Reviewerly/exHarmony huggingface dataset:
Towards Few-shot Image Captioning with Cycle-based Compositional Semantic Enhancement Framework

Many efforts paid attention to the multi-modal task, of which image captioning is a classic work. Especially the Clip model improves the performance of image captioning; meantime, its few-shot and zero-shot problems have become a significant research project. In this work, aiming at the image captioning task, we design the new few-shot and zero-shot settings different from popular directions. The direction focuses on the impact of the exited dataset for captioning model ability. According to analysis, we discover the frequency of the word combination can directly influence the performance of the captioning model. Based on this, we define the new few-shot and zero-shot settings. In terms of this, a Cycle-based captioning framework based on data augmentation is proposed to overcome this problem, of which the novelty switcher module is the critical component. Finally, experiments demonstrate that our framework can achieve state-of-the-art performance on both traditional, few-shot and zero-shot settings.
Medical Named Entity Recognition Fusing Part-of-Speech and Stroke Features

It is highly significant from a research standpoint and a valuable practice to identify diseases, symptoms, drugs, examinations, and other medical entities in medical text data to support knowledge maps, question and answer systems, and other downstream tasks that can provide the public with knowledgeable answers. However, when contrasted with other languages like English, Chinese words lack a distinct dividing line, and medical entities have problems such as long length and multiple entity types nesting. Therefore, to address these issues, this study suggests a medical named entity recognition (NER) approach that combines part-of-speech and stroke features. First, the text is fed into the BERT pre-training model to get the semantic representation of the text, while the part-of-speech feature vector is obtained using the part-of-speech dictionary, and the stroke feature of the text is extracted through a convolution neural network (CNN). The word vector is then joined with the part-of-speech and stroke feature vectors, respectively, and input into the BiLSTM and CRF layer for training. Additionally, to balance the disparity in data volume across several types of entities, the class-weighted loss function is included in the loss function. According to the experimental findings, our model’s F1 score on the CCKS2019 dataset reaches 78.65%, and the recognition performance exceeds many existing algorithms.
Multi-Scale Depth-Aware Unsupervised Domain Adaption in Semantic Segmentation

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the domain-invariant knowledge from the labeled source domain to the unlabeled target domain. Leveraging highly relevant tasks as auxiliary tasks has become a common approach to UDA tasks because it contributes to the mutual promotion of the tasks. However, when applying task interactions on a single scale, the model fails to perceive the overall context of the image. To address this issue, we propose a multi-scale depth-aware (Mti-DA) method for domain adaption. In particular, we use the channel attention mechanism to distill the task features and then fuse them with other task features as a complement. The semantic features will better perceive the shape and edges of the objects when they are enhanced by the depth features. We perform task interaction on every scale to deliver the full potential of multi-task learning. Exploiting the depth perception on each scale in the source domain to guide the target domain contributes to enhanced segmentation performance because the complementary relationships of different tasks in the target domain are available. Extensive experiments on two benchmarks (GTA5 to Cityscapes and SYNTHIA to Cityscapes) demonstrate that Mti-DA achieves state-of-the-art performance.
Extending Interpolation Consistency Training for Unsupervised Domain Adaptation

Interpolation consistency training (ICT) is a semi-supervised learning method that encourages predictions of interpolated samples to be consistent with the interpolation of predictions of the corresponding original samples. It has achieved highly impressive results on semi-supervised learning benchmarks, but has not been evaluated in domain adaptation settings where the distributions of labeled and unlabeled data are different. We extend the ICT principle for domain adaptation tasks, by combining ICT with a gradient reversal mechanism that accounts for the domain shift in an adversarial manner. We show that ICT alone is not sufficient for handling the distribution shift and even deteriorates the performance, but the proposed method achieves good performance on visual domain adaptation benchmarks.
Self-adaptive Prompt-tuning for Event Extraction in Ancient Chinese Literature

Extracting different types of war events from ancient Chinese literature is significant, as war is an important factor in driving the development of Chinese history. The existing trend of event extraction models utilizes template-based generative approaches, which do not take into account the brevity and obscurity of ancient Chinese, as well as the diversity of templates for similar event types. In this paper, we propose a novel Knowledge Graph-based generative event extraction framework with a self-Adaptive Prompt (KGAP) for ancient Chinese war. Specifically, we construct a self-adaptive prompt, which considers its unique trigger words for different types of wars and is designed to solve the problem of the similarity in events. Moreover, we construct a semantic knowledge graph of ancient literature, assisting the pre-trained language model to better understand the ancient Chinese text. Since there is no public dataset for the ancient Chinese event extraction task, we provide an event extraction dataset and conduct experiments on it. Experimental results show that our model is more state-of-the-art than both the classification-based and generative-based methods for event extraction in ancient Chinese literature.
CAKT: Coupling contrastive learning with attention networks for interpretable knowledge tracing

In intelligent systems, knowledge tracing (KT) plays a vital role in providing personalized education. Existing KT methods often rely on students' learning interactions to trace their knowledge states by predicting future performance on the given questions. While deep learning-based KT models have achieved improved predictive performance compared with traditional KT models, they often lack interpretability into the captured knowledge states. Furthermore, previous works generally neglect the multiple semantic information contained in knowledge states and sparse learning interactions. In this paper, we propose a novel model named CAKT that couples contrastive learning with attention networks for interpretable knowledge tracing. Specifically, we use three attention-based encoders to model three dynamic factors of the Item Response Theory (IRT) model, based on designed learning sequences. Then, we identify two key properties related to the knowledge states and learning interactions: consistency and separability. We utilize contrastive learning to incorporate the semantic information of the above properties into the representations of knowledge states and learning interactions. With the training goal of contrastive learning, we can obtain more representative representations of them. Extensive experiments demonstrate the excellent predictive performance of CAKT and the positive effects of considering the two properties. Additionally, CAKT can exhibit high interpretability for captured knowledge states.
Multi-source Multi-label Feature Selection

Feature selection for multi-source multi-label data has attracted much attention, because there are a lot of scenarios that produce multiple source data with multi-labels in the real-world applications, which aggravates the problems of dimensional disaster and the label skewness. However, most of existing multi-source feature selection methods miss the multi-label issue while existing multi-label feature selection methods can not select the optimal feature set in the multi-source environment. Motivated by this, we propose a novel feature selection method, called MSMLFS. To be specific, the Inf-FS algorithm is firstly introduced to handle multi-label feature selection for each data source, which considers the label weight in the feature selection. Secondly, the over-sampling mechanism and the inter-source feature fusion method are used to handle the label skewness of multi-label data and the feature selection in multiple sources respectively. Finally, extensive experiments conducted on synthetic and real-world multi-source multi-label data sets demonstrate that the proposed method outperforms several state-of-the-art multi-source or multi-label feature selection methods.
Graphcut Textures: Image and Video Synthesis Using Graph Cuts

In this paper we introduce a new algorithm for image and video texture synthesis. In our approach, patch regions from a sample image or video are transformed and copied to the output and then stitched together along optimal seams to generate a new (and typically larger) output. In contrast to other techniques, the size of the patch is not chosen a-priori, but instead a graph cut technique is used to determine the optimal patch region for any given offset between the input and output texture. Unlike dynamic programming, our graph cut technique for seam optimization is applicable in any dimension. We specifically explore it in 2D and 3D to perform video texture synthesis in addition to regular image synthesis. We present approximative offset search techniques that work well in conjunction with the presented patch size optimization. We show results for synthesizing regular, random, and natural images and videos. We also demonstrate how this method can be used to interactively merge different images to generate new scenes.
Introducing Undergraduates to Research Data Management: A Marine Science Case Study-Based Asynchronous Express Course

ABSTRACTExperiential learning can connect undergraduate students with research projects where they create and analyze data. Research Data Management (RDM) competencies are needed to ensure that research data are effectively organized, described, stored, and curated for future use. In recent years, undergraduates have been taught RDM in credit classes and workshops, but opportunities to learn RDM competencies are not widespread in undergraduate education. This study describes a one-credit asynchronous express RDM course that was developed and taught by an academic librarian as a 100-level undergraduate class between 2021 and 2023. Students applied RDM theory and best practices to marine science case study-based assignments to experience organizing and describing research data firsthand. The course culminated with students writing proposals to implement RDM practices at a research institution. Evaluation of the course is described from the librarian-instructor perspective and using enrollment, grade, and student course evaluation data. Teaching asynchronously at an express pace presented challenges in keeping some students engaged and progressing throughout the seven-week course, requiring proactive outreach and instructor attentiveness. While the asynchronous mode adds flexibility to student schedules, a reduced scope or slower pace is recommended for providing undergraduates a meaningful experiential introduction to data management.KEYWORDS: Data literacy competencieslibrary special topics coursesonline learningresearch data managementundergraduates AcknowledgmentsAppreciation is expressed to Dr. Amanda Whitmire, Britney Prebis, and Autum Blanchard for their review of the manuscript during its development.Disclosure statementNo potential conflict of interest was reported by the author(s).
Can ChatGPT pass Glycobiology?

The release of text-generating applications based on interactive Large Language Models (LLMs) in late 2022 triggered an unprecedented and ever-growing interest worldwide. The almost instantaneous success of LLMs stimulated lively discussions in public media and in academic fora alike not only on the value and potentials of such tools in all areas of knowledge and information acquisition and distribution but also on the dangers posed by their uncontrolled and indiscriminate use. This conversation is now particularly active in the higher education sector, where LLMs are seen as a potential threat to academic integrity at all levels, from facilitating cheating by students in assignments to plagiarizing academic writing in the case of researchers and administrators. Within this framework, we are interested in testing the boundaries of the LLM ChatGPT (www.openai.com) in areas of our scientific interest and expertise and in analyzing the results from different perspectives, i.e. of a final year BSc student, of a research scientist, and of a lecturer in higher education. To this end, in this paper, we present and discuss a systematic evaluation on how ChatGPT addresses progressively complex scientific writing tasks and exam-type questions in Carbohydrate Chemistry and Glycobiology. The results of this project allowed us to gain insight on: (i) the strengths and limitations of the ChatGPT model to provide relevant and (most importantly) correct scientific information, (ii) the format(s) and complexity of the query required to obtain the desired output, and (iii) strategies to integrate LLMs in teaching and learning.
The Virtual Cinematographer: A Paradigm for Automatic Real-Time Camera Control and Directing

This paper presents a paradigm for automatically generating complete camera specifications for capturing events in virtual 3D environments in real-time. We describe a fully implemented system, called the Virtual Cinematographer, and demonstrate its application in a virtual "party" setting. Cinematographic expertise, in the form of film idioms, is encoded as a set of small hierarchically organized finite state machines. Each idiom is responsible for capturing a particular type of scene, such as three virtual actors conversing or one actor moving across the environment. The idiom selects shot types and the timing of transitions between shots to best communicate events as they unfold. A set of camera modules, shared by the idioms, is responsible for the low-level geometric placement of specific cameras for each shot. The camera modules are also responsible for making subtle changes in the virtual actors? positions to best frame each shot. In this paper, we discuss some basic heuristics of filmmaking and show how these ideas are encoded in the Virtual Cinematographer.
Trading patterns in the bitcoin market

Despite the growing literature on Bitcoin and other cryptocurrencies, we know relatively little about who are involved in trading, transacting and using these assets and how they behave. Examining millions of Bitcoin transaction records, we show that less than 1% of Bitcoin users contribute to more than 95% of the market volumes. These ‘whales’ are often associated with strategic trading/transaction volumes, market reactions and timing patterns. Using K-means clustering on a comprehensive transaction dataset, we establish a typology of traders by learning their trading exchange patterns, strategies and impact risk and market microstructure. Our approach ‘learns’ and identifies five distinct groups or types of Bitcoin users, which are somewhat, though not entirely, comparable to popular categorisations used in conventional market such as fundamental, technical, retail and institutional traders as well as market makers. Four of these groups present distinguishable trading patterns with a strong impact on liquidity provision and trading signals.
Prioritising paths: An improved cost function for local path planning for UAV in medical applications

Abstract Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH * local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH * uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.
Cascade Speech Translation for the Kazakh Language

Speech translation systems have become indispensable in facilitating seamless communication across language barriers. This paper presents a cascade speech translation system tailored specifically for translating speech from the Kazakh language to Russian. The system aims to enable effective cross-lingual communication between Kazakh and Russian speakers, addressing the unique challenges posed by these languages. To develop the cascade speech translation system, we first created a dedicated speech translation dataset ST-kk-ru based on the ISSAI Corpus. The ST-kk-ru dataset comprises a large collection of Kazakh speech recordings along with their corresponding Russian translations. The automatic speech recognition (ASR) module of the system utilizes deep learning techniques to convert spoken Kazakh input into text. The machine translation (MT) module employs state-of-the-art neural machine translation methods, leveraging the parallel Kazakh-Russian translations available in the dataset to generate accurate translations. By conducting extensive experiments and evaluations, we have thoroughly assessed the performance of the cascade speech translation system on the ST-kk-ru dataset. The outcomes of our evaluation highlight the effectiveness of incorporating additional datasets for both the ASR and MT modules. This augmentation leads to a significant improvement in the performance of the cascade speech translation system, increasing the BLEU score by approximately 2 points when translating from Kazakh to Russian. These findings underscore the importance of leveraging supplementary data to enhance the capabilities of speech translation systems.
Research on Quantum-Attack-Resistant Strong Forward-Secure Signature Schemes

The security of digital signatures depends significantly on the signature key. Therefore, to reduce the impact of leaked keys upon existing signatures and subsequent ones, a digital signature scheme with strong forward security could be an effective solution. Most existing strong forward-secure digital signature schemes rely on traditional cryptosystems, which cannot effectively resist quantum attacks. By introducing lattice-based delegation technology into the key-iteration process, a two-direction and lattice-based key-iteration algorithm with strong forward security is proposed. In the proposed algorithm, a unique key pair is assigned to the signer in every period. Based on the proposed algorithm, a strong forward-secure signature scheme is further put forward, which achieves resistance to quantum attacks. Performance analysis shows that under the security assumption of the SIS problem on the lattice, the proposed strong forward-secure signature scheme is existentially unforgeable under the random oracle model. Ultimately, based on the proposed strong forward-secure signature scheme, a remote identity-authentication scheme that is resistant to quantum attacks is proposed, ensuring post-quantum security in the user-authentication process.
Early diagnosis and personalised treatment focusing on synthetic data modelling: Novel visual learning approach in healthcare

The early diagnosis and personalised treatment of diseases are facilitated by machine learning. The quality of data has an impact on diagnosis because medical data are usually sparse, imbalanced, and contain irrelevant attributes, resulting in suboptimal diagnosis. To address the impacts of data challenges, improve resource allocation, and achieve better health outcomes, a novel visual learning approach is proposed. This study contributes to the visual learning approach by determining whether less or more synthetic data are required to improve the quality of a dataset, such as the number of observations and features, according to the intended personalised treatment and early diagnosis. In addition, numerous visualisation experiments are conducted, including using statistical characteristics, cumulative sums, histograms, correlation matrix, root mean square error, and principal component analysis in order to visualise both original and synthetic data to address the data challenges. Real medical datasets for cancer, heart disease, diabetes, cryotherapy and immunotherapy are selected as case studies. As a benchmark and point of classification comparison in terms of such as accuracy, sensitivity, and specificity, several models are implemented such as k-Nearest Neighbours and Random Forest. To simulate algorithm implementation and data, Generative Adversarial Network is used to create and manipulate synthetic data, whilst, Random Forest is implemented to classify the data. An amendable and adaptable system is constructed by combining Generative Adversarial Network and Random Forest models. The system model presents working steps, overview and flowchart. Experiments reveal that the majority of data-enhancement scenarios allow for the application of visual learning in the first stage of data analysis as a novel approach. To achieve meaningful adaptable synergy between appropriate quality data and optimal classification performance while maintaining statistical characteristics, visual learning provides researchers and practitioners with practical human-in-the-loop machine learning visualisation tools. Prior to implementing algorithms, the visual learning approach can be used to actualise early, and personalised diagnosis. For the immunotherapy data, the Random Forest performed best with precision, recall, f-measure, accuracy, sensitivity, and specificity of 81%, 82%, 81%, 88%, 95%, and 60%, as opposed to 91%, 96%, 93%, 93%, 96%, and 73% for synthetic data, respectively. Future studies might examine the optimal strategies to balance the quantity and quality of medical data.
Exploring the Challenges and Adoption Hurdles of Blockchain Technology in Agri-Food Supply Chain

The need for a transparent and secure system in the agri-food supply chain has led to the exploration of blockchain technology as a feasible solution. Food contamination, counterfeiting, and insecurity have become major concerns globally, and the implementation of blockchain technology has been identified as a way to minimize food waste and risks to the public. However, there are implementation challenges and barriers associated with the adoption of this technology. This study aims to identify and understand the barriers and implementation issues related to blockchain technology in the agri-food supply chain. The research employs qualitative methods, including case studies from Trace X and expert interviews, to identify the most common implementation barriers. The study also utilizes the analytical hierarchical model to rank the barriers and prioritize the most significant ones hindering the adoption process. Overall, this chapter provides valuable insights into the implementation challenges of blockchain technology in the agri-food sector.