Reviewerly/exHarmony huggingface dataset:
Image deraining based on dual-channel component decomposition

Image deraining aims to remove rain streaks from images and reduce information loss in outdoor images caused by rain. As a fundamental task in image processing, image deraining not only enhances the visibility of images but also provides necessary image restoration for advanced vision tasks. Existing image deraining models mostly train end-to-end models by minimizing the similarity between the output image of the model and the rain-free ground truth. Although these methods have achieved significant results, they often perform poorly in the face of dense and changing rain streak scenes. In this paper, we propose a novel method, called Dual-Channel Component Decomposition Network (DCD-Net). The basic idea of DCD-Net is to leverage the separability prior of rainy images, treats the rain-free background layer and the rain streak mask layer as two parallel component extraction tasks. To this end, it builds a dual-branch parallel networks that extract the rain-free background image and decouple the reconstruction information of the rain streak mask, respectively. It finally applies a composite multi-level contrastive supervision to the output of the above dual-branch parallel network, thereby achieving rain streak removal. Extensive experiments on various datasets demonstrate that the proposed model outperforms existing methods in deraining dense rain streak images.
Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning

Abstract Motivation Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedical named entity recognition (BioNER) often encounters limited human-labeled data in real-world scenarios, leading to poor generalization performance when training only a few labeled instances. Recent approaches either leverage cross-domain high-resource data or fine-tune the pre-trained masked language model using limited labeled samples to generate new synthetic data, which is easily stuck in domain shift problems or yields low-quality synthetic data. Therefore, in this article, we study a more realistic scenario, i.e. few-shot learning for BioNER. Results Leveraging the domain knowledge graph, we propose knowledge-guided instance generation for few-shot BioNER, which generates diverse and novel entities based on similar semantic relations of neighbor nodes. In addition, by introducing question prompt, we cast BioNER as question-answering task and propose prompt contrastive learning to improve the robustness of the model by measuring the mutual information between query–answer pairs. Extensive experiments conducted on various few-shot settings show that the proposed framework achieves superior performance. Particularly, in a low-resource scenario with only 20 samples, our approach substantially outperforms recent state-of-the-art models on four benchmark datasets, achieving an average improvement of up to 7.1% F1. Availability and implementation Our source code and data are available at https://github.com/cpmss521/KGPC.
A Reconfigurable CMOS Ising Machine With Three-Body Spin Interactions for Solving Boolean Satisfiability With Direct Mapping

Ising machines have recently emerged as efficient computers for nondeterministic polynomial-time hard (NPhard) combinatorial optimization problems (COPs). While most prior works have built their Ising machines with spins arranged in a graph with only two-body interactions, many real-world COPs, including a popular Boolean satisfiability problem (SAT), often involve many-body spin interactions. This letter presents a novel Ising machine that can directly map and solve 3-SAT problems (i.e., SAT problems with at most three literals per clause). The proposed Ising machine eliminates the hardware overhead due to the ancillary spins required for approximate mapping used for the prior Ising machines with two-body interactions only. For evaluation, a prototype chip is fabricated using 65nm and solved 3-SAT problems and their variants (a weighted max 3-SAT). The 65nm chip occupies 0.345mm2 for 128-1024 embedded spins with 8-bit interaction coefficients and consumes 3.73μW per spin at 1.2V and 64MHz.
Contextualized Knowledge Graph Embedding for Explainable Talent Training Course Recommendation

Learning and development, or L&amp;D, plays an important role in talent management, which aims to improve the knowledge and capabilities of employees through a variety of performance-oriented training activities. Recently, with the rapid development of enterprise management information systems, many research efforts and industrial practices have been devoted to building personalized employee training course recommender systems. Nevertheless, a widespread challenge is how to provide explainable recommendations with the consideration of different learning motivations from talents. To this end, we propose CKGE, a contextualized knowledge graph (KG) embedding approach for developing an explainable training course recommender system. A novel perspective of CKGE is to integrate both the contextualized neighbor semantics and high-order connections as motivation-aware information for learning effective representations of talents and courses. Specifically, in CKGE, for each entity pair (i.e., the talent-course pair), we first construct a meta-graph, including the neighbors of each entity and the meta-paths between entities as motivation-aware information. Then, we develop a novel KG-based Transformer, which can serialize entities and paths in the meta-graph as a sequential input, with the specially designed relational attention and structural encoding mechanisms to better model the global dependence of KG structured data. Meanwhile, the local path mask prediction can effectively reveal the importance of different paths. As a result, CKGE not only can make precise predictions but also can discriminate the saliencies of meta-paths in characterizing corresponding preferences. Extensive experiments on real-world and public datasets clearly validate the effectiveness and interpretability of CKGE compared with state-of-the-art baselines.
Limits for learning with language models

With the advent of large language models (LLMs), the trend in NLP has been to train LLMs on vast amounts of data to solve diverse language understanding and generation tasks.The list of LLM successes is long and varied.Nevertheless, several recent papers provide empirical evidence that LLMs fail to capture important aspects of linguistic meaning.Focusing on universal quantification, we provide a theoretical foundation for these empirical findings by proving that LLMs cannot learn certain fundamental semantic properties including semantic entailment and consistency as they are defined in formal semantics.More generally, we show that LLMs are unable to learn concepts beyond the first level of the Borel Hierarchy, which imposes severe limits on the ability of LMs, both large and small, to capture many aspects of linguistic meaning.This means that LLMs will continue to operate without formal guarantees on tasks that require entailments and deep linguistic understanding.
Modelling the barriers to blockchain implementation in human resource function

Purpose The aim of this paper is to explore and analyse the challenges in effective implementation of blockchain by human resource management (HRM) functions. This paper also aims to assess the interplay between the barriers in causing the challenges during blockchain execution. Design/methodology/approach Ten barriers are discovered from the past studies. Based on the expert views on the identified barriers interpretive structural modelling (ISM) is administered to understand the interplay of these 10 challenges resulting in ineffective or non-implementation of HR blockchain. Findings The application of ISM has helped in categorizing the variables into strategic, operational and performance outcomes. Results of ISM indicate key barriers like lack of expertise, data privacy, technical infeasibility, complexity in implantation and lack of used cases. Research limitations/implications The research is limited to 10 barriers. There can be other barriers that can also be studied. Second, the research is proposing a conceptual model that needs further validation. Practical implications This paper has significant implications for the theoretical and practical body of knowledge. So far, most studies are exploring and describing HRM from a digital perspective. Most HR studies are on artificial intelligence, the Internet of Things and smart HRM. Previous studies on blockchain for HRM are mostly describing the advantages of going for it. Social implications Based on the findings, it can also be suggested that policy formulators must advance the technical regulatory framework. Blockchain technology can be effectively implemented only if the top management is committed to it because they can only frame the rules and right control framework, affirm the governance process and strategize improvement. Originality/value The study offers insights into the organization's decision makers for effectively implementing blockchain into their HR systems. Some specific recommendations based on the results are also made. The paper is an innovative attempt to analyse the barriers to HR blockchain.
Distinguishing Fact from Fiction: A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era.

As generative NLP can now produce content nearly indistinguishable from human writing, it becomes difficult to identify genuine research contributions in academic writing and scientific publications.Moreover, information in NLP-generated text can potentially be factually wrong or even entirely fabricated.This study introduces a novel benchmark dataset, containing human-written and machine-generated scientific papers from SCIgen, GPT-2, GPT-3, Chat-GPT, and Galactica.After describing the generation and extraction pipelines, we also experiment with four distinct classifiers as a baseline for detecting the authorship of scientific text.A strong focus is put on generalization capabilities and explainability to highlight the strengths and weaknesses of detectors.We believe our work serves as an important step towards creating more robust methods for distinguishing between human-written and machine-generated scientific papers, ultimately ensuring the integrity of scientific literature.
Improving domain generalization performance for medical image segmentation via random feature augmentation

Deep convolutional neural networks (DCNNs) have shown remarkable performance in medical image segmentation tasks. However, medical images frequently exhibit distribution discrepancies due to variations in scanner vendors, operators, and image quality, which pose significant challenges to the robustness of trained models when applied to unseen clinical data. To address this issue, domain generalization methods have been developed to enhance the generalization ability of DCNNs. Feature space-based data augmentation methods have been proven effective in improving domain generalization, but they often rely on prior knowledge or assumptions, which can limit the diversity of source domain data. In this study, we propose a novel random feature augmentation (RFA) method to diversify source domain data at the feature level without prior knowledge. Specifically, our RFA method perturbs domain-specific information while preserving domain-invariant information, thereby adequately diversifying the source domain data. Furthermore, we propose a dual-branches invariant synergistic learning strategy to capture domain-invariant information from the augmented features of RFA, enabling DCNNs to learn a more generalized representation. We evaluate our proposed method on two challenging medical image segmentation tasks, optic cup/disc segmentation on fundus images and prostate segmentation on MRI images. Extensive experimental results demonstrate the superior performance of our method over state-of-the-art domain generalization methods.
A Survey on Out-of-Distribution Evaluation of Neural NLP Models

Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. This survey will 1) compare the three lines of research under a unifying definition; 2) summarize their data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work.
Hyperspectral Image Denoising Using Uncertainty-Aware Adjustor

Hyperspectral image (HSI) denoising has achieved promising results with the development of deep learning. A mainstream class of methods exploits the spatial-spectral correlations and recovers each band with the aids of neighboring bands, collectively referred to as spectral auxiliary networks. However, these methods treat entire adjacent spectral bands equally. In theory, clearer and nearer bands tend to contain more reliable spectral information than noisier and farther ones with higher uncertainties. How to achieve spectral enhancement and adaptation of each adjacent band has become an urgent problem in HSI denoising. This work presents the UA-Adjustor, a comprehensive adjustor that enhances denoising performance by considering both the band-to-pixel and enhancement-to-adjustment aspects. Specifically, UA-Adjustor consists of three stages that evaluate the importance of neighboring bands, enhance neighboring bands based on uncertainty perception, and adjust the weight of spatial pixels in adjacent bands through estimated uncertainty. For its simplicity, UA-Adjustor can be flexibly plugged into existing spectral auxiliary networks to improve denoising behavior at low cost. Extensive experimental results validate that the proposed solution can improve over recent state-of-the-art (SOTA) methods on both simulated and real-world benchmarks by a large margin.
CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo

The core of Multi-view Stereo(MVS) is the matching process among reference and source pixels. Cost aggregation plays a significant role in this process, while previous methods focus on handling it via CNNs. This may inherit the natural limitation of CNNs that fail to discriminate repetitive or incorrect matches due to limited local receptive fields. To handle the issue, we aim to involve Transformer into cost aggregation. However, another problem may occur due to the quadratically growing computational complexity caused by Transformer, resulting in memory overflow and inference latency. In this paper, we overcome these limits with an efficient Transformer-based cost aggregation network, namely CostFormer. The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions. Furthermore, Residual Regression Transformer(RRT) is proposed to enhance spatial attention. The proposed method is a universal plug-in to improve learning-based MVS methods.
OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking

Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese tracking paradigm, which first generates proposals and then predicts corresponding proposal-wise scores. However, such a network suffers from tedious hyper-parameter tuning and task misalignment, limiting the tracking performance. Towards these concerns, we propose a simple yet effective one-stage point-to-box network for point cloud-based 3D single object tracking. It synchronizes 3D proposal generation and center-ness score prediction by a parallel predictor without tedious hyper-parameters. To guide a task-aligned score ranking of proposals, a center-aware focal loss is proposed to supervise the training of the center-ness branch, which enhances the network's discriminative ability to distinguish proposals of different quality. Besides, we design a binary target classifier to identify target-relevant points. By integrating the derived classification scores with the center-ness scores, the resulting network can effectively suppress interference proposals and further mitigate task misalignment. Finally, we present a novel one-stage Siamese tracker OSP2B equipped with the designed network. Extensive experiments on challenging benchmarks including KITTI and Waymo SOT Dataset show that our OSP2B achieves leading performance with a considerable real-time speed.
Multi-Scale Subgraph Contrastive Learning

Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usually simply assume that a graph and its augmented graph as a positive pair, otherwise as a negative pair. However, it is well known that graph structure is always complex and multi-scale, which gives rise to a fundamental question: after graph augmentation, will the previous assumption still hold in reality? By an experimental analysis, we discover the semantic information of an augmented graph structure may be not consistent as original graph structure, and whether two augmented graphs are positive or negative pairs is highly related with the multi-scale structures. Based on this finding, we propose a multi-scale subgraph contrastive learning architecture which is able to characterize the fine-grained semantic information. Specifically, we generate global and local views at different scales based on subgraph sampling, and construct multiple contrastive relationships according to their semantic associations to provide richer self-supervised signals. Extensive experiments and parametric analyzes on eight graph classification real-world datasets well demonstrate the effectiveness of the proposed method.
Study on the Synchronous Performance of Vocal Music and Piano

The study discusses how singers can maintain and enhance their ability to simultaneously offer supportive and expressive piano accompaniment.We'll explore the negatives and positives of self-accompanying on the piano for singers, including Alicia keys & john Mayer's if I ain't got you at times square in 2016.While some may find the piano sound limited in its colouristic nature, I personally find the variety of the simultaneous vocal/piano arrangement in Alicia keys' live performance of if I ain't got you refreshing.In addition, she strikes a reasonable balance between the technique of the piano/voice and the expression of emotion in the music.More specifically, I will be exploring and analysing if I ain't got you from the point of view of musical and physical considerations, musical conception and the relation between voice and piano.
Usability of a Collaborative Virtual Reality Environment Earthwork Exercises

received his Ph.D. in civil engineering from Georgia Institute of Technology with a concentration in construction management and with a minor in computer engineering and strong statistical
Work-in-Progress: Using Modern Mobile Technologies in STEM Education

With advances in modern mobile devices and the increasing use of smart phones and tablets by students, there is an opportunity to provide enhanced and personalized educational experiences and convenient remote access to course material.This paper describes an education research endeavor aimed at developing, packaging and assessing course content, interactive learning modules, educational simulations, and visualization examples for smart phones and tablets.Our efforts focus on integrating existing and new tools, optimized for smart phones and tablets, in undergraduate Science, Technology, Engineering and Mathematics (STEM) courses.The project brings together several technologies including a Java simulation environment, an Android operating system with several dedicated apps, Blackboard Mobile TM Learn, Sprint ID and others.The hypothesis here is that the creation of a "central" education site with maximum mobility will be compelling to undergraduate students that seem fascinated by the exciting multi-tasking possibilities that smart phones and tablets bring these days.In this paper, we will work on the development and assessment of a portal for mobile devices that will enable students to operate several state of the art education mobile applications (apps) seamlessly.These apps include visualization modules, mobile labs, lecture video viewing, quiz taking, homework/lab submission applets, and customized educational web site access.An assessment task will be a senior level signal processing class to examine several aspects of the mobile student learning experience.
Development of Green Technology Curriculum

This paper will discuss the development of a training program for area professionals and a BS degree in Sustainable Energy Systems Management at Southeast Missouri State University (Southeast).The curriculum is designed to serve as a career pathway toward developing a future workforce in green energy technologies.The process involved the assessment of needs of the region, selection of advisory board members, identification of core competencies, development of course and lab curricula, development of training modules for business and industry professionals, and purchase of necessary equipment.The green technology skills that were targeted were energy efficiency and management, planning and design of sustainable facilities, sustainable and green construction, sustainable and green manufacturing, and renewable energy sources such as biomass, solar power, and wind energy technologies. The curriculum development effort was