Reviewerly/exHarmony huggingface dataset:
SPDB Innovation Lab at SemEval-2022 Task 10: A Novel End-to-End Structured Sentiment Analysis Model based on the ERNIE-M

Sentiment analysis is a classical problem of natural language processing.SemEval 2022 sets a problem on the structured sentiment analysis in task 10, which is also a study-worthy topic in research area.In this paper, we propose a method which can predict structured sentiment information on multiple languages with limited data.The ERNIE-M pretrained language model is employed as a lingual feature extractor which works well on multiple language processing, followed by a graph parser as a opinion extractor.The method can predict structured sentiment information with high interpretability.We apply data augmentation as the given datasets are so small.Furthermore, we use K-fold cross-validation and DeBERTaV3 pretrained model as extra English embedding generator to train multiple models as our ensemble strategies.Experimental results show that the proposed model has considerable performance on both monolingual and cross-lingual tasks.
Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering

Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB.Despite sophisticated retrieval algorithms, the impact of the low-resource (incomplete) KB is not fully exploited, where contributing components (i.e.key entities and/or relations) may be absent for question answering.To effectively address this problem, we propose a contrastive regularization based method, which is motivated by the learn-by-analogy capability from human readers.Specifically, the proposed work includes two major modules: the knowledge extension and sMoCo module.The former aims at exploiting the latent knowledge from the context KB and generating auxiliary information in the form of question-answer pairs.The later module utilizes those additional pairs and applies the contrastive regularization to learn informative representations, that making hard positive pairs attracted and hard negative pairs separated.Empirically, we achieved the state-of-the-art performance on the We-bQuestionsSP dataset and the effectiveness of proposed modules is also evaluated.
The Curious Case of Adversarially Robust Models: More Data Can Help,  Double Descend, or Hurt Generalization

Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy. To mitigate this issue, it is commonly believed that more training data will eventually help such adversarially robust models generalize better on the benign/unperturbed test data. In this paper, however, we challenge this conventional belief and show that more training data can hurt the generalization of adversarially robust models in the classification problems. We first investigate the Gaussian mixture classification with a linear loss and identify three regimes based on the strength of the adversary. In the weak adversary regime, more data improves the generalization of adversarially robust models. In the medium adversary regime, with more training data, the generalization loss exhibits a double descent curve, which implies the existence of an intermediate stage where more training data hurts the generalization. In the strong adversary regime, more data almost immediately causes the generalization error to increase. Then we move to the analysis of a two-dimensional classification problem with a 0-1 loss. We prove that more data always hurts the generalization performance of adversarially trained models with large perturbations. To complement our theoretical results, we conduct empirical studies on Gaussian mixture classification, support vector machines (SVMs), and linear regression.
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this paper, we present a scalable approximation to a general family of learning objectives including score matching, by observing a new connection between these objectives and Wasserstein gradient flows. We present applications with promise in learning neural density estimators on manifolds, and training implicit variational and Wasserstein auto-encoders with a manifold-valued prior.
Online Covariance Matrix Estimation in Stochastic Gradient Descent

The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge data sets and online learning. While this recursive algorithm is popular for computation and memory efficiency, quantifying variability and randomness of the solutions has been rarely studied. This paper aims at conducting statistical inference of SGD-based estimates in an online setting. In particular, we propose a fully online estimator for the covariance matrix of averaged SGD iterates (ASGD) only using the iterates from SGD. We formally establish our online estimator's consistency and show that the convergence rate is comparable to offline counterparts. Based on the classic asymptotic normality results of ASGD, we construct asymptotically valid confidence intervals for model parameters. Upon receiving new observations, we can quickly update the covariance matrix estimate and the confidence intervals. This approach fits in an online setting and takes full advantage of SGD: efficiency in computation and memory.
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley  Transform

Strictly enforcing orthonormality constraints on parameter matrices has been shown advantageous in deep learning. This amounts to Riemannian optimization on the Stiefel manifold, which, however, is computationally expensive. To address this challenge, we present two main contributions: (1) A new efficient retraction map based on an iterative Cayley transform for optimization updates, and (2) An implicit vector transport mechanism based on the combination of a projection of the momentum and the Cayley transform on the Stiefel manifold. We specify two new optimization algorithms: Cayley SGD with momentum, and Cayley ADAM on the Stiefel manifold. Convergence of Cayley SGD is theoretically analyzed. Our experiments for CNN training demonstrate that both algorithms: (a) Use less running time per iteration relative to existing approaches that enforce orthonormality of CNN parameters; and (b) Achieve faster convergence rates than the baseline SGD and ADAM algorithms without compromising the performance of the CNN. Cayley SGD and Cayley ADAM are also shown to reduce the training time for optimizing the unitary transition matrices in RNNs.
The Research for Software Programming of English Online Learning

Everyone appreciates the importance of software programming. It is the process of letting computer code solve a certain problem, running a certain calculation method for a certain calculation system, making the calculation system work according to the calculation method, and finally obtaining the expected result. Major determinants of software programming product are costs, procedures, and qualities. In this article, the author aims to discuss the software programming, process improvement, as well as the suggestions for English online learning software apps.
Using Knowledge Tracing to Predict Students’ Performance in Cognitive Training and Math

AbstractCognitive training aims to improve skills such as working memory capacity and spatial ability, which have been linked to math skills. In this study, we fit Deep Knowledge Tracing with Transformers (DKTT), Dynamic Key-Value Memory Networks (DKVMN), and Knowledge Tracing Machines (KTM) to a large dataset from a cognitive training system. DKVMN achieved the highest AUC (0.739) of the algorithms. To explore connections between math skills and cognitive skills, the data was split into cognitive and math items. DKVMN’s AUC on the math items was higher (0.745) than on the cognitive (0.706). Notably, the split model AUCs did not differ from skill-level AUCs produced by a model trained on the entire dataset, suggesting that math performance did not improve DKVMN’s cognitive predictions and vice versa.KeywordsKnowledge tracingCognitive trainingMath learning
Software Sentiment Analysis Using Machine Learning with Different Word-Embedding

Software sentiment analysis has applications in numerous software engineering tasks ranging from code suggestions to evaluating app reviews which help to save the development team valuable time and increase productivity. In recent years, sentiment analysis has been used to study the emotional state of developers through sources like commit messages. State-of-the-art sentiment analysis techniques have been employed to accomplish these tasks with varying results. The goal of this paper is to provide a comparison between the performance of various models for possible applications of sentiment analysis in software engineering. We have used three different datasets to account for the possible applications: JIRA, AppReviews, and StackOverflow. In this work, six word embedding techniques have been applied on above datasets to represent the text as n-dimensional vectors. To handle the skewed distribution of classes present in the data, we have employed two class balancing techniques in the form of SMOTE and Borderline-SMOTE. The resulting data is subjected to six feature selection techniques, and finally, the sentiment of the text is classified using 14 different classifiers. The experimental results suggest that some models are very successful in accurately classifying the sentiment of the text, whereas choosing the wrong combination of ML techniques can lead to disappointing performance.
Method of automatIc text annoting based on hybrid intelligent technologies

In the article, the analysis of modern methods of automatic annotation and abstracting of texts is carried out, the disadvantages and advantages of existing methods are noted. The problem of automatic annotation is formulated. The main stages of automatic processing of information during annotation are determined. A model for processing text sources using multi-agent organizations is proposed. A method based on the hypothesis of the existence of meaning at the level of syntactic collocations has been developed. A model for solving the problem based on the extraction of N-grams and their subsequent grammatical processing is proposed.
X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection

Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios.This paper describes the X-PuDu system that participated in SemEval-2022 Task 6, iSarcasmEval -Intended Sarcasm Detection in English and Arabic, which aims at detecting intended sarcasm in various settings of natural language understanding.Our solution finetunes pre-trained language models, such as ERNIE-M and De-BERTa, under the multilingual settings to recognize the irony from Arabic and English texts.Our system ranked second out of 43, and ninth out of 32 in Task A: one-sentence detection in English and Arabic; fifth out of 22 in Task B: binary multi-label classification in English; first out of 16, and fifth out of 13 in Task C: sentence-pair detection in English and Arabic.
taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning

We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes.We adapt multitask learning for the multimodal subtasks of the MAMI challenge to transfer knowledge among the correlated subtasks.We also leverage on ensemble learning for synergistic integration of models individually trained for the subtasks.We finally discuss about errors of the system to provide useful insights for future work.
SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model

Synonyms and antonym practices are the most common practices in our early childhood.It correlated our known words to a better place deep in our intuition.At the beginning of life for a machine, we would like to treat the machine as a baby and build a similar training for it as well to present a qualified performance.In this paper, we present an ensemble model for sentence logistics classification, which outperforms the state-of-art methods.Our approach essentially builds on two models including ERNIE-M and DeBERTaV3.With crossvalidation and random seed tuning, we select the top performance models for the last soft ensemble and make them vote for the final answer, achieving the top 6 performance.
A Proposed Method for Assessing Cluster Heterogeneity

Assessing how adequate clusters fit a dataset and finding an optimum number of clusters is a difficult process. A membership matrix and the degree of membership matrix is suggested to determine the homogeneity of a cluster fit. Maximisation of the ratio of the overall degree of membership at cluster number lag 1 is also suggested as a method to optimise the number of clusters in a dataset. A threshold factor upon the degree of membership is also suggested for homogeneous clusters. Cluster simulations were given to compare how well the proposed method compares against established methods. This method may be applied to the output of both hierarchical and k-means clustering.
Differentially Private Combinatorial Cloud Auction

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time.
$4$-uniform BCT permutations from generalized butterfly structure

As a generalization of Dillon's APN permutation, butterfly structure and generalizations have been of great interest since they generate permutations with the best known differential and nonlinear properties over the field of size $2^{4k+2}$. Complementary to these results, we show in this paper that butterfly structure, more precisely the closed butterfly also yields permutations with the best boomerang uniformity, a new and important parameter related to boomerang-style attacks. This is the sixth known infinite family of permutations in the literature with the best known boomerang uniformity over such fields.
The Digital Poetics of Lost Waterscapes in Coimbatore, South India

This chapter explores the potential for digital literature and creative visualisations for work in the DEH. This work emerges from a project on water stress in the Southern Indian district of Coimbatore. Our aim was to compile an open-access dataset of water in the district in the past and present and to find ways of making that data available and resonant in new ways. Working with local activists and communities, the interdisciplinary project aims to create robust digital resources for activists, educators, and scholars and, in doing so, frame datasets about lost and precarious landscapes of water in ways that challenge the presumption of imperial pasts and state science. A key outcome of the project is a series of digital literary visualisations. The rationale and development of these visualisations are presented here as a means to explore their potential in the DEH.
Jacqueline Campbell: Studying Oceans from Above

Planetary scientist studies oceans with a combination of laboratory work and satellite imagery.
Qualitative Analysis Method for Training of College Students’ Entrepreneurial Resilience from the Perspective of Entrepreneurial Ecological Environment

&#x0D; Due to the constraints of the entrepreneurial ecological environment and the heterogeneity of the individual college students and their interpersonal relationships and situations, some specific problems like psychological potential development and stress buffering effect in the process of college students’ entrepreneurial resilience cultivation have not been fundamentally resolved. Therefore, this paper explores the qualitative analysis method for cultivation of college students’ entrepreneurial resilience from the perspective of entrepreneurial ecological environment. First, the relationship among the main roles in the entrepreneurial ecosystem was shown, the homogeneity-driven integration process of the strategies for college students’ entrepreneurial resilience cultivation was expounded, and a knowledge interaction model for college students’ entrepreneurial resilience cultivation was constructed. Then, a conceptual model of college students’ entrepreneurial heterogeneity was constructed, and a dynamic evolution analysis was performed on the homogenization-oriented complementary adjustment of college students’ entrepreneurial ecological environment. The experiment showed that the empirical results of the constructed model basically met the theoretical expectations. Finally, the experimental results under the completely heterogeneous, generally homogenized and completely homogeneous states were given and discussed, which verified the effectiveness of the homogenization-oriented complementation strategy.
Preface

The International Conference on Smart Technologies for Sustainable Development (ICSTSD2021) organized by JIS College of Engineering, the oldest Autonomous Institute of West Bengal, India during 28 th – 29 th October 2021 focussing on a cornucopia full of diverse issues pertaining to Smart Technologies and Intelligent Solutions to the challenges enforced by Industry 4.0. The Conference was a perfect platform of researchers, scientists, technocrats, industrialists; for sharing novel ideas, innovations, and solutions to some of the contemporary issues. Major areas of the Conference have been Electronic and Devices, Computational Science, Environmental &amp; Earth Science, and Instrumentation &amp; Measurement. About 40 manuscripts after stringent review process were accepted to be presented in the conference. The research papers are in the area of Automation, Sensor Interfacing, Image Analysis, Artificial Intelligence, Electronic Device Designing, and Machine Learning for various data analysis. The Conference was held at the Digital Studio of JIS College of Engineering, Kalyani, Nadia, West Bengal, India. List of Brief Profiles of Editors, Conference Committee are available in this pdf.
Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs

Uncertain graphs have been widely used to model complex linked data in many real-world applications, such as guaranteed-loan networks and power grids, where a node or edge may be associated with a probability. In these networks, a node usually has a certain chance of default or breakdown due to self-factors or the influence from upstream nodes. For regulatory authorities and companies, it is critical to efficiently identify the vulnerable nodes, i.e., nodes with high default risks, such that they could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the problem of top-$k$ vulnerable nodes detection in uncertain graphs. We formally define the problem and prove its hardness. To identify the $k$ most vulnerable nodes, a sampling-based approach is proposed. Rigorous theoretical analysis is conducted to bound the quality of returned results. Novel optimization techniques and a bottom-$k$ sketch based approach are further developed in order to scale for large networks. In the experiments, we demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks. The evaluation results show that the proposed methods can achieve up to 2 orders of magnitudes speedup compared with the baseline approach. Moreover, to further verify the advantages of our model in real-life scenarios, we integrate the proposed techniques with our current loan risk control system, which is deployed in the collaborated bank, for more evaluation. Particularly, we show that our proposed new model has superior performance on real-life guaranteed-loan network data, which can better predict the default risks of enterprises compared to the state-of-the-art techniques.
Adaptive Distraction Context Aware Tracking Based on Correlation Filter

The Discriminative Correlation Filter (CF) uses a circulant convolution operation to provide several training samples for the design of a classifier that can distinguish the target from the background. The filter design may be interfered by objects close to the target during the tracking process, resulting in tracking failure. This paper proposes an adaptive distraction context aware tracking algorithm to solve this problem. In the response map obtained for the previous frame by the CF algorithm, we adaptively find the image blocks that are similar to the target and use them as negative samples. This diminishes the influence of similar image blocks on the classifier in the tracking process and its accuracy is improved. The tracking results on video sequences show that the algorithm can cope with rapid changes such as occlusion and rotation, and can adaptively use the distractive objects around the target as negative samples to improve the accuracy of target tracking.
An End-to-End Dialogue State Tracking System with Machine Reading  Comprehension and Wide & Deep Classification

This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking. The goal of this task is to predict the intents and slots in each user turn to complete the dialogue state tracking (DST) based on the information provided by the task's schema. Different from traditional stage-wise DST, we propose an end-to-end DST system to avoid error accumulation between the dialogue turns. The DST system consists of a machine reading comprehension (MRC) model for non-categorical slots and a Wide & Deep model for categorical slots. As far as we know, this is the first time that MRC and Wide & Deep model are applied to DST problem in a fully end-to-end way. Experimental results show that our framework achieves an excellent performance on the test dataset including 50% zero-shot services with a joint goal accuracy of 0.8652 and a slot tagging F1-Score of 0.9835.