Reviewerly/exHarmony huggingface dataset:
Topic-aware Heterogeneous Graph Neural Network for Link Prediction

Heterogeneous graphs (HGs), consisting of multiple types of nodes and links, can characterize a variety of real-world complex systems. Recently, heterogeneous graph neural networks (HGNNs), as a powerful graph embedding method to aggregate heterogeneous structure and attribute information, has earned a lot of attention. Despite the ability of HGNNs in capturing rich semantics which reveal different aspects of nodes, they still stay at a coarse-grained level which simply exploits structural characteristics. In fact, rich unstructured text content of nodes also carries latent but more fine-grained semantics arising from multi-facet topic-aware factors, which fundamentally manifest why nodes of different types would connect and form a specific heterogeneous structure. However, little effort has been devoted to factorizing them.
PSNet: Perspective-sensitive convolutional network for object detection

Multi-view object detection is challenging due to the influence of the different view-angles on intra-class similarity. The uniformed feature representation of traditional detectors couples the object’s perspective attribute and semantic feature, and the variances of perspective will cause intra-class differences. In this paper, a robust perspective-sensitive network (PSNet) is proposed to overcome the above problem. The uniformed feature is replaced by the perspective-specific structural feature, which makes the network perspective sensitive. Its essence is to learn multiple perspective spaces. In each perspective space, the semantic feature is decoupled from the perspective attribute and is robust to perspective variances. Perspective-sensitive RoI pooling and loss function are proposed for perspective-sensitive learning. Experiments on Pascal3D + and SpaceNet MOVI show the effectiveness and superiority of the PSNet.
Hybrid Convolutional Optoelectronic Reservoir Computing for Image Recognition

Photonic delay-based reservoir computers (RC) have emerged as an attractive high-speed, low-power alternative to traditional digital hardware for AI. We demonstrate experimentally a novel hybrid RC scheme in which input data is first preprocessed through several convolutional layers, either trained or untrained, digitally to generate novel feature maps. These random feature maps are then processed through an optoelectronic implementation of delay-based RC. Using the MNIST dataset of handwritten digits, experiments of our proposed hybrid scheme achieve classification error of 1.6% using untrained convolutions, and an error of 1.1% using trained convolutions, results comparable to that of state-of-the-art machine learning algorithms. Additionally, our experimental implementation can offer a potential 10x decrease in model training time, compared to that of common digital alternatives.
Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection

Over the last few years, contextualized pre-trained transformer models such as BERT have provided substantial improvements on information retrieval tasks. Traditional sparse retrieval methods such as BM25 rely on high-dimensional, sparse, bag-of-words query representations to retrieve documents. On the other hand, recent approaches based on pre-trained transformer models such as BERT, fine-tune dense low-dimensional contextualized representations of queries and documents in embedding space. While these dense retrievers enjoy substantial retrieval effectiveness improvements compared to sparse retrievers, they are computationally intensive, requiring substantial GPU resources, and dense retrievers are known to be more expensive from both time and resource perspectives. In addition, sparse retrievers have been shown to retrieve complementary information with respect to dense retrievers, leading to proposals for hybrid retrievers. These hybrid retrievers leverage low-cost, exact-matching based sparse retrievers along with dense retrievers to bridge the semantic gaps between query and documents. In this work, we address this trade-off between the cost and utility of sparse vs dense retrievers by proposing a classifier to select a suitable retrieval strategy (i.e., sparse vs. dense vs. hybrid) for individual queries. Leveraging sparse retrievers for queries which can be answered with sparse retrievers decreases the number of calls to GPUs. Consequently, while utility is maintained, query latency decreases. Although we use less computational resources and spend less time, we still achieve improved performance. Our classifier can select between sparse and dense retrieval strategies based on the query alone. We conduct experiments on the MS MARCO passage dataset demonstrating an improved range of efficiency/effectiveness trade-offs between purely sparse, purely dense or hybrid retrieval strategies, allowing an appropriate strategy to be selected based on a target latency and resource budget.
GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data

Anomaly detection in spatiotemporal data is a problem encountered in a variety of applications including urban traffic monitoring. For urban traffic data, anomalies refer to unusual events such as traffic congestion and unexpected crowd gatherings. Detecting these anomalies is challenging due to the scarcity of anomalous events and the dependence of anomaly definition on time and space. Existing spatiotemporal anomaly detection methods cannot preserve the spatial and temporal correlations and do not take the structure of anomalies into account. In this paper, we introduce a temporally regularized, locally consistent, robust low-rank plus sparse tensor model for spatiotemporal anomaly detection. The proposed method takes the spatially sparse and temporally smooth structure of urban anomalies into account by modeling the anomalies as the sparse part of the tensor and minimizing the total variation across the temporal mode of this part. The local consistency of the low-rank part is ensured using a manifold embedding approach. The proposed approach is referred to as Graph Regularized Low-rank plus Temporally Smooth Sparse decomposition (GLOSS) and is evaluated on synthetic and real spatiotemporal urban traffic data. The results illustrate the accuracy and robustness of the proposed method with respect to missing data, noise and anomaly strength.
AUV path planning for coverage search of static target in ocean environment

Aiming at the path planning problem for searching static target by autonomous underwater vehicles (AUVs) in ocean environment, this paper proposes a hierarchical method based on the maximum cumulative detection reward or search efficiency. First, the mission sea region is described in the form of target probability map, and the subregions with high search value are extracted by the method of expectation-maximization (EM). For multiple AUVs, we propose the bidirectional negotiation with biased min-consensus (BN-BMC) algorithm to determine the allocation and prioritization of subregions to be searched. On this basis, we utilize an adaptive oval spiral coverage (AOSC) strategy to plan the coverage path within each subregion, and take the Dubins path satisfying the AUV kinematic constraint as the transition path between subregions. Finally, the simulation results validate the advantage of our method.
What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?

Geopolitical forecasting tournaments have stimulated the development of methods for improving probability judgments of real-world events. But these innovations have focused on easier-to-quantify variables, like personnel selection, training, teaming, and crowd aggregation—bypassing messier constructs, like qualitative properties of forecasters’ rationales. Here, we adapt methods from natural language processing (NLP) and computational text analysis to identify distinctive reasoning strategies in the rationales of top forecasters, including: (a) cognitive styles, such as dialectical complexity, that gauge tolerance of clashing perspectives and efforts to blend them into coherent conclusions and (b) the use of comparison classes or base rates to inform forecasts. In addition to these core metrics, we explore metrics derived from the Linguistic Inquiry and Word Count (LIWC) program. Applying these tools to multiple tournaments and to forecasters of widely varying skill (from Mechanical Turkers to carefully culled “superforecasters”) revealed that: (a) top forecasters show higher dialectical complexity in their rationales and use more comparison classes; (b) experimental interventions, like training and teaming, that boost accuracy also influence NLP profiles of rationales, nudging them in a “superforecaster” direction.
Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group

Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast Cancer Working Group (IKWG) for Ki67 scoring and to evaluate the prognostic potential of this platform in an independent cohort. Four algorithms were independently built by four pathologists based on our study cohort using an open-source digital image analysis (DIA) platform (QuPath) following the detailed guideline of the IKWG. The algorithms were applied on an ER+ breast cancer study cohort of 157 patients with 15 years of follow-up. The reference Ki67 score was obtained by a DIA algorithm trained on a subset of the study cohort. Intraclass correlation coefficient (ICC) was used to measure reproducibility. High interobserver reliability was reached with an ICC of 0.938 (CI: 0.920–0.952) among the algorithms and the reference standard. Comparing each machine-read score against relapse-free survival, the hazard ratios were similar (2.593–4.165) and showed independent prognostic potential (p ≤ 0.018, for all comparisons). In conclusion, we demonstrate high reproducibility and independent prognostic potential using the IKWG DIA instructions to score Ki67 in breast cancer. A prospective study is needed to assess the clinical utility of the IKWG DIA Ki67 instructions.
Fine-tuning GPT-2 on annotated RPG quests for NPC dialogue generation

GPT-2, a neural language model trained on a large dataset of English web text, has been used in a variety of natural language generation tasks because of the language quality and coherence of its outputs. In order to investigate the usability of GPT-2 for text generation for video games, we fine-tuned GPT-2 on a corpus of video game quests and used this model to generate dialogue lines for quest-giver NPCs in a role-playing game. We show that the model learned the structure of quests and NPC dialogue, and investigate how the temperature parameter influences the language quality and creativity of the output artifacts. We evaluated our approach with a crowdsource experiment in which human judges were asked to rate hand-written and generated quest texts on language quality, coherence and creativity.
CauSeR

Recommender Systems (RS) tend to recommend more popular items instead of the relevant long-tail items. Mitigating such popularity bias is crucial to ensure that less popular but relevant items are part of the recommendation list shown to the user. In this work, we study the phenomenon of popularity bias in session-based RS (SRS) obtained via deep learning (DL) models. We observe that DL models trained on the historical user-item interactions in session logs (having long-tailed item-click distributions) tend to amplify popularity bias. To understand the source of this bias amplification, we consider potential sources of bias at two distinct stages in the modeling process: i. the data-generation stage (user-item interactions captured as session logs), ii. the DL model training stage. We highlight that the popularity of an item has a causal effect on i. user-item interactions via conformity bias, as well as ii. item ranking from DL models via biased training process due to class (target item) imbalance. While most existing approaches in literature address only one of these effects, we consider a comprehensive causal inference framework that identifies and mitigates the effects at both stages. Through extensive empirical evaluation on simulated and real-world datasets, we show that our approach improves upon several strong baselines from literature for popularity bias and long-tailed classification. Ablation studies show the advantage of our comprehensive causal analysis to identify and handle bias in data generation as well as training stages.
Enhancing EFL vocabulary learning with multimodal cues supported by an educational robot and an IoT-Based 3D book

Three-dimensional (3D) books, which feature interactive learning environments for immersing learners in situated learning, have proven to be an effective mediating tool for vocabulary acquisition. However, traditional 3D books lack affordances for two-way communication and multimodal language input for language learners. This study aims to apply multimodal cues in a task-based learning system consisting of an educational robot and a 3D book supported by the Internet of Things (IoT) to enhance EFL children's vocabulary learning. Two fourth graders participated in two phases of research (Exploratory and Enhancement Phases) separated by a six-month systematic enhancement period. In the Enhancement Phase, the two learners took pre- and post-tests on vocabulary recognition and oral production. Results showed a significant improvement in oral production, which can be attributed to elicitation cues applied in the robot- and 3D book-supported learning environment. Further analysis revealed a learner difference in reacting to auditory cues. Pedagogical implications for similar future efforts in using multimodal cues to enhance vocabulary learning among EFL children are also provided.
Perceptual Variousness Motion Deblurring with Light Global Context Refinement

Deep learning algorithms have made significant progress in dynamic scene deblurring. However, several challenges are still unsettled: 1) The degree and scale of blur in different regions of a blurred image can have a considerable variation in a large range. However, the traditional input pyramid or downscaling-upscaling, is designed to have limited and inflexible perceptual variousness to cope with large blur scale variation. 2) The nonlocal block is proved to be effective in the image enhancement tasks, but it requires high computation and memory cost. In this paper, we are the first to propose a light-weight globally-analyzing module into the image deblurring field, named Light Global Context Refinement (LGCR) module. With exponentially lower cost, it achieves even better performance than the nonlocal unit. Moreover, we propose the Perceptual Variousness Block (PVB) and PVB-piling strategy. By placing PVB repeatedly, the whole method possesses abundant reception field spectrum to be aware of the blur with various degrees and scales. Comprehensive experimental results from the different benchmarks and assessment metrics show that our method achieves excellent performance to set a new state-of-the-art in motion deblurring. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Resource Analysis of Ethereum 2.0 Clients

Scalability is a common issue among the most used permissionless blockchains, and several approaches have been proposed to solve this issue.Tackling scalability while preserving the security and decentralization of the network is an important challenge.To deliver effective scaling solutions, Ethereum is on the path of a major protocol improvement called Ethereum 2.0 (Eth2), which implements sharding.As the change of consensus mechanism is an extremely delicate matter, this improvement will be achieved through different phases, the first of which is the implementation of the Beacon Chain.For this, a specification has been developed, and multiple groups have implemented clients to run the new protocol.This work analyzes the resource usage behavior of different clients running as Eth2 nodes, comparing their performance and analyzing differences.Our results show multiple important network perturbations and how different clients react to them.We discuss the differences between Eth2 clients and their limitations.
Self-Supervised Synthesis Ranking for Deep Metric Learning

The core purpose of deep metric learning is to construct an embedding space, where objects belonging to the same class are gathered together and the ones from different classes are pushed apart. Most existing approaches typically insist to inter-class characteristics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , class-level information or instance-level similarity, to obtain semantic relevance of data points and get a large margin between different classes in the embedding space. However, the intra-class characteristics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , local manifold structure or relative relationship within the same class, are usually overlooked in the learning process. Hence the output embeddings have limitation in retrieving a good ranking result if existing multiple positive samples. And the local data structure of embedding space cannot be fully exploited since lack of relative ranking information. As a result, the model is prone to overfitting on a train set and get low generalization on the test set (unseen classes) when losing sight of intra-class variance. This paper presents a novel self-supervised synthesis ranking auxiliary framework, which captures intra-class characteristics as well as inter-class characteristics for better metric learning. Our method designs a synthetic samples generation of polar coordinates to generate measurable intra-class variance with different strength and diversity in the latent space, which can simulate the various local structure change of intra-class in the initial data domain. And then formulates a self-supervised learning procedure to fully exploit this property and preserve it in the embedding space. As a result, the learned embedding space not only keeps inter-class discrimination but also owns subtle intra-class diversity, leading to better global and local embedding structures. Extensive experiments on five benchmarks show that our method significantly improves and outperforms the state-of-the-art methods on the performances of both retrieval and ranking by 2%-4% (personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org).
Decentralized Self-Auditing Scheme With Errors Localization for Multi-Cloud Storage

With the popularity of cloud storage, increasing users begin to outsource data to the cloud. In order to resist possible data analysis for centralized outsourced data and improve the fault tolerance, users prefer to distribute data to cloud servers of different cloud service providers. However, once the data have been outsourced, it will be out of user’s control and many security issues may occur, such as outsourced data being illegally tamper with, or rarely accessed data being secretly deleted. In this article, we propose a decentralized self-auditing scheme for multi-cloud storage, called DSAS. First, based on the symmetric balanced incomplete block design, DSAS achieves integrity verification for outsourced data via the interactions of cloud servers and the auditing costs are shared by the participating CSs. Second, DSAS can locate misbehavior cloud server with low computation costs, and resist denial of service attack initiated by malicious cloud servers which attempts to destroy the audit. Third, DSAS can recover the corrupted data without fetching data, and support the revocation of cloud servers and batch auditing. Finally, security proof and function evaluation show that DSAS has comprehensive security and functionality, and performance simulations and experiment results show that DSAS is efficient.
Impact of Colour on Robustness of Deep Neural Networks

Convolutional neural networks have become the most widely used tool for computer vision applications like image classification, segmentation, object localization, etc. Recent studies have shown that the quality of images has a significant impact on the performance of these deep neural networks. The accuracy of the computer vision tasks gets significantly influenced by the image quality due to the shift in the distribution of the images on which the networks are trained on. Although, the effects of perturbations like image noise, image blur, image contrast, compression artifacts, etc. on the performance of deep neural networks on image classification have been studied, the effects of colour and quality of colour in digital images have been a mostly unexplored direction. One of the biggest challenges is that there is no particular dataset dedicated to colour distortions and colour aspects of images in image classification. The main aim of this paper is to study the impact of colour distortions on the performance of image classification using deep neural networks. Experiments performed using multiple state-of–of-the–the-art deep convolutional neural architectures on a proposed colour distorted dataset are presented and the impact of colour on image classification task is demonstrated.
Distributed Bayesian optimisation framework for deep neuroevolution

Neuroevolution is a machine learning method for evolving neural networks parameters and topology with a high degree of flexibility that makes them applicable to a wide range of architectures. Neuroevolution has been popular in reinforcement learning and has also shown to be promising for deep learning. The major feature of Bayesian optimisation is in reducing computational load by approximating the actual model with an acquisition function (surrogate model) that is computationally cheaper. A major limitation of neuroevolution is the high computational time required for convergence since learning (evolution) typically does not utilize gradient information. Bayesian optimisation, which is also known as surrogate-assisted optimisation, has been popular for expensive engineering optimisation problems and hyper-parameter tuning in machine learning. It has potential for training deep learning models via neuroevolution given large datasets and complex models. Recent advances in parallel and distributed computing have enabled efficient implementation of neuroevolution for complex and computationally expensive neural models. In this paper, we present a Bayesian optimisation framework for deep neuroevolution using a distributed architecture to provide computational efficiency in training. Our results demonstrate promising results for simple to deep neural network models such as convolutional neural networks which motivates further applications.