Reviewerly/exHarmony huggingface dataset:
Evolved Transistor Array Robot Controllers

For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.
Teacher Education in Canada: Current State and Development Trends

During the last few years, higher education has been the subject of constant transformation, innovations and reforms. In response to the new demands of the society, universities increase academic mobility, attract international students, introduce information technologies and new approaches to learning: personalized learning, problem-based learning, flipped classroom, distance learning, blended learning and so on. The role of a teacher in introducing new educational technologies and preparing the future generation is of great importance, so the main priority all over the world is to improve the quality of teacher education. The purpose of this paper is to analyze the current state of initial teacher training in Canada, a country where a larger proportion of students performed at the highest levels of proficiency according to the most recent PISA study, which indicates the quality of teacher training. The paper discusses the main features of Canadian teacher education programs: the teacher education models, the program structure and duration, the program content as well as different methods for assessing student learning outcomes with particular attention paid to modern approaches to assessing the student professional competencies at Quebec teacher faculties. Based on a literature review, the results of comparative studies of Canadian researches as well as universities regulations and official websites information, we analyze the main trends in the teacher education in Canada.
Alert-Confirm Track Confirmation for Radar

Alert-confirm detection is a refinement of sequential detection that has been popular for electronically-scanned-array radar since its discovery in the 1960s. The alert-confirm approach was adapted for track confirmation in a recent paper by the authors [1]. The purpose of this paper is to propose a further extension of the approach, adapted specifically to a radar model. Tentative tracks are formed on an alert detection. If the alert occurs within a specified range, the radar scan is interrupted and the hypothesised target is briefly tracked with a number of track-confirmation dwells. The track is confirmed if the track quality metric exceeds a threshold. Using the tracker operating characteristic for Integrated Probabilistic Data Association, also recently derived by the authors [2], which we apply here to a radar model, the performance of alert-confirm track-confirmation can be analysed and optimised. Our theoretical results are supported by numerical simulations.
Adaptive UAS Route Planner Based Upon Evidence Accrual

With the continual increase in governmental and commercial uses of unmanned aerial systems (UASs), flight planning systems that provide both an initial flight plan and a flight plan that is modified as current information is provided become necessary. With UAS operations, flight paths will need to be able change rapidly as restricted airspace can vary quickly, with flight traffic that can increase at a moment's notice, and due to environmental conditions, that can adversely affect the performance of a UAS, i.e., wind corridors in downtown areas. Planning algorithms are needed that better take into account onboard sensor reports, information from off-board reports such as weather and traffic patterns, and information regarding known fixed locations, such as maps of buildings, restricted space, and GPS-dark zones. A technique that was developed for military replanning provides the core for a computationally lightweight and rapidly adaptive UAS route planning. The proposed UAS planning system is described that can offer adaptations from the current plan in response to observations in flight, based upon game-theoretic assessment. The system overlays a map with a hex grid and ranks each hex using evidence accrual based on its effect on the UAS to achieve its goal. The evidence accrual system generates both a score and uncertainty. The data is incorporated using a fuzzy Kalman filter and then is fused via a linear systems-theory approach. With both a score and an uncertainty, the plan can be weighted as possible solution based on achieving the goal of UAS (e.g, delivering a package and returning to base) with a good probability of being correct. As new information is provided, the planning system can change its route based on its current location. This effort includes a description of the route planning system and subcomponents. A simulation of the system is provided with a variety of conditions that modify the original plan both from the start and while in transit.
Natural Language Parser for Physician’s Handwritten Prescription

Drug-prescription recognizing mistakes usually can be attributed to the error in recognizing the physicians prescription. This is because physicians usually write the names of the medicines in an incomprehensible way. To avoid the problems that arise because of the illegible handwriting of the physician, the paper proposes a system that addresses this issue. Since the handwriting of physicians is hard to peruse out, the proposed system simplifies the process of comprehending such handwriting by the methods for handwriting recognition utilizing the OCR techniques. The function of the currently existing input device-a pen is that it is be used by a physician to write the prescription which is simultaneously transform the handwritten text into a printed form and display it online. The contribution of the paper is creating a system that will take the printed form and will then align and parse and format it using formatting tools and thus explore utilization of OCR techniques in field of medicine.
Towards building knowledge by merging multiple ontologies with CoMerger: A partitioning-based approach

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a complete representation of a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present Co Merger, a scalable multiple ontologies merging method. It takes as input a set of source ontologies and existing mappings across them and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. In both steps, user-specified subsets of generic merge requirements (GMRs) are taken into account and used to optimize outputs. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.
Ensembling Imbalanced-Spatial-Structured Support Vector Machine

The support vector machine (SVM) and its extensions have been widely used in various areas. However, these methods cannot effectively handle imbalanced data with spatial association. The ensembling imbalanced-spatial-structured support vector machine (EISS-SVM) method is proposed to handle such data. Not only the proposed method accommodates the relationship between the response and predictors, but also accounts for the spatial correlation existing in data which may be imbalanced. The EISS-SVM classifier embraces the usual SVM as a special case. Numerical studies show satisfactory performance of the proposed method, and the analysis results are reported for the application of the proposed method to handling the imaging data from an ongoing prostate cancer research conducted in Canada.
New Optimal Difference Systems of Sets from Ideal Sequences and Perfect Ternary Sequences

Difference systems of sets (DSSs) introduced by Levenstein are combinatorial structures used to construct comma-free codes for synchronization. In this letter, two classes of optimal DSSs are presented. One class is obtained based on q-ary ideal sequences with d-form property and difference-balanced property. The other class of optimal and perfect DSSs is derived from perfect ternary sequences given by Ipatov in 1995. Compared with known constructions (Zhou, Tang, Optimal and perfect difference systems of sets from q-ary sequences with difference-balanced property, Des. Codes Cryptography, 57(2), 215-223, 2010), the proposed DSSs lead to comma-free codes with nonzero code rate.
Generation of Stereo Images Based on a View Synthesis Network

The conventional warping method only considers translations of pixels to generate stereo images. In this paper, we propose a model that can generate stereo images from a single image, considering both translation as well as rotation of objects in the image. We modified the appearance flow network to make it more general and suitable for our model. We also used a reference image to improve the inpainting method. The quality of images resulting from our model is better than that of images generated using conventional warping. Our model also better retained the structure of objects in the input image. In addition, our model does not limit the size of the input image. Most importantly, because our model considers the rotation of objects, the resulting images appear more stereoscopic when viewed with a device.
Wavefront Marching Methods: A Unified Algorithm to Solve Eikonal and Static Hamilton-Jacobi Equations

This paper presents a unified propagation method for dealing with both the classic Eikonal equation, where the motion direction does not affect the propagation, and the more general static Hamilton-Jacobi equations, where it does. While classic Fast Marching Method (FMM) techniques achieve the solution to the Eikonal equation with a O(M log M) (or O(M) assuming some modifications), solving the more general static Hamilton-Jacobi equation requires a higher complexity. The proposed framework maintains the O(M log M) complexity for both problems, while achieving higher accuracy than available state-of-the-art. The key idea behind the proposed method is the creation of ‘mini wave-fronts’, where the solution is interpolated to minimize the discretization error. Experimental results show how our algorithm can outperform the state-of-the-art both in precision and computational cost.
Augmented Reality Tourism using Tele-Rover

In the present generation, nothing can sound more interesting than having to experience something without actually spending time and money to be there. For example, in the Airforce Wing, pilot trainees can perform their flight maneuvering and other training programs without having to crash a million-dollar plane using Virtual Reality (VR) technology. There are many such instances where we would prefer experiencing certain things without physically present there. In order to make things more emotionally relatable, we are going to integrate VR with robotics to enable us to see and communicate at a place remote from where we presently are. We make use of a rover with a high resolution camera mounted on it along with a microphone module. On the user end, we provide a VR experience through virtual reality headset along with earphones, enabling end to end communication through the Internet Of Things (IoT). Additionally, the user is also provided with a joystick to control the movement of the rover. This rover is equipped with a rocker-bogie mechanism that enables it with an ability to climb staircase effortlessly and navigate through rough terrains easily. The design of rocker-bogie has proven mobility application known for its obstacle-climbing capability and superior vehicle stability. The gyroscope is used for tracking the user's head position. This information is transmitted to the rover, with the use of IoT and the camera on the rover changes its position in both the X and the Y-axis based on the readings received, analogous to the position of user's head.
Guided Exploration of Human Intentions for Human-Robot Interaction

Robot understanding of human intentions is essential for fluid human-robot interaction. Intentions, however, cannot be directly observed and must be inferred from behaviors. We learn a model of adaptive human behavior conditioned on the intention as a latent variable. We then embed the human behavior model into a principled probabilistic decision model, which enables the robot to (i) explore actively in order to infer human intentions and (ii) choose actions that maximize its performance. Furthermore, the robot learns from the demonstrated actions of human experts to further improve exploration. Preliminary experiments in simulation indicate that our approach, when applied to autonomous driving, improves the efficiency and safety of driving in common interactive driving scenarios.
New Approach on Steganalysis

Recently, steganography tools for concealing messages in images have been widely used, and internal mechanisms for hiding messages using steganography tools are mostly unknown to public. Therefore, we adopted the reverse engineering for unveiling the information hiding algorithm and operation mechanism applied to 'Steg' steganography software using IDA tool. By reversing the steganography software, we disclosed the internal steganographic mechanism used on 'Steg' software. Therefore, we can propose a new approach for efficiently detecting and automatically extracting hidden messages in stego data using the software reversing technique.
A statistical study on daily solar radiation and meteorological parameters

In this paper, the daily solar radiation and meteorological parameters such as temperature, dew point temperature, relative humidity, rainfall and wind speed were analyzed for the period of one year (2014) data-set for Dehradun, India. The maximum values of temperature, wind speed and relative humidity, ranging from 35∼39 °C, 3∼4 m/s and 55∼70 % were found in monsoon months. The maximum value of rainfall (46.30 mm) was recorded on 21 June 2014. The statistical parameters such as standard deviation, kurtosis and skewness were calculated to check the normality of used data set. Regression and correlation analysis was also used to model the relationship between daily solar radiation and meteorological parameters.
Prototyping Interaction

Mobile and social technologies have had a dramatic impact on the way we communicate, compressing time and space and enabling novel human interactions around, and through, technology. The promise of hyperconnectivity through an internet of physical and wearable things could further alter the way we interact with each other, with governments, and with corporations. Yet many of these technologies were not designed with this type of communication in mind. They were developed by engineers and scientists at the forefront of material and computational exploration, primarily interested in technical possibility, not human interaction. In communications research there is a tendency to understand new technologies in terms of the way they have changed our social interactions and impacted our culture. Scholars have noted a focus on media texts, industries and audiences as a means of understanding the implications of new platforms. Less attention has been paid to prospective approaches that focus on the creation of new technologies and how they could change communication. This chapter argues that prototyping interaction not only allows us to imagine new forms of communication, but gives insight into the implications of new technologies in use.
Web Assembled Benchmark for Image Visual Quality Assesment, Prediction and Improvement

Visual quality of an image is important factor at all stages of its processing. Images taken by modern devices, such as, digital cameras or smartphones can be distorted by the noise. Currently various effective denoising methods are available to suppress noise of different types. A purpose of applying these techniques is to improve image visual quality in terms of human perception and effective automatic post-processing. This paper considers the following stages of image processing under noisy conditions: initial and intermediate image quality assessment (estimation) during treatment using metrics; prediction of such estimates without a priori information and reference data; and reasonable denoising. As the result, a benchmark is developed to deploy all aforementioned processing stages to a web platform and provide their functionality regardless of user device type and operating system. The proposed web-application allows a user to employ images represented in different format; set the noise model and its characteristics, get image quality assessment parameters; tune denoising methods and obtain a rationale of its applying. In addition, a rationale for filtering running predicted values of visual quality metrics and their improvement after denoising without its applying are provided using neural networks. The benchmark is implemented using WebAssembly for all calculations and evaluations and TensorFlow.js to run neural networks.
Bounds on the discrepancy of linear recurring sequences over Galois rings

Abstract We study the discrepancy of linear recurring sequences over Galois rings. By means of an estimate of an exponential sum some nontrivial bounds on the discrepancy are derived. It is shown that these bounds are asymptotically not worse than known estimates for maximal period linear recurring sequences over prime fields.
Using Online Handwritten Character Recognition in Assistive Tool for Students with Hearing and Speech Impairment

Students with hearing and speech impairment are approximately two years behind school due to their disability and the lack of research, few resources and documented practices on the aforementioned conditions that may provide guidance for classroom teachers and Persons with Disabilities (PWDs). Assistive technology helps resolve problems faced by people with physical, mental, intellectual and sensory impairments. It is any device, software or equipment that helps people work around their disability. This paper concentrates on implementing online handwritten character recognition (OHCR) in the web-based learning application as an assistive tool in developing the learning process of deaf and hard-of-hearing (HoH) students. OHCR is the ability of a computer to receive and interpret the handwriting input wherein the movement of the stylus or fingertip can be sensed as it accepts a string of coordinate pairs from the stylus or finger touching a pressure sensitive digital tablet. The OHCR feature in the application will aid students in learning how to complete a given basic Statistics problem and bypass an area of difficulty to achieve the academic outcome intended. Testing shows that the developed system can read 95 characters including numbers, lower and upper case letters, and special characters.
Limitations of Partial Discharge De-noising of Power Transformer Using Adaptive Singular Value Decomposition

De-noising of partial discharge (PD) is the most important task in PD detection and localization for power transformers and power cables. The performance of adaptive singular value decomposition (ASVD) method, as one of newly used de-noising method, has been shown in power cable PD denoising. However, this approach has some limitations which should be considered by users before applying it. This paper investigates limitations of ASVD method for PD de-noising, especially in power transformers.
Quantifying mass changes during hydrothermal alteration in listwaenite-type mercury mineralization, Tavreh area, northwestern Iran

The Tavreh mercury prospect, a listwaenite-type alteration/mineralization system, is located c . 90 km west of Khoy in northwestern Iran. Tavreh is hosted within the Khoy ophiolite zone. Three types of listwaenites have been recognized in the Khoy ophiolite: silica, silica-carbonate and carbonate. Of these three, Hg mineralization at Tavreh is spatially and genetically associated with the silica-type listwaenite, also known as berberite. Alteration and mineralization at Tavreh are restricted to a faulted contact between shale and serpentinite. The Tavreh listwaenite is inferred to form from the hydrothermal alteration of brecciated serpentinite. Major mineralogical changes resulting from this alteration include the decomposition of serpentine-group minerals and the formation of silica phases. In this study, the mass changes of 18 listwaenites from Tavreh were assessed relative to the least altered serpentinites. To illustrate these changes quantitatively a comparative analysis of three different methods of calculating mass change was undertaken using Grant's isocon analysis, MacLean's equation and Gresens’ equation. Results from the three methods are similar. Listwaenite alteration was associated with a large increase in SiO 2 (44.4, 36.2, 63.9%, respectively). MgO and loss on ignition were depleted (−34.8, −36.9, −36.6; −8.5, −9.3, −8.3%, respectively) and Al 2 O 3 was relatively unchanged (0.7, 0.6, 0.9%). Mercury is the most enriched rare element in altered rock (375.1, 346.8, 474.6 ppm). Arsenic, Pb, Au and Sb were also enriched. The intensity of mass changes of the various alteration components increases significantly from the serpentinite wall rock towards the listwaenite alteration and the ore-bearing zone. Therefore, the mass balance method can probably be used to locate mineral deposits from a few hundred metres and to explore for blind mineral deposits.