Reviewerly/exHarmony huggingface dataset:
‘To tweet or not to tweet?’ A study of the use of Twitter by scholarly book publishers in Social Sciences and Humanities

Publishers might believe the use of Twitter will help promote their scholarly books. In this study, we analyzed 18,691 books indexed by the Book Citation Index (BKCI) in the Social Sciences and Humanities, published between 2014 and 2018, and proposed two indicators describing the Twitter engagement of publishers: relative coverage and relative receptivity. The results show significant disciplinary and year differences in publishers’ Twitter engagement For instance, in all 10 disciplines, small and medium-sized publishers generally prefer to promote their books (high relative coverage) on Twitter, but the majority of publishers have low relative coverage in these 5 years. In addition, results show that books mentioned by their publishers’ Twitter accounts get significantly higher Twitter mentions (high relative receptivity) as compared to books mentioned by non-publisher’ Twitter accounts. The results suggest that scholarly book publishers should engage in social media activity to increase Twitter mentions and visibility of their books.
Learning Association Between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions

It has been shown that answering questions contributes to students learning effectively. However, generating questions is an expensive task and requires a lot of effort. Although there has been research reported on the automation of question generation in the literature of Natural Language Processing, these technologies do not necessarily generate questions that are useful for educational purposes. To fill this gap, we propose QUADL, a method for generating questions that are aligned with a given learning objective. The learning objective reflects the skill or concept that students need to learn. The QUADL method first identifies a key concept, if any, in a given sentence that has a strong connection with the given learning objective. It then converts the given sentence into a question for which the predicted key concept becomes the answer. The results from the survey using Amazon Mechanical Turk suggest that the QUADL method can be a step towards generating questions that effectively contribute to students’ learning.
Effects of classical random external field on the dynamics of entanglement in a four-qubit system

We investigate the dynamics of entanglement through negativity and witness operators in a system of four non-interacting qubits driven by a classical phase noisy laser characterized by a classical random external field (CREF). The qubits are initially prepared in the GHZ-type and W-type states and interact with the CREF in two different qubit-field configurations, namely, common environment and independent environments in which the cases of equal and different field phase probabilities are distinguished. We find that entanglement exhibits different decaying behavior, depending on the input states of the qubits, the qubit-field coupling configuration, and field phase probabilities. On the one hand, we demonstrate that the coupling of the qubits in a common environment is an alternative and more efficient strategy to completely shield the system from the detrimental impacts of the decoherence process induced by a CREF, independent of the input state and the field phase probabilities considered. Also, we show that GHZ-type states have strong dynamics under CREF as compared to W-type states. On the other hand, we demonstrate that in the model investigated the system robustness’s can be greatly improved by increasing the number of qubits constituting the system.
Towards a continuous frequency band chirp material measure for surface topography measuring instrument calibration

To experimentally determine the transfer behavior and the resolution limits of areal surface topography measuring instruments, numerous approaches have been suggested with the two most common ones being based on either rectangular (type ASG material measure) or sinusoidal (chirp material measure) test geometries, imaging various spatial frequencies. Each of the methods carries individual advantages and disadvantages. Here, we describe the design of a varied chirp material measure which provides sufficient amplitudes throughout a broad range of spatial frequencies. For its evaluation, two different approaches based on either geometrical fitting or a direct analysis in the frequency domain are described and compared with each other. The material measure provides a steady spectrum in the spatial frequency domain that can be evaluated with both approaches. Their advantages and disadvantages are composed to derive information for a reliable implementation that leads to a standardized evaluation routine with small uncertainty. The examination of the two methods results in an evaluation with a high statistical reliability that allows an unambiguous determination of the spatial transfer behavior.
Blockchain-Based Internet-of-Vehicle

Internet-of-vehicle (IoV) is a heterogeneous environment involving information exchange between system components such as road infrastructure devices, vehicular embedded sensors and other vehicular elements. In IoV network, devices communications and data exchange need to be secure, efficient and transparent to achieve the platform’s goals. Blockchain technology is proved to provide decentralization, immutability, security and transparency properties due to the features of its distributed ledger. In this chapter, we introduce the integration of blockchain technology into IoV networks to support the essential data exchange and storage requirements such as decentralizing, security and transparency. We describe blockchain terminologies and how blockchain can be adopted in IoV environments. Then, we explain blockchain support for IoV data sharing. Besides, blockchain support for IoV trust and verification is also clarified. We also explore the most popular blockchain-based IoV applications the researchers have developed.
Enhancing Adversarial Examples Via Self-Augmentation

Recently, adversarial attacks pose a challenge for the security of Deep Neural Networks, which motivates researchers to establish various defense methods. However, do current defenses really achieve real security? To answer the question, we propose self-augmentation method (SA) for circumventing defenders to transferable adversarial examples. Concretely, self-augmentation includes two strategies: (1) self-ensemble, which applies additional convolution layers to an existing model to build diverse virtual models that be fused for achieving an ensemble-model effect and preventing overfitting; and (2) deviation-augmentation, which based on the observation of defense models that the input data is surrounded by highly curved loss surfaces, thus inspiring us to apply deviation vectors to input data for escaping from their vicinity space. Extensive experiments conducted on four vanilla models and ten defenses suggest the superiority of our method compared with the state-of-the-art transferable attacks. The source code is public available at https://github.com/zhuangwz/ICME2021_self_augmentation.
Salient Region Guided Blind Image Sharpness Assessment

Salient regions provide important cues for scene understanding to the human vision system. However, whether the detected salient regions are helpful in image blur estimation is unknown. In this study, a salient region guided blind image sharpness assessment (BISA) framework is proposed, and the effect of the detected salient regions on the BISA performance is investigated. Specifically, three salient region detection (SRD) methods and ten BISA models are jointly explored, during which the output saliency maps from SRD methods are re-organized as the input of BISA models. Consequently, the change in BISA metric values can be quantified and then directly related to the difference in BISA model inputs. Finally, experiments are conducted on three Gaussian blurring image databases, and the BISA prediction performance is evaluated. The comparison results indicate that salient region input can help achieve a close and sometimes superior performance to a BISA model over the whole image input. When using the center region input as the baseline, the detected salient regions from the saliency optimization from robust background detection (SORBD) method lead to consistently better score prediction, regardless of the BISA model. Based on the proposed hybrid framework, this study reveals that saliency detection benefits image blur estimation, while how to properly incorporate SRD methods and BISA models to improve the score prediction will be explored in our future work.
Survey on Civil Complaints Management System by Using Machine Learning Techniques

Today’s Government Complaints Registration System is totally Human Operable hence it consumes lot of time to resolve each of those complaint. The proposed system is being developed for the government offices to create a helpful complaint registering platform which will be efficient. The Framework model of civil complaint handling system is done by using sentimental analysis and ML techniques to speed up the process of categorization and prioritization of complaints. The system will analyses the citizen sentiment to prioritize the complaints. Later then the categorization of complaints done by using clustering method and give them priority based upon urgency of each one respectively. The municipality can use this method to identify citizen’s needs and estimate their satisfaction.
Research on Diversified Model of PE Teaching Evaluation in Universities Based on Modern Information Technology

With the development of technology, advanced technology is also used in physical education in China in order to improve the teaching quality. College students are mostly exposed to some information products. It is difficult to arouse students' interest if universities still adopt traditional teaching modes and equipment. College physical education mainly aims at improving students' physical quality and promoting their all-round development. People's recognition of quality education has promoted the important development of physical education subjects, and many traditional physical education teaching methods have been difficult to keep up with the trend of the times, which has been gradually questioned by the public. Therefore, the introduction of information technology in modern physical education is extremely important. This paper mainly studies the development of modern educational technology and analyzes its advantages in the evaluation of college physical education teaching, aiming at continuously strengthening the teaching level of Chinese college physical education and promoting its sustainable development.
Personalized recommendation algorithm in social networks based on representation learning

Recommendation algorithm is not only widely used in entertainment media, but also plays an important role in national strategy, such as the recommendation algorithm of byte beating company. This paper studies the personalized recommendation algorithm based on representation learning. The data in social network is complex, and the data mainly exists in various platforms. This paper introduces AI (Artificial Intelligence) algorithm to guide the algorithm of representation learning, and integrates the algorithm steps of representation learning, to realize the implementation of personalized recommendation algorithm in social network, and compares the representation learning algorithm. Finally, this paper designs a method based on heat conduction and text mining to provide users with webpage recommendations and help users better mine interesting popular webpages. Research shows that the performance of IMF is better than that of PMF because it overcomes the sparsity of data by pre-filling. The accuracy of IMF is 3.69% higher than that of PMF on the epinions data set, and 6.24% higher than that of PMF on the double data set. Rtcf, socialmf, tcars, CSIT, isrec, and hesmf have better performance than PMF and IMF. Among them, rtcf, socialmf, tcars, CSIT, isrec, and hesmf improve the MAE performance of PMF by 7.6%, 6.3%, 8.8%, 7.9%, 9.5% and 14.2%, respectively.
Modeling Password Guessability via Variational Auto-Encoder

Human-chosen text passwords remain an important form of authentication. To better understand properties of passwords chosen by users and password guessing models adopted by attackers, a lot of researches on password guessing are carried out. In recent studies, people try to guess passwords through deep learning models. However, existing deep learning-based guessing models (such as RNN and GAN) show unsatisfactory performance under limited guesses. In this paper, we propose PGVAE, a password guessing model based on variational autoencoder. The model can learn highly structured and continuous latent representations of passwords and then generate highquality candidate guesses. The effectiveness of PGVAE is verified in multiple leaked dataset. Results show that our model is capable of modeling the guessability of passwords, detecting vulnerable passwords and strengthening the security of authentication systems.
Multi-way Arabic Sentiment Classification Using Genetic Algorithm and Logistic Regression

Sentiment analysis has become a promising field, given its importance to support decision-making in many areas. It is winning its popularity from the abundance of data accessible through web2.0 and social networks. Our study focuses on the Arabic language considering its high penetration rate and its huge growth on the internet. We treat data from Twitter to categorize sentiments into multiple classes. To this end, we are proposing an innovative model based on multinomial logistic Regression using a Genetic Algorithm to select the most relevant features for our task. Our model performs very well as it exceeds the state-of-the-art accuracy measures.
Approach of Protecting IHE-XDS-I oriented Medical Image Information

IHE-XDS-I (Integrating the Health-care Enterprise-Cross Enterprise Document Sharing for Images) is an inter-organization, involving multiple users, information sharing for medical image integration specifications. The medical image information that shared between the medical institutions mainly include medical images and the medical reports. This information is an important part of the patients medical records and plays an important role in clinical diagnosis, research and teaching. In this case, how to ensure the safety of this data is the key problem to be solved. This article proposes a scheme of IHE-XDS-I medical image information security protection and design an access control algorithm -- Based on cryptographic properties of multi access control algorithm to allocate minimum privileges to users of different institutions IHE-XDS-I, medical image information can be safe and flexible to be shared.
Activation Map Saliency Guided Filtering for Efficient Image Compression for Vision Tasks

With the development of Internet of things applications, there will be ever more visual data generated for machine vision tasks. Traditional image compression, which focus on recovering pixels from compressed representations for human consumption, could waste bits on non-essential information, as the compression pipeline is designed around a pixel-level loss function. How to design new compression schemes that directly reflect vision task losses, becomes the key in extracting new compression efficiency in machine vision applications. Recent research in the field of machine learning understanding provides us activation map saliency as a kind of deep in-sight to deep-learning-based inference. In this paper, we propose an image compression scheme based on activation map guided filtering to encode images with higher compression ratio while maintaining classification accuracy. Specifically, a pre-filter is adopted at encoder side to improve traditional image compression standards. This prefilter is designed to preserve value and edge information of salient area for analysis and smooth the unimportant pixels for bits saving. The images compressed by our scheme could be decoded for vision task without any modification to standard decoder. The experimental results show that, compared with traditional compression methods, our method could improve compression efficiency while maintaining the classification accuracy.
TSAR-based Expert Recommendation Mechanism for Community Question Answering

Community Question Answering (CQA) provides a platform to share knowledge for users. With the increasing number of users and questions, askers have to wait a long time for an answer with high quality while responders may not be interested in assigned questions. Current methods usually try to address this issue based on text or link analysis. However, most of them suffer from delayed answers or low coverage of best answer. In this paper, we design a novel expert recommendation mechanism by incorporating the deep structured semantic model (DSSM) [20] with our proposed graph-based algorithm, a topic sensitive answerer rank algorithm (TSAR). In the process of constructing transition probability matrix, we not only take into account both the number of questions answered by the user and question difficulty, but also consider the user's average response time for providing the answer. The experiments carried out on Yahoo! Answers and Stack Overflow datasets demonstrate that the proposed mechanism outperforms the current typical algorithms [9] on multiple metrics and achieves the best answer coverages, which are 61.5% and 53.8%, respectively.
Keyframe Extraction Using Sobel Fuzzified Weighted Approach

Nowadays, progress in technology and the application of internet has led to exponential growth of video data. This drastic increase stipulates efficient video analysis techniques. Keyframe extraction is one of the technique that provides a succinct representation of video and are useful in various applications like video indexing and retrieval. In this direction, an efficient approach for keyframe extraction is proposed. The process begins by converting gray scale images of shots into gradient magnitude images using Sobel operator and establishing fuzzification. Further, 3*3 mask of sliding window is utilized in both overlapping and non-overlapping fashion to obtain Binary Weighted Codes (BWC) on a fuzzified edge image. In the subsequent step, feature set is obtained by applying entropy measure on BWC values of every frame within a shot. Finally, frame having highest entropy value is chosen as a keyframe of the corresponding shot. To verify the effectiveness of the proposed approach experiments were conducted on Open Video Project dataset. The experimental results shows that the proposed method outperforms some of the state-of-the-art algorithms with average of 93% fidelity measure.
Deep Interest Network for Taobao advertising data Click-Through Rate Prediction

Click-through rate prediction has been widely used in recommendation system, search engine optimization, advertising and other fields. Traditional machine learning methods such as LR, FM, and GBDT require a large amount of artificial feature construction processes and only learn the linear relationship between features. With the rapid development of deep learning in natural language processing, computer vision, many studies such as DeepFM, PNN, and Wide&Deep, apply DNN models to Click-through rate prediction estimation. In this work, I use the modified deep interest network (DIN) for the click-through rate prediction task. The mixed loss is proposed as the training loss to fit the DIN model. Attention mechanism is improved according to different input features. I evaluate proposed deep interest network model on Taobao ad click data. Results show the model has superior performance than other deep learning methods. Experiments show that the modified DIN model is effective for the Taobao advertising data click-through rate prediction task.
GDot: Drawing Graphs with Dots and Circles

This paper presents a new visual representation of graphs, inspired by the dot painting style of Central Australia. This painting style is established as a powerful medium for communicating information with abstraction, and has a long history of supporting storytelling.We propose a general framework GDot to visually represent in-formation as dot paintings. We describe computational techniques as well as the rendering effects to produce painterly representations of graphs and networks. We present visualization examples with various networks from diverse domains, from pure mathematics to social systems. Further, we briefly describe the extension of our dot painting visualization style to multi-dimensional data, dynamic data and geo-referenced data.
Analysis of Different Encoder-decoder-based Approaches for Biomedical Imaging Segmentation

Recently, CNNs (convolutional neural networks) have been widely used in the field of medical image segmentation. In particular, the encoder-decoder architectures represented by U-Net have achieved state-of-art segmentation effects and inspired many more elaborated networks, which adopt newer and more advanced network designs. To our knowledge, the comprehensive and detailed comparison among these improved versions from a multiplicity of points of view has not been conducted up to now.
Identification of Shape Using Circularity Approach for Medical Image Analysis

Cancer is one of the most serious health problems where patient’s condition deteriorates day by day rapidly. In medical diagnosis, cancer cell should be detected by using such an image processing technique which gives quick result. For doing this, we introduce an approach where images can be divided into several geometrical shapes for proper analysis. By using this process, the operation is much easier as we can get better information about the cancer-affected cells. The image contains basic geometric shapes. The geometric shapes of the cell can be recognized by measuring circularity of the cell objects present in the image. It also gives the number of basic objects in image as results. Here, circularity is used as an invariant shape descriptor.
A Container Cloud Based PaaS Solution for Supplying GPU Computing Resources

With the increasing number of deep learning models started to rely on the high computing throughput of GPUs, many institutes, such as universities, have invested a lot in purchasing GPU devices and servers and building data centers. However, providing usable development environments for end-users can be quite a challenge for them due to the lack of specific expertise. In this paper, we designed and implemented a platform as a service (PaaS) solution, which enables end-users to provision an integrated multi-framework development environment with shared GPU computing resources using a few clicks. Our actual use shows that the proposed platform is capable of supporting nearly 100 development environments through a Kubernetes cluster consisting of 3 nodes and each node equipped with 2 Nvidia Tesla V100 datacenter-class GPUs with 32GB device memory, and can be scaled up and down as needed.