Reviewerly/exHarmony huggingface dataset:
Identifying Five Archetypes of Interaction Design Professionals and Their Universal Design Expertise

Abstract Systems and services based on Information and Communications Technology (ICT) are now prevalent in our daily lives. Digital transformations have been, and are still being, initiated across private and public sectors. As such, the consequences of digital exclusion are severe and may block access to key aspects of modern life, such as education, employment, consumerism and health services. In order to combat this, regions and countries such as the USA, Canada, EU and Scandinavia have all legislated universal design (UD) in relation to ICT, in order to ensure as many citizens as possible have the opportunity to access and use digital information and services. However, there has been limited research into how higher educational programs address legislated accessibility responsibilities. This paper looks into the discipline of interaction design (IxD). IxD is the design domain focused on ‘how human beings relate to other human beings through the mediating influence of products’ (Buchanan, R. (2001) Designing research and the new learning. Des. Issues, 17, 3–23). The study presents an analysis of Norwegian higher educational programs within IxD. Based on document analysis, we map the skillsets the study programs state to deliver and investigate to what degree UD expertise is included. Our findings indicate the study programs do not deliver adequate training in UD, in order to fulfill the professional responsibilities related to ICT accessibility. From our findings, we extrapolate five ‘archetypes’ of interaction designers. These personas-like analytical constructs hold slightly different characteristics. For each of the five, we propose UD expertise fitting key skillsets. We hope our contributions are useful both for the higher education sector and the industry and will contribute to raised awareness of UD skills so they can educate interaction designers in their different industry roles with required competences. RESEARCH HIGHLIGHTS We indicate the current content of interaction design (IxD) programs in higher education and document the lacking focus on universal design (UD). We identify five different archetypes of interaction designers being educated in such programs. We describe key skillsets and strengths for each archetype. We propose UD expertise for the (IxD) profession and link UD expertise to archetype skillsets to emphasize relevance.
Workshop on recommender systems in fashion (fashionXrecsys2019)

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. However, relatively little research has been done on these complex problems. The very First fashionXrecsys Workshop aims at addressing these issues by providing a avenue for discussing novel approaches to recommendations in fashion and e-commerce applications.
EyeControl

Due to the explicit and implicit facets of gaze-based interaction, eye tracking is a major area of interest within the field of cognitive industrial assistance systems. In this position paper, we describe a scenario which includes a wearable platform built around a mobile eye tracker, which can support and guide an industrial worker throughout the execution of a maintenance task. The potential benefits of such a solution are discussed and the key components are outlined.
Comparative Analysis of Cloud Security Classifications, Taxonomies, and Ontologies

Various classifications have been proposed to describe and organize the cloud security landscape. While some are inherited from general security concepts like security attributes (confidentiality, integrity, and availability), others are based on cloud specific characteristics such as cloud service models and stakeholders' roles. Despite the extensive body of research on developing classifications for cloud computing security, a study on the principles and dimensions of these classifications have largely escaped rigorous analysis. This paper presents a comprehensive survey of the existing cloud computing security classifications, taxonomies, and ontologies. We explore their strengths, limitations, and effective use in an attempt to draw the general properties for a holistic framework that can provide a comprehensive view of cloud computing security and help better understand this evolving field. Researchers may find in this paper a road map and an overview of what exists in terms of cloud security classifications, taxonomies, and ontologies.
Darwin &amp; Goliath

Recommendations-as-a-Service (RaaS) ease the process for small and medium-sized enterprises (SMEs) to offer product recommendations to their customers. Current RaaS, however, suffer from a one-size-fits-all concept, i.e. they apply the same recommendation algorithm for all SMEs. We introduce Darwin & Goliath, a RaaS that features multiple recommendation frameworks (Apache Lucene, TensorFlow, ...), and identifies the ideal algorithm for each SME automatically. Darwin & Goliath further offers per-instance algorithm selection and a white label feature that allows SMEs to offer a RaaS under their own brand. Since November 2018, Darwin & Goliath has delivered more than 1m recommendations with a CTR = 0.5%.
Robust and Invisible Watermarking Technique Based on Frei-Chen Bases

In recent decades, many researchers are focusing on digital image watermarking to provide secure methods to protect images from an unauthorized usage and distribution. The use of digital watermarking is preferred over the use of digital signatures and other methods since it reduces the overhead and provides more robustness. Robustness and imperceptibility of the watermark are highly considered factors which researchers focus on when developing new watermarking algorithms. In this paper, we propose an image watermarking technique that embeds the watermark on the edges of the image. The Edges of the image are found using the Frei-Chen masks. The simulation results show the invisibility and the robustness of the proposed algorithm.
Gradient-Based Learning of Compositional Dynamics with Modular RNNs

Learning compositional dynamics with recurrent neural networks (RNNs) trained with back-propagation through time (BPTT) is usually a difficult task. Typically RNNs learn the consecutive shape along target sequences from time step to time step, focusing on local temporal correlations. When the challenge is to identify and model independent, unknown data subcomponents, that is, data generating causes on-the-fly during training, however, this local temporal shape-oriented inductive learning bias is obstructive. We propose a modular, compositional RNN architecture and derive simple procedures to automatically infer the source subdynamics that generate the data. We show that the involved error signal separation can be used for both teacher forcing and model-distinct target signal provision in the compositional RNN architecture. As a result, the entire network is able to learn compositional dynamics, developing emergent, flexibly adaptable signal decompositions within the distributed modules. We demonstrate that in this way simple RNNs trained with BPTT can learn sequences that could so far only be solved effectively with reservoir computing approaches. Moreover we show that these RNNs are much more robust against signal noise when compared to traditional BPTT or reservoir computing approaches.
Slanted Stixels: A Way to Represent Steep Streets

This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a Fully Convolutional Network (FCN), which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.
Simple Implementation of Fuzzy Controller for Low Cost Microcontroller

Fuzzy logic inference system requires a long enough computational process, starting from fuzzification, matching rule base, drawing conclusions and defuzzification. By using C programming, fuzzy logic systems can be carried out by a low capability microcontrollers, for example PIC16F877A that has a flash memory capacity of 8KB and the maximum clock speed is 20 MHz. But if it will be applied to a microcontroller that has a smaller size of flash memory, there is a possibility that it cannot be realized, especially if it uses the gaussian membership function and each membership function inputs was devided in to more than three memberships. To overcome this problem we need a method to accelerate the fuzzy logic computation process. One of that method is using a linear interpolation from the table of input-output fuzzy system. In this paper, the linear interpolation was illustrated by using bilinear fuzzy interpolation. Based on the GNU Octave simulation, it was obtained data that the time being needed to complete the simulation for fuzzy bilinear Look Up Table has 7 times faster than the fuzzy system computation, whereas from the real implementation, it was obtained that fuzzy bilinear Look Up Table gives 2 times faster and can save program memory space around 25% compared to ordinary fuzzy system.
Transferring Reservoir Computing: Formulation and Application to Fluid Physics

We propose a transfer learning for reservoir computing, and verify the effectivity of the proposed methods for the standard inference task of the Lorenz system. Applying the proposed methods to an inference task of fluid physics, we show the inference accuracy is drastically improved compared with the conventional reservoir computing method if available training data size is highly limited.
Towards multi-person motion forecasting

Forecasting body motion has a lot of potential applications such as sports and entertainment. Previous studies have mainly employed cameras and optical motion captures to measure the joint positions of person, and predicted them about 0.5 seconds before by using deep neural networks. However, following two difficulties have to be solved to install the forecasting system into the real world: One is that camera and optical based methods have to take into account the environmental settings and occlusion problems, and the other is that previous studies have not considered plural persons. In this paper, we propose a multi-person motion forecasting system by using inertial measurement unit (IMU) motion captures to overcome these difficulties simultaneously, and demonstrate a preliminary result.
A Flexible Query Answering System for Movie Analytics

With advances in technologies, huge volumes of a wide variety of valuable data—which may be of different levels of veracity—are easily generated or collected at a high velocity from a homogenous data source or various heterogeneous data sources in numerous real-life applications. Embedded in these big data are rich sources of information and knowledge. This calls for data science solutions to mine and analyze various types of big data for useful information and valuable knowledge. Movies are examples of big data. In this paper, we present a flexible query answering system (FQAS) for movie analytics. To elaborate, nowadays, data about movies are easy accessible. Movie analytics help to give insights about useful revenues, trends, marketing related to movies. In particular, we analyze movie datasets from data sources like Internet Movie Database (IMDb). Our FQAS makes use of our candidate matching process to generate a prediction of a movie IMDb rating as a response to user query on movie. Users also have flexibility to tune querying parameters. Evaluation results show the effectiveness of our data science approach—in particular, our FQAS—for movie analytics.
Model-Driven Context Configuration in Business Process Management Systems: An Approach Based on Knowledge Graphs

Business Process Management Systems (BPMSs) are inherently model-driven, relying on machine-readable process repositories that are typically standards-based. However, a requirement for semantic agility is emerging as knowledge-driven applications become less blueprint-oriented and more context-aware. The integration of process knowledge with contextual data can be subjected to this agility requirement – i.e., having the process modelling environment customised in terms of (expanding) its knowledge space and in terms of model-data interoperability. Such customisations may capture any of the enterprise perspectives proposed by the Zachman Framework (among which the How, Who and Where facets are in our particular focus) towards the benefit of establishing a hybrid knowledge-data fabric underlying flexible, context-driven BPMSs. This paper presents a project-based technical solution, based on the interplay of semantic technology and agile modelling methods, for setting up a hybrid knowledge base derived from several heterogeneous sources: diagrammatic models, semantically lifted legacy data and open geospatial data, with reasoning rules on top of this conglomerate. Together, these sources cover the How, Who and Where facets of the Zachman Framework concepts in a Knowledge Graph that drives the front-end Task Management panel of a BPMS. The proposal advocates complementarity and integration of paradigms that rarely converge – i.e., knowledge representation, open data and process-aware information systems.
Design and Deployment of a Better Course Search Tool: Inferring Latent Keywords from Enrollment Networks

Liberal arts universities possess a vast catalog of courses from which students can choose. The common approach to surfacing these courses has been through traditional keyword matching information retrieval. The course catalog descriptions used to match on may, however, be overly brief and omit important topics covered in the course. Furthermore, even if the description is verbose, novice students may use search terms that do not match relevant courses, due to their catalog descriptions being written in the specialized language of a discipline outside of their own. In this work, we design and user test an approach intended to help mitigate these issues by augmenting course catalog descriptions with topic keywords inferred to be relevant to the course by analyzing the information conveyed by student co-enrollment networks. We tune a neural course embedding model based on enrollment sequences, then regress the embedding to a bag-of-words representation of course descriptions. Using this technique, we are able to infer keywords, in a system deployed for a user study, that students (N = 75) rated as more relevant than a word drawn at random from a course’s description.
Some Problems in Using Prospect Theory to Explain Strategic Management Issues

Prospect theory has had an immense impact on strategic management scholarship, stemming from an apparent belief that the theory leads to relatively straightforward general hypotheses relating perfo...
Unsupervised classification of high-dimension and low-sample data with variational autoencoder based dimensionality reduction

In data mining research and development, one of the defining challenges is to perform classification or clustering tasks for relatively limited-samples with high-dimensions data, also known as high-dimensional limited-sample size (HDLSS) problem. Due to the limited-sample-size, there is a lack of enough training data to train classification models. Also, the ‘curse of dimensionality’ aspect is often a restriction on the effectiveness of many methods for solving HDLSS problem. Classification model with limited-sample dataset lead to overfitting and cannot achieve a satisfactory result. Thus, the unsupervised method is a better choice to solve such problems. Due to the emergence of deep learning, their plenty of applications and promising outcome, it is required an extensive analysis of the deep learning technique on HDLSS dataset. This paper aims at evaluating the performance of variational autoencoder (VAE) based dimensionality reduction and unsupervised classification on the HDLSS dataset. The performance of VAE is compared with two existing techniques namely PCA and NMF on fourteen datasets in term of three evaluation metrics namely purity, Rand index, and NMI. The experimental result shows the superiority of VAE over the traditional methods on the HDLSS dataset.
Skeleton Based Fall Detection with Convolutional Neural Network

There are more and more elderly people living alone in the society today, and the supervision of the elderly has become a social problem. In order to enable elderly people living alone to be found at home when they fall, minimize the damage to them. We propose a skeleton-based real-time fall detection system. Firstly, we encode the sequence of motions into an RGB image and retain the original spatial structure information and temporal dynamic information. Then, we designed a lightweight convolutional neural network (CNN) to recognize the encoded image. We collected a fall dataset and merged it with the MSRDaily Activity3D dataset. We use two data enhancement methods to extend the dataset. The results of our model on the test set show that the recall is 93.9% and the accuracy is 93.75%. Finally, we also propose an online detection algorithm. The experimental results show that the recall is 1, and the accuracy is 95%.
Compositional network embedding for link prediction

Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the existing methods from being applied in real cases. Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem. The heterogeneous network usually requires extra work to encode node types, as node type is not able to be identified by node ID. Node ID carries rare information, resulting in the criticism that the existing methods are not robust to noise. To address this issue, we introduce Compositional Network Embedding, a general inductive network representation learning framework that generates node embeddings by combining node features based on the "principle of compositionally". Instead of directly optimizing an embedding lookup based on arbitrary node IDs, we learn a composition function that infers node embeddings by combining the corresponding node attribute embeddings through a graph-based loss. For evaluation, we conduct the experiments on link prediction under three different settings. The results verified the effectiveness and generalization ability of compositional network embeddings, especially on unseen nodes.
An Improved GMM based Video Foreground Separation

Surveillance video analysis has applied to kinds of areas. Mixed GMM is a traditional target separation method. In order to overcome the object detection performance under dynamic scene, an improved algorithm is adopted to improve the mixed Gaussian model algorithm. A different initialization and model parameter updating strategy are adopted to detect the object under dynamic scene. The experiments validate the effectiveness of the algorithm at the lakeside and geyser.
Automatic Generation of Coherent Learning Pathways for Open Educational Resources

Learners and educators all over the world have been increasingly relying on the internet for education, thus generating and consuming vast amounts of online learning resources. Selecting appropriate learning resources among them and structuring them in a way that maximises comprehension and skill building is a challenging task. In this work, we propose a model to automatically generate learning pathways from available open learning resources, such that the generated pathways are semantically coherent and pedagogically progressive. The proposed model has two components– a Greedy Generator and a Validator based on Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models respectively. The Greedy Generator chooses the next resource in the learning pathway based on local considerations and the Validator validates the learning pathway as a whole. They work in tandem with each other connected by a feedback loop. Since we work with open educational resources that lack standard meta-data, we also propose methods to generate metrics that compare a pair of learning resources. The learning pathways generated by our model from a corpus of open learning resources show promising results.
Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks

In this paper, we present an approach to Spanish talk shows summarization. Our approach is based on the use of Siamese Neural Networks on the transcription of the show audios. Specifically, we propose to use Hierarchical Attention Networks to select the most relevant sentences for each speaker about a given topic in the show, in order to summarize his opinion about the topic. We train these networks in a siamese way to determine whether a summary is appropriate or not. Previous evaluation of this approach on summarization task of English newspapers achieved performances similar to other state-of-the-art systems. In the absence of enough transcribed or recognized speech data to train our system for talk show summarization in Spanish, we acquire a large corpus of document-summary pairs from Spanish newspapers and we use it to train our system. We choose this newspapers domain due to its high similarity with the topics addressed in talk shows. A preliminary evaluation of our summarization system on Spanish TV programs shows the adequacy of the proposal.
Exploring Transfer Learning to Reduce Training Overhead of HPC Data in Machine Learning

Nowadays, scientific simulations on high-performance computing (HPC) systems can generate large amounts of data (in the scale of terabytes or petabytes) per run. When this huge amount of HPC data is processed by machine learning applications, the training overhead will be significant. Typically, the training process for a neural network can take several hours to complete, if not longer. When machine learning is applied to HPC scientific data, the training time can take several days or even weeks. Transfer learning, an optimization usually used to save training time or achieve better performance, has potential for reducing this large training overhead. In this paper, we apply transfer learning to a machine learning HPC application. We find that transfer learning can reduce training time without, in most cases, significantly increasing the error. This indicates transfer learning can be very useful for working with HPC datasets in machine learning applications.