Reviewerly/exHarmony huggingface dataset:
Anomaly-Based Risk Detection Using Digital News Articles

Enterprise risk management is a well established methodology used in industry. This area relies heavily on risk owners and their expert opinion. In this work, we present an approach to a semi-automated risk detection for companies using anomaly detection. We present various anomaly detection algorithms and present an approach on how to apply them on multidimensional data sources like news articles and stock data to automatically extract possible risks. To do so, NLP methods, including sentiment analysis, are used to extract numeric values from news articles, which are needed for anomaly analysis. The approach is evaluated by conducting interview questionnaires with domain experts. The results show that the presented approach is a useful tooling that helps risk owners and domain expert to find and detect potential risks for their companies.
Robust Rule Based Neural Network Using Arithmetic Fuzzy Inference System

Deep Neural Networks (DNNs) are currently one of the most important research areas of Artificial Intelligence (AI). Various type of DNNs have been proposed to solve practical problems in various fields. However the performance of all these types of DNNs degrades in the presence of feature noise. Expert systems are also a key area of AI that are based on rules. In this work we wish to combine the advantages of these two areas. Here, we present Rule-Based Neural Networks (RBNNs) where each neuron is a Fuzzy Inference System (FIS). RBNN can be trained to learn various regression and classification tasks. It has relatively a few trainable parameters. It is robust to (input) feature noise and it provides a good prediction accuracy even in the presence of large feature noise. The learning capacity of the RBNN can be enhanced by increasing the number of neurons, number of rules and number of hidden layers. The effectiveness of RBNNs is demonstrated by learning real world regression and classification tasks.
Quantum Displacements Dictated by Machine Learning Principles: Towards Optimization of Quantum Paths

In Physics the energy of any system represents a sensitive variable because of it depends the functionality and evolution of system at time. Thus the deep knowledge of the interactions of system might be a remarkable advantage as to anticipate stochastic fluctuations as well as minimize the errors at the done measurements. Thus, in this paper a particular attention is paid on the mathematical characteristics of the quantum mechanics evolution operator when it is projected onto a full scenario of principles based at Machine Learning. In concrete the case of pass of charged particle through a bunch of charged particles can be perceived as a system exhibiting oscillations because the attraction and repulsion forces experienced along the space-time trajectory. The fact that the energy can be controllable by using free parameters can be advantageous in the sense of providing a learning to the system in order to optimize the total energy at key space-time coordinates.
Evaluation of Deep Learning Techniques in Human Activity Recognition

The increasingly acceptance of Internet of Things (IoT) devices adversely contribute to the accumulation of massive amounts of data which challenges for the adoption of techniques capable to handle it. This paper first presents overall points of deep learning, and IoT principles. After that, this paper uses the human recognition activity scenario to evaluate two DL models: the Convolutional Neural Network (CNN), and the Recurrent Neural Network. At last, a benchmark with the state-of-the-art is presented. The main findings evidenced the suitability of the proposed model; the CNN performed a mean accuracy rate of 93%, and therefore it is likely to be embedded in an IoT device. There is room for improvements, namely, the ability to recognize additional human activities, and to include more testing scenarios.
HiSAT: Hierarchical Framework for Sentiment Analysis on Twitter Data

Social media websites such as Twitter have become so indispensable today that people use them almost on a daily basis for sharing their emotions, opinions, suggestions and thoughts. Motivated by such behavioral tendencies, the purpose of this study is to define an approach to automatically classify the tweets on Twitter data into two main classes, namely, hate speech and non-hate speech. This provides a valuable source of information in analyzing and understanding target audiences and spotting marketing trends. We thus propose HiSAT, a Hierarchical framework for Sentiment Analysis on Twitter data. Sentiments/opinions in tweets are highly unstructured-and do not have a proper defined sequence. They constitute a heterogeneous data from many sources having different formats, and express either positive or negative, or neutral sentiment. Hence, in HiSAT we conduct Natural Language Processing encompassing tokenization, stemming and lemmatization techniques that convert text to tokens; as well as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques that convert text sentences into numeric vectors. These are then fed as inputs to Machine learning algorithms within the HiSAT framework; more specifically, Random Forest, Logistic Regression and Naïve Bayes are used as text-binary classifiers to detect hate speech and non-hate speech from the tweets. Results of experiments performed with the HiSAT framework show that Random Forest outperforms the others with a better prediction in estimating the correct labels (with accuracy above the 95% range). We present the HiSAT approach, its implementation and experiments, along with related work and ongoing research.
Experimental Design of a Quantum Convolutional Neural Network Solution for Traffic Sign Recognition

Quantum convolutional neural networks encapsulate and combine complex features of both Artificial intelligence and principles of Quantum Mechanics to develop complex systems capable of solving intricate and detailed machine learning problems such as object recognition. In this paper, we propose an experimental design for a quantum convolutional neural network to be used in conjunction with the GTSRB dataset and compare the results to that of both a traditional neural network and a hybrid neural network, enabling an accurate comparison between the various methods. A detailed look into the mathematical and architectural constructs of the QCNN will be depicted alongside the potential flaws that Quantum computing may incur.
Design and Implementation of Virtual Roaming Control Method Based on Pen Tablet

A natural game interaction method can bring a good game experience to users, but pen-type interaction, which is one of the natural human-computer interaction methods, rarely appears in games, especially three-dimensional games. The main reason is that most pen-type interactive devices are developed for two-dimensional applications such as writing and painting. Whether the pen-based interaction can be extended from two-dimensional to three-dimensional is the key to whether the pen-based interaction can become one of the leading interactive methods of the game. The pen tablet is one of the most widely used pen-type interactive devices, and a virtual tour is an essential part of the game. Therefore, this paper takes this as a breakthrough and proposes a virtual roaming control method based on the pen tablet. First, we analyze the performance characteristics of the pen tablet and get the data information available for the pen tablet. Then, we design various virtual roaming actions such as walking, running, jumping, viewing angle rotation, view zooming, space-free displacement, acceleration, and deceleration and establish an association with the operation of the pen tablet. At the same time, the maximum likelihood function constraint was added to improve the error tolerance rate of viewing angle rotation. Finally, two games were developed to verify their effectiveness. The experimental results show that the pen tablet performs well in completing virtual roaming tasks, especially when users need to achieve similar writing and roaming tasks without switching devices back and forth. This maintains the user’s habit of writing with pen and paper and improves the interactive experience. The method is a new exploration of the application of pen-based interaction in games, which lays the foundation for future game development based on pen-based interaction.
Application of Virtual Reality Based on Multisensor Data Fusion in Theater Space and Installation Art

The application of Virtual Reality (VR) in theater space and installation art is the general trend, and it can be seen in large stage plays and installation art exhibitions. However, as the current VR is not mature enough, it is difficult to perfectly fulfill the exhibition requirements of large theaters, so this paper aims to change this situation by using VR based on multisensor data fusion. In this paper, a data fusion algorithm based on multisensors is designed, which improves the data transmission efficiency and delay of the VR system, so that VR can have a better viewing experience in theater space and installation art. And, through the questionnaire survey and actual interview, the actual feelings of VR audience in theater space and installation art are investigated and studied. Through the experimental analysis of this paper, the algorithm in this paper has high reliability and can improve the experience of using VR. The interview results and results show that the main application of VR in theater space is manifested in three aspects: multiangle and all-round viewing, multiroute viewing, and man-machine interaction in art galleries. The application of VR in installation art is mainly reflected in the perception of installation materials.
Exploring Latent Space Using a Non-linear Dimensionality Reduction Algorithm for Style Transfer Application

A latent space represents data by embedding them in a multidimensional vector space. In this way an abstract estimation of any complex domain could be created. Empirical approach for exploring the latent space generated by known pre-trained model of human face images using a nonlinear dimensionality reduction algorithm is presented in this paper. One aim was to find more detailed entangled features (beard and hair color) between the real images and their representation, in artistic face portrait application. Experimental results showed that sparse vectors in the latent space could be useful to obtain optimal results with relatively low effort. To evaluate our work, we present the results of a survey that was sent to 25 thousand subscribers of the real world application and got around 360 responses. The main goal of the survey was to find some quantitative measurements that can be used in our research.
Forecasting POI Occupation with Contextual Machine Learning

The increasing availability of historical information has emphasized the importance to explore, understand and extract value from it in order to achieve both short-term goals and strategic objectives. Intelligent techniques to handle heterogeneous data, together with user preferences, may be beneficial for end users; among them we can mention recommendation systems, which are able to guide users through huge catalogues of alternative items. This kind of systems represent an invaluable help not only for the users, who can feel disoriented in presence of so many alternatives at their disposal, but also for service providers or sellers, which can benefit from inferred hidden knowledge and guide towards particular items the choices of specific groups of users sharing some common preferences. This influence capability of recommendation systems can be particularly useful in the touristic domain, where the need to control and manage the level of crowding of POIs (Points Of Interest) has become a pressing need in the recent years. In this paper we study the role of contextual information in determining POI occupations and we explore how machine learning and deep learning technologies can help in producing good POI occupation forecasters by enriching historical information with contextual one. Throughout the paper we refer to a real-world application scenario regarding the touristic visits performed in Verona, a municipality in Northern Italy, between 2014 and 2019.
Feature Ranking from Random Forest Through Complex Network’s Centrality Measures

The volume of available data in recent years has rapidly increased. In consequence, datasets commonly end up with many irrelevant features. That increase may disturb human understanding and even lead to poor machine learning models. This research proposes a novel feature ranking method that employs trees from a Random Forest to transform a dataset into a complex network to which centrality measures are applied to rank the features. That process takes place by representing each tree as a graph where all the tree features are vertices on this graph, and the links within the nodes (father $$\rightarrow $$ child) of the tree are represented by a weighted edge between the two respective vertices. The union of all graphs from individual trees leads to the complex network. Then, three centrality measures are applied to rank the features in the complex network. Experiments were performed in eighty-five supervised classification datasets, with a variation in the feature noise level, to evaluate our novel method. Results show that centrality measures in non-oriented complex networks are comparable and may be correlated to the Random Forest's variable importance ranking algorithm. Vertex strength and eigenvector outperformed the Random Forest in 40% noise datasets, with a not statistically different result at a 95% confidence level.
On an Extensible Conceptual Data Model by a Non-formal Example

The investigation subject of this paper is an extensible conceptual data model. Formal semantics of an algebra of hierarchical relations is proposed. Extensibility concept of conceptual data model is discussed. A non-formal example of extending the conceptual data model is considered. Conceptual schema is defined as a set of OPENMath attribution objects.
Automatic Classification of Stigmatizing Articles of Mental Illness: The Case of Portuguese Online Newspapers

The stigma related to mental health continues to be present in online newspapers, where mental diseases are often used metaphorically to refer to entities or situations outside the clinical of mental health. This project explores the implementation of Artificial Intelligence and Natural Language Processing techniques for the task of automatically classifying stigmatizing articles with references to the mental disorders of schizophrenia and psychosis. This work is implemented in Portuguese online news articles, collected from the Arquivo.pt repository, a public repository of archived Portuguese web pages, and can be adapted to other languages or similar problems. Nine machine and deep learning algorithms were implemented, most of them yielding results with a precision above 90%. In addition, the automatic detection of articles topics was also performed, through topic modeling with the top2vec model, which allowed concluding that the stigmatization of mental health occurs, essentially, in Economics and Politics related news. The results confirm the existence of stigma in Portuguese newspapers (52% of the 978 articles collected) and the effectiveness of the use of Artificial Intelligence models to detect it. Additionally, a set of 978 articles collected and manually classified with the classes ["stigmatizing", "literal"] is obtained.
Models and Query Languages for Temporal Property Graph Databases

Although property graphs are increasingly being studied by the research community, most authors do not consider the evolution of such graphs over time. However, this is needed to capture a wide range of real-world situations, where changes normally occur. In this work, we propose a temporal model and a high level query language for property graphs and analyse the real-world cases where they can be useful, with focus on transportation networks (like road and river networks) equipped with sensors that measure different variables over time. Many kinds of interesting paths arise in this scenario. To efficiently compute these paths, also path indexing techniques must be studied.
Neural Word Sense Disambiguation to Prune a Large Knowledge Graph of the Italian Cultural Heritage

In this paper, we describe our recent findings in interlinking the ArCo Italian cultural heritage entities to the well known Getty Art and Architecture (GVP) Thesaurus through the automated extraction of candidate entities from textual descriptions and the subsequent pruning of ambiguous out-of-domain entities using Neural Word Sense Disambiguation. The disambiguation task is particularly complex since, as detailed in this paper, we map Italian entities in the Arco cultural heritage onto lexical concepts in English (such as those in the GVP Thesaurus). To date, the majority of entity linking and word sense disambiguation systems are designed to work with English and to operate with general purpose sense inventories and knowledge bases, such as DBpedia, BabelNet and WordNet. To address this challenging entity linking and disambiguation task, we adapted a state-of-the-art Neural Word Sense Disambiguation to work in this multi-language setting. We here describe our adaptation process and discuss preliminary experimental results.
Summarizing Edge-Device Data via Core Items

In this work, we consider the problem of summarizing a data stream through an item-based summary using core items. We consider an IoT setting, where computing such summaries at the edge devices instead of emitting the whole data stream can drastically reduce the network traffic and speed up further processing. Core items of a data stream are the items with the highest values for a given monotone submodular utility function. To create stream summaries, we propose the SoftSieving approach for parallel processing with low memory consumption and fast execution time while attaining acceptable utility gain. Through extensive experiments with real-world datasets, we show the suitability of our approach and its superiority over state-of-the-art competitors.
Interactive Knowledge Graph Querying Through Examples and Facets

Knowledge graphs are a highly useful form of information representation. To assist end users in understanding the contents of a given graph, multiple lines of research have proposed and studied various data exploration tools. Despite major advancements, it remains highly non-trivial to find entities of interest in a large-scale graph where the user requirements may depend on the initially unknown contents and structure of the graph. We provide in this paper a formal approach for the problem, which combines in a novel way ideas from two approaches: query-by-example and faceted search. We first provide a novel model for user interaction that includes different formal semantics for interpreting the answers. The semantics correspond to natural interpretations of feedback in faceted search. We show that for each of these semantics, any sequence of user feedback may be encoded as a SPARQL query under standard closed-world semantics. We then turn to the problem of iteratively choosing which user feedback to prompt in order to optimize the expected length of interaction. We show that depending on the probabilities of user answers, the optimal choice of question may depend on the semantics; in contrast, we show that for a natural way of estimating the probabilities, the optimal choices coincide.
Generalization Aware Compression of Molecular Trajectories

Molecular Dynamics (MD) simulation is often used to study properties of various chemical interactions in domains such as drug development when executing real experimental studies are costly and/or unsafe. Studying trajectories generated from MD simulations provides detailed atomic level location data of every atom in the experiment. The analysis of this data leads to an atomic and molecular level understanding of interactions among the constituents of the system-of-interest, however, the data is extremely large and poses formidable storage and processing challenges in the analyses and querying of associated atom level motion trajectories. We take a first step towards applying domain-specific generalization techniques for trajectory compression algorithms towards reducing the storage requirements and speeding up the processing of within-distance queries over MD simulation data. We demonstrate that this generalization-aware compression, when applied to the dataset used in this case study yields significant efficiency improvements, without sacrificing the effectiveness of within-distance queries for threshold-based detection of molecular events of interest, such as the formation of hydrogen-bonds (H-Bonds).
An Algebra for Path Manipulation in Graph Databases

A key characteristic of current graph query languages is their support for path queries. Although a path query looks for paths in a graph database, current graph query languages are restricted to return just the source and target nodes connected by each solution path. Therefore, the user is not able to manipulate the elements (nodes and edges) of the resulting paths. In order to overcome such restriction, this paper presents an algebra for path manipulation. Inspired by the relational algebra, we defined the operators of selection, projection, node-based join, edge-based join, node-based cartesian product, edge-based cartesian product, union, intersection and difference. These operators are closed under sets of paths, i.e. the input and the output are sets of paths. We study the algebraic properties of the operators and describe use cases that justify the usefulness of the algebra.
Data Narrative Crafting via a Comprehensive and Well-Founded Process

Data narration is the activity of crafting narratives supported by facts extracted from data exploration and analysis, using interactive visualizations. While data narration has recently attracted much attention, the process of crafting data narratives is loosely documented and has not yet been formally described. In this article, we propose a comprehensive and well-founded process to fill this need. It aims at (i) supporting the complete cycle of data narration, from the exploration of data to the visual rendering of the narrative, (ii) being flexible enough to cover a wide range of crafting practices, and (iii) being well founded upon with a conceptual model of the domain.
Termination and Confluence of an Extended CHASE Algorithm

Many requirements of systems managing and analyzing large volumes of data are interconnected and should therefore be realized together. In this research project, we use a fundamental algorithm of database theory – the CHASE – to address those requirements in a unified manner. While highly expressive, the language of the CHASE is still inadequate to formulate many problems of practical importance. Extending the CHASE with additional features would increase its range of applications, but might jeopardize its key features confluence, safe termination and efficiency. In this work, we demonstrate that calculating basic linear algebra operations with the CHASE is feasible after extending the algorithm with negation and a restricted set of scalar functions. We discuss how confluence and termination of the CHASE are influenced by these extensions.