Reviewerly/exHarmony huggingface dataset:
Learning the micro deformations by max-pooling for offline signature verification

For signature verification systems, micro deformations can be defined as the small differences in the same strokes of signatures or special writing habits of different signers. These micro deformations can reveal the core distinction between the genuine signatures and skilled forgeries. In this paper, we prove that Convolutional Neural Networks (CNNs) have the potential to extract those micro deformations by max-pooling. More specifically, the micro deformations can be determined by watching the location coordinates of the maximum values in pooling windows of max-pooling. Extensive analysis and experiments demonstrate that it is possible to achieve state-of-the-art performance by using this location information as a new feature for capturing micro deformations, along with convolutional features. The proposed method outperforms the state-of-the-art systems on four publicly available datasets of different languages, i.e., English (GPDSsynthetic, CEDAR), Persian (UTSig), and Hindi (BHSig260).
Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start Problem

E-learning recommender systems are gaining significance nowadays due to its ability to enhance the learning experience by providing tailor-made services based on learner preferences. A Personalized Learning Environment (PLE) that automatically adapts to learner characteristics such as learning styles and knowledge level can recommend appropriate learning resources that would favor the learning process and improve learning outcomes. The pure cold-start problem is a relevant issue in PLEs, which arises due to the lack of prior information about the new learner in the PLE to create appropriate recommendations. This article introduces a semantic framework based on ontology to address the pure cold-start problem in content recommenders. The ontology encapsulates the domain knowledge about the learners as well as Learning Objects (LOs). The semantic model that we built has been experimented with different combinations of the key learner parameters such as learning style, knowledge level, and background knowledge. The proposed framework utilizes these parameters to build natural learner groups from the learner ontology using SPARQL queries. The ontology holds 480 learners’ data, 468 annotated learning objects with 5,600 learner ratings. A multivariate k-means clustering algorithm, an unsupervised machine learning technique for grouping similar data, is used to evaluate the learner similarity computation accuracy. The learner satisfaction achieved with the proposed model is measured based on the ratings given by the 40 participants of the experiments. From the evaluation perspective, it is evident that 79% of the learners are satisfied with the recommendations generated by the proposed model in pure cold-start condition.
An Open-source Library of Large Integer Polynomial Multipliers

Polynomial multiplication is a bottleneck in most of the public-key cryptography protocols, including Elliptic-curve cryptography and several of the post-quantum cryptography algorithms presently being studied. In this paper, we present a library of various large integer polynomial multipliers to be used in hardware cryptocores. Our library contains both digitized and non-digitized multiplier flavours for circuit designers to choose from. The library is supported by a C++ generator that automatically produces the multipliers' logic in Verilog HDL that is amenable for FPGA and ASIC designs. Moreover, for ASICs, it also generates configurable and parameterizable synthesis scripts. The features of the generator allow for a quick generation and assessment of several architectures at the same time, thus allowing a designer to easily explore the (complex) optimization search space of polynomial multiplication.
Beyond the Command

Machines, from artificially intelligent digital assistants to embodied robots, are becoming more pervasive in everyday life. Drawing on feminist science and technology studies (STS) perspectives, we demonstrate how machine designers are not just crafting neutral objects, but relationships between machines and humans that are entangled in human social issues such as gender and power dynamics. Thus, in order to create a more ethical and just future, the dominant assumptions currently underpinning the design of these human-machine relations must be challenged and reoriented toward relations of justice and inclusivity. This paper contributes the "social machine" as a model for technology designers who seek to recognize the importance, diversity and complexity of the social in their work, and to engage with the agential power of machines. In our model, the social machine is imagined as a potentially equitable relationship partner that has agency and as an "other" that is distinct from, yet related to, humans, objects, and animals. We critically examine and contrast our model with tendencies in robotics that consider robots as tools, human companions, animals or creatures, and/or slaves. In doing so, we demonstrate ingrained dominant assumptions about human-machine relations and reveal the challenges of radical thinking in the social machine design space. Finally, we present two design challenges based on non-anthropomorphic figuration and mutuality, and call for experimentation, unlearning dominant tendencies, and reimagining of sociotechnical futures.
Efficient set-valued prediction in multi-class classification

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with the highest expected utility. For this problem, which is computationally challenging as there are exponentially (in the number of classes) many predictions to choose from, we propose efficient algorithms that can be applied to a broad family of utility functions. Our theoretical results are complemented by experimental studies, in which we analyze the proposed algorithms in terms of predictive accuracy and runtime efficiency.
An efficient multi-signature wallet in blockchain using bloom filter

A blockchain wallet is a financial application which provides an interface for managing a digital asset. In the blockchain wallet, a digital signature (single or multiple signatures) is used to sign a transaction. However, the wallet is often vulnerable to security attacks when a single-signature scheme is used, and there are performance issues when a multi-signature scheme is employed. To address these issues, we propose a new efficient multi-signature wallet that improves performance, storage efficiency, and privacy without modifying the blockchain protocol. The steps in the development of the wallet are as follows, we first design a blockchain wallet that supports multi-signature as a single-signature by using a threshold elliptic curve digital signature algorithm (T-ECDSA) for high validation performance. Second, for achieving high storage efficiency and privacy, we use a bloom-filter for transactions for ensuring a small transaction size and identifying the participant of a transactions without exposing participant information. Experimental results indicate that the proposed multi-signature wallet shows higher verification performance in a blockchain, with a small transaction size compared with existing wallets.
Pseudo Video and Refocused Images-Based Blind Light Field Image Quality Assessment

The commercial light field camera is able to capture four-dimensional Light Field Image (LFI), which can be visualized to LFI contents on 2D displays by means of the Pseudo Video (PV) or the Refocused Images (RIs) generated with the refocusing function of LFI. However, the quality degradation of LFI will affect user’s visual experience of LFI contents. Hence, it is crucial to develop an effective LFI quality assessment method to monitor the LFI quality. Most existing subjective databases of LFI use PV and RIs visualization techniques to assess the quality of LFI. Therefore, as the way of presenting LFI on 2D display, PV and RIs are closely related to the subjective perception of LFI by human eyes. Based on these two visualization techniques, this article proposes a novel PV and RIs based blind LFI quality assessment method, in which the feature extraction is divided into two parts. In the first part, the PV’s structure, motion and disparity information are extracted with multi-scale and multi-directional Shearlet transform. In the other part, the spatial structure, depth and semantic information of the RIs are obtained. Finally, support vector regression is used to nonlinear map the perceptual features to quality score of LFI. The experimental results on four LFI databases show that the proposed method has better correlation with human visual perception, compared with the classical 2D image quality assessment methods as well as the state-of-the-art LFI quality assessment methods.
Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration

Abstract Broad Bayesian learning (BBL), a novel probabilistic Bayesian neural network methodology with optimized architecture configuration, is proposed. It has an expandable feedforward broad learning network. Therefore, the uncertain estimates can be quantified in terms of probability distributions and network architecture augmentation can be adopted incrementally by use of the inherited information from the previously trained network. Furthermore, a learning network architecture configuration optimization scheme is proposed to determine the optimal architecture configuration. Based on the plausibilities of the concerned configurations, the most plausible one can be obtained, and it indicates the proper augmentation to develop the optimal configuration. To demonstrate the proposed methodology, three simulation examples and an application with in‐field structural health monitoring measurement are presented.
Two-Level Attention Model of Representation Learning for Fraud Detection

Fraud detection has attracted significant attention in financial institutions, especially utilizing some artificial intelligent methods to automatically detect fraudulent transactions. With the study and application of intelligent fraud detection technology, scholars found that the representation learning method can reveal more information about fraud patterns, which is also crucial for detection task. Therefore, in this work, we present a novel method for detecting fraud transactions by combining two modules learning hidden information at different levels of data in a unified framework. To address and explore the deep representation of features of transaction behaviors, we propose a two-level attention model to capture them by integrating two data embeddings at the data sample level and the feature level. In particular, the sample-level attention model captures the detailed information more centrally that is difficult to determine; the feature-level attention model extends the information of feature dependences. We further combine them to train a final fraud detection model. Extensive experiments are conducted using a data set provided by a financial company in China and several public financial data sets. The results confirm the effectiveness of our proposed method in detecting fraudulent transactions compared with other state-of-the-art methods.
Mixed-Initiative Level Design with RL Brush

This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation. The tool uses reinforcement-learning-based models to augment manual human level-design through the addition of AI-generated suggestions. Here, we apply RL Brush to designing levels for the classic puzzle game Sokoban. We put the tool online and tested it in 39 different sessions. The results show that users using the AI suggestions stay around longer and their created levels on average are more playable and more complex than without.
Low-Resource Named Entity Recognition via the Pre-Training Model

Named entity recognition (NER) is an important task in the processing of natural language, which needs to determine entity boundaries and classify them into pre-defined categories. For low-resource languages, most state-of-the-art systems require tens of thousands of annotated sentences to obtain high performance. However, there is minimal annotated data available about Uyghur and Hungarian (UH languages) NER tasks. There are also specificities in each task—differences in words and word order across languages make it a challenging problem. In this paper, we present an effective solution to providing a meaningful and easy-to-use feature extractor for named entity recognition tasks: fine-tuning the pre-trained language model. Therefore, we propose a fine-tuning method for a low-resource language model, which constructs a fine-tuning dataset through data augmentation; then the dataset of a high-resource language is added; and finally the cross-language pre-trained model is fine-tuned on this dataset. In addition, we propose an attention-based fine-tuning strategy that uses symmetry to better select relevant semantic and syntactic information from pre-trained language models and apply these symmetry features to name entity recognition tasks. We evaluated our approach on Uyghur and Hungarian datasets, which showed wonderful performance compared to some strong baselines. We close with an overview of the available resources for named entity recognition and some of the open research questions.
A novel Byzantine fault tolerance consensus for Green IoT with intelligence based on reinforcement

To enhance the consensus performance of Blockchain in the Green Internet of Things (G-IoT) and improve the static network structure and communication overheads in the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, in this paper, we propose a Credit Reinforce Byzantine Fault Tolerance (CRBFT) consensus algorithm by using reinforcement learning. The CRBFT algorithm divides the nodes into three types, each with different responsibilities: master node, sub-nodes, and candidate nodes, and sets the credit attribute to the node. The node’s credit can be adjusted adaptively through the reinforcement learning algorithm, which can dynamically change the state of nodes. CRBFT algorithm can automatically identify malicious nodes and invalid nodes, making them exit from the consensus network. Experimental results show that the CRBFT algorithm can effectively improve the consensus network’s security. Besides, compared with the PBFT algorithm, in CRBFT, the consensus delay is reduced by about 40%, and the traffic overhead is reduced by more than 45%. This reduction is conducive to save energy and reduce emissions.
Light-weight UAV object tracking network based on strategy gradient and attention mechanism

Most existing object tracking methods have poor adaptability to complex scenes, and cannot achieve a good balance between tracking accuracy and real-time performance. To solve the above problems, this paper proposes a lightweight UAV object real-time tracking algorithm based on strategy gradient and attention. Firstly, a lightweight E-Mobile Net is designed as the backbone network of feature extraction; secondly, a feature enhanced attention assistant module is designed to enhance the adaptability and discrimination ability of the model; with multi-layer feature fusion regional suggestion network, foreground background classification and boundary box regression response map are obtained by cross-correlation, and the tracking results are calculated. The strategy network based on strategy gradient is used to optimize the template update and re detection strategy, which improves the overall tracking accuracy and efficiency of the model. Simulation experiments on an embedded device and multiple standard data sets show that compared with the current mainstream algorithms, the tracking accuracy is significantly improved 20%∼30%, the algorithm robustness also has obvious advantages, and the tracking speed on an embedded device is 56 fps can meet the real-time requirements.
UAV Image Multi-Labeling with Data-Efficient Transformers

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.
The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface

The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal with strong randomness, effective feature extraction and data mining techniques are urgently required for EEG classification of BCI. In this paper, the new bionic whale optimization algorithms (WOA) are proposed to promote the improved extreme learning machine (ELM) algorithms for EEG classification of BCI. Two improved WOA-ELM algorithms are designed to compensate for the deficiency of random weight initialization for basic ELM. Firstly, the top several best individuals are selected and voted to make decisions to avoid misjudgment on the best individual. Secondly, the initial connection weights and bias between the input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy (BNAS) and shrinking encircling mechanism (SEM), and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix to promote the generalization performance of the algorithm.As shown in the contrast results, the average accuracy of the proposed method can reach 93.67%, which is better than other methods on BCI dataset.
Variable-weighted FDA combined with t-SNE and multiple extreme learning machines for visual industrial process monitoring

The visualization of an operating state of industrial processes allows operators to identify and diagnose faults intuitively and quickly. The identification and diagnosis of faults are important for ensuring industrial production safety. A method that combines variable-weighted Fisher discriminant analysis (VWFDA), t-distributed stochastic neighbor embedding (t-SNE), and multiple extreme learning machines (ELMs) is proposed for visual process monitoring. First, the VWFDA weighs variables on the basis of their contribution to the fault, thereby amplifying the fault information. The VWFDA is used to extract feature vectors from industrial data, and normal state and various fault states can be separated from each other in the space formed by these feature vectors. Second, t-SNE is used to visualize these feature vectors. Third, given that t-SNE lacks a transformation matrix during dimension reduction, one ELM is used for each class data of t-SNE to obtain the mapping relation from its input data to its mapping points. Finally, the VWFDA and multiple trained ELMs are combined for online process monitoring. The performance of the proposed approach is compared with that of FDA-t-SNE and other methods on the basis of the Tennessee Eastman process, thereby confirming that the proposed approach is advantageous for visual industrial process monitoring.
A Survey of Needs and Features for Augmented Reality Collaborations in Collocated Spaces

In this paper we contribute a literature review and organization framework for classifying the collaboration needs and features that should be considered when designing headset-based augmented reality (AR) experiences for collocated settings. In recent years augmented reality technology has been experiencing significant growth through the emergence of headsets that allow gestural interaction, and AR designers are increasingly interested in using this technology to enhance collaborative activities in a variety of physical environments. However, collaborative AR applications need to contain features that enhance collaboration and satisfy needs that are present during the group activities. When AR designers lack an understanding of what collaborators need during an interaction, or what features have already been designed to solve those needs, then AR creators will spend time redesigning features that have already been created, or worse, create applications that do not contain necessary features. While much work has been done on designing virtual reality (VR) collaborative environments, AR environments are a relatively newer design space, and designers are lacking a comprehensive framework for describing needs that arise during collaborative activities and the features that could be designed into AR applications to satisfy those needs. In this paper we contribute a literature review of 92 papers in the areas of augmented reality and virtual reality, and we contribute a list of design features and needs that are helpful to consider when designing for headset-based collaborative AR experiences.
Gaussian Constrained Attention Network for Scene Text Recognition

Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduces an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. The proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. The experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https://github.com/Pay20Y/GCAN.