Reviewerly/exHarmony huggingface dataset:
Fun with interfaces (SVG interfaces for musical expression)

In this paper we address the design and implementation of custom controller interfaces, bridging the issue of user mapping between action and sound in interactive music systems. A simple framework utilizing functional specifications for musical interfaces and their mappings is presented, in terms of a subset of Scalable Vector Graphics (SVG); interfaces can be described using a simple Haskell based `controller DSL' or equally using a vector drawing application (i.e. Illustrator).
AM <sub>clr</sub>

Ant-Miner, a rule-based classifier, is a powerful algorithm used to extract classification rules. It has the ability to classify the complex data accurately but it has limitations of high selective pressure, and pre-mature convergence or very slow convergence. One of the big limitations is the correct prediction based evaluation of the rules that has not yet been addressed. In this paper, we propose a new ant-miner (named as AMclr) based on a new quality function, rank based term selection and on rule rejection thresholds. The new quality function is based on rule length, coverage and correct prediction. The rule rejection thresholds are based on coverage and quality of the rule. The proposed algorithm has been tested on various benchmark datasets and compared with other state of the art algorithms. The experimental results showed that the proposed approach achieved better results in terms of accuracy, convergence speed, and comprehensibility.
Self-similarity Based Multi-layer DEM Image Up-Sampling

As one of the basic data of GIS, DEM data which expresses the surface elevation data is widely used in many fields. How to obtain a wide range of high-precision elevation data is a big challenge, the simple interpolation algorithm currently used is less accurate. Due to the fractal data characteristics of terrain data, DEM data shows strong self-similarity. Based on this feature, this paper proposes a multi-layer Dem image up-sampling method. Image up-sampling is performed multiple times in layers on the low-resolution DEM image, therefore, high-precision DEM information with less error is obtained. In this paper, elevation data of 30 m is expanded to elevation data of 10 m by gradually using this method. Experimental results show that the algorithm can achieve good results and has a small deviation from the real elevation data of 10 m.
Blockchain Based Informed Consent with Reputation Support

Digital economy relies on global data exchange flows. On May 25th 2018 the GDPR came into force, representing a shift in data protection legislation by tightening data protection rules. This paper introduces an innovative solution that aims to diminish the burden resulting from new regulatory demands on all stakeholders. The presented solution allows the data controller to collect the consent, of a European citizen, in accordance to the GDPR and persist proof of said consent on public a blockchain. On the other hand, the data subject will be able to express his consent conveniently through his smartphone and evaluate the data controller’s performance. The regulator’s role was also contemplated, meaning that he can leverage certain system capabilities specifically designed to gauge the status of the relationships between data subjects and data controllers.
Prediction of SQL Injection Attacks in Web Applications

As web applications become increasingly complex and connected, it becomes imperative to reduce the vulnerabilities in applications. SQLIA is a part of OWASP vulnerabilities and it is extremely important to prevent them. The proposed system aims to predict the occurrence of SQLIA on a given server, with applications deployed on it, from a given source, at a particular time. This prediction can be done with the help of JMeter tool. Apache JMeter is used to simulate logs data. From this, one can pre-process, extract features, and classify, which is then fed to a model for prediction of SQLIA.
Fast and robust Fourier domain ranging for light field

A light-field camera combines optics and computation to provide the ability to perform ranging. Many methods and algorithms, which are time-consuming and noise-sensitive, have been proposed for per-pixel depth estimation. We present a Fourier domain ranging method for light fields (LFs). Instead of estimating the depth for every pixel, we attempt to detect the depths of the different planes at which objects are located. The method is somewhat like using the energy in the focal stack, but it is carried out in a more efficient way. Our method has the advantages of speediness and robustness compared with traditional per-pixel depth estimation methods. In addition to the ranging algorithm, we also demonstrate the idea and application of a region adaptive denoising filter, in which the depth parameters are tuned by the proposed method. We include results for synthetic LFs, the Stanford LF archives, and LFs captured with a Lytro camera, exploiting the algorithm complexity, accuracy, depth resolution, and noise performance of our method. Our method uses less computation and is more robust than per-pixel depth estimation, making it appropriate for many applications, such as autorefocusing and autodenoising.
Copy-Move Forgery Detection in Transform Domain

In this chapter, we present a copy-move forgery detection technique which utilizes Undecimated Dyadic Wavelet Transform (DyWT) for its operation. Dyadic Wavelet Transform (DyWT) is advantageous because it does not involve signal down-sampling and hence provides a better approximation compared to other wavelet transforms. Subsequently, we propose a technique to reduce the number of false positives obtained through application of this technique, to further improve the efficiency of the proposed scheme.
Possibilistic Investigation of Mechanical Control Systems Under Uncertainty

Uncertainty in linear control systems is often modeled by real- or complex-valued perturbations. The key idea of robust control techniques is to check whether the design specifications are satisfied even for the worst-case uncertainty. A quite intuitive and apparently natural way to model uncertainty is using fuzzy sets and arithmetic. The resulting uncertain system models do not only consider worst-case scenarios, but also the shape of uncertainty modeled by imprecise quantities. Possibilistic measures can then be employed to investigate the satisfaction of stability and performance objectives and to draw conclusions about the robustness of the resulting controller. The gradualness in the quantification of uncertainty allows for more comprehensive statements than just worst-case scenarios.
Application of Noise Reduction Technique in the Inverse Analysis Using Kalman Filter

The inverse techniques usually employ the sensor measurement to estimate the unknown quantities. Regardless of sensor accuracy, the measurements contain some degrees of uncertainty and error, inadvertently. Inasmuch as, the inverse problems are ill-conditioned in general term, the measurement errors cause instabilities, perturbations, and excursions in the solution procedure. To handle the noise difficulties, a novel approach is proposed in the current study. In this method, the measurement errors are filtered to alleviate the noise priori to utilization of inverse method. The Kalman filter is implemented to remove the noise from the original sensor readings. Thereafter, the Levenberg–Marquardt method is implemented to predict the unknown. To evaluate the accuracy and robustness of the developed approach, a high nonlinear test case containing moving boundary heat conduction problem is investigated. Comparing the obtained results illustrates the improvement of inverse solution procedure by employing the noise filtering technique.
Low Complexity "Smart" Per-Scene Video Encoding

Recently, Netflix proposed a perceptual per-scene video encoding framework for over-the-top (OTT) adaptive streaming applications. Given a full-matrix of fixed quantization (fixed-QP) scene encodes (i.e., all scenes encoded at all QP values for all resolutions), it selects scene encodes which reduce the average bitrate for a target quality value. This paper proposes a low complexity alternative for achieving per-scene encoding. At first, using a small set of scene encodes, a bitrate-resolution-quality model of the complete video is constructed. Using this model, per-scene video encoding is posed as a global optimization problem. The proposed method adapts the instantaneous bitrate and encoding resolution to the complexity of the scene, enabling lower overall bitrate compared to the adaptive-QP approach [2]. Additionally, thanks to our bitrate-resolution-quality modeling, the proposed method significantly reduces the computational complexity without the need to compute a full-matrix of scene encodes and their respective quality computations.
Using Game Theory to Address New Security Risks in the IoT

An Internet of things (IoT) botnet is a network formed by connecting together a number of computers, smart devices and appliances connected to the Internet. This new type of network does not come without risk, since devices participating in the IoT can easily be infected with malicious software in order to be controlled by authorities that are not the owners of the devices. The botnet is in turn used, without the knowledge of the devices’ owner(s), and forced to act as a set of transmitting devices on behalf of the hackers. It is often the case that owners of smart devices and IoT-connected appliances do not consider the significance of having strong security when connecting these devices to the Internet, even if it is as simple as making sure that a strong password is set. It is very important to note here that strong passwords that are updated often can quite satisfactorily safeguard access to the device. The chapter uses game theory to model a coalition that would handle the password generation process in such an IoT scenario, in order to reduce the risk of IoT nodes being hijacked.
Multi-pruning and Restricted Multi-pruning of Decision Trees

In this chapter, we consider two questions related to decision trees: (i) how to construct decision trees with reasonable number of nodes and reasonable number of misclassifications when they are used for knowledge representation, and (ii) how to improve the prediction accuracy of decision trees when they are used as classifiers. We created so-called multi-pruning approach based on dynamic programming algorithms for bi-criteria optimization of CART-like decision trees relative to the number of nodes and the number of misclassifications. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments with decision tables from the UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in the number of misclassifications. Multi-pruning approach includes a procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. We considered a modification of multi-pruning approach (restricted multi-pruning) that requires less memory and time but usually keeps the quality of constructed trees as classifiers or as a way for knowledge representation. Based on the uncertainty measure abs which is applicable both to decision tables with single- and many-valued decisions, we extended the considered approaches to the case of decision tables with many-valued decisions.
The Framework of Personalized Ranking on Poisson Factorization

Matrix factorization (MF) has earned great success on recommender systems. However, the common-used regression-based MF not only is sensitive to outliers but also unable to guarantee that the predicted values are in line with the user preference orders, which is the basis of common measures of recommender systems, e.g., nDCG. To overcome the aforementioned drawbacks, we propose a framework for personalized ranking of Poisson factorization that utilizes learning-to-rank based posteriori instead of the classical regression-based ones. Owing to the combination, the proposed framework not only preserves user preference but also performs well on a sparse matrix. Since the posteriori that combines learning to rank and Poisson factorization does not follow the conjugate prior relationship, we estimate variational parameters approximately and propose two optimization approaches based on variational inference. As long as the used learning-to-rank model has the 1st and 2nd order partial derivatives, by exploiting our framework, the proposed optimizing algorithm can maximize the posteriori whichever the used learning-to-rank model is. In the experiment, we show that the proposed framework outperforms the state-of-the-art methods and achieves promising results on consuming log and rating datasets for multiple recommendation tasks.
A genetic algorithm hybridisation scheme for effective use of parallel workers

Genetic algorithms are simple to accelerate by evaluating the fitness of individuals in parallel. However, when fitness evaluation is expensive and time to evaluate each individual is highly variable, workers can be left idle while waiting for long-running tasks to complete. This paper proposes a local search hybridisation scheme which guarantees 100% utilisation of parallel workers. Separate work queues are maintained for individuals produced by genetic crossover and local search neighbourhood operators, and a priority rule determines which queue should be used to distribute work at each step. Hill-climbing local search and a conventional genetic algorithm can both be derived as special cases of the algorithm.
Fuzzy Logic for Speed Control in Object Tracking Inside a Restricted Area Using a Drone

This article presents the autonomous speed and positioning control for a drone using fuzzy logic to track a target within a restricted area. Classical controllers present a problem, as in general they only support one input and one output and a system model is always required. For this application, it is necessary to analyze two inputs, the position in “x” and the position in “y” of an object that will be recognized by the drone through artificial vision. The goal is to control the speed at which the drone moves according to the position of the object detected by the machine vision within a restricted area, resulting in a faster or slower movement that will improve the tracking of a moving target by delivering real-time object monitoring information to the user in order to take some action based on this information.
Remaining Semper Paratus: Augmented Reality in the Coast Guard

Over the past decade, augmented reality (AR) has emerged into the mainstream with numerous studies conducted on its effectiveness in both military and public learning environments. Compatible with constructivist learning theory, AR scenarios help instructors grab and maintain learner attention, close the realism gap between learning and job environments, enhance practice and rehearsal opportunities, and support transfer of learning. AR integration into the U.S. Coast Guard training system can enhance learning while managing the ever-rising challenges of limited time and resources.
Gesture Control for Human-Robot Interaction Based on Three-Way Decision Model

The Gesture interaction is a new Human-Computer Interaction method which has gradually become the heated topic and attracted much attention. As the gesture is intuitive and natural, it has been widely used in the area of human-robot interaction. However, most existing gesture control processes focus on the effectiveness of successful operation. The consideration of the risk of misoperation is usually ignored. It may cause a great risks and much trouble to the robot control system. Therefore, this paper introduces three-way decision model into human-robot interaction process. In the three-way decisions, it is important to acquire the thresholds and decision costs. In this paper, the thresholds of the three-way decision model are the combination of decision risk and classification accuracy. The decision costs come from the aid of group decision making process. Moreover, as the gesture control process is a multi-category problem, it is transformed into several two-category problems. Because the transformed problems may cause conflicts in the decision actions, a divided threshold is considered to deal with the problem. To demonstrate the effectiveness of our proposed method, an illustration is presented. Finally, our experimental results show that the performance of the proposed method is better that that of the method based on two-way decision model.
A Double Auction VM Migration Approach

Virtualization technology plays an important role in cloud computing. Virtual machine (VM) migration can reduce the cost of cloud computing data centers. In this paper, a double auction-based VM migration algorithm is proposed, which takes the cost of communication between VMs into account under normal operation situation. The algorithm of VM migration is divided into two parts: (1) selecting the VMs to be migrated according to the communication and occupied resources factors of VMs, (2) determining the destination host for VMs which to be migrated. We proposed VMs greedy selection algorithm (VMs-GSA) and VM migration double auction mechanism (VMM-DAM) to select VMs and obtain the mappings between VMs and underutilized hosts. Compared with other existing works, the algorithms we proposed have advantages.
A Classification Method of Landform Elements Based on Douglas-Peucker Algorithm for Multi-scale Morphology

Landform elements (e.g. ridge, valley) are the basic morphologic types of earth's surface. All types of geomorphy are formed by different spatial combination of landform elements, so landform elements are the foundation of studying geomorphy. Using a pre-assigned set of topographic attributes derived from the digital elevation model (DEM), most of existing classification methods of landform elements are likely to encounter a problem that local topographic attributes can not catch the terrain context well in the process of landform elements mapping. To solve the problem, we improve the Geomorphons classification method and propose a new classification method of landform elements based on Douglas-Peucker algorithm for the multi-scale morphology in this paper. The proposed method effectively overcomes the problem of the Geomorphons classification method which can not show the multi-scale characteristics of landform elements and thus might result in misclassification in rugged area. The experimental results show that the proposed method can get more reasonable results than that by the Geomorphons classification method.
Differences in Emotional Reactions to Social Media Content

Individuals in diaspora tend to engage and interact with the people of their origin. Afghans in the 1980s and 90s, still one of the largest diaspora communities, used traditional channels such as Radio and Television to communicate with their loved ones inside Afghanistan. The Internet facilitated changing their mode of communication to near real-time updates. Diaspora and local communities engage in the same Social Awareness Streams (SAS), but there is a lack of research focusing on difference and similarities of these communities. This paper investigates the differences in emotional disclosure and expression between diaspora and local communities in Afghanistan using data from 'Afghanistan My Passion', the largest public Afghan Facebook community page. We investigate a corpus of 2,165 Persian language words considering the location and gender of the social media user. This work provides the first analysis towards understanding differences in diaspora and local communities' emotional reactions to social media content and extends the body of literature on Persian language sentiment analysis.
Designing a Recommender System for Touristic Activities in a Big Data as a Service Platform

Designing e-services forBellandi, Valerio tourist today implies to deal with a large amount of data and metadata that developers should be able to exploit for generating user perceived values. By integrating aCeravolo, Paolo Recommender System on a Big Data platform, we constructed the horizontal infrastructure for managing these services in an application-neutral layer. In this chapter, we reviseDamiani, Ernesto the design choices followed to implement this service layer, highlighting the data processing and architectural patterns we selected. More specifically, we first introduce the relevantTacchini, Eugenio notions related to Big Data technologies, we discussed the evolving trends in Tourism, and we introduce fundaments for designing Recommender Systems. This part provides us with a set of requirements to be fulfilled in order to integrate these different components. We then propose an architecture and a set of algorithms to support these requirements. This design process guided the implementation of an innovative e-service platform for tourist operators in Italy.
A School Violence Detection Algorithm Based on a Single MEMS Sensor

School violence has become more and more frequent in today’s school life and caused great harm to the social and educational development in many countries. This paper used a MEMS sensor which is fixed on the waist to collect data and performed feature extraction on the acceleration and gyro data of the sensors. Altogether nine kinds of activities were recorded, including six daily-life kinds and three violence kinds. A filter-based Relief-F feature selection algorithm was used and Radial Basis Function (RBF) neural network classifier was applied on them. The results showed that the algorithm could distinguish physical violence movements from daily-life movements with an accuracy of 90%.
The Cluster Analysis in Big Data Mining

The purpose of this chapter is the consideration of modern methods of the cluster analysis, crisp methods and fuzzy methods, robust probabilistic and possibilistic clustering methods, their properties and application. The problem of cluster analysis is formulated, main criteria and metrics are considered and discussed. Classification of cluster analysis methods is presented, several crisp methods are considered, in particular, hard C-means method and Ward’s method. Fuzzy clustering methods are considered and analyzed: fuzzy C-means method and its generalization Gustavsson-Kessel’s method of cluster analysis which is used when metrics of distance differs from Euclidian. The methods of initial location of cluster centers are considered: peak and differential grouping and their properties analyzed. Adaptive robust clustering algorithms are presented and analyzed which are used when initial data is distorted by high level of noise, or by outliers. In the Sect. 1.7 robust probabilistic algorithms of fuzzy clustering are considered and investigated for batch processing mode and on-line mode which may be used for clustering in BD bases. Experimental investigations of the considered clustering methods are presented, including clustering of UNO countries by indicators of sustainable development.