Reviewerly/exHarmony huggingface dataset:
Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data

Detection of non-wear periods is an important step in accelerometer data processing. This study evaluated five non-wear detection algorithms for wrist accelerometer data and two rules for non-wear detection when non-wear and sleep algorithms are implemented in parallel. Non-wear algorithms were based on the standard deviation (SD), the high-pass filtered acceleration, or tilt angle. Rules for differentiating sleep from non-wear consisted of an override rule in which any overlap between non-wear and sleep was deemed non-wear; and a 75% rule in which non-wear periods were deemed sleep if the duration was < 75% of the sleep period. Non-wear algorithms were evaluated in 47 children who wore an ActiGraph GT3X+ accelerometer during school hours for 5 days. Rules for differentiating sleep from non-wear were evaluated in 15 adults who wore a GeneActiv Original accelerometer continuously for 24 hours. Classification accuracy for the non-wear algorithms ranged between 0.86–0.95, with the SD of the vector magnitude providing the best performance. The override rule misclassified 37.1 minutes of sleep as non-wear, while the 75% rule resulted in no misclassification. Non-wear algorithms based on the SD of the acceleration signal can effectively detect non-wear periods, while application of the 75% rule can effectively differentiate sleep from non-wear when examined concurrently.
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation

Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii) solely rely on cross-entropy loss that leads to issues like exposure bias and inconsistency between train/test measurement, or (iii) fail to fully exploit the answer information. To address these limitations, in this paper, we propose a reinforcement learning (RL) based graph-to sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator with a novel Bidirectional Gated Graph Neural Network based encoder to embed the passage, and a hybrid evaluator with a mixed objective function that combines both the cross-entropy and RL loss to ensure the generation of syntactically and semantically valid text. We also introduce an effective Deep Alignment Network for incorporating the answer information into the passage at both the word and contextual level. Our model is end-to-end trainable and achieves new state-of-the-art scores, outperforming existing methods by a significant margin on the standard SQuAD benchmark for QG.
AIM 2019 Challenge on RAW to RGB Mapping: Methods and Results

This paper reviews the first AIM challenge on mapping camera RAW to RGB images with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map the original low-quality RAW images from the Huawei P20 device to the same photos captured with the Canon 5D DSLR camera. The considered problem embraced a number of computer vision subtasks, such as image demosaicing, denoising, gamma correction, image resolution and sharpness enhancement, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved baseline results, defining the state-of-the-art for RAW to RGB image restoration.
An On-Line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers

As data centers are consuming massive amount of energy, improving the energy efficiency of cloud computing has emerged as a focus of research. However, it is challenging to reduce energy consumption while maintaining system performance without increasing the risk of Service Level Agreement violations. Most of the existing consolidation approaches for virtual machines (VMs) consider system performance and Quality of Service (QoS) metrics as constraints, which usually results in large scheduling overhead and impossibility to achieve effective improvement in energy efficiency without sacrificing some system performance and cloud service quality. In this article, we first define the metrics of peak power efficiency and optimal utilization for heterogeneous physical machines (PMs). Then we propose Peak Efficiency Aware Scheduling (PEAS), a novel strategy of VM placement and reallocation for achieving dual improvement in performance and energy conservation from the perspective of server clusters. PEAS allocates and reallocates VMs in an on-line manner and always attempts to maintain PMs working in their peak power efficiency via VM consolidation. Extensive experiments on Cloudsim show that PEAS outperforms several energy-aware consolidation algorithms with regard to energy consumption, system performance as well as multiple QoS metrics.
Text Detection and Recognition for Images of Medical Laboratory Reports With a Deep Learning Approach

The adoption of electronic health records (EHRs) is an important step in the development of modern medicine. However, complete health records are not often available during treatment because of the functional problem of the EHR system or information barriers. This paper presents a deep-learning-based approach for textual information extraction from images of medical laboratory reports, which may help physicians solve the data-sharing problem. The approach consists of two modules: text detection and recognition. In text detection, a patch-based training strategy is applied, which can achieve the recall of 99.5% in the experiments. For text recognition, a concatenation structure is designed to combine the features from both shallow and deep layers in neural networks. The experimental results demonstrate that the text recognizer in our approach can improve the accuracy of multi-lingual text recognition. The approach will be beneficial for integrating historical health records and engaging patients in their own health care.
Thematic Analysis for Classifying the Main Challenges and Factors Influencing the Successful Implementation of E-learning System Using NVivo

The problem of low usage of e-learning system still exists due to some factors that cause students' reluctance to use this system.However, the successful implementation of e-learning system relies on understanding the adoption factors as well as the main challenges that face the current e-learning systems.There is lack of agreement about the main factors that shape the successful adoption of e-learning system; hence, a clear gap has been identified in the knowledge on the critical factors of e-learning adoption.Therefore, this research seeks to explore the main challenges that face the current e-learning systems and investigate the main factors that support the adoption of e-learning system.This study employed the interview approach using thematic analysis through NVivo software.The interview was conducted with 31 experts in e-learning systems at five Jordanian universities.The results highlight 20 e-learning adoption factors and challenges that strengthen the findings from previous literature.The study reveals e-learning system adoption factors and challenges, namely, change management issues, financial support issues, e-learning system quality factors, culture factors, Self-efficacy factors and trust factors, which unexplored in e-learning system adoption literature previously.
Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP-DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP-DDPG can stably complete a variety of tasks in complex, unknown environments.
Blockchain-based access control management for Decentralized Online Social Networks

Online Social Networks (OSNs) represent today a big communication channel where users spend a lot of time to share personal data. Unfortunately, the big popularity of OSNs can be compared with their big privacy issues. Indeed, several recent scandals have demonstrated their vulnerability. Decentralized Online Social Networks (DOSNs) have been proposed as an alternative solution to the current centralized OSNs. DOSNs do not have a service provider that acts as central authority and users have more control over their information. Several DOSNs have been proposed during the last years. However, the decentralization of the social services requires efficient distributed solutions for protecting the privacy of users. During the last years the blockchain technology has been applied to Social Networks in order to overcome the privacy issues and to offer a real solution to the privacy issues in a decentralized system. However, in these platforms the blockchain is usually used as a storage, and content is public. In this paper, we propose a manageable and auditable access control framework for DOSNs using blockchain technology for the definition of privacy policies. The resource owner uses the public key of the subject to define auditable access control policies using Access Control List (ACL), while the private key associated with the subject’s Ethereum account is used to decrypt the private data once access permission is validated on the blockchain. We provide an evaluation of our approach by exploiting the Rinkeby Ethereum testnet to deploy the smart contracts. Experimental results clearly show that our proposed ACL-based access control outperforms the Attribute-based access control (ABAC) in terms of gas cost. Indeed, a simple ABAC evaluation function requires 280,000 gas, instead our scheme requires 61,648 gas to evaluate ACL rules.
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition

Many studies have applied reinforcement learning to train a dialog policy and show great promise these years.One common approach is to employ a user simulator to obtain a large number of simulated user experiences for reinforcement learning algorithms.However, modeling a realistic user simulator is challenging.A rule-based simulator requires heavy domain expertise for complex tasks, and a data-driven simulator requires considerable data and it is even unclear how to evaluate a simulator.To avoid explicitly building a user simulator beforehand, we propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents.Two agents interact with each other and are jointly learned simultaneously.The method uses the actorcritic framework to facilitate pretraining and improve scalability.We also propose Hybrid Value Network for the role-aware reward decomposition to integrate role-specific domain knowledge of each agent in task-oriented dialog.Results show that our method can successfully build a system policy and a user policy simultaneously, and two agents can achieve a high task success rate through conversational interaction.
Learning for Graph Matching and Related Combinatorial Optimization Problems

This survey gives a selective review of recent development of machine learning (ML) for combinatorial optimization (CO), especially for graph matching. The synergy of these two well-developed areas (ML and CO) can potentially give transformative change to artificial intelligence, whose foundation relates to these two building blocks. For its representativeness and wide-applicability, this paper is more focused on the problem of weighted graph matching, especially from the learning perspective. For graph matching, we show that many learning techniques e.g. convolutional neural networks, graph neural networks, reinforcement learning can be effectively incorporated in the paradigm for extracting the node features, graph structure features, and even the matching engine. We further present outlook for the new settings for learning graph matching, and direction towards more integrated combinatorial optimization solvers with prediction models, and also the mutual embrace of traditional solver and machine learning components.
Blockchain-based verification framework for data integrity in edge-cloud storage

With the popularity of the Internet of Things (IoT), data integrity verification in the edge cloud storage attracts attentions from many researchers. Due to the over dependence of the Third Party Auditor (TPA) and the dynamical nature of the IoT data, the traditional data integrity verification framework for cloud storage can hardly work. To satisfy the characteristics of the IoT and avoid the over dependence of the TPA, we propose a blockchain-based framework without TPA for data integrity verification in a decentralized edge-cloud storage (ECS) scenario in this paper. In our framework, we employ the Merkle tree with random challenging numbers for data integrity verification and analyze different Merkle tree structures to optimize the system performance. To solve the problem of limited resources and high real-time requirements, we further propose sampling verification and develop rational sampling strategies to make sampling verification more effective. The overhead and precision of the verification in ECS are studied by an optimal sample size strategy. Finally, a prototype system is implemented based on our framework. We conduct a series of experiments to evaluate the effectiveness of the proposed schemes. The experimental results show that our schemes can effectively improve the performance of data integrity verification.
A Novel Hierarchical BERT Architecture for Sarcasm Detection

Online discussion platforms are often flooded with opinions from users across the world on a variety of topics.Many such posts, comments, or utterances are often sarcastic in nature, i.e., the actual intent is hidden in the sentence and is different from its literal meaning, making the detection of such utterances challenging without additional context.In this paper, we propose a novel deep learning-based approach to detect whether an utterance is sarcastic or non-sarcastic by utilizing the given contexts in a hierarchical manner.We have used datasets from two online discussion platform -Twitter and Reddit 1 for our experiments.Experimental and error analysis shows that the hierarchical models can make full use of history to obtain a better representation of contexts and thus, in turn, can outperform their sequential counterparts.
Trust Management in Decentralized IoT Access Control System

Heterogeneous and dynamic IoT environments require a lightweight, scalable, and trustworthy access control system for protection from unauthorized access and for automated detection of compromised nodes. Recent proposals in IoT access control systems have incorporated blockchain to overcome inherent issues in conventional access control schemes. However, the dynamic interaction of IoT networks remains uncaptured. Here, we develop a blockchain based Trust and Reputation System (TRS) for IoT access control, which progressively evaluates and calculates the trust and reputation score of each participating node to achieve a self-adaptive and trustworthy access control system. Trust and reputation are explicitly incorporated in the attribute-based access control policy, so that different nodes can be assigned to different access right levels, resulting in dynamic access control policies. We implement our proposed architecture in a private Ethereum blockchain comprised of a Docker container network. We benchmark our solution using various performance metrics to highlight its applicability for IoT contexts.
Relevance assignation feature selection method based on mutual information for machine learning

With the complication of the subjects and environment of the machine learning, feature selection methods have been used more frequently as an effective mean of dimension reduction. However, existing feature selection methods are deficient in striking a balance between the relevance evaluation accuracy with the searching efficiency. In this regard, the characteristics of the relevance between the feature set and the classification result are analyzed. Then, we propose our Relevance Assignation Feature Selection (RAFS) method based on the mutual information theory, which assigns the relevance evaluation according to the redundancy. With this method, we can estimate the contribution of each feature in a feature set, which is regarded as value of the feature and is used as the heuristic index in searching of the relevant features. A special dataset ("Grid World") with strong interactive features is designed. Using the Grid World and six other natural datasets, the proposed method is compared with six other feature selection methods. Results show that in the Grid World dataset, the RAFS method can find correct relevant features with the probability above 90%, much higher than the others. In six other datasets, the RAFS method also has the best performance in the classification accuracy.
Continuous-Time Dynamic Graph Learning via Neural Interaction Processes

Dynamic graphs such as the user-item interactions graphs and financial transaction networks are ubiquitous nowadays. While numerous representation learning methods for static graphs have been proposed, the study of dynamic graphs is still in its infancy. A main challenge of modeling dynamic graphs is how to effectively encode temporal and structural information into nonlinear and compact dynamic embeddings. To achieve this, we propose a principled graph-neural-based approach to learn continuous-time dynamic embeddings. We first define a temporal dependency interaction graph(TDIG) that is induced from sequences of interaction data. Based on the topology of this TDIG, we develop a dynamic message passing neural network named TDIG-MPNN, which can capture the fine-grained global and local information on TDIG. In addition, to enhance the quality of continuous-time dynamic embeddings, a novel selection mechanism comprised of two successive steps, i.e., co-attention and gating, is applied before the above TDIG-MPNN layer to adjust the importance of the nodes by considering high-order correlation between interactive nodes' k-depth neighbors on TDIG. Finally, we cast our learning problem in the framework of temporal point processes (TPPs) where we use TDIG-MPNN to design a neural intensity function for the dynamic interaction processes. Our model achieves superior performance over alternatives on temporal interaction prediction (including tranductive and inductive tasks) on multiple datasets.
Multi-task Learning for Multilingual Neural Machine Translation

While monolingual data has been shown to be useful in improving bilingual neural machine translation (NMT), effectively and efficiently leveraging monolingual data for Multilingual NMT (MNMT) systems is a less explored area. In this work, we propose a multi-task learning (MTL) framework that jointly trains the model with the translation task on bitext data and two denoising tasks on the monolingual data. We conduct extensive empirical studies on MNMT systems with 10 language pairs from WMT datasets. We show that the proposed approach can effectively improve the translation quality for both high-resource and low-resource languages with large margin, achieving significantly better results than the individual bilingual models. We also demonstrate the efficacy of the proposed approach in the zero-shot setup for language pairs without bitext training data. Furthermore, we show the effectiveness of MTL over pre-training approaches for both NMT and cross-lingual transfer learning NLU tasks; the proposed approach outperforms massive scale models trained on single task.
“You are grounded!”: Latent Name Artifacts in Pre-trained Language Models

Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models.We focus on artifacts associated with the representation of given names (e.g., Donald), which, depending on the corpus, may be associated with specific entities, as indicated by next token prediction (e.g., Trump).While helpful in some contexts, grounding happens also in underspecified or inappropriate contexts.For example, endings generated for 'Donald is a' substantially differ from those of other names, and often have more-than-average negative sentiment.We demonstrate the potential effect on downstream tasks with reading comprehension probes where name perturbation changes the model answers.As a silver lining, our experiments suggest that additional pre-training on different corpora may mitigate this bias.
Noise Augmented Double-Stream Graph Convolutional Networks for Image Captioning

Image captioning, aiming at generating natural sentences to describe image contents, has received significant attention with remarkable improvements in recent advances. The problem nevertheless is not trivial for cross-modal training due to the two challenges: 1) image detectors often consider only salient areas in an image and seldom explore the rich background context; 2) the language model is highly vulnerable to small but intentional perturbation attacks. To alleviate these issues, we propose the Noise Augmented Double-stream Graph Convolutional Networks (NADGCN) that novelly exploits the additional background context and enhances the generalization of the language model. Technically, NADGCN capitalizes on grid-stream GCN as a supplementary to the region stream, following the recipe that a rescaled grid graph can encode the relationship across grid areas over the full image rather than salient areas only. Moreover, we devise a noise module and integrate into the double-stream GCN to augment the capability of the basic generator. Such noise module introduces adaptive noise into the Recurrent Neural Networks (RNN) and is learnt through regarding the module as an agent with a stochastic Gaussian policy in Reinforcement Learning (RL). Extensive experiments on MSCOCO validate the design of the grid-stream GCN and the noise agent, and our generator outperforms the comparative baselines clearly.