The effect of crowding on the reading of program code for programmers with dyslexia

Eye track recording data and meta data for 30 participants. There were 14 participants with dyslexia, consisting of 3 female and 11 male. Dyslexia group mean age was 21.50 years (SD = 1.61) and mean programming experience was 3.67 years (SD = 1.87). There were 16 participants in the control group, 2 female and 14 male. Control group mean age was 22.19 years (SD = 2.99) and mean programming experience was 4.75 years (SD = 1.96)

Analysis of General Practitioner Prescribing Behaviours Local Data Store

Open Prescription data used in Postgraduate project into Northern Ireland General Practice prescribing

InSync

Version 1.0 ====================================== Abstract: ====================================== The InSync data set was collected at the Pervasive Computing lab at Ulster University. It consists of subjects performing activities of daily living (ADLs) in an atmosphere that mimics a real-life environment while data is collected using three different sensing technologies: inertial, image, and audio. The data set can be used to research human activity recognition algorithms to tackle problems on classification, transfer learning, data fusion, data segmentation, feature extraction, so on and so forth. Number of instances: ====================================== 16,959 (inertial data points) + 650 (thermal images) + 16,986 (audio files) Relevant information: ====================================== InSync contains 12 hours of data from ten subjects, consisting of 78 runs (times that a subject performed the scripted protocol). Sensor data from three different technologies (inertial, images and audio) captured the performance (not simulation) of the subjects performing ADLs. All the activities were annotated a posteriori using a video stream. ***** ACTIVITIES OF DAILY LIVING As the data set aimed at recording the subject's physical activity performance. The tasks consisted of ADLs and well-known scenarios. Three general scenarios were chosen, a bedroom-related scenario in which the subjects performed two of the ADLs, namely, personal hygiene and dressing, a breakfast-related scenario was chosen to embrace the ADL of feeding as it has extensively been used in literature, and free of obstacle scenario in which the subjects can walk alongside to demonstrate their transferring capabilities. The script was designed with nine high-level activities: Bedroom: (1) Napping (2) Wearing joggers (3) Combing hair (4) Brushing teeth Corridor: (5) Operating door Kitchen: (6) Drinking water (7) Eating cereal Livingroom: (8) Transporting (i.e. walking) (9) Resting (i.e. sitting in a chair) Details of the room's dimensions and sensor locations are available in the Relevant Papers. ***** SENSING TECHNOLOGY The deployed sensing technology included thirteen shimmer devices enabled with 3-axis accelerometers, four Matrix Voice ESP32 consisting of eight embedded microphones and four Thermal Vision Sensor (TVS). The sensing technology was placed as described next: Shimmers wore by the subject: - Right wrist - Left wrist - Lower back - Upper back - Right shoe Shimmers mounted on everyday items: - Comb - Toothbrush - Glass - Spoon - Jogger - Belt - Strap to mimic a watch - Strap to mimic smart shoe Matrix Voice ESP32 (one located in each room): - Bedroom - Corridor - Kitchen - Livingroom Thermal sensor (one located in each room): - Bedroom - Corridor - Kitchen - Livingroom Attribute information: ====================================== The data set comprises the readings of inertial sensors, thermal images, and audio files to recorded performed ADLs. There is a total of 60 attributes for the inertial data which includes the mean value and root-mean-square (RMS) from x, y, and z-axis. The thermal data consists of grayscale images in 32x32 pixels, and the audio data consists of 44.1 kHz waveform audio files. A list of videos of the experiment can be seen in the following links. Bedroom: (1) napping: https://youtu.be/IqWLKsgch6A (2) wearing joggers: https://youtu.be/FJBjO9C4Q4U (3) combing hair: https://youtu.be/bYKrKbVBNos (4) brushing teeth: https://youtu.be/wuVkrWlsmSs Corridor: (5) operating door: https://youtu.be/pWJjx3TH6Q4 Kitchen: (6) drinking water: https://youtu.be/wS9OBKK_LFY (7) eating cereal: https://youtu.be/nOK8TuyCXBA Livingroom: (8) transporting (i.e. walking): https://youtu.be/45MGsYS9cYg (9) resting (i.e. sitting in a chair): https://youtu.be/45MGsYS9cYg IMPORTANT: The videos previously provided were recorded using conventional webcams. The videos were used as ground truth; they were not used for training nor testing purposes. Note that the participant's identity has been considered by blurring their face. The speed of the videos varies as different sampling rates were used when recording the videos

Dataset for the ReSolve Project

The RE:Solve project aim was to develop a process/proof of concept (POC), and to tackle the issue of contaminated plastic packaging from the food waste management sector in order to divert this substantial (and potentially valuable and useful) waste stream from landfill via effective separation of the material contaminated with organic residues from the plastic packaging

Industry and faculty surveys call for increased collaboration to prepare information technology graduates

Academic and industry collaborations can help improve computing curricula and student learning experiences. Such collaborations are formally encouraged by accreditation standards. Through the auspices of ACM and IEEE-CS, the IT2017 task group is updating curriculum guidelines for information technology undergraduate degree programs, similar to the regular updates for other computing disciplines. The task group surveyed curriculum preferences of both faculty and industry. The authors, with the group\u27s cooperation, compare US faculty and US industry preferences in mathematics, IT knowledge areas, and student workplace skill sets. Faculty and industry share common ground, which supports optimism about their productive collaboration, but are also distinct enough to justify the effort of actively coordinating with each other

Representative Names of Computing Degree Programs Worldwide

Through the auspices of ACM and with support from the IEEE Computer Society, a task group charged to prepare the IT2017 report conducted an online international survey of computing faculty members about their undergraduate degree programs in computing. The purpose of this survey was to clarify the breadth of and disparities in nomenclature used by diverse communities in the computing field, where a word or phrase can mean different things in different computing communities. This paper examines the English-language words and phrases used to name the computing programs of almost six hundred survey respondents, and the countries in which those names are used. Over eight hundred program names analysed in this paper reveal six program names that together account for more than half of all program names. The paper goes on to consider possible correspondence between reported program names and the five areas of computing identified by the ACM. Names such as computer science and information technology appear to dominate, but with different meanings, while the names of other computing disciplines show clear geographic preferences. Convergence towards a very small number of highly representative program names in computing education worldwide might be deceptive. The paper calls for further examination and international collaborations to align program names with program curriculum content

Smoothing the Transition to Mandatory Electronic Theses

After a year of voluntary submissions, Caltech is requiring electronic thesis submission for all graduate students effective July 1, 2002. Website development, user education, collaboration between library and campus computing staff, and with faculty and the dean's office are all integral to the transition