Reviewerly/exHarmony huggingface dataset:
Decentralized Identity and Trust Management Framework for Internet of Things

Today, Internet of Things (IoT) devices mostly operate in enclosed, proprietary environments. To unfold the full potential of IoT applications, a unifying and permissionless environment is crucial. All IoT devices, even unknown to each other, would be able to trade services and assets across various domains. In order to realize those applications, uniquely resolvable identities are essential. However, quantifiable trust in identities and their authentication are not trivially provided in such an environment due to the absence of a trusted authority. This research presents a new identity and trust framework for IoT devices, based on Distributed Ledger Technology (DLT). IoT devices assign identities to themselves, which are managed publicly and decentralized on the DLT's network as Self Sovereign Identities (SSI). In addition to the Identity Management System (IdMS), the framework provides a Web of Trust (WoT) approach to enable automatic trust rating of arbitrary identities. For the framework we used the IOTA Tangle to access and store data, achieving high scalability and low computational overhead. To demonstrate the feasibility of our framework, we provide a proof-of-concept implementation and evaluate the set objectives for real world applicability as well as the vulnerability against common threats in IdMSs and WoTs.
Time-Varying Graph Signal Denoising via Median Filters

Graph Signal Processing (GSP) leverages pair-wise relationship between nodes of a graph to formulate operators on signals defined over the nodes. Most existing graph signal operators in the literature are linear, and can be described by linear transformation matrices. Recently, works are emerging that consider the time correlation of graph signals, leading to time-vertex signal processing. By exploiting the joint correlations across the graph topology and time, better results can be obtained. In this brief, we propose a median operator that will leverage the joint correlation, for denoising time-varying graph signals. The median operator, known for its robustness to outliers in statistics, has been very successful in traditional signal processing, especially for images. The efficient and highly localised graph median filters developed here are applied to denoising real world sensor network data. Real world sensor nodes are usually resource limited in terms of their computational and communication capacity, hence the imperative requirement for efficient localised filters. Comprehensive experimental results will show that in some cases, the performance is significantly better than the equivalent linear operator counterpart.
Machine translation and fair access to information

Abstract This article contributes to the discussion on fairness and ethics in MT by highlighting efforts that have been made to use MT for the humanitarian purpose of increasing access to information for groups that are underserved. The article provides an overview of example projects in which MT has been implemented for this purpose in three contexts: civic participation, public health and safety, and media and culture. In addition, the article examines some of the ethical issues surrounding efforts to use MT for accessibility, including issues of quality, acceptability, and the need to involve stakeholders in development.
Learning discriminative update adaptive spatial-temporal regularized correlation filter for RGB-T tracking

The RGB-T trackers based on correlation filter framework have been extensively investigated for that they can track targets more accurately in most complex scenes. However, the performance of these trackers is limited when facing some specific challenging scenarios, such as occlusion and background clutter. For different tracking targets, most of these trackers utilize fixed regularization constraint to build the filter model, which is obviously unreasonable to effectively present the appearance changes and characteristics of a specific target. In addition, they adopt a simple model update mechanism based on linear interpolation, which can easily lead to model degradation in challenging scenarios, resulting in tracker drift. To solve the above problems, we propose a novel adaptive spatial-temporal regularized correlation filter model to learn an appropriate regularization for achieving robust tracking and a relative peak discriminative method for model updating to avoid the model degradation. Besides, to make better integrate the unique advantages of the two modes and adapt the changing appearance of the target, an adaptive weighting ensemble scheme and a multi-scale search mechanism are adopted, respectively. To optimize the proposed model, we designed an efficient ADMM algorithm, which greatly improved the efficiency. Extensive experiments have been carried out on two available datasets, RGBT234 and RGBT210, and the experimental results indicate that the tracker proposed by us performs favorably in both accuracy and robustness against the state-of-the-art RGB-T trackers.
Blockchain Based Sensitive Data Management by Using Key Escrow Encryption System From the Perspective of Supply Chain

The security of operation and maintenance phase in systems that share long-life cycles like weapon systems is of great importance. Even if the system passes the security evaluation at the development stage before release, it can be adversely affected by the penetration of counterfeit components (parts) during the operation and maintenance phase. Such security issues are concatenated with data related to supply chain, accordingly, system parts need to fulfill the traceability on a fundamental basis. In addition to traceability, supply chains should also meet the data security standards of availability, integrity and confidentiality in the long run. Also, even without trusted third party, these data should be available to users. In this paper, we, therefore, propose a framework that utilizes blockchain and key escrow encryption system in a bid to optimize the security of supply chains for long-lifecycle systems and provide better measures to improve services for global business survivability.
Scalability of Network Visualisation from a Cognitive Load Perspective

Node-link diagrams are widely used to visualise networks. However, even the best network layout algorithms ultimately result in 'hairball' visualisations when the graph reaches a certain degree of complexity, requiring simplification through aggregation or interaction (such as filtering) to remain usable. Until now, there has been little data to indicate at what level of complexity node-link diagrams become ineffective or how visual complexity affects cognitive load. To this end, we conducted a controlled study to understand workload limits for a task that requires a detailed understanding of the network topology-finding the shortest path between two nodes. We tested performance on graphs with 25 to 175 nodes with varying density. We collected performance measures (accuracy and response time), subjective feedback, and physiological measures (EEG, pupil dilation, and heart rate variability). To the best of our knowledge this is the first network visualisation study to include physiological measures. Our results show that people have significant difficulty finding the shortest path in high density node-link diagrams with more than 50 nodes and even low density graphs with more than 100 nodes. From our collected EEG data we observe functional differences in brain activity between hard and easy tasks. We found that cognitive load increased up to certain level of difficulty after which it decreased, likely because participants had given up. We also explored the effects of global network layout features such as size or number of crossings, and features of the shortest path such as length or straightness on task difficulty. We found that global features generally had a greater impact than those of the shortest path.
CMPD: Using Cross Memory Network With Pair Discrimination for Image-Text Retrieval

Cross-modal retrieval using deep neural networks aims to retrieve relevant data between the two different modalities. The performance of cross-modal retrieval is still unsatisfactory for two problems. First, most of the previous methods failed to incorporate the common knowledge among modalities when predicting the item representations. Second, the semantic relationships indicated by class label are still insufficiently utilized, which is an important clue for inferring similarities between the cross modal items. To address the above issues, we propose a novel cross memory network with pair discrimination (CMPD) for image-text cross modal retrieval, where the main contributions lie in two-folds. First, we propose the cross memory as a set of latent concepts to capture the common knowledge among different modalities. It is learnable and can be fused into each modality through attention mechanism, which aims to discriminatively predict representations. Second, we propose the pair discrimination loss to discriminate modality labels and class labels of item pairs, which can efficiently capture the semantic relationships among these modality labels and class labels. Comprehensive experimental results show that our method outperforms the state-of-the-art approaches in image-text retrieval.
Learning dynamic simultaneous clustering and classification via automatic differential evolution and firework algorithm

Semi-supervised learning is significant data analysis method in the age of Big Data. The Bayesian-based classifier is a classical classification method in semi-supervised learning. Wherein, the classifier with clustering and classification technology have experienced the transformation from sequential structure to simultaneous structure. There are two main difficulties in simultaneous structure: the limited accuracy and diversity caused by rigid optimization algorithm; and the imbalance status between clustering and classification processes caused by insufficiently structure. To overcome these difficulties, a novel multi-objective differential evolution and firework algorithm for automatic simultaneous clustering and classification algorithm (MASCC-DE/FWA) is proposed. The main contributions of MASCC-DE/FWA contain: (1) Combination searching strategy for dynamic searching (2) Rapid and low-complexity silhouette coefficient as redesigned clustering objective function (3) Automatic clustering, opposition-based learning and adjusted mutual information for strengthening SCC-MOEA framework. The experimental result demonstrates that MASCC-DE/FWA performs better than other 8 state-of-art classification algorithms on synthetic dataset, 19 UCI datasets and image segmentation tasks.
Investigation on Works and Military Applications of Artificial Intelligence

Technology determines tactics, and tactics promote the development of technology.Artificial intelligence technology is a multiplier that accelerates the innovation and development of military theory.In this paper, we first list the different intelligence levels by introducing their corresponding applications.Then we review the technical classification based on the related concepts, Finally, we discuss technical and practical difficulties and give some solutions from the aspects of strengthening knowledge engineering, building simulation systems, and accumulating data engineering knowledge.The development of Artificial intelligence technology has a profound impact on military development trends, leading to major changes in the forms and modes of war.
An Adaptive Two-Layer Light Field Compression Scheme Using GNN-Based Reconstruction

As a new form of volumetric media, Light Field (LF) can provide users with a true six degrees of freedom immersive experience because LF captures the scene with photo-realism, including aperture-limited changes in viewpoint. But uncompressed LF data is too large for network transmission, which is the reason why LF compression has become an important research topic. One of the more recent approaches for LF compression is to reduce the angular resolution of the input LF during compression and to use LF reconstruction to recover the discarded viewpoints during decompression. Following this approach, we propose a new LF reconstruction algorithm based on Graph Neural Networks; we show that it can achieve higher compression and better quality compared to existing reconstruction methods, although suffering from the same problem as those methods—the inability to deal effectively with high-frequency image components. To solve this problem, we propose an adaptive two-layer compression architecture that separates high-frequency and low-frequency components and compresses each with a different strategy so that the performance can become robust and controllable. Experiments with multiple datasets 1 show that our proposed scheme is capable of providing a decompression quality of above 40 dB, and can significantly improve compression efficiency compared with similar LF reconstruction schemes.
A Parallel World Framework for scenario analysis in knowledge graphs

Abstract This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO 2 reduction modeling and planning at scale due to its distributed architecture.
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation

Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common practice to perform negative sampling to generate negative samples. However, this leads to a consequence that many potential positive samples are mislabeled as negative ones and data sparsity would exacerbate the mislabeling problem.
Reusing digital collections from GLAM institutions

For some decades now, Galleries, Libraries, Archives and Museums (GLAM) institutions have published and provided access to information resources in digital format. Recently, innovative approaches have appeared such as the concept of Labs within GLAM institutions that facilitates the adoption of innovative and creative tools for content delivery and user engagement. In addition, new methods have been proposed to address the publication of digital collections as data sets amenable to computational use. In this article, we propose a methodology to create machine actionable collections following a set of steps. This methodology is then applied to several use cases based on data sets published by relevant GLAM institutions. It intends to encourage institutions to adopt the publication of data sets that support computationally driven research as a core activity.
Hierarchical Attention Propagation for Healthcare Representation Learning

Medical ontologies are widely used to represent and organize medical terminologies. Examples include ICD-9, ICD-10, UMLS etc. The ontologies are often constructed in hierarchical structures, encoding the multi-level subclass relationships among different medical concepts, allowing very fine distinctions between concepts. Medical ontologies provide a great source for incorporating domain knowledge into a healthcare prediction system, which might alleviate the data insufficiency problem and improve predictive performance with rare categories. To incorporate such domain knowledge, Gram, a recent graph attention model, represents a medical concept as a weighted sum of its ancestors' embeddings in the ontology using an attention mechanism. Although showing improved performance, Gram only considers the unordered ancestors of a concept, which does not fully leverage the hierarchy thus having limited expressibility. In this paper, we propose Hierarchical Attention Propagation (HAP), a novel medical ontology embedding model that hierarchically propagate attention across the entire ontology structure, where a medical concept adaptively learns its embedding from all other concepts in the hierarchy instead of only its ancestors. We prove that HAP learns more expressive medical concept embeddings -- from any medical concept embedding we are able to fully recover the entire ontology structure. Experimental results on two sequential procedure/diagnosis prediction tasks demonstrate HAP's better embedding quality than Gram and other baselines. Furthermore, we find that it is not always best to use the full ontology. Sometimes using only lower levels of the hierarchy outperforms using all levels.
Neural Networks Identification and Control of Mobile Robot Using Adaptive Neuro Fuzzy Inference System

This paper developed and investigates the performance of intelligent algorithms in order to stabilize the robot when it is tracking to the reference signal. One type of robot is a Two Wheeled Balancing Mobile Robot (TWBMR) that requires control for both balancing and maneuvering. Combination artificial intelligence, Neural Networks (NNs) and Fuzzy Logic Control (FLC) have been recognized as the main tools to improve the performance of coupling nonlinear robot system without using any mathematical model. The input-output data of TWBMR generated from closed loop control system is used to develop a neural network model. In this study, neural networks model can be trained offline and then transferred into a process where an adaptive online learning is carried out using Adaptive Network Based Fuzzy Inference System ANFIS to improve the system performance. The simulation results verify that the considered identification and control strategies can achieve favorable control performance. The ANFIS control design approach does not require an accurate model of the plant. In addition, high-level knowledge of the system is not needed to build a set of rules for a fuzzy controller.
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences

We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of Renyí differential privacy (RDP). Our result is based on the joint range of two f-divergences that underlie the approximate and the Renyi variations of differential privacy. We apply our result tó the moments accountant framework for characterizing privacy guarantees of stochastic gradient descent. When compared to the state-of-the-art, our bounds may lead to about 100 more stochastic gradient descent iterations for training deep learning models for the same privacy budget.
Multi-Label Graph Convolutional Network Representation Learning

Knowledge representation of networked systems is fundamental in many disciplines. To date, existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels. For example, a user may belong to diverse interest groups of a social network, resulting in multi-label networks for many applications. A multi-label network not only has multiple labels for each node, the labels are often highly correlated making existing methods ineffective or even fail to handle such correlation for node representation learning. In this article, we propose a novel multi-label graph convolutional network (MuLGCN) for learning node representation. To fully explore label-label correlation and network topology structures, we propose to model a multi-label network as two Siamese GCNs: a node-node-label graph and a label-label-node graph. The two GCNs each handle one aspect of representation learning for nodes and labels, respectively, and are seamlessly integrated in one objective function. The learned label representations can effectively preserve the intra-label interaction and node label properties, and are aggregated to enhance the node representation learning under a unified training framework. Experiments and comparisons on multi-label node classification validate the effectiveness of our proposed approach.
Application and Practice of VR Virtual Education Platform in Improving the Quality and Ability of College Students

The competition among countries in the world is becoming more and more fierce, among which the most important is the talent competition with core competence. Therefore, it is a top priority for the world education to cultivate talents who can master the core competence in line with the world development trend. The purpose of this study is to make a beneficial practice for the application of virtual reality technology in students' education and for the teaching points at this stage, so as to open up the ideas for further research. Based on VR virtual education platform, this paper first introduces the current research status, some important concepts of virtual reality technology, and clarifies the importance of education and some disadvantages under the traditional education mode. Then, through the collected literature and survey data, the feasibility of combining virtual reality technology with education is analyzed to provide theoretical basis. The subjects were selected from a key university in our city, and the questionnaire survey was conducted among freshmen to juniors by random sampling. The survey of quality education activities in colleges and universities, the survey of students' favorite education methods, the survey of virtual education platform curriculum arrangement and the comparison of quality and ability were carried out. The experimental results show that the traditional course learning is still the main mode of quality-oriented education in Colleges and universities, but lectures, publicity and social practice are also used to educate students, but the proportion is not very high. All three-degree pupils tend towards the traditional way of school education, which represents more than fifty%. The proportion of young students is the highest of the three grades, which corresponds to 62.3%.In addition, the quality and ability of students improved significantly after learning the virtual learning platform.