Reviewerly/exHarmony huggingface dataset:
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning

Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown improvements in the quality of generated responses. However, the number of utterances for different types of fine-grained sentence functions is extremely imbalanced. Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent. Consequently, dialogue generation conditioned on these infrequent sentence functions suffers from data deficiency. In this paper, we investigate a structured meta-learning (SML) approach for dialogue generation on infrequent sentence functions. We treat dialogue generation conditioned on different sentence functions as separate tasks, and apply model-agnostic meta-learning to high-resource sentence functions data. Furthermore, SML enhances meta-learning effectiveness by promoting knowledge customization among different sentence functions but simultaneously preserving knowledge generalization for similar sentence functions. Experimental results demonstrate that SML not only improves the informativeness and relevance of generated responses, but also can generate responses consistent with the target sentence functions. Code will be public to facilitate the research along this line.
Device Target Checking for Power Patrol Robot Based on Objectness Estimation

In order to free people from daily patrol tasks in power substations, patrol robots are designed to check power equipment status and read various electric meters. According to the requirements of patrol tasks, a hierarchical, coarse-to-fine, fast device detection and recognition method is proposed. The coarse detection is based on objectness, using 8 × 8 Binarized Normed Gradients (BING) feature to generate proposals and filtering them using Support Vector Machine(SVM) trained by Local Binary Pattern(LBP) feature combining with histogram matching. The fine detection utilizes the color and contour feature of the basic elements of a certain device to obtain the status information or meter data. In this paper, instances of the device status checking and the pointer meter data reading are developed. The experiments validate the effectiveness, accuracy and real-time of this method.
Surrogate Data for Deep Learning Architectures in Rehabilitative Edge Systems

The age of Big Data came about with the profitable mining of large amounts of data stored on the Cloud data servers accessible through the Internet. Much of these data were provided by users of extensive social networks. The availability of these data for training, coupled with advances in processing power have led to the surge in deep learning applications.While the Cloud provides wide scale data storage and analytic facilities, there is a move to perform data analyses closer to where data is gathered. Low resource but powerful processors have led the move toward these systems at the “edge“ rather than remotely.However, certain environments which can benefit from edge systems do not produce large volumes of data, such as in clinical applications. We augment such data using surrogate time series and compare various neural network architectures which would allow data analysis in rehabilitative edge systems.We show that a neural network using temporal data provides excellent classification results while using a fraction of the resources in an earlier work. Our novel framework shows how deep learning tools can be trained in a data scarce environment and then deployed in resource constrained edge systems.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures

Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fundamental problem of machine learning. Regularization techniques are versatile, as they are task- and architecture-agnostic, but they do not exploit a large amount of data available. Transfer learning methods learn to transfer knowledge from one domain to another, but may not generalize across tasks and architectures, and may introduce new training cost for adapting to the target task. To bridge the gap between the two, we propose a transferable perturbation, MetaPerturb, which is meta-learned to improve generalization performance on unseen data. MetaPerturb is implemented as a set-based lightweight network that is agnostic to the size and the order of the input, which is shared across the layers. Then, we propose a meta-learning framework, to jointly train the perturbation function over heterogeneous tasks in parallel. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures. We validate the efficacy and generality of MetaPerturb trained on a specific source domain and architecture, by applying it to the training of diverse neural architectures on heterogeneous target datasets against various regularizers and fine-tuning. The results show that the networks trained with MetaPerturb significantly outperform the baselines on most of the tasks and architectures, with a negligible increase in the parameter size and no hyperparameters to tune.
Pyrocko - A Versatile Software Framework for Seismology

&amp;lt;p&amp;gt;Pyrocko is an open source seismology toolbox and library, written in the Python programming language. It can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, modelling of waveforms, InSAR or GPS displacement data, or for seismic source characterization. At its core, Pyrocko is a &amp;amp;#160;library &amp;amp;#160;and &amp;amp;#160;framework &amp;amp;#160;providing &amp;amp;#160;building &amp;amp;#160;blocks &amp;amp;#160;for researchers &amp;amp;#160;and &amp;amp;#160;students &amp;amp;#160;wishing &amp;amp;#160;to &amp;amp;#160;develop &amp;amp;#160;their &amp;amp;#160;own applications. Pyrocko contains a few standalone applications for everyday seismological practice. These include the Snuffler program, an extensible seismogram browser and workbench, the Cake tool, providing travel-time and ray-path computations for 1D layered earthmodels, Fomosto, a tool to manage pre-calculated Green&amp;amp;#8217;s function stores, Jackseis, a command-line tool for common waveform archive data manipulations, Colosseo, a tool to create synthetic earthquake scenarios, serving waveforms and static displacements, and new, Sparrow, a 3D geophysical data visualization tool. This poster gives a glimpse of Pyrocko&amp;amp;#8217;s features, for more examples and tutorials visit https://pyrocko.org/.&amp;lt;/p&amp;gt;
Relations among k-ME concurrence, negativity, polynomial invariants, and tangle

The k-ME concurrence as a measure of multipartite entanglement (ME) unambiguously detects all k-nonseparable states in arbitrary dimensions and satisfies many important properties of an entanglement measure. Negativity is a simple computable bipartite entanglement measure. Invariant and tangle are useful tools to study the properties of the quantum states. In this paper, we mainly investigate the internal relations among the k-ME concurrence, negativity, polynomial invariants, and tangle. Strong links between k-ME concurrence and negativity as well as between k-ME concurrence and polynomial invariants are derived. We obtain the quantitative relation between k-ME $$(k=n)$$ concurrence and negativity for all n-qubit states, give an exact value of the n-ME concurrence for the mixture of n-qubit GHZ states and white noise, and derive an connection between k-ME concurrence and tangle for n-qubit W state. Moreover, we find that for any 3-qubit pure state the k-ME concurrence $$(k=2, 3)$$ is related to negativity, tangle, and polynomial invariants, while for 4-qubit states the relations between k-ME concurrence (for $$k = 2, 4)$$ and negativity, and between k-ME concurrence and polynomial invariants also exist. Our work provides clear quantitative connections between k-ME concurrence and negativity, and between k-ME concurrence and polynomial invariants.
Convolutional Neural Network Architecture with Exact Solution

Convolutional Neural Networks (CNNs) have been explored rigorously, due to their complex image classification capabilities and applied in many real-world applications. In majority of such applications, training of CNN using a back-propagation type iterative learning has become a standard practice, but this makes training of CNN very inefficient and uncertain because of various problems such as local minima and paralysis. Other iterative and non-iterative learning including exact solution-based learning might be more efficient in terms of accuracy and certainty in training, however, potential of this type of combined learning has not been fully explored by CNN researchers. Therefore, in this paper an exact solution based new convolutional neural network architecture is proposed. In proposed architecture, a novel concept is introduced in which the weights of CNN layers are updated using iterative process for a fixed number of epochs and then the weights of fully connected layer are calculated using an exact solution process. Both iterative and calculated weights are then used for training the full architecture. The proposed approach has been evaluated on three benchmark datasets such as CIFAR-10, MNIST and Digit. The experimental results have demonstrated that the proposed approach can achieve higher accuracy than the standard CNN. Statistical significance test was carried out to prove the efficacy of proposed approach.
Texture Interpolation for Probing Visual Perception.

Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (i.e. the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures. Compared to other CNN-based approaches, our interpolation method appears to match more closely the geometry of texture perception, and our mathematical framework is better suited to study its statistical nature. We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.
New Functions for Designing Supervisory Control Systems for NPPs

It proposes adding several functions based on the use of modern types of computer technology for the upper level of NPP APCS of new generations. A list of functions, their placement at NPPs, the personnel involved, implementation methods are providing. Issues of ergonomics and cybersecurity discussed.
Deep Automodulators

We introduce a new category of generative autoencoders called automodulators.These networks can faithfully reproduce individual real-world input images likeregular autoencoders, but also generate a fused sample from an arbitrarycombination of several such images, allowing instantaneous 'style-mixing' andother new applications. An automodulator decouples the data flow of decoderoperations from statistical properties thereof and uses the latent vector tomodulate the former by the latter, with a principled approach for mutualdisentanglement of decoder layers. Prior work has explored similar decoderarchitecture with GANs, but their focus has been on random sampling. Acorresponding autoencoder could operate on real input images. For the firsttime, we show how to train such a general-purpose model with sharp outputs inhigh resolution, using novel training techniques, demonstrated on four imagedata sets. Besides style-mixing, we show state-of-the-art results inautoencoder comparison, and visual image quality nearly indistinguishable fromstate-of-the-art GANs. We expect the automodulator variants to become a usefulbuilding block for image applications and other data domains.
A Novel GAN-Based Synthesis Method for In-Air Handwritten Words

In recent years, with the miniaturization and high energy efficiency of MEMS (micro-electro-mechanical systems), in-air handwriting technology based on inertial sensors has come to the fore. Most of the previous works have focused on character-level in-air handwriting recognition. In contrast, few works focus on word-level in-air handwriting tasks. In the field of word-level recognition, researchers have to face the problems of insufficient data and poor generalization performance of recognition methods. On one hand, the training of deep neural learning networks usually requires a particularly large dataset, but collecting data will take a lot of time and money. On the other hand, a deep recognition network trained on a small dataset can hardly recognize samples whose labels do not appear in the training set. To address these problems, we propose a two-stage synthesis method of in-air handwritten words. The proposed method includes a splicing module guided by an additional corpus and a generating module trained by adversarial learning. We carefully design the proposed network so that it can handle word sample inputs of arbitrary length and pay more attention to the details of the samples. We design multiple sets of experiments on a public dataset. The experimental results demonstrate the success of the proposed method. What is impressive is that with the help of the air-writing word synthesizer, the recognition model learns the context information (combination information of characters) of the word. In this way, it can recognize words that have never appeared in the training process. In this paper, the recognition model trained on synthetic data achieves a word-level recognition accuracy of 62.3% on the public dataset. Compared with the model trained using only the public dataset, the word-level accuracy is improved by 62%. Furthermore, the proposed method can synthesize realistic samples under the condition of limited of in-air handwritten character samples and word samples. It largely solves the problem of insufficient data. In the future, mathematically modeling the strokes between characters in words may help us find a better way to splice character samples. In addition, we will apply our method to various datasets and improve the splicing module and generating module for different tasks.
Using Affective Features from Media Content Metadata for Better Movie Recommendations

This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview. We apply a text-based Emotion Detection and Recognition model, which trained by tweets short messages and transfers the learned model to detect movie overviews' implicit affective features. We vectorized the affective movie tags to represent the mood embeddings of the movie. We obtain the user's emotional features by taking the average of all the movies' affective vectors the user has watched. We apply five-distance metrics to rank the Top-N movie recommendations against the user's emotion profile. We found Cosine Similarity distance metrics performed better than other distance metrics measures. We conclude that by replacing the top-N recommendations generated by the Recommender with the reranked recommendations list made by the Cosine Similarity distance metrics, the user will effectively get affective aware top-N recommendations while making the Recommender feels like an Emotion Aware Recommender.
Integration of Ontologies with Decentralized Autonomous Organizations Development: A Systematic Literature Review

This paper presents a systematic literature review of the integration of ontologies into the Decentralized Autonomous Organization (DAO) development process. The review extracted data from 34 primary studies dealing with ontologies in the blockchain domain. DAO has become a key concept for the development of blockchain-based decentralized software systems. DAOs are seen as a positive alternative for organizations interested in the adoption of decentralized, reliable and transparent governance, as well as attracting the interest of academic research. However, there is no common understanding or generally accepted formal definition of a DAO, and the guidelines that provide support for the adoption and development of DAOs are limited to a few key references that lack the computational semantics needed to enable their automated validation, simulation or execution. Thus, the objective of this paper is to provide an unbiased and up-to-date review related to the integration of ontologies within DAOs which helps to identify new research opportunities and take advantage of this integration from a blockchain-based decentralized perspective.
An Efficient Data Hiding Approach on Digital Color Image

A concealing picture is worked of various assortments of a comparative scene, one for each repulsive section. These dim level picture gives the gathering of light, each at the unequivocal awful part and at the circumstance of every pixel. Red, Green and Blue are three frightful gatherings. Each image is created of three diminish measurement pictures, three gatherings.We’ll conceal the substance in diminish pictures. Using Image improvement methodology, when we change the power estimations of basic tones by then the disguised substance is undeniable. From the preliminary outcomes we get 96% exactness
An Insight into Fault Propagation in Deep Neural Networks: Work-in-Progress

Reliability is of critical importance for Deep Neural Networks (DNNs) applied in safety-critical applications. Traditional analysis of fault propagation in DNNs is not suitable for such applications. In this paper we approach to give the theory-driven analysis of fault propagation in DNN. Specifically, the perturbation on weights of layers are formulated and the propagation conditions of faults are obtained through theoretical derivation. All the analysis is based on DNNs with 32-bit float numbers. Finally, initial experiments on three typical DNNs are conducted to evaluate our theoretical results.
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'. Through experiments on many different GAN variants, we show that this `top-k update' procedure is a generally applicable improvement. In order to understand the nature of the improvement, we conduct extensive analysis on a simple mixture-of-Gaussians dataset and discover several interesting phenomena. Among these is that, when gradient updates are computed using the worst-scoring batch elements, samples can actually be pushed further away from their nearest mode. We also apply our method to recent GAN variants and improve state-of-the-art FID for conditional generation from 9.21 to 8.57 on CIFAR-10.
Intelligent Dynamic Grouping for Collaborative Activities in Learning Management System

In a sustainable education environment, Learning is fixed everyone and learning time may vary with individual capability. To provide additional learning space, Learning Management System (LMS) is a perfect solution. All kind of learners can make use of the additional learning space and attain the objectives of the content. Learning Management System is an integral component of the e-Learning design and development, and to manage the learning process. LMS would provide pre-defined templates for development as well as to deploy e-content, integrate multimedia content such as audio, video, images, graphics and animat ion, organi se assessments, evaluation procedures and analyze the learner style by including the learning analytics. Most of the Learning Management Systems offer convenient tools that allow to create custom courses and thus implement educational, teaching, training and management processes in an organization. Today learners are more towards active and team-oriented learning. LMS would improve a ctive and collaborative learning by providing varieties of activities and group projects. While giving the group assignments and projects to the learners, an effective and dynamic grouping mechanism need to be integrated. Based on the group size, the group should consist of mixed kind of learners to learn from group activity and to lead a group activity. The formation of the group must be dynamic for every activity. The groups should be dynamic in nature and every learner of the course must have active learning with each other in the same course. To prepare an intelligent grouping mechanism, a framework was developed and deployed in the MOODLE LMS. This intelligent dynamic grouping mechanism improve the overall learning activities in the LMS environment.
SelfGuard: semi-automated activity tracking for enhancing self-protection against the COVID-19 pandemic

Contagious diseases like COVID-19 spread periodically and threaten our lives. Self-protection, such as washing hands, wearing a mask, and staying home, are simple and practical solutions to safeguard against these diseases. Most governments and health departments recommend that people maintain self-protection. Although continuous self-protection effectively prevents the spread of infection, only the intent to self-protect is unsustainable in the long term. In this study, we design, develop, and deploy an application to track users' daily activities semi-automatically and enhance self-protection behavior using mobile sensing and gamified feedback techniques. Currently, more than 324 people have installed the app via AppStore, and 52 users have shared their activity data to our research group.
Learning Global Pose Features in Graph Convolutional Networks for 3D Human Pose Estimation

As the human body skeleton can be represented as a sparse graph, it is natural to exploit graph convolutional networks (GCNs) to model the articulated body structure for 3D human pose estimation (HPE). However, a vanilla graph convolutional layer, the building block of a GCN, only models the local relationships between each body joint and their neighbors on the skeleton graph. Some global attributes, e.g., the action of the person, can be critical to 3D HPE, especially in the case of occlusion or depth ambiguity. To address this issue, this paper introduces a new 3D HPE framework by learning global pose features in GCNs. Specifically, we add a global node to the graph and connect it to all the body joint nodes. On one hand, global features are updated by aggregating all body joint features to model the global attributes. On the other hand, the feature update of each body joint depends on not only their neighbors but also the global node. Furthermore, we propose a heterogeneous multi-task learning framework to learn the local and global features. While each local node regresses the 3D coordinate of the corresponding body joint, we force the global node to classify an action category or learn a low-dimensional pose embedding. Experimental results demonstrate the effectiveness of the proposed approach.