Reviewerly/exHarmony huggingface dataset:
Holocene background concentrations and actual enrichment factors of metals in sediments from Ria Formosa, Portugal

Metal history in a natural system was described since the early Holocene by analysing the preserved subsurface sediment record and present-day surface sediments. Normalized geochemical data from six sediment cores (129 samples) was compared with 28 surface samples. Pre-anthropic sedimentary environments allowed the definition of local natural Background Values. Enrichment Factors were then used for elements discrimination in terms of natural and anthropogenic inputs to the system. While subsurface results displayed a similar behaviour in all cores, pointing to largely undisturbed system, surface sediments show significant contamination, with high enrichment factors for As, Cu, Pb, and Zn. Other metal pollutants have generally low enrichment values, suggesting natural conditions. Surface results were reproduced as metal enrichment maps which evidenced anthropogenic sources in specific locations. This work reveals the importance of combining subsurface and surface geochemical data with mapping techniques in order to better evaluate the environmental quality of a natural system.
Local Average of Nearest Neighbors: Univariate Time Series Imputation

The imputation of time series is one of the most important tasks in the homogenization process, the quality and precision of this process will directly influence the accuracy of the time series predictions.This paper proposes two simple algorithms, but quite powerful for univariate time series imputation process, which are based on the means of the nearest neighbors for the imputation of missing data.The first of them Local Average of Neighbors Neighbors (LANN) calculates the missing value from the average of the previous neighbor and the following neighbor to the missing value.The second Local Average of Neighbors Neighbors+ (LANN+), considers a threshold parameter, which allows to differentiate the calculation of the missing values according to the difference between the neighbors: for the differences less than or equal to the threshold the missing value is calculated through of LANN and for major differences the missing value is calculated from the average of the four closest neighbors, two previous and two subsequent to the missing value.Imputation results on different time series are promising.
Apply event extraction techniques to the judicial field

While hearing a case, the judge must fully understand the case and make clear the disputed issues between parties, which is the cornerstone of a fair trial. However, manual mining the key of the case from the statements of the litigious parties is a bottleneck, which currently relies on methods like keyword searching and regular matching. To complete this time-consuming and laborious task, judges need to have sufficient prior knowledge of cases belonging to different causes of action. We try to apply the technology of event extraction to faster capture the focus of the case. However, there is no proper definition of events that contains types of focus in the judicial field. And existing event extraction methods can't solve the problem of multiple events sharing the same arguments or trigger words in a single sentence, which is very common in case materials. In this paper, we present a mechanism to define focus events, and a two-level labeling approach, which can solve multiple events sharing the same argument or trigger words, to automatically extract focus events from case materials. Experimental results demonstrate that the method can obtain the focus of case accurately. As far as we know, this is the first time that event extraction technology has been applied to the judicial field.
Biclustering with missing data

Biclustering is a statistical learning methodology that simultaneously partitions rows and columns of a rectangular data array into homogeneous subsets. Biclustering is known to be an NP-hard problem, and therefore various heuristic approaches have been proposed. These strategies break down when dealing with any degree of missing data in a two-way table of data values. To address this issue, we propose a new biclustering method based on the work of Li in 2014 [18]. Numerical results show our approach performs well on moderate-sized test cases with even a large missing-value percentage (99%+). To illustrate the practical usefulness of the method we provide two case studies. The first is an agricultural application where rows represent plant varieties, columns represent planting locations, and data are yield values. The other is a well-known movie rater application where rows represent raters, columns represent movies, and data are ratings.
A PHD-Based Particle Filter for Detecting and Tracking Multiple Weak Targets

Joint detection and tracking weak target is a challenging problem whose complexity is intensified when there are multiple targets present at the same time. Some Probability Hypothesis Density (PHD) based track-before-detect (TBD) particle filters (PHD-TBD) are proposed to solve this issue; however, the performance is unsatisfactory especially when the number of targets is large because some assumptions in PHD are violated. We propose to modify the general PHD-TBD filter in two aspects to make the PHD processing available for TBD scenarios. First, the distribution of false alarms is approximated as the Poisson distribution through a threshold method, and then a clustering technique is proposed to solve the overestimation of the target number. A typical TBD scenario is used to test the effectiveness of the proposed method. Simulation results indicate that the proposed method outperforms the general method in terms of estimation accuracy and computational complexity.
PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data

Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volume and fast veracity. Most of the current BN structure learning algorithms have shortcomings facing big data. First, learning a BN structure from the entire big dataset is an expensive task which often ends in failure due to memory constraints. Second, it is quite difficult to select a learner from numerous BN structure learning algorithms to consistently achieve good learning accuracy. Lastly, there is a lack of an intelligent method that merges separately learned BN structures into a well structured BN network. To address these shortcomings, we introduce a novel parallel learning approach called PEnBayes (Parallel Ensemble-based Bayesian network learning). PEnBayes starts with an adaptive data preprocessing phase that calculates the Appropriate Learning Size and intelligently divides a big dataset for fast distributed local structure learning. Then, PEnBayes learns a collection of local BN Structures in parallel using a two-layered weighted adjacent matrix-based structure ensemble method. Lastly, PEnBayes merges the local BN Structures into a global network structure using the structure ensemble method at the global layer. For the experiment, we generate big data sets by simulating sensor data from patient monitoring, transportation, and disease diagnosis domains. The Experimental results show that PEnBayes achieves a significantly improved execution performance with more consistent and stable results compared with three baseline learning algorithms.
A Hierarchy of Monadic Effects for Program Verification Using Equational Reasoning

One can perform equational reasoning about computational effects with a purely functional programming language thanks to monads. Even though equational reasoning for effectful programs is desirable, it is not yet mainstream. This is partly because it is difficult to maintain pencil-and-paper proofs of large examples. We propose a formalization of a hierarchy of effects using monads in the Coq proof assistant that makes equational reasoning practical. Our main idea is to formalize the hierarchy of effects and algebraic laws like it is done when formalizing hierarchy of traditional algebras. We can then take advantage of the sophisticated rewriting capabilities of Coq to achieve concise proofs of programs. We also show how to ensure the consistency of our hierarchy by providing rigorous models. We explain the various techniques we use to formalize a rich hierarchy of effects (with nondeterminism, state, probability, and more), to mechanize numerous examples from the literature, and we furthermore discuss extensions and new applications.
A Neural-Network based Code Summarization Approach by Using Source Code and its Call Dependencies

Code summarization aims at generating natural language abstraction for source code, and it can be of great help for program comprehension and software maintenance. The current code summarization approaches have made progress with neural-network. However, most of these methods focus on learning the semantic and syntax of source code snippets, ignoring the dependency of codes. In this paper, we propose a novel method based on neural-network model using the knowledge of the call dependency between source code and its related codes. We extract call dependencies from the source code, transform it as a token sequence of method names, and leverage the Seq2Seq model for code summarization using the combination of source code and call dependency information. About 100,000 code data is collected from 1,000 open source Java proejects on github for experiment. The large-scale code experiment shows that by considering not only the code itself but also the codes it called, the code summarization model can be improved with the BLEU score to 33.08.
Group maturity, team efficiency, and team effectiveness in software development: A case study in a CMMI‐DEV Level 5 organization

Abstract Group aspects are essential to the success of software projects; however, almost no research regarding group maturity in CMMI‐DEV Level 5 organizations has been conducted. The objective of this work is to investigate group maturity and its relationship to the efficiency and effectiveness of Agile Software Development teams in a CMMI‐DEV Level 5 organization. A case study was conducted, and three data collection methods were used: a validated questionnaire was given to nine agile software teams (59 people) to measure specific aspects of group maturity, team efficiency, and team effectiveness; the effort to develop Use Case Points and a group rate (related to productivity and salary) were collected to investigate team efficiency, and focus group sessions and semi‐structured interviews were conducted with project managers and team leaders to explore group issues in software development. Aspects of group maturity were correlated with team effectiveness and team efficiency measures. Communication was found to be strongly correlated with team effectiveness and group rate, and conformity and the clarity of roles and goals were found to be strongly correlated with team efficiency. The results suggest that group maturity has a positive influence on software teams in CMMI‐DEV Level 5 organizations.
Joint Spatial and Radical Analysis Network For Distorted Chinese Character Recognition

Recently, a novel radical analysis network (RAN) has been proposed for Chinese character recognition (CCR). The key idea is treating a Chinese character as a composition of radicals rather than a single character class. Compared with traditional learning ways, two serious issues in CCR, i.e., enormous categories and limited training data, can be effectively alleviated. In this paper, we further excavate the potential capability of RAN. First, we validate RAN can reduce the equivariant requirement of regular convolutional neural network (CNN) owing to finer modeling and a local-to-global recognition process, especially considering the rotation transformation. This modeling approach of RAN can be regarded as one instance of compositional models. Second, we propose a joint spatial and radical analysis network (JSRAN) to handle more general situation in which the test data includes kinds of affine transformations. No matter for rotated printed Chinese character or natural scene, JSRAN can outperform RAN and traditional CNN. Finally, according to visualization analysis, we empirically explain why JSRAN can yield a remarkable improvement.
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model

In this paper, we address the ice-start problem, i.e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs. This setting is representative of the real-world machine learning applications. For instance, in the health care domain, obtaining every single measurement comes with a cost. We propose Icebreaker, a principled framework for elementwise training data acquisition. Icebreaker introduces a full Bayesian Deep Latent Gaussian Model (BELGAM) with a novel inference method, which combines recent advances in amortized inference and stochastic gradient MCMC to enable fast and accurate posterior inference. By utilizing BELGAM’s ability to fully quantify model uncertainty, we also propose two information acquisition functions for imputation and active prediction problems. We demonstrate that BELGAM performs significantly better than previous variational autoencoder (VAE) based models, when the data set size is small, using both machine learning benchmarks and real world recommender systems and health-care applications. Moreover, Icebreaker not only demonstrates improved performance compared to baselines, but it is also capable of achieving better test performance with less training data available.
Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Human Activity Recognition (HAR) is a challenging task in the field of human-related signal processing. Owing to the development of wearable sensing technology, an emerging research approach in HAR is to identify user-performed tasks by using data collected from wearable sensors. In this paper, we propose a novel system for monitoring and recognizing daily living activities using an off-the-shelf smart band and two smart shoes. The system aims at providing a useful tool for solving problems regarding body part placement, fusion of multimodal sensors and feature selection for a specific set of activities. The system collects inertial and plantar pressure data at wrist and foot to analyze and then, extract, select important features for recognition. We construct and compare two predictive models of classifying activities from the reduced feature set. A comparison of the classification for each wearable device and a fusion scheme is provided to identify the best body part for activity recognition: either the wrist or the feet. This comparison also demonstrated the effective HAR performance of the proposed system.
Agents Teaching Agents: A Survey on Inter-agent Transfer Learning

While reinforcement learning (RL) has helped artificial agents solve challenging tasks, high sample complexity is still a major concern. Inter-agent teaching -- endowing agents with the ability to respond to instructions from others -- has been responsible for many developments towards scaling up RL. RL agents that can leverage instructions can learn tasks significantly faster than agents that cannot take advantage of such instruction. That said, the inter-agent teaching paradigm presents many new challenges due to, among other factors, differences between the agents involved in the teaching interaction. This paper is a summary of our JAAMAS article, where we propose two frameworks that provide a comprehensive view of the challenges associated with inter-agent teaching. We highlight state-of-the-art solutions, open problems, prospective applications, and argue that new research in this area should be developed in the context of the proposed frameworks.
Can AI Fix Medical Records?

Digitized patient charts were supposed to revolutionize medical practice. Artificial intelligence could help unlock their potential. Digitized patient charts were supposed to revolutionize medical practice. Artificial intelligence could help unlock their potential.
Tell Me More: A Dataset of Visual Scene Description Sequences

We present a dataset consisting of what we call image description sequences, which are multi-sentence descriptions of the contents of an image. These descriptions were collected in a pseudo-interactive setting, where the describer was told to describe the given image to a listener who needs to identify the image within a set of images, and who successively asks for more information. As we show, this setup produced nicely structured data that, we think, will be useful for learning models capable of planning and realising such description discourses.
Generalized Hidden Parameter MDPs:Transferable Model-Based RL in a Handful of Trials

There is broad interest in creating RL agents that can solve many (related) tasks and adapt to new tasks and environments after initial training. Model-based RL leverages learned surrogate models that describe dynamics and rewards of individual tasks, such that planning in a good surrogate can lead to good control of the true system. Rather than solving each task individually from scratch, hierarchical models can exploit the fact that tasks are often related by (unobserved) causal factors of variation in order to achieve efficient generalization, as in learning how the mass of an item affects the force required to lift it can generalize to previously unobserved masses. We propose Generalized Hidden Parameter MDPs (GHP-MDPs) that describe a family of MDPs where both dynamics and reward can change as a function of hidden parameters that vary across tasks. The GHP-MDP augments model-based RL with latent variables that capture these hidden parameters, facilitating transfer across tasks. We also explore a variant of the model that incorporates explicit latent structure mirroring the causal factors of variation across tasks (for instance: agent properties, environmental factors, and goals). We experimentally demonstrate state-of-the-art performance and sample-efficiency on a new challenging MuJoCo task using reward and dynamics latent spaces, while beating a previous state-of-the-art baseline with &gt; 10× less data. Using test-time inference of the latent variables, our approach generalizes in a single episode to novel combinations of dynamics and reward, and to novel rewards.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference.Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.The code is available at https://github.com/matenure/GNN_planner.
Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation

With the rapid development of Role-Playing Games (RPGs), players are now allowed to edit the facial appearance of their in-game characters with their preferences rather than using default templates. This paper proposes a game character auto-creation framework that generates in-game characters according to a player's input face photo. Different from the previous methods that are designed based on neural style transfer or monocular 3D face reconstruction, we re-formulate the character auto-creation process in a different point of view: by predicting a large set of physically meaningful facial parameters under a self-supervised learning paradigm. Instead of updating facial parameters iteratively at the input end of the renderer as suggested by previous methods, which are time-consuming, we introduce a facial parameter translator so that the creation can be done efficiently through a single forward propagation from the face embeddings to parameters, with a considerable 1000x computational speedup. Despite its high efficiency, the interactivity is preserved in our method where users are allowed to optionally fine-tune the facial parameters on our creation according to their needs. Our approach also shows better robustness than previous methods, especially for those photos with head-pose variance. Comparison results and ablation analysis on seven public face verification datasets suggest the effectiveness of our method.
A fusion framework to extract typical treatment patterns from electronic medical records

Electronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify “right patient”, “right drug”, “right dose”, “right route”, and “right time” from doctor order information. We propose a fusion framework to extract typical treatment patterns based on multi-view similarity Network Fusion (SNF) method. The multi-view SNF method involves three similarity measures: content-view similarity, sequence-view similarity and duration-view similarity. An EMR dataset and two metrics were utilized to evaluate the performance and to extract typical treatment patterns. Experimental results on a real-world EMR dataset show that the multi-view similarity network fusion method outperforms all the single-view similarity measures and also outperforms the existing similarity measure methods. Furthermore, we extract and visualize typical treatment patterns by clustering analysis. The extracted typical treatment patterns by combining doctor order content, sequence, and duration views can provide data-driven guidelines for artificial intelligence in medicine and help clinicians make better decisions in clinical practice.
GA and PSO hybrid algorithm for ANN training with application in Medical Diagnosis

Since the diagnosis of a disorder in patients is very important for proper treatment, so it is essential that the results of these diagnostic tests are properly categorized. ANNs have the computational capability of efficiently mapping the data collected from medical tests to the results. In this paper, it is analyzed that how hybridizing the training process of ANNs with Evolutionary algorithms such as PSO and GA can enhance the mapping capability of ANNs and overcome the shortcomings of Gradient Descent training algorithm. When compared to Gradient Descent, the proposed GA-PSO-BP hybrid training algorithm, demonstrated an average increment of 5% in the average testing accuracies. While the PSO-BP hybrid training algorithm displayed an average improvement of 4% as compared to Gradient Descent.