Reviewerly/exHarmony huggingface dataset:
Explaining Answers with Entailment Trees.

Our goal, in the context of open-domain textual question-answering (QA), is to explain answers by showing the line of reasoning from what is known to the answer, rather than simply showing a fragment of textual evidence (a “rationale”). If this could be done, new opportunities for understanding and debugging the system’s reasoning become possible. Our approach is to generate explanations in the form of entailment trees, namely a tree of multipremise entailment steps from facts that are known, through intermediate conclusions, to the hypothesis of interest (namely the question + answer). To train a model with this skill, we created ENTAILMENTBANK, the first dataset to contain multistep entailment trees. Given a hypothesis (question + answer), we define three increasingly difficult explanation tasks: generate a valid entailment tree given (a) all relevant sentences (b) all relevant and some irrelevant sentences, or (c) a corpus. We show that a strong language model can partially solve these tasks, in particular when the relevant sentences are included in the input (e.g., 35% of trees for (a) are perfect), and with indications of generalization to other domains. This work is significant as it provides a new type of dataset (multistep entailments) and baselines, offering a new avenue for the community to generate richer, more systematic explanations.
Interactions between nonlinear resonators represented in Volterra series

This paper aims at introducing nonlinear interactions in the sound synthesis of nonlinear resonators. Sound synthesis is performed using the formalism of Volterra series which is used to describe and simulate weakly nonlinear models using their Volterra kernels. Interactions are constraints imposed to the considered models and are used to excite (e.g. bowing a string or striking a plate) or connect resonators together (such as connecting a string to a bridge). This requires to resolve an inverse problem giving the interaction force as a function of a given velocity. In the case of Volterra series, kernels of the inverse problem have been defined by Schetzen. Using this definition, a structure of simulation of the direct and inverse problems at each time step will be defined. Application to the case of a Kirchhoff-Carrier string connected to a Berger plate will be presented including computation of the kernels (direct and inverse), structure of simulation and numerical results.
Regularising Fisher Information Improves Cross-lingual Generalisation

Many recent works use ‘consistency regularisation’ to improve the generalisation of fine-tuned pre-trained models, both multilingual and English-only. These works encourage model outputs to be similar between a perturbed and normal version of the input, usually via penalising the Kullback–Leibler (KL) divergence between the probability distribution of the perturbed and normal model. We believe that consistency losses may be implicitly regularizing the loss landscape. In particular, we build on work hypothesising that implicitly or explicitly regularizing trace of the Fisher Information Matrix (FIM), amplifies the implicit bias of SGD to avoid memorization. Our initial results show both empirically and theoretically that consistency losses are related to the FIM, and show that the flat minima implied by a small trace of the FIM improves performance when fine-tuning a multilingual model on additional languages. We aim to confirm these initial results on more datasets, and use our insights to develop better multilingual fine-tuning techniques.
Diseño y valoración de un kit de laboratorio en entornos virtuales usando: Working Model en el aprendizaje de la enseñanza de las ciencias físicas

espanolEl proposito de este trabajo fue el diseno y la valoracion de un modulo de laboratorio virtual como estrategia de aprendizaje de un curso de ciencias fisicas. Se uso el metodo de indagacion guiada. Se trabajo con los estudiantes del semestre 2020-II, a los cuales se les dividio en dos grupos: un grupo de control y un grupo experimental. El 71,09% de los estudiantes del grupo experimental aprobaron el curso satisfactoriamente, mientras que en el grupo de control solo aprobo el 35,93%. Ademas, los estudiantes del grupo experimental adquirieron habilidades cognitivas al interactuar con el simulador, obteniendo un factor de Hake de 0,58, indicando una ganancia de aprendizaje. EnglishThe purpose of this work was the design and assessment of a virtual laboratory kit as learning strategy of a physics science course. The method used was guided inquiry. We worked with the 2020-II semester students, divided into a control group and an experimental group. The 71,09 % of experimental group students passed the course successfully, while the control group students got 35,93%. In addition, experimental group students acquired cognitive skills by interacting with the simulator, obtaining a Hake factor of 0.58, indicating a learning gain.
Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers

We present a new perspective of achieving image synthesis by viewing this task as a visual token generation problem. Different from existing paradigms that directly synthesize a full image from a single input (e.g., a latent code), the new formulation enables a flexible local manipulation for different image regions, which makes it possible to learn content-aware and fine-grained style control for image synthesis. Specifically, it takes as input a sequence of latent tokens to predict the visual tokens for synthesizing an image. Under this perspective, we propose a token-based generator (i.e.,TokenGAN). Particularly, the TokenGAN inputs two semantically different visual tokens, i.e., the learned constant content tokens and the style tokens from the latent space. Given a sequence of style tokens, the TokenGAN is able to control the image synthesis by assigning the styles to the content tokens by attention mechanism with a Transformer. We conduct extensive experiments and show that the proposed TokenGAN has achieved state-of-the-art results on several widely-used image synthesis benchmarks, including FFHQ and LSUN CHURCH with different resolutions. In particular, the generator is able to synthesize high-fidelity images with 1024x1024 size, dispensing with convolutions entirely.
Routing Protocols of Mobile Ad-hoc Network MANET

Feature-based methods are resistant to various types of attacks including tearing, and the syntactic property of the watermarking is retained in the watermark along with prominent image features. These image features may be the main image, local areas, or feature points such as its edges, corners, texture and color. The title of second generation of watermarking is introduced based on feature points.This category of watermarking uses prominent image features in both insertion and extraction operations of watermark as a reference. Features must have properties such as resistance to image transfer, rotation, resizing, as well as resistance to noise and local changes. As a result, due to the unchangeability properties of feature points and important areas, this method is resistant to geometric attacks and image processing. Another advantage of this method is its combination in the areas of location and frequency. This paper reviewed two resistant methods of surf and Harris which are widely used in feature-based watermarking.
Semantic Analysis and Retrieval of User-Generated Text

Author(s): Le, Nhat Xuan Thong | Advisor(s): Hristidis, Vagelis | Abstract: With the ever-increasing volume of user-generated text (e.g., product reviews, doctor notes, chat logs), there is a need to distill valuable semantic information from such un-structured sources. We initially focus on product reviews, which conceptually consist of concepts (or aspects) such as “screen brightness”, and user opinions on these concepts such as very positive. First, we present a novel review summarization framework that advances the state-of-the-art by leveraging a domain hierarchy of concepts to handle the semantic overlap among the aspects, and by accounting for different opinion levels. Second, we argue and empirically show that the current style of soliciting customer opinion by asking them to write free-form text reviews is suboptimal, as few aspects receive most of the ratings. Therefore, we propose various techniques to dynamically select which aspects to ask users to rate given the current review history of a product.The last body of work leverages user chat logs to continuously optimize the workflow of a goal-oriented chatbot, such as a pizza ordering bot. On one hand, diagram-based chatbots are simple and interpretable but only support limited predefined conversation scenarios. On the other hand, the state-of-the-art Reinforcement Learning (RL) models can handle more scenarios but are not interpretable. We propose a hybrid method, which enforces workflow constraints in a chatbot, and uses RL to select the best chatbot response given the specified constraints.
I wish I would have loved this one, but I didn’t: A multilingual dataset for counterfactual detection in product reviews

Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering counterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.
DualNet: Continual Learning, Fast and Slow

According to Complementary Learning Systems (CLS) theory~\citep{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics and individual experiences, and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment. Motivated by this theory, we propose a novel continual learning framework named DualNet, which comprises a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for unsupervised representation learning of task-agnostic general representation via a Self-Supervised Learning (SSL) technique. The two fast and slow learning systems are complementary and work seamlessly in a holistic continual learning framework. Our extensive experiments on two challenging continual learning benchmarks of CORE50 and miniImageNet show that DualNet outperforms state-of-the-art continual learning methods by a large margin. We further conduct ablation studies of different SSL objectives to validate DualNet's efficacy, robustness, and scalability. Code will be made available upon acceptance.
Evolution of Homomorphic Encryption

Data encryption has been a major tool for ensuring data confidentiality and integrity.Data encryption is mostly applied to data at rest, and data in motion.For data in use, encrypted data is usually decrypted by the user before performing any operation on the data.The security of such data is usually compromised when the user is not trusted.In cloud computing, for instance, where computing services are delegated, decrypting data by a third party before performing any operation is not appropriate.The operation must be performed on the data in its encrypted form; hence, the need for homomorphic encryption.This paper presents the evolution of homomorphic encryption schemes as a basis for research into modern homomorphic encryption schemes.The various stages identified are Privacy homomorphism, Partial homomorphism, Somewhat homomorphism and fully homomorphic encryption.
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model

The curse of dimensionality is a widely known issue in reinforcement learning (RL). In the tabular setting where the state space $\mathcal{S}$ and the action space $\mathcal{A}$ are both finite, to obtain a nearly optimal policy with sampling access to a generative model, the minimax optimal sample complexity scales linearly with $|\mathcal{S}|\times|\mathcal{A}|$, which can be prohibitively large when $\mathcal{S}$ or $\mathcal{A}$ is large. This paper considers a Markov decision process (MDP) that admits a set of state-action features, which can linearly express (or approximate) its probability transition kernel. We show that a model-based approach (resp.$~$Q-learning) provably learns an $\varepsilon$-optimal policy (resp.$~$Q-function) with high probability as soon as the sample size exceeds the order of $\frac{K}{(1-\gamma)^{3}\varepsilon^{2}}$ (resp.$~$$\frac{K}{(1-\gamma)^{4}\varepsilon^{2}}$), up to some logarithmic factor. Here $K$ is the feature dimension and $\gamma\in(0,1)$ is the discount factor of the MDP. Both sample complexity bounds are provably tight, and our result for the model-based approach matches the minimax lower bound. Our results show that for arbitrarily large-scale MDP, both the model-based approach and Q-learning are sample-efficient when $K$ is relatively small, and hence the title of this paper.
The Genesis of University Education in Ukraine in the Second Galf of the XIX – Early XX Century

Modernization of the higher education system in Ukraine requires the study and generalization of promising author’s ideas, the experience of scientists, in particular the second half of the XIX – early XX century. The main problems of higher university education of that period were: autonomy of universities, determining the predominant type of higher education institutions, training of teaching staff, defining the timing of the educational process in universities, the formation of women’s higher educaton, relevant in modern conditions. Previously unresolved parts of the general problem to which the article is devoted are the traditions of teaching disciplines in Ukrainian universities in the second half of XIX – early XX centuries. The purpose of the article is to characterize the genesis of university education in Ukraine in the second half of the XIX – early XX century. Object of research: higher university education of Ukraine in the second half of the XIX – beginning of the XX century. Subject of research: formation and development of university education in Ukraine in the second half of the XIX – early XX century. Conclusions. Thus: 1. University education in Ukraine in the late nineteenth – early twentieth century was a stage of formation and development. 2. University education in Ukraine during this period is beginning to become an independent branch of culture, growing tendencies to independence, autonomy. 3. Due to the lack of teachers since 1899, universities were allowed to announce a competition to fill vacancies in the departments, and admitted to the competition and persons with a master's degree. 4. In the early twentieth century. the general term of study at universities was established: 4 years (8 semesters), or 5 years (10 semesters). As an exception, it was allowed to continue studies up to 6 years (12 semesters), or to reduce them to 2 years (4 semesters). 5. Higher education for women was launched: Kyiv Higher Women's Courses were opened in 1878.
Cross-Lingual Wolastoqey-English Definition Modelling

Definition modelling is the task of automatically generating a dictionary-style definition given a target word.In this paper, we consider cross-lingual definition generation.Specifically, we generate English definitions for Wolastoqey (Malecite-Passamaquoddy) words.Wolastoqey is an endangered, low-resource polysynthetic language.We hypothesize that sub-word representations based on byte pair encoding (Sennrich et al., 2016) can be leveraged to represent morphologically-complex Wolastoqey words and overcome the challenge of not having large corpora available for training.Our experimental results demonstrate that this approach outperforms baseline methods in terms of BLEU score.
A Multi-class Magnitude Classifying Sparse Source Model for Compressible Sources

Source modelling is a gateway to the fascinating world of source coding. Many real-world sources are sparse or have a sparse representation. According to this fact, this work has focused on providing a new model to represent real-world non-strictly sparse (compressible) sources. To this aim, a novel model has been evolved from a simple sparse binary source to reflect the characteristics of compressible sources. The model is capable to represents real-world compressible sources by classifying samples into different classes based on their magnitudes. The model parameters are estimated using an innovative approach, a combination of a clustering technique and the binary genetic algorithm. The ability of the new approach has been assessed in modeling DCT coefficients of still images and video sequences. The proposed model also inspires an efficient coding approach to compress a wide range of sources including compressible sources. Comparison with classical well-known distributions including Laplace, Cauchy, and generalized Gaussian distribution and also with the most recent Noisy BG model reveals the capabilities of the proposed model in describing the characteristics of sparse sources. The numerical results based on the “chi-square goodness of fit” show that the proposed model provides a better fit to reflect the statistical characteristics of compressible sources.
Machine Learning and Implementation of KNN Algorithm in a Quantum Setting

This book contains abstracts of the various research papers of the academic & research community presented at the International Conference on Innovations and Challenges in Computing, Analytics and Security (ICICCAS-2020). ICICCAS-2020 has served as a platform for researchers, professionals to meet and exchange ideas on computing, data analytics, and security. The conference has invited papers in seven main tracks of Data Science, Networking Technologies, Sequential, Parallel, Distributed and Cloud Computing, Advances in Software Engineering, Multimedia, Image Processing, and Embedded Systems, Security and Privacy, Special Track (IoT, Smart Technologies and Green Engineering). The Technical and Advisory Committee Members were from various countries that have rich Research and Academic experience.Conference Title: TEQIP - III Sponsored First International Conference on Innovations and Challenges in Computing, Analytics and SecurityConference Acronym: ICICCAS-2020Conference Date: 29-30 July 2020Conference Location: Pondicherry Engineering College, Puducherry – 605014, India (Virtual Mode)Conference Organizer: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India.Conference Sponsor: TEQIP-III NPIU (A Unit of the Ministry of Human Resource Development, India)
R-Drop: Regularized Dropout for Neural Networks

Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in model training, namely R-Drop, which forces the output distributions of different sub models generated by dropout to be consistent with each other. Specifically, for each training sample, R-Drop minimizes the bidirectional KL-divergence between the output distributions of two sub models sampled by dropout. Theoretical analysis reveals that R-Drop reduces the freedom of the model parameters and complements dropout. Experiments on $\bf{5}$ widely used deep learning tasks ($\bf{18}$ datasets in total), including neural machine translation, abstractive summarization, language understanding, language modeling, and image classification, show that R-Drop is universally effective. In particular, it yields substantial improvements when applied to fine-tune large-scale pre-trained models, e.g., ViT, RoBERTa-large, and BART, and achieves state-of-the-art (SOTA) performances with the vanilla Transformer model on WMT14 English$\to$German translation ($\bf{30.91}$ BLEU) and WMT14 English$\to$French translation ($\bf{43.95}$ BLEU), even surpassing models trained with extra large-scale data and expert-designed advanced variants of Transformer models. Our code is available at GitHub{\url{this https URL}}.
Research on monitoring system of subgrade slope dangerous rock based on Harris corner detection algorithm

Due to the frequent occurrence of geological disasters in our country, which seriously endangers the safety of people’s lives and properties, in view of the slope disasters caused by the slide and collapse of dangerous rock slopes, this paper proposes a set of Harris corner detection algorithm based on digital image processing technology. The characteristic corner detection is studied to improve the algorithm. At the same time, the real-time video sequence of the slope scene is collected, and the target slope area is monitored for collapse using the characteristic corner detection and matching algorithm, so as to monitor the safety status of the slope. The ground is suitable for real-time monitoring of dangerous rocks on subgrade slopes, and effectively prevents dangerous rock disasters on slopes.
Learning Models for Actionable Recourse

As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely impacted by predicted outcomes (e.g., an applicant denied a loan) with recourse -- i.e., a description of how they can change their features to obtain a positive outcome. We propose a novel algorithm that leverages adversarial training and PAC confidence sets to learn models that theoretically guarantee recourse to affected individuals with high probability. We demonstrate the efficacy of our approach with extensive experiments on real data.
Surrogate-Assisted Multitask Genetic Programming for Learning Scheduling Heuristics

This chapter shows how to use surrogate techniques to improve the transfer effectiveness between tasks in multitask learning. This chapter introduces how surrogates are built and used to help share knowledge between tasks. The results show that the proposed surrogate-assisted multitask genetic programming can learn effective scheduling heuristics for all tasks simultaneously. In addition, the results show that the proposed algorithm can improve the diversity of individuals for tasks and the proportions of individuals from different tasks are different. Last, this chapter studies the sizes of the learned scheduling heuristics and finds that the proposed algorithm has a bigger effect on the learned routing rule than the sequencing rule. The analyses of the structures of the learned rules show that the learned scheduling heuristics for different tasks share common knowledge.