Reviewerly/exHarmony huggingface dataset:
Research on Artificial Intelligence Customer Service on Consumer Attitude and Its Impact during Online Shopping

Abstract Currently, AI customer service is gaining popularity at a high rate, with over 92% of online shoppers having experience with AI customer service. The question is: what attitudes do consumers have towards AI customer service? A randomly selected sample of 670 consumers was surveyed and the study found that 71.5% of consumers accept or at least do not resist AI customer service. The main reasons AI customer service is so popular are that it is fulltime responsive, absolutely neutral, more objective, and represents a future trend. Nevertheless, 28.5% of consumers are still resistant to AI chatbot, mainly because they are not as relevant, effective, and smooth as a human customer service agent. Besides, there are prevalent obstacles to seamlessly bridge AI chatbots with human agents. In terms of explicitly specified AI chatbots versus concealed AI chatbots, merchants should have them specified since consumers have strong antipathy with AI chatbots disguising as human agents. Furthermore, there are differences in attitudes towards AI customer service across age and educational background. Accordingly, we recommend that, in the context of the overall improvement of service quality, to slow down the pace of AI replacing human agents, to do so in a step-by-step and orderly manner, and to give consumers adequate choice for human agents versus AI chatbots. Finally, to fully promote AI customer service, it is an essential measure to increase its media coverage and publicity.
Achieve Thumbnail Preserving Encryption by Sum Preserving Approaches for Images

A large number of images are generated and uploaded to the cloud, due to the rapid development of IT and networking technologies. To prevent unauthorized access, image encryption is a way that can ensure the safety of images. However, the encrypted image is noisy, which is not suitable for further management and application. Some scholars have recently proposed the concept of Thumbnail Preserving Encryption (TPE). The encrypted image not only achieves privacy protection, but also preserves the properties of thumbnails. Therefore, when an image is encrypted by TPE, it is possible for the thumbnail image to retain similar appearance after encryption, allowing a quick overview of the encrypted image. In this paper, we propose a new TPE method to resolve the problems that may arise in methods used by cryptography scholars such as Tajik. Experimental results show that our TPE method retained the same thumbnail appearance before and after encryption.
Do the stars align?: Stakeholders and strategies in libraries' curation of an astronomy dataset

Abstract When developing university‐based research data curation services, libraries face critical decisions around organization and sustainability that can affect dataset producers' satisfaction with these services. We present a study, involving interviews ( n = 43) and ethnographic observation, of how two libraries partnered with the Sloan Digital Sky Survey (SDSS) to curate a significant astronomy dataset. Each library took different decisions: one library assigned activities to a unit specializing in digital curation, while the other distributed activities across its existing units. Neither approach proved a silver bullet. While library staff members felt the outcomes largely met their expectations, SDSS leaders expressed mixed opinions. We identify three factors that contributed to these differences in perspective: differing strategic motivations for undertaking this Data Transfer Process, SDSS leaders' misperceptions about libraries, and organizational mismatches. These factors contributed to four differences in perspective between SDSS leaders and library staff: provenance as technical information or as information about social context, dataset as a live research object or as a static object to be preserved, systems and services tailored to the dataset or easily adaptable to other datasets, and obstacles as setbacks or as opportunities. Only those differences that emerged when SDSS collaboration members and library staff communicated frequently were resolved.
Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior

Open navigation online learning systems allow learners to choose the next learning activity. These systems can be instrumented to provide learners with feedback to help them choose the next learning activity. One type of feedback is providing an estimate of the learner's current skill proficiency. A learner can then choose to skip the remaining learning activities for that skill after achieving proficiency in that skill. In this paper, we investigate whether predicting proficiency and communicating it to learners can save time for learners within a course. We evaluate the accuracy of the BKT based proficiency pre- diction framework for learner's proficiency prediction which considers one attempt per question. We extend the proficiency prediction framework to include multiple attempts at individual questions and show that it is more accurate in proficiency prediction than BKT based proficiency prediction framework. We discuss the potential implications of attempt enhanced framework on the learners' behavior for open navigation on- line learning systems.
Few-shot Image Recognition for UAV Sports Cinematography

The goal of few-shot image learning is to utilize a very small amount of training examples in order to train a machine learning model to recognize a given number of image classes. While humans can perform such a task pretty much effortlessly, applying the same mechanism to deep learning visual recognition systems is a much more difficult task, having a wide range of real-world visual recognition applications. In this paper, we investigate the behavior of such few-shot methods in the context of drone vision cinematography for sports event filming, in order to recognize new image classes by taking into consideration the fact that this new class we wish to identify is a subclass of an already known class. More specifically we use UAV footage to recognize certain types of athletes, belonging to a subset of an original athlete class, utilizing only a handful of recorded images of this athlete subclass. We examine the effects of such methods on image recognition accuracy while proposing a novel approach for accuracy optimizations. The overall task is evaluated on actual cycling race UAV footage.
Standard Deviation Based Adaptive Gradient Compression For Distributed Deep Learning

Distributed deep learning has been proposed to train large scale neural network models with huge amounts of datasets using multiple workers. Since workers need to frequently communicate with each other to exchange gradients for updating parameters, communication overhead is always a major challenge in distributed deep learning. To cope with this challenge, gradient compression has been used to reduce the amount of data to be exchanged. However, existing compression methods, including both gradient quantization and gradient sparsification, either hurt model performance significantly or suffer from inefficient compression. In this paper, we propose a novel approach, called Standard Deviation based Adaptive Gradient Compression (SDAGC), which can simultaneously achieve model training with low communication overhead and high model performance in synchronous training. SDAGC uses the standard deviation of gradients in each layer of the neural network to dynamically calculate a suitable threshold according to the training process. Moreover, several associated methods, including residual gradient accumulation, local gradient clipping, adaptive learning rate revision and momentum compensation, are integrated to guarantee the convergence of the model. We verify the performance of SDAGC on various machine learning tasks: image classification, language modeling and speech recognition. The experiment results show that, compared with other existing works, SDAGC can achieve a gradient compression ratio from 433× to 2021× with similar or even better accuracy.
Representing Structural Language Features in Formal Meta-languages

Structural language features are those that introduce new kinds of declarations as opposed to those that only add expressions. They pose a significant challenge when representing languages in meta-languages such as standard formats like OMDoc or logical frameworks like LF. It is desirable to use shallow representations where a structural language feature is represented by the analogous feature of the meta-language, but the richness of structural language features in practical languages makes this difficult. Therefore, the current state of the art is to encode unrepresentable structural language features in terms of more elementary ones, but that makes the representations difficult to reuse and verify. This challenge is exacerbated by the fact that many languages allow users to add new structural language features that are elaborated into a small trusted kernel, which allows for a large and growing set of features. In this paper we extend the Mmt representation framework with a generic concept of structural features. This allows defining exactly the language features needed for elegant shallow embeddings of object languages. The key achievement here is to make this concept expressive enough to cover complex practical features while retaining the simplicity of existing meta-languages. We exemplify our framework with representations of various important structural features including datatype definitions and theory instantiations.
Large Universe CCA2 CP-ABE With Equality and Validity Test in the Standard Model

Abstract Attribute-based encryption with equality test (ABEET) simultaneously supports fine-grained access control on the encrypted data and plaintext message equality comparison without decrypting the ciphertexts. Recently, there have been several literatures about ABEET proposed. Nevertheless, most of them explore the ABEET schemes in the random oracle model, which has been pointed out to have many defects in practicality. The only existing ABEET scheme in the standard model, proposed by Wang et al., merely achieves the indistinguishable against chosen-plaintext attack security. Considering the aforementioned problems, in this paper, we propose the first direct adaptive chosen-ciphertext security ciphertext-policy ABEET scheme in the standard model. Our method only adopts a chameleon hash function and adds one dummy attribute to the access structure. Compared with the previous works, our scheme achieves the security improvement, ciphertext validity check and large universe. Besides, we further optimize our scheme to support the outsourced decryption. Finally, we first give the detailed theoretical analysis of our constructions in computation and storage costs, then we implement our constructions and carry out a series of experiments. Both results indicate that our constructions are more efficient in Setup and Trapdoor and have the shorter public parameters than the existing ABEET ones do.
Enhancement Framework of Semantic Query Expansion Using Mapped Ontology

In the present information age, providing correct information to the query with a limited concept is a challenging task. The modern adaptive Information Retrieval (IR) system is needed to provide valid information. Ontology-based on IR can provide a better solution. Ontology is a perception of shared conceptualization, represented by classes, properties, and objects. Ontology mapping offers a solution to integrate inter-domain knowledge. This paper presents a method for using ontologies to handle inter-domain query in information retrieval. Using ontology in IR for Query Expansion (QE) and document ranking seems to be the ultimate goal. The Multi-Domain Specific Ontology Mapping (MDOM) proposes the concepts derived from ontology for query expansion and information retrieval. The result shows the improvement in terms of better document ranking.
Optimal Learning with Local Nonlinear Parametric Models over Continuous Designs

We consider the problem of optimizing an unknown function over a multidimensional continuous domain, where function evaluation is noisy and expensive. We assume that a globally accurate model of the function is not available, but there exist some parametric models that can well approximate the function in local regions. In this paper, we propose an algorithm in the optimal learning framework that learns the shape of the function and finds the optimal design with a limited number of measurements. We construct belief functions using a radial basis function--based local approximation technique, and use the knowledge gradient policy to decide where to measure, aiming at maximizing the value of information from each measurement. Experiments on both synthetic test problems and a real materials science application show the strong performance of our algorithm.
Image recognition technology based on cloud computing platform

The rapid development of computers makes people’s production and life rich and colorful, and people communicate with each other in the world of the Internet. The daily downloads and uploads of network pictures are countless. The existing image recognition technology alone cannot meet the currently required functions, so technology is needed to meet the retrieval requirements. The purpose of this paper is to study the image recognition technology based on the computer platform. This paper takes vehicle image recognition as an example. By performing a deblurring operation on the vehicle image, the edge detection method is used to separate the target vehicle image from the background, and the image is binary. Processing. Based on different eigenvalue categories, intelligent recognition of vehicle models is achieved through Bayesian classifiers. Collect experimental data through simulation experiments. Experimental data shows that after a certain number of nodes, the recognition efficiency is higher than the image recognition technology running on a stand-alone platform. The experimental data show that the image recognition technology based on a cloud computing platform is conducive to the development of image recognition technology. It can quickly solve the problems of traditional image detection systems in terms of computing efficiency and data processing ability, and has guiding significance for the development of image recognition technology.
The Barcan formulas and necessary existence: the view from Quarc

Abstract The Modal Predicate Calculus gives rise to issues surrounding the Barcan formulas, their converses, and necessary existence. I examine these issues by means of the Quantified Argument Calculus (Quarc), a recently developed, powerful formal logic system. Quarc is closer in syntax and logical properties to Natural Language than is the Predicate Calculus, a fact that lends additional interest to this examination, as Quarc might offer a better representation of our modal concepts. The validity of the Barcan formulas and their converses is shown by Quarc to be a result of the specific incorporation of quantification in the Predicate Calculus, and not as reflecting a feature of the interaction of quantification and modality more generally. Necessary existence is shown to follow from the identification, in the Predicate Calculus on its canonical interpretation, of particular quantification, ascription of existence and the ‘there is’ construction, three constructions which are distinguished in both Quarc and Natural Language. The issues surrounding the Barcan formulas, their converses and necessary existence are thus shown to be an artefact of a specific logic system, not an essential feature of our relevant modal concepts or of formal logic.
Group-based Personalization Using Topical User Profile

Although user profiles are indicative of the user's interests, they can be incomplete to reflect all the user's interests and in more times it is needed to use a group of personalized user profiles to re-rank the returned results by search engines. One of the disadvantages of the personalization based on the user profile is that it is built by considering only the documents that the user has clicked. The set of the clicked documents might be sparse for some users. Data sparsity can be resolved by backing off to the group of users with similar behavior to the user. In this paper, we present a group-based personalization model using topical user-profiles and compare the result of the proposed ranking methods based on the group and user profiles. To cluster the groups of users we use the Kmeans clustering algorithm and the similarity between users is measured by symmetric Kullback-Leibler divergence between their latent topic distributions. Using the proposed group-based personalization model, we can improve the ranking result using group-based profiles and solve the cold start problem of users without history. In the issues related to privacy concerns, group profiles are also more secure than user profiles because both the computation and the storage of the user information are done as a group of users. The result reveals that the group-based personalization using topical user profile improves the Mean Reciprocal Rank and the Normalized Discounted Cumulative Gain by 7% and 6% respectively in all short, long and session term profiles while the short term user profile obtains more effective than the others profiles.
A new methodological approach (QEMSCAN<sup>®</sup>) in the mineralogical study of Polish loess: Guidelines for further research

Abstract This article presents in detail the methodology dedicated strictly to loess mineralogical investigation by automated mineralogy system QEMSCAN ® (quantitative evaluation of minerals by scanning electron microscopy (SEM)), which couples SEM and energy dispersive X-ray spectrometry to automatically deliver mineral and phase mapping. The present study provides guidelines for further loess investigation in Poland, in order to maintain the complete comparability of results which will be obtained. The methodology is then used to obtain the data on complex mineralogical composition (heavy, light, transparent and opaque phases). In total 1,159,107 particles have been measured for five bulk loess samples and 4–6% of them were heavy minerals (c.a. 10,000 per sample). The bulk samples are dominated by quartz (57.3–62.9%) and contain plagioclase (7.8–9.2%), K-feldspar (7.9–8.7%), carbonates (5.0–7.8%), muscovite (3.2–6.2%), biotite (4.2–7.5%), heavy minerals (4.3–5.8%) and clay minerals (0.9–1.6%). The heavy minerals (as a group recalculated to 100%) are mainly represented by phases such as clinopyroxene (38–51%), garnets (14–21%), TiO 2 polymorphs (8–12%), Al2SiO5 polymorphs (3–7%), ilmenite (3–6%), iron oxides, e.g., hematite and magnetite (2–5%) and zircon (∼2%). Nearly 50% of the heavy minerals is classified in the 16–31 µm fraction, which determine the changes in the current research procedure traditionally used for Polish loess.
A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory

As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by wearable sensors, and a fine-grained evidence reasoning approach has been proposed to produce a timely and reliable result. First, the basic time unit of input data is selected by finding a tradeoff between accuracy and time cost. Then, the approach uses Long Short Term Memory to extract features and project raw multidimensional data into probability assignments, followed by trainable evidence combination and inference network that reduce uncertainly to improve the classification accuracy. Experiments validate the effectiveness of fine granularity and evidence reasoning while the final results indicate that the recognition accuracy of this approach can reach 96.4% with no additional complexity in training.
EnResNet: ResNets Ensemble via the Feynman--Kac Formalism for Adversarial Defense and Beyond

Empirical adversarial risk minimization is a widely used mathematical framework to robustly train deep neural nets that are resistant to adversarial attacks. However, both natural and robust accuracies, in classifying clean and adversarial images, respectively, of the trained robust models are far from satisfactory. In this work, we unify the theory of optimal control of transport equations with the practice of training and testing of ResNets. Based on this unified viewpoint, we propose a simple yet effective ResNets ensemble algorithm to boost the accuracy of the robustly trained model on both clean and adversarial images. The proposed algorithm consists of two components: First, we modify the base ResNets by injecting a variance-specified Gaussian noise to the output of each residual mapping. Second, we average over the production of multiple jointly trained modified ResNets to get the final prediction. These two steps give an approximation to the Feynman--Kac formula for representing the solution of a transport equation with viscosity, or a convection-diffusion equation. For the CIFAR10 benchmark, this simple algorithm leads to a robust model with a natural accuracy of 85.62% on clean images and a robust accuracy of 57.94% under the 20 iterations of the iterative fast gradient sign method (IFGSM) attack, which outperforms the current state of the art in defending against attack on the CIFAR10. Both natural and robust accuracies of the proposed ResNets ensemble can be improved dynamically as the building block ResNet advances. The code is available at https://github.com/BaoWangMath/EnResNet.
SEEVis: A Smart Emergency Evacuation Plan Visualization System with Data‐Driven Shot Designs

Abstract Despite the significance of tracking human mobility dynamics in a large‐scale earthquake evacuation for an effective first response and disaster relief, the general understanding of evacuation behaviors remains limited. Numerous individual movement trajectories, disaster damages of civil engineering, associated heterogeneous data attributes, as well as complex urban environment all obscure disaster evacuation analysis. Although visualization methods have demonstrated promising performance in emergency evacuation analysis, they cannot effectively identify and deliver the major features like speed or density, as well as the resulting evacuation events like congestion or turn‐back. In this study, we propose a shot design approach to generate customized and narrative animations to track different evacuation features with different exploration purposes of users. Particularly, an intuitive scene feature graph that identifies the most dominating evacuation events is first constructed based on user‐specific regions or their tracking purposes on a certain feature. An optimal camera route, i.e., a storyboard is then calculated based on the previous user‐specific regions or features. For different evacuation events along this route, we employ the corresponding shot design to reveal the underlying feature evolution and its correlation with the environment. Several case studies confirm the efficacy of our system. The feedback from experts and users with different backgrounds suggests that our approach indeed helps them better embrace a comprehensive understanding of the earthquake evacuation.