OpenRaiser/Intern-Atlas huggingface dataset:
Towards Open-Set Semantic Segmentation of Aerial Images
Venue: arXiv cs.CV 2020
Year: 2020

Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However, the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increasing mainly due to the low cost of drones, cheapening of imaging satellite launch costs, and novel public datasets. Usually remote sensing applications employ computer vision techniques strictly modeled for classification tasks in closed set scenarios. However, real-world tasks rarely fit into closed set contexts, frequently presenting previously unknown classes, characterizing them as open set scenarios. Focusing on this problem, this is the first paper to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of a novel approach for open set semantic segmentation. Our method yielded competitive results when compared to closed set methods for the same dataset.
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
Venue: arXiv cs.LG 2020
Year: 2020

We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for structure learning) and a weight learner (for parameter learning) sequentially, we develop a gradient-boosted approach that performs both simultaneously. Our approach learns a set of weak relational regression trees, whose paths from root to leaf are conjunctive clauses and represent the structure, and whose leaf values represent the parameters. When the learned relational regression trees are transformed into a lifted RBM, its hidden nodes are precisely the conjunctive clauses derived from the relational regression trees. This leads to a more interpretable and explainable model. Our empirical evaluations clearly demonstrate this aspect, while displaying no loss in effectiveness of the learned models.
SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks
Venue: arXiv cs.CL 2020
Year: 2020

The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multi-purpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. In this study, we developed a semantically annotated corpus using clinical texts from multiple medical specialties, document types, and institutions. We present the following: (1) a survey listing common aspects and lessons learned from previous research, (2) a fine-grained annotation schema which could be replicated and guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. The result of this work is the SemClinBr, a corpus that has 1,000 clinical notes, labeled with 65,117 entities and 11,263 relations, and can support a variety of clinical NLP tasks and boost the EHR's secondary use for the Portuguese language.
ABCTracker: an easy-to-use, cloud-based application for tracking multiple objects
Venue: arXiv cs.CV 2020
Year: 2020

Visual multi-object tracking has the potential to accelerate many forms of quantitative analyses, especially in research communities investigating the motion, behavior, or social interactions within groups of animals. Despite its potential for increasing analysis throughput, complications related to accessibility, adaptability, accuracy, or scalable application arise with existing tracking systems. Several iterations of prototyping and testing have led us to a multi-object tracking system -- ABCTracker -- that is: accessible in both system as well as technical knowledge requirements, easily adaptable to new videos, and capable of producing accurate tracking data through a mixture of automatic and semi-automatic tracking features.
Business Process Full Compliance with Respect to a Set of Conditional Obligation in Polynomial Time
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, we present a new methodology to evaluate whether a business process model is fully compliant with a regulatory framework composed of a set of conditional obligations. The methodology is based failure delta-constraints that are evaluated on bottom-up aggregations of a tree-like representation of business process models. While the generic problem of proving full compliance is in coNP-complete, we show that verifying full compliance can be done in polynomial time using our methodology, for an acyclic structured process model given a regulatory framework composed by a set of conditional obligations, whose elements are restricted to be represented by propositional literals
Extraction of Templates from Phrases Using Sequence Binary Decision Diagrams
Venue: arXiv cs.CL 2020
Year: 2020

The extraction of templates such as ``regard X as Y'' from a set of related phrases requires the identification of their internal structures. This paper presents an unsupervised approach for extracting templates on-the-fly from only tagged text by using a novel relaxed variant of the Sequence Binary Decision Diagram (SeqBDD). A SeqBDD can compress a set of sequences into a graphical structure equivalent to a minimal DFA, but more compact and better suited to the task of template extraction. The main contribution of this paper is a relaxed form of the SeqBDD construction algorithm that enables it to form general representations from a small amount of data. The process of compression of shared structures in the text during Relaxed SeqBDD construction, naturally induces the templates we wish to extract. Experiments show that the method is capable of high-quality extraction on tasks based on verb+preposition templates from corpora and phrasal templates from short messages from social media.
Axiomatizations for the Shapley-Shubik power index for games with several levels of approval in the input and output
Venue: arXiv cs.GT 2020
Year: 2020

The Shapley-Shubik index is a specialization of the Shapley value and is widely applied to evaluate the power distribution in committees drawing binary decisions. It was generalized to decisions with more than two levels of approval both in the input and the output. The corresponding games are called $(j,k)$ simple games. Here we present a new axiomatization for the Shapley-Shubik index for $(j,k)$ simple games as well as for a continuous variant, which may be considered as the limit case.
Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet
Venue: arXiv cs.NI 2020
Year: 2020

Tactile Internet is an emerging concept that focuses on supporting high-fidelity, ultra-responsive, and widely available human-to-machine interactions. To reduce the transmission latency and alleviate Internet congestion, fog computing has been advocated as an important component of the Tactile Internet. In this paper, we focus on energy-efficient design of fog computing networks that support low-latency Tactile Internet applications. We investigate two performance metrics: Service response time of end-users and power usage efficiency of fog nodes. We quantify the fundamental tradeoff between these two metrics and then extend our analysis to fog computing networks involving cooperation between fog nodes. We introduce a novel cooperative fog computing concept, referred to as offload forwarding, in which a set of fog nodes with different computing and energy resources can cooperate with each other. The objective of this cooperation is to balance the workload processed by different fog nodes, further reduce the service response time, and improve the efficiency of power usage. We develop a distributed optimization framework based on dual decomposition to achieve the optimal tradeoff. Our framework does not require fog nodes to disclose their private information nor conduct back-and-forth negotiations with each other. Two distributed optimization algorithms are proposed. One is based on the subgradient method with dual decomposition and the other is based on distributed ADMM-VS. We prove that both algorithms can achieve the optimal workload allocation that minimizes the response time under the given power efficiency constraints of fog nodes.
Subsumption Demodulation in First-Order Theorem Proving
Venue: arXiv cs.LO 2020
Year: 2020

Motivated by applications of first-order theorem proving to software analysis, we introduce a new inference rule, called subsumption demodulation, to improve support for reasoning with conditional equalities in superposition-based theorem proving. We show that subsumption demodulation is a simplification rule that does not require radical changes to the underlying superposition calculus. We implemented subsumption demodulation in the theorem prover Vampire, by extending Vampire with a new clause index and adapting its multi-literal matching component. Our experiments, using the TPTP and SMT-LIB repositories, show that subsumption demodulation in Vampire can solve many new problems that could so far not be solved by state-of-the-art reasoners.
Efficient Logging for Blockchain Applications
Venue: arXiv cs.SE 2020
Year: 2020

Second generation blockchain platforms, like Ethereum, can store arbitrary data and execute user-defined smart contracts. Due to the shared nature of blockchains, understanding the usage of blockchain-based applications and the underlying network is crucial. Although log analysis is a well-established means, data extraction from blockchain platforms can be highly inconvenient and slow, not least due to the absence of logging libraries. To close the gap, we here introduce the Ethereum Logging Framework (ELF) which is highly configurable and available as open source. ELF supports users (i) in generating cost-efficient logging code readily embeddable into smart contracts and (ii) in extracting log analysis data into common formats regardless of whether the code generation has been used during development. We provide an overview of and rationale for the framework's features, outline implementation details, and demonstrate ELF's versatility based on three case studies from the public Ethereum blockchain.
Mining social media data for biomedical signals and health-related behavior
Venue: arXiv cs.CY 2020
Year: 2020

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.
Distributed Resource Allocation for Network Slicing over Licensed and Unlicensed Bands
Venue: arXiv cs.NI 2020
Year: 2020

Network slicing is considered one of the key enabling technologies for 5G due to its ability to customize and "slice" a common resource to support diverse services and verticals.This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can coordinate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For licensed band slicing, we propose an inter-operator spectrum aggregation method that allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Because all MNOs enjoy equal rights to accessing these bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a {\em modified back-of-the-envelope (mBoE) method} for MNOs to evaluate their {\em Value-of-Rights (VoR)} when coexisting with other wireless technologies. A {\em network slicing game} based on the overlapping coalition formation game is formulated to investigate cooperation between MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To implement our proposed framework without requiring MNOs to reveal private information to other MNOs, we develop a distributed algorithm called D-ADMM-PVS. Performance evaluation of our proposed framework is provided using a discrete-event simulator that is driven by real MNO deployment scenarios based on over 400 base station locations deployed by two primary cellular operators in the city of Dublin.Numerical results show that our proposed framework can almost double the capacity for all supported services for each MNO in an urban setting.
Margin Maximization as Lossless Maximal Compression
Venue: arXiv cs.LG 2020
Year: 2020

The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional margin maximization -- correctly classifying as many training examples as possible with maximal confidence --has been known to construct models with good generalization guarantees. This work gives an information-theoretic interpretation of a margin maximizing model on a noiseless training dataset as one that achieves lossless maximal compression of said dataset -- i.e. extracts from the features all the useful information for predicting the label and no more. The connection offers new insights on generalization in supervised machine learning, showing margin maximization as a special case (that of classification) of a more general principle and explains the success and potential limitations of popular learning algorithms like gradient boosting. We support our observations with theoretical arguments and empirical evidence and identify interesting directions for future work.
Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging
Venue: arXiv cs.LG 2020
Year: 2020

Retail food packaging contains information which informs choice and can be vital to consumer health, including product name, ingredients list, nutritional information, allergens, preparation guidelines, pack weight, storage and shelf life information (use-by / best before dates). The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks. Consequently, erroneous or illegible labeling has the potential to be highly detrimental to consumers and many other stakeholders in the supply chain. In this paper, a multi-source deep learning-based domain adaptation system is proposed and tested to identify and verify the presence and legibility of use-by date information from food packaging photos taken as part of the validation process as the products pass along the food production line. This was achieved by improving the generalization of the techniques via making use of multi-source datasets in order to extract domain-invariant representations for all domains and aligning distribution of all pairs of source and target domains in a common feature space, along with the class boundaries. The proposed system performed very well in the conducted experiments, for automating the verification process and reducing labeling errors that could otherwise threaten public health and contravene legal requirements for food packaging information and accuracy. Comprehensive experiments on our food packaging datasets demonstrate that the proposed multi-source deep domain adaptation method significantly improves the classification accuracy and therefore has great potential for application and beneficial impact in food manufacturing control systems.
Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations
Venue: arXiv cs.IR 2020
Year: 2020

Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at \url{https://github.com/lola63/HDE-Python}
Investigating Classification Techniques with Feature Selection For Intention Mining From Twitter Feed
Venue: arXiv cs.IR 2020
Year: 2020

In the last decade, social networks became most popular medium for communication and interaction. As an example, micro-blogging service Twitter has more than 200 million registered users who exchange more than 65 million posts per day. Users express their thoughts, ideas, and even their intentions through these tweets. Most of the tweets are written informally and often in slang language, that contains misspelt and abbreviated words. This paper investigates the problem of selecting features that affect extracting user's intention from Twitter feeds based on text mining techniques. It starts by presenting the method we used to construct our own dataset from extracted Twitter feeds. Following that, we present two techniques of feature selection followed by classification. In the first technique, we use Information Gain as a one-phase feature selection, followed by supervised classification algorithms. In the second technique, we use a hybrid approach based on forward feature selection algorithm in which two feature selection techniques employed followed by classification algorithms. We examine these two techniques with four classification algorithms. We evaluate them using our own dataset, and we critically review the results.
Nonlocal PageRank
Venue: arXiv cs.SI 2020
Year: 2020

In this work we introduce and study a nonlocal version of the PageRank. In our approach, the random walker explores the graph using longer excursions than just moving between neighboring nodes. As a result, the corresponding ranking of the nodes, which takes into account a \textit{long-range interaction} between them, does not exhibit concentration phenomena typical of spectral rankings which take into account just local interactions. We show that the predictive value of the rankings obtained using our proposals is considerably improved on different real world problems.
OAuth 2.0 authorization using blockchain-based tokens
Venue: arXiv cs.CR 2020
Year: 2020

OAuth 2.0 is the industry-standard protocol for authorization. It facilitates secure service provisioning, as well as secure interoperability among diverse stakeholders. All OAuth 2.0 protocol flows result in the creation of an access token, which is then used by a user to request access to a protected resource. Nevertheless, the definition of access tokens is transparent to the OAuth 2.0 protocol, which does not specify any particular token format, how tokens are generated, or how they are used. Instead, the OAuth 2.0 specification leaves all these as design choices for integrators. In this paper, we propose a new type of OAuth 2.0 token backed by a distributed ledger. Our construction is secure, and it supports proof-of-possession, auditing, and accountability. Furthermore, we provide added-value token management services, including revocation, delegation, and fair exchange by leveraging smart contracts. We realized a proof-of-concept implementation of our solution using Ethereum smart contracts and the ERC-721 token specification.
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
Venue: arXiv cs.CL 2020
Year: 2020

Attention-based encoder-decoder neural network models have recently shown promising results in goal-oriented dialogue systems. However, these models struggle to reason over and incorporate state-full knowledge while preserving their end-to-end text generation functionality. Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input. The model provides an additional integration of user intent along with text generation, trained with a multi-task learning paradigm along with an additional regularization technique to penalize generating the wrong entity as output. The model further incorporates a Knowledge Graph entity lookup during inference to guarantee the generated output is state-full based on the local knowledge graph provided. We finally evaluated the model using the BLEU score, empirical evaluation depicts that our proposed architecture can aid in the betterment of task-oriented dialogue system`s performance.