Reviewerly/exHarmony huggingface dataset:
Analysis of the Attitude of Teachers and Students to the Distance Learning Format

the article considers the results of the analysis of a survey conducted among teachers and students on their attitude to distance education technology. Patterns are identified and conclusions are drawn.
Basic Principles and Methods of Using Media Education for Forming Students’ Analytical Thinking

This article deals with the basic principles and techniques of media education to form analytical thinking of students It also examines film analysis, creative and imitation media education exercises. The development of students’ skills to communicate and discuss, to prove their point of view with reason is always a relevant task in the educational landscape of modern higher school. One of the optimal ways to develop analytical interdisciplinary competence is media education, which allows the students to carry out the process of analysis and synthesis of information on the example of works of media culture of various types and genres. The authors consider the possibilities of using media educational methods and technologies in the process of civil and patriotic education, give examples of creative tasks of various types (literary-imitation, theatrical-situational, visual-imitation, etc.) for the formation of analytical thinking of the younger generation. In this way the student body gets an opportunity to evaluate works of media culture independently, develop their creative potential, learn to perceive media projects thoroughly, get new knowledge, develop communicative competences. The data provided by the authors of the paper can confirm the need of use of the educational, educational and developing media education potential for the Russian educational organizations, relevance of the questions connected with strengthening of interaction of various educational structures and scientific education centers in the field of media pedagogy for further development of methodical tools in the solution of the problems of development of media competence of the younger generation.
Cross-Lingual Leveled Reading Based on Language-Invariant Features

Leveled reading (LR) aims to automatically classify texts according to different reading capabilities and provide appropriate reading materials to readers.However, most state-of-theart LR methods rely on the availability of copious annotated resources, which prevents their adaptation to low-resource languages like Chinese.In our work, to tackle Chinese LR, we explore to perform different language transfer methods on English-Chinese LR.Specifically, we focus on adversarial training and cross-lingual pre-training method to transfer the LR knowledge learned from annotated data in the rich-resource English language to Chinese.For evaluation, we introduce the agebased standard to align datasets with different leveling standards, and conduct experiments in both zero-shot and few-shot settings.Experiments show that the cross-lingual pre-training method can capture language-invariant features more effectively than adversarial training.We also conduct analysis to propose further improvement in cross-lingual LR.
Fairness-aware Class Imbalanced Learning

Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases.
Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models

We use a dataset of U.S. first names with labels based on predominant gender and racial group to examine the effect of training corpus frequency on tokenization, contextualization, similarity to initial representation, and bias in BERT, GPT-2, T5, and XLNet. We show that predominantly female and non-white names are less frequent in the training corpora of these four language models. We find that infrequent names are more self-similar across contexts, with Spearman’s rho between frequency and self-similarity as low as -.763. Infrequent names are also less similar to initial representation, with Spearman’s rho between frequency and linear centered kernel alignment (CKA) similarity to initial representation as high as .702. Moreover, we find Spearman’s rho between racial bias and name frequency in BERT of .492, indicating that lower-frequency minority group names are more associated with unpleasantness. Representations of infrequent names undergo more processing, but are more self-similar, indicating that models rely on less context-informed representations of uncommon and minority names which are overfit to a lower number of observed contexts.
Foundations of Symbolic Languages for Model Interpretability

Several queries and scores have recently been proposed to explain individual predictions over ML models. Given the need for flexible, reliable, and easy-to-apply interpretability methods for ML models, we foresee the need for developing declarative languages to naturally specify different explainability queries. We do this in a principled way by rooting such a language in a logic, called FOIL, that allows for expressing many simple but important explainability queries, and might serve as a core for more expressive interpretability languages. We study the computational complexity of FOIL queries over two classes of ML models often deemed to be easily interpretable: decision trees and OBDDs. Since the number of possible inputs for an ML model is exponential in its dimension, the tractability of the FOIL evaluation problem is delicate but can be achieved by either restricting the structure of the models or the fragment of FOIL being evaluated. We also present a prototype implementation of FOIL wrapped in a high-level declarative language and perform experiments showing that such a language can be used in practice.
Where the Path Leads : State of the Art and Challenges of Graph Database Systems

Compared to relational databases, graph database systems provide a novel way of processing and analyzing highly interconnected data. Due to their unique properties, graph databases embody an interesting area of research in academic circles. For this reason, this work is fundamentally concerned with examining the state of the industry and current challenges. In this regard, we revisit the basic concepts and highlight the tremendous heterogeneity of available systems using the example of differing path semantics. Based on this insight, we explore algorithmic advancements for graph query processing regarding path finding and worst-case optimal joins. Subsequently, we discuss issues regarding performance and support for graph analytics. Finally, we provide an overview of GQL, a joint standardization effort towards unification of property graph databases.
Lifelong Explainer for Lifelong Learners

Lifelong Learning (LL) black-box models are dynamic in that they keep learning from new tasks and constantly update their parameters. Owing to the need to utilize information from previously seen tasks, and capture commonalities in potentially diverse data, it is hard for automatic explanation methods to explain the outcomes of these models. In addition, existing explanation methods, e.g., LIME, which are computationally expensive when explaining a static black-box model, are even more inefficient in the LL setting. In this paper, we propose a novel Lifelong Explanation (LLE) approach that continuously trains a student explainer under the supervision of a teacher – an arbitrary explanation algorithm – on different tasks undertaken in LL. We also leverage the Experience Replay (ER) mechanism to prevent catastrophic forgetting in the student explainer. Our experiments comparing LLE to three baselines on text classification tasks show that LLE can enhance the stability of the explanations for all seen tasks and maintain the same level of faithfulness to the black-box model as the teacher, while being up to 10ˆ2 times faster at test time. Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer. Our code is available at https://github.com/situsnow/LLE.
LibGuides: A LibGuide for ECE's: Digital Resources

Are you in the Early Childhood Education program at Algonquin College? Check out this guide for information to help you with your program!
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives

Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents. In this work, we present a rule-based and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets.
High Dimensional Deep Data Clustering Architecture Towards Evolving Concept

Data Clustering on the evolving data stream has becoming primary and vital tasks in the natural language processing in data driven application domains. Performance analysis of data clustering techniques depends on the quality of data representation on the evolving data streams. Machine learning algorithm plays a primary role in high dimensional data clustering of the evolving data streams from social media. Despite of large benefits on implementing those algorithms, it suffers on various aspects such as low accuracy and distribution of the data points in the time varying clusters. In order to resolve above mentioned issues, Deep learning architectures has been analysed on various aspects to develop a optimized model for better data representation of data clustering. In this paper, an optimized deep learning architecture which termed as Concept Based High Dimensional Deep Clustering has been projected for analysing unstructured and high dimensional evolving data in the data streams. Proposed architectures use the hidden layer for better effective identification of the hidden feature. Further the evolving data uses transform learning for data transformation. Those learning transforms high dimensional to low dimensional deep feature space. Finally these deep feature spaces extract the concept specific features on the employment of the max layer using principle component analysis (PCA). PCA on the max layer determines the salient features on ensuring the minimum reconstruction error. Deep architecture is eliminating the NP hard problem and over fitting issues of the clustered results. Further all parameters are fine tuned with respect to certain criterion on cross validation. The Softmax layer is used to map the data points into accurate cluster representations. Finally it is helpful to find a better initialization of the parameters. Extensive experiments have been conducted on real datasets to compare proposed model with several state-of-the-art approaches. The experimental results show that proposed deep clustering model can achieve both effectiveness and good scalability on high dimensional data.
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement  Learning

Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations. To address such a problem, we introduce a novel formulation, combinatorial construction, which requires a building agent to assemble unit primitives (i.e., LEGO bricks) sequentially -- every connection between two bricks must follow a fixed rule, while no bricks mutually overlap. To construct a target object, we provide incomplete knowledge about the desired target (i.e., 2D images) instead of exact and explicit volumetric information to the agent. This problem requires a comprehensive understanding of partial information and long-term planning to append a brick sequentially, which leads us to employ reinforcement learning. The approach has to consider a variable-sized action space where a large number of invalid actions, which would cause overlap between bricks, exist. To resolve these issues, our model, dubbed Brick-by-Brick, adopts an action validity prediction network that efficiently filters invalid actions for an actor-critic network. We demonstrate that the proposed method successfully learns to construct an unseen object conditioned on a single image or multiple views of a target object.
Scaling Vision with Sparse Mixture of Experts

Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent scalability in Natural Language Processing. In Computer Vision, however, almost all performant networks are dense, that is, every input is processed by every parameter. We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks. When applied to image recognition, V-MoE matches the performance of state-of-the-art networks, while requiring as little as half of the compute at inference time. Further, we propose an extension to the routing algorithm that can prioritize subsets of each input across the entire batch, leading to adaptive per-image compute. This allows V-MoE to trade-off performance and compute smoothly at test-time. Finally, we demonstrate the potential of V-MoE to scale vision models, and train a 15B parameter model that attains 90.35% on ImageNet.
Measuring Similarity of Opinion-bearing Sentences

For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicability to opinion texts. We address this gap in the literature, studying: (1) how humans judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of “opinion similarity” but not “opinion difference”. Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence-embedding network based on review text and weak supervision by review ratings. Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

We introduce a curriculum learning algorithm, Variational AutomaticCurriculum Learning (VACL), for solving challenging goal-conditionedcooperative multi-agent reinforcement learning problems. We motivate ourparadigm through a variational perspective, where the learning objective can bedecomposed into two terms: task learning on the current task distribution, andcurriculum update to a new task distribution. Local optimization over thesecond term suggests that the curriculum should gradually expand the trainingtasks from easy to hard. Our VACL algorithm implements this variationalparadigm with two practical components, task expansion and entity progression,which produces training curricula over both the task configurations as well asthe number of entities in the task. Experiment results show that VACL solves acollection of sparse-reward problems with a large number of agents.Particularly, using a single desktop machine, VACL achieves 98% coverage ratewith 100 agents in the simple-spread benchmark and reproduces the ramp-usebehavior originally shown in OpenAI's hide-and-seek project. Our projectwebsite is at https://sites.google.com/view/vacl-neurips-2021.
Computing an Order-Complete Basis for $$M^{\infty }(N)$$ and Applications

This paper gives a quick way to construct all modular functions for the group \(\Gamma _0(N)\) having only a pole at \(\tau = i \infty \). We assume that we are given two modular functions for \(\Gamma _0(N)\) with poles only at \(i \infty \) and coprime pole orders. As an application we obtain two new identities from which one can derive that \(p(11n+6)\equiv 0\pmod {11}\), where p(n) is the usual partition function.
The relation between project team conflict and user resistance in software projects

This study aims to explore the relation between conflict in the project team and user resistance to change in software projects. Following a cross-sectional research design, a survey was conducted among 1,000 largest companies in Slovenia (N = 114). The results of PLS-SEM analysis indicate that task and process conflicts in the project team are associated with user resistance. This study is among the first to associate conflict within the project team and user resistance in the implementing organization. It is also one of the first studies to investigate the relations between different types of conflict and user resistance. Project managers may invest resources into adequately managing conflicts within the project team related to tasks in which the project team interacts with users of developed software to lower user resistance. Project with poorly defined roles (e.g., agile and information security projects) may be more prone to user resistance than projects with clearly defined roles.
Developmental Studies for a Fuzzy Rule based System for Various Engineering Applications

rule-based systems are rule-based systems that employ fuzzy sets and fuzzy logic to describe various types of information about the problem at hand, as well as to simulate the interactions and relationships that exist between its variables. The fuzzy logic relies on rules to generate the fuzzy output. The rule-based system differs from expert systems in that the rules that make up the rule-based system are derived from sources other than human experts, making it distinct from expert systems. rules are linguistic IF-THEN structures with the general form IF A THEN B, where A and B are (groups of) propositions with linguistic variables. The rule's premise is A, and the rule's consequence is B. A fuzzy rule-based system is made up of several rules. Classical set theory differs from fuzzy set theory in that the latter allows for partial set membership. A classical or crisp set, on the other hand, is a fuzzy set whose membership values are limited to 0 and 1, the unit interval's endpoints. It has been modelled and applied to a range of technical domains over a period of more than 50 years since the concept of Fuzzy Logic and Fuzzy Rule based System was created. Particularly useful in circumstances when there are high levels of uncertainty and ambiguity in the behaviour of the variables under investigation. This research attempts to investigate the development and use of Rule Based Systems in a variety of engineering applications.
Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization

While much research has been done in text-to-image synthesis, little work has been done to explore the usage of linguistic structure of the input text. Such information is even more important for story visualization since its inputs have an explicit narrative structure that needs to be translated into an image sequence (or visual story). Prior work in this domain has shown that there is ample room for improvement in the generated image sequence in terms of visual quality, consistency and relevance. In this paper, we first explore the use of constituency parse trees using a Transformer-based recurrent architecture for encoding structured input. Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story. Third, we also incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images within a dual learning setup. We show that off-the-shelf dense-captioning models trained on Visual Genome can improve the spatial structure of images from a different target domain without needing fine-tuning. We train the model end-to-end using intra-story contrastive loss (between words and image sub-regions) and show significant improvements in visual quality. Finally, we provide an analysis of the linguistic and visuo-spatial information.
Few-Shot Object Detection via Association and DIscrimination

Object detection has achieved substantial progress in the last decade. However, detecting novel classes with only few samples remains challenging, since deep learning under low data regime usually leads to a degraded feature space. Existing works employ a holistic fine-tuning paradigm to tackle this problem, where the model is first pre-trained on all base classes with abundant samples, and then it is used to carve the novel class feature space. Nonetheless, this paradigm is still imperfect. Durning fine-tuning, a novel class may implicitly leverage the knowledge of multiple base classes to construct its feature space, which induces a scattered feature space, hence violating the inter-class separability. To overcome these obstacles, we propose a two-step fine-tuning framework, Few-shot object detection via Association and DIscrimination (FADI), which builds up a discriminative feature space for each novel class with two integral steps. 1) In the association step, in contrast to implicitly leveraging multiple base classes, we construct a compact novel class feature space via explicitly imitating a specific base class feature space. Specifically, we associate each novel class with a base class according to their semantic similarity. After that, the feature space of a novel class can readily imitate the well-trained feature space of the associated base class. 2) In the discrimination step, to ensure the separability between the novel classes and associated base classes, we disentangle the classification branches for base and novel classes. To further enlarge the inter-class separability between all classes, a set-specialized margin loss is imposed. Extensive experiments on Pascal VOC and MS-COCO datasets demonstrate FADI achieves new SOTA performance, significantly improving the baseline in any shot/split by +18.7. Notably, the advantage is most announced on extremely few-shot scenarios.