Reviewerly/exHarmony huggingface dataset:
Hijaiyah Letter Interactive Learning for Mild Mental Retardation Children using Gillingham Method and Augmented Reality

Assistive technology for children with special needs is a problem that is interesting to study.Collaboration between methods and latest technology can be used as a learning aid for them.Learning of Hijaiyah letters is the first step to being able to read the Holy Qur'an.Mentally retarded children have IQs below the average normal child, so their learning process is slower and requires special methods.This study aims to develop an application by using the Gillingham and augmented reality methods to help mentally retarded children recognize Hijaiyah letters.The Gillingham method uses a visual, auditory, kinestetic, and tactile (VAKT) approach, that can be used to facilitate mentally retarded children.While augmented reality is used to develop more interesting and interactive applications.Based on the results of research and testing, it can be concluded that the learning application that was built can improve children's memory and understanding of Hijaiyah letters, The results of the pretest and posttest testing, showed an increase of 12% for children who were difficult to receive learning material and 6% for children who are classified as easy to receive learning material.
Parallel Heuristics for Balanced Graph Partitioning Based on Richness of Implicit Knowledge

Balanced graph partitioning (BGP) has a wide range of applications that involve many large-scale distributed data processing problems. However, most of the existing approaches to parallel graph partitioning neglect the problem of the richness of implicit knowledge (RIK) residing in big graph and the knowledge cohesion after graph partitioning. In this paper, we propose a parallel balanced graph partitioning framework called JA-BE-JA-RIK, which improves the original heuristic JA-BE-JA algorithm by evaluating RIK such that parallel BGP can be more efficiently made under the condition of obtaining the good edge-cut value and high knowledge cohesion. We introduce the concept about RIK and further present a quantitative measure to dynamically evaluate the RIK values during parallel BGP. Based on the Hadoop platform, we also implemented both frameworks and JA-BE-JA. The extensive experiments were made for illustrating efficacy of our JA-BE-JA-RIK approach in comparison with the traditional JA-BE-JA algorithm. The experimental results show that our parallel BGP approach can obtain the RIK value as higher as possible within a closer number of edge-cuts in comparison with the traditional JA-BE-JA algorithm.
Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis

Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and adjusts the ensemble weight based on loss value in this study. We theoretically demonstrate that the ensemble weight converges to the limiting distribution, and, thus, minimizes the average total loss from a new regret measure based on adversarial assumption. We also present an overall framework that can be applied to analyze time series. In the experiments, we focused on the on-line phase, in which the ensemble models predict the binary class for the simulated data and the financial and non-financial real data. The proposed method outperformed other ensemble approaches. Moreover, our method was not only robust to the intentional attacks but also sustainable in data distribution changes. In the future, our algorithm can be extended to regression and multiclass classification problems.
The Mixed Reality Toolkit as the Next Step in the Mass Customization Co-Design Experience

The mass customization (MC) toolkit is the major enabler of relational value for consumers.Not a new concept, but nascent in the context of the MC co-design experience, mixed reality (MR) merges the real and virtual world.While the literature extols the significance of MR components -augmented reality (AR) and virtual reality (VR) -in manufacturing, IT, education and, to a limited extent, retail, few if any studies address its relevance to MC and the consumer's perceived value of the co-design experience.Is a mixed reality configurator viable as the enabler of relational benefit in the consumer co-design experience?With visual and feedback features critical to the structure of successful webbased configurators, what characteristics must a MR toolkit possess to deliver optimal experiential value to the MC consumer?What is the nature of the MR toolkit and its bearing upon perceived complexity, control and enjoyment of the co-design process?Are these perceptions the most relevant to consider in designing a MC toolkit utilizing MR technology?This conceptual paper attempts to define a mixed reality toolkit and explore its potential influence on the consumer's perception of value of the MC co-design experience in the mixed reality context. .
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval

While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA). In our proposed ExAG, a human user needs to guess a secret image picked by the VQA agent by asking natural language questions to it. We show that overall, when AI explains its answers, users succeed more often in guessing the secret image correctly. Notably, a few correct explanations can readily improve human performance when VQA answers are mostly incorrect as compared to no-explanation games. Furthermore, we also show that while explanations rated as “helpful” significantly improve human performance, “incorrect” and “unhelpful” explanations can degrade performance as compared to no-explanation games. Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanation on a human-AI collaborative task.
Cervical Histopathology Image Classification Using Ensembled Transfer Learning

In recent years, Transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in Cervical Histopathology Image Classification (CHIC) becomes a new research domain. In this paper, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderately and poorly differentiated cervical histopathology images. In this ETL framework, Inception-V3 and VGG-16 based transfer learning structures are first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from these two structures. Finally, a late fusion based ensemble learning strategy is designed for the final classification. In the experiment, a practical dataset with 100 VEGF stained cervical histopathology images is applied to test the proposed ETL method in the CHIC task, and an average accuracy of 80% is achieved.
Static Knowledge-Based Authentication Mechanism for Hadoop Distributed Platform using Kerberos

With the quickened phenomenal expansion of data, storing massive data has become important and increasingly growing day by day. Thus, big data came to express this large data and handling it properly under three important characteristics such as volume, veracity, and Variety. One practical of big data problems is user and services authentication. Kerberos v5 protocol provided a new solution to such this problem in the Hadoop-distributed platform (HDP). In this paper, we suggest a credible scheme by adding one more level of protection and authentication security to the Kerberos v5 protocol by using a static knowledge-based authentication (SKBA). Where in the login and verification phase by using Kerberos protocol, the KDC will replay with a question to the user-side to check the actual presence of user which the user already answered this question in his registration phase. Our credible scheme is useful in case of capturing messages that enable an eavesdropper to get the ticket that allows getting access to the HDFS as well as to avoid the common attacks with less computation, communication and storage cost. The proposed scheme works seriously and strictly to ensure the registration by delivery of user information over an insecure network in a safe manner and store this information in the KDC-database to be used later for getting access with HDFS.
Second-order contexts from lexical substitutes for few-shot learning of word representations

There is a growing awareness of the need to handle rare and unseen words in word representation modelling.In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts.We introduce a substitute-based context representation technique that can be applied on an existing word embedding space.Previous context-based approaches to modelling unseen words only consider bag-of-word firstorder contexts, whereas our method aggregates contexts as second-order substitutes that are produced by a sequence-aware sentence completion model.We experimented with three tasks that aim to test the modelling of emerging concepts.We found that these tasks show different emphasis on first and second order contexts, and our substitute-based method achieved superior performance on naturallyoccurring contexts from corpora.
Language-Agnostic Model for Aspect-Based Sentiment Analysis

In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis.The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features.We define three different architectures for the successful combination of word embeddings and hand-crafted features.We evaluate the proposed approach for six languages (i.e.English, Spanish, French, Dutch, German and Hindi) and two problems (i.e.aspect term extraction and aspect sentiment classification).Experiments show that the proposed model attains state-of-the-art performance in most of the settings.
L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations

Building large datasets annotated with semantic information, such as FrameNet, is an expensive process.Consequently, such resources are unavailable for many languages and specific domains.This problem can be alleviated by using unsupervised approaches to induce the frames evoked by a collection of documents.That is the objective of the second task of SemEval 2019, which comprises three subtasks: clustering of verbs that evoke the same frame and clustering of arguments into both frame-specific slots and semantic roles.We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments.Using such representations is appropriate in the context of this task, since they provide cues for word-sense disambiguation.Thus, they can be used to identify different frames evoked by the same words.Using this approach we were able to outperform all of the baselines reported for the task on the test set in terms of Purity F 1 , as well as in terms of BCubed F 1 in most cases.
Inductive Zero-Shot Image Annotation via Embedding Graph

Conventional image annotation systems can only handle those images having labels within the exist library, but cannot recognize those novel labels.In order to learn new concepts, one has to gather large amount of labeled images and train the model from scratch.More importantly, it can come with a high price to collect those labeled images.For these reasons, we put forward a zero-shot image annotation model, to reduce the demand for the images with novel labels.In this paper, we focus on the two big challenges of zero-shot image annotation: polysemous words and a strong bias in the generalized zero-shot setting.For the first problem, instead of training on large corpus datasets as previous methods, we propose to adopt Node2Vec to obtain contextualized word embeddings, which can easily produce word vectors of the polysemous words.For the second problem, we alleviate the strong bias in two ways: on one hand, we utilize a model based on graph convolutional network (GCN) to make target images involved in the training process; on the other hand, we put forward a novel semantic coherent (SC) loss to capture the semantic relations of the source and target labels.The extensive experiments on NUSWIDE, COCO, IAPR TC-12, and Corel5k datasets show the superiority of the proposed model and the annotation performance get improved by 4%-6% comparing with state-of-the-art methods.
Isabelle/DOF: Design and Implementation

DOF is a novel framework for defining ontologies and enforcing them during document development and document evolution. A major goal of DOF is the integrated development of formal certification documents (e. g., for Common Criteria or CENELEC 50128) that require consistency across both formal and informal arguments. To support a consistent development of formal and informal parts of a document, we provide Isabelle/DOF, an implementation of DOF on top of Isabelle/HOL. Isabelle/DOF is integrated into Isabelle’s IDE, which allows for smooth ontology development as well as immediate ontological feedback during the editing of a document. In this paper, we give an in-depth presentation of the design concepts of DOF’s Ontology Definition Language (ODL) and key aspects of the technology of its implementation. Isabelle/DOF is the first ontology language supporting machine-checked links between the formal and informal parts in an LCF-style interactive theorem proving environment. Sufficiently annotated, large documents can easily be developed collaboratively, while ensuring their consistency, and the impact of changes (in the formal and the semi-formal content) is tracked automatically.
CAMsterdam at SemEval-2019 Task 6: Neural and graph-based feature extraction for the identification of offensive tweets

We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offensive language identification in Twitter data.Our proposed model learns to extract textual features using a multi-layer recurrent network, and then performs text classification using gradient-boosted decision trees (GBDT).A self-attention architecture enables the model to focus on the most relevant areas in the text.We additionally learn globally optimised embeddings for hashtags using node2vec, which are given as additional tweet features to the GBDT classifier.Our best model obtains 78.79% macro F1-score on detecting offensive language (subtask A), 66.32% on categorising offence types (targeted/untargeted; subtask B), and 55.36% on identifying the target of offence (subtask C).
Emotion Impacts Speech Recognition Performance

It has been established that the performance of speech recognition systems depends on multiple factors including the lexical content, speaker identity and dialect. Here we use three English datasets of acted emotion to demonstrate that emotional content also impacts the performance of commercial systems. On two of the corpora, emotion is a bigger contributor to recognition errors than speaker identity and on two, neutral speech is recognized considerably better than emotional speech. We further evaluate the commercial systems on spontaneous interactions that contain portions of emotional speech. We propose and validate on the acted datasets, a method that allows us to evaluate the overall impact of emotion on recognition even when manual transcripts are not available. Using this method, we show that emotion in natural spontaneous dialogue is a less prominent but still significant factor in recognition accuracy.
Emad at SemEval-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approaches

In this paper, the used methods and the results obtained by our team, entitled Emad, on the OffensEval 2019 shared task organized at Se-mEval 2019 are presented.The OffensEval shared task includes three sub-tasks namely Offensive language identification, Automatic categorization of offense types and Offense target identification.We participated in subtask A and tried various methods including traditional machine learning methods, deep learning methods and also a combination of the first two sets of methods.We also proposed a data augmentation method using word embedding to improve the performance of our methods.The results show that the augmentation approach outperforms other methods in terms of macro-f1.
Weakly Supervised Cervical Histopathological Image Classification Using Multilayer Hidden Conditional Random Fields

In this paper, a novel Multilayer Hidden Conditional Random Fields based weakly supervised Cervical Histopathological Image Classification framework is proposed to classify well, moderately and poorly differentiation stages of cervical cancer. First, color, texture and Deep Learning features are extracted to represent the histopathological image patches. Then, based on the extracted features, Artificial Neural Network, Support Vector Machine and Random Forest classifiers are designed to calculate the patch-level classification probability. Thirdly, effective features are selected to generate unary and binary potentials of the proposed Multilayer Hidden Conditional Random Fields framework. Lastly, using the generated potentials, the final image-level classification result is predicted by our Multilayer Hidden Conditional Random Fields model, and an accuracy of $$88\%$$ is obtained on a practical histopathological image dataset with more than 100 AQP stained samples.
Writing Anxiety among Indonesian EFL College Students: Levels, Causes, and Coping Strategies

This research aimed at investigating foreign language writing anxiety experienced by Indonesian EFL learners studying at Universitas Negeri Padang. The numbers of participants involved in this survey were 132 students comprising of sophomores, junior, and senior students. This research employed three research instruments: Second Language Writing Anxiety Inventory (SLWAI) to collect data on students’ levels of writing anxiety; Causes of Writing Anxiety Inventory (CWAI) to gauge information about sources of students’ writing anxiety; typologies of tactics to cope with writing anxiety. The result of this research reveals that the level of students’ writing anxiety is moderate. In addition, no significant relationship between students’ writing anxiety level and their academic level is revealed suggesting moderately anxious students outnumbering those with high and low writing anxiety at any academic level. In addition, the sources of students’ writing anxiety are problems with topic choice, linguistic difficulties, and lack of writing practice. The coping strategies used by students to overcome writing anxiety are preparation, positive thinking, relaxation, peer-seeking, and resignation (most-frequent to least frequent respectively).
Graph Reconstruction - Some New Developments*

The reconstruction conjecture claims that every graph on at least three vertices is uniquely determined by its collection of vertex deleted subgraphs. Even though it is one of the foremost unsolved...
AI for Ancient Games

This report summarises the Digital Ludeme Project, a recently launched 5-year research project being conducted at Maastricht University. This computational study of the world’s traditional strategy games seeks to improve our understanding of early games, their development, and their role in the spread of related mathematical ideas throughout recorded human history.
The Permutable k-means for the Bi-Partial Criterion

The here applied objective function for clustering consists of two parts, where the first one takes into account intra-cluster relations, and the second – inter-cluster ones. In the case of k -means algorithm, such bi-partial objective function combines cluster dispersions with inter-cluster similarity, to be jointly minimized. The first part only of such objective function provides the “standard” quality of clustering based on distances between objects (the well-known classical k -means). To improve the clustering quality based on the bi-partial objective function, we need to develop the permutable version of k -means algorithm. This paper shows the permutable k -means that appears to be a new type of clustering procedure.
Deformable Face Net: Learning Pose Invariant Feature with Pose Aware Feature Alignment for Face Recognition

Face recognition plays an important role in computer vision. It still remains a challenging task due to pose, expression, illumination, partial occlusion, etc. In this work, we propose a novel Deformable Face Net (DFN) to handle the pose variations in face recognition. The Deformable Face Net introduces deformable convolution modules to simultaneously learn face recognition oriented alignment and feature extraction. Specifically, two loss functions, namely displacement consistency loss (DCL) and identity consistency loss (ICL) are designed to minimize the intra-class feature variation caused by different poses. These two loss functions jointly learn pose-aware displacement fields for deformable convolutions in the DFN. Different from the existing methods, the DFN focuses on aligning features across different poses rather than frontalizing the input faces. Extensive experiments show that the proposed DFN outperforms the state-of-the-art methods, especially on the datasets with large poses.
Open Data Visualization in Danish Schools: A case Study

Increasingly public bodies and organizations are publishing Open Data for citizens to improve their quality of life and solving public problems.But having Open Data available is not enough.Public engagement is also important for successful Open Data initiatives.There is an increasing demand for strategies to actively involve the public exploiting Open Data, where not only the citizens but also school pupils and young people are able to explore, understand and extract useful information from the data, grasp the meaning of the information, and to visually represent findings.In this research paper, we investigate how we can equip our younger generation with the essential future skills using Open Data as part of their learning activities in public schools.We present the results of a survey among Danish school teachers and pupils.The survey focuses on how we can introduce Open Data visualizations in schools, and what are the possible benefits and challenges for pupils and teachers to use Open Data in their everyday teaching environment.We briefly review Copenhagen city's Open Data and existing open source software suitable for visualization, to study which open source software pupils can easily adapt to visualize Open Data and which data-sets teachers can relate to their teaching themes.Our study shows that introducing Open Data visualizations in schools make everyday teaching interesting and help improving pupils learning skills and that to actively use Open Data visualizations in schools, teachers and pupils need to boost their digital skills.