OpenRaiser/Intern-Atlas huggingface dataset:
Multilayer network simplification: approaches, models and methods
Venue: arXiv cs.SI 2020
Year: 2020

Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their size, or because traditional methods defined to analyze simple networks do not have a straightforward extension able to handle multiple layers. Therefore, a number of methods have been devised in the literature to simplify multilayer networks with the objective of improving our ability to analyze them. In this article we provide a unified and practical taxonomy of existing simplification approaches, and we identify categories of multilayer network simplification methods that are still underdeveloped, as well as emerging trends.
PrIC3: Property Directed Reachability for MDPs
Venue: arXiv cs.LO 2020
Year: 2020

IC3 has been a leap forward in symbolic model checking. This paper proposes PrIC3 (pronounced pricy-three), a conservative extension of IC3 to symbolic model checking of MDPs. Our main focus is to develop the theory underlying PrIC3. Alongside, we present a first implementation of PrIC3 including the key ingredients from IC3 such as generalization, repushing, and propagation.
Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding
Venue: arXiv cs.LG 2020
Year: 2020

A pharmacological effect of a drug on cells, organs and systems refers to the specific biochemical interaction produced by a drug substance, which is called its mechanism of action. Drug repositioning (or drug repurposing) is a fundamental problem for the identification of new opportunities for the use of already approved or failed drugs. In this paper, we present a method based on a multi-relation unsupervised graph embedding model that learns latent representations for drugs and diseases so that the distance between these representations reveals repositioning opportunities. Once representations for drugs and diseases are obtained we learn the likelihood of new links (that is, new indications) between drugs and diseases. Known drug indications are used for learning a model that predicts potential indications. Compared with existing unsupervised graph embedding methods our method shows superior prediction performance in terms of area under the ROC curve, and we present examples of repositioning opportunities found on recent biomedical literature that were also predicted by our method.
6G White Paper on Edge Intelligence
Venue: arXiv cs.DC 2020
Year: 2020

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.
Fully-Dynamic Coresets
Venue: arXiv cs.DS 2020
Year: 2020

With input sizes becoming massive, coresets -- small yet representative summary of the input -- are relevant more than ever. A weighted set $C_w$ that is a subset of the input is an $\varepsilon$-coreset if the cost of any feasible solution $S$ with respect to $C_w$ is within $[1 {\pm} \varepsilon]$ of the cost of $S$ with respect to the original input. We give a very general technique to compute coresets in the fully-dynamic setting where input points can be added or deleted. Given a static $\varepsilon$-coreset algorithm that runs in time $t(n, \varepsilon, λ)$ and computes a coreset of size $s(n, \varepsilon, λ)$, where $n$ is the number of input points and $1 {-}λ$ is the success probability, we give a fully-dynamic algorithm that computes an $\varepsilon$-coreset with worst-case update time $O((\log n) \cdot t(s(n, \varepsilon/\log n, λ/n), \varepsilon/\log n, λ/n) )$ (this bound is stated informally), where the success probability is $1{-}λ$. Our technique is a fully-dynamic analog of the merge-and-reduce technique that applies to insertion-only setting. Although our space usage is $O(n)$, we work in the presence of an adaptive adversary, and we show that $Ω(n)$ space is required when adversary is adaptive. As a consequence, we get fully-dynamic $\varepsilon$-coreset algorithms for $k$-median and $k$-means with worst-case update time $O(\varepsilon^{-2}k^2\log^5 n \log^3 k)$ and coreset size $O(\varepsilon^{-2}k\log n \log^2 k)$ ignoring $\log \log n$ and $\log(1/\varepsilon)$ factors and assuming that $\varepsilon, λ= Ω(1/$poly$(n))$. These are the first fully-dynamic algorithms for $k$-median and $k$-means with worst-case update times $O($poly$(k, \log n, \varepsilon^{-1}))$. We also give conditional lower bound on update/query time for any fully-dynamic $(4 - δ)$-approximation algorithm for $k$-means.
Extracting Layered Privacy Language Purposes from Web Services
Venue: arXiv cs.SE 2020
Year: 2020

Web services are important in the processing of personal data in the World Wide Web. In light of recent data protection regulations, this processing raises a question about consent or other basis of legal processing. While a consent must be informed, many web services fail to provide enough information for users to make informed decisions. Privacy policies and privacy languages are one way for addressing this problem; the former document how personal data is processed, while the latter describe this processing formally. In this paper, the socalled Layered Privacy Language (LPL) is coupled with web services in order to express personal data processing with a formal analysis method that seeks to generate the processing purposes for privacy policies. To this end, the paper reviews the background theory as well as proposes a method and a concrete tool. The results are demonstrated with a small case study.
Pain and Physical Activity Association in Critically Ill Patients
Venue: arXiv cs.CY 2020
Year: 2020

Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring administration of analgesics and sedation. Such medications generally exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. Thus, it is important not only to minimize pain levels, but also to optimize analgesic strategies in order to maximize mobility and activity of ICU patients. Currently, we lack an understanding of the relation between pain and physical activity on a granular level. In this study, we examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device. We found that average, standard deviation, and maximum physical activity counts are significantly higher before high pain reports compared to before low pain reports during both daytime and nighttime, while percentage of time spent immobile was not significantly different between the two pain report groups. Clusters detected among patients using extracted physical activity features were significant in adjusted logistic regression analysis for prediction of pain report group.
A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
Venue: arXiv cs.CL 2020
Year: 2020

We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional diverse references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU's capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.
Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs
Venue: arXiv cs.CL 2020
Year: 2020

Sequence-based neural networks show significant sensitivity to syntactic structure, but they still perform less well on syntactic tasks than tree-based networks. Such tree-based networks can be provided with a constituency parse, a dependency parse, or both. We evaluate which of these two representational schemes more effectively introduces biases for syntactic structure that increase performance on the subject-verb agreement prediction task. We find that a constituency-based network generalizes more robustly than a dependency-based one, and that combining the two types of structure does not yield further improvement. Finally, we show that the syntactic robustness of sequential models can be substantially improved by fine-tuning on a small amount of constructed data, suggesting that data augmentation is a viable alternative to explicit constituency structure for imparting the syntactic biases that sequential models are lacking.
Efficiently Reclaiming Space in a Log Structured Store
Venue: arXiv cs.DB 2020
Year: 2020

A log structured store uses a single write I/O for a number of diverse and non-contiguous pages within a large buffer instead of using a write I/O for each page separately. This requires that pages be relocated on every write, because pages are never updated in place. Instead, pages are dynamically remapped on every write. Log structuring was invented for and used initially in file systems. Today, a form of log structuring is used in SSD controllers because an SSD requires the erasure of a large block of pages before flash storage can be reused. No update-in-place requires that the storage for out-of-date pages be reclaimed (garbage collected or "cleaned"). We analyze cleaning performance and introduce a cleaning strategy that uses a new way to prioritize the order in which stale pages are garbage collected. Our cleaning strategy approximates an "optimal cleaning strategy". Simulation studies confirm the results of the analysis. This strategy is a significant improvement over previous cleaning strategies.
Context based Text-generation using LSTM networks
Venue: arXiv cs.CL 2020
Year: 2020

Long short-term memory(LSTM) units on sequence-based models are being used in translation, question-answering systems, classification tasks due to their capability of learning long-term dependencies. In Natural language generation, LSTM networks are providing impressive results on text generation models by learning language models with grammatically stable syntaxes. But the downside is that the network does not learn about the context. The network only learns the input-output function and generates text given a set of input words irrespective of pragmatics. As the model is trained without any such context, there is no semantic consistency among the generated sentences. The proposed model is trained to generate text for a given set of input words along with a context vector. A context vector is similar to a paragraph vector that grasps the semantic meaning(context) of the sentence. Several methods of extracting the context vectors are proposed in this work. While training a language model, in addition to the input-output sequences, context vectors are also trained along with the inputs. Due to this structure, the model learns the relation among the input words, context vector and the target word. Given a set of context terms, a well trained model will generate text around the provided context. Based on the nature of computing context vectors, the model has been tried out with two variations (word importance and word clustering). In the word clustering method, the suitable embeddings among various domains are also explored. The results are evaluated based on the semantic closeness of the generated text to the given context.
UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection
Venue: arXiv cs.CL 2020
Year: 2020

We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic drift over time. We analyse the performance of two contextualising architectures (BERT and ELMo) and three change detection algorithms. We find that the most effective algorithms rely on the cosine similarity between averaged token embeddings and the pairwise distances between token embeddings. They outperform strong baselines by a large margin (in the post-evaluation phase, we have the best Subtask 2 submission for SemEval-2020 Task 1), but interestingly, the choice of a particular algorithm depends on the distribution of gold scores in the test set.
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
Venue: arXiv cs.CL 2020
Year: 2020

The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pre-training. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pre-trained multilingual model to a new language. MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. Our code and adapters are available at AdapterHub.ml
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binarized weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework. To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent). In the search stage, the Child-Parent model uses an indicator generated by the child and parent model accuracy to evaluate the performance and abandon operations with less potential. In the training stage, a kernel-level CP loss is introduced to optimize the binarized network. Extensive experiments demonstrate that the proposed CP-NAS achieves a comparable accuracy with traditional NAS on both the CIFAR and ImageNet databases. It achieves the accuracy of $95.27\%$ on CIFAR-10, $64.3\%$ on ImageNet with binarized weights and activations, and a $30\%$ faster search than prior arts.
AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages
Venue: arXiv cs.CL 2020
Year: 2020

We present the IndicNLP corpus, a large-scale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families. We share pre-trained word embeddings trained on these corpora. We create news article category classification datasets for 9 languages to evaluate the embeddings. We show that the IndicNLP embeddings significantly outperform publicly available pre-trained embedding on multiple evaluation tasks. We hope that the availability of the corpus will accelerate Indic NLP research. The resources are available at https://github.com/ai4bharat-indicnlp/indicnlp_corpus.
On the Spontaneous Emergence of Discrete and Compositional Signals
Venue: arXiv cs.CL 2020
Year: 2020

We propose a general framework to study language emergence through signaling games with neural agents. Using a continuous latent space, we are able to (i) train using backpropagation, (ii) show that discrete messages nonetheless naturally emerge. We explore whether categorical perception effects follow and show that the messages are not compositional.
Learning to Rank Intents in Voice Assistants
Venue: arXiv cs.LG 2020
Year: 2020

Voice Assistants aim to fulfill user requests by choosing the best intent from multiple options generated by its Automated Speech Recognition and Natural Language Understanding sub-systems. However, voice assistants do not always produce the expected results. This can happen because voice assistants choose from ambiguous intents - user-specific or domain-specific contextual information reduces the ambiguity of the user request. Additionally the user information-state can be leveraged to understand how relevant/executable a specific intent is for a user request. In this work, we propose a novel Energy-based model for the intent ranking task, where we learn an affinity metric and model the trade-off between extracted meaning from speech utterances and relevance/executability aspects of the intent. Furthermore we present a Multisource Denoising Autoencoder based pretraining that is capable of learning fused representations of data from multiple sources. We empirically show our approach outperforms existing state of the art methods by reducing the error-rate by 3.8%, which in turn reduces ambiguity and eliminates undesired dead-ends leading to better user experience. Finally, we evaluate the robustness of our algorithm on the intent ranking task and show our algorithm improves the robustness by 33.3%.
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
Venue: arXiv cs.CL 2020
Year: 2020

Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document. Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction. The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs. Compared to plain HANs, accuracy increases on all three domains. On the computation and language domain our new model works best overall, and increases accuracy 4.7% over the best literature result. We also obtain improvements when introducing the tags for prediction of the number of citations for 88k scientific publications that we compiled from the Allen AI S2ORC dataset. For our HAN-system with structure-tags we reach 28.5% explained variance, an improvement of 1.8% over our reimplementation of the BiLSTM-based model as well as 1.0% improvement over plain HANs.
Towards scalable user-deployed ultra-dense networks: Blockchain-enabled small cells as a service
Venue: arXiv cs.NI 2020
Year: 2020

Neutral Host Small Cell Providers (SCP) represent a key element of the 5G vision of ultra-dense mobile networks. However, current business models mostly focus on multi-year agreements for large venues, such as stadiums and hotel chains. These business agreements are regulated through binding Service Level Agreements (SLAs), which tend to be too cumbersome and costly for smaller scale SCPs. As a result, the neutral host model does not scale up to its full potential. In this paper, we propose a framework to enable the participation of small- to medium-sized players in the cellular market as providers offering network resources to Mobile Network Operators (MNOs). To this purpose, we review the current and emerging spectrum and technology opportunities that SCPs can use for neutral host deployments. We also propose the use of blockchain-enabled smart contracts as a simple and cost-efficient alternative to traditional SLAs for small-scale SCPs. To demonstrate this, we describe a proof of concept implementation of an Ethereum-based smart contract platform for best-effort service between an SCP and an MNO. Our simulations on potential smart contract-based deployments in city centre Dublin show that the received signal strength in the considered area will increase by an average of $10$ percent.
Interpretable Entity Representations through Large-Scale Typing
Venue: arXiv cs.CL 2020
Year: 2020

In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret. In this paper, we present an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box. Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types, indicating the confidence of a typing model's decision that the entity belongs to the corresponding type. We obtain these representations using a fine-grained entity typing model, trained either on supervised ultra-fine entity typing data (Choi et al. 2018) or distantly-supervised examples from Wikipedia. On entity probing tasks involving recognizing entity identity, our embeddings used in parameter-free downstream models achieve competitive performance with ELMo- and BERT-based embeddings in trained models. We also show that it is possible to reduce the size of our type set in a learning-based way for particular domains. Finally, we show that these embeddings can be post-hoc modified through a small number of rules to incorporate domain knowledge and improve performance.