Reviewerly/exHarmony huggingface dataset:
A Synopsis of Monocular Depth Estimation

Depth information is necessary for automated devices or software to accomplish tasks which require the knowledge of the surrounding environment without error. Monocular depth estimation uses a single 2D image to estimate depth, which makes it an exigent method. However, recent studies in monocular depth estimation based on convolutional neural networks indicate favorable performance in accuracy. Research on monocular depth training traverses through increasingly complex model architectures, and optimization of loss functions, all of these have recently helped to close the gap between the accuracy percentage of the traditional methods and trained models. In this review paper, we survey numerous existing literature on monocular depth estimation along with various datasets, as well as several supervised, unsupervised and semi-supervised algorithms. Furthermore, we figured out the drawbacks of the existing traditional methods and discussed contemporary methods using convolutional neural networks which may already have improved upon the said drawbacks. Lastly, we present our own findings on further scope in monocular depth estimation.
Dynamic Causal Bayesian Optimization

This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time. This problem arises in a variety of domains e.g. system biology and operational research. Dynamic Causal Bayesian Optimization (DCBO) brings together ideas from sequential decision making, causal inference and Gaussian process (GP) emulation. DCBO is useful in scenarios where all causal effects in a graph are changing over time. At every time step DCBO identifies a local optimal intervention by integrating both observational and past interventional data collected from the system. We give theoretical results detailing how one can transfer interventional information across time steps and define a dynamic causal GP model which can be used to quantify uncertainty and find optimal interventions in practice. We demonstrate how DCBO identifies optimal interventions faster than competing approaches in multiple settings and applications.
Uncertainty Quantification and Deep Ensembles

Deep Learning methods are known to suffer from calibration issues: they typically produce over-confident estimates. These problems are exacerbated in the low data regime. Although the calibration of probabilistic models is well studied, calibrating extremely over-parametrized models in the low-data regime presents unique challenges. We show that deep-ensembles do not necessarily lead to improved calibration properties. In fact, we show that standard ensembling methods, when used in conjunction with modern techniques such as mixup regularization, can lead to less calibrated models. In this text, we examine the interplay between three of the most simple and commonly used approaches to leverage deep learning when data is scarce: data-augmentation, ensembling, and post-processing calibration methods. We demonstrate that, although standard ensembling techniques certainly help to boost accuracy, the calibration of deep-ensembles relies on subtle trade-offs. Our main finding is that calibration methods such as temperature scaling need to be slightly tweaked when used with deep-ensembles and, crucially, need to be executed after the averaging process. Our simulations indicate that, in the low data regime, this simple strategy can halve the Expected Calibration Error (ECE) on a range of benchmark classification problems when compared to standard deep-ensembles.
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection.

The paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus. Previous works have tried to mitigate this problem with additional pre-training, either on the downstream corpus itself when it is large enough, or on a manually curated unlabeled corpus of a similar domain. In this paper, we address the problem for the case when the downstream corpus is too small for additional pre-training. We propose TADPOLE, a task adapted pre-training framework based on data selection techniques adapted from Domain Adaptation. We formulate the data selection as an anomaly detection problem that unlike existing methods works well when the downstream corpus is limited in size. It results in a scalable and efficient unsupervised technique that eliminates the need for any manual data curation. We evaluate our framework on eight tasks across four different domains: Biomedical, Computer Science, News, and Movie reviews, and compare its performance against competitive baseline techniques from the area of Domain Adaptation. Our framework outperforms all the baseline methods. On small datasets with less than 5K training examples, we get a gain of 1.82% in performance with additional pre-training for only 5% steps compared to the originally pre-trained models. It also compliments some of the other techniques such as data augmentation known for boosting performance when downstream corpus is small; highest performance is achieved when data augmentation is combined with task adapted pre-training.
Super-Class Mixup for Adjusting Training Data

Mixup is one of data augmentation methods for image recognition task, which generate data by mixing two images. Mixup randomly samples two images from training data without considering the similarity of these data and classes. This random sampling generates mixed samples with low similarities, which makes a network training difficult and complicated. In this paper, we propose a mixup considering super-class. Super-class is a superordinate categorization of object classes. The proposed method tends to generate mixed samples with the almost same mixing ratio in the case of the same super-class. In contrast, given two images having different super-classes, we generate samples largely containing one image’s data. Consequently, a network can train the features between similar object classes. Furthermore, we apply the proposed method into a mutual learning framework, which would improve the network output used for mutual learning. The experimental results demonstrate that the proposed method improves the recognition accuracy on a single model training and mutual training. And, we analyze the attention maps of networks and show that the proposed method also improves the highlighted region and makes a network correctly focuses on the target object.
KnowMAN: Weakly Supervised Multinomial Adversarial Networks

The absence of labeled data for training neu-ral models is often addressed by leveragingknowledge about the specific task, resultingin heuristic but noisy labels. The knowledgeis captured in labeling functions, which detectcertain regularities or patterns in the trainingsamples and annotate corresponding labels fortraining. This process of weakly supervisedtraining may result in an over-reliance on thesignals captured by the labeling functions andhinder models to exploit other signals or togeneralize well. We propose KnowMAN, anadversarial scheme that enables to control in-fluence of signals associated with specific la-beling functions. KnowMAN forces the net-work to learn representations that are invari-ant to those signals and to pick up other sig-nals that are more generally associated with anoutput label. KnowMAN strongly improvesresults compared to direct weakly supervisedlearning with a pre-trained transformer lan-guage model and a feature-based baseline
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers.

Recent work demonstrates that deep neural networks trained using Empirical Risk Minimization (ERM) can generalize under distribution shift, outperforming specialized training algorithms for domain generalization. The goal of this paper is to further understand this phenomenon. In particular, we study the extent to which the seminal domain adaptation theory of Ben-David et al. (2007) explains the performance of ERMs. Perhaps surprisingly, we find that this theory does not provide a tight explanation of the out-of-domain generalization observed across a large number of ERM models trained on three popular domain generalization datasets. This motivates us to investigate other possible measures-that, however, lack theory-which could explain generalization in this setting. Our investigation reveals that measures relating to the Fisher information, predictive entropy, and maximum mean discrepancy are good predictors of the out-of-distribution generalization of ERM models. We hope that our work helps galvanize the community towards building a better understanding of when deep networks trained with ERM generalize out-of-distribution.
SEMANTIC SEGMENTATION FOR SELF DRIVING CARS

This paper proposes a novel approach for Semantic segmentation which is one of the biggest challenge increasing in an order and have been making humans hold keen active interest to result in fast and accurate semantic segmentation. Whereas At present, we are trying to solve this problem of semantic segmentation using the segnet which makes its more accurate interms of accuracy, computational time, and inference time. and here we are using segnet model to take this to the next level which includes max-pooling, Batch normalization techniques to map low-resolution features to input resolution for pixelwise classification and the architecture here consists of an encoder which takes the input image and is identical to 13 convolutional layers and a decoder that uses segnet followed by pixel-wise classification layer. and also when compared with other architectures segnet provides good performance with competitive inference time and most efficient memory. So, therefore here we are presenting deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet.
Work-in-Progress: Detecting Deepfake Videos by Visual-Audio Synchronism

Different to traditional works on frame-level features and temporal characteristics, we propose a deepfake video detection method based on visual-audio synchronism, which compares the audio stream and the visual stream by an improved siamese neural network. We combine the audio stream and visual stream as a 2-channel input and design a 2-branches network to achieve the visual-audio synchronism detection. Preliminary experiments demonstrate the efficiency of the proposed method, which can achieve the highest accuracy compared with other existing methods.
A Unified Approach to Discourse Relation Classification in nine Languages

This paper presents efforts to solve the shared task on discourse relation classification (disrpt task 3).The intricate prediction task aims to predict a large number of classes from the Rhetorical Structure Theory (RST) framework for nine target languages.Labels include discourse relations such as background, condition, contrast and elaboration.We present an approach using euclidean distance between sentence embeddings that were extracted using multlingual sentence BERT (sBERT) and directionality as features.The data was combined into five classes which were used for initial prediction.The second classification step predicts the target classes.We observe a substantial difference in results depending on the number of occurrences of the target label in the training data.We achieve the best results on Chinese, where our system achieves 70 % accuracy on 20 labels.
Implementation of Value-Decomposition Networks Based Algorithm for Multiple Units Combat in StarCraft

In this paper, we use a multi-agent reinforcement learning (MARL) method to solve multiple units combat problem in StarCraft. A group of agents are trained under the value-decomposition networks (VDN) structure which can make them learn to cooperation in the combat scenario. By applying the centralized training and decentralized execution (CTDE) approach, each agent in the group can take behaviours from its own observation without communicate with others. In order to make the cooperation more efficient, the parameter and learning policy are both shared among the units. Finally, the experimental results show that the controlled units can use cooperative strategies to defeat the built-in opponent in a StarCraft combat scenario.
Meta-Learning Reliable Priors in the Function Space

Meta-Learning promises to enable more data-efficient inference by harnessing previous experience from related learning tasks. While existing meta-learning methods help us to improve the accuracy of our predictions in face of data scarcity, they fail to supply reliable uncertainty estimates, often being grossly overconfident in their predictions. Addressing these shortcomings, we introduce a novel meta-learning framework, called F-PACOH, that treats meta-learned priors as stochastic processes and performs meta-level regularization directly in the function space. This allows us to directly steer the probabilistic predictions of the meta-learner towards high epistemic uncertainty in regions of insufficient meta-training data and, thus, obtain well-calibrated uncertainty estimates. Finally, we showcase how our approach can be integrated with sequential decision making, where reliable uncertainty quantification is imperative. In our benchmark study on meta-learning for Bayesian Optimization (BO), F-PACOH significantly outperforms all other meta-learners and standard baselines. Even in a challenging lifelong BO setting, where optimization tasks arrive one at a time and the meta-learner needs to build up informative prior knowledge incrementally, our proposed method demonstrates strong positive transfer.
Automatic medieval charters structure detection : A Bi-LSTM linear segmentation approach

This paper presents a model aiming to automatically detect sections in medieval Latin charters. These legal sources are some of the most important sources for medieval studies as they reflect economic and social dynamics as well as legal and institutional writing practices. An automatic linear segmentation can greatly facilitate charter indexation and speed up the recovering of evidence to support historical hypothesis by the means of granular inquiries on these raw, rarely structured sources. Our model is based on a Bi-LSTM approach using a final CRF-layer and was trained using a large, annotated collection of medieval charters (4,700 documents) coming from Lombard monasteries: the CDLM corpus (11th-12th centuries). The evaluation shows a high performance in most sections on the test-set and on an external evaluation corpus consisting of the Montecassino abbey charters (10th-12th centuries). We describe the architecture of the model, the main problems related to the treatment of medieval Latin and formulaic discourse, and we discuss some implications of the results in terms of record-keeping practices in High Middle Ages.
Low-light image restoration using discrete cosine transform and conditional generative adversarial network

In the process of low-light imaging, some part of useful information of an image is overwhelmed by a noise.When interference is large, the signal-to-noise ratio (SNR) detected in a system is reduced to a very low level.We study the lowlight imaging under condition when the detection SNR is equal to 1 dB.Taking into account that the noise is often located in the high-frequency spectral part, we use discrete cosine transform (DCT) to remove the noise or, at least, filter out its some part.Then we use an algorithm of conditional generative adversarial network (CGAN) to improve the image quality.The simulation results testify that the DCT and CGAN algorithms combined together improve significantly the restoration results and the final quality of images.The latter is high enough, with the average peak SNR being higher than 22 dB and the structural similarity index measure amounting to about 0.8.
Don’t Handicap AI without Explicit Knowledge : Keynote 3

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Knowledge representation as expert system rules or using frames and variety of logics, played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning are part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.
Quantifying entanglement in cluster states built with error-prone interactions

Measurement-based quantum computing is an alternative paradigm to the circuit-based model. This approach can be advantageous in certain scenarios, such as when read-out is fast and accurate, but two-qubit gates realized via inter-particle interactions are slow and can be parallelized to efficiently create a cluster state. However, understanding how two-qubit errors impact algorithm accuracy and developing experimentally viable approaches to characterize cluster-state fidelity are outstanding challenges. Here, we consider one-dimensional cluster states built from controlled phase, Ising, and XY interactions with slow two-qubit error in the interaction strength, consistent with error models of interactions found in a variety of qubit architectures. We detail an experimentally viable teleportation fidelity that offers a measure of the impact of these errors on the cluster state. Our fidelity calculations show that the error has a distinctly different impact depending on the underlying interaction used for the two-qubit entangling gate. In particular, the Ising and XY interactions can allow perfect teleportation through the cluster state even with large errors, but the controlled phase interaction does not. Nonetheless, we find that teleportation through cluster state chains of size $N$ has a maximum two-qubit error for teleportation along a quantum channel that decreases as ${N}^{\ensuremath{-}1/2}$. To enable the construction of larger cluster states, we design lowest-order refocusing pulses for correcting these slow errors in the interaction strength. Our work generalizes to higher-dimensional cluster states and sets the stage for experiments to monitor the growth of entanglement in cluster states built from error-prone interactions.
Towards Realistic Few-Shot Relation Extraction

In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification is solved. In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types.
A Novel Approach of Multi-Objective Based Feature Selection Method for Face Recognition

In Face Recognition (FR), image representation is a longstanding practice. FR is complex due to the rich context and a large number of details in photographs. As a result, the image features must be correctly extracted and understood. It’s challenging to come up with decent picture descriptors. One of the significant issues in pattern recognition is the dimensionality of extracted feature vectors; hence feature selection becomes a necessity. Feature selection is an essential and necessary step for use in bio-metrics as bio-metrics tends to degrade over time. Thus to solve this problem, a multi-objective feature selection method has been proposed. The prime objectives of the feature selection method were to minimize the variance within the same group so that it is homogeneous while maximizing the variance between the groups to be well separated. Feature selection is coupled with the three widely used Feature Extraction(FE) methods Principle Component Analysis (PCA), Wavelet Transform (WT), and Local Binary Pattern (LBP). A maximum of 95.03 percent recognition rate was achieved when wavelet FE was used combined with the hybrid FS method. In contrast, a 94.21 percent recognition rate was achieved when the feature selection method was not used.
Design of Small Unmanned Aerial Vehicle Navigation Algorithm Based on Control PID

The purpose of the application of UAV navigation system is to accurately judge the position of UAV in horizontal space and ensure that it can fly according to the expected set course. Although the inertia-satellite (GPS) navigation system design proposed in the past is in line with the development needs of the traditional market, in the new era, as small unmanned aerial vehicles enter the field of vision of researchers, experts begin to carry out a new design of the countermeasures and algorithms of the navigation control system. Therefore, this paper analyzes how to design and implement the navigation algorithm of small unmanned aerial vehicles (U AVs) based on expert PID combined with the design method of expert PID navigation control law while understanding the design scheme and basic principles of the existing navigation algorithm of small unmanned aerial vehicles (UAVs).