Reviewerly/exHarmony huggingface dataset:
Integration of biplanar X-ray, three-dimensional animation and particle simulation reveals details of human ‘track ontogeny’

The emergence of bipedalism had profound effects on human evolutionary history, but the evolution of locomotor patterns within the hominin clade remains poorly understood. Fossil tracks record in vivo behaviours of extinct hominins, and they offer great potential to reveal locomotor patterns at various times and places across the human fossil record. However, there is no consensus on how to interpret anatomical or biomechanical patterns from tracks due to limited knowledge of the complex foot-substrate interactions through which they are produced. Here, we implement engineering-based methods to understand human track formation with the ultimate goal of unlocking invaluable information on hominin locomotion from fossil tracks. We first developed biplanar X-ray and three-dimensional animation techniques that permit visualization of subsurface foot motion as tracks are produced, and that allow for direct comparisons of foot kinematics to final track morphology. We then applied the discrete element method to accurately simulate the process of human track formation, allowing for direct study of human track ontogeny. This window lets us observe how specific anatomical and/or kinematic variables shape human track morphology, and it offers a new avenue for robust hypothesis testing in order to infer patterns of foot anatomy and motion from fossil hominin tracks.
Explaining Architectural Design Tradeoff Spaces: A Machine Learning Approach

In software design, guaranteeing the correctness of run-time system behavior while achieving an acceptable balance among multiple quality attributes remains a challenging problem. Moreover, providing guarantees about the satisfaction of those requirements when systems are subject to uncertain environments is even more challenging. While recent developments in architectural analysis techniques can assist architects in exploring the satisfaction of quantitative guarantees across the design space, existing approaches are still limited because they do not explicitly link design decisions to satisfaction of quality requirements. Furthermore, the amount of information they yield can be overwhelming to a human designer, making it difficult to distinguish the forest through the trees. In this paper, we present an approach to analyzing architectural design spaces that addresses these limitations and provides a basis to enable the explainability of design tradeoffs. Our approach combines dimensionality reduction techniques employed in machine learning pipelines with quantitative verification to enable architects to understand how design decisions contribute to the satisfaction of strict quantitative guarantees under uncertainty across the design space. Our results show feasibility of the approach in two case studies and evidence that dimensionality reduction is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.
AI And Web-Based Interactive College Enquiry Chatbot

AI and web-based interactive College Enquiry Chatbot is a straightforward web application that aims to supply knowledge regarding college. The chatbot created here may be a web-based application that uses tongue Processing Libraries and AI terminology to possess conversations with humans. “Eliza” and “Cleverbot” are several online applications that are created within the past. The College Enquiry Chatbot will engage in friendly conversations, respond to the course and college information, provide a link to the tutorial calendar, and answer frequently asked questions, among other things. This project is specialized in creating a chatbot to be employed by students to urge their queries responded to easily from the college website. A chatbot may be a program that may do real conversations with textual and/or auditory methods. Using AI, chatbots can simulate human conversations. Humans respond to others depending on their mood and emotions. Whereas chatbots are bound by some rules, resulting in them treating a customer most politely and perfectly. Students can ask questions to the chatbot at any time of the day and get a reply very quickly. At any time of day, chatbots can have simultaneous conversations with thousands of people. A chatbot can work 24x7 without getting tired. It is subjected to minimal errors thus increasing productivity.
Application of Data Mining in English Online Learning Platform

Abstract Since the formation of global economic integration, in order to better exchanges and cooperation with countries around the world, master the necessary international common language - English is very necessary. In the context of today’s big era with a variety of English learning methods, it is logical that data mining is applied to the English online platform As for data mining, it has made remarkable achievements in the commercial field. With the continuous application of data mining technology and the continuous development of online learning system, its application in the field of education is also more and more extensive. It promotes the development of educational decision support model based on evidence and “data”. Based on the analysis of the application of data mining technology in the field of education and teaching at home and abroad, this paper summarizes and summarizes the application and development of various data mining technologies in English online learning system. Finally, combined with the actual situation in the field of education in China, this paper puts forward some challenges in the application of data mining technology in this field, and gives the direction and prospect of further development in the future, this paper hopes to improve the learning effect of English online learning platform effectively [1].
What is the degree of teacher centrality in optimal teaching of digital literacy in a technology-enhanced environment? Typology of teacher prototypes

This study explored the degree of teachers’ centrality in the classroom while developing digital literacies of their students and the pedagogical strategies which are employed by teachers in this process. We conducted a bottom-up analysis on 65 semi-structured interviews (n = 4372 statements) based on two conceptual frameworks: the Digital Literacy framework and the 5Cs framework with a nation-wide sample of elementary and secondary-teachers. The findings offer a typology of four teacher prototypes regarding the degree of their centrality in a technology-enhanced classroom and the mapped pedagogical strategies employed by each prototype. Based on bottom-up coding, the study expands the above-mentioned frameworks regarding re-production thinking, socio-emotional thinking, e-communication, and collaboration literacies. We discuss the implications for educational theory and practice.
Learning with smooth Hinge losses

Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain a faster convergence rate with modern optimization algorithms. In this paper, we introduce two smooth Hinge losses ψG(α;σ) and ψM(α;σ) which are infinitely differentiable and converge to the Hinge loss uniformly in α as σ tends to 0. By replacing the Hinge loss with these two smooth Hinge losses, we obtain two smooth support vector machines (SSVMs), respectively. Solving the SSVMs with the Trust Region Newton method (TRON) leads to two quadratically convergent algorithms. Experiments in text classification tasks show that the proposed SSVMs are effective in real-world applications. We also introduce a general smooth convex loss function to unify several commonly-used convex loss functions in machine learning. The general framework provides smooth approximation functions to non-smooth convex loss functions, which can be used to obtain smooth models that can be solved with faster convergent optimization algorithms.
A degradation model for simultaneous brightness and sharpness enhancement of low-light image

Although a large number of methods have been proposed for low-light image enhancement, there still remain challenges for these methods to simultaneously achieve excellent sharpness/resolution, high calculation efficiency as well as visual pleasure requirements.In this communication, we propose a new low-light image enhancement method based on the degradation model to overcome this dilemma.Specifically, we regard the low-light image enhancement as a special inverse problem of image degradation, and then the task of low-light enhancement is logically embedded in the iterative back-projection (IBP) framework.Meanwhile, an adaptive gamma correction is utilized to adaptively adjust the brightness, and then the IBP framework is transferred to the logarithmic domain instead of the spatial domain for further acceleration.Besides, a simple and effective pre-processing strategy is proposed to pre-enhance the low-light input while making the enhanced image clarify (or visual pleasure).Extensive experimental results on public databases and seven state-of-the-art benchmarks consistently demonstrate the effectiveness and efficiency of the proposed method both visually and quantitatively.
Retinex Underwater Image Enhancement With Multiorder Gradient Priors

We develop a variational retinex algorithm for enhancing single underwater image with multiorder gradient priors of reflectance and illumination. First, a simple yet effective color correction approach is used to remove color casts and recover naturalness. Then, a variational retinex model for enhancing the color-corrected underwater image is established by imposing multiorder gradient priors of reflectance and illumination. According to structural sparsity difference between illumination and reflectance, the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm is accurately adopted to model piecewise and piecewise linear approximations on the reflectance, while the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm is appropriately employed to enforce spatial smoothness and spatial linear smoothness on the illumination. Next, a complex underwater image enhancement issue is turned into simple denoising subproblems, which can be addressed by an efficient optimization algorithm that is fast performed on pixel-wise operations without requiring additional prior knowledge about underwater imaging conditions. Final experiments demonstrate that the proposed method yields better results of qualitative and quantitative assessments than several traditional and leading underwater image enhancement approaches.
ChatrEx: Designing Explainable Chatbot Interfaces for Enhancing Usefulness, Transparency, and Trust

When breakdowns occur during a human-chatbot conversation, the lack of transparency and the “black-box” nature of task-oriented chatbots can make it difficult for end users to understand what went wrong and why. Inspired by recent HCI research on explainable AI solutions, we explored the design of in-application explainable chatbot interfaces (ChatrEx) that explain the underlying working of a chatbot during a breakdown. ChatrEx-VINC provides visual example-based step-by-step explanations in-context of the chat window whereas ChatrEx-VST provides explanations as a visual tour overlaid on the application interface. We implemented these chatbots for complex spreadsheet tasks and our comparative observational study (N=14) showed that the explanations provided by both ChatrEx-VINC and ChatrEx-VST enhanced users' understanding of the reasons for a breakdown and improved users' perceptions of usefulness, transparency, and trust. We identify several opportunities for future research to exploit explainable chatbot interfaces and better support human-chatbot interaction.
Ensemble Deep Learning Using Faster R-CNN and Genetic Algorithm for Vehicle Detection in UAV Images

In this paper, an ensemble deep transfer learning (EDTL) based on Faster R-CNN is introduced for the vehicle detection in UAV images. We perform a weighted-averaging ensemble transfer learning comprising six base learners using a ResNet50 that have already pre-trained on ImageNet dataset. The weights of the six base learners as well as the final decision threshold are adaptively optimized via genetic algorithm, to maximize the total accuracy, precision, and recall. Simulation results on AU-AIR dataset demonstrate the superiority of the EDTL against the existing techniques, in terms of the total accuracy, and the trade-off between precision and recall.
Location Privacy Protection Scheme for LBS in IoT

The widespread use of Internet of Things (IoT) technology has promoted location-based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client-server-to-user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G-V anonymity algorithm is proposed to find <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> neighbors in an anonymous area so that the user’s location data meets the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> -anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.
A Faceted Taxonomy of Requirements Changes in Agile Contexts

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Originally, developers aimed to identify most software requirements upfront in software development projects. However, agile methods explicitly encourage software requirements to be changed throughout development, i.e., many Requirements Changes (RCs) occur. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> The objective of this study is to better understand RCs and produce a taxonomy of RCs in agile contexts. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> We ran a mixed-methods approach comprising a series of studies: an interview-based study (10 participants from New Zealand and Australia), a focused literature review, and an in-depth survey (40 participants world-wide). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Key characteristics of RCs in agile we found relate to different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">types</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">forms</i> , agile RCs have multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reasons</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sources</i> , they are brought by different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">carriers</i> , and their emergence in agile is via a variety of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">events.</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Summary:</i> The presented taxonomy provides a guide for software practitioners to use to help manage RC-related issues in agile contexts.
Degree Tables for Secure Distributed Matrix Multiplication

We consider the problem of secure distributed matrix multiplication (SDMM) in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers. We construct polynomial codes for SDMM by studying a recently introduced combinatorial tool called the degree table. For a fixed partitioning, minimizing the total communication cost of a polynomial code for SDMM is equivalent to minimizing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> , the number of distinct elements in the corresponding degree table. We propose new constructions of degree tables with a low number of distinct elements. These new constructions lead to a general family of polynomial codes for SDMM, which we call <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {GASP}_{r}$ </tex-math></inline-formula> (Gap Additive Secure Polynomial codes) parameterized by an integer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> . <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {GASP}_{r}$ </tex-math></inline-formula> outperforms all previously known polynomial codes for SDMM under an outer product partitioning. We also present lower bounds on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> and prove the optimality or asymptotic optimality of our constructions for certain regimes. Moreover, we formulate the construction of optimal degree tables as an integer linear program and use it to prove the optimality of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {GASP}_{r}$ </tex-math></inline-formula> for all the system parameters that we were able to test.
Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring

We address the problem of learning self-supervised representations from unlabeled image collections. Unlike existing approaches that attempt to learn useful features by maximizing similarity between augmented versions of each input image or by speculatively picking negative samples, we instead also make use of the natural variation that occurs in image collections that are captured using static monitoring cameras. To achieve this, we exploit readily available context data that encodes information such as the spatial and temporal relationships between the input images. We are able to learn representations that are surprisingly effective for downstream supervised classification, by first identifying high probability positive pairs at training time, i.e. those images that are likely to depict the same visual concept. For the critical task of global biodiversity monitoring, this results in image features that can be adapted to challenging visual species classification tasks with limited human supervision. We present results on four different camera trap image collections, across three different families of self-supervised learning methods, and show that careful image selection at training time results in superior performance compared to existing baselines such as conventional self-supervised training and transfer learning.
Securing private information by data perturbation using statistical transformation with three dimensional shearing

Privacy is very important in shared data for the knowledge based applications. However it causes serious privacy concerns, when the sensitive data is stored and moved to other applications. It is vital to incorporate privacy in the sensitive data for the data mining process. While preserving privacy, certain protocols allow the knowledge extraction from the modified data without revealing the original information. In this work, a series of steps like, Weight of Evidence, Information Value, Min–Max normalization and 3D shearing are applied to perturb the quasi-identifiers in the data. The classification techniques such as Decision Tree, Random Forest, Extreme Gradient Boost and Support Vector Machines are employed in adult income, bank marketing and lung cancer datasets to analyze the performance of the original and perturbed data. Accuracy, variance and sensitivity-specificity are being considered as performance measures of the classifiers. This research work is compared with 2D rotation and 3D rotation algorithms. The experimental results clearly show that the proposed work preserves the data utility with higher data transformation capacity and privacy preserving capacity than the existing geometric transformation techniques.
SwarmPlay: Interactive Tic-tac-toe Board Game with Swarm of Nano-UAVs driven by Reinforcement Learning

Reinforcement learning (RL) methods have been actively applied in the field of robotics, allowing the system itself to find a solution for a task otherwise requiring a complex decision-making algorithm. In this paper, we present a novel RL-based Tic-tac-toe scenario, i.e. SwarmPlay, where each playing component is presented by an individual drone that has its own mobility and swarm intelligence to win against a human player. Thus, the combination of challenging swarm strategy and human-drone collaboration aims to make the games with machines tangible and interactive. Although some research on AI for board games already exists, e.g., chess, the SwarmPlay technology has the potential to offer much more engagement and interaction with the user as it proposes a multi-agent swarm instead of a single interactive robot. We explore user’s evaluation of RL-based swarm behavior in comparison with the game theory-based behavior. The preliminary user study revealed that participants were highly engaged in the game with drones (70% put a maximum score on the Likert scale) and found it less artificial compared to the regular computer-based systems (80%). The affection of the user’s game perception from its outcome was analyzed and put under discussion. User study revealed that SwarmPlay has the potential to be implemented in a wider range of games, significantly improving human-drone interactivity.