Reviewerly/exHarmony huggingface dataset:
Service Robots in the Hospitality Industry: An Exploratory Literature Review

The service sector is changing drastically due the use of robotics and other technologies, such as Artificial Intelligence (AI), Internet of things (IoT), Big Data and Biometrics. Consequently, further research opportunities in the service industry domain are also expected. In light of the above, the purpose of this paper is to explore the potentialities and limitations of service robots in the hospitality industry. To this end, this paper uses a conceptual approach based on a literature review. As a result, we found that in contexts of high customer contact, service robots should be considered to perform standardized tasks due to social/emotional and cognitive/analytical complexity. The hospitality industry is therefore considered closely related to empathic intelligence, as the integration of service robots has not yet reached the desired stage of service delivery. In a seemingly far-fetched context of our reality, organizations will have to decide whether the AI will allow the complete replacement of humans with robots capable of performing the necessary cognitive and emotional tasks. Or investing in balanced capacities by integrating robot-human systems that seems a reasonable option these days.
Heterogeneous graph neural networks for noisy few-shot relation classification

Relation classification is an essential and fundamental task in natural language processing. Distant supervised methods have achieved great success on relation classification, which improve the performance of the task through automatically extending the dataset. However, the distant supervised methods also bring the problem of wrong labeling. Inspired by people learning new knowledge from only a few samples, we focus on predicting formerly unseen classes with a few labeled data. In this paper, we propose a heterogeneous graph neural network for few-shot relation classification, which contains sentence nodes and entity nodes. We build the heterogeneous graph based on the message passing between entity nodes and sentence nodes in the graph, which can capture rich neighborhood information of the graph. Besides, we introduce adversarial learning for training a robust model and evaluate our heterogeneous graph neural networks under the scene of introducing different rates of noise data. Experimental results have demonstrated that our model outperforms the state-of-the-art baseline models on the FewRel dataset.
Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection

The emergence of online social network invokes social actors to share their personal information digitally. Moreover, it provides the facility to maintain their links with people of same interest globally. Take advantage of these services; it has become a fascinating testbed to invite various threats like a spammer. Detection of spammer in OSN is one of the most critical tasks. Spammer not only spreads unwanted or bad advertisement but does certain malicious activity in others' profiles. By clearly understanding the activities of different threats, some incremental and accurate approaches are needed for detecting spammer content and profiles involved in these activities by using social network services. Therefore, the focus of this article is to detect spammer content and account, specifically on the leading microblogging platform called Twitter. We propose a hybrid approach which leverages the capabilities of various machine learning algorithms to separate spammer and nonspammer contents and account. Initially, the optimisation algorithm called genetic algorithm analyses the various features and selects the best suitable features that influence the behaviour of user account, and these features are then used to train classifiers. Our framework achieved to severalise spammer and nonspammer content in an effective way. Finally, to prove the efficiency of our proposed framework, a comparative analysis is conducted with some existing state-of-art techniques. The experimental analysis shows that our approach achieves a high detection rate of 99.6%, which is better than other state-of-art techniques.
GAN-Based Key Secret-Sharing Scheme in Blockchain

In this article, we propose a key secret-sharing technology based on generative adversarial networks (GANs) to address three major problems in the blockchain: 1) low security; 2) hard recovery of lost keys; and 3) low communication efficiency. In our scheme, the proposed network plays the role of a dealer and treats the secret-sharing process as a classification issue. The key idea is to view the secret as an image during the secret-sharing process. If the user's private key is text, we can covert the key text into an image called the original image. Specifically, we first divide the original image into original subimages by the image segmentation. Next, we encode each original subimage by DNA coding. Finally, we train the proposed network to find the key secret-sharing results. Our proposed scheme is not only a significant extension of the GANs but also a new direction for the key secret-sharing technology. The simulation results show that the scheme is secure, and both flexible and efficient in communication.
Multi-level feature fusion based Locality-Constrained Spatial Transformer network for video crowd counting

Video-based crowd counting can leverage the spatial-temporal information between neighboring frames, and thus this information would improve the robustness of crowd counting. Therefore, this solution is more practical than single image-based crowd counting in real applications. Since severe occlusions, translation, rotation, and scaling of persons will give rise to the change of density map of heads between neighboring frames, video-based crowd counting is a very challenging task. To alleviate these issues in video crowd counting, a Multi-Level Feature Fusion Based Locality-Constrained Spatial Transformer Network (MLSTN) is proposed, which consists of two components, namely density map regression module and Locality-Constrained Spatial Transformer (LST) module. Specifically, we first estimate the density map of each frame by utilizing the combination of the low-level, middle-level and high-level features of the Convolutional Neural Networks. This is because the low-level features may be more effective in the extraction of small head information, while the middle and high level features are more effective in the extraction of medium and large head information. Then to measure the relationship of the density maps between neighboring frames, the LST module is proposed, which estimates the density map of the next frame by concatenating several regression density maps. To facilitate the performance evaluation for video crowd counting, we have collected and labeled a large-scale video crowd counting dataset which includes 100 five-second-long sequences with 394,081 annotated heads from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments show the effectiveness of our proposed approach for crowd counting on our dataset and other video-based crowd counting datasets. All our dataset are released online.1
The Lattice-Based Digital Signature Scheme qTESLA

We present qTESLA, a post-quantum provably-secure digital signature scheme that exhibits several attractive features such as simplicity, strong security guarantees against quantum adversaries, and built-in protection against certain side-channel and fault attacks. qTESLA—selected for round 2 of NIST’s post-quantum cryptography standardization project—consolidates a series of recent schemes originating in works by Lyubashevsky, and Bai and Galbraith. We provide full-fledged, constant-time portable C implementations consisting of only about 300 lines of C code, which showcases the code compactness of the scheme. Our results also demonstrate that a conservative, provably-secure signature scheme can be efficient and practical, even with a compact and portable implementation. For instance, our C-only implementation executes signing and verification in approximately 0.9 ms on an x64 Intel processor using the proposed level 1 parameter set. Finally, we also provide AVX2-optimized assembly implementations that achieve an additional factor-1.5 speedup.
Evolutionary and adaptive inheritance enhanced Grey Wolf Optimization algorithm for binary domains

This paper introduces a new binary Grey Wolf Optimization (GWO) algorithm, which is one of the recent swarm intelligence-based metaheuristic algorithms. Its various extensions have been reported in the related literature. Despite numerous successful applications in real-valued optimization problems, the canonical GWO algorithm cannot directly handle binary optimization problems. To this end, transformation functions are commonly employed to map the real-valued solution vector to the binary values; however, this approach brings about the undesired problem of spatial disconnect. In this study, evolutionary and adaptive inheritance mechanisms are employed in the GWO algorithm so as to operate in the binary domain directly. To mimic the leadership hierarchy procedure of the GWO, multi-parent crossover with two different dominance strategies is developed while updating the binary coordinates of the wolf pack. Furthermore, adaptive mutation with exponentially decreasing step-size is adopted to avoid premature convergence and to establish a balance between intensification and diversification. The performance of the proposed algorithm is tested on the well-known binary benchmark suites comprised of the Set-Union Knapsack Problem (SUKP) that extends the 0–1 Knapsack Problem and the Uncapacitated Facility Location Problem (UFLP). Comprehensive experimental study including real-life applications and statistical analyses demonstrate the effectiveness of the proposed algorithm.
Learning Review Representations from user and Product Level Information for Spam Detection

Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex global semantics of reviews. Although recent works apply deep learning methods to learn review-level semantic features, their models ignore the impact of the user-level and product-level information on learning review semantics and the inherent user-review-product relationship information. In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from user and product level. Specifically, we design a multiattention unit to extract user(product)-related review information. Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information. Finally, we take the review as a relation between user and product entity and apply TransH to jointly encode this relationship into review representation. Experimental results obtained more than 10% absolute precision improvement over the state-of-the-art performances on four real-world datasets, which show the effectiveness and versatility of the model.
ICDAR2019 Competition on Recognition of Documents with Complex Layouts - RDCL2019

This paper presents an objective comparative evaluation of page segmentation and region classification methods for docu-ments with complex layouts. It describes the competition (mo-dus operandi, dataset and evaluation methodology) held in the context of ICDAR2019, presenting the results of the evaluation of twelve methods - nine submitted, three state-of-the-art sys-tems (commercial and open-source). Three scenarios are re-ported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmenta-tion and region classification. Text recognition was a bonus challenge and was not taken up by all participants. The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-textual content.
Evaluating Performance of Web Application Security Through a Fuzzy Based Hybrid Multi-Criteria Decision-Making Approach: Design Tactics Perspective

Design of software can have a major impact on the overall security of the software.Developing a secure website design is a challenge for architectures.It depends on different and tough decisions which determine the security of website.Increasing number of vulnerabilities increase the level of security requirements.Hence, security design tactics are to be adopted to satisfy these security requirements.Security design tactics are the mechanisms to define, detect and mitigate vulnerabilities and attacks.Therefore, faults in the application of security tactics or their weakening during website maintenance could be one of the key reasons behind the emergence of new and severe vulnerabilities that can be targeted by the hackers.There is a need for in-depth analysis of security tactics and its prioritization for the sake of determining the most prioritized factor.This will further help in gaining a more secure system.In this research study, the authors have used the hybrid method of Fuzzy AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order Preference by Similarity Ideal Solution) for the evaluation of security design tactics and its attributes.The efficiency of this approach has been tested on a real time web application of Babasaheb Bhimrao Ambedkar University, Lucknow, India.Further, different web applications of the University have been used to validate the obtained results.This study's evaluation of the most impactful web application design for improving security will help the architects to secure systems by using security tactics.
Robot Localization via Odometry-assisted Ultra-wideband Ranging with Stochastic Guarantees

We consider the problem of accurate and high-rate self-localization for a mobile robot. We adaptively combine the speed information acquired by proprioceptive sensors with intermittent positioning samples acquired via ultra-wideband (UWB) radios. These are triggered only if and when needed to reduce the positioning uncertainty, itself modeled by a probabilistic cost function. Our formulation is agnostic w.r.t. the source of uncertainty and enables an intuitive specification of user navigation requirements along with stochastic guarantees on the system operation. Experimental results in simulation and with a real platform show that our approach i) meets these guarantees in practice ii) achieves the same accuracy of a fixed periodic sampling but with significantly higher scalability and lower energy consumption iii) is resilient to errors in UWB estimates, enabling the use of low-accuracy ranging schemes which further improve these two performance metrics.
Unsupervised domain adaptation with adversarial learning for mass detection in mammogram

Many medical image datasets have been collected without proper annotations for deep learning training. In this paper, we propose a novel unsupervised domain adaptation framework with adversarial learning to minimize the annotation efforts. Our framework employs a task specific network, i.e., fully convolutional network (FCN), for spatial density prediction. Moreover, we employ a domain discriminator, in which adversarial learning is adopted to align the less-annotated target domain features with the well-annotated source domain features in the feature space. We further propose a novel training strategy for the adversarial learning by coupling data from source and target domains and alternating the subnet updates. We employ the public CBIS-DDSM dataset as the source domain, and perform two sets of experiments on two target domains (i.e., the public INbreast dataset and a self-collected dataset), respectively. Experimental results suggest consistent and comparable performance improvement over the state-of-the-art methods. Our proposed training strategy is also proved to converge much faster.
Graph convolutional networks with multi-level coarsening for graph classification

Graph convolutional networks (GCNs) have attracted increasing attention in recent years. Many important tasks in graph analysis involve graph classification which aims to map a graph to a certain category. However, as the number of convolutional layers increases, most existing GCNs suffer from the problem of over-smoothing, which makes it difficult to extract the hierarchical information and global patterns of graphs when learning its representations. In this paper, we propose a multi-level coarsening based GCN (MLC-GCN) for graph classification. Specifically, from the perspective of graph analysis, we develop new insights into the convolutional architecture of image classification. Inspired by this, the two-stage MLC-GCN architecture is presented. In the architecture, we first introduce an adaptive structural coarsening module to produce a series of coarsened graphs and then construct the convolutional network based on these graphs. In contrast to existing GCNs, MLC-GCN has the advantages of learning graph representations at multiple levels while preserving the local and global information of graphs. Experimental results on multiple benchmark datasets demonstrate that the proposed MLC-GCN method is competitive with the state-of-the-art graph classification methods.
Impact of sample preparation methods for characterizing the geochemistry of soils and sediments by portable X‐ray fluorescence

Abstract We examined the impact of three different sample preparation methods on bulk soil geochemistry data obtained from a hand‐held, portable X‐ray fluorescence (pXRF) spectrometer. We generated data from a soil core recovered from the surface, downward into unaltered loess, and into a buried soil at a site in eastern Iowa. Samples were scanned (i) directly from field‐moist soil cores; (ii) after drying, grinding, and being loosely massed in plastic cups; and (iii) as pressed‐powder pellets. Data derived using these methods were compared with data obtained from a standard benchtop X‐ray fluorescence (XRF) unit. Generally, the results indicated that data from pressed powder pellets often provide the best correlation to benchtop XRF data, although the results were sometimes element or compound specific. Calcium oxide, Fe 2 O 3 , and K 2 O generally provided the strongest correlations between pXRF‐ and XRF‐reported values; SiO 2 data were more problematic. Field‐moist pXRF scans generally underestimated element concentrations, but the correlations between pXRF and benchtop XRF measurements were greatly improved after applying pXRF‐derived calibration standards. In summary, although element/compound data provided by pXRF showed significant relationships to benchtop XRF data, the results are improved with proper sample preparation (i.e., drying, grinding, pressing) and usually by calibrating the pXRF data against known standards.
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation

When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image. The temporal variations in a video allow different regions of the target object to be activated. We obtain an activated region in each frame of a video, and then aggregate the regions from successive frames into a single image, using a warping technique based on optical flow. The resulting localization maps cover more of the target object, and can then be used as proxy ground-truth to train a segmentation network. This simple approach outperforms existing methods under the same level of supervision, and even approaches relying on extra annotations. Based on VGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4, respectively, on PASCAL VOC 2012 test images, which represents a new state-of-the-art.
The mediating role of presence differs across types of spatial learning in immersive technologies

The effects of immersive technology on learning have been mixed. It is therefore important to determine the factors that affect when and why immersive technologies are and are not effective. One psychological construct proposed to explain why higher levels of immersive technology may lead to better learning compared to lower immersion is presence, or the subjective feeling of “being there.” Participants completed a spatial task in three levels of immersive technology, reported the amount of presence felt, and completed learning outcome tasks measuring three levels of spatial knowledge: landmark, route, and survey knowledge. The relationships between the level of immersive technology, presence, and spatial learning outcomes were examined. The highest immersion condition led to better performance on landmark, route, survey, and overall spatial knowledge, and also led to higher levels of presence. Higher presence led to better performance on route, survey, and overall spatial knowledge. However, presence only significantly mediated the relationship for survey knowledge, and effects of low vs. medium immersion condition on learning and presence often did not differ, despite the devices having largely different affordances. The relationship between immersion and learning is thus complex, depends on type of learning, and may be mediated by both presence and non-presence effects on cognitive load.
QoE-Based Mobility-Aware Collaborative Video Streaming on the Edge of 5G

Today's Internet traffic is dominated by video streaming applications transmitted through wireless/cellular interfaces of mobile devices. Although ultrahigh-definition videos are now easily transmitted through mobile devices, video quality level that users perceive is generally lower than expected due to distance-based high latency between sources and end-users. Mobile edge computing (MEC) paradigm is expected to address this issue and provide users with higher perceived quality of experience (QoE) for latency-critical applications, deploying MEC servers at edges. However, due to capacity concerns on MEC servers, a more comprehensive approach is needed to meet users' expectations applying all possible operations over the resources such as caching, prefetching, and task offloading policies depending on the data repetition or memory/CPU utilization. To address these issues, this article proposes a novel collaborative QoE-based mobility-aware video streaming scheme deployed at MEC servers. Throughout the article, we demonstrate how the proposed scheme can be implemented so as to preserve the desired QoE level per user during entire video sessions. Performance of the proposed scheme has been investigated by extensive simulations. In comparison to existing schemes, the results illustrate that high efficiency is achieved through collaboration among MEC servers, utilizing explicit window size adaptation, collaborative prefetching, and handover among the edges.
Education 4.0 – Jump to Innovation with IoT in Higher Education

Artificial Intelligence (AI) will play a key role in Higher Education. Our contribution leads the road to innovation within IoT for Education 4.0, using a system comprising smartwatch data, health data, learning analytics and Artificial Intelligence. By using embedded sensors from wearable devices, we can add value to Education 4.0. Smartwatches are IoT devices, equipped with a multitude of embedded sensors collecting distraction-free huge amounts of real-time data during students' learning. In our paper we highlight advantages of wrist-based wearables like smartwatches for Education 4.0. We develop a User Experience Questionnaire for the measurement of acceptance of advanced electronic technology in Higher Education. We identify as experiment's results the most important sensors and protocols for Education 4.0.