Reviewerly/exHarmony huggingface dataset:
Deep Knowledge Tracing with Side Information

Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models’ ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing.
Combining Distant and Direct Supervision for Neural Relation Extraction

Iz Beltagy, Kyle Lo, Waleed Ammar. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Retrieval of Structured and Unstructured Data with vitrivr

With the increase in sensory capability of mobile devices, the data that can be generated and used in a lifelogging context gets increasingly diverse. Such data is special in the context of multimedia, not only because of its close personal relationship with its originator, but also because of its diverse multimodality and its composition from structured, semi-structured, and unstructured data. This diversity poses retrieval challenges that are unique to lifelog data but which also have implications for retrieval activity in other multimedia domains. In this paper, we present the extensions made to the vitrivr open-source multimedia retrieval stack, in order to address some of these unique lifelogging challenges. For the participation to the 2019 Lifelog Search Challenge (LSC), we have extended vitrivr with the capability to process Boolean query expressions alongside content-based query descriptions in order to leverage the structural diversity inherent to lifelog data.
Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms.
Avatar Embodiment Enhances Haptic Confidence on the Out-of-Body Touch Illusion

In the real world, our bodies influence how we perceive ourselves and how others perceive us. Our body can also affect estimations of object sizes and distances. But how does our body affect our haptic experience? Here, we examined the modulation of a visuo-haptic illusion of touch on a virtual stick in virtual reality, when participants were embodied in an avatar and when they were not. During the experiments participants (n = 49) received successions of three taps delivered from two independent controllers while they saw visual stimuli presented sequentially along the virtual stick. The stimulation pattern resulted in a robust illusion of tapping directly on the virtual stick. After each trial, participants were asked to report where they perceived the taps. We found that participants in both the body and no-body conditions displaced the second tap toward the center of the stick, and reported similar levels of certainty about their reported location. However, the illusion of touch on the stick, as measured by the reported location of the tap, was significantly stronger for those who had a virtual body than those who did not. Therefore, our study shows that avatar embodiment can change haptic perception.
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study imageto-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the image-to-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors’ own photos), our model can generate high-quality facial expression videos of which about 50% are labeled as real by Amazon Mechanical Turk workers.
Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM

The increasing number of breast cancer in recent years has attracted numerous researchers' attention.Several techniques of Computer Aided Diagnosis System have been proposed as alternative solutions to diagnose breast cancer.The flaw of simply using the naked eye to see the differences between normal and with cancer mammogram images makes the texture analysis play an important role in classifying breast cancer.In this study, the results of the classification were compared using various methods of texture analysis in extracting a feature of the mammogram image.Some texture analysis methods, including first order, which consist of GLCM, GLRLM, and GLDM, have successfully extracted features based on their characteristics.The statistical features of these methods are used as input for the ECOC SVM classification, which three kernel comparisons; linear, RBF, and polynomial, build the classification.The results show that the best kernel is polynomial kernels with statistical features built by GLRLM with 93.9757% accuracy value.
How is Digitalisation Affecting the Flexibility and Openness of Higher Education Provision? Results of a Global Survey Using a New Conceptual Model

The adoption of open, online, flexible and technology-enhanced modes of learning (in short: of OOFAT) differs by higher education institution, despite the general cries of revolution and disruption due to digitalisation. This paper presents a new conceptual model for framing difference in three key educational processes (content, delivery and recognition) related to the potential of digitalisation to make these processes more flexible and more open. It is based on the results of a global survey of 69 higher education providers. The findings reveal six distinct archetypes of technology-enhanced higher education which vary according to the extent to which digitalisation is harnessed for content, delivery and recognition, and suggest different institutional strategies of digital adoption. It is hoped that this contribution will support comparative analysis of digitalisation strategies and peer learning between institutions.
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks

With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. Following recent progresses along this line, we investigate a number of watermark-based DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on the ownership verification by forging counterfeit watermarks. It is shown that ambiguity attacks pose serious threats to existing DNN watermarking methods. As remedies to the above-mentioned loophole, this paper proposes novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. The gist of embedding digital passports is to design and train DNN models in a way such that, the DNN inference performance of an original task will be significantly deteriorated due to forged passports. In other words, genuine passports are not only verified by looking for the predefined signatures, but also reasserted by the unyielding DNN model inference performances. Extensive experimental results justify the effectiveness of the proposed passport-based DNN ownership verification schemes. Code and models are available at https://github.com/kamwoh/DeepIPR
Answering Conversational Questions on Structured Data without Logical Forms

We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation.We encode tables as graphs using a graph neural network model based on the Transformer architecture.The answers are then selected from the encoded graph using a pointer network.This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references.We demonstrate the validity of this approach with competitive results on the Sequential Question Answering (SQA) task (Iyyer et al., 2017).
Association of Microcalcification Clusters with Short-term Invasive Breast Cancer Risk and Breast Cancer Risk Factors

Abstract Using for-presentation and for-processing digital mammograms, the presence of microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for microcalcification cluster detection from for-presentation digital mammograms. Here, we focus on digitised mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative mammogram between three months and two years before diagnosis, from which we measured microcalcifications) and 728 controls (also with prior mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of microcalcification clusters and short-term (within 3–24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of microcalcification clusters (p = 4 × 10 −04 ). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.
Applying landmarks to enhance memory-based collaborative filtering

Memory-based Collaborative Filtering (CF) has been a widely used approach for personalised recommendation with considerable success in many applications. An important issue regarding memory-based CF lies in similarity computation: the sparsity of the rating matrix leads to similarity computations based on few co-rated items between users, resulting in high sensitive predictions. Additionally, the 'sparse' similarity computation has high computational cost, due to the dimensionality of the item space. In this paper, we pursue both these issues. We propose a new model to compute similarity by representing users (or items) through their distances to preselected users, named landmarks. Such user modelling allows the introduction of more ratings into similarity computations through transitive relations created by the landmarks. Unlike conventional memory-based CF, the proposal builds a new user space defined by distances to landmarks, avoiding sensitivity in similarity computations. Findings from our experiments show that the proposed modelling achieves better accuracy than the 'sparse' similarity representation in all tested datasets, and has also yielded competitive accuracy results against the compared model-based CF algorithms. Furthermore, the proposed implementation has beaten all compared methods in terms of computational performance, becoming a promising alternative to memory-based CF algorithms for large datasets.
The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

Preprocessing is an essential task for sentiment analysis since textual information carries a lot of noisy and unstructured data. Both stemming and stopword removal are pretty popular preprocessing techniques for text classification. However, the prior research gives different results concerning the influence of both methods toward accuracy on sentiment classification. Therefore, this paper conducts further investigations about the effect of stemming and stopword removal on Indonesian language sentiment analysis. Furthermore, we propose four preprocessing conditions which are with using both stemming and stopword removal, without using stemming, without using stopword removal, and without using both. Support Vector Machine was used for the classification algorithm and TF-IDF as a weighting scheme. The result was evaluated using confusion matrix and k-fold cross-validation methods. The experiments result show that all accuracy did not improve and tends to decrease when performing stemming or stopword removal scenarios. This work concludes that the application of stemming and stopword removal technique does not significantly affect the accuracy of sentiment analysis in Indonesian text documents.
What’s up with WhatsApp? A Critical Analysis of Mobile Instant Messaging Research in Language Learning

This paper is devoted to exploring the way how the mobile instant messaging WhatsApp is deployed to enhance the learning of a second or foreign language. Therefore, this study set out to investigate the empirical studies related to WhatsApp and language learning published in peer-reviewed journals. Thirty-seven studies were selected after a four-phase article identification procedure and a systematic review was conducted to investigate the effectiveness of WhatsApp on language learning. The analysis focused on the keywords, sample sizes, participants, data sets, duration, and language learning benefits of the studies. The results have shown that WhatsApp has been used diversely in language learning. The studies found evidence that WhatsApp can be used to improve the four language skills (i.e. reading, listening, writing, and speaking), integrated language skills, and vocabulary. Moreover, WhatsApp was found to be effective in increasing motivation and language attitudes, fostering learner autonomy, increasing interaction, and lowering language anxiety. The study also provided future research directions and recommendations for practice concerning how to appropriately employ mobile instant messaging in language learning.
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue

Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018).
The effect of innovation and strategic planning on enhancing organizational performance of Dubai Police

Purpose The purpose of this study is to examine empirically the joint effect of innovation and strategic planning on organizational performance of Dubai Police. Design/methodology/approach To examine the hypothesized model of the study, a survey questionnaire was used. The data were collected from the general department of total quality of the Dubai Police. The total number of questionnaires distributed was 150, out of which only 95 usable questionnaires were returned and ready for analysis. The regression approach through SPSS was used to analyze the data and test the hypotheses. Findings The statistical results confirm the effect of strategic planning and innovation on the organizational performance of Dubai Police. Research limitations/implications Further details and valuable implications are discussed throughout the study. The results have many practical implications, in that it can help managers to make proper decisions when deciding to implement innovation and strategic planning in their organizations. Originality/value This study is a rare and unique empirical study that examines the effect of innovation and strategic planning on the organizational performance of Dubai Police.
TensorLog: A Probabilistic Database Implemented Using Deep-Learning Infrastructure

We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neural-network infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning frameworks to be used for tuning the parameters of a probabilistic logic. The integration with these frameworks enables use of GPU-based parallel processors for inference and learning, making TensorLog the first highly parallellizable probabilistic logic. Experimental results show that TensorLog scales to problems involving hundreds of thousands of knowledge-base triples and tens of thousands of examples.
Goal-Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space

In this paper, we propose a goal-oriented obstacle avoidance navigation system based on deep reinforcement learning that uses depth information in scenes, as well as goal position in polar coordinates as state inputs. The control signals for robot motion are output in a continuous action space. We devise a deep deterministic policy gradient network with the inclusion of depth-wise separable convolution layers to process the large amounts of sequential depth image information. The goal-oriented obstacle avoidance navigation is performed without prior knowledge of the environment or a map. We show that through the proposed deep reinforcement learning network, a goal-oriented collision avoidance model can be trained end-to-end without manual tuning or supervision by a human operator. We train our model in a simulation, and the resulting network is directly transferred to other environments. Experiments show the capability of the trained network to navigate safely around obstacles and arrive at the designated goal positions in the simulation, as well as in the real world. The proposed method exhibits higher reliability than the compared approaches when navigating around obstacles with complex shapes. The experiments show that the approach is capable of avoiding not only static, but also dynamic obstacles.
Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem, the high utility pattern mining approaches from non-binary database have been proposed and actively studied by many researchers. Lately, new data is progressively created with the passage of time in diverse area such as biometric data of a patient diagnosed in a medical device and log data of an internet user, and the volume of a database is gradually increasing. A database with these characteristics is called a dynamic database. Under these circumstances, high utility mining techniques suitable for analyzing dynamic databases have recently been extensively studied. In this paper, we propose a new list-based algorithm that mines high utility patterns considering the arrival time of each transaction in an incremental database environment. That is, our algorithm efficiently performs pattern pruning by using a damped window model that considers the importance of the previously inputted data lower than that of recently inserted data and identifies high utility patterns. Experimental results indicate that our proposed method has better performance than the state-of-the-art techniques in terms of runtime, memory, and scalability.
Virtual Reality: Development of an Integrated Learning Environment for Education

The new paradigm shift of education where learners should learn real-life scenarios and solve real-life problems has created a major challenge.Students are passive and detached and may struggle to see the relevance of what they are learning to their lives.The recent growth of emerging technologies has launched terms such as virtual reality (VR), augmented reality (AR) and mixed reality (MR) into mainstream topic.Virtual reality, an integrated, hands-on tool for learning, can play a unique role in addressing these educational challenges.The proposed research demonstrates using the knowledge of integrated disciplines with virtual reality.COSPACES and MERGE cube add-on have been used to develop a simulation platform to teach water cycle process for young kids.This type of integration develops learners' 21st century skills to be decision makers, problem solvers, lifelong learners, creators, innovators, and to think critically in solving problems.
py-pde: A Python package for solving partial differential equations

Partial differential equations (PDEs) play a central role in describing the dynamics of physical systems in research and in practical applications.However, equations appearing in realistic scenarios are typically non-linear and analytical solutions rarely exist.Instead, such systems are solved by numerical integration to provide insight into their behavior.Moreover, such investigations can motivate approximative solutions, which might then lead to analytical insight.