Reviewerly/exHarmony huggingface dataset:
A mutual communicated model based on multi-parametric MRI for automated prostate segmentation and prostate cancer classification

Deep learning methods for multi-parametric MRI hold the greatest promise for automated computer-aided diagnosis of prostate cancer, including classification and segmentation. In this work, we propose a new model (MC-DSCN) for classification and segmentation simultaneously. MC-DSCN contains three components: the coarse segmentation component based on the residual U-net with attention blocks, the classification component based on the stacked residual blocks and multi-parametric fusion mechanism, and the fine segmentation component that incorporates the information about lesion location (cancer response map, CRM) arising from the classification component. Extensive experiments are performed to demonstrate that the proposed method could effectively transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way, thus outperforming the methods designed to perform only one task.
Research on the Display of VR Animation Art in Historical Relics–Take the Example of the Eastern Han Dynasty Silver Copper Bull Lamp

This takes VR animation display of the Eastern Han Dynasty silver copper bull lamp as an example to study the advantages of VR animation in the display of historical heritage. Combined with historical documents and field pictures, the digital restoration of The Eastern Han Dynasty silver copper bull lamp was carried out in MAYA and Z-Brush, materials and maps required for topological relics in Photoshop, construction of simulated display scenes in SketchUp, and output of final renderings in 3ds Max. The final effect compared with traditional historical relics display, the biggest advantage of VR animation display is that it can break through the limitation of time, space and particularity of cultural relics, and watch through VR equipment anytime and anywhere, which has certain positive significance for the promotion and promotion of Chinese historical relics and the spirit of traditional craftsmen.
Fractal Analysis of Radon Coefficients for No-Reference Video Quality Assessment (NR-VQA)

Forecast video quality in the absence of a reference video is a difficult task. This paper proposes a novel method for evaluating the quality of videos using the Radon transform. We propose fractal analysis of Radon coefficients to determine the video quality in the absence of reference data. Fractal analysis is a mathematical technique for characterizing the properties of objects that have an irregular or complex structure. It is used to extract the structural changes that occur in video frames as a result of various distortions. Additionally, a support vector regression model is used to predict the quality score of the video. Three widely used and publicly available video quality databases are used to validate the proposed NR-VQA model. In terms of quality prediction performance, the proposed model outperforms the majority of existing state-of-the-art methods.
Visual Object Tracking Based on Adaptive Background-Awareness and Spatial Constraint

The correlation filter method is effective in visual tracking tasks, whereas it suffers from the boundary effect and filter degradation in complex situations, which can result in suboptimal performance. Aiming at the solving above problem, this study proposes an object tracking method with a discriminant correlation filter, which combines an adaptive background perception and a spatial dynamic constraint. In this method, an adaptive background-awareness strategy is used to filter the background information trained by the interference filter to improve the discriminability between the object and the background. In addition, the spatial regularization term is introduced, and the dynamic change of the real filter and the predefined spatial constraint template is used to optimize filter learning to enhance the spatial information capture ability of the filter model. Experiments on the OTB100, VOT2018, and TrackingNet standard datasets demonstrate that our method achieves favorable accuracy and success rates. Compared with the current popular correlation filter methods, the proposed method can still maintain stable tracking performance with a scene scale variation, complex background, motion blur, and fast motion.
Blockchain-Based Authentication and Trust Computation Security Solution for Internet of Vehicles (IoV)

A vehicular ad hoc network (VANET) is one of the applications of mobile ad hoc network (MANET). VANET is used to reduce road traffic and accidents to save millions of lives by providing safety applications. Whereas, the current evolution of the Internet is the Internet of Things (IoT), where billions of smart computing devices, including smart vehicles are connected to the Internet to transfer data without human intervention. The existing two technologies VANETs and IoT define new technology called internet of vehicles (IoV). The IoT enables various value-added services; however, IoV is susceptible to various security threats from malicious entities. The collective efforts of researchers enhance security against internal and external attacks. To secure the IoV against the attacks from malicious nodes, most of the existing technologies provided centralized and computation overhead-based solutions. Many researchers have proposed cryptography and trust management-based security solutions. These schemes have their own limitations. The cryptography-based schemes provide security over external attacks, whereas trust management schemes provide security over internal attacks. The new Blockchain technology enables decentralized and distributed operations. The chapter has proposed blockchain-based security solution for IoV to authenticate vehicles, calculate reward points and 158compute new trust value. The proposed Blockchain networks employ both private and public Blockchain networks. The first uniqueness of proposed scheme, it employs the combination of both public and private Blockchains, it is called consortium blockchain And second uniqueness, the transmission and computation overhead in IoV are overcome by making use of deep neural networks at RSUs and ATA. The simulation results and comparative analysis of V2V, V21, private, and public Blockchain networks are presented and it shows performance of the proposed scheme is comparably enhanced by minimizing transmission and computation overheads.
An Augmented Neural Network for Sentiment Analysis Using Grammar

Understanding human sentiment from their expressions is very important in human-robot interaction. But deep learning models are hard to represent grammatical changes for natural language processing (NLP), especially for sentimental analysis, which influence the robot's judgment of sentiment. This paper proposed a novel sentimental analysis model named MoLeSy, which is an augmentation of neural networks incorporating morphological, lexical, and syntactic knowledge. This model is constructed from three concurrently processed classical neural networks, in which output vectors are concatenated and reduced with a single dense neural network layer. The models used in the three grammatical channels are convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected dense neural networks. The corresponding output in the three channels is morphological, lexical, and syntactic results, respectively. Experiments are conducted on four different sentimental analysis corpuses, namely, hotel, NLPCC2014, Douban movie reviews dataset, and Weibo. MoLeSy can achieve the best performance over previous state-of-art models. It indicated that morphological, lexical, and syntactic grammar can augment the neural networks for sentimental analysis.
Adaptive spectral affinity propagation clustering

Affinity propagation (AP) is a classic clustering algorithm. To improve the classical AP algorithms, we propose a clustering algorithm namely, adaptive spectral affinity propagation (AdaSAP). In particular, we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine famous arbitrary-shaped clustering algorithms. We propose a strategy of extending AP in non-spherical clustering by constructing category similarity of objects. Leveraging the monotonicity that the clusters' number increases with the self-similarity in AP, we propose a model selection procedure that can determine the number of clusters adaptively. For the parameters introduced by extending AP in non-spherical clustering, we provide a grid-evolving strategy to optimize them automatically. The effectiveness of Ada-SAP is evaluated by experiments on both synthetic datasets and real-world clustering tasks. Experimental results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.
Single‐image human mesh reconstruction by parallel spatial feature aggregation

Abstract Recovering human mesh from a single image with natural postures is a challenging task in human modeling and animation. Model‐free methods regress the mesh vertices from the input image directly to avoid the 6‐DoF human joint extraction from the 2D image. However, the missing of the global information in spatial feature aggregation of the existing GNNs may result in the undesired deformity and inaccuracy of the recovered human mesh. To address this issue, we propose a parallel‐aggregating network with a novelly designed global layer for spatial feature extracting from random walk normalized matrix. Moreover, the coarse body mesh (head, hand, foot, etc.) provided by the coarsening network can add the human characteristic to the mesh. The local and global spatial features are aggregated to update vertice coordinates following an iterative, coarse‐to‐fine process to obtain an accurate and smooth human mesh. Experiments validated the effectiveness and robustness of the proposed approaches for single‐image human mesh recovery.
Combining Learner Model and Reinforcement Learning for Adaptive Sequencing of Learning Activities

In this paper, we present an approach for adapting the sequencing of learning activities that relies on the Q-learning, a reinforcement learning algorithm. The Q-learning learns a sequencing policy to select learning activities that improves the knowledge states of students. In this research, we rely on the student knowledge state inferred by the Bayesian Knowledge Tracing (BKT) at every testing activity to calculate the reward of the Q-Learning. The more the Q-Learning decision improves the student knowledge state the greater the reward received by the Q-Learning. In addition, we propose a 3-step method aiming to ensure that the use of the Q-Learning is education domain compliant. It consists on training the Q-Learning first on simulated students to answer the “cold start” problem of the Q-Learning. We present empirical results showing that the sequencing policy resulting from the 3-step method provides the ITS with an efficient strategy to improve the students’ knowledge states.
東亞國家國民小學教師工作滿意度之相關因素研究：以TALIS資料為例

&lt;p&gt;本研究以2018年OECD的教與學國際調查資料探討臺灣、日本、韓國的國小教師工作滿意度因素，各以3,441、3,062、3,058名教師，透過迴歸分析獲得結論如下：學校教師團隊愈創新，愈認為教職可以影響學生，工作滿意度愈高；然而教師教育程度、教職是安定工作、教職可以幫助弱勢與工作滿意度沒有關聯。這三國的差異包括：1.日本的女教師工作滿意度明顯低於男性，而臺韓則沒有差異。2.臺日的教師年資愈長，工作滿意度愈低，韓國沒有關聯。3.臺日的教師把教職作為首選，工作滿意度較高，而韓國沒有關聯。4.日本的教師工作時間愈長，工作滿意度愈低，而臺韓則沒有關聯。5.日本教師愈認為教職是可靠收入，工作滿意度愈低，而臺韓在此項沒有關聯。6.臺灣的教師愈認為教職容易安排生活時間，工作滿意度較高；日本的教師與工作滿意度為負向關聯。7.臺日的教師愈認為教職可對社會貢獻，工作滿意度愈高，然而韓國的教師沒有關聯。本研究貢獻在於儒家文化圈的學校團隊創新和教職，與學生和工作滿意度是重要的關聯因素，並針對結論提出具體建議。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;Based on the 2018 OECD&amp;rsquo;s Teaching and Learning International Survey, this research explored factors related to the job satisfaction of elementary school teachers in Taiwan, Japan, and South Korea from 3,441, 3,062, and 3,058 teachers, respectively, and performed regression analyses. The following conclusions were obtained. The more innovative the school teacher team of school teachers in the three countries was, the more the school teachers believed that teaching positions can affect students, and the higher is their job satisfaction. However, the teacher’s educational level, teaching positions being stable jobs, and teaching positions can all help the disadvantaged, nd there was no significant correlation with job satisfaction. The differences in the three countries included the following. 1) The job satisfaction of female teachers in Japan was significantly lower than that of male teachers, while there was no significant difference between male and female teachers in Taiwan and South Korea. 2) The more senior the teachers in Taiwan and Japan were, the lower was their job satisfaction, while in South Korea, there existed no significant correlations. 3) Teachers in Taiwan and Japan prioritized teaching positions and had higher job satisfaction, whereas, in South Korea, there existed no significant correlations. 4) The longer the working week of Japanese teachers was, the lower was their job satisfaction. However, there was no significant correlation between working hours and job satisfaction of teachers in Taiwan and South Korea. 5) The more Japanese teachers thought that teaching was a reliable income, the lower was their job satisfaction, while this relationship was not significant for teachers in Taiwan and South Korea. 6) Teachers in Taiwan thought that teaching posts made it easier to arrange their lifestyle and had higher job satisfaction. Japanese teachers had a negative correlation with job satisfaction and teaching posts made it easier to arrange their lifestyle. 7) The more teachers in Taiwan and Japan believed that they can contribute to society, the higher was their job satisfaction. However, this variable was not obvious on teachers in South Korea. The contributions of this research lay in the Confucian cultural circle, school team innovation and teaching positions can affect students, and job satisfaction was an important factor. An in-depth discussion of the results and specific suggestions were offered herein.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Target-Oriented Knowledge Distillation with Language-Family-Based Grouping for Multilingual NMT

Multilingual NMT has developed rapidly, but still has performance degradation caused by language diversity and model capacity constraints. To achieve the competitive accuracy of multilingual translation despite such limitations, knowledge distillation, which improves the student network by matching the teacher network’s output, has been applied and shown enhancement by focusing on the important parts of the teacher distribution. However, existing knowledge distillation methods for multilingual NMT rarely consider the knowledge, which has an important function as the student model’s target, in the process. In this article, we propose two distillation strategies that effectively use the knowledge to improve the accuracy of multilingual NMT. First, we introduce a language-family-based approach, guiding to select appropriate knowledge for each language pair. By distilling the knowledge of multilingual teachers that each processes a group of languages classified by language families, the multilingual model overcomes accuracy degradation caused by linguistic diversity. Second, we propose target-oriented knowledge distillation, which intensively focuses on the ground-truth target of knowledge with a penalty strategy. Our method provides a sensible distillation by penalizing samples without actual targets, while additionally targeting the ground-truth targets. Experiments using TED Talk datasets demonstrate the effectiveness of our method with BLEU scores increment. Discussions of distilled knowledge and further observations of the methods also validate our results.
Proficient Evaluation of Visual Cryptography using Transposition Cipher and Bit Reversal Techniques

This paper aims to secure visual information present in an image using cryptographic techniques. Transposition cipher and Bit reversal image encryption are the two techniques demonstrated in this paper and have implemented the same using python programming knowledge. The transposition cipher refers to scrambling of pixels in a key controlled manner in both horizontal and vertical ways. The used in this inscription can be provided by users. To hide information regarding the color composition of the image, a bit reversal image encryption algorithm is implemented which is a keyless encryption technique. Furthermore, this paper compares the original image and the image encrypted with transposition cipher and bit reversal technique and comparative measurements of entropy, PSNR, MSE and SSIM are computed.
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging?

Previous Part-Of-Speech (POS) induction models usually assume certain independence assumptions (e.g., Markov, unidirectional, local dependency) that do not hold in real languages.For example, the subject-verb agreement can be both long-term and bidirectional.To facilitate flexible dependency modeling, we propose a Masked Part-of-Speech Model (MPoSM), inspired by the recent success of Masked Language Models (MLM).MPoSM can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction.We achieve competitive results on both the English Penn WSJ dataset as well as the universal treebank containing 10 diverse languages.Though modeling the long-term dependency should ideally help this task, our ablation study shows mixed trends in different languages.To better understand this phenomenon, we design a novel synthetic experiment that can specifically diagnose the model's ability to learn tag agreement.Surprisingly, we find that even strong baselines fail to solve this problem consistently in a very simplified setting: the agreement between adjacent words.Nonetheless, MPoSM achieves overall better performance.Lastly, we conduct a detailed error analysis to shed light on other remaining challenges. 1
Power Industry Big Data Privacy Protection Processing Method Based on Fuzzy Logic and Intelligent Clustering

The power industry is the corporate globe’s backbone, delivering vital energy to industrial, manufacturing, promotional, and residential clients worldwide. Investment has been prompted by the shift of fuel and energy sources, rising environmental laws, and an ageing generating fleet and transmission infrastructure in industrialized nations with mature power systems. The evolving requirement in the power industry is causing several workforce challenges, including massive shifts in skills required, a skills gap for delivering. Performing newer technologies, changes occur when the Industry is experiencing high retirements and challenges recruiting a strengthening workforce. In this paper, Big Data based on the fuzzy logic method has been suggested to protect sensitive information. BD-FLM proposes a large data clustering-based privacy preservation probabilistic model that aims to cause the least disruption while maintaining the greatest amount of privacy. To alter or generalize sensitive data, use a methodology that secures sensitive information after detecting sensitive data clusters. In terms of conventional performance assessment metrics, the model’s privacy protection of individual data in large data with little disruption and effective reconstruction underlines its importance. This paper can automatically cluster based on the power characteristic factor and efficiently identify the power-related aspects of distinct user groups. The simulation results demonstrate that the proposed technique outperforms the comparison algorithm regarding prediction accuracy. As a result of anticipating the accurate values, individual privacy is preserved, but data accuracy is improved.
A visual analytics approach for the assessment of information quality of performance models—a software review

Abstract In this paper we provide a review of the main functionalities of a Visual Analytics Environment (VAE) developed for the assessment of data and information quality in the context of performance evaluation of research organizations. Performing data and information quality tests are necessary procedures to ensure the bibliometric and research performance evaluation analysis of organizations have the necessary robustness. The proposed environment is helpful to guide the user to an Information Quality-aware development of Performance models. This interactive visual analytics environment offers to the user the possibility to produce and compare information quality-aware indicators, exploring and defining correct behavior, identifying anomalous cases from both data quality and information quality perspectives, and supporting the user in forming hypotheses on possible causes for those anomalies. The proposed approach, exploiting visual interactive exploration results in a more efficient process, minimizing the number of cases for which a manual investigation is needed. The illustration on European higher education institutions data demonstrates the use of the presented functionalities and their benefits.
Homomorphisms of Lifted Planning Tasks: The Case for Delete-Free Relaxation Heuristics

Classical planning tasks are modelled in PDDL which is a schematic language based on first-order logic. Most of the current planners turn this lifted representation into a propositional one via a grounding process. However, grounding may cause an exponential blowup. Therefore it is important to investigate methods for searching for plans on the lifted level. To build a lifted state-based planner, it is necessary to invent lifted heuristics. We introduce maps between PDDL tasks preserving plans allowing to transform a PDDL task into a smaller one. We propose a novel method for computing lifted (admissible) delete-free relaxed heuristics via grounding of the smaller task and computing the (admissible) delete-free relaxed heuristics there. This allows us to transfer the knowledge about relaxed heuristics from the grounded level to the lifted level.
Operator-Potential Heuristics for Symbolic Search

Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive approach to optimal planning. Yet heuristic search in this context remains challenging. The many advances on admissible planning heuristics are not directly applicable, as they evaluate one state at a time. Indeed, progress using heuristic functions in symbolic search has been limited and even very informed heuristics have been shown to be detrimental. Here we show how this connection can be made stronger for LP-based potential heuristics. Our key observation is that, for this family of heuristic functions, the change of heuristic value induced by each operator can be precomputed. This facilitates their smooth integration into symbolic search. Our experiments show that this can pay off significantly: we establish a new state of the art in optimal symbolic planning.