Reviewerly/exHarmony huggingface dataset:
Learning Analytics Data Flow and Visualizing for Ubiquitous Learning Logs in LMS and Learning Analytics Dashboard

In this paper, we describe about a kind of data flow design that between ubiquitous learning log system called SCROLL and learning analytics and visualizing system called Learning Analytics Dashboard (LAD). SCROLL is a ubiquitous learning system what is logging students’ learning behaviors data in database, and SCROLL can provide students suitable learning method and location to learn efficiently. Lots of paper show that it is appreciate to share the learning data in SCROLL to the other learning analytics system like LTI, Bookroll, Moodle and so on. Learning Analytics Dashboard (LAD) is also a learning data analytics and visualizing system. So share students’ learning data from SCROLL to LAD to show and help students to know their students’ learning situation is the proposal of this paper.
Use of an Emotional Chatbot for the Analysis of a Discussion Forum for the Improvement of an E-Learning Platform

In this work, we try to show that the contribution of a chatbot in an e-learning platform can bring more efficiency in learning. The observation being that many learners either abandon their learning. They judge that it’s not very effective, because often their questions are not answered adequately. Our emotional chatbot proposal shows that if we take into account the emotional state of the learner, we can offer him an answer adapted to his concerns that appear during their learning processes
Whose Risk Is It Anyway: How Do Risk Perception and Organisational Commitment Affect Employee Information Security Awareness?

Since information security (InfoSec) incidents often involve human error, businesses are investing greater resources into improving staff awareness and compliance with best-practice InfoSec behaviours. This research examined whether employees who feel that they may be personally affected by workplace InfoSec incidents are more likely to behave in accordance with those best-practice behaviours. To further understand this, we also examined organisational commitment and risk perception. Data collection involved an online questionnaire measuring these constructs in relation to three workplace cyber threats: phishing, malware, and mobile devices. The questionnaire was completed by 269 employed Australians. Participants who felt more personally affected by attacks associated with mobile devices were more likely to report following best-practice behaviours in that context at work. This was not the case for phishing and malware attacks. Other variables, including age, gender, employment level and InfoSec training, were also found to predict reported compliance with best-practice behaviours, and employees with more frequent training self-reported poorer compliance. Theoretical and practical implications are discussed.
Communication-Efficient Weighted Reservoir Sampling from Fully Distributed Data Streams

We consider weighted random sampling from distributed data streams presented as a sequence of mini-batches of items. This is a natural model for distributed streaming computation, and our goal is to showcase its usefulness. We present and analyze a fully distributed, communication-efficient algorithm for weighted reservoir sampling in this model. An experimental evaluation on up to 256 nodes (5120 processors) shows good speedups, while theoretical analysis promises further scaling to much larger machines.
Temperature Prediction Using Recurrent Neural Network for Internet of Things Room Controlling Application

Prediction has several benefits in improving our lives such as in health, weather, seismology, and safety in smart building that able to forecast future values based on past information. Deep learning models for prediction on time series sensor data and choose the appropriate model that fit our case is analyzed. Smart buildings have to make distributed analyze for time series from various sensors in rooms. In this paper, a Recurrent Neural Network (RNM) in particular with the Long Short Term Memory (LSTM) techniques used for advanced analysis of predicting the temperature. The performance of LSTM NN was measured using root mean square error (RMSE) and the results show the model is significantly more accurate.
Systems Engineering and Design Science Research for Effective Product Development

The value of Design Science Research (DSR) is well known for producing artifacts from research. The context is shifted slightly in this paper to show the value of using a combination of Systems Engineering (SE) and DSR in a product development context. SE is a well-established framework for bringing systems into being, with well-defined resources to support development in a system's full life cycle. SE is useful to place focus on system relevance by using requirements management in a formal way, while research provides novelty and decision support in the SE and business processes. A case is provided for development of mine safety equipment. SE was used to develop a winch signaling system, with research conducted on the system's effectiveness in terms of reducing both production downtime and hazardous exposure. As the developed system was a safety system, it was imperative to conduct research in a simulated environment on relevant simulation models to prevent harm to end-users. For different technology options, end-user risk appetite was translated to behavioral failures and each technology option's effectiveness evaluated for an integrated system as if in operation. Operational risk and productivity were thus modeled using a new method called Activity Based Risk (ABR). The ABR method showed the sensitivity of an integrated production system with respect to technology variation in high-risk environments. It was concluded that combining SE and research resulted in a well-defined DSR framework that produced both a physical artifact and an improved acquisition process for mines.
Generating Graphic Representations of Spoken Interactions Revisited: The Tension Factor and Information Not Uttered in Journalistic Data

The proposed interactive and semi-automatic processing in distinctive modules facilitates the correct perception and evaluation of pragmatic features and paralinguistic features in spoken interaction, especially in discussions and interactions beyond a defined agenda and specified protocol, such as interviews and live conversations in Skype or in the Media. We propose a processing and evaluation framework including a generation of graphical representations and tags corresponding to values and benchmarks depicting the degree of information not uttered and non-neutral elements - including tension - in Speaker behavior in spoken text segments.
Thoughts Reflection Machine

This blue sky paper presents the Thoughts Reflection Machine (TRM) which combines hypertext technologies and intelligent components. Using hypertext, the TRM provides means to its users to express or communicate their thoughts and ideas. Furthermore, the machine suggests relevant information that trigger users' creative thinking. The TRM is an approach towards a tight cooperation between human and machine supporting both in their specific tasks in which they are most excellent in: creative problem solving respective computation of huge data sets.
Applying Emotional Team Coaching to Software Development

In this article, we explain how we applied team coaching and team emotional intelligence techniques to improve an outsourced IT team. The experience was conducted by an interdisciplinary team, including experts on psychology and on IT lean/agile development.
Comparing a Mouse and a Free Hand Gesture Interaction Technique for 3D Object Manipulation

Interacting in 3D has considerable and growing importance today in several areas and most computer systems are equipped with both a mouse or touchscreen and one or more cameras that can be readily used for gesture-based interaction. Literature comparing mouse and free-hand gesture interaction, however, is still somewhat sparse in regards to user satisfaction, learnability and memorability, which can be particularly important attributes for applications in education or entertainment. In this paper, we compare the use of mouse-based interaction with free-hand bimanual gestures to translate, scale and rotate a virtual 3D object. We isolate object manipulation from its selection and focus particularly on those three attributes to test whether the related variables show significant differences when comparing both modalities. To this end, we integrate a gesture recognition system, Microsoft’s Kinect and an educational virtual atlas of anatomy, then design and perform an experiment with 19 volunteers, combining data from self-reported metrics and event logs. Our results show that, in this context, the difference is indeed significant and favors the use of gestures for most experimental tasks and evaluated attributes.
Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency

Shearlet Transform (ST) has been instrumental for the Densely-Sampled Light Field (DSLF) reconstruction, as it sparsifies the underlying Epipolar-Plane Images (EPIs). The sought sparsification is implemented through an iterative regularization, which tends to be slow because of the time spent on domain transformations for dozens of iterations. To overcome this limitation, this letter proposes a novel self-supervised DSLF reconstruction method, CycleST, which employs ST and cycle consistency. Specifically, CycleST is composed of an encoder-decoder network and a residual learning strategy that restore the shearlet coefficients of densely-sampled EPIs using EPI-reconstruction and cycle-consistency losses. CycleST is a self-supervised approach that can be trained solely on Sparsely-Sampled Light Fields (SSLFs) with small disparity ranges (≤8 pixels). Experimental results of DSLF reconstruction on SSLFs with large disparity ranges (16 - 32 pixels) demonstrate the effectiveness and efficiency of the proposed CycleST method. Furthermore, CycleST achieves ~ 9x speedup over ST, at least.
Exploring Personal Credit Evaluation Model Based on Blockchain

With the fast development of financial systems, there are many deficiencies appeared in traditional credit evaluation models which can't satisfy the needs of reliability and efficiency. In this paper, we firstly analyze the deficiencies such as excessive centralization, fake data identification and data transmission lag in existing credit models which brought some difficulties of credit evaluation in different types of customers. For solving these problems, we have introduced "Coin-day Destruction" credit model in blockchain and used decentralized data sources, periodically historical data destruction and other methods to explore a blockchain based personal credit model. In the experiment with real-world dataset, it has shown a better performance comparing to traditional statistical credit models.
Learning With Selected Features

The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innovation, due to its computational and storage bottlenecks. This article presents a scalable algorithm based on subsampling, called learning with selected features (LSF), to reduce the computational burden of RLS. Almost the optimal learning rate together with a sufficient condition on selecting kernels and centers to guarantee the optimality is derived. Our theoretical assertions are verified by numerical experiments, including toy simulations, UCI standard data experiments, and a real-world massive data application. The studies in this article show that LSF can reduce the computational burden of RLS without sacrificing its generalization ability very much.
Dynamic Visual Identities: Exploring Variation Mechanisms to Achieve Flexibility

Dynamic visual identities differ from the conventional ones by the occurrence of variation in at least one of the elements of their visual system. The type of dynamism depends on the variation mechanisms used, which may confer different features to the visual identity. Flexibility is one of these features and consists in the ability to adapt to different content and formats. We focus on the study of how flexibility can be achieved using different variation mechanisms by conducting practical experiments.
Towards syntax-aware token embeddings

Abstract Distributional semantic word representations are at the basis of most modern NLP systems. Their usefulness has been proven across various tasks, particularly as inputs to deep learning models. Beyond that, much work investigated fine-tuning the generic word embeddings to leverage linguistic knowledge from large lexical resources. Some work investigated context-dependent word token embeddings motivated by word sense disambiguation, using sequential context and large lexical resources. More recently, acknowledging the need for an in-context representation of words, some work leveraged information derived from language modelling and large amounts of data to induce contextualised representations. In this paper, we investigate S yntax- A ware word Tok en E mbeddings ( SATokE ) as a way to explicitly encode specific information derived from the linguistic analysis of a sentence in vectors which are input to a deep learning model. We propose an efficient unsupervised learning algorithm based on tensor factorisation for computing these token embeddings given an arbitrary graph of linguistic structure. Applying this method to syntactic dependency structures, we investigate the usefulness of such token representations as part of deep learning models of text understanding. We encode a sentence either by learning embeddings for its tokens and the relations between them from scratch or by leveraging pre-trained relation embeddings to infer token representations. Given sufficient data, the former is slightly more accurate than the latter, yet both provide more informative token embeddings than standard word representations, even when the word representations have been learned on the same type of context from larger corpora (namely pre-trained dependency-based word embeddings). We use a large set of supervised tasks and two major deep learning families of models for sentence understanding to evaluate our proposal. We empirically demonstrate the superiority of the token representations compared to popular distributional representations of words for various sentence and sentence pair classification tasks.
A Contextual Usability Exploration of Cash and Ticket Machines

Today, many services, such as ticket purchase and cash withdrawal, are provided by stationary machines such as ATM and ticket machines placed in train stations and travel centers. Personalized service is not always available, and users are referred to machines and automats. Previous research on ATM and ticket machines mostly focus on user interface and do not take the contextual conditions into consideration. A well-designed interface may not work under conditions such as poor lighting and time limitations of the service which affects the usability. The focus of this paper is not the physical or digital design of ATM and ticket automats as such, but rather how they function in their environment, and how that will affect users; especially old people with some sensory limitations such as limited eyesight and partial motor skills. The empirical study indicates that the physical environment of these machines and automats is crucial for how the service works. Identified parameters that affect usability are light conditions, auditory conditions, the liveliness of the setting and time limitations of services. If the contextual conditions surrounding the automat or machine does not work, it will be inaccessible to many users, however deliberate the design of their physical and digital interface.
A characterization of 2-threshold functions via pairs of prime segments

A {0,1}-valued function on a two-dimensional rectangular grid is called threshold if its sets of zeros and ones are separable by a straight line. In this paper we study 2-threshold functions, i.e. functions representable as the conjunction of two threshold functions. We provide a characterization of 2-threshold functions by pairs of oriented prime segments, where each such segment is defined by an ordered pair of adjacent integer points.
Human Cyber Risk Management by Security Awareness Professionals: Carrots or Sticks to Drive Behaviour Change?

Cyber crime is rising at an unprecedented rate. Organisations are spending more than ever combating the human element through training and other interventions, such as simulated phishing. Organisations employ “carrots” (rewards) and “sticks” (sanctions) to reduce risky behaviour. Sanctions (such as locking computers and informing one’s line manager) are problematic as they lead to unintended consequences towards employee trust and productivity. This study explored how organisations use rewards and sanctions both in their campaigns and specifically following simulated phishing. We also assessed what factors (such as control over rewards, tendency to blame users) influenced security awareness professionals’ use of rewards and sanctions. The findings revealed that organisations use a variety of rewards and sanctions within their campaigns, with sanctions being used across 90% of the organisations. We did not find any factors that influence security awareness professionals’ usage of rewards and sanctions. Our findings suggest the need for a greater consideration of the human element of cyber security. In particular, campaigns should take a more informed approach to use of behaviour change strategies that consider the organisational structure in which they are implemented and the role (and influence) of security awareness professionals within that structure.
Fitness landscape analysis to understand and predict algorithm performance for single- and multi-objective optimization

tutorial Fitness landscape analysis to understand and predict algorithm performance for single- and multi-objective optimization Share on Authors: Bilel Derbel Univ. Lille - CRIStAL, Inria, Lille, France Univ. Lille - CRIStAL, Inria, Lille, FranceView Profile , Sébastien Verel Univ. Littoral Côte d'Opale - LISIC, Calais, France Univ. Littoral Côte d'Opale - LISIC, Calais, FranceView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 993–1042https://doi.org/10.1145/3377929.3389893Published:08 July 2020 1citation129DownloadsMetricsTotal Citations1Total Downloads129Last 12 Months49Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improving Dialogue Design and Control for Smartwatches by Reinforcement Learning Based Behavioral Acceptance Patterns

Dialogue control for health-oriented smartwatch apps is a multi-dimensional task. In our application scenario, the intended purpose of the smartwatch app is the prevention and detection of health hazards jeopardizing the smartwatch wearer (e.g. exsiccosis because of insufficient drinking); the designated target group of the app are elderly people. The dimension of a potential simultaneity of health hazards and ethical considerations how to position the wearer always in control of the app have been presented before. In this paper we focus on the third dimension of the mandatory acceptance conditions of the app. The intended assistance functionality of the app can be only realized, if the interventions of the app occur only in daily life situations, when the wearer will accept such interventions. We present a machine learning approach, by which the app will learn from the wearer over time, when such interventions are appropriate and accepted - and when the app will be expected to remain silent. Of course, this decision has to take into account also the urgency of the intervention with respect to the severity of the threating health hazard.
Cloud-Based Business Intelligence for a Cellular IoT Network

This paper presents a cloud-based business intelligence (BI) implementation for a cellular Internet of Things (IoT) network. A Design Science Research (DSR) paradigm, combined with elaborated Action Design Research (eADR) was used to ensure a workable artifact is delivered. The real-world problem is that, in the cellular network considered here, network health status was not initially visible in an intelligent and actionable way. The network health status is used to ensure service availability and includes different health indicators, of which measurements are made at regular intervals. Not all IoT edge devices have health indicators available, but the network under evaluation provided sufficient data from which to extract anomalies. Experiments were conducted to identify the most appropriate anomaly detection technique from three options, namely SARIMA, SVM and LSTM techniques. Anomalies were linked to system operational failures, in turn to be addressed by appropriate standard operating procedures of a larger main-tenance system. Finally, a clustering algorithm was evaluated for automated recognition of anomalous events, showing that anomalies may be clustered in a useful way using the Mean-Shift clustering algorithm, and also identifying additional health indicators that support anomaly classification.
Semantics-aware typographical choices via affective associations

With the tens of thousands of fonts that are now readily available, it is non-trivial to select the most suitable font for a given use case. Considering the impact of the choice of font on human perception of the text, there is a strong need for semantic font search and recommendation. Aiming to fulfill this need, we induce a typographical lexicon providing associations between words and fonts. For this purpose, we determine font vectors for basic and complex emotions, based on word similarities, antonymy information, and Plutchik's Wheel of Emotions. We create a large font lexicon, named FontLex, relying on emotion associations between the words and the fonts. We evaluate our results through user studies and find that for the majority of the evaluated words, the fonts recommended by FontLex are preferred. We also further extend the dataset using synonyms of font attributes and emotion names. Finally, using CNN embeddings of the fonts, we expand our attribute score assignment to new fonts. The resulting FontLex resource provides mappings between 6.7K words and 2K fonts. Our proof of concept application demonstrates how FontLex can be invoked to obtain semantic font recommendation for poster design.