Reviewerly/exHarmony huggingface dataset:
Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation

Autoregressive models recently achieved comparable results versus state-of-the-art Generative Adversarial Networks (GANs) with the help of Vector Quantized Variational AutoEncoders (VQ-VAE). However, autoregressive models have several limitations such as exposure bias and their training objective does not guarantee visual fidelity. To address these limitations, we propose to use Reinforced Adversarial Learning (RAL) based on policy gradient optimization for autoregressive models. By applying RAL, we enable a similar process for training and testing to address the exposure bias issue. In addition, visual fidelity has been further optimized with adversarial loss inspired by their strong counterparts: GANs. Due to the slow sampling speed of autoregressive models, we propose to use partial generation for faster training. RAL also empowers the collaboration between different modules of the VQ-VAE framework. To our best knowledge, the proposed method is first to enable adversarial learning in autoregressive models for image generation. Experiments on synthetic and real-world datasets show improvements over the MLE trained models. The proposed method improves both negative log-likelihood (NLL) and Fréchet Inception Distance (FID), which indicates improvements in terms of visual quality and diversity. The proposed method achieves state-of-the-art results on Celeba for 64 $$\times $$ 64 image resolution, showing promise for large scale image generation.
Testing self-adaptive software with probabilistic guarantees on performance metrics

This paper discusses the problem of testing the performance of the adaptation layer in a self-adaptive system. The problem is notoriously hard, due to the high degree of uncertainty and variability inherent in an adaptive software application. In particular, providing any type of formal guarantee for this problem is extremely difficult. In this paper we propose the use of a rigorous probabilistic approach to overcome the mentioned difficulties and provide probabilistic guarantees on the software performance. We describe the set up needed for the application of a probabilistic approach. We then discuss the traditional tools from statistics that could be applied to analyse the results, highlighting their limitations and motivating why they are unsuitable for the given problem. We propose the use of a novel tool – the scenario theory – to overcome said limitations. We conclude the paper with a thorough empirical evaluation of the proposed approach, using two adaptive software applications: the Tele-Assistance Service and the Self-Adaptive Video Encoder. With the first, we empirically expose the trade-off between data collection and confidence in the testing campaign. With the second, we demonstrate how to compare different adaptation strategies.
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.However, most existing methods either use handcrafted features or learn features using deep learning with simple text corpus as input for review rating prediction, ignoring the hierarchies among data.In this paper, we propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation, which can serve as an effective decision-making tool for the academic paper review process.Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and interreview encoder (level three).Each encoder first derives contextual representation of each level, then generates a higher-level representation, and after the learning process, we are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.Furthermore, we introduce two new metrics to evaluate models in data imbalance situations.Extensive exper
Evaluation of English online teaching based on remote supervision algorithms and deep learning

The English online teaching automatic evaluation system is unstable in actual teaching evaluation. Therefore, how the automatic evaluation system can better adapt to high school teaching also needs a more in-depth theoretical and practical discussion. According to the actual needs of English online teaching, this article combines remote supervision and deep learning algorithms, builds a system structure for the English online teaching evaluation process, and simulates and analyzes the application of supervision algorithms in the teaching process. Moreover, this article evaluates the actions and status of the student’s learning process from the aspects of teacher evaluation and student evaluation, and also scores the teacher’s teaching process. In order to study the practical effect of this system in English online teaching, this paper designs experiments to evaluate the model online English teaching effect. The research results show that the model constructed in this paper has good performance.
Privacy-Enhancing Decentralized Anonymous Credential in Smart Grids

Decentralized Anonymous Credential (DAC) has posed enormous potential in smart grids for protecting user privacy (e.g. preventing user’s daily life from being sketched out). However, the existing DAC protocols still compromise user privacy for a full list of attributes are disclosed during showing credentials. In this paper, to construct a privacy-enhancing decentralized anonymous credential (PEDAC) protocol, we first design a more efficient range proof to hide user’s attributes. Specifically, we are inspired by Camenisch et al. (ASIACRYPT 2008), but replace their adopted Boneh-Boyen signature scheme with the Chinese standard SM2 signature scheme (incorporated in the ISO/IEC 14888-3) to propose pairing-free set membership and range proof protocols. These protocols can be executed non-interactively upon the Fiat-Shamir heuristic (INDOCRYPT 2012), and then be used to construct our PEDAC protocol. By compared with the protocols of Camenisch et al. (ASIACRYPT 2008) and Poelstra et al. (FC 2018), our proposed range proofs are with less communication and computation costs and hence more practical for constructing PEDAC protocols in smart grids.
Features of modern distance learning for students

The study examines the features of distance learning for university students from different countries. The current problem has escalated during the pandemic to ensure the safety of students and university staff. The authors pay great attention to the peculiarities of distance learning in Russia, based on an analysis of the literature and regulatory documents. The results obtained show that most universities provide distance learning on modern technical, software, subject, methodological, administrative and management basis and cloud of service support.
Tracking Emotions: Intrinsic Motivation Grounded on Multi - Level Prediction Error Dynamics

We present an intrinsic motivation architecture that generates behaviors towards self-generated and dynamic goals and that regulates goal selection and the balance between exploitation and exploration through multi-level monitoring of prediction error dynamics. This architecture modulates exploration noise and leverages computational resources according to the dynamics of the overall performance of the learning system. Results show that this architecture outperforms intrinsic motivation approaches where exploratory noise and goals are fixed. We suggest that the tracking of prediction error dynamics allows an artificial agent to be intrinsically motivated to seek new experiences but constrained to those that generate reducible prediction error. We argue about the potential relationship between emotional valence and rates of progress toward a goal.
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements

Understanding image advertisements is a challenging task, often requiring non-literal interpretation.We argue that standard image-based predictions are insufficient for symbolism prediction.Following the intuition that texts and images are complementary in advertising, we introduce a multimodal ensemble of a state of the art image-based classifier, a classifier based on an object detection architecture, and a fine-tuned language model applied to texts extracted from ads by OCR.The resulting system establishes a new state of the art in symbolism prediction.
Automatic Modulation Recognition for Secondary Modulated Signals

Automatic modulation recognition (AMR) for secondary modulated signals, remains as a bottleneck, since both analog and digital modulation are adopted in one signal. To address this issue, we focus on the original signal spectrum and the 2nd order spectrum of the pre-demodulated signal, which imply modulation features. Based on this idea, a novel statistic for discrimination is proposed and the feature vector is formed. Finally, the support vector machine (SVM) is adopted to implement the classification work.
Stopping Criterion during Rendering of Computer-Generated Images Based on SVD-Entropy

The estimation of image quality and noise perception still remains an important issue in various image processing applications. It has also become a hot topic in the field of photo-realistic computer graphics where noise is inherent in the calculation process. Unlike natural-scene images, however, a reference image is not available for computer-generated images. Thus, classic methods to assess noise quantity and stopping criterion during the rendering process are not usable. This is particularly important in the case of global illumination methods based on stochastic techniques: They provide photo-realistic images which are, however, corrupted by stochastic noise. This noise can be reduced by increasing the number of paths, as proved by Monte Carlo theory, but the problem of finding the right number of paths that are required in order to ensure that human observers cannot perceive any noise is still open. Until now, the features taking part in the human evaluation of image quality and the remaining perceived noise are not precisely known. Synthetic image generation tends to be very expensive and the produced datasets are high-dimensional datasets. In that case, finding a stopping criterion using a learning framework is a challenging task. In this paper, a new method for characterizing computational noise for computer generated images is presented. The noise is represented by the entropy of the singular value decomposition of each block composing an image. These Singular Value Decomposition (SVD)-entropy values are then used as input to a recurrent neural network architecture model in order to extract image noise and in predicting a visual convergence threshold of different parts of any image. Thus a new no-reference image quality assessment is proposed using the relation between SVD-Entropy and perceptual quality, based on a sequence of distorted images. Experiments show that the proposed method, compared with experimental psycho-visual scores, demonstrates a good consistency between these scores and stopping criterion measures that we obtain.
Monitoring Health Parameters of Elders to Support Independent Living and Improve Their Quality of Life

Improving the well-being and quality of life of the elderly population is closely related to assisting them to effectively manage age-related conditions such as chronic illnesses and anxiety, and to maintain their independence and self-sufficiency as much as possible. This paper presents the design, architecture and implementation structure of an adaptive system for monitoring the health and well-being of the elderly. The system was designed following best practices of the Human-Centred Design approach involving representative end-users from the early stages.
Improved algorithm of ID card detection by a priori knowledge of the document aspect ratio

In this work, we consider a problem of quadrilateral document borders detection in images captured by a mobile device’s camera. State-of-the-art algorithms for the quadrilateral document borders detection are not designed for cases when one of the document borders is either completely out of the frame, obscured, or of low contrast. We propose the algorithm which correctly processes the image in such cases. It is built on the classical contour-based algorithm. We modify the latter using the document’s aspect ratio which is known a priori. We demonstrate that this modification reduces the number of incorrect detections by 34% on an open dataset MIDV-500.
A pooling-based feature pyramid network for salient object detection

How to effectively utilize and fuse deep features has become a critical point for salient object detection. Most existing methods usually adopt the convolutional features based on U-shape structures and fuse multi-scale convolutional features without fully considering the different characteristics between high-level features and low-level features. Furthermore, existing salient object detection methods rarely consider the role of pooling in convolutional neural networks. Moreover, there is still much room to improve the detection performance for objects in complex scenes. To address the problems mentioned above, we propose a pooling-based feature pyramid (PFP) network to boost salient object detection performance in this paper. First, we design two U-shaped feature pyramid modules to capture rich semantic information from high-level features and to obtain clear saliency boundaries from low-level features respectively. Second, a pyramid pooling refinement module is designed to utilize the pooling to capture more semantic information. Third, a universal channel-wise attention (UCA) module is designed to select effective high-level features of multi-scale and multi-receptive-field for rich semantic information, even in complex scenes. Finally, we fuse the selected high-level features and low-level features together, followed by an edge preservation loss to obtain accurate boundary location. Extensive experiments are conducted on five datasets and the experimental results indicate that our proposed method has the ability to get better salient object detection performance compared to the state-of-the-art methods.
Localization-Aware Adaptive Pairwise Margin Loss for Fine-Grained Image Recognition

Fine-grained image recognition is a highly challenging problem due to subtle differences between images. There are many attempts to solve fine-grained image recognition problems using data augmentation, jointly optimizing deep metric learning. CutMix is one of the excellent data augmentation strategies which crops and merges to generate new images. However, it sometimes generates meaningless and obscured object images that degrade recognition performance. We propose a novel framework that solves the above problem and expands the CutMix leveraging localizing method. Also, we improve the recognition accuracy to joint optimizing with a pairwise margin loss using generated images from the improved CutMix. There are some images similar to the reference image among the generated images. They are generated by replacing similar parts from the reference image. Those generated images should not be located much farther than the margin value in embedding space because those generated images and a reference image have similar semantic meaning. However, the conventional margin loss can not consider those images which are located much farther than the margin. To solve this problem, we propose an additional margin loss to consider those generated images. The proposed framework consists of two stages: the part localization-aware CutMix and an adaptive pairwise margin loss. The proposed method achieves state-of-the-art performance on the CUB-200-2011, FGVC-Aircraft, Stanford Cars, and DeepFashion datasets. Furthermore, extensive experiments demonstrate that each stage improves the final performance.
How Value is Created in Tokenized Assets

A tidal wave of change is coming to the world of Economic Science. Digital tokens — including bitcoin, altcoins, and cryptocurrencies — will require a fundamental rethinking of valuation, in the same way that the introduction of the stock market required a new understanding of value. As of this writing, the total value of all tokens stands at $500 billion. How do investors place value on computer code, with no central bank or physical asset to support it? Drawing from the literature on behavioral economics and cognitive psychology, we provide a clear understanding of how investors are valuing these new digital assets, making this the first study of applied behavioral economics on token valuation. Using a new instrument called the Framework for Token Confidence, we show how value can be created out of “thin air,” and how tokens — indeed, our entire economic system — operate as something like a “vote of confidence.”
An Immersive Node-Link Visualization of Artificial Neural Networks for Machine Learning Experts

The black box problem of artificial neural networks (ANNs) is still a very relevant issue. When communicating basic concepts of ANNs, they are often depicted as node-link diagrams. Despite this being a straight forward way to visualize them, it is rarely used outside an educational context. However, we hypothesize that large-scale node-link diagrams of full ANNs could be useful even to machine learning experts. Hence, we present a visualization tool that depicts convolutional ANNs as node-link diagrams using immersive virtual reality. We applied our tool to a use-case in the field of machine learning research and adapted it to the specific challenges. Finally, we performed an expert review to evaluate the usefulness of our visualization. We found that our node-link visualization of ANNs was perceived as helpful in this professional context.
Medical Video Super-Resolution Based on Asymmetric Back-Projection Network With Multilevel Error Feedback

Medical video is important for medical diagnosis. However, due to the influence of the network bandwidth and hardware equipment, some medical videos have low resolution, which is not conducive for diagnosing early diseases with small lesions. Medical videos with super-resolutions can help doctors clearly observe small lesions and increase the likelihood of diagnosing and curing diseases. In this paper, we propose an advanced medical video super-resolution method based on an asymmetric back-projection network. We construct a single-frame medical video super-resolution model as the benchmark model, combine the optical flow algorithm and multiframe fusion strategy to propose a medical video super-resolution method based on an asymmetric back-projection network with multilevel error feedback, and train high-quality and high-speed medical video super-resolution models. Finally, we use the proposed method to create super-resolution versions of different types of low-resolution medical videos and evaluate the quality and efficiency of reconstruction. The experimental results show that the proposed medical video super-resolution method achieves superior performance compared to that of other methods considered in this work.
AlBERTo: Modeling Italian Social Media Language with BERT

Natural Language Processing tasks recently achieved considerable interest and progresses following the development of numerous innovative artificial intelligence models released in recent years. The increase in available computing power has made possible the application of machine learning approaches on a considerable amount of textual data, demonstrating how they can obtain very encouraging results in challenging NLP tasks by generalizing the properties of natural language directly from the data. Models such as ELMo, GPT/GPT-2, BERT, ERNIE, and RoBERTa have proved to be extremely useful in NLP tasks such as entailment, sentiment analysis, and question answering. The availability of these resources mainly in the English language motivated us towards the realization of AlBERTo, a natural language model based on BERT and trained on the Italian language. We decided to train AlBERTo from scratch on social network language, Twitter in particular, because many of the classic tasks of content analysis are oriented to data extracted from the digital sphere of users. The model was distributed to the community through a repository on GitHub and the Transformers library (Wolf et al. 2019) released by the development group huggingface.co. We have evaluated the validity of the model on the classification tasks of sentiment polarity, irony, subjectivity, and hate speech. The specifications of the model, the code developed for training and fine-tuning, and the instructions for using it in a research project are freely available.
A feasibility study of the 1.5-axis tracking model in utility-scale solar PV plants

Many solar tracking technologies have been developed with the aim of improving the energy performance of solar PV installations. Among these, single-axis tracking (SAT) systems give good economic prospect for ground-mounted PV plants but experience inevitable power loss when the trackers diverge from the incident solar irradiance during winter. The 1.5-axis tracking (1.5AT) model is developed in recent years to achieve a balance between the benefits and shortcomings of the SAT and dual-axis tracking (DAT) systems. Despite the anticipated superiority of the 1.5AT, there is a lack of literature on the methodology to analytically study the potential and feasibility of the 1.5AT model, especially from a global perspective. In this paper, a comprehensive feasibility study has been conducted to evaluate the energy performance and shading analysis of the 1.5AT model. The developed methodology serves as a general benchmark to explore the prospects of the 1.5AT model at different geographical locations and seasons. This encourages further research and deployment of the 1.5AT model as a potential alternative to improve the return of investment for various PV system stakeholders.
An Investigation Into the Effect of the Learning Rate on Overestimation Bias of Connectionist Q-learning

In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation bias, which may lead to poor performance or unstable learning. In this paper, we present a novel analysis of this problem using various control tasks. For solving these tasks, Q-learning is combined with a multilayer perceptron (MLP), experience replay, and a target network. We focus our analysis on the effect of the learning rate when training the MLP. Furthermore, we examine if decaying the learning rate over time has advantages over static ones. Experiments have been performed using various maze-solving problems involving deterministic or stochastic transition functions and 2D or 3D grids and two Open-AI gym control problems. We conducted the same experiments with Double Q-learning using two MLPs with the same parameter settings, but without target networks. The results on the maze problems show that for Q-learning combined with the MLP, the overestimation occurs when higher learning rates are used and not when lower learning rates are used. The Double Q-learning variant becomes much less stable with higher learning rates and with low learning rates the overestimation bias may still occur. Overall, decaying learning rates clearly improves the performances of both Q-learning and Double Q-learning.