Reviewerly/exHarmony huggingface dataset:
Causal Discovery with Reinforcement Learning

Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local heuristics to search for a Directed Acyclic Graph (DAG) according to a predefined score function. While these methods, e.g., greedy equivalence search, may have attractive results with infinite samples and certain model assumptions, they are less satisfactory in practice due to finite data and possible violation of assumptions. Motivated by recent advances in neural combinatorial optimization, we propose to use Reinforcement Learning (RL) to search for the DAG with the best scoring. Our encoder-decoder model takes observable data as input and generates graph adjacency matrices that are used to compute rewards. The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity. In contrast with typical RL applications where the goal is to learn a policy, we use RL as a search strategy and our final output would be the graph, among all graphs generated during training, that achieves the best reward. We conduct experiments on both synthetic and real datasets, and show that the proposed approach not only has an improved search ability but also allows for a flexible score function under the acyclicity constraint.
Toxic Comment Detection in Online Discussions

Comment sections of online news platforms are an essential space to express opinions and discuss political topics. In contrast to other online posts, news discussions are related to particular news articles, comments refer to each other, and individual conversations emerge. However, the misuse by spammers, haters, and trolls makes costly content moderation necessary. Sentiment analysis can not only support moderation but also help to understand the dynamics of online discussions. A subtask of content moderation is the identification of toxic comments. To this end, we describe the concept of toxicity and characterize its subclasses. Further, we present various deep learning approaches, including datasets and architectures, tailored to sentiment analysis in online discussions. One way to make these approaches more comprehensible and trustworthy is fine-grained instead of binary comment classification. On the downside, more classes require more training data. Therefore, we propose to augment training data by using transfer learning. We discuss real-world applications, such as semi-automated comment moderation and troll detection. Finally, we outline future challenges and current limitations in light of most recent research publications.
Measuring the Deployment of Network Censorship Filters at Global Scale

Content filtering technologies are often used for Internet censorship, but even as these technologies have become cheaper and easier to deploy, the censorship measurement community lacks a systematic approach to monitor their proliferation.Past research has focused on a handful of specific filtering technologies, each of which required cumbersome manual detective work to identify.Researchers and policymakers require a more comprehensive picture of the state and evolution of censorship based on content filtering in order to establish effective policies that protect Internet freedom.In this work, we present FilterMap, a novel framework that can scalably monitor content filtering technologies based on their blockpages.FilterMap first compiles in-network and new remote censorship measurement techniques to gather blockpages from filter deployments.We then show how the observed blockpages can be clustered, generating signatures for longitudinal tracking.FilterMap outputs a map of regions of address space in which the same blockpages appear (corresponding to filter deployments), and each unique blockpage is manually verified to avoid false positives.By collecting and analyzing more than 379 million measurements from 45,000 vantage points against more than 18,000 sensitive test domains, we are able to identify filter deployments associated with 90 vendors and actors and observe filtering in 103 countries.We detect the use of commercial filtering technologies for censorship in 36 out of 48 countries labeled as 'Not Free' or 'Partly Free' by the Freedom House "Freedom on the Net" report.The unrestricted transfer of content filtering technologies have led to high availability, low cost, and highly effective filtering techniques becoming easier to deploy and harder to circumvent.Identifying these filtering deployments highlights policy and corporate social responsibility issues, and adds accountability to filter manufacturers.Our continued publication of FilterMap data will help the international community track the scope, scale and evolution of content-based censorship.
The Spatial and Spectral Resolution of ASTER Infrared Image Data: A Paradigm Shift in Volcanological Remote Sensing

During the past two decades, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra satellite has acquired nearly 320,000 scenes of the world’s volcanoes. This is ~10% of the data in the global ASTER archive. Many of these scenes captured volcanic activity at never before seen spatial and spectral scales, particularly in the thermal infrared (TIR) region. Despite this large archive of data, the temporal resolution of ASTER is simply not adequate to understand ongoing eruptions and assess the hazards to local populations in near real time. However, programs designed to integrate ASTER into a volcanic data sensor web have greatly improved the cadence of the data (in some cases, to as many as 3 scenes in 48 h). This frequency can inform our understanding of what is possible with future systems collecting similar data on the daily or hourly time scales. Here, we present the history of ASTER’s contributions to volcanology, highlighting unique aspects of the instrument and its data. The ASTER archive was mined to provide statistics including the number of observations with volcanic activity, its type, and the average cloud cover. These were noted for more than 2000 scenes over periods of 1, 5 and 20 years.
A Progressive Content-Based Image Retrieval in JPEG 2000 Compressed Remote Sensing Archives

Due to the dramatically increased volume of remote sensing (RS) image archives, images are usually stored in a compressed format to reduce the storage size. Existing content-based RS image retrieval (CBIR) systems require as input fully decoded images, thus resulting in a computationally demanding task in the case of large-scale CBIR problems. To overcome this limitation, in this article, we present a novel CBIR system that achieves a coarse-to-fine progressive RS image description and retrieval in the partially decoded Joint Photographic Experts Group (JPEG) 2000 compressed domain. The proposed system initially: 1) decodes the code blocks associated only to the coarse wavelet resolution and 2) discards the most irrelevant images to the query image based on the similarities computed on the coarse resolution wavelet features of the query and archive images. Then, the code blocks associated with the subsequent resolution of the remaining images are decoded and the most irrelevant images are discarded by computing similarities considering the image features associated with both resolutions. This is achieved by using the pyramid match kernel similarity measure that assigns higher weights to the features associated with the finer wavelet resolution than to those related to the coarse wavelet resolution. These processes are iterated until the codestreams associated with the highest wavelet resolution are decoded. Then, the final retrieval is performed on a very small set of completely decoded images. Experimental results obtained on two benchmark archives of aerial images point out that the proposed system is much faster while providing a similar retrieval accuracy than the standard CBIR systems.
Condition Aware and Revise Transformer for Question Answering

The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applications. Although existing question answering systems have considered the external information, such as categorical attributes and triples in knowledge base, they all assume that the external information is correct and complete. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer. Experimental results on a real-world customer service dataset demonstrate that the CAR-Transformer can still select an appropriate reply when conditions corresponding to the question exist wrong or missing values, and substantially outperforms baseline models on automatic and human evaluations. The proposed CAR-Transformer can be extended to other NLP tasks which need to consider conditioning information.
Action Recognition Based on Two-Stream Convolutional Networks With Long-Short-Term Spatiotemporal Features

Human action recognition is an important research topic in the field of computer vision due to its application values. Recently, a variety of approaches based on deep learning features have been proposed due to the effectiveness of deep neural networks. But most of these approaches are not able to fully extract spatiotemporal features from videos, because of the lack of consideration of the diversity of scales in temporal domain. In this paper, we propose a two-stream convolutional network with long-short-term spatiotemporal features (LSF CNN) for human action recognition task. The network is mainly composed of two subnetworks. One is long-term spatiotemporal features extraction network (LT-Net) that takes the stacked RGB images as inputs. Another one is short-term spatiotemporal features extraction network (ST-Net) that takes the optical flow as input, which is estimated from two adjacent frames. The two-scale spatiotemporal features are fused in the fully-connected layer and fed into the linear support vector machine (SVM). We also propose a new expression for optical flow field, which is proved to have better performance than traditional expression in action recognition problem. With two-stream architecture, the network can fully learn deep features in both spatial and temporal domains. The experimental results on HMDB51 and UCF101 datasets indicated that the proposed approach improves the action recognition accuracy by using the long-short-term spatiotemporal information.
Super-Resolution using GANs for Medical Imaging

Generative Adversarial Models (GANs) have been quite popular and are currently and active area of research. They can be used for generative new data and study adversarial samples and attacks. We have used the similar approach to apply super-resolution to medical images. In Radiology MRI is a commonly used method to produce medical imaging but the limitations of lab equipment and health hazard of being in an MRI radiation environment to obtain good quality scans lead to lower quality scans and also it takes a lot of time to get a high-resolution data. This problem can be solved by using super-resolution using deep learning as a post-processing step to improve the resolution of the scans. Super-resolution is a process of generating higher resolution images from lower resolution data. For this, we are proposing a generative adversarial network architecture which is a dual neural network designed to generate lifelike images. In this deep learning algorithm, two neural networks compete with each other to improve alternatively. Given a training set, this technique learns to generate new data with the same statistics as the training set. To apply this technique to our problem statement we are using generator as the network to improve the resolution and discriminator as a network to train generator better. We used transfer learning in our generative neural network and training our discriminator from scratch and using the perceptual loss [1] to train our network. This will help in improving the performance of the network. We are using Lung MRI scans of tuberculosis with a set of 216 MRI samples containing around 60-130 channels each and each channel having 512x512 dimensions.
The essence of the content of the concept of digital educational resources and its role in primary education

The article is devoted to the current problem of the use of digital educational resources in elementary school. The work touches on the conditions for the effective use of digital educational resources in the educational process of primary school. The author studies the opinion of primary school teachers on the appropriateness of using digital educational resources in teaching younger students and the difficulties that arise. The article presents possible measures to create favorable conditions for the fruitful use of digital educational resources in educational institutions. The article may be of interest to future primary school teachers, teachers of pedagogical universities.
Robustness of Accelerated First-Order Algorithms for Strongly Convex Optimization Problems

We study the robustness of accelerated first-order algorithms to stochastic uncertainties in gradient evaluation. Specifically, for unconstrained, smooth, strongly convex optimization problems, we examine the mean-squared error in the optimization variable when the iterates are perturbed by additive white noise. This type of uncertainty may arise in situations where an approximation of the gradient is sought through measurements of a real system or in a distributed computation over a network. Even though the underlying dynamics of first-order algorithms for this class of problems are nonlinear, we establish upper bounds on the mean-squared deviation from the optimal solution that are tight up to constant factors. Our analysis quantifies fundamental tradeoffs between noise amplification and convergence rates obtained via any acceleration scheme similar to Nesterov's or heavy-ball methods. To gain additional analytical insight, for strongly convex quadratic problems, we explicitly evaluate the steady-state variance of the optimization variable in terms of the eigenvalues of the Hessian of the objective function. We demonstrate that the entire spectrum of the Hessian, rather than just the extreme eigenvalues, influences robustness of noisy algorithms. We specialize this result to the problem of distributed averaging over undirected networks and examine the role of network size and topology on the robustness of noisy accelerated algorithms.
Analysing the Role of Supervised and Unsupervised Machine Learning in IoT

To harness the value of data generated from IoT, there is a crucial requirement of new mechanisms. Machine learning (ML) is among the most suitable paradigms of computation which embeds strong intelligence within IoT devices. Various ML techniques are being widely utilised for improving network security in IoT. These techniques include reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. This report aims to critically analyse the role played by supervised and unsupervised ML for the enhancement of IoT security.
Neurological divide

Software engineering involves writing new code or editing existing code. Recent efforts have investigated the neural processes associated with reading and comprehending code --- however, we lack a thorough understanding of the human cognitive processes underlying code writing. While prose reading and writing have been studied thoroughly, that same scrutiny has not been applied to code writing. In this paper, we leverage functional brain imaging to investigate neural representations of code writing in comparison to prose writing. We present the first human study in which participants wrote code and prose while undergoing a functional magnetic resonance imaging (fMRI) brain scan, making use of a full-sized fMRI-safe QWERTY keyboard.
Sentiment Analysis from Bengali Depression Dataset using Machine Learning

Nowadays, Sentiment Analysis is one of the advanced matters of natural language processing. Sentiment analysis determines a particular pole of a paragraph. Our purpose is to find the sentiment from the Bengali paragraph which is happy or sad using various types of machine learning classification analysis algorithms. For doing this we are collecting data from various social network sites, Bengali blogs, etc. To get a compatible result, we passed through many difficulties. Bengali text preprocessing is one of the complex parts of all. After preprocessing the data, we tokenized the data by using Countvectorizer. After that, we applied six different algorithms to predict almost high accuracy. Among them, the Multinomial Naive Bayes provide us the maximum accuracy which is 86.67%.
Cotatron: Transcription-Guided Speech Encoder for Any-to-Many Voice Conversion Without Parallel Data

We propose Cotatron, a transcription-guided speech encoder for speaker-independent linguistic representation. Cotatron is based on the multispeaker TTS architecture and can be trained with conventional TTS datasets. We train a voice conversion system to reconstruct speech with Cotatron features, which is similar to the previous methods based on Phonetic Posteriorgram (PPG). By training and evaluating our system with 108 speakers from the VCTK dataset, we outperform the previous method in terms of both naturalness and speaker similarity. Our system can also convert speech from speakers that are unseen during training, and utilize ASR to automate the transcription with minimal reduction of the performance. Audio samples are available at this https URL, and the code with a pre-trained model will be made available soon.
Bayesian Deep Learning and a Probabilistic Perspective of Generalization

The key distinguishing property of a Bayesian approach is marginalization, rather than using a single setting of weights. Bayesian marginalization can particularly improve the accuracy and calibration of modern deep neural networks, which are typically underspecified by the data, and can represent many compelling but different solutions. We show that deep ensembles provide an effective mechanism for approximate Bayesian marginalization, and propose a related approach that further improves the predictive distribution by marginalizing within basins of attraction, without significant overhead. We also investigate the prior over functions implied by a vague distribution over neural network weights, explaining the generalization properties of such models from a probabilistic perspective. From this perspective, we explain results that have been presented as mysterious and distinct to neural network generalization, such as the ability to fit images with random labels, and show that these results can be reproduced with Gaussian processes. We also show that Bayesian model averaging alleviates double descent, resulting in monotonic performance improvements with increased flexibility. Finally, we provide a Bayesian perspective on tempering for calibrating predictive distributions.
Space-Time Correspondence as a Contrastive Random Walk

This paper proposes a simple self-supervised approach for learning a representation for visual correspondence from raw video. We cast correspondence as prediction of links in a space-time graph constructed from video. In this graph, the nodes are patches sampled from each frame, and nodes adjacent in time can share a directed edge. We learn a representation in which pairwise similarity defines transition probability of a random walk, so that long-range correspondence is computed as a walk along the graph. We optimize the representation to place high probability along paths of similarity. Targets for learning are formed without supervision, by cycle-consistency: the objective is to maximize the likelihood of returning to the initial node when walking along a graph constructed from a palindrome of frames. Thus, a single path-level constraint implicitly supervises chains of intermediate comparisons. When used as a similarity metric without adaptation, the learned representation outperforms the self-supervised state-of-the-art on label propagation tasks involving objects, semantic parts, and pose. Moreover, we demonstrate that a technique we call edge dropout, as well as self-supervised adaptation at test-time, further improve transfer for object-centric correspondence.
Sovereignty, privacy, and ethics in blockchain-based identity management systems

Self-sovereign identity (SSI) solutions implemented on the basis of blockchain technology are seen as alternatives to existing digital identification systems, or even as a foundation of standards for the new global infrastructures for identity management systems. It is argued that 'self-sovereignty' in this context can be understood as the concept of individual control over identity relevant private data, capacity to choose where such data is stored, and the ability to provide it to those who need to validate it. It is also argued that while it might be appealing to operationalise the concept of 'self-sovereignty' in a narrow technical sense, depreciation of moral semantics obscures key challenges and long-term repercussions. Closer attention to the normative substance of the 'sovereignty' concept helps to highlight a range of ethical issues pertaining to the changing nature of human identity in the context of ubiquitous private data collection.
Convolutional Neural Network and Data Augmentation for Behavioral-Based Biometric User Identification

One classification problem that is especially challenging is biometric identification, which links cybersecurity to the analysis of human behavior. Biometric data can be collected through the use of wearable devices, especially smartphones that incorporate a variety of sensors, during the performance of activities by the users. In recent research, numerous identification systems using machine learning classification algorithms have been proposed to provide solutions to this classification problem. However, their ability to perform identification is limited to only suitable selected features of time-series data from the biometric raw data. Therefore, in this study, an architecture for the biometric user identification using walking patterns that employs a convolutional neural network is proposed. Validation of the proposed framework for biometric identification was accomplished through the generation of synthetic data from datasets of samples from public walking. As a result, the framework described in this study provides more effective outcomes in terms of the accuracy of the model and additional metrics than the conventional machine learning utilized for biometric user identification.