Reviewerly/exHarmony huggingface dataset:
Towards an open-domain chatbot for language practice

State-of-the-art chatbots for English are now able to hold conversations on virtually any topic (e.g.Adiwardana et al., 2020;Roller et al., 2021).However, existing dialogue systems in the language learning domain still use handcrafted rules and pattern matching, and are much more limited in scope.In this paper, we make an initial foray into adapting opendomain dialogue generation for second language learning.We propose and implement decoding strategies that can adjust the difficulty level of the chatbot according to the learner's needs, without requiring further training of the chatbot.These strategies are then evaluated using judgements from human examiners trained in language education.Our results show that re-ranking candidate outputs is a particularly effective strategy, and performance can be further improved by adding sub-token penalties and filtering.
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias

Media news framing bias can increase political polarization and undermine civil society.The need for automatic mitigation methods is therefore growing.We propose a new task, a neutral summary generation from multiple news articles of the varying political leanings to facilitate balanced and unbiased news reading.In this paper, we first collect a new dataset, illustrate insights about framing bias through a case study, and propose a new effective metric and model (NEUS-TITLE) for the task.Based on our discovery that title provides a good signal for framing bias, we present NEUS-TITLE that learns to neutralize news content in hierarchical order from title to article.Our hierarchical multi-task learning is achieved by formatting our hierarchical data pair (title, article) sequentially with identifier-tokens ("TI-TLE=>", "ARTICLE=>") and fine-tuning the auto-regressive decoder with the standard negative log-likelihood objective.We then analyze and point out the remaining challenges and future directions.One of the most interesting observations is that neural NLG models can hallucinate not only factually inaccurate or unverifiable content but also politically biased content.
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations

Recent work has shown that deep learning models in NLP are highly sensitive to lowlevel correlations between simple features and specific output labels, leading to overfitting and lack of generalization.To mitigate this problem, a common practice is to balance datasets by adding new instances or by filtering out "easy" instances (Sakaguchi et al., 2020), culminating in a recent proposal to eliminate single-word correlations altogether (Gardner et al., 2021).In this opinion paper, we identify that despite these efforts, increasingly-powerful models keep exploiting ever-smaller spurious correlations, and as a result even balancing all single-word features is insufficient for mitigating all of these correlations.In parallel, a truly balanced dataset may be bound to "throw the baby out with the bathwater" and miss important signal encoding common sense and world knowledge.We highlight several alternatives to dataset balancing, focusing on enhancing datasets with richer contexts, allowing models to abstain and interact with users, and turning from large-scale fine-tuning to zero-or few-shot setups.
Improving negation detection with negation-focused pre-training

Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text.Recent work has shown that state-of-the-art NLP models underperform on samples containing negation in various tasks, and that negation detection models do not transfer well across domains.We propose a new negation-focused pre-training strategy, involving targeted data augmentation and negation masking, to better incorporate negation information into language models.Extensive experiments on common benchmarks show that our proposed approach improves negation detection performance and generalizability over the strong baseline Neg-BERT (Khandelwal and Sawant, 2020).
Unsupervised Knowledge Graph Generation Using Semantic Similarity Matching

Knowledge Graphs (KGs) are directed labeled graphs representing entities and the relationships between them.Most prior work focuses on supervised or semi-supervised approaches which require large amounts of annotated data.While unsupervised approaches do not need labeled training data, most existing methods either generate too many redundant relations or require manual mapping of the extracted relations to a known schema.To address these limitations, we propose an unsupervised method for KG generation that requires neither labeled data nor manual mapping to the predefined relation schema.Instead, our method leverages sentence-level semantic similarity for automatically generating relations between pairs of entities.Our proposed method outperforms two baseline systems when evaluated over four datasets.
Transition to the Online Education in the Content of Pandemic: Initial Outcomes

The paper is aimed at reviewing the first outcomes of the forced transition to the online higher education within two years of COVID-19 pandemic. The study focuses on advantages and disadvantages of the online education as perceived by the main stakeholders of the education process. The author summarizes the results of the large-scale sociological surveys of teachers and students of the Russian universities. These surveys were conducted by the HSE University, Russian Presidential Academy of National Economy and Public Administration, Moscow State University, and RUDN University. New unpublished data from an HSE University Monitoring of Teachers’ and Researchers’ Life conducted in December 2021 is also attracted. The author examines contradictory views of teachers and students regarding the distant learning. Special attention is paid to the opportunities and shortages of the online education, comparative quality of distant learning and psychological conditions of students. Finally, general conclusions are made with regard to the increasing de-subjectivation of relationships in the teaching process and prospects for the distant learning after the pandemic.
SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning

We introduce SLM Lab, a software framework for reproducible reinforcement learning (RL) research. SLM Lab implements a number of popular RL algorithms, provides synchronous and asynchronous parallel experiment execution, hyperparameter search, and result analysis. RL algorithms in SLM Lab are implemented in a modular way such that differences in algorithm performance can be confidently ascribed to differences between algorithms, not between implementations. In this work we present the design choices behind SLM Lab and use it to produce a comprehensive single-codebase RL algorithm benchmark. In addition, as a consequence of SLM Lab's modular design, we introduce and evaluate a discrete-action variant of the Soft Actor-Critic algorithm (Haarnoja et al., 2018) and a hybrid synchronous/asynchronous training method for RL agents.
Deep Double Descent: Where Bigger Models and More Data Hurt

We show that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.
A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.
H&amp;E Multi-Laboratory Staining Variance Exploration with Machine Learning

In diagnostic histopathology, hematoxylin and eosin (H&amp;E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists’ time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&amp;E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue samples stained by the method routinely used in each laboratory. The samples were digitized and summarized as red, green, and blue channel histograms. Dimensions were reduced using principal component analysis. The data projected by principal components were inserted into the k-means clustering algorithm and the k-nearest neighbors classifier with the laboratories as the target. The k-means silhouette index indicated that K = 2 clusters had the best separability in all tissue types. The supervised classification result showed laboratory effects and tissue-type bias. Both supervised and unsupervised approaches suggested that tissue type also affected inter-laboratory variance. We suggest tissue type to also be considered upon choosing the staining and color-normalization approach.
Who’s the Expert? On Multi-source Belief Change

Consider the following belief change/merging scenario. A group of information sources give a sequence of reports about the state of the world at various instances (e.g. different points in time). The true states at these instances are unknown to us. The sources have varying levels of expertise, also unknown to us, and may be knowledgeable on some topics but not others. This may cause sources to report false statements in areas they lack expertise. What should we believe on the basis of these reports? We provide a framework in which to explore this problem, based on an extension of propositional logic with expertise formulas. This extended language allows us to express beliefs about the state of the world at each instance, as well as beliefs about the expertise of each source. We propose several postulates, provide a couple of families of concrete operators, and analyse these operators with respect to the postulates.
REPUTABLE–A Decentralized Reputation System for Blockchain-Based Ecosystems

Reputation systems are an important means to facilitate trustworthy interactions between on-and off-chain services and users. However, contemporary reputation systems are typically dependent on a trusted central authority to preserve privacy of raters or on adding noise into the user feedback. Moreover, the accuracy of reputation values relies on the integrity of user feedback or input; this feedback should not be tampered with or misused for other purposes. This paper presents blockchain-based reputation system named REPUTABLE (A Decentralized Reputation System for blockchain-based Ecosystems), which computes the reputation of service providers and external services within a blockchain ecosystem through decentralized on-chain and off-chain implementation. Specifically, REPUTABLE not only ensures privacy, but also reliability, integrity and accuracy of reputation values, while incurring minimal overhead. It also enables performing certain data or statistical analytics functions on user feedback, whilst preserving security, privacy, accountability and unlinkability of participants and their feedback. We present a proof-of-concept implementation and a demonstration of the REPUTABLE system. Finally, by means of formal and empirical evaluation, we show the effectiveness of our proposed system to preserve the anonymity of user feedback and the high performance of its blockchain-based implementation.
Novel Dominant Color Subband Image Encryption in Visual Sensor Network for Smart Military Surveillance System

Internet of Things plays a major role in improving the levels of intelligence for several defense and military applications. There are myriads of visual sensor nodes deployed in various domain areas to acquire complete situational awareness. These sensor nodes must transmit the real-time information obtained from multiple locations to identify the threats in the danger-prone zones. The keyframes obtained from the visual sensor nodes should reach the command and control room in a confidentially manner. The lightweight scheme proposed for encryption in the visual sensor network is the novel Dominant color subband image encryption. This scheme involves encrypting only the dominant subband from an entire RGB image using the novel hybrid Chaotic method. The hybrid chaotic method combines the distinctive features of Quadratic, Ginger breadman, and Baker’s map. This hybrid scheme develops a complex, diverse chaotic structure that is unpredictable. A comparative analysis of this encryption scheme against other chaotic methods was done. It was found that the Novel Dominant color subband image encryption algorithm performs significantly better than the other existing methods to achieve real-time security in vision sensors. This method takes advantage of having lesser computation time and increased PSNR values, which indicates the higher quality of the images after decryption.
Web Archive Analytics

Web archive analytics is the exploitation of publicly accessible web pages and their evolution for research purposes -- to the extent organizationally possible for researchers. In order to better understand the complexity of this task, the first part of this paper puts the entirety of the world's captured, created, and replicated data (the "Global Datasphere") in relation to other important data sets such as the public internet and its web pages, or what is preserved thereof by the Internet Archive. Recently, the Webis research group, a network of university chairs to which the authors belong, concluded an agreement with the Internet Archive to download a substantial part of its web archive for research purposes. The second part of the paper in hand describes our infrastructure for processing this data treasure: We will eventually host around 8 PB of web archive data from the Internet Archive and Common Crawl, with the goal of supplementing existing large scale web corpora and forming a non-biased subset of the 30 PB web archive at the Internet Archive.
Would You Like to Hear the News?

One of the key benefits of voice-based personal assistants is the potential to proactively recommend relevant and interesting information. One of the most valuable sources of such information is the News. However, in order for the user to hear the news that is useful and relevant to them, it must be recommended in an interesting and informative way. However, to the best of our knowledge, how to present a news item for a voice-based recommendation remains an open question. In this paper, we empirically compare different ways of recommending news, or specific news items, in a voice-based conversational setting. Specifically, we study the user engagement and satisfaction with five different variants of presenting news recommendations: (1) a generic news briefing; (2) news about a specific entity relevant to the current conversation; (3) news about an entity from a past conversation; (4) news on a trending news topic; and (5) the default - a suggestion to talk about news in general. Our results show that entity-based news recommendations exhibit 29% higher acceptance compared to briefing recommendations, and almost 100% higher acceptance compared to recommending generic or trending news. Our investigation into the presentation of news recommendations and the resulting insights could make voice assistants more informative and engaging.
Empowering Face Recognition Methods using a GAN-based Single Image Super-Resolution Network

Face recognition is one of the most common authentication techniques widely used due to its easy access. In many face recognition applications, captured images are often of low resolution. Face recognition methods perform poorly on low resolution images because they are trained on high resolution face images. Although existing face hallucination methods may generate visually pleasing images, they cannot improve the performance of face recognition methods at low resolution as the structure of the face image and high-frequency details are not sufficiently preserved. Recent advances in deep learning have been used in this paper to propose a new face super-resolution approach to empower face recognition methods. In this paper, a Generative Adversarial Network is used to empower face recognition in low-resolution images. This network considers image edges and reconstructs high-frequency details to preserve the face structure. The proposed technique to generate super-resolved features is usable in any face recognition method. We have used some state-of-the-art face recognition methods to evaluate the proposed method. The results showed a significant impact of the proposed method on the accuracy of face recognition of low resolution images.
Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as "fill-in-the-blank" cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA.
Experimental Semi-quantum Key Distribution With Classical Users

Quantum key distribution, which allows two distant parties to share an unconditionally secure cryptographic key, promises to play an important role in the future of communication. For this reason such technique has attracted many theoretical and experimental efforts, thus becoming one of the most prominent quantum technologies of the last decades. The security of the key relies on quantum mechanics and therefore requires the users to be capable of performing quantum operations, such as state preparation or measurements in multiple bases. A natural question is whether and to what extent these requirements can be relaxed and the quantum capabilities of the users reduced. Here we demonstrate a novel quantum key distribution scheme, where users are fully classical. In our protocol, the quantum operations are performed by an untrusted third party acting as a server, which gives the users access to a superimposed single photon, and the key exchange is achieved via interaction-free measurements on the shared state. We also provide a full security proof of the protocol by computing the secret key rate in the realistic scenario of finite-resources, as well as practical experimental conditions of imperfect photon source and detectors. Our approach deepens the understanding of the fundamental principles underlying quantum key distribution and, at the same time, opens up new interesting possibilities for quantum cryptography networks
Student's $t$ Information Filter with Adaptive Degree of Freedom for Multi-Sensor Fusion

Safety-critical applications such as autonomous driving require a high-integrity localization system that bounds the errors of the estimation process.In this paper, the classical Kalman filter used for multi-sensor data fusion, which is unable to consistently bound estimation errors with a low probability risk, is replaced by a Student's t filter.The degree of freedom of the t distribution offers a way of shaping the heavy tail of the distribution that makes the estimation process more robust in the presence of non-detectable bias and results in a more consistent confidence interval computation.We make use of the heavy-tailed property of the t distribution by introducing a novel real-time adaptive computation of the degree of freedom.The filtering process is formalized through an informational form, since this makes it easier to include a fault detection and exclusion step where a bank of filters is generated.The performance of the proposed approach is evaluated through a localization problem using data acquired from an experimental vehicle equipped with multiple sensors: a GNSS receiver, wheelspeed sensors, a yaw rate gyro and a smart camera that can detect several lane markings, together with high-definition maps.
Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF

In this paper we tackle multilingual named entity recognition task. We use the BERT Language Model as embeddings with bidirectional recurrent network, attention, and NCRF on the top. We apply multilingual BERT only as embedder without any fine-tuning. We test out model on the dataset of the BSNLP shared task, which consists of texts in Bulgarian, Czech, Polish and Russian languages.