Reviewerly/exHarmony huggingface dataset:
Create Dashboards and Data Story with the Data &amp; Analytics Frameworks

In recent years, many data visualization tools have appeared on the market that can potentially guarantee citizens and users of the Public Administration (PA) the ability to create dashboards and data stories with just a few clicks, using open and unopened data from the PA. The Data Analytics Framework (DAF), a project of the Italian government launched at the end of 2017 and currently being tested, has the goal to improve and simplify the interoperability and exchange of data between Public Administrations, thanks to its big data platform and the integrated use of data visualization tools and semantic technologies. The DAF also has the objective of facilitating data analysis, improving the management of Open Data and facilitating the spread of linked open data (LOD) thanks to the integration of OntoPiA, a network of controlled vocabularies and ontologies, such as "IoT Events", an ontology for representing and modelling the knowledge within the domain of the Internet of Things. This paper contributes to the enhancement of the project by introducing a case study created by the author, concerns tourism of Sardinia (a region of Italy). The case study follows a process in the DAF in 5 steps, starting from selection of the dataset to the creation phase of the real dashboard through Apache Superset (a business intelligence tool) and the related data story. This case study is one of the few demonstrations of use on a real case of DAF and highlights the ability of this national platform to transform the analysis of a large amount of data into simple visual representations with clear and effective language.
UNARI

Over the last two decades, several algorithms have been proposed to infer the type of relationship between Autonomous Systems (ASes). While the recent works have achieved increasingly higher accuracy, there has not been a systematic study on the uncertainty of AS relationship inference. In this paper, we analyze the factors contributing to this uncertainty and introduce a new paradigm to explicitly model the uncertainty and reflect it in the inference result. We also present UNARI, an exemplary algorithm implementing this paradigm, that leverages a novel technique to capture the interdependence of relationship inference across AS links.
Automatic detonator code recognition via deep neural network

Detonators are hazardous and must be strictly controlled to strengthen public security since they contain explosive. To deal with the stressful management of detonators by manually recording detonator codes, an expert and intelligent system for detonator code recognition is an alternative management scheme for automatically recording detonator codes. How to achieve accurate and reliable recognition performance is also a challenging problem in such an intelligent system. In this study, we develop an intelligent vision-based system based on deep learning for identifying detonator codes. Specifically, we design a detonator image acquisition subsystem to construct the detonator code image dataset, and an intelligent image processing subsystem named as automatic detonator code recognition network (ADCR-Net). The ADCR-Net is a pipeline cascading a deep localization network and a deep recognition network. The multi-scale concatenation block and the features integration module are proposed for the localization network of the ADCR-Net to improve the features expressions. The multi-level progressive self-attention block is put forward for the recognition network of the ADCR-Net to help with focusing on multi-level activation maps. Also, a multi-label loss function for the recognition network is designed to balance the significance of the classifiers of the intelligent system during training. Experiments on our released dataset demonstrate the effectiveness and efficiency of the proposed expert and intelligent system, which achieves 99.18% accuracy in end-to-end recognition. Our work provides an effective resolution to the expert and intelligent system for automatic detonator code recognition. Also, we provide an alternative framework to deal with the tradeoff between inference time and recognition accuracy in deep learning which is becoming a popular method in many expert and intelligent systems. Furthermore, the released dataset is available for distribution to the related researchers to develop their expert systems in the field of industrial optical character recognition.
Strategic negotiations for extensive-form games

When studying extensive-form games it is commonly assumed that players make their decisions individually. One usually does not allow the possibility for the players to negotiate their respective strategies and formally commit themselves to future moves. As a consequence, many non-zero-sum games have been shown to have equilibrium outcomes that are suboptimal and arguably counter-intuitive. For this reason we feel there is a need to explore a new line of research in which game-playing agents are allowed to negotiate binding agreements before they make their moves. We analyze what happens under such assumptions and define a new equilibrium solution concept to capture this. We show that this new solution concept indeed yields solutions that are more efficient and, in a sense, closer to what one would expect in the real world. Furthermore, we demonstrate that our ideas are not only theoretical in nature, but can also be implemented on bounded rational agents, with a number of experiments conducted with a new algorithm that combines techniques from Automated Negotiations, (Algorithmic) Game Theory, and General Game Playing. Our algorithm, which we call Monte Carlo Negotiation Search, is an adaptation of Monte Carlo Tree Search that equips the agent with the ability to negotiate. It is completely domain-independent in the sense that it is not tailored to any specific game. It can be applied to any non-zero-sum game, provided that its rules are described in Game Description Language. We show with several experiments that it strongly outperforms non-negotiating players, and that it closely approximates the theoretically optimal outcomes, as defined by our new solution concept.
Explainable Machine Learning for Breast Cancer Diagnosis

Cancer is already the leading cause of death in most Brazilian cities and in the world. The understanding of its internal mechanisms and the design of computational models capable of improving its diagnosis will have strong benefits for humanity. New technologies have made available a wealth of data, which can be used to improve the diagnosis of cancer. As a manual analysis of this data is impracticable, many black-box machine learning algorithms have been employed successfully for cancer diagnosis. Despite their high accuracy prediction abilities, black-box models sacrifice transparency and accountability. In contrast, interpretable machine learning algorithms are powerful tools for understanding the underlying mechanism present within a large corpus of data. In this work, Linear Projections and Radviz were used as visualization techniques for data exploration and feature selection. Further, Decision Tree induction algorithms were used to create models that are able to differentiate between Malignant and Benign breast tumors from breast mass images. These models can be considered white-box models which means their inner workings are easier to explain and interpret. The result shows Classification and Regression Trees achieved an accuracy of 96% in predicting breast cancer.
Incremental and dynamic graph construction with application to image classification

In this paper, we propose a dynamic graph construction technique that inserts new samples into a previously constructed graph, which reduces the computational time and complexity of the classic batch construction schemes. The basic assumption of the proposed method is that by adding one sample into a graph, only its close nodes will be affected, hence, it is not necessary to update the whole graph but only the weights of close nodes. The proposed method has two steps of insertion and updating. In the insertion phase, the similarity between the new sample and the available data is calculated. The similarity vector is retrieved either from a distance function or a coding scheme. Then, in the update phase, by evaluating the similarity vector of the new sample, close nodes which will be affected are identified and the graph weights of these close (similar) nodes are updated. By adopting this scenario, in each insertion of a node (or a set of nodes), only the weights of very few nodes have to be updated. It is worthy to mention that since the proposed method does not invoke labels of the samples, it can be adopted by any unsupervised, semi-supervised or supervised technique. A set of extensive experiments for the task of classification on different image datasets show that, in various post-graph learning tasks (i.e., Label propagation and Manifold learning), the graph which is constructed by the proposed method, even after hundreds of insertions and updates, has a similar performance (in some cases it can be better) compared to the graph which is constructed from scratch.
A Method for Decimal Number Recovery from its Residues Based on the Addition of the Product Modules

The following topics are dealt with: learning (artificial intelligence); particle swarm optimisation; wireless sensor networks; optimisation; feature extraction; evolutionary computation; convolutional neural nets; image classification; neural nets; search problems.
Identifying Traffic Event Types from Twitter by Multi-Label Classification

Social networks have been recently employed as a source of information for traffic event detection, with particular reference to traffic incidents. However, extracting meaningful and actionable knowledge from user-generated content is a complex endeavor, because social media texts are usually short, informal, with a lot of abbreviations, jargon, slang, idioms, and usually belong multiple labels or topics, so the classification of an only topic is difficult to be predicted. In this paper, we present an approach for classifying traffic event from the social network Twitter. The system fetches publications from Twitter (i.e., tweets) according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the multi-label classification of tweets. The aim is to assign the appropriate class labels to each tweet, as related to traffic event types. The proposed model was employed in the Brazilian city of Porto Alegre, allowing for classification of traffic event types. Extensive experiments show the effectiveness of our proposed method for multi-label classification of Twitter posts, achieving high precision at substantial overall coverage.
Fuzzy and PID controllers applied to ball and plate system

This document shows the design of a Fuzzy controller and a PID controller applied to the Ball and Plate “CE 151” system. The design method chosen for the PID controller was Root Locus Design and because it requires a mathematical model, it was obtained using Lagrangian mechanics. This model was validated with the real data of the system in order to have a model closer to the real dynamics. The Fuzzy controller was designed using an inference system with Takagi-Sugeno-Kang logic, which has as inputs the error and the derivative of the error. This inference system was complemented by an integral action to eliminate the steady state error. The performance of the two designed controllers is compared via the application of tests such as, step response, setpoint tracking, disturbances response and robustness.
Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

The predictive performance of different granular models (GMs) was compared and analyzed for methods that evenly divide linguistic context in information granulation-based GMs and perform flexible partitioning. GMs are defined by input and output space information transformations using context-based fuzzy C-means clustering. The input space information transformation is directly induced by the output space context. Usually, the output space context is evenly divided. In this paper, the linguistic context was flexibly divided by stochastically distributing data in the output space. Unlike most fuzzy models, this GM yielded information segmentation. Their performance is usually evaluated using the root mean square error, which utilizes the difference between the model’s output and ground truth. However, this is inadequate for the performance evaluation of information innovation-based GMs. Thus, the GM performance was compared and analyzed using the linguistic context partitioning by selecting the appropriate performance evaluation method for the GM. The method was augmented by the coverage and specificity of the GMs output as the performance index. For the GM validation, its performance was compared and analyzed using the auto MPG dataset. The GM with flexible partitioning of linguistic context performed better. Performance evaluation using the coverage and specificity of the membership function was validated.
Mutichain Enabled EHR Management System and Predictive Analytics

One of the challenges in biomedical research and clinical practice is that we need to consolidate tremendous efforts in order to use all kinds of medical data for improving work processes, to increase capacity while lessening costs and enhancing efficiencies. Very few medical centers in India have digitized their patient records. Because of less interoperability among themselves, they end up having scattered and incomplete data. Health data is proprietary and being a personal asset of the patient, its distribution or use should be accomplished only with the patient’s consent and for a specific duration. This research proposes multichain as a secure, decentralized network for storing Electronic Health Records. The architecture provides users with a holistic, transparent view of their medical history by disintermediation of trust while insuring data integrity among medical facilities. This will open up new horizons of vital trends and insights for research, innovation, and development through robust analysis. The platform focuses on an interactive dashboard containing year, month, and season wise statistics of various diseases which are used to notify the users and the medical authorities on a timely basis. Prediction of epidemics using machine learning techniques will facilitate users by providing personalized care and the medical institutions for managing inventory and procuring medicines. Vital insights like patient to doctor ratio, infant mortality rates, and prior knowledge of the forthcoming epidemics will help government institutions to analyze and plan infrastructural requirements and services to be provided.
Joint Wasserstein Autoencoders for Aligning Multimodal Embeddings

One of the key challenges in learning joint embeddings of multiple modalities, e.g. of images and text, is to ensure coherent cross-modal semantics that generalize across datasets. We propose to address this through joint Gaussian regularization of the latent representations. Building on Wasserstein autoencoders (WAEs) to encode the input in each domain, we enforce the latent embeddings to be similar to a Gaussian prior that is shared across the two domains, ensuring compatible continuity of the encoded semantic representations of images and texts. Semantic alignment is achieved through supervision from matching image-text pairs. To show the benefits of our semi-supervised representation, we apply it to cross-modal retrieval and phrase localization. We not only achieve state-of-the-art accuracy, but significantly better generalization across datasets, owing to the semantic continuity of the latent space.
Songverse: A Music-Loop Authoring Tool Based on Virtual Reality

Digital Musical Instruments (DMIs) can be defined as hardware-software solutions that are interactive and crafted to output sound according to users' input. DMIs are well known to unleash users' creativity but also to allow different and innovative experiences for the creation process, for example smoothing the learning curve towards musical concepts such as rhythm and composition. On the other hand, Virtual Reality (VR) allows users to explore spatial interfaces in a natural and limitless way, which shows potential synergy towards the rise of new DMIs. In this paper, we introduce Songverse, an immersive DMI placed in a Virtual Reality scenario that allows users to create music by interacting with an environment designed to resemble the outer space. By adding systems, planets, and satellites to the virtual environment the user is able to shape the produced sound through interactions that were extensively tested during the development phase. We then evaluated the instrument with musicians and non-musicians by interviewing and applying the System Usability Scale (SUS) to assess the easiness for people to create music using Songverse. As results, users reported the use of the DMI as intuitive and easy to use, also highlighting the produced song as enjoyable.
Residual Neural Network Vs Local Binary Convolutional Neural Networks for Bilingual Handwritten Digit Recognition

Most of the public and government documents in Arabic states are typed or written in bilingual forms; Arabic and Latin languages; such as railway reservation slips, bank withdrawal slips, etc. Using a bilingual system is better than using two systems for every language that is not a practical solution. In this paper, a bilingual digit recognition system is developed using Residual Neural Network (ResNet) and Local Binary Convolutional Neural Networks (LBCNN). The proposed systems are evaluated using a bilingual dataset generated from AHDBase and MNIST datasets. The recognition rate of ResNet or LBCNN is 99.38%. In addition, the proposed systems are applied to MNIST and AHDBase datasets separately. The obtained accuracies for MNIST are 99.27% and 99.51% and for AHDBase are 99.29% and 99.38%, respectively. The resulting performance of ResNet and LBCNN are the highest when they are compared against several state-of-the-art techniques.
Hybrid short term prediction to address limited timeliness of public transport data streams

In spite of the diversity of solutions developed in the Internet of Things (IoT) domain, some features are shared by numerous IoT deployments and the data they process. These include incompleteness and latency in data transmission from multiple distributed objects. Among others, the systems tracking the location of vehicles are affected by these problems. The primary objective of this work is to address the way the latency in location data acquisition, referred to also as timeliness, can be dealt with. We propose a hybrid method combining machine learning models such as multilayer perceptrons trained in batch mode and online learning methods to perform short-term prediction of vehicle delay data. In this way, stream instances that have not arrived yet from the sensors can be temporarily replaced with predicted values. The method we propose successfully integrates stream mining methods developed for stationary and non-stationary conditions i.e. also the methods developed for concept drifting data streams. For all examined reference data sets and hybridised stream methods, the method reduced prediction error and addressed the risk of using static prediction models not matching or no longer matching the evolving process for which the prediction is performed.
Use of HL7 FHIR to structure data in epilepsy self-management applications

The expansion of health infrastructure to support the networking of actors in the healthcare system is rapidly growing and is forced by law in several nations. The solutions are based on international standards to ensure interoperability of existing and new health applications and future viability. In the EPItect project, a telemedicine infrastructure for epilepsy care was developed. This paper presents part of the conceptual work and focuses on the structural and semantic standardization of epilepsy data based on the international standard HL7 FHIR. The main contribution of this work is an implementation guide for the exchange of epilepsy related data between epilepsy self-management applications and health infrastructures.
Lifelong Machine Learning and root cause analysis for large-scale cancer patient data

Abstract Introduction This paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimensional data from scratch is computationally infeasible since constant data stream ingestion on top of a historical data pool increases the training time exponentially. Therefore, the need arises on how to retain past learning and fast update the model incrementally based on the new data. Also, the current machine learning approaches do the model prediction without providing a comprehensive root cause analysis. To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) model. Case description A cancer patient’s pathological tests like blood, DNA, urine or tissue analysis provide a unique signature based on the DNA combinations. Our analysis allows personalized and targeted medications and achieves a therapeutic response. Model is evaluated through data from The National Cancer Institute’s Genomic Data Commons unified data repository. The aim is to prescribe personalized medicine based on the thousands of genotype and phenotype parameters for each patient. Discussion and evaluation The model uses a dimension reduction method to reduce training time at an online sliding window setting. We identify the Gleason score as a determining factor for cancer possibility and substantiate our claim through Lilliefors and Kolmogorov–Smirnov test. We present clustering and Random Decision Forest results. The model’s prediction accuracy is compared with standard machine learning algorithms for numeric and categorical fields. Conclusion We propose an ensemble framework of stream and batch data for incremental lifelong learning. The framework successively applies first streaming clustering technique and then Random Decision Forest Regressor/Classifier to isolate anomalous patient data and provides reasoning through root cause analysis by feature correlations with an aim to improve the overall survival rate. While the stream clustering technique creates groups of patient profiles, RDF further drills down into each group for comparison and reasoning for useful actionable insights. The proposed MALA architecture retains the past learned knowledge and transfer to future learning and iteratively becomes more knowledgeable over time.
Comparative Study of Syntactic Search Engine and Semantic Search Engine: A Survey

Search engine symbolizes an extremely powerful and valuable tool for fetching any sort of information from Internet. There has been numerous researches carried on search engines techniques, the major ones are syntactic and semantic. Referring to the Syntactic web, the results obtained are purely as per the keyword match. That is the query outputs numerous web pages against the keyword match that may not even be relevant or meaningful. Whereas, unlike the syntactic web, the semantic web is a revised or upgraded version of the web which produces quiet meaningful and specific output as it has the potential to comprehend the query effectively. Few examples of Semantic based search engines include Kosmix, Hakia, Cognition, Swoogle and Lexxe. Whereas syntactic based search engines are Google, Yahoo, Ask. The work performs a comparison amidst the performance of semantic and syntactic based search engine and evaluates them by employing certain queries.