Reviewerly/exHarmony huggingface dataset:
Jointly Optimized Neural Coreference Resolution with Mutual Attention

Coreference resolution aims at recognizing different forms in a document which refer to the same entity in the real world. Although many models have been proposed and achieved success, there still exist some challenges. Recent models that use recurrent neural networks to obtain mention representations ignore dependencies between spans and their proceeding distant spans, which will lead to predicted clusters that are locally consistent but globally inconsistent. In addition, these models are trained only by maximizing the marginal likelihood of gold antecedent spans from coreference clusters, which will make some gold mentions undetectable and cause unsatisfactory coreference results. To address these challenges, we propose a neural coreference resolution model. It employs mutual attention to take into account the dependencies between spans and their proceeding spans directly (use attention mechanism to capture global information between spans and their proceeding spans). And our model is trained by jointly optimizing mention clustering and imbalanced mention detection, which enables it to detect more gold mentions in a document to make more accurate coreference decisions. Experimental results on the CoNLL-2012 English dataset show that our model can detect the most gold mentions and achieve the state-of-the-art coreference performance compared with baselines.
Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning

Models have been proposed to extract temporal patterns from longitudinal electronic health records (EHR) for clinical predictive models. However, the common relations among patients (e.g., receiving the same medical treatments) were rarely considered. In this paper, we propose to learn patient similarity features as phenotypes from the aggregated patient-medical service matrix using non-negative matrix factorization. On real-world medical claim data, we show that the learned phenotypes are coherent within each group, and also explanatory and indicative of targeted diseases. We conducted experiments to predict the diagnoses for Chronic Lymphocytic Leukemia (CLL) patients. Results show that the phenotype-based similarity features can improve prediction over multiple baselines, including logistic regression, random forest, convolutional neural network, and more.
Resilient Logic Programs: Answer Set Programs Challenged by Ontologies

We introduce resilient logic programs (RLPs) that couple a non-monotonic logic program and a first-order (FO) theory or description logic (DL) ontology. Unlike previous hybrid languages, where the interaction between the program and the theory is limited to consistency or query entailment tests, in RLPs answer sets must be ‘resilient’ to the models of the theory, allowing non-output predicates of the program to respond differently to different models. RLPs can elegantly express ∃∀∃-QBFs, disjunctive ASP, and configuration problems under incompleteness of information. RLPs are decidable when a couple of natural assumptions are made: (i) satisfiability of FO theories in the presence of closed predicates is decidable, and (ii) rules are safe in the style of the well-known DL-safeness. We further show that a large fragment of such RLPs can be translated into standard (disjunctive) ASP, for which efficient implementations exist. For RLPs with theories expressed in DLs, we use a novel relaxation of safeness that safeguards rules via predicates whose extensions can be inferred to have a finite bound. We present several complexity results for the case where ontologies are written in some standard DLs.
Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior

We introduce a method to learn imitative policies from expert demonstrations that are interpretable and manipulable. We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic. We achieve manipulability by integrating this automaton into planning, so that changes to the automaton have predictable effects on the learned behavior. These qualities allow a human user to first understand what the model has learned, and then either correct the learned behavior or zero-shot generalize to new, similar tasks. We build upon previous work by no longer requiring additional supervised information which is hard to collect in practice. We achieve this by using a deep Bayesian nonparametric hierarchical model. We test our model on several domains and also show results for a real-world implementation on a mobile robotic arm platform.
Factors Of Forming Regional Policy: Expectations Of Consumers Of Educational Services

The research is devoted to the problem of expectations of consumers of educational services. In the work, such notions as “the market of educational services”, “educational service”, “actors of educational services”, “the consumer of educational services”, “satisfaction with the education quality” were analyzed. I it is the mentioned data that must be one of the most important factors of forming the educational policy of a region. Education is one of the main factors forming the society of the state and its regions, and ensuring its development. Prerequisite of progressive development of the society is the extended reproduction of labor and education potential of the state which can be implemented due to the development of the marketing system of educational services in modern conditions. This is why the educational field, particularly the higher professional one, faces new tasks, the most important of them being the provision of production and infrastructure areas with highly qualified specialists. Till the end of the 20th century, the educational model aimed at producers (traditional) has reigned - the state was the chief customer, the source of financing, and the only consumer of specialists trained. In recent years, due to the development of modern information technologies, the initiative has passed to customers of educational services, and a new model of the market of educational services focused completely on customers has been formed.
Blockchain-Based Privacy Protection Unified Identity Authentication

Blockchain is a specific type of distributed ledger with the features of high transparency and publicly verifiable which may threaten users' data privacy. Due to this concern, we propose a blockchain-based privacy protection identity authentication scheme in this paper. On the one hand, we take advantage of the decentralized feature of blockchain to safely store the hash value of users' identity information in the block of the blockchain, so that these information cannot be tampered and can be verified by the verifier. On the other hand, we introduce a set of key derivation algorithms into the scheme to provide anonymity and unlinkability. The scheme is applicable to scenarios with unified authentication and information privacy protection requirements.
FACT: Fused Attention for Clothing Transfer with Generative Adversarial Networks

Clothing transfer is a challenging task in computer vision where the goal is to transfer the human clothing style in an input image conditioned on a given language description. However, existing approaches have limited ability in delicate colorization and texture synthesis with a conventional fully convolutional generator. To tackle this problem, we propose a novel semantic-based Fused Attention model for Clothing Transfer (FACT), which allows fine-grained synthesis, high global consistency and plausible hallucination in images. Towards this end, we incorporate two attention modules based on spatial levels: (i) soft attention that searches for the most related positions in sentences, and (ii) self-attention modeling long-range dependencies on feature maps. Furthermore, we also develop a stylized channel-wise attention module to capture correlations on feature levels. We effectively fuse these attention modules in the generator and achieve better performances than the state-of-the-art method on the DeepFashion dataset. Qualitative and quantitative comparisons against the baselines demonstrate the effectiveness of our approach.
Siamese Visual Tracking With Deep Features and Robust Feature Fusion

Trackers based on fully-convolutional Siamese networks regard tracking as a process of learning a similarity function. By utilizing shallow networks and off-line training, Siamese trackers can achieve high tracking speed and perform well in some simple scenes. However, due to the less semantic information and the invariant template, Siamese trackers still have a gap compared with the state-of-the-art methods in complex scenes and other challenging problems (Occlusion, Deformation, etc.). In this paper, we propose a Siamese tracking algorithm with deep features and robust feature fusion (SiamDF). The improved ResNet-18 network is utilized to replace the traditional shallow network and extract the deep features with more semantic information. For eliminating the negative effect of padding and making better use of the deep network, the proposed algorithm adopts the spatial aware sampling strategy to overcome the strict translation invariance. Meanwhile, a final response map with high quality can be obtained by using the multi-layer feature fusion. Thus, the tracker can significantly reduce the impact of the distractors in complex scenes. In addition, an adaptive feature information fusion is adopted to update the template, so that the algorithm can adapt to various changes of the target appearance. Objective evaluation on the OTB100 dataset shows that the precision and the overlap success can reach 0.852 and 0.658 respectively. Moreover, the EAO value evaluated on the VOT2016 database can reach 0.336. These results demonstrate that our algorithm can effectively improve the tracking performance and perform favorably in both robustness and accuracy.
An eikonal equation based path planning method using polygon decomposition and curve evolution

Abstract Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.
Optimal Prefix Free Codes with Partial Sorting

We describe an algorithm computing an optimal prefix free code for n unsorted positive weights in time within O ( n ( 1 + lg α ) ) ⊆ O ( n lg n ) , where the alternation α ∈ [ 1 . . n − 1 ] approximates the minimal amount of sorting required by the computation. This asymptotical complexity is within a constant factor of the optimal in the algebraic decision tree computational model, in the worst case over all instances of size n and alternation α . Such results refine the state of the art complexity of Θ ( n lg n ) in the worst case over instances of size n in the same computational model, a landmark in compression and coding since 1952. Beside the new analysis technique, such improvement is obtained by combining a new algorithm, inspired by van Leeuwen’s algorithm to compute optimal prefix free codes from sorted weights (known since 1976), with a relatively minor extension of Karp et al.’s deferred data structure to partially sort a multiset accordingly to the queries performed on it (known since 1988). Preliminary experimental results on text compression by words show α to be polynomially smaller than n, which suggests improvements by at most a constant multiplicative factor in the running time for such applications.
A Pilot Study on Mandarin Chinese Cued Speech

Cued Speech (CS) is a communication system developed for deaf people, which exploits hand cues to complement speechreading at the phonetic level. Currently, it is estimated that CS has been adapted to over 60 languages; however, no official CS system is available for Mandarin Chinese. This article proposes a novel and efficient Mandarin Chinese CS system, satisfying the main criterion that the hand coding constitutes a complement to the lips' movements. We propose to code vowels [i, u, y] as semiconsonants when they are followed by other Mandarin finals, which reduces the number of Mandarin finals to be coded from 36 to 16. We establish a coherent similarity between Mandarin Chinese and French vowels for the remaining 16 finals/vowels, which allows us to take advantage of the French CS system. Furthermore, by investigating the lips viseme distribution based on a new corpus, an optimal allocation of the 16 Mandarin vowels to different hand positions is obtained. A Gaussian classifier was used to evaluate the average separability of different allocated vowel groups, which gives 92.08%, 92.33%, and 92.73% for the three speakers, respectively. The consonants are mainly designed according to their similarities with the French CS system, as well as some considerations on the special Mandarin consonants. In our system, the tones of Mandarin are coded with head movements.
Generating Well-Formed Answers by Machine Reading with Stochastic Selector Networks

Question answering (QA) based on machine reading comprehension has been a recent surge in popularity, yet most work has focused on extractive methods. We instead address a more challenging QA problem of generating a well-formed answer by reading and summarizing the paragraph for a given question.For the generative QA task, we introduce a new neural architecture, LatentQA, in which a novel stochastic selector network composes a well-formed answer with words selected from the question, the paragraph and the global vocabulary, based on a sequence of discrete latent variables. Bayesian inference for the latent variables is performed to train the LatentQA model. The experiments on public datasets of natural answer generation confirm the effectiveness of LatentQA in generating high-quality well-formed answers.
Asymptotic Risk of Bézier Simplex Fitting

The B&amp;apos;ezier simplex fitting is a novel data modeling technique which utilizes geometric structures of data to approximate the Pareto set of multi-objective optimization problems. There are two fitting methods based on different sampling strategies. The inductive skeleton fitting employs a stratified subsampling from skeletons of a simplex, whereas the all-at-once fitting uses a non-stratified sampling which treats a simplex as a single object. In this paper, we analyze the asymptotic risks of those B&amp;apos;ezier simplex fitting methods and derive the optimal subsample ratio for the inductive skeleton fitting. It is shown that the inductive skeleton fitting with the optimal ratio has a smaller risk when the degree of a B&amp;apos;ezier simplex is less than three. Those results are verified numerically under small to moderate sample sizes. In addition, we provide two complementary applications of our theory: a generalized location problem and a multi-objective hyper-parameter tuning of the group lasso. The former can be represented by a B&amp;apos;ezier simplex of degree two where the inductive skeleton fitting outperforms. The latter can be represented by a B&amp;apos;ezier simplex of degree three where the all-at-once fitting gets an advantage.
Finite-time stabilization of fractional-order delayed bidirectional associative memory neural networks

This paper investigates the finite-time stabilization problem of fractional-order delayed bidirectional associative memory neural networks with the fractional-order α ∈ (1, 2).Based on feedback control, a sufficient condition is derived to realize the finite-time stabilization of systems by using the Cauchy-Schwartz inequality and the generalized Gronwall inequality.Furthermore, two sufficient conditions are directly given to realize the finite-time stabilization of systems via partial feedback control.In particular, these conditions can be expressed as some algebraic inequalities, so the settling time can be easily calculated in practical applications.Finally, some numerical examples are provided to present the feasibility and effectiveness of our main results.
Optimized interval 2-tuple linguistic aggregation operator based on PGSA and its application in MAGDM

This study proposes a multiple attribute group decision- making (MAGDM) approach on the basis of the plant growth simulation algorithm (PGSA) and interval 2-tuple weighted average operators for uncertain linguistic weighted aggregation (ULWA).We provide an example for illustration and verification and compare several aggregation operators to indicate the optimality of the assembly method.In addition, we present two comparisons to demonstrate the practicality and effectiveness of the proposed method.The method can be used not only to aggregate MAGDM problems but also to solve multi-granularity uncertain linguistic information.Its high reliability, easy programming, and high-speed calculation can improve the efficiency of ULWA characteristics.Finally, the proposed method has the exact characteristics for linguistic information processing and can effectively avoid information distortion and loss.
The Impact of Treewidth on Grounding and Solving of Answer Set Programs

In this paper, we aim to study how the performance of modern answer set programming (ASP) solvers is influenced by the treewidth of the input program and to investigate the consequences of this relationship. We first perform an experimental evaluation that shows that the solving performance is heavily influenced by treewidth, given ground input programs that are otherwise uniform, both in size and construction. This observation leads to an important question for ASP, namely, how to design encodings such that the treewidth of the resulting ground program remains small. To this end, we study two classes of disjunctive programs, namely guarded and connection-guarded programs. In order to investigate these classes, we formalize the grounding process using MSO transductions. Our main results show that both classes guarantee that the treewidth of the program after grounding only depends on the treewidth (and the maximum degree, in case of connection-guarded programs) of the input instance. In terms of parameterized complexity, our findings yield corresponding FPT results for answer-set existence for bounded treewidth (and also degree, for connection-guarded programs) of the input instance. We further show that bounding treewidth alone leads to NP-hardness in the data complexity for connection-guarded programs, which indicates that the two classes are fundamentally different. Finally, we show that for both classes, the data complexity remains as hard as in the general case of ASP.
MAP-Vis: A Distributed Spatio-Temporal Big Data Visualization Framework Based on a Multi-Dimensional Aggregation Pyramid Model

During the exploration and visualization of big spatio-temporal data, massive volume poses a number of challenges to the achievement of interactive visualization, including large memory consumption, high rendering delay, and poor visual effects. Research has shown that the development of distributed computing frameworks provides a feasible solution for big spatio-temporal data management and visualization. Accordingly, to address these challenges, this paper adopts a proprietary pre-processing visualization scheme and designs and implements a highly scalable distributed visual analysis framework, especially targeted at massive point-type datasets. Firstly, we propose a generic multi-dimensional aggregation pyramid (MAP) model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid. The proposed MAP model can support the simultaneous hierarchical aggregation of time, space, and attributes, and also later transformation of the derived aggregates into discrete key-value pairs for scalable storage and efficient retrieval. Using the generated MAP datasets, we develop an open-source distributed visualization framework (MAP-Vis). In MAP-Vis, a high-performance Spark cluster is used as a parallel preprocessing platform, while distributed HBase is used as the massive storage for the generated MAP data. The client of MAP-Vis provides a variety of correlated visualization views, including heat map, time series, and attribute histogram. Four open datasets, with record numbers ranging from the millions to the tens of billions, are chosen for system demonstration and performance evaluation. The experimental results demonstrate that MAP-Vis can achieve millisecond-level query response and support efficient interactive visualization under different queries on the space, time, and attribute dimensions.
Active learning in multiple-class classification problems via individualized binary models

We propose a unified algorithm for both categorical and ordinal labeled data in multiclass classification problems, where each subject belongs to one class only. In training an effective classification rule, it is critical that one have and rely on a sufficient amount of reliably labeled data. As information on the training sample sizes needed to obtain satisfactory performance is lacking, we adopt an adaptive subject recruiting scheme with an experimental design criterion to shorten the training process. Because this kind of active learning method is naturally conducted in a sequential manner, we adopt sequential analysis to control the required sample size and ensure the performance of the final classifier. Additionally, we report its statistical properties and numerical results from using synthesized and real data.
Artificial God Optimization - A Creation

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence, the term &amp;quot;Artificial God Optimization (AGO)&amp;quot; is coined in this paper. AGO is a new field, which is invented in this work. A new Algorithm titled &amp;quot;God Particle Swarm Optimization (GoPSO)&amp;quot; is created and applied on various benchmark functions. The World&amp;#39;s first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm, which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.