Reviewerly/exHarmony huggingface dataset:
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis

In this work, we present the SOMOS dataset, the first large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples. It can be employed to train automatic MOS prediction systems focused on the assessment of modern synthesizers, and can stimulate advancements in acoustic model evaluation. It consists of 20K synthetic utterances of the LJ Speech voice, a public domain speech dataset which is a common benchmark for building neural acoustic models and vocoders. Utterances are generated from 200 TTS systems including vanilla neural acoustic models as well as models which allow prosodic variations. An LPCNet vocoder is used for all systems, so that the samples' variation depends only on the acoustic models. The synthesized utterances provide balanced and adequate domain and length coverage. We collect MOS naturalness evaluations on 3 English Amazon Mechanical Turk locales and share practices leading to reliable crowdsourced annotations for this task. We provide baseline results of state-of-the-art MOS prediction models on the SOMOS dataset and show the limitations that such models face when assigned to evaluate TTS utterances.
Artificial intelligence (AI)-driven services

This chapter aims to give a brief introduction to how artificial intelligence (AI) is utilized to drive service. As an emerging technology, artificial intelligence has become a game-changer in many industries. In the service industry, it provides personalized service by tracking customers’ personal information and purchase behavior. Although previous studies focused on AI from different perspectives, the way of AI driving services is not clear. Thus, this chapter wants to fulfill the research gap. This chapter divided AI based on how it is utilized for service, including communication support, assistance for decision-making, and enhanced customer experience service. Examples and explanations were given for each type of AI. Based on the literature review, the authors also propose suggestions and guidelines for the future development of AI in the service industry. This chapter has both practical implications and theoretical implications. In practical view, this chapter gave marketers information and future development tips that may help them to create strategies focusing on AI. Besides, as a summary of the previous literature, scholars can get direct information from this chapter and also be inspired by it for their future research.
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction

Magnetic resonance imaging (MRI) with high resolution (HR) provides more detailed information for accurate diagnosis and quantitative image analysis. Despite the significant advances, most existing super-resolution (SR) reconstruction network for medical images has two flaws: 1) All of them are designed in a black-box principle, thus lacking sufficient interpretability and further limiting their practical applications. Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images. 2) most existing SR reconstruction approaches only use a single contrast or use a simple multi-contrast fusion mechanism, neglecting the complex relationships between different contrasts that are critical for SR improvement. To deal with these issues, in this paper, a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction is proposed. The Model-Guided image SR reconstruction approach solves manually designed objective functions to reconstruct HR MRI. We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix and explicit multi-contrast relationship matrix into account during the end-to-end optimization. Extensive experiments on the multi-contrast IXI dataset and BraTs 2019 dataset demonstrate the superiority of our proposed model.
An Effective Sharding Consensus Algorithm for Blockchain Systems

Sharding is the widely used approach to the trilemma of simultaneously achieving decentralization, security, and scalability in traditional blockchain systems. However, existing schemes generally involve problems such as uneven shard arithmetic power and insecure cross-shard transaction processing. In this study, we used the Practical Byzantine Fault Tolerance (PBFT) as the intra-shard consensus and, here, we propose a new sharding consensus mechanism. Firstly, we combined a jump consistent hash algorithm with signature Anchorhash to minimize the mapping of the node assignment. Then, we improved the process of the cross-shard transaction and used the activity of nodes participating in intra-shard transactions as the criterion for the shard reconfiguration, which ensured the security of the blockchain system. Meanwhile, we analyzed the motivation mechanism from two perspectives. Finally, through theoretical analysis and related experiments, we not only verified that the algorithm can ensure the security of the entire system, but also further clarified the necessary conditions to ensure the effectiveness of the shards and the system on the original basis.
Cloud Nowcasting with Structure-Preserving Convolutional Gated Recurrent Units

Nowcasting of clouds is a challenging spatiotemporal task due to the dynamic nature of the atmosphere. In this study, the use of convolutional gated recurrent unit networks (ConvGRUs) to produce short-term cloudiness forecasts for the next 3 h over Europe is proposed, along with an optimisation criterion able to preserve image structure across the predicted sequences. This approach is compared against state-of-the-art optical flow algorithms using over two and a half years of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation satellite. We show that the ConvGRU trained using our structure-preserving loss function significantly outperforms the optical flow algorithms with an average change in R2, mean absolute error and structural similarity of 12.43%, −8.75% and 9.68%, respectively, across all time steps. We also confirm that merging multiple optical flow algorithms into an ensemble yields significant short-term performance increases (&lt;1 h), and that nowcast skill can vary significantly across different European regions. Furthermore, our results show that blurry images resulting from using globally oriented loss functions can be avoided by optimising for structural similarity when producing nowcasts. We thus showcase that deep-learning-based models using locally oriented loss functions present a powerful new way to produce accurate cloud nowcasts, with important applications to be found in solar power forecasting.
Criminal Behavior Identification Using Social Media Forensics

Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.
Temperature Prediction Model for a Regenerative Aluminum Smelting Furnace by a Just-in-Time Learning-Based Triple-Weighted Regularized Extreme Learning Machine

In a regenerative aluminum smelting furnace, real-time liquid aluminum temperature measurements are essential for process control. However, it is often very expensive to achieve accurate temperature measurements. To address this issue, a just-in-time learning-based triple-weighted regularized extreme learning machine (JITL-TWRELM) soft sensor modeling method is proposed for liquid aluminum temperature prediction. In this method, a weighted JITL method (WJITL) is adopted for updating the online local models to deal with the process time-varying problem. Moreover, a regularized extreme learning machine model considering both the sample similarities and the variable correlations was established as the local modeling method. The effectiveness of the proposed method is demonstrated in an industrial aluminum smelting process. The results show that the proposed method can meet the requirements of prediction accuracy of the regenerative aluminum smelting furnace.
A dense light field reconstruction algorithm for four-dimensional optical flow constraint equation

Dense light field sampling is an important basis for refocusing, depth estimation and 3-D imaging. It is difficult to obtain high resolution dense light field with a large-scale camera array and expensive equipment. At the same time, the current storage devices and transmission bandwidth also limit this technology's post-processing and application. In order to effectively reconstruct the angle domain of the light field based on the sparse light field data, this paper analyzes the correlation and constraint relationship between the optical flow field and the motion field of multi-view images in the same scene, extends the traditional optical flow constraint equation of two-dimensional imaging to the optical flow constraint equation of four-dimensional light field, and establishes an effective mathematical model. The coordinate position of the original pixel in the new angle image is determined by coordinate search, and its intensity is reconstructed. The experimental results of multi-scene dense reconstruction show that the proposed method can reconstruct the texture, shadow and color information in the light field of a long-baseline scene with high quality. The quantitative evaluation results show that the algorithm can be applied to dense light field reconstruction of complex scenes. The algorithm in this paper is only suitable for the case of optical flow constraint in a linear light field, and the follow-up research will focus on the case of nonlinear optical flow constraint.
DOMFN: A Divergence-Orientated Multi-Modal Fusion Network for Resume Assessment

In talent management, resume assessment aims to analyze the quality of a job seeker's resume, which can assist recruiters to discover suitable candidates and benefit job seekers improving resume quality in return. Recent machine learning based methods on large-scale public resume datasets have provided the opportunity for automatic assessment for reducing manual costs. However, most existing approaches are still content-dominated and ignore other valuable information. Inspired by practical resume evaluations that consider both the content and layout, we construct the multi-modalities from resumes but face a new challenge that sometimes the performance of multi-modal fusion is even worse than the best uni-modality. In this paper, we experimentally find that this phenomenon is due to the cross-modal divergence. Therefore, we need to consider when is it appropriate to perform multi-modal fusion? To address this problem, we design an instance-aware fusion method, i.e., Divergence-Orientated Multi-Modal Fusion Network (DOMFN), which can adaptively fuse the uni-modal predictions and multi-modal prediction based on cross-modal divergence. Specifically, DOMFN computes a functional penalty score to measure the divergence of cross-modal predictions. Then, the learned divergence can be used to decide whether to conduct multi-modal fusion and be adopted into an amended loss for reliable training. Consequently, DOMFN rejects multi-modal prediction when the cross-modal divergence is too large, avoiding the overall performance degradation, so as to achieve better performance than uni-modalities. In experiments, qualitative comparison with baselines on real-world dataset demonstrates the superiority and explainability of the proposed DOMFN, e.g., we find a meaningful phenomenon that multi-modal fusion has positive effects for assessing resumes from UI Designer and Enterprise Service positions, whereas affects the assessment of Technology and Product Operation positions.
Smart Home-Based Complex Interwoven Activities for Cognitive Health Assessment

With the prevalence of cognitive diseases, the health industry is facing newer challenges since cognitive health deteriorates gradually over time, and clear signs and symptoms appear when it is too late. Smart homes and the IoT (Internet of Things) have given hope to the health industry to monitor and manage the elderly and the less-abled in the comfort of their homes. Smart homes have been most influential in detecting and managing cognitive diseases like dementia. They can give a comprehensive view of the ADL (Activities of Daily Living) of dementia patients. ADLs are categorized as activities of daily life and complex interwoven activities. First signs of cognitive decline appear when a cognitively impaired individual tries to perform complex activities involving planning, analyzing, calculating, and decision making. Therefore, we analyze individuals’ performance while performing complex activities as opposed to Simple ADL. Artificial Intelligence has been one of health-care’s most promising techniques for prediction and diagnosis. When applied to ADL data, machine learning and deep learning algorithms can conveniently and accurately analyze activity patterns and predict the first signs of cognitive decline. Our proposed work uses machine and deep learning classifiers to classify dementia and healthy individuals by analyzing complex interwoven activity data. We use the subset of the CASAS (Centre of Advanced Studies in Adaptive Systems) dataset for eight complex activities performed by 179 individuals in a smart home setting. decision tree, Naive Bayes, support vector, multilayer perceptron classifiers, and deep neural networks have been used for classification. Their results and performances are compared to determine the best classifier. It is observed that deep neural networks and multilayer perceptron show the best results for classifying dementia vs. healthy individuals when evaluating their complex interwoven activities.
Are the Instructions Clear? Evaluating the Visual Characteristics of Augmented Reality Content for Remote Guidance

Augmented Reality (AR) solutions are emerging in multiple scenarios of application as Industry 4.0 takes shape. In particular, for remote collaboration, flexible mechanisms such as authoring tools can be used to generate instructions and assist human operators as they experience increased complexity in their daily tasks. In addition to the traditional handicap of ensuring instructions can be intuitively created without having to understand complicated AR concepts, another relevant topic is the fact that the quality of said instructions is not properly analyzed prior to the tools being evaluated. This means that the characteristics of the visual content are not adequately assessed beforehand. Hence, it is essential to be aware of the cognitive workload associated with AR instructions to assert if they can be easily understood and accepted before being deployed in real-world scenarios. To address this, we focused on AR during sessions of remote guidance. Based on a participatory process with domain experts from the industry sector, a prototype for creating AR-based instructions was developed, and a user study with two parts was conducted: (1) first, a set of step-by-step instructions was produced, and their visual characteristics were evaluated by 129 participants based on a set of relevant dimensions; (2) afterward, these instructions were used by nine participants to understand if they could be used to assist on-site collaborators during real-life remote maintenance tasks. The results suggest that the AR instructions offer low visual complexity and considerable visual impact, clarity, and directed focus, thus improving situational understanding and promoting task resolution.
New Cloaking Region Obfuscation for Road Network-Indistinguishability and Location Privacy

The development of location-based services (LBS) leads to the rapid growth of location data, potentially increasing the threat to location privacy. Existing location obfuscation techniques focus on two-dimensional (2D) planar areas and overlook the features of road networks. In this paper, we leverage differential privacy and propose a new notion of Road Network-Indistinguishability (RN-Indistinguishability) to measure the indistinguishability of locations in road networks. With the RN-Indistinguishability, we design a Cloaking Region Obfuscation (CRO) mechanism to protect the location privacy of vehicles on roads. With the CRO mechanism, vehicle locations in a cloaking region are obfuscated following the same obfuscation distribution. The proposed CRO mechanism is proved to achieve RN-Indistinguishability and can be generalized with road network features holding the triangle inequality. Comprehensive experiments show that the CRO mechanism outperforms existing 2D obfuscation mechanisms in real-world road networks.
A new Copy-Move forgery detection method using LIOP

• An approach is proposed for detecting copy-move forgery in the image. • As a feature, a combination of LIOP descriptors extracted from each color channel in the YCbCr color space is used. • The parallelism condition was used with random sample consensus to reduce the false detection rate. • CoMoFoD and GRIP datasets were used in the experiments. • The proposed CMFD method displayed robustness and was successful against post-processing attacks. The most prevalent type of digital image falsification occurs when a portion of a image is copied and pasted onto another section of the same image. Falsification of the image made in this way is called copy-move forgery (CMF). This study presents a new and effective approach for copy-move forgery detection (CMFD) using the Local Intensity Order Pattern (LIOP) to overcome the restrictions of existing CMFD techniques. The input image is first converted to a YCbCr color space and then split into Y, Cb, and Cr color channels. The LIOP features are then extracted from each color channel and all the features are combined. The feature vectors are ordered lexicographically and related features are detected by comparing the LIOP features. Although the LIOP feature has rarely been used in CMFD prior to this study, the success rate of the proposed method is high. In addition, since the channels are not correlated to each other in the YCbCr color space, each color channel is considered as a gray image, and the success rate is increased by combining the features extracted from each of the color channels. The proposed approach was assessed using the CoMoFoD and GRIP datasets. Experimental findings demonstrated that the suggested method was successful and displayed robustness in post-processing attacks.
Interpretable hierarchical symbolic regression for safety-critical systems with an application to highway crash prediction

We introduce a framework to discover interpretable regression models for high-stakes decision making in the context of safety-critical systems. The core of our proposal is a multi-objective hierarchical symbolic regression algorithm able to compute cluster-specific rankings of regression models ordered by increasing complexity. We discover the hierarchical structure by clustering the features’ importances of a post-hoc explainability framework (viz., SHAP) applied to a highly flexible predictive model (viz., XGBoost). We rely on a symbolic regression algorithm based on the simulated annealing meta-heuristic to infer sparse linear models which may include non-linear effects (e.g., log-transforms, multiplicative interactions...). This search is guided by two objectives: Maximizing predictive performance and minimizing complexity. It ends on a list of Pareto-optimal models that fosters a dynamic interpretative process: the user navigates from the least to the most complex model and decides the ones he can trust depending on whether he understands them, and whether he is satisfied by the quantified uncertainty of their parameters and predictions. Our approach achieves promising results when compared to more than ten other interpretable or black-box predictive models on eleven public regression datasets of various volumes, dimensionalities or domains, and on a proprietary dataset for highway crash prediction. On this last dataset, we demonstrate the usefulness of our new ranking-by-complexity of inherently interpretable models.
Sentinel-1 and ALOS data for lineament extraction: a comparative study

GREENPEG is a H2020 project developing exploration toolsets for critical materials in pegmatites. Lineament mapping is a powerful tool to identify geological structures at different scales, which is commonly applied in mineral exploration. To extract lineament information, different types of data can be employed, namely: (i) optical remote sensing data, (ii) radar remote sensing data, and (iii) digital elevation models (DEMs). All these data have been used by GREENPEG, where image processing of Sentinel-1 and ALOS (DEM) datasets were evaluated to identify regional-scale tectonic structures, such as faults, that may have controlled pegmatite melt emplacement. Automated geomatics methods to extract lineament information and perform lineament mapping were employed and the mean direction of the extracted lineaments was evaluated through rose diagrams. In general, the results of the radar image processing were affected by noise in the coastal areas and by the topography of the region. ALOS data presented some advantages, due to less influence from topography and human structures (saving time in the visual inspection of the results). Moreover, even though higher thresholds for the minimum length to consider as a lineament were applied to Sentinel-1 images, ALOS-derived lineaments were longer. The main trend of the extracted lineaments is in agreement between the two datasets, although specific trends are clearer in the ALOS-derived lineaments because the shaded reliefs produced already filtered the azimuth of lineaments to be extracted according to light incidence angle. For all three project demonstration sites (Leinster, Ireland; Wolfsberg, Austria; and Tysfjord, Norway) it was possible to identify lineaments that may relate to structures that controlled pegmatite emplacement. This study demonstrates the usefulness of different satellite data in structural mapping and mineral exploration.