Reviewerly/exHarmony huggingface dataset:
Average Approximate Hashing-Based Double Projections Learning for Cross-Modal Retrieval

Cross-modal retrieval has attracted considerable attention for searching in large-scale multimedia databases because of its efficiency and effectiveness. As a powerful tool of data analysis, matrix factorization is commonly used to learn hash codes for cross-modal retrieval, but there are still many shortcomings. First, most of these methods only focus on preserving locality of data but they ignore other factors such as preserving reconstruction residual of data during matrix factorization. Second, the energy loss of data is not considered when the data of cross-modal are projected into a common semantic space. Third, the data of cross-modal are directly projected into a unified semantic space which is not reasonable since the data from different modalities have different properties. This article proposes a novel method called average approximate hashing (AAH) to address these problems by: 1) integrating the locality and residual preservation into a graph embedding framework by using the label information; 2) projecting data from different modalities into different semantic spaces and then making the two spaces approximate to each other so that a unified hash code can be obtained; and 3) introducing a principal component analysis (PCA)-like projection matrix into the graph embedding framework to guarantee that the projected data can preserve the main energy of data. AAH obtains the final hash codes by using an average approximate strategy, that is, using the mean of projected data of different modalities as the hash codes. Experiments on standard databases show that the proposed AAH outperforms several state-of-the-art cross-modal hashing methods.
The World of NFTs (Non-Fungible Tokens)

Tokenizing assets through the use of blockchain is the next big thing in digital currency markets. Securing the assets in the world of the internet is challenging as most of them can easily be copied and sold in the secondary market. Protecting the rights of the asset owner is one of the challenging research areas. NFTs (non-fungible tokens) are very useful in representing the ownership of unique items for any assets. NFTs ensure that an asset can have only one official owner at any point in time with the help of Ethereum-based blockchain network. Ethereum NFTs can ensure that no one can modify the ownership rights or copy and paste the digital assets. NFTs are a boon to the artists, musicians, and others who want to create impressive digital assets. The objective of this chapter is to take you to the world of NFTs and to explain how the NFTs are going to impact digital transactions in a bigger way in the future. This chapter covers the introduction, technical aspects, security impacts, use cases, and successful implementations of NFTs in various realms.
Leveraging context-awareness for Internet of Things ecosystem: Representation, organization, and management of context

Present-day devices are becoming increasingly smarter than their predecessors. From a simple passive light switch to an intelligent wristwatch, great strides have been made in networking smart devices, creating an autonomous ecosystem, the so-called Internet of Things. In an increasingly information-driven world, context-awareness supports the intended applications as well as their constituent devices, making them conscious of and adaptive to the specific scenario in real-time. Moreover, heterogeneous devices in the Internet of Things ecosystem peruse disparate data formats and semantics, giving rise to interoperability and information sharing challenges. Context modeling is a core feature that facilitates interoperability and information sharing between applications. Although generic context models exist, they do not consider pertinent dimensions of context to provide a generic vocabulary, and therefore, they cannot be extended to generalize situations commonly encountered in the Internet of Things environment. An extensible, generic modeling and representation of context is required to manage pertinent context dimensions in various ecosystems by being dynamically aware of the situation. This paper presents Context Model for Internet of Things, an extensible and generic ontology-based context modeling approach that provides relevant information at the right time. This work encompasses Context Ontology for Internet of Things, an ontology-based context organization approach, which provides an abstract and overarching vocabulary that fosters knowledge reusability and sharing. The proposed model has been implemented and evaluated with a use case to validate its adaptability, effectiveness, and viability. Our evaluation based on generality, effectiveness, and consistency shows that the proposed model can effectively represent, organize, and manage the context in different Internet of Things ecosystems.
Reliable Requirements Engineering Practices for COVID-19 Using Blockchain

Improvement in the requirements for engineering practices is needed in areas such as requirement elicitation, validation, prioritization, and negotiations between stakeholders to create successful projects for COVID-19 (coronavirus disease 2019) software. Many algorithms and techniques are used to create quality software projects, but they still need more improvement to work effectively for global pandemic COVID-19 software. By improving the reliability of requirement engineering practices using blockchain-based technology, the software will be reliable and will make it easier for the users working in a lockdown situation because of COVID-19. Therefore, our purpose is to identify the factors for reliable software engineering practices using blockchain-oriented technology for COVID-19 software. A systematic literature review is conducted to identify challenges and offer solutions. Through using blockchain-based technology for requirement engineering practices, the requirements will be gathered accurately and validated, and the conflicts between stakeholders will also be solved. It will improve the quality and reliability of COVID-19 software projects, which will help society work effectively from home. Improvement in the quality and reliability of COVID-19 software will improve users’ interest, and their working capacity will be increased.
Long Short-Term Session Search: Joint Personalized Reranking and Next Query Prediction

DR and next query prediction (NQP) are two core tasks in session search. They are often driven by the same search intent and, hence, it is natural to jointly optimize both tasks. So far, most models proposed for jointly optimizing document reranking (DR) and NQP have focused on users' short-term intent in an ongoing search session. Because of this limitation, these models fail to account for users' long-term intent as captured in their historical search sessions. In contrast, we consider a personalized mechanism for learning a user's profile from their long-term and short-term behavior to simultaneously enhance the performance of DR and NQP in an ongoing search session.
The FinSim-2 2021 Shared Task: Learning Semantic Similarities for the Financial Domain

The FinSim-2 is a second edition of FinSim Shared Task on Learning Semantic Similarities for the Financial Domain, colocated with the FinWeb workshop. FinSim-2 proposed the challenge to automatically learn effective and precise semantic models for the financial domain. The second edition of the FinSim offered an enriched dataset in terms of volume and quality, and interested in systems which make creative use of relevant resources such as ontologies and lexica, as well as systems which make use of contextual word embeddings such as BERT[4]. Going beyond the mere representation of words is a key step to industrial applications that make use of Natural Language Processing (NLP). This is typically addressed using either unsupervised corpus-derived representations like word embeddings, which are typically opaque to human understanding but very useful in NLP applications or manually created resources such as taxonomies and ontologies, which typically have low coverage and contain inconsistencies, but provide a deeper understanding of the target domain. Finsim is inspired from previous endeavours in the Semeval community, which organized several competitions on semantic/lexical relation extraction between concepts/words. This year, 18 system runs were submitted by 7 teams and systems were ranked according to 2 metrics, Accuracy and Mean rank. All the systems beat our baseline 1 model by over 15 points and the best systems beat the baseline 2 by over 1 ∼ 3 points in accuracy.
The economic dependency of bitcoin security

We studied the extent to which bitcoin blockchain security permanently depends on the underlying distribution of cryptocurrency market outcomes using daily blockchain and bitcoin data for 2014–2019 and employing the autoregressive-distributed lag (ARDL) approach. We tested three equilibrium hypotheses: (i) sensitivity of the bitcoin blockchain to mining reward, (ii) security outcomes of the bitcoin blockchain and the proof-of-work cost, and (iii) the speed of adjustment of the bitcoin blockchain security to deviations from the equilibrium path. Our results suggest that bitcoin price and mining rewards were intrinsically linked to bitcoin security outcomes.The bitcoin blockchain security’s dependency on mining costs was geographically differenced – it was more significant for the global mining leader China than for other world regions. Bitcoin blockchain security tended to revert relatively fast to its equilibrium security level after the input or output of price shocks.
Forecast Model of Breast Cancer Diagnosis Based on RF-AdaBoost

Breast cancer is a female malignant tumor with the highest incidence, which seriously affects women's health. Therefore, early and accurate diagnosis of breast cancer patients is particularly crucial. This paper took the Wisconsin female breast cancer tumor data set as the research object, through the integration of Random Forest and AdaBoost algorithms, proposed a breast cancer classification prediction model that can give a diagnosis result of benign or malignant. Finally, compared the model with single Support Vector Machine, Logistic Regression, K-Nearest Neighbor, Decision Tree algorithms. The test results have shown that the ensemble model's prediction accuracy has been increased by 4.3% on average compared to the single algorithm models, with the highest increase up to 9.8%, which has provided a new reference model for breast cancer prediction.
Taskology: Utilizing Task Relations at Scale

Many computer vision tasks address the problem of scene understanding and are naturally interrelated e.g. object classification, detection, scene segmentation, depth estimation, etc. We show that we can leverage the inherent relationships among collections of tasks, as they are trained jointly, supervising each other through their known relationships via consistency losses. Furthermore, explicitly utilizing the relationships between tasks allows improving their performance while dramatically reducing the need for labeled data, and allows training with additional unsupervised or simulated data. We demonstrate a distributed joint training algorithm with task-level parallelism, which affords a high degree of asynchronicity and robustness. This allows learning across multiple tasks, or with large amounts of input data, at scale. We demonstrate our framework on subsets of the following collection of tasks: depth and normal prediction, semantic segmentation, 3D motion and egomotion estimation, and object tracking and 3D detection in point clouds. We observe improved performance across these tasks, especially in the low-label regime.
Multi-Scale Fused SAR Image Registration Based on Deep Forest

SAR image registration is a crucial problem in SAR image processing since the registration results with high precision are conducive to improving the quality of other problems, such as change detection of SAR images. Recently, for most DL-based SAR image registration methods, the problem of SAR image registration has been regarded as a binary classification problem with matching and non-matching categories to construct the training model, where a fixed scale is generally set to capture pair image blocks corresponding to key points to generate the training set, whereas it is known that image blocks with different scales contain different information, which affects the performance of registration. Moreover, the number of key points is not enough to generate a mass of class-balance training samples. Hence, we proposed a new method of SAR image registration that meanwhile utilizes the information of multiple scales to construct the matching models. Specifically, considering that the number of training samples is small, deep forest was employed to train multiple matching models. Moreover, a multi-scale fusion strategy is proposed to integrate the multiple predictions and obtain the best pair matching points between the reference image and the sensed image. Finally, experimental results on four datasets illustrate that the proposed method is better than the compared state-of-the-art methods, and the analyses for different scales also indicate that the fusion of multiple scales is more effective and more robust for SAR image registration than one single fixed scale.
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs

Graph neural networks (GNNs) are powerful tools for learning from graph data and are widely used in various applications such as social network recommendation, fraud detection, and graph search. The graphs in these applications are typically large, usually containing hundreds of millions of nodes. Training GNN models on such large graphs efficiently remains a big challenge. Despite a number of sampling-based methods have been proposed to enable mini-batch training on large graphs, these methods have not been proved to work on truly industry-scale graphs, which require GPUs or mixed CPU-GPU training. The state-of-the-art sampling-based methods are usually not optimized for these real-world hardware setups, in which data movement between CPUs and GPUs is a bottleneck. To address this issue, we propose Global Neighborhood Sampling that aims at training GNNs on giant graphs specifically for mixed CPU-GPU training. The algorithm samples a global cache of nodes periodically for all mini-batches and stores them in GPUs. This global cache allows in-GPU importance sampling of mini-batches, which drastically reduces the number of nodes in a mini-batch, especially in the input layer, to reduce data copy between CPU and GPU and mini-batch computation without compromising the training convergence rate or model accuracy. We provide a highly efficient implementation of this method and show that our implementation outperforms an efficient node-wise neighbor sampling baseline by a factor of 2× ~ 4× on giant graphs. It outperforms an efficient implementation of LADIES with small layers by a factor of 2× ~ 14× while achieving much higher accuracy than LADIES. We also theoretically analyze the proposed algorithm and show that with cached node data of a proper size, it enjoys a comparable convergence rate as the underlying node-wise sampling method.
Designing Individualised Virtual Reality Applications for Supporting Depression: A Feasibility Study

Mental health conditions pose a major challenge for individuals, healthcare systems and society – and the COVID-19 pandemic has likely worsened this issue. According to the Mental Health Foundation of New Zealand, one in five people will develop a serious mood disorder, including depression, at some time in their life. Co-designed solutions to increase resilience and well-being in young people have specifically been recognised as part of the National Suicide Prevention Strategy and the New Zealand Health Strategy. Virtual Reality (VR) in mental health is an innovative field. Recent studies support the use of VR technology in the treatment of anxiety, phobia, and pain management. However, there is little research on using VR for supporting, treating and preventing depression. There is also very little work done in offering an individualised VR experience to improve mental health. In our earlier work, we presented iVR, a novel individualised VR experience for enhancing peoples' self-compassion, and in the long run, their mental health, and described its design and architecture. In this paper, we outline the results of a feasibility study conducted recently. Most participants believed introducing elements of choice within iVR enhanced their user experience and that iVR had the potential to enhance people's self-compassion. We also approached seven mental health professionals for feedback, who felt that introducing elements of choice within iVR would increase their knowledge of clients. Our contribution can pave the way for large-scale efficacy testing, clinical use, and cost-effective delivery of intelligent individualised VR technology for mental health therapy in future.
CILEA-NET: Curriculum-Based Incremental Learning Framework for Remote Sensing Image Classification

In this article, we address class incremental learning (IL) in remote sensing image analysis. Since remote sensing images are acquired continuously over time by Earth's observation sensors, the land-cover/land-use classes on the ground are likely to be found in a gradational manner. This process restricts the deployment of stand-alone classification approaches, which are trained for all the classes together in one iteration. Therefore, for every new set of categories discovered, the entire network consisting of old and new classes requires retraining. This procedure is often impractical, considering vast volumes of data, limited resources, and the complexity of learning models. In this respect, we propose a convolutional-neural-network-based framework (called CILEA-NET, curriculum-based incremental learning framework for remote sensing image classification) to efficiently resolve the difficulties associated with incremental learning paradigm. The framework includes new classes in the already trained model to avoid catastrophic forgetting for the old while ensuring improved generalization for the newly added classes. To manage the IL's stability-plasticity dilemma, we introduce a novel curriculum learning-based approach where the order of the new classes is devised based on their similarity to the already trained classes. We then perform the training in that given order. We notice that the curriculum learning setup distinctly enhances the training time for the new classes. Experimental results on several optical datasets: PatternNet and NWPU-RESISC45, and a hyperspectral dataset: Indian Pines, validate the robustness of our technique.
H2MN

Graph similarity learning, which measures the similarities between a pair of graph-structured objects, lies at the core of various machine learning tasks such as graph classification, similarity search, etc. In this paper, we devise a novel graph neural network based framework to address this challenging problem, motivated by its great success in graph representation learning. As the vast majority of existing graph neural network models mainly concentrate on learning effective node or graph level representations of a single graph, little effort has been made to jointly reason over a pair of graph-structured inputs for graph similarity learning. To this end, we propose Hierarchical Hypergraph Matching Networks (H2sup>MN) to calculate the similarities between graph pairs with arbitrary structure. Specifically, our proposed H2MN learns graph representation from the perspective of hypergraph, and takes each hyperedge as a subgraph to perform subgraph matching, which could capture the rich substructure similarities across the graph. To enable hierarchical graph representation and fast similarity computation, we further propose a hyperedge pooling operator to transform each graph into a coarse graph of reduced size. Then, a multi-perspective cross-graph matching layer is employed on the coarsened graph pairs to extract the inter-graph similarity. Comprehensive experiments on five public datasets empirically demonstrate that our proposed model can outperform state-of-the-art baselines with different gains for graph-graph classification and regression tasks.
Principal component analysis in the wavelet domain

This paper proposes a new principal component analysis method in the wavelet domain, which is useful for dimension reduction and feature extraction of multiple non-stationary time series. The proposed method is constructed using a novel combination of eigenanalysis and the local wavelet spectrum defined in the locally stationary wavelet process. Therefore, we can expect the proposed method to reflect a more generalized non-stationary time series beyond some limited types of signals that existing methods have performed. We investigate the theoretical results of estimated principal components and their loadings. The results of numerical examples, including the analysis of real seismic data and financial data, show the promising empirical properties of the proposed approach.
Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling

One of the difficulties of conversion rate (CVR) prediction is that the conversions can delay and take place long after the clicks. The delayed feedback poses a challenge: fresh data are beneficial to continuous training but may not have complete label information at the time they are ingested into the training pipeline. To balance model freshness and label certainty, previous methods set a short waiting window or even do not wait for the conversion signal. If conversion happens outside the waiting window, this sample will be duplicated and ingested into the training pipeline with a positive label. However, these methods have some issues. First, they assume the observed feature distribution remains the same as the actual distribution. But this assumption does not hold due to the ingestion of duplicated samples. Second, the certainty of the conversion action only comes from the positives. But the positives are scarce as conversions are sparse in commercial systems. These issues induce bias during the modeling of delayed feedback. In this paper, we propose DElayed FEedback modeling with Real negatives (DEFER) method to address these issues. The proposed method ingests real negative samples into the training pipeline. The ingestion of real negatives ensures the observed feature distribution is equivalent to the actual distribution, thus reducing the bias. The ingestion of real negatives also brings more certainty information of the conversion. To correct the distribution shift, DEFER employs importance sampling to weigh the loss function. Experimental results on industrial datasets validate the superiority of DEFER. DEFER have been deployed in the display advertising system of Alibaba, obtaining over 6.0% improvement on CVR in several scenarios. The code and data in this paper are now open-sourced (https://github.com/gusuperstar/defer.git).
Learning by Planning: Language-Guided Global Image Editing

Recently, language-guided global image editing draws increasing attention with growing application potentials. However, previous GAN-based methods are not only confined to domain-specific, low-resolution data but also lacking in interpretability. To overcome the collective difficulties, we develop a text-to-operation model to map the vague editing language request into a series of editing operations, e.g., change contrast, brightness, and saturation. Each operation is interpretable and differentiable. Furthermore, the only supervision in the task is the target image, which is insufficient for a stable training of sequential decisions. Hence, we propose a novel operation planning algorithm to generate possible editing sequences from the target image as pseudo ground truth. Comparison experiments on the newly collected MA5k-Req dataset and GIER dataset show the advantages of our methods. Code is available at https://github.com/jshi31/T2ONet.