Reviewerly/exHarmony huggingface dataset:
Putting “mobile” into mathematics: Results of a randomised controlled trial

There is an increasing use of mobile technologies in the classroom, particularly its use in supporting contextual learning, but comparative research on the effects of mobile learning in mathematics are few. The aim of this research was to examine student perceptions of using mobile technologies and their effect on mathematics achievement in a randomised controlled trial. Seventy-four Grade 5 and 6 students and three teachers participated in the study. Both groups participated in six weeks of active and collaborative learning activities on math. The experimental group used tablets to support them in their activities while the control group had similarly designed activities without the tablets. The tablets were observed to have facilitated constructivist learning activities as students moved in and out of different learning contexts. Most of the experimental group had positive evaluations but their end activity ratings were not significantly different from the control group. Gender differences were found in terms of how students perceived the mobile learning activities. There was no difference found in the groups’ post-test achievement scores following an analysis of covariance with pre-test as covariate. For items relating to student misconception, students in the experimental group performed better. Overall, the study highlights that the success of a mobile learning intervention is dependent on various factors, such as student characteristics, stability of the technology and content compatibility. Implications for practice and future research are discussed.
A Socially Assistive Robotic Platform for Upper-Limb Rehabilitation: A Longitudinal Study With Pediatric Patients

In motor rehabilitation therapies, especially with pediatric patients, there is often a loss of interest and motivation due to the lack of variety in exercises and therapeutic activities. NAOTherapist (NT) is a socially assistive robotics (SAR) platform designed to support hands-off rehabilitation sessions through two interactive games: mirror and Simon. It integrates a cognitive architecture with an NAO humanoid robot that acts as a coach, as well as a red-green-blue-depth (RGB-D) sensor that eases body capture. This work aims to deploy and evaluate the developed technology for a long-term experience.
Optimization of a frame structure using the Coulomb force search strategy-based dragonfly algorithm

This article proposes an adaptive dragonfly algorithm (DA) for the structural optimization of frame structures. This algorithm, named the Coulomb force search strategy-based dragonfly algorithm (CFSSDA), combines the Coulomb force search strategy (CFSS) with the DA method. The CFSS is applied to automatically determine an adaptive search step in each iteration, thus enhancing the exploration capability of the CFSSDA. The performance of the CFSSDA is investigated on the basis of three widely used benchmark numerical examples. The results indicate that the proposed CFSSDA achieves a markedly improved convergence rate while maintaining a high accuracy. Furthermore, the CFSSDA is utilized to find the optimal parameters for an engineering frame structure problem with discrete variables under stress constraints. These results also show that the CFSSDA can rapidly generate an optimal structure with a weight that is reduced by 27.97% compared with the initial design.
Automatic Kidney Lesion Detection for CT Images Using Morphological Cascade Convolutional Neural Networks

The CT scan image is one of the most useful tools for diagnosing and locating lesions in the kidney. It can provide precise information about the location and size of lesions in many medical applications. Manual and traditional medical testings are labor-consuming and time-costing. Nowadays, detecting lesions in CT automatically is an integral assignment to the paramount importance of clinical diagnosis. Computer-aided diagnosis (CAD) is needed to develop and improve medical testing efficiency. However, it is still a tremendous challenge to the extant low precision and incomplete detection algorithm. In this paper, we proposed a lesion detection tool using multi intersection over union (IOU) threshold based on morphological cascade convolutional neural networks (CNNs). For improving the detection of small lesions (1–5 mm) and increasing the stableness of network, we proposed two morphology convolution layers and modified feature pyramid networks (FPNs) in the faster RCNN and combined four IOU threshold cascade RCNNs. In this lesion detection task, the modified CNN was trained in pytorch framework. The experiments were conducted in CT kidney images of DeepLesion that are published by hospitals’ picture archiving and communication systems (PACSs). Finally, our method achieved AP of 0.840 and AUC of 0.871, and the results demonstrated that our proposed detector is an outstanding tool for detecting lesions in CT and outperformed in the data set.
Chasing Play Potentials

User involvement is well established in game and play design. But in a time when play design is becoming relevant in domains beyond pure entertainment, and play blends into everyday activity in diverse ways, we need to revisit old, and develop new, user involvement methods. Using a situated perspective and Research through Design, we present Situated Play Design (SPD), a novel approach for the design of playful interventions aimed at open-ended, everyday activities that are non-entertainment based. Like user-centered game and play design methods, our contribution leverages user engagement; like Participatory Design methods, our method acknowledges the co-creating role of end users. SPD extends those approaches by focusing on uncovering existing manifestations of contextual playful engagement and using them as design material. Through two case studies, we illustrate our approach and the design value of using existing and emergent playful interactions of users in context as inspirations for future designs. This allows us to provide actionable strategies to design for in-context playful engagement.
Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area

Nowadays there are lots of fatal diseases are growing at a rapid rate. We consider about four primary diseases like jaundice, diabetes mellitus, yellow fever, and cholera. In this paper, we design a novel method with the help of fuzzy and FPGA system for prediction multiple diseases in a rural area . Association rule mining technique helps to define fuzzy rules, which implemented on both Spartan3-E and Artix-7 FPGA kit. Due to this implementation, it is easier to design a cost-effective and portable system for multi-disease prediction. The innovation lies in design a low power FPGA and meticulousness method for identification, prediction of four fatal diseases. The whole plan has tested on Xilinx and Cadence tool for generating RTL model and Layout design.
Mitigating coherent noise using Pauli conjugation

Coherent noise can be much more damaging than incoherent (probabilistic) noise in the context of quantum error correction. One solution is to use twirling to turn coherent noise into incoherent Pauli channels. In this Article, we show that some of the coherence of the noise channel can actually be used to improve its logical fidelity by simply sandwiching the noise with a chosen pair of Pauli gates, which we call Pauli conjugation. Using the optimal Pauli conjugation, we can achieve a higher logical fidelity than using twirling and doing nothing. We devise a way to search for the optimal Pauli conjugation scheme and apply it to Steane code, 9-qubit Shor code and distance-3 surface code under global coherent $Z$ noise. The optimal conjugation schemes show improvement in logical fidelity over twirling while the weights of the conjugation gates we need to apply are lower than the average weight of the twirling gates. In our example noise and codes, the concatenated threshold obtained using conjugation is consistently higher than the twirling threshold and can be up to 1.5 times higher than the original threshold where no mitigation is applied. Our simulations show that Pauli conjugation can be robust against gate errors. With the help of logical twirling, the undesirable coherence in the noise channel can be removed and the advantages of conjugation over twirling can persist as we go to multiple rounds of quantum error correction.
Analysing and Demonstrating Tool-Supported Customizable Task Notations

When task descriptions are precise they can be analysed to yield a variety of insights about interaction, such as the quantity of actions performed, the amount of information that must be perceived, and the cognitive workload involved. Task modelling notations and associated tools provide support for precise task description, but they generally provide a fixed set of constructs, which can limit their ability to model new and evolving application domains and technologies. This article describes challenges involved in using fixed notations for describing tasks. We use examples of recognized tasks analysis processes and their phases to show the need for customization of task notations, and through a series of illustrative examples, we demonstrate the benefits using our extensible task notation and tool (HAMSTERS-XL).
Benchmarking in classification and regression

Abstract The article presents an overview of the status quo in benchmarking in classification and nonlinear regression. It outlines guidelines for a comparative analysis in machine learning, benchmarking principles, accuracy estimation, and model validation. It provides references to established repositories and competitions and discusses the objectives and limitations of benchmarking. Benchmarking is key to progress in machine learning as it allows an unprejudiced comparison among alternative methods. This article presents guidelines and best practices for benchmarking in classification and regression. It reviews state‐of‐the‐art approaches in machine learning, establishes benchmarking principles and discusses performance metrics for a sound statistical comparative analysis. This article is categorized under: Technologies &gt; Computational Intelligence Fundamental Concepts of Data and Knowledge &gt; Key Design Issues in Data Mining Technologies &gt; Machine Learning Technologies &gt; Classification
Semi-supervised multi-view clustering based on constrained nonnegative matrix factorization

Most existing clustering approaches address multi-view clustering problems by graph regularized nonnegative matrix factorization to obtain the new representation of each view separately. And then, the additional regularization term is introduced to fuse these representations. However, the graph regularization is no guarantee that the representation obtained by mapping data points has the consistent label information with the original data. In addition, we may have a number of labeled samples in practice. This paper proposes a new semi-supervised multi-view clustering method based on Constrained Nonnegative Matrix Factorization with sparseness constraint, which is called MVCNMF. Our method first learns the representation by introducing an auxiliary matrix for each view and constructs the label constraint matrix shared among all views, where the label constraint matrix can guarantee that the label information is fused into the new representation of each view. Then, by applying the co-regularization and the sparseness constraint in auxiliary matrices, the complementary information from different views is integrated, and the robust feature of each view is extracted. Further, we also present the convergence proof and the computational complexity analysis of our method. Finally, extensive experiments on real datasets have demonstrated that our proposed approach can achieve a better clustering quality as compared to state-of-the-art multi-view clustering methods.
Contract Analytics in Auditing

SYNOPSIS This paper proposes the Contract Analytics Framework (CAF), which provides guidance for incorporating textual analysis into audits of large numbers of contracts. Contracts are used extensively in auditing for risk assessment, analytical procedures, substantive testing, and audit review. While material contracts are carefully examined to assess financial implications, contracts perceived to be low risk are typically audited using standard sampling techniques. This proposed CAF facilitates effective and efficient audit analyses on full populations of contracts. We identify and describe six functional areas in the CAF: (1) Document Management, (2) Content Identification, (3) Cutoff Testing, (4) Record Confirmation, (5) Term Verification, and (6) Additional Audit Tasks. Based on auditing standards, we discuss the application of the CAF in the audit stages of risk assessment, substantive tests, and review. The framework is then implemented on a group of reinsurance contracts to demonstrate the feasibility of auditing full populations of contracts. JEL Classifications: M42; C88; O39.
Interoperability of disparate engineering domain ontologies using basic formal ontology

As engineering applications require management of ever larger volumes of data, ontologies offer the potential to capture, manage, and augment data with the capability for automated reasoning and semantic querying. Unfortunately, considerable barriers hinder wider deployment of ontologies in engineering. Key among these is lack of a shared top-level ontology to unify and organise disparate aspects of the field and coordinate co-development of orthogonal ontologies. As a result, many engineering ontologies are limited to their scope, and functionally difficult to extend or interoperate with other engineering ontologies. This paper demonstrates how the use of a top-level ontology, specifically the Basic Formal Ontology (BFO), greatly facilitates interoperability of multiple engineering-related ontologies. We constructed a system of formal linked ontologies by re-engineering legacy ontologies to be conformant with BFO and developing new BFO-conformant ontologies to capture knowledge in the engineering design, enterprise, human factors, manufacturing, and application domain of additive manufacturing. The resulting Integrated Framework for Additively Manufactured Products (IFAMP), including the body knowledge instantiated on its basis, serve as the basis for a proposed Design with Additive Manufacturing Method (DWAM), which we believe can support the design of innovative products with semantically enhanced ideation tools and enhanced access to application domain knowledge. The method and its facilitation through the ontological framework are demonstrated using a case study in medicine.
Characterising postgraduate students’ corpus query and usage patterns for disciplinary data-driven learning

Abstract Data-driven learning (DDL; Johns, 1991), involving students’ hands-on use of corpora for self-guided language learning, is a methodology now increasingly used in many tertiary contexts to enhance the teaching of disciplinary postgraduate thesis writing. However, there are still few studies tracking students’ actual engagement with corpora for DDL. This mixed-methods study reports on the tracking of students’ corpus use via a purpose-built corpus query and data visualisation platform integrated into a large postgraduate disciplinary thesis writing program at a university in Hong Kong. Data on corpus usage history (e.g. times of access, duration of use), query syntax (e.g. query lexis/phraseology and use of wildcards and part-of-speech tags), query function (e.g. frequency lists/distribution, concordance sorting and collocation) and query filters (e.g. searches by faculty, discipline, or thesis section) were collected from 327 students spanning over 11,000 individual corpus queries. The results show significant interdisciplinary and inter-/intra-user trends and variation in the use of particular corpus functions and query syntax adopted by corpus users. Students varied in the type of knowledge (e.g. domain-specific, language-specific) they were accessing, and frequently went beyond the exemplars of the DDL course materials to generate unique queries under their own initiative. Qualitative case study data from three corpus users’ activity logs also show distinctive individual corpus engagement by query frequency and function. These data provide a clearer insight into what students actually do during DDL and the different directions and trajectories that individual users take as a result of DDL. All accompanying DDL tasks are also included as supplementary materials.
Political Bots: Disrupting Canada’s Democracy

Bots—automated online agents that mimic human behaviour—attempting to influence public opinion through social media, provide a useful case of the challenges for regulating disruptive technologies. This article articulates four problems associated with bots in politics: identification, evidence, attribution, and enforcement. It then explores policy solutions, arguing the solutions must align with the type of disruption bots contribute to.
Empowered to Make a Change

This paper scrutinizes how children can be empowered to make a change through acquiring skills in digital technology design. We propose a framework that integrates theoretical understanding from literature on nexus analysis, values, and value as well as empowerment and genuine participation of children, and a related tool for educators and researchers advocating empowerment and inclusion. They should benefit from this tool when planning, analyzing, and evaluating their projects. We argue that the tool is useful beyond studies with children and can be used as a practical tool when planning and implementing digital technology design projects with any group of people and as a theoretical tool when studying such endeavors, especially when working with vulnerable or underserved participants.
Benchmarks for Automata Learning and Conformance Testing

We describe a large collection of benchmarks, publicly available through the wiki automata.cs.ru.nl, of different types of state machine models: DFAs, Moore machines, Mealy machines, interface automata and register automata. Our repository includes both randomly generated state machines and models of real protocols and embedded software/hardware systems. These benchmarks will allow researchers to evaluate the performance of new algorithms and tools for active automata learning and conformance testing.
nofu — A Lightweight No-Reference Pixel Based Video Quality Model for Gaming Content

Popularity of streaming services for gaming videos has increased tremendously over the last years, e.g. Twitch and Youtube Gaming. Compared to classical video streaming applications, gaming videos have additional requirements. For example, it is important that videos are streamed live with only a small delay. In addition, users expect low stalling, waiting time and in general high video quality during streaming, e.g. using http-based adaptive streaming. These requirements lead to different challenges for quality prediction in case of streamed gaming videos. We describe newly developed features and a no-reference video quality machine learning model, that uses only the recorded video to predict video quality scores. In different evaluation experiments we compare our proposed model nofu with state-of-the-art reduced or full reference models and metrics. In addition, we trained a no-reference baseline model using brisque+niqe features. We show that our model has a similar or better performance than other models. Furthermore, nofu outperforms VMAF for subjective gaming QoE prediction, even though nofu does not require any reference video.
Assessing Diffusion and Perception of Test Smells in Scala Projects

Test smells are, analogously to code smells, defined as the characteristics exhibited by poorly designed unit tests.Their negative impact on test effectiveness, understanding, and maintenance has been demonstrated by several empirical studies.However, the scope of these studies has been limited mostly to JAVA in combination with the JUNIT testing framework.Results for other language and framework combinations are -despite their prevalence in practice-few and far between, which might skew our understanding of test smells.The combination of SCALA and SCALATEST, for instance, offers more comprehensive means for defining and reusing test fixtures, thereby possibly reducing the diffusion and perception of fixture-related test smells.This paper therefore reports on two empirical studies conducted for this combination.In the first study, we analyse the tests of 164 open-source SCALA projects hosted on GITHUB for the diffusion of test smells.This required the transposition of their original definition to this new context, and the implementation of a tool (SOCRATES) for their automated detection.In the second study, we assess the perception by and the ability of 14 SCALA developers to identify test smells.For this context, our results show (i) that test smells have a low diffusion across test classes, (ii) that the most frequently occurring test smells are LAZY TEST, EAGER TEST, and ASSERTION ROULETTE, and (iii) that many developers were able to perceive but not to identify the smells.
Masked Graph Attention Network for Person Re-Identification

The mainstream methods for person re-identification (ReID) mainly focus on the correspondence between individual sample images and labels, while ignoring rich global mutual information resides in the whole sample set. We propose a method called Masked Graph Attention Network (MGAT) to address this problem. MGAT operates on the complete graph constructed with the extracted features, where nodes are able to directionally attend over other nodes' features under the guidance of label information in the form of mask matrix. By using MGAT module, the previously neglected global mutual information is exploited to generate an optimized feature space with more discriminant power. Meanwhile, we propose to feedback the optimization information learned by MGAT module to the feature-embedding network to enhance the mapping capability, thus avoiding the difficulty to handle large-scale graphs in testing phase. To evaluate our method, we conduct experiments on three commonly used ReID datasets. The results show that our method outperforms most mainstream methods, and is highly comparable to the state-of-the-art method.
Position Paper

WebAssembly (Wasm) is a low-level platform-independent bytecode language. Today, developers can compile C/C++ to Wasm and run it everywhere, at almost native speeds. Unfortunately, this compilation from C/C++ to Wasm also preserves classic memory safety vulnerabilities, such as buffer overflows and use-after-frees.
Real-Time Semantic Segmentation-Based Stereo Reconstruction

In this paper, we propose a novel semantic segmentation-based stereo reconstruction method that can keep up with the accuracy of the state-of-the art approaches while running in real time.The solution follows the classic stereo pipeline, each step in the stereo workflow being enhanced by additional information from semantic segmentation.Therefore, we introduce several improvements to computation, aggregation, and optimization by adapting existing techniques to integrate additional surface information given by each semantic class.For the cost computation and optimization steps, we propose new genetic algorithms that can incrementally adjust the parameters for better solutions.Furthermore, we propose a new postprocessing edge-aware filtering technique relying on an improved convolutional neural network (CNN) architecture for disparity refinement.We obtain the competitive results at 30 frames/s, including segmentation.