Reviewerly/exHarmony huggingface dataset:
MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification

Few-shot learning (FSL) that acquires new knowledge with little supervision, attracts much attention due to expensive cost of data annotation. Various meta-learning methods have made a great progress for few-shot problem in image and text data. In reality, data samples are not independent but rich in link relations. Large amounts of data exists in the form of graph structure such as citation, social, and biological networks. However, FSL study on graph data is still in its infancy because of the obstacle on extracting meta-knowledge from a meta node classification task. Current research just simply combines the FSL methods experienced in computer vision with node representation models together, but ignores the effect of rich links among support and query nodes in few-shot meta-task. For this issue, we propose a novel Multi-Level Graph Relation Network (MuL-GRN) for the challenging few-shot node classification. MuL-GRN extracts node embeddings through the popular graph neural networks (GNNs). And it includes a relation learning module to mine the deep node relations from three views, namely node-level, global subgraph-level, and local subgraph-level relations. For any two nodes, the node-level relation is computed on their node embeddings, global subgraph-level relation is measured on their subgraph embeddings, and the local subgraph-level relation is mined according to the pairwise node comparison information in their subgraphs. The three-view relation vectors are fused together with an interesting relation fusion module, which measures the importance of relation vector for the current few-shot classification task automatically. Extensive experiments on five real datasets show that MuL-GRN significantly outperforms existing state-of-the-art methods by a large margin.
ASSESSMENT OF CURRENT COMPETENCY LEVEL OF AGRICULTURAL EXTENSION ADVISORS IN FEDERAL NEPAL

In the present context of 21st century, modernization in the agriculture sector demands a higher level of competency to fulfill the needs and wants of people around the globe. Competency is amalgamation of skills, knowledge, attitude and behavior that trigger to perform the delegated services in a precise and systematic manner. The objective of this study was to determine the existing competency level of extension advisors of Nepalese extension service. Total 6 districts from province 1, 2 and Bagmati were selected for the study based on a questionnaire. The questionnaire was composed of open and close-ended questions including the nine core competencies. Descriptive statistics was used to analyze the findings. The findings indicated respondent competency in its all nine core competencies namely program planning, program implementation, communication skills, extension education and information technology, program evaluation, personal and professional development, diversity, subject matter expertise and emotional intelligence as moderately competent in general. The extension advisors perceived most competent in extension education and information technology and poor in pluralism in agriculture. Subject matter expertise, extension education and information technology skills are getting better with increase in educational level. Current level of core competency of program planning, program implementation skills, communication skills, personal and professional development and pluralism were found to have increased with increased years of experience. The exposure visit and inservice training are effective methods for competencies acquisition.
Smart Information Retrieval using Query Transformation based on Ontology and Semantic-Association

A notable problem with current information retrieval systems is that the input queries cannot express user information needs properly.This imprecise representation of the query hampers the effectiveness of the retrieval system.One method to solve this problem is to transform the original query into a more meaningful form.This paper proposes an ontologybased retrieval system that transforms initial user queries using domain ontologies and applies semantic association during the indexing process.The proposed system performs a semantic matching between an ontologically enhanced query and index to capture query-related terms.To show the performance of the proposed system, it is evaluated using standard parameters like precision, recall, and NDCG.In addition, the authors presented a comparison between the proposed and existing retrieval systems on three test datasets.Experimental results on these datasets indicate that the use of ontology and semantics has significantly increased the retrieval efficiency obtained by baseline.This work highlights the importance of ontology and semantics in information retrieval.
EGraph: Efficient Concurrent GPU-based Dynamic Graph Processing

In many applications of the analysis of dynamic graph, many <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Timing iterative Graph Processing</i> (TGP) jobs usually need to be generated for the processing of the corresponding snapshots of the dynamic graph to obtain the results at different points of time. For high throughput of such applications, it is expected to run the TGP jobs on the GPU concurrently. Although many GPU-based systems have been recently developed, for out-of-GPU-memory dynamic graph processing, this concurrent way suffers from significant data access overhead due to a large volume of data transfer between CPU and GPU and the interference between these concurrently running jobs, which eventually incurs low GPU utilization ratio. In this work, we observed that the TGP jobs have strong temporal and spatial similarity when they access different snapshots for their own processing as most parts of the snapshots are the same and only a few parts are changing with time. It creates ideal opportunities for efficient concurrent execution of the TGP jobs by dramatically reducing CPU-GPU graph data transfer cost. Based on this observation, we develop the first GPU-based dynamic graph processing system <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EGraph</i> , which can be integrated into the existing out-of-GPU-memory static graph processing systems to enable them to efficiently support concurrent execution of TGP jobs on dynamic graphs with the help of GPU accelerators. Different from the existing approaches, we propose in EGraph an effective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Loading-Processing-Switching</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPS</i> ) execution model. It is able to effectively reduce the overhead of CPU-GPU data transfer and ensures a higher GPU utilization ratio for efficient execution of the TGP jobs by fully utilizing the data access similarity between the TGP jobs. Experimental results show that the existing GPU-accelerated systems achieve performance improvements of 2.3-3.5 times after being integrated with EGraph.
Learn to Adapt for Generalized Zero-Shot Text Classification

Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes.Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationary in predicting procedures.To address these challenges, we propose a novel Learn to Adapt (LTA) network using a variant meta-learning framework.Specifically, LTA trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning (GZSL) scenario in accordance with the test time, and simultaneously learns to calibrate the class prototypes and sample representations to make the learned parameters adaptive to incoming unseen classes.We claim that the proposed model is capable of representing all prototypes and samples from both classes to a more consistent distribution in a global space.Extensive experiments on five text classification datasets show that our model outperforms several competitive previous approaches by large margins.
Bitcoin Price Prediction Using Ml

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Fine-grained Ship Image Recognition Based on BCNN with Inception and燗M-Softmax

The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features from ships. Secondly, by adding margin values to the decision boundary, the AM-Softmax function can better extend the inter-class differences and reduce the intra-class differences. In addition, as there are few publicly available datasets for fine-grained ship image recognition, we construct a Ship-43 dataset containing 47,300 ship images belonging to 43 categories. Experimental results on the constructed Ship-43 dataset demonstrate that our method can effectively improve the accuracy of ship image recognition, which is 4.08% higher than the BCNN model. Moreover, comparison results on the other three public fine-grained datasets (Cub, Cars, and Aircraft) further validate the effectiveness of the proposed method.
Long-range Sequence Modeling with Predictable Sparse Attention

Self-attention mechanism has been shown to be an effective approach for capturing global context dependencies in sequence modeling, but it suffers from quadratic complexity in time and memory usage.Due to the sparsity of the attention matrix, much computation is redundant.Therefore, in this paper, we design an efficient Transformer architecture, named Fourier Sparse Attention for Transformer (FSAT), for fast long-range sequence modeling.We provide a brand-new perspective for constructing sparse attention matrix, i.e. making the sparse attention matrix predictable.Two core submodules are: (1) A fast Fourier transform based hidden state cross module, which captures and pools L 2 semantic combinations in O(L log L) time complexity.(2) A sparse attention matrix estimation module, which predicts dominant elements of an attention matrix based on the output of the previous hidden state cross module.By reparameterization and gradient truncation, FSAT successfully learned the index of dominant elements.The overall complexity about the sequence length is reduced from O(L 2 ) to O(L log L).Extensive experiments (natural language, vision, and math) show that FSAT remarkably outperforms the standard multi-head attention and its variants in various long-sequence tasks with low computational costs, and achieves new state-of-the-art results on the Long Range Arena benchmark.
Review on Major Cyber security Issues in Educational Sector

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Max-Sum with Quadtrees for Decentralized Coordination in Continuous Domains

In this paper we put forward a novel extension of the classic Max-Sum algorithm to the framework of Continuous Distributed Constrained Optimization Problems (Continuous DCOPs), by utilizing a popular geometric algorithm, namely Quadtrees. In its standard form, Max-Sum can only solve Continuous DCOPs with an a priori discretization procedure. Existing Max-Sum extensions to continuous multiagent coordination domains require additional assumptions regarding the form of the factors, such as access to the gradient, or the ability to model them as continuous piecewise linear functions. Our proposed approach has no such requirements: we model the exchanged messages with Quadtrees, and, as such, the discretization procedure is dynamic and embedded in the internal Max-Sum operations (addition and marginal maximization). We apply Max-Sum with Quadtrees to lane-free autonomous driving. Our experimental evaluation showcases the effectiveness of our approach in this challenging coordination domain.
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition

Nested named entity recognition (nested NER) is a fundamental task in natural language processing. Various span-based methods have been proposed to detect nested entities with span representations. However, span-based methods do not consider the relationship between a span and other entities or phrases, which is helpful in the NER task. Besides, span-based methods have trouble predicting long entities due to limited span enumeration length. To mitigate these issues, we present the Propose-and-Refine Network (PnRNet), a two-stage set prediction network for nested NER. In the propose stage, we use a span-based predictor to generate some coarse entity predictions as entity proposals. In the refine stage, proposals interact with each other, and richer contextual information is incorporated into the proposal representations. The refined proposal representations are used to re-predict entity boundaries and classes. In this way, errors in coarse proposals can be eliminated, and the boundary prediction is no longer constrained by the span enumeration length limitation. Additionally, we build multi-scale sentence representations, which better model the hierarchical structure of sentences and provide richer contextual information than token-level representations. Experiments show that PnRNet achieves state-of-the-art performance on four nested NER datasets and one flat NER dataset.
Fallacious Argument Classification in Political Debates

Fallacies play a prominent role in argumentation since antiquity due to their contribution to argumentation in critical thinking education. Their role is even more crucial nowadays as contemporary argumentation technologies face challenging tasks as misleading and manipulative information detection in news articles and political discourse, and counter-narrative generation. Despite some work in this direction, the issue of classifying arguments as being fallacious largely remains a challenging and an unsolved task. Our contribution is twofold: first, we present a novel annotated resource of 31 political debates from the U.S. Presidential Campaigns, where we annotated six main categories of fallacious arguments (i.e., ad hominem, appeal to authority, appeal to emotion, false cause, slogan, slippery slope) leading to 1628 annotated fallacious arguments; second, we tackle this novel task of fallacious argument classification and we define a neural architecture based on transformers outperforming state-of-the-art results and standard baselines. Our results show the important role played by argument components and relations in this task.
Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation

Current semi-supervised semantic segmentation methods mainly focus on designing pixel-level consistency and contrastive regularization. However, pixel-level regularization is sensitive to noise from pixels with incorrect predictions, and pixel-level contrastive regularization has a large memory and computational cost. To address the issues, we propose a novel region-level contrastive and consistency learning framework (RC^2L) for semi-supervised semantic segmentation. Specifically, we first propose a Region Mask Contrastive (RMC) loss and a Region Feature Contrastive (RFC) loss to accomplish region-level contrastive property. Furthermore, Region Class Consistency (RCC) loss and Semantic Mask Consistency (SMC) loss are proposed for achieving region-level consistency. Based on the proposed region-level contrastive and consistency regularization, we develop a region-level contrastive and consistency learning framework (RC^2L) for semi-supervised semantic segmentation, and evaluate our RC^2L on two challenging benchmarks (PASCAL VOC 2012 and Cityscapes), outperforming the state-of-the-art.
Double-Check Soft Teacher for Semi-Supervised Object Detection

In the semi-supervised object detection task, due to the scarcity of labeled data and the diversity and complexity of objects to be detected, the quality of pseudo-labels generated by existing methods for unlabeled data is relatively low, which severely restricts the performance of semi-supervised object detection. In this paper, we revisit the pseudo-labeling based Teacher-Student mutual learning framework for semi-supervised object detection and identify that the inconsistency of the location and feature of the candidate object proposals between the Teacher and the Student branches are the fatal cause of the low quality of the pseudo labels. To address this issue, we propose a simple yet effective technique within the mainstream teacher-student framework, called Double Check Soft Teacher, to overcome the harm caused by insufficient quality of pseudo labels. Specifically, our proposed method leverages teacher model to generate pseudo labels for the student model. Especially, the candidate boxes generated by the student model based on the pseudo label will be sent to the teacher model for "double check", and then the teacher model will output probabilistic soft label with background class for those candidate boxes, which will be used to train the student model. Together with a pseudo labeling mechanism based on the sum of the TOP-K prediction score, which improves the recall rate of pseudo labels, Double Check Soft Teacher consistently surpasses state-of-the-art methods by significant margins on the MS-COCO benchmark, pushing the new state-of-the-art. Source codes are available at https://github.com/wkfdb/DCST.
Ancestral Instrument Method for Causal Inference without Complete Knowledge

Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement on a standard IV is strict and untestable. Conditional IVs have been proposed to relax the requirement of standard IVs by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for finding a conditioning set for a conditional IV needs a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it challenging to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) for causal inference with latent variables, we study the graphical properties of ancestral IVs, a type of conditional IVs using MAGs, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in data under the pretreatment variable assumption. Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data. Extensive experiments on synthetic and real-world datasets demonstrate the performance of the algorithm in comparison with existing IV methods.
CADET: Calibrated Anomaly Detection for Mitigating Hardness Bias

The detection of anomalous samples in large, high-dimensional datasets is a challenging task with numerous practical applications. Recently, state-of-the-art performance is achieved with deep learning methods: for example, using the reconstruction error from an autoencoder as anomaly scores. However, the scores are uncalibrated: that is, they follow an unknown distribution and lack a clear interpretation. Furthermore, the reconstruction error is highly influenced by the `hardness' of a given sample, which leads to false negative and false positive errors. In this paper, we empirically show the significance of this hardness bias present in a range of recent deep anomaly detection methods. To mitigate this, we propose an efficient and plug-and-play error calibration method which mitigates this hardness bias in the anomaly scoring without the need to retrain the model. We verify the effectiveness of our method on a range of image, time-series, and tabular datasets and against several baseline methods.
Towards Discourse-Aware Document-Level Neural Machine Translation

Current document-level neural machine translation (NMT) systems have achieved remarkable progress with document context. Nevertheless, discourse information that has been proven effective in many NLP tasks is ignored in most previous work. In this work, we aim at incorporating the coherence information hidden within the RST-style discourse structure into machine translation. To achieve it, we propose a document-level NMT system enhanced with the discourse-aware document context, which is named Disco2NMT. Specifically, Disco2NMT models document context based on the discourse dependency structures through a hierarchical architecture. We first convert the RST tree of an article into a dependency structure and then build the graph convolutional network (GCN) upon the segmented EDUs under the guidance of RST dependencies to capture the discourse-aware context for NMT incorporation. We conduct experiments on the document-level English-German and English-Chinese translation tasks with three domains (TED, News, and Europarl). Experimental results show that our Disco2NMT model significantly surpasses both context-agnostic and context-aware baseline systems on multiple evaluation indicators.