Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Inference on Random Dot Product Graphs: a Survey

The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate a wide range of random graphs, from relatively simple stochastic block models to complex latent position graphs. In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices. We examine the analogues, in graph inference, of several canonical tenets of classical Euclidean inference: in particular, we summarize a body of existing results on the consistency and asymptotic normality of the adjacency and Laplacian spectral embeddings, and the role these spectral embeddings can play in the construction of single- and multi-sample hypothesis tests for graph data. We investigate several real-world applications, including community detection and classification in large social networks and the determination of functional and biologically relevant network properties from an exploratory data analysis of the Drosophila connectome. We outline requisite background and current open problems in spectral graph inference.
Modeling Linguistic and Personality Adaptation for Natural Language Generation

Previous work has shown that conversants adapt to many aspects of their partners’ language. Other work has shown that while every person is unique, they often share general patterns of behavior. Theories of personality aim to explain these shared patterns, and studies have shown that many linguistic cues are correlated with personality traits. We propose an adaptation measure for adaptive natural language generation for dialogs that integrates the predictions of both personality theories and adaptation theories, that can be applied as a dialog unfolds, on a turn by turn basis. We show that our measure meets criteria for validity, and that adaptation varies according to corpora and task, speaker, and the set of features used to model it. We also produce fine-grained models according to the dialog segmentation or the speaker, and demonstrate the decaying trend of adaptation.
Patient Views on Advance Care Planning in Cirrhosis: A Qualitative Analysis

Aim To investigate patient experiences and perceptions of advance care planning (ACP) process in cirrhosis. Methods Purposive sampling was used to identify and recruit participants (N = 17) from discrete patient groups: compensated with no prior decompensation, decompensated and not yet listed for transplant, transplant wait listed, medical contraindications for transplant, and low socioeconomic status. Review and discussion of local ACP videos, documents, and experiences with ACP occurred in two individual interviews and four focus groups. Data were analyzed using inductive content analysis including iterative processes of open coding, categorization, and abstraction. Results Three overarching categories emerged: (1) lack of understanding about disease trajectories and ACP processes, (2) roles of alternate decision makers, and (3) preferences for receiving ACP information. Most patients desired advanced care-planning conversations before the onset of decompensation (specifically hepatic encephalopathy) with a care provider with whom they had a trusting, preexisting relationship. Involvement of the alternate decision makers was of critical importance to participants, as was the use of direct, easy to understand patient education tools that address practical issues. Conclusion Our findings support the need for early advance care planning in the outpatient setting. Outpatient clinicians may play a key role in facilitating these discussions.
Estimating User Interest from Open-Domain Dialogue

Dialogue personalization is an important issue in the field of open-domain chat-oriented dialogue systems. If these systems could consider their users’ interests, user engagement and satisfaction would be greatly improved. This paper proposes a neural network-based method for estimating users’ interests from their utterances in chat dialogues to personalize dialogue systems’ responses. We introduce a method for effectively extracting topics and user interests from utterances and also propose a pre-training approach that increases learning efficiency. Our experimental results indicate that the proposed model can estimate user’s interest more accurately than baseline approaches.
A privacy-preserving model for multi-agent propositional planning

Abstract Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents’ privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents’ privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics.
A two‐stage sampling for robust feature matching

For any visual feature‐based SLAM (simultaneous localization and mapping) solutions, to estimate the relative camera motion between two images, it is necessary to find “correct” correspondence between features extracted from those images. Given a set of feature correspondents, one can use a n‐point algorithm with robust estimation method, to produce the best estimate to the relative camera pose. The accuracy of a motion estimate is heavily dependent on the accuracy of the feature correspondence. Such a dependency is even more significant when features are extracted from the images of the scenes with drastic changes in viewpoints and illuminations and presence of occlusions. To make a feature matching robust to such challenging scenes, we propose a new feature matching method that incrementally chooses a five pairs of matched features for a full DoF (degree of freedom) camera motion estimation. In particular, at the first stage, we use our 2‐point algorithm to estimate a camera motion and, at the second stage, use this estimated motion to choose three more matched features. In addition, we use, instead of the epipolar constraint, a planar constraint for more accurate outlier rejection. With this set of five matching features, we estimate a full DoF camera motion with scale ambiguity. Through the experiments with three, real‐world data sets, our method demonstrates its effectiveness and robustness by successfully matching features (1) from the images of a night market where presence of frequent occlusions and varying illuminations, (2) from the images of a night market taken by a handheld camera and by the Google street view, and (3) from the images of a same location taken daytime and nighttime.
Toxicity and Safety Evaluation of Doxorubicin-Loaded Cockleshell-Derived Calcium Carbonate Nanoparticle in Dogs

Doxorubicin (DOX) is a potent anticancer agent with cytotoxic effects which limit its clinical usage. This effect is due to its nonselective nature causing injury to the cells as a result of reactive free oxygen radical's release. Cockleshell-derived calcium carbonate nanoparticle (CS-CaCO3NP) is a pH-responsive carrier with targeted delivery potentials. This study aimed at evaluating the toxicity effects of repeated dose administration of DOX-loaded CS-CaCO3NP in healthy dogs. Fifteen dogs with an average body weight of 15 kg were randomized equally into 5 groups. Dogs were subjected to 5 doses at every 3-week interval with (i) normal saline, (ii) DOX, 30 mg/m2, and the experimental groups: CS-CaCO3NP-DOX at (iii) high dose, 50 mg/m2, (iv) clinical dose, 30 mg/m2, and (v) low dose, 20 mg/m2. Radiographs, electrocardiography, and blood samples were collected before every treatment for haematology, serum biochemistry, and cardiac injury assessment. Heart and kidney tissues were harvested after euthanasia for histological and ultrastructural evaluation. The cumulative dose of DOX 150 mg/m2 over 15 weeks revealed significant effects on body weight, blood cells, functional enzymes, and cardiac injury biomarkers with alterations in electrocardiogram, myocardium, and renal tissue morphology. However, the dogs given CS-CaCO3NP-DOX 150 mg/m2 and below did not show any significant change in toxicity biomarker as compared to those given normal saline. The study confirmed the safety of repeated dose administration of CS-CaCO3NP-DOX (30 mg/m2) for 5 cycles in dogs. This finding offers opportunity to dogs with cancer that might require long-term administration of DOX without adverse effects.
Cost-Sensitive Active Learning for Dialogue State Tracking

Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data. Since dialogue state annotation usually requires labelling all turns of a single dialogue and utilizing context information, it is very expensive to annotate all available unlabelled data. In this paper, a novel cost-sensitive active learning framework is proposed based on a set of new dialogue-level query strategies. This is the first attempt to apply active learning for dialogue state tracking. Experiments on DSTC2 show that active learning with mixed data query strategies can effectively achieve the same DST performance with significantly less data annotation compared to traditional training approaches.
A Comprehensive Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation

Neural machine translation (NMT) has shown very promising results when there are large amounts of parallel corpora. However, for low resource domains, vanilla NMT cannot give satisfactory performance due to overfitting on the small size of parallel corpora. Two categories of domain adaptation approaches have been proposed for low resource NMT, i.e., adaptation using out-of-domain parallel corpora and in-domain monolingual corpora. In this paper, we conduct a comprehensive empirical comparison of the methods in both categories. For domain adaptation using out-of-domain parallel corpora, we further propose a novel domain adaptation method named mixed fine tuning, which combines two existing methods namely fine tuning and multi domain NMT. For domain adaptation using in-domain monolingual corpora, we compare two existing methods namely language model fusion and synthetic data generation. In addition, we propose a method that combines these two categories. We empirically compare all the methods and discuss their benefits and shortcomings. To the best of our knowledge, this is the first work on a comprehensive empirical comparison of domain adaptation methods for NMT.
Pythagorean fuzzy CODAS and its application to supplier selection in a manufacturing firm

PurposeThe purpose of this paper is to develop the Pythagorean fuzzy extension of CODAS method.Design/methodology/approachSupplier selection is a critical issue for manufacturing companies since it is a multidimensional problem including several conflicting criteria. A suitable multi criteria decision making (MCDM) method that could consider vagueness and impreciseness in the assessments should be used for this kind of problems. Pythagorean fuzzy sets (PFSs) are characterized by a membership degree and a non-membership degree satisfying the condition that their square sum is equal to or less than 1. PFSs extend the concept of intuitionistic fuzzy sets (IFSs). COmbinative Distance-based Assessment (CODAS) method is relatively a new MCDM technique introduced by Keshavarz Ghorabaee et al. (2016). FindingsPythagorean fuzzy CODAS gives better results than ordinary fuzzy CODAS since it considers the hesitancy of decision makers and presents a larger space for membership and non-membership definition.Originality/valueThe value of this paper is the proposal of a new method to use for the solutions of MCDM problems under vagueness and impreciseness. To show validity and effectiveness of the proposed method, an application to the supplier selection problem is given.
Challenges for digital transformation – towards a conceptual decision support guide for managers

Abstract Digital technologies are transforming operations, products and services in organisations large and small. The digital transformation of organisations is extolled as a solution to organisational challenges related to both efficiency and effectiveness. To date, there has been limited consideration of the challenges of successfully operationalising a transformative digital transformation approach. This article examines some of the common dilemmas facing managers that may be relevant across a range of organisational scenarios. Seven factors are analysed to provide a starting point for creating successful digital transformations in business. To begin to tackle these challenges, this research-in-progress paper proposes a digital transformation decision support guide for managers. This tool may be utilised by managers, supporting them to move from an ad hoc technology-driven approach to a more systematic integrated approach to digital transformation.
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bi-directional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.
Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks

In cooperative dialogues, identifying the intent of ones conversation partner and acting accordingly is of great importance. While this endeavour is facilitated by phrasing intentions as directly as possible, we can observe in human-human communication that a number of factors such as cultural norms and politeness may result in expressing one’s intent indirectly. Therefore, in human-computer communication we have to anticipate the possibility of users being indirect and be prepared to interpret their actual meaning. Furthermore, a dialogue system should be able to conform to human expectations by adjusting the degree of directness it uses to improve the user experience. To reach those goals, we propose an approach to differentiate between direct and indirect utterances and find utterances of the opposite characteristic that express the same intent. In this endeavour, we employ dialogue vector models and recurrent neural networks.
Autonomous legged hill ascent

This paper reports on an autonomous ascent by a legged robotic platform in outdoor forested terrain. Two controllers govern the integration of online inertial measurement unit and light detection and ranging sensor signals into commands for climbing by means of an abstracted (unicycle) representation of the platform in support of different performance goals: a kinematic version for endurance and a dynamic version for speed. These control laws, backed by a suite of formal correctness guarantees, encourage a stripped‐down sensory suite supporting a simplified world model whose departures from the actual physical environment are handled by the mechanical competence of the legged platform. Both behaviors are implemented on a version of the legged RHex platform, and experiments spanning almost a kilometer (thousands of body lengths) in various challenging settings are conducted.
Feudal Dialogue Management with Jointly Learned Feature Extractors

Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains. Recently, Feudal Dialogue Management (FDM), has shown to increase the scalability to large domains by decomposing the dialogue management decision into two steps, making use of the domain ontology to abstract the dialogue state in each step. In order to abstract the state space, however, previous work on FDM relies on handcrafted feature functions. In this work, we show that these feature functions can be learned jointly with the policy model while obtaining similar performance, even outperforming the handcrafted features in several environments and domains.
Towards a knowledge-based probabilistic and context-aware social recommender system

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).
A survey on real-time event detection from the Twitter data stream

The proliferation of social networking services has resulted in a rapid growth of their user base, spanning across the world. The collective information generated from these online platforms is overwhelming, in terms of both the amount of content produced every moment and the diversity of topics discussed. The real-time nature of the information produced by users has prompted researchers to analyse this content, in order to gain timely insight into the current state of affairs. Specifically, the microblogging service Twitter has been a recent focus of researchers to gather information on events occurring in real time. This article presents a survey of a wide variety of event detection methods applied to streaming Twitter data, classifying them according to shared common traits, and then discusses different aspects of the subtasks and challenges involved in event detection. We believe this survey will act as a guide and starting point for aspiring researchers to gain a structured view on state-of-the-art real-time event detection and spur further research in this direction.
A Context-aware Convolutional Natural Language Generation model for Dialogue Systems

Natural language generation (NLG) is an important component in spoken dialog systems (SDSs). A model for NLG involves sequence to sequence learning. State-of-the-art NLG models are built using recurrent neural network (RNN) based sequence to sequence models (Ondřej Dušek and Filip Jurčíček, 2016a). Convolutional sequence to sequence based models have been used in the domain of machine translation but their application as Natural Language Generators in dialogue systems is still unexplored. In this work, we propose a novel approach to NLG using convolutional neural network (CNN) based sequence to sequence learning. CNN-based approach allows to build a hierarchical model which encapsulates dependencies between words via shorter path unlike RNNs. In contrast to recurrent models, convolutional approach allows for efficient utilization of computational resources by parallelizing computations over all elements, and eases the learning process by applying constant number of nonlinearities. We also propose to use CNN-based reranker for obtaining responses having semantic correspondence with input dialogue acts. The proposed model is capable of entrainment. Studies using a standard dataset shows the effectiveness of the proposed CNN-based approach to NLG.
Neural Dialogue Context Online End-of-Turn Detection

This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker’s utterances and collocutor’s utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker’s and collocutor’s multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker’s acoustic sequential features and collocutor’s linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker’s utterances and collocutor’s utterances into consideration.
Effect of the solar wind conditions on the ionospheric equivalent current systems

We employ a global magnetohydrodynamics (MHD) model, namely the PPMLR-MHD model, to investigate the effect of the solar wind conditions, such as the interplanetary magnetic field (IMF) clock angle, southward IMF magnitude and solar wind speed, on the average pattern of the ionospheric equivalent current systems (ECS). A new method to derive ECS from the MHD model is proposed and applied, which takes account of the oblique magnetic field line effects. The model results indicate that when the IMF is due northward, the ECS are very weak while the current over polar region is stronger than the lower latitude; when the IMF rotates southward, the two-cell current system dominates, the eastward electrojet on the afternoon sector and the westward electrojet on the dawn sector increase rapidly while the westward electrojet is stronger than the eastward electrojet. Under southward IMF, the intensity of the westward electrojet and eastward electrojet both increase with the increase of the southward IMF magnitude and solar wind speed, and the increase is very sharp for the westward electrojet. Furthermore, we compare the geomagnetic perturbations on the ground represented by the simulated average ECS with the observation-based statistical results under similar solar wind conditions. It is found that the model results generally match with the observations, but the underestimation of the eastward equivalent current on the dusk sector is the main limitation of the present model.