Reviewerly/exHarmony huggingface dataset:
Interactive task learning via embodied corrective feedback

Abstract This paper addresses a task in Interactive Task Learning (Laird et al. IEEE Intell Syst 32:6–21, 2017). The agent must learn to build towers which are constrained by rules, and whenever the agent performs an action which violates a rule the teacher provides verbal corrective feedback: e.g. “No, red blocks should be on blue blocks”. The agent must learn to build rule compliant towers from these corrections and the context in which they were given. The agent is not only ignorant of the rules at the start of the learning process, but it also has a deficient domain model, which lacks the concepts in which the rules are expressed. Therefore an agent that takes advantage of the linguistic evidence must learn the denotations of neologisms and adapt its conceptualisation of the planning domain to incorporate those denotations. We show that by incorporating constraints on interpretation that are imposed by discourse coherence into the models for learning (Hobbs in On the coherence and structure of discourse, Stanford University, Stanford, 1985; Asher et al. in Logics of conversation, Cambridge University Press, Cambridge, 2003), an agent which utilizes linguistic evidence outperforms a strong baseline which does not.
Incorporating Boundary and Category Feature for Nested Named Entity Recognition

In the natural language processing (NLP) field, it is fairly common that an entity is nested in another entity. Most existing named entity recognition (NER) models focus on flat entities but ignore nested entities. In this paper, we propose a neural model for nested named entity recognition. Our model employs a multi-label boundary detection module to detect entity boundaries, avoiding boundary detection conflict existing in the boundary-aware model. Besides, our model with a boundary detection module and a category detection module detects entity boundaries and entity categories simultaneously, avoiding the error propagation problem existing in current pipeline models. Furthermore, we introduce multitask learning to train the boundary detection module and the category detection module to capture the underlying association between entity boundary information and entity category information. In this way, our model achieves better performance of entity extraction. In evaluations on two nested NER datasets and a flat NER dataset, we show that our model outperforms previous state-of-the-art models on nested and flat NER.
Investigation of Mutual-Influence among Blockchain Development Communities and Cryptocurrency Price Changes

This paper aims to identify and model relationships between cryptocurrencies market price changes and topic discussion occurrences on social media. The considered cryptocurrencies are the two highest in value at the moment, Bitcoin and Ethereum. At the same time, topics were realized through a classification of the comments gained from the Reddit social media platform, implementing a Hawkes model. The results highlight that it is possible to identify some interactions among the considered features, and it appears that some topics are indicative of certain types of price movements. Specifically, the discussions concerning issues about government, trading and Ethereum cryptocurrency as an exchange currency, appear to affect Bitcoin and Ethereum prices negatively. The discussions of investment appear to be indicative of price rises, while the discussions related to new decentralized realities and technological applications is indicative of price falls.
Question-Answering using Keyword Entries in the Oil&amp;Gas Domain

To improve the efficiency and accuracy of knowledge access in the oil industry, we propose to build a question-answer system for domain users. The system applies the state-of-the-art natural language processing techniques to analyze the domain knowledge in the form of keywords and their explanations. To this end, we collect over 20,000 keyword entries from reliable resources in the oil domain. The system then uses four different feature extraction techniques, including deep learning models, over these keyword entries to generate features, which are used to compute similarity between any given user question and keyword entries. Eventually, the keyword entries with the highest similarity scores are chosen as the answers and presented to the user. Our empirical evaluation shows that the traditional TF-IDF methods for feature extraction outperforms the other methods in our testing dataset.
A Spark-based Distributed Dragonfly Algorithm for Feature Selection

Dragonfly algorithm is an intelligent group optimization algorithm. In this paper, dragonfly algorithm is well used in the field of feature selection. However, dragonfly algorithm has the problem of falling into local optimal solution, which reduces the performance of feature selection and classification. Therefore, this paper proposes a binary Dragonfly optimization algorithm based on spark, which integrates the global optimization ability of dragonfly algorithm with the parallel computing ability of spark, greatly improving the performance of the algorithm. Experimental results show that binary Dragonfly algorithm has better performance than traditional particle swarm optimization algorithm and genetic algorithm. The binary Dragonfly algorithm based on spark solves the problem of falling into the local optimal solution, improves the running speed of the algorithm, can effectively deal with massive data, and greatly enhances the performance of the algorithm.
New Approaches and Tools for Ship Detection in Optical Satellite Imagery

Abstract Ship detection using optical satellite images is a very important task for the field of maritime security, either in search of lost ships or in maritime control of a commercial or military type. Added to this are the advances in the field of Computer Vision, especially in the use of models based on Artificial Intelligence, which allow the construction of robust and more precise detection systems. However, geographic scenarios, typical of a satellite image, limit the development of this type of system since they require the availability of a large number of images in different scenarios. In this paper, a new approach to Ship Detection is proposed using two new data sets labeled with horizontal bounding boxes (HBB). Likewise, a new labeling tool (DATATOOL) is presented that allows better organization and distribution of data. The new data sets, Peruvian Ship Dataset (PSDS) and Mini Ship Dataset (MSDS), have been generated from optical satellite images obtained from different sources. PSDS is created from 22 satellite images of PERUSAT-1 with 0.7m spatial resolution, giving a total of 1310 images. MSDS has been generated using Google Earth satellite images, generating 2993 images of 900x900 pixels. Ships are found both at sea or inshore. Finally, results of the tests using Deep Learning Algorithms such as YOLT and YOLOv4 are presented, following the approach and the proposed tools. Resource and source code available at https://gitlab.com/williamccondori/datatool
Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks

Measuring the productivity of an agent in a call center domain is a challenging task. Subjective measures are commonly used for evaluation in the current systems. In this paper, we propose an objective framework for modeling agent productivity for real estate call centers based on speech signal processing. The problem is formulated as a binary classification task using deep learning methods. We explore several designs for the classifier based on convolutional neural networks (CNNs), long-short-term memory networks (LSTMs), and an attention layer. The corpus consists of seven hours collected and annotated from three different call centers. The result shows that the speech-based approach can lead to significant improvements (1.57% absolute improvements) over a robust text baseline system.
Deep Neo-Fuzzy Neural Network and its Accelerated Learning

The using of a neo-fuzzy neuron as the main component of a neural network is considered. A deep neo-fuzzy neural network architecture and corresponding backpropagation algorithm are described.
Chapter-level entity relationship extraction method based on joint learning

In this paper, we explored the relationship extraction on the coarse granularity of chapter-level, and proposed an entity relationship extraction method (CH-MEL) for chapter-level joint learning. First, the self-attention mechanism is used to learn the chapter representation based on the chapter structure information. Secondly, an improved joint annotation strategy is adopted to realize the annotation of entity relationship. Then, the joint decoding of entity and relationship is realized by introducing the minimize expected loss(MEL) training function. The experimental results in ACE05 and Semeval2010 Task 8 data sets show that the proposed joint relationship extraction method (Ch-MEL) is superior to the existing joint learning model.
Improved feature decay algorithms for statistical machine translation

Abstract In machine-learning applications, data selection is of crucial importance if good runtime performance is to be achieved. In a scenario where the test set is accessible when the model is being built, training instances can be selected so they are the most relevant for the test set. Feature Decay Algorithms (FDA) are a technique for data selection that has demonstrated excellent performance in a number of tasks. This method maximizes the diversity of the n -grams in the training set by devaluing those ones that have already been included. We focus on this method to undertake deeper research on how to select better training data instances. We give an overview of FDA and propose improvements in terms of speed and quality. Using German-to-English parallel data, first we create a novel approach that decreases the execution time of FDA when multiple computation units are available. In addition, we obtain improvements on translation quality by extending FDA using information from the parallel corpus that is generally ignored.
Deep Neural Networks Characterization Framework for Efficient Implementation on Embedded Systems

Bio-inspired machine learning algorithms, such as Convolutional Neural Networks (CNNs), offer interesting solutions to complex real-life problems that cannot be simply modeled. Applications involving image recognition and object detection can greatly benefit from these approaches. Furthermore, their intrinsic and regular parallel structure offer opportunities regarding hardware acceleration. However, moving compute and memory-intensive CNNs to embedded systems while maintaining high energy-efficiency remains challenging. This paper presents the first step of a generic framework targeting the characterization of neural network algorithms to improve their implementation on embedded systems. The presented approach aims at reducing the gap between the fast-changing landscape of applications based on artificial intelligence and the hardware targets. The framework computes different metrics from neural network descriptions (such as computation and memory needs or data locality and reuse) to derive appropriate implementation strategies, or configurations of target architectures. Based on the outputs of the framework, new neural networks topologies can be quickly studied to reduce time-to-market of new systems.
Modeling Periodic Pattern with Self-Attention Network for Sequential Recommendation

Repeat consumption is a common phenomenon in sequential recommendation tasks, where a user revisits or repurchases items that (s)he has interacted before. Previous researches have paid attention to repeat recommendation and made great achievements in this field. However, existing studies rarely considered the phenomenon that the consumers tend to show different behavior periodicities on different items, which is important for recommendation performance. In this paper, we propose a holistic model, which integrates Graph Convolutional Network with Periodic-Attenuated Self-Attention Network (GPASAN) to model user's different behavior patterns for a better recommendation. Specifically, we first process all the users' action sequences to construct a graph structure, which captures the complex item connection and obtains item representations. Then, we employ a periodic channel and an attenuated channel that incorporate temporal information into the self-attention mechanism to model the user's periodic and novel behaviors, respectively. Extensive experiments conducted on three public datasets show that our proposed model outperforms the state-of-the-art methods consistently.
Quantum dynamics of a qutrit in a cavity filled with Kerr-like medium and intrinsic noise

We study the dynamics of the intrinsic noise (IN) model of a three-level atom (qutrit) in V-configuration. The qutrit is inside an open cavity with a Kerr-like medium. The quantifiers of the atomic population, entanglement and mixedness are evaluated under the physical parameters of the photon multiplicity, IN and the nonlinearity of both the unitary interaction and the Kerr-like medium. It is found that the generated entanglement and mixedness may be enhanced by filling the cavity with a nonlinear medium. The transfer of the energy between the two upper states and the lower state are very sensitive to the physical parameters. The entanglement and mixedness depend on the initial coherent intensity, the IN and the Kerr-like medium.
Research on classroom expression recognition based on deep circular convolution self-encoding network

The detection and recognition of facial expressions has been widely used in various fields of social life in recent years. When students are in class, it is very useful to strengthen the monitoring and management of students during class by using methods such as expression recognition. For student expression recognition, we propose a cycle convolution self-coding deep learning network. This network combines the ideas of convolutional self-encoding networks and cyclic consistency. Our experiments were performed on the classroom student expression recognition dataset, and the recognition accuracy reached 98.12%.
A Homomorphic Encryption Approach in a Voting System in a Distributed Architecture

It is considered that Electronic Votes is an alternative of safer systems, which provides greater confidence and transparency to the general public, but the problems exist in the organisms that manage this process. The main objective is to describe the Homomorphic encryption, understand why this algorithm is the basis for different schemes mentioned in the document. There are several implementations of homomorphic properties that generate the possibility to continue analyzing the different encryption schemes and application areas as visualized in electronic voting. We use the deductive methodology, logical analytics, and exploratory research to analyze the description of three schemes that have a Homomorphic base. It is a basis for properly selecting a homomorphic scheme that fits a specific need. It is concluded that the homomorphic schemes presented in this analysis have different uses of algorithms, but they do not leave behind the efficiency that all present, at the same time the reference is made to a distributed architecture that helps to have an additional point when dealing with the information obtained in electoral.
Multi-Scale Region-based Fully Convolutional Networks

A multi-scale R-FCN detection algorithm is presented to solve the problem of region-based full convolution network (R-FCN) in multi-scale object detection. Firstly, in order to solve the problem that R-FCN algorithm has limited receptive field and semantic information in single feature map detection, a multi-scale proposal box (proposal) is obtained by using multi-scale feature maps in the main network. At the same time, in order to make the feature maps of different layers have rich semantic information at the same time during the two-stage detection, the feature maps of different layers are fused from top to bottom. Finally, in order to make the two-stage position-sensitive score map also have good multiscale representation ability, a multi-layer shared convolution method is used to generate the position-sensitive score map. The experimental results on PASCAL VOC dataset show that the multiscale R-FCN algorithm in this paper is better than the original R-FCN algorithm in detection accuracy. At the same time, the detection map shows that the algorithm performs better in multi-scale object detection.
Uranium in animals, vegetables and minerals: landscape geochemical and biogeochemical expressions of the Four Mile West sedimentary uranium deposit, South Australia

The sediment-hosted uranium deposit at Four Mile West is buried by 130–150 m of sediments yet has a landscape as well as a surficial geochemical and biogeochemical expression of the mineral system. The landscape expression includes exposure of stratigraphy with now-oxidised redox overprinting (including ‘roll-fronts’) equivalent to those in the buried mineralisation. The landscape also includes morpho-tectonic expressions of faults that have been responsible for the development and preservation of mineralisation. Most importantly, this faulting has produced an embayment in the range front corresponding to a down-faulted area on the plains that has hosted throughflow of surface and groundwater and preservation of underlying, chemically reduced, ore-hosting Mesozoic and Cenozoic sediments. Landscape geochemical and biogeochemical expressions of buried mineralisation are best represented in U2/Th and selected trace elements, rather than simply elevated U contents in isolation. These associations are especially important for distinguishing between the U-rich expressions of buried, sediment-hosted mineralisation and laterally transported U-rich detritus from the adjacent ranges. The surficial geochemical and biogeochemical expression of the underlying mineralised substrate, with higher U2/Th, is best expressed in regolith carbonates, river red gums and potentially ants and macropod droppings. The mechanism proposed for this vertical geochemical transfer is in part tree-root depth penetration (or burrowing in the case of ants) but most importantly upward groundwater flux. Important groundwater fluctuations are connected to low-frequency–high-intensity rainfall periods that drive groundwater recharge and significantly raises the water-table towards the land surface, especially along faults and fractures near the range front and the immediately adjacent plains. During seasons with high rainfall, this groundwater rise may even express itself as water discharge at springs along the range front. Other media, such as stream sediments, soils and inland tea tree are more closely associated with expressing lateral physical dispersion and re-accumulation of U within the landscape. This study shows that surficial mapping, geochemistry and biogeochemistry within a landscape processes/landscape system context can be invaluable for the detection of, and exploration for, buried sediment-hosted uranium mineral systems, and most especially understanding more about the behaviour of chemical elements in the Australian landscape.
ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering

Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.
The New Normal: We Cannot Eliminate Cuts in Coinductive Calculi, But We Can Explore Them

Abstract In sequent calculi, cut elimination is a property that guarantees that any provable formula can be proven analytically. For example, Gentzen’s classical and intuitionistic calculi LK and LJ enjoy cut elimination. The property is less studied in coinductive extensions of sequent calculi. In this paper, we use coinductive Horn clause theories to show that cut is not eliminable in a coinductive extension of LJ , a system we call CLJ . We derive two further practical results from this study. We show that CoLP by Gupta et al. gives rise to cut-free proofs in CLJ with fixpoint terms, and we formulate and implement a novel method of coinductive theory exploration that provides several heuristics for discovery of cut formulae in CLJ .
Spotify Tailoring for Architectural Governance

Abstract Organisations usually tailor Agile methods to fit their needs best. Spotify has developed its own Agile culture to facilitate software development for hundreds of developers across multiple cities. The Spotify model has become influential among agile proponents and hence formed the basis of methods used in other organisations. We have identified a lack of research into agile architecture using the Spotify model. To explore How can architectural governance increase the autonomy of teams when using the Spotify model? , an intervention embedded case study was conducted in a multinational FinTech organisation, using the Spotify model. New processes were introduced by developing and evaluating an approach to Agile architectural governance. This approach incorporates a structural change and a change management process. We conducted 6 semi-structured open-ended interviews and direct observations of Agile practices. The collected data was analysed using Thematic Analysis and informed by some Grounded Theory techniques. The practitioners in our study report benefits of this evaluated approach. These benefits include transforming architectural based decision into decentralised based decision-making, strengthening the autonomy of squads through aligning architectural based decisions, sharing the architectural knowledge among the squads, and other benefits. We identify the characteristics and benefits of our evaluated approach to Agile architectural governance using the Spotify model. Also, we identify guidelines and challenges for those wishing to adopt this approach.
Intraoperative 3-dimensional Projection of Blood Vessels on Body Surface Using an Augmented Reality System

Preoperative understanding of the running pattern of blood vessels is an important factor to approach surgical fields safely. In 2 cases where the vascular abnormalities were estimated, we projected the blood vessels onto the surgical field using an augmented reality device HoloLens. A splint was made to allow the patient to be fixed while undergoing computed tomographic angiography. Three-dimensional (3D) data on the blood vessels, skin surfaces, bones, and the 3 chosen points for alignment were segmented and then projected onto the body surfaces as holograms using the HoloLens. Two types of projection for holograms were used: projection type 1-where the body contours were projected as a line, and projection type 2-where the body surface was projected as meshed skin type. By projecting projection type 2 rather than projection type 1, we gained a better understanding of the 3D anatomic findings and deformation characteristics, including the anatomic blood vessel variation and positional relationships between the organs and body surfaces. To some extent, we could make sure that the depth perception can be obtained by recognizing the bone, vessels, or tumor inside the meshed skin surface. Our new method allows the 3D visualization of blood vessels from the body surface, and helps understand the 3D anatomic variation of the blood vessels to be applied as long as the blood vessels can be visualized.