Reviewerly/exHarmony huggingface dataset:
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models

Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs.Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge.To utilize these unexploited graph-level knowledge, we propose an approach to model subgraphs in a medical KG.Then, the learned knowledge is integrated with a pre-trained language model to do the knowledge generalization.Experimental results demonstrate that our model achieves the state-of-the-art performance on several medical NLP tasks, and the improvement above MedERNIE indicates that graph contextualized knowledge is beneficial.
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks. We present a practical workflow describing how to use deep learning to develop continuous-risk models that can predict various adverse outcomes from structured electronic health records.
The Relationship between Artificial Intelligence and Internet of Things: A quick review

Internet Of Things (IOT) is a network of various devices that are connected over the internet, and they can collect and exchange data with each other. These IOT devices generate a lot of data that needs to be collected and mined for actionable results through use artificial intelligence (AI) to manage huge data flows and storage in the IOT network. In this paper we briefly discussed about what IOT is, what AI is, Algorithm of AI, Challenge AI with IOT, application of artificial intelligence system in the IOT. The self-optimizing network and software defined network are parts of the important parameters in the Artificial Intelligence IoT System. This paper provides a general discussion about importance of the IoT in different applications. The paper covers different applications of IoT and shows the relationship between AI and IoT. The role of the AI in IoT applications is extensively discussed. In the future work, we are planning to work on improving the performance of IoT applications using advanced AI methods and algorithms such as Machine Learning and Deep Learning.
Dual Global Enhanced Transformer for image captioning

Transformer-based architectures have shown great success in image captioning, where self-attention module can model source and target interaction (e.g., object-to-object, object-to-word, word-to-word). However, the global information is not explicitly considered in the attention weight calculation, which is essential to understand the scene content. In this paper, we propose Dual Global Enhanced Transformer (DGET) to incorporate global information in the encoding and decoding stages. Concretely, in DGET, we regard the grid feature as the visual global information and adaptively fuse it into region features in each layer by a novel Global Enhanced Encoder (GEE). During decoding, we proposed Global Enhanced Decoder (GED) to explicitly utilize the textual global information. First, we devise the context encoder to encode the existing caption generated by classic captioner as a context vector. Then, we use the context vector to guide the decoder to generate accurate words at each time step. To validate our model, we conduct extensive experiments on the MS COCO image captioning dataset and achieve superior performance over many state-of-the-art methods.
Multiauthority CP-ABE-based Access Control Model for IoT-enabled Healthcare Infrastructure

The exponential growth of the Internet of Things (IoT) technologies requires high data security. Here, data security is very critical as all IoT devices transfer data over the Internet. The fine-grained access control provided by the ciphertext policy attribute-based encryption (CP-ABE) technique can be considered as a potential solution to this issue. However, most of the CP-ABE schemes use bilinear pairing operations for its internal working, which is expensive for any resource constraint device. An elliptic curve cryptography (ECC) based CP-ABE scheme can be well suited for resource constraint IoT framework because ECC takes less computational time. This article proposes a novel CP-ABE technique based on ECC to achieve fine-grained access control over data or resources. The proposed technique includes multiple attribute authorities to manage attributes and key generation, which can reduce the work overhead of having a single authority in traditional CP-ABE systems. In addition, the proposed scheme outsources the decryption process to a user assistant entity to reduce the decryption overhead of the end-users. To prove the efficiency of the proposed scheme, both formal security analysis and performance comparisons are presented in this article. The result and findings prove the effectiveness of the proposed scheme over some well-known schemes.
Blockchain-Based Trusted Electronic Records Preservation in Cloud Storage

Cloud storage represents the trend of intensive, scale and specialization of information technology, which has changed the technical architecture and implementation method of electronic records management. Moreover, it will p... | Find, read and cite all the research you need on Tech Science Press
A robust classification algorithm for separation of construction waste using NIR hyperspectral system

To improve the utilization rate of construction waste, reduce processing costs, and improve processing efficiency, we used near-infrared hyperspectral technology to extract and classify typical construction waste types. We proposed the pythagorean wavelet transform (PWT) to get the characteristic reflectivity to avoid the redundancy of hyperspectral data. Compared with the results from the wavelet transform (WT), we were able to retain more detailed information, and we observed the enhancement of differences between different species. To adapt to the complex conditions present in actual situations and to improve our ability to distinguish similar spectrum, we extracted, in addition to the characteristic reflectivity, four potential features. After classified verification, we found out that the first derivative and the intrinsic mode function (IMF) were effective features. At the same time the random forest (RF) algorithm was best at identifying trend-features, and the extreme learning machine (ELM) was better at identifying amplitude-features. We proposed a complementary troubleshooting (CT) method for the online identification of construction waste. After using the ELM to identify the characteristic reflectivity, the RF was used to identify first derivative for supplemental verification, which reduced errors due to working conditions and improved the overall model robustness and correctness. The accuracy of proposed method can reach 100% in identifying 180 samples with 6 types including woods, plastics, bricks, concretes, rubbers and black bricks.
Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Defect prediction at early stages of software development life cycle is a crucial activity of quality assurance process and has been broadly studied in the last two decades.The early prediction of defective modules in developing software can help the development team to utilize the available resources efficiently and effectively to deliver high quality software product in limited time.Until now, many researchers have developed defect prediction models by using machine learning and statistical techniques.Machine learning approach is an effective way to identify the defective modules, which works by extracting the hidden patterns among software attributes.In this study, several machine learning classification techniques are used to predict the software defects in twelve widely used NASA datasets.The classification techniques include: Naïve Bayes (NB), Multi-Layer Perceptron (MLP).Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF).Performance of used classification techniques is evaluated by using various measures such as: Precision, Recall, F-Measure, Accuracy, MCC, and ROC Area.The detailed results in this research can be used as a baseline for other researches so that any claim regarding the improvement in prediction through any new technique, model or framework can be compared and verified.
Studying the Inductive Biases of

Shauli Ravfogel, Yoav Goldberg, Tal Linzen. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system

A novel Four-channels reservoir computing (RC) based on polarization dynamics in mutually coupled vertical cavity surface emitting lasers (MDC-VCSELs) is proposed and demonstrated numerically. Here, the four channels are realized in two orthogonal polarization modes (x-polarization and y-polarization modes) of two VCSELs for the first time. A chaotic time series prediction task is employed to quantitatively evaluated the prediction performance of the proposed system. It is found that the Four-channels RC based on MDC-VCSELs can produce comparable prediction performance with One-channel RC, and it is possible to increase four times information processing rate by using the Four-channels RC. Besides, the effects of injection current, external injection strength, frequency detuning, coupling strength, as well as internal parameters on the prediction performance of the Four-channels RC based on MDC-VCSELs are carefully examined. Moreover, the influences of sampled period of input signal and the number of virtual nodes are also considered. The proposed Four-channels RC based on MDC-VCSELs is valuable for further enhancing the information processing rate of RC-based neuromorphic photonic systems.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. Unfortunately, finding these ticket'' initializations is computationally expensive. One potential solution is to reuse the same winning tickets across a variety of datasets and optimizers. However, the generality of winning ticket initializations remains unclear. Here, we attempt to answer this question by generating winning tickets for one training configuration (optimizer and dataset) and evaluating their performance on another configuration. Perhaps surprisingly, we found that, within the natural images domain, winning ticket initializations generalized across a variety of datasets, including Fashion MNIST, SVHN, CIFAR-10/100, ImageNet, and Places365, often achieving performance close to that of winning tickets generated on the same dataset. Moreover, winning tickets generated using larger datasets consistently transferred better than those generated using smaller datasets. We also found that winning ticket initializations generalize across optimizers with high performance. These results suggest that winning ticket initializations generated by sufficiently large datasets contain inductive biases generic to neural networks more broadly which improve training across many settings and provide hope for the development of better initialization methods.
Distributionally Robust Neural Networks

Overparameterized neural networks trained to minimize average loss can be highly accurate on average on an i.i.d. test set, yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that do not hold at test time). Distributionally robust optimization (DRO) provides an approach for learning models that instead minimize worst-case training loss over a set of pre-defined groups. We find, however, that naively applying DRO to overparameterized neural networks fails: these models can perfectly fit the training data, and any model with vanishing average training loss will also already have vanishing worst-case training loss. Instead, the poor worst-case performance of these models arises from poor generalization on some groups. As a solution, we show that increased regularization---e.g., stronger-than-typical weight decay or early stopping---allows DRO models to achieve substantially higher worst-group accuracies, with 10% to 40% improvements over standard models on a natural language inference task and two image tasks, while maintaining high average accuracies. Our results suggest that regularization is critical for worst-group performance in the overparameterized regime, even if it is not needed for average performance. Finally, we introduce and provide convergence guarantees for a stochastic optimizer for this group DRO setting, underpinning the empirical study above.
Drone Chi: Somaesthetic Human-Drone Interaction

Somaesthetics - motivated by improving life quality via appreciation for bodily and sensory experiences - is increasingly influencing HCI designs. Investigating the potential of drones as a material for somaesthetic HCI, we designed Drone Chi: a Tai Chi-inspired close-range human-drone interaction experience. The design process for Drone Chi has been informed by the soma design approach and the Somaesthetic Appreciation concept from HCI literature. The artifact expands somaesthetic HCI by exemplifying dynamic and intimate somaesthetic interactions with a robotic design material, and body movements in expansive 3D space. To characterize the Drone Chi experience, we conducted an empirical study with 32 participants. Analysis of participant accounts revealed 4 themes that articulate different aspects of the experience: Looping Mental States, Environment, Agency vs. Control, and Physical Narratives. From these accounts and our craft knowledge, we derive 5 design implications to guide the development of movement-based close-range drone interactions.
Temporal-Contextual Recommendation in Real-Time

Personalized real-time recommendation has had a profound impact on retail, media, entertainment and other industries. However, developing recommender systems for every use case is costly, time consuming and resource-intensive. To fill this gap, we present a black-box recommender system that can adapt to a diverse set of scenarios without the need for manual tuning. We build on techniques that go beyond simple matrix factorization to incorporate important new sources of information: the temporal order of events [Hidasi et al., 2015], contextual information to bootstrap cold-start users, metadata information about items [Rendle 2012] and the additional information surrounding each event. Additionally, we address two fundamental challenges when putting recommender systems in the real-world: how to efficiently train them with even millions of unique items and how to cope with changing item popularity trends [Wu et al., 2017]. We introduce a compact model, which we call hierarchical recurrent network with meta data (HRNN-meta) to address the real-time and diverse metadata needs; we further provide efficient training techniques via importance sampling that can scale to millions of items with little loss in performance. We report significant improvements on a wide range of real-world datasets and provide intuition into model capabilities with synthetic experiments. Parts of HRNN-meta have been deployed in production at scale for customers to use at Amazon Web Services and serves as the underlying recommender engine for thousands of websites.
Policy-GNN: Aggregation Optimization for Graph Neural Networks

Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors with stackable network modules. Motivated by the observation that different nodes often require different iterations of aggregation to fully capture the structural information, in this paper, we propose to explicitly sample diverse iterations of aggregation for different nodes to boost the performance of GNNs. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. Moreover, it is not straightforward to derive an efficient algorithm since we need to feed the sampled nodes into different number of network layers. To address the above challenges, we propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process. Specifically, Policy-GNN uses a meta-policy to adaptively determine the number of aggregations for each node. The meta-policy is trained with deep reinforcement learning~(RL) by exploiting the feedback from the model. We further introduce parameter sharing and a buffer mechanism to boost the training efficiency. Experimental results on three real-world benchmark datasets suggest that Policy-GNN significantly outperforms the state-of-the-art alternatives, showing the promise in aggregation optimization for GNNs.
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression

The use of $\ell_p$ $(p=1,2)$ norms has largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess the loss of visual information, these simple norms are not very consistent with human perception. Here, we describe a different "proximal" approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, broadly termed ProxIQA, which mimics the perceptual model while serving as a loss layer of the network. We experimentally demonstrate how this optimization framework can be applied to train an end-to-end optimized image compression network. By building on top of an existing deep image compression model, we are able to demonstrate a bitrate reduction of as much as $31\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.
Prototypical Contrastive Learning of Unsupervised Representations

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it implicitly encodes semantic structures of the data into the learned embedding space. Specifically, we introduce prototypes as latent variables to help find the maximum-likelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive learning, which encourages representations to be closer to their assigned prototypes. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks with substantial improvement in low-resource transfer learning. Code and pretrained models are available at https://github.com/salesforce/PCL.
P2P energy trading: Blockchain-enabled P2P energy society with multi-scale flexibility services

A deeper decarbonization perspective is undergoing with the increasing engagement of new distributed players and the deployment of innovative behind-the-meter flexibility services. Peer-to-Peer (P2P) trading has emerged as an innovative mechanism to foster the direct energy sharing among multi-level market players with pre-determined responsibility and privacy. However, compared with other P2P assets trading, P2P energy trading is facing huge challenges in achieving a large-scale commercialization due to the cooperation obstacles between non-professional distributed players and regulated players as Distributed System Operator (DSO), Transmission System Operator (TSO) and utilities. It is related to not only business and marketing, but also energy system operation to keep secure and reliable with injection of new roles, new utilization patterns, and new markets. This paper investigates the socio-technical interaction and mechanism for the sustainable P2P energy trading from the social dimension on the cooperation of multi-level market players, the technical dimension on the cutting-edge exchange of flexibility, and the economical dimension on the inter-operative decentralized/regulated marketplaces. Three questions are targeted on: (1) How Information and Communication Technology (ICT) enables co-creation of decentralized heterogeneous user-centered digital frameworks for the large-scale P2P trading interaction, (2) What specific energy services drive the cross-border interactions for exchange of multi-scale P2P flexibility, (3) How operational framework in P2P energy trading achieves the inter-operative marketplaces with the formation of trusted P2P energy society and the injection of multi-scale flexibility services. Finally, regulation challenges on P2P energy trading implementation are discussed for the guide of future work.
Metaverses and DeMetaverses: From Digital Twins in CPS to Parallel Intelligence in CPSS

A total of 12 years have been passed since this Department was created in 2010 as the first academic forum dedicated to cyber-physical-social systems (CPSS), with the first CPSS research article on the field: “The Emergence of Intelligent Enterprises: From CPS to CPSS.” What has happened and changed during the past decade? A brief reflection and review are presented here with a focus on digital twins in CPS versus parallel intelligence in CPSS, and their relationship to blockchain intelligence, smart contracts, metaverses, DAO, Web3, and decentralized science. The concept of DeMetaverses is thus introduced and interpreted as a DAO-based decentralized autonomous metaverse. The characteristics, mechanism, and impact of DeMetaverses are discussed with a vision for achieving an integrated human, artificial, natural, and organizational intelligence that would transform our world into “6S” societies.