Pre-saccadic perception: separate time courses for enhancement and spatial pooling at the saccade target

We interact with complex scenes using eye movements to select targets of interest. Studies have shown that the future target of a saccadic eye movement is processed differently by the visual system. A number of effects have been reported, including a benefit for perceptual performance at the target (“enhancement”), reduced influences of backward masking (“unmasking”), reduced crowding (“un-crowding”) and spatial compression towards the saccade target. We investigated the time course of these effects by measuring orientation discrimination for targets that were spatially crowded or temporally masked. In four experiments, we varied the target-flanker distance, the presence of forward/backward masks, the orientation of the flankers and whether participants made a saccade. Masking and randomizing flanker orientation reduced performance in both fixation and saccade trials. We found a small improvement in performance on saccade trials, compared to fixation trials, with a time course that was consistent with a general enhancement at the saccade target. In addition, a decrement in performance (reporting the average flanker orientation, rather than the target) was found in the time bins nearest saccade onset when random oriented flankers were used, consistent with spatial pooling around the saccade target. We did not find strong evidence for un-crowding. Overall, our pattern of results was consistent with both an early, general enhancement at the saccade target and a later, peri-saccadic compression/pooling towards the saccade target

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.Comment: Related to arXiv:1702.0083

Motivating Power System Protection Course Students by Practical and Computer-Based Activities

This paper presents several methods for motivating students taking a power system protection (PSP) course. The paper reviews the laboratory activities developed for the PSP course at Curtin University, Australia; these methods are applicable and can be used for PSP course instruction at any institution. These activities were developed to improve the learning experience of the electrical engineering undergraduate and postgraduate students enrolled in this course. Initially, the PSP course at Curtin University consisted of lectures and tutorials accompanied by two sessions of software-based simulations and one session of laboratory demonstration. To motivate the students, several computer-based simulations and practical laboratory experiments were developed. PSCAD and ETAP power system analysis software tools are introduced and used to demonstrate the performance and coordination of different protection relays from steady-state and dynamic points of view. Also, a practical setup composed of a LabVolt power system simulator and industrial relays is used to carry out several practical experiments. The experiments help students observe the performance of protection systems for transformers and induction motors during faults and abnormal operating conditions. Finally, the concept of relay testing and commissioning is introduced by relay standalone experiments using a secondary injection relay test set. The results of these activities were evaluated according to the students’ satisfaction, comments, total scores, and interest in PSP

An adaptive learning control system for aircraft

A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions

Gamification of The Future: An Experiment on Gamifying Education of Forecasting

In this study, we developed a gamied learning platform called F-LauReLxp that employed three gamification strategies (called Horses for Courses, JudgeIt and Metrics to Escape) to help educate statistical, judgmental forecasting and forecasting accuracy respectively. This study presents a quantitative analysis of experimental design concerning learning performance of 261 students of an undergraduate and a MBA course. Treatment and control groups were compared in a series of experiments. The results show that using gamified applications as a complementary teaching tool in a forecasting course had a positive impact on students’ learning performance

Student risk identification learning model using machine learning approach

Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall

A Query, a Minute: Evaluating Performance Isolation in Cloud Databases

Several cloud providers offer reltional databases as part of their portfolio. It is however not obvious how resource virtualization and sharing, which is inherent to cloud computing, influence performance and predictability of these cloud databases. Cloud providers give little to no guarantees for consistent execution or isolation from other users. To evaluate the performance isolation capabilities of two commercial cloud databases, we ran a series of experiments over the course of a week (a query, a minute) and report variations in query response times. As a baseline, we ran the same experiments on a dedicated server in our data center. The results show that in the cloud single outliers are up to 31 times slower than the average. Additionally, one can see a point in time after which the average performance of all executed queries improves by 38 %