Reviewerly/exHarmony huggingface dataset:
LMQF: Hyperledger Fabric Concurrent Transaction Conflict Solution Based on Distributed Lock and Message Queue

Hyperledger Fabric, which satisfies decentralization and security, is one of the most popular blockchain distributed ledger technologies, but in high-throughput, high-concurrency distributed scenarios, concurrency conflicts can cause transactions to fail, resulting in significant network bandwidth and resource waste. In this paper, we deeply analyze the operation flow of Fabric’s underlying transactions, and propose a solution to concurrent transaction conflicts in Fabric based on distributed locks and message queues. Redis is used to implement the locking mechanism of concurrent keys, and the ChaincodeEvent event listener is used to detect the transactions and realize the unlocking operation; on this basis, the message asynchronous retry mechanism, i.e., use the message queue RabbitMQ to re-consume the conflicting transactions to solve the concurrent transaction conflicts encountered against Fabric at present. Experiments show that the proposed approach can significantly improve the concurrency capability of the system, including transaction success rate and effective transaction throughput, under different levels of transaction conflicts; it does not affect the performance of the system in the absence of transaction conflicts.
NFT Cross-Chain Transfer Method Under the Notary Group Scheme

With the development of blockchain, the demand for the cross-chain transfer of the on-chain digital assets, such as Non-Fungible Tokens (NFTs), is increasing. The notary scheme is a kind of cross-chain mechanisms with low complexity to achieve the above demand. Although many NFT cross-chain methods and protocols have been invented, the method based on the notary scheme has not been widely studied at this stage. Therefore, we improve the traditional notary scheme and propose an NFT cross-chain transfer model with it. In this paper, we introduce NCTN, a trusted NFT cross-chain transfer model based on the notary group and design the cross-chain transaction protocol in detail according to NCTN’s architecture. To ensure the availability of the model, we then present an effective reputation value model, which is used as the basis for the reliable election in the notary group. Experiments show that our model can well complete the tasks of cross-chain transfers of NFTs and has good performance in transaction response time; our reputation value model can well avoid the problem of excessive centralization and can ensure the reliability of notaries.
Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation

Knowledge concept recommendation is a kind of fine-grained recommendation in massive open online courses (MOOCs) scenario, user interaction data has the characteristics of strong collaborative signals and imbalanced interactions. This leads to a single recommendation and reduced accuracy. Recently, the ability of contrastive learning (CL) in mitigating interaction imbalance in recommender systems has received widespread attention. CL requires the use of augmentation methods to generate different views. Existing augmentation methods (1) augment only at the topology or feature level ignoring semantic or structural information, and (2) undifferentiated augmentation tends to lose the critical information. In this paper, we propose Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation (GCARec). Specifically, (1) topology level adaptive augmentation based on degree centrality captures critical structural information, and then (2) feature level adaptive augmentation based on degree centrality captures critical semantic information. Comprehensive experiments show that our proposed approach can outperform other baselines. Our implementations are available at https://github.com/DingZhaoyuan/GCARec.
Low Light Image Illumination Adjustment Using Fusion of MIRNet and Deep Illumination Curves

Images that are captured in low lighting conditions present a number of challenges, including a lack of brightness and contrast as well as color distortion. The purpose of low-light image enhancement is to improve the visual effects of an image for the purpose of benefiting later image processing and computer vision tasks; yet, it offers a difficult challenge. Enhancing low-light images may be done quickly and effectively using the technique that is proposed in this work. The proposed model consists of two phases. Phase one produces a bright image from a low light image using MIRNet. The conventional deep learning-based models have difficulty in reproducing the color and texture features. This is because of the limited availability of image pairs during training. In the second phase, deep illumination curves are used to produce pixel wise higher order tonal curves for image illumination adjustment. This procedure over-illuminates certain parts of the image with light sources. The results of the two phases are fused to obtain a uniform high illumination adjusted output. The proposed model uses contrast based fusion which eliminated the drawbacks of both the traditional approaches and produces results with higher efficiency.
Assessing Reading Patterns of Learners Through Eye Tracking

Advancement in technology brings about the advent of E-learning which replaces the traditional way of conducting examination using paper and pen. Determining student academic growth merely based on assessment scores is inadequate to help them learn and grow from their mistakes. Non-verbal inkling such as eye gaze can provide insights on the motive and contemplation of the participants that may have affected their performance. The addition of this knowledge can help in obtaining a more realistic system for determining participant performance. This research focuses on 3 different reading patterns of participants and to label them into various categories. It was observed that most of the participants belonged to the novice category, while there was an equal distribution among the participants exhibiting organized and unorganized reading pattern classes, and participants that employed the scanning method tend to answer questions correctly.
Addressing Challenges in Healthcare Big Data Analytics

The exponential growth of healthcare data poses significant challenges for clinical researchers who strive to identify meaningful patterns and correlations. The complexity of this data arises from its high dimensionality, sparsity, inaccuracy, incompleteness, longitudinality, and heterogeneity. While conventional pattern recognition algorithms can partially address issues related to high dimensionality, sparsity, inaccuracy, and longitudinality, the problems of incompleteness and heterogeneity remain a persistent challenge, particularly when analyzing electronic health records (EHRs). EHRs often encompass diverse data types, such as clinical notes (text), blood pressure readings (longitudinal numerical data), MR scans (images), and DCE-MRIs (longitudinal video data), and may only include a subset of data for each patient at any given time interval. To tackle these challenges, we propose a kernel-based framework as the most suitable approach for handling heterogeneous data formats by representing them as matrices of equal terms. Our research endeavours to develop methodologies within this framework to construct a decision support system (DSS). To achieve this, we advocate for the incorporation of preprocessing mechanisms to address the challenges of incompleteness and heterogeneity prior to integration into the kernel framework.
Conversational Artificial Intelligence in Digital Healthcare: A Bibliometric Analysis

Conversational artificial intelligence is a subfield of the broader spectrum of AI that refers to tools and technologies, such as chatbots and virtual agents, that can converse with humans. Conversational AI uses natural language understanding, language processing, machine learning, and reasoning capabilities to mimic human cognition. These agents may find applications in sectors such as education, healthcare, and marketing, to name a few. Recent years witnessed exponential growth in conversational AI research and development, introducing more intelligent agents with near-human-like conversation capabilities. Healthcare is one area that witnessed the rapid adoption of healthcare agents, including patient-facing use cases. This bibliometric analysis summarizes and analyzes recent and prominent research in conversational AI in healthcare. This work poses many research questions and attempts to answer them using the derived insights. This may be highly useful for researchers and practitioners of various avenues of the digital healthcare sector to understand the research trends in conversational artificial intelligence.
A Hybrid Intelligent Cryptography Algorithm for Distributed Big Data Storage in Cloud Computing Security

Present Cloud computing is the most adaptable and popular technology. Through the services, customer gets efficiency, competence and quickness etc. in their work. Large data centers are available in the cloud to process the large volume of data. Cloud Computing (CC) is used by businesses to manage their large information load. It is becoming increasingly popular for organizations to store their data in the cloud, which provides reliable access from anywhere. Because numerous customers use the same cloud, data security is the primary issue with cloud computing. Security in cloud computing is a subject of current research issues. Hence in order to solve these issues, a hybrid intelligent cryptography algorithm for distributed big data storage in cloud computing security is presented. The RSA (Rivest-Shamir-Adleman) asymmetric algorithm covers authentication, whereas the Blowfish symmetric algorithm manages data confidentiality. Both symmetric and asymmetric cryptographic techniques have been used in the creation and construction of this hybrid cryptosystem. The experimental evaluations demonstrate that approach successfully protects the system and requires a reasonable amount of time to implement. Both security and efficiency performance will be evaluated during in the experimental results.
Improving Software Effort Estimation with Heterogeneous Stacked Ensemble Using SMOTER over ELM and SVR Base Learners

Software engineering projects can be complex and involve many different tasks and activities, such as collecting requirements, design, coding, testing, and deployment. Estimating the effort needed for the project is one of the first phases in the creation of software projects. For a software engineering project to be successfully completed, the amount of work needed to execute it must be precisely estimated. The paper suggests a novel method to calculate the effort needed to construct a software project by employing a heterogeneous stacked ensemble comprising of two base learners, namely Extreme Learning Machine (ELM) and Support Vector Regressor (SVR) and it also aims to investigate the effectiveness of Synthetic Minority Over-Sampling Technique for Regression (SMOTER) in predicting software project effort. The results indicate that the proposed model enhances the performance of the base models, resulting in a reduction of 35.7% in MAE, 24.1% in RMSE, and 18.3% in R-value. After the implementation of SMOTER, a noteworthy reduction in the error of the proposed model has been observed.
Artificial Intelligence as a Service: Providing Integrity and Confidentiality

As Artificial Intelligence technologies are being vigorously used, there are major concerns about privacy, security and compression of the data. Bulk amounts of data are being stored in the cloud and the same will be transmitted to the parties that offer AI software services or platform services. The three key features that are to considered while transferring the data from cloud to Artificial Intelligence as a service (AIaaS) or Machine Learning as a service (MLaaS) are Data Compression, Data Integrity and Data Confidentiality. There is high demand for data processing which in turn is driving us to perform data compression. Data compression has to be done whether it is with Artificial Intelligence or Cloud Computing or Machine learning algorithms. Because without compressing the data such huge amounts of data whether it is text or multimedia application cannot be stored as it is. In this paper we have used an optimized lossless compression algorithm. When bulk amounts of data is being transferred from platform services to cloud, the foremost thing that has to be done is categorization of data i.e. the data that is critical and which needs integrity and the data that can be read by the users on the network. The critical data which needs AI services should be checked whether they are transmitted as it is. The data that is being sent by the cloud user should reach the service providing platforms without being modified. To maintain such integrity to the data, Hashing can be used. In this paper we have proposed a hashing algorithm that is implemented after performing data compression. The generated hash value is used as an attribute in generating keys for encryption.
Redefining the World of Medical Image Processing with AI – Automatic Clinical Report Generation to Support Doctors

In this study, we focus on generating readable automatic assisted clinical reports using medical scans/ images. This project is beneficial when it comes to quick analysis of the medical condition and providing on-time treatment. The challenge in this project is the achievement of clinical accuracy and reduce data bias. On the other hand, this endeavor is essential in meeting medical demands in nations with limited resources due to the shortage of radiologists and radiology training programs. Medical picture captioning or report production is the issue that we'll be solving in this case study. Fundamentally, we must use a Convolutional Neural Network (CNN) or a transfer learning algorithm to extract features (bottleneck features) from the photos (preferable as we have less amount of data). Use these traits that were extracted afterward to anticipate the captions. The result would be a string of words resembling a radiologist's report, where a typical report generated by radiologists includes a summary of the findings, a rationale for the examination, and a history of the patient. We were able to create the Python code with this goal in mind. The predictions were analyzed using greedy and beam search techniques. While beam search was shown to create proper phrases, a greedy search was discovered to be significantly faster. In conclusion, a custom final model that uses greedy search is determined to be the best model for this project based on the Bleu score.
Unified Transformer With Isomorphic Branches for Natural Language Tracking

Natural language tracking aims to localize the target object referred to by a language description using a sequence of bounding boxes in video frames. Compared with traditional visual single object tracking initialized with only a bounding box (BBox), this task introduces high-level semantic information to reduce the ambiguity of BBox and enhance the ability to retrieve the target in a global manner. Thus, it can yield more accurate and robust tracking results. Previous methods usually adopt off-the-shelf grounding and tracking branches to tackle this task, where feature representations are learned in isolation without benefiting each other. The two branches can associate with each other to discover crucial clues since the language description and the template image provide information from different sources. Therefore, we propose a unified transformer method for natural language tracking named TransNLT, which utilizes isomorphic Transformer structures for grounding and tracking branches where collaborative learning is enabled to construct comprehensive features for the target. In addition, we propose a Selective Feature Gathering (SFG) module which can integrate cross-modal global information of the tracking target from the visual template and language description. Through effective interaction of visual and language information, we can achieve better results than tracking with only a single modality. Extensive experiments on three popular natural language tracking benchmarks show our proposed TransNLT outperforms previous state-of-the-art methods.
Visualization Recommendation for Incremental Data Based on Intent

Data Visualization is a pictorial representation that transforms data into visual elements. Visualizations are generally recommended for a dataset based on the user’s requirement. Visualization Recommendation is done generally with the help of experts in their domain field which is time consuming and very expensive. Recommendation can also be done based on statistical features extracted from the dataset and applying the machine learning model. There are existing works of Visualization Recommendation based on intent from the user. In this approach visualization is categorized into visualization type and visualized columns. Visualization type (e.g., Pie, Bar, and Line charts) is predicted using the BiDA model, Visualized columns are identified by leveraging the pretrained model BERT. The incremental data could correspond to big data where volume and velocity of data is added which is a challenging task to handle in visualization recommendation. To overcome this challenge we proposed Incremental Visualization Recommendation (IVR) where the recommendation algorithm is not used from scratch (updated data) rather, we use the recommendation algorithm only for the incremental segment of the data. Our experiments on benchmark datasets consume less time and show good results.
Lightweight Blockchain-Based Secure Spectrum Sharing in Space–Air–Ground-Integrated IoT Network

Unmanned aerial vehicles (UAVs) will be widely deployed due to their flexibility, mobility, and miniaturization, providing the necessary support for spectrum sharing between different communication systems in the space–air–ground-integrated IoT network (SAGIN). However, there are potential security threats to spectrum sharing among different communication systems due to the openness of a wireless network, the unreliability of node behavior, and the trust barriers of the networks. In this article, a secure spectrum sharing scheme based on lightweight UAV-blockchain (LUBC) is proposed to address the above security issues. First, a spectrum sharing model based on the overlay mode is developed to improve the spectrum efficiency of SAGIN, where UAVs relay signals from the satellite to the ground users in exchange for spectrum access opportunities and serve their own users simultaneously in the nonorthogonal multiple access (NOMA) mode. Second, a secure spectrum sharing framework based on LUBC is proposed to solve the security and privacy issues of spectrum trading in SAGIN. Then, aiming at maximizing the primary user’s throughput under the premise of meeting the minimum power allocation factor of UAV network, the spectrum auction based on NOMA is formulated as a multirelay selection optimization problem, which is solved by the blockchain-based sequential Vickrey auction mechanism. Finally, the security evaluation and numerical results are conducted to verify the security and effectiveness of the proposed spectrum sharing scheme for SAGIN.
Using Non-Monotonic Logics to Create a Dynamic Framework for a Behavior Support Agent

Behavior support agents can assist humans in accomplishing a variety of goals by suggesting actions that promote the desired outcome while being in line with the user’s needs and preferences. In order to make these agents more effective, flexible and responsible, this research aims to create a framework which allows for more interaction between the agent and the user. By using techniques from non-monotonic reasoning, this work aims to model the knowledge base of the agent so that it aligns with the user’s mental model and is able to be modified by the user through new input. In order for the agent to be able to explain its output to the user, the reasoning process needs to be explicit and traceable, which this work intends to incorporate into a logical framework.
A collusion-resistant certificateless provable data possession scheme for shared data with user revocation

Cloud storage service can provide a lot convenience for users to collect, store and share data within a group. However, there are some secure issues, one of which is that the cloud server may cheat users for a good reputation when the data is lost. A classical solution to deal with it is provable data possession (PDP) protocols. Most of PDP protocols are relied on either the public key infrastructure or identity-based cryptography. However, certificate management and key escrow issues place a significant burden on this. Furthermore, revoking the group's illegal users is a critical issue for PDP schemes. To address these problems, we put forward a certificateless PDP scheme for shared data with user revocation. Our proposed scheme not only achieves efficient user revocation but also withstands collusion attack. We also give a formal proof of security of our proposed scheme without random oracle model. Experimental results and analysis show that our scheme is quite effective in data auditing, verification, and user revocation.
Programming an n-Back task in Qualtrics using HTML and JavaScript

The n-back task is an extensively used cognitive test that assesses working memory.The task is well-suited to virtual administration as it reliably produces similar results to in-person administration and is easily adapted to asynchronous operation.However, the procedure to program this task into various computer programs and software is not widely known.Therefore, this tutorial aims to provide researchers with simple yet detailed step-by-step instructions on how to program an n-back task in Qualtrics using HTML and JavaScript.This tutorial is meant to be easily followed by the layperson without extensive knowledge of computer programming.
Correction to: A Line Search Based Proximal Stochastic Gradient Algorithm with Dynamical Variance Reduction

This file contains a revised version of the proofs of Theorems 3 and 4 of the paper [1]. In particular, a more correct argument is employed to obtain the inequality (A11) from (A10), provided that a stronger hypothesis on the sequence $$\{\varepsilon _k\}$$ is included. The practical implementation of the algorithm (Section 3) remains as it is and all the numerical experiments (Section 4) are still valid since the stronger hypothesis on $$\{\varepsilon _k\}$$ was already satisfied by the selected setting of the hyperparameters.
Turning the Cacophony of the Internet's Tower of Babel into a Coherent General Collective Intelligence

Increasing the number, diversity, or uniformity of opinions in a group does not necessarily imply that those opinions will converge into a single more “intelligent” one, if an objective definition of the term intelligent exists as it applies to opinions. However, a recently developed approach called human-centric functional modeling provides what might be the first general model for individual or collective intelligence. In the case of the collective intelligence of groups, this model suggests how a cacophony of incoherent opinions in a large group might be combined into coherent collective reasoning by a hypothetical platform called “general collective intelligence” (GCI). When applied to solving group problems, a GCI might be considered a system that leverages collective reasoning to increase the beneficial insights that might be derived from the information available to any group. This GCI model also suggests how the collective reasoning ability (intelligence) might be exponentially increased compared to the intelligence of any individual in a group, potentially resulting in what is predicted to be a collective superintelligence.