Reviewerly/exHarmony huggingface dataset:
Reduce Information Loss in Transformers for Pluralistic Image Inpainting

Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer from information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficiency consideration, incurring information loss and extra misalignment for the boundaries of masked regions. 2) They quantize 256 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> RGB pixels to a small number (such as 512) of quantized pixels. The indices of quantized pixels are used as tokens for the inputs and prediction targets of transformer. Although an extra CNN network is used to upsample and refine the low-resolution results, it is difficult to retrieve the lost information back. To keep input information as much as possible, we propose a new transformer based framework “PUT”. Specifically, to avoid input downsampling while maintaining the computation efficiency, we design a patch-based auto-encoder P-VQVAE, where the encoder converts the masked image into non-overlapped patch tokens and the decoder recovers the masked regions from the inpainted tokens while keeping the unmasked regions unchanged. To eliminate the information loss caused by quantization, an Un-Quantized Transformer (UQ-Transformer) is applied, which directly takes the features from P-VQVAE encoder as input without quantization and regards the quantized tokens only as prediction targets. Extensive experiments show that PUT greatly outperforms state-of-the-art methods on image fidelity, especially for large masked regions and complex large-scale datasets.
Conjunction of Active and Semi-Supervised Learning for Wireline Logs-Based Automatic Lithology Identification

The sample labeling is the basis for wireline logs-based automatic lithology identification (WLALI). Due to the paucity of core data, the expert-annotation accounts for most labeling workload. In the traditional workflow of WLALI, the samples for labeling are passively selected. The labeled samples thus fail to reflect the overall distribution and feature of whole data set, leading to the difficulty in training the prediction model with robust generalization performance. To solve this problem, a novel workflow of hybrid active and semi-supervised learning-based WLALI has been proposed. First, we alter the conventional labeling process to active selection mode by employing the approach of active learning. In such way the labeled samples are representative of whole data features. Second, we propose a novel active learning algorithm based on density difference of Gaussian probability (DDGP) to obtain a better performance of sample query. Third, the semi-supervised learning method is introduced to combine the active learning algorithm, attempting to minimize the involvement of hand-crafted labeling under the target performance. In the evaluation, the proposed workflow not only has achieved favorable result in the uppercase image dataset, but also the practical application shows great promise. When compared with the workflow of random sampling + semi-supervised learning that is similar to the traditional mode, the average F1-score of proposed workflow of DDGP + semi-supervised learning can increase by approximately 3.47%.
Media comparison studies dominate comparative research on augmented reality in education

Research on the use of augmented reality (AR) in education has received a lot of attention in recent years. Based on many systematic reviews and meta-analyses, it has been concluded that AR is effective. Recently, however, researchers have criticized the fact that the empirical basis for this conclusion is based on results from methodologically problematic media comparison studies. However, an analysis of the literature and quantitative evidence for this claim are lacking. In this research project, this research gap was addressed using the Systematic Review method. A total of 92 primary studies from the top 12 Educational Technology journals were coded and analyzed. The results show that research on AR in education is based on media comparison studies: 80% of the studies compare AR to another medium or technology. Few studies examine how and when learning with AR is effective. In addition, results show that over the years, since 2009, more media comparison studies have been published than other research types. We summarize why media comparison studies are problematic and discuss directions for future research on AR in education. This research shifts from the question if AR can be used in instruction to the more important questions of how and when learning and teaching with AR works.
Framework for A Foreign Language Teaching Software for Children Utilizing AR, Voicebots and ChatGPT (Large Language Models)

The cognitive capabilities of children develop during the early years of their life. Research shows that learning a foreign language helps develop cognitive skills. Moreover, learning a foreign language has become essential and an increasing number of parents would like their kids to start learning a foreign language at an early age. However, engaging little kids with learning activities is challenging. In this study, we propose a framework for developing a language learning software tool utilizing Augmented Reality (AR), Voicebots, and ChatGPT (an AI utilizing the Large Language Model) technologies to provide a unique product for small kids to teach a foreign language. With AR and Voicebots, the product will grab attention, motivate and provide an entertaining learning environment. The capabilities of ChatGPT will be utilized to efficiently prepare the content for the software tool. We utilize the capabilities of ChatGPT to generate interactive dialogs that will be hosted at Google DialogFlow. We believe the framework and the design principles we propose in this study can be a blueprint for developing highly effective foreign language teaching software.
Sustainable finance and blockchain: A systematic review and research agenda

Sustainable finance and blockchain studies have garnered considerable interest recently. but there has been no systematic analysis of blockchain in sustainable finance to far. To fill this gap, based on the theme structure of blockchain research in the field of renewable finance from November 1, 2008 to January 31, 2022, this paper proposes a multi-level and all-round comprehensive bibliometric method (Co-occurrence Analysis method, Natural Language Processing method, and Exploratory Factor Analysis method) to comprehend the mode, process, and mechanism of the integration of them. The findings indicate that: (1) Blockchain has been widely used in many industries involved in sustainable finance; (2) Blockchain will have a long-term impact on Sustainable Finance in the fields of smart city and sharing economy; (3) Blockchain can be deeply integrated with other technologies to promote the diversified development of sustainable finance. Additionally, we highlight trends and research directions regarding blockchain in sustainable finance research.
Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis

Aspect-based Sentiment Analysis (ABSA) is a crucial natural language understanding (NLU) research field which aims to accurately recognize reviewers’ opinions on different aspects of products and services. Despite the prominence of recent ABSA applications, mainstream ABSA approaches inevitably rely on large-scale supervised corpora, and their final performances is susceptible to the quality of the training datasets. However, annotating sufficient data is labour intensive, which presents a significant barrier for generalizing a high-quality sentiment analysis model. Nonetheless, humans can make more accurate judgement based on their external background knowledge, such as factoid triples knowledge and event causality. Inspired by the investigations on external knowledge enhancement strategies in other popular NLP research, we propose a novel knowledge augmentation framework for ABSA, named the Oxford Dictionary descriptive knowledge-infused aspect-based sentiment analysis (DictABSA). Comprehensive experiments with many state-of-the-art approaches on several widely used benchmarks demonstrate that our proposed DictABSA significantly outperforms previous mainstream ABSA methods. For instance, compared with the baselines, our BERT-based knowledge infusion strategy achieves a substantial 6.42% and 5.26% absolute accuracy gain when adopting BERT-SPC on SemEval2014 and ABSA-DeBERTa on ACLShortData, respectively. Furthermore, to effectively make use of dictionary knowledge we devise several alternative knowledge infusion strategies. Extensive experiments using different knowledge infused strategies further demonstrate that the proposed knowledge infusion strategies effectively enhance the sentiment polarity identification capability. The Python implementation of our DictABSA is publicly available at https://github.com/albert-jin/DictionaryFused-E2E-ABSA.
Blockchain for Healthcare Management Systems: A Survey on Interoperability and Security

n recent years it has been shown that the secure exchange of medical information significantly benefits people’s life quality, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge, and even more, with all the risks posed to the security of healthcare information. Blockchain technology is emerging as one of the main alternatives when it comes to finding a balance in the healthcare ecosystem. However, the constant development of new Blockchain technologies and the evolution of healthcare systems make it difficult to find established proposals. From an architectural point of view, the design of blockchain-based solutions requires trade-offs e.g., security and interoperability. This paper focuses on two main objectives, in the first one, it was carried out a Systematic Literature Review for exploring architectural mechanisms used to support the interoperability and security of Blockchain-based Health Management Systems. Taking into account of results, a series of scenarios were generated where these mechanisms can be used along with their context, issues, and various architectural concerns (interoperability and security). In the second objective, a high-level architecture and its validation were proposed through an experiment for the whole process of developing a Domain Specific Language, using the Model Driven Engineering methodology for specific Smart Contracts.
Digital Learning and Digital Institution in Higher Education

Higher education institutions are going through major changes in their education and operations. Several influences are driving these major changes. Digital transformation, online courses, digital-navy students, operational costs, and micro and nano degrees are just some examples of these influences. Digital technologies show a range of tools selected to include formalized learning environments in teaching in higher education, and students utilize these tools to promote their learning. The Industrial Revolution 4.0’s technological growth has penetrated higher education institutions (HEIs), forcing them to deal with the digital transformation (DT) in all of its dimensions. As they enable us to characterize the various interrelationships among stakeholders in a digitally enabled context of teaching and learning, applying digital transformation techniques to the education sector is an emerging field that has attracted attention recently. The aim of this study is to provide an overview of the distinguishing features of the digital transformation implementation process that has occurred at higher education institutions. In addition, how digital learning can be seen as part of the ecosystem of modern higher education. Further study is necessary to determine how higher education institutions can comprehend digital transformation and meet the demands imposed by the fourth Industrial Revolution.
Eye Tracking in Virtual Reality: a Broad Review of Applications and Challenges

Eye tracking is becoming increasingly available in head-mounted virtual reality displays with various headsets with integrated eye trackers already commercially available. The applications of eye tracking in virtual reality are highly diversified and span multiple disciplines. As a result, the number of peer-reviewed publications that study eye tracking applications has surged in recent years. We performed a broad review to comprehensively search academic literature databases with the aim of assessing the extent of published research dealing with applications of eye tracking in virtual reality, and highlighting challenges, limitations and areas for future research.
Metaverse-enabled entrepreneurship

Metaverse is expected to be a trillion-dollar market within this decade and to change the nature of entrepreneurship. However, research on metaverse, its opportunities, and challenges, as well as the nature of entrepreneurship remains scarce. This study lays out a framework to explore metaverse-enabled entrepreneurship with its enablers for supply and demand, and technological and social enablers. We show how metaverse enables transformational pathways, i.e., purely virtual, physical to virtual, virtual to physical, and hybrid, and shapes offerings, ventures, and processes. We discuss the implications for entrepreneurship research and lay out a future research agenda so that research can lead and inform practice.
Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems

Meta-heuristic algorithms have been widely used to solve different optimization problems. There have always been ongoing efforts to develop new and efficient algorithms. In this paper, the Orchard Algorithm (OA) is designed and introduced, inspired by fruit gardening. In this process, various actions such as irrigation, fertilization, trimming, and grafting lead to a fruit orchard where most trees grow and produce fruit adequately. In OA, both explorations of the search space and exploitation of the best solutions are achieved using personal and social behavior. By introducing various operators such as annual growth, screening, and grafting, the algorithm can efficiently search and explore the search space. The performance of the proposed OA algorithm was evaluated on CEC2005, IEEE CEC06 2019,test functions, and five real-world engineering problems compared with 13 widely used and competitive algorithms. Thirty benchmark functions were used to compare the capabilities of the OA algorithm with other research. The OA yields far better results in many aspects than the other algorithms. The results show the OA's superiority and this algorithm's capability in solving optimization problems.
The Voice of Monetary Policy

We develop a deep learning model to detect emotions embedded in press conferences after the Federal Open Market Committee meetings and examine the influence of the detected emotions on financial markets. We find that, after controlling for the Federal Reserve’s actions and the sentiment in policy texts, a positive tone in the voices of Federal Reserve chairs leads to significant increases in share prices. Other financial variables also respond to vocal cues from the chairs. Hence, how policy messages are communicated can move the financial market. Our results provide implications for improving the effectiveness of central bank communications. (JEL D83, E31, E44, E52, E58, F31, G14)
Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches

Navigation/positioning systems have become critical to many applications, such as autonomous driving, Internet of Things (IoT), Unmanned Aerial Vehicle (UAV), and smart cities. However, it is difficult to provide a robust, accurate, and seamless solution with single navigation/positioning technology. For example, the Global Navigation Satellite System (GNSS) cannot perform satisfactorily indoors; consequently, multi-sensor integrated systems provide the solution, as they compensate for the limitations of single technology by using the complementary characteristics of different sensors. This article describes a thorough investigation into multi-sensor data fusion, which over the last ten years has been used for integrated positioning/navigation systems. In this article, different navigation/positioning systems are classified and elaborated upon from three aspects: (1) sources, (2) algorithms and architectures, and (3) scenarios, which we further divide into two categories: (i) analytics-based fusion and (ii) learning-based fusion. For analytics-based fusion, we discuss the Kalman filter and its variants, graph optimization methods, and integrated schemes. For learning-based fusion, several supervised, unsupervised, reinforcement learning, and deep learning techniques are illustrated in multi-sensor integrated positioning/navigation systems. Design consideration of these integrated systems is discussed in detail from several aspects and their application scenarios are categorized. Finally, future directions for their research and implementation are discussed.
Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators’ algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy. In fact, WSA-KNN outperforms traditional ML methods by about 18% and ensemble ML algorithms by 9%. Moreover, WSA-KNN achieves comparable or slightly better solutions when compared with neural networks hybridized with metaheuristics. These findings highlight the importance and potential of WSA for feature selection in modern-day data processing systems.
Can ChatGPT Boost Artistic Creation: The Need of Imaginative Intelligence for Parallel Art

ChatGPT, one of the leading Large Language Models (LLMs), has acquired linguistic capabilities such as text comprehension and logical reasoning, enabling it to engage in natural conversations with humans. As illustrated in “What Does ChatGPT Say: The DAO from Algorithmic Intelligence to Linguistic Intelligence” [1], there are three levels of intelligence: 1) Algorithmic Intelligence (AI), 2) Linguistic Intelligence (LI), 3) Imaginative Intelligence (II). ChatGPT is a powerful demonstration of Linguistic Intelligence, another milestone after AlphaGo for Algorithmic Intelligence [1]–[3]. We believe the next break-through in intelligence should be Imaginative Intelligence for artistic creation.
MEALPY: An open-source library for latest meta-heuristic algorithms in Python

Meta-heuristic algorithms are becoming more prevalent and have been widely applied in various fields. There are numerous reasons for the success of such techniques in both science and industry, including but not limited to simplicity in search/optimization mechanisms, implementation readiness, black-box nature, and ease of use. Although the solutions obtained by such algorithms are not guaranteed to be exactly global optimal, they usually find reasonably good solutions in a reasonable time. Many algorithms have been proposed and developed in the last two decades. However, there is no library implementing meta-heuristic algorithms, which is easy to use and has a vast collection of algorithms. This paper proposes an open-source and cross-platform Python library for nature-inspired optimization algorithms called Mealpy. To propose Mealpy, we analyze the features of existing libraries for meta-heuristic algorithms. After, we propose the designation and the structure of Mealpy and validate it with a case study discussion. Compared with other libraries, our proposed Mealpy has the largest number of classical and state-of-the-art meta-heuristic algorithms, with more than 160 algorithms. Mealpy is an open-source library with well-documented code, has a simple interface, and benefits from minimum dependencies. Mealpy includes a wide range of well-known and recent meta-heuristics algorithms capable of optimizing challenge benchmark functions (e.g. CEC-2017). Mealpy can also be used for practical problems such as optimizing parameters for machine learning models. We invite the research community for widespread evaluations of this comprehensive library as a promising tool for research study and real-world optimization. The source codes, supplementary materials, and guidance is publicly available on GitHub: https://github.com/thieu1995/mealpy.
How do virtual streamers affect purchase intention in the live streaming context? A presence perspective

The rise of live streaming commerce has attracted attention from scholars and practitioners as a new opportunity to reach consumers. However, few studies have focused on the role of virtual streamers in this context. Based on the stimulus organism response framework, we explore the impact of three characteristics of virtual streamers—likeability, animacy, and responsiveness—on social presence, telepresence, and purchase intention. We use partial least squares structural equation modeling analysis with online survey data from 378 consumers concerning their experiences of viewing virtual streamers. The findings indicate that likeability, animacy, and responsiveness enhance social presence and telepresence, which then promote purchase intention. Likeability and responsiveness directly increase purchase intention, but animacy does not. Multi-group analysis reveals differences between the effects of humanoid and zoonotic virtual streamers on social presence and telepresence. These results shed light on the role of virtual streamer characteristics and contribute to the literature on live streaming commerce, presence, and virtual humans.