

The Borrowers: Researching the cognitive aspects of translation

The paper considers the interdisciplinary interaction of research on the cognitive aspects of translation. Examples of influence from linguistics, psychology, neuroscience, cognitive science, reading and writing research and language technology are given, with examples from specific sub-disciplines within each one. The breadth of borrowing by researchers in cognitive translatology is made apparent, but the minimal influence of cognitive translatology on the respective disciplines themselves is also highlighted. Suggestions for future developments are made, including ways in which the domain of cognitive translatology might exert greater influence on other disciplines

Development of an Ada programming support environment database SEAD (Software Engineering and Ada Database) administration manual

Software Engineering and Ada Database (SEAD) was developed to provide an information resource to NASA and NASA contractors with respect to Ada-based resources and activities which are available or underway either in NASA or elsewhere in the worldwide Ada community. The sharing of such information will reduce duplication of effort while improving quality in the development of future software systems. SEAD data is organized into five major areas: information regarding education and training resources which are relevant to the life cycle of Ada-based software engineering projects such as those in the Space Station program; research publications relevant to NASA projects such as the Space Station Program and conferences relating to Ada technology; the latest progress reports on Ada projects completed or in progress both within NASA and throughout the free world; Ada compilers and other commercial products that support Ada software development; and reusable Ada components generated both within NASA and from elsewhere in the free world. This classified listing of reusable components shall include descriptions of tools, libraries, and other components of interest to NASA. Sources for the data include technical newletters and periodicals, conference proceedings, the Ada Information Clearinghouse, product vendors, and project sponsors and contractors