Reviewerly/exHarmony huggingface dataset:
Student perceptions of virtual reality use in higher education

Purpose This paper provides an analysis and insight into undergraduate student views concerning the use of virtual reality technology towards whether it has the potential to support and provide novel pedagogical avenues towards teaching and learning in higher education. The purpose of this paper is to ascertain student views about the application of VR technology within their degree programmes from a pedagogical perspective in addition to identifying potential challenges to VR adoption. Design/methodology/approach The research design adopted a mixed methods approach through the use of a questionnaire that was disseminated to undergraduate students studying in the discipline area of the creative industries. Through a series of open and closed questions, student views on VR adoption in higher education were analysed both quantitatively and qualitatively. The results were analysed statistically through a series of Mann–Whitney and Kruskal–Wallis tests. The qualitative statements were contextualised in the overall perspective of the research with the more relevant viewpoints identified to coincide with aspects of VR discovered in the literature. Findings The predominant findings of the research indicated that the majority of the students considered the use of VR to have useful pedagogical implications though not all findings were positive. The findings provided a sound overview of the benefits and potential drawbacks of VR use in general with a more specific focus in an educational context. Research limitations/implications Limitations of the research include the lack of overall generalisations that can be formed from the study due to the sample size and the fact that the results were based from one specific academic institution. Practical implications The findings of the research will provide educators with an insight into various perceptions of VR adoption within higher education. This will aid towards allowing them to reflect on whether VR is an appropriate tool to integrate within their curriculum and pedagogical approaches towards course delivery. Originality/value Though several studies have explored the use of VR in multiple contexts and subject areas, there still needs to be more research towards its potential drawbacks in a teaching and learning scenario and how to resolve these issues.
Convolutional neural networks as aid in core lithofacies classification

Artificial intelligence methods have a very wide range of applications. From speech recognition to self-driving cars, the development of modern deep-learning architectures is helping researchers to achieve new levels of accuracy in different fields. Although deep convolutional neural networks (CNNs) (a kind of deep-learning technique) have reached or surpassed human-level performance in image recognition tasks, little has been done to transport this new image classification technology to geoscientific problems. We have developed what we believe to be the first use of CNNs to identify lithofacies in cores. We use highly accurate models (trained with millions of images) and transfer learning to classify images of cored carbonate rocks. We found that different modern CNN architectures can achieve high levels of lithologic image classification accuracy (approximately 90%) and can aid in the core description task. This core image classification technique has the potential to greatly standardize and accelerate the description process. We also provide the community with a new set of labeled data that can be used for further geologic/data science studies.
BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis

We propose a novel BIRADS-SSDL network that integrates clinically-approved breast lesion characteristics (BIRADS features) into task-oriented semi-supervised deep learning (SSDL) for accurate diagnosis of ultrasound (US) images with a small training dataset. Breast US images are converted to BIRADS-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the converted BFMs are used as the input of an SSDL network, which performs unsupervised stacked convolutional auto-encoder (SCAE) image reconstruction guided by lesion classification. This integrated multi-task learning allows SCAE to extract image features with the constraints from the lesion classification task, while the lesion classification is achieved by utilizing the SCAE encoder features with a convolutional network. We trained the BIRADS-SSDL network with an alternative learning strategy by balancing the reconstruction error and classification label prediction error. To demonstrate the effectiveness of our approach, we evaluated it using two breast US image datasets. We compared the performance of the BIRADS-SSDL network with conventional SCAE and SSDL methods that use the original images as inputs, as well as with an SCAE that use BFMs as inputs. The experimental results on two breast US datasets show that BIRADS-SSDL ranked the best among the four networks, with a classification accuracy of around 94.23 ± 3.33% and 84.38 ± 3.11% on two datasets. In the case of experiments across two datasets collected from two different institutions/and US devices, the developed BIRADS-SSDL is generalizable across the different US devices and institutions without overfitting to a single dataset and achieved satisfactory results. Furthermore, we investigate the performance of the proposed method by varying the model training strategies, lesion boundary accuracy, and Gaussian filter parameters. The experimental results showed that a pre-training strategy can help to speed up model convergence during training but with no improvement of the classification accuracy on the testing dataset. The classification accuracy decreases as the segmentation accuracy decreases. The proposed BIRADS-SSDL achieves the best results among the compared methods in each case and has the capacity to deal with multiple different datasets under one model. Compared with state-of-the-art methods, BIRADS-SSDL could be promising for effective breast US computer-aided diagnosis using small datasets.
Deterministic Preparation of Dicke States

The Dicke state is an equal-weight superposition of all n-qubit states with Hamming Weight k (i.e. all strings of length n with exactly k ones over a binary alphabet). Dicke states are an important class of entangled quantum states that among other things serve as starting states for combinatorial optimization quantum algorithms. We present a deterministic quantum algorithm for the preparation of Dicke states. Implemented as a quantum circuit, our scheme uses $$\mathcal {O}(kn)$$ gates, has depth $$\mathcal {O}(n)$$ and needs no ancilla qubits. The inductive nature of our approach allows for linear-depth preparation of arbitrary symmetric pure states and – used in reverse – yields a quasilinear-depth circuit for efficient compression of quantum information in the form of symmetric pure states, improving on existing work requiring quadratic depth. All of these properties even hold for Linear Nearest Neighbor architectures.
Security Analysis and Enhancement of a Certificateless Searchable Public Key Encryption Scheme for IIoT Environments

With the fast development of Industrial Internet of Things (IIoT), IIoT storage, which provides a lower cost, higher reliability of data, remote access services, and expandable storage space, is received much attention among enterprises and individual users. However, providing a secure IIoT storage service could be a challenging task for some situations, for example, how to search encrypted data in the IIoT. In this paper, we examine the security of a recent proposed certificateless searchable public key encryption scheme for IIoT environments by Ma et al. We find that their scheme is insecure against an off-line keyword guessing attack. Then, we propose an enhancement based on the scheme and prove the security of our enhancement with a formal model.
Adaptive Self-paced Deep Clustering with Data Augmentation

Deep clustering gains superior performance than conventional clustering by jointly performing feature learning and cluster assignment. Although numerous deep clustering algorithms have emerged in various applications, most of them fail to learn robust cluster-oriented features which in turn hurts the final clustering performance. To solve this problem, we propose a two-stage deep clustering algorithm by incorporating data augmentation and self-paced learning. Specifically, in the first stage, we learn robust features by training an autoencoder with examples that are augmented by random shifting and rotating the given clean examples. Then, in the second stage, we encourage the learned features to be cluster-oriented by alternatively finetuning the encoder with the augmented examples and updating the cluster assignments of the clean examples. During finetuning the encoder, the target of each augmented example in the loss function is the center of the cluster to which the clean example is assigned. The targets may be computed incorrectly, and the examples with incorrect targets could mislead the encoder network. To stabilize the network training, we select most confident examples in each iteration by utilizing the adaptive self-paced learning. Extensive experiments validate that our algorithm outperforms the state of the arts on four image datasets.
Towards a more efficient and cost-sensitive extreme learning machine: A state-of-the-art review of recent trend

In spite of the prominence of extreme learning machine model, as well as its excellent features such as insignificant intervention for learning and model tuning, the simplicity of implementation, and high learning speed, which makes it a fascinating alternative method for Artificial Intelligence, including Big Data Analytics, it is still limited in certain aspects. These aspects must be treated to achieve an effective and cost-sensitive model. This review discussed the major drawbacks of ELM, which include difficulty in determination of hidden layer structure, prediction instability and Imbalanced data distributions, the poor capability of sample structure preserving (SSP), and difficulty in accommodating lateral inhibition by direct random feature mapping. Other drawbacks include multi-graph complexity, global memory size, one-by-one or chuck-by-chuck (a block of data), global memory size limitation, and challenges with big data. The recent trend proposed by experts for each drawback is discussed in detail towards achieving an effective and cost-sensitive model.
An Overview of Unsupervised Deep Feature Representation for Text Categorization

High-dimensional features are extensively accessible in machine learning and computer vision areas. How to learn an efficient feature representation for specific learning tasks is invariably a crucial issue. Due to the absence of class label information, unsupervised feature representation is exceedingly challenging. In the last decade, deep learning has captured growing attention from researchers in a broad range of areas. Most of the deep learning methods are supervised, which is required to be fed with a large amount of accurately labeled data points. Nevertheless, acquiring sufficient accurately labeled data is unaffordable in numerous real-world applications, which is suggestive of the needs of unsupervised learning. Toward this end, quite a few unsupervised feature representation approaches based on deep learning have been proposed in recent years. In this paper, we attempt to provide a comprehensive overview of unsupervised deep learning methods and compare their performances in text categorization. Our survey starts with the autoencoder and its representative variants, including sparse autoencoder, stacked autoencoder, contractive autoencoder, denoising autoencoder, variational autoencoder, graph autoencoder, convolutional autoencoder, adversarial autoencoder, and residual autoencoder. Aside from autoencoders, deconvolutional networks, restricted Boltzmann machines, and deep belief nets are introduced. Then, the reviewed unsupervised feature representation methods are compared in terms of text clustering. Extensive experiments in eight publicly available data sets of text documents are conducted to provide a fair test bed for the compared methods.
Virtual Performance Augmentation in an Immersive Jump &amp; Run Exergame

Human performance augmentation through technology has been a recurring theme in science and culture, aiming to increase human capabilities and accessibility. We investigate a related concept: virtual performance augmentation (VPA), using VR to give users the illusion of greater capabilities than they actually have. We propose a method for VPA of running and jumping, based on in place movements, and studied its effects in a VR exergame. We found that in place running and jumping in VR can be used to create a somewhat natural experience and can elicit medium to high physical exertion in an immersive and intrinsically motivating manner. We also found that virtually augmenting running and jumping can increase intrinsic motivation, perceived competence and flow, and may also increase motivation for physical activity in general. We discuss implications of VPA for safety and accessibility, with initial evidence suggesting that VPA may help users with physical impairments enjoy the benefits of exergaming.
AdaCAD

Woven smart textiles are useful in creating flexible electronics because they integrate circuitry into the structure of the fabric itself. However, there do not yet exist tools that support the specific needs of smart textiles weavers. This paper describes the process and development of AdaCAD, an application for composing smart textile weave drafts. By augmenting traditional weaving drafts, AdaCAD allows weavers to design woven structures and circuitry in tandem and offers specific support for common smart textiles techniques. We describe these techniques, how our tool supports them alongside feedback from smart textiles weavers. We conclude with a reflection on smart textiles practice more broadly and suggest that the metaphor of coproduction can be fruitful in creating effective tools and envisioning future applications in this space.
Ways of Knowing When Research Subjects Care

This paper investigates a hidden dimension of research with real world stakes: research subjects who care -- sometimes deeply -- about the topic of the research in which they participate. They manifest this care, we show, by managing how they are represented in the research process, by exercising politics in shaping knowledge production, and sometimes in experiencing trauma in the process. We draw first-hand reflections on participation in diversity research on Wikipedia, transforming participants from objects of study to active negotiators of research process. We depict how care, vulnerability, harm, and emotions shape ethnographic and qualitative data. We argue that, especially in reflexive cultures, research subjects are active agents with agendas, accountabilities, and political projects of their own. We propose ethics of care and collaboration to open up new possibilities for knowledge production and socio-technical intervention in HCI.
StoryBlocks

Block-based programming languages can support novice programmers through features such as simplified code syntax and user-friendly libraries. However, most block-based programming languages are highly visual, which makes them inaccessible to blind and visually impaired students. To address the inaccessibility of block-based languages, we introduce StoryBlocks, a tangible block-based game that enables blind programmers to learn basic programming concepts by creating audio stories. In this paper, we document the design of StoryBlocks and report on a series of design activities with groups of teachers, Braille experts, and students. Participants in our design sessions worked together to create accessible stories, and their feedback offers insights for the future development of accessible, tangible programming tools.
Mobile Robot Platform with Arduino Uno and Raspberry Pi for Autonomous Navigation

Mobile robots represent a very attractive and multidisciplinary field with a lot of applications and scientific research possibilities. The development of microcomputers, single board computers and embedded systems has helped to deploy low-cost solutions for this domain. In this paper is proposed such a low cost mobile robot platform with fixed four wheel chassis, commended by Raspberry Pi and Arduino Uno interfaces. The mobile robot has the ability to move into 2D environments as line follower robot with mapping, navigation, and obstacle avoidance features. The platform contains also one degree of freedom (DOF) robotic arm for lifting and transport of the obstacles.
Effectiveness assessment of ship navigation safety countermeasures using fuzzy cognitive maps

Abstract The fuzzy cognitive map method is adopted to construct the systematic mapping relationship between ship navigational risk factors and safety level for assessing the effectiveness of ship navigation safety countermeasures. The proposed approach integrates fuzzy inference system and evidential reasoning to convert initial input values of variables and obtain the strength of interconnections, respectively. Non-linear Hebbian learning algorithm is employed to refine the model. The constructed model is then applied with an illustrative case study associated with the middle stream region of the Yangtze river, where the current safety situation and several scenarios for improving navigational safety in the future are evaluated. The logicality and robustness constructed model and the assessing results are validated by applying sensitivity analysis. Through this research, it is found that the proposed method has the characteristics of high efficiency, lightweight and stability, which simulates the safety dynamics of the ship navigational system intuitively like the mode of natural thinking. This work provides an alternative for the shipping safety decision maker to understand the navigation safety status and control the evolving trend of it for a foreseeable period.
TDPP-Net: Achieving three-dimensional path planning via a deep neural network architecture

Path planning plays a significant role in autonomous navigation for robots in complex environments and hence has been extensively studied for decades. However, the computational time of most existing methods are dependent on the scale and complexity of environment, which leads to the compromise between time efficiency and path quality. To tackle this challenge, deep neural network based (DNN-based) planning methods have been actively explored. However, despite the success of DNN-based 2D planner, 3D path planning, which is a significant primitive for quite a few autonomous robots, is rarely handled by DNNs. In this paper, we propose a novel end-to-end neural network architecture named Three-Dimensional Path Planning Network (TDPP-Net) to realize DNN-based 3D path planning. Embedding the action decomposition and composition concept, our network predicts 3D actions merely through 2D convolutional neural networks (CNNs). Besides, the computational time of TDPP-Net is almost independent of environmental scale and complexity for each action prediction. The experimental results demonstrate that our approach exhibits remarkable performance for planning real-time paths in unseen 3D environments.
A recent survey of self-embedding fragile watermarking scheme for image authentication with recovery capability

The rapid developments of storage technology and information exchange encourage the development of research in the field of information security. In the process of sending information, tamper and issues about data ownership may occur. The fragile watermarking is one technique that can overcome the problem, in which this technique exploits sensitivity to tamper with the inserted watermark components. Therefore, it is not tolerable to change even only one bit. Self-embedding can be defined as some important features obtained from a selected image as a watermark by modifying the pixel value of the original image. Once a picture which has been inserted with a watermark is modified by other users, it can be used for tamper detection and recovery images. Due to the importance of this fragile watermarking scheme, this paper discusses the principles and characteristics of a fragile watermarking algorithm. The main contribution of this paper survey is that it summarises the current mechanisms of selection, generation, method of watermark insertion, detection and tamper localisation and recovery procedures. Comparison of several watermarking techniques was analysed and presented in tabular form, as well as experimental evaluation of four watermarking schemes in many graphs to show the performance of the self-embedding fragile watermarking scheme.
A Novel Audio Encryption Algorithm with Permutation-Substitution Architecture

In this paper, a new cryptographic method is proposed, designed for audio files’ security. The encryption algorithm is based on classic symmetric models using pseudo-random number generator composed with chaotic circle map and modified rotation equations. The scheme of a new pseudo-random generator is presented and used as basis for chaotic bit-level permutations and substitutions applied to audio files structure for successful encryption. The audio encryption and decryption algorithms are described and explained. Proving the high level of security we provide extensive cryptographic analysis including key sensitivity analysis, key-space analysis, waveform and spectrogram analysis, correlation analysis, number of sample change rate analysis, level of noise analysis and speed performance test.
Crowdsourcing Subjective Tasks: The Case Study of Understanding Toxicity in Online Discussions

Discussing things you care about can be difficult, especially via online platforms, where sharing your opinion leaves you open to the real and immediate threats of abuse and harassment. Due to these threats, people stop expressing themselves and give up on seeking different opinions. Recent research efforts focus on examining the strengths and weaknesses (e.g. potential unintended biases) of using machine learning as a support tool to facilitate safe space for online discussions; for example, through detecting various types of negative online behaviors such as hate speech, online harassment, or cyberbullying. Typically, these efforts build upon sentiment analysis or spam detection in text. However, the toxicity of the language could be a strong indicator for the intensity of the negative behavior. In this paper, we study the topic of toxicity in online conversations by addressing the problems of subjectivity, bias, and ambiguity inherent in this task. We start with an analysis of the characteristics of subjective assessment tasks (e.g. relevance judgment, toxicity judgment, sentiment assessment, etc). Whether we perceive something as relevant or as toxic can be influenced by almost infinite amounts of prior or current context, e.g. culture, background, experiences, education, etc. We survey recent work that tries to understand this phenomenon, and we outline a number of open questions and challenges which shape the research perspectives in this multi-disciplinary field.