Reviewerly/exHarmony huggingface dataset:
Distributed Deep Learning Using Volunteer Computing-Like Paradigm

Use of Deep Learning (DL) in commercial applications such as image classification, sentiment analysis and speech recognition is increasing. When training DL models with large number of parameters and/or large datasets, cost and speed of training can become prohibitive. Distributed DL training solutions that split a training job into subtasks and execute them over multiple nodes can decrease training time. However, the cost of current solutions, built predominantly for cluster computing systems, can still be an issue. In contrast to cluster computing systems, Volunteer Computing (VC) systems can lower the cost of computing, but applications running on VC systems have to handle fault tolerance, variable network latency and heterogeneity of compute nodes, and the current solutions are not designed to do so. We design a distributed solution that can run DL training on a VC system by using a data parallel approach. We implement a novel asynchronous SGD scheme called VC-ASGD suited for VC systems. In contrast to traditional VC systems that lower cost by using untrustworthy volunteer devices, we lower cost by leveraging preemptible computing instances on commercial cloud platforms. By using preemptible instances that require applications to be fault tolerant, we lower cost by 70-90% and improve data security.
On the Intersection Distribution of Degree Three Polynomials and Related Topics

The intersection distribution of a polynomial $f$ over finite field $\mathbb{F}_q$ was recently proposed by Li and Pott [\emph{Finite Fields and Their Applications, 66 (2020)}], which concerns the collective behaviour of a collection of polynomials $\{f(x)+cx \mid c \in\mathbb{F}_q\}$. The intersection distribution has an underlying geometric interpretation, which indicates the intersection pattern between the graph of $f$ and the lines in the affine plane $AG(2,q)$. When $q$ is even, the long-standing open problem of classifying o-polynomials can be rephrased in a simple way, namely, classifying all polynomials which have the same intersection distribution as $x^2$. Inspired by this connection, we proceed to consider the next simplest case and derive the intersection distribution for all degree three polynomials over $\mathbb{F}_q$ with $q$ both odd and even. Moreover, we initiate to classify all monomials having the same intersection distribution as $x^3$, where some characterizations of such monomials are obtained and a conjecture is proposed. In addition, two applications of the intersection distributions of degree three polynomials are presented. The first one is the construction of nonisomorphic Steiner triple systems and the second one produces infinite families of Kakeya sets in affine planes with previously unknown sizes.
GAN Ensemble for Anomaly Detection

When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works apply Generative Adversarial Networks (GANs) to anomaly detection tasks and show good performances from these models. Motivated by the observation that GAN ensembles often outperform single GANs in generation tasks, we propose to construct GAN ensembles for anomaly detection. In the proposed method, a group of generators and a group of discriminators are trained together, so every generator gets feedback from multiple discriminators, and vice versa. Compared to a single GAN, a GAN ensemble can better model the distribution of normal data and thus better detect anomalies. Our theoretical analysis of GANs and GAN ensembles explains the role of a GAN discriminator in anomaly detection. In the empirical study, we evaluate ensembles constructed from four types of base models, and the results show that these ensembles clearly outperform single models in a series of tasks of anomaly detection.
Speckle phase map denoising based on empirical wavelet transform and cross correlation

In order to resolve the problem of speckle interference phase map noise interference, a phase denoising method of digital speckle pattern interferometry based on empirical wavelet transform (EWT) and cross correlation is proposed. First, the EWT is used to decompose the speckle phase map, and a series of components is obtained. Then the noise-free components containing phase information are extracted based on the cross correlation. After that, combined with the particle swarm optimization algorithm and sine–cosine denoising method, an improved sine–cosine denoising method is proposed to process the noise components. Finally, the noiseless components are reconstructed to obtain the denoised speckle phase map. Simulation and experimental results show that the proposed method can effectively reduce the noise interference and obtain accurate phase information.
Here and There with Arithmetic

In the theory of answer set programming, two groups of rules are called strongly equivalent if, informally speaking, they have the same meaning in any context. The relationship between strong equivalence and the propositional logic of here-and-there allows us to establish strong equivalence by deriving rules of each group from rules of the other. In the process, rules are rewritten as propositional formulas. We extend this method of proving strong equivalence to an answer set programming language that includes operations on integers. The formula representing a rule in this language is a first-order formula that may contain comparison symbols among its predicate constants, and symbols for arithmetic operations among its function constants. The paper is under consideration for acceptance in TPLP.
A Vertical Federated Learning Framework for Graph Convolutional Network

Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data. However in most industries, data exists in the form of isolated islands and the data privacy and security is also an important issue. In this paper, we propose FedVGCN, a federated GCN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GCN models. Specifically, we split the computation graph data into two parts. For each iteration of the training process, the two parties transfer intermediate results to each other under homomorphic encryption. We conduct experiments on benchmark data and the results demonstrate the effectiveness of FedVGCN in the case of GraphSage.
Extended goal recognition: A planning-based model for strategic deception

Goal recognition is the problem of determining an agent’s intent by observing its actions. In the context of AI research, the problem is tackled for two quite different purposes: to determine an agent’s most probable goal or, for human-aware planning including planned—or strategic—deception, to determine an observer’s most likely belief about that goal. Making no distinction, contemporary models tend to assume an infallible observer, deceived only while it has limited access to information or if the environment itself is only partially observable. Focusing on the second purpose, we propose an extended framework that incorporates formal definitions of confirmation bias, selective attention and memory decay. In contrast to pre-existing models, our approach combines explicit consideration of prior probabilities with a principled representation of observer confidence and distinguishes between potential observations—i.e., every observable event within the observer’s frame of reference—and recalled observations which we model as a function of attention and memory. We show that when these factors are taken into consideration, false beliefs may arise and can be made to persist, even in a fully observable environment—thus providing a perceptual model readily incorporated into the “thinking” of an adversarial agent for the purpose of strategic deception.
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers

We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.
Isolated Word Speech Recognition based on BNN and Its Hardware Implementation

In this paper, a binary convolution neural network (BNN) is proposed to realize isolated word speech recognition task, which greatly reduces the model training parameters and training time. For isolated word data sets, the rectangular convolution kernel is designed to replace the traditional square convolution kernel, and batch normalization layer is integrated into the convolution layer to realize the lossless acceleration of the inference process. The binary convolution neural network is deployed on FPGA to realize the edge calculation.
Deep demosaicking considering inter-channel correlation and self-similarity

The effectiveness of utilizing inter-channel correlation and self-similarity for demosaicking has been reported in many literatures. On the other hand, many convolutional neural network (CNN)-based demosaicking techniques have also been proposed to achieve state-of-the-art accuracy. In CNN-based demosaicking, one of the most important issue is how to consider the correlations using neural network. In this paper, we propose a novel CNN-based demosaicking method that considers an effective combination of both inter-channel correlation and self-similarity. Specifically, we apply the CNN to predict the color differences R-G and B-G, then the demosaicked image is obtained from the predicted color differences and the input color filter array (CFA) image. At the same time, our network considers the self-similarity in the color difference domain by applying non-local attention for high-level feature map. Experimental results show that our method provides the better accuracy and visual performance compared with conventional demosaicking methods. In addition, the versatility of the proposed framework is demonstrated by experiments with images sampled by various CFA patterns.
Real-Time Hand Gesture Recognition

With the rapid development of computer vision, the demand for interaction between humans and machines is becoming more and more extensive. Since hand gestures can express enriched information, hand gesture recognition is widely used in robot control, intelligent furniture, and other aspects. The paper realizes the segmentation of hand gestures by establishing the skin color model and AdaBoost classifier based on haar according to the particularity of skin color for hand gestures and the denaturation of hand gestures with one frame of video being cut for analysis. In this regard, the human hand is segmented from a complicated background. The camshaft algorithm also realizes real-time hand gesture tracking. Then, the area of hand gestures detected in real-time is recognized by a convolutional neural network to discover the recognition of 10 common digits. Experiments show 98.3% accuracy.
Visual navigation algorithm for asteroid lander based on irregular curve matching

<p indent="0mm">In order to solve the visual navigation problem of the landing process of the small celestial body lander, this paper proposes an algorithm to estimate the position and attitude of the lander by using point-tangent line on the irregular curve. First, the irregular curve matching in the landing image is completed by constructing the irregular curve descriptor. Second, the constraint relationship between the position attitude and the point-tangent is established. Then, the curve parameterization method is used to solve the higher order equations, and the constraint conditions are used to obtain the unique solution of position attitude parameters and the least squares solution of multiple matching curves. Finally, the influence of noise intensity and the number of matching curves on the navigation accuracy is verified by simulation, and the feasibility of the algorithm is proved.
Action Advising with Advice Imitation in Deep Reinforcement Learning

Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Recently proposed student-initiated approaches have obtained promising results. However, due to being in the early stages of development, these also have some substantial shortcomings. One of the abilities that are absent in the current methods is further utilising advice by reusing, which is especially crucial in the practical settings considering the budget and cost constraints in peer-to-peer. In this study, we present an approach to enable the student agent to imitate previously acquired advice to reuse them directly in its exploration policy, without any interventions in the learning mechanism itself. In particular, we employ a behavioural cloning module to imitate the teacher policy and use dropout regularisation to have a notion of epistemic uncertainty to keep track of which state-advice pairs are actually collected. As the results of experiments we conducted in three Atari games show, advice reusing via generalisation is indeed a feasible option in deep RL and our approach can successfully achieve this while significantly improving the learning performance, even when paired with a simple early advising heuristic.
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics exhibited with temporally-ordered and multi-level inter-dependent relation structures. These methods largely overlook the relation hierarchy of item transitional patterns. In this paper, we propose a multi-task learning framework with Multi-level Transition Dynamics (MTD), which enables the jointly learning of intra- and inter-session item transition dynamics in automatic and hierarchical manner. Towards this end, we first develop a position-aware attention mechanism to learn item transitional regularities within individual session. Then, a graph-structured hierarchical relation encoder is proposed to explicitly capture the cross-session item transitions in the form of high-order connectivities by performing embedding propagation with the global graph context. The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space. Extensive experiments on three real-world datasets demonstrate the superiority of MTD as compared to state-of-the-art baselines.
Exploration of College English Blended Learning Model under the Background of Educational Information

Driven by the rapid development of computer network application technology, various technologies such as cloud computing, big data, and artificial intelligence have appeared in the public's field of vision, and are quickly and widely used in various fields. Among them, the theory of education and teaching integration information technology and technological innovation are among them. The model. The introduction of Blended Learning Model realizes the organic integration of information technology theory and ideological and political education and teaching, as well as the scientific planning and full use of learning time and space inside and outside the class, which enriches the method of education and teaching system. The importance of blended learning models under the background of educational informationization is becoming increasingly prominent. This article attempts to explore the blended learning mode of college English from three aspects: online learning, offline learning and extracurricular development. It aims to promote students' initiative, autonomy and cooperation in learning, and achieve effective learning results.
Evaluating the potential of enhancing teachers’ digital competencies to meet the needs of diversification of education and learning

Introduction. The article presents the results of reflection on the readiness of the education system to respond to the transformational challenges of digital economy through the formation of relevant human resources. It identifies an imbalance in the response of the education system to the demand of society and business for training human resources with digital competences. The imbalance makes it difficult to implement new in-demand educational trajectories based on the principle of individualization of employee’s professional and personal self-realization. The purpose of the study is to identify the potential of enhancing teachers' digital competence in accordance with the needs of education diversification within digital transformation. Materials and Methods. The methodological basis for the study consists of reflexive modernity concepts (E. Giddens, W. Beck), reflecting qualitative changes of the social world in the context of digitalization; digital sociology of changes in social relations influenced by digital technologies (V. F. Nitsevich), mediating ordinary social contacts in everyday communication and creating new digital opportunities and risks. Existing and required digital competences of teachers were identified on the basis of desk research of teachers’ digital skills and competences models and empirical research on technical university teachers' opinions (survey method). Statistical methods were used to analyze and interpret the research data. Results. The study has identified the potential of the education system to adjust to the challenges of educational and career transformations. It is emphasized that the ways of potential realization can vary from ‘soft’ ones which imply gradual mastering based on several program versions and formats to the so called stress-options of forced competence mastering within a limited period of time. The necessity of introducing regulating measures for the transition to a new role of a teacher in the digital model of education is justified (e.g. teacher as a manager, teacher as a facilitator of the educational process). Conclusions. The article concludes that differentiation based on the level of digital competences and the degree of readiness to master digital technologies limits the speed of relevant response of the education system to the transformational demands of society. The need for content elaboration and teaching health-saving technologies within the digital culture is highlighted. Since the level of digital competencies of teaching staff is the basis for the development of educational institutions and the competitiveness of the country, measures are to be taken in order to facilitate the development of digital competencies.
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification

In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately. The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks. Unfortunately, none of the existing approaches consider the two important sources of information simultaneously. In this paper, we propose a Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two tasks. The core module is a proposed co-interactive graph interaction layer where a cross-utterances connection and a cross-tasks connection are constructed and iteratively updated with each other, achieving to consider the two types of information simultaneously. Experimental results on two public datasets show that our model successfully captures the two sources of information and achieve the state-of-the-art performance. In addition, we find that the contributions from the contextual and mutual interaction information do not fully overlap with contextualized word representations (BERT, Roberta, XLNet).
Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network

Skills gained from vocational or higher education form an essential component of country’s economy, determining the structure of the national labor force. Therefore, knowledge on how people’s education converts to jobs enables data-driven choices concerning human resources within an ever-changing job market. Moreover, the relationship between education and occupation is also relevant in times of global crises, such as the COVID-19 pandemic. Healthcare system overload and skill shortage on one hand, and job losses related to lock-downs on the other, have exposed a necessity to identify target groups with relevant education backgrounds in order to facilitate their occupational transitions. However, the relationship between education and employment is complex and difficult to model. This study aims to propose the methodology that would allow us to model education-to-skill mapping. Multiple challenges arising from administrative datasets, namely imbalanced data, complex labeling, hierarchical structure and textual data, were addressed using six neural network-based algorithms of incremental complexity. The final proposed mathematical model incorporates the textual data from descriptions of education programs that are transformed into embeddings, utilizing transformer neural networks. The output of the final model is constructed as the hierarchical classification task. The effectiveness of the proposed model is demonstrated using experiments on national level data, which covers whole population of Lithuania. Finally, we provide the recommendations for the usage of proposed model. This model can be used for practical applications and scenario forecasting. Some possible applications for such model usage are demonstrated and described in this article. The code for this research has been made available on GitHub.
Axis alignment method in the rotating photogrammetric system

In large-scale imaging or measurement, a rotating photogrammetric system with a camera fixed on the surface of the turntable can be used to extend the field of view of the camera with the rotation of the turntable. The camera’s projection center needs to be located at the rotation axis of the turntable, so the images captured during the sequentially rotating share the same projection center. It is very convenient to merge the subimages into a panoramic image. Due to the complexity of the camera’s lens system, the position of the projection center is usually unknown, which makes it difficult to find the projection center and align it with the rotation axis of the turntable. We propose a method to find the distance and azimuth angle between the projection center and rotation axis to solve the alignment problem using the parameters of the camera. In theory, as long as the intrinsic and extrinsic parameters of the camera are known, the distance and angle can be obtained by rotating the turntable once. With the translation values calculated from the distance and angle, the camera can be moved in two directions perpendicular to each other, so its projection center passes through the rotation axis. The theoretical analysis of the alignment problem is given. Experiments verify the feasibility of the proposed method are conducted. The alignment uncertainty of the proposed method is <0.2 mm.