Reviewerly/exHarmony huggingface dataset:
Investigating pedagogical challenges of mobile technology to English teaching

Digital education with mobile technology is becoming a global trend. This study considers language teachers' challenges related to open distance teaching in higher education. Twenty-eight university lecturers participated in the semi-structured interviews and expressed their opinions on how mobile technologies impacted the nature of teaching and learning in tertiary education. In particular, four themes associated with teaching English in the context of mobile technologies were discussed: (1) technology mediation, (2) disruptive pedagogy, (3) English teaching, and (4) learning flexibility. The results show that mobile technology and pedagogical innovation are not challenging for teachers. Instead, psychological anxiety, extended pedagogical roles, and learning flexibility are the challenging issues. Based on the results, a theoretical framework for digital education and a learning model for digital native students are proposed. This study suggests further work to promote pedagogy that connects students with higher-level thinking.
GeCNs: Graph Elastic Convolutional Networks for Data Representation

Graph representation and learning is a fundamental problem in machine learning area. Graph Convolutional Networks (GCNs) have been recently studied and demonstrated very powerful for graph representation and learning. Graph convolution (GC) operation in GCNs can be regarded as a composition of feature aggregation and nonlinear transformation step. Existing GCs generally conduct feature aggregation on a full neighborhood set in which each node computes its representation by aggregating the feature information of all its neighbors. However, this full aggregation strategy is not guaranteed to be optimal for GCN learning and also can be affected by some graph structure noises, such as incorrect or undesired edge connections. To address these issues, we propose to integrate elastic net based selection into graph convolution and propose a novel graph elastic convolution (GeC) operation. In GeC, each node can adaptively select the optimal neighbors in its feature aggregation. The key aspect of the proposed GeC operation is that it can be formulated by a regularization framework, based on which we can derive a simple update rule to implement GeC in a self-supervised manner. Using GeC, we then present a novel GeCN for graph learning. Experimental results demonstrate the effectiveness and robustness of GeCN.
Management and Digitalization Strategy for Transforming Education Sector

The digitalization of the education sector has led to continuous learning that is beneficial for the students as well as teachers despite facing the medical emergency in the country due to the COVID-19 crisis. It was found that the impact of COVID-19 on the transformation of education has both positive and negative outlooks. The negative impact was that all educational organizations/institutions shut down and examinations got postponed or delayed, but nothing stopped totally. It was also found that due to this pandemic, the educational system has taken a new turn and got evolved in an unimagined way. Teachers and faculty members are positively adapting and managing the digitalization strategy for education and make optimum use of digital media. COVID-19 has provided an opportunity for teachers to upgrade their skills and knowledge by joining multidisciplinary courses available online, and it was also found that computer courses taught online were much more effective than theoretical subjects taught via online applications, but lengthy online lectures cause fatigue and boredom.
Mining Partially-Ordered Episode Rules in an Event Sequence

Episode rule mining is a popular data mining task for analyzing a sequence of events or symbols. It consists of identifying subsequences of events that frequently appear in a sequence and then to combine them to obtain episode rules that reveal strong relationships between events. But a key problem is that each rule requires a strict ordering of events. As a result, similar rules are treated differently, though they in practice often describe a same situation. To find a smaller set of rules that are more general and can replace numerous episode rules, this paper introduces a novel type of rules called partially-ordered episode rules, where events in a rule are partially ordered. To efficiently find all these rules in a sequence, an efficient algorithm named POERM (Partially-Ordered Episode Rule Miner) is presented. An experimental evaluation on several benchmark dataset shows that POERM has excellent performance.
Optimizing multiple non-invasive techniques (PXRF, pMS, IA) to characterize coarse-grained igneous rocks used as building stones

We present a workflow to conduct a full characterization of medium to coarse-grained igneous rocks, using portable, non-invasive, and reproducible approaches. This includes: (i) Image Analysis (IA) to quantify mineral phase proportions, grain size distribution using the Weka trainable machine learning algorithm. (ii) Portable X-ray fluorescence spectrometer (PXRF, Bruker Tracer IV) to quantify the whole-rock's chemical composition. For this purpose, a specific calibration method dedicated to igneous rocks using the open-source CloudCal app was developed. It was then validated for several key elements (Si, Al, K, Ti, Ca, Fe, Mn, Sr, Ga, Ba, Rb, Zn, Nb, Zr, and Y) by analyzing certified standard reference igneous rocks. (iii) Portable Magnetic Susceptibilimeter (pMS, Bartington MS2K system) to constrain the mineralogical contribution of the samples. The operational conditions for these three methods were tested and optimized by analyzing five unprepared surfaces of igneous rocks ranging from a coarse-grained alkaline granite to a fine-grained porphyric diorite and hence, covering variable grain sizes, mineralogical contents, and whole-rock geochemical compositions. For pMS and PXRF tools, one hundred analyses were conducted as a 10 cm × 10 cm square grid on each sample. Bootstrap analysis was implemented to establish the best grid size sampling to reach an optimized reproducibility of the whole-rock signature. For PXRF analysis, averaged compositions were compared to PXRF analysis on press-pellets and laboratory WD-XRF analysis on fused disk and solution ICP-OES (for major) and solution-ICPMS (for trace element concentrations). Ultimately, this workflow was applied in the field on granitoids from three Roman quarrying sites in the Lavezzi archipelago (southern Corsica) and tested against the Bonifacio granitic War Memorial, for which its provenance is established. Our results confirm this information and open the door to geoarchaeological provenance studies with a high spatial resolution.
Facial Attribute Transformers for Precise and Robust Makeup Transfer

In this paper, we address the problem of makeup transfer, which aims at transplanting the makeup from the reference face to the source face while preserving the identity of the source. Existing makeup transfer methods have made notable progress in generating realistic makeup faces, but do not perform well in terms of color fidelity and spatial transformation. To tackle these issues, we propose a novel Facial Attribute Transformer (FAT) and its variant Spatial FAT for high-quality makeup transfer. Drawing inspirations from the Transformer in NLP, FAT is able to model the semantic correspondences and interactions between the source face and reference face, and then precisely estimate and transfer the facial attributes. To further facilitate shape deformation and transformation of facial parts, we also integrate thin plate splines (TPS) into FAT, thus creating Spatial FAT, which is the first method that can transfer geometric attributes in addition to color and texture. Extensive qualitative and quantitative experiments demonstrate the effectiveness and superiority of our proposed FATs in the following aspects: (1) ensuring high-fidelity color transfer; (2) allowing for geometric transformation of facial parts; (3) handling facial variations (such as poses and shadows) and (4) supporting high-resolution face generation.
Learning with Memory-based Virtual Classes for Deep Metric Learning

The core of deep metric learning (DML) involves learning visual similarities in high-dimensional embedding space. One of the main challenges is to generalize from seen classes of training data to unseen classes of test data. Recent works have focused on exploiting past embeddings to increase the number of instances for the seen classes. Such methods achieve performance improvement via augmentation, while the strong focus on seen classes still remains. This can be undesirable for DML, where training and test data exhibit entirely different classes. In this work, we present a novel training strategy for DML called MemVir. Unlike previous works, MemVir memorizes both embedding features and class weights to utilize them as additional virtual classes. The exploitation of virtual classes not only utilizes augmented information for training but also alleviates a strong focus on seen classes for better generalization. Moreover, we embed the idea of curriculum learning by slowly adding virtual classes for a gradual increase in learning difficulty, which improves the learning stability as well as the final performance. MemVir can be easily applied to many existing loss functions without any modification. Extensive experimental results on famous benchmarks demonstrate the superiority of MemVir over state-of-the-art competitors. Code of MemVir is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Privacy Leakage of SIFT Features via Deep Generative Model Based Image Reconstruction

Many practical applications, e.g., content based image retrieval and object recognition, heavily rely on the local features extracted from the query image. As these local features are usually exposed to untrustworthy parties, the privacy leakage problem of image local features has received increasing attention in recent years. In this work, we thoroughly evaluate the privacy leakage of Scale Invariant Feature Transform (SIFT), which is one of the most widely-used image local features. We first consider the case that the adversary can fully access the SIFT features, i.e., both the SIFT descriptors and the coordinates are available. We propose a novel end-to-end, coarse-to-fine deep generative model for reconstructing the latent image from its SIFT features. The designed deep generative model consists of two networks, where the first one attempts to learn the structural information of the latent image by transforming from SIFT features to Local Binary Pattern (LBP) features, while the second one aims to reconstruct the pixel values guided by the learned LBP. Compared with the state-of-the-art algorithms, the proposed deep generative model produces much improved reconstructed results over three public datasets. Furthermore, we address more challenging cases that only partial SIFT features (either SIFT descriptors or coordinates) are accessible to the adversary. It is shown that, if the adversary can only have access to the SIFT descriptors while not their coordinates, then the modest success of reconstructing the latent image might be achieved for highly-structured images (e.g., faces) and probably would fail in general settings. In addition, the latent image usually can be reconstructed with acceptable quality solely from the SIFT coordinates. Our results would suggest that the privacy leakage problem can be avoided to a certain extent if the SIFT coordinates can be well protected.
Factors Affecting the Success of Internet of Things for Enhancing Quality and Efficiency Implementation in Hospitals Sector in Jordan During the Crises of Covid-19

The rapid growth of Internet of things (IoT) has vigorously affected healthcare applications over the last decade particularly in the health information technology sector. This is done through improving healthcare delivery by increasing efficiency, reducing cost and time involved. However, the applicability of DeLone and McLean IS success model achievement model for health welfare implementation in IoT remains unknown. Thus, this research amid to establish the significance of IS application and its associations with user intention, client fulfilment and net advantages of IoT in five hospitals in a developing country such as Jordan. Furthermore, this research emphasized on the technological and infrastructural facilities factors that are considered imperative in improving the social healthcare procedures of resident's health care. Hope that this discovery would convince the administration of hospitals to focus on important viewpoints that impact the utilization of Internet of things (IoT) in healthcare services. The fundamental of this research is to determine the variables that impact the achievement of Internet of things (IoT) in the execution of human services from the viewpoint of medical clinic residents in Jordan. The fundamental point of the current paper was to examine the features that impact the achievement of Internet of things (IoT) in the implementation of health care from the perspective of hospitals in Jordan. An aggregate of 700 questionnaires will be circulated to five (5) hospitals in various regions in Jordan, out of which 417 questionnaire surveys returned, representing a response rate of between 50 and 60 per cent. This study presented elements of technological and infrastructural features of technological and infrastructural factors in the DeLone and McLean success model data framework and how these variables impacted user intention and citizen satisfaction as parts of medicinal healthcare services in Internet of things (IoT). This research used the PLS-SEM analysis techniques to test fourteen hypotheses. Variables are technological and infrastructural factors, estimation of data, framework quality and nature of administration. The research results supported technological and infrastructural factors, quality of the data and system service quality as significant aspects affecting the successful implementation of the Internet of things (IoT) for health care in Jordan. The study extended the DeLone and McLean IS success model by implementing variables of technological and infrastructural facilities factors impact that are critical factors in the Arab regions.
Deep learning-based Technique for Image Tamper Detection

The objective of the research work is to thoroughly study existing methodologies for detecting passive image tampering using deep learning techniques. Here, survey is conducted predominantly focusing on tampering detection using deep learning techniques. Different image tampering datasets such as MICC, CASIA, and UCID, etc. have been used by existing tampering detection methodologies for validating tampering detection accuracies. From the study, it is identified that not all method obtains good accuracies for all kind of attack such as splicing, compression, rotation, resampling, copy-move, etc. From the study it is identified for detecting tampering efficiently it is important to design an efficient deep learning-based feature extraction mechanism that learns correlation among pixels more efficiently. In contrast with another recent survey, this paper covers significant developments in passive image forensic analysis methods adopting deep learning techniques. Existing methodologies are studied concerning benefit, limitation, the dataset used, and kind of attack considered. The paper further highlights future challenges and open issues, and also provides the possible future solution in building efficient tampering detection mechanism using deep learning technique. Experiment outcomes show good performance in connection with TPR, FPR, and F1-Score.
A Comparison of Self-Supervised Speech Representations As Input Features For Unsupervised Acoustic Word Embeddings

Many speech processing tasks involve measuring the acoustic similarity between speech segments. Acoustic word embeddings (AWE) allow for efficient comparisons by mapping speech segments of arbitrary duration to fixed-dimensional vectors. For zero-resource speech processing, where unlabelled speech is the only available resource, some of the best AWE approaches rely on weak top-down constraints in the form of automatically discovered word-like segments. Rather than learning embeddings at the segment level, another line of zero-resource research has looked at representation learning at the short-time frame level. Recent approaches include self-supervised predictive coding and correspondence autoencoder (CAE) models. In this paper we consider whether these frame-level features are beneficial when used as inputs for training to an unsupervised AWE model. We compare frame-level features from contrastive predictive coding (CPC), autoregressive predictive coding and a CAE to conventional MFCCs. These are used as inputs to a recurrent CAE-based AWE model. In a word discrimination task on English and Xitsonga data, all three representation learning approaches outperform MFCCs, with CPC consistently showing the biggest improvement. In cross-lingual experiments we find that CPC features trained on English can also be transferred to Xitsonga.
Telling the What while Pointing to the Where: Multimodal Queries for Image Retrieval

Most existing image retrieval systems use text queries as a way for the user to express what they are looking for. However, fine-grained image retrieval often requires the ability to also express where in the image the content they are looking for is. The text modality can only cumbersomely express such localization preferences, whereas pointing is a more natural fit. In this paper, we propose an image retrieval setup with a new form of multimodal queries, where the user simultaneously uses both spoken natural language (the what) and mouse traces over an empty canvas (the where) to express the characteristics of the desired target image. We then describe simple modifications to an existing image retrieval model, enabling it to operate in this setup. Qualitative and quantitative experiments show that our model effectively takes this spatial guidance into account, and provides significantly more accurate retrieval results compared to text-only equivalent systems.
Composable Augmentation Encoding for Video Representation Learning

We focus on contrastive methods for self-supervised video representation learning. A common paradigm in contrastive learning is to construct positive pairs by sampling different data views for the same instance, with different data instances as negatives. These methods implicitly assume a set of representational invariances to the view selection mechanism (e.g., sampling frames with temporal shifts), which may lead to poor performance on downstream tasks which violate these invariances (fine-grained video action recognition that would benefit from temporal information). To overcome this limitation, we propose an ‘augmentation aware’ contrastive learning framework, where we explicitly provide a sequence of augmentation parameterisations (such as the values of the time shifts used to create data views) as composable augmentation encodings (CATE) to our model when projecting the video representations for contrastive learning. We show that representations learned by our method encode valuable information about specified spatial or temporal augmentation, and in doing so also achieve state-of-the-art performance on a number of video benchmarks.
Combinatorial Clustering Based on an Externally-Defined One-Hot Constraint

Recently, a clustering method using a combinatorial optimization problem, called combinatorial clustering, has been drawing attention due to the rapid spreads of quantum annealing and simulated annealing. Combinatorial clustering is performed by minimizing an objective function under a condition to satisfy a constraint. The objective function and the constraint function are generally formulated to a unified objective function of a QUBO (Quadratic Unconstrained Binary Optimization) problem by the method of the Lagrange multiplier.Although the Lagrange multiplier needs to be large enough to avoid violating the one-hot constraint, the Lagrange multiplier usually cannot be set appropriately due to the limitation of the bit precision. The latest quantum annealer can handle values represented by only six or fewer bits. Even conventional computing systems cannot handle the larger value of the Lagrange multiplier as the number of data points increases.To solve this problem, this paper proposes combinatorial clustering that overcomes the limitations of the method of the Lagrange multiplier. The proposed method uses a QUBO solver that can externally define the one-hot constraint independent from the objective function, and the externally-defined constraint is satisfied by the bit-flip operations. Since the constraint function is not included in the objective function, it is no longer needed to determine the Lagrange multiplier. The experimental results show that the proposed method can improve the quality of clustering results.
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation

Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success in many complex tasks, these algorithms need a large number of samples to learn meaningful policies. In this letter, we present a task for navigating a marble to the center of a circular maze. While this system is very intuitive and easy for humans to solve, it can be very difficult and inefficient for standard reinforcement learning algorithms to learn meaningful policies. We present a model that learns to move a marble in the complex environment within minutes of interacting with the real system. Learning consists of initializing a physics engine with parameters estimated using data from the real system. The error in the physics engine is then corrected using Gaussian process regression, which is used to model the residual between real observations and physics engine simulations. The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon. To the best of our knowledge, this is the first time that a hybrid model consisting of a full physics engine along with a statistical function approximator has been used to control a complex physical system in real-time using nonlinear model-predictive control (NMPC).
Structural Learning Techniques for Bayesian Attack Graphs in Cyber Physical Power Systems

Updating the structure of attack graph templates based on real-time alerts from Intrusion Detection Systems (IDS), in an Industrial Control System (ICS) network, is currently done manually by security experts. But, a highly-connected smart power systems, that can inadvertently expose numerous vulnerabilities to intruders for targeting grid resilience, needs automatic fast updates on learning attack graph structures, instead of manual intervention, to enable fast isolation of compromised network to secure the grid. Hence, in this work, we develop a technique to first construct a prior Bayesian Attack Graph (BAG) based on a predefined threat model and a synthetic communication network for a cyber-physical power system. Further, we evaluate a few score-based and constraint-based structural learning algorithms to update the BAG structure based on real-time alerts, based on scalability, data dependency, time complexity and accuracy criteria.