Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Synchronization of Fractional-Order Uncertain Chaotic Systems Based on Output Feedback Sliding Mode Control

This paper mainly focuses on the robust synchronization issue for drive-response fractional-order chaotic systems (FOCS) when they have unknown parameters and external disturbances. In order to achieve the goal, the sliding mode control scheme only using output information is designed, and at the same time, the structures of a sliding mode surface and a sliding mode controller are also constructed. A sufficient criterion is presented to ensure the robust synchronization of FOCS according to the stability theory of the fractional calculus and sliding mode control technique. In addition, the result can be applied to identical or non-identical chaotic systems with fractional-order. In the end, we build two practical examples to illustrate the feasibility of our theoretical results.
Distributional Models with Syntactic Contexts for the Measurement of Word Similarity in Brazilian Portuguese

The similarity between words constitutes significant support to tasks in natural language processing. Several works use Lexical resources such as WordNet for semantic similarity and synonym identification. Nevertheless, words out-of-vocabulary or missing links between senses are perceived problems of this approach. Distributional-based proposals like word embeddings have successfully been used to meet such problems, but the lack of contextual information can prevent the achievement of even better results. The distributional models that include contextual information can bring advantages to this area, but these models are still scarcely explored. Therefore, this work studies the advantages of incorporating syntactic information in the distributional models, fostering for better results in semantic similarity approaches. For that purpose, the current work explore existing lexical and distributional techniques regarding the measurement of word similarity in Brazilian Portuguese. Experiments were carried out with the lexical database WordNet, using different techniques over a standard dataset. The results indicate that word embeddings can cover words out of vocabulary and have better results in comparison with lexical approaches. The main contribution of this article is a new approach to apply syntactic context in the training process of word embeddings to a Brazilian Portuguese corpus. The comparison of this model with the outcome of the previous experiments shows sound results and presents relevant complementary aspects.
An analysis of procedural piano music composition with mood templates using genetic algorithms

Creating music in an automatic way has been studied since the beginning of artificial intelligence. One of the biggest obstacles of music generation is the vagueness and subjectivity of the mood or emotion transmitted by a music piece. In this work, we experiment with the generation of piano music using template pieces, represented in MIDI format, as a mood directive. We generated a population of random pieces for templates of two opposing moods - happy and sad - and evolved them with a genetic algorithm until their intended mood was close enough to their respective templates. The fitness function that we implemented uses MIDI statistical features to calculate the distance between the given piece and the template. A questionnaire applied to human listeners showed that the generated pieces could overall meet the objective, which was to express the same mood as the template. However, they still sounded computer-generated, probably due to the lack of rhythm regularity and synchronicity. We believe that the weights in the fitness function still need to be balanced, and that new rhythm features could make the pieces sound more natural.
Detection of Man-Made Constructions Using LiDAR Data and Decision Trees

Real estate monitoring is very important aspect of country economics, but old manual methods of land survey are time and resources consuming processes as geodata actualization tasks. Actual, precise, multidimensional and detailed information is the main instrument of geospatial intelligence to understand current economic situation and to make effective decision. Actualization of geoinformation using remote sensing is the modern approach of the computer age to complete Earth observation and human environment monitoring. This article describes multi-stage classification model, which detects man-made constructions in LiDAR point cloud. Proposed classification model applies decision tree and geometrical features of shape to remove noises. The goal of study is to experimentally compare decision trees with crisp and fuzzy logic (ID3 algorithms) to select the more suitable algorithm for noise reduction task. Algorithms are compared using total accuracy and Cohen’s Kappa coefficient.
On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library

Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions that must be taken with regard to any particular analyses. This paper proposes on-demand data cubes, which are constructed on the fly when data users process the data. We introduce the open-source C++ library and R package gdalcubes for the construction and processing of on-demand data cubes from satellite image collections, and show how it supports interactive method development workflows where data users can initially try methods on small subsamples before running analyses on high resolution and/or large areas. Two study cases, one on processing Sentinel-2 time series and the other on combining vegetation, land surface temperature, and precipitation data, demonstrate and evaluate this implementation. While results suggest that on-demand data cubes implemented in gdalcubes support interactivity and allow for combining multiple data products, the speed-up effect also strongly depends on how original data products are organized. The potential for cloud deployment is discussed.
UJI DIAGNOSTIK PLASMODIUM MALARIA MENGGUNAKAN METODE IMUNOKROMATOGRAFI DIPERBANDINGKAN DENGAN PEMERIKSAAN MIKROSKOPIS

Microscopic examination is still a gold standard for malaria diagnostic tests. Immunochromatographic (IC) technique can be used as an alternative examination. The aim is to compare the identification value of immunochromatographic method diagnostic test to microscopic examination of malaria in laboratory examination. Cross sectional diagnostic study approach was applied to those with symptoms of: (1) fever (temperature > 38°C) or intermittent fever lasting for two days or more (2) cephalgia/myalgia, 604 samples were taken consecutively in January to July 2005 from primary health centres of Sukaraja and Keruak, as well as Clinic Nugraha at Lombok Timur district , province of Nusa Tenggara Barat . From these samples, there were 37 samples with P.vivax, 45 samples with P. falciparum, 5 samples with mixed infection and 517 samples without Plasmodium sp. From IC examination, results were reserved for 36 samples with P. vivax, 69 samples with P. falciparum, 5 samples with mixed infection and 503 samples without Plasmodium sp. Diagnostic test data analysis showed that the immunochromatographic test revealed 100% sensitivity, 96.7% specificity, 83.2% positive predictive value and 100% negative predictive value. It can be concluded that malaria IC test is reliable to be used as a routine malaria test.
An Empirical Analysis of Network Traffic: Device Profiling and Classification

Time and again we have seen the Internet grow and evolve at an unprecedented scale. The number of online users in 1995 was 40 million but in 2020, number of online devices are predicted to reach 50 billion, which would be 7 times the human population on earth. Up until now, the revolution was in the digital world. But now, the revolution is happening in the physical world that we live in; IoT devices are employed in all sorts of environments like domestic houses, hospitals, industrial spaces, nuclear plants etc., Since they are employed in a lot of mission-critical or even life-critical environments, their security and reliability are of paramount importance because compromising them can lead to grave consequences. IoT devices are, by nature, different from conventional Internet connected devices like laptops, smart phones etc., They have small memory, limited storage, low processing power etc., They also operate with little to no human intervention. Hence it becomes very important to understand IoT devices better. How do they behave in a network? How different are they from traditional Internet connected devices? Can they be identified from their network traffic? Is it possible for anyone to identify them just by looking at the network data that leaks outside the network, without even joining the network? That is the aim of this thesis. To the best of our knowledge, no study has collected data from outside the network, without joining the network, with the intention of finding out if IoT devices can be identified from this data. We also identify parameters that classify IoT and non-IoT devices. Then we do manual grouping of similar devices and then do the grouping automatically, using clustering algorithms. This will help in grouping devices of similar nature and create a profile for each kind of device.
Entity matching on unstructured data : an active learning approach

With the growing number of data sources in enterprises, entity matching becomes a crucial part of every data integration project. In order to reduce the human effort involved in identifying matching entities between different database tables, typically machine learning algorithms are applied. Moreover, active learning is often combined with supervised machine learning methods to further reduce the effort of labeling entities as true or false matches. However, while state-of-the-art active learning algorithms have proven to work well on structured data sets, unstructured data still poses a challenge in entity matching. This paper proposes an end-to-end entity matching pipeline to minimize the human labeling effort for entity matching on unstructured data sets. We use several natural language processing techniques such as soft tf-idf to pre-process the record pairs before we classify them using a novel Active Learning with Uncertainty Sampling (ALWUS) algorithm. We designed our algorithm as a plugin system to work with any state-of-the-art classifier such as support vector machines, random forests or deep neural networks. Detailed experimental results demonstrate that our end-to-end entity matching pipeline clearly outperforms comparable entity matching approaches on an unstructured realword data set. Our approach achieves significantly better scores (F1-score) while using 1 to 2 orders of magnitude fewer human labeling efforts than existing state-of-the-art algorithms.
Dynamic Knowledge Tracing Models for Large-Scale Adaptive Learning Environments

— Large-scale data about learners’ behavior are being generated at high speed on various online learning platforms. Knowledge Tracing (KT) is a family of machine learning sequence models that use these data to identify the likelihood of future learning performance. KT models hold great potential for the online education industry by enabling the development of personalized adaptive learning systems. This study provides an overview of five KT models from both a technical and an educational point of view. Each model is chosen based on the inclusion of at least one adaptive learning property. These are the recency effects of engagement with the learning resources, dynamic sequences of learning resources, inclusion of students’ differences, and learning resources dependencies. Furthermore, the study outlines for each model, the data representation, evaluation, and optimization component, together with their advantages and potential pitfalls. The aforementioned dimensions and the underlying model assumptions reveal potential strengths and weaknesses of each model with regard to a specific application. Based on the need for advanced analytical methods suited for large-scale data, we briefly review big data analytics along with KT learning algorithms’ scalability. Challenges and future research directions regarding learners’ performance prediction are outlined. The provided overview is intended to serve as a guide for researchers and system developers, linking the models to the learner’s knowledge acquisition process modeled over time.
Statistical approaches for resting state fMRI data analysis

This doctoral dissertation investigates the methodology to explore brain dynamics from resting state fMRI data. A standard resting state fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. There are two main objectives in the analysis of these noisy data: establishing the link between neural activity and the measured signal, and determining distributed brain networks that correspond to brain function. These measures can then be used as indicators of psychological, cognitive or pathological states. Two main issues will be addressed: retrieving and interpreting the hemodynamic response function (HRF) at rest, and dealing with the redundancy inherent to fMRI data. Novel approaches are introduced, discussed and validated on simulated data and on real datasets, in health and disease, in order to track modulation of brain dynamics and HRF across different pathophysiological conditions.
Stackelberg-Game-Based Defense Analysis Against Advanced Persistent Threats on Cloud Control System

In this paper, the security problem for a cloud control system (CCS) is studied. In the CCS, so-called advanced persistent threats (APTs) can be launched by a malicious attacker to reduce the quality of service of cloud and deteriorate the system performance further. To defend against APTs and create a security as a service scheme, a defender needs to allocate defense resource to different units serving to different plants for improving the overall system performance when the CCS accommodates multiple physical plants simultaneously. After observing the defender's action, the attacker decides which serve units to comprise. Considering that both defender and attacker are subject to resource constraints, the interaction of two sides is modeled by a Stackelberg game. The optimal solutions for two players under different types of budget constraints are investigated. Simulation examples and comparison results are provided to verify the main results of this paper.
Experimenting the Influence of Input Modality on Involvement Load Hypothesis

As far as incidental vocabulary learning is concerned, the basic contention of the Involvement Load Hypothesis (ILH) is that retention of unfamiliar words is, generally, conditional upon the degree of involvement in processing them. This study examined input modality and incidental vocabulary uptake in a task-induced setting whereby three variously loaded task types (marginal glosses, fill-intask, and sentence-writing) were alternately assigned to one group of students at Allameh Tabataba’i University (n=2l) during six classroom sessions. While one round of exposure was comprised of the audiovisual medium (TV talk shows), the second round consisted of textual materials with approximately similar subject matter (reading texts). In both conditions, however, the tasks were equivalent to one another. Taken together, the study pursued the dual objectives of establishing a litmus test for the ILH and its proposed values of ‘need’, ‘search’ and ‘evaluation’ in the first place. Secondly, it sought to bring to light the superiority issue of exposure to audiovisual input versus the written input as far as the incorporation of tasks is concerned. At the end of each treatment session, a vocabulary active recall test was administered to measure their incidental gains. Running a one-way analysis of variance revealed that the audiovisual intervention yielded higher gains than the written version even when differing tasks were included. Meanwhile, task 'three' (sentence-writing) turned out the most efficient in tapping learners' active recall of the target vocabulary items. In addition to shedding light on the superiority of audiovisual input over the written input when circumstances are relatively held constant, this study for the most part, did support the underlying tenets of ILH. Keywords—Evaluation, incidental vocabulary learning, input mode, involvement load hypothesis, need, search.
Switching fuzzy control for nonlinear systems

This paper presents a controller design condition based on a switching Lyapunov function for a class of nonlinear systems. In our previous papers we proposed a switching fuzzy model and a switching Lyapunov function for open-loop systems. The switching Lyapunov function is constructed by mirroring structure of the switching fuzzy model. In this paper, stabilization conditions to design a switching fuzzy controller are derived. To avoid discontinuity of the switching control inputs, we derive a continuity condition of control inputs. A design example illustrates the utility of this approach.
Evolution and Fragility of Mobile Automated Test Suites

Context. Android applications have reached a level of diffusion and complexity that was once exclusive of desktop computing, hence demanding a thorough validation and verification phase to ensure that they meet their requirements. Such need especially applies to their GUIs (i.e., Graphical User Interfaces) through which most of the interaction with the final user is performed. However, although many approaches, techniques and tools exist to test Android apps, evidence from the literature suggests that GUI testing is generally limited among practitioners. Goal. The main goals of the studies reported in this thesis have been: (i) assess the usability of existing GUI testing techniques applicable to Android apps; (ii) quantify the adoption of existing tools by developers from the open-source environment; (iii) investigate the amount of effort needed in maintaining test suites during their coevolution with the AUT (Application Under Test) and the principal reasons behind the interventions; (iv) identify the main issues faced by Android testers, and provide guidelines and tools to aid reducing their impact on testing design and maintenance. Method. These goals were pursued by performing five different studies, after a preliminary exploratory study on a popular open-source app. An experiment with Graduate students and a survey with developers from the industry were conducted in order to gather qualitative information about the usability of testing tools and techniques, and to understand what are the needs and difficulties felt by different categories of users of those tools. Starting from a mining of all tested Android applications published on GitHub, a set of metrics for quantifying testware evolution and maintenance and a taxonomy of maintenance reasons were defined and validated. Finally, a tool that leverages the benefits of two GUI testing techniques (Layoutbased and Visual) was implemented and validated. Results. The studies showed that the diffusion of testing tools is limited on the set of projects mined from GitHub, the largest context for this kind of quantitative exper-
Advancing mouse-tracking research : new solutions for study design, implementation and analysis

This thesis addresses the topic of mouse-tracking, that is, the recording and analysis of mouse movements in computerized experiments. Mouse-tracking is an increasingly popular process tracing method in many psychological disciplines as it allows capturing the temporal development of the relative attraction to and conflict between response alternatives. It thus provides the opportunity to test psychological theories about factors that influence the conflict involved in making a decision, and how this conflict develops over time. So far, researchers have faced a number of difficulties when conducting mouse-tracking studies: There has been no easy-to-use, flexible and open software for creating experiments and no general-purpose package for analysis. Researchers also have had to make many choices regarding the study setup, with no evidence-based guidelines to support their decisions. This thesis aims to provide solutions for these challenges. First, this thesis introduces free and open-source software packages for creating and analyzing mouse-tracking experiments. The mousetrap plugin enables researchers to implement mouse-tracking in their experiments without programming and, through integration with the experiment builder OpenSesame, offers a graphical user interface that makes it easy to create a variety of experiments and designs. The mousetrap R package provides extensive functionality for processing, analyzing, and visualizing mouse-tracking raw data of all major formats. It implements most of the commonly used preprocessing procedures and mouse-tracking indices, as well as a set of novel visualization and classification procedures for analyzing trajectory shapes. Second, this thesis presents results from a series of experiments that investigate how the methodological setup influences mouse-tracking data. In separate experiments, I manipulated the design factors starting procedure, mouse sensitivity, and response indication and investigated their impact on trajectory curvature and shape. An additional study investigated the effects of the starting procedure on movement consistency and also included dynamic analyses. While central cognitive effects on trajectory curvature were replicated in all setups, their size varied considerably between some of the setups. In addition, the setup strongly influenced the trajectory shapes and dynamic analyses. Based on this evidence, I discuss implications for interpreting mouse-tracking data and offer preliminary recommendations for conducting mouse-tracking experiments. In sum, I hope this thesis will contribute to advancing mouse-tracking research and making the method accessible to a broader audience.