Reviewerly/exHarmony huggingface dataset:
Introducing Graph Neural Networks for Few-Shot Relation Prediction in Knowledge Graph Completion Task

Relation prediction is a key problem for graph-structured data, to many applications such as knowledge graph completion (KGC), while embedding-based methods, effective for relation prediction in KGs, perform poorly on relations that only have a few associative triples. Nowadays, many works focus on leveraging Graph Neural Network (GNN) for alleviating few-shot learning problems in relation prediction task. However, existing work usually only averages the neighbors of the target nodes, which limits model’s performance and capability. Besides, although the Graph Attention Network (GAT) can generate more powerful feature representations through the expansion of multi-head attention, unfortunately it only considers the relationship between neighbors in the same layer, and does not make full use of hierarchical information (e.g., layer-wise information), resulting in excessive over-smoothing problem. Hence, this paper develops a novel GNN-based model for few-shot relation prediction, by leveraging intra-layer neighborhood attention and inter-layer memory attention, to alleviate over-smoothing tendencies of deep GNNs. Empirical studies on two real-world datasets confirm the superiority of the proposed model. The proposed model addresses the few-shot relation prediction and outperforms competitive state-of-the-art models.
A Framework for Edge Intelligent Smart Distribution Grids via Federated Learning

Recent advances in distributed data processing and machine learning provide new opportunities to enable critical, time-sensitive functionalities of smart distribution grids in a secure and reliable fashion. Combining the recent advents of edge computing (EC) and edge intelligence (EI) with existing advanced metering infrastructure (AMI) has the potential to reduce overall communication cost, preserve user privacy, and provide improved situational awareness. In this paper, we provide an overview for how EC and EI can supplement applications relevant to AMI systems. Additionally, using such systems in tandem can enable distributed deep learning frameworks (e.g., federated learning) to empower distributed data processing and intelligent decision making for AMI. Finally, to demonstrate the efficacy of this considered architecture, we approach the non-intrusive load monitoring (NILM) problem using federated learning to train a deep recurrent neural network architecture in a 2-tier and 3-tier manner. In this approach, smart homes locally train a neural network using their metering data and only share the learned model parameters with AMI components for aggregation. Our results show this can reduce communication cost associated with distributed learning, as well as provide an immediate layer of privacy, due to no raw data being communicated to AMI components. Further, we show that FL is able to closely match the model loss provided by standard centralized deep learning where raw data is communicated for centralized training.
An empirical study on the joint impact of feature selection and data resampling on imbalance classification

Many real-world datasets exhibit imbalanced distributions, in which the majority classes have sufficient samples, whereas the minority classes often have a very small number of samples. Data resampling has proven to be effective in alleviating such imbalanced settings, while feature selection is a commonly used technique for improving classification performance. However, the joint impact of feature selection and data resampling on two-class imbalance classification has rarely been addressed before. This work investigates the performance of two opposite imbalanced classification frameworks in which feature selection is applied before or after data resampling. We conduct a large-scale empirical study with a total of 9225 experiments on 52 publicly available datasets. The results show that both frameworks should be considered for finding the best performing imbalanced classification model. We also study the impact of classifiers, the ratio between the number of majority and minority samples (IR), and the ratio between the number of samples and features (SFR) on the performance of imbalance classification. Overall, this work provides a new reference value for researchers and practitioners in imbalance learning.
Adaptive Hybrid Robust Filter for Multi-Sensor Relative Navigation System

This paper provides an adaptive hybrid robust filter (AHRF) for multi-sensor relative navigation systems that can be used to support cooperative intelligent transport systems. It is known that Huber’s M-estimation based robust filter and the fault detection and exclusion (FDE) based RAIM filter each has its own drawbacks, depending on the nature of the observation error biases. Based on the interactive multiple model (IMM) framework, our proposed AHRF in this paper can take advantage of both filters in a complementary sense. A new adaptive IMM (AIMM) algorithm with Markov transition probability prediction is proposed to allow AHRF to switch efficiently between the two filters. We consider the relative navigation system with Global Navigation Satellite System (GNSS) and ultra-wideband (UWB) as observations to verify AHRF in three cases of possible failure modes and multipath-induced errors. Our results show that AHRF outperforms both the FDE and robust filter in all cases. AHRF framework can be further adapted to include many other fault-tolerant filters to improve the robustness of multi-sensor relative navigation system even further.
Robust face alignment by dual-attentional spatial-aware capsule networks

Face alignment in-the-wild still faces great challenges due to that i) partial occlusion blurs the inter-features spatial relations of faces and ii) traditional CNN makes the network more difficult to capture the spatial positional relations between landmarks. To address the issues above, we propose a face alignment algorithm named Dual-attentional Spatial-aware Capsule Network (DSCN). Firstly, the spatial-aware module builds a more accurate inter-features spatial constrained model with the hourglass capsule network (HGCaps) as the backbone, which can effectively enhance its robustness against occlusions. Then, two sorts of attention mechanisms, namely capsule attention and spatial attention, are added to the attention-guided module to make the network focus more on the advantageous features and suppress other unrelated ones for more effective feature recalibration. Our method achieves 1.08% failure rate on the COFW dataset, which is much lower than the current state-of-the-art algorithms. The mean error under 300W dataset and WFLW dataset are respectively 3.91% and 5.66%, which shows that DSCN is more robust to occlusion and outperforms state-of-the-art methods in the literature.
Multisource Heterogeneous User-Generated Contents-Driven Interactive Estimation of Distribution Algorithms for Personalized Search

Personalized search is essentially a complex qualitative optimization problem, and interactive evolutionary algorithms (EAs) have been extended from EAs to adapt to solving it. However, the multisource user-generated contents (UGCs) in the personalized services have not been concerned on in the adaptation. Accordingly, we here present an enhanced restricted Boltzmann machine (RBM)-driven interactive estimation of distribution algorithms (IEDAs) with multisource heterogeneous data from the viewpoint of effectively extracting users’ preferences and requirements from UGCs to strengthen the performance of IEDA for personalized search. The multisource heterogeneous UGCs, including users’ ratings and reviews, items’ category tags, social networks, and other available information, are sufficiently collected and represented to construct an RBM-based model to extract users’ comprehensive preferences. With this RBM, the probability model for conducting the reproduction operator of estimation of distribution algorithms (EDAs) and the surrogate for quantitatively evaluating an individual (item) fitness are further developed to enhance the EDA-based personalized search. The UGCs-driven IEDA is applied to various publicly released Amazon datasets, e.g., recommendation of Digital Music, Apps for Android, Movies, and TV, to experimentally demonstrate its performance in efficiently improving the IEDA in personalized search with less interactions and higher satisfaction.
Rebuilding trust in the construction industry: a blockchain-based deployment framework

Trust has been acknowledged as an essential element of inter-organizational relationships, and it also affects the efficiency of work operations. Although the construction industry has been pursuing trust for a long time, trust is still lacking and fragile among stakeholders. Blockchain seems to provide a promising approach through which trust can be rebuilt. This study aims to develop a deployment framework by following which construction stakeholders can develop blockchain-based solutions for rebuilding trust. A design science research (DSR) method is used to develop a blockchain-based framework. Then, the framework is prototyped and illustrated through a case study of construction supervision. It is discovered that the proposed framework can provide a promising approach to fostering trust. One contribution of this research is to articulate a normative vs. naturalistic trust-building spectrum and position blockchain-based trust-building in it. Practically, this research develops a framework that stakeholders can develop blockchain-based solutions.
Quantum master equations and steady states for the ultrastrong-coupling limit and the strong-decoherence limit

In the framework of the theory of open quantum systems, we derive quantum master equations for the ultrastrong system-bath coupling regime and, more generally, the strong-decoherence regime. In this regime, the strong decoherence is complemented by slow relaxation processes. We use a generalization of the F\"orster and modified Redfield perturbation theories known in the theory of excitation-energy transfer. Also, we show that the mean-force Gibbs state in the corresponding limits is stationary for the derived master equations.
Secure Data Sharing: Blockchain-Enabled Data Access Control Framework for IoT

As Internet-of-Things (IoT) service becomes richer, data sharing among different IoT systems gets popular. The traditional IoT system provides data storage and access service with the central cloud, which faces serious trust and security challenges. To provide a cross-system data sharing service, we adopt blockchain to build a multicenter data management (DM) framework and construct a trustable environment for data sharing. As regards to a security problem, attribute-based encryption (ABE) has been applied to the IoT system, but it still relies on the central server. Therefore, we design an ABE algorithm that could be used for multicenter scenario and shift DM to blockchain instead of a central server. Moreover, IoT devices always cannot afford complex encrypt computations as they have limited computing resource. To solve this, we design an obfuscating policy to shift encryption computations to the cloud instead of terminals. In this way, IoT devices could encrypt data with low computation cost. Security analysis and simulations prove that the algorithm we designed could reduce computation burdens of IoT terminals in data encryption and decryption phases effectively and safely.
Understanding embodied immersion in technology‐enabled embodied learning environments

Abstract Objective The aim of this current paper is to investigate the salient embodiment features in the empirical studies on technology‐enabled embodied learning environments (TEELEs), and to contribute a comprehensive conceptual framework to direct the understanding and the designing of TEELEs – to embody and how to embody. Method In this thematic paper, we carefully selected and reviewed 28 empirical studies (peer‐reviewed journal articles published in the recent decade between 2010 and 2020) on TEELEs, and analyzed their salient features concerning embodiment. Results and Implications Synthesizing the characteristics distilled from the selected studies, we propose the conceptual framework of embodied immersion , which consists of three major dimensions, namely, physical, sensory, and cognitive immersions. Eight factors – means of embodiment, amount of motoric movements, point of view, media effects, haptic effects, operational congruency, learning congruency, and personalization – contribute to these three dimensions, and are carefully articulated, discussed, and explicated. The discussions around the embodied immersion framework are rooted in the concepts of presence, immersion, and agency. We regard our framework as a trinity borrowing the ingredients from each of those three that will guide through the understanding and the designing of TEELEs.
Managing ML pipelines

The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed to manage and standardize the engineer's workflow in this end-to-end pipeline, focusing on traditional tabular feature data. In recent years, however, model development has shifted towards using self-supervised pretrained embeddings as model features. Managing these embeddings and the downstream systems that use them introduces new challenges with respect to managing embedding training data, measuring embedding quality, and monitoring downstream models that use embeddings. These challenges are largely unaddressed in standard feature stores. Our goal in this tutorial is to introduce the feature store system and discuss the challenges and current solutions to managing these new embedding-centric pipelines.
Autonomous angles-only multitarget tracking for spacecraft swarms

This paper presents a new algorithm for autonomous multitarget tracking of resident space objects using optical angles-only measurements from a spaceborne observer. To enable autonomous angles-only navigation of spacecraft swarms, an observer must be able to identify and track multiple known or unknown target space objects in view, without reliance on a-priori relative orbit knowledge. Extremely high assignment precision is necessary with low measurement frequencies and limited computational resources. The new "Spacecraft Angles-only MUltitarget tracking System" (SAMUS) algorithm has been developed to meet these objectives and constraints. It combines domain-specific modeling of target kinematics with multi-hypothesis techniques to autonomously track multiple unknown targets using only sequential camera images. A measurement transform ensures that target motion in the observer reference frame follows consistent parametric models; curve fitting is used to predict track behavior; and kinematically-derived track gating and scoring criteria are applied to improve the efficiency and accuracy of the multi-hypothesis approach. Monte-Carlo testing with high-fidelity simulations demonstrates close to 100% measurement assignment precision and strong recall across a range of multi-spacecraft formations, in both near-circular and eccentric orbits. Tracking is maintained in the presence of eclipse periods, significant measurement noise, and partially known swarm maneuvers. A comparison to other tracking algorithms reveals strong advantages in precision, robustness and computation time, crucial for spaceborne angles-only navigation.
A Large-scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search

Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this article, we help to position it with respect to other research areas within conversational artificial intelligence (AI) by analysing the structural properties of an information-seeking dialogue. To this end, we perform a large-scale dialogue analysis of more than 150K transcripts from 16 publicly available dialogue datasets. These datasets were collected to inform different dialogue-based tasks including conversational search. We extract different patterns of mixed initiative from these dialogue transcripts and use them to compare dialogues of different types. Moreover, we contrast the patterns found in information-seeking dialogues that are being used for research purposes with the patterns found in virtual reference interviews that were conducted by professional librarians. The insights we provide (1) establish close relations between conversational search and other conversational AI tasks and (2) uncover limitations of existing conversational datasets to inform future data collection tasks.
Speech Emotion Recognition by Late Fusion for Bidirectional Reservoir Computing With Random Projection

Many researchers are inspired by studying Speech Emotion Recognition (SER) because it is considered as a key
Dissecting word embeddings and language models in natural language processing

AbstractAbstractNatural language processing (NLP) is an area in artificial intelligence that deals with understanding, interpretation and development of human language for computers to carry out tasks such as sentiment analysis, summarization of text in a document, developing conversational agents, machine translation and speech recognition. From conversational agents called catboats deployed on various websites that interact with consumers digitally to understand the needs of the consumers to reading summarized content delivered through apps in smartphones, NLP has had some major achievements in transforming the digital world that is increasingly gearing towards artificial intelligence. One area that has seen remarkable growth in recent times is language modelling, a statistical technique to compute the probability of tokens or words in a given sentence. In this paper, we attempt to present an overview of various representations with respect to language modelling, from neural word embeddings such as Word2Vec and GloVe to deep contextualized pre-trained embedding such as ULMFit, ELMo, OpenAI GPT and BERT.Subject Classification: 68 Mxx68 PxxKeywords: Natural language processingLanguage modelsWord embedding
Augmented reality-based visual-haptic modeling for thoracoscopic surgery training systems

Compared with traditional thoracotomy, video-assisted thoracoscopic surgery (VATS) has less minor trauma, faster recovery, higher patient compliance, but higher requirements for surgeons. Virtual surgery training simulation systems are important and have been widely used in Europe and America. Augmented reality (AR) in surgical training simulation systems significantly improve the training effect of virtual surgical training, although AR technology is still in its initial stage. Mixed reality has gained increased attention in technology-driven modern medicine but has yet to be used in everyday practice. This study proposed an immersive AR lobectomy within a thoracoscope surgery training system, using visual and haptic modeling to study the potential benefits of this critical technology. The content included immersive AR visual rendering, based on the cluster-based extended position-based dynamics algorithm of soft tissue physical modeling. Furthermore, we designed an AR haptic rendering systems, whose model architecture consisted of multi-touch interaction points, including kinesthetic and pressure-sensitive points. Finally, based on the above theoretical research, we developed an AR interactive VATS surgical training platform. Twenty-four volunteers were recruited from the First People's Hospital of Yunnan Province to evaluate the VATS training system. Face, content, and construct validation methods were used to assess the tactile sense, visual sense, scene authenticity, and simulator performance. The results of our construction validation demonstrate that the simulator is useful in improving novice and surgical skills that can be retained after a certain period of time. The video-assisted thoracoscopic system based on AR developed in this study is effective and can be used as a training device to assist in the development of thoracoscopic skills for novices.
Graph Based Visualisation Techniques for Analysis of Blockchain Transactions

Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.
Secure and Robust Optical Multi-Stage Medical Image Cryptosystem

Due to the rapid growth of telemedicine and healthcare services, color medical image security applications have been expanded precipitously. In this paper, an asymmetric PTFrFT (Phase Truncated Fractional Fourier Transform)-based color medical image cryptosystem is suggested. Two different phases in the fractional Fourier and output planes are provided as deciphering keys. Accordingly, the ciphering keys will not be employed for the deciphering procedure. Thus, the introduced PTFrFT algorithm comprises asymmetric ciphering and deciphering processes in contrast to the traditional optical symmetric OSH (Optical Scanning Holography) and DRPE (Double Random Phase Encoding) algorithms. One of the principal impacts of the introduced asymmetric cryptosystem is that it eliminates the one-dimensionality aspects of the related symmetric cryptosystems due to its remarkable feature of phase nonlinear truncation components. More comparisons on various color medical images are examined and analyzed to substantiate the cryptosystem efficacy. The achieved experimental outcomes ensure that the introduced cryptosystem is robust and secure. It has terrific cryptography performance compared to conventional cryptography algorithms, even in the presence of noise and severe channel attacks.
Representation, learning, and planning algorithms for geometric task and motion planning

We present a framework for learning to guide geometric task-and-motion planning (G-TAMP). G-TAMP is a subclass of task-and-motion planning in which the goal is to move multiple objects to target regions among movable obstacles. A standard graph search algorithm is not directly applicable, because G-TAMP problems involve hybrid search spaces and expensive action feasibility checks. To handle this, we introduce a novel planner that extends basic heuristic search with random sampling and a heuristic function that prioritizes feasibility checking on promising state–action pairs. The main drawback of such pure planners is that they lack the ability to learn from planning experience to improve their efficiency. We propose two learning algorithms to address this. The first is an algorithm for learning a rank function that guides the discrete task-level search, and the second is an algorithm for learning a sampler that guides the continuous motion-level search. We propose design principles for designing data-efficient algorithms for learning from planning experience and representations for effective generalization. We evaluate our framework in challenging G-TAMP problems, and show that we can improve both planning and data efficiency.