Reviewerly/exHarmony huggingface dataset:
Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation

Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs ​​of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended.
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding

Finetuning deep pre-trained language models has shown state-of-the-art performances on a wide range of Natural Language Processing (NLP) applications.Nevertheless, their generalization performance drops under domain shift.In the case of Arabic language, diglossia makes building and annotating corpora for each dialect and/or domain a more challenging task.Unsupervised Domain Adaptation tackles this issue by transferring the learned knowledge from labeled source domain data to unlabeled target domain data.In this paper, we propose a new unsupervised domain adaptation method for Arabic cross-domain and crossdialect sentiment analysis from Contextualized Word Embedding.Several experiments are performed adopting the coarse-grained and the fine-grained taxonomies of Arabic dialects.The obtained results show that our method yields very promising results and outperforms several domain adaptation methods for most of the evaluated datasets.On average, our method increases the performance by an improvement rate of 20.8% over the zero-shot transfer learning from BERT.
Model-driven engineering for multi-party business processes on multiple blockchains

As a disruptive technology, the blockchain is continuously finding novel application contexts, bringing new opportunities and radical changes. In this paper, we use blockchain as a communication infrastructure to support multi-party business processes. In particular, through smart contracts specifically generated by the mentioned business process, it is possible to derive a trustable infrastructure enabling the interaction among parties. Moreover, the emergence of different blockchain technologies, satisfying different characteristics, gives the possibility to support the same business process dealing with different non-functional needs. In this paper, we propose a novel engineering methodology supported by a practical framework called Multi-Chain. It permits to derive, using a model-driven strategy, a blockchain-based infrastructure, that can be deployed over a specific blockchain technology (e.g., Ethereum or Hyperledger Fabric). The objective is to permit the single definition and multiple deployments of the business process, to deliver the same functionalities, but satisfying different non-functional needs. In such a way, organisations willing to cooperate can select the multi-party business process and the blockchain technology they would like to use to satisfy their needs. Using Multi-Chain, they will be able to automatically derive from a Business Process Modelling Notation (BPMN) choreography diagram a blockchain infrastructure ready to be used. This overcomes the need to get acquainted with many details of the specific technology.
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions

Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks.However, existing models require a large amount of parallel image-caption data for pre-training.Such data are costly to collect and require cumbersome curation.Inspired by unsupervised machine translation, we investigate if a strong V&L representation model can be learned through unsupervised pre-training without image-caption corpora.In particular, we propose to conduct "mask-and-predict" pre-training on text-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities.We find that such a simple approach achieves performance close to a model pre-trained with aligned data, on four English V&L benchmarks.Our work challenges the widely held notion that aligned data is necessary for V&L pre-training, while significantly reducing the amount of supervision needed for V&L models.
MSFC: Deep Feature Compression in Multi-Task Network

With the remarkable success of deep learning, a novel AI-deployment strategy on mobile devices called collaborative intelligence (CI) is proposed recently, which can greatly improve the efficiency of neural network by distributing work-loads between mobile devices and the cloud. In order to reduce transmission overhead, feature maps obtained from mobile devices need to be compressed before being transmitted to the cloud. In this paper, we propose a multi-scale feature compression (MSFC) framework for applying complex multi-task learning network in CI deployment scenarios, which consists of a multi-scale feature fusion (MSFF) module, a single-stream feature codec (SSFC) and a multi-scale feature reconstruction (MSFR) module. When applied to the popular multi-task network Mask R-CNN, experimental results show that with less than 2% accuracy degradation, the proposed MSFC can compress the 32-bit floating point feature to 0.012 bits on average.
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation

We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformerbased sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts.We adapt the generative BART architecture (Lewis et al., 2020) to a multimodal model with visual and textual inputs.We further develop novel pretraining tasks to improve the model performance on the Visual Commonsense Generation (VCG) task.In particular, our pretraining task of Knowledge-based Commonsense Generation (KCG) boosts model performance on the VCG task by leveraging commonsense knowledge from a large language model pretrained on external commonsense knowledge graphs.To the best of our knowledge, we are the first to propose a dedicated task for improving model performance on the VCG task.Experimental results show that our model reaches state-of-the-art performance on the VCG task (Park et al., 2020) by applying these novel pretraining tasks. Decoder Encoder
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains

Pre-trained language models have been applied to various NLP tasks with considerable performance gains.However, the large model sizes, together with the long inference time, limit the deployment of such models in realtime applications.One line of model compression approaches considers knowledge distillation to distill large teacher models into small student models.Most of these studies focus on single-domain only, which ignores the transferable knowledge from other domains.We notice that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation.Hence we propose a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students.Specifically, we explicitly force the meta-teacher to capture transferable knowledge at both instance-level and feature-level from multiple domains, and then propose a meta-distillation algorithm to learn singledomain student models with guidance from the meta-teacher.Experiments on public multidomain NLP tasks show the effectiveness and superiority of the proposed Meta-KD framework.Further, we also demonstrate the capability of Meta-KD in the settings where the training data is scarce.
DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations

This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes; and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject. These two cases are developed experimentally for individual digits and handwriting signatures, respectively, achieving in both cases remarkable results. Also, we provide experimental results for the task of on-line signature verification showing the high potential of DeepWriteSYN to improve significantly one-shot learning scenarios. To the best of our knowledge, this is the first synthesis approach capable of generating realistic on-line handwriting in the short term (including handwritten signatures) via deep learning. This can be very useful as a module toward long-term realistic handwriting generation either completely synthetic or as natural variation of given handwriting samples.
Few-Shot One-Class Classification via Meta-Learning

Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot OCC problem and presents a method to modify the episodic data sampling strategy of the model-agnostic meta-learning (MAML) algorithm to learn a model initialization particularly suited for learning few-shot OCC tasks. This is done by explicitly optimizing for an initialization which only requires few gradient steps with one-class minibatches to yield a performance increase on class-balanced test data. We provide a theoretical analysis that explains why our approach works in the few-shot OCC scenario, while other meta-learning algorithms fail, including the unmodified MAML. Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples. Moreover, we successfully train anomaly detectors for a real-world application on sensor readings recorded during industrial manufacturing of workpieces with a CNC milling machine, by using few normal examples. Finally, we empirically demonstrate that the proposed data sampling technique increases the performance of more recent meta-learning algorithms in few-shot OCC and yields state-of-the-art results in this problem setting.
DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation

The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from the unlabeled data. In this paper, we propose a novel deep hidden backdoor (DeHiB) attack scheme for SSL-based systems. In contrast to the conventional attacking methods, the DeHiB can inject malicious unlabeled training data to the semi-supervised learner so as to enable the SSL model to output premeditated results. In particular, a robust adversarial perturbation generator regularized by a unified objective function is proposed to generate poisoned data. To alleviate the negative impact of the trigger patterns on model accuracy and improve the attack success rate, a novel contrastive data poisoning strategy is designed. Using the proposed data poisoning scheme, one can implant the backdoor into the SSL model using the raw data without hand-crafted labels. Extensive experiments based on CIFAR10 and CIFAR100 datasets demonstrated the effectiveness and crypticity of the proposed scheme.
Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System

Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven.
IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks

We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framework to the optimization of GAN, thereby named IB-GAN. The architecture of IB-GAN is partially similar to that of InfoGAN but has a critical difference; an intermediate layer of the generator is leveraged to constrain the mutual information between the input and the generated output. The intermediate stochastic layer can serve as a learnable latent distribution that is trained with the generator jointly in an end-to-end fashion. As a result, the generator of IB-GAN can harness the latent space in a disentangled and interpretable manner. With the experiments on dSprites and Color-dSprites dataset, we demonstrate that IB-GAN achieves competitive disentanglement scores to those of state-of-the-art β-VAEs and outperforms InfoGAN. Moreover, the visual quality and the diversity of samples generated by IB-GAN are often better than those by β-VAEs and Info-GAN in terms of FID score on CelebA and 3D Chairs dataset.
MARTA: Leveraging Human Rationales for Explainable Text Classification

Explainability is a key requirement for text classification in many application domains ranging from sentiment analysis to medical diagnosis or legal reviews. Existing methods often rely on "attention" mechanisms for explaining classification results by estimating the relative importance of input units. However, recent studies have shown that such mechanisms tend to mis-identify irrelevant input units in their explanation. In this work, we propose a hybrid human-AI approach that incorporates human rationales into attention-based text classification models to improve the explainability of classification results. Specifically, we ask workers to provide rationales for their annotation by selecting relevant pieces of text. We introduce MARTA, a Bayesian framework that jointly learns an attention-based model and the reliability of workers while injecting human rationales into model training. We derive a principled optimization algorithm based on variational inference with efficient updating rules for learning MARTA parameters. Extensive validation on real-world datasets shows that our framework significantly improves the state of the art both in terms of classification explainability and accuracy.
Progressive Joint Low-Light Enhancement and Noise Removal for Raw Images

Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in low image quality. Most of the previous works on low-light imaging focus either only on a single task such as illumination adjustment, color enhancement, or noise removal; or on a joint illumination adjustment and denoising task that heavily relies on short-long exposure image pairs from specific camera models. These approaches are less practical and generalizable in real-world settings where camera-specific joint enhancement and restoration is required. In this paper, we propose a low-light imaging framework that performs joint illumination adjustment, color enhancement, and denoising to tackle this problem. Considering the difficulty in model-specific data collection and the ultra-high definition of the captured images, we design two branches: a coefficient estimation branch and a joint operation branch. The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint operation branch works in a full-resolution space and progressively performs joint enhancement and denoising. In contrast to existing methods, our framework does not need to recollect massive data when adapted to another camera model, which significantly reduces the efforts required to fine-tune our approach for practical usage. Through extensive experiments, we demonstrate its great potential in real-world low-light imaging applications.
Proposal for Entangling Gates on Fluxonium Qubits via a Two-Photon Transition

We propose a family of microwave-activated entangling gates on two capacitively coupled fluxonium qubits. A microwave pulse applied to either qubit at a frequency near the half-frequency of the $|00\rangle - |11\rangle$ transition induces two-photon Rabi oscillations with a negligible leakage outside the computational subspace, owing to the strong anharmonicity of fluxoniums. By adjusting the drive frequency, amplitude, and duration, we obtain the gate family that is locally equivalent to the fermionic-simulation gates such as $\sqrt{\rm SWAP}$-like and controlled-phase gates. The gate error can be tuned below $10^{-4}$ for a pulse duration under 100 ns without excessive circuit parameter matching. Given that the fluxonium coherence time can exceed 1 ms, our gate scheme is promising for large-scale quantum processors.
MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing

Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by the drawing process of experienced manga artists, MangaGAN generates geometric features and converts each facial region into the manga domain with a tailored multi-GANs architecture. For training MangaGAN, we collect a new data-set from a popular manga work with extensive features. To produce high-quality manga faces, we propose a structural smoothing loss to smooth stroke-lines and avoid noisy pixels, and a similarity preserving module to improve the similarity between domains of photo and manga. Extensive experiments show that MangaGAN can produce high-quality manga faces preserving both the facial similarity and manga style, and outperforms other reference methods.
Stakeholder perspectives and requirements on cybersecurity in Europe

This article presents an overview and analysis of the key cybersecurity problems, challenges and requirements to be addressed in the future, which we derived through 63 interviews with European stakeholders from security-critical sectors including Open Banking, Supply Chain, Privacy-preserving Identity Management, Security Incident Reporting, Maritime Transport, Medical Data Exchange, and Smart Cities. We show that common problems, challenges and requirements across these sectors exist in relation to building trust, implementing privacy and identity management including secure and useable authentication, building resilient systems, standardisation and certification, achieving security and privacy by design, secure and privacy-compliant data and information sharing, and government regulations. Our results also indicate cybersecurity trends and allow to derive directions for future research and innovation activities that will be of high importance for Europe.
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating

Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL applications, usually only one or partial parties hold labels, which makes it challenging for all parties to collaboratively learn the model without privacy leakage. Meanwhile, most existing VFL algorithms are trapped in the synchronous computations, which leads to inefficiency in their real-world applications. To address these challenging problems, we propose a novel VFL framework integrated with new backward updating mechanism and bilevel asynchronous parallel architecture (VFB^2), under which three new algorithms, including VFB^2-SGD, -SVRG, and -SAGA, are proposed. We derive the theoretical results of the convergence rates of these three algorithms under both strongly convex and nonconvex conditions. We also prove the security of VFB^2 under semi-honest threat models. Extensive experiments on benchmark datasets demonstrate that our algorithms are efficient, scalable, and lossless.