Reviewerly/exHarmony huggingface dataset:
Multi-modal Graph and Sequence Fusion Learning for Recommendation

Multi-modal recommendation aims to leverage multi-modal information for mining users’ latent preferences. Existing multi-modal recommendation approaches primarily exploit graph structures and multi-modal information to explore the graph information derived from user-item interactions, overlooking the underlying sequence information. Furthermore, by treating items solely as coarse-grained entities, the latent relationships of items within each modality are disregarded, impeding the effective extraction of latent user preferences. To address the limitations, we propose a novel approach called Multi-modal Graph and Sequence Fusion Learning Architecture for Recommendation (MMGCF). In MMGCF, we first construct dynamic item-item graphs to enhance item features and capture relationships within each modality. Subsequently, according to the influence between modalities, we design a self attention network to fuse multi-modal features. Finally, in addition to regular graph convolution, we also devise a sequence-aware learning layer to preserve and capture sequence information for model to learn user preferences from a sequential perspective. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our method over various state-of-the-art baselines.
Multi-modal Instance Refinement for Cross-Domain Action Recognition

Unsupervised cross-domain action recognition aims at adapting the model trained on an existing labeled source domain to a new unlabeled target domain. Most existing methods solve the task by directly aligning the feature distributions of source and target domains. However, this would cause negative transfer during domain adaptation due to some negative training samples in both domains. In the source domain, some training samples are of low-relevance to target domain due to the difference in viewpoints, action styles, etc. In the target domain, there are some ambiguous training samples that can be easily classified as another type of action under the case of source domain. The problem of negative transfer has been explored in cross-domain object detection, while it remains under-explored in cross-domain action recognition. Therefore, we propose a Multi-modal Instance Refinement (MMIR) method to alleviate the negative transfer based on reinforcement learning. Specifically, a reinforcement learning agent is trained in both domains for every modality to refine the training data by selecting out negative samples from each domain. Our method finally outperforms several other state-of-the-art baselines in cross-domain action recognition on the benchmark EPIC-Kitchens [4] dataset, which demonstrates the advantage of MMIR in reducing negative transfer.
Lightweight Separable Convolutional Dehazing Network to Mobile FPGA

The advancement of deep learning has significantly increased the efficiency of picture dehazing techniques. Convolutional neural networks can’t, however, be implemented on portable FPGA devices because to their high computing, storage, and energy needs. In this paper, we propose a generic solution for image dehazing from CNN models to mobile FPGAs. The proposed solution designs lightweight network using depth-wise separable convolution and channel attention mechanism, and uses an accelerator to increase the system’s processing efficiency. We implemented the entire system on a custom and low-cost FPGA SOC platform (Xilinx Inc. ZYNQ $$^{TM}$$ XC7Z035). Experiments can conclude that our approach has compatible performance to GPU-based methods with much lower resource usage.
ScaleNet: Rethinking Feature Interaction from a Scale-Wise Perspective for Medical Image Segmentation

Recently, vision transformers have become outstanding segmentation structures for their remarkable global modeling capability. In current transformer-based models for medical image segmentation, convolutional layers are often replaced by transformers, or transformers are added to the deepest layer of the encoder to learn the global context. However, for the extracted multi-scale feature information, most existing methods tend to ignore the multi-scale dependencies, which leads to inadequate feature learning and fails to produce rich feature representations. In this paper, we propose ScaleNet from the perspective of feature interaction at different scales that can alleviate mentioned problems. Specifically, our approach consists of two multi-scale feature interaction modules: the spatial scale interaction (SSI) and the channel scale interaction (CSI). SSI uses a transformer to aggregate patches from different scale features to enhance the feature representations at the spatial scale. CSI uses a 1D convolutional layer and a fully connected layer to perform a global fusion of multi-level features at the channel scale. The combination of CSI and SSI enables ScaleNet to emphasize multi-scale dependencies and effectively resolve complex scale variations.
A Fuzzy Error Based Fine-Tune Method for Spatio-Temporal Recognition Model

The spatio-temporal convolution model is widely recognized for its effectiveness in predicting action in various fields. This model typically uses video clips as input and employs multiple clips for inference, ultimately deriving a video-level prediction through an aggregation function. However, the model will give a high confidence prediction result, regardless of whether the input clips have sufficient spatio-temporal information to indicate its class or not. The inaccurate high confidence prediction errors can subsequently affect the accuracy of the video-level results. Although the current approach to mitigating this problem involves increasing the number of clips used, it fails to address this problem from its root causes. To solve this issue, we propose a fine-tuning framework based on Fuzzy error loss, aimed at further refining the well-trained spatio-temporal convolution model that relies on dense sampling. By giving a low confidence prediction output for clips with insufficient spatio-temporal information, our framework strives to enhance the accuracy of video-level motion recognition. We conducted extensive experiments on two motion recognition datasets, namely UCF101 and Kinetics-Sounds, to evaluate the effectiveness of our proposed framework. The results indicate a significant improvement in motion recognition accuracy at the video level on both data sets.
PPI-NET: End-to-End Parametric Primitive Inference

In engineering applications, line, circle, arc, and point are collectively referred to as primitives, and they play a crucial role in path planning, simulation analysis, and manufacturing. When designing CAD models, engineers typically start by sketching the model’s orthographic view on paper or a whiteboard and then translate the design intent into a CAD program. Although this design method is powerful, it often involves challenging and repetitive tasks, requiring engineers to perform numerous similar operations in each design. To address this conversion process, we propose an efficient and accurate end-to-end method that avoids the inefficiency and error accumulation issues associated with using auto-regressive models to infer parametric primitives from hand-drawn sketch images. Since our model samples match the representation format of standard CAD software, they can be imported into CAD software for solving, editing, and applied to downstream design tasks.
Camera Motion Correction with PGA

In this paper we study the geometry and kinematics of stabilizing a moving camera in order to track a stationary scene both in the 2D and 3D setting. This is being done initially in a rather straightforward manner, using the tools of analytic and differential geometry, after which we discuss the advantages of the so-called ‘projective geometric algebra’ (PGA) approach in this context. In the planar case one can easily get equivalent results with complex numbers, but in 3D it is a convenient substitute for Plücker line geometry and the theory of screws. While a lot can be done using quaternions and differential geometry, PGA is quite handy when there are different rotation or screw axes involved. Its basic constructions and properties are briefly summarized in the appendix.
Spatio-Temporal Self-supervision for Few-Shot Action Recognition

Few-shot action recognition aims to classify unseen action classes with limited labeled training samples. Most current works follow the metric learning technology to learn a good embedding and an appropriate comparison metric. Due to the limited labeled data, the generalization of embedding networks is limited when employing the meta-learning process with episodic tasks. In this paper, we aim to repurpose self-supervised learning to learn a more generalized few-shot embedding model. Specifically, a Spatio-Temporal Self-supervision (STS) framework for few-shot action recognition is proposed to generate self-supervision loss at the spatial and temporal levels as auxiliary losses. By this means, the proposed STS can provide a robust representation for few-shot action recognition. Furthermore, we propose a Spatio-Temporal Aggregation (STA) module that accounts for the spatial information relationship among all frames within a video sequence to achieve optimal video embedding. Experiments on several challenging few-shot action recognition benchmarks show the effectiveness of the proposed method in achieving state-of-the-art performance for few-shot action recognition.
RecFormer: Recurrent Multi-modal Transformer with History-Aware Contrastive Learning for Visual Dialog

Recently, benefiting from the powerful representation ability learned from large-scale image-text pre-training, pre-trained vision-language models show significant improvements in visual dialog task. However, these works suffer from two main challenges: 1) how to incorporate the sequential nature of multi-turn dialog systems for better capturing temporal dependencies of visual dialog; 2) how to align the semantics among different modal-specific features for better multi-modal interactions and understandings. To address the above issues, we propose a recurrent multi-modal transformer (named RecFormer) to capture temporal dependencies between utterances via encoding dialog utterances and interacting with visual information turn by turn. Specifically, we equip a pre-trained transformer with a recurrent function that maintains cross-modal history encoding for the dialog agent. Thus, the dialog agent can make better predictions by considering temporal dependencies. Besides, we also propose history-aware contrastive learning as an auxiliary task to align visual features and dialog history features for improving visual dialog understanding. The experimental results demonstrate that our RecFormer can achieve new state-of-the-art performances on both VisDial v0.9 (72.52 MRR score and 60.47 R@1 on val split) and VisDial v1.0 (69.29 MRR score and 55.90 R@1 on test-std split) datasets.
Large Language Model for Geometric Algebra: A Preliminary Attempt

Geometric algebra serves as the unified language of mathematics, physics, and engineering in the 21st century. Coinciding with the era of artificial intelligence, the utilization of a Large Language Model (LLM) can significantly benefit the learning and application of geometric algebra. This study develops a representative application called PrivateGPT, based on the ggml-ggml-nous-gpt4-vicuna-13b model, to explore the integration of geometric algebra and LLM by building a knowledge base of geometric algebra expertise. The Geometric Algebra Knowledge Base was created by collecting 20,711 papers and data, categorizing them by topics. This application possesses the capability of iterative refinement, enhancing its understanding and reasoning of geometric algebra knowledge. It accomplishes the textual summarization of research content, methods, innovations, and conclusions. It facilitates the development of tailored learning plans for students from diverse fields to acquire knowledge of geometric algebra in their specific domains. Additionally, we compared the performance of PrivateGPT and ChatGPT in providing personalized learning paths for the same group of learners and evaluated their responses through a questionnaire survey. The results showed that PrivateGPT has an advantage in devising tailored learning plans for learners from various disciplines.
Face-to-Face and Online Learning in Higher Education: Academic Achievements and Learners’ Experience in the Chinese SFL Context

As a result of the COVID-19 virus outbreak, there has been a dramatic change in the educational landscape, and traditional classroom instruction has shifted to the online environment. Previous research has yielded contradictory results regarding the effect of different instructional modalities on student achievement. This study aimed to examine the impact of teaching mode (online vs. face-to-face) on academic outcomes in the context of Spanish as a foreign language in China. A comparative analysis was conducted between two cohorts of students, one receiving online courses and the other undergoing in-person instruction, and their respective scores for Spanish tests are reported. Additionally, a questionnaire was administered to the online cohort to gain insight into their learning motivation. The results showed that students who received online instruction outperformed those who received face-to-face instruction. Regarding the potential factors underlying the superior academic outcomes of online learners, examination of questionnaire responses pertaining to their learning experience reveals that students’ motivation, learning strategies, self-regulation, and self-efficacy are crucial factors of successful learning results and may have positively contributed to such outcomes.
Embedding Principle for Rings and Abelian Groups

Summary The article concerns about formalizing a certain lemma on embedding of algebraic structures in the Mizar system, claiming that if a ring A is embedded in a ring B then there exists a ring C which is isomorphic to B and includes A as a subring. This construction applies to algebraic structures such as Abelian groups and rings.
Trends in AI-powered Classification of Thyroid Neoplasms Based on Histopathology Images - a Systematic Review

Background: Assessment of thyroid nodules histopathology using AI is crucial for an accurate diagnosis. This systematic review analyzes recent works employing deep learning approaches for classifying thyroid nodules based on histopathology images, evaluating their performance, and identifying limitations. Methods: Eligibility criteria focused on peer-reviewed English papers published in the last 5 years, applying deep learning to categorize thyroid histopathology images. The PubMed database was searched using relevant keyword combinations. Results: Out of 103 articles, 11 studies met inclusion criteria. They used convolutional neural networks to classify thyroid neoplasm. Most studies aimed for basic tumor subtyping; however, 3 studies targeted the prediction of tumor-associated mutation. Accuracy ranged from 77% to 100%, with most over 90%. Discussion: The findings from our analysis reveal the effectiveness of deep learning in identifying discriminative morphological patterns from histopathology images, thus enhancing the accuracy of thyroid nodule histopathological classification. Key limitations were small sample sizes, subjective annotation, and limited dataset diversity. Further research with larger diverse datasets, model optimization, and real-world validation is essential to translate these tools into clinical practice.
The Case for a Database Approach

The management of research data in the humanities and social sciences is a genuine, non-trivial challenge from a computational perspective. In this chapter, a case is made for a database approach that allows for the integration of all project data in a way that fits productively into the research program, and which makes the resulting data maximally useful for analysis, sharing and publication. A research database platform should accommodate highly diverse data that is dispersed over space and time, that is characterized by high variability, that is semi-structured, and that contains uncertainty and disagreements. Integration of spatial, temporal, textual, lexical, and multi-media data should be supported naturally and intuitively. In this chapter, an evaluation of traditional options for managing data leads to a discussion of a hybrid model, as implemented by OCHRE using XML, that is inspired by all the major database paradigms—the hierarchical, the relational, and the graph/network—taking advantage of the best features of each. An appropriately generic upper ontology provides an underlying framework for managing data of all kinds.
Conclusion: Can the “Fempire” Strike Back?

This volume has brought together research from all over the globe to ask the question—who is FemTech for? Significantly, the chapters here have shown that future research and innovation into the sector requires deeper thinking on issues of access, intersectionality, and equity. However, they have also demonstrated the various ways in which FemTech is flourishing at the so-called “margins.” Unlike Western and Northern markets, which are no doubt growing exponentially but where the baseline user remains the same—white, heterosexual, and middle to upper class—it is the Global South that is challenging the status quos of digital health and widening our scope of what feminine technologies mean, and include, and disrupting the market with regard to gender and sexuality, race, class, and ability. Much like Seddig argues in her chapter on the “silicon Savannah,” there is much potential in these “high need, low resource” communities (page ref). But this is not to discount, of course, the realities of those marginalized in and within white, western communities, as Roberts and Roberson note, respectively, in their chapters on AI interventions for autoimmune disease, and the surveillance of black, female bodies in sport. Indeed, even in America, where FemTech investments are highest (FemTech Analytics, 2021), there is much need to consider how the industry could benefit those traditionally excluded from technological health innovation. Within heavily industrialized nations, race, class, and gender converge in ways that make access to potentially transformative tools infinitely more complex.
OCHRE: An Item-Based Approach

OCHRE’s item-based approach begins by breaking data down into its granular parts—the most minimal meaningful parts to be identified and studied. From these components, any number of appropriate data structures and configurations can be assembled as determined by the nature of the data itself or by the goals of the researcher. Having building blocks to work with as the primary structural units of a computational platform creates more flexibility in design, more potential for customization, and more options for fitting in with other structures. This process of itemization (or atomization) begs the question How far is far enough? But whether constructing a composite view or a compelling argument, this chapter sets the foundation for the ensuing discussion that seeks to demonstrate how an item-based approach proves to be particularly useful and flexible for addressing research questions.
Digital History Case Study: Greek Coin Hoards

The digital history case study focuses on the Hoard Analysis Research Project, a sub-project of the CRESCAT initiative. When the CRESCAT-HARP project began, it was faced with legacy data in print and digital formats that required considerable attention to be useable in a study of coin circulation. HARP leveraged the OCHRE data model to atomize and organize the data into individually identifiable items—hoards, hoard items, coin groups, and coins—contextualized in space and time. The project borrowed from existing taxonomic metadata properties and created customized variables and values where necessary—the data landing soundly in an ultimately rigorous and intuitive representation within the OCHRE platform, becoming available for meaningful, quantitative analysis and publication. While HARP was a narrowly focused project, it involved experts from a wide variety of backgrounds, from Classics to modern monetary theory to data analytics and computing. The OCHRE Data Service played a major role in advising on data management and in performing much of the data curation, integration, and publication, helping to bring this data set to life.
Digital Archaeology Case Study: Tell Keisan, Israel

The digital archaeology comprehensive case study describes the application of OCHRE to the archaeological excavation at Tell Keisan (Israel) operated jointly by the University of Chicago and Ben-Gurion University of the Negev. In 2016 this project pushed the limits of digital archaeology using OCHRE’s ability to run in offline mode onsite at an excavation. Using the strategies discussed in this chapter, data collected by the project was visible, editable, query-able, and accessible to all members of the project team on a day-to-day basis throughout the excavation season. A tight cycle of data collection and data integration, combined with the means of coherent graphical visualization in a unified digital environment, greatly enabled the continuous evaluation, re-evaluation, exploration, and discussion of the excavation results in real-time.
Functions of Family: Indispensable in Leading to Up-gradation of one’s Living Conditions

The individuals, belonging to all communities, categories and socio-economic backgrounds consider their family valuable. The family puts into operation number of tasks and activities, which are considered vital in leading to up-gradation of overall standards of living. The individuals need to ensure, they maintain cordial and amiable terms and relationships with family members. The family members need to put into operation effective communication processes with each other. These are considered vital in augmenting information in terms of different types of subjects and concepts. It is apparently understood that individuals are overwhelmed by different types of dilemmas and challenging situations, they obtain ideas and suggestions from family members. The individuals will render an important contribution in bringing about improvements in their overall quality of lives, when they obtain ideas and suggestions from family members. Hence, individuals need to realise that their family members carry out different types of functions, which are facilitating in promoting enhancement of overall standards of living. One needs to augmenting communication skills and treat each other with respect and courtesy in order to reinforce cordial relationships with family members. Furthermore, up-gradation of listening skills will be facilitating to a major extent. Therefore, functions of family are indispensable in leading to up-gradation of one’s living conditions. The main concepts that are taken into account in this research paper are, functions of family and advantages of acknowledging the functions of family.