Reviewerly/exHarmony huggingface dataset:
Design Process for Human-Data Interaction: Combining Guidelines with Semio-participatory Techniques

The complexity of analytically reasoning to extract and identify useful knowledge from large masses of data requires that the design of visual analytics tools addresses challenges of facilitating human-data interaction (HDI). Designing data visualisation based on guidelines is fast and low-cost, but does not favour the engagement of people in the process. In this paper, we propose a design process to integrate design based on guidelines with participatory design practices. We investigate, and when necessary, adapt existing practices for each step of our design process. The process was evaluated on a design problem involving a visual analytics tool supporting decisions related to the production strategy in oil reservoirs with the participation of key stakeholders. The generated prototype was tested with adapted participatory evaluation practices. The obtained results indicate participants’ satisfaction with the design practices used and detected the fulfilment of users’ needs. The design process and the associated practices may serve as a basis for improving the HDI in other contexts.
An MDD‐based method for building context‐aware applications with high reusability

Abstract Adding context ‐awareness capabilities to modern mobile and pervasive computing applications (apps) is becoming a mainstream activity in the software engineering community. In this respect, many context models and middleware architectures have been proposed with the aim to provide the developers with tools and abstractions that make it easier to produce context‐aware applications. However, current solutions suffer from relatively low reusability and lack ease‐of‐use. In this paper, we propose a two‐layer approach based on model‐driven development: At the higher layer, we introduce the design of reusable context plug‐ins, which can be used to monitor low‐level context data and to infer higher‐level information about the users, their computing infrastructure, and their interaction. At the lower layer, the plug‐ins themselves are synthesized using more elementary, reusable components. We argue that this development approach provides significant advantages to the developers, as it enables them to design, implement, reuse, and maintain the codebase of context‐aware apps more efficiently. To evaluate this approach, we demonstrate it in the context of a two‐part example application and assess it both qualitatively and quantitatively.
Incremental Algorithm Based on Split Technique

Most clustering algorithms become ineffective when provided with unsuitable parameters or applied to data-sets which are composed of clusters with diverse shapes, sizes, and densities. In our paper we present a new version of k-means method, that allows adding one new cluster to the k cluster we already had with out retraining from scratch. This method is based on the splitting process, we are looking for the cluster that had the highest score to be split, our score is based on three criteria; SSE, Dispersion-index and the size of cluster. Finally, the split process is performed by using standard K. Experimental results demonstrate the effectiveness of our approach both on simulated and real data-sets.
Cloth Recommender System Based on Item Matching

Abstract Recommender systems have become an essential part in our life. Most social Websites use recommender systems to enhance user experience. In online shopping Websites such as Amazon, Clothing is one of the most popular domains, therefore a recommender is of great significance. Previous recommender systems were often focused on retrieving items based on user preference, i.e. similarity to the previous items purchased by the user. However, in Clothing domain, the matching relationships between candidate items and the previously purchased is also important for recommendation. For example, a user may want to buy a new jean rather than suit pants if he/she has just purchased a shirt. This kind of matching relationships also frequently occurs in other life contexts. In this paper, we aim to recommend new clothes that can better match the clothes purchased by a user. This new recommendation strategy would work better in the Clothing domain and complement the current recommendation literature. Experiment results show that our method can lead to better recommendation performance in the Clothing domain.
A Comprehensive Study on Virtual Machine Migration Techniques of Cloud Computing

Cloud computing emergence is based on virtualization technologies which comes out with more opportunities without investing any cost on infrastructure. Every cloud data center comprises of heterogeneous servers which are hosting different VMs with different specifications and instances. It may cause the imbalance of resources. This paper exemplifies the concept of virtualization in cloud environment with virtual machine (VM) migration techniques. It also presents the survey and comparative analysis of VM migration algorithms given by eminent researchers in this field.
Some Evaluations on a Digital Watermarking Technique for Music Data Using Distortion Effect

We have proposed a novel concept of a digital watermarking technique for music data that focuses on the use of sound synthesis and sound effect techniques. This paper describes the details of our proposed technique that employs the distortion effect, one of the most common sound effects frequently utilized especially for guitar and bass instruments. This paper describes the experimental results of evaluating the resistance of the proposed technique against some basic malicious attacks utilizing MP3 coding, tempo alteration, pitch alteration, and high-pass filtering. It is demonstrated that the proposed technique potentially has appropriate resistance against such attacks except for the high-pass filtering attack. A technique for increasing the resistance against the high-pass filtering attack is also supplementarily discussed.
EEkNN: k-Nearest Neighbor Classifier with an Evidential Editing Procedure for Training Samples

The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms applied in many fields because it is very simple to understand and easy to design. However, one of the major problems encountered in using the kNN rule is that all of the training samples are considered equally important in the assignment of the class label to the query pattern. In this paper, an evidential editing version of the kNN rule is developed within the framework of belief function theory. The proposal is composed of two procedures. An evidential editing procedure is first proposed to reassign the original training samples with new labels represented by an evidential membership structure, which provides a general representation model regarding the class membership of the training samples. After editing, a classification procedure specifically designed for evidently edited training samples is developed in the belief function framework to handle the more general situation in which the edited training samples are assigned dependent evidential labels. Three synthetic datasets and six real datasets collected from various fields were used to evaluate the performance of the proposed method. The reported results show that the proposal achieves better performance than other considered kNN-based methods, especially for datasets with high imprecision ratios.
Web Summarization and Browsing Through Semantic Tag Clouds

The need to manage electronic documents is an open issue in the digital era. It becomes a challenging problem on the internet where a large amount of data needs even more efficient and effective methods and techniques for mining and representing information. In this context, document summarization, browsing processes and visualization techniques have had a great impact on several dimensions of user information perception. In this context, the use of ontologies for knowledge representation has rapidly grown in the last years in several application domains together with social-based techniques such as tag clouds. This form of visualization tool is becoming particularly useful in the interaction process between users and social applications where a huge amount of data needs to have effective and efficient interfaces. In this article, the authors propose a novel methodology based on a combination of ontologies and Tag Clouds for web document collections browsing and summarizing, they call this tool Semantic Tag Cloud.
An Effective Text Classification Model Based on Ensemble Strategy

Abstract Automatic text classification is a classic topic for natural language processing. Text classification research mainly focuses on feature representation of text documents or designing an efficient machine learning model. Although various approaches have been proposed to address these problems, they are still far from being solved. In this paper, we proposed a novel method called LAC_DNN to achieve the text classification based on diverse feature representation approaches and classifiers. More specifically, LAC_DNN firstly introduces a novel feature representation approach called LATW to extract feature information of the documents, which integrates the feature information extracted by LSI model, TF-IDF weighted vector space model (TF-IDF_VSM), TF-IDF weighted word2vec (TF-IDF_word2vec) and average word2vec (Avg_word2vec), respectively. Secondly, it trains different classifiers including support vector machine, k nearest neighbor, logistic regression and convolutional neural networks based on the feature encoded by LATW. Finally, LAC_DNN integrates these classifiers into an ensemble predictor to leverage complimentary information of feature representation methods and classifiers, and predict the topic of text documents. LAC_DNN achieves superior performance with accuracy of 97.44% and 97.43% on the text datasets of Fudan and Netease news, respectively. Extensive experiments show that LAC_DNN is prominent and useful for text classification.
Extreme Reverse Projection Learning for Zero-Shot Recognition

Zero-shot learning (ZSL) aims to transfer knowledge from a set of seen classes to a set of unseen classes so that the latter can be recognised without any training samples. It faces two main challenges: the projection domain shift problem and the hubness problem. Existing models have been proposed to address one of the two problems, but not both. In this paper, we propose a novel ZSL model termed extreme reverse projection learning (ERPL) to solve both problems. It has a simple linear formulation that casts ZSL into a min-min optimization problem. An efficient and robust gradient-based algorithm is also introduced to find an extremely generalizable projection for ZSL. Extensive experiments on five benchmark datasets show that the proposed model outperforms the state-of-the-art alternatives on all datasets, often by significant margins. Furthermore, the obtained improvements are particularly salient under the more challenging but realistic ‘pure’ and generalized ZSL settings.
Iris

Advances in eye tracking technology have enabled new interaction techniques and gaze-based applications. However, the techniques for visualizing gaze information have remained relatively unchanged. We developed Iris, a tool to support the design of contextually relevant gaze visualizations. Iris allows users to explore displaying different features of gaze behavior including the current fixation point, duration, and saccades. Stylistic elements such as color, opacity, and smoothness can also be adjusted to give users creative and detailed control over the design of their gaze visualization. We present the Iris system and perform a user study to examine how participants can make use of the tool to devise contextually relevant gaze visualizations for a variety of collaborative tasks. We show that changes in color and opacity as well as variation in gaze trails can be adjusted to create meaningful gaze visualizations that fit the context of use.
A novel gaze event detection metric that is not fooled by gaze-independent baselines

Eye movement classification algorithms are typically evaluated either in isolation (in terms of absolute values of some performance statistic), or in comparison to previously introduced approaches. In contrast to this, we first introduce and thoroughly evaluate a set of both random and above-chance baselines that are completely independent of the eye tracking signal recorded for each considered individual observer. Surprisingly, our baselines often show performance that is either comparable to, or even exceeds the scores of some established eye movement classification approaches, for smooth pursuit detection in particular. In these cases, it may be that (i) algorithm performance is poor, (ii) the data set is overly simplistic with little inter-subject variability of the eye movements, or, alternatively, (iii) the currently used evaluation metrics are inappropriate. Based on these observations, we discuss the level of stimulus dependency of the eye movements in four different data sets. Finally, we propose a novel measure of agreement between true and assigned eye movement events, which, unlike existing metrics, is able to reveal the expected performance gap between the baselines and dedicated algorithms.
Design for System Change: Developing Digital Competences of Vocational Teachers

Digital transformation shapes the educational system in many ways. It has also far-reaching implications for teachers as their job description may fundamentally change in the future. In this light, it is important (1) to identify necessary digital competences of teachers and (2) to find ways to foster those competences in an efficient way. By means of a literature review and expert interviews, we developed a framework of teachers’ digital competences. In line with Baumert and Kunter (2006) as well as Koehler and Mishra (2009), it comprises content knowledge, pedagogical content knowledge and pedagogical knowledge. However, these facets have extended meaning in the context of digital transformation. Moreover, our framework considers the official EU competence framework (Carretero et al., 2017) and hence covers instrumental skills and knowledge in handling digital media. We successfully validated our framework by means of structural equation modelling with a sample of 215 Swiss teachers. Utilising an Importance Performance Map Analysis, we identified competence facets that show the highest effects on the (self-reported) use of digital media and content. For efficiently fostering those facets, we established a webinar series in order to provide further education service regarding topics like digital teaching and learning.
Towards Building an Arabic Plagiarism Detection System: Plagiarism Detection in Arabic

This article describes a plagiarism detection system for the Arabic language that combines different similarity-measure techniques to uncover plagiarism in Arabic documents. The proposed system consists of two main components, one document-retrieval and the other detailed similarity analysis. The document-retrieval component generates queries from a given suspicious document and makes use of Google search API to retrieve candidate source documents from the Web. The similarity analysis component takes each source document in turn and attempts to identify the plagiarized parts in the suspicious document. The proposed system is thoroughly evaluated using an indigenous corpus. At the document-retrieval level, the system achieved above 75% accuracy in terms of f-score, whereas at the detailed similarity-computation level, the f-score is above 70%.
Immanent Interbodies: Composing Disability through Embodied Choragraphy

In this article, I argue that many praxes of composition stumble into pitfalls of ableism built into the default computer technologies that classrooms employ. Writing software and hardware writ large typically conflate the particularities of embodiment with the generality of "the body" equipped to succeed by standards of normalcy. Therefore, I propose a trajectory away from idealized interfaces, and toward immanent "interbodies," which more fully account for embodiment's contradictive mutabilities. Such work requires strategies for composing disability to draw attention to the embodied ways that many composition practices are performed in writing processes. Composing disability, I argue, makes our writing more like our bodies by subverting the standard use of writing technologies that construct classroom discourses. These praxes contribute to embodied choragraphy, which calls into question ableist pedagogies. Through wide citation of diverse scholarship and description of classroom exercises utilizing videogames and related media, this article challenges the fields' commitment to computers and composition, and questions what versions of embodiment it finds value in.
Interaction graphs

Eye tracking studies have been conducted to understand the visual attention in different scenarios like, for example, how people read text, which graphical elements in a visualization are frequently attended, how they drive a car, or how they behave during a shopping task. All of these scenarios - either static or dynamic - show a visual stimulus in which the spectators are not able to change the visual content they see. This is different if interaction is allowed like in (graphical) user interfaces (UIs), integrated development environments (IDEs), dynamic web pages (with different user-defined states), or interactive displays in general as in human-computer interaction, which gives a viewer the opportunity to actively change the stimulus content. Typically, for the analysis and visualization of time-varying visual attention paid to a web page, there is a big difference for the analytics and visualization approaches - algorithmically as well as visually - if the presented web page stimulus is static or dynamic, i.e. time-varying, or dynamic in the sense that user interaction is allowed. In this paper we discuss the challenges for visual analysis concepts in order to analyze the recorded data, in particular, with the goal to improve interactive stimuli, i.e., the layout of a web page, but also the interaction concept. We describe a data model which leads to interaction graphs, a possible way to analyze and visualize this kind of eye movement data.
Center affine filter based adaptive image despeckling method with preserving details

Speckle noises inherently exist in radar, sonar, ultrasound and optical coherent tomography images. Such noises greatly degrade image quality, and thus make the tasks such as object detection and image registration difficult. With the Center Affine Filter (CAF), this paper proposes a new adaptive method for suppressing speckle noises. The proposed method adopts both local arithmetic mean and local geometric mean around an image pixel to compute the dispersion parameter of the CAF. The metrics of arithmetic and geometric mean not only intimately relate to the probability density function of speckle noise, but also reflect the image homogeneity; therefore, the designed CAF is capable of adaptively suppressing the speckle noises while maintaining the useful details in an image. Then, to speed up the proposed method, the technique of the integral image is used to get the local arithmetic and local geometric mean. The proposed method is experimentally tested on both synthetic and real images, which shows a prospective performance over Lee filter, SRAD filter, and the state-of-the-art BNLM filter.
Graph encryption for all‐path queries

Summary Since cloud computing and cloud storage have become a common practice in our daily life, people are paying more attention on protecting the privacy of their sensitive data. In this paper, we consider the problem of graph encryption in such a way that one can perform all‐path queries over an encrypted graph in a privacy‐preserving manner. A solution for such problem has many potential applications in network virtualization since graph data structures are commonly used to represent the topology of substrate and virtual networks. We propose a searchable symmetric encryption scheme for graph data that support all‐path queries. The scheme allows a client to encrypt a sensitive graph and outsource it to an untrusted server for storage. After that, the client can perform arbitrary all‐path queries between a source and a destination. For each query, the server computes and returns all the paths that satisfied the query. We prove that our scheme is secure against adaptive chosen‐query attacks in the semi‐honest setting.
PasOnto: Ontology for Learning Pascal Programming Language

The Pascal programming language is one of the most pedagogical languages to learn coding. The best way to improve coding skills is through practice. In that regard, we give in our programming course the opportunity to practice the Pascal programming language concepts by providing exercises with solutions. To enhance learners' understanding, research suggests to represent exercises querying using ontologies. Nevertheless, such research focuses mainly on object-oriented programming (Java, C#) and C programming language. For this reason, this work proposes PasOnto ontology to describe exercise solutions essentially in Pascal programming. We also propose a web and mobile interface around this ontology so as to query and highlight Pascal keywords in displayed exercise solution.
ROVETS

As a result of the tremendous proliferation of sessions at academic conferences, recommending appropriate venues for researchers has become a considerable problem. In this article, the authors propose an innovative recommender algorithm called Recommendation of Venues and Environments Through Social-Awareness (ROVETS). ROVETS seeks to enhance the social awareness of attendees at a smart conference. ROVETS initially employs closeness centrality and Breadth First Search (BFS) to detect potential presenters for a target attendee. Then, the accurate tie strength between the attendees and presenters as well as the degree centrality of the presenters are computed based on similarity of their research interests. Using the computations above, ROVETS generates effective recommendations pertaining to venues for attendees who have high tie strength with presenters. Through a relevant real-world dataset, this article evaluates the proposed recommender algorithm. These experimental results validate that ROVETS exhibits favorable enhancements over other existing state-of-the-art methods.
Towards Using Natural User Interfaces for Robotic Arm Manipulation

In this paper, we propose a Natural User Interface (NUI) based on Leap Motion controller which enables intuitive manipulation of a 6-DOF Jaco robotic arm. By using this NUI interface, we aim to study the effects of hand movements and gestures for interaction with a robotic arm. We present a qualitative evaluation of the proposed NUI system by trials with subjects carrying out a standard robotic arm manipulation session using both the conventional joystick interface and the NUI based one. The results are discussed in the last part of the paper.
Media Competence: Enabling for Digitization

Increasing digitization in work and learning environment is changing the requirements for employees. Digital learning is considered more important for vocational training. This is a chance for employees who could so far rarely participate in traditional vocational training. But for successful digital learning media competence constitute a key factor. In the research project MeQ:ino - media competence qualification: individual and organizational a holistic model of media competence was developed. It contains various dimensions of media competence required to keep up with digitization. Based on these different dimensions, several learning nuggets were created to enable educationally disadvantaged employees to meet the requirements of digital learning.