Reviewerly/exHarmony huggingface dataset:
Research on Literature Searching Website Design Based on Usability Test

Based on the basic theory of usability test, a problem about how to improve the usability and UX (user experience) of literature searching websites are explored in this paper. Taking the CNKI website as an example, the usability test of the existing version of CNKI was carried out. The factors which affect the usability and UX of CNKI were obtained, and an iterative design were designed. Then, by comparing data of usability test between 2 versions. It is concluded that the optimized UX design can improve the usability of the website. Finally, key factors and guidance of optimizing the UX design of literature searching websites were proposed in this paper.
Challenges in Future Data Interchange in Transport and Logistics Sector

Data integration in today’s supply chain becomes more and more challenging for companies. In the age when technology is not so much obstacle for integration the few others appeared, related to: different systems in use, organization’s aspects, standards in use, growing number of players in global supply chains and continues business changes. These article shows how data integration has been changing through years from one hand adopting new technologies and from the other adapting to business requirements. Thanks a wide spectrum of researches, surveys, consultation with IT managers responsible in their organization for data integration and EDI on-boarding projects. Authors were able to conclude on some of near future data integration aspects as well as to assess what solutions would have to be compromised and which bring true values for companies and trading partners. The professional experience of authors brings the true value to the article because it is based on real daily examples of data integration in global supply chains, also an experience in research projects for e-administration and idea of building blocks for partner integrations shows even more ways of where the future of data integration is heading to.
Extending Forgetting-Based Abduction Using Nominals

Abductive reasoning produces hypotheses to explain new observations with respect to some background knowledge. This paper focuses on ABox abduction in ontologies, where knowledge is expressed in description logics and both the observations and hypotheses are ground statements. The input is expressed in the description logic $$\mathcal {ALC}$$ and the observation can contain any set of $$\mathcal {ALC}$$ concept or role assertions. The proposed approach uses forgetting to produce hypotheses in the form of a disjunctive set of axioms, where each disjunct is an independent explanation for the observation and the overall hypothesis is semantically minimal, i.e., makes the least assumptions required. Previous work on forgetting-based abduction is combined with the semantic forgetting method of the system FAME. The hypotheses produced are expressed in an extension of $$\mathcal {ALC}$$ which uses nominals, role inverses and fixpoints: $$\mathcal {ALCOI}\mu (\nabla )$$ . This combination overcomes the inability of the existing forgetting-based approach to allow role assertions in observations and hypotheses, and enables the computation of other previously unreachable hypotheses. An experimental evaluation is performed using a prototype implementation of the method on a corpus of real world ontologies.
Keywords-Based Auxiliary Information Network for Abstractive Summarization

Automatic text summarization is an important research task in the field of natural language processing (NLP). The abstractive approach to automatic text summarization produces the condensed version of the source text by generating new words and phrases. Recently, the attentional sequence-to-sequence models have shown good ability in abstractive text summarization. Nevertheless, these neural network models are still hard to cover most key points of the source text and may produce unfactual details. To address these issues, we proposed a keywords-based auxiliary information model to guide the process of encoding and decoding. Firstly, we proposed an auxiliary information network based on the keywords of the document, which aims to generate the modified encoded representation. In addition, we designed a novel selective beam search mechanism to keep more keywords and reduce redundancy in the decoded summaries. We evaluated our model on different datasets including the benchmark CNN/Daily Mail dataset. The experimental results show that our model leads to significant improvements compared with abstractive baseline models.
Statistical Archetypal Analysis for Cognitive Categorization

Human knowledge develops through complex relationships between categories. In the era of Big Data, the concept of categorization implies data summarization in a limited number of well-separated groups that must be maximally and internally homogeneous at the same time. This proposal exploits archetypal analysis capabilities by finding a set of extreme points that can summarize entire data sets in homogeneous groups. The archetypes are then used to identify the best prototypes according to Rosch’s definition. Finally, in the geometric approach to cognitive science, the Voronoi tessellation based on the prototypes is used to define categorization. An example using a well-known wine dataset by Forina et al. illustrates the procedure.
An image caption model incorporating high-level semantic features

Encoder-decoder framework attracts great interests in image caption. It focuses on the extraction of low-level features and achieves good results. The performance can be further improved if high-level semantics are considered. In this work, we propose a new image caption model incorporating high-level semantic features through an revised Convolutional Neural Network(CNN). Both the low-level image features and high-level semantic features are fed into the Long-Short Term Memory networks(LSTMs) to acquire natural sentence descriptions. We show in a number of experiments on Flickr8K and Flickr30K datasets that our method outperforms most standard network baseline for image caption.
Usability of Skin Texture Biometrics for Mixed-Resolution Images

There is a growing demand for alternative biometric modalities that can handle various real world challenges such as recognizing partially occluded individuals. Skin texture has been proposed as a potential alternative; however, such skin texture analysis can become difficult when captured images are at varying resolutions (due to different distances or devices). This paper explores the prospect of using mixed-resolution facial skin images as a source of biometric information. The four facial skin regions investigated here are the forehead, right cheek, left cheek, and chin which were selected because at least one of these are expected to be captured in real-world scenarios. The proposed framework first localises and assesses the usability of the extracted region of interest (ROI) for subsequent analysis. Local Binary Pattern (LBP) descriptors are then used for feature matching because of their reported effectiveness in extracting skin texture information. Experiments conducted using the XM2VTS database suggest that mixed resolution skin texture images can provide adequate information for biometric applications.
An integration de-noising method based on self-adaptive manifold filtering and text contour for ancient Chinese calligraphy tablet image

This paper proposed an integration de-noising method based on self-adaptive manifold filtering and text contour for ancient Chinese calligraphy tablet image. It consists of two main operations in sequence: image smoothing with a non-local means (NL-means) based self-adaptive manifold filter and isolated blocks removal based on text contour. Experiments demonstrate that the proposed method is superior to several recent published stele image de-noising techniques in terms of preserving the character structures.
Spatio-Temporal Correlation Graph for Association Enhancement in Multi-object Tracking

Due to the frequent interaction between targets in real-world scenarios, various data association problems, such as association ambiguities and association failure, are caused by potential correlation between interactive tracklets, especially during crowded and cluttered scenes. To overcome the non-intuitionistic of tracklet interaction, spatio-temporal correlation graph (STCG) is proposed to model the potential correlation between pairwise tracklets. Three primitive interactions (aggregation, abruption, stability) are defined to model the completed period of the tracklet interaction. Furthermore, STCG model is applied into network flow tracking to exploit the potential correlation between tracklets and enhance the association of the interactive tracklets, especially when overlapping or occlusion is happened. Our method is effective on MOT challenge benchmarks and achieves considerable competitive results with current state-of-the-art trackers.
Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection

Humans can intuitively understand the content of images, and often reach a consensus that some images are more difficult to visual search tasks than others. However, this is quite challenging for computers as it is a subjective task which may be influenced by human emotional factors. Instead of focusing on how the models make reactions on datasets, our method has a capability of assigning scores to samples respectively within a dataset that estimating the difficulty of visual search tasks. Our model shows better performance for predicting visual search difficulty scores of samples produced by human annotators in PASCAL VOC2012. Eventually, we demostrate with experiments that our method has an ability of selecting suitable samples to improve the performance of detectors in a semi-supervised task.
Development of Operational Technology for Meteorological High Performance Computing

As an important branch of information technology, high-performance computing has expanded its application field and its influence has been expanding.High-performance computing is always a key area of application in meteorology.We used field research and literature review methods to study the application of high performance computing in China's meteorological department, and obtained the following results: 1) China Meteorological Department gradually established the first high-performance computer system since 1978.High-performance computing services can support operational numerical weather prediction models.2) The Chinese meteorological department has always used the relatively advanced high-performance computing technology, and the business system capability has been continuously improved.The computing power has become an important symbol of the level of meteorological modernization.3) High-performance computing technology and meteorological numerical forecasting applications are increasingly integrated, and continue to innovate and develop.4) In the future, high-performance computing resource management will gradually transit from the current local pre-allocation mode to the local remote unified scheduling and shared use.In summary, we have come to the conclusion that the performance calculation business of the meteorological department will usher in a better tomorrow.
Detecting Temporal Sentiment-Oriented Difference for Crowdsourced Time-Sync Comments

Recently, with the rapid development of online shared media, a novel time-sync comment occurs. Time-sync comment is a type of crowdsourced user review embedded in online video website and provides better real-time user interaction than traditional mode. User is probably willing to write comments and then send as time-sync comment when reading interested time-sync comments by others or watching attractive video shots. In this paper, we propose temporal sentiment-oriented difference graph (TSoDG) to detect different areas which exist sentiment-oriented difference over time. We attain the TSoDG by calculating the sentiment-oriented score of time-sync comments, and then use semantic similarity to deal with lagged comments. Finally, we analysis the temporal difference in graph by rule. The result can be regarded as a baseline for further research.
Rectified Encoder Network for High-Dimensional Imbalanced Learning

Many existing works have studied the learning on imbalanced data, however, it is still very challenging to handle high-dimensional imbalanced data. One key challenge of learning on imbalanced data is that most learning models usually have a bias towards the majority and its performance will deteriorate in the presence of underrepresented data and severe class distribution skews. One solution is to synthesize the minority data to balance the class distribution, but it may lead to more overlapping, especially in the high-dimensional setting. To alleviate the above challenges, in this paper, we present a novel Rectified Encoder Network (REN) for high-dimensional imbalanced learning tasks. The main contribution is that: (1) To deal with high-dimensionality, REN encodes high-dimensional imbalanced data into low dimensional latent codes as a latent representation. (2) To obtain a discriminative representation, we introduce a Rectifier to match the latent codes with our proposed Predefined Codes, which disentangles the overlapping among classes. (3) During rectification, in the Predefined Latent Distribution, we can efficiently identify and generate informative samples to maintain the balance of class distribution, so that the minority classes will not be neglected. The experimental results on several high-dimensional and image imbalanced data sets indicate that our REN obtains good representation code for classification and visualize the reason why REN gets better performance in high-dimensional imbalanced learning.
A Hierarchical Attention Headline Generation Model with a Filter

The recent researches demonstrate the methods of generating text headline can tackle the problem of information overload. However, the popular neural network based framework, namely, encoder-decoder framework is difficult to handle the headline generation task of long source text. In this paper, we establish a hierarchical attention headline generation model with a filter to solve this problem. This model first relies on a filter to extract crucial contents of the source text, and then the hierarchical attention mechanism accurately identifies important words. Finally, our model generates a high-quality headline. Experimental results show that the ROUGE scores of our model are higher than those of classical models. Furthermore, our model achieves better performance than classical model in dealing with long text.
Foreground Mask Guided Network for Crowd Counting

Crowd counting in unconstrained scenes is a challenging task due to large scale variations, complex background clutters and severe occlusions, etc. The performance of current networks utilizing multi-path based architectures for better multi-scale representation is constrained by the number of paths. In many cases, existing methods suffer from false responses background such as buildings and trees in complex scenes. To address these issues, we propose an end-to-end network, called Foreground Mask Guided Network (FMGNet), for high-quality density map generation and accurate crowd counting. By employing deep fusion in intermediate layers, the proposed network aggregates multi-scale features in a more efficient way. Moreover, foreground mask features representing the differences between crowd foreground and background are used as guidance information to suppress false responses in background. Extensive experiments on three challenging benchmarks have well demonstrated the effectiveness of the proposed method as well as the superior performance over prior state-of-the-arts.
Single image super resolution based on generative adversarial networks

Deep neural networks based on SRGAN single image super-resolution reconstruction can generate more realistic images than CNN-based super-resolution deep neural networks. However, when the network is deeper and more complex, unpleasant artifacts can result. Through a lot of experiments, we can use the ESRGAN model to avoid such problems. When using the ESRGAN model for super-resolution reconstruction, the perceived index of the resulting results does not reach a lower value. There are two reasons for this: (1)ESRGAN does not expand the feature maping. ESRGAN uses 128*128 to obtain the feature information of the image by default, and can't get more image information better. (2) ESRGAN did not re-optimize the generated image. Therefore, we propose ESRGAN-Pro to optimize ESRGAN for the above two aspects, combined with a large amount of training data, and get a better perception index and texture.
A study of graduate on time (GOT) for Ph.D students using decision tree model

Over the years, there has been exponential growth in the number of Doctor of Philosophy (Ph.D) graduates in most of the universities all around the world. The increment of Ph.D students causes both university and government bodies concern about the capability of the Ph.D students to accomplish the mission of Graduate on Time (GOT) that is stipulated by the university. Therefore, this study aims to classify the Ph.D students into the group of “GOT achiever” and “non-GOT achiever” by using decision tree models. Historical data that related to all Ph.D students in a public university in Malaysia has been obtained directly from the database of Graduate Academic Information System (GAIS) in order to develop and compare the performance of decision tree models (Chi-square algorithm, Gini index algorithm, Entropy algorithm and an interactive decision tree). The result gained in four decision tree models illustrated that the attributes of English background, gender and the Ph.D students’ entry Cumulative Grade Point Average (CGPA) result are the core in impacting the students’ success. Among all models, decision tree model with Entropy algorithm perform the best by scoring the highest accuracy rate (72%) and sensitivity rate (95%). Therefore, it has been selected as the best model for predicting the ability of the Ph.D students in achieving GOT. The outcome can certainly ease the burden of universities in handling and controlling the GOT issue. Also, the model can be used by the university to uncover the restriction in this issue so that better plans can be carried out to boost the number of GOT achiever in future.
Comparing two- and three-view computer vision

Abstract To reconstruct the points in three dimensional space, we need at least two images. In this paper we compared two different methods: the first uses only two images, the second one uses three. During the research we measured how camera resolution, camera angles and camera distances influence the number of reconstructed points and the dispersion of them. The paper presents that using the two-view method, we can reconstruct significantly more points than using the other one, but the dispersion of points is smaller if we use the three-view method. Taking into consideration the different camera settings, we can say that both the two- and three-view method behaves the same, and the best parameters are also the same for both methods.
Using Sentiment Analysis for Comparing Attitudes between Computer Professionals and Laypersons on the Topic of Artificial Intelligence

Most research in investigating computer professionals and laypersons' attitudes toward artificial intelligence (AI) are limited to online or offline surveys. This paper analyzes computer professionals' and laypersons' attitudes toward AI by using a sentiment lexicon developed by Wilson et al. To explore whether there is a correlation between the occupation categories (computer-related versus non-computer-related occupations) and people's attitudes toward artificial intelligence, I conducted a polarity classification of over 0.6 million tweets containing references to "AI", "artificial intelligence", or both. The result did not provide evidence of a relationship between public attitudes toward AI and the occupation categories. In the end, several future directions in the data collection and the data analysis are discussed.
Comparison of Deep Learning-Based Recognition Techniques for Medical and Biomedical Images

The recognition and classification of medical and biomedical images typically suffer from the problem of a low number of annotated samples. This comes along with the problem of efficient training of the current deep learning frameworks. Therefore, many researchers opt for various techniques which could substitute the traditional training of convolutional neural networks (CNN) from scratch. In this article, we are comparing multiple of these methods, including transfer learning and using the CNNs as feature extractors. The paper contains results on two datasets with different modalities and three different CNN architectures. We demonstrate the high effectiveness of transfer learning and suggest that, in some cases, it is worth retraining more layers at the end of the network for achieving higher accuracy.
A Novel Implementation Method for GRP Instruction

In order to solve the problem of large resources of existing GRP instruction, this paper proposes a reverse parallel extract-shift operation, and studies the method of implementing GRP instruction function by combining its result with parallel decimation. Then we design a hardware architecture, which can support GRP, reverse parallel extract-shift, parallel extract-shift, parallel extraction and cyclic shift. The results of experiment show that our design in this paper is improved by 0~30% and the resource is reduced by 35.2%~43.7% compared with the existing design.
Selective Metamorphosis for Growth Modelling with Applications to Landmarks

We present a framework for shape matching in computational anatomy allowing users control of the degree to which the matching is diffeomorphic. The control is a function defined over the domain describing where to violate the diffeomorphic constraint. The location can either be specified from prior knowledge of the growth location or learned from data. We consider landmark matching and infer the distribution of a finite dimensional parameterisation of the control via Markov chain Monte Carlo. Preliminary analytical and numerical results are shown and future paths of investigation are laid out.
Identification of Heterogeneous Identifiers of Objects Based on Support Vector Machin

In the applications of the Internet of things, the identifiers of objects are beneficial for authentication and management of objects, and are effective to query the status or data of the object, which greatly accelerate the development and deployment of the Internet of things. At the same time, more and more identification systems, which are supported by an organization or an industry, are gradually formed. With the increasing demand for cross-industry applications, the heterogeneous identifiers of objects bring some problems about research and development. For example, the repeated identifiers of an object will increase the redundant information in the database. Aiming at the heterogeneity of the identification systems, this paper describes the related features of the object identifier through the subset search and subset evaluation methods, and then trains the support vector machine multi-class classifier to identify the heterogeneous identifiers of object. Finally, the simulation experiment is carried out by using the LIBSVM software combined with MATLAB. The experimental results show that the method can effectively identify the heterogeneous identifiers of object.