Reviewerly/exHarmony huggingface dataset:
Region Similarity Representation Learning

We present Region Similarity Representation Learning (ReSim), a new approach to self-supervised representation learning for localization-based tasks such as object detection and segmentation. While existing work has largely focused on solely learning global representations for an entire image, ReSim learns both regional representations for localization as well as semantic image-level representations. ReSim operates by sliding a fixed-sized window across the overlapping area between two views (e.g., image crops), aligning these areas with their corresponding convolutional feature map regions, and then maximizing the feature similarity across views. As a result, ReSim learns spatially and semantically consistent feature representation throughout the convolutional feature maps of a neural network. A shift or scale of an image region, e.g., a shift or scale of an object, has a corresponding change in the feature maps; this allows downstream tasks to leverage these representations for localization. Through object detection, instance segmentation, and dense pose estimation experiments, we illustrate how ReSim learns representations which significantly improve the localization and classification performance compared to a competitive MoCo-v2 baseline: $+2.7$ AP$^{\text{bb}}_{75}$ VOC, $+1.1$ AP$^{\text{bb}}_{75}$ COCO, and $+1.9$ AP$^{\text{mk}}$ Cityscapes. Code and pre-trained models are released at: \url{https://github.com/Tete-Xiao/ReSim}
Improving Authorship Verification using Linguistic Divergence

We propose an unsupervised solution to the Authorship Verification task that utilizes pre-trained deep language models to compute a new metric called DV-Distance. The proposed metric is a measure of the difference between the two authors comparing against pre-trained language models. Our design addresses the problem of non-comparability in authorship verification, frequently encountered in small or cross-domain corpora. To the best of our knowledge, this paper is the first one to introduce a method designed with non-comparability in mind from the ground up, rather than indirectly. It is also one of the first to use Deep Language Models in this setting. The approach is intuitive, and it is easy to understand and interpret through visualization. Experiments on four datasets show our methods matching or surpassing current state-of-the-art and strong baselines in most tasks.
Unified Batch All Triplet Loss for Visible-Infrared Person  Re-identification

Visible-Infrared cross-modality person re-identification (VI-ReID), whose aim is to match person images between visible and infrared modality, is a challenging cross-modality image retrieval task. Batch Hard Triplet loss is widely used in person re-identification tasks, but it does not perform well in the Visible-Infrared person re-identification task. Because it only optimizes the hardest triplet for each anchor image within the mini-batch, samples in the hardest triplet may all belong to the same modality, which will lead to the imbalance problem of modality optimization. To address this problem, we adopt the batch all triplet selection strategy, which selects all the possible triplets among samples to optimize instead of the hardest triplet. Furthermore, we introduce Unified Batch All Triplet loss and Cosine Softmax loss to collaboratively optimize the cosine distance between image vectors. Similarly, we rewrite the Hetero Center Triplet loss, which is proposed for VI-ReID task, into a batch all form to improve model performance. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.
Reinforcement Learning Trajectory Generation and Control for Aggressive  Perching on Vertical Walls with Quadrotors

Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. The trained low-level reinforcement learning controller would manipulate quadrotor toward the perching point in simulation environment. Once the simulated quadrotor has successfully perched, the relative trajectory information in simulation will be sent to tracking controller on real quadrotor and start the actual perching task. Generating feasible trajectories via the trained reinforcement learning controller requires less time, and the traditional trajectory tracking controller could easily be modified to control the quadrotor and mathematically analysis its stability and robustness. We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with high precision.
Limits of Probabilistic Safety Guarantees when Considering Human  Uncertainty

When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Many data-driven methods have explored learning probabilistic bounds over human agents' trajectories (i.e. confidence tubes that contain trajectories with probability $\delta$), which can then be used to guarantee safety with probability $1-\delta$. However, almost all existing works consider $\delta \geq 0.001$. The purpose of this paper is to argue that (1) in safety-critical applications, it is necessary to provide safety guarantees with $\delta < 10^{-8}$, and (2) current learning-based methods are ill-equipped to compute accurate confidence bounds at such low $\delta$. Using human driving data (from the highD dataset), as well as synthetically generated data, we show that current uncertainty models use inaccurate distributional assumptions to describe human behavior and/or require infeasible amounts of data to accurately learn confidence bounds for $\delta \leq 10^{-8}$. These two issues result in unreliable confidence bounds, which can have dangerous implications if deployed on safety-critical systems.
Distilling Causal Effect of Data in Class-Incremental Learning

We propose a causal framework to explain the catastrophic forgetting in Class-Incremental Learning (CIL) and then derive a novel distillation method that is orthogonal to the existing anti-forgetting techniques, such as data replay and feature/label distillation. We first 1) place CIL into the framework, 2) answer why the forgetting happens: the causal effect of the old data is lost in new training, and then 3) explain how the existing techniques mitigate it: they bring the causal effect back. Based on the framework, we find that although the feature/label distillation is storage-efficient, its causal effect is not coherent with the end-to-end feature learning merit, which is however preserved by data replay. To this end, we propose to distill the Colliding Effect between the old and the new data, which is fundamentally equivalent to the causal effect of data replay, but without any cost of replay storage. Thanks to the causal effect analysis, we can further capture the Incremental Momentum Effect of the data stream, removing which can help to retain the old effect overwhelmed by the new data effect, and thus alleviate the forgetting of the old class in testing. Extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Sub&Full, show that the proposed causal effect distillation can improve various state-of-the-art CIL methods by a large margin (0.72%--9.06%).
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement  Learning Agents

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR for decentralized execution. Then, to handle the temporal nature of the stochastic outcomes during executions, we propose a dynamic risk level predictor for risk level tuning. Finally, we optimize the CVaR policies with CVaR values used to estimate the target in TD error during centralized training and the CVaR values are used as auxiliary local rewards to update the local distribution via Quantile Regression loss. Empirically, we show that our method significantly outperforms state-of-the-art methods on challenging StarCraft II tasks, demonstrating enhanced coordination and improved sample efficiency.
Tackling Instance-Dependent Label Noise via a Universal Probabilistic  Model

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning \mbox{methods} with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing an alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts.
Style Normalization and Restitution for Domain Generalization and  Adaptation

For many practical computer vision applications, the learned models usually have high performance on the datasets used for training but suffer from significant performance degradation when deployed in new environments, where there are usually style differences between the training images and the testing images. An effective domain generalizable model is expected to be able to learn feature representations that are both generalizable and discriminative. In this paper, we design a novel Style Normalization and Restitution module (SNR) to simultaneously ensure both high generalization and discrimination capability of the networks. In the SNR module, particularly, we filter out the style variations (e.g, illumination, color contrast) by performing Instance Normalization (IN) to obtain style normalized features, where the discrepancy among different samples and domains is reduced. However, such a process is task-ignorant and inevitably removes some task-relevant discriminative information, which could hurt the performance. To remedy this, we propose to distill task-relevant discriminative features from the residual (i.e, the difference between the original feature and the style normalized feature) and add them back to the network to ensure high discrimination. Moreover, for better disentanglement, we enforce a dual causality loss constraint in the restitution step to encourage the better separation of task-relevant and task-irrelevant features. We validate the effectiveness of our SNR on different computer vision tasks, including classification, semantic segmentation, and object detection. Experiments demonstrate that our SNR module is capable of improving the performance of networks for domain generalization (DG) and unsupervised domain adaptation (UDA) on many tasks. Code are available at https://github.com/microsoft/SNR.
Outcome Prediction from Behaviour Change Intervention Evaluations using a Combination of Node and Word Embedding.

Findings from randomized controlled trials (RCTs) of behaviour change interventions encode much of our knowledge on intervention efficacy under defined conditions. Predicting outcomes of novel interventions in novel conditions can be challenging, as can predicting differences in outcomes between different interventions or different conditions. To predict outcomes from RCTs, we propose a generic framework of combining the information from two sources - i) the instances (comprised of surrounding text and their numeric values) of relevant attributes, namely the intervention, setting and population characteristics of a study, and ii) abstract representation of the categories of these attributes themselves. We demonstrate that this way of encoding both the information about an attribute and its value when used as an embedding layer within a standard deep sequence modeling setup improves the outcome prediction effectiveness.
Multi-task Transfer Learning for Bayesian Network Structures

We consider the interest of leveraging information between related tasks for learning Bayesian network structures. We propose a new algorithm called Multi-Task Max-Min Hill Climbing (MT-MMHC) that combines ideas from transfer learning, multi-task learning, constraint-based and search-and-score techniques. This approach consists in two main phases. The first one identifies the most similar tasks and uses their similarity to learn their corresponding undirected graphs. The second one directs the edges with a Greedy Search combined with a Branch-and-Bound algorithm. Empirical evaluation shows that MT-MMHC can yield better results than learning the structures individually or than the state-of-the-Art MT-GS algorithm in terms of structure learning accuracy and computational time.
SLA-Aware Cloud Query Processing with Reinforcement Learning-Based Multi-objective Re-optimization

Query processing on cloud database systems is a challenging problem due to the dynamic cloud environment. In cloud database systems, besides query execution time, users also consider the monetary cost to be paid to the cloud provider for executing queries. Moreover, a Service Level Agreement (SLA) is signed between users and cloud providers before any service is provided. Thus, from the profit-oriented perspective for the cloud providers, query re-optimization is multi-objective optimization that minimizes not only query execution time and monetary cost but also SLA violations. In this paper, we introduce ReOptRL and SLAReOptRL, two novel query re-optimization algorithms based on deep reinforcement learning. Experiments show that both algorithms improve query execution time and query execution monetary cost by 50% over existing algorithms, and SLAReOptRL has the lowest SLA violation rate among all the algorithms.
Unifying Discourse Resources with Dependency Framework

For text-level discourse analysis, there are various discourse schemes but relatively few labeled data, because discourse research is still immature and it is labor-intensive to annotate the inner logic of a text. In this paper, we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by designing semi-automatic methods to convert them into dependency structures. We also implement several benchmark dependency parsers and research on how they can leverage the unified data to improve performance.
Data Security on Internet of Things Devices Using the Public-Key Cryptography Method

The growth of IoT across many industries has resulted in a new set of issues, including data security. Various issues with information theft and hacking in IoT systems are attributed to the system's security flaws. IoT devices could be part of a system that aims to address such issues specifically, and this study focuses on information security in IoT devices, which are similar to microcontrollers with limited resources. First and foremost, it uses the RSA rule to implement public-key cryptography victimization within the device. Then come up with a way for evaluating the tactic's effectiveness and potency. Finally, the findings of this study suggest that using public-key cryptography in IoT devices for information security makes the device practical and cost-effective to figure out even for minor keys.
Knowledge Discovery in Web Usage Patterns Using Pageviews and Data Mining Association Rule

Web browsing and navigation behavior of web users are assessed with web usage patterns. The browsing details that are stored in weblogs consists of the fields namely IP Address, Session, URL, User agent, Status code and Bytes transferred. The three categories of Web mining are Usage mining, Content mining and. Structure mining which are used to analyze usage patterns, content searching and locates the link structure, navigation pattern of the web within the user respectively. The primary data sources for web analysis are server logs, access logs and application server logs. The two most used data abstraction are page view and session abstraction. This work creates different types of page view abstraction and analyzes the web patterns to find the navigation behavior using data mining technique FP-Growth algorithm that generates itemsets and rules with the user accessed pattern. Frequent sets are generated and then association rules are formed with minimum support, confidence. Variant user minsupport values are applied with the dataset to analyze the number of frequent itemsets and association rules for each support value. The proposed analysis creates a multi view of web analysis with variant pageviews and improves the existing FP-Growth algorithm by producing more frequent sets and best rules. This leads to personalize the content of the site as per user needs. This research work is implemented and the result is visualized in Rapid miner tool.
Segmentation and Classification Approach to Improve Breast Cancer Screening

The most common type of cancer among women is breast cancer. The early analysis is pivotal in the treatment interaction. The radiology emotionally supportive network in the indicative interaction permits quicker and more exact radiographic moulding. The point of the work done is to upgrade the consequence of the location and acquire outcomes that are more precise. Division, Pre-Processing, Extraction and Classification of Feature table and different significant computations were performed. Recent studies have developed a deep underlying connection between mammographic parenchymal examples and breast cancer risk. Notwithstanding, there is an absence of freely accessible information and programming for genuine examination and clinical approval. This paper presents an open and versatile execution of a completely programmed automated system for mammographic picture detection for breast cancer. The methodology employs mammographic image analysis in four stages: breast segmentation, detection of Region-Of-Interests, feature extraction and risk scoring. This is tried on a bunch of 305 full-field computerized mammography pictures relating to 84 patients (51 cases and 49 controls) from the breast malignant growth advanced storehouse (BCDR). The results accomplish an AUC of 0.847 for the malignant growth inside the breast. Furthermore, utilized together with generally acknowledged dangerous factors like patient age and bosom thickness, mammographic picture examination involving this methodology shows a genuinely critical improvement in execution with an AUC of 0.867 (p < 0.001). The proposed structure will be made openly accessible, and it is not difficult to fuse new strategies. The Dice Index calculated in most of analysed cases was greater than 92%. Various Techniques like SVM, Region Growing, ST GLCM, GLCM were used for making the model produce better results.
Survey on Handwritten Characters Recognition in Deep Learning

Handwritten characters in a document are used in every part of life. The document is a proof of communication and need to restore digitally. Digital format of the handwritten document when scanned, the system should be able to recognize each character in the document and store. This pattern recognition in deep learning needs to be effectively performed. Using deep learning, the model adapts multiple neural networks to learn and test the enormous observations. The scope of the paper presents the survey on various techniques used to recognize handwritten characters in south regional languages, several datasets used in the process of training and testing the model is presented with the statistics report on the number of works done for two consecutive years in four regional languages in South India.
Web Based Voice Assistant for Railways Using Deep Learning Approach

Speech recognition is the ability of machine or program to respond to spoken commands. It works using algorithms through acoustic and language modelling. It is a technique which enables hands-free control of various devices to interact with machines. This project deals with developing a speaking machine using the deep learning algorithm named Linear Predictive Coding (LPC) or Linear Predictive Speech Synthesis (LPSS) which can be used by customers or passengers in a railway station for an enquiry. This helps the people to enquire the details of trains for the limited questionnaire, identified by the name of the train with reference station location and its availability of trains at the real-time for required destinations. This system gives the information about the platform number for all the train arrivals with the names of the trains and the details of the ticket fare for different destinations. These can be done through giving the voice input to the developed system.
The Effect of Learning Using Videos on Online Learning of Private University During the Covid-19 Pandemic

In Indonesia, the number of cases of Covid-19 has increased over time. Therefore, several sectors such as the business sector, agriculture, plantations and others have to adapt to Covid-19. Even in the education sector, there are several regulations that students and students must comply with including keeping a distance, wearing masks, diligently washing hands, and others. In the education sector, students and students also conduct online learning. There are several ways to do this, such as through video learning. Video learning is a learning method by conveying knowledge or theory in the form of video learning. Many private university students felt that the video learning effects them in the negative way. Therefore, we conduct this study to determine the effect of video learning on students of private university in learning during the covid-19 pandemic. This research method is quantitative research. The population in this study were students of private university majoring in information systems. While the sample taken is 16 students. The technique that we use is an online survey using google form, this survey is distributed to random respondents consisting of our friends and strangers from private university. According to the respondents from our survey, the problems that can arise during video learning are difficulty focusing on learning activities that are being carried out, the internet often lags, it is difficult to ask questions when there is material that is not understood, discussion activities with friends become more difficult, etc.