Reviewerly/exHarmony huggingface dataset:
LODGVis

The amount of structured data available on the Web in semantic formats have been growing enormously in recent years. In this scenario, Linked Data effort is essential to retrieve detailed information from the Web, especially if we consider the importance of the Web in our daily lives and activities. However, there is a lack of tools and techniques that allow users to properly navigate on Linked Data formats, such as Resource Description Framework (RDF) documents. This work aims to present LODGVis, a novel interactive visualization technique that enables navigation throughout RDF documents. This technique was developed as an open source Web application and provides a visual exploratory approach that allows users to freely browse semantic data through graphs-base visualization considering several features, including customization, aggregation and multiple level navigation. Our study case considers DBpedia data set, but our technique can be easily extended to any RDF-based data set.
Side-Scan Sonar Data-Driven Coverage Path Planning: A Comparison of Approaches

Side-scan sonar mapping of an unknown large-scale seafloor area by a marine vehicle is nowadays very common. It is also important that a-priori unknown interesting parts of the seafloor area are scanned in more detail, i.e. sonified from both sides. However, completely autonomous and time-efficient coverage path (re)planning for such missions is still an open issue. In contrast to the standard overlap-all-sonar-ranges lawnmower pattern offline static coverage problem solution for side-scan sonar missions, in this paper two online sonar data-driven coverage algorithms are proposed as extensions of authors’ prior work. Analytical upper and lower bounds on performance of the proposed coverage planning algorithms are given and validated through extensive mission parameters variation simulations. Statistical performance analysis of the proposed coverage planning algorithms’ performance shows significant complete coverage time efficiency improvements w.r.t. the classical unadaptive lawnmower approach. Also, a detailed comparison of coverage planning algorithms proposed by the authors so far is provided.
Estimating CDMs Using MCMC

In this chapter, we provide a brief survey of Markov chain Monte Carlo (MCMC) methods used in estimating Cognitive Diagnostic Models (CDMs). MCMC techniques have been widely used for the Bayesian estimation of psychometric models. MCMC algorithms and general purpose MCMC software has been facilitating the development of modern psychometric models that are otherwise difficult to fit (Levy R, J Probab Stat 1–18, 2009. Retrieved from http://www.hindawi.com/journals/jps/2009/537139/ , https://doi.org/10.1155/2009/ 537139 ). We introduce a Gibbs sampler for fitting the saturated Log-linear CDM model (LCDM, Henson RA, Templin JL, Willse JT, Psychometrika, 74(2):191–210, 2009. Retrieved from https://doi.org/10.1007/s11336-008-9089-5 ). The utility of Bayesian inference is demonstrated by analyzing the Examination for the Certificate of Proficiency in English (ECPE) dataset.
Analytic expression of Mamdani fuzzy system constructed by fuzzy similarity degree and its output algorithm

Fuzzy similarity degree is a measurement of the similarity between fuzzy sets through local information, it plays an important role in the design of fuzzy system and controller. This article first proposes a new computational formula for membership functions of a consequent fuzzy set based on fuzzy similarity degree, and an analytic representation of the Mamdani fuzzy system is obtained through the Gauss fuzzification, product inference engine and center average defuzzification. Next, a specific Mamdani fuzzy system constructed by Gauss fuzzifier or singleton fuzzification be expressed through a given fuzzy similarity degree in practice. Finally, the output algorithm of the proposed fuzzy system is given by the space positioning method. The result shows that the Mamdani fuzzy system constructed by fuzzy similarity degree and Gauss fuzzification is superior to that based on singleton fuzzification in terms of approximation capability.
An artificial neural network approach in predicting career strand of incoming senior high school students

Abstract The K to 12 program has been implemented in the Philippines by the Department of Education which implicated an additional two years in the students’ basic education. These ancillary years allow senior high school students to take courses under the core curriculum and the track of choice. Each student must select one track to pursue that can equip him/her with skills to prepare for the future. Prediction of choice of career track in senior high school is advantageous for educational institutions since it gives insights that can help them develop vital programs beneficial for students’ learning in school. In this study, we applied artificial neural network (ANN) to predict the career strand based on the students’ grades in five major subjects. Different ANN models have been considered and compared. In training and testing the models, a sample of 293 student data information was used. The highest accuracy recorded among all the models was 74.1 %.
Designing an Interface for Sharing Quantitative Ethnographic Research Data

Recently, there have been growing calls to make research data more widely available. While the potential benefits of sharing research data are many, there are also many challenges, including the interpretability, attendability, and complexity of the data. These challenges are particularly salient for research data associated with quantitative ethnographic analyses, which often use relatively novel and sophisticated techniques. In this paper, we explore design considerations for an interface for sharing research data that attempts to address these challenges for quantitative ethnographic analyses. These considerations include: (a) maintaining the consistency of the interpretive space, (b) simplifying model details, (c) including example results and interpretations, and (d) highlighting key affordances in the user interface. To explore these considerations, we describe the design of an interactive visualization of the thematic networks present in the HBO television series, Game of Thrones.
Design of a 3D Vision-based Micro-Force Sensing Probe

This paper presents a vision-based micro-force sensing probe that is capable of μN level force sensing in three dimensions. The sensor is mounted on a standard micromanipulation probe and can be easily integrated into many systems. Furthermore, it is cheap, reliable, and can be specifically tailored for a desired application. Tests were conducted to demonstrate the accuracy of the system. It is capable of achieving sub-μN force resolution, with a range of 175 μN and an accuracy in the x, y, and z directions of 3.63%,0.58%, and 0.61%, respectively. Two case studies using the vision-based micro-force sensing probe were performed to demonstrate the efficacy of the system.
Ontology-Driven Processing of Unstructured Text

A lot of projects on ontologies focus on describing some aspect of reality: objects, relations, states of affairs, events, and processes in the world. Another approach is using ontologies for problem-solving. In this paper we discuss an approach for designing NLP tasks based on a multilevel system of ontological models. We developed a system of ontological models which is used for ontology-driven computational processing of unstructured texts. The components of the system are the ontology of task designing, the ontology of applied models, and the domain ontology. We discuss the general schema of designing solutions of applied tasks and some applications.
Adaptive Model Update Strategy for Correlation Filter Trackers

Model update is an important module in target trackers. It plays an important role in adaptive tracking. Many researches have proven that different model update strategies should be adopted, when tracking in different scenes, especially in occlusion and deformation. Though many strategies have been proposed in recent years, few of them make high improvement and good combination on trackers. In this paper, we first proved there is a close relationship between the tracking scenes and the response maps. Then, we proposed an adaptive model update strategy for calculating model update rate based on the response map. Many experiments have been done to compare the proposed model update strategy with some state-of-the-art strategies, and the results have shown that the proposed model update strategy outperforms the best model update strategy by 7% on the test of Kernel Correlation Filter tracker. Furthermore, the proposed model update strategy was evaluated on some state-of-the-art correlation filter trackers. Results have shown the proposed strategy was well integrated into many trackers, and improved the tracking accuracy effectively.
Optimal Mapping Function for Predictions of the Subjective Quality Evaluation Using Artificial Intelligence

With the growth of QoE interest, IPTV providers need a method to control QoE. The paper describes the correlation between the results of objective and subjective methods in video quality assessment. The authors proposed the optimal mapping function for predictions of the subjective quality evaluation based on the objective evaluation to determine the perception of the video quality by the human brain. Our model using artificial intelligence, it is based on a neural network which can simulate and predicts the subjective quality of the scene. It also can predict subjective or objective video quality for video sequences defined by spatial, temporal information, which is the critical and key variable of a given scene, and by the qualitative parameters of the scene. The results from the model are verified by comparing predicted video quality using the proposed classifier with the required value. The two most common statistical parameters related to express performance are Pearson’s correlation coefficient and Root Mean Square Error.
Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input Fuzzy Sets (FSs), Type-2 (T2) input FSs provide the ability to also capture variation in uncertainty levels by means of their extra degrees of freedom. Specifically, in this paper, we propose a strategy to design Interval Type-2 (IT2) input Membership Functions (MFs) in an online manner to ensure the parameters of input MFs are updated dynamically, thus capturing varying levels of uncertainty affecting systems' inputs. In this strategy, first, uncertainty detection is performed over a given time-frame (the Uncertainty Estimation Time-frame) and Type-1 (T1) input MFs are constructed by utilising the detected uncertainty level. Second, the variation of the uncertainty levels over a sliding window (the Uncertainty Variation Window) is used to capture the degree of variation in the detected uncertainty levels over time, which in turn informs the size of the Footprint of Uncertainty (FOU) of the IT2 MF associated with the T1 principal MF. Using time-series prediction experiments as an initial evaluation and demonstration platform for the proposed architecture, we show that the proposed strategy of designing IT2 input MFs has the potential to deliver performance benefits. Specifically, it allows systems to not only adapt to specific uncertainty levels but also to be more resilient to the variation of said uncertainty levels over time, thus offering a pathway to robust performance in real-world applications.
A Preliminary Study on Symbolic Fuzzy Cognitive Maps for Pattern Classification

Within the neural computing field, Fuzzy Cognitive Maps (FCMs) are attractive simulation tools to model dynamic systems by means of well-defined neural concepts and causal relationships, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle by experts. Recently, we proposed a symbolic FCM scheme (termed FCM-TFN) in which both weights and activation values are described by triangular fuzzy numbers. In spite of the promising results, the model’s performance in solving prediction problems remains uncertain. In this paper, we explore the prediction capabilities of the FCM-TFN model in pattern classification and concluded that our method is able to perform well when compared with traditional classifiers.
A Recommendation Model for Medical Data Visualization Based on Information Entropy and Decision Tree Optimized by Two Correlation Coefficients

Medical practitioners usually have difficulties in obtaining information effectively from massive data due to limited time and energy. This paper proposes a novel recommendation model for medical data visualization based on decision tree and information entropy optimized by two correlation coefficients, that is, Pearson's correlation coefficient and Kendall's correlation coefficient(P&K.CC). After investigating visualization techniques under different medical scenarios, we construct a medical domain knowledge-based decision tree which employs two correlation coefficients as new measures of feature quality to confirm the optimal splitting attributes and points in its growth, as well as prioritize the medical datasets based on improved information entropy. Finally, in contrast to several traditional decision tree classifiers, the results indicated that the proposed method achieves a better accuracy of the scenario classification of medical data. At the same time, the method can find the datasets that perform better in knowledge presentation and visualization.
Hyperparameter Tuning for Enhanced Authorship Identification Using Deep Neural Networks

Authorship Identification as a task has been long studied and explored. Historically, authorship claims were ratified for copyright issues after the death of the author for unpublished work through style matching. The immense growth in the reach of internet technologies has once again brought to the fore the importance of authorship identification. An application opening up in areas like Intellectual Property Right settlement, Copyrights, Plagiarism, Cyber Crime and Forensics, authorship identification is now an area of active research. The current work presents a Deep Neural Network based approach to authorship identification from a large corpus. The experiments carried out bring out the applicability of Deep Neural Networks for the task and also highlights the importance of hyperparameter tuning for the purpose. Results show that a proper choice and balance in the hyperparameter setting can improve already established outcomes.
Implementing VESA Display Stream Compression Encoder in FPGAs

In this paper, an architecture for implementing VESA DSC image compression standard in FPGAs is proposed. DSC uses a very small 3-pixel wide coding unit that restricts the HW architecture of the prediction part to only three pipeline stages. The proposed architecture optimizes the pipeline distribution and performs algorithmic optimizations in order to reduce the critical path inside the prediction modes of DSC. It achieves 62 MHz and requires 18595 slices on Virtex 7 spl. It can process full HD (1920×1080) 4:4:4 images at 30 frames per second and full HD 4:2:2 or 4:2:0 images at 60 frames per second, while it has sub-line latency.
Implicit Related-Key Factorization Problem on the RSA Cryptosystem

In this paper, we address the implicit related-key factorization problem on the RSA cryptosystem. Informally, we investigate under what condition it is possible to efficiently factor RSA moduli in polynomial time given the implicit information of related private keys. We propose lattice-based attacks using Coppersmith’s techniques. We first analyze the special case given two RSA instances with known amounts of shared most significant bits (MSBs) and least significant bits (LSBs) of unknown related private keys. Subsequently a generic attack is proposed using a heuristic lattice construction when given more RSA instances. Furthermore, we conduct numerical experiments to verify the validity of the proposed attacks.
An Approach for Optimization of Intuitionistic and Type-2 Fuzzy Systems in Pattern Recognition Applications

Traditional mathematical models work with type-0, which means using precise numbers in the models, but since the seminal work of Prof. Zadeh in 1965, type-1 fuzzy models emerged as a powerful way to represent human knowledge and natural phenomena. Later type-2 fuzzy models were also proposed by Prof. Zadeh in 1975 and more recently have been studied and applied in real world problems by many researchers. In addition, as another extension of the original type-1 fuzzy logic, Prof. Atanassov proposed Intuitionistic Fuzzy Logic, which is a very powerful theory in its own right. Previous works of the author and other researchers have shown that certain problems can be appropriately solved by using type-1, and others by interval type-2, while others by using intuitionistic fuzzy logic. Bio-inspired and meta-heuristic optimization algorithms have been commonly used to find the optimal design of type-1, type-2 or intuitionistic fuzzy models for applications in control, robotics, pattern recognition, time series prediction, just to mention a few. However, the question still remains about if even more complex problems may require even higher types, orders or extensions of type-1 fuzzy models to obtain better solutions to real world problems. In this paper a framework for solving this problem of finding the optimal fuzzy model for a particular problem is presented. To the knowledge of the author, this is the first work to propose a systematic approach to solve this problem, and we envision that in the future this approach will serve as a basis for more efficient algorithms for the same task of finding the optimal fuzzy system.
A Non-Overlapping View Human Tracking Algorithm using HSV Colour Space

Object tracking done by multiple cameras is commonly used to monitor a wide area such as airport, the resident area of a large city or any large public area. Multiple targets tracking across camera views is challenging since there might be a significant appearance change of a target across camera views caused by variations in illumination conditions, poses and camera image characteristics. To address this problem, we propose an object re-identification technique to track the targets through multiple non-overlapping views camera by using Bhattacharyya matching method. To handle the variation of lighting condition and photometric settings of cameras, we have utilised the layers in the HSV colour space as the feature to represent the appearance of the object. Few testbeds were setup indoors and outdoors to better evaluate the accuracy of the cross camera human tracking algorithm. Extensive experiments on the proposed algorithms demonstrate the effectiveness and reliability of our approach by able to track the human using only one of the HSV layers.
Semantics and multimedia

This short course is an introduction to symbolic artificial intelligence (AI) applied to multimedia. We assume no previous knowledge of AI and only basic knowledge of multimedia. The short course is divided into three parts. In the first part, we present general notions of AI and discuss the challenges involved in the construction of intelligent multimedia applications, that is, applications enriched with the capacity for semantic query, reasoning, and recommendation. In the second part, we detail the main technologies for symbolic AI and discuss their application to multimedia. In particular, we present the ontology languages RDF(S) and OWL, the description logics (DLs) which give these languages a formal basis, the SPARQL query language, and the vocabularies and extensions that allow us to apply these technologies to multimedia data. In the third and last part, we discuss the Hyperknowledge model: a hybrid model for the representation of knowledge and content. The Hyperknowledge model extends the NCM model, a classic hypermedia model, with the notion of semantic links, which allow for the integrated representation of multimedia content together with its semantic description.
Ontologies to Support Patients with Dementia

With the changing demographics toward aging population, also the number of people suffering from dementia increases. To allow the prolonged independent and socially active life of patients with dementia (PwD), some works propose the development of intelligent assistive systems that aim to support the PwD during their everyday activities. With the help of a structured knowledge base such systems are able to reason about the person׳s behavior, causes of deviations from normal behavior, and the appropriate intervention strategies. The knowledge base is usually modeled in the form of an ontology, allowing its reuse in other applications aiming to support the independent life of PwD. In this article, we describe how assistive systems use ontologies in order to support the PwD, and we present ontologies that contain domain-specific knowledge used by healthcare and monitoring systems for PwD. Furthermore, we discuss how these ontologies can be linked to other ontologies in order to extend functionality and applicability to different problems from the domain of dementia.
Bitmap filter: Speeding up exact set similarity joins with bitwise operations

The Exact Set Similarity Join problem aims to find all similar sets between two collections of sets, with respect to a threshold and a similarity function such as Overlap, Jaccard, Dice or Cosine. The naïve approach verifies all pairs of sets and it is often considered impractical due the high number of combinations. So, Exact Set Similarity Join algorithms are usually based on the Filter-Verification Framework, that applies a series of filters to reduce the number of verified pairs. This paper presents a new filtering technique called Bitmap Filter, which is able to accelerate state-of-the-art algorithms for the exact Set Similarity Join problem. The Bitmap Filter uses hash functions to create bitmaps of fixed b bits, representing characteristics of the sets. Then, it applies bitwise operations (such as xor and population count) on the bitmaps in order to infer a similarity upper bound for each pair of sets. If the upper bound is below a given similarity threshold, the pair of sets is pruned. The Bitmap Filter benefits from the fact that bitwise operations are efficiently implemented by many modern general-purpose processors and it was easily applied to four state-of-the-art algorithms implemented in CPU: AllPairs, PPJoin, AdaptJoin and GroupJoin. Furthermore, we propose a Graphic Processor Unit (GPU) algorithm based on the naïve approach but using the Bitmap Filter to speedup the computation. The experiments considered 12 collections containing from 100 thousands up to 10 million sets and the joins were made using Jaccard thresholds from 0.50 to 0.95. The Bitmap Filter was able to improve 85% of the experiments in CPU, with speedups of up to 4.50× and 1.35× on average. Using the GPU algorithm, the experiments were able to speedup the original CPU algorithms by up to 577× using an Nvidia Geforce GTX 980 Ti.