Reviewerly/exHarmony huggingface dataset:
Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain

Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model’s performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods.
How does serif <i>vs</i> sans serif typeface impact the usability of e-commerce websites?

This study tries to find evidence that points towards the best typeface to use in e-commerce websites to maximize usability, trust, loyalty, appearance and overall user satisfaction. We tested the difference between serif and sans serif inside the same font family. A total of 246 volunteers participating in the experiment were asked to complete a set of tasks and a questionnaire on an e-commerce website prototype. We measured task completion time, reading speed and reading comprehension. From the results, using multiple linear regression, we deduced that only gender determines user preferences. Females tend to prefer the serif version of the typeface under study. Although most e-commerce websites use sans serif typefaces, we could not find evidence supporting this decision. The serif and sans serif characteristic inside the same font family does not affect usability on a website, as it was found that it has no impact on reading speed and user preference.
CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage Assessment

Disaster damage assessment (DDA) has emerged as an important application in disaster response and management, which aims to assess the damage severity of an affected area by leveraging AI (e.g., deep learning) techniques to examine the imagery data posted on social media during a disaster event. In this paper, we focus on a crowd-guided neural architecture searching (NAS) problem in DDA applications. Our goal is to leverage human intelligence from crowdsourcing systems to guide the discovery of the optimal neural network architecture in the design space to achieve the desirable damage assessment performance. Our work is motivated by the limitation that the deep neural network architectures in current DDA solutions are mainly designed by AI experts, which is known to be both time-consuming and error-prone. Two critical technical challenges exist in solving our problem: i) it is challenging to design a manageable NAS space for crowd-based solutions; ii) it is non-trivial to transfer the imperfect crowd knowledge to effective decisions in identifying the optimal neural network architecture of a DDA application. To address the above challenges, we develop CrowdNAS, a crowd-guided NAS framework that develops novel techniques inspired by AI, crowdsourcing, and estimation theory to address the NAS problem. The evaluation results from two real-world DDA applications show that CrowdNAS consistently outperforms the state-of-the-art AI-only, crowd-AI, and NAS baselines by achieving the highest classification accuracy in the damage assessment while maintaining a low computational cost under various evaluation scenarios.
Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

In recent years, knowledge representation in the Artificial Intelligence (AI) domain is able to help people understand the semantics of data and improve the interoperability between diverse knowledge-based applications. Semantic Web (SW), as one of the methods of knowledge representation, is the new generation of World Wide Web (WWW), which integrates AI with web techniques and dedicates to implementing the automatic cooperations among different intelligent applications. Ontology, as an information exchange model that defines concepts and formally describes the relationships between two concepts, is the core technique of SW, implementing semantic information sharing and data interoperability in the Internet of Things (IoT) domain. However, the heterogeneity issue hampers the communications among different ontologies and stops the cooperations among ontology-based intelligent applications. To solve this problem, it is vital to establish semantic relationships between heterogeneous ontologies, which is the so-called ontology matching. Ontology metamatching problem is commonly a complex optimization problem with many local optima. To this end, the ontology metamatching problem is defined as a multiobjective optimization model in this work, and a multiobjective particle swarm optimization (MOPSO) with diversity enhancing (DE) (MOPSO-DE) strategy is proposed to better trade off the convergence and diversity of the population. The well-known benchmark of the Ontology Alignment Evaluation Initiative (OAEI) is used in the experiment to test MOPSO-DE’s performance. Experimental results prove that MOPSO-DE can obtain the high-quality alignment and reduce the MOPSO’s memory consumption.
PSGAN: Revisit the binary discriminator and an alternative for face frontalization

Generative adversarial network (GAN) based face frontalization is a cheap and convenient way to eliminate the impact of pose variance on face recognition. The sigmoid cross-entropy loss function is usually employed for the discriminator in those GAN based face synthesis methods. There are two disadvantages for this loss function: 1) The discriminator always wins the generator easily at the beginning of training because the convergence of the discriminator and the generator is unbalanced; 2) The training of GANs becomes unstable due to the prediction boundary uncertainty and massive parameters of the traditional binary discriminator. In order to eliminate the impacts caused by the traditional discriminator in the general GANs, a Bayesian induced perceptual self-representation discriminator (i.e. PSD) is proposed, which can also maintain the identity information, and simultaneously reduce the model parameters and training difficulty. There are three key contributions in this work: 1) On the basis of PSD, a perceptual self-representation GAN (i.e. PSGAN) with a new architecture is proposed, which reduces the training difficulty without lowering the synthetic quality; 2) In order to further improve the performance of our method, multiple features extracted from different layers are adopted to constitute a multi-perceptual self-representation discriminator (i.e. MPSD); 3) The proposed PSD discriminator is more lightweight with fewer parameters and can also be easily plugged and played in various GANs. Extensive qualitative and quantitative experiments on both restricted and unrestricted face databases and non-facial datasets demonstrate its superiority.
The Impact of Stochastic Perturbations in Physics Variables for Predicting Surface Solar Irradiance

We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m2) than in the other models (22.7–23.6 W/m2). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided.
Marine Extended Target Tracking for Scanning Radar Data Using Correlation Filter and Bayes Filter Jointly

As the radar resolution improves, the extended structure of the targets in radar echoes can make a significant contribution to improving tracking performance, hence specific trackers need to be designed for these targets. However, traditional radar target tracking methods are mainly based on the accumulation of the target’s motion information, and the target’s appearance information is ignored. In this paper, a novel tracking algorithm that exploits both the appearance and motion information of a target is proposed to track a single extended target in maritime surveillance scenarios by incorporating the Bayesian motion state filter and the correlation appearance filter. The proposed algorithm consists of three modules. Firstly, a Bayesian module is utilized to accumulate the motion information of the target. Secondly, a correlation module is performed to capture the appearance features of the target. Finally, a fusion module is proposed to integrate the results of the former two modules according to the Maximum A Posteriori Criterion. In addition, a feedback structure is proposed to transfer the fusion results back to the former two modules to improve their stability. Besides, a scale adaptive strategy is presented to improve the tracker’s ability to cope with targets with varying shapes. In the end, the effectiveness of the proposed method is verified by measured radar data. The experimental results demonstrate that the proposed method achieves superior performance compared with other traditional algorithms, which simply focus on the target’s motion information. Moreover, this method is robust under complicated scenarios, such as clutter interference, target shape changing, and low signal-to-noise ratio (SNR).
Resource-based theory perspective in the textile industry: The impact of the digital supply chain on operational performance

In a developing nation such as Pakistan, the textile sector is crucial for employment creation and poverty reduction. As a result of digitization, standard industrial operating processes have altered. The study, which is built on resource-based theory, seeks to determine how the digital supply chain (comprised of blockchain, the internet of things, and big data analytics) influences operational performance. In addition, the relationship between the innovative climate and the operational success of the digital supply chain has been analyzed. Data was collected from 270 respondents, i.e., 65 top level managers and 205 middle level managers. The results indicate that the digitization of the supply chain, which includes the application of big data analytics, the internet of things, and blockchain, greatly enhances operational performance. The innovation climate has not been proven to affect operational performance or digitalization. At the end theoretical and practical implication are highlighted.
A Mixed Visual Encoding Model Based on the Larger-Scale Receptive Field for Human Brain Activity

Research on visual encoding models for functional magnetic resonance imaging derived from deep neural networks, especially CNN (e.g., VGG16), has been developed. However, CNNs typically use smaller kernel sizes (e.g., 3 × 3) for feature extraction in visual encoding models. Although the receptive field size of CNN can be enlarged by increasing the network depth or subsampling, it is limited by the small size of the convolution kernel, leading to an insufficient receptive field size. In biological research, the size of the neuronal population receptive field of high-level visual encoding regions is usually three to four times that of low-level visual encoding regions. Thus, CNNs with a larger receptive field size align with the biological findings. The RepLKNet model directly expands the convolution kernel size to obtain a larger-scale receptive field. Therefore, this paper proposes a mixed model to replace CNN for feature extraction in visual encoding models. The proposed model mixes RepLKNet and VGG so that the mixed model has a receptive field of different sizes to extract more feature information from the image. The experimental results indicate that the mixed model achieves better encoding performance in multiple regions of the visual cortex than the traditional convolutional model. Also, a larger-scale receptive field should be considered in building visual encoding models so that the convolution network can play a more significant role in visual representations.
A new genetic algorithm method based on statistical-based replacement for the training of multiplicative neuron model artificial neural networks

Abstract In this study, we propose a new genetic algorithm that uses a statistical-based chromosome replacement strategy determined by the empirical distribution of the objective function values. The proposed genetic algorithm is further used in the training process of a multiplicative neuron model artificial neural network. The objective function value for the genetic algorithm is the root mean square error of the multiplicative neuron model artificial neural network prediction. This combination of methods is proposed for a particular type of problems, that is, time-series prediction. We use different subsets of three stock exchange time series to test the performance of the proposed method and compare it against similar approaches, and the results prove that the proposed genetic algorithm for the multiplicative neuron model of the artificial neural network works better than many other artificial intelligence optimization methods. The ranks of the proposed method are 1.78 for the Nikkei data sets, 1.55 for the S&amp;P500 data sets and 1.22 for the DOW JONES data sets for data corresponding to different years, according to the root mean square error, respectively. Moreover, the overall mean rank is 1.50 for the proposed method. Also, the proposed method obtains the best performance overall as well as the best performance for all the individual tests. The results certify that our method is robust and efficient for the task investigated.
WTASR: Wavelet Transformer for Automatic Speech Recognition of Indian Languages

Automatic speech recognition systems are developed for translating the speech signals into the corresponding text representation. This translation is used in a variety of applications like voice enabled commands, assistive devices and bots, etc. There is a significant lack of efficient technology for Indian languages. In this paper, an wavelet transformer for automatic speech recognition (WTASR) of Indian language is proposed. The speech signals suffer from the problem of high and low frequency over different times due to variation in speech of the speaker. Thus, wavelets enable the network to analyze the signal in multiscale. The wavelet decomposition of the signal is fed in the network for generating the text. The transformer network comprises an encoder decoder system for speech translation. The model is trained on Indian language dataset for translation of speech into corresponding text. The proposed method is compared with other state of the art methods. The results show that the proposed WTASR has a low word error rate and can be used for effective speech recognition for Indian language.
Expressiveness of real-time motion captured avatars influences perceived animation realism and perceived quality of social interaction in virtual reality

Using motion capture to enhance the realism of social interaction in virtual reality (VR) is growing in popularity. However, the impact of different levels of avatar expressiveness on the user experience is not well understood. In the present study we manipulated levels of face and body expressiveness of avatars while investigating participant perceptions of animation realism and interaction quality when disclosing positive and negative experiences in VR. Moderate positive associations were observed between perceptions of animation realism and interaction quality. Post-experiment questions revealed that many of our participants (approximately 40%) indicated the avatar with the highest face and body expressiveness as having the most realistic face and body expressions. The same proportion also indicated the avatar with the highest face and body expressiveness as being the most comforting and enjoyable avatar to interact with. Our results suggest that higher levels of face and body expressiveness are important for enhancing perceptions of realism and interaction quality within a social interaction in VR using motion capture.
Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features

This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on the architecture of the various internal processing modules of the ASR System. The speaker recognition system proposed in the article, has been compared very closely to other competing systems, achieving improved speaker identification and verification results, on known certified voice dataset. The ASR System owes this to the dual use of genetic algorithms both in the feature selection process and in the optimization of the system’s internal parameters. This was also influenced by the proprietary feature generation and corresponding classification process using Gaussian mixture models. This allowed the development of a system that makes an important contribution to the current state of the art in speaker recognition systems for telephone transmission applications with known speech coding standards.
Online Booking Services Assisted by Technology to Improve Customer Loyalty in Jordanian Five-Star Hotels

Purpose: This study's goal was to examine how customer satisfaction at Jordanian five-star hotels served as a mediator between the effects of website quality and e-trust on customer loyalty. Design/methodology/approach: Questionnaires were the primary method used in the study for gathering data. The survey was created using data from earlier research and given to visitors staying at several five-star hotels in Amman, the country's capital. A convenience sample of 401 people received questionnaires from the researchers. The valid data was examined using Structural Equation Modeling (SEM) and SPSS V.25 (AMOS V.20). Findings: The study found a favorable relationship between website quality and customer loyalty as well as a positive correlation between e-trust and consumer satisfaction. Additionally, there is a direct correlation between customer satisfaction and loyalty. Research implications: Hotels should take steps to gain the public's trust if they want their websites to be regularly preferred by visitors. The website should also show a relationship between its target audience and the information and images about the hotel by consistently updating both. It has been demonstrated that users of online hotel booking websites exhibit higher levels of trust in websites. When a customer sees what to expect when staying at the hotel, their sense of risk reduces. Originality/value: In order to research the marketing function of hotel services, which today heavily rely on online hotel reservations, this study is a ground-breaking endeavor to improve the worldwide tourism industry. The results should spur Jordanian hotels to research the factors that influence customer loyalty for online reservations.
Tuning block-parallel all-pairs shortest path algorithm for efficient multi-core implementa- tion.

Finding shortest paths in a weighted graph is one of the key problems in computer-science, which has numerous practical applications in multiple domains. This paper analyzes the parallel blocked all-pairs shortest path algorithm at the aim of evaluating the influence of the multi-core system and its hierarchical cache memory on the parameters of algorithm implementation depending on the size of the graph and the size of distance matrix’s block. It proposes a technique of tuning the block-size to the given multi-core system. The technique involves profiling tools in the tuning process and allows the increase of the parallel algorithm throughput. Computational experiments carried out on a rack server equipped with two Intel Xeon E5-2620 v4 processors of 8 cores and 16 hardware threads each have convincingly shown for various graph sizes that the behavior and parameters of the hierarchical cache memory operation don’t depend on the graph size and are determined only by the distance matrix’s block size. To tune the algorithm to the target multi-core system, the preferable block size can be found once for the graph size whose in-memory matrix representation is larger than the size of cache shared among all processor’s cores. Then this blocksize can be reused on graphs of bigger size for efficient solving the all-pairs shortest path problem