Reviewerly/exHarmony huggingface dataset:
Semantics perception and refinement network for aspect-based sentiment analysis

With the proliferation of user-generated content on the web, sentiment analysis of text has become a current research hotspot. Aspect-based sentiment analysis (ABSA) is a type of fine-grained sentiment analysis that aims to identify the sentiment polarities of an aspect given in a context sentence. To date, most ABSA methods are based on recurrent neural networks (RNNs) and attention mechanism. However, RNNs suffer from losing long-distance dependency as the sentence length increases. In contrast, self-attention (SA) concentrates on extracting the dependencies among words regardless of the distance. However, similar to RNNs, SA is not sensitive enough to perceive the local semantics information for the ABSA task. In addition, the attention mechanism employed in ABSA may introduce some noise, which is not conducive to capturing important sentiment expressions. To address these issues, in this paper, we propose a semantics perception and refinement network (SPRN) for sentiment analysis based on aspects. In the SPRN, a novel structure named the dual gated multichannel convolution (DGMCC) is utilized to acquire the aspect-related semantics features of a sentence. Specifically, based on the sentence’s global dependency obtained by the SA-based mechanism as the context information and aspect information over the sentence, a multichannel convolution (MCC) is designed with multiple semantic spaces to extract informative local semantic features. Moreover, a dual refinement gate (DRG) is proposed to strengthen the interaction between the aspect and context at a fine granularity and enhance the noise filter. To verify the effect of the SPRN, we conduct abundant experiments on 5 benchmark datasets with pretrained BERT and GloVe embeddings. The experimental results demonstrate that the SPRN performs better than 14 state-of-the-art ABSA methods in terms of various evaluation indices such as accuracy, marco-F1 and AUROC.
On removing potential redundant constraints for SVOR learning

As an extension of support vector machine in ordinal regression problem, support vector ordinal regression (SVOR) finds (r−1) parallel hyperplanes by solving a quadratic programming which has n(r−1) constraints. Here, n and r represent the number of training sample and ranks, respectively. Therefore, it would costs much more time to train SVOR than SVC or SVR. Fortunately, the solution of SVOR is only decided by minor constraints which are associated with non-zero Lagrange multipliers. Other constraints with zero Lagrange multipliers have no influence on the solution. Because a training sample is associated with (r−1) constraints, retaining potential support vector may still induce that many redundant constraints are reserved. In this paper, we try to remove these potential redundant constraints for SVOR learning. For the jth parallel hyperplane, the potential constraints with non-zero Lagrange multipliers are associated with the samples near the jth parallel hyperplane. These samples can be identified by a chain near the jth parallel hyperplane. Then, other constraints for the jth parallel hyperplane can be discarded before learning. The number of the constraints can be reduced to less than 17 percent of the original in our experiments. Additionally, it only executes once to find potential critical constraints. Obviously, it is easy to tune parameters after removing potential redundant constraints. The experimental results on several datasets demonstrate that SVOR becomes much faster after removing potential redundant constraints and the performance does not degrade seriously.
Co-Creating New Directions for Service Robots in Hospitality and Tourism

Research on the relationship between automation services and tourism has been rapidly growing in recent years and has led to a new service landscape where the role of robots is gaining both practical and research attention. This paper builds on previous reviews and undertakes a comprehensive analysis of the research literature to discuss opportunities and challenges presented by the use of service robots in hospitality and tourism. Management and ethical issues are identified and it is noted that practical and ethical issues (roboethics) continue to lack attention. Going forward, new directions are urgently needed to inform future research and practice. Legal and ethical issues must be proactively addressed, and new research paradigms developed to explore the posthumanist and transhumanist transitions that await. In addition, closer attention to the potential of “co-creation” for addressing innovations in enhanced service experiences in hospitality and tourism is merited. Among others, responsibility, inclusiveness and collaborative human-robot design and implementation emerge as important principles to guide future research and practice in this area.
Motion saliency based multi-stream multiplier ResNets for action recognition

In this paper, we propose a Motion Saliency based multi-stream Multiplier ResNets (MSM-ResNets) for action recognition. The proposed MSM-ResNets model consists of three interactive streams: the appearance stream, motion stream and motion saliency stream. Similar to conventional two-stream CNNs models, the appearance stream and motion stream are responsible for capturing the appearance information and motion information, respectively, while the motion saliency stream is responsible for capturing the salient motion information. In particular, to effectively utilize the spatiotemporal interactive information between different streams, the proposed MSM-ResNets model establishes interactive connections between different streams instead of fusing three streams at the final output layer. Two kinds of different multiplicative connections are injected, the first one is to inject multiplicative connections from the motion stream to the appearance stream, while the second one is to inject multiplicative connections from the motion saliency stream to the motion stream. Experimental results verify the effectiveness of the proposed MSM-ResNets on two standard action recognition datasets: UCF101 and HMDB51.
An Examination of People’s Privacy Concerns, Perceptions of Social Benefits, and Acceptance of COVID-19 Mitigation Measures That Harness Location Information: A Comparative Study of the U.S. and South Korea

This paper examines people’s privacy concerns, perceptions of social benefits, and acceptance of various COVID-19 control measures that harness location information using data collected through an online survey in the U.S. and South Korea. The results indicate that people have higher privacy concerns for methods that use more sensitive and private information. The results also reveal that people’s perceptions of social benefits are low when their privacy concerns are high, indicating a trade-off relationship between privacy concerns and perceived social benefits. Moreover, the acceptance by South Koreans for most mitigation methods is significantly higher than that by people in the U.S. Lastly, the regression results indicate that South Koreans (compared to people in the U.S.) and people with a stronger collectivist orientation tend to have higher acceptance for the control measures because they have lower privacy concerns and perceive greater social benefits for the measures. These findings advance our understanding of the important role of geographic context and culture as well as people’s experiences of the mitigation measures applied to control a previous pandemic.
Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification

Text classification is a fundamental and important area of natural language processing for assigning a text into at least one predefined tag or category according to its content. Most of the advanced systems are either too simple to get high accuracy or centered on using complex structures to capture the genuinely required category information, which requires long time to converge during their training stage. In order to address such challenging issues, we propose a dynamic embedding projection-gated convolutional neural network (DEP-CNN) for multi-class and multi-label text classification. Its dynamic embedding projection gate (DEPG) transforms and carries word information by using gating units and shortcut connections to control how much context information is incorporated into each specific position of a word-embedding matrix in a text. To our knowledge, we are the first to apply DEPG over a word-embedding matrix. The experimental results on four known benchmark datasets display that DEP-CNN outperforms its recent peers.
Part-Object Relational Visual Saliency

Recent years have witnessed a big leap in automatic visual saliency detection attributed to advances in deep learning, especially Convolutional Neural Networks (CNNs). However, inferring the saliency of each image part separately, as was adopted by most CNNs methods, inevitably leads to an incomplete segmentation of the salient object. In this paper, we describe how to use the property of part-object relations endowed by the Capsule Network (CapsNet) to solve the problems that fundamentally hinge on relational inference for visual saliency detection. Concretely, we put in place a two-stream strategy, termed Two-Stream Part-Object RelaTional Network (TSPORTNet), to implement CapsNet, aiming to reduce both the network complexity and the possible redundancy during capsule routing. Additionally, taking into account the correlations of capsule types from the preceding training images, a correlation-aware capsule routing algorithm is developed for more accurate capsule assignments at the training stage, which also speeds up the training dramatically. By exploring part-object relationships, TSPORTNet produces a capsule wholeness map, which in turn aids multi-level features in generating the final saliency map. Experimental results on five widely-used benchmarks show that our framework consistently achieves state-of-the-art performance. The code can be found on https://github.com/liuyi1989/TSPORTNet.
Research on the design of analytical communication and information model for teaching resources with cloud‐sharing platform

Abstract The recent growth in the adoption by several organizations of cloud computing services has created the challenge to determine their performance of information model. The ability of cloud computing systems to provide the required services in line with the quality of service and service level agreement constraints is a major problem for cloud service providers and consumers. To that end, it is possible to develop the means of ensuring that the desired levels of success are achieved in simulated environments. In the eventual stage of commissioning and running cloud computing, this will reduce the disrupted service and performance degradation problems using analytical communication and information model of cloud technology. However, several modelers and simulators for simulating cloud computing systems are available in conventional research. This paper, therefore, provides a critical assessment with state of the art cloud computing modeling and simulation frameworks. It compares the prominent frameworks of simulation regarding application programming interface features, programming flexibility, operating system requirements, services supported, and licensing needs with 95% reliability. It offers recommendations to the researchers, administrators, and managers of cloud computing systems regarding the choice of the most appropriate framework.
Multi-faceted impact and outcome of COVID-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies

The shock of Coronavirus Disease 2019 (COVID-19) has disrupted food systems worldwide. Such disruption, affecting multiple systems interfaces in smallholder agriculture, is unprecedented and needs to be understood from multi-stakeholder perspectives. The multiple loops of causality in the pathways of impact renders the system outcomes unpredictable. Understanding the nature of such unpredictable pathways is critical to identify present and future systems intervention strategies. Our study aims to explore the multiple pathways of present and future impact created by the pandemic and "Amphan" cyclonic storm on smallholder agricultural systems. Also, we anticipate the behaviour of the systems elements under different realistic scenarios of intervention. We explored the severity and multi-faceted impacts of the pandemic on vulnerable smallholder agricultural production systems through in-depth interactions with key players at the micro-level. It provided contextual information, and revealed critical insights to understand the cascading effect of the pandemic and the cyclone on farm households. We employed thematic analysis of in-depth interviews with multiple stakeholders in Sundarbans areas in eastern India, to identify the present and future systems outcomes caused by the pandemic, and later compounded by "Amphan". The immediate adaptation strategies of the farmers were engaging family labors, exchanging labors with neighbouring farmers, borrowing money from relatives, accessing free food rations, replacing dead livestock, early harvesting, and reclamation of waterbodies. The thematic analysis identified several systems elements, such as harvesting, marketing, labor accessibility, among others, through which the impacts of the pandemic were expressed. Drawing on these outputs, we employed Mental Modeler, a Fuzzy-Logic Cognitive Mapping tool, to develop multi-stakeholder mental models for the smallholder agricultural systems of the region. Analysis of the mental models indicated the centrality of "Kharif" (monsoon) rice production, current farm income, and investment for the next crop cycle to determine the pathways and degree of the dual impact on farm households. Current household expenditure, livestock, and soil fertility were other central elements in the shared mental model. Scenario analysis with multiple stakeholders suggested enhanced market access and current household income, sustained investment in farming, rapid improvement in affected soil, irrigation water and livestock as the most effective strategies to enhance the resilience of farm families during and after the pandemic. This study may help in formulating short and long-term intervention strategies in the post-pandemic communities, and the methodological approach can be used elsewhere to understand perturbed socioecological systems to formulate anticipatory intervention strategies based on collective wisdom of stakeholders.
Sustainable Utilization of Financial and Institutional Resources in Reducing Income Inequality and Poverty

This study aims to determine the role of globalization, electronic government, financial development, concerning the moderation of institutional quality in reducing income inequality and poverty in One Belt One Road countries. The electronic government and regional integration of the economies of the One Belt One Road countries has increased globalization and can play a vital role in reducing income inequality and poverty. However, this globalization and digital transformation of government systems can only be beneficial in the presence of good institutional quality. The sample includes 64 One Belt One Road countries from 2003 to 2018. We employed a two-step system generalized method of moment (Sys-GMM) and a robustness check through Driscoll–Kraay standard errors regression. Our findings show that globalization, economic growth, e-government development, government expenditure, and inflation have a statistically significant and negative impact on income inequality and are key to eradicating income inequality and poverty. On the other hand, financial development, gross capital formation, and population size positively influence income inequality, which causes an increase in poverty and income inequality as financial development and population levels increase. Moderating variable institutional quality also positively impacts income inequality, which means that institutional quality in Belt and Road Countries is weak, as they are mostly developing countries that need to improve their systems. Moreover, the marginal effect also revealed that institutional quality has a corrective effect on the factors’ relationship with income inequality. Our findings endorse and conclude that globalization and e-government development improve economic growth and eradicate poverty and income inequality by boosting digitalization, investments, job creation, and wage increases for semi-skilled and unskilled human capital in Belt and Road countries. The sustainable utilization of financial and institutional resources plays a vital role in reducing income inequality and poverty in Belt and Road countries.
Robotics in Education: A Scientific Mapping of the Literature in Web of Science

The technological revolution has created new educational opportunities. Today, robotics is one of the most modern systems to be introduced in educational settings. The main objective of this research was to analyze the evolution of the “robotics” concept in the educational field while having, as a reference point, the reported literature in the Web of Science (WoS). The methodology applied in this research was bibliometrics, which we used to analyze the structural and dynamic development of the concept. The collection of WoS studies on robotics in education began in 1975. Its evolution has been irregular, reaching peak production in 2019. Although the focus was on collecting studies with educational knowledge areas, other knowledge areas were also present, such as engineering and computing. It was found that the types of manuscript most commonly used to present scientific results in this area are proceedings papers. The country with the highest level of production in this field of study is the United States. The results confirm the potential of this type of study in the scientific field. The importance of this technology in the training of future surgeons and in the results they produce in their own learning was also detected.
Air-to-Air Visual Detection of Micro-UAVs: An Experimental Evaluation of Deep Learning

This letter studies the problem of air-to-air visual detection of micro unmanned aerial vehicles (UAVs) by monocular cameras. This problem is important for many applications such as vision-based swarming of UAVs, malicious UAV detection, and see-and-avoid systems for UAVs. Although deep learning methods have exhibited superior performance in many object detection tasks, their potential for UAV detection has not been well explored. As the first main contribution of this letter, we present a new dataset, named Det-Fly, which consists of more than 13 000 images of a flying target UAV acquired by another flying UAV. Compared to the existing datasets, the proposed one is more comprehensive in the sense that it covers a wide range of practical scenarios with different background scenes, viewing angles, relative distance, flying altitude, and lightning conditions. The second main contribution of this letter is to present an experimental evaluation of eight representative deep-learning algorithms based on the proposed dataset. To the best of our knowledge, this is the first comprehensive experimental evaluation of deep learning algorithms for the task of visual UAV detection so far. The evaluation results highlight some key challenges in the problem of air-to-air UAV detection and suggest potential ways to develop new algorithms in the future. The dataset is available at https://github.com/Jake-WU/Det-Fly.
Deep k-NN Defense Against Clean-Label Data Poisoning Attacks

Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference. Although defenses have been proposed for general poisoning attacks, no reliable defense for clean-label attacks has been demonstrated, despite the attacks’ effectiveness and realistic applications. In this work, we propose a simple, yet highly-effective Deep k-NN defense against both feature collision and convex polytope clean-label attacks on the CIFAR-10 dataset. We demonstrate that our proposed strategy is able to detect over 99% of poisoned examples in both attacks and remove them without compromising model performance. Additionally, through ablation studies, we discover simple guidelines for selecting the value of k as well as for implementing the Deep k-NN defense on real-world datasets with class imbalance. Our proposed defense shows that current clean-label poisoning attack strategies can be annulled, and serves as a strong yet simple-to-implement baseline defense to test future clean-label poisoning attacks. Our code is available on GitHub .
Slim-CNN: A Light-Weight CNN for Face Attribute Prediction

We introduce a computationally-efficient CNN micro-architecture Slim Module to design a lightweight deep neural network, Slim-CNN, for face attribute prediction. Slim Modules are constructed by assembling depthwise separable convolutions with pointwise convolution to produce a computationally efficient module. The problem of facial attribute prediction is challenging because of the large variations in pose, background, illumination, and dataset imbalance. We stack multiple Slim Modules to devise a compact CNN, which still maintains very high accuracy. Additionally, Slim-CNN has a very low memory footprint, which makes it suitable for mobile and embedded applications. Experiments on the CelebA dataset show that Slim-CNN achieve an accuracy of 91.24% with 25x fewer parameters compared to MCNN-AUX and 100x fewer parameters when compared to DTML. This reduces the memory storage requirement of Slim-CNN by at least 87%. Furthermore, we compare Slim Modules with other well-known micro-architectures, such as Inception modules, residual blocks, Shuffle-Unit, and Inverted Residual units, and show it outperforms them in performance and in memory size, making it suitable for face-related tasks in embedded applications.
RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning

Today's high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage heuristic priority functions to prioritize and schedule jobs. But, once configured and deployed by the experts, such priority functions can hardly adapt to the changes of job loads, optimization goals, or system settings, potentially leading to degraded system efficiency when changes occur. To address this fundamental issue, we present RLScheduler, an automated HPC batch job scheduler built on reinforcement learning. RLScheduler relies on minimal manual interventions or expert knowledge, but can learn high-quality scheduling policies via its own continuous `trial and error'. We introduce a new kernel-based neural network structure and trajectory filtering mechanism in RLScheduler to improve and stabilize the learning process. Through extensive evaluations, we confirm that RLScheduler can learn high-quality scheduling policies towards various workloads and various optimization goals with relatively low computation cost. Moreover, we show that the learned models perform stably even when applied to unseen workloads, making them practical for production use.