Reviewerly/exHarmony huggingface dataset:
Learning General Gaussian Mixture Model with Integral Cosine Similarity

Gaussian mixture model (GMM) is a powerful statistical tool in data modeling, especially for unsupervised learning tasks. Traditional learning methods for GMM such as expectation maximization (EM) require the covariance of the Gaussian components to be non-singular, a condition that is often not satisfied in real-world applications. This paper presents a new learning method called G$^2$M$^2$ (General Gaussian Mixture Model) by fitting an unnormalized Gaussian mixture function (UGMF) to a data distribution. At the core of G$^2$M$^2$ is the introduction of an integral cosine similarity (ICS) function for comparing the UGMF and the unknown data density distribution without having to explicitly estimate it. By maximizing the ICS through Monte Carlo sampling, the UGMF can be made to overlap with the unknown data density distribution such that the two only differ by a constant scalar, and the UGMF can be normalized to obtain the data density distribution. A Siamese convolutional neural network is also designed for optimizing the ICS function. Experimental results show that our method is more competitive in modeling data having correlations that may lead to singular covariance matrices in GMM, and it outperforms state-of-the-art methods in unsupervised anomaly detection.
MuiDial: Improving Dialogue Disentanglement with Intent-Based Mutual Learning

The main goal of dialogue disentanglement is to separate the mixed utterances from a chat slice into independent dialogues. Existing models often utilize either an utterance-to-utterance (U2U) prediction to determine whether two utterances that have the “reply-to” relationship belong to one dialogue, or an utterance-to-thread (U2T) prediction to determine which dialogue-thread a given utterance should belong to. Inspired by mutual leaning, we propose MuiDial, a novel dialogue disentanglement model, to exploit the intent of each utterance and feed the intent to a mutual learning U2U-U2T disentanglement model. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness and generalizability of our approach.
Information Injection to Deep Learning Solutions in Knowledge Transfer

Nowadays, with the thrive of AutoML techniques, it is expected that Machine Learning algorithms will work well without any human intervention. This is why the biggest focus is on introducing new neural network architectures, especially those capable of learning to learn. Recently, the Data-Centric AI Challenge was proposed by Andrew Ng whose goal was to change the paradigm and instead of having a fixed dataset and modifying the model, now the model is fixed and the data is preprocessed so that the model results in the best performance. In my thesis, I would like to focus on another approach, where I would not modify the given data nor introduce new architectures, instead, I would like to propose new ways of injecting additional information into knowledge transfer models to increase their performance.
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning

In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently remedied it via encoding invariances from raw pixels. Nevertheless, we empirically find that not all samples are equally important and hence simply injecting more augmented inputs may instead cause instability in Q-learning. In this paper, we approach this problem systematically by developing a model-agnostic Contrastive-Curiosity-driven Learning Framework (CCLF), which can fully exploit sample importance and improve learning efficiency in a self-supervised manner. Facilitated by the proposed contrastive curiosity, CCLF is capable of prioritizing the experience replay, selecting the most informative augmented inputs, and more importantly regularizing the Q-function as well as the encoder to concentrate more on under-learned data. Moreover, it encourages the agent to explore with a curiosity-based reward. As a result, the agent can focus on more informative samples and learn representation invariances more efficiently, with significantly reduced augmented inputs. We apply CCLF to several base RL algorithms and evaluate on the DeepMind Control Suite, Atari, and MiniGrid benchmarks, where our approach demonstrates superior sample efficiency and learning performances compared with other state-of-the-art methods. Our code is available at https://github.com/csun001/CCLF.
Enhancing Unsupervised Domain Adaptation via Semantic Similarity Constraint for Medical Image Segmentation

This work proposes a novel unsupervised cross-modality adaptive segmentation method for medical images to tackle the performance degradation caused by the severe domain shift when neural networks are being deployed to unseen modalities. The proposed method is an end-2-end framework, which conducts appearance transformation via a domain-shared shallow content encoder and two domain-specific decoders. The feature extracted from the encoder is enhanced to be more domain-invariant by a similarity learning task using the proposed Semantic Similarity Mining (SSM) module which has a strong help of domain adaptation. The domain-invariant latent feature is then fused into the target domain segmentation sub-network, trained using the original target domain images and the translated target images from the source domain in the framework of adversarial training. The adversarial training is effective to narrow the remaining gap between domains in semantic space after appearance alignment. Experimental results on two challenging datasets demonstrate that our method outperforms the state-of-the-art approaches.
Efficient Budgeted Graph Search

Iterative Budgeted Exponential Search (IBEX) is a general search algorithm that can limit the number of re-expansions performed in common problems like iterative-deepening tree search and search with inconsistent heuristics. IBEX has been adapted into a specific tree algorithm, Budgeted Tree Search (BTS), which behaves like IDA* when the problem instance is well-behaved but keeps the worst-case guarantees when problems are not well-behaved. The analogous algorithms on graphs, Budgeted Graph Search (BGS), do not have these same properties. This paper reformulates BGS into Efficient Budgeted Graph Search (BGSe), showing how to implement the algorithm so that it behaves identically to A* when problems are well-behaved, and retains the best-case performance otherwise. Experimental results validate the performance of BGSe on a range of theoretical and practical problem instances.
Learning Label Initialization for Time-Dependent Harmonic Extension.

Node classification on graphs can be formulated as the Dirichlet problem on graphs where the signal is given at the labeled nodes, and the harmonic extension is done on the unlabeled nodes. This paper considers a time-dependent version of the Dirichlet problem on graphs and shows how to improve its solution by learning the proper initialization vector on the unlabeled nodes. Further, we show that the improved solution is at par with state-of-the-art methods used for node classification. Finally, we conclude this paper by discussing the importance of parameter t, pros, and future directions.
Self-Supervised Mutual Learning for Dynamic Scene Reconstruction of Spiking Camera

Mimicking the sampling mechanism of the primate fovea, a retina-inspired vision sensor named spiking camera has been developed, which has shown great potential for capturing high-speed dynamic scenes with a sampling rate of 40,000 Hz. Unlike conventional digital cameras, the spiking camera continuously captures photons and outputs asynchronous binary spikes with various inter-spike intervals to record dynamic scenes. However, how to reconstruct dynamic scenes from asynchronous spike streams remains challenging. In this work, we propose a novel pretext task to build a self-supervised reconstruction framework for spiking cameras. Specifically, we utilize the blind-spot network commonly used in self-supervised denoising tasks as our backbone, and perform self-supervised learning by constructing proper pseudo-labels. In addition, in view of the poor scalability and insufficient information utilization of the blind-spot network, we present a mutual learning framework to improve the overall performance of the network through mutual distillation between a non-blind-spot network and a blind-spot network. This also enables the network to bypass constraints of the blind-spot network, allowing state-of-the-art modules to be used to further improve performance. The experimental results demonstrate that our methods evidently outperform previous unsupervised spiking camera reconstruction methods and achieve desirable results compared with supervised methods.
Deep Neural Network for Click-Through Rate Prediction

Predicted click-through rate is one of the most frequently used criteria to determine the effectiveness of an ads. In advertising production, click-through predictions are very influential for the company that places the ads. Click-through rates need to be predicted accurately because accurate prediction results determine whether the click-through rate is exactly clicked or not by the viewing consumer. Predicted click-through can be done on advertising and social network datasets. The use of these two datasets is intended to make the comparison results more convincing from the proposed method. The purpose of this study is to compare two advertising and social network datasets, by proposing the application of the Deep Neural Network (DNN) model by testing hyperparameter variations to find a better architecture in predicting whether or not users click on an advertisement. The hyperparameter variations include 3 variations of the hidden layer, 2 variations of the activation function, namely ReLuand Sigmoid, 3 variations of the optimization (RMSprop, Adam, and Adagrad),and 3 variations of the learning rate (0.1, 0.01, and 0.001). The results of experiments conducted with the advertising parameter dataset with hidden layer of 3, learning rate of 0.01,and Adam optimization with an accuracy value of 99.90%, AUC of 99.90% and Precision-Recallof99.89% while the data for social network ads parameters with hidden layer of 5, learning rate of 0.1 and Adam optimization with accuracy of 92.25%, AUC of 92.72%,andPrecision-Recallof 89.70%.
Landmark Heuristics for Lifted Classical Planning

While state-of-the-art planning systems need a grounded (propositional) task representation, the input model is provided "lifted", specifying predicates and action schemas with variables over a finite object universe. The size of the grounded model is exponential in predicate/action-schema arity, limiting applicability to cases where it is small enough. Recent work has taken up this challenge, devising an effective lifted forward search planner as basis for lifted heuristic search, as well as a variety of lifted heuristic functions based on the delete relaxation. Here we add a novel family of lifted heuristic functions, based on landmarks. We design two methods for landmark extraction in the lifted setting. The resulting heuristics exhibit performance advantages over previous heuristics in several benchmark domains. Especially the combination with lifted delete relaxation heuristics to a LAMA-style planner yields good results, beating the previous state of the art in lifted planning.
Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning

Vision-based reinforcement learning requires efficient and robust representations of image-based observations, especially when the images contain distracting (task-irrelevant) elements such as shadows, clouds, and light. It becomes more important if those distractions are not exposed during training. We design a Self-Predictive Dynamics (SPD) method to extract task-relevant features efficiently, even in unseen observations after training. SPD uses weak and strong augmentations in parallel, and learns representations by predicting inverse and forward transitions across the two-way augmented versions. In a set of MuJoCo visual control tasks and an autonomous driving task (CARLA), SPD outperforms previous studies in complex observations, and significantly improves the generalization performance for unseen observations. Our code is available at https://github.com/unigary/SPD.
Explicit Alignment Learning for Neural Machine Translation

Even though neural machine translation (NMT) has become the state-of-the-art solution for end-to-end translation, it still suffers from a lack of translation interpretability, which may be conveniently enhanced by explicit alignment learning (EAL), as performed in traditional statistical machine translation (SMT). To provide the benefits of both NMT and SMT, this paper presents a novel model design that enhances NMT with an additional training process for EAL, in addition to the end-to-end translation training. Thus, we propose two approaches an explicit alignment learning approach, in which we further remove the need for the additional alignment model, and perform embedding mixup with the alignment based on encoder--decoder attention weights in the NMT model. We conducted experiments on both small-scale (IWSLT14 De-&gt;En and IWSLT13 Fr-&gt;En) and large-scale (WMT14 En-&gt;De, En-&gt;Fr, WMT17 Zh-&gt;En) benchmarks. Evaluation results show that our EAL methods significantly outperformed strong baseline methods, which shows the effectiveness of EAL. Further explorations show that the translation improvements are due to a better spatial alignment of the source and target language embeddings. Our method improves translation performance without the need to increase model parameters and training data, which verifies that the idea of incorporating techniques of SMT into NMT is worthwhile.
Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language

Through this paper, we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study. The dataset is a pioneer dataset for the Nigerian Sign Language and was created in collaboration with relevant stakeholders. We pre-processed the data in readiness for two different object detection models and a classification model and employed diverse evaluation metrics to gauge model performance on sign-language to text conversion tasks. Finally, we convert the predicted sign texts to speech and deploy the best performing model in a lightweight application that works in real-time and achieves impressive results converting sign words/phrases to text and subsequently, into speech.
Filtration-Enhanced Graph Transformation

Graph kernels and graph neural networks (GNNs) are widely used for the classification of graph data. However, many existing graph kernels and GNNs have limited expressive power, because they cannot distinguish graphs if the classic 1-dimensional Weisfeiler-Leman (1-WL) algorithm does not distinguish them. To break the 1-WL expressiveness barrier, we propose a novel method called filtration-enhanced graph transformation, which is based on a concept from the area of topological data analysis. In a nutshell, our approach first transforms each original graph into a filtration-enhanced graph based on a certain pre-defined filtration operation, and then uses the transformed graphs as the inputs for graph kernels or GNNs. The striking feature of our approach is that it is a plug-in method and can be applied in any graph kernel and GNN to enhance their expressive power. We theoretically and experimentally demonstrate that our solutions exhibit significantly better performance than the state-of-the art solutions for graph classification tasks.
SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities

As deep learning advances, there is an ever-growing demand for models capable of synthesizing information from multi-modal resources to address the complex tasks raised from real-life applications. Recently, many large multi-modal datasets have been collected, on which researchers actively explore different methods of fusing multi-modal information. However, little attention has been paid to quantifying the contribution of different modalities within the proposed models. In this paper, we propose the SHapley vAlue-based PErceptual (SHAPE) scores that measure the marginal contribution of individual modalities and the degree of cooperation across modalities. Using these scores, we systematically evaluate different fusion methods on different multi-modal datasets for different tasks. Our experiments suggest that for some tasks where different modalities are complementary, the multi-modal models still tend to use the dominant modality alone and ignore the cooperation across modalities. On the other hand, models learn to exploit cross-modal cooperation when different modalities are indispensable for the task. In this case, the scores indicate it is better to fuse different modalities at relatively early stages. We hope our scores can help improve the understanding of how the present multi-modal models operate on different modalities and encourage more sophisticated methods of integrating multiple modalities.
Anchors Selection for Cross-lingual Embedding Alignment through Time

In recent years, vector representations of words have proven to be extremely useful across a wide range of NLP applications. Because of the broad interest in the topic, it became essential to answer the following question: is it possible to align different embeddings, in order to compare terms belonging to different vector spaces and their relations? While embedding alignment received considerable attention in the literature, how to find the best anchors for this process is still an open problem; in this paper, we propose an unsupervised, automatic method to select words belonging to different corpora that are close from a semantic point of view, and can be used as anchors for aligning their respective embedding spaces.
Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback

We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N recommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learning objectives (such as pointwise or pairwise loss) with the benchmark metrics for ranking quality (e.g. Average Precision, AP), resulting in sub-optimal accuracy. To this end, we propose a Discrete Listwise Personalized Ranking (DLPR) model that optimizes AP under discrete constraints for fast and accurate top-N recommendation. To resolve the challenging DLPR problem, we devise an efficient algorithm that can directly learn binary codes in a relaxed continuous solution space. Specifically, theoretical analysis shows that the optimal solution to the relaxed continuous optimization problem is exactly the same as that of the original discrete DLPR problem. Through extensive experiments on two real-world datasets, we show that DLPR consistently surpasses state-of-the-art hashing methods for top-N recommendation.
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs significant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly configure and periodically update. Security negligence and human errors often lead to misconfiguring IAM policies which may open a backdoor for attackers. To address these challenges, first, we develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP). We identify reducing dormant permissions of cloud users as an optimality criterion, which intuitively implies minimizing unnecessary datastore access permissions. Second, to make IAM policies interpretable, we use graph representation learning applied to historical access patterns of users to augment our CP model with similarity constraints: similar users should be grouped together and share common IAM policies. Third, we describe multiple attack models and show that our optimized IAM policies significantly reduce the impact of security attacks using real data from 8 commercial organizations, and synthetic instances.
Explaining the Behaviour of Hybrid Systems with PDDL+ Planning

The aim of this work is to explain the observed behaviour of a hybrid system (HS). The explanation problem is cast as finding a trajectory of the HS that matches some observations. By using the formalism of hybrid automata (HA), we characterize the explanations as the language of a network of HA that comprises one automaton for the HS and another one for the observations, thus restricting the behaviour of the HS exclusively to trajectories that explain the observations. We observe that this problem corresponds to a reachability problem in model-checking, but that state-of-the-art model checkers struggle to find concrete trajectories. To overcome this issue we provide a formal mapping from HA to PDDL+ and show how to use an off-the-shelf automated planner. An experimental analysis over domains with piece-wise constant, linear and nonlinear dynamics reveals that the proposed PDDL+ approach is much more efficient than solving directly the explanation problem with model-checking solvers.
Clickbait Detection via Contrastive Variational Modelling of Text and Label

Clickbait refers to deliberately created sensational or deceptive text for tricking readers into clicking, which severely hurts the web ecosystem. With a growing number of clickbaits on social media, developing automatic detection methods becomes essential. Nonetheless, the performance of existing neural classifiers is limited due to the underutilization of small labelled datasets. Inspired by related pedagogy theories that learning to write can promote comprehension ability, we propose a novel Contrastive Variational Modelling (CVM) framework to exploit the labelled data better. CVM models the conditional distributions of text and clickbait labels by predicting labels from text and generating text from labels simultaneously with Variational AutoEncoder and further differentiates the learned spaces under each label by a mixed contrastive learning loss. In this way, CVM can capture more underlying textual properties and hence utilize label information to its full potential, boosting detection performance. We theoretically demonstrate CVM as learning a joint distribution of text, clickbait label, and latent variable. Experiments on three clickbait detection datasets show our method's robustness to inadequate and biased labels, outperforming several recent strong baselines.
Computing Concept Referring Expressions for Queries on Horn ALC Ontologies

Classical instance queries over an ontology only consider explicitly named individuals. Concept referring expressions (CREs) also allow for returning answers in the form of concepts that describe implicitly given individuals in terms of their relation to an explicitly named one. Existing approaches, e.g., based on tree automata, can neither be integrated into state-of-the-art OWL reasoners nor are they directly amenable for an efficient implementation. To address this, we devise a novel algorithm that uses highly optimized OWL reasoners as a black box. In addition to the standard criteria of singularity and certainty for CREs, we devise and consider the criterion of uniqueness of CREs for Horn ALC ontologies. The evaluation of our prototypical implementation shows that computing CREs for the most general concept (Top) can be done in less than one minute for ontologies with thousands of individuals and concepts.