Reviewerly/exHarmony huggingface dataset:
Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity

Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable. To address this problem, we propose a disparity-based MVS method based on the epipolar disparity flow (E-flow), called DispMVS, which infers the depth information from the pixel movement between two views. The core of DispMVS is to construct a 2D cost volume on the image plane along the epipolar line between each pair (between the reference image and several source images) for pixel matching and fuse uncountable depths triangulated from each pair by multi-view geometry to ensure multi-view consistency. To be robust, DispMVS starts from a randomly initialized depth map and iteratively refines the depth map with the help of the coarse-to-fine strategy. Experiments on DTUMVS and Tanks\&amp;Temple datasets show that DispMVS is not sensitive to the depth range and achieves state-of-the-art results with lower GPU memory.
Gradient Corner Pooling for Keypoint-Based Object Detection

Detecting objects as multiple keypoints is an important approach in the anchor-free object detection methods while corner pooling is an effective feature encoding method for corner positioning. The corners of the bounding box are located by summing the feature maps which are max-pooled in the x and y directions respectively by corner pooling. In the unidirectional max pooling operation, the features of the densely arranged objects of the same class are prone to occlusion. To this end, we propose a method named Gradient Corner Pooling. The spatial distance information of objects on the feature map is encoded during the unidirectional pooling process, which effectively alleviates the occlusion of the homogeneous object features. Further, the computational complexity of gradient corner pooling is the same as traditional corner pooling and hence it can be implemented efficiently. Gradient corner pooling obtains consistent improvements for various keypoint-based methods by directly replacing corner pooling. We verify the gradient corner pooling algorithm on the dataset and in real scenarios, respectively. The networks with gradient corner pooling located the corner points earlier in the training process and achieve an average accuracy improvement of 0.2%-1.6% on the MS-COCO dataset. The detectors with gradient corner pooling show better angle adaptability for arrayed objects in the actual scene test.
Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis

Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both image global structures and object details remains a challenging task. In this paper, we present Frido, a Feature Pyramid Diffusion model performing a multi-scale coarse-to-fine denoising process for image synthesis. Our model decomposes an input image into scale-dependent vector quantized features, followed by a coarse-to-fine gating for producing image output. During the above multi-scale representation learning stage, additional input conditions like text, scene graph, or image layout can be further exploited. Thus, Frido can be also applied for conditional or cross-modality image synthesis. We conduct extensive experiments over various unconditioned and conditional image generation tasks, ranging from text-to-image synthesis, layout-to-image, scene-graph-to-image, to label-to-image. More specifically, we achieved state-of-the-art FID scores on five benchmarks, namely layout-to-image on COCO and OpenImages, scene-graph-to-image on COCO and Visual Genome, and label-to-image on COCO.
Recurrent Structure Attention Guidance for Depth Super-resolution

Image guidance is an effective strategy for depth super-resolution. Generally, most existing methods employ hand-crafted operators to decompose the high-frequency (HF) and low-frequency (LF) ingredients from low-resolution depth maps and guide the HF ingredients by directly concatenating them with image features. However, the hand-designed operators usually cause inferior HF maps (e.g., distorted or structurally missing) due to the diverse appearance of complex depth maps. Moreover, the direct concatenation often results in weak guidance because not all image features have a positive effect on the HF maps. In this paper, we develop a recurrent structure attention guided (RSAG) framework, consisting of two important parts. First, we introduce a deep contrastive network with multi-scale filters for adaptive frequency-domain separation, which adopts contrastive networks from large filters to small ones to calculate the pixel contrasts for adaptive high-quality HF predictions. Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery. In addition, we fuse the features of HF maps to enhance the edge structures in the decomposed LF maps. Extensive experiments show that our approach obtains superior performance compared with state-of-the-art depth super-resolution methods. Our code is available at: https://github.com/Yuanjiayii/DSR-RSAG.
Detecting Exclusive Language during Pair Programming

Inclusive team participation is one of the most important factors that aids effective collaboration and pair programming. In this paper, we investigated the ability of linguistic features and a transformer-based language model to detect exclusive and inclusive language. The task of detecting exclusive language was approached as a text classification problem. We created a research community resource consisting of a dataset of 40,490 labeled utterances obtained from three programming assignments involving 34 students pair programming in a remote environment. This research involves the first successful automated detection of exclusive language during pair programming. Additionally, this is the first work to perform a computational linguistic analysis on the verbal interaction common in the context of inclusive and exclusive language during pair programming.
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-resolution

Image super-resolution (SR) serves as a fundamental tool for the processing and transmission of multimedia data. Recently, Transformer-based models have achieved competitive performances in image SR. They divide images into fixed-size patches and apply self-attention on these patches to model long-range dependencies among pixels. However, this architecture design is originated for high-level vision tasks, which lacks design guideline from SR knowledge. In this paper, we aim to design a new attention block whose insights are from the interpretation of Local Attribution Map (LAM) for SR networks. Specifically, LAM presents a hierarchical importance map where the most important pixels are located in a fine area of a patch and some less important pixels are spread in a coarse area of the whole image. To access pixels in the coarse area, instead of using a very large patch size, we propose a lightweight Global Pixel Access (GPA) module that applies cross-attention with the most similar patch in an image. In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a spatial convolution is applied to process the finest details. In addition, a Cascaded Patch Division (CPD) strategy is proposed to enhance perceptual quality of recovered images. Extensive experiments suggest that our method outperforms state-of-the-art lightweight SR methods by a large margin. Code is available at https://github.com/passerer/HPINet.
Self-Supervised Graph Learning for Long-Tailed Cognitive Diagnosis

Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts. Despite the effectiveness of existing efforts, previous methods always considered the mastery level on the whole students, so they still suffer from the Long Tail Effect. A large number of students who have sparse interaction records are usually wrongly diagnosed during inference. To relieve the situation, we proposed a Self-supervised Cognitive Diagnosis (SCD) framework which leverages the self-supervised manner to assist the graph-based cognitive diagnosis, then the performance on those students with sparse data can be improved. Specifically, we came up with a graph confusion method that drops edges under some special rules to generate different sparse views of the graph. By maximizing the cross-view consistency of node representations, our model could pay more attention on long-tailed students. Additionally, we proposed an importance-based view generation rule to improve the influence of long-tailed students. Extensive experiments on real-world datasets show the effectiveness of our approach, especially on the students with much sparser interaction records. Our code is available at https://github.com/zeng-zhen/SCD.
Classifying Course Discussion Board Questions using LLMs

Large language models (LLMs) can be used to answer student questions on course discussion boards, but there is a risk of LLMs answering questions they are unable to address. We propose and evaluate an LLM-based system that classifies student questions into one of four types: conceptual, homework, logistics, and not answerable. We then prompt an LLM using a type-specific prompt. Using GPT-3, we achieve 81% classification accuracy across the four categories. Furthermore, we achieve 93% accuracy on classifying not answerable questions. This indicates that our system effectively ignores questions that it cannot address.
Acceptance of social assistant robots for the older adults living in the community in China

Social assistant robots (SARs) are an important part of providing high quality health and social care for older people, and are an effective measure to promote the development of smart aging. Therefore, it is important to understand the factors that influence the acceptance of assistive robots by older adults. To investigate the acceptance of SARs for older adults living in the community and explore its influencing factors. 207 elderly was invited to answer a questionnaire after watching a SAR video and discussing it. Participants’ characteristics, physical health status, general self-efficacy, personality trait and acceptance toward SARs were recorded and analyzed using multiple linear regression analysis. showed that the degree of acceptance among older adults living in the community was moderate(2.55±0.86), and the acceptance rate was 51.0%. Whether to use mobile devices(smartphones, computers, robots), the experience of using mobile service devices, perceived usefulness, perceived enjoyment, perceived ease of use and attitude were the main affecting factors (P<0.05). The elderly Chinese in the community have a low acceptance of SARs. The higher the perceived usefulness, perceived enjoyment and perceived ease of use, the more positive attitude to using. The elderly who has the experience of using mobile service devices have a higher acceptance of SARs.
Basic Python Programming for Data Science

Python is a general-purpose language so that it can be used for a wide range of applications, such as data science, computer science, software engineering, mathematics, life science, linguistics, and journalism. However, learning Python programming for data science requires its unique specific knowledge tailored to its use in that field.
Bayesian Networks for Named Entity Prediction in Programming Community Question Answering

Within this study, we propose a new approach for natural language processing using Bayesian networks to predict and analyze the context and show how this approach can be applied to the Community Question Answering domain, such as Stack Overflow questions. We compared the Bayesian networks with different score metrics, such as the BIC, BDeu, K2, and Chow-Liu trees. Our proposed approach outperforms the baseline model on the precision metric. We also discuss the influence of penalty terms on the structure of Bayesian networks and how they can be used to analyze the relationships between entities. In addition, we examine the visualization of directed acyclic graphs to analyze semantic relationships. The article further identifies issues with detecting certain semantic classes that are separated by the structure of directed acyclic graphs.
A Textual Representation Based on Bag-of-Concepts and Thesaurus for Legal Information Retrieval

The retrieval of similar textual documents is a challenging task for the legal area due to its peculiar language with unique characteristics. This paper presents a new approach, called BoC-Th, proposed to represent legal documents based on the Bag-of-Concept (BoC) approach, which generates concept through clustering word vectors generated from a basic neural network model, and compute the frequencies of these concept clusters to represent document vectors. The novel contribution of the BoC-Th is to generate weighted histograms of concepts defined from the distance of the word to its respective similar term within a thesaurus. The idea is to emphasize those words that have more significance for the context, thus generating more discriminative vectors. Experimental evaluations were performed by comparing the proposed approach with the traditional BoW and BoC approaches, both popular techniques for document representation. The proposed method obtained the best result among the evaluated techniques for retrieving judgments and jurisprudence documents. The BoC-Th increased the mAP (mean Average Precision) in 51% compared to the traditional BoC approach, while being up to 3.4 times faster than the traditional BoW representation.
The study of the personification of the actor as a psychological phenomenon

Introduction. The article addresses the problem of ensuring the personification of the individual in the conditions of preparing for professional career. The aim of the study is to identify the main structural components of personification and to clarify their role in the transformation of the internal structure of the personality that determines the social and professional success of the actor (the subject of the activity). Materials and Methods. The methodological basis of the study was V. A. Petrovsky’s conception of personalization and the conception of personification by A. B. Orlov, as well as the theory of psychological science, which considers a person as an active subject capable of self-determination and self-improvement. The main approaches to the scientific analysis were the subject-activity and system ones. The research methods included theoretical and methodological analysis of scholarly literature, the author's interpretation of the basic provisions of the concepts of personalization and personification, methods of design, modeling, specification and classification, generalization and prognostics. Results. Based on the deep essence and psychological nature of personification, the authors identified three basic semantic components of this phenomenon and justified the content of each of them. It was established that the identified components of personification provide the formation of such new psychological formations as personality traits that determine full functioning of the personality and its professional and personal self-efficacy as a subject of activity. The interrelations between the components of personification and personality characteristics that ensure its full functioning were revealed. As a result of the research, a matrix of personification subject was constructed, which comprehensively reflects the integral abilities of the individual, ensuring social and professional success of the subject of activity. Conclusions. Personification as an integral characteristic of individual psychological traits of a person ensures the stability of subjectivity, individuality and identity of personality – new psychological formations that make it possible to consider this phenomenon as an internal leading activity in enhancing social maturity by fulfilling personal potential and excessive social and professional activity of the subject. In the conditions of professional preparation in a new personalized format of education, where the main driving force is the personality of the student, it is important to form such properties that will ensure the professional and personal productivity of the actor. It is the personification of the individual that is the most important predictor of the socio-professional success of the actor in the conditions of preparation for professional activity. The study showed that the personification of personality is constructive and can serve as a psychological basis for a comprehensive study of the subject of activity, their self-development and self-fulfillment.
The role of human resource management practices on organizational innovation: The importance of innovation driven human resource practices

The purpose of this study is to propose a bundle of HRM practices. Specifically, this study segments HRM practices in non-overlapping practices to examine their role in organizational innovation. The methodological approach is a quantitative approach using a convenient sampling technique to collect valid data of 126 service sector employees across five sales and service centers. The findings of this study reveal that both commitment and innovation driven HRM practices positively impact organizational innovation. Meanwhile, the results have shown that the High Performance Work System does not impact organizational innovation. Thus, this study argues that to confront the challenges associated with an ever-evolving nature of organizations, top management must use integrative HRM strategies that can yield a cumulative effect in driving organizational innovation.
Research on the competency level of teachers' intelligent educational technology in private colleges and universities in Shandong province

The development of a new generation of information technology combined with teaching has not only changed the sharing of teaching resources, the construction of the environment, the setting of disciplines and majors, and students' learning, but also had a profound impact on the reform of the school's philosophy and mechanism.In order to promote the construction of the teaching staff of private colleges and universities in Shandong Province and promote intelligent teaching in the new era, the author analyzes and discusses the concept of "adaptation".The research found that in terms of intellectual education technology quality, the intellectual quality level of teachers in private colleges and universities across the province is above the average level.In terms of innovation ability, the professional skills and skills of teachers in private colleges and universities are an effective way to explore and innovate intelligent education technology.
The Impact of Applying Re-Engineering Application Process on Critical Performance Criteria in Jordanian Islamic Banks

Purpose: The importance of the study is that it provides an understanding of the management approach to re-engineering the application process in the banking industry to deal with significant and quick changes in the environment by implementing superior and radical changes that aid it in increasing its efficiency and lowering costs as well as in shifting the focus of employees to meet the needs and desires of customers by redesigning banking processes based on customer perceptions and needs, during the period 2019. Theoretical Framework: The Study of the management method in re-engineering the application process in the banking sector to face the large and rapid changes in the environment is critical, as is shifting the focus of employees to meet the needs and desires of customers by redesigning the banking process based on customer perceptions and needs. Design/methodology/approach: All three Jordanian Islamic banks comprise the study's study community, and the study's sample includes sixty-three (63) members of these banks' managerial ranks. The study used a questionnaire to gather data and analytical methodology, which helped explain the study problem. Findings: After conducting the analysis process of the study data and hypotheses, Findings show There is a statistically significant influence of the application of the re-engineering process (redesigning jobs, redesigning process, reducing control levels, using new technology, empowering employees) on performance criteria (costs, quality, speed ) in Jordanian Islamic banks. Research, Practical &amp; Social implications: To identify and analyze the re-engineering application process in Jordanian Islamic banks. The following elements are part of the study methodology: Cost, Quality, and Speed are the dependent variables in the crucial performance criteria; the study relied on choosing the dependent variables according to the objectives that re-engineering the application process can accomplish. Application re-engineering process dimensions, such as redesigning jobs and processes redesign, reducing control levels, using new technology, and empowering employees act as independent variables. Originality/value: The Study recommends additional future research that could involve all levels of bank management in the re-engineering process and create an effective technology infrastructure and communication system that effectively contributes to the success of the re-engineering process.
SEML: Self-Supervised Information-Enhanced Meta-learning for Few-Shot Text Classification

Abstract Training a deep-learning text classification model usually requires a large amount of labeled data, yet labeling data are usually labor-intensive and time-consuming. Few-shot text classification focuses on predicting unknown samples using only a few labeled samples. Recently, metric-based meta-learning methods have achieved promising results in few-shot text classification. They use episodic training in labeled samples to enhance the model’s generalization ability. However, existing models only focus on learning from a few labeled samples but neglect to learn from a large number of unlabeled samples. In this paper, we exploit the knowledge learned by the model in unlabeled samples to improve the generalization performance of the meta-network. Specifically, we introduce a novel knowledge distillation method that expands and enriches the meta-learning representation with self-supervised information. Meanwhile, we design a graph aggregation method that efficiently interacts the query set information with the support set information in each task and outputs a more discriminative representation. We conducted experiments on three public few-shot text classification datasets. The experimental results show that our model performs better than the state-of-the-art models in 5-way 1-shot and 5-way 5-shot cases.
Free space continuous variable Quantum Key Distribution with discrete phases

Quantum Key Distribution (QKD) offers unconditional security in principle. Many QKD protocols have been proposed and demonstrated to ensure secure communication between two authenticated users. Continuous variable (CV) QKD offers many advantages over discrete variable (DV) QKD since it is cost-effective, compatible with current classical communication technologies, efficient even in daylight, and gives a higher secure key rate. Keeping this in view, we demonstrate a discrete modulated CVQKD protocol in the free space which is robust against polarization drift. We also present the simulation results with a noise model to account for the channel noise and the effects of various parameter changes on the secure key rate. These simulation results help us to verify the experimental values obtained for the implemented CVQKD.