Reviewerly/exHarmony huggingface dataset:
Bayesian Belief Networks for Integrating Scientific and Stakeholders’ Knowledge to Support Nature-Based Solution Implementation

There is a growing interest worldwide on the potential of nature-based solutions (NBSs) as measures for dealing with water-related risks while producing multiple co-benefits that can contribute to several societal challenges and many of the sustainable development goals. However, several barriers still hamper their wider implementation, such as mainly the lack of stakeholders’ engagement and the limited integration of stakeholders’ knowledge throughout the phases of NBS design and implementation. This is a crucial aspect to guarantee that the multidimensional implications of NBSs are adequately understood and considered by decision-makers. Innovative methods and tools for improving NBS design and supporting decision-makers in overcoming the main barriers to implementation, ultimately enhancing their effectiveness, are therefore needed. The present work proposes a combined approach based on the integration of fuzzy cognitive maps, hydraulic modeling, and participatory Bayesian belief networks aiming to facilitate the stakeholders’ engagement and the knowledge integration process in NBS design and assessment. The approach was developed and implemented within the NAIAD project in the Lower Danube demo site, specifically oriented to support the process of the Potelu Wetland restoration. First, fuzzy cognitive maps are adopted as a problem structuring method for eliciting stakeholders’ risk perception and problem understanding, and for constructing a causal model describing the system as a whole, with specific attention to the expected role of the NBS in reducing flood risk and addressing the key local challenges. Second, hydraulic modeling is used to analyze the effect of extreme floods starting from the retrospective analysis of a specific event and to model the potential benefits of risk reduction measures. Last, a Bayesian belief network is used to support the model integration process and a scenario analysis with a user-friendly tool. The whole process can be replicated in other areas and is particularly suitable to support an active engagement of stakeholders (both institutional and not) in the process of NBS design and assessment.
Adaptive Hypergraph Auto-Encoder for Relational Data Clustering

The embedded representation and clustering tasks both play important roles in relational data analysis and mining. Traditional methods mainly employ graph structure to describe relational data, but intuitive pairwise connections among nodes are insufficient to model high-order data in the real-world, such as the relations between proteins and polypeptide chains. Hypergraphs are a generalization of graphs, and hypergraphs can well model high-order data. When modeling relational data in the real world, hypergraphs are often accompanied by node attributes, i.e. attributed hypergraphs. Besides this, how to integrate the structural information and attribute information appropriately is another important task, while has not been investigated systematically. In this paper, we propose Adaptive Hypergraph Auto-Encoder(AHGAE) to learn node embeddings in low-dimensional space. Our method can utilize the high-order relation to generate embedding for clustering. It is composed of two procedures, i.e. the adaptive hypergraph Laplacian smoothing filter and the relational reconstruction auto-encoder. It has the advantage of integrating more complex data relations compared with graph-based methods, which leads to better modeling and clustering performance. The proposed method has been evaluated on hypergraph datasets and benchmark graph datasets. Experimental results and comparison with the state-of-the-art methods have demonstrated the effectiveness of our proposed method.
Blockchain Technology for Convergence

Blockchain technologies are drawing attention after the success of cryptocurrency. Due to the inherent features, such as decentralization, transparency, security, immutability, and integrity, they have already become the prime choice of researchers and scientists. Blockchain is among the most disruptive innovations which have the potential to reshape the behavior of many businesses and industries. Blockchain applications are based on DLT in which public ledger can be accessed by everyone by eliminating the need of third party. Although the power of AI allows the intelligence and decision-making powers of machines in the same way as humans, it relies on a unified model for training and validating datasets. However, the unified nature of AI poses many threats to data privacy and data tempering. Thus, the unique features of blockchain technology makes its application attractive in almost every field including financial services, healthcare, IoT, and many more. This chapter presents a comprehensive overview on blockchain and its integration with AI to explore numerous capabilities.
Two-Level Genetic Algorithm for Evolving Convolutional Neural Networks for Pattern Recognition

The aim of Neuroevolution is to find neural networks and convolutional neural network (CNN) architectures automatically through evolutionary algorithms. A crucial problem in neuroevolution is search time, since multiple CNNs must be trained during evolution. This problem has led to fitness acceleration approaches, generating a trade-off between time and fitness fidelity. Also, since search spaces for this problem usually include only a few parameters, this increases the human bias in the search. In this work, we propose a novel two-level genetic algorithm (GA) for addressing the fidelity-time trade-off problem for the fitness computation in CNNs. The first level evaluates many individuals quickly, and the second evaluates only those with the best results more finely. We also propose a search space with few restrictions, and an encoding with unexpressed genes to facilitate the crossover operation. This search space allows CNN architectures to have any sizes, shapes, and skip-connections among nodes. The two-level GA was applied to the pattern recognition problem on seven datasets, five MNIST-Variants, Fashion-MNIST, and CIFAR-10, achieving significantly better results than all those previously published. Our results show an improvement of 39.89% (4.2% error reduction) on the most complex dataset of MNIST (MRDBI), and on average 30.52% (1.35% error reduction) on all the five datasets. Furthermore, we show that our algorithm performed as well as precise-training GA, but took only the time of a fast-training GA. These results can be relevant and useful not only for image classification problems but also for GA-related problems.
Generative Adversarial Neural Networks and Deep Learning: Successful Cases and Advanced Approaches

Cross-domain artificial intelligence (AI) frameworks are the keys to amplify progress in science. Cutting edge deep learning methods offer novel opportunities for retrieving, optimizing, and improving different data types. AI techniques provide new ways for enhancing and polishing existing models that are used in applied sciences. New breakthroughs in generative adversarial neural networks (GANNs/GANs) and deep learning allow to drastically increase the quality of diverse graphic samples obtained with research equipment. All these innovative approaches can be compounded into a unified academic and technological pipeline that can radically elevate and accelerate scientific research and development. The authors analyze a number of successful cases of GAN and deep learning applications in applied scientific fields (including observational astronomy, health care, materials science, deep fakes, bioinformatics, and typography) and discuss advanced approaches for increasing GAN and DL efficiency in terms of performance calibration using modified data samples, algorithmic enhancements, and various hybrid methods of optimization.
VideoDG: Generalizing Temporal Relations in Videos to Novel Domains

This paper introduces video domain generalization where most video classification networks degenerate due to the lack of exposure to the target domains of divergent distributions. We observe that the global temporal features are less generalizable, due to the temporal domain shift that videos from other unseen domains may have an unexpected absence or misalignment of the temporal relations. This finding has motivated us to solve video domain generalization by effectively learning the local-relation features of different timescales that are more generalizable, and exploiting them along with the global-relation features to maintain the discriminability. This paper presents the VideoDG framework with two technical contributions. The first is a new deep architecture named the Adversarial Pyramid Network, which improves the generalizability of video features by capturing the local-relation, global-relation, and cross-relation features progressively. On the basis of pyramid features, the second contribution is a new and robust approach of adversarial data augmentation that can bridge different video domains by improving the diversity and quality of augmented data. We construct three video domain generalization benchmarks in which domains are divided according to different datasets, different consequences of actions, or different camera views, respectively. VideoDG consistently outperforms the combinations of previous video classification models and existing domain generalization methods on all benchmarks.
Deep Learning in Multi-step Forecasting of Chaotic Dynamics

Abstract The prediction of chaotic dynamical systems’ future evolution is widely debated and represents a hot topic in the context of nonlinear time series analysis. Recent advances in the field proved that machine learning techniques, and in particular artificial neural networks, are well suited to deal with this problem. The current state-of-the-art primarily focuses on noise-free time series, an ideal situation that never occurs in real-world applications. This chapter provides a comprehensive analysis that aims at bridging the gap between the deterministic dynamics generated by archetypal chaotic systems, and the real-world time series. We also deeply explore the importance of different typologies of noise, namely observation and structural noise. Artificial intelligence techniques turned out to provide robust predictions, and potentially represent an effective and flexible alternative to the traditional physically-based approach for real-world applications. Besides the accuracy of the forecasting, the domain-adaptation analysis attested the high generalization capability of the neural predictors across a relatively heterogeneous spatial domain.
Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection

The information captured by the gist signal, which refers to radiologists' first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 (p = 0.82, CI: -0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 (p < 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62-0.86), 0.79 (CI: 0.63-0.89), and 0.88 (CI: 0.79-0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection.
Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

Abstract This paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium .
Digital Transformation, AI Applications and IoTs in Blockchain Managing Commerce Secrets: And Cybersecurity Risk Solutions in the Era of Industry 4.0 and Further

Once again in this study, we emphasizes strongly on applications of mathematical solutions for industrial uses. By using qualitative analysis, synthesis and inductive methods, together with statistic data, our paper target is to analyze aspects of SWOT analysis on blockchain technology and its opportunities for various industries and sectors in emerging markets such as Vietnam. This is based on the original foundation theory indicating that blockchain tech functioned as a mathematical solutions that can be applied for solving commerce secrets, with high data security and can be used for intellectual property. Among our research results is findings on the connection between IoTs - Internet of Things and blockchain tech, as well as relationship between AI - Artificial intelligence and blockchain tech. Last but not least authors propose cybersecurity risk solutions and there are policies implications attached.
Scalable Double Blockchain Architecture for IoT Information and Reputation Management

In this paper, we propose a decentralised double blockchain (DB) architecture for scalable, lightweight and secure Internet-of-Things (IoT) information and reputation management. We consider a heterogeneous cloud and fog IoT communication network where the information blockchain (IB) stores IoT data in the cloud layer due to limited storage resources in the fog layer and a lightweight reputation blockchain (RB) stores reputations in the fog layer to quickly identify the trust levels of IoT devices. Furthermore, we propose a fog layer DB node classification algorithm to improve the efficiency of IB and RB generation and RB storage in the fog layer. To improve security, we propose a mapping algorithm between the short RB and long IB to securely validate the reputation data based on corresponding IoT device data, and quickly identify malicious tampering of the DB. Experimental simulation results highlight the storage scalability, low latency propagation, and security of our DB architecture compared to conventional single blockchains.
Low-threshold power and tunable integrated optical limiter based on an ultracompact VO2/Si waveguide

Optical limiters are nonlinear devices that encompass applications from device protection to activation functionalities in neural networks. In this work, we report an optical limiter on silicon photonics based on an ultracompact VO2/Si waveguide. Our 20-μm-long experimental device features a thermal tunable threshold power of only ∼3.5 mW while being spectrally broadband. Our work provides a new pathway to achieve integrated optical limiters for dense and low-power photonic integrated circuits.
Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity

We present a new scientific document similarity model based on matching fine-grained aspects of texts.To train our model, we exploit a naturally-occurring source of supervision: sentences in the full-text of papers that cite multiple papers together (co-citations).Such cocitations not only reflect close paper relatedness, but also provide textual descriptions of how the co-cited papers are related.This novel form of textual supervision is used for learning to match aspects across papers.We develop multi-vector representations where vectors correspond to sentence-level aspects of documents, and present two methods for aspect matching: (1) A fast method that only matches single aspects, and (2) a method that makes sparse multiple matches with an Optimal Transport mechanism that computes an Earth Mover's Distance between aspects.Our approach improves performance on document similarity tasks in four datasets.Further, our fast single-match method achieves competitive results, paving the way for applying finegrained similarity to large scientific corpora.
Cryptocurrency, green and fossil fuel investments

This paper investigates the time-varying spillovers among cryptocurrency, green and fossil fuel investments. Using a TVP-VAR network connectedness model, we find that the spillovers among cryptocurrency, green and fossil fuel assets vary over time and that they are more pronounced during crisis periods. Our results also reveal asymmetric spillovers among these assets, where negative return spillovers are larger than positive return spillovers. We conclude our paper by discussing the implications of our results for policymakers and investors.
UAV-Assisted Privacy-Preserving Online Computation Offloading for Internet of Things

Unmanned aerial vehicle (UAV) plays a more and more important role in Internet of Things (IoT) for remote sensing and device interconnecting. Due to the limitation of computing capacity and energy, the UAV cannot handle complex tasks. Recently, computation offloading provides a promising way for the UAV to handle complex tasks by deep reinforcement learning (DRL)-based methods. However, existing DRL-based computation offloading methods merely protect usage pattern privacy and location privacy. In this paper, we consider a new privacy issue in UAV-assisted IoT, namely computation offloading preference leakage, which lacks through study. To cope with this issue, we propose a novel privacy-preserving online computation offloading method for UAV-assisted IoT. Our method integrates the differential privacy mechanism into deep reinforcement learning (DRL), which can protect UAV’s offloading preference. We provide the formal analysis on security and utility loss of our method. Extensive real-world experiments are conducted. Results demonstrate that, compared with baseline methods, our method can learn cost-efficient computation offloading policy without preference leakage and a priori knowledge of the wireless channel model.
JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection

The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly unbalanced and longtailed distributed action labels from a stream of sensory data captured from a mobile robot platform remains a significant challenge, not least owing to the lack of a reflective large-scale dataset. In this paper, we introduce JRDB-Act, as an extension of the existing JRDB, which is captured by a social mobile manipulator and reflects a real distribution of human daily-life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labeled with one pose-based action label and multiple (optional) interaction-based action labels. Moreover JRDB-Act provides social group annotation, conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities (common activities in each social group). Each annotated label in JRDB-Act is tagged with the annotators' confidence level which contributes to the development of reliable evaluation strategies. In order to demonstrate how one can effectively utilise such annotations, we develop an end-to-end trainable pipeline to learn and infer these tasks, i.e. individual action and social group detection. The data and the evaluation code will be publicly available at https://jrdb.erc.monash.edu/
Exploration of Using Modern Information Technology to Realize College English Distance Teaching

China is gradually evolving to a knowledge-based society of computer information technology, knowledge economy, and learning. The modern information construction of college English is to realize the distance teaching of College English with the help of modern information technology, which is conducive to the continuous development of network education in China in the direction of modern information. This paper summarizes the research objectives and contents of college English modern information technology construction. We analyze the significance of implementing college English modern information technology and realize distance teaching. We put forward important measures for college English information technology to realize distance teaching, in order to bring some reference to relevant professionals. Finally, the sharing of high-quality educational resources will be realized, so as to provide reference for further improving the situation of rural English teaching.