Reviewerly/exHarmony huggingface dataset:
Understanding Contexts Inside Robot and Human Manipulation Tasks through Vision-Language Model and Ontology System in Video Streams

Manipulation tasks in daily life, such as pouring water, unfold through human intentions. Being able to process contextual knowledge from these Activities of Daily Living (ADLs) over time can help us understand manipulation intentions, which are essential for an intelligent robot to transition smoothly between various manipulation actions. In this paper, to model the intended concepts of manipulation, we present a vision dataset under a strictly constrained knowledge domain for both robot and human manipulations, where manipulation concepts and relations are stored by an ontology system in a taxonomic manner. Furthermore, we propose a scheme to generate a combination of visual attentions and an evolving knowledge graph filled with commonsense knowledge. Our scheme works with real-world camera streams and fuses an attention-based Vision-Language model with the ontology system. The experimental results demonstrate that the proposed scheme can successfully represent the evolution of an intended object manipulation procedure for both robots and humans. The proposed scheme allows the robot to mimic human-like intentional behaviors by watching real-time videos. We aim to develop this scheme further for real-world robot intelligence in Human-Robot Interaction.
Simultaneous Scene Reconstruction and Whole-Body Motion Planning for Safe Operation in Dynamic Environments

Recent work has demonstrated real-time mapping and reconstruction from dense perception, while motion planning based on distance fields has been shown to achieve fast, collision-free motion synthesis with good convergence properties. However, demonstration of a fully integrated system that can safely re-plan in unknown environments, in the presence of static and dynamic obstacles, has remained an open challenge. In this work, we first study the impact that signed and unsigned distance fields have on optimisation convergence, and the resultant error cost in trajectory optimisation problems in 2D path planning, arm manipulator motion planning, and whole-body loco-manipulation planning. We further analyse the performance of three state-of-the-art approaches to generating distance fields (Voxblox, Fiesta, and GPU-Voxels) for use in realtime environment reconstruction. Finally, we use our findings to construct a practical hybrid mapping and motion planning system which uses GPU-Voxels and GPMP2 to perform receding- horizon whole-body motion planning that can smoothly avoid moving obstacles in 3D space using live sensor data. Our results are validated in simulation and on a real-world Toyota Human Support Robot (HSR).
Role of Knowledge Creation and Absorptive Capacity: A Panel Data Study of Innovation

Purpose- Knowledge creation refers to the ability of firms to create new knowledge that starts from individuals to integrating the firms and then the overall economy. This study suggests that knowledge acquisition in a country has a significant relationship with innovative performance. Design/Methodology- Data from 48 highly HDI countries is taken from World Bank and World Economic Forum. Based on 480 country-year observations in a panel mediator model, it is revealed that the national efforts of boosting knowledge acquisition influence the firms’ innovative performance. Findings- Further, it is found that absorptive capacity in the employability of knowledgeable workers works as a mediator between knowledge acquisition and innovation. Whereby higher knowledge acquisition leads to higher absorptive capacity and higher innovation. Practical Implications- This study builds a quantitative model for the macroeconomic context of knowledge-based view.
Differential evolution with quasi-reflection-based mutation

&lt;abstract&gt;&lt;p&gt;Differential evolution (DE) is one of the most successful evolutionary algorithms. However, the performance of DE is significantly influenced by its mutation strategies. Generally, different mutation strategies may obtain different search directions. The improper search direction misleads the search and results in the poor performance of DE. Therefore, it is vital to consider the search direction when designing new mutation strategies. Based on this consideration, in this paper, the quasi-reflection-based mutation is proposed to enhance the performance of DE. The quasi-reflection-based mutation is able to provide the promising search direction to guide the search. To extensively evaluate the performance of our approach, $ 30 $ benchmark functions are chosen as the test suite. Combined with SHADE, Re-SHADE is presented. Compared with different advanced DE methods, Re-SHADE can obtain better results in terms of the accuracy and the convergence rate. Additionally, further experiments on the CEC2013 test suite also confirm the effectiveness of the proposed method.&lt;/p&gt;&lt;/abstract&gt;
Relation extraction for coal mine safety information using recurrent neural networks with bidirectional minimal gated unit

Abstract The data of coal mine safety field are massive, multi-source and heterogeneous. It is of practical importance to extract information from big data to achieve disaster precaution and emergency response. Existing approaches need to build more features and rely heavily on the linguistic knowledge of researchers, leading to inefficiency, poor portability, and slow update speed. This paper proposes a new relation extraction approach using recurrent neural networks with bidirectional minimal gated unit (MGU) model. This is achieved by adding a back-to-front MGU layer based on original MGU model. It does not require to construct complex text features and can capture the global context information by combining the forward and backward features. Evident from extensive experiments, the proposed approach outperforms the existing initiatives in terms of training time, accuracy, recall and F value.
Smartwatch User Authentication by Sensing Tapping Rhythms and Using One-Class DBSCAN

As important sensors in smart sensing systems, smartwatches are becoming more and more popular. Authentication can help protect the security and privacy of users. In addition to the classic authentication methods, behavioral factors can be used as robust measures for this purpose. This study proposes a lightweight authentication method for smartwatches based on edge computing, which identifies users by their tapping rhythms. Based on the DBSCAN clustering algorithm, a new classification method called One-Class DBSCAN is presented. It first seeks core objects and then leverages them to perform user authentication. We conducted extensive experiments on 6110 real data samples collected from more than 600 users. The results show that our method achieved the lowest Equal Error Rate (EER) of only 0.92%, which was lower than those of other state-of-the-art methods. In addition, a statistical method for detecting the security level of a tapping rhythm is proposed. It can prevent users from setting a simple tapping rhythm password, and thus improve the security of smartwatches.
Adversarial Data Augmentation on Breast MRI Segmentation

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.
Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction

Drones have become a common tool, which is utilized in many tasks such as aerial photography, surveillance, and delivery. However, operating a drone requires more and more interaction with the user. A natural and safe method for Human-Drone Interaction (HDI) is using gestures. In this paper, we introduce an HDI framework building upon skeleton-based pose estimation. Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance. We also propose a monocular distance estimation method, which is entirely based on image features and does not require any additional depth sensors. To perform comprehensive experiments and quantitative analysis, we create a customized testing dataset. The experiments indicate that our HDI framework can achieve an average of 93.5% accuracy in the recognition of 11 common gestures. The code will be made publicly available to foster future research.
Discrete Argument Representation Learning for Interactive Argument Pair Identification

In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic.Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior).Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge.Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines.Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.
Resource sharing of mobile edge computing networks based on auction game and blockchain

Abstract The edge clouds in mobile edge computing networks are isolate which may belong to different companies or organizations, and hence the communication, computation, and storage resources are not efficiently utilized. To solve this problem, we propose the resource-sharing model of edge clouds which is based on blockchain technology and auction game. In this model, the blockchain platform is regarded as the bridge of the resource sharing, composed of edge clouds, clouds, third-party spectrum and computation management, identity authentication institutions, etc. It is used to record the users’ transaction information and broadcast the intelligent terminals’ resource requirements to all edge clouds in the blockchain platform through smart contracts. Then, an optimization problem of the joint allocation of communication and computation resources is formulated to maximize the utility of intelligent terminals. And an efficient improved sealed second-price auction game is proposed to allocate communication and computation resources and determine the optimal price of resources under the intelligent terminals’ QoS constraints. Simulation results show that the model can effectively improve the system resources utilization and the successful transaction rate.
The Effect of Pretraining on Extractive Summarization for Scientific Documents

Large pretrained models have seen enormous success in extractive summarization tasks.We investigate, here, the influence of pretraining on a BERT-based extractive summarization system for scientific documents.We derive performance improvements using an intermediate pretraining step that leverages existing summarization datasets and report state-of-theart results on a recently released scientific summarization dataset, SCITLDR.We systematically analyze the intermediate pretraining step by varying the size and domain of the pretraining corpus, changing the length of the input sequence in the target task and varying target tasks.We also investigate how intermediate pretraining interacts with contextualized word embeddings trained on different domains.
CRDet: Improving Signet Ring Cell Detection by Reinforcing the Classification Branch

Detecting signet ring cells on histopathologic images is a critical computer-aided diagnostic task that is highly relevant to cancer grading and patients' survival rates. However, the cells are densely distributed and exhibit diverse and complex visual patterns in the image, together with the commonly observed incomplete annotation issue, posing a significant barrier to accurate detection. In this article, we propose to mitigate the detection difficulty from a model reinforcement point of view. Specifically, we devise a Classification Reinforcement Detection Network (CRDet). It is featured by adding a dedicated Classification Reinforcement Branch (CRB) on top of the architecture of Cascade RCNN. The proposed CRB consists of a context pooling module to perform a more robust feature representation by fully making use of context information, and a feature enhancement classifier to generate a superior feature by leveraging the deconvolution and attention mechanism. With the enhanced feature, the small-sized cell can be better characterized and CRDet enjoys a more accurate signet ring cell identification. We validate our proposal on a large-scale real clinical signet ring cell data set. It is shown that CRDet outperforms several popular convolutional neural network-based object detection models on this particular task.
Financial Inclusion and Labour Market Participation of Women in Selected Countries in Africa

Abstract Research purpose : This study investigates the impact of financial inclusion on female labour force participation in Africa. It also complements the existing studies by evaluating how advancement in information and communication technology (ICT) and Trade openness (TO) modulate the relationship between financial inclusion and female economic participation in selected African countries. Design/methodology/approach : The study focuses on twelve African countries while the empirical evidence is based on Fixed Effects, Random Effects and Generalised Least Square estimators (GLS). Data over the period of 2005-2016 are sourced from the World Bank Development database and IMF international Financial Statistics. Findings: The results show that financial inclusion has a non-monotonic relationship with female labour force participation. The study establishes that if the level of financial inclusion can be increased to the range of 33-57 per cent, it would improve the level of women participation in economic activities. The results further show that ICT moderates the nexus between financial inclusion and female economic participation at a threshold level of 38.17 per cent. These findings persist when the TO is used as the moderating factor at a threshold value of 80.90 per cent. The results are robust enough to suggest an alternative proxy for female labour force participation and alternative estimation techniques. Originality/value/practical implications: Ending gender inequality has become one of the priorities in the global development policies in which most African nations domesticate the same for their national planning. There are voices at every corner in Africa demanding the possibility of achieving gender equality in employment, among others. This article is one of the few articles that evaluate whether financial inclusion can be used to accelerate female economic participation in Africa.
Improving Causal Discovery By Optimal Bayesian Network Learning

Many widely-used causal discovery methods such as Greedy Equivalent Search (GES), although with asymptotic correctness guarantees, have been reported to produce sub-optimal solutions on finite data, or when the causal faithfulness condition is violated. The constraint-based procedure with Boolean satisfiability (SAT) solver, and the recently proposed Sparsest Permutation (SP) algorithm have shown superb performance, but currently they do not scale well. In this work, we demonstrate that optimal score-based exhaustive search is remarkably useful for causal discovery: it requires weaker conditions to guarantee asymptotic correctness, and outperforms well-known methods including PC, GES, GSP, and NOTEARS. In order to achieve scalability, we also develop an approximation algorithm for larger systems based on the A* method, which scales up to 60+ variables and obtains better results than existing greedy algorithms such as GES, MMHC, and GSP. Our results illustrate the risk of assuming the faithfulness assumption, the advantages of exhaustive search methods, and the limitations of greedy search methods, and shed light on the computational challenges and techniques in scaling up to larger networks and handling unfaithful data.
Low-Light-Level Image Super-Resolution Reconstruction Based on a Multi-Scale Features Extraction Network

Wide field-of-view (FOV) and high-resolution (HR) imaging are essential to many applications where high-content image acquisition is necessary. However, due to the insufficient spatial sampling of the image detector and the trade-off between pixel size and photosensitivity, the ability of current imaging sensors to obtain high spatial resolution is limited, especially under low-light-level (LLL) imaging conditions. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize pixel-super-resolved LLL imaging. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, the calculation of the high-frequency components can receive greater attention. Compared with other networks, the peak signal-to-noise ratio of the reconstructed image was increased by 1.67 dB. Extensions of the MSFE network are investigated for scene-based color mapping of the gray image. Most of the color information could be recovered, and the similarity with the real image reached 0.728. The qualitative and quantitative experimental results show that the proposed method achieved superior performance in image fidelity and detail enhancement over the state-of-the-art.
Corpus Linguistics Methods for Building ESP Word Lists, Glossaries and Dictionaries on the Example of a Marine Engineering Word List

In addition to the general English knowledge required for nearly any human occupation today, vocabulary competence has been especially focused on seeking to keep pace with boosting Englishes for Specific Purposes. Owing to the possibilities offered by contemporary software solutions, corpus linguistics has been able to answer some specific questions on the vocabulary demand of texts, as well as to provide concentrated vocabulary lists according to their frequency in real-life texts (corpora). Aiming to provide our target learners of English for marine engineering purposes with a practical vocabulary tool to help them reach an adequate reading comprehension text coverage of 95%, we developed a marine engineering word list of 337 word families, accompanied by a list of 73 transparent compounds, which were derived from the corpus of marine engineering instruction books with 1,769,821 running words. The list can be studied in university classes or training courses for seafarers, through various types of vocabulary exercises, but it might also assist in building technical glossaries and dictionaries. The methodology used and procedures applied in the paper should hopefully be of assistance to other authors and language instructors working on other areas of technical English.
Invertible Resampling-Based Layered Image Compression

Flow-based generative models are successfully applied in image generation tasks, where an invertible neural network (INN) is built up based on flow steps. Learning-based compression commonly transforms the input into a compact space and then implements a reconstruction network in the decoder accordingly. By utilizing low-resolution images, traditional or adaptive downsamplers with their corresponding traditional or learned upsamplers usually achieve better coding quality at low bit-rate. This paper proposes a novel image compression framework named Invertible Resampling-based Layered Image Compression (IRLIC). A rescaling network is built by splitting the input image into a downsampled image and a high-frequency part which is transformed into pre-defined distribution by INN, with symmetrical upsampling. Thus, reliable rescaling is applied in the total lossy compression framework, where only the downsampled image and the reconstruction residual are needed to recover a compressed image. Our IRLIC achieves superior performance to the current methods like BPG and other learning-based image compressions at the bit-rate below 1.8bpp.
Virtual Migration in Cloud Computing: A Survey

In this era of everything available online, the user can get access to shared services and resources such as hardware, network, and infrastructure through the Internet. In contrast without the use of cloud, these services are available in expensive manner. To resolve this issue, virtualization concept is used in order to help clients to get efficient management of all resources or virtual machines. In virtualization, one of the most prominent features is the ability of virtual machines to migrate, that allows transferring the data of one virtual machine to another virtual machine for backup. This process gives rise to various features such as maintenance of virtual machines, energy efficiency, power management, and fault tolerance. While transferring data from virtual machines, various techniques are followed basis on their required transferred time, downtime, and latency. In this paper, the concept of virtualization, Migration of machine data, and their different techniques are discussed. This paper mainly focuses on the comparison of various approaches and the challenges faced during virtual machine migration.
Multitask Balanced and Recalibrated Network for Medical Code Prediction

Human coders assign standardized medical codes to clinical documents generated during patients’ hospitalization, which is error prone and labor intensive. Automated medical coding approaches have been developed using machine learning methods, such as deep neural networks. Nevertheless, automated medical coding is still challenging because of complex code association, noise in lengthy documents, and the imbalanced class problem. We propose a novel neural network, called the Multitask Balanced and Recalibrated Neural Network, to solve these issues. Significantly, the multitask learning scheme shares the relationship knowledge between different coding branches to capture code association. A recalibrated aggregation module is developed by cascading convolutional blocks to extract high-level semantic features that mitigate the impact of noise in documents. Also, the cascaded structure of the recalibrated module can benefit learning from lengthy notes. To solve the imbalanced class problem, we deploy focal loss to redistribute the attention on low- and high-frequency medical codes. Experimental results show that our proposed model outperforms competitive baselines on a real-world clinical dataset called the Medical Information Mart for Intensive Care (MIMIC-III).