Reviewerly/exHarmony huggingface dataset:
Medical Dialogue Response Generation with Pivotal Information Recalling

Medical dialogue generation is an important yet challenging task. Most previous works rely on the attention mechanism and large-scale pretrained language models. However, these methods often fail to acquire pivotal information from the long dialogue history to yield an accurate and informative response, due to the fact that the medical entities usually scatters throughout multiple utterances along with the complex relationships between them. To mitigate this problem, we propose a medical response generation model with Pivotal Information Recalling (MedPIR), which is built on two components, i.e., knowledge-aware dialogue graph encoder and recall-enhanced generator. The knowledge-aware dialogue graph encoder constructs a dialogue graph by exploiting the knowledge relationships between entities in the utterances, and encodes it with a graph attention network. Then, the recall-enhanced generator strengthens the usage of these pivotal information by generating a summary of the dialogue before producing the actual response. Experimental results on two large-scale medical dialogue datasets show that MedPIR outperforms the strong baselines in BLEU scores and medical entities F1 measure.
Composition of Short Stories Using Book Recommendations

A challenge in the domain of creative story generation is generating stories that reflect a consistent theme or genre. We present GREEN, a computational creative (CC) system designed to generate short stories whose genre is customized to a user’s tastes in literature. Given a book title, GREEN uses association rule mining to identify appropriate book recommendations from which to train a long short-term memory model (LSTM) to then produce a new short story. The system forms its intention based on the user’s input, thereby aiming to generate artifacts that will be of value and interest to the user. We report the results of a preliminary survey that serves to demonstrate that as a proof of concept the system shows promise in its ability to achieve its defined intentions.Source: https://github.com/delaneyemoore/GREEN-system
Dual-track Protocol Reverse Analysis Based on Share Learning

Private protocols, whose specifications are agnostic, are widely used in the Industrial Internet. While providing customized service, they also raise essential security concerns as well, due to their agnostic nature. The Protocol Reverse Analysis (PRA) techniques are developed to infer the specifications of private protocols. However, the conventional PRA techniques are far from perfection for the following reasons: (i) Error propagation: Canonical solutions strictly follow the "from keyword extraction to message clustering" serial structure, which deteriorates the performance for ignoring the interplay between the sub-tasks, and the error will flow and accumulate through the sequential workflow. (ii) Increasing diversity: As the protocols’ diversities of characteristics increase, tailoring for specific types of protocols becomes infeasible. To address these issues, we design a novel dual-track framework SPRA, and propose Share Learning, a new concept of protocol reverse analysis. Particularly, based on the share layer for protocol learning, SPRA builds a parallel workflow to co-optimize both the generative model for keyword extraction and the probability-based model for message clustering, which delivers automatic and robust syntax inference across diverse protocols and greatly improves the performance. Experiments on five real-world datasets demonstrate that the proposed SPRA achieves better performance compared with the state-of-art PRA methods.
Batch Adaptative Streaming for Video Analytics

Video streaming plays a critical role in the video analytics pipeline and thus its adaptation scheme has been a focus of optimization. As machine learning algorithms have become main consumers of video contents, the streaming adaptation decision should be made to optimize their inference performance. Existing video streaming adaptation schemes for video analytics are usually designed to adapt to bandwidth and content variations separately, which fail to consider the coordination between transmission and computation. Given the nature of batch transmission in video streaming and batch processing in deep learning-based inference, we observe that the choices of the batch sizes directly affects the bandwidth efficiency, the response delay and the accuracy of the deep learning inference in video analytics. In this work, we investigate the effect of the batch size in transmission and processing, formulate the optimal batch size adaptation problem, and further develop the deep reinforcement learning-based solution. Practical issues are further addressed for Implementation. Extensive simulations are conducted for performance evaluation, whose results demonstrate the superiority of our proposed batch adaptive streaming approach over the baseline streaming approaches.
Object Detection and Navigation Strategy for Obstacle Avoidance Applied to Autonomous Wheel Chair Driving

The primary aim of this study is to develop machine learning or deep-learning aided procedures that enhances the capability of a commercial non-autonomous wheelchair towards autonomy. The paper addresses the computer vision work for obstacle detection applied to an autonomous wheelchair operation. The computer vision tasks including the depth image classification are accommodated in a small form factored and resource constraint computers such as Raspberry Pie and Google Coral. The tasks and strategies also include classifying the images using a pretrained model (TensorFlow lite), detecting and measure the degree of obstacle avoidance by pairing RGB image classification with depth images. The objective has been further extended to develop a simulation platform for autonomous wheelchair driving where navigation and path mapping construction algorithm evaluations are visually offered using MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> .
Med-SRNet: GAN-Based Medical Image Super-Resolution via High-Resolution Representation Learning

High-resolution (HR) medical imaging data provide more anatomical details of human body, which facilitates early-stage disease diagnosis. But it is challenging to get clear HR medical images because of the limiting factors, such as imaging systems, imaging environments, and human factors. This work presents a novel medical image super-resolution (SR) method via high-resolution representation learning based on generative adversarial network (GAN), namely, Med-SRNet. We use GAN as backbone of SR considering the advantages of GAN that can significantly reconstruct the visual quality of the images, and the high-frequency details of the images are more realistic in the image SR task. Furthermore, we employ the HR network (HRNet) in GAN generator to maintain the HR representations and repeatedly use multi-scale fusions to strengthen HR representations for facilitating SR. Moreover, we adopt deconvolution operations to recover high-quality HR representations from all the parallel lower resolution (LR) streams with the aim to yield richer aggregated features, instead of simple bilinear interpolation operations used in HRNetV2. When evaluated on a home-made medical image dataset and two public COVID-19 CT datasets, the proposed Med-SRNet outperforms other leading edge methods, which obtains higher peak signal to noise ratio (PSNR) values and structural similarity (SSIM) values, i.e., maximum improvement of 1.75 and minimum increase of 0.433 on the PSNR metric for "Brain" test sets under 8× and maximum improvement of 0.048 and minimum increase of 0.016 on the SSIM metric for "Lung" test sets under 8× compared with other methods.
Cutting Tail Latency in Commodity Datacenters with Cloudburst

Long tail latency of short flows (or messages) greatly affects user-facing applications in datacenters. Prior solutions to the problem introduce significant implementation complexities, such as global state monitoring, complex network control, or non-trivial switch modifications. While promising superior performance, they are hard to implement in practice.This paper presents Cloudburst, a simple, effective yet readily deployable solution achieving similar or even better results without introducing the above complexities. At its core, Cloudburst explores forward error correction (FEC) over multipath — it proactively spreads FEC-coded packets generated from messages over multipath in parallel, and recovers them with the first few arriving ones. As a result, Cloudburst is able to obliviously exploit underutilized paths, thus achieving low tail latency. We have implemented Cloudburst as a user-space library, and deployed it on a testbed with commodity switches. Our testbed and simulation experiments show the superior performance of Cloudburst. For example, Cloudburst achieves 63.69% and 60.06% reduction in 99th percentile message/flow completion time (FCT) compared to DCTCP and PIAS, respectively.
Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems

Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures.
Intellectual property protection, religious culture and dynamic capabilities of enterprises: evidence from Chinese listed companies

Using the panel data of Chinese A-share listed companies from 2010 to 2019, this paper empirically tests the impact of intellectual property protection and religious culture on enterprise dynamic capabilities. It is found that intellectual property protection and religious culture have a significant promoting effect on the dynamic capabilities of enterprises. And there is a substitution effect between intellectual property protection and religious culture. After the robustness test, the benchmark research conclusion is still valid. Heterogeneity analysis shows that intellectual property protection and religious culture have heterogeneous effects on firms with different financing constraints. Further analysis shows that intellectual property protection can improve the innovation level of enterprises by improving dynamic capabilities.
Formal verification of persistence and liveness in the trust-based blockchain crowdsourcing consensus protocol

Crowdsourcing is a potential computing paradigm that exploits collective human intelligence to solve complex tasks, but it suffers from various safety and security problems. Blockchain has emerged as a promising technology to address most of the security issues, however, it is challenging to find an appropriate and trusted blockchain-based consensus protocol for crowdsourcing services. This work proposes a novel Trust-based Blockchain Crowdsourcing consensus protocol that selects a leader and validators based on various trust factors. The proposed protocol addresses a major issue of ensuring correctness associated with the safety and security-critical systems which has a vital importance because failure of such systems may lead to adverse consequences. Mainly it is focused on persistence and liveness properties preventing invalid block insertion and consensus delay attacks. Model checking technique is utilized because of its effectiveness and automatic nature to perform formal verification. The proposed protocol is specified using Communicating Sequential Programs, and the persistence and liveness properties are specified through Linear Temporal Logic. The model verification is performed by giving the formal model and the properties as input to the Process Analysis Toolkit which checks for the satisfaction or violation of the properties.
Underwater image enhancement based on color restoration and dual image wavelet fusion

Due to the severe light absorption and scattering, underwater images often exhibit problems such as low contrast, detail blurring, color attenuation, and low illumination. To address these issues, this paper presents a two-step strategy based on color restoration and image fusion by combining deep learning and conventional image enhancement technologies to improve the visual performance of underwater images. First, an adaptive color compensation method is proposed to make up for the loss of severely attenuated channels. Color restoration is further implemented to estimate the illuminant color cast caused by the selective attenuation of light. Since the underwater image after color restoration still suffers from scattering and blurring, an effective method based on dual image wavelet fusion (DIWF) and Generative Adversarial Network (GAN) is designed to further enhance the edge details and improve the contrast of the color restored image. Experiments demonstrate that the proposed method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities. The approach can achieve better performance of color restoration, blur removal, and low illumination enhancement. • The paper presents an approach by integrating data-driven deep learning and hand-crafted image enhancement for the single underwater image enhancement. We argue that it is impractical only to use one method to deal with the complex underwater imaging environment. By combining deep learning and image enhancement technology, the model can process images obtained in various underwater scenes. • The paper presents an adaptive color compensation method to make up for the loss of severely attenuated channels, and color restoration is further implemented to estimate the illuminant color cast caused by the selective attenuation of light. • Since the underwater image after color restoration still suffers from scattering and blurring, an effective method based on dual image wavelet fusion (DIWF) and Generative Adversarial Network (GAN) is designed to further enhance the edge details and improve the contrast of the color restored image.
Tripartite Quantum Key Distribution Implemented with Imperfect Sources

Multipartite quantum key distribution (QKD) is a promising area of quantum networks that provides unconditional secret keys among multiple parties, enabling only legitimate users to decrypt the encrypted message. However, security proofs of existing multipartite QKD typically assume perfect state preparation devices of legitimate users and neglect the relative rotation of reference frames. These presumptions are, nevertheless, very difficult to meet in practice, and thus the security of current multipartite QKD implementations is not guaranteed. By combining the idea of a loss tolerant technique, introduced by Tamaki et al. (K. Tamaki et al., Phys. Rev. A, 90, 052314, 2014), and the concept of a reference frame-independent protocol, we propose a three-party QKD protocol that considers state preparation flaws and the slow drift of reference frames. Through a numerical simulation, the influence of misaliged reference frames on the protocol’s stability was examined by drifting reference frames through angles β=π/5, β=π/6 and β=π/7. In addition, the performance of the proposed protocol was examined for the encoding flaws set at δ=0.35, δ=0.20, and δ=0.10. The results show that the protocol is robust against state preparation flaws, and is insignificantly impacted by misalignment of the reference frames because the achieved transmission distances and secret key rates are comparable to the perfect scenarios. This work dramatically contributes toward the realization of practical and secure multipartite QKD. The proposed protocol has direct applications in quantum communication network environments that involve unknown and slowly varying reference frames, web conferences, and online communications.
Sustainable knowledge management during crisis: Focus on Covid-19 pandemic

This paper consolidates literature that justifies effective knowledge management as a precursor for mitigating the effects of a crisis, Covid-19 pandemic in particular, through key antecedents of leadership, culture, and information and communication technology (ICT). A thorough review of retrieved literature relevant to the topic was conducted. The study materials were rigorously screened to limit any potential biases regarding their selection. Through the study, the paper concludes that the fight against Covid-19 crisis indeed requires knowledge to, among other things, find a lasting solution, mitigate the impacts, limit misinformation, revert to normalcy, and plan for similar crises in future. Further, the paper concludes that sustainable knowledge management during the Covid-19 crisis largely depends on a decisive leadership style that puts employees at the centre; a culture that embraces knowledge as a core asset, and supportive ICT infrastructure. Furthermore, the study reveals that relevance of ICT in the process of managing knowledge, largerly depends on a culture that accepts knowledge as a critical resource. The study establishes some challenges associated with ICT where a way forward for migrating from knowledge capture to knowledge creation and sharing has been re-affirmed. The present paper has led to the development of a model that further explains the relationships between the determinants of leadership, culture and ICT against effective management of a crisis using knowledge as a strategic resource. Further, six propositions have been put forward to provide clarity on the relationships.
Unsupervised Insurance Fraud Prediction Based on Anomaly Detector Ensembles

The detection of anomalous data patterns is one of the most prominent machine learning use cases in industrial applications. Unfortunately very often there are no ground truth labels available and therefore it is good practice to combine different unsupervised base learners with the hope to improve the overall predictive quality. Here one of the challenges is to combine base learners that are accurate and divers at the same time, where another challenge is to enable model explainability. In this paper we present BHAD, a fast unsupervised Bayesian histogram anomaly detector, which scales linearly with the sample size and the number of attributes and is shown to have very competitive accuracy compared to other analyzed anomaly detectors. For the problem of model explainability in unsupervised outlier ensembles we introduce a generic model explanation approach using a supervised surrogate model. For the problem of ensemble construction we propose a greedy model selection approach using the mutual information of two score distributions as a similarity measure. Finally we give a detailed description of a real fraud detection application from the corporate insurance domain using an outlier ensemble, we share various feature engineering ideas as well as discuss practical challenges.
SVM classifier of cervical histopathology images based on texture and morphological features

Cervical histopathology image classification is a crucial indicator in cervical biopsy results.The objective of this study is to identify histopathology images of cervical cancer at an early stage by extracting texture and morphological features for the Support Vector Machine (SVM) classifier.We extract three different texture features and one morphological feature of cervical histopathology images: first-order histogram, K-means clustering, Gray Level Co-occurrence Matrix (GLCM) and nucleus feature. The original dataset used in our experiment is obtained from 20 patients diagnosed with cervical cancer, including 135 whole slide images (WSIs). Given an entire WSI, the patches on its tissue region are extracted randomly.We finally obtain 3,000 patches, including 1,000 normal, 1,000 hysteromyoma and 1,000 cancer images. Among them, 80% of the entire data set is randomly selected as training set and the remaining 20% as test set. The accuracy of SVM classification using first-order histogram, K-means clustering, GLAM and nucleus feature for extracting features are respectively 87.4%, 90.6%, 91.6% and 93.5%.The classification accuracy of the SVM combining the four features is 96.8%, and the proposed nucleus feature plays a key role in the SVM classification of cervical histopathology images.
The phenomenon of “clip thinking” in the educational and cognitive activities of students of natural and physical-mathematical educational profile

Abstract The article presents an analysis of the phenomenon of “clip thinking” in the educational and cognitive activities of students of natural and physical-mathematical educational profile. The involvement of clip thinking in the initial stage of cognition in comparison with rational and theoretical thinking is theoretically substantiated. An empirical verification of the originality of cognitive processes of students of natural and physical-mathematical educational profile in the situation of comparing traditional linear (textual) and hypertextual way of presenting information is established. Deficiency of cognitive processes characteristic of clip thinking for results is revealed, which is characterized by characteristics of mental (theoretical) level of cognition. Therefore, the next stage of cognition should be the understanding of information through the mechanisms of comparison, generalization, categorization and re-synthesis.
Incorporating Digital Competences in Social Sciences and Humanities Subjects Curricula: a Course Design Framework

Development of competences demanded in the technologically rich environment has become a challenging task for universities. This task should not be regarded as an information technology disciplines’ prerogative. On the contrary, developers of curriculum documentation and teaching materials should adhere to the interdisciplinary approach to course design. A review of publications on digital competence conceptions allowed the authors to highlight digital skills and abilities that are in immediate connection with key social, communicative, cognitive competences and competences for lifelong learning. The principles that enable teachers to achieve efficient integration of digital competences in a non-technical subject curriculum are formulated in the article. These principles are accompanied by step-by-step guidelines that will help update teaching programmes and materials. The examples presented in the article illustrate how course objectives, teaching techniques, and assessment criteria are reconsidered in line with digital transformation of higher education.