Reviewerly/exHarmony huggingface dataset:
Blockchain as a Cyber Defense: Opportunities, Applications, and Challenges

Targets of cyber crime are not exclusive to the private sector. Successful cyber attacks on nation-states have proved that cyber threats can jeopardize significant national interests. In response, nation-states have begun to handle cyber threats at the national defense level, which is titled &#x2018;cyber defense.&#x2019; The cyber defense sector is related to national security, therefore requires robust security technology. Contrary to normal systems, blockchain provides strong security properties without a centralized control entity, and as such its application in the cyber defense field is under the spotlight. In this paper, we present opportunities blockchain provides for cyber defense, research and national projects, and limitations. We constructed a survey of government documents, interviews, related news, technical reports, and research papers from 2016 to 2021. As a result, our research contributes to reducing the gap in blockchain for cyber defense by systematically conducting research and analysis. In our research, we found that not only research but also government-led plans are actively promoting blockchain, which demonstrates that blockchain will play a remarkable role in cyber defense. This paper concludes with suggestions for future research in aspects of the blockchain technology, evaluation, and survey.
Journalistic Guidelines Aware News Image Captioning

The task of news article image captioning aims to generate descriptive and informative captions for news article images.Unlike conventional image captions that simply describe the content of the image in general terms, news image captions follow journalistic guidelines and rely heavily on named entities to describe the image content, often drawing context from the whole article they are associated with.In this work, we propose a new approach to this task, motivated by caption guidelines that journalists follow.Our approach, Journalistic Guidelines Aware News Image Captioning (JoGANIC), leverages the structure of captions to improve the generation quality and guide our representation design.Experimental results, including detailed ablation studies, on two large-scale publicly available datasets show that JoGANIC substantially outperforms state-of-the-art methods both on caption generation and named entity related metrics.
Decentralized Machine Autonomy for Manufacturing Servitization

Blockchain ecosystems are rapidly maturing and meeting the needs of business environments (e.g., industry, manufacturing, and robotics). The decentralized approaches in industries enable novel business concepts, such as machine autonomy and servitization of manufacturing environments. Introducing the distributed ledger technology principles into the machine sharing and servitization economy faces several challenges, and the integration opens new interesting research questions. Our research focuses on data and event models and secure upgradeable smart contract platforms for machine servitization. Our research indicates that with the proposed approaches, we can efficiently separate on- and off-chain data and assure scalability of the DApp without compromising the trust. We demonstrate that the secure upgradeable smart contract platform, which was adapted for machine servitization, supports the business workflow and, at the same time, assures common identification and authorization of all the participants in the system, including people, devices, and legal entities. We present a hybrid decentralized application (DApp) for the servitization of 3D printing. The solution can be used for or easily adapted to other manufacturing domains. It comprises a modular, upgradeable smart contract platform and off-chain machine, customer and web management, and monitoring interfaces. We pay special attention to the data and event models during the design, which are fundamental for the hybrid data storage and DApp architecture and the responsiveness of off-chain interfaces. The smart contract platform uses a proxy contract to control the access of smart contracts and role-based access control in function calls for blockchain users. We deploy and evaluate the DApp in a consortium blockchain network for performance and privacy. All the actors in the solution, including the machines, are identified by their blockchain accounts and are compeers. Our solution thus facilitates integration with the traditional information-communication systems in terms of the hybrid architectures and security standards for smart contract design comparable to those in traditional software engineering.
An Causal XAI Diagnostic Model for Breast Cancer Based on Mammography Reports

Breast cancer has become one of the most common malignant tumors in women worldwide, and it seriously threatens women’s physical and mental health. In recent years, with the development of Artificial Intelligence(AI) and the accumulation of medical data, AI has begun to be deeply integrated with mammography, MRI, ultrasound, etc. to assist physicians in disease diagnosis. However, the existing breast cancer diagnosis model based on Computer Vision(CV) is greatly affected by the image quality; on the other hand, the breast cancer diagnosis model based on Natural Language Processing(NLP) cannot effectively extract the semantic information of the mammography report. The lack of model interpretability also makes the existing diagnostic models have low confidence. In this paper, we proposed Breast Cancer Causal XAI Diagnostic Model(BCCXDM). Specifically, we first structured the mammography report. Then find the causal graph based on the structured table. We combine the existing tabular learning method TabNet with causal graphs(Causal-TabNet) to enable reasoning in the graphs to preserve the correlation between features. More importantly, we use GNN and node transition probability to aggregate node information. We evaluate our model on the real-world mammography report, and compare it with other popular interpretable methods. The experimental results show that our interpretable results are closer to the diagnostic criteria of clinicians.
Exploring the specification of educational compatibility of virtual reality within a technology acceptance model

This study investigated the specification of educational compatibility within a technology acceptance model (TAM) suited to engaging educational technologies. Attitudes towards virtual reality (VR) for learning was used to test the experimental model. One hundred and seventy-nine valid survey responses were collected from 517 potential participants with the majority from first-year university students. The independent variables were educational compatibility, cognitive engagement, social influence, system attributes, perceived anxiety and facilitating conditions. Exploratory factor analysis showed that educational compatibility and attitude were collinear, and therefore were combined into one construct. Confirmatory factor analysis indicated that the combined educational compatibility-attitude construct and perceived usefulness were not discriminant. Two structural models were therefore compared: one where educational compatibility-attitude items were incorporated within perceived usefulness, and another where educational compatibility-attitude items were excluded entirely. The results showed that incorporating educational compatibility-attitude items within perceived usefulness affected the influence of cognitive engagement and system attributes on perceived usefulness, though overall model power was unchanged. The results suggested that (a) educational compatibility and attitude could be redundant, and (b) incorporating educational compatibility into perceived usefulness may help specify educationally focused TAMs. Implications for practice or policy: Researchers may regard educational compatibility and attitude to be redundant and exclude them from TAMs as separate constructs. Researchers could consider tailoring the perceived usefulness construct to make it more specific to the educational context, for example by including one or more educational compatibility items.
PRIDE: Predicting Relationships in Conversations

Automatically extracted interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots.To infer speakers' relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation.PRIDE utilizes the dialogue structure and augments it with external knowledge about speaker features and conversation style.Unlike prior works, we address multi-label prediction of fine-grained relationships.We release largescale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants.Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-theart baselines.
Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks

Systematic compositionality is an essential mechanism in human language, allowing the recombination of known parts to create novel expressions.However, existing neural models have been shown to lack this basic ability in learning symbolic structures.Motivated by the failure of a Transformer model on the SCAN compositionality challenge (Lake and Baroni, 2018), which requires parsing a command into actions, we propose two auxiliary sequence prediction tasks as additional training supervision.These automatically-generated sequences are more representative of the underlying compositional symbolic structures of the input data.During inference, the model jointly predicts the next action and the next tokens in the auxiliary sequences at each step.Experiments on the SCAN dataset show that our method encourages the Transformer to understand compositional structures of the command, improving its accuracy on multiple challenging splits from ≤ 10% to 100%.With only 418 (5%) training instances, our approach still achieves 97.8% accuracy on the MCD1 split.Therefore, we argue that compositionality can be induced in Transformers given minimal but proper guidance.We also show that a better result is achieved using less contextualized vectors as the attention's query, providing insights into architecture choices in achieving systematic compositionality.Finally, we show positive generalization results on the grounded-SCAN task (Ruis et al., 2020).
News topic detection based on capsule semantic graph

Most news topic detection methods use word-based methods, which easily ignore the relationship among words and have semantic sparsity, resulting in low topic detection accuracy.In addition, the current mainstream probability methods and graph analysis methods for topic detection have high time complexity.For these reasons, we present a news topic detection model on the basis of capsule semantic graph (CSG).The keywords that appear in each text at the same time are modeled as a keyword graph, which is divided into multiple subgraphs through community detection.Each subgraph contains a group of closely related keywords.The graph is used as the vertex of CSG.The semantic relationship among the vertices is obtained by calculating the similarity of the average word vector of each vertex.At the same time, the news text is clustered using the incremental clustering method, where each text uses CSG; that is, the similarity among texts is calculated by the graph kernel.The relationship between vertices and edges is also considered when calculating the similarity.Experimental results on three standard datasets show that CSG can obtain higher precision, recall, and F1 values than several latest methods.Experimental results on large-scale news datasets reveal that the time complexity of CSG is lower than that of probabilistic methods and other graph analysis methods.
Flexible clustering via hidden hierarchical Dirichlet priors

Abstract The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distribution. A popular Bayesian nonparametric model for clustering probability distributions is the nested Dirichlet process, which however has the drawback of grouping distributions in a single cluster when ties are observed across samples. With the goal of achieving a flexible and effective clustering method for both samples and observations, we investigate a nonparametric prior that arises as the composition of two different discrete random structures and derive a closed‐form expression for the induced distribution of the random partition, the fundamental tool regulating the clustering behavior of the model. On the one hand, this allows to gain a deeper insight into the theoretical properties of the model and, on the other hand, it yields an MCMC algorithm for evaluating Bayesian inferences of interest. Moreover, we single out limitations of this algorithm when working with more than two populations and, consequently, devise an alternative more efficient sampling scheme, which as a by‐product, allows testing homogeneity between different populations. Finally, we perform a comparison with the nested Dirichlet process and provide illustrative examples of both synthetic and real data.
ActressMAS, a .NET Multi-Agent Framework Inspired by the Actor Model

Multi-agent systems show great promise in the actual state of increasing interconnectedness and autonomy of computer systems. This paper presents a .NET multi-agent framework for experimenting with agents and building multi-agent simulations. Its main advantages are conceptual simplicity and ease of use, which make it suitable for teaching agent-based notions. Several algorithms, protocols and simulations using this framework are also presented.
Communication-Free MPC-Based Neighbors Trajectory Prediction for Distributed Multi-UAV Motion Planning

In an environment with multiple static obstacles, UAVs usually communicate with each other to avoid collisions during trajectory planning.However, such communication may become infeasible or unreliable due to interference or jam in practice.This paper introduces a neighbors trajectory prediction algorithm based on model predictive control (MPC), which enables each UAV to predict the motion behavior of its neighbors without communication.By solving the MPC model of its neighbors, an UAV can predict their trajectories and then avoid collision with them in the future.To prove the practicability, we integrate the proposed algorithm into distributed model predictive control (DMPC) framework to realize multi-UAV trajectory planning without communication and with static obstacles.The performance of our method is verified by simulation experiments in two scenes.
How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

With the increasing number of items in electronic retailers, news websites, etc., finding interesting items concerning the taste of users is becoming more challenging. Recommender Systems (RS) are a well-known solution to this issue. Collaborative filtering (CF) is a widely accepted and popular technique to implement an RS. However, cold-start and data sparsity problems reduce the performance of CF methods. One promising solution for these issues is to use the social trust information. However, how to properly use social trust information is a hot and still open question. In this paper, we propose a similarity measure and a simple link prediction method to address this question and employ them in trust-aware matrix factorization. Especially, our proposed similarity measure is asymmetric to consider the nature of social relationships. Also, to have a more accurate similarity estimation, we have considered both the user’s historical ratings and trust relations, and we have determined the weight of each source. Finally, we have used the item-based model and the level of interest a user’s trustee have for an item to improve the performance of the proposed method for sparse datasets. We conduct extensive performance evaluations in terms of rate prediction and interesting items found. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method, especially in terms of Mean Absolute Error.
Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis

To investigate whether artificial-intelligence-based, computer-aided diagnosis (AI-CAD) could facilitate the detection of missed cancer on digital mammography, a total of 204 women diagnosed with breast cancer with diagnostic (present) and prior mammograms between 2018 and 2020 were included in this study. Two breast radiologists reviewed the mammographic features and classified them into true negative, minimal sign or missed cancer. They analyzed the AI-CAD results with an abnormality score and assessed whether the AI-CAD correctly localized the known cancer sites. Of the 204 cases, 137 were classified as true negative, 33 as minimal signs, and 34 as missed cancer. The sensitivity, specificity and diagnostic accuracy of AI-CAD were 84.7%, 91.5% and 86.3% on diagnostic mammogram and 67.2%, 91.2% and 83.38% on prior mammogram, respectively. The AI-CAD correctly localized 27 cases from 34 missed cancers on prior mammograms. The findings in the preceding mammography of AI-CAD-detected missed cancer were common in the order of calcifications, focal asymmetry and asymmetry. Asymmetry was the most common finding among the seven cases, which could not be detected by AI-CAD in the missed cases (5/7). The assistance of AI-CAD can be helpful in the early detection of breast cancer in mammography screenings.
Characterizing the Reproducibility of Noisy Quantum Circuits

The ability of a quantum computer to reproduce or replicate the results of a quantum circuit is a key concern for verifying and validating applications of quantum computing. Statistical variations in circuit outcomes that arise from ill-characterized fluctuations in device noise may lead to computational errors and irreproducible results. While device characterization offers a direct assessment of noise, an outstanding concern is how such metrics bound the reproducibility of a given quantum circuit. Here, we first directly assess the reproducibility of a noisy quantum circuit, in terms of the Hellinger distance between the computational results, and then we show that device characterization offers an analytic bound on the observed variability. We validate the method using an ensemble of single qubit test circuits, executed on a superconducting transmon processor with well-characterized readout and gate error rates. The resulting description for circuit reproducibility, in terms of a composite device parameter, is confirmed to define an upper bound on the observed Hellinger distance, across the variable test circuits. This predictive correlation between circuit outcomes and device characterization offers an efficient method for assessing the reproducibility of noisy quantum circuits.
A Qualitative Survey on Deep Learning Based Deep fake Video Creation and Detection Method

The rapid growth of Deep Learning (DL) based applications is taking place in this modern world. Deep Learning is used to solve so many critical problems such as big data analysis, computer vision, and human brain interfacing. The advancement of deep learning can also causes some national and some international threats to privacy, democracy, and national security. Deepfake videos are growing so fast having an impact on political, social, and personal life. Deepfake videos use artificial intelligence and can appear very convincing, even to a trained eye. Often obscene videos are made using deepfakes which tarnishes people's reputation. Deepfakes are a general public concern, thus it's important to develop methods to detect them. This survey paper includes a survey of deepfake creation algorithms and, more crucially we added some approaches of deepfake detection that proposed by researchers to date. Here we go over the problems, trends in the field, and future directions for deepfake technology in detail. This paper gives a complete overview of deepfake approaches and supports the implementation of novel and more reliable methods to cope with the highly complicated deepfakes by studying the background of deepfakes and state-of-the-art deepfake detection methods.
One View Is Not Enough: Review of and Encouragement for Multiple and Alternative Representations in 3D and Immersive Visualisation

The opportunities for 3D visualisations are huge. People can be immersed inside their data, interface with it in natural ways, and see it in ways that are not possible on a traditional desktop screen. Indeed, 3D visualisations, especially those that are immersed inside head-mounted displays are becoming popular. Much of this growth is driven by the availability, popularity and falling cost of head-mounted displays and other immersive technologies. However, there are also challenges. For example, data visualisation objects can be obscured, important facets missed (perhaps behind the viewer), and the interfaces may be unfamiliar. Some of these challenges are not unique to 3D immersive technologies. Indeed, developers of traditional 2D exploratory visualisation tools would use alternative views, across a multiple coordinated view (MCV) system. Coordinated view interfaces help users explore the richness of the data. For instance, an alphabetical list of people in one view shows everyone in the database, while a map view depicts where they live. Each view provides a different task or purpose. While it is possible to translate some desktop interface techniques into the 3D immersive world, it is not always clear what equivalences would be. In this paper, using several case studies, we discuss the challenges and opportunities for using multiple views in immersive visualisation. Our aim is to provide a set of concepts that will enable developers to perform critical thinking, creative thinking and push the boundaries of what is possible with 3D and immersive visualisation. In summary developers should consider how to integrate many views, techniques and presentation styles, and one view is not enough when using 3D and immersive visualisations.
A Blockchain and NLP Based Electronic Health Record System: Indian Subcontinent Context

The healthcare system in the Indian subcontinent is plagued with numerous issues related to the access, transfer, and storage of patient's medical records. The lack of infrastructure to properly communicate and track records between all key participants has allowed the distribution of counterfeit drugs, dependency on unsafe methods of communication, and lack of trust between patients and providers. During the global COVID-19 pandemic, the need for a robust communication and record tracking system has been further emphasized. To facilitate efficient communication and mitigate the mentioned issues, a nationwide EHR (electronic health record) system must be introduced to bring the healthcare system into digital space. To further enhance security, efficiency, and cost, the innovation of Blockchain is introduced. Blockchain is a decentralized data structure that allows secure transactions between untrusted parties without needing a central authority. In this paper, a Hyperledger fabric-based Blockchain Electronic Healthcare Record (EHR) system is proposed. The system is integrated with technologies such as NLP (Natural Language Processing), and Machine Learning to provide users with practical features.