Reviewerly/exHarmony huggingface dataset:
Multiple Views: different meanings and collocated words

Abstract We report on an in‐depth corpus linguistic study on ‘multiple views’ terminology and word collocation. We take a broad interpretation of these terms, and explore the meaning and diversity of their use in visualisation literature. First we explore senses of the term ‘multiple views’ (e.g., ‘multiple views’ can mean juxtaposition, many viewport projections or several alternative opinions). Second, we investigate term popularity and frequency of occurrences, investigating usage of ‘multiple’ and ‘view’ (e.g., multiple views, multiple visualisations, multiple sets). Third, we investigate word collocations and terms that have a similar sense (e.g., multiple views, side‐by‐side, small multiples). We built and used several corpora, including a 6‐million‐word corpus of all IEEE Visualisation conference articles published in IEEE Transactions on Visualisation and Computer Graphics 2012 to 2017. We draw on our substantial experience from early work in coordinated and multiple views, and with collocation analysis develop several lists of terms. This research provides insight into term use, a reference for novice and expert authors in visualisation, and contributes a taxonomy of ‘multiple view’ terms.
Towards Better Support for Exploratory Search through an Investigation of Notes-to-self and Notes-to-share

Recently, there has been interest in integrating information retrieval systems more closely with users' knowledge development processes, especially to support exploratory search. In this work, we investigated how people organize and structure information they discover during exploratory searches. In a lab study, we asked 24 participants to take hand-written notes they could use in the future while they were completing an exploratory search. We then asked participants to organize their findings to share with someone else who wants to explore the topic. Finally, we conducted post-session interviews to gain insights into the types of information saved and how participants organized the information they found. In our qualitative analysis of the notes and interviews, we found that the notes included background information about the topic, key concepts, specific details, useful information sources, and information to help with the broader work task. Notes were primarily structured in lists, and reflected a combination of linear note-taking strategies and grouping by information source or topical themes. Participants changed the content and structure of the shared notes to make them easier to understand and to provide a more thorough overview of the topic. Our findings have implications for the design of search tools to help current searchers organize, structure, and synthesize information and to help future searchers engaged in similar information seeking tasks.
Predicting risk of Cervical Cancer : A case study of machine learning

AbstractAbstractHuman Papillomavirus (HPV) is the cause for 90% of cases of Cervical Cancer which can only be cured if diagnosed in early stage. Years of clinical experience in testing the tissue slide and examination of various risk factors thoroughly helps a physician to confirm the presence of Cervical Cancer, which is very time-consuming process. Tissue slides contain a vast amount of complex data, which can sometimes can be missed by human observation during microscopic examination. In this research, we have used machine learning algorithms to make the detection process a lot faster and accurate. The rationale is to develop an efficient prediction system which can predict whether the new sample is cancerous or not. The experiments are carried out on high dimensional biopsy samples of cancer patients. After training different machine learning models with the collected data, Bayes Net algorithm achieved an accuracy and AUC of 96.38% and 0.95 respectively. When the execution of the framework is set and performance of BayeNet is compared with standard classifiers like SVM, random forest, etc., using different evaluation metrics like accuracy, sensitivity, etc., the results are quite promising. In the field of cancer detection, machine learning can play a big role in saving lives.Subject Classification: (2010) 68M12Keywords: Machine learningRisk analysisData analyticsCervical cancerPredictionRandom forest
A new enhancement of elliptic curve digital signature algorithm

AbstractAbstractElliptic Curve Digital Signature Algorithm (ECDSA) is a public key cryptographic algorithm based on the hardness of the Elliptic Curve Discrete Logarithm Problem (ECDLP), it is used to ensure users' authentication, data integrity and transactions non-repudiation. However, its weakness is to derive the signer's private key in case he uses the same random number for to generate two signatures for two different messages. Firstly, in this paper we present the ECDSA and analyse its variants proposed in the literature.Also, we propose a new improvement technique for ECDSA of Liao and Shen and ECDSA of Chande and Lee. We intend to do so by introducing the unknown parameters which are used in the verification of the signature. Our aim is to overcome the problem which has not been explained in ECDSA schemes of both Liao and Shen and Chande and Lee.Subject Classification: 14H5294A60Keywords: Elliptic CurvesElliptic Curve CryptographyElliptic Curve Discrete Logarithm ProblemElliptic Curve Digital Signature Algorithm
Removal of Design Problems through Refactorings: Are We Looking at the Right Symptoms?

A design problem is the result of design decisions that negatively impact quality attributes. For example, a stakeholder introduces a design problem when he decides to addresses multiple unrelated responsibilities in a single class, impacting the modifiability and reusability of the system. Given their negative consequences, design problems should be identified and refactored. The literature still lacks evidence on which symptoms' characteristics can be used as strong indicators of design problems. For example, it is unknown if the density and diversity of certain symptoms (e.g., violations of object-oriented principles) are correlated with the occurrence of design problems. Thus, in this paper, we report a case study involving two C# systems. We evaluated the impact of refactoring, focused on removing design problems, on the density and diversity of symptoms. Results indicate that refactored classes usually present higher density and diversity of symptoms. However, the density and diversity of some symptoms, such as the violation of object-oriented principles, was not predominantly higher in refactored classes. Moreover, contrary to our expectations, refactorings caused almost no positive impact on the density and diversity of symptoms.
Design of Seamless Multi-modal Interaction Framework for Intelligent Virtual Agents in Wearable Mixed Reality Environment

In this paper, we present the design of a multimodal interaction framework for intelligent virtual agents in wearable mixed reality environments, especially for interactive applications at museums, botanical gardens, and similar places. These places need engaging and no-repetitive digital content delivery to maximize user involvement. An intelligent virtual agent is a promising mode for both purposes. Premises of framework is wearable mixed reality provided by MR devices supporting spatial mapping. We envisioned a seamless interaction framework by integrating potential features of spatial mapping, virtual character animations, speech recognition, gazing, domain-specific chatbot and object recognition to enhance virtual experiences and communication between users and virtual agents. By applying a modular approach and deploying computationally intensive modules on cloud-platform, we achieved a seamless virtual experience in a device with limited resources. Human-like gaze and speech interaction with a virtual agent made it more interactive. Automated mapping of body animations with the content of a speech made it more engaging. In our tests, the virtual agents responded within 2-4 seconds after the user query. The strength of the framework is flexibility and adaptability. It can be adapted to any wearable MR device supporting spatial mapping.
Understanding Large-Scale Software – A Hierarchical View

Program comprehension accounts for a large portion of software development costs and effort. The academic literature contains research on program comprehension of short code snippets, but comprehension at the system level is no less important. We claim that comprehending a software system is a distinct activity that differs from code comprehension. We interview experienced developers, architects, and managers in the software industry and open-source community, to uncover the meaning of program comprehension at the system level. The interviews demonstrate, among other things, that system comprehension is detached from code and programming language, and includes scope that is not captured in the code. It focuses on the structure of the system and less on the code itself. This is a continuous, iterative process, which mixes white-box and black-box approaches at different layers of the system, and combines both bottom-up and top-down comprehension strategies.
Do Smart Contract Languages Need to Be Turing Complete?

Blockchain based systems become more and more prominent. While starting by developing (crypto)currency payment schemes, a lot of the latest development goes in the direction of executing source code directly in the peer-to-peer network blockchains are usually built on. These so called smart contracts have become popular in order to reduce the amount of necessary middle-mans involved in different processes. Despite the large amount of research already invested in the design of languages which support smart contracts, there are still a lot of problems in the existing approaches, regularly resulting in security flaws. One of these problems is the complexity of the used languages. Therefore, this paper provides an evaluation of currently deployed smart contracts with respect of the requirements of those contracts concerning computability. The finding is that most of the currently deployed smart contracts do not need Turing complete languages, but could also be implemented based on a simpler design of the underlaying language.
Using Bayesian networks with hidden variables for identifying trustworthy users in social networks

The popularity and broad accessibility of online social networks (OSNs) have facilitated effective communication among people, but such networks also pose potential risks that should not be ignored. Interaction through OSNs is complex and can be unsafe, as individuals can be contacted by strangers at any time. This makes the notion of trust a crucial issue in the use of OSNs. However, compared with decision-making processes associated with whether to trust a stranger encountered in everyday life, this task is more difficult to address with regard to OSNs due to the lack of face-to-face communication and prior knowledge between people. In this article, trust evaluation is formalised as a classification problem. We demonstrate how user profiles and historical records can be organised into a logical structure based on Bayesian networks to recognise the trustworthy people without the need to build trust relationships in OSNs. This is possible when a more detailed description of features denoted by hidden variables is considered. We compare the performance of our method with those of six other machine learning methods using Facebook and Twitter datasets, and our results show that our method achieves higher values in accuracy, recall and F1 score.
Security analysis of practical continuous-variable quantum key distribution systems under laser seeding attack

Here, we investigate the security of the practical one-way CVQKD and CV-MDI-QKD systems under laser seeding attack. In particular, Eve can inject a suitable light into the laser diodes of the light source modules in the two kinds of practical CVQKD systems, which results in the increased intensity of the generated optical signal. The parameter estimation under laser seeding attack shows that the secret key rates of these two schemes may be overestimated, which indicates that this attack can open a security loophole for Eve to successfully obtain information about secret key in these practical CVQKD systems. To close this loophole, we propose a real-time monitoring scheme to precisely evaluate the secret key rates of these schemes. The analysis results indicate the implementation of the proposed monitoring scheme can effectively resist this potential attack.
DenseNet With Deep Residual Channel-Attention Blocks for Single Image Super Resolution

This paper proposes a DenseNet with deep Residual Channel Attention (DRCA) for single image super resolution. Recent works have shown that skip connections between layers improve the performance of the convolutional neural network such as ResNet and DenseNet. We have interpreted the role of ResNet (feature value refinement by addition) and DenseNet (feature value memory by concatenation). The contribution of the proposed network is dense connections between residual groups rather than convolution layers. In terms of feature value refinement and memory, the proposed method refines the feature values sufficiently (by residual group) and memorizes the refined feature values intermittently (by dense connections between residual groups). Experimental results show that the proposed DRCA (14.2M) achieved better performance than the state-of-the-art methods with fewer parameters.
Success Factors for Effective Process Metrics Operationalization in Agile Software Development: A Multiple Case Study

Existing literature proposes success factors for establishing metrics programs. However, very few studies focus on factors that could ensure long-term use of metrics, and even fewer studies investigate such factors in the context of Agile Software Development (ASD). Motivated by this knowledge gap, we aim to identify success factors for operationalizing metrics in ASD, particularly, factors that could help in the long-term use of metrics. We conducted a multiple case study, where we operationalized process metrics at two software-intensive companies using ASD. We learned that data availability and development process are the two fundamental success factors for process metrics operationalization, albeit less prominent in literature. Companies prefer iterative and incremental operationalization of stable and functional process metrics, which is analogous to the agile way of working. Metrics trustworthiness plays a key role in successful operationalization of process metrics, and is potentially vital to ensuring their long-term use. By comparing the identified success factors with the existing literature, we conclude that success factors concerning data availability, development process, and metrics trustworthiness warrant greater attention, especially to maximize the chances of long-term use of process metrics.
Effects of a Ubiquitous Guide-Learning System on Cultural Heritage Course Students’ Performance and Motivation

In most cultural heritage courses, students physically visit several renowned heritage sites for educational purposes. However, because of time and manpower limitations, many teachers use traditional outdoor instruction methods to transmit vital information regarding these sites and buildings. This approach could result in students merely memorizing knowledge for test-taking purposes, rather than engaging in active thinking. Therefore, this study proposed using a ubiquitous guide-learning system to enhance students' performance and active participation in cultural heritage courses. To evaluate the effectiveness of the proposed system, a trial experiment was conducted to visit eight heritage sites featured in the cultural heritage course offered by a local university. In total, 62 students were randomly assigned to either an experimental group testing the proposed system or a control group using traditional outdoor instruction methods. The experimental results indicated that our ubiquitous guide-learning system significantly improved students' learning achievements. The outcome of the Instructional Materials Motivation Survey questionnaire indicated that the experimental group participants were motivated to learn and interested in using the proposed system. Moreover, the two groups had statistically significant results for the attention, confidence, and satisfaction factors. Therefore, implementing our proposed system in conjunction with cultural heritage education courses may yield a significant learning advantage for students by improving overall learning performance and motivation.
Hermes: An Open and Transparent Marketplace for IoT Sensor Data over Distributed Ledgers

Internet-of-Things (IoT) sensors have become cheaper and more ubiquitous than ever [1]. From smart thermometers to home assistants and smartphones, massive amounts of data are being streamed in real-time by sensors that are continuously connected to the web. However, these data tend to be amassed in private silos that are difficult to access. Many private companies and public institutions could build valuable services with these data but so far few solutions have been proposed on how to bridge this gap. In this paper we introduce Hermes, a platform for trading sensor data using distributed ledgers as intermediaries to add safeguards against malicious behavior.
FAQ Retrieval Using Attentive Matching

The task of ranking question-answer pairs in response to an input query, aka FAQ (Frequently Asked Question) Retrieval, has traditionally been focused mostly on extracting relevance signals between query and questions based on extensive manual feature engineering. In this paper we propose multiple deep learning architectures designed for FAQ Retrieval that eliminate the need for feature engineering and are able to elegantly combine both query-question and query-answer similarities. We present experimental results showing that models that effectively combine both query-question and query-answer representations using attention mechanisms in a hierarchical manner yield the best results from all proposed models. We further verify the effectiveness of attention mechanisms for FAQ Retrieval by conducting experiments on a completely different attention-based architecture, originally designed for question duplicate detection tasks, and observing equally impressive experimental ranking results.
An Interpretable Disease Onset Predictive Model Using Crossover Attention Mechanism From Electronic Health Records

Analysis of patients' Electronic Health Records (EHRs) can help guide the prevention of diseases and personalization of treatment. Therefore, it is an important task to predict the disease onset information (referred to as medical codes in this paper) within the upcoming visit based on patients' EHR data. In order to achieve this objective, the real-time nature and high dimensionality of EHR data must be addressed. Moreover, the prediction results of the model must be interpretable. Existing methods mainly use Recurrent Neural Networks (RNNs) to model EHR data and adopt attention mechanism to provide interpretability. However, diagnosis and treatment information have usually been regarded as the same kind of information, the difference and relationship between the two parts being ignored. This has led to unclear analysis about the patient's disease development and inaccurate prediction results. To address this limitation, we propose a CrossOver Attention Model (COAM). This model adopts two RNNs to process diagnosis and treatment information, respectively, and then deploys a crossover attention mechanism to improve prediction accuracy by leveraging the correlation between the two parts of information. It can learn effective representations of personal medical diagnosis and treatment, and provide interpretable prediction results. Experiments demonstrate that COAM can significantly improve the accuracy of prediction and provide clinically meaningful explanations.
Creating Value with Proto-Research Persona Development

To stay competitive in marketplace, companies have to design compelling products that are successfully adopted by their intended users. To achieve this goal, companies must establish and validate a shared understanding of their customer base. In this paper we propose a persona development process for creating value in an organization. This process, which can be conducted relatively quickly and cost-effectively, provides a roadmap for continual assessment of organizational understanding of customer base. It creates value for companies by allowing them (1) to build consensus internally around their customers' needs, goals, and preferences, and (2) to validate/test the accuracy of the assumptions they make about their customers. We tested this process in a fortune 500 company. Our results suggest that our process is both effective and efficient in establishing a shared understanding of the customer base and in testing the accuracy of assumptions made by organizations about their customers.
Global technologies, local practices

This special issue of Learning, Media and Technology contributes to a growing need, not only for critical accounts of digital education that resist the global, but also for a more diverse representation of the multiplicitous practices of teaching and learning with technology across the globe. It builds on important work that has examined open education initiatives in the Global South [Arinto, P. B., C. Hodgkinson-Williams, and H. Trotter. 2017. "OER and OEP in the Global South: Implications and Recommendations for Social Inclusion." Adoption and Impact of OER in the Global South, 577–592] and highlighted ‘missing voices’ in educational technology development [Davis, R. 2015. The Missing Voices in EdTech: Bringing Diversity into EdTech. Thousand Oaks, CA: Corwin Press]. It is through such critical enquiry that research can continue to cut through the discursive regimes of simplistic disruption and enhancement from a pervasive education technology industry. This means aligning research in this area, less with grand narratives that portray digital education as an external and universal force capable of radical transformation, and more with accounts that acknowledge an already-present political economy of educational technology, in which specific devices and applications succumb to everyday practices of negotiation, consumption, and resistance, particular to the contexts in which they manifest.
Consolidating Segmentwise Non-Rigid Structure from Motion

This paper introduces a new segmentwise technique which consolidates multiple principles for nonrigid structure from motion (NRSfM) into a single energy-based framework. The energy functional of our Consolidating Monocular Dynamic Reconstruction (CMDR) approach is optimised by nonlinear least squares and includes terms allowing to define the deformation model and additional constraints simultaneously in the metric and trajectory spaces. The proposed method achieves high accuracy on several tested sequences while providing robustness and scalability due to the spatial scene segmentation and the new lifted spatial Laplacian term. CMDR is flexible and easy to implement, thanks to the unified optimisation framework. It allows for scenario-specific extensions and can be used for rapid prototyping of new NRSfM methods.