Reviewerly/exHarmony huggingface dataset:
Touch the Collections: A 3D UI Prototype in DSpace-7

Institutions have focused on the digitization process, metadata workflow, and digital infrastructure development for digital librarianship in recent decades. The undergoing digital shift that is accelerated by the pandemic crisis has signified the importance of visualizing digital collections because digital presence is no longer a preview of the physical collection; instead, the digital presence has become the primary access point. Replacing the heavily text-based browsing features, visualization and interactive design in digital repositories are used to facilitate communication, presentation, discovery, and comprehension of digital and digitized heritage. This project explores and demos the first 3D user interface that visually outlines and interactively exhibits communities and collections in DSpace-7 and discusses the applicable scenarios. The integration of the developed modules will be released as open sources on GitHub once completed.
Deciphering the Reviewer's Aspectual Perspective: A Joint Multitask Framework for Aspect and Sentiment Extraction from Scholarly Peer Reviews

Peer reviews are one of the most important artifacts in scholarly communications. Peer reviews can serve as a rich source of knowledge discovery from texts that are human-generated and also opinionated on the paper under scrutiny. Reviewers comment on several implicit aspects of the paper (Originality, Soundness, Clarity, Appropriateness, etc.) where they sometimes appreciate, sometimes discuss, or sometimes question or criticize the work. Hence, correctly understanding the reviewer's aspectual perspective on the paper is crucial for chairs/editors to take a stand and also for the authors to respond or revise accordingly. In this paper, we introduce MASEPR, a novel multitask deep neural architecture to jointly discover the aspects and associated sentiments from the peer review texts. Our proposed approach leverages the knowledge sharing between aspect and sentiment lexicons to generate predictions. We outperform the standard baselines by a significant margin. We also make our codes available at https://github.com/cruxieu17/MASEPR.
Enriching Simple Keyword Queries for Domain-Aware Narrative Retrieval

Providing effective access paths to content is a key task in digital libraries. Oftentimes, such access paths are realized through advanced query languages, which, on the one hand, users may find challenging to learn or use, and on the other, requires libraries to convert their content into a high quality structured representation. As a remedy, narrative information access proposes to query library content through structured patterns directly, to ensure validity and coherence of information. However, users still find it challenging to express their information needs in such patterns. Therefore, this work bridges the gap by introducing a method that deduces patterns from keyword searches. Moreover, our user studies with participants from the biomedical domain show their acceptance of our prototypical system.
Bibliometric Data Fusion for Biomedical Information Retrieval

Digital libraries in the scientific domain provide users access to a wide range of information to satisfy their diverse information needs. Here, ranking results play a crucial role in users' satisfaction. Exploiting bibliometric metadata, e.g., publications' citation counts or bibliometric indicators in general, for automatically identifying the most relevant results can boost retrieval performance. This work proposes bibliometric data fusion, which enriches existing systems' results by incorporating bibliometric metadata such as citations or altmetrics. Our results on three biomedical retrieval benchmarks from TREC Precision Medicine (TREC-PM) show that bibliometric data fusion is a promising approach to improve retrieval performance in terms of normalized Discounted Cumulated Gain (nDCG) and Average Precision (AP), at the cost of the Precision at 10 (P@10) rate. Patient users especially profit from this light-weight, data-sparse technique that applies to any digital library.
The Library of Babel as a Digital Library of Human Mistakes

Mistakes appear in every area of life. Analyzing these mistakes can yield valuable insights to prevent possible mistakes in the future. However, until now there is a lack of infrastructure for collecting these mistakes. In this paper, we present suggestions towards a conceptual model for storing human mistakes as digital objects. We argue, that these digital libraries need special infrastructure, to make the information useful not only for humans but for machines as well. To ensure the usability of such a library of mistakes, the mistakes themselves should follow the FAIR principles.
History-Related Content Recommendation in Social Networks Based on User's Interest Estimation

Knowing and analyzing the events of the past is important as it allows us to understand the formation process of modern society. Twitter users are able to acquire and share information of their interests including also content on history. We propose an algorithm to recommend history-related content according to the interests of Twitter users. Our approach analyzes users' past tweets to determine their interests by modeling the forgetting curve of memory. It determines the users' current interests by considering the time passage and the repetition of posted content on particular topics.
Persistent Identifiers and Research Data

Persistent Identifiers are essential for identifying, citing, and tracking the usage of research data. This poster will examine the unique identifier requirements of research data, and evaluate how existing systems meet these requirements. We hope rethinking the requirements of dataset identifiers will lead to identifier systems that are a better fit for data; that will reduce the institutional and organization cost of maintaining the identification; and will better support data identification, discovery, and preservation.
Perseus 6.0: Towards a Next Generation Reading Environment for Born-Digital Editions and Corpora

The Beyond Translation Project has developed a new reading environment for multilingual sources to address several challenges. First, we wanted to create a new digital library system for the Perseus Digital Library that could support new classes of borndigital data about historical sources. Second, we wanted to assess the feasibility of integrating openly licensed data of different types from multiple, independent projects from various countries. Third, we wanted to create a reading environment in which audiences use a growing range of annotations to go from translations into sources composed in languages that they do not know. We report here on Perseus 6.0, the initial release of work by Beyond Translation and the first iteration of the sixth generation system for the Perseus Digital Library.
SMAuC - The Scientific Multi-Authorship Corpus

The rapidly growing volume of scientific publications offers an interesting challenge for research on methods for analyzing the authorship of documents with one or more authors. However, most existing datasets lack scientific documents or the necessary metadata for constructing new experiments and test cases. We introduce SMAuC, a comprehensive, metadata-rich corpus tailored to scientific authorship analysis. Comprising over 3 million publications across various disciplines from over 5 million authors, SMAuC is the largest openly accessible corpus for this purpose. It encompasses scientific texts from humanities and natural sciences, accompanied by extensive, curated metadata, including unambiguous author IDs. SMAuC aims to significantly advance the domain of authorship analysis in scientific texts.
Prediction Model of Short Video Danmaku Emotion Recognition

In short video communication, the user's emotional analysis is the basis of exploring the communication law of video media. The short video danmaku contains important information to analyze the user's emotional tendency. So identifying the emotional tendency of the audience via short video danmaku is significant for the video media communication. A novel prediction model for short video danmaku emotion recognition is designed. Firstly, the short video audience's emotions are divided into five categories to construct the user's emotional space. Then the video danmaku is preprocessed by shallow learning model. Then the preprocessed text vector generated by the preprocessor is input into Bi(LSTM+GRU)-Att model to extract high level features. Finally the shallow learning model completes the danmaku emotion recognition. The prediction model not only fully extracts the text and semantic features of the context, but also improves the model's interpretability. The experimental results show that the classification accuracy reaches 87.7% and the accuracy on the test set reaches 99.6%. Therefore this model has high accuracy in short video danmaku emotion recognition. The proposed model can be used as a basic tool for short video propagation mechanism analysis.
Prompt Learning Under the Large Language Model

With the emergence of models such as chatGPT and Baidu AI Wenxin Yiyan, the research and application of NLP (Natural Language Processing) is increasingly centered on PLM (Pretrained Language Model),It marks that the current machine learning model has reached a new height. This article first introduces the background of the large language model, and introduces pre-training + fine-tuning and the current popular Prompt from the four major paradigms of NLP. Understand the workflow and function of Prompt, focusing on Prompt engineering, and structures, and looking ahead to future challenges for Prompt.
Inner Connection Among Translation Psychology, Probability Theory and Machine Translation

The development of the recurrent neural network (RNN) in machine translation (MT) has greatly improved its translation speed and accuracy. Although a larger proportion of the discourse power of MT is increasingly taken in translation activities, MT cannot monopolize human translation due to the probability algorithm. There are two reasons. First, the order and format of some texts will affect the accuracy of machine translation according to the algorithm of probability theory. Second, “memory” is not only an indispensable core technique in machine translation but also an insurmountable component in translation psychology. Although the current research on MT based on RNNs has made some achievements, it is still impossible to obtain the most scientific combination mode of the parts of speech according to probabilistic computing; therefore, the machine itself cannot distinguish the correctness of the source language, which leads to the fact that the input of the source language with the wrong structure may still obtain the correct translation. This paper explores possible ways to optimize MT by researching the internal relations among probability theory, translation psychology and MT.
Research on Improving Neural Machine Translation Performance with Multi-Source Input Data

In this thesis, we present a new approach to improving NMT performance by incorporating data from many sources.Our findings demonstrate the method's use for improving translation quality under constraints and expanding the NMT system's adaptability to different language contexts.Our method offers a constructive way forward for NMT study and has significant implications for the development of more reliable and precise NLP models.
Collision Detection Technique Based on the Spatial Decomposition Method

In this paper, we propose a collision detection algorithm for a dynamic simulation system. This algorithm first conducts global search for obj ects, optimizes global detection through spatial decomposition, and uses spatial segmentation technology to map objects to spatial grids and quickly exclude adjacent but disjoint objects. In the process of accurate collision detection, the data structure of the hash table is used to save the geometric information of the objects, and the parallel algorithm is used to update the hash table to further improve the rate of the parallel algorithm. The experiment shows that the algorithm can ensure the authenticity of flexible fabric collision detection, and the space complexity and time complexity are relatively low, which can solve the flexible fabric simulation problem of time-consuming and large space.
Source of Intelligent Manufacturing and Industrial Big Data

The concept of “big data” in basic scientific research, such as meteorological map analysis, big physics true computing, and gene map analysis. In the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century, the Internet, e-commerce, mobile Internet, social network, Internet of things, and other technologies are developing rapidly, big data has become the inevitable product of the development of this new generation of information technology. Big data has the characteristics of a large amount of data, complex data types, and high realtime requirements of data processing. The wide application of big data analysis in the field of the Internet and e-commerce has produced great commercial value and has been highly valued by all countries in the world. Big data is the next area of innovation, competition, and productivity, according to McKinsey, a world-famous strategic consulting company.
A Neighborhood Decision Based Mean Filter for Video Image Denoising

Denoising Gaussian noise generated in digital images during acquisition and transmission requires balancing the denoising effect with the loss of picture details, and existing spatial domain filtering methods have their advantages and disadvantages. In this work, we propose an improved method based on mean filtering, which uses a depth-first search algorithm to find connected blocks with similar colors to the center point of the current filter window, and only counts the values of these pixel points for the filtering operation. This method improves the inherent defects of mean filtering by considering the correlation of colors between pixel points. Moreover, we conducted an experimental design considering the acceptance characteristics of film and television viewers. The quantitative and qualitative results of the experiments show that our method has certain advantages in preserving the edge clarity of color blocks and the sharpness of the image when applied to the noise reduction of film and television image frames.
Few-Shot Incremental Event Detection Method Based on External Knowledge for Sample Preservation

The few-shot incremental event detection task requires model can incrementally learning with a limited sample. This task facilitates rapid text categorization. Existing methods for sample retention have been found to produce prototype bias issues resulting from the small sample size. To address this problem, we introduce a novel approach that utilizes external knowledge for sample retention. Our method involves the use of encoded external knowledge and samples as prototypes, from which we select a retention sample. Experimental results demonstrate the efficacy of our method in reducing prototype bias.
Batch and Lossless Image Hiding Via Super Resolution

Batch image hiding refers to the technique of embedding and extracting multiple secret images within a cover image in a covert manner. This process is typically used in applications that require the secure transmission of multiple secret images over a public channel while keeping the sensitive information hidden from unauthorized viewers. However, existing batch image steganography methods prioritize the visual effect of the resulting “stego” images over the constraint of lossless extraction of secret images, and often sacrifice detection resistance. In this paper, we propose a new steganography framework that can embed batch images into images and extract them without loss, thus overcoming the aforementioned challenges in image hiding. To achieve lossless data revealing, we formulate the data hiding as an image super-resolution problem. We consider the cover image as the object of the super-resolution process, while the batch secret images are mapped as the high-frequency information of the super-resolution images. Additionally, we design an invertible neural network that simultaneously learns the super-resolution process and the corresponding inverse transformation, enabling the lossless extraction of secret images. Finally, through qualitative and quantitative experiments and analysis, we demonstrate that our method achieves 100% revealing accuracy in different scenarios and is resistant to third-party detection.
Free Space Detection Method Based On SENet Attention

The free space based on deep learning object detection still has some uncertainties when detecting static boundary or dynamic obstacle boundary, this will make it impossible to make effective track planning and state decision for vehicle running state, therefore, we need to modify the semantic edge information by matching the results of curb, lane line and target box and define the free space from the vector envelope or raster diagram. Secondly, the detection of driving area may be affected by light, dust, heavy snow and fog, therefore, it is necessary to fully combine vision and radar for obstacle detection to ensure its detection stability. In the process of free space detection, sensors such as cameras and lidar are easily affected by the environment, light conditions and extreme weather will affect the accuracy of the detection results of free space, in the traffic jam section, the occlusion caused by the surrounding vehicles will also affect the accuracy of the detection results of the free space. This paper refers to a CNN data fusion architecture, called RoadSeg, it can from the RGB image feature extraction and fusion, and deduce the surface normal information supplied, so as to realize the free space of accurate detection [1]. We introduced SENet on RoadSeg[2] called SE-RoadSeg to make some results more accurate.
Socio-pedagogical design in the training of students of pedagogical universities in the context of the formation of a coherent environment of the future

The author considers the problem of using socio-pedagogical design as a condition for training students of pedagogical universities in the context of the formation of a coherent environment of the future, reveals the features of such socio-pedagogical design, its structure, presents a model for implementing a system of training students of pedagogical universities of socio-pedagogical design as a guarantor of readiness for future professional activity and conditions for maintaining a harmonious educational environment as a means of formation of personality. The need to develop a model for the use of socio-pedagogical design in the training of students of pedagogical universities in the context of the formation of a coherent environment of the future is caused by a number of reasons: the uneven development of the environment with the priority requirement of the organization of effective education and personal development. Instability, inconsistency of environments leads to a negative impact on the personality both in psychological terms (discomfort, contradictory, ambivalent experiences) and in the pedagogical aspect (lack of a stable idea of values, problems with choosing and building a priority of values, difficulties in forming a stable worldview). A coherent educational environment, or, in other words, a balanced, harmonized one, is able to maximize the capabilities to solve the problems of the educational process, maintain concentration, adequate interactions of all environments with each other. This is possible with the extraction of its potential by specialists who possess advising competencies.
Simulation of Medical Service Robot Based on ROS

For the distribution of materials in infectious disease isolation areas and the regular elimination of corridors in infected areas, it is currently mainly done manually. In order to reduce labor costs and the possibility of cross-infection, this paper proposes a medical service robot based on ROS(robot operating system). Firstly, the medical service robot system is simulated, and the establishment of the robot structure and the analysis of the kinematic model are completed. Secondly, import the built medical service robot into the Gazebo simulation platform to create a simulation environment. Then, a two-dimensional grid map of the simulated environment is constructed. Finally, the navigation simulation experiment is completed for this environment, and obstacles are added to test its obstacle avoidance ability, so that the medical service robot can complete related tasks and operations. The experimental results show that the medical service robot can complete tasks such as map construction, real-time positioning, path planning, dynamic obstacle avoidance, and fixed-point navigation in the constructed simulation environment. Thus, when equipped with different modules, the medical service robots can eventually perform tasks such as the distribution of supplies in isolation areas and the disinfection of corridors.
Terminal Path Planning of Industrial Robot

To improve the traditional method of teaching paths for industrial robots, a path planning method using the repulsive force field of artificial potential field method is proposed to improve the A* algorithm in space to plan a moving path with high security. Extending the A* algorithm to space for global search, adding repulsive influence factors, improving the cost function, modifying the cost of moving the end of the manipulator near the obstacle, and improving the path safety. Comparing the performance of the traditional A* algorithm, Artificial Potential Field(APF) method and improved algorithm in the same environment, the improved algorithm has significantly improved in path security.
Design of Target Tracking Ball Gear Pan-Tilt System Based on Machine Vision

The trend of intelligent and unmanned industries benefited from the rapid development of information technology is more and more obvious. The unmanned combat platform has become a new combat force in anti-terrorism operations, relying on sensor, machine vision, communication, information fusion and other technologies. A design scheme of pan-tilt target tracking system based on machine vision is proposed here for the visual perception requirements of unmanned combat platform. A combination of YOLOv4-tiny and DeepSORT is used to identify and track personnel with cameras mounted on ball gears. When the target moves to the edge of view, the position information will be sent to the control circuit through serial communication, and the camera will be adjusted with the PID control algorithm to keep the target in the center of view. The experimental results showed that this system can achieve target tracking effectively in real time and the tracking success rate can reach 80% by optimizing with K-Means++ algorithm and PID controller.
Intelligent Sampling Method for Aluminum Ingot in Electrolytic Cell

Due to the dangerous working environment and high working intensity of electrolytic aluminum workshop, aluminum ingot transfer robot is used to assist workers to sample and transport aluminum ingot, among which, accurate positioning of aluminum ingot is an important link. In the process of robot grasping aluminum ingot, there are some problems, such as uneven brightness, low contrast and small target to be detected, which affect the accuracy of identification and positioning. In order to solve the above problems, an optical correction method of aluminum ingot image based on adaptive gamma rays is proposed in this paper to improve the accuracy of target detection under extreme illumination conditions. After the YOLOX network detects the target, the space motion function is proposed to realize the conversion from two-dimensional(2D) plane coordinates to three-dimensional(3D) space coordinates. This algorithm can effectively solve the problem of low recognition accuracy caused by illumination. Through the calculation of space motion function, the positioning accuracy error of the manipulator is less than 5mm.