Reviewerly/exHarmony huggingface dataset:
CLVIN: Complete language-vision interaction network for visual question answering

The emergence of the Transformer optimizes the interactive modeling of multimodal information in visual question answering (VQA) tasks, helping machines better understand multimodal information. The existing Transformer-based end-to-end methods have made some achievements in applying the Encoder-Decoder (E-D) mode or realizing complete interaction. However, almost no methods combine the advantages of the two well and give full play to them. Thus, this paper designs a complete language-vision interaction network (CLVIN) for VQA based on the implementation of the quadratic E-D mode. Based on the core framework of the modular co-attention network (MCAN), CLVIN achieves the complete interaction of multimodal information by using the E-D mode again, realizing the rational distribution of the question words’ weight information. In addition, to reduce the additional consumption of time and memory caused by introducing the quadratic E-D mode, this paper proposes a compact method called CLVIN-c through optimizing the underlying implementation of the scaled dot-product attention in Transformer. Finally, a series of experimental results based on the dataset VQA-v2.0 and CLEVR show that CLVIN has a significant performance improvement, and CLVIN-c achieves further optimizations in model size and performance. Code is available at https://github.com/RainyMoo/myvqa.
Designing a Private and Secure Personal Health Records Access Management System: A Solution Based on IOTA Distributed Ledger Technology

The privacy and security of patients' health records have been an ongoing issue, and researchers are in a race against technology to design a system that can help stop the compromising of patient data. Many researchers have proposed solutions; however, most solutions have not incorporated potential parameters that can ensure private and secure personal health records management, which is the focus of this study. To design and develop a solution, this research thoroughly investigated existing solutions and identified potential key contexts. These include IOTA Tangle, Distributed Ledger Technology (DLT), IPFS protocols, Application Programming Interface (API), Proxy Re-encryption (PRE), and access control, which are analysed and integrated to secure patient medical records, and Internet of Things (IoT) medical devices, to develop a patient-based access management system that gives patients full control of their health records. This research developed four prototype applications to demonstrate the proposed solution: the web appointment application, the patient application, the doctor application, and the remote medical IoT device application. The results indicate that the proposed framework can improve healthcare services by providing immutable, secure, scalable, trusted, self-managed, and traceable patient health records while giving patients full control of their own medical records.
A multi-view clustering algorithm based on deep semi-NMF

Multi-view clustering (MVC) aims to fuse the information among multiple views to achieve effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization (SNMF) typically have two issues: (1) their optimization schemes are not flexible enough; and (2) the variables are updated only rely on the data but not guided by learning rate. These problems can result in very poor clusters generated. In this paper, we present a multi-view clustering algorithm based on deep SNMF (MCDS) to resolve these issues. Specifically, we first design two types of activation functions to restrict the value domain of the element in the low-dimensional matrix to eliminate the constraint. Then, the SGD algorithm is used to implement element update guided by the learning rate. After obtaining the corresponding weight matrix and bias matrix, we combine them with the activation functions to construct a deep SNMF (DSNMF) network. This network is to update the element in the corresponding low-dimensional matrix for each view and obtain the consensus matrix. To validate the proposed algorithm, numerous experiments are performed on six multi-view datasets including both normal and large-scale datasets. The results demonstrate that MCDS can achieve excellent clustering results and outperform other competitive methods.
FinTech finance and social-environmental performance around the world

We examine the influence of the country-level value of FinTech finance on social-environmental performance across 58 countries during the period of 2013–2019. Our analysis incorporates additional economic, institutional, and social factors to capture the multifaceted nature of the effect. Our findings reveal a small yet statistically significant positive impact of FinTech finance on social-environmental performance. This effect remains robust across various measures and estimation methods, as well as through sensitivity and endogeneity analyses. While we do not observe nonlinear effects, we do identify lagged effects. Our study contributes to the limited existing literature by utilizing a large sample of countries and employing aggregate values of FinTech finance to predict long-term social-environmental performance. As a result, our findings hold implications for policy considerations.
The importance of resource awareness in artificial intelligence for healthcare

Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide range of healthcare applications, from medical image computing and analysis to continuous health monitoring and management. Recent data have demonstrated a clear trend that AI/ML model sizes, as well as their computational complexity, memory consumption and the scale of the required training data and costs, are experiencing an exponential increase. The developments in current computing hardware platforms, storage infrastructure, networking and domain expertise cannot keep up with this exponential growth in resources demanded by the AI/ML models. Here, we first analyse this recent trend and highlight that there are resource sustainability issues in AI/ML for healthcare. We then present various algorithm/system innovations that will help address these issues. We finally outline future directions to proactively and prospectively tackle these resource sustainability issues. Sustainability awareness is lacking in the development of AI systems and algorithms for healthcare. The authors discuss resource sustainability issues in energy, storage and domain knowledge, and present potential solutions.
Design implications of generative AI systems for visual storytelling for young learners

The study examines the design implications of leveraging generative AI tools such as ChatGPT, Stable Diffusion, Midjourney for literacy development and creative expression for children [6, 8, 18]. We sought to elicit insights on the applicability of generative AI for educational purposes from various stakeholders (i.e., parents, teachers, and AI researchers). We recruited nine participants to elicit their perspectives on designing a visual narrative app with generative AI. We examined the opportunities and limitations of the current generative AI tools. Using the implications from our evaluation, we propose AIStory, an AI-powered visual storytelling application prototype that can be used for children’s creative expression, storytelling, and literacy development.
Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.
Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Abstract Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.
CSI-SharK: CSI-FiSh with Sharing-friendly Keys

CSI-FiSh is one of the most efficient isogeny-based signature schemes, which is proven to be secure in the Quantum Random Oracle Model (QROM). However, there is a bottleneck in CSI-FiSh in the threshold setting, which is that its public key needs to be generated by using $$k-1$$ secret keys. This leads to very inefficient threshold key generation protocols and also forces the parties to store $$k-1$$ secret shares. We present CSI-SharK, a new variant of CSI-FiSh that has more Sharing-friendly Keys and is as efficient as the original scheme. This is accomplished by modifying the public key of the ID protocol, used in the original CSI-FiSh, to the equal length Structured Public Key (SPK), generated by a single secret key, and then proving that the modified ID protocol and the resulting signature scheme remain secure in the QROM. We translate existing CSI-FiSh-based threshold signatures and Distributed Key Generation (DKG) protocols to the CSI-SharK setting. We find that DKG schemes based on CSI-SharK outperform the state-of-the-art actively secure DKG protocols from the literature by a factor of about 3, while also strongly reducing the communication cost between the parties. We also uncover and discuss a flaw in the key generation of the actively secure CSI-FiSh based threshold signature Sashimi, that can prevent parties from signing. Finally, we discuss how (distributed) key generation and signature schemes in the isogeny setting are strongly parallelizable and we show that by using C independent CPU threads, the total runtime of such schemes can basically be reduced by a factor C. As multiple threads are standard in modern CPU architecture, this parallelizability is a strong incentive towards using isogeny-based (distributed) key generation and signature schemes in practical scenarios.
The graph attention network and its post-hoc explanation for recognizing mineralization-related geochemical anomalies

Deep learning algorithms have become a cutting-edge technology for mining geochemical survey data to identify geochemical patterns related to mineralization. Similarities in the origins of the same types of mineral deposits may result in similarities in the observed geochemical anomalies to some extent. However, image-based models have limited ability to model the relationships between samples because fixed-size pixel patches are not connected. Graphs have enormous potential to capture spatial information, which can model the complex nonlinear spatial relationship between vertices and edges and effectively measure the spatial relationships between metallogenic information and geochemical survey sites. The graph-based model considers both labeled and unlabeled data as inputs, allowing the relationships between individual samples to be incorporated into the network. In this study, we used a graph-based model, the graph attention network (GAT), to recognize geochemical anomalies associated with gold mineralization in the Xiaoqinling–Xiong'ershan region of the western Henan Province, China. The GNNExplainer, a method for explaining the predictions of graph-based deep learning tasks, was used to determine the importance of the input features of the trained GAT model. The results indicated that the model regards Bi, Cd, Mn, Zn, and Mo as important geochemical elements for mineralization. Meanwhile, a comparative study of GAT with a convolutional neural network suggests that the graph-based model can be applied to effectively identify mineralization-related geochemical patterns, and geochemical anomalies recognized by GAT are more representative of gold mineralization.
Chinese mineral question and answering system based on knowledge graph

Mineral knowledge acquisition is indispensable in geological research. Currently, mineral knowledge can be acquired though a search engine like Google, or though some mineral databases. But usually, the answers from the search engines are not professional enough, and the mineral databases cannot answer the questions input in natural language. In this paper, a Chinese mineral question and answering system based on knowledge graph is proposed with the aim of answering the mineral questions in Chinese natural language professionally, accurately and efficiently. Two deep learning models based on BERT are built to obtain mineral entities and relationships to construct the mineral knowledge graph. After the mineral knowledge graph is constructed, accordingly question templates are defined and another deep learning model based on BERT is built to recognize the intent and the entity/attribute of the input question. The query statement is generated according to the question template pre-defined and is input to the mineral knowledge graph constructed. Then the answer to the question is returned by the knowledge graph. The knowledge graph we constructed has 22,568 entities and 91,699 relationships, which is richer than most of the geological knowledge graph. The question and answering system achieve a 91.2% accuracy on the 2,000 test questions.
Security and privacy protection technologies in securing blockchain applications

As a key technology for building trust networks and implementing value interconnections, blockchain has received extensive attention. There are numerous technical use cases for blockchains in a variety of areas, such as digital currency systems, Internet of Things (IoT), smart grids, supply blockchain and finance. However, the widespread application of blockchain also encounters many security and privacy issues. This article presents an overview and analysis of privacy protection, security supervision and data exchange in blockchain-based applications, providing a comprehensive analysis of the wide application of blockchain. Moreover, blockchain's future research directions and development trends are discussed.
Cryptanalyzing an image cipher using multiple chaos and DNA operations

Recently, an improved color image cipher (ICIC-DNA) based on multiple deoxyribonucleic acid (DNA) sequence operations with DNA synthetic image and chaos was proposed. ICIC-DNA features the use of multiple chaotic systems and diverse DNA operations. However, after our careful cryptanalysis, we found that ICIC-DNA has several fatal security flaws. First, despite the use of multiple chaotic systems, the corresponding encryption sequences are independent of the plaintext, such that equivalent keys exist. Second, the diverse DNA operation is essentially a 2-bit data substitution process, and thus can be equivalently simplified. Third, ICIC-DNA includes substitution and permutation of DNA domains, and based on the equivalent simplification operation, the substitution and permutation can be attacked separately. Based on these, we propose a chosen-plaintext attack method to attack ICIC-DNA. Differential analysis is firstly adopted to break the DNA-base permutation process, and then the DNA domain encryption is eliminated, and finally the equivalent key is used to achieve complete cracking. Theoretical analysis and experimental results show that the proposed attack method is effective and has low computational complexity and data complexity.
Logical English for Law and Education

In this paper we present the key features of Logical English as syntactic sugar for logic programming languages such as pure Prolog, ASP and s(CASP); and we highlight two application areas, coding legal rules, and teaching logic as a computer language for children.
Some Thoughts on How to Teach Prolog

Prolog, and (Constraint) Logic Programming in general, represent a unique programming paradigm. Prolog has many characteristics that are not present in other styles of programming, and this is one of the reasons why it is taught. At the same time, and precisely because of this uniqueness, teaching Prolog presents some special challenges. In this paper we present some lessons learned over many years of teaching Prolog, and (C)LP in general, mostly to CS college students, at several universities. We address how to show the beauty and usefulness of the language, and also how to avoid some common pitfalls, misconceptions, and myths about it. The emphasis of our discussion is on how, rather than what. Despite some focus on CS college students, we believe that many of the ideas that we propose also apply to teaching Prolog at any other education level.
An intelligent routing algorithm for energy prediction of 6G-powered wireless sensor networks

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling.
High‐fidelity direct contrast synthesis from magnetic resonance fingerprinting

This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations.To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. The performance of our proposed method is demonstrated on in vivo MRF scans from healthy volunteers. Quantitative metrics, including normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID), were used to evaluate the performance of the proposed method and compare it with others.In-vivo experiments demonstrated excellent image quality with respect to that of simulation-based contrast synthesis and previous DCS methods, both visually and according to quantitative metrics. We also demonstrate cases in which our trained model is able to mitigate the in-flow and spiral off-resonance artifacts typically seen in MRF reconstructions, and thus more faithfully represent conventional spin echo-based contrast-weighted images.We present N-DCSNet to directly synthesize high-fidelity multicontrast MR images from a single MRF acquisition. This method can significantly decrease examination time. By directly training a network to generate contrast-weighted images, our method does not require any model-based simulation and therefore can avoid reconstruction errors due to dictionary matching and contrast simulation (code available at:https://github.com/mikgroup/DCSNet).