Reviewerly/exHarmony huggingface dataset:
Adaptive collaborative fusion for multi-view semi-supervised classification

Multi-view semi-supervised classification is inherently a challenging task in multi-view learning due to the lack of label information. Existing methods generally suffer from insufficient data fusion, expensive computation cost in the solution procedure and fail in tackling unseen samples directly, intensively limiting their applicability and efficiency in real scenarios. To address these issues, we propose an adaptive collaborative fusion method, seeking for an appropriate representation and fusion for multi-view data. The main advantage of the proposed method is that it simultaneously fuses both multiple feature projections and similarity graphs to learn a joint projection subspace as well as a unified similarity graph that fully preserve the correlation and distinction among views. Meanwhile, our method can coalesce different views in an adaptive-weighting manner, making the learned subspace more discriminative and facilitating label propagation on the fused graph. Furthermore, an acceleration strategy has been designed to reduce the computational complexity, thereby making the proposed method scalable to relatively large-scale data. Finally, an alternating optimization has been adopted to solve the formulated objective function. Extensive experiments on synthetic and real-world datasets are conducted to demonstrate the effectiveness and superiority of our proposed method.
Remote sensing image compression with long-range convolution and improved non-local attention model

It is a challenge to achieve high compression rates for remote sensing images because they have rich information and complex backgrounds. Long-range context information can help the model to identify spatial redundant features. Therefore, we propose a long-range convolution compression network (LRCompNet) for remote sensing images. In order to capture long-range context information, we first propose a long-range convolution, and a light-weight compression model is designed. Additionally, an improved non-local attention model is proposed to reduce the computation complexity in order to accommodate remote sensing image compression. We also collect two remote sensing datasets (GoogleMap dataset and GF7 dataset) to test the proposed method and existing codecs, such as JPEG, JPEG2000 and learned compression models. We assess the overall performance of our method in terms of rate-distortion curves and execution time, respectively. Results on both datasets show that the proposed compression network achieves improved rate-distortion performance. Furthermore, the proposed method significantly reduces the time complexity when compared to other methods.
The Importance of Conceptualising the Human-Centric Approach in Maintaining and Promoting Cybersecurity-Hygiene in Healthcare 4.0

The cyberspace depicts an increasing number of difficulties related to security, especially in healthcare. This is evident from how vulnerable critical infrastructures are to cyberattacks and are unprotected against cybercrime. Users, ideally, should maintain a good level of cyber hygiene, via regular software updates and the development of unique passwords, as an effective way to become resilient to cyberattacks. Cyber security breaches are a top priority, and most users are aware that their behaviours may put them at risk; however, they are not educated to follow best practices, such as protecting their passwords. Mass cyber education may serve as a means to offset poor cyber security behaviours; however, mandatory education becomes a questionable point if the content is not focused on human factors, using human-centric approaches and taking into account end users’ behaviours, which is currently the case. The nature of the present paper is largely exploratory, and the purpose is two-fold: To present and explore the cyber hygiene definition, context and habits of end users in order to strengthen our understanding of users. Our paper reports the best practices that should be used by healthcare organisations and healthcare professionals to maintain good cyber hygiene and how these can be applied via a healthcare use case scenario to increase awareness related to data privacy and cybersecurity. This is an issue of great importance and urgency considering the rapid increase of cyberattacks in healthcare organisations, mainly due to human errors. Further to that, based on human-centric approaches, our long-term vision and future work involves facilitating the development of efficient practices and education associated with cybersecurity hygiene via a flexible, adaptable and practical framework.
Deep Cross-Modal Hashing Based on Semantic Consistent Ranking

The amount of multi-modal data available on the Internet is enormous. Cross-modal hash retrieval maps heterogeneous cross-modal data into a single Hamming space to offer fast and flexible retrieval services. However, existing cross-modal methods mainly rely on the feature-level similarity between multi-modal data and ignore the relationship between relative rankings and label-level fine-grained similarity of neighboring instances. To overcome these issues, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> eep <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ross-modal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u> ashing based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> emantic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> onsistent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> anking (DCH-SCR) that comprehensively investigates the intra-modal semantic similarity relationship. Firstly, to the best of our knowledge, it is an early attempt to preserve semantic similarity for cross-modal hashing retrieval by combining label-level and feature-level information. Secondly, the inherent gap between modalities is narrowed by developing a ranking alignment loss function. Thirdly, the compact and efficient hash codes are optimized based on the common semantic space. Finally, we use the gradient to specify the optimization direction and introduce the Normalized Discounted Cumulative Gain (NDCG) to achieve varying optimization strengths for data pairs with different similarities. Extensive experiments on three real-world image-text retrieval datasets demonstrate the superiority of DCH-SCR over several state-of-the-art cross-modal retrieval methods.
Linking blockchain technology and digital advertising: How blockchain technology can enhance digital advertising to be more effective, efficient, and trustworthy

Blockchain technology is often touted as a transformative solution for improving the current over-centralized systems in our economy, where all tangible and intangible assets are traded. Driven by computer science and engineering, blockchain technology is believed to decentralize the current system with its simple yet effective property of distributed ledger technology, which guarantees data transparency that can be seen by anyone in the network. With its robustness, blockchain technology can be applied to the centralized and multi-party-controlled current advertising ecosystem. Despite its potential, there is a lack of scientific discussion linking blockchain technology and advertising. In this essay, we examine the current challenges that render the measurement of advertising effects inefficient and untrustworthy in the advertising ecosystem. By focusing on such challenges as data transparency, consumer privacy, and ad fraud, we explain how blockchain technology can enhance the advertising system, making it more trustworthy from both technical and consumer standpoints.
Second-Generation Functional Data

Modern studies from a variety of fields record multiple functional observations according to either multivariate, longitudinal, spatial, or time series designs. We refer to such data as second-generation functional data because their analysis—unlike typical functional data analysis, which assumes independence of the functions—accounts for the complex dependence between the functional observations and requires more advanced methods. In this article, we provide an overview of the techniques for analyzing second-generation functional data with a focus on highlighting the key methodological intricacies that stem from the need for modeling complex dependence, compared with independent functional data. For each of the four types of second-generation functional data presented—multivariate functional data, longitudinal functional data, functional time series and spatially functional data—we discuss how the widely popular functional principal component analysis can be extended to these settings to define, identify main directions of variation, and describe dependence among the functions. In addition to modeling, we also discuss prediction, statistical inference, and application to clustering. We close by discussing future directions in this area.
Fast spectral clustering with self-adapted bipartite graph learning

Spectral Clustering (SC) is a widespread used clustering algorithm in data mining, image processing, etc. It is a graph-based algorithm capable of handling arbitrarily distributed data. However, the distances of all samples in the high-dimensional space tend to be equal, so the similarity matrix of SC may not be reasonable in high-dimensional data. In addition, the similarity matrix and clustering results in SC are performed in two steps. To solve these problems, a novel joint clustering method called fast spectral clustering with self-adapted bipartite graph learning (FSBGL), is proposed. It is capable of obtaining low-dimensional representations from high dimensional data, thus decreasing the complexity and increase the efficiency of the algorithm. In contrast to traditional spectral clustering algorithms that obtain clustering results in a two-step process, FSBGL obtains clustering results directly from the concatenated components of the optimized similarity matrix while learning the optimized bipartite graph. This eliminates the effect of performing these two steps separately in SC. In effect, a more discriminative low-dimensional representation may be derived from the adaptively learned bipartite graph, while the better low-dimensional representation can continue to be used to learn the structure of the graph. The learning of the bipartite graph alternates with the iteration of the low-dimensional representation, which allows the algorithm to obtain more accurate clustering results. Furthermore, by means of a low-dimensional representation on the basis of fast spectral embedding, the algorithm has better performance on some large-scale datasets. The results of the experiment indicate that the FSBGL is better than other comparative methods in various data sets.
Bi-RRNet: Bi-level recurrent refinement network for camouflaged object detection

In this paper, we present a lightweight Bi-level Recurrent Refinement Network (Bi-RRNet) for Camouflaged Object Detection (COD) that consists of a Lower-level RRNet (L-RRN) and an Up-level RRNet (U-RRN) to progressively refine the multi-level context features for precise dense prediction. In particular, the L-RRN recursively refines the deeper layer high-level semantic features with the high-resolution low-level features from the earlier layers in a top-down manner, and the U-RRN progressively polishes the refined features from the L-RRN in a recurrent manner, producing the high-resolution semantic features that are essential to accurate COD. Moreover, we develop a Multi-scale Scene Perception Module (MSPM) that, in order to deal with target appearance variation, first compresses the global scene context information at each layer into a learnable weight vector and then modulates the multi-scale context features produced by a filter bank with various local receptive fields using the learned weights. Meanwhile, we design a Region-Consistency Enhancement Module (RCEM) that makes use of high-level semantic features to direct filtering out the cluttered information in the lower-layer features. This module can highlight the regions of camouflaged objects, maximizing the inter-class contrast between the objects and their surroundings. Extensive experiments on four challenging benchmark datasets, including CHAMELEON, CAMO, COD10K, and NC4K, show that our Bi-RRNet outperforms a variety of state-of-the-art methods in terms of accuracy and model parameters. Our Bi-RRNet, in particular, is lightweight, with 14.95M parameters that are only half the size of the state-of-the-art BSA-Net.
Scams and Solutions in Cryptocurrencies—A Survey Analyzing Existing Machine Learning Models

With the emergence of cryptocurrencies and Blockchain technology, the financial sector is turning its gaze toward this latest wave. The use of cryptocurrencies is becoming very common for multiple services. Food chains, network service providers, tech companies, grocery stores, and so many other services accept cryptocurrency as a mode of payment and give several incentives for people who pay using them. Despite this tremendous success, cryptocurrencies have opened the door to fraudulent activities such as Ponzi schemes, HYIPs (high-yield investment programs), money laundering, and much more, which has led to the loss of several millions of dollars. Over the decade, solutions using several machine learning algorithms have been proposed to detect these felonious activities. The objective of this paper is to survey these models, the datasets used, and the underlying technology. This study will identify highly efficient models, evaluate their performances, and compile the extracted features, which can serve as a benchmark for future research. Fraudulent activities and their characteristics have been exposed in this survey. We have identified the gaps in the existing models and propose improvement ideas that can detect scams early.
Designing Robot Sound-In-Interaction

Horns and sirens are important tools for communicating on the road, which are still understudied in autonomous vehicles. While HRI has explored different ways in which robots could sound, we focus on the range of actions that a single sound can accomplish in interaction. In a Research through Design study involving autonomous shuttle buses in public transport, we explored sound design with the help of voice-overs to video recordings of the buses on the road and Wizard-of-Oz tests in live traffic. The buses are slowed down by (unnecessary) braking in response to people getting close. We found that prolonged jingles draw attention to the bus and invite interaction, while repeated short beeps and bell sounds can instruct the movement of others away from the bus. We highlight the importance of designing sound in sequential interaction and describe a new method for embedding video interaction analysis in the design process.
Exploring blockchain-based innovations for economic and sustainable development in the global south: A mixed-method approach based on web mining and topic modeling

Information and Communication Technologies such as blockchain can significantly contribute to achieving the Sustainable Development Goals (SDGs) in the Global South (GS). Grounded in the innovation forecasting literature, this paper explores blockchain-based innovations and research in the context of economic and sustainable development to answer the following research questions: what is the status quo of blockchain innovations and research in the Global South?; and further, how can blockchain innovations contribute to economic and sustainable development? We follow a mixed-method approach based on web mining and topic modeling with unstructured empirical data on blockchain innovators from Crunchbase, their websites, and academic publications to address these research questions. Our results indicate that blockchain innovators already engage in various application areas beyond the financial sector, addressing critical development issues. However, the empirical data on blockchain research and innovations suggests a largely untapped potential in various promising application areas relevant for the SDGs, such as sustainable energy or integrity verification. The major contributions of this paper are an overview and status quo of entrepreneurial innovation activities and research on blockchain technology in the GS, an analysis of these areas in the context of the SDGs, and a lean methodological approach for innovation forecasting.
Conserved charges in the quantum simulation of integrable spin chains

When simulating the time evolution of quantum many-body systems on a digital quantum computer, one faces the challenges of quantum noise and of the Trotter error due to time discretization. The Trotter error in integrable spin chains can be under control if the discrete time evolution preserves integrability. In this work we implement, on a real quantum computer and on classical simulators, the integrable Trotterization of the spin-1/2 Heisenberg XXX spin chain. We study how quantum noise affects the time evolution of several conserved charges, and observe the decay of the expectation values. We in addition study the early time behaviors of the time evolution, which can potentially be used to benchmark quantum devices and algorithms in the future. We also provide an efficient method to generate the conserved charges at higher orders.
Automatic classification of construction safety reports using semi-supervised YAKE-Guided LDA approach

Literature on supervised Machine-Learning (ML) approaches for classifying text-based safety reports for the construction sector has been growing. Recent studies have emphasized the need to build ML approaches that balance high classification accuracy and performance on management criteria, such as resource intensiveness. However, despite being highly accurate, the extensively focused, supervised ML approaches may not perform well on management criteria as many factors contribute to their resource intensiveness. Alternatively, the potential for semi-supervised ML approaches to achieve balanced performance has rarely been explored in the construction safety literature. The current study contributes to the scarce knowledge on semi-supervised ML approaches by demonstrating the applicability of a state-of-the-art semi-supervised learning approach, i.e., Yet, Another Keyword Extractor (YAKE) integrated with Guided Latent Dirichlet Allocation (GLDA) for construction safety report classification. Construction-safety-specific knowledge is extracted as keywords through YAKE, relying on accessible literature with minimal manual intervention. Keywords from YAKE are then seeded in the GLDA model for the automatic classification of safety reports without requiring a large quantity of prelabeled datasets. The YAKE-GLDA classification performance (F1 score of 0.66) is superior to existing unsupervised methods for the benchmark data containing injury narratives from Occupational Health and Safety Administration (OSHA). The YAKE-GLDA approach is also applied to near-miss safety reports from a construction site. The study demonstrates a high degree of generality of the YAKE-GLDA approach through a moderately high F1 score of 0.86 for a few categories in the near-miss data. The current research demonstrates that, unlike the existing supervised approaches, the semi-supervised YAKE-GLDA approach can achieve a novel possibility of consistently achieving reasonably good classification performance across various construction-specific safety datasets yet being resource-efficient. Results from an objective comparative and sensitivity analysis contribute to much-required knowledge-contesting insights into the functioning and applicability of the YAKE-GLDA. The results from the current study will help construction organizations implement and optimize an efficient ML-based knowledge-mining strategy for domains beyond safety and across sites where the availability of a pre-labeled dataset is a significant limitation.
QCSH: A full quantum computer nuclear shell-model package

The nuclear system is a promising area for demonstrating practical quantum advantage. A comprehensive computation of a nuclear system in a classical computer is beyond the capacity of current classical computers. With the rapid development of hardware, the prospect of using quantum computers in nuclear physics is close at hand. In this paper, we report a full quantum package, QCSH, for solving a nuclear shell model in a quantum computer. QCSH uses the linear combination of the unitary formalism of quantum computing and performs all calculations in a quantum computer. The complexities of qubit resource and the number of basic gates of QCSH are both polynomials to the number of nucleons in nuclei. For example, QCSH is used to calculate the binding energies of 12 light nuclei (i.e., 2H, 3H, 3He, 4He, 6Li, 7Li, 12C, 14N, 16O, 17O, 23Na, and 40Ca). Moreover, we experimentally demonstrate the calculation of deuteron binding energy using a superconducting quantum processor. The result indicates that QCSH can provide meaningful results already in near-term quantum devices.
Contrastive Graph Convolutional Networks With Generative Adjacency Matrix

Semi-supervised node classification with Graph Convolutional Network (GCN) is an attractive topic in social media analysis and applications. Recent studies show that GCN-based classification methods can facilitate the accuracy increase of learning algorithms. However, most of the existing methods do not conduct adequate explorations of the complementary information within the topology structure. Besides, they also suffer from the insufficient excavation of useful information among nodes and the scarcity of labeled samples, resulting in undesired classification performance. To cope with these issues, this paper proposes a contrastive GCN-based framework to jointly leverage the topology graph and the self-adaptive topology graph with feature information in semi-supervised information. In order to extract more valid potential information in the topology graph and increase the flexibility of the framework, we learn an adjacency matrix supervised by a flexible loss that exploits node embeddings to reinforce the topological representation capability of the adjacency matrix. To maximize the homogeneity of these two distinct graphs, we design an improved semi-supervised contrastive loss. In order to enrich scarce label information, we propose a self-supervised mechanism to generate reliable pseudo labels from abundant unlabeled data, which further refines the learnable adjacency matrix. With these modules, both unlabeled and labeled samples jointly furnish the supervision signals, thereby improving the accuracy of the proposed model. Extensive experimental results on real-world datasets demonstrate the effectiveness and superiority of the proposed algorithm against state-of-the-arts.