Reviewerly/exHarmony huggingface dataset:
Digital Transformation and Cybersecurity in the Context of COVID-19 Proliferation

The Coronavirus pandemic has given a powerful impetus to the mass adoption of digital technologies, which will bring unprecedented changes in the social and economic fabric of our society. More than ever, the digitalization of companies is accelerating. This is reflected in the massive dematerialization of information systems towards the cloud, the explosion of the Internet of Things, and the accumulation of data from users in Big Data. The ongoing measures of social exclusion in most countries of the world have forced a large part of the world's trade, in terms of goods and services, to go online. Soon, the world is likely to see further explosive growth in the capitalization of online service providers as commodity companies decline [1]. At the same time, these openings are opportunities for companies to deploy innovative and more efficient services.
From OpenAPI Fragments to API Pattern Primitives and Design Smells

In the past few years, the OpenAPI Specification (OAS) has emerged as a standard description language for accurately modeling Web APIs. Today, thousands of OpenAPI descriptions can be found by mining open source repositories. In this paper, we attempt to exploit these artifacts to extract commonly occurring building blocks used in Web API structures, in order to assist Web API designers in their modelling task. Our work is based on a fragmentation mechanism, that starts from OpenAPI descriptions of Web APIs to extract their structures, then fragment these structures into smaller blocks. This approach enabled us to extract a large dataset of reoccurring fragments from a collection of 6619 API specifications. Such fragments have been found multiple times in the same or across different APIs. We have classified the most reoccurring fragments into four pattern primitives used to expose in the API access to collections of items. We distinguish for each primitive variants from design smells. This classification is based on the specific combinations of operations associated with the collection items and on an in-depth analysis of their natural language labels and descriptions. The resulting pattern primitives are intended to support designers who would like to introduce one or more collections for a specific class of items in their HTTP-based API.
Disruptive innovation, the episteme and technology-enhanced learning in higher education

This paper combines the theory of disruptive innovation with Foucault's concept of the episteme, in order to analyse the extent to which the integration of Foucauldian analysis clarifies understandings of disruptive innovation; the process by which innovation happens; and its applications in higher education. The theory of disruptive innovation is summarized, as is the episteme, and the idea of skeuomorphic design is used to link disruptive innovation and the episteme, showing how innovation can happen. Disruptive innovation, the episteme and skeuomorphic design are applied to three, specific technologies – Second Life, the massive open online course and the virtual learning environment – arguing that all three offer little or no innovation. The paper contributes to studies on innovation in technology-enhanced learning by applying a novel theoretical framework with the potential for new and predictive insights. The paper links disruptive innovation with Foucault's concept of the episteme and with skeuomorphic design to argue for the emergence of a new, neoliberal episteme in which technology itself is central.
An Assessment of Nature-Inspired Algorithms for Text Feature Selection

This paper provides a comprehensive assessment of feature selection (FS) methods that are originated from nature-inspired (NI) meta-heuristics, where two well-known filter-based FS methods are also included for comparison. The performances of the considered methods are compared on two different high-dimensional and real-world text datasets against the accuracy, the number of selected features, and computation time. This study differs from existing studies in terms of the extent of experimental analyses performed under different circumstances where classifier, feature model, and term weighting scheme are different. The results of the extensive experiments indicate that NI algorithms produce slightly different results than filter-based methods for the problem of the text FS. However, filter-based methods often provide better results by using a lower number of features and computation times.
Theoretical model of solar incident angle for an optionally oriented fixed flat surface

The apparent position of the Sun in relation to any surface is defined by the solar incident angle. The solar incident angle forms the vector of the position of the Sun and the vector of normal to the observed surface. The spatial position of the aforementioned vectors is determined by solar coordinates, which include: declination, latitude, hour angle, longitude, solar elevation angle, solar azimuth angle, azimuth angle of the observed surface, angle of inclination of the observed surface in the north-south direction and angle of inclination of the observed surface in the east-west direction. This article describes in detail the theoretical model for determining the solar incident angle for an arbitrarily placed fixed, flat surface using vector algebra and analytical geometry, as well as solar coordinates. The study model, which was developed by combining, modifying, and supplementing existing models, can be used to determine the thermal performance of solar devices such as: solar collectors, PV panels, and hybrid solar systems.
Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Abstract The COVID-19 is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to discard the masked face region. Next, we apply pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.
FACTORS CONTRIBUTING TO STUDENTS DIFFICULTIES IN ACADEMIC WRITING CLASS: STUDENTS PERCEPTIONS

Writing is one of the most difficult skills in learning English as a Second Language (ESL) or English Foreign Language (EFL). Many language learners encountered difficulties from the writing process. At the tertiary level, students are required to produce academic essays, which are difficult and may frustrate students. The purpose of this study was to analyze factors contributing to students difficulties in an Academic Writing course based on the students perceptions. This study used a qualitative method. The instruments used were open-ended questionnaires and interview protocol. In this study, all the participants answers were coded and analyzed descriptively. The participants were 22 students who were taking Academic Writing class for repeaters in Semester I, 2018/2019 academic year, in an English Language Education Program, in a university in Central Java, Indonesia. Questionnaire data were coded and classified by the researcher. In conducting the interview, data were audio-recorded and transcribed. Those data were analyzed to answer the research questions in this study. Based on the findings, there were two factors that caused students difficulties in writing academically. The two factors were internal and external. Internal factors consisted of self-motivation, self-confidence, lack of knowledge and feeling of under pressure. External factors consisted of the teachers teaching style, classroom atmosphere, materials, and writing aspects. This research hopefully can help teachers and instructors in developing appropriate teaching techniques in Academic Writing and minimize students' difficulties in Academic Writing.
Developing Visual-Assisted Decision Support Systems across Diverse Agricultural Use Cases

Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise uncertainty and are evaluated with end-users. To address these gaps, we developed six web-based visual-assisted DSSs for various agricultural use cases, including biological efficacy correlation analysis, water stress and irrigation requirement analysis, product price prediction, etc. We then evaluated our DSSs with domain experts, focusing on usability, workload, acceptance and trust. Results showed that our systems were easy to use and understand, and participants perceived them as highly performant, even though they required a slightly high mental demand, temporal demand and effort. We also published the source code of our proposed systems so that they can be re-used or adapted by the agricultural community.
Modeling long-term dependencies and short-term correlations in patient journey data with temporal attention networks for health prediction

Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive visits into account, where irregular time intervals, incorporated as auxiliary information, are fed into health prediction models to capture latent progressive patterns of patient journeys. We present a novel deep neural network with four modules to take into account the contributions of various variables for health prediction: i) the Stacked Attention module strengthens the deep semantics in clinical events within each patient journey and generates visit embeddings, ii) the Short-Term Temporal Attention module models short-term correlations between consecutive visit embeddings while capturing the impact of time intervals within those visit embeddings, iii) the Long-Term Temporal Attention module models long-term dependencies between visit embeddings while capturing the impact of time intervals within those visit embeddings, iv) and finally, the Coupled Attention module adaptively aggregates the outputs of Short-Term Temporal Attention and Long-Term Temporal Attention modules to make health predictions. Experimental results on MIMIC-III demonstrate superior predictive accuracy of our model compared to existing state-of-the-art methods, as well as the interpretability and robustness of this approach. Furthermore, we found that modeling short-term correlations contributes to local priors generation, leading to improved predictive modeling of patient journeys.
Dynamic Sparse Training for Deep Reinforcement Learning

Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions with the environment. This leads to a long training time for dense neural networks to achieve good performance. Hence, prohibitive computation and memory resources are consumed. Recently, learning efficient DRL agents has received increasing attention. Yet, current methods focus on accelerating inference time. In this paper, we introduce for the first time a dynamic sparse training approach for deep reinforcement learning to accelerate the training process. The proposed approach trains a sparse neural network from scratch and dynamically adapts its topology to the changing data distribution during training. Experiments on continuous control tasks show that our dynamic sparse agents achieve higher performance than the equivalent dense methods, reduce the parameter count and floating-point operations (FLOPs) by 50%, and have a faster learning speed that enables reaching the performance of dense agents with 40−50% reduction in the training steps.
Doubly Sparse Asynchronous Learning for Stochastic Composite Optimization

Parallel optimization has become popular for large-scale learning in the past decades. However, existing methods suffer from huge computational costs, memory usage, and communication burden in high-dimensional scenarios. To address the challenges, we propose a new accelerated doubly sparse asynchronous learning (DSAL) method for stochastic composite optimization, under which two algorithms are proposed on shared-memory and distributed-memory architecture respectively, which only conducts gradient descent on the nonzero coordinates (data sparsity) and active set (model sparsity). The proposed algorithm can converge much faster and achieve significant speedup by simultaneously enjoying the sparsity of the model and data. Moreover, by sending the gradients on the active set only, communication costs are dramatically reduced. Theoretically, we prove that the proposed method achieves the linear convergence rate with lower overall complexity and can achieve the model identification in a finite number of iterations almost surely. Finally, extensive experimental results on benchmark datasets confirm the superiority of our proposed method.
On Preferred Abductive Explanations for Decision Trees and Random Forests

Abductive explanations take a central place in eXplainable Artificial Intelligence (XAI) by clarifying with few features the way data instances are classified. However, instances may have exponentially many minimum-size abductive explanations, and this source of complexity holds even for ``intelligible'' classifiers, such as decision trees. When the number of such abductive explanations is huge, computing one of them, only, is often not informative enough. Especially, better explanations than the one that is derived may exist. As a way to circumvent this issue, we propose to leverage a model of the explainee, making precise her / his preferences about explanations, and to compute only preferred explanations. In this paper, several models are pointed out and discussed. For each model, we present and evaluate an algorithm for computing preferred majoritary reasons, where majoritary reasons are specific abductive explanations suited to random forests. We show that in practice the preferred majoritary reasons for an instance can be far less numerous than its majoritary reasons.
Learning First-Order Rules with Differentiable Logic Program Semantics

Learning first-order logic programs (LPs) from relational facts which yields intuitive insights into the data is a challenging topic in neuro-symbolic research. We introduce a novel differentiable inductive logic programming (ILP) model, called differentiable first-order rule learner (DFOL), which finds the correct LPs from relational facts by searching for the interpretable matrix representations of LPs. These interpretable matrices are deemed as trainable tensors in neural networks (NNs). The NNs are devised according to the differentiable semantics of LPs. Specifically, we first adopt a novel propositionalization method that transfers facts to NN-readable vector pairs representing interpretation pairs. We replace the immediate consequence operator with NN constraint functions consisting of algebraic operations and a sigmoid-like activation function. We map the symbolic forward-chained format of LPs into NN constraint functions consisting of operations between subsymbolic vector representations of atoms. By applying gradient descent, the trained well parameters of NNs can be decoded into precise symbolic LPs in forward-chained logic format. We demonstrate that DFOL can perform on several standard ILP datasets, knowledge bases, and probabilistic relation facts and outperform several well-known differentiable ILP models. Experimental results indicate that DFOL is a precise, robust, scalable, and computationally cheap differentiable ILP model.
MetaFinger: Fingerprinting the Deep Neural Networks with Meta-training

As deep neural networks (DNNs) play a critical role in various fields, the models themselves hence are becoming an important asset that needs to be protected. To achieve this, various neural network fingerprint methods have been proposed. However, existing fingerprint methods fingerprint the decision boundary by adversarial examples, which is not robust to model modification and adversarial defenses. To fill this gap, we propose a robust fingerprint method MetaFinger, which fingerprints the inner decision area of the model by meta-training, rather than the decision boundary. Specifically, we first generate many shadow models with DNN augmentation as meta-data. Then we optimize some images by meta-training to ensure that only models derived from the protected model can recognize them. To demonstrate the robustness of our fingerprint approach, we evaluate our method against two types of attacks including input modification and model modification. Experiments show that our method achieves 99.34% and 97.69% query accuracy on average, surpassing existing methods over 30%, 25% on CIFAR-10 and Tiny-ImageNet, respectively. Our code is available at https://github.com/kangyangWHU/MetaFinger.
SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech

The recent progress in non-autoregressive text-to-speech (NAR-TTS) has made fast and high-quality speech synthesis possible. However, current NAR-TTS models usually use phoneme sequence as input and thus cannot understand the tree-structured syntactic information of the input sequence, which hurts the prosody modeling. To this end, we propose SyntaSpeech, a syntax-aware and light-weight NAR-TTS model, which integrates tree-structured syntactic information into the prosody modeling modules in PortaSpeech. Specifically, 1) We build a syntactic graph based on the dependency tree of the input sentence, then process the text encoding with a syntactic graph encoder to extract the syntactic information. 2) We incorporate the extracted syntactic encoding with PortaSpeech to improve the prosody prediction. 3) We introduce a multi-length discriminator to replace the flow-based post-net in PortaSpeech, which simplifies the training pipeline and improves the inference speed, while keeping the naturalness of the generated audio. Experiments on three datasets not only show that the tree-structured syntactic information grants SyntaSpeech the ability to synthesize better audio with expressive prosody, but also demonstrate the generalization ability of SyntaSpeech to adapt to multiple languages and multi-speaker text-to-speech. Ablation studies demonstrate the necessity of each component in SyntaSpeech. Source code and audio samples are available at https://syntaspeech.github.io.
Towards Robust Dense Retrieval via Local Ranking Alignment

Dense retrieval (DR) has extended the employment of pre-trained language models, like BERT, for text ranking. However, recent studies have raised the robustness issue of DR model against query variations, like query with typos, along with non-trivial performance losses. Herein, we argue that it would be beneficial to allow the DR model to learn to align the relative positions of query-passage pairs in the representation space, as query variations cause the query vector to drift away from its original position, affecting the subsequent DR effectiveness. To this end, we propose RoDR, a novel robust DR model that learns to calibrate the in-batch local ranking of query variation to that of original query for the DR space alignment. Extensive experiments on MS MARCO and ANTIQUE datasets show that RoDR significantly improves the retrieval results on both the original queries and different types of query variations. Meanwhile, RoDR provides a general query noise-tolerate learning framework that boosts the robustness and effectiveness of various existing DR models. Our code and models are openly available at https://github.com/cxa-unique/RoDR.
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions

Recent studies have shown that the training samples can be recovered from gradients, which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of extensive surveys covering recent advances and thorough analysis of this issue. In this paper, we present a comprehensive survey on GradInv, aiming to summarize the cutting-edge research and broaden the horizons for different domains. Firstly, we propose a taxonomy of GradInv attacks by characterizing existing attacks into two paradigms: iteration- and recursion-based attacks. In particular, we dig out some critical ingredients from the iteration-based attacks, including data initialization, model training and gradient matching. Second, we summarize emerging defense strategies against GradInv attacks. We find these approaches focus on three perspectives covering data obscuration, model improvement and gradient protection. Finally, we discuss some promising directions and open problems for further research.
Hierarchical Diffusion Scattering Graph Neural Network

Graph neural network (GNN) is popular now to solve the tasks in non-Euclidean space and most of them learn deep embeddings by aggregating the neighboring nodes. However, these methods are prone to some problems such as over-smoothing because of the single-scale perspective field and the nature of low-pass filter. To address these limitations, we introduce diffusion scattering network (DSN) to exploit high-order patterns. With observing the complementary relationship between multi-layer GNN and DSN, we propose Hierarchical Diffusion Scattering Graph Neural Network (HDS-GNN) to efficiently bridge DSN and GNN layer by layer to supplement GNN with multi-scale information and band-pass signals. Our model extracts node-level scattering representations by intercepting the low-pass filtering, and adaptively tunes the different scales to regularize multi-scale information. Then we apply hierarchical representation enhancement to improve GNN with the scattering features. We benchmark our model on nine real-world networks on the transductive semi-supervised node classification task. The experimental results demonstrate the effectiveness of our method.
Abstraction for Deep Reinforcement Learning

We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the need for end-to-end differentiability. We review developments in AI and machine learning that could facilitate their adoption.