Reviewerly/exHarmony huggingface dataset:
Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape

The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.
Analyzing education based on metaverse technology

The Metaverse is a post-reality universe, a perpetual multi-user environment that combines physical reality and digital virtuality. It is based on the convergence of technologies that enable multisensory interactions with virtual environments, digital objects, and people, such as virtual reality (VR) and augmented reality (AR). As a result, Metaverse is a web of social, networked immersive environments on persistent multi-user platforms. It allows for real-time, embodied user communication and dynamic interactions with digital artifacts. Its first incarnation was a web of virtual worlds between which avatars could teleport. The modern Metaverse includes social, immersive VR platforms that are compatible with massive multiplayer online video games, open game worlds, and AR collaborative spaces.&#x0D; The aim of the research is to thoroughly examine the perceptions and educational requirements of Metaverse-based education and to investigate its educational applicability. Using a mixed research method, the study investigated instructors' and students' perceptions of the use of the Metaverse in education, as well as the support system for classes that use the Metaverse. And determining the implications for action. plans and directions for its application in education.
The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.
Do Strategic Human Resources and Artificial Intelligence Help to Make Organisations More Sustainable? Evidence from Non-Governmental Organisations

Uncertainty and a lack of stability are among the difficulties non-governmental organisations face. However, certain strategies for ensuring their performance’s sustainability have not been empirically demonstrated in the literature. Using strategic resource management practises and artificial intelligence, this study examines the effect of organisational learning and corporate social responsibility on the sustainability of non-governmental organisations’ performance. The survey gathered data from 171 participants representing 21 United Nations organisations and 70 non-governmental organisations in Jordan to accomplish this goal. The data were analysed using WarpPLS and PLS-SEM. The study demonstrates that organisational learning, artificial intelligence, strategic human resource management practises, and corporate social responsibility all contribute to the long-term viability of non-governmental organisations. Furthermore, the study discovered that strategic resource management practises and artificial intelligence significantly mediate the relationship between organisational learning and sustainable organisational performance on the one hand, and corporate social responsibility on the other. Finally, the study provides theoretical and practical guidance on how to apply the findings to assist non-profit organisations’ management in utilising organisational learning, corporate social responsibility, artificial intelligence, and strategic resource management practices to help them run their internal operations in a more efficient and sustainable manner over time.
On the Existence of Simpler Machine Learning Models

It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple model. Finding optimal, sparse, accurate models of various forms (linear models with integer coefficients, decision sets, rule lists, decision trees) is generally NP-hard. We often do not know whether the search for a simpler model will be worthwhile, and thus we do not go to the trouble of searching for one. In this work, we ask an important practical question: can accurate-yet-simple models be proven to exist, or shown likely to exist, before explicitly searching for them? We hypothesize that there is an important reason that simple-yet-accurate models often do exist. This hypothesis is that the size of the Rashomon set is often large, where the Rashomon set is the set of almost-equally-accurate models from a function class. If the Rashomon set is large, it contains numerous accurate models, and perhaps at least one of them is the simple model we desire. In this work, we formally present the Rashomon ratio as a new gauge of simplicity for a learning problem, depending on a function class and a data set. The Rashomon ratio is the ratio of the volume of the set of accurate models to the volume of the hypothesis space, and it is different from standard complexity measures from statistical learning theory. Insight from studying the Rashomon ratio provides an easy way to check whether a simpler model might exist for a problem before finding it, namely whether several different machine learning methods achieve similar performance on the data. In that sense, the Rashomon ratio is a powerful tool for understanding why and when an accurate-yet-simple model might exist. If, as we hypothesize in this work, many real-world data sets admit large Rashomon sets, the implications are vast: it means that simple or interpretable models may often be used for high-stakes decisions without losing accuracy.
Knowledge Bridging for Empathetic Dialogue Generation

Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with external knowledge and construct an emotional context graph. Then we learn emotional context representations from the knowledge-enriched emotional context graph and distill emotional signals, which are the prerequisites to predicate emotions expressed in responses. Finally, to generate the empathetic response, we propose an emotional cross-attention mechanism to learn the emotional dependencies from the emotional context graph. Extensive experiments conducted on a benchmark dataset verify the effectiveness of the proposed method. In addition, we find the performance of our method can be further improved by integrating with a pre-trained model that works orthogonally.
FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos

Deepfake has ignited hot research interests in both academia and industry due to its potential security threats. Many countermeasures have been proposed to mitigate such risks. Current Deepfake detection methods achieve superior performances in dealing with low-visual-quality Deepfake media which can be distinguished by the obvious visual artifacts. However, with the development of deep generative models, the realism of Deepfake media has been significantly improved and becomes tough challenging to current detection models. In this paper, we propose a frame inference-based detection framework (FInfer) to solve the problem of high-visual-quality Deepfake detection. Specifically, we first learn the referenced representations of the current and future frames’ faces. Then, the current frames’ facial representations are utilized to predict the future frames’ facial representations by using an autoregressive model. Finally, a representation-prediction loss is devised to maximize the discriminability of real videos and fake videos. We demonstrate the effectiveness of our FInfer framework through information theory analyses. The entropy and mutual information analyses indicate the correlation between the predicted representations and referenced representations in real videos is higher than that of high-visual-quality Deepfake videos. Extensive experiments demonstrate the performance of our method is promising in terms of in-dataset detection performance, detection efficiency, and cross-dataset detection performance in high-visual-quality Deepfake videos.
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base

Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribute comparison, set operation.Existing benchmarks have some shortcomings that limit the development of Complex KBQA: 1) they only provide QA pairs without explicit reasoning processes; 2) questions are poor in diversity or scale.To this end, we introduce KQA Pro, a dataset for Complex KBQA including ~120K diverse natural language questions.We introduce a compositional and interpretable programming language KoPL to represent the reasoning process of complex questions.For each question, we provide the corresponding KoPL program and SPARQL query, so that KQA Pro serves for both KBQA and semantic parsing tasks.Experimental results show that SOTA KBQA methods cannot achieve promising results on KQA Pro as on current datasets, which suggests that KQA Pro is challenging and Complex KBQA requires further research efforts.We also treat KQA Pro as a diagnostic dataset for testing multiple reasoning skills, conduct a thorough evaluation of existing models and discuss further directions for Complex KBQA.
Engineering an effective three-spin Hamiltonian in trapped-ion systems for applications in quantum simulation

Abstract Trapped-ion quantum simulators, in analog and digital modes, are considered a primary candidate to achieve quantum advantage in quantum simulation and quantum computation. The underlying controlled ion–laser interactions induce all-to-all two-spin interactions via the collective modes of motion through Cirac–Zoller or Mølmer–Sørensen schemes, leading to effective two-spin Hamiltonians, as well as two-qubit entangling gates. In this work, the Mølmer–Sørensen scheme is extended to induce three-spin interactions via tailored first- and second-order spin–motion couplings. The scheme enables engineering single-, two-, and three-spin interactions, and can be tuned via an enhanced protocol to simulate purely three-spin dynamics. Analytical results for the effective evolution are presented, along with detailed numerical simulations of the full dynamics to support the accuracy and feasibility of the proposed scheme for near-term applications. With a focus on quantum simulation, the advantage of a direct analog implementation of three-spin dynamics is demonstrated via the example of matter-gauge interactions in the U(1) lattice gauge theory within the quantum link model. The mapping of degrees of freedom and strategies for scaling the three-spin scheme to larger systems, are detailed, along with a discussion of the expected outcome of the simulation of the quantum link model given realistic fidelities in the upcoming experiments. The applications of the three-spin scheme go beyond the lattice gauge theory example studied here and include studies of static and dynamical phase diagrams of strongly interacting condensed-matter systems modeled by two- and three-spin Hamiltonians.
Global Entity Disambiguation with BERT

We propose a global entity disambiguation (ED) model based on BERT (Devlin et al., 2019).To capture global contextual information for ED, our model treats not only words but also entities as input tokens, and solves the task by sequentially resolving mentions to their referent entities and using resolved entities as inputs at each step.We train the model using a large entity-annotated corpus obtained from Wikipedia.We achieve new state-of-the-art results on five standard ED datasets: AIDA-CoNLL, MSNBC, AQUAINT, ACE2004, and WNED-WIKI.The source code and model checkpoint are available at https: //github.com/studio-ousia/luke.
Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models

Hate speech detection models are typically evaluated on held-out test sets.However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate speech datasets.To enable more targeted diagnostic insights, recent research has thus introduced functional tests for hate speech detection models.However, these tests currently only exist for English-language content, which means that they cannot support the development of more effective models in other languages spoken by billions across the world.To help address this issue, we introduce MULTILINGUAL HATECHECK (MHC), a suite of functional tests for multilingual hate speech detection models.MHC covers 34 functionalities across ten languages, which is more languages than any other hate speech dataset.To illustrate MHC's utility, we train and test a highperforming multilingual hate speech detection model, and reveal critical model weaknesses for monolingual and cross-lingual applications.
Cross-modal Contrastive Learning for Speech Translation

How can we learn unified representations for spoken utterances and their written text?Learning similar representations for semantically similar speech and text is important for speech translation.To this end, we propose ConST, a cross-modal contrastive learning method for end-to-end speech-to-text translation.We evaluate ConST and a variety of previous baselines on a popular benchmark MuST-C.Experiments show that the proposed ConST consistently outperforms the previous methods, and achieves an average BLEU of 29.4.The analysis further verifies that ConST indeed closes the representation gap of different modalities -its learned representation improves the accuracy of cross-modal speechtext retrieval from 4% to 88%.Code and models are available at https://github.com/ReneeYe/ConST.
Multiplex Heterogeneous Graph Convolutional Network

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on five real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/NSSSJSS/MHGCN.
ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

(1)People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved.(2)In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pretrained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs.(3)We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively.(4)The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods.
Systematic Review on AI-Blockchain Based E-Healthcare Records Management Systems

Electronic health records (EHRs) are digitally saved health records that provide information about a person&#x2019;s health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of EHR management and create a more secure, decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. When smart contracts are used to make decisions and conduct analytics with machine-learning algorithms, the results may be trusted and unquestioned. However, Blockchain is not always indestructible and suffers from scalability and complexity issues that might render it inefficient. Combining AI and blockchain technology can handled some of the drawbacks of these two technical ecosystems effectively. AI algorithms rely on data or information to learn, analyze, and reach conclusions. The performance of AI algorithms is enhanced through the data obtained from a data repository or a reliable, secure, trustworthy, and credible platform. Researchers have identified three categories of blockchain-based potential solutions for the management of electronic health records: conceptual, prototype, and implemented. The purpose of this research work is to conduct a Systematic Literature Review (SLR) to identify and assess research articles that were either conceptual or implemented to manage EHRs using blockchain technology. The study conducts a comprehensive evaluation of the literature on blockchain technology and enhanced health record management systems utilizing artificial intelligence technologies. The study examined 189 research papers collected from various publication categories. The in-depth analysis focuses on the privacy, security, accessibility, and scalability of publications. The SLR has illustrated that blockchain technology has the potential to deliver decentralization, security, and privacy that are frequently lacking in traditional EHRs. Additionally, the outcomes of the extensive analysis inform future researchers about the type of blockchain to use in their research. Additionally, methods used in healthcare are summarized per application area while their pros and cons are highlighted. Finally, the emphasized taxonomy combines blockchain and artificial intelligence, which enables us to analyze possible blockchain and artificial intelligence applications in health records management systems. The article ends with a discussion on open issues for research and future directions.
Redefining food safety traceability system through blockchain: findings, challenges and open issues

In the last few decades, there has been an increase in food safety and traceability issues. To prevent accidents and misconduct, it became essential to establish Food Safety Traceability System (FSTS) to trace the food from producer to consumer. The traceability systems can help track food in supply chains from farms to retail. Numerous technologies such as Radio Frequency Identification (RFID), sensor networks, and data mining have been integrated into traditional food supply chain systems to remove unsafe food products from the chain. But, these are not adequate for the current supply chain market. The emerging technology of blockchain can overcome safety and tracking issues. This can be possible with the help of blockchain features like transparent, decentralized, distributed, and immutable. Most of the previous works missed the discussion of the systematic process and technology involved in implementing the FSTS using blockchain. In this paper, we have discussed an organized state of research of the existing FSTS using blockchain. This survey paper aims to outline a detailed analysis of blockchain technology, FSTS using blockchain, consensus algorithms, security attacks, and solutions. Several survey papers and solutions based on blockchain are included in this research paper. Also, this work discusses some of the open research issues related to FSTS.