Reviewerly/exHarmony huggingface dataset:
Performance evaluation of college laboratories based on fusion of decision tree and BP neural network

Abstract Performance evaluation can promote the continuous improvement of the laboratories in a college. It is necessary to take into account the scientific evaluation method during the process of the performance evaluation. In this paper, a performance evaluation method based on the fusion of the decision tree and BP neural network is presented. In detail, the decision tree model is used to select performance evaluation indexes with high weight. The BP neural network was adopted aiming to reduce the impact of assessment prediction of classification by non-core factors. First, the data were pre-processed by trapezoidal membership function. Then, the decision tree was generated by the C4.5 algorithm to select the evaluation indexes with high weight. Then, the BP neural network was trained with as many samples as possible by evaluation indexes; it possesses experts’ experience which can be used to predict the performance evaluation results. The method overcomes the shortages of the separate model, eliminates the disturbance of human factors and improves the accuracy of the evaluation. Experiments show that the model is feasible and effective in performance evaluation of college laboratories. The outcomes of this work can provide a scientific evaluation method for people such as researchers, college administrators and laboratory managers. Also, this paper will help them to improve the management of laboratories and provide them with decision references for constructing the laboratories.
A Fully-Autonomous Framework of Unmanned Surface Vehicles in Maritime Environments Using Gaussian Process Motion Planning

Unmanned surface vehicles (USVs) are of increasing importance to a growing number of sectors in the maritime industry, including offshore exploration, marine transportation and defence operations. A major factor in the growth in use and deployment of USVs is the increased operational flexibility that is offered through use of autonomous navigation systems that generate optimised trajectories. Unlike path planning in terrestrial environments, planning in the maritime environment is more demanding as there is need to assure mitigating action is taken against the significant, random and often unpredictable environmental influences from winds and ocean currents. With the focus of these necessary requirements as the main basis of motivation, this paper proposes a novel motion planner, denoted as GPMP2*, extending the application scope of the fundamental GP-based motion planner, GPMP2, into complex maritime environments. An interpolation strategy based on Monte-Carlo stochasticity has been innovatively added to GPMP2* to produce a new algorithm named GPMP2* with Monte-Carlo stochasticity (MC-GPMP2*), which can increase the diversity of the paths generated. In parallel with algorithm design, a ROS based fully-autonomous framework for an advanced unmanned surface vehicle, the WAM-V 20 USV, has been proposed. The practicability of the proposed motion planner as well as the fully-autonomous framework have been functionally validated in a simulated inspection missions for an offshore wind farm in ROS.
EMT-NET: Efficient Multitask Network for Computer-Aided Diagnosis of Breast Cancer

Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that easily controls the balance between the sensitivity and specificity of cancer detection. The proposed approach is evaluated using a breast ultrasound dataset with 1511 images. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively. We validate the model using a virtual mobile device, and the average inference time is 0.35 seconds per image.
C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval

Pretrained language models have improved effectiveness on numerous tasks, including ad-hoc retrieval. Recent work has shown that continuing to pretrain a language model with auxiliary objectives before fine-tuning on the retrieval task can further improve retrieval effectiveness. Unlike monolingual retrieval, designing an appropriate auxiliary task for cross-language mappings is challenging. To address this challenge, we use comparable Wikipedia articles in different languages to further pretrain off-the-shelf multilingual pretrained models before fine-tuning on the retrieval task. We show that our approach yields improvements in retrieval effectiveness.
Iterative Design of Gestures During Elicitation: Understanding the Role of Increased Production

Previous gesture elicitation studies have found that user proposals are influenced by legacy bias which may inhibit users from proposing gestures that are most appropriate for an interaction. Increasing production during elicitation studies has shown promise moving users beyond legacy gestures. However, variety decreases as more symbols are produced. While several studies have used increased production since its introduction, little research has focused on understanding the effect on the proposed gesture quality, on why variety decreases, and on whether increased production should be limited. In this paper, we present a gesture elicitation study aimed at understanding the impact of increased production. We show that users refine the most promising gestures and that how long it takes to find promising gestures varies by participant. We also show that gestural refinements provide insight into the gestural features that matter for users to assign semantic meaning and discuss implications for training gesture classifiers.
Probing Simile Knowledge from Pre-trained Language Models

Simile interpretation (SI) and simile generation (SG) are challenging tasks for NLP because models require adequate world knowledge to produce predictions.Previous works have employed many hand-crafted resources to bring knowledge-related into models, which is time-consuming and labor-intensive.In recent years, pre-trained language models (PLMs) based approaches have become the defacto standard in NLP since they learn generic knowledge from a large corpus.The knowledge embedded in PLMs may be useful for SI and SG tasks.Nevertheless, there are few works to explore it.In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time.The backbone of our framework is to construct masked sentences with manual patterns and then predict the candidate words in the masked position.In this framework, we adopt a secondary training process (Adjective-Noun mask Training) with the masked language model (MLM) loss to enhance the prediction diversity of candidate words in the masked position.Moreover, pattern ensemble (PE) and pattern search (PS) are applied to improve the quality of predicted words.Finally, automatic and human evaluations demonstrate the effectiveness of our framework in both SI and SG tasks.
Artificial Intelligence (AI) and Internet of Things (IoT) in Healthcare: Opportunities and Challenges

The potential of Artificial Intelligence (AI) enabled Internet of Things(IoT) healthcare is expanded upon to theorize how IoT and AI can improve the accessibility of preventative public health services and transition our current secondary and tertiary healthcare to be a more proactive, continuous, and coordinated system. Also IoT and AI helps in getting obligation and fulfillment by engaging patients to contribute greater imperativeness collaborating with their specialists. Apart from this, IoT has many advantages of streamlining and enhancing healthcare delivery to efficiently predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. This study proposes a comprehensive yet brief review of the challenges and opportunities in healthcare sector by using AI and IoT. It also provides an overview of AI and IoT, its applicability, some insights about current trends, outlook on future developments, and challenges of healthcare systems.
A study of the virtual reality cybersickness impacts and improvement strategy towards the overall undergraduate students’ virtual learning experience

Purpose Virtual reality (VR) technologies have expanded their application domains towards education with pedagogical benefits including fully immersive learning environment and in-depth user engagement through scenario-based virtual simulations. Motion sickness (MS), however, has become one of the long-standing key challenges of the VR utilisation, even in gaming industries. Thus, this paper aims to present a preliminary study on understanding the VR MS, referred as cybersickness, in the teaching and learning (T&amp;L) context at the University of Nottingham Ningbo China. Design/methodology/approach A VR-based virtual classroom content was developed and tested for 60 undergraduate students having equal access to the same VR equipment. A two-step data collection, comprising qualitative and quantitative measures, was conducted for the participants. The aspects of how gender influences the cybersickness severity and how academic background affects the learning experience were investigated and analysed using analysis of variance F -test statistical approach. Findings The results demonstrated approximately 47% of the participants had experienced cybersickness, where 64% of them were females. With confidence level of 95% ( a = 5%), the obtained p -value and F -statistical value for the respective gender and study discipline categories against the cybersickness symptoms confirmed the significance level between the two compared variables. Moreover, it is worth highlighting that the virtual movement speed, perspective angle and visual properties of the virtual environment were selected as the top three factors that caused the cybersickness. Originality/value The study is hoped to provide valuable pointers to current and future VR developers in minimising the cybersickness symptoms that would enable an effective T&amp;L environment in higher education.
Medical Image Segmentation Algorithm for Three-Dimensional Multimodal Using Deep Reinforcement Learning and Big Data Analytics

To avoid the problems of relative overlap and low signal-to-noise ratio (SNR) of segmented three-dimensional (3D) multimodal medical images, which limit the effect of medical image diagnosis, a 3D multimodal medical image segmentation algorithm using reinforcement learning and big data analytics is proposed. Bayesian maximum a posteriori estimation method and improved wavelet threshold function are used to design wavelet shrinkage algorithm to remove high-frequency signal component noise in wavelet domain. The low-frequency signal component is processed by bilateral filtering and the inverse wavelet transform is used to denoise the 3D multimodal medical image. An end-to-end DRD U-Net model based on deep reinforcement learning is constructed. The feature extraction capacity of denoised image segmentation is increased by changing the convolution layer in the traditional reinforcement learning model to the residual module and introducing the multiscale context feature extraction module. The 3D multimodal medical image segmentation is done using the reward and punishment mechanism in the deep learning reinforcement algorithm. In order to verify the effectiveness of 3D multimodal medical image segmentation algorithm, the LIDC-IDRI data set, the SCR data set, and the DeepLesion data set are selected as the experimental data set of this article. The results demonstrate that the algorithm's segmentation effect is effective. When the number of iterations is increased to 250, the structural similarity reaches 98%, the SNR is always maintained between 55 and 60 dB, the training loss is modest, relative overlap and accuracy all exceed 95%, and the overall segmentation performance is superior. Readers will understand how deep reinforcement learning and big data analytics test the effectiveness of 3D multimodal medical image segmentation algorithm.
Improved Adaptive Belief Propagation Decoding of Reed-Solomon Codes With SPC Codes

In this letter, two novel single parity-check aided (SPC-aided) adaptive belief propagation (ABP) decoding algorithms are proposed for Reed-Solomon (RS) codes. SPC codes can be used to evaluate the reliability of the information bits in the received RS codewords and modify the log-likelihood ratio (LLR) values of the probably incorrect bits, which can greatly reduce the influence of the pseudo-equilibrium points on the traditional ABP algorithm. Based on this idea, we propose an SPC-aided ABP decoding algorithm. In addition, a new selection scheme of unreliable bits to be sparsified is proposed with the aid of SPC codes. Combining the SPC-aided ABP and this new selection scheme, an enhanced SPC-aided (E-SPC-aided) ABP algorithm is further proposed. The complexity and latency analysis results demonstrate that the two proposed algorithms have lower complexity and decoding latency than the dynamic scheduling strategies. Compared with the traditional ABP, LRBP and HD-P-ABP, simulation results show that the E-SPC-aided ABP algorithm can provide error-correction performance gains of up to 1.1 dB, 0.7 dB and 0.5 dB for RS(255,239) code, respectively at the FER = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> .
Multistage Spatio-Temporal Networks for Robust Sketch Recognition

Sketch recognition relies on two types of information, namely, spatial contexts like the local structures in images and temporal contexts like the orders of strokes. Existing methods usually adopt convolutional neural networks (CNNs) to model spatial contexts, and recurrent neural networks (RNNs) for temporal contexts. However, most of them combine spatial and temporal features with late fusion or single-stage transformation, which is prone to losing the informative details in sketches. To tackle this problem, we propose a novel framework that aims at the multi-stage interactions and refinements of spatial and temporal features. Specifically, given a sketch represented by a stroke array, we first generate a temporal-enriched image (TEI), which is a pseudo-color image retaining the temporal order of strokes, to overcome the difficulty of CNNs in leveraging temporal information. We then construct a dual-branch network, in which a CNN branch and a RNN branch are adopted to process the stroke array and the TEI respectively. In the early stages of our network, considering the limited ability of RNNs in capturing spatial structures, we utilize multiple enhancement modules to enhance the stroke features with the TEI features. While in the last stage of our network, we propose a spatio-temporal enhancement module that refines stroke features and TEI features in a joint feature space. Furthermore, a bidirectional temporal-compatible unit that adaptively merges features in opposite temporal orders, is proposed to help RNNs tackle abrupt strokes. Comprehensive experimental results on QuickDraw and TU-Berlin demonstrate that the proposed method is a robust and efficient solution for sketch recognition.
Robust method for certifying genuine high-dimensional quantum steering with multimeasurement settings

Genuine high-dimensional (HD) quantum steering was recently proposed for certifying HD entanglement in the one-sided device-independent setting, as quantified by the Schmidt number [ Phys. Rev. Lett. 126 , 200404 ( 2021 ) PRLTAO 0031-9007 10.1103/PhysRevLett.126.200404 ]. As one of the central challenges in the steering test is the tolerated noise threshold, here we develop a more robust method for certifying genuine HD steering in noisy environments. We derive genuine HD steering criteria using multiple mutually unbiased bases, surpassing the previous restriction of only two measurement settings. Although the present multisetting criteria are not tight, we theoretically show that they are more robust to noise than the tight two-setting criteria. To test the practicality of our method under realistic conditions, we report an experimental demonstration using photonic orbital angular momentum entangled states in dimensions <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>d</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> <mml:mo>,</mml:mo> <mml:mn>4</mml:mn> <mml:mo>,</mml:mo> <mml:mn>5</mml:mn> </mml:math> . Our work offers a more robust way to witness the entanglement dimension in practical one-sided device-independent quantum information processing.
A Novel Approach for Asymmetric Quantum Error Correction With Syndrome Measurement

Most of the quantum error correction methods are symmetric.Symmetric methods are implemented by considering the amplitude of bit flip(X) and phase flip(Z) errors as same.With the quantum experiments, it is observed that the amplitude of Z errors are more compared to X errors.Due to which the need of asymmetric error correction has increased.This paved a path for the development of asymmetric error correction methods.In this paper, we discussed the concept of asymmetric quantum error correction (AQEC) and proposed an efficient approach for AQEC with encoding, syndrome measurement and decoding operations with increased fidelity to 85.89% and reduced circuit depth to 48%.
RStudio Projects

What we have learned so far adds a great deal of integration to R programming. But we are missing a way to bring all of that together in a format that we can reuse. This is where RStudio projects come into play.
Cyber Risk Measurement in the Hyperconnected World

This chapter introduces cyber risk as a critical business risk spilling over into strategic risk, credit risk, and regulatory risk on the entity level, as well as market risk and systemic risk on the portfolio level. It then analyses the uniqueness of cyber risk, the need for cyber risk measurement and its current challenges, followed by a review on cost of cybercrime, cyber incident loss categories, and models for measuring expected loss from cyber incidents, including Annual Loss Expectancy, Standard Deviation of Loss, and Perceived Composite Risk. It then covers current methods for cyber risk measurement, e.g., Common Vulnerability Scoring System (CVSS), CORAS, stochastic modeling, Monte Carlo simulation, Cyber Value at Risk, and Factor Analysis of Information Risk (FAIR). The Cyber Risk Quadrant is introduced in this chapter, applying medical risk measurement to the cyber context. It categorizes risk factors into technological, nontechnological, inherent (nonmodifiable), and control (modifiable) factors. Examples of scenario analysis for control assessment and loss quantification are also provided.
Fast, Lifetime-Preserving Readout for High-Coherence Quantum Annealers

We demonstrate, for the first time, that a quantum flux parametron (QFP) is capable of acting as both isolator and amplifier in the readout circuit of a capacitively shunted flux qubit (CSFQ). By treating the QFP like a tunable coupler and biasing it such that the coupling is off, we show that $T_1$ of the CSFQ is not impacted by Purcell loss from its low-Q readout resonator ($Q_e = 760$) despite being detuned by only $40$ MHz. When annealed, the QFP amplifies the qubit's persistent current signal such that it generates a flux qubit-state-dependent frequency shift of $85$ MHz in the readout resonator, which is over $9$ times its linewidth. The device is shown to read out a flux qubit in the persistent current basis with fidelities surpassing $98.6\%$ with only $80$ ns integration, and reaches fidelities of $99.6\%$ when integrated for $1$ $\mu$s. This combination of speed and isolation is critical to the readout of high-coherence quantum annealers.
Linear Model Selection and Regularization

In the regression setting, the standard linear model $$ Y = \beta_{0} + \beta_{1}X_{1} + \cdots + \beta_{p}X_{p} + \epsilon $$ is commonly used to describe the relationship between a response Y and a set of variables X1, X2,…,Xp.
Emerging edge-of-things computing for smart cities: Recent advances and future trends

There is a rising need for healthcare as a result of rising public awareness of health issues. Electronic medical records include extremely confidential and sensitive information, and blockchain technology can enable the safe exchange of these records across various medical organizations. The current blockchain system is susceptible to quantum computer attacks, nevertheless, as a result of the advent of quantum computers. This research designs a novel distributed quantum electronic medical record system and suggests a new private quantum blockchain network based on security considerations. The blocks in this quantum blockchain’s data structure are linked via entangled states. The time stamp is automatically formed by connecting quantum blocks with controlled activities, which lowers the amount of storage space needed. Each block’s hash value is recorded using just one qubit. The quantum information processing is detailed in depth in the quantum electronic medical record protocol. Every medical record can be tracked, and the security and privacy of electronic medical records in Internet of Medical Things systems can be guaranteed. The protocol also ditches the traditional encryption and digital signature algorithms in favor of a quantum authentication system. According to the mathematical analysis, the quantum blockchain network has strong security against attacks from quantum computers since it can withstand External attack, Intercept-Measure-Repeat attack and Entanglement-Measure attack. The quantum circuit diagram for deriving the hash value is provided, along with the correctness and traceability analysis of the quantum block. The comparison between the proposed quantum blockchain model and a few other quantum blockchain models is also included.
Learning Backward Compatible Embeddings

Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding team that trains an embedding model to solve intended tasks (e.g., product recommendation). The produced embeddings are then widely consumed by consumer teams to solve their unintended tasks (e.g., fraud detection). However, as the embedding model gets updated and retrained to improve performance on the intended task, the newly-generated embeddings are no longer compatible with the existing consumer models. This means that historical versions of the embeddings can never be retired or all consumer teams have to retrain their models to make them compatible with the latest version of the embeddings, both of which are extremely costly in practice. Here we study the problem of embedding version updates and their backward compatibility. We formalize the problem where the goal is for the embedding team to keep updating the embedding version, while the consumer teams do not have to retrain their models. We develop a solution based on learning backward compatible embeddings, which allows the embedding model version to be updated frequently, while also allowing the latest version of the embedding to be quickly transformed into any backward compatible historical version of it, so that consumer teams do not have to retrain their models. Under our framework, we explore six methods and systematically evaluate them on a real-world recommender system application. We show that the best method, which we call BC-Aligner, maintains backward compatibility with existing unintended tasks even after multiple model version updates. Simultaneously, BC-Aligner achieves the intended task performance similar to the embedding model that is solely optimized for the intended task.