Reviewerly/exHarmony huggingface dataset:
Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification

Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure. Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions. Spurious edges may be included; other edges may be missing between nodes that have very strong relationships. In this paper we adopt a Bayesian approach, viewing the observed graph as a realization from a parametric family of random graphs. We then target inference of the joint posterior of the random graph parameters and the node (or graph) labels. We present the Bayesian GCNN framework and develop an iterative learning procedure for the case of assortative mixed-membership stochastic block models. We present the results of experiments that demonstrate that the Bayesian formulation can provide better performance when there are very few labels available during the training process.
A Comparative Study: Blockchain Technology Utilization Benefits, Challenges and Functionalities

Blockchain technology enables users to verify, preserve, and synchronize the contents of a data sheet (a transaction ledger) replicated by multiple users.Blockchain technology has provided considerable advantages and incentives to industries in terms of enabling better services.This review aims to explore the benefits, challenges and functionalities that affect blockchain applications in different sectors.This article is constructed as a systematic literature review study.From 1976 articles, 168 final articles were selected and classified into three main dimensions, that is, benefits, challenges, and functionalities, in four different sectors: government, financial, manufacturing, and healthcare.The results were extracted and compared based on factors in three dimensions, which were categorized as benefits (informational, technological, economic, organizational, and strategic), challenges (technological, organizational, adoption, operational, and environmental and sustainability), and functionalities (point-to-point transmission, data ownership, data protection, and transaction processing).The results of this review study aim to support professionals, practitioners, and stakeholders who wish to implement and manage transformation projects related to blockchain in their sectors.Moreover, helping these possible blockchain users to understand the implied factors associated with blockchain would be beneficial for the decision-making processes of their organizations.
KILT: a Benchmark for Knowledge Intensive Language Tasks

Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources.While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure.To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT).All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the reuse of components, as well as accelerating research into task-agnostic memory architectures.We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text.
Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods

Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive to collect. To address it, many efforts have been made on training sophisticated models with few labeled data in an unsupervised and semi-supervised fashion. In this paper, we will review the recent progresses on these two major categories of methods. A wide spectrum of models will be categorized in a big picture, where we will show how they interplay with each other to motivate explorations of new ideas. We will review the principles of learning the transformation equivariant, disentangled, self-supervised and semi-supervised representations, all of which underpin the foundation of recent progresses. Many implementations of unsupervised and semi-supervised generative models have been developed on the basis of these criteria, greatly expanding the territory of existing autoencoders, generative adversarial nets (GANs) and other deep networks by exploring the distribution of unlabeled data for more powerful representations. We will discuss emerging topics by revealing the intrinsic connections between unsupervised and semi-supervised learning, and propose in future directions to bridge the algorithmic and theoretical gap between transformation equivariance for unsupervised learning and supervised invariance for supervised learning, and unify unsupervised pretraining and supervised finetuning. We will also provide a broader outlook of future directions to unify transformation and instance equivariances for representation learning, connect unsupervised and semi-supervised augmentations, and explore the role of the self-supervised regularization for many learning problems.
Systematic reviews in sentiment analysis: a tertiary study

Abstract With advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.
A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention

Together with the fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing HAR methods depend on exploratory case-based shallow feature learning architectures, which struggle with correct activity recognition when put into real-life practice. To tackle this problem, we propose a novel approach that utilizes the convolutional neural networks (CNNs) and the attention mechanism for HAR. In the presented method, the activity recognition accuracy is improved by incorporating attention into multihead CNNs for better feature extraction and selection. Proof of concept experiments are conducted on a publicly available data set from wireless sensor data mining (WISDM) lab. The results demonstrate a higher accuracy of our proposed approach in comparison with the current methods.
Smart augmented reality instructional system for mechanical assembly towards worker-centered intelligent manufacturing

Quality and efficiency are crucial indicators of any manufacturing company. Many companies are suffering from a shortage of experienced workers across the production line to perform complex assembly tasks. To reduce time and error in an assembly task, a worker-centered system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The integrated AR is designed to provide on-site instructions including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is trained on a synthetic tool dataset. The dataset is generated using CAD models of tools and displayed onto a 2D scene without using real tool images. By experimenting the system to a mechanical assembly of a CNC carving machine, the result of a designed experiment shows that the system helps reduce the time and errors of the given assembly tasks by 33.2 % and 32.4 %, respectively. With the integrated system, an efficient, customizable smart AR instruction system capable of sensing, characterizing requirements, and enhancing worker’s performance has been built and demonstrated.
Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction

We propose a novel attention-based framework for 3D human pose estimation from a monocular video. Despite the general success of end-to-end deep learning paradigms, our approach is based on two key observations: (1) temporal incoherence and jitter are often yielded from a single frame prediction; (2) error rate can be remarkably reduced by increasing the receptive field in a video. Therefore, we design an attentional mechanism to adaptively identify significant frames and tensor outputs from each deep neural net layer, leading to a more optimal estimation. To achieve large temporal receptive fields, multi-scale dilated convolutions are employed to model long-range dependencies among frames. The architecture is straightforward to implement and can be flexibly adopted for real-time applications. Any off-the-shelf 2D pose estimation system, e.g. Mocap libraries, can be easily integrated in an ad-hoc fashion. We both quantitatively and qualitatively evaluate our method on various standard benchmark datasets (e.g. Human3.6M, HumanEva). Our method considerably outperforms all the state-of-the-art algorithms up to 8% error reduction (average mean per joint position error: 34.7) as compared to the best-reported results. Code is available at: (https://github.com/lrxjason/Attention3DHumanPose)
Recent Advances in End-to-End Automatic Speech Recognition

Recently, the speech community is seeing a significant trend of moving from deep neural network based hybrid modeling to end-to-end (E2E) modeling for automatic speech recognition (ASR). While E2E models achieve the state-of-the-art results in most benchmarks in terms of ASR accuracy, hybrid models are still used in a large proportion of commercial ASR systems at the current time. There are lots of practical factors that affect the production model deployment decision. Traditional hybrid models, being optimized for production for decades, are usually good at these factors. Without providing excellent solutions to all these factors, it is hard for E2E models to be widely commercialized. In this paper, we will overview the recent advances in E2E models, focusing on technologies addressing those challenges from the industry's perspective.
Grounded Language-Image Pre-training

This paper presents a grounded language-image pretraining (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representations semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many supervised baselines. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Supervised baselines on COCO object detection: Faster-RCNN w/ ResNet50 (40.2) or ResNet101 (42.0), and DyHead w/ Swin-Tiny (49.7). 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. Code will be released at https://github.com/microsoft/GLIP.
Nature-Inspired Optimization Algorithms

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm
Cooperative and Distributed Computation Offloading for Blockchain-Empowered Industrial Internet of Things

Offloading computation-intensive blockchain mining tasks to the edge servers (ESs) is a promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the computing capabilities in IIoT are usually limited, whereas the blockchain mining tasks are computationally intensive. However, the computation offloading solutions for data processing tasks and for blockchain mining tasks have been studied separately. Moreover, most of the existing solutions for offloading assume that all IIoT devices can directly connect to the ESs or cloud data centers. To address these issues, in this paper, we propose a multihop cooperative and distributed computation offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT. First, we study the multihop computation offloading problem for both the data processing tasks and the mining tasks to minimize the economic cost of IIoT devices. Second, we formulate the offloading problem as a potential game in which the IIoT devices can make their decisions autonomously and prove the existence of Nash equilibrium (NE) for the game. Third, we design an efficient distributed algorithm based on exchanging messages between IIoT devices to achieve the NE with low computational complexity. Lastly, our experimental results demonstrate that our distributed algorithm scales well as the number of IIoT devices increases and has the minimum system cost compared with other approaches.
Transaction-based classification and detection approach for Ethereum smart contract

Blockchain technology brings innovation to various industries. Ethereum is currently the second blockchain platform by market capitalization, it’s also the largest smart contract blockchain platform. Smart contracts can simplify and accelerate the development of various applications, but they also bring some problems. For example, smart contracts are used to commit fraud, vulnerability contracts are deliberately developed to undermine fairness, and there are numerous duplicative contracts that waste performance with no actual purpose. In this paper, we propose a transaction-based classification and detection approach for Ethereum smart contract to address these issues. We collected over 10,000 smart contracts from Ethereum and focused on the data behavior generated by smart contracts and users. We identified four behavior patterns from the transactions by manual analysis, which can be used to distinguish the difference between different types of contracts. Then 14 basic features of a smart contract are constructed from these. To construct the experimental dataset, we propose a data slicing algorithm for slicing the collected smart contracts. After that, we use an LSTM network to train and test our datasets. The extensive experimental results show that our approach can distinguish different types of contracts and can be applied to anomaly detection and malicious contract identification with satisfactory precision, recall, and f1-score.
Structured optimal graph based sparse feature extraction for semi-supervised learning

Graph-based feature extraction is an efficient technique for data dimensionality reduction, and it has gained intensive attention in various fields such as image processing, pattern recognition, and machine learning. However, conventional graph-based dimensionality reduction algorithms usually depend on a fixed weight graph called similarity matrix, which seriously affects the subsequent feature extraction process. In this paper, a novel structured optimal graph based sparse feature extraction (SOGSFE) method for semi-supervised learning is proposed. In the proposed method, the local structure learning, sparse representation, and label propagation are simultaneously framed to perform data dimensionality reduction. In particular the similarity matrix and the projection matrix are obtained by an iterative calculation manner. The experimental results on several public image datasets demonstrate the robustness and effectiveness of the proposed method.
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2

The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal.The traditional approach to building such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually.However, such an optimization scheme does not necessarily yield an overall performance improvement of the whole system.On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus.This scheme makes it difficult for goal-oriented dialogues where the system needs to be integrated with external systems or to provide interpretable information about why the system generated a particular response.In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above.Our dialogue system achieved the success rate of 68.32%, the language understanding score of 4.149, and the response appropriateness score of 4.287 in human evaluations, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8).
Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics

Automatic metrics are fundamental for the development and evaluation of machine translation systems.Judging whether, and to what extent, automatic metrics concur with the gold standard of human evaluation is not a straightforward problem.We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, which often leads to falsely confident conclusions about a metric's efficacy.Finally, we turn to pairwise system ranking, developing a method for thresholding performance improvement under an automatic metric against human judgements, which allows quantification of type I versus type II errors incurred, i.e., insignificant human differences in system quality that are accepted, and significant human differences that are rejected.Together, these findings suggest improvements to the protocols for metric evaluation and system performance evaluation in machine translation.
Blockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing

The vehicular networks constructed by interconnected vehicles and transportation infrastructure are vulnerable to cyber-intrusions due to the expanded use of software and the introduction of wireless interfaces. Intrusion detection systems (IDSs) can be customized efficiently in response to this increased attack surface. There has been significant progress in detecting malicious attack traffic using machine learning approaches. However, existing IDSs require network devices with powerful computing capabilities to continuously train and update complex network models, which reduces the efficiency and defense capability of intrusion detection systems due to limited resources and untimely model updates. This work proposes a cooperative intrusion detection mechanism that offloads the training model to distributed edge devices (e.g., connected vehicles and roadside units (RSUs). Distributed federated-based approach reduces resource utilization of the central server while assuring security and privacy. To ensure the security of the aggregation model, blockchain is used for the storage and sharing of the training models. This work analyzes common attacks and shows that the proposed scheme achieves cooperative privacy-preservation for vehicles while reducing communication overhead and computation cost.