Reviewerly/exHarmony huggingface dataset:
P2P-O: A Purchase-To-Pay Ontology for Enabling Semantic Invoices

Small and medium-sized enterprises increasingly adopt electronic invoices and digitized purchase-to-pay processes. A purchase-to-pay process begins with making a purchase order and ends with completing the payment process. Even when organizations adopt electronic invoices, knowledge work in such processes is characterized by assimilating information distributed over heterogeneous sources among different stages in the process. By integrating such information and enabling a shared understanding of stakeholders in such processes, ontologies and knowledge graphs can serve as an appropriate infrastructure for enabling knowledge services. However, no suitable ontology is available for current electronic invoices and digitized purchase-to-pay processes. Therefore, this paper presents P2P-O, a dedicated purchase-to-pay ontology developed in cooperation with industry domain experts. P2P-O enables organizations to create semantic invoices, which are invoices following linked data principles. The European Standard EN 16931-1:2017 for electronic invoices was the main non-ontological resource for developing P2P-O. The evaluation approach is threefold: (1) to follow ontology engineering best practices, we applied OOPS! (OntOlogy Pitfall Scanner!) and OntoDebug; (2) to evaluate competency questions, we constructed a purchase-to-pay knowledge graph with RML technologies and executed corresponding SPARQL queries; (3) to illustrate a P2P-O-based knowledge service and use case, we implemented an invoicing dashboard within a corporate memory system and thus enabled an entity-centric view on invoice data. Organizations can immediately start experimenting with P2P-O by generating semantic invoices with provided RML mappings.
One-Shot Imitation Drone Filming of Human Motion Videos

Imitation learning has recently been applied to mimic the operation of a cameraman in existing autonomous camera systems. To imitate a certain demonstration video, existing methods require users to collect a significant number of training videos with a similar filming style. Because the trained model is style-specific, it is challenging to generalize the model to imitate other videos with a different filming style. To address this problem, we propose a framework that we term "one-shot imitation filming", which can imitate a filming style by "seeing" only a single demonstration video of the target style without style-specific model training. This is achieved by two key enabling techniques: 1) filming style feature extraction, which encodes sequential cinematic characteristics of a variable-length video clip into a fixed-length feature vector; and 2) camera motion prediction, which dynamically plans the camera trajectory to reproduce the filming style of the demo video. We implemented the approach with a deep neural network and deployed it on a 6 degrees of freedom (DOF) drone system by first predicting the future camera motions, and then converting them into the drone's control commands via an odometer. Our experimental results on comprehensive datasets and showcases exhibit that the proposed approach achieves significant improvements over conventional baselines, and our approach can mimic the footage of an unseen style with high fidelity.
A Block-Level RNN Model for Resume Block Classification

Resume block classification is the most significant step in resume information extraction. However, the existing algorithms applied to resume block classification are all the general text classification algorithms, which failed to consider the contextual order of each block within a resume. In order to improve the performance of resume block classification, we propose in this paper a block-level bidirectional recurrent neural network model that makes full use of the contextual order relationship among different resume blocks. The experimental results show that the average F1-score value of our model on three 1,400 real resume datasets is 6% to 9% higher than the existing methods.
Scalable Coding of 360-degree Video for Streaming Adaptation at 5G Network Edges

The huge amount of data that is necessary to capture the full field-of-view (FoV) in omnidirectional video, i.e., 360°, imposes the use of highly efficient compressed formats as well as adaptive broadcast and streaming mechanisms, such as those foreseen for 5G networks. To cope with the demanding requirements of 360° video streaming over 5G networks, this work proposes a scalable 360° video coding architecture, by enabling adaptation through the Multi-Access Edge Computing (MEC) server in two different domains of the spherical visual content, namely spatial resolution and FoV. In the proposed architecture two-layers are encoded from the input 360° video content: (i) the base-layer (BL), encoding each 360° image as a whole, at a lower spatial resolution; (ii) the enhancement-layer (EL) encoding each spherical image as a set of multiple FoVs with higher spatial resolution. Such arrangement enables flexible stream adaptation for the smart decision algorithms to be implemented at the MEC server, enabling significant reduction of the overall bit rate through the radio interface. The simulation results show that the proposed scalable coding scheme allows a great deal of bit rate savings across the 5G network, achieving 36% of bit rate saving, on average, for a 90° FoV in comparison with conventional single-layer coding.
Pattern Analysis for Transaction Fraud Detection

Many people have dealt with fraud through their bank or credit card company, but are unaware of how it is detected by these entities. This work aims to clearly explore the possible methods used in exposing fraud and form an idea as to what qualities are attributed to it. The data sets we used had multiple useful attributes as well as plenty of entries to train our machine learning algorithms. Our research first led us to find what attributes have the highest correlation to fraud. This was found by creating a correlation chart in our descriptive analysis. That information was then used to train our machine learning algorithms during our predictive and prescriptive analyses. Decision Tree, Linear Regression, and Logistic Regression algorithms were used on the data in combination to reach a surprising level of accuracy. This line of research gives more in-depth information to the general populous who may be struggling with fraud related issues.
Three New Constructions of Asymptotically Optimal Periodic Quasi-Complementary Sequence Sets With Small Alphabet Sizes

Quasi-complementary sequence sets (QCSSs) play an important role in multi-carrier code-division multiple-access (MC-CDMA) systems. They can support more users than perfect complementary sequence sets in MC-CDMA systems. It is desirable to design QCSSs with good parameters that are a trade-off of large set size, small periodic maximum magnitude correlation and small alphabet size. The main results are to construct new infinite families of QCSSs that all have small alphabet size and asymptotically optimal periodic maximum magnitude correlation. In this paper, we propose three new constructions of QCSSs using additive characters over finite fields. Notably, these QCSSs have new parameters and small alphabet sizes. Using the properties of characters and character sums, we determine their maximum periodic correlation magnitudes and prove that these QCSSs are asymptotically optimal with respect to the lower bound.
Dynamic Quaternion Extreme Learning Machine

Quaternion random neural network trained by extreme learning machine (Q-ELM) becomes attractive for its good learning capability and generalization performance in 3 or 4-dimensional (3/4-D) hypercomplex data learning. But how to determine the optimal network architecture is always challenging in Q-ELM. To this end, a novel error-minimization-based Q-ELM (QEM-ELM) that only needs to optimize the output weights of the newly added neuron is developed in this brief. On this basis, a dynamic network construction scheme is further extended on Q-ELM, leading to a novel DQ-ELM, where the hidden nodes can be dynamically recruited or deleted according to the significance to network performance. The network parameters can be optimized and the architecture can be self-adapted simultaneously. Simulation results on many benchmark datasets demonstrate that the proposed QEM-ELM and DQ-ELM achieve good generalization performance by preserving a compact network size.
MuLan: Multilevel Language-based Representation Learning for Disease Progression Modeling

Modeling patient disease progression using Electronic Health Records (EHRs) is crucial to assist clinical decision making. In recent years, deep learning models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) have shown great success in handling sequential multivariate data, such as EHRs. Despite their great success, it is often difficult to interpret and visualize patient disease progression learned from these models in a meaningful yet unified way. In this work, we present MuLan: a Multilevel Language-based representation learning framework that can automatically learn a hierarchical representation for EHRs at entry, event, and visit levels. We validate MuLan on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results showed that these unified multilevel representations can be utilized not only for interpreting and visualizing the latent mechanism of patients' septic shock progressions but also for early detection of septic shock.
Graph Force Learning

Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning.
Ship Detection in Satellite Optical Imagery

Deep learning ship detection in satellite optical imagery suffers from false positive occurrences with clouds, landmasses, and man-made objects that interfere with correct classification of ships, typically limiting class accuracy scores to 88%. This work explores the tensions between customization strategies, class accuracy rates, training times, and costs in cloud-based solutions. We demonstrate how a custom U-Net can achieve 92% class accuracy over a validation dataset and 68% over a target dataset. Trade-offs with extensions to this effort, such as a refining method of offline hard example mining, are also considered.
A Quantified Coalgebraic van Benthem Theorem

Abstract The classical van Benthem theorem characterizes modal logic as the bisimulation-invariant fragment of first-order logic; put differently, modal logic is as expressive as full first-order logic on bisimulation-invariant properties. This result has recently been extended to two flavours of quantitative modal logic, viz. fuzzy modal logic and probabilistic modal logic. In both cases, the quantitative van Benthem theorem states that every formula in the respective quantitative variant of first-order logic that is bisimulation-invariant, in the sense of being nonexpansive w.r.t. behavioural distance, can be approximated by quantitative modal formulae of bounded rank. In the present paper, we unify and generalize these results in three directions: We lift them to full coalgebraic generality, thus covering a wide range of system types including, besides fuzzy and probabilistic transition systems as in the existing examples, e.g. also metric transition systems; and we generalize from real-valued to quantale-valued behavioural distances, e.g. nondeterministic behavioural distances on metric transition systems; and we remove the symmetry assumption on behavioural distances, thus covering also quantitative notions of simulation.
Legal professionalism in a context of Uberisation

From around the millennial turn, Australia was to the fore among common law countries in the liberalisation of legal practice with a range of radical reforms, such as the ownership of firms by non-lawyers and listing on the stock exchange. Albeit not peculiar to Australia, technological innovations, including remote working, digitalised platforms and artificial intelligence (AI), are also dramatically changing the way law is practised. Invariably motivated by profit maximisation, the impact of these reforms poses discomfiting questions for the underlying values of legal professionalism. This article will overview the reforms that have occurred, drawing on a small study of NewLaw firms in Australia and the UK, to illustrate how the "Uberisation" of contemporary legal practice is contributing to a new incarnation of postprofessionalism. The article will also show how the injunction to work at home in response to COVID-19 has given "Uberisation" an adrenalin shot in the arm.
Denoising distant supervision for ontology lexicalization using semantic similarity measures

Ontology lexicalization aims to provide information about how the elements of an ontology are verbalized in a given language. Most ontology lexicalization techniques require labeled training data, which are usually generated automatically using the distant supervision technique. This technique is based upon the assumption that if a sentence contains two entities of a triple in a knowledge base, it expresses the relation stated in that triple. This assumption is very simplistic and would lead to generating wrong mappings between sentences and knowledge base triples. In other words, a sentence may contain two entities of a triple, but the relation of entities in the sentence differs from the relation of the triple. Such wrong mappings cause to generating wrong ontology lexicon entries. In this paper, a new method, called denoising distant supervision, is presented to reduce the wrong mappings between sentences and triples by taking the semantic similarity between sentences and the label of triples’ predicate into account. For this purpose, different semantic similarity measures are proposed, which use pre-trained word embeddings to calculate the semantic similarity between the sentences and the label of the triples relation. Then, the sentences whose semantic similarity is low are removed from the mapping. The proposed solution is evaluated in the M−ATOLL framework. The experimental results show that the quality of the generated ontology lexicon under the proposed solution is improved compared to state-of-the-art techniques.
SaccadeCam: Adaptive Visual Attention for Monocular Depth Sensing

Most monocular depth sensing methods use conventionally captured images that are created without considering scene content. In contrast, animal eyes have fast mechanical motions, called saccades, that control how the scene is imaged by the fovea, where resolution is highest. In this paper, we present the SaccadeCam framework for adaptively distributing resolution onto regions of interest in the scene. Our algorithm for adaptive resolution is a self-supervised network and we demonstrate results for end-to-end learning for monocular depth estimation. We also show preliminary results with a real SaccadeCam hardware prototype.
Augmenting Deep Learning with Relational Knowledge from Markov Logic Networks

Neuro-symbolic learning, where deep networks are combined with symbolic knowledge can help regularize the model and control overfitting. In particular, for applications where data instances are not independent, domain knowledge can be used to specify relational dependencies which may be hard to infer purely from the data. Symbolic AI models such as Markov Logic networks (MLNs) which are based on first-order logic are designed to represent and reason with uncertain background knowledge. However learning and inference algorithms in such models is known to be slow and inaccurate. In this paper, we develop a novel model that combines the best of both worlds, namely, the scalable learning capabilities of DNNs and symbolic knowledge specified in MLNs. To do this, we infer symmetries in the data based on the relational knowledge encoded in an MLN knowledge base and train a Convolutional Neural Network (CNN) to learn kernels combining symmetrical variables. However, by doing this, we are forced to split the relational data into independent instances for CNN training which may result is a loss of relational dependencies adding noise/uncertainty to the learned model. Therefore, instead of a single model, we learn a distribution over the model parameters. Our experiments illustrate that our model outperforms purely-MLN or purely-DNN based models in several different problem domains.
Accurate feature point detection method exploiting the line structure of the projection pattern for 3D reconstruction

The 3D imaging methods using a grid pattern can satisfy real-time applications since they are fast and accurate in decoding and capable of producing a dense 3D map. However, like the other spatial coding methods, it is difficult to achieve high accuracy as is the case for time multiplexing due to the effects of the inhomogeneity of the scene. To overcome those challenges, this paper proposes a convolutional-neural-network-based method of feature point detection by exploiting the line structure of the grid pattern projected. First, two specific data sets are designed to train the model to individually extract the vertical and horizontal stripes in the image of a deformed pattern. Then the predicted results of trained models with images from the test set are fused in a unique skeleton image for the purpose of detecting feature points. Our experimental results show that the proposed method can achieve higher location accuracy in feature point detection compared with previous ones.
Query expansion based on term distribution and DBpedia features

Query Expansion (QE) approaches that involve the reformulation of queries by adding new terms to the initial user query, are intended to ameliorate the vocabulary mismatch between the query keywords and the documents’ in Information Retrieval Systems (IRS). One big issue in QE is the selection of the right candidate terms for expansion. For this purpose Linked Data can be used, as a valuable resource, for providing additional expansion features such as the values of sub- and super classes of resources. The underlying research question is whether interlinked data and vocabulary items provide features which can be taken into account for query expansion. In this paper, we introduced a new QE approach that aimed at improving IRS by using the well-known distribution based method Bose-Einstein statistics (Bo1) as well as Linked Data from the knowledge base DBpedia using different numbers of expansion terms. We evaluated the effectiveness of each method individually as well as their combinations using two Text REtrieval Conference (TREC) test collections. Our approach has lead to significant improvement in terms of precision, recall, Mean Average Precision (MAP) at rank 10, and normalized Discounted Cumulative Gain (nDCG) at different ranks compared to Pseudo Relevance Feedback (PRF) that we used as a baseline. The results show that the inclusion of semantic annotations clearly improves the retrieval performance over the baseline method.
Data Reduction and Deep-Learning Based Recovery for Geospatial Visualization and Satellite Imagery

The storage, retrieval, and distribution of data are some critical aspects of big data management. Data scientists and decision-makers often need to share large datasets and make decisions on archiving or deleting historical data to cope with resource constraints. A potential approach to mitigate such problems is to reduce big datasets into smaller ones, which will not only lower storage requirements but also allow light load transfer over the network. Carefully prepared data by removing redundancies, along with a machine learning model capable of reconstructing the whole dataset from its reduced version, can improve the storage scalability, data transfer, and speed up the overall data management pipeline. In this paper, we explore some data reduction strategies for big datasets, while ensuring that the data can be transferred and used ubiquitously by all stakeholders, i.e., the entire dataset can be reconstructed with high quality whenever necessary. Our approach guarantees a minimum of 75% data size reduction, where the reconstruction accuracy observed is as high as 98.75% on an average for geospatial meteorological data (e.g., soil moisture and albedo), and 99.09% for satellite imagery. We propose a novel variance based reduction technique that can further reduce the data size without losing the accuracy significantly, and adopt various deep learning approaches for high-quality reconstruction.
Experimental optimal generation of hybrid entangled states in photonic quantum walks

While the existence of disorders is commonly believed to weaken the unique properties of quantum systems, recent progress has predicted that it can exhibit a counterintuitive enhanced effect on the behavior of entanglement generation, which is even independent of the chosen initial conditions and physical platforms. However, to achieve a maximally entangled state in such disordered quantum systems, the key limitation of this is the scarcity of an infinite coherence time, which makes its experimental realization challenging. Here, we experimentally investigate the entanglement entropy dynamics in a photonic quantum walk with disorders in time. Through the incorporation of a classic optimization algorithm, we experimentally demonstrate that such disordered systems can relax to a high-entanglement hybrid state at any given time step. Moreover, this prominent entangling ability is universal for a wide variety of initial conditions. Our results may inspire achieving a well-controlled entanglement generator for quantum computation and information tasks.
Application of the hierarchy analysis method for the choice of the computer mathematics system for the IT-sphere specialists preparation

Abstract Peculiarities of the usage of the hierarchy analysis method for the making decision on the choice of the most efficient computer mathematics system (CMS) used for the preparation of the IT-sphere specialists are in the focus of the research. Eight alternatives were selected for the hierarchy analysis method and seven criteria were considered. The alternative is chosen by means of figuring of the priority vector corresponding to each alternative. The alternative with the highest value is considered the right decision. The scale of impact was used that is the scale of evaluations for the paired comparisons of the advantage of the first object over other with meaning from 1 to 9. The basic criteria for which the alternatives are calculated were described in the course research; their characteristics were chosen, in particular functionalities, studying materials, on-line mode, mobile application, license, language support and an open code. The matrix of the paired comparisons for each criterion was constructed and numerical characteristics of these matrices were calculated – the highest own value, the index of compatibility and the index of the consistency of formulas. Every matrix is composed of the expert evaluations for the alternatives, which are the systems of computer mathematics used in the courses of mathematics. Based on the results of the calculations carried out the impact of the alternative was shown and the system having the highest impact was chosen.