Reviewerly/exHarmony huggingface dataset:
Variable-Based Spatiotemporal Trajectory Data Visualization Illustrated

As a frontier research topic in the field of scientific visualization, trajectory data visualization extracts valuable patterns and knowledge from trajectory data for decision support via spatiotemporal trajectory visualization techniques. We propose the concept of multivariate trajectory data and interpret two categories of attributes that are based on geographical space and abstract space. Properly analyzing multivariate trajectory data depends on many factors such as visualization task and data sparsity. Therefore, we generalize rich interactions to explore the evolution of trajectory events and transform the issue into a more intelligibly perceptual task, which derives our discussion regarding advantages and limitations of the analytical methods. This review endeavors to provide a quick and thorough cognition and comprehension with regard to fundamental features and numerous outcomes in visual analytics for trajectory data, seeks to promote comparisons and criticisms about the descriptive framework for multivariate spatiotemporal trajectory data visualization, and aims to encourage the exploration of emerging methods and techniques.
Self-Tune Linear Adaptive-Genetic Algorithm for Feature Selection

Genetic algorithm (GA) is an established machine learning technique used for heuristic optimisation purposes.However, this natural selection-based technique is prone to premature convergence, especially of the local optimum event.The presence of stagnant performance is due to low population diversity and fixed genetic operator setting.Therefore, an adaptive algorithm, the Self-Tune Linear Adaptive-GA (STLA-GA), is presented in order to avoid suboptimal solutions in feature selection case studies.STLA-GA performs parameter tuning for mutation probability rate, population size, maximum generation number and novel convergence threshold while simultaneously updating the stopping criteria by adopting an exploration-exploitation cycle.The exploration-exploitation cycle embedded in STLA-GA is a function of the latest classifier performance.Compared to standard feature selection practice, the proposed STLA-GA delivers multi-fold benefits, including overcoming local optimum solutions, yielding higher feature subset reduction rates, removing manual parameter tuning, eliminating premature convergence and preventing excessive computational cost, which is due to unstable parameter tuning feedback.
A Privacy-Preserving and Identity-Based Personalized Recommendation Scheme for Encrypted Tasks in Crowdsourcing

Personalized task recommendation can guarantee that tasks are pushed to the right workers, and thus, requesters gain better-quality output from a crowdsourcing system.Requesters' tasks and workers' interests exposed to the remote crowdsourcing platform in the form of plaintext have raised serious privacy concerns.Encrypting tasks and interests is a feasible solution to protect the privacy of both the requesters and the workers.However, data encryption renders the existing crowdsourcing task recommendation techniques ineffective.To address this challenge, in this study, we transform the personalized task recommendation problem into a task access control and keyword-based search problem for encrypted tasks.On the basis of this idea, we first develop a new technique called multi-authority attribute-based searchable encryption by equipping the searchable capacity for Lewko and Waters's multi-authority attribute -based encryption.Then, we utilize the new technique to construct a secure and personalized recommendation scheme in crowdsourcing, which achieves accurate personalized task recommendation in a privacy-preserving manner by a seamless combination of attribute-based encryption and searchable encryption.We provide rigorous security proof and thorough security analysis for the proposed scheme.Extensive experiments demonstrate the correctness and practicality of the proposed scheme.
A Data Stratification Process for Instances Selection in Semi-Supervised Learning

This paper presents a study in the field of semi-supervised learning and, more specifically, it proposes changes in the self-training algorithm in order to apply a data stratification method in the labeling process of this algorithm. Therefore, this work proposes a method, called FlexCon-CS, whose objective is to apply data stratification in the inclusion of new instances in the training data set. In this sense, the representativeness and class distribution will be maintained throughout the labeling process, with the same proportions of the initially labeled dataset. In order to evaluate this proposal, we performed experiments on 27 databases with different data distribution features. Each dataset was trained with four different classification algorithms, Naive Bayes, Decision Tree, ripper, and K-Nearest Neighbor classifiers. Moreover, the Friedman statistical test was applied to provide a statistically significant analysis of the obtained results. Our findings indicate that, in most cases, the proposed methods perform better than the original self-training method.
Toward Measuring Knowledge Loss due to Ontology Modularization

This paper formalizes the graphical modularization technique, View Traversal, for an ontology-based system represented using the Domain Information System (DIS). Our work is motivated by the need for autonomous agents, within an ontology-based system, to automatically create their own views of the ontology to address the problems of ontology evolution and data integration found in an enterprise setting. Through DIS, we explore specific ontologies that give Cartesian perspectives of the domain, which allows modularization to be a means for agents to extract views of specific combinations of data. The theory of ideals from Boolean algebra is used to formalize a module. Then, with the use of homomorphisms, the quantity of knowledge within the module can be measured. More specifically, through the first isomorphism theorem, we establish that the loss of information is quantified by the kernel of the homomorphism. This constitutes a foundational step towards theories related to reasoning on partial domain knowledge, and is important for applications where an agent needs to quickly extract a view that contains a specific set of knowledge.
Reversible Steganographic Scheme for AMBTC-Compressed Image Based on (7,4) Hamming Code

In recent years, compression steganography technology has attracted the attention of many scholars. Among all image compression method, absolute moment block truncation coding (AMBTC) is a simple and effective compression method. Most AMBTC-based reversible data hiding (RDH) schemes do not guarantee that the stego AMBTC compression codes can be translated by the conventional AMBTC decoder. In other words, they do not belong to Type I AMBTC-based RDH scheme and easily attract malicious users’ attention. To solve this problem and enhance the hiding capacity, we used (7,4) hamming code to design a Type I AMBTC-based RDH scheme in this paper. To provide the reversibility feature, we designed a prediction method and judgement mechanism to successfully select the embeddable blocks during the data embedding phase and data extraction and recovery phase. In comparing our approach with other BTC-based schemes, it is confirmed that our hiding capacity is increased while maintaining the limited size of the compression codes and acceptable image quality of the stego AMBTC-compressed images.
Text Mining in Hotel Reviews: Impact of Words Restriction in Text Classification

Text Mining is the process of extracting interesting and non-trivial patterns or knowledge from unstructured text documents. Hotel Reviews are used by hotels to verify client satisfaction regarding their own services or facilities. However, we can’t deal with this type of big and unstructured data manually, so we should use OLAP techniques and Text Cube for modelling and manage text data. But then, we have a problem, we must separate the reviews in two classes, positive and negative, and for that, we use Sentiment Analysis technique. Nevertheless, do we really need all the words of a review to make the right classification? In this paper, we will study the impact of word restriction on text classification. To do that, we create some words domains (words that belong to a Hotel Domain). First, we use an algorithm that will pre-process the text (where we use our created domains like stop words). In the experimental evaluation, we use four classifiers to classify the text, Naive-Bayes, Decision-Tree, Random-Forest, and Support Vector Machine.
Fireworks Algorithm versus Plant Propagation Algorithm

In recent years, the field of Evolutionary Algorithms has seen a tremendous increase in novel methods. Whilethese algorithmic innovations often show excellent results on relatively limited domains, they are less oftenrigorously cross-tested or compared to other state-of-the-art developments. Two of these methods, quite similarin their appearance, are the Fireworks Algorithm and Plant Propagation Algorithm.This study compares the similarities and differences between these two algorithms, from both quantitative andqualitative perspectives, by comparing them on a set of carefully chosen benchmark functions. The FireworksAlgorithm outperforms the Plant Propagation Algorithm on the majority of these, but when the functionsare shifted slightly, Plant Propagation gives better results. Reasons behind these surprising differences arepresented, and comparison methods for evolutionary algorithms are discussed in a wider context. All sourcecode, graphs, test functions, and algorithmic implementations have been made publicly available for referenceand further reuse.
Efficiently Generating Geometric Inhomogeneous and Hyperbolic Random Graphs.

Hyperbolic random graphs (HRG) and geometric inhomogeneous random graphs (GIRG) are two similar generative network models that were designed to resemble complex real world networks. In particular, they have a power-law degree distribution with controllable exponent beta, and high clustering that can be controlled via the temperature T.We present the first implementation of an efficient GIRG generator running in expected linear time. Besides varying temperatures, it also supports underlying geometries of higher dimensions. It is capable of generating graphs with ten million edges in under a second on commodity hardware. The algorithm can be adapted to HRGs. Our resulting implementation is the fastest sequential HRG generator, despite the fact that we support non-zero temperatures. Though non-zero temperatures are crucial for many applications, most existing generators are restricted to T = 0. We also support parallelization, although this is not the focus of this paper. Moreover, we note that our generators draw from the correct probability distribution, i.e., they involve no approximation.Besides the generators themselves, we also provide an efficient algorithm to determine the non-trivial dependency between the average degree of the resulting graph and the input parameters of the GIRG model. This makes it possible to specify the desired expected average degree as input.Moreover, we investigate the differences between HRGs and GIRGs, shedding new light on the nature of the relation between the two models. Although HRGs represent, in a certain sense, a special case of the GIRG model, we find that a straight-forward inclusion does not hold in practice. However, the difference is negligible for most use cases.
Bias-Resilient Neural Network

Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased. The code is available at this https URL.
EOG-Based Gaze Angle Estimation Using a Battery Model of the Eye

In this work, a novel method to estimate the gaze angles using electrooculographic (EOG) signals is presented. Specifically, this work investigates the use of a battery model of the eye, which relates the recorded EOG potential with the distances between the corresponding electrode and the centre points of the cornea and retina, for gaze angle estimation. Using this method a cross-validated horizontal and vertical gaze angle error of 2.42±0.91° and 2.30±0.50° respectively was obtained across six subjects, demonstrating that the proposed methods and the battery model may be used to estimate the user's ocular pose reliably.
Development of Android Version Active Textbook System

The authors have proposed the Active Textbook System (A-txt) as the next generation learning material system. A-txt is a system to enhance the textbook by adding digital content to the figures and formulas in a general book by using AR technology. Production of content and registration to the server can be done relatively easily using a general-purpose personal computer, and the content added to the book can be browed using the users’ own smart terminal. Therefore, the introduction cost can be reduced without the need for a dedicated machine. Also, once content is downloaded, it can be used offline. This point is the other advantage that other systems do not have. So far, A-txt was limited to iOS devices that hardware are standardized, but this time the system for Android devices, that have more users, have been developed. Although challenges remain for AR and 3D display, an application that meets practical requirements has been completed. Also, since the Android OS is free, there are a wide variety of terminals adopted. Therefore, recommended hardware specifications have been formulated by benchmark evaluation. Moreover, in order to solve some problems that became apparent in the development process, it was decided to change to an OS-independent system by using Unity.
Search for a substring of characters using the theory of non-deterministic finite automata and vector-character architecture

The paper proposed an algorithm which purpose is searching for a substring of characters in a string. Principle of its operation is based on the theory of non-deterministic finite automata and vector-character architecture. It is able to provide the linear computational complexity of searching for a substring depending on the length of the searched string measured in the number of operations with hyperdimensional vectors when repeatedly searching for different strings in a target line. None of the existing algorithms has such a low level of computational complexity. The disadvantages of the proposed algorithm are the fact that the existing hardware implementations of computing systems for performing operations with hyperdimensional vectors require a large number of machine instructions, which reduces the gain from this algorithm. Despite this, in the future, it is possible to create a hardware implementation that can ensure the execution of operations with hyperdimensional vectors in one cycle, which will allow the proposed algorithm to be applied in practice.
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition

Object detection and instance recognition play a central role in many AI applications like autonomous driving, video surveillance and medical image analysis. However, training object detection models on large scale datasets remains computationally expensive and time consuming. This paper presents an efficient and open source object detection framework called SimpleDet which enables the training of state-of-the-art detection models on consumer grade hardware at large scale. SimpleDet supports up-to-date detection models with best practice. SimpleDet also supports distributed training with near linear scaling out of box. Codes, examples and documents of SimpleDet can be found at this https URL .
Transformation of Supply Chain Activities in Blockchain Environment

Blockchain is one of the breakthrough technologies having the potential to dramatically transform the supply chains. In this study, potential impact of the blockchain technology on the supply chain management (SCM) will be investigated to reveal the nature of transformation that it can result in the domain. Based on a comprehensive review of recent literature at the intersection of blockchain and SCM, effects of blockchain on SCM are analysed under four titles: (a) basic characteristics, (b) transparency/visibility and traceability, (c) automated controls with smart contracts; and (d) trust-building and collaboration. Findings reveal that the technology is expected to provide accurate and trustable transaction infrastructure as well as true visibility and traceability across partners. With its potential to provide a transparent and trustable multi-partner ecosystem, it appears that blockchain will accelerate and strengthen the realization of a collaborative, IT-based network paradigm. Hence, findings of the study support that the blockchain technology will be a critical enabler of the transformation of the supply chains into tightly coupled, transparent collaborative ecosystems.
Cross-document coreference: An approach to capturing coreference without context

In this paper, we discuss a cross-document coreference annotation schema that we developed to further automatic extraction of timelines in the clinical domain.Lexical senses and coreference choices are determined largely by context, but cross-document work requires reasoning across contexts that are not necessarily coherent.We found that an annotation approach that relies less on context-guided annotator intuitions and more on schematic rules was most effective in creating meaningful and consistent cross-document relations.
Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking

As one of the core contents of intelligent monitoring, target tracking is the basis for video content analysis and processing. In visual tracking, due to occlusion, illumination changes, and pose and scale variation, handling such large appearance changes of the target object and the background over time remains the main challenge for robust target tracking. In this paper, we present a new robust algorithm (STC-KF) based on the spatio-temporal context and Kalman filtering. Our approach introduces a novel formulation to address the context information, which adopts the entire local information around the target, thereby preventing the remaining important context information related to the target from being lost by only using the rare key point information. The state of the object in the tracking process can be determined by the Euclidean distance of the image intensity in two consecutive frames. Then, the prediction value of the Kalman filter can be updated as the Kalman observation to the object position and marked on the next frame. The performance of the proposed STC-KF algorithm is evaluated and compared with the original STC algorithm. The experimental results using benchmark sequences imply that the proposed method outperforms the original STC algorithm under the conditions of heavy occlusion and large appearance changes.
Evaluation of Ten Open-Source Eye-Movement Classification Algorithms in Simulated Surgical Scenarios

Despite providing several insights into visual attention and evidence regarding certain brain states and psychological functions, classifying eye movements is a highly demanding process. Currently, there are several algorithms to classify eye movement events which use different approaches. However, to date, only a limited number of studies have assessed these algorithms under specific conditions, such as those required for surgical training programmes. This study presents an investigation of ten open-source eye-movement classification algorithms using the Eye Tribe eye-tracker. The algorithms were tested on the eye-movement records obtained from 23 surgical residents, who performed computer-based surgical simulation tasks under different hand conditions. The aim was to offer data for the improvement of surgical training programmes. According to the results, due to the different classification methods and default threshold values, the ten algorithms produced different results. Considering the fixation duration, the only common event for all of the investigated algorithms, the binocular-individual threshold (BIT) algorithm resulted in a different clustering compared to the other algorithms. Based on the other set of common events, three clusters were determined by eight algorithms (except BIT and event detection (ED)), distinguishing dispersion-based, velocity-based and modified versions of velocity-based algorithms. Accordingly, it was concluded that dispersion-based and velocity-based algorithms provided different results. Additionally, as it individually specifies the threshold values for the eye-movement data, when there is no consensus about the threshold values to be set, the BIT algorithm can be selected. Especially for such cases like simulation-based surgical skill-training, the use of individualised threshold values in the BIT algorithm can be more beneficial in classifying the raw eye data and thus evaluating the individual progress levels of trainees based on their eye movement behaviours. In conclusion, the threshold values had a critical effect on the algorithm results. Since default values may not always be suitable for the unique features of different data sets, guidelines should be developed to indicate how the threshold values are set for each algorithm.
Evaluation of vector embedding models in clustering of text documents

The paper presents an evaluation of word embedding models in clustering of texts in the Polish language.Authors verified six different embedding models, starting from widely used word2vec, across fast-Text with character n-grams embedding, to deep learning-based ELMo and BERT.Moreover, four standardisation methods, three distance measures and four clustering methods were evaluated.The analysis was performed on two corpora of texts in Polish classified into subjects.The Adjusted Mutual Information (AMI) metric was used to verify the quality of clustering results.The performed experiments show that Skipgram models with n-grams character embedding, built on KGR10 corpus and provided by Clarin-PL, outperforms other publicly available models for Polish.Moreover, presented results suggest that Yeo-Johnson transformation for document vectors standardisation and Agglomerative Clustering with a cosine distance should be used for grouping of text documents.