Reviewerly/exHarmony huggingface dataset:
A new approach for localization of copy-move forgery in digital images

In this study, newly localization approach is presented to reveal copy-move forgery regions. Firstly, Speed-Up Robust Features (SURF) keypoints are utilized to label whether the suspicious input image is original or forged. With the presence of a sufficient number of the most similar keypoint matches, the image is labeled as forged and the location stage of the forged region is started. In determining the boundaries of the forged region, the blocks received around the matched keypoints are considered seed forged blocks. 8 overlapping neighboring blocks of these blocks in the target and source regions are labeled as candidate blocks. If the candidate blocks corresponding to each other meet the similarity requirement, they are labeled as forged blocks and the candidate block label is removed. The location phase is completed by evaluating the candidate blocks located in the neighborhood of all forged blocks. The high performance of the method has been proved in the freely available GRIP dataset with the experiments.
Automating the Evaluation of Trustworthiness

Digital services have a significant impact on the lives of many people and organisations. Trust influences decisions regarding potential service providers, and continues to do so once a service provider has been selected. There is no globally accepted model to describe trust in the context of digital services, nor to evaluate the trustworthiness of entities. We present a formal framework to partially fill this gap. It is based on four building blocks: a data model, rulebooks, trustworthiness evaluation functions and instance data. An implementation of this framework can be used by a potential trustor to evaluate the trustworthiness of a potential trustee.
A Handwritten Text Detection Model Based on Cascade Feature Fusion Network Improved by FCOS

In this paper, we propose a method for detecting handwritten ancient texts. The challenges in detecting this type of data are: the complexity of the layout of handwritten ancient texts, the varying text sizes, mixed arrangement of pictures and texts, the high number of hand-drawn patterns and the high background noise. Unlike general scene text detection tasks (ICDAR, TotalText, etc.), the texts in the images of ancient books are more densely distributed. For the features of the dataset, we propose a detection model based on cascade feature fusion called DFCOS, which aims to improve the fusion of localization information in lower layers. Specifically, bottom-up paths are created to use more localization signals from low-levels, and we incorporate skip connections to better extract information in the backbone, and then improve our model by parallel cascading. We verified the effectiveness of our DFCOS on HWAD (Handwritten Ancient-Books Dataset), a dataset containing four languages - Yi, Chinese, Tibetan and Tangut - provided by the Institute of Yi of Guizhou University of Engineering Science and National Digital Library of China, and its precision, recall and F-measure outperformed most of the existing text detection models.
Research on the Methods of Speech Synthesis Technology

An important technology to realize human computer interaction is the technology of converting a given text into natural speech, that is speech synthesis. This paper succinctly expounds the development process of speech synthesis, analyzes the shortcomings of traditional speech synthesis technology, and highlights the advantages and disadvantages of various vocoders. Due to the overwhelming contribution of deep learning to the field of speech synthesis, this paper introduces several pioneering research results in the field of speech synthesis, expounds its main ideas, advantages and disadvantages, and inspires new ideas on this basis. Finally, it objectively discusses and analyzes the problems of speech synthesis technology and puts forward the direction that can be further studied.
An Ahp Based Server Selection Scheme For Migrating Crawler

Migrating crawlers are able to move to the resource which needs to be accessed in order to take advantage of local data access however this process incurs migration cost and have security risks. Therefore to increase the efficiency the crawler must be able to select the best possible server out of multiple copies of a web page present on WWW. In this work a novel credit based server selection mechanism has been proposed for appropriate server selection which considers network parameters like Network distance, Bandwidth availability, and existent load on the server to select a particular server for migrant. This technique provides enhanced security, minimum communication cost and efficient use of network resources.
The Interactive Functions of Questions in Embodied Collaborative Work

Researchers have been interested in the investigation of the interactive functions of questions in conversational contexts. However, limited research has been conducted on the interactive functions of questions in embodied collaborative work, that is, work that involves the manipulation of physical objects. This study aimed to identify the interactive functions of questions in embodied collaborative work. To do so, we conducted a systematic qualitative analysis of a dataset of 1,751 question-answer sequences collected from an experimental study where pairs of participants (N = 67) completed a collaborative food preparation task. The qualitative analysis enabled us to identify three functions of questions: anticipation questions, exploration questions, and confirmation questions. We have discussed in this study how the types of questions identified are associated with: (i) the accomplishment of interactional goals and (ii) complementary temporalities in the collaborative activities.
Handwriting Recognition using Deep Learning with Effective Data Augmentation Techniques

Machine learning techniques have been successfully used in deciphering handwritten text. Deep learning has made further improvements in this regard. However, they require substantial amounts of training data. This research aims to improve the effectiveness of classification accuracy in the presence of limited training data on handwriting recognition. The main focus thus involves enabling deep models to converge during training on smaller datasets using data augmentation. This will allow for broader use of these systems across more regions, greater accessibility, and future related systems to be less reliant on the amount of data available. Therefore, the proposed research includes an image processing and machine learning approach to handwriting recognition while generating more sample data in various ways. Applying random cropping as an augmentation technique resulted in higher accuracy than several other augmentation techniques examined in this paper. Some of these techniques performed worse than on unaugmented data.
Stock Market Crisis Early Warning Model Based on Network Information Text Mining

In order to improve the accuracy of stock market crisis warning model. This paper proposes a stock market crisis early warning model based on network information text mining. Firstly, the method introduces network information variables and uses principal component analysis to reduce dimension. Then, BP neural network based on particle swarm optimization is used to establish and optimize the stock market crisis warning model. Compared with the traditional method, the new method has higher warning effect.
Research on Participatory Skills in Tang Shipwreck Collection Exhibition from the Perspective of SDT

This paper takes the Tang Shipwreck Collection Exhibition of Shanghai Museum as the carrier, relies on the digital intelligent navigation system and modern interactive installation designed based on Internet of Things, Wi-Fi positioning and other technologies in the exhibition, and according to SDT designs a set of participatory exhibition viewing process for education, whose process and educational effect are analyzed.
Application of Computer in Mixed Teaching of Higher Mathematics

In today's society, because of the development of higher information education technology, computer technology is rapidly changing the learning methods and methods of college students. A variety of novel learning methods have also changed the learning concept and characteristics of college students. As an important part of university teaching curriculum system, higher mathematics plays a very important role in cultivating and developing students' thinking ability and creative ability. In order to improve the learning effect of the course and cultivate the students' active learning ability, some colleges and universities have carried out a new teaching method — mixed teaching on the basis of integrating online knowledge resources and offline educational resources. In order to further promote the development of the mixed teaching mode of “Advanced Mathematics ”, this paper takes the application of computer as the starting point, and through analyzing the concrete application of computer in each link of the mixed teaching mode, establishes the important position of computer in the mixed teaching, thus provides the direction for further promoting the development of the mixed teaching mode.
DeepComp: A Deep Comparator for Improving Facial Age-Group Estimation

We introduce an age-group estimation scheme known as DeepComp. It is a combination of an Early Information-Sharing Feature Aggregation (EISFA) mechanism and a ternary classifier. The EISFA part is a feature extractor that applies a siamese layer to input images and an aggregation module that sums up all the images. The ternary process compares the image representations into three possible outcomes corresponding to younger, similar, or older. From the comparisons, we arrive at a score indicating the similarity between an input and reference images: the higher the score, the closer the similarity. Experimentation shows that our DeepComp scheme achieves an impressive 94.9% accuracy on the Adience benchmark dataset using a minimum number of reference images per age group. Moreover, we demonstrate the generality of our method on the MORPH II dataset, and the result is equally impressive. Altogether, we show that, among other schemes, our method exemplifies facial age-group estimation.
The Problem of Journal Supplements in a Sharing World

This quantitative longitudinal study cross-analyzes a shared print repository’s periodical supplement records against physical copies to determine the accuracy of bibliographic representation for bound supplements. The authors relate the study’s findings to collection managers and library administrators by highlighting prominent issues discovered, and by discussing the challenges this descriptive disconnect may present for shared repositories, as well as any library collection containing periodical supplements. By requesting that participating libraries provide catalog records for materials set to be submitted, the shared print repository central to this study was well positioned to access and analyze large samples of records and materials from an assortment of independent library collections. It was discovered that many periodical supplement records failed to fully represent actual supplement content held, particularly when involving supplements that were bound together with serial volumes. As a result, the authors pose that if relying only on standard supplement cataloging requirements to represent periodical supplement holdings, it may be particularly difficult for those overseeing collection management to know exactly what supplement content is held and bound within or apart from the affiliated parent resource materials. In turn, this issue may result in libraries inadvertently mislaying or discarding valuable supplement content, particularly when supplement information is not recorded and cataloged at the item level.
Context-Aware Anomaly Detection in Attributed Networks

Anomaly detection in attributed networks has received increasing attention due to its broad applications in various high-impact domains. Compared to traditional anomaly detection, the main challenge of this task lies in how to integrate the network structure and node attributes to spot anomalies. However, existing methods attempt to integrate two kinds of information into a fixed representation and neglect the contextual information. Specifically, a fixed feature vector is directly adopted to evaluate its abnormality without considering the node’s diverse roles when interacting with different neighbors. In this paper, we propose a novel Context-Aware Anomaly Detection (CAAD) framework in attributed networks. CAAD derives context-aware embeddings for each node pair with a mutual attention mechanism. The embeddings extracted by feature interactions can concentrate on the most relevant attributes of network structures. Numerous context information provides us with multiple perspectives to understand the structure connection and detect local anomaly structure. Moreover, we develop an anomaly gated mechanism to assign global anomalous scores to node pairs. The anomalous scores are learnable and applied to reduce the adverse effect of anomalies during the training process. By jointly optimizing network embeddings and anomaly gated mechanism, our model can spot anomalies in local and global collaborations. Experiments on various real-world network datasets indicate that the proposed model achieves state-of-the-art results.
Visual Center Recognition in Game Concept Art Based on Art Principle

In the image analysis, the visual center is an area that has a high weighting factor for investigation. For instance, visual saliency is essential in image analysis. Furthermore, the visual center and visual guidance are necessary factors in composition when the 2D artist designs the concept art. Previous research is based on experiments or different saliency methods to identify visual interest rules. This research is inspired by bags of feature models and analyzes the frequency and color contrast in local regions to recognize the visual center. The authors intend to improve accuracy by using the bags of color model, and demonstrating the relationship between artists' pictorial skills and visual interest.
MediTables: A New Dataset and Deep Network for Multi-category Table Localization in Medical Documents

Localizing structured layout components such as tables is an important task in document image analysis. Numerous layout datasets with document images from various domains exist. However, healthcare and medical documents represent a crucial domain that has not been included so far. To address this gap, we contribute MediTables, a new dataset of 200 diverse medical document images with multi-category table annotations. Meditables contains a wide range of medical document images with variety in capture quality, layouts, skew, occlusion and illumination. The dataset images include pathology, diagnostic and hospital-related reports. In addition to document diversity, the dataset includes implicitly structured tables that are typically not present in other datasets. We benchmark state of the art table localization approaches on the MediTables dataset and introduce a custom-designed U-Net which exhibits robust performance while being drastically smaller in size compared to strong baselines. Our annotated dataset and models represent a useful first step towards the development of focused systems for medical document image analytics, a domain that mandates robust systems for reliable information retrieval. The dataset and models can be accessed at https://github.com/atmacvit/meditables.
Decidability and Np-Completeness for Somelanguages Which Extend Boolean Set Theory

We prove the decidability for a class of languages which extend BSTand NP-completeness for a subclass of them. The languages BST ex-tended with unordered cartesian product, BST extended withordered cartesian product and BST extended with powerset fall in this last subclass.
Petrography, Sedimentology and Geochemistry of Rocks and Sediments from Three Key Localities from the South-East Region of the Yaound&amp;#233; Group (Centre Region, Cameroon): Implications for Rutile Exploration

The Otele, Matomb and Nguibassal localities correspond to the SW part of the Yaoundé Group.Field observations, rocks and stream sediments of the study area were examined using rock tin sections, granulometric, morphoscopic and X-ray fluorescence analysis to identify their distribution, the nature of the basement, provenance useful minerals and some weathering process that affect them to show the exploration significance of these outcomes.From these it appears that, three rocks types occur in the areas: gneisses, micaschists and amphibolite, respectively with the following minerals assemblage: (Fks + Bt + Qz + Ky + Pl + Msc + Grt + Op), (Bt + Qz + Msc + Fks + Op + Ky + Grt) and (Amp + Bt + Pl + Kfs + Qz + Px + Grt + Op).Sedimentological analyses reveal the dominance of unworn grains of rutile quartz and kyanite that suggest a short transport; while less abundant minor shiny dulls and sub-rounded grains suggest a long transport.Sediments collected are homometric, well sorted, well classified, with immature rutile due to their angular shapes.Silica is the most important major element ranging in concentration between 47.56 wt% SiO 2 in amphibolite to 61.21 wt% SiO 2 in gneiss.Stream sediments chemistry equally showed that silica and alumina are the remarkable elements with values of 53.92 wt% and 13.33 wt% respectively with important increase of TiO 2 ranging between 51.77 and 95.03 wt%.The TiO 2 percentage met in minerals of the global fraction and rutile concentrate come from the same minerals in the rock, and percentage increases when linked to heavy minerals observation suggested the Ti percentage to mainly come from rutile.Impurities present in the rutile How to cite this paper:
Optical Flow Estimation with Foreground Attention Guided Network

Optical flow is used to describe the variations between adjacent images of a sequence. Although the pixels belonging to a same object have similar displacements, most existing optical flow estimation methods focus on processing each pixel indiscriminately, while neglecting the semantic information of pixels. Consider the foreground objects usually have large displacement and outstanding to viewer, we in this paper propose a foreground attention guided network to strengthen the foreground target feature for optical flow estimation. Specifically, we first adopt a foreground attention network to obtain a map of foreground objects. Then the attention map of foreground is utilized to strengthen the features in multiple scales via 3D convolution for optical flow estimation progression. Finally, the strengthening features are concatenated with the original feature maps in multi-scale deconvolution operation to achieve the final optical flow. To the end, we pretrain our proposed framework on Flying Chairs dataset, and then execute comparison experiments on MPI Sintel and KITTI benchmark datasets. The experimental results verify that our proposed framework is comparable with state-of-the-art methods with a miniature network.
A Few-shot Learning Method Based on Bidirectional Encoder Representation from Transformers for Relation Extraction

Relation extraction is one of the fundamental subtasks of the information extraction. The purpose is to determine the implicit relation between two entities in a sentence. Therefore, Convolutional Neural Networks and Feature Attention-based Prototypical Networks (CNN-Proto-FATT), a typical few-shot learning method, is proposed and achieve competitive performance. However, convolutional neural networks suffer from the insufficient instances of relation in real scenes, leading to undesirable results. To extract long-distance features more comprehensively, the pre-trained model Bidirectional Encoder Representation from Transformers (BERT) is incorporated into CNN-Proto-FATT. In this model, named Bidirectional Encoder Representation from Transformers and Feature Attention-based Prototypical Networks (BERT-Proto-FATT), the multi-head attention helps the network extract semantic features cross long- and short-distance to enhance the encoded representations. Experimental results indicate that BERT-Proto-FATT demonstrates significant improvements on the FewRel dataset.
Switching fluctuation compensation of the multi-view optical positioning system under camera occlusion

The advances of multi-view optical tracking technology led to the rise of image-guided robotic-assisted surgery. The positioning accuracy of multi-view system is extremely crucial in ensuring the safety of the surgery process. The calibration error and camera occlusion may lead to the system switching fluctuation problem, which affects the system stability and limits its usage in situations requiring high safety standards. In this paper, the direct linear transformation (DLT) method is applied to solve multi-view triangulation problem and the influence of the calibration parameter error on the measurement accuracy is analyzed in the semi-physical simulation environment. The results show that the system switching fluctuation has a great influence on the positioning accuracy of the system. In order to improve the measurement stability of the system under camera occlusion, this paper proposes an error compensation strategy based on neural network nonlinear fitting. The measurement data at different locations in the workspace are used as learning samples. The trained network model is used to predict the occlusion points of the system and the measurement results are compensated for errors. Finally, the effectiveness of the compensation method is verified by simulations and real experiments.
Fostering Open Innovation by Linking Entrepreneurial Leadership and Knowledge Management: An Empirical Study in the Tunisian Context

Entrepreneurial leadership is universally recognized and there are divergences in its effectiveness, which suggests several promising areas of inquiry. The success of this leadership style depends on interrelations between leaders, followers, and the knowledge context. This study offers an interpretative reading of entrepreneurship and leadership theories to describe an innovative approach. It investigates the link between entrepreneurial leadership and open innovation considering the knowledge management importance. A new conceptual model is proposed through a thorough review of the literature. A qualitative research method was used. Five in-depth case studies were conducted. Data was collected from small and medium-sized enterprises belonging to the Tunisian industrial sector. The findings of this study show that entrepreneurial leadership affects open innovation through knowledge management. Moreover, the results show that entrepreneurial leaders encounter difficulties in the use of knowledge into open innovation. In addition, the results reveal the contextual specificities of the Tunisian entrepreneurial leadership. This work would be an opportunity for practitioners allowing them to discover the mechanisms and processes ensuring the maintenance of entrepreneurial intensity in an innovative company but also, opening the door to action when this intensity presents deficiencies.