Reviewerly/exHarmony huggingface dataset:
The Benefits of HTML Slides for Programming Lectures

Many instructors use slide formats like Keynote, Powerpoint, Google Docs, or PDF for their lectures. When the topic is programming, those slides formats are highly problematic: it is hard for the presenter to format code snippets with helpful syntax highlighting and hard for students to copy/paste code snippets and preserve indentation. The result is hard-to-read code and students that are less likely to try code for themselves. A better format is HTML slides. The slides are written with simple HTML tags, and a JavaScript library renders them as interactive slides. As a result, all of the code snippets have automatic syntax highlighting plus a one-click copy-to-clipboard button. For some programming languages, the code can even be executed on the slide itself. The slides still have the features of traditional formats: they can be converted to PDF for easy printing and note-taking, slide fragments can be animated in sequence (just by adding a single class name to a tag). But they have even more benefits, thanks to being web pages. HTML slides can be fully accessible for students with disabilities by following WCAG guidelines when writing the HTML, and they can be easily indexed by web search engines, making it easier for students across the world to learn from them. This lightning talk will take you on a tour of HTML slides and share a webpage with additional tips for how to get started with HTML slides in your own programming classes.
A Path Planning Algorithm Based on Improved RRT

To address the problems of long search time and non-optimal generated paths when the original fast expanding random tree (RRT) algorithm is used for path planning, this paper proposes an improved RRT algorithm based on greedy strategy combined with adaptive sampling area and node approaching to obstacles. Firstly, the greedy idea is introduced on the basis of the original RRT algorithm to improve the node expansion strategy; secondly, the sampling area is limited by introducing the adaptive sampling factor to optimize the search time and improve the efficiency; finally, the path length is further optimized by removing the redundant nodes of the path and using the optimization algorithm to make the nodes approach the obstacles. The simulation results show that the improved algorithm can solve a better path with fewer expansions and faster convergence speed for different complexity scenarios.
Adaptive NN based Visual Servoing Control for Robot Manipulator with Field of View Constraints and Dynamic Uncertainties

This paper studies the constraint control of a visual servoing system which composed of a 6DOF manipulator with a camera sensor mounted on the end-effector subject to the limitations of the field of view (FOV). In visual servoing control, losing information about the visual features can easily lead to task failure. In this work, a barrier Lyapunov function (BLF) is utilized to design the controller and render certain features continuously stay within the field of view, also guarantee the uniform ultimate boundedness of the closed-loop system. No path planning nor information about the actual system dynamics is required. The Moore-penrose inverse term is introduced to ensure the asymptotic stability of the system. In the presence of system uncertainties, an adaptive neural network is adopted to compensate for uncertainties. The theoretical findings are demonstrated via comparative simulation studies based on a 6 DOF robot.
Statistical Significance Tests

In this book, we are interested in the process of comparing performance of different NLP algorithms in a statistically sound manner. How is this goal related to the calculation of the p-value? Well, calculating the p-value is inextricably linked to statistical significance testing, as we will attempt to explain next. Recall the definition of δ(X) in Equation (2.3). δ(X) is our test statistic for the hypothesis test defined in Equation (2.3).
Towards Digital Thinking and Practices: Experiences of Sri Lankan Teachers and Students

Commonwealth Digital Education Leadership Training in Action (C-DELTA), an open and free online programme of the Commonwealth of Learning, provides a framework to foster digital education. The Open University of Sri Lanka implemented an intervention during 2020-2021 to promote digital education in Sri Lankan secondary schools, through the adoption of C-DELTA. This paper presents how C-DELTA supported developing digital thinking and practices among teachers and students, challenges faced and supports received by them, and impacts of the intervention. Participants’ experiences were captured through questionnaire surveys, concept maps, focus group interviews, reflective stories, and video narratives. The findings revealed that the intervention has enhanced developing digital learning skills of teachers and students, and changing their thinking and practices, yet, amid various challenges. While the implementation of C-DELTA in schools has been slow during the COVID-19 pandemic, the pandemic itself has shown the significance of improving digital literacy and digital practices.
Advisory

Security has become a crucial factor in the development of software systems. The number of dependencies in software systems is becoming a source of countless bugs and vulnerabilities. In the past, the product line community has proposed several techniques and mechanisms to cope with the problems that arise when dealing with variability and dependency management in such systems. In this paper, we present Advisory, a solution that allows automated dependency analysis for vulnerabilities within software projects based on techniques from the product line community. Advisory first inspects software dependencies, then generates a dependency graph, to which security information about vulnerabilities is attributed and translated into a formal model, in this case, based on SMT. Finally, Advisory provides a set of analysis and reasoning operations on these models that allow extracting helpful information about the location of vulnerabilities of the project configuration space, as well as details for advising on the security risk of these projects and their possible configurations.
Poisson Distribution

The Poisson distribution was developed by the French mathematician Simeon Denis Poisson in 1837. The Poisson distribution is a discrete probability distribution. It is used to approximate the count of events that occur randomly and independently. The Poisson distribution may calculate number of instances that should occur in a certain amount of time, distance, area, or volume. For instance, the random variable could be used to estimate the number of radioactive decays in a given period of time for a certain amount of the radioactive material. If you know the rate of decay for that amount of the material you can use the Poisson distribution as a good estimate of the amount of decays. We shall elaborate on Poisson statistical properties, the derivation of the mean and the variance of a Poisson distribution, and an application of the Poisson distribution. Its practicality shall be demonstrated in the worked examples.
OP09 Lord’s ‘paradox’ explained: The 50-year warning on the analyses of ‘change scores’*

<h3>Background</h3> In 1967, Frederick Lord posed a conundrum that has confused scientists for over half a century. Since termed Lord’s ‘paradox’, the puzzle concerns the setting of analyses of change in an outcome measured at two times. In most studies, such data are examined either by analyzing the follow-up adjusted for baseline (Method 1) or analyzing the outcome ‘change score’ (Method 2). Alas, in observational data, these two approaches can return paradoxically different – even sign-discordant – results. Which, if either, is correct? <h3>Methods</h3> At the heart of Lord’s ‘paradox’ lies another more general puzzle concerning the analyses of ‘change scores’ in observational data. By exploring the philosophy of change, and using directed acyclic graphs and data simulations, we examine the performance of the two standard approaches to analyses of change between two time points and discuss the wider lessons for contemporary data science. <h3>Results</h3> The solution to Lord’s ‘paradox’ begins with recognising and distinguishing between the three reasons that a variable can change over time. First, endogenous (or ‘pre-determined’) change represents scale changes due to the realisation of past events. Second, random change represents all changes due to random or enigmatic processes. Third, exogenous change represents all non-random changes beyond what was expected. In almost all situations, particularly where there is a wish to understand and/or influence change, exogenous change is the only element of interest. Alas, neither Method 1 nor Method 2 are suitable for robustly estimating the causes of exogenous change in observational data. For Method 1, this is because of susceptibility to mediator-outcome confounding and, with only two datapoints, there is insufficient data to distinguish exogenous change from random change. For Method 2, this is because analyses of ‘change scores’ evaluate obscure estimands with little, if any, real-world interpretation. Accurate and precise estimates instead require the use of appropriate causal inference methods (such as g-methods) and more than two measures of the outcome variable. <h3>Conclusion</h3> Despite some eccentricities with his original example, Lord’s ‘paradox’ has surprising relevance to several aspects of contemporary data science. Like the Birthweight ‘paradox’ before it, the puzzle particularly demonstrates the benefits of considering causal questions within a formal causal framework and clearly identifying your estimand before conducting your data analysis. It also repeats the dangers of conducting and interpreting analyses of ‘change scores’ in observational data.
Upsampling, a comparative study with new ideas

We give a brief tour of sampling techniques not uncommon in dealing with imbalanced data having binary tags. We mention the rather &#x2018;naive&#x2019; upsampling techniques, classical bootstrap (B. Efron, 1979) and a more recent technique called SMOTE (Chawla, et al, 2002). We demonstrate those techniques using features derived from the Android OS source code with tags derived from the common vulnerabilities &#x0026; exposures public database (CVE, Mitre) which contains known software bugs in widely-used applications. Evidently, the data which contains approximately 0.5&#x0025; positive tags with remaining approximately 99.5&#x0025; negative tags is highly, in fact extremely imbalanced. A new idea is to synthesize samples from a minority class using an idea from the Bayesian domain. Forthcoming is an empirical analysis of the procedure. Keywords: Unstructured Data, Android Source Code Imbalanced Data, Binary Tags, Binary Classification, ROC, AUC, Lift, Specificity, Recall, Upsampling, Boot-strapping, SMOTE, Bayesian Upsampling, EM, Autoregressive
An Improved Explainable Point Cloud Classifier (XPCC)

Classification of objects from 3-D point clouds has become an increasingly relevant task across many computer-vision applications. However, few studies have investigated explainable methods. In this article, a new prototype-based and explainable classification method called eXplainable point cloud classifier (XPCC) is proposed. The XPCC method offers several advantages over previous explainable and nonexplainable methods. First, the XPCC method uses local densities and global multivariate generative distributions. Therefore, the XPCC provides comprehensive and interpretable object-based classification. Furthermore, the proposed method is built on recursive calculations, thus, is computationally very efficient. Second, the model learns continuously without the need for complete retraining and is domain transferable. Third, the proposed XPCC expands on the underlying learning method explainable deep neural networks (xDNN), and is specific to 3-D. As such, the following three new layers are added to the original xDNN architecture: 1) the 3-D point cloud feature extraction, 2) the global compound prototype weighting, and 3) the SoftMax function. Experiments were performed with the ModelNet40 benchmark, which demonstrated that XPCC is the only one to increase classification accuracy relative to the base algorithm when applied to the same problem. In addition, this article proposes a novel prototype-based visual representation that provides model- and object-based explanations. The prototype objects are superimposed to create a prototypical class representation of their data density within the feature space, called the compound prototype cloud. They allow a user to visualize the explainable aspects of the model and identify object regions that contribute to the classification in a human-understandable way.
Comparison of various deep learning inpainting methods in smart colposcopy images

The specular reflection appears as white area on the cervical images that covers certain region of the images. It affects the quality of the cervical images causing difficulty for the physician to analyse the smart colposcopy images. In this paper, specular reflection is detected and removed from the cervical images, and these removed pixels are refilled using the inpainting methods. To fill the removed pixels, the deep learning inpainting algorithms like partial convolutional neural network, generative multicolumn convolutional neural network, and the dilated convolutional neural network to were used to get the complete and enhanced cervical images. The enhanced images are considered for lesion identification using the Bayes classifier. Based on the analysis, the partial convolution inpainting method gives higher quality with the PSNR value of 48.25 dB and SSIM value of 0.984. The enhanced images using the partial inpainting method identify the neoplasm with an accuracy of 98%.
Moving Object Detection Using Optical Flow and HSV

Detection of moving object in a video sequence has become a demanding task due to changing background, varying appearance of the moving object and when moving object share similar colour with the background. In this paper, combination of two methods is done to detect moving object. An optical flow method along with Hue Saturation and Value colour space is proposed to detect camouflage moving object. The algorithm is tested on various standard data sets. Both the qualitative and quantitative results were obtained. The experimental results show that the method adjust itself according to various challenges and gives best qualitative and quantitative results.
A secure hash function based on sponge construction and chaos-maps

This work introduces a new hash function based on the sponge structure and two chaotic maps. It aims to avoid the major problems of Merkle-Damgård construction and ad hoc structures such as multi-collision and length extension attacks. The proposed hash function consists of three phases: message processing, absorbing and squeezing. In the message processing phase, the message is divided into n blocks, each block is processed by a standard chaotic map, then the absorbing phase takes the result of the message as input and generates a new output sequence using the PWLCM chaotic map, this process being repeated until all blocks of the message are processed. The final hash value is generated by the squeezing phase. The theoretical and experimental analysis shows that the proposed hash function has good statistical properties, high resistance to collision attacks, high sensitivity to any small change in the control parameter and message, and can be easily implemented and used for digital signatures, data management and password hashing in cloud computing.
Research on the Design of Intelligent Chatbot Based on Deep Learning

Abstract At present, chat robots still have the problems of insufficient utilization of multi-modal information, weak emotion expression ability, and lack of model fusion mechanism. With the application of deep learning in the fields of natural language understanding, word vector representation, machine translation, sentiment analysis, and Chinese word segmentation, people have begun to study the key technologies of chat robots and apply deep learning to chat robots. In view of the problem that the traditional chat robot Seq2Seq model training will produce too safe, common, and repetitive answers to the speech corpus, which affects the interactive experience with users, this paper combines the idea of mutual information to improve the objective function of the model so that the model can produce richer and more diverse answers and give users a better experience. Experiments show that the model has improved many indicators compared with the single-modal model in the microblog data set experiment.
Influence of motivational sphere on professional self-determination of medical college students

The aim of the study is to study the influence of the motivational sphere on the professional self-determination of medical college students. Results. A theoretical analysis of scientific psychological literature was carried out, the concepts of «professional self-determination», «professional self-determination of students», «motivation of professional self-determination» were defined; the features of professional self-determination of students of secondary professional institutions were revealed; the influence of the motivational sphere of students on their professional self-determination was studied; a set of diagnostic methods was selected in order to identify the influence of the motivational sphere of students on their professional self-determination; a quantitative and qualitative analysis of the results of the empirical study was carried out. Conclusion. The study of the problem of the influence of the motivational sphere on the professional self-determination of students of secondary vocational educational institutions is an urgent problem at the present stage of development of psychological science. The educational and professional activity of students becomes successful if they have a high level of motivation for this type of activity.
A Blockchain Implementation to Improve Collaboration Between Original Equipment Manufacturers (OEM) and Partnering Organizations

Original Equipment Manufacturers (OEMs) need to collaborate within and outside their organizations to improve product quality and time to market. However, legacy systems built over decades using different technology stacks make information sharing and maintaining consistency challenging. Distributed ledger technologies (DLTs) can improve efficiency and provide trust, thus helping to achieve a more streamlined and unified collaboration infrastructure. However, most of the work done is theoretical or conceptual and lacks implementation. This paper elaborates on architecture and implementing a proof of concept (POC) of blockchain-based interoperability and data sharing system that allows OEMs to collaborate seamlessly and share information in real-time.
Q-percentile Bandwidth Billing Based Geo-Scheduling Algorithm

Current IaaS providers deploy cheaper computing resources in newly built data centers and provide cross-regional network services to improve the interoperability of computing resources in different regions. Third-party service providers can use part of their budget to purchase cross-regional communication resources to use cheaper resources in remote areas to reduce the cost of processing massive task requests. The Q-percentile charging model is widely used in cross-regional communication resources billing, but there is little task scheduling research on that billing method. Therefore, this paper studies a geo-distributed task scheduling scenario using the Q-percentile charging model. We design a geo-scheduling algorithm specifically for Q-percentile charging model to allocate resources in the two dimensions of computing resources and communication resources. Furthermore, referring to three existing communication resource allocation strategies, we design three bandwidth allocation algorithms considering the Q-percentile charging characteristics to provide suitable solutions for different scenarios. We conducted experiments based on public well-known datasets such as LIGO workflow. Results show that, compared with the baseline, the scheduling algorithm proposed in this paper can reduce the task scheduling cost between geo-distributed data centers by 10%-20% based on various task loads and show differences in the applicability of different communication resource allocation strategies.
Towards User Experience Heuristics for Engagement and Interaction

In the human computer interaction domain, there are few to no design guidelines that take into consideration the holistic end-user engagement and interaction with the ICT product. The extract from the study presented in this paper aimed at achieving two objectives: to determine the parts of a process for the development of end-user experience (UX) heuristics, and to identify UX heuristics for engagement and interaction using the heuristics development process. The study employed the use of qualitative data collection and analysis techniques. The primary contributions in this paper are the formulated heuristics development approach, and the UX heuristics for engagement and interaction aligned to the three dimensions of UX engagement and interaction namely emotional, cognitive, and behavioural. Many of the guidelines support at least two UX engagement and interaction dimensions, thus showing that designing for an engaging experience needs to consider not just a single dimension, but a dual or more holistic approach.
Point-Mass Filter in Land and Air Terrain-Aided Navigation: Performance Evaluation

This paper discusses a recently developed efficient solution to the nonlinear filtering problem that arises in terrain-aided navigation of land and air vehicles. The specific implementation of a Rao-Blackwellised point-mass filter has been field-tested and its performance has been validated on multiple data sets and maps of variable accuracy. The filter implementation for the air vehicles is supplemented with a radar altimeter model that predicts the location of the most probable radar beam reflection point to be used in measurement consistency test.
Privacy Preserving Multi-class Fall Classification Based on Cascaded Learning And Noisy Labels Handling

With an increasingly ageing population in the world, fall detection and classification for elderly people becomes an imperative problem that needs to be addressed for assisted living. Currently, most of the fall detection algorithms are based on wearable and non-wearable sensors, such as accelerometer and video camera respectively. In this work, different from previous vision-based methods where the whole images are used, to mitigate the privacy protection problem and detect different types of fall events, we utilize only the skeleton data to achieve the classification of different fall events by using a deep neural network (DNN). The cost of manually labelling and due to varieties of annotators, for a recorded dataset, there always exist errors which will deteriorate the performance. To address this issue, we introduce the confident learning to remove wrong labelled samples and propose a new cascaded learning method to solve the noisy labelled data problem. To confirm the efficacy of the proposed method, we compare different algorithms on the UP-Fall dataset to show that the proposed method performs better than the state-of-the-art.