Reviewerly/exHarmony huggingface dataset:
Image Captioning with Style Using Generative Adversarial Networks

Image captioning research, which initially focused on describing images factually, is currently being developed in the direction of incorporating sentiments or styles to produce natural captions that reflect human-generated captions. The problem this research tries to solve the problem that captions produced by existing models are rigid and unnatural due to the lack of sentiment. The purpose of this research is to design a reliable image captioning model that incorporates style based on state-of-the-art SeqCapsGAN architecture. The materials needed are MS COCO and SentiCaps datasets. Research methods are done through literature studies and experiments. While many previous studies compare their works without considering the differences in components and parameters being used, this research proposes a different approach to find more reliable configurations and provide more detailed insights into modelsâ€™ behavior. This research also does further experiments on the generator part that have not been thoroughly investigated. Experiments are done on the combinations of feature extractor (VGG-19 and ResNet-50), discriminator model (CNN and Capsule), optimizer (Adam, Nadam, and SGD), batch size (8, 16, 32, and 64), and learning rate (0.001 and 0.0001) by doing a grid search. In conclusion, more insights into the modelsâ€™ behavior can be drawn, and better configuration and result than the baseline can be achieved. Our research implies that research in comparative studies of image recognition models in image captioning context, automated metrics, and larger datasets suited for stylized image captioning might be needed for furthering the research in this field.
Using Approximately Coupled Tensor Factorization to Model Changing User Preferences for Movie Recommendations

In movie recommendation systems, users usually tend to change their preferences over time. Some recent studies suggest that modeling the temporal dynamics of user preferences can improve the quality of recommendations. In this paper, we propose a time-dynamic model of user preferences based on approximately coupled tensor factorization. First, we model the user-item interaction information as a tensor and downweight the user’s historical preferences using an individual exponential decay factor. Second, we extract similarity information from the interaction information as auxiliary information to mitigate the cold-start and data sparsity problems. Then, we use approximately coupled tensor factorization to jointly analyze the obtained data to generate the top-K recommendations. We validate the effectiveness of our proposed method on the MovieLens dataset, and the experimental results show that our method performs better than other competitive methods.
Cost-Effective Task Scheduling Strategy in Vehicular Cloud Computing

With the development of vehicle cloud computing technology, vehicle cloud has more extensive application scenarios. It can provide users with computing resources to process requests of mobile devices, so as to improve the defect of weak computing and storage capacity of mobile devices. Due to the distributed resources of vehicle cloud, it is a challenge to schedule workflow tasks with deadline constraint and high cost efficiency. In this paper, we propose a cost-effective and deadline-constrained strategy (CEDCS) to implement task scheduling in vehicular cloud computing. Our strategy firstly proposes an initial task allocation strategy to create a schedule to meet deadline constraint. Then, a task rescheduling algorithm is proposed to achieve lower service cost. Our strategy is compared with existing genetic algorithm (GA) through simulation experiments. Experimental results show that our strategy can achieve higher service success ratio and lower service cost.
Literature in the Digital Space: Features and Problems of Reading

Purpose The aim of the paper is to deal with the problems of changes, which have recently occurred in reading culture, and with a question, how digitalization has influenced a book industry including all processes connected with it. Design/methodology/approach During the research, a descriptive method was used, in which direction Internet resources related to the literature in varying degrees are consistently analyzed. The method of sociological observations, accounting for the results of surveys was also used. Content analysis as a method of bibliometry made it possible to investigate the content of text arrays on the Internet. The elements of the analytical-synthetic method made it possible to identify the features and components of the modern process of literature functioning in digital space in their unity and correlation. All observations are systematized in accordance with the set research objective. Findings Reading act as a cultural phenomenon that is transforming—there is a transition from traditional reading culture to consumption of specific content on the Internet. However, authors of some Internet resources create projects, which are aimed at the development of interest in books and forming a thoughtful reader. Originality/value The process of literature digitalization is rapidly developing. In the majority of cases, a modern reader is oriented on digital content, which meets any readership request. Nevertheless, a decrease in reading culture among mass readers can be noticed. In this regard, they predict a situation, when serious literature becomes elite: open in terms of access, but hidden from many people in terms of understanding.
A User Experience Investigation on Using Augmented Reality Technology for Explaining Step-by-Step Instructions

Since Augmented Reality (AR) technology is able to help young children read storybooks and understand the content more interestingly (Tuli &amp; Mantri, 2019), as well as can help students in learning abstract geometry (Flores-Bascuñana et al., 2019), AR technology has the potential capacity to extend the representation from paper to digital, and to bring ideas from virtual to reality. In order to use AR to import step-by-step information more effectively and to provide a friendly user experience, we use AR as an auxiliary tool for step-by-step instructions to help users receive and understand the content of information in diversified ways. However, the differences between real and virtual environments often affect the user's ability to operate the devices, to read the information, and to understand the content. In order to explore how the import of AR-assisted step-by-step instructions affects users' experience during operation, we conducted a task-based test and evaluated how the viewing angle and the viewing distance have an effect on test-takers' reading of documents. We analyzed the relationship between devices and human behaviors to understand users' experience in operation, this study explores 1) what is the best viewing angle and distance when users are reading documents and operating AR system; and 2) how the relative positional relationship of the AR developing between paper-based step-by-step instructions affect users' operation. The results show that: 1) Although all test-takers have no significant difference in terms of operation of viewing angle (p=0.535) and viewing distance (p=0.489), there are significant differences in terms of time spent by test-takers on completing this task (p=0.048); 2) During this experiment, the operating range of a test-taker's viewing angle is about 70~87 degrees, while the operating range of the viewing distance is about 16~23 cm; and 3) The differences between the relative positional relationship of AR developing block and the step-by-step instructions in text has an impact on the test-taker's operating experience. The AR developing block laid out on the right-hand side of the text box allows users to have greater adaptability in operational performance among other layout arrangements.
The Effects of Conversational Agents’ Emotion Cues on Their Perceived Responsiveness and Consumers’ Intention to Act: An Abstract

Conversational agents allow products and services to be sold through real-time one-on-one conversations. However, consumers tend to engage with humans and resist conversational agents. Further, conversation abandonment without leaving personal information (e.g., email) leaves marketers without the ability to re-engage with potential customers. Drawing on the Social Information Processing theory and Affect-as-Information model, this study investigates how emotion cues: a higher positive tone from the conversational agent and emojis influence (1) the agent’s responsiveness perception, and (2) consumers’ intention to disclose personally-identifying information (email) to the agent retailer. The research uses a computerized text analysis of the chat scenarios and a 2 (low vs higher emotional tone) × 2 (emoji use vs. no emoji use) between-subjects experimental design. This research focuses on the effect of emotional tone and emoji use on the conversational agent’s perceived responsiveness and consumers’ intention to disclose their personal information to the agent in a text-based conversational commerce context. When the conversational agent’s emotional tone was higher, it was perceived as being more responsive. However, the effect of emoji use on the perception of agents’ responsiveness was not significant. Finally, the present research found that the effect of higher emotional tone, emoji use, and the interaction term was significant for consumers’ intention to disclose their email to the agent retailer. This study provides several managerial implications; although emoji use did not result in higher perceived responsiveness, emojis and higher emotional tone can be used to promote email disclosure to the agent retailers and thus enable marketers to collect valuable consumer data. Further, an emotional tone can benefit a conversational website, particularly one in need of incorporating customers’ personal information to provide personalized products. The findings contribute to the growing research stream on integrating emotion cues into conversational agents and their impact on consumers’ perceptions and personal information disclosure.
SPARQL Endpoints and Web API (SWApi)

The success of Semantic Web technology has boosted the publication of Knowledge Graphs in the Web of Data, and several technologies to access them have become available covering different spots in the spectrum of expressivity: from the highly expressive SPARQL to the controlled access of Linked Data APIs, with GraphQL in between. Many of these technologies have reached industry-grade maturity. Finding the trade-offs between them is often difficult in the daily work of developers, interested in quick API deployment and easy data ingestion. This tutorial covers this in-between technology space, with the main goal of providing strategies and tools for publishing Web APIs that ensure the easy consumption of data coming from SPARQL endpoints. Together with an overview of state-of-the-art technologies, the tutorial focuses on two novel technologies: SPARQL Transformer, which allows to get a more compact JSON structure for SPARQL results, decreasing the effort required by developers in interfacing JavaScript and Python applications; and grlc, an automatic way of building APIs on top of SPARQL endpoints by sharing queries on collaborative platforms. Moreover, recent developments are presented to combine the two, offering a complete resource for developers and researchers. Hands-on sessions are proposed to internalise those concepts with practical exercises.
Version Control

Version control, in its simplest form, is used for tracking changes to your software. It is also an efficient way of collaborating on software development since it often allows several developers to make changes to the software and merge it with changes from other developers. RStudio supports two version control systems, Subversion and git. Of these, git is the most widely used, and although these things are very subjective of course, I think that it is also the better system. It is certainly the system we will use here.
Image-Based Physics Rendering for 3D Surface Reconstruction: A Survey

Obtaining 3D surface information and physical material information of an object from images is an essential research prospect in computer vision and computer graphics. Image-based 3D reconstruction is to extract the 3D depth information of the scene and objects from single or multiple images through specific algorithms to reconstruct the 3D model of objects or locations with robust realism, which has fast reconstruction speed, simple equipment, realistic effect, and minor technical data, which can better realize the virtualization of natural objects.
Opportunities and Challenges Facing AI Voice-Based Assistants: Consumer Perceptions and Technology Realities: An Abstract

Where AI has become especially pivotal for users’ interactions is in the case of voice-based assistants (VAs), such as Apple’s Siri and Amazon’s Alexa. From what is initially perceived as being a tool to play music, read out news reports and set timers, VAs have developed considerably in recent years and their functionality goes way beyond initial perceptions. For example, Amazon Alexa will be able to give out health advice to users in the United Kingdom as health-related questions will be automatically searched for using the official NHS website (MIT Technology Review, 2020). Some reports go as far to discuss how Amazon has plans to be able to run someone’s entire life from the Alexa on the basis that the systems are getting so sophisticated and the data being collected is so vast that the Alexa will be capable of predicting needs (Hao, 2019). Nevertheless, users still appear to be resistant to use VAs, with some even reluctant to engage with this technology entirely (PwC, 2018). Most of these constraints to VA adoption primarily relate to lack of trust, perceived data privacy and security concerns and lack of knowledge or understanding. Despite the benefits and opportunities of AI software, it is inherently limited by capabilities surrounding planning, reasoning, knowledge, natural language processing, ability to move and to empathise. It is this lack of emotional connection that is a fundamental component of users being less trusting towards AI voice-based assistants. This research investigates the role of emerging capabilities, privacy concerns and trust towards VAs using a mixed-methodology design. Results from the qualitative element of the study reveal that it is more about trusting the capabilities and functionalities than being scared over personal data. This theme will subsequently be examined further in a follow-up experimental study to examine the role of context towards these perceptions. The focus of the experimental design will subsequently consider the direct relationship between perceived behavioural control and intention to continue to use VAs with consideration into the indirect effects of privacy concerns and trust and the moderating role of perceived capabilities. The findings, thus far, show how VAs, as new technologies and leaders of machine learning capabilities, can enhance customer experiences, improve customer relationships and add value to firms. As privacy concerns are often dismissed, if customers benefit from the sharing of their information, yet their capabilities being under-appreciated, firms can focus attention to the high-quality functions of these devices to encourage their more frequent use.
Enrichment of Gold Above Background

Enrichment of an element above its average abundance in the Earth’s crust is required to form any ore deposit. For gold, that enrichment is around 10,000 times which is more than virtually any other element, and the high enrichment implies special partitioning in Nature. Enrichment follows provinciality as the second of five characteristics that needs to be accommodated when deciding how gold deposits may form.
From Security-as-a-Hindrance Towards User-Centred Cybersecurity Design

Cybersecurity controls in the workplace are viewed by many people as a hindrance that results in wasted time. End-users often bypass controls to get their work done and because of this, even the technically most secure systems can become unsecured. One crucial reason for this could be a lack of attention paid to usability factors by the software development teams that de-sign controls. In this paper I investigate how to design cybersecurity controls in such a way that the user is more likely to behave in a secure manner when confronted with these controls. I put forward three practices that, when employed alongside each other, hold the promise to produce usable and effective cybersecurity controls.
Introduction to R Programming

We will use R for our data analysis, so we need to know the basics of programming in the R language. R is a full programming language with both functional programming and object-oriented programming features, and learning the complete language is far beyond the scope of this chapter. We return to it later, when we have a little more experience using R. The good news is, though, that to use R for data analysis, we rarely need to do much programming. At least, if you do the right kind of programming, you won’t need much.
Innovative Teaching Theory Supportive of Mandarin Chinese Teaching Quality: New Theory of Children’s Thinking Development

Language is the expression form of thinking. Language and thinking are inseparable. Whether in understanding the developmental process of thinking and relationship between them, language will exert a vital restrictive effect on the teaching of national language (namely the teaching of mother tongue, commonly known as the teaching of Mandarin or Putonghua). If the understanding is scientific and objective, it will play a good role in promoting Chinese teaching. Otherwise, it will seriously reduce the quality and efficiency of mother tongue teaching, and greatly delay and block the process of mother tongue teaching reform.
An Enhanced Depth Motion Map and Deep Learning Method to Identify Human Actions from Depth Action Sequences

Research on human action recognition in view of depth action sequences has numerous applications and is one of the most notable exploration regions. Compared with RGB videos, depth action videos are more efficient due to their insensitivity to illumination variations. However, the major problem is the presence of some undefined regions, which do not have much significance in motion representation. This paper proposed another activity descriptor called Enhanced Depth Motion Map (EDMM) to take care of this issue. Then, we develop a Convolutional Neural Network (CNN) model that extracts a decent set of features and more correctly simplifies the activities. The CNN model perceives even activities with the most comparable developments with its nine layers. We tried our proposed strategy by utilizing two openly accessible datasets, MSR activity 3D and UTD-MHAD (UTD-Multimodal Human Action Data Set). Test results show that the proposed technique is better than a few past strategies.
Project 2: Bayesian Linear Regression

The project for this chapter is building an R package for Bayesian linear regression. The model we will work with is somewhat a toy example of what we could imagine we could build an R package for, and the goal is not to develop all the bells and whistles of Bayesian linear regression. We will just build enough to see the various aspects that go into building a real R package.
The Case of Imperfect Negation Cues: A Two-Step Approach for Automatic Negation Scope Resolution

Negation is a complex grammatical phenomenon that has received considerable attention in the biomedical natural language processing domain. While neural network-based methods are the state-of-the-art in negation scope resolution, they often use the unrealistic assumption that negation cue information is completely accurate. Even if this assumption holds, there remains a dependency on engineered features from state-of-the-art machine learning methods. To tackle this issue, in this study, we adopted a two-step negation resolving approach to assess whether a neural network-based model, here a bidirectional long short-term memory, can be a an alternative for cue detection. Furthermore, we investigate how inaccurate cue predictions would affect the scope resolution performance. We ran various experiments on the open access Bio-Scope corpus. Experimental results suggest that word embeddings alone can detect cues reasonably well, but there still exist better alternatives for this task. As expected, scope resolution performance suffers from imperfect cue information, but remains acceptable on the Abstracts subcorpus. We also found that the scope resolution performance is most robust against inaccurate information for models with a recurrent layer only, compared to extensions with a conditional random field layer and extensions with a post-processing algorithm. We advocate for more research into the application of automated deep learning on the effect of imperfect information on scope resolution.
Using Convolutional Neural Network to Handle Word Shape Similarities in Handwritten Cursive Arabic Scripts of Pashto Language

The Inter-Class Word Similarities in combination with Intra-Class Variations make it a difficult task for an OCR or any other machine learning system to recognize the handwritten characters and words with high accuracy, especially in the domain of cursive Arabic scripts. Convolutional Neural Network was originally designed to handle the problems of shape resemblance, position shift and distortion in the domain of handwritten characters and digit recognition. In this paper, we have used Convolutional Neural Network (CNN) to recognize multi class handwritten words written in cursive Arabic scripts of Pashto Language. The handwritten word images in Pashto Database [3] contain a high level of shape resemblance, and position shift in terms of diacritic marks. Hence it has been proved to be a good source for properly analyzing the performance of any CNN based recognition system. The model has successfully handled the problem and has not been affected by the level of complexity of Inter-class resemblances. The CNN model has been well tested on three sub data sets of Pashto Database. In each data set the number of classes and level of complexity, i.e., Shape Similarity, increase from one data set to another, i.e., 25, 40 and 68 classes of handwritten words. The average accuracies for the three Data Sets are 97.26%, 96.25% and 95.84%, respectively.
A Class of General Type-2 Fuzzy Controller Based on Adaptive Alpha-Plane for Nonlinear Systems

This study introduces a general type-2 Takagi–Sugeno–Kang fuzzy controller (GT2-TSKFC) for controlling the uncertain systems. The proposed GT2-TSKFC uses equidistant type-2 triangular membership functions (MFs) for the antecedents, Larsen’s implication method, type-1 fuzzy sets for the consequent’s parameters, and direct defuzzification method. From the analytical structure of the proposed controller, the alpha-plane and apex of the secondary MFs have noticeable effect on calculating the control signal. The adaptation of alpha and apex of the secondary MFs are performed using Lyapunov function to achieve the stability of the controlled system. The proposed controller is applied to an uncertain nonlinear inverted pendulum system. The results of the proposed control algorithm are compared with the general type-2 fuzzy controller with a certain number of alpha-planes, the quasi type-2 fuzzy controller, interval type-2 fuzzy controller and type-1 fuzzy controller to show the robustness and superiority of the proposed scheme.