Reviewerly/exHarmony huggingface dataset:
Detecting Branching Nodes of Multiply Connected 3D Structures

In order to detect and analyze branching nodes in 3D images of micro-structures, the topology preserving thinning algorithm of Couprie and Zrour is jointly used with a constraint set derived from the spherical granulometry image. The branching points in the thus derived skeleton are subsequently merged guided by the local structure thickness as provided by the spherical granulometry. This enables correct merging of nodes and thus a correct calculation of the nodes’ valences. The algorithm is validated using two synthetic foam structures with known vertices and valences. Subsequently, the algorithm is applied to micro-computed tomography data of a rigid aluminium foam where the valence is known, to the pore space of polar firn samples, and to corrosion casts of mice livers.
Bridging identity challenges: why and how one library plugged ORCiD into their enterprise

Purpose The purpose of this paper is to describe the implementation and impact of a locally customized Open Researcher and Contributor ID (ORCiD) profile wizard. It also provides a broader context for adopting ORCiD as an identity and single sign-on solution. Design/methodology/approach A custom web application was designed by a library team and implemented using a combination of the OAuth protocol and the ORCiD web services API. The tool leveraged a rich, curated set of local publication data, and exposed integration hooks that allowed other enterprise systems to connect ORCiD IDs with an internal employee identifier. Findings Initially the tool saw only modest use. Ultimately its success depended upon integration with other enterprise systems and the requirement of an ORCiD ID for internal funding requests, rather than exclusively on the merits of the tool. Since introduction, it has been used to generate over 1,660 ORCiDs from a population of 4,000 actively publishing researchers. Practical implications Organizations that desire to track publications by many affiliated authors would likely benefit from some sort of integration with ORCiD web services. This is particularly true for organizations that have many publishing researchers and/or track publications spanning many decades. Enterprise integration is crucial to the success of such a project. Originality/value Research inputs and research products are now primarily digital objects. So having a reliable system for associating researchers with their output is a big challenge that, if solved, could increase researcher impact and enhance digital scholarship. ORCiD IDs are a potential glue for many aspects of this problem. The design and implementation of the wizard eased and quickened adoption of ORCiD Ids by local researchers due in part to the ease with which a researcher can push publication information already held by the library to their profile. Subsequent integration of researcher ORCiD IDs with local enterprise systems has enabled real-time propagation of ORCiD IDs across research proposal workflow, publication review and content discovery systems.
Cross-modal Collaborative Manifold Propagation for Image Recommendation

With the rapid evolution of social networks, the increasing user intention gap and visual semantic gap both bring great challenge for users to access satisfied contents. It becomes promising to investigate users' customized multimedia recommendation. In this paper, we propose cross-modal collaborative manifold propagation (CMP) for image recommendation. CMP leverages users' interest distribution to propagate images' user records, which lets users know the trend from others and produces interest-aware image candidates upon users' interests. Visual distribution is investigated simultaneously to propagate users' visual records along dense semantic visual manifold. Visual manifold propagation helps to estimate semantic accurate user-image correlations for the candidate images in recommendation ranking. Experimental performance demonstrate the collaborative user-image inferring ability of CMP with effective user interest manifold propagation and semantic visual manifold propagation in personalized image recommendation.
A general framework for scalable spectral clustering based on document models

We propose a new, general framework for fast, approximate spectral clustering on large data sets. We first consider the special setting of cosine similarity for clustering sparse data (e.g., documents under the bag-of-words model) or data of at most a few hundred dimensions (e.g., small images). We show that in those cases various versions of spectral clustering, such as the Ng-Jordan-Weiss algorithm (NIPS 2001), Normalized Cut (Shi and Malik, 2000), and Diffusion Maps (Coifman and Lafon, 2006), can be implemented solely based on three kinds of efficient operations on the data matrix – elementwise manipulation, matrix-vector multiplication and low-rank SVD, thus eliminating the need to compute the weight matrix. For general similarity and any kind of data, we present a landmark-based technique that first converts the given data (or a landmark set selected from them) to a collection of "documents" and then applies to them the scalable implementation of spectral clustering with cosine similarity. Our algorithm is simple to implement and fast to run, with additional benefits such as a naturally embedded outliers removal step. We conduct extensive experiments to compare our algorithm with a few existing methods to demonstrate its superior performance.
A note on application of kernel derivatives in density estimation with the univariate case

This paper considers the application of kernel density derivatives to real life data. Kernel density derivatives estimation is very fundamental and critical in statistical data analysis especially for exploratory and visualization purposes. As a result of the wide range of its applications, appropriate estimation of the kernel derivatives from its function and locating some statistical features such as bumps and modes of a set of observation is of a great importance. We consider the first and second derivative of the Gaussian kernel and compare their results in terms of performance using asymptotic mean integrated squared error as the error criterion function. The results of the comparison shows that as the derivative of the kernel function increases, the AMISE decreases with an increase in the smoothing parameter.
Data Visualisation as a Tool for Informed Structural Design

The architectural design process is characterised by continuously comparing different design alternatives throughout the whole design process. Collaborators from different fields of expertise need to work closely together and they are expected to give quick but sufficient feedback on the available design alternatives in order to proceed in the collaborative process. However, for structural engineers making these comparisons on the structural behaviour and efficiency is hard to do. The lack of appropriate tools disables the structural engineer to gain quickly and qualitatively insight into a set of design alternatives. This paper discusses a novel mind-set for structural designers, that fills this gap by informed decision-making based on data visualisation. This kind of data exploration is only rarely used in the structural design field. Since visual displays of data allow for better decision-making, this opportunity should be leveraged by structural designers. Five different bow-string bridge geometries are compared as a case study with the help of data visualisation. As a consequence, this paper focuses on the comparing practice of structural parametric models of the same structural typology. This research illustrates that the use of data visualisations can help designers to quickly understand the structural behaviour and performance of different design alternatives. This way, informed decision-making through comparing is facilitated.
Analysis of Weather and Exercise Steps Based on LSTM Neural Network

The analysis of exercise and health has become a hot issue that people are paying attention to today. This paper presents an automated method for the complete analysis of weather and air quality and daily exercise volume (number of steps). Firstly, a clustering method based on Fourier transform is used to separate users of different exercise styles, and then the LSTM neural network is used to train the step data and the corresponding weather and air quality data to find the correlation. After testing the real data with multiple exercise styles, the validity of this analysis method can be confirmed. At the same time, the training verification time of the model is also collected, which proves that it has certain advantages in the analysis of large-scale data.
Developing a Vehicle Networking Platform Based on Blockchain Technology

Blockchain technology has been developing very rapidly through the entire world and is widely seen as one of the most disruptive forces in modern businesses. The applications of blockchain technology to the finance, insurance and automobile industries create extra values in innovation and motivation to the business. This paper is to describe the research and development work called iCarChain which is based on vehicle networking and blockchain technology, through distributed ledger, consensus mechanism, smart contract and other underlying architectures, using open and transparent value acquisition and an artificial intelligence based allocation algorithm to achieve value realization of data on vehicle and driving behavior. Each user (vehicle owner or driver) is referred to install an intelligent OBD device. The device captures the real-time data from vehicles to produce value identification. With such technology, the values of data acquisition and transparent can be transferred through the token carriers called iCarToken which is an incentive unit launched for users to contribute their behavior value, which is generated by blockchain and smart contract, and is gained through the value of contribution behavior.
Modelling and Simulation of a Cloud Platform for Sharing Distributed Digital Fabrication Resources

Fabrication as a Service (FaaS) is a new concept developed within the framework of the NEWTON Horizon 2020 project. It is aimed at empowering digital fabrication laboratories (Fab Labs) by providing hardware and software wrappers to expose numerically-controlled expensive fabrication equipment as web services. More specifically, FaaS leverages cloud and IoT technologies to enable a wide learning community to have remote access to these labs’ computer-controlled tools and equipment over the Internet. In such context, the fabrication machines can be seen as networked resources distributed over a wide geographical area. These resources can communicate through machine-to-machine protocols and a centralized cloud infrastructure and can be digitally monitored and controlled through programmatic interfaces relying on REST APIs. This paper introduces FaaS in the context of Fab Lab challenges and describes FaaS deployment within NEWTON Fab Labs, part of the NEWTON European Horizon 2020 project on technology enhanced learning. The NEWTON Fab Labs architecture is described in detail targeting software, hardware and network architecture. The system has been extensively load-tested simulating real use-case scenarios and it is presently in production. In particular, this paper shows how the measured data has been used to build a simulation model to estimate system performance and identify possible bottlenecks. The measurements performed show that the platform delays exhibit a tail distribution with Pareto-like behaviour; this finding has been used to build a simple mathematical model and a simulator on top of CloudSim to estimate the latencies of the critical paths of the NEWTON Fab Lab platform under several load conditions.
A Concept Map Based Assessment of Free Student Answers in Tutorial Dialogues

Typical standard Semantic Textual Similarity (STS) solutions assess free student answers without considering context. Furthermore, they do not provide an explanation for why student answers are similar, related or unrelated to a benchmark answer. We propose a concept map based approach that incorporates contextual information resulting in a solution that can both better assess and interpret student responses. The approach relies on a novel tuple extraction method to automatically map student responses to concept maps. Using tuples as the unit of learning (learning components) allows us to track students’ knowledge at a finer grain level. We can thus better assess student answers beyond the binary decision of correct and incorrect as we can also identify partially-correct student answers. Moreover, our approach can easily detect missing learning components in student answers. We present experiments with data collected from dialogue-based intelligent tutoring systems and discuss the added benefit of the proposed method to adaptive interactive learning systems such as the capability of providing relevant targeted feedback to students which could significantly improve the effectiveness of such intelligent tutoring systems.
Improving Performance of Automatic Duplicate Bug Reports Detection using Longest Common Sequence : Introducing New Textual Features for Textual Similarity Detection

Automatic duplicate bug reports detection is a famous problem in mining software repositories since 2004 for software triage systems e.g. Bugzilla. Textual features are the most important type of features in similarity and duplicate detection e.g. BM25F which indicate the common term frequency in two reports. Sometimes a common sequence can show more similarity in two texts, thus new features based on longest common sequence (LCS) of two bug reports proposed in this paper as new textual features for text similarity detection. Android, Eclipse, Mozilla, and Open Office dataset are used for evaluation of proposed features and the experimental results show LCS-based features are important and the accuracy, precision and recall of classifier prediction models improved 4.5, 2.5 and 2.5 percent respectively on average after using LCS and get up to 96, 98 and 97 percent respectively on average using different classifiers.
Scalable Machine Learning Algorithms for a Twitter Followee Recommender System

Recently, machine learning (ML) algorithms have been employed in social networking recommender systems. In this paper, a Twitter recommender system is simulated by a multi-agent system that can be used to provide the users with a list of useful recommendations, specifically a list of users (i.e., followees) that a user is interested in following. The simulator is used to test the scalability of a machine learning algorithm (i.e., Neural Network, Multilayer Perceptron) for data analysis with parallel implementation on multi-node distributed systems. The distributed environment is simulated by a multi-agent modeling. The initial parameters that should be set up on the simulator include the number of nodes, the algorithm employed in the simulated recommender system, and the actual followees and followers information. The experimental results were obtained on three distinct datasets for evaluating the accuracy and the execution time of a simulated recommender system when testing the ML algorithm in different scenarios.
Exploring Interaction with Remote Autonomous Systems using Conversational Agents

Autonomous vehicles and robots are increasingly being deployed to remote, dangerous environments in the energy sector, search and rescue and the military. As a result, there is a need for humans to interact with these robots to monitor their tasks, such as inspecting and repairing offshore wind-turbines. Conversational Agents can improve situation awareness and transparency, while being a hands-free medium to communicate key information quickly and succinctly. As part of our user-centered design of such systems, we conducted an in-depth immersive qualitative study of twelve marine research scientists and engineers, interacting with a prototype Conversational Agent. Our results expose insights into the appropriate content and style for the natural language interaction and, from this study, we derive nine design recommendations to inform future Conversational Agent design for remote autonomous systems.
The Logic of Imagination Acts: A Formal System for the Dynamics of Imaginary Worlds

Imagination has received a great deal of attention in different fields such as psychology, philosophy and the cognitive sciences, in which some works provide a detailed account of the mechanisms involved in the creation and elaboration of imaginary worlds. Although imagination has also been formalized using different logical systems, none of them captures those dynamic mechanisms. In this work, we take inspiration from the Common Frame for Imagination Acts, that identifies the different processes involved in the creation of imaginary worlds, and we use it to define a dynamic formal system called the Logic of Imagination Acts. We build our logic by using a possible-worlds semantics, together with a new set of static and dynamic modal operators. The role of the new dynamic operators is to call different algorithms that encode how the formal model is expanded in order to capture the different mechanisms involved in the creation and development of imaginary worlds. We provide the definitions of the language, the semantics and the algorithms, together with an example that shows how the model is expanded. By the end, we discuss some interesting features of our system, and we point out to possible lines of future work.
Lifting Kirigami Actuators Up Where They Belong

Kirigami Actuators are two-dimensional patterns that allow the translation of a simple actuation in one dimension into a complex transformation in another. Kirigami Actuators represent one metamaterial strategy that designers of Shape-changing Interfaces could utilize to minimize the size and complexity costs of actuation. Metamaterials yield great promise for HCI and Shape-changing Interfaces in particular. In an effort to reveal the promise of Kirigami Actuators for the design of Shape-changing Interfaces, this pictorial presents several design tactics for a specific Kirigami Actuator: The Lift pattern. These tactices outline how different components of the pattern can be changed to strategically alter the transformational qualities of the pattern. Initial concept sketches are presented as inspiration for future work.
Towards Automated Configuration of Cloud Storage Gateways: A Data Driven Approach

Cloud storage gateways (CSGs) are an essential part of enterprises to take advantage of the scale and flexibility of cloud object store. A CSG provides clients the impression of a locally configured large size block-based storage device, which needs to be mapped to remote cloud storage which is invariably object based. Proper configuration of the cloud storage gateway is extremely challenging because of numerous parameters involved and interactions among them. In this paper, we study this problem for a commercial CSG product that is typical of offerings in the market. We explore how machine learning techniques can be exploited both for the forward problem (i.e. predicting performance from the configuration parameters) and backward problem (i.e. predicting configuration parameter values from the target performance). Based on extensive testing with real world customer workloads, we show that it is possible to achieve excellent prediction accuracy while ensuring that the model is not overfitted to the data.
Font Style Transfer Using Neural Style Transfer and Unsupervised Cross-domain Transfer

In this paper, we study about font generation and conversion. The previous methods dealt with characters as ones made of strokes. On the contrary, we extract features, which are equivalent to the strokes, from font images and texture or pattern images using deep learning, and transform the design pattern of font images. We expect that generation of original font such as hand written characters will be generated automatically by the proposed approach. In the experiments, we have created unique datasets such as a ketchup character image dataset and improve image generation quality and readability of character by combining neural style transfer with unsupervised cross-domain learning.
New approaches to dynamic evaluation of quality indicators

Abstract The paper proposes a mathematical model for evaluating time-varying quality indicators of an arbitrary nature. The model is based on the Rasch’s method of estimating latent variables. Cases of single-criterion and multi-criteria evaluation are considered. The obtained estimates are measured on a linear interval scale and do not depend on a set of indicators, time periods and evaluation criteria. Comparison of results with traditional assessment methods.
A New Approach for Authorship Verification Using Information Retrieval Features

Authorship Verification is the process of verifying an author by checking whether the document is written by the suspected author or not. The performance of the Authorship Verification mainly depends on the features used for differentiating the writing style in the documents. The researchers extracted various types of stylistic features for author identification based on the writing style of a particular author. In this work, a new approach is proposed by using the information retrieval features for author identification. In this approach, the test document was treated as a query and the training data as a document database. Cosine similarity measure is used to compute the similarity between the test document and training data. The accuracy of author verification depends on the scores of the similarity measures. The proposed approach performance is good when compared with most of the state-of-the-art approaches in Authorship Verification.
MoCaDiX

This paper presents MoCaDiX, a method for designing cross-device graphical user interfaces of an information system based on its UML class diagram, structured as a four-step process: (1) a UML class diagram of the information system is created in a model editor, (2) how the classes, attributes, methods, and relationships of this class diagram are presented across devices is then decided based on user interface patterns with their own parametrization, (3) based on these parameters, a Concrete User Interface model is generated in QuiXML, a lightweight fit-to-purpose User Interface Description Language, and (4) based on this model, HTML5 cross-device user interfaces are semi-automatically generated for four configurations: single/multipledevice single/multiple-display on a smartphone, a tablet, and a desktop. From the practitioners' viewpoint, a first experiment investigates effectiveness, efficiency, and subjective satisfaction of three intermediate and three expert designers, using MoCaDiX on a representative class diagram. From the end user's viewpoint, a second experiment compares subjective satisfaction and preference of twenty end users assessing layout strategies for interfaces generated on two devices.