Reviewerly/exHarmony huggingface dataset:
Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies

A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security.This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS.A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS.A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies.This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.
7‐1: <i>Invited Paper:</i> UDC Technology for OLED Display

UDC (Under‐Display Camera) technology is a technology breakthrough that can most likely solve the contradiction between the display integrity and front camera. It can improve the integrity of the display and the proportion of display screen. This paper analyzes the difficulties and challenges of the UDC technology, presents the progress of each key technical point and explores potential solutions. In the end, the paper introduces the UDC technology development and productions in Visionox Company.
Serverless FaaS Scalability Evaluation: An ECG Signal Processing Use Case

The serverless approach provides a completely new way of developing cloud services, service scalability, and elasticity by utilizing the container-level virtualization and abstraction. In this paper, we present a use case of an application that processes streaming electrocardiograms by implementing the Function as a Service approach. Several experiments are conducted to evaluate the scalability and elasticity performance by checking the following hypothesis: The system will be highly scalable keeping the same response time and throughput, for up to 7000 data streams. To check the validity of the hypothesis we will provide an experimental research on the following research questions to analyze the performance behaviour of response and throughput with the increased load of parallel data streams testing the following cases: A) Function that will generate a variable number of data streams, B) Functions that are invoked sequentially, and C) Functions that are invoked in parallel. Our use case proved that system scales horizontally and satisfies all requests. When the workload has linearly increased the system has linearly increased throughput and results in a similar response time for each individual request.
Intelligent Request Strategy Design in Recommender System

Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a stream of recommended items consisting of successive pages that can be browsed by scrolling. In waterfall RS, when a user finishes browsing a page, the edge (e.g., mobile phones) would send a request to the cloud server to get a new page of recommendations, known as the paging request mechanism. RSs typically put a large number of items into one page to reduce excessive resource consumption from numerous paging requests, which, however, would diminish the RSs' ability to timely renew the recommendations according to users' real-time interest and lead to a poor user experience. Intuitively, inserting additional requests inside pages to update the recommendations with a higher frequency can alleviate the problem. However, previous attempts, including only non-adaptive strategies (e.g., insert requests uniformly), would eventually lead to resource overconsumption. To this end, we envision a new learning task of edge intelligence named Intelligent Request Strategy Design (IRSD). It aims to improve the effectiveness of waterfall RSs by determining the appropriate occasions of request insertion based on users' real-time intention. Moreover, we propose a new paradigm of adaptive request insertion strategy named Uplift-based On-edge Smart Request Framework (AdaRequest). AdaRequest 1) captures the dynamic change of users' intentions by matching their real-time behaviors with their historical interests based on attention-based neural networks. 2) estimates the counterfactual uplift of user purchase brought by an inserted request based on causal inference. 3) determines the final request insertion strategy by maximizing the utility function under online resource constraints. We conduct extensive experiments on both offline dataset and online A/B test to verify the effectiveness of AdaRequest. Remarkably, AdaRequest has been deployed on the Waterfall RS of Taobao and brought over 3% lift on Gross Merchandise Value (GMV).
Visual Tracking with FPN Based on Transformer and Response Map Enhancement

Siamese network-based trackers satisfy the balance between performance and efficiency for visual tracking. However, they do not have enough robustness to handle the challenges of target occlusion and similar objects. In order to improve the robustness of the tracking algorithm, this paper proposes visual tracking with FPN based on Transformer and response map enhancement. In this paper, a feature pyramid structure based on Transformer is designed to encode robust target-specific appearance features, as well as the response map enhanced module to improve the tracker’s ability to distinguish object and background. Extensive experiments and ablation experiments are conducted on many challenging benchmarks such as UAV123, GOT-10K, LaSOT and OTB100. These results show that the tracking algorithm we proposed in this paper can effectively improve the tracking robustness against the challenges of target occlusion and similar object, and thus improve the precision rate and success rate of the tracking algorithm.
Polyglot studies in spring 2022

This paper is a review of academic conferences on polyglottery that took place in spring 2022, namely a paper at the conference “Integrative and Cross-Cultural Approaches to the Study of Thinking and Language”, the 2nd International Seminar on Linguistics &amp; Polyglot Studies, and the round table “Research on Polyglottery: Theoretical Analysis and Practice” as part of the 20th International Symposium on Psycholinguistics and Communication Theory. Discussed are polyglots’ cognitive skills, self-monitoring instruments and learning strategies, the hyperintensive method, cognitive metaphors, social perception of polyglottery, common principles behind language processing by brain and by machine, and other issues.
Systems research on artificial intelligence

Systems Research and Behavioral ScienceVolume 39, Issue 3 p. 359-360 EDITORIAL Systems research on artificial intelligence Li Da Xu, Li Da Xu Special Issue Editor orcid.org/0000-0002-3263-5217 Old Dominion University, Norfolk, Virginia, USASearch for more papers by this author Li Da Xu, Li Da Xu Special Issue Editor orcid.org/0000-0002-3263-5217 Old Dominion University, Norfolk, Virginia, USASearch for more papers by this author First published: 29 June 2022 https://doi.org/10.1002/sres.2839Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume39, Issue3Special Issue: Systems Research on Artificial IntelligenceMay/June 2022Pages 359-360 RelatedInformation
Word frequency and text complexity: an eye-tracking study of young Russian readers

Although word frequency is often associated with the cognitive load on the reader and is widely used for automated text complexity assessment, to date, no eye-tracking data have been obtained on the effectiveness of this parameter for text complexity prediction for the Russian primary school readers. Besides, the optimal ways for taking into account the frequency of individual words to assess an entire text complexity have not yet been precisely determined. This article aims to fill these gaps. The study was conducted on a sample of 53 children of primary school age. As a stimulus material, we used 6 texts that differ in the classical Flesch readability formula and data on the frequency of words in texts. As sources of the frequency data, we used the common frequency dictionary based on the material of the Russian National Corpus and DetCorpus - the corpus of literature addressed to children. The speed of reading the text aloud in words per minute averaged over the grades was employed as a measure of the text complexity. The best predictive results of the relative reading time were obtained using the lemma frequency data from the DetCorpus. At the text level, the highest correlation with the reading speed was shown by the text coverage with a list of 5,000 most frequent words, while both sources of the lists - Russian National Corpus and DetCorpus - showed almost the same correlation values. For a more detailed analysis, we also calculated the correlation of the frequency parameters of specific word forms and lemmas with three parameters of oculomotor activity: the dwell time, fixations count, and the average duration of fixations. At the word-by-word level, the lemma frequency by DetCorpus demonstrated the highest correlation with the relative reading time. The results we obtained confirm the feasibility of using frequency data in the text complexity assessment task for primary school children and demonstrate the optimal ways to calculate frequency data.
A new approach to compute deficiency number of Mahjong configurations

• An efficient algorithmic approach to compute deficiency number of Mahjong configuration is designed, i.e., the number of tiles the player needs to change minimally for the winning. • The algorithm is implemented by using the programming language Python, and its performance is measured through more than 1.2 million test cases without duplication. • The algorithm can deal with each test case within one fiftieth of a second, and the average number of computation branches is only 7.784. Mahjong is a traditional Chinese tile-based game with long history, and now it is world-widely played in many different countries. An important issue on Mahjong topic is how many tiles the player needs to change minimally for the winning, i.e., the so-called deficiency number , which plays a significant role in AI development for Mahjong game. However, deficiency number cannot be easily computed due to a large amount of possible combinations of tiles. In this work, we propose an efficient algorithmic approach to compute the deficiency number of the tiles in players’ hands with the assistance of the programming language Python . In order to evaluate the performance of the approach, we established more than 1.2 million test cases for various deficiency numbers. Finally, the experimental results show the efficiency of our approach.
Personas Characterising Secondary School Mathematics Students: Development and Applications to Educational Technology

Information technology plays an increasingly prominent role in our personal and professional lives. It also plays an important role in schools, and especially in mathematics education. To realise their full potential in education, technologies should be designed in a way that addresses the characteristics of the target students. In the context of learning mathematics, students often experience anxiety with regard to the content that needs to be learnt; therefore, this work considers mathematics anxiety as a characteristic that should be particularly relevant when designing technologies that assist with teaching and learning mathematics. The development of personas, i.e., prototypical and simplified user descriptions, is a widely used tool in user experience (UX) research that supports design processes. In this methodological paper, we combine this UX methodology with a grounded theory approach. This combination of methodologies could facilitate the establishment of guidelines for developing personas representing secondary school mathematics students, which constitutes the goal of this paper. We review existing persona development techniques in other fields and adapt them to the context of secondary mathematics education using qualitative secondary data. Our aim is to provide a research approach that produces a better understanding of students’ characteristics, needs, and fears. Despite limitations in terms of the amount of data included in developing and piloting this methodology, this work forms the basis for increasing the usability of educational technologies and for adapting them to individual learning styles to reduce mathematics anxiety.
Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

Image representation learning has been a key challenge to promote the performance of the histopathological whole slide images analysis. The previous representation learning methods followed the supervised learning paradigm. However, manual annotation for large-scale WSIs is time-consuming and labor-intensive. Hence, the self-supervised contrastive learning has recently attracted intensive attention. In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis. We built a lesion queue based on the memory bank structure to store the representations of different classes of WSIs, which allowed the contrastive model to selectively define the negative pairs during the training. Moreover, We designed a queue refinement strategy to purify the representations stored in the lesion queue. The experimental results demonstrate that LACL achieves the best performance in histopathology image representation learning on different datasets, and outperforms state-of-the-art methods under different WSI classification benchmarks. The code is available at https://github.com/junl21/lacl .
Transfer Learning for Robust Masked Face Recognition

Over the past three years, it has seen many outbreaks of different coronavirus diseases around the world. One of the ways of transmission of COVID 19 is air transport. This transmission occurs when humans breathe in droplets released by an infected person by breathing, speaking, coughing or sneezing. The World Health Organization (WHO) has given orders to wear a face mask in the public places.Despite the impressive results achieved by deep learning methods in face recognition, the performance of these methods deteriorates when wearing a mask. The issue of masked face recognition is attracting more attention.In this work, a simple and effective method is proposed to deal with the recognition of people who wear a mask. We divide the problem into three phases: feature detection, feature extraction and recognition. The first stage of our model with MediaPipe Face Mesh automatically produces a segmentation of the masked area as feature detection and several points for cropping the area of interest. Then, the second stage extracts the features gained using Resnet50. The selected network, based on ResNet-50, is modified so that the third stage performs the classification process with Soft Max as an redaction and activation function.To train our system, we used the Labeled Faces in the Wild (LFW). From the original facial recognition data set, a masked copy is generated using data augmentation, and the two data sets are combined during the training process. Our model achieved a test accuracy of 98%.
YOLO-Based Face Mask Detection on Low-End Devices Using Pruning and Quantization

Deploying Deep Learning (DL) based object detection (OD) models in low-end devices, such as single board computers, may lead to poor performance in terms of frames-per-second (FPS). Pruning and quantization are well-known compression techniques that can potentially lead to a reduction of the computational burden of a DL model, with a possible decrease of performance in terms of detection accuracy. Motivated by the widespread introduction of face mask mandates by many institutions during the Covid-19 pandemic, we aim at training and compressing an OD model based on YOLOv4 to recognize the presence of face masks, to be deployed on a Raspberry Pi 4. We investigate the capability of different kinds of pruning and quantization techniques of increasing the FPS with respect to the uncompressed model, while retaining the detection accuracy. We quantitatively assess the pruned and quantized models in terms of Mean Average Precision (mAP) and FPS, and show that with proper pruning and quantization, the FPS can be doubled with a moderate loss in mAP. The results provide guidelines for compression of other OD models based on YOLO.
FPGA-based Reservoir Computing with Optimized Reservoir Node Architecture

Updating the state of reservoir nodes is one of the essential operations of reservoir computing (RC), which highly affects the system’s performance. In an echo state network (ESN), one of the primary types of RC, the process of state renewal can be divided into two stages: multiplication of the weight matrix with the input-state vector and applying a nonlinear activation function on the sum of products. The weight matrix is typically large and sparse, providing opportunities for optimizing the matrix multiplication; the choices of activation functions may also affect hardware resource utilization. This paper introduces an optimized reservoir node architecture for FPGA-based RC systems. Specifically, we adopt the bit-serial matrix multiplier and direct spatial implementation of the weight matrix to fully exploit the sparseness property. The canonical signed digit representation is also employed to further optimize the multiplier logic. Furthermore, a hyperbolic tangent activation function is designed and optimized to maintain the nonlinearity of the neural network without affecting its accuracy. Compared with existing hardware ESN designs, our reservoir node architecture significantly reduces resource utilization while maintaining comparable performance.
A Generalized Continuous Collision Detection Framework of Polynomial Trajectory for Mobile Robots in Cluttered Environments

In this letter, we introduce a generalized continuous collision detection (CCD) framework for the mobile robot along the polynomial trajectory in cluttered environments including various static obstacle models. Specifically, we find that the collision conditions between robots and obstacles could be transformed into a set of polynomial inequalities, whose roots can be efficiently solved by the proposed solver. In addition, we test different types of mobile robots with various kinematic and dynamic constraints in our generalized CCD framework and validate that it allows the provable collision checking and can compute the exact time of impact. Furthermore, we combine our architecture with the path planner in the navigation system. Benefiting from our CCD method, the mobile robot is able to work safely in some challenging scenarios.
Defense against backdoor attack in federated learning

As a new distributed machine learning framework, Federated Learning (FL) effectively solves the problems of data silo and privacy protection in the field of artificial intelligence. However, for its independent devices, heterogeneous data and unbalanced data distribution, it is more vulnerable to adversarial attack, especially backdoor attack. In this paper, we investigate typical backdoor attacks in FL, containing model replacement attack and adaptive backdoor attack. Based on attack initiating round, we divide backdoor attack into convergence-round attack and early-round attack. In addition, we respectively design a defense scheme with model pre-aggregation and similarity measurement to detect and remove backdoor model under convergence-round attack and a defense scheme with backdoor neuron activation to remove backdoor under early-round attack. Experiments and performance analysis show that compared to benchmark schemes, our defense scheme with similarity measurement obtains the highest backdoor detection accuracy under model replacement attack (25% increase) and adaptive backdoor attack (67% increase) at the convergence round. Moreover, detection effect is the most stable. Compared to defense of participant-level differential privacy and adversarial training, our defense scheme with backdoor neuron activation can rapidly remove malicious effects of backdoor without reducing the main task accuracy under early-round attack. Thus, the robustness of FL can be improved greatly with our defense schemes. We make our key codes public at Github https://github.com/lsw3130104597/Backdoor_detection.
How to improve the problem of hotel manpower shortage in the COVID-19 epidemic environment? Exploring the effectiveness of the hotel practice training system

This study aimed to examine the role of effective employee training in the sustainable growth and corporate social responsibility of hotels during the postpandemic period. An initial respondent pool was selected using purposive sampling, and 280 questionnaires were finally obtained by snowball sampling from September 2019 to February 2020. The sample was analyzed using basic statistical tests, the Pearson correlation coefficient, and multivariate regression. We then interpreted the sample data through consultation with scholars and practitioners in hotel management. Finally, the data were analyzed using multivariate verification. The results indicated that China Binhai Hotel could not foster consistent employee enthusiasm and fulfill its corporate social responsibilities during the postpandemic period due to deficiencies in its human resources training, employee benefit, job rotation, and incentive systems. We suggest for hotels to promote corporate culture, improve the system of promotion, increase employee benefits, and adjust the workplace environment and equipment provided to employees. These will improve employee attitudes toward hotel management, improve work efficiency, increase retention, and solve the problem of personnel shortage during the postpandemic period.