Reviewerly/exHarmony huggingface dataset:
AI-News Personalization System Combining Complete Content Characterization and Full Term Interest Portrayal in the Big Data Era

In order to leverage the advantages of the large sample capacity in the era of big data and improve the performance of contemporary news recommendation methods, we have characterized complete information such as news textual details, explicit themes, and implicit themes, and thoroughly portrayed users’ mixed full term interest including long-term and short-term interests. As a result, we propose an artificial intelligence (AI)-News Personalization system that combines Complete news Content Characterization and user Full term interest portrayal, i.e., NP-3C-FIP. The NP-3C-FIP system first utilizes Latent Dirichlet Allocation (LDA) to extract the implicit theme distribution from the news textual content. Then, it learns the unified news characterizations based on the headline, summary, category, subcategory, and implicit themes. Using these news characterizations, the proposed method transforms the historical clicked news into the representation vectors. Subsequently, the obtained sequence of news representation vectors is fed into a Gate Recurrent Unit (GRU) network to capture the sequential interest features of the user. Furthermore, this paper introduces a personalized attention mechanism to model the stable tastes. Finally, the system concatenates the portrayals of the full term to obtain a unified user representation vector, and calculates the click probability for candidate articles using vector dot product. Experimental results demonstrate the effectiveness of the proposed method in improving news recommendation performance.
Logarithmic Potential Field: A New Leader– Follower Robotic Control Mechanism to Enhance the Execution Speed and Safety Attributes

The leader-follower formation approach is a commonly used strategy in multi-robot systems, usually implemented with a hierarchical control architecture combining path planning and formation control. The leader robot determines the desired trajectory while the follower robots track the motion of the leader robot using a control system. However, this hierarchical architecture does not ensure successful obstacle avoidance for follower robots. Several solutions proposed adding an obstacle avoidance layer, but this can increase the system complexity and reduce the computational speed, hindering real-time performance. Improving the opposing attributes, namely the execution speed, path length, safety, and smoothness, together is a challenging path-planning problem in robotics. This paper proposes a novel leader-follower control mechanism that combines formation control and obstacle avoidance in one step. The new path planning technique focuses on enhancing execution speed and safety while ensuring the generation of smooth paths with acceptable path lengths. The main contribution of the proposed technique lies in the development of a novel potential field modeling approach specifically designed for follower robots in a multi-robot system. The proposed potential field model consists of three terms, namely, the Gaussian term, the Euclidean term, and the Logarithmic term, which are all optimized later using Particle Swarm Optimization (PSO) to generate the path. The Gaussian term, acting as a repulsive force, represents the Gaussian distance to each obstacle in the environment. It exhibits a strong value in close proximity to obstacles, while it gradually decays exponentially as the distance from the obstacles increases. The second term, the Euclidean term, which is the Euclidean distance to the leader robot, is responsible to find the shortest path to the leader robot. Finally, to ensure follower robot safety, a logarithmic term is integrated into the potential field model, facilitating automatic switching between attractive and repulsive forces generated by the leader robot. The incorporation of a logarithmic term into the potential field model stands as a significant innovation in the proposed technique. This inclusion enables the leader robot to generate an initial attractive force towards the followers, which dynamically transitions into a repulsive force as the follower robots approach. This automatic switching behavior enhances processing efficiency while ensuring collision avoidance. A kinematic control strategy is applied to the system in order to test the proposed path planning technique. The experimental results have proven the effectiveness of the proposed system, which has shown superior performance over the well-known techniques A*, RRT*, PRM, and also Hybrid-A* in terms of execution speed and the path length.
SSRN: A Nonlocal Sparse Attention and Multiscale Fusion Super Resolution Network for Spacecraft ISAR Image

Inverse Synthetic Aperture Radar (ISAR) is a popular space object detection method. However, due to the complex flight conditions of spacecraft and limited observation time, the resolution of spacecraft ISAR images are reduced. Although there are many methods of ISAR image super resolution, but none of them aim at spacecraft. To this end, in this letter, we analyze the characteristics of spacecraft ISAR images, and firstly propose an approach Space Super Resolution Network(SSRN), suitable for spacecraft ISAR images. Specially, our approach is designed to utilize the repetitive structures commonly observed in spacecraft ISAR images. So we introduce Non-Local Sparse Attention(NLSA) to capture the long-distance self-similarity in the spacecraft ISAR image. Spherical Locality Sensitive Hashing is used to construct multiple attention buckets, and the query feature and features in the same and adjacent buckets are used for attention operations. In order to effectively extract multi-scale features and contrast features from spacecraft ISAR image, we design the Residual Atrous Spatial Pyramid Pooling block(ResASPPblock), connect a number of atrous convolution layers with different dilation rates, and add the skip connection. The experiment on the real ISAR image of a certain type spacecraft proves the effectiveness of our approach, and the performance of our approach is higher than the popular ISAR super resolution method. Overall, our approach provides a promising solution for improving the resolution of spacecraft ISAR images.
What is the Real Need for Scene Text Removal? Exploring the Background Integrity and Erasure Exhaustivity Properties

As a crucial application in privacy protection, scene text removal (STR) has received amounts of attention in recent years. However, existing approaches coarsely erasing texts from images ignore two important properties: the background texture integrity (BI) and the text erasure exhaustivity (EE). These two properties directly determine the erasure performance, and how to maintain them in a single network is the core problem for STR task. In this paper, we attribute the lack of BI and EE properties to the implicit erasure guidance and imbalanced multi-stage erasure respectively. To improve these two properties, we propose a new ProgrEssively Region-based scene Text eraser (PERT). There are three key contributions in our study. First, a novel explicit erasure guidance is proposed to enhance the BI property. Different from implicit erasure guidance modifying all the pixels in the entire image, our explicit one accurately performs stroke-level modification with only bounding-box level annotations. Second, a new balanced multi-stage erasure is constructed to improve the EE property. By balancing the learning difficulty and network structure among progressive stages, each stage takes an equal step towards the text-erased image to ensure the erasure exhaustivity. Third, we propose two new evaluation metrics called BI-metric and EE-metric, which make up the shortcomings of current evaluation tools in analyzing BI and EE properties. Compared with previous methods, PERT outperforms them by a large margin in both BI-metric ( ↑ 6.13 %) and EE-metric ( ↑ 1.9 %), obtaining SOTA results with high speed (71 FPS) and at least 25% lower parameter complexity. Code will be available at https://github.com/wangyuxin87/PERT.
An adversarial training method for text classification

Text classification is an emerging topic in the field of text data mining, but the current methods of deducing sentence polarity have two major shortcomings: on the one hand, there is currently a lack of a large and well-curated corpus; on the other hand, current solutions based on deep learning are particularly vulnerable to attacks from adversarial samples. To overcome the limitations above, we propose an adversarial training method HNN-GRAT (Hierarchical Neural Network and Gradient Reversal) for text classification. Firstly, A Robustly Optimized BERT Pretraining Approach (RoBERTa) pretraining model is used to extract text features and feature gradient information; secondly, the original gradient information is passed through the gradient reversal layer designed to obtain the inverted gradient information; finally, the original gradient information and the inverted gradient information are fused to obtain the new gradient of the model. HNN-GRAT method are tested on three real datasets and five attack methods, compared with RoBERTa pretraining model, HNN-GRAT improves the robustness accuracy and reduces the probability of the model being attacked. In addition, using six text defense methods, HNN-GRAT achieves the best Boa and Succ (Such as, DeepWordBug attack, for AGNEWS, IMDB, SST-2 datasets, with an improvement of Boa up to 41.50%, 67.50%, 28.15% and Succ drop to 55.90%, 27.45%, 69.89%, respectively).
Manuals and Reference Books for Typographers of the Late XIX – Early XX Centuries

A brief description of publications for employees of printing houses is presented: reference books, calendars, which made it possible for typographers to learn the basic rules of typing, historical information on the printing business, as well as the addresses of printing houses in St. Petersburg. Special attention is paid to the characteristics of publications devoted to the printing business, from which information can be gleaned about ancient book-making technologies. In almost every publication, you can get bibliographic information about both printing manuals and small brochures intended for students. The authors shared their long experience of work in printing houses, thus helping the successors to avoid mistakes and perform works efficiently. Publications became necessary as reference and information materials, especially for owners of printing establishments, because from them it was possible to learn about factory legislation, insurance laws and customs tariff. After 1918, the governing bodies changed, so the directories contained information about the main state management bodies for the press and publishing houses of the Supreme Economic Council (Supreme Council of the National Economy) and for Petrograd, the structure of the Union of Workers of Printing Production, etc. There are no modern studies on the information support of the process of printing books in the late XIX – early XX centuries. The purpose of this article is to generalize information about reference literature aimed at typography workers of that period.
Reference Services in University Libraries: the Union of Science and Education

The article discusses the main issues related to the organization of reference services in university libraries all over the world in the framework of the traditional reference bureau and virtual reference service. The implementation of the concept of digital reference service requires high professionalism, expert knowledge of digital tools and methods of working with them on the part of the staff of reference services. The development of information technologies changes the format of communication between the bibliographer and the user. Traditional reference services are complemented by specialized research assistance carried out in accordance with research practices of universities. The provision of real-time and deferred virtual reference services, digital transformation of libraries, and the involvement of catboats with artificial intelligence in reference services are current issues in library theory and practice.The turning point in the evolution of reference services has been the university libraries strategic initiative for online learning and additional professional education. Due to the increasing educational mission of university libraries in the professional library community, the role of information curator that the reference librarian can assume is being actively discussed. Reference librarians, alongside with other information professionals, shape the library’s research and educational space. The article bases on the analysis of publications in foreign professional periodicals.
Formation of the Sverdlovsk Regional Universal Scientific Library Named After V. G. Belinsky Collections in 1940

The article is devoted to the history of the collections acquisition of the Sverdlovsk Regional Universal Scientific Library named after V. G. Belinsky (SOUNB) in 1940 as a part of the study devoted to the history of the library collections formation. The efficiency of any library depends on the quality and quantity of the book collections, so the selection of documents is the main function of librarians throughout the entire period of the library existence. The author aims at tracing how approaches to the creation of collections have been changing for the space of work of the Library named after V. G. Belinsky. The purpose of the study is to present the history of the library through the collections acquisition. The task was to find documentary and statistical materials about the activities of librarians working with collections. The history of the acquisition is unstudied yet, therefore, official government documents, publications in the local periodical press and documents from the archive of SOUNB served as sources for research. For the first time, data revealed in the library archive are introduced into scientific circulation, and an attempt is made to recreate the history of the most important component of library activities – the acquisition of the largest library in the Middle Urals. Special attention is paid to the topic of obtaining the mandatory copy, opening the department for blind readers, creating an exchange stock, also the problems of current acquisition, preservation of book collections and lack of the necessary space for their placement are considered. To study the topic, the method of historical alignment of the chronological sequence of events has been applied.
Self-reflection proposal on digital competence for regular primary education teachers: the Selfie For Teachers model

Expansion of Digital Information and Communication Technologies (DICT) applied to education requires increasingly prepared educators to interact and produce knowledge, thus avoiding the mere transposition of analogue information to digital. Accordingly, analysis of the digital competence of teachers through self-reflection is a necessity in the face of the contradictions that the 21st century experiences, primarily with the Covid-19 challenges. The purpose of this study is to analyze the importance of the Selfie for Teachers model in the construction/improvement processes of digital competences of educators in primary education, providing them with a self-reflection on their level of digital skills. To this end, we opted for the methodological strategy of an exploratory and descriptive study, using bibliographic research on the subject as data source. The article concludes that the theoretical Selfie for Teachers model encourages teachers and suggests, through real-time returns, possibilities to constantly build on and improve their digital competence.
Underwater Fish Tracking-by-Detection: An Adaptive Tracking Approach

High distortion and complex marine environment pose severe challenges to underwater tracking. In this paper, we propose a simple, template-free Adaptive Euclidean Tracking (AET) approach for underwater fish tracking by regarding tracking as a specific case of instance detection. The proposed method exploits the advanced detection framework to track the fish in underwater imagery without any image enhancement techniques. The proposed method achieves comparable performance on the DeepFish dataset, with 22% and 14% improvement in precision and success over state-of-art trackers.
Graph-Based Analysis of Hierarchical Embedding Generated by Deep Neural Network

In a previous work, we have developed a framework for the multimodal and hierarchical classification of images from soil remediation reports. We extended this work using Deep Metric Learning (DML) as an additional training step to improve embeddings quality and obtained 84.24% of weighted F1 score for the level 5th hierarchical level. However, the standard classifier performance metrics are insufficient to explain the decision process reasoning. So far of our knowledge, there are no methods to analyze hierarchical classification algorithms. In this work, we propose a method of graph analysis to describe the embeddings that represent the extended classifier, which we believe properly interprets the obtained results than classification metrics. We illustrate the method of analyzing hierarchical classification algorithms on private dataset, but the method remains generic enough to be used in other contexts.
Operation fidelity explored by numerical range of Kraus operators

Present-day quantum devices require precise implementation of desired quantum channels. To characterize the quality of implementation one uses the average operation fidelity $F$, defined as the fidelity between an initial pure state and its image with respect to the analyzed operation, averaged over an ensemble of pure states. We analyze the statistical properties of the operation fidelity for low-dimensional channels and study its extreme values and probability distribution. These results are obtained with the help of the joint numerical range of the set of Kraus operators representing a channel. Analytic expressions for the density $P(F)$ are derived in some particular cases including unitary and mixed unitary channels as well as quantum maps represented by commuting Kraus operators. We propose a scheme for fidelity-based channel estimation which uses the distribution of operation fidelity.
Visualization of sorting algorithms in the virtual reality environment

This study examines the use of virtual reality (VR) in programming, specifically in visualization of sorting methods. Addressing students’ needs to better understand and implement sorting methods, “VR sorting” application was developed to visualize the bubble sorting and selection sorting abstract methods in the VR environment. The effects of visualization were evaluated drawing on an extended taxonomy, specifically developed by the authors of this study. The results indicate that VR might significantly enhance students’ understanding of sorting tasks, further allowing them to employ these skills in practice. Specifically, 76.9% of students, who studied sorting methods in virtual environment drawing on “VR sorting” application, demonstrated higher outcomes in implementing sorting tasks. VR visualization of sorting methods, differs from existing ways of visualizing learning in the context of constructivism. Since VR allows the student to construct the algorithm himself directly interacting machine memory in the form of cells where the data is stored and managing the progress of sorting. These results shed some light to future research avenues on VR enabled constructive visualization.
A Multi-modal Registration and Visualization Software Tool for Artworks Using CraquelureNet

For art investigations of paintings, multiple imaging technologies, such as visual light photography, infrared reflectography, ultraviolet fluorescence photography, and x-radiography are often used. For a pixel-wise comparison, the multi-modal images have to be registered. We present a registration and visualization software tool, that embeds a convolutional neural network to extract cross-modal features of the crack structures in historical paintings for automatic registration. The graphical user interface processes the user’s input to configure the registration parameters and to interactively adapt the image views with the registered pair and image overlays, such as by individual or synchronized zoom or movements of the views. In the evaluation, we qualitatively and quantitatively show the effectiveness of our software tool in terms of registration performance and short inference time on multi-modal paintings and its transferability by applying our method to historical prints.
From TrashCan to UNO: Deriving an Underwater Image Dataset to Get a More Consistent and Balanced Version

The multiplication of publicly available datasets makes it possible to develop Deep Learning models for many real-world applications. However, some domains are still poorly explored, and their related datasets are often small or inconsistent. In addition, some biases linked to the dataset construction or labeling may give the impression that a model is particularly efficient. Therefore, evaluating a model requires a clear understanding of the database. Moreover, a model often reflects a given dataset’s performance and may deteriorate if a shift exists between the training dataset and real-world data. In this paper, we derive a more consistent and balanced version of the TrashCan [6] image dataset, called UNO, to evaluate models for detecting non-natural objects in the underwater environment. We propose a method to balance the number of annotations and images for cross-evaluation. We then compare the performance of a SOTA object detection model when using TrashCAN and UNO datasets. Additionally, we assess covariate shift by testing the model on an image dataset for real-world application. Experimental results show significantly better and more consistent performance using the UNO dataset. The UNO database and the code are publicly available at: https://www.lirmm.fr/uno and https://github.com/CBarrelet/balanced_kfold .
Techno-Social Systems—A Value-Based Model for Digitalisation

In the age of global challenges, digitalisation in any social system must be shaped for the common good and not for particular economic interests. In order to do so, it is incumbent upon studies of information to explicate the possibility of such a design. How can the interplay of social and technological factors be modelled to allow for digital humanism? The author proposes the model of so-called “techno-social systems”. By terming them so, they shall be differentiated from usually being called—in reverse order—“socio-technical systems”. This study shall make clear that a techno-social system is a specification of a social system and not the other way around. The shaping of such a system can then be understood by a cycle of ideal values, value dispositions and value qualities. It is this cycle that can be influenced by social actors. It can materialise digital humanism through compliance with imperatives concerning the technological support for worldwide governance, worldwide dialogue and worldwide netizenship.