Reviewerly/exHarmony huggingface dataset:
Branding in the time of virtual reality: Are virtual store brand perceptions real?

This research investigates whether consumers display similar brand perceptions between physical and virtual store environments. Specifically, it explores the set of causal relationships through which the virtual store experience affects consumers’ perceptions and intentions toward the retailer’s brand. The results from an experimental study manipulating the store environment (virtual vs. physical) reveal that individuals exposed to a virtual-reality-based retail environment perceive higher levels of presence than those exposed to a more traditional, physical store environment; moreover, this positive effect does not depend on individuals’ technological self-efficacy perceptions. Higher levels of presence positively affect the shopping experience, which then produces a positive change in value perceptions, which ultimately lead to higher patronage intentions and WOM referral. Despite the presence of inattentional blindness found in the virtual environment, the results show that such an image transfer from the store environment to patronage intention holds even when individuals cannot correctly recall the store brand.
Meta-Learning based prototype-relation network for few-shot classification

Pattern recognition has made great progress under large amount of labeled data, while performs poorly on a very few examples obtained, named few-shot classification, where a classifier can identify new classes not encountered during training. In this paper, a simple framework named Prototype-Relation Network is presented for the few-shot classification. Moreover, a novel loss function compared with prototype networks is proposed which takes both inter-class and intra-class distance into account. During meta-learning, the model is optimized by end-to-end episodes, each of which is to imitate the test few-shot setting. The trained model is used to classify new classes by computing min distance between query images and the prototype of each class. Extensive experimental results demonstrate that our proposed meta-learning model is competitive and effective, which achieves the state-of-the-art performance on Omniglot and miniImageNet datasets.
Dense and Small Object Detection in UAV Vision Based on Cascade Network

With the development of Unmanned Aerial Vehicles, drones are being deployed in a number of commercial and civil government applications ranging from remote surveillance and infrastructure maintenance among others. However, processing the videos captured by drones for the extracting meaningful information is hindered by multitude of challenges that include, the appearance of small objects, changes in viewpoint of these objects, illumination changes, large-scale resolution of the captured video, occlusion and truncation. Addressing these challenges, there is a critical need to develop algorithms that is able to efficiently process the videos that can result in robust detection and recognition of small objects. In this paper, we propose a novel processing pipeline, that brings together several key contributions including (i) the introduction of DeForm convolution layers within backbone; (ii) use of the interleaved cascade architecture; (iii) data augmentation process based on crop functionality and (iv) multi-model fusion of sub-category detection networks. The proposed approach has been exhaustively benchmarked against VisDrone-DET object detection dataset, which includes 10,209 images for training, validation and testing. The evaluation of the proposed approach has resulted in 22.61 average precision on the test-challenge set in VisDrone-DET 2019.
Multiobjective Optimal Parking Maneuver Planning of Autonomous Wheeled Vehicles

This article proposes a computational trajectory optimization framework for solving the problem of multiobjective automatic parking motion planning. Constrained automatic parking maneuver problem is usually difficult to solve because of some practical limitations and requirements. This problem becomes more challenging when multiple objectives are required to be optimized simultaneously. The designed approach employs a swarm intelligent algorithm to produce the tradeoff front along the objective space. In order to enhance the local search ability of the algorithm, a gradient operation is utilized to update the solution. In addition, since the evolutionary process tends to be sensitive with respect to the flight control parameters, a novel adaptive parameter controller is designed and incorporated in the algorithm framework such that the proposed method can dynamically balance the exploitation and exploration. The performance of using the designed multiobjective strategy is validated and analyzed by performing a number of simulation and experimental studies. The results indicate that the present approach can provide reliable solutions and it can outperform other existing approaches investigated in this article.
Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards

Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.
Online Distributed IoT Security Monitoring With Multidimensional Streaming Big Data

Internet of Things (IoT) enables extensive connections between cyber and physical "things". Nevertheless, the streaming data among IoT sensors bring "big data" issues, for example, large data volumes, data redundancy, lack of scalability and so on. Under "big data" circumstances, IoT system monitoring becomes a challenge. Furthermore, cyberattacks which threaten IoT security are hard to be detected. In this paper, we propose an online distributed IoT security monitoring algorithm (ODIS). An advanced influential point selection operation extracts important information from multidimensional time series data across distributed sensor nodes based on the spatial and temporal data dependence structure. Then, an accurate data structure model is constructed to capture the IoT system behaviors. Next, hypothesis testing is carried out to quantify the uncertainty of the monitoring tasks. Besides, the distributed system architecture solves the scalability issue. Using a real sensor network testbed, we commit cyberattacks to an IoT system with different patterns and strengths. The proposed ODIS algorithm demonstrates promising detection and monitoring performances.
A Hybrid Ensemble Model for Interval Prediction of Solar Power Output in Ship Onboard Power Systems

Application of solar energy into ship power systems has been increasingly drawing attention. Accordingly, an accurate prediction of solar power plays a significant role in the shipboard power system operation. However, a photovoltaic (PV) generation system on the shipboard, different from the one on land, has to suffer more dramatic power fluctuations caused by weather variations and motions of the ships, which increase the uncertainty of PV power outputs. This paper proposes a hybrid ensemble method for optimal interval prediction of onboard solar power based on a stochastic ship motion model. A set of machine learning techniques are combined together with the particle swarm optimization (PSO) to constitute a hybrid forecasting model, including a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), an extreme learning machine (ELM) and an Elman neural network. Furthermore, for different learning algorithms, an ensemble strategy is employed to reduce the forecasting error and various environmental variables along with ship moving and rolling impacts are taken into account. The developed model has been practically tested on a power system on a large oil tanker penetrated with PV energy and the data along the typical navigation route from Dalian in China to Aden in Yemen are selected for solar power prediction. The simulation results demonstrate its high accuracy, which provides a reliable reference for ship power system operators to achieve a better energy management.
SEM-ACSIT: Secure and Efficient Multiauthority Access Control for IoT Cloud Storage

Data access control in a cloud storage system is regarded as a promising technique for enhanced efficiency and security utilizing a ciphertext-policy attribute-based encryption (CP-ABE) approach. However, due to a large number of data users as well as limited resources and heterogeneity of data devices in Internet of Things (IoT), existing access control schemes for the cloud storage are not effectively applicable to IoT applications. In this article, we construct a new CP-ABE-based storage model for data storing and secure access in a cloud for IoT applications. Our new framework introduces an attribute authority management (AAM) module in the cloud storage system functioned as an agent that provides a user-friendly access control and highly reduces the storage overhead of public keys. Then, we propose a novel secure and efficient multiauthority access control scheme of the cloud storage system for IoT, namely, SEM-ACSIT, which obtains both backward security and forward security when an attribute of a user is revoked. By exploiting encryption outsourcing, simplified key structuring and the AAM module, the computational overhead of a user is immensely decreased. Moreover, a user access control list (UACL) in the cloud server is constructed newly to support authorization access for a specific user. The analysis and simulation results demonstrate that our SEM-ACSIT scheme achieves powerful security with less computational overhead and lower storage costs than the existing schemes.
Quantum Neuromorphic Platform for Quantum State Preparation

We develop a scheme of quantum reservoir state preparation, based on a quantum neural network framework, which takes classical optical excitation as input and provides desired quantum states as output. We theoretically demonstrate the broad potential of our scheme by explicitly preparing a range of intriguing quantum states, including single-photon states, Schrödinger's cat states, and two-mode entangled states. This scheme can be used as a compact quantum state preparation device for emerging quantum technologies.
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations

Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of d_χ-privacy designed to achieve geo-indistinguishability in location data. Our approach applies carefully calibrated noise to vector representation of words in a high dimension space as defined by word embedding models. We present a privacy proof that satisfies d_χ-privacy where the privacy parameter $\varepsilon$ provides guarantees with respect to a distance metric defined by the word embedding space. We demonstrate how $\varepsilon$ can be selected by analyzing plausible deniability statistics backed up by large scale analysis on GloVe and fastText embeddings. We conduct privacy audit experiments against $2$ baseline models and utility experiments on 3 datasets to demonstrate the tradeoff between privacy and utility for varying values of varepsilon on different task types. Our results demonstrate practical utility (< 2% utility loss for training binary classifiers) while providing better privacy guarantees than baseline models.
Breast tumors recognition based on edge feature extraction using support vector machine

Nowadays, it is important for the detection of ultrasound images of breast tumors. In this paper, a new ultrasonic image feature extraction algorithm combining edge-based features and morphologic feature information is proposed, which has good effect on benign and malignant identification of breast tumors. This paper mainly studies three features (Sum of maximum curvature, Sum of maximum curvature and peak, Sum of maximum curvature and standard deviation) according to the shape histogram of ultrasound breast tumors from a local perspective. Based on the results of SVM classifier, it was found that the edge-based features have higher classification accuracy. The recognition system would perform better when morphologic features (Roughness, Regularity, Aspect ratio, Ellipticity, Roundness) were incorporated, compared with the control group whose input only with morphologic features. The results show that edge-based features can well describe breast tumors in ultrasound images, and have the potential to be used in breast ultrasound computer-aided design.
Window Selection of the Savitzky–Golay Filters for Signal Recovery From Noisy Measurements

The Savitzky-Golay (SG) filtering is a widely used denoising method employed in different applications. The SG filter has two design parameters: the window length and the filter order. As the window length increases, the estimation variance is reduced, but at the same time, the bias error is increased. In this article, we obtain the optimal window length of an SG filter with an arbitrary order, based on minimizing the mean square error (mse), a well-known performance measure considering both the estimation bias and variance. To achieve the optimal window length, we propose an algorithm the performance of which is much better than the existing methods. In this article, we follow the viewpoint proposed by Persson and Strang and design the filter on the basis of the Chebyshev orthogonal polynomials.
Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.
Hyper-heuristic method for multilevel thresholding image segmentation

In digital image processing, one of the most relevant tasks is to classify pixels depending on their intensity level. To perform this process there exist different traditional methods as Otsu or Kapur, such methods are used to compute the thresholds that divide the histogram of the image into different groups. These methods are easy to implement for a single threshold; however, the computational effort is affected when more thresholds are required. Therefore, different meta-heuristic based approaches have been proposed, but each of them has its properties and limitations. So, this paper introduces an alternative concept to the image segmentation which is called hyper-heuristic that at each iteration determines the optimal execution sequence of meta-heuristic algorithms that provides the optimal thresholds. The proposed method consists of two layers, in the first layer, the genetic algorithm (GA) is used to determine the execution sequence of the meta-heuristic algorithms. While the second layer contains the set of four meta-heuristic algorithms that executed in a specific order, assigned by the current solution of GA, to update the threshold population. In order to evaluate the performance of the proposed approach, it has been tested over a set of benchmark images and the results provide a good performance in terms of quality of segmentation. Moreover, experimental comparisons support that the proposed hyper-heuristic is able to find more accurate solutions than other algorithms.
An empirical characterization of bad practices in continuous integration

Continuous Integration (CI) has been claimed to introduce several benefits in software development, including high software quality and reliability. However, recent work pointed out challenges, barriers and bad practices characterizing its adoption. This paper empirically investigates what are the bad practices experienced by developers applying CI. The investigation has been conducted by leveraging semi-structured interviews of 13 experts and mining more than 2,300 Stack Overflow posts. As a result, we compiled a catalog of 79 CI bad smells belonging to 7 categories related to different dimensions of a CI pipeline management and process. We have also investigated the perceived importance of the identified bad smells through a survey involving 26 professional developers, and discussed how the results of our study relate to existing knowledge about CI bad practices. Whilst some results, such as the poor usage of branches, confirm existing literature, the study also highlights uncovered bad practices, e.g., related to static analysis tools or the abuse of shell scripts, and contradict knowledge from existing literature, e.g., about avoiding nightly builds. We discuss the implications of our catalog of CI bad smells for (i) practitioners, e.g., favor specific, portable tools over hacking, and do not ignore nor hide build failures, (ii) educators, e.g., teach CI culture, not just technology, and teach CI by providing examples of what not to do, and (iii) researchers, e.g., developing support for failure analysis, as well as automated CI bad smell detectors.
A stream processing architecture for heterogeneous data sources in the Internet of Things

The number of Internet of Things (IoT) and smart devices capable of producing, consuming and exchanging information is constantly increasing. It is estimated there will be around 30 billion of them in 2020. In most cases, the structures of the information produced by such devices are completely different, thus providing heterogeneous information. This is becoming a challenge for researchers working on IoT, who need to perform homogenisation and pre-processing tasks before using the IoT data. This paper aims to provide an architecture for processing and analysing data from heterogeneous sources with different structures in IoT scopes, allowing researchers to focus on data analysis, without having to worry about the structure of the data sources. This architecture combines the real-time stream processing paradigm for information processing and transforming, together with the complex event processing for information analysis. This provides us with capability of processing, transforming and analysing large amounts of information in real time. The results obtained from the evaluation of a real-world case study about water supply network management show that the architecture can be applied to an IoT water management scenario to analyse the information in real time. Additionally, the stress tests successfully conducted for this architecture highlight that a large incoming rate of input events could be processed without latency, resulting in efficient performance of the proposed architecture. This novel software architecture is adequate for automatically detecting situations of interest in the IoT through the processing, transformation and analysis of large amounts of heterogeneous information in real time.
Creativity Inspired Zero-Shot Learning

Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of zero-shot learning, we model the visual learning process of unseen categories with an inspiration from the psychology of human creativity for producing novel art. We relate ZSL to human creativity by observing that zero-shot learning is about recognizing the unseen and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. Empirically, we show consistent improvement over the state of the art of several percents on the largest available benchmarks on the challenging task or generalized ZSL from a noisy text that we focus on, using the CUB and NABirds datasets. We also show the advantage of our loss on Attribute-based ZSL on three additional datasets (AwA2, aPY, and SUN). Code is available at https://github.com/mhelhoseiny/CIZSL.
Identifying microseismic events in a mining scenario using a convolutional neural network

Microseismic monitoring in mining can potentially forecast catastrophic disasters, and help in the optimization of day-to-day operational and safety issues. However, it is difficult to fully utilize microseismic data due to the large quantity of small microseismic events and because automatically detecting events is frustrated by the pollution of the microseismic signal by instrument noise and mining activities. Recent research has demonstrated that convolutional neural networks (CNNs) trained on vast seismic data sets can accurately detect seismic events. We train a CNN on hand-labeled, modestly sized, multi-channel microseismic data from a coal mine. We make the labeled data available as part of this paper. Using a k-fold cross validation approach, we demonstrate that the CNN surpasses the accuracy of a human microseismic expert, both in picking more true events and in eliminating more spurious (false) events. This demonstrates the feasibility of including a CNN in an automated system to detect, classify and locate microseismic events, for use in mine safety and operation.