Reviewerly/exHarmony huggingface dataset:
A Study on the Structure of the Creative Fusion Platform in the Engineering : Centered on analysis of Capstone design class based on reflective thinking education

The purpose of this study is to analyze the structure of creative fusion platform in the engineering. For this study, pieces of related literature were reviewed focusing on analyzing the structure of creative fusion learning platform through the analysis of thinking education-based Capstone design class operation. In addition, interviews with five professors in the field of engineering, aesthetics, and social science were analyzed. The key of analysis was the five phases of thinking education process used for Capstone design class. The findings of this study were as follows. Most of the professors recognized the importance of thinking education. However, it was found that the 5-phase thinking education process did not apply systematically to the operation of Capstone design class. Consequently, there was a discussion about the ways to operate Capstone design class in order to improve the class quality through experience-based thinking education instead of simply focusing on outcome-oriented creative learning experience, as well as about valuable outcome expectations for creative products. In addition, based on the study findings, the structure of creative fusion learning platform was proposed as basic structure, process structure, and outcome structure.
Automatic recommendation of medical departments to outpatients based on text analyses and medical knowledge graph

In many countries, outpatients generally visit a major hospital without a referral from health professionals due to the shortage of family physicians. Not knowing at which medical specialty department to register, outpatients have to wait in long queues to consult receptionists. We propose to alleviate this situation via a computer system offering an automatic recommendation of departments (ARD) to outpatients, which identifies the appropriate medical department for outpatients according to their chief complaints. Besides, ARD systems can boost the emerging services of online hospital registration and online medical diagnosis, which require that the outpatients know the correct department first. ARD is a typical problem of text classification. Nevertheless, off-the-shelf tools for text processing may not suit ARD, because the chief complaints of outpatients are generally brief and contain much noisy information. To solve this problem, we propose ARD-K, a deep learning framework incorporating external medical knowledge sources. We also propose a dual-attention mechanism to mitigate the interference of noisy words and knowledge entities. The performance of ARD-K is compared with some off-the-shelf techniques on a real-world dataset. The results demonstrate the effectiveness of ARD-K for the automatic recommendation of departments to outpatients.
Design and application of the prevention model based on the examination of academic plagiarism

Currently, the plagiarism is continuously seen in the academic fields. The one of the reasons is thought that the accurate application of plagiarism results in obscurity. To solve this issue related to the plagiarism, this study examines the typical examples of the plagiarism in recent years and surveys the relevant references of plagiarism, and proposes the general definition of the plagiarism from the aspect of "uniqueness." Moreover, this study considers the prevention of the plagiarism along the idea that attempts to prevent the plagiarism at the stage before plagiarism occurring. It is different from the ordinary idea that focuses on how to detect the plagiarism after it happened. We conceive the prevention model of the plagiarism based on that idea, design the application utilizing the model and show its mechanism.
Defocused projection model for phase-shifting profilometry with a large depth range

Phase-shifting 3D profilometry is widely combined with defocused projection, but the accuracy of defocused projection could be far below expectations especially in the case of large depth range measurement. In this paper, a new defocus-induced error related to the shape of the measured object is pinpointed and a novel defocused projection model is established to cope with such a error to improve the accuracy of defocusing phase-shifting profilometry. Supplemented with a specialized calibration and reconstruction procedure, the phase is well corrected to obtain accurate measurement results. Furthermore, the impact of the defocus-induced error is analyzed through simulations, and the feasibility of our method is verified by experiments. Faced with issues involving a large measurement range, the proposed method is expected to give a competitive performance.
A Language for Modelling False Data Injection Attacks in Internet of Things

Internet of Things (IoT) is now omnipresent in all aspects of life and provides a large number of potentially critical services. For this, Internet of Things relies on the data collected by objects. Data integrity is therefore essential. Unfortunately, this integrity is threatened by a type of attack known as False Data Injection Attack. This consists of an attacker who injects fabricated data into a system to modify its behaviour. In this work, we dissect and present a method that uses a Domain-Specific Language (DSL) to generate altered data, allowing these attacks to be simulated and tested.
Popularity Measuring and Prediction Mining of IPL Team Using Machine Learning

The Indian Premier League (IPL) is a Twenty20 cricket league held in India each year between March and May. Eight teams participating from Indian cities or states. The Board of Control in India (BCCI) started the league in 2007. The Indian Premier League (IPL) is the most popular cricket league in the world, ranking sixth among all sports leagues in terms of average appearance in 2014. The IPL was the first sporting event in the world to be broadcast live on YouTube in 2010. Since the IPL is so common, In order to be more competitive, it is necessary to assess how people will react to the league. Machine Learning techniques can be used to resolve the problem of predicting outcomes. Public opinion, as well as the causes behind it, has been discovered to be political. Among the many social media channels available, one of the popular social service websites is Twitter, which allows users to post events from their daily lives. 7 lakh People's feedbacks are taken from the IPL-2020 Twitter website using Application Interface. This paper is split into two sections. First, we divided 7 lakh tweets into different teams based on hashtag attributes, analyzed people's feelings in neutral, positive, and negative, checked the most popular team during IPL-2020, and measured accuracy using Multinomial Naïve Bayes, Logistic Regression, Ridge Classifier, Random Forest, SGD Classifier, and Decision Tree on those tweets. In the second part, 50k data were collected at random from 7 lakh tweets and F1-score, precision, accuracy, and recall was measured using Multinomial Naïve Bayes, Logistic Regression, Ridge Classifier, Random Forest, SGD Classifier, and Decision Tree. This paper aims to correctly assess people's feedback for an event such as the Indian Premier League 2020.
Amazon SageMaker Model Parallelism: A General and Flexible Framework for  Large Model Training

With deep learning models rapidly growing in size, systems-level solutions for large-model training are required. We present Amazon SageMaker model parallelism, a software library that integrates with PyTorch, and enables easy training of large models using model parallelism and other memory-saving features. In contrast to existing solutions, the implementation of the SageMaker library is much more generic and flexible, in that it can automatically partition and run pipeline parallelism over arbitrary model architectures with minimal code change, and also offers a general and extensible framework for tensor parallelism, which supports a wider range of use cases, and is modular enough to be easily applied to new training scripts. The library also preserves the native PyTorch user experience to a much larger degree, supporting module re-use and dynamic graphs, while giving the user full control over the details of the training step. We evaluate performance over GPT-3, RoBERTa, BERT, and neural collaborative filtering, and demonstrate competitive performance over existing solutions.
Attention-based Adversarial Partial Domain Adaptation

With the rapid development of vision-based deep learning (DL), it is an effective method to generate large-scale synthetic data to supplement real data to train the DL models for domain adaptation. However, previous vanilla domain adaptation methods generally assume the same label space, and such an assumption is no longer valid for a more realistic scenario where it requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes. To handle this problem, we propose an attention-based adversarial partial domain adaptation (AADA). Specifically, we leverage adversarial domain adaptation to augment the target domain by using source domain, then we can readily turn this task into a vanilla domain adaptation. Meanwhile, to accurately focus on the transferable features, we apply attention-based method to train the adversarial networks to obtain better transferable semantic features. Experiments on four benchmarks demonstrate that the proposed method outperforms existing methods by a large margin, especially on the tough domain adaptation tasks, e.g. VisDA-2017.
Blockchain and IoT Integration for Pollutant Emission Control

A recent and prominent trend is the integration of Blockchain and the Internet of Things (IoT). Blockchain can enable completely decentralised IoT applications where autonomous IoT devices securely interact without the need of a centralised trusted party. The perfect match of these two technologies still needs many challenges to be faced. IoT devices can produce a huge amount of data which can pose blockchain scalability problems in terms of fees and memory. IoT transactions should occur in real-time, but in reality it seldom happens due to transactions validation and involved consensus techniques. In this paper we introduce a novel industrial IoT case study scenario, where Blockchain technology has been used to check the compliance of industrial incinerators with the Italian emission regulations of pollutant. We have explored various blockchain based alternatives that are on-chain, off-chain and permissioned based solutions. These have been compared in order to find a viable one.
The Interview Box: Notes on a Prototype System for Video-Recording Remote Interviews

Video-recording remote interviews can sometimes be necessary or desirable, such as in news broadcasting or documentary-making. However, remote interviews are not currently well-supported by digital tools. Unresolved questions about best practices and the kinds of support needed to facilitate remote interviews have become increasingly relevant since the Covid-19 pandemic. To reflect on these questions and explore the design space for systems to support high quality, remote video-recorded interviews, we conducted an exploratory Research through Design study, drawing on professional media-making techniques, novel interviewing methods and a bespoke intervention: The Interview Box. We provide a detailed summary of our design process and, reflecting on both the successes and failures of our interventions, construct two contributions: technical insights relating to the practical challenges of designing and implementing a remote video interview system, and general insights into the broader interaction design challenges of designing for remote video-recorded interviews.
Exploiting Residual and Illumination with GANs for Shadow Detection and Shadow Removal

Residual image and illumination estimation have been proven to be helpful for image enhancement. In this article, we propose a general framework, called RI-GAN, that exploits residual and illumination using generative adversarial networks (GANs). The proposed framework detects and removes shadows in a coarse-to-fine fashion. At the coarse stage, we employ three generators to produce a coarse shadow-removal result, a residual image, and an inverse illumination map. We also incorporate two indirect shadow-removal images via the residual image and the inverse illumination map. With the residual image, the illumination map, and the two indirect shadow-removal images as auxiliary information, the refinement stage estimates a shadow mask to identify shadow regions in the image, and then refines the coarse shadow-removal result to the fine shadow-free image. We introduce a cross-encoding module to the refinement generator, in which the use of feature-crossing can provide additional details to promote the shadow mask and the high-quality shadow-removal result. In addition, we apply data augmentation to the discriminator to reduce the dependence between representations of the discriminator and the quality of the predicted image. Experiments for shadow detection and shadow removal demonstrate that our method outperforms state-of-the-art methods. Furthermore, RI-GAN exhibits good performance in terms of image dehazing, rain removal, and highlight removal, demonstrating the effectiveness and flexibility of the proposed framework.
Dynamic ConvNets on Tiny Devices via Nested Sparsity

This work introduces a new training and compression pipeline to build nested sparse convolutional neural networks (ConvNets), a class of dynamic ConvNets suited for inference tasks deployed on resource-constrained devices at the edge of the Internet of Things. A nested sparse ConvNet consists of a single ConvNet architecture, containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> sparse subnetworks with nested weights subsets, like a Matryoshka doll, and can trade accuracy for latency at runtime, using the model sparsity as a dynamic knob. To attain high accuracy at training time, we propose a gradient masking technique that optimally routes the learning signals across the nested weight subsets. To minimize the storage footprint and efficiently process the obtained models at inference time, we introduce a new sparse matrix compression format with dedicated compute kernels that fruitfully exploit the characteristic of the nested weights subsets. Tested on image classification and object detection tasks on an off-the-shelf ARM-M7 microcontroller unit (MCU), nested sparse ConvNets outperform variable-latency solutions naively built assembling single sparse models trained as stand-alone instances, achieving 1) comparable accuracy; 2) remarkable storage savings; and 3) high performance. Moreover, when compared to state-of-the-art dynamic strategies, such as dynamic pruning and layer width scaling, nested sparse ConvNets turn out to be Pareto optimal in the accuracy versus latency space.
Pegasus 2022

The 2022 Pegasus was held on January 29 at Gulfstream Park and featured three major races (9, 11 and 12) plus an outstanding undercard
Efficient discrete cross-modal hashing with semantic correlations and similarity preserving

With its merits in query speed and memory footprint, hashing has elicited considerable monument in cross-media similarity retrieval applications. Many label-dependent supervised hashing methods have been proposed for similarity searching across modality boundaries. However, the current cross-modal hashing (CMH) works are subjected to severe information loss, and their performances may dramatically degrade because of the expensive costs of constructing affinity graphs, inadequate mining of label information, and disregarding label correlations. To facilitate these problems, we propose an Efficient Discrete Cross-Modal Hashing (EDCH) in which an asymmetric model is introduced, which not only conveys external semantic information via embedding high-order labels but also preserves internal modality attributes by introducing binary representations and common subspace. To fully use label semantic information, we integrate the semantic supervised intersection scheme and the category correlations embedding in a shallow framework. Moreover, we elaborately develop an efficient and effective discrete optimization strategy to learn binary representations and a novel mutual linear projection to strengthen the capability and effectiveness of hash functions. Comprehensive experiments are conducted on three representative datasets to evaluate our method. The results validated that our method achieved promising and competitive retrieval performance and surpasses several typical and cutting-edge approaches.
Service robot’s responses in service recovery and service evaluation: the moderating role of robots’ social perception

ABSTRACTService robots are used in all aspects of the service industry and improve business efficiency dramatically. However, service robot failures are also prevalent. This paper explores the interaction effect between symbolic service recovery and service robot social perception on service evaluations, the underlying mechanism, and the boundary conditions. Through three experiments with multiple service scenarios, such as hotel, restaurant, and tourist attraction, we find that service robots with warm perceptions using humor responses for a service recovery result in better service evaluations, whereas service robots with competent perceptions using rational responses for a service recovery result in better service evaluations. In addition, our research verifies the mediating role of perceived relief and perceived sincerity and identifies the moderating role of the locus of responsibility. We conclude by discussing the implications of these findings for marketers in the services and hospitality industries.服务机器人已经应用在服务行业的各个方面并显著提高企业的效率，但服务机器人服务失败也很普遍。本文探讨了象征性服务补救与服务机器人社会感知对消费者服务评价的交互作用，影响机制及边界条件。通过3个不同服务场景的实验，如酒店、餐馆和旅游景点，我们发现温暖感知的服务机器人使用幽默的回复方式来进行服务补救会使消费者有更好的服务评价; 而能力感知的服务机器人使用理性的回复方式来进行服务补救则更佳。此外，我们的研究验证了感知宽慰和感知真诚的中介作用，并确定了责任点的调节作用。最后，我们讨论了这些发现对服务和酒店业营销人员的影响。KEYWORDS: Robot social perceptionservice robotsservice failure and recoveryservice evaluation Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The warm service robot image was from https://www.veer.com/photo/334513466?utm_source=baidu&utm_medium=imagesearch&chid=902. The competent service robot image was from https://www.sohu.com/a/648768636_625367.2. The warm service robot image was from https://www.niusnews.com/=P1z96c79. The competent service robot image was from https://new.qq.com/rain/a/20230108A05T6L00.3. The warm service robot image was from http://mt.sohu.com/20171221/n525862640.shtml. The competent service robot image was from https://nb.zol.com.cn/640/6406287.html.Additional informationFundingThe work was supported by the Natural Science Foundation of Shandong Province [ZR2021MG036].
One-to-Few Label Assignment for End-to-End Dense Detection

One-to-one (o2o) label assignment plays a key role for transformer based end-to-end detection, and it has been recently introduced in fully convolutional detectors for end-to-end dense detection. However, o2o can degrade the feature learning efficiency due to the limited number of positive samples. Though extra positive samples are introduced to mitigate this issue in recent DETRs, the computation of self- and cross- attentions in the decoder limits its practical application to dense and fully convolutional detectors. In this work, we propose a simple yet effective one-to-few (o2f) label assignment strategy for end-to-end dense detection. Apart from defining one positive and many negative anchors for each object, we define several soft anchors, which serve as positive and negative samples simultaneously. The positive and negative weights of these soft anchors are dynamically adjusted during training so that they can contribute more to “representation learning” in the early training stage, and contribute more to “duplicated prediction removal” in the later stage. The detector trained in this way can not only learn a strong feature representation but also perform end-to-end dense detection. Experiments on COCO and CrowdHuman datasets demonstrate the effectiveness of the o2f scheme. Code is available at https://github.com/strongwolf/o2f
Learning Conditional Attributes for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute “wet” in “wet apple” and “wet cat” is different. As a solution, we provide analysis and argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings by a proposed attribute learning framework containing an attribute hyper learner and an attribute base learner. By encoding conditional attributes, our model enables to generate flexible attribute embeddings for generalization from seen to unseen compositions. Experiments on CZSL benchmarks, including the more challenging C-GQA dataset, demonstrate better performances compared with other state-of-the-art approaches and validate the importance of learning conditional attributes. Code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Gllee:https://gitee.com/wqshmzh/canet-czsl is available at https://github.com/wqshmzh/CANet-CZSL.
Context-Sensitive Thresholding Technique Using ABC for Aerial Images

Image anatomization is a remarkable notch in image processing which entails the scrutinization of the number of non-overlapping and homogeneous regions that exist in the input image. Thresholding is the most popular algorithm of image segmentation. In this article, the authors have utilized energy curve to incorporate spatial contextual information to inspect the regions where most favourable threshold(s) exist. The thresholding technique automatically computes the count of objects present in input image. To determine the optimal thresholds present in the image, artificial bee colony algorithm has been deployed. The results achieved have been compared with GA-based technique to ensure the efficacy of the proposed technique.
Survey of Pedestrian Action Recognition in Unmanned-Driving

With the development of unmanned-driving car technology, there are higher requirements for the intelligence, safety and stability of intelligent vehicle driving. Especially in a complex and uncertain environment, the driverless car can accurately detect the pedestrian action, which can effectively realize the autonomous driving of the vehicle. This requires that vehicles detect pedestrians firstly, then identify pedestrian body language and try to understand their intentions, predict pedestrian’s actions, which form a good interaction cognition between human and vehicle. In this paper, we give a detailed survey about the recent and state-of-the-art research methods in the filed of human action recognition and discuss their advantages and limitations. We analysis the main framework of motion recognition, and summarize the common datasets of this filed. Finally, suggestions for future research directions are offered, which is expected to benefit the follow research.
A Probabilistic Model for Collaborative Filtering

We propose a probabilistic model that uses the early data to generate the prior distribution and the recent data to capture the change of the states of both users and items in collaborative filtering system. It keeps updating every time it receives new data and has a constant limit of the time cost of every updating, which is suitable to deal with large scale data for online recommendation. Experiments on real datasets show the improvement performance of our model over the existing time-aware recommender systems.