Reviewerly/exHarmony huggingface dataset:
Removing Atmospheric Turbulence via Deep Adversarial Learning

Restoring images degraded due to atmospheric turbulence is challenging as it consists of several distortions. Several deep learning methods have been proposed to minimize atmospheric distortions that consist of a single-stage deep network. However, we find that a single-stage deep network is insufficient to remove the mixture of distortions caused by atmospheric turbulence. We propose a two-stage deep adversarial network that minimizes atmospheric turbulence to mitigate this. The first stage reduces the geometrical distortion and the second stage minimizes the image blur. We improve our network by adding channel attention and a proposed sub-pixel mechanism, which utilizes the information between the channels and further reduces the atmospheric turbulence at the finer level. Unlike previous methods, our approach neither uses any prior knowledge about atmospheric turbulence conditions at inference time nor requires the fusion of multiple images to get a single restored image. Our final restoration models DT-GAN+ and DTD-GAN+ outperform the general state-of-the-art image-to-image translation models and baseline restoration models. We synthesize turbulent image datasets to train the restoration models. Additionally, we also curate a natural turbulent dataset from YouTube to show the generalisability of the proposed model. We perform extensive experiments on restored images by utilizing them for downstream tasks such as classification, pose estimation, semantic keypoint estimation, and depth estimation. We observe that our restored images outperform turbulent images in downstream tasks by a significant margin demonstrating the restoration model’s applicability in real-world problems.
Didactic Prototype for Teaching the MQTT Protocol Based on Free Hardware Boards and Node-RED

Due to the constant evolution of the technologies related to the Internet of Things and the great expansion of the protocols and devices that possibilitate their use, there is a need to enforce and to approach the study of its concepts and applications to the students of electronic engineering and technical university careers. In this work, a weather station is designed and built based on low-cost technologies, whose data can be visible through an online platform designed for this purpose. The weather station is oriented to be used in laboratory practices and for teaching of subjects related to IoT, sensing platforms, communication platforms, etc. The general objective of the low-cost weather station platform is to provide a didactic, portable and simple design for replication in classroom in order to provide a whole system from the beginning to the students. Thanks to the use of this low cost platform it was possible to perform better approach to this technology obtaining great improvements in the learning process of the students.
An IoT-oriented real-time storage mechanism for massive small files based on Swift

In the internet of things (IoT), large amounts of small files are generated from various structure sensors in cloud storage platforms. Real-time storage of massive small files will put great pressures to the traditional file system. By studying the real-time storage strategy of massive small files based on the object storage architecture Swift, we proved a unique aggregation storage strategy, called sequential data aggregation strategy (SDAS), for storage of small files. We designed a two-level index structure to improve writing rate by transferring randomly write to sequentially write. To improve overall data access efficiency and solve the performance bottleneck of proxy node, we utilise a file's potential relevance of timing to prefetch related files that are merged in other blocks. Simulation results show that relative to the original system, SDAS has shorter response time of writing operation, lower cost of index maintenance cost, more balanced node load and 30% reduction in disk overhead.
Let The Sunshine in: Sun Glare Detection on Automotive Surround-view Cameras

Sun glare is a commonly encountered problem in both manual and automated driving. Sun glare causes over-exposure in the image and significantly impacts visual perception algorithms. For higher levels of automated driving, it is essential for the system to understand that there is sun glare which can cause system degradation. There is very limited literature on detecting sun glare for automated driving. It is primarily based on finding saturated brightness areas and extracting regions via image processing heuristics. From the perspective of a safety system, it is necessary to have a highly robust algorithm. Thus we designed two complementary algorithms using classical image processing techniques and CNN which can learn global context. We also discuss how sun glare detection algorithm will efficiently fit into a typical automated driving system. As there is no public dataset, we created our own and will release it publicly via theWoodScape project [1] to encourage further research in this area.
Definitions and Applications of Augmented/Virtual Reality: A Survey

Reality is shaped differently in software environments through Virtual Reality VR and augmented Reality AR, it has a remarkable position and an important background with its role of ensuring contact between the software environment and the user. It was popular in the entertainment sector, in particularly industry, but over time, it becomes apparent that there would be a much greater need for VR/AR technologies in different areas dealing with tasks/issues in the real world. In This article we provide an overview of virtual and augmented reality systems and their principal domains of applications.
Neural labeled LDA: a topic model for semi-supervised document classification

Recently, some statistical topic modeling approaches based on LDA have been applied in the field of supervised document classification, where the model generation procedure incorporates prior knowledge to improve the classification performance. However, these customizations of topic modeling are limited by the cumbersome derivation of a specific inference algorithm for each modification. In this paper, we propose a new supervised topic modeling approach for document classification problems, Neural Labeled LDA (NL-LDA), which builds on the VAE framework, and designs a special generative network to incorporate prior information. The proposed model can support semi-supervised learning based on the manifold assumption and low-density assumption. Meanwhile, NL-LDA has a consistent and concise inference method while semi-supervised learning and predicting. Quantitative experimental results demonstrate our model has outstanding performance on supervised document classification relative to the compared approaches, including traditional statistical and neural topic models. Specially, the proposed model can support both single-label and multi-label document classification. The proposed NL-LDA performs significantly well on semi-supervised classification, especially under a small amount of labeled data. Further comparisons with related works also indicate our model is competitive with state-of-the-art topic modeling approaches on semi-supervised classification.
Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?

Global Covariance Pooling (GCP) aims at exploiting the second-order statistics of the convolutional feature.Its effectiveness has been demonstrated in boosting the classification performance of Convolutional Neural Networks (CNNs).Singular Value Decomposition (SVD) is used in GCP to compute the matrix square root.However, the approximate matrix square root calculated using Newton-Schulz iteration [14] outperforms the accurate one computed via SVD [15].We empirically analyze the reason behind the performance gap from the perspectives of data precision and gradient smoothness.Various remedies for computing smooth SVD gradients are investigated.Based on our observation and analyses, a hybrid training protocol is proposed for SVD-based GCP meta-layers such that competitive performances can be achieved against Newton-Schulz iteration.Moreover, we propose a new GCP metalayer that uses SVD in the forward pass, and Padé approximants in the backward propagation to compute the gradients.The proposed meta-layer has been integrated into different CNN models and achieves state-of-the-art performances on both large-scale and fine-grained datasets.
Using Distance EdTech for Remote Foreign Language Teaching During the COVID-19 Lockdown in Ukraine

The situation with a coronavirus challenged the education system and forced educators to shift to a fully E-learning. Many academic institutions were not ready for such a rapid change. The remote learning of foreign languages during the COVID-19 lockdown caused modern teaching technologies to expand the scope of the educational process, broaden its practical orientation, contribute to the intensification of students' independent work, increasing their cognitive activity. The goals of the article include: to define types and specifics of modern edTech for the educational process on the way of transition from no E-Learning to fully E-Learning; to eliminate the role of the Internet as means of information and communication edTech in teaching foreign languages at higher education establishments; to find out the ways of improving students' learning at higher educational establishments, the importance of using distance edTechs and Strengths, Weaknesses, Opportunities, &amp; Challenges (SWOC) analysis of E-learning techniques during the pandemic lockdown. This article also provides the examples of usage of different edTechs in Ukrainian higher education establismens in general. Drohobych Ivan Franko State Pedagogical University, Lviv State University of Life Safety, Odesa National Maritime University in particular.
Prescribed-Time Convergent Adaptive ZNN for Time-Varying Matrix Inversion under Harmonic Noise

Harmonic noises widely exist in industrial fields and always affect the computational accuracy of neural network models. The existing original adaptive zeroing neural network (OAZNN) model can effectively suppress harmonic noises. Nevertheless, the OAZNN model’s convergence rate only stays at the exponential convergence, that is, its convergence speed is usually greatly affected by the initial state. Consequently, to tackle the above issue, this work combines the dynamic characteristics of harmonic signals with prescribed-time convergence activation function, and proposes a prescribed-time convergent adaptive ZNN (PTCAZNN) for solving time-varying matrix inverse problem (TVMIP) under harmonic noises. Owing to the nonlinear activation function used having the ability to reject noises itself and the adaptive term also being able to compensate the influence of noises, the PTCAZNN model can realize double noise suppression. More importantly, the theoretical analysis of PTCAZNN model with prescribed-time convergence and robustness performance is provided. Finally, by varying a series of conditions such as the frequency of single harmonic noise, the frequency of multi-harmonic noise, and the initial value and the dimension of the matrix, the comparative simulation results further confirm the effectiveness and superiority of the PTCAZNN model.
Group public key encryption supporting equality test without bilinear pairings

In cloud computing scenarios, it is a common approach to encrypt the data before uploading in order to maintain the privacy. Public key encryption with equality test (PKEET) provides a generic solution to equality comparisons on encrypted data . Considering the practical requirements of PKEET in group user scenarios, Ling et al. presented a group public key encryption scheme with equality test (G-PKEET) by utilizing the computationally expensive bilinear pairing operations. In this paper, we propose an efficient and verifiable group public key encryption with equality test construction without bilinear pairings (GPKEET/BP). Our proposal is developed from a basic observation that two points determine a straight line, and is proven to be indistinguishable against the chosen-ciphertext attacks (IND-CCA) in the random oracle model under the computational Diffie-Hellman (CDH) assumption. Theoretical analysis shows that our construction only involves several modular exponentiations in the encryption, decryption and test algorithms. The theoretical results are well supported by experimental simulations, which show that the test algorithm of GPKEET/BP performs about 92 % faster than that of Ling et al.’s scheme. So our GPKEET/BP scheme turns out to be better suited for group ciphertext equality test situations.
An intelligent approach for Arabic handwritten letter recognition using convolutional neural network

Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transformation of manual documentation from hardcopy to digital formats. The transformation of handwritten documents into electronic files is one of the principal aspects of digitization and represents a common need shared by today’s businesses. Generally, handwriting recognition poses a complex digitization challenge, and Arabic handwriting recognition, specifically, proves inordinately challenging due to the nature of Arabic scripts and the excessive diversity in human handwriting. This study presents an intelligent approach for recognizing handwritten Arabic letters. In this approach, a convolution neural network (CNN) model is proposed to recognize handwritten Arabic letters. The model is regularized using batch normalization and dropout operations. Moreover, the model was tested with and without dropout, resulting in a significant difference in the performance. Hence, the model overfitting has been prevented using dropout regularization. The proposed model was applied to the prominent, publicly-available Arabic handwritten characters (AHCD) dataset with 16,800 letters, and the performance was measured using several evaluation measures. The experimental results show the best fit of the proposed model in terms of higher accuracy results that reached 96.78%; additionally, other evaluation measures compared to popular domain-relevant approaches in the literature.
The landscape of facial processing applications in the context of the European AI Act and the development of trustworthy systems

This work focuses on facial processing, which refers to artificial intelligence (AI) systems that take facial images or videos as input data and perform some AI-driven processing to obtain higher-level information (e.g. a person's identity, emotions, demographic attributes) or newly generated imagery (e.g. with modified facial attributes). Facial processing tasks, such as face detection, face identification, facial expression recognition or facial attribute manipulation, are generally studied as separate research fields and without considering a particular scenario, context of use or intended purpose. This paper studies the field of facial processing in a holistic manner. It establishes the landscape of key computational tasks, applications and industrial players in the field in order to identify the 60 most relevant applications adopted for real-world uses. These applications are analysed in the context of the new proposal of the European Commission for harmonised rules on AI (the AI Act) and the 7 requirements for Trustworthy AI defined by the European High Level Expert Group on AI. More particularly, we assess the risk level conveyed by each application according to the AI Act and reflect on current research, technical and societal challenges towards trustworthy facial processing systems.
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction

Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple domains is effective to improve the overall performance. However, it is still challenging to improve generalization across domains under limited training data, and hard to deploy current solutions due to computational complexity. In this paper, we propose AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better generalization across domains with lower computational cost. We introduce domain-aware neuron-level weighting factors to measure the importance of neurons, with that for each domain our model can prune redundant neurons to improve generalization. We further add flexible sparsity regularizations to control the sparsity ratio of learned structures. Offline and online experiments show that AdaSparse outperforms previous multi-domain CTR models significantly.
One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia

NLP research is impeded by a lack of resources and awareness of the challenges presented by underrepresented languages and dialects.Focusing on the languages spoken in Indonesia, the second most linguistically diverse and the fourth most populous nation of the world, we provide an overview of the current state of NLP research for Indonesia's 700+ languages.We highlight challenges in Indonesian NLP and how these affect the performance of current NLP systems.Finally, we provide general recommendations to help develop NLP technology not only for languages of Indonesia but also other underrepresented languages.
Word Graph Guided Summarization for Radiology Findings

Radiology reports play a critical role in communicating medical findings to physicians.In each report, the impression section summarizes essential radiology findings.In clinical practice, writing impression is highly demanded yet time-consuming and prone to errors for radiologists.Therefore, automatic impression generation has emerged as an attractive research direction to facilitate such clinical practice.Existing studies mainly focused on introducing salient word information to the general text summarization framework to guide the selection of the key content in radiology findings.However, for this task, a model needs not only capture the important words in findings but also accurately describe their relations so as to generate highquality impressions.In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSUM) is designed to generate impressions with the help of the word graph.Experimental results on two datasets, OPENI and MIMIC-CXR, confirm the validity and effectiveness of our proposed approach, where the state-of-the-art results are achieved on both datasets.Further experiments are also conducted to analyze the impact of different graph designs to the performance of our method. 1
Enhancing Security Patch Identification by Capturing Structures in Commits

With the rapid increasing number of open source software (OSS), the majority of the software vulnerabilities in the open source components are fixed silently, which leads to the deployed software that integrated them being unable to get a timely update. Hence, it is critical to design a security patch identification system to ensure the security of the utilized software. However, most of the existing works for security patch identification just consider the changed code and the commit message of a commit as a flat sequence of tokens with simple neural networks to learn its semantics, while the structure information is ignored. To address these limitations, in this paper, we propose our well-designed approach E-SPI, which extracts the structure information hidden in a commit for effective identification. Specifically, it consists of the code change encoder to extract the syntactic of the changed code with the BiLSTM to learn the code representation and the message encoder to construct the dependency graph for the commit message with the graph neural network (GNN) to learn the message representation. We further enhance the code change encoder by embedding contextual information related to the changed code. To demonstrate the effectiveness of our approach, we conduct the extensive experiments against six state-of-the-art approaches on the existing dataset and from the real deployment environment. The experimental results confirm that our approach can significantly outperform current state-of-the-art baselines.
Improving the bidirectional steerability between two accelerated partners via filtering process

In this paper, the bidirectional steering between two accelerated partners sharing initially different classes of entangled states is discussed. Due to the decoherence, the steerability and its degree decrease either as the acceleration increases or the partners share initially a small amount of quantum correlations. The possibility of increasing the steerability is investigated by applying the filtering process. Our results show that by increasing the filtering strength, one can improve the upper bounds of the steerability and the range of acceleration at which the steerability is possible. Steering large coherent states is much better than steering less coherent ones.
Two-Stage Hybrid Deep Learning With Strong Adaptability for Detailed Day-Ahead Photovoltaic Power Forecasting

Deep learning (DL) has been widely used in photovoltaic (PV) power forecasting due to its advantages in nonlinear processing and feature extraction. However, it faces an overfitting challenge when daily weather change, resulting in poor adaptability for practical applications. Conversely, the mapping strategy primarily applied in plant sacrifices the PV power fluctuation details for forecasting under complex weather conditions to improve adaptation. To keep long-term adaptations and accurately forecast fluctuation detail, this study proposes a novel two-stage hybrid DL (HDL) framework for day-ahead PV power forecasting with 15-min intervals. In the first numerical weather prediction (NWP) information mapping stage, motivated by forecasting strategies in plants, a mapping model is developed based on long short-term memory (LSTM) networks to forecast the general power trends with strong adaptation. Then, in the second stage of historical features (adjacency and similarity features) extraction, wavelet decomposition, LSTM, and a convolutional neural network are used to forecast more fluctuation details accurately. Moreover, a hyperparameter optimization method based on grid search and Bayesian optimization is proposed, which facilitates an unbiased development of DL. The developed framework is implemented on 10 MW and 100 kW plants and the results show that the proposed method can achieve correctness and adaptation among the rivals.
Digitized counterdiabatic quantum optimization

Polynomial enhancement with digitized-counterdiabatic quantum optimization over finite-time adiabatic quantum protocols for combinatorial optimization is presented. The role of nonstoquastic counterdiabatic terms and their effect on a minimal energy gap during the evolution is analyzed.