Reviewerly/exHarmony huggingface dataset:
Latent Space Secrets of Denoising Text-Autoencoders.

Generative autoencoders offer a promising approach for controllable text generation by leveraging their latent sentence representations. However, current models struggle to maintain coherent latent spaces required to perform meaningful text manipulations via latent vector operations. Specifically, we demonstrate by example that neural encoders do not necessarily map similar sentences to nearby latent vectors. A theoretical explanation for this phenomenon establishes that high capacity autoencoders can learn an arbitrary mapping between sequences and associated latent representations. To remedy this issue, we augment adversarial autoencoders with a denoising objective where original sentences are reconstructed from perturbed versions (referred to as DAAE). We prove that this simple modification guides the latent space geometry of the resulting model by encouraging the encoder to map similar texts to similar latent representations. In empirical comparisons with various types of autoencoders, our model provides the best trade-off between generation quality and reconstruction capacity. Moreover, the improved geometry of the DAAE latent space enables zero-shot text style transfer via simple latent vector arithmetic.
SDQO AND SFO, ONTOLOGIES FOR SPATIAL DATA QUALITY ASSESSMENT

Abstract. Spatial quality assessment is based on the conformance of data to its specifications or fitness for users’ purpose. These specifications and the users’ purposes include the rules and constraints that a dataset should comply with. Assessing the compliance of data to the rules is still an active research subject and rule-based approach is the common method. For the efficient rule-based system implementation, it is desired to automate assessment process with a domain-independent and web-based approach. Reasoning capability and re-usability of semantic web components are expected to promote efficient implementation. In literature, many domains such as agriculture, music, Linked Data and geospatial domain etc. apply ontology-based methods for quality management. There is a need to model geospatial quality concepts and rules in a domain-independent way to automate the quality management process. In our model of rule formalism, we use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We devise two types of ontologies. These are; the specification ontologies (SfO) and the Spatial Data Quality Ontology (SDQO). SfO is to be created by domain experts/users to define rules according to specifications. SDQO is responsible with quality assessment; it is domain independent and makes assessment based on the rules defined by any SfO for the related domain. The quality elements are domain and toposemantic consistency that assessed by SWRL. In this paper, the design considerations of the ontologies for quality assessment are explained with an example.
Phys-Sketch: Sketching 3D Dynamic Objects in Immersive Virtual Reality

Sketching was traditionally a 2D task. Even when the new generation of VR devices allowed to sketch in 3D, the drawn models remained essentially static representations. In this paper, we introduce a new physics-inspired sketching technique built on the top of Position-based Dynamics to enrich the 3D drawings with dynamic behaviors. A particle-based method allows interacting in real time with a wide range of materials including fluids, rigid bodies, soft bodies and clothes. Users can interact with the dynamic sketches and sculpt them while they move, deform and fall. We analyze the expressiveness of the system from the regard of two experienced artists. Thus, this paper also gives a starting point to move towards an improved generation of physics-enabled sketching applications.
A Hybrid Approach for Financial Sentiment Analysis Using Artificial Intelligence and Cuckoo Search

The newly altered read-write network has resulted in a fast development of the content written by the users that results in a large quantity of un-useful data. Opinion mining and Sentiment analysis (SA) are the two mostly used techniques in the last couple of years that processed the available online unstructured data and obtained important information as well as recognize the sentiments of user. In this research work, we will train the proposed sentiment analysis system based on Artificial Neural Network (ANN) as classification algorithm for data such as positive, negative and neutral sentiments. But ANN works mainly in two phases initially train the system as per the data available, if the training data is unique then the classification accuracy will be high so we propose cuckoo search algorithm to optimize the extracted features with a novel healthy function. On the basis of objective function, cuckoo search returns appropriate feature sets according to the data type such as positive and negative sentiments. In any sentiment analysis system work, optimization technique is needed because of unstructured processed data but in proposed work we will utilize the pre-processing steps such as normalization, stop words removal technique.
Positive Personas: Integrating Well-being Determinants into Personas.

System design for well-being needs an appropriate tool to help designers to determine relevant requirements that can help human well-being to flourish. Personas come as a simple yet powerful tool in the early development stage of the user interface design. Considering well-being determinants in the early design process provide benefits for both the user and the development team. Therefore, in this short paper, we performed a literature study to provide a conceptual model of well-being in personas and propose positive design interventions in the personas creation process.
LARA: A Design Concept for Lattice-Based Encryption

Lattice-based encryption schemes still suffer from a low message throughput per ciphertext and inefficient solutions towards realizing enhanced security properties such as CCA1- or CCA2-security. This is mainly due to the fact that the underlying schemes still follow a traditional design concept and do not tap the full potentials of LWE. Furthermore, the desired security features are also often achieved by costly approaches or less efficient generic transformations. Recently, a novel encryption scheme based on the A-LWE assumption (relying on the hardness of LWE) has been proposed, where data is embedded into the error term without changing its target distributions. By this novelty it is possible to encrypt much more data as compared to the classical approach. In this paper we revisit this approach and propose several techniques in order to improve the message throughput per ciphertext. Furthermore, we present a very efficient trapdoor construction of reduced storage size. More precisely, the secret and public key sizes are reduced to just 1 polynomial, as opposed to $$O( \log q)$$ polynomials following previous constructions. Finally, we give an efficient implementation of the scheme instantiated with the new trapdoor construction. In particular, we attest high message throughputs and low ciphertext expansion factors at efficient running times. Our scheme even ensures CCA (or RCCA) security, while entailing a great deal of flexibility to encrypt arbitrary large messages or signatures by use of the same secret key.
Perron stability and its study at the first approximation

Natural concepts of Perron stability, asymptotic stability, and complete instability of the trivial solution to a differential system are introduced. Their features and relationships with Perron exponents and similar Lyapunov ones are indicated. An absolute and unique coincidence of research opportunities for Perron and Lyapunov stability and asymptotic stability at the first approximation is found.
Importância do profissional de enfermagem nos cuidados ao paciente com transtorno mental: uma revisão integrativa

Nursing care for the mentally ill patient should be developed respecting the principles of dignity, interaction with others, freedom from prejudice, personal beliefs and values, with which nursing has sought to offer individualized care in its more complex actions.The present study aims to analyze nursing care for the mentally ill, and the changes that have occurred over the years and the new public policies.The present study uses the integrative literature review method.We searched for articles with the following health descriptors: nursing care, nurse and mental disorder, and after the exclusion criteria there were 12 articles with eligibility for the same.It is important to emphasize that in order to achieve good results in the nurses' work, it is important that there is a close relationship between the person in care, their families and the professional, allowing an integral care, since it is no longer based only on technical care, have a broad look at working the relationship between team, community and family.
Multiple software product lines to configure applications of internet of things

Software product lines (SPL) emulate the industrial production lines that are capable of generating large volumes of products through reuse schemes and mass production. A multi product line (MPL) aims to reuse of several SPL. Feature models are often used to manage the existing resources of SPLs and define valid products through notations and relationships such as mandatory, optional, and alternative features. The main contribution of this study is a method to manage the variability of multiple SPL and generate a new portfolio of products for Internet of Things (IoT). For this, the problem of developing a universal feature model (FM) for an MPL from merging the FMs of the individual SPLs with a Search-Based Software Engineering (SBSE) technique is addressed. In addition, the authors propose a multi-objective optimisation model to maximise the reusability and compatibility between features and minimise the development cost. The model facilitates the design of an MPL-feature model. Authors’ empirical results show that the proposed model solved by genetic algorithms allows to configure a variety of software products and to determine the scope of the MPL.
Multimodality and Gamification

The term multimodality refers to a particular mode wherein something is expressed or experienced. In the context of gamification, it implies the diverse dimensions the gamification is used for across various domains and technology sectors. Classic gamification used on the consumer end has often been connected to reward-based models while the modality in gamification in modern times is related to even more diverse, more comprehensive, and perhaps more complicated experiences. This chapter reveals those areas including hapticsHaptics” \b \i , augmented realityAugmented reality” \b \i , mixed realityMixed reality” \b \i , virtual realityVirtual reality” \b \i , sensorsSensors” \b \i , and other relevant concepts and technologies.
An IPVO-based reversible data hiding scheme using floating predictors

This work optimizes an improved high-fidelity reversible data hiding scheme of Peng et al. which is based on improved pixel-value-ordering (IPVO) and prediction-error expansion. In Peng et al.'s method, the difference between the maximum and second largest value (or, the minimum and second smallest value) of a block is defined considering the pixel locations of maximum and second the largest value (or, the pixel locations of minimum and second the smallest value). When the difference between the maximum and second largest value (or, the minimum and second smallest value) of a block is equal to 0 or 1, the block can be exploited to embed data. Otherwise, the block should be shifted or remain unchanged. However, different prediction-error used to embed information can lead to different histogram modification and different pixel shift rate, to further reduces the change in the carrier image. In this work, we list all the different prediction-error, which are used as the selection object for the embedded error when hiding information. As a prerequisite of meeting the demand of the embedding capacity, some appropriate prediction-errors are selected for embedding to reduce the number of the pixel shifted in the marked image as small as possible. An IPVO-based reversible data hiding scheme with floating predictor is also extended. Experimental results show that the proposed scheme yields a superior performance than the state-of-the-art works, under the condition of same embedding capacity, especially for relatively rough images.
Aorta Centerline Smoothing and Registration Using Variational Models

In this work we present an application of variational techniques to the smoothing and registration of aorta centerlines. We assume that a 3D segmentation of the aorta lumen and an initial estimation of the aorta centerline are available. The centerline smoothing technique aims to maximize the distance of the centerline to the boundary of the aorta lumen segmentation but keeping the curve smooth. The proposed registration technique computes a rigid transformation by minimizing the squared Euclidean distance between the points of the curves, using landmarks and taking into account that the curves can be of different lengths. We present a variety of experiments on synthetic and real scenarios in order to show the performance of the methods.
On parallelisation of image dehazing with OpenMP

In this paper, we present our learning experience on the design and implementation of image dehazing parallel code with OpenMP developed from existing fast sequential version. The aim of this work is to present an analysis of a case study showing the development of parallel haze removal with practical and efficient use of shared memory multi-core servers. Implementation technique and result discussions in terms of program improvements that may be needed to support parallel application developers with similar high performance goals are presented. Preliminary studies, results and experiments on haze removal application program are executed on multi-core shared memory platforms, and results show that the performance of the proposed parallel code is promising.
Class Reconstruction Driven Adversarial Domain Adaptation for Hyperspectral Image Classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach.
Structured and uncertainty-aware data storytelling

Data stories are an increasingly popular medium for delivering information in the field of journalism. More and more journalists are telling stories directly from data, by creating and combining data visualisations. A good data story, as with any other types of news story, should convey the author’s message accurately, and without distorting the underlying data. In this paper, we introduce our ongoing project, which aims to help data journalists create accurate stories by providing a logical story structure and informing authors of data-uncertainty through the whole life cycle of story creation. We identify key challenges in this space, and present our design considerations for addressing them.
ICRODI: Image Compression of Region of Diagnostics Interest (RODI) using Layer Segmentation and Wavelet

Robotic guided medical system requires efficient mechanism of compression of Region of Diagnostics Interest (RODI) in medical images to overcome the tradeoff among efficiency and time which is a computationally challenging task. This task involves the requirement of suitable noise filtering, segmentation, critical feature selection especially at corners of RODI and encoding process. This paper proposes a framework namely ICRODI to evaluate a hybrid approach of compression for region of diagnostic interest in Brain MRI as well as for rest of the region. The approaches used are median filter, thresholding as pre-processing and fuzzy c-mean clustering, Harris corner detection, s-shape fuzzy for segmentation and feature point selection optimization. Further alpha hull of the convex hull is used for getting the volume of the mass and finally the wavelet co-efficient based compression is applied. The effectiveness of the proposed ICRODI is validated by evaluating MSE and PSNR for both RODI and Non-ROSI. The average value of the PSRN for RODI is found approximately 49 % higher as compared to the non-RODI and MSE of the RODI is reduced by approximately 33% as compared to the non-RODI after simulating the process on a numerical simulation platform. The achieved results are quite promising and could be optimized for the VLSI implementation in future.
Efficient Method for Inverse Dynamics of Robot Manipulators by using Adaptive-network-based Fuzzy Inference System

The inverse dynamics calculation of a robot manipulator is essential in a robot manipulator control system. The commonly used approaches to formulate dynamic models of robot manipulators are Newton-Euler and Lagrange-Euler methods. Since both methods are numerical recursive methods, they are computationally expensive and not suitable to be used directly in real time applications. In this paper, the adaptive-network-based fuzzy inference system (ANFIS) is used to construct the input-output mapping of inverse dynamics model of a robot manipulator. To create the training data sets for the ANFIS systems, the dynamic model of a 5 degree-of-freedom robot manipulator is computed using Newton-Euler dynamic formulation. The proposed method is tested in formulating the required torques of a point-to-point trajectory. The results show that the proposed method possesses shorter operational time and its performance is comparable to numerical method. The proposed method can be used for the rigid-body robot manipulators whose dynamical characteristics are known.
Game Theoretic-Based Approaches for Cybersecurity-Aware Virtual Machine Placement in Public Cloud Clusters

Allocating several Virtual Machines (VMs) onto a single server helps to increase cloud computing resource utilization and to reduce its operating expense. However, multiplexing VMs with different security levels on a single server gives rise to major VM-to-VM cybersecurity interdependency risks. In this paper, we address the problem of the static VM allocation with cybersecurity loss awareness by modeling it as a two-player zero-sum game between an attacker and a provider. We first obtain optimal solutions by employing the mathematical programming approach. We then seek to find the optimal solutions by quickly identifying the allocation strategies in our formulated zero-sum game. We mean by equilibrium that none of the provider nor the attacker has any incentive to deviate from one's chosen strategy. Specifically, we study the characteristics of the game model, based on which, to develop effective and efficient allocation algorithms. Simulation results show that our proposed cybersecurity-aware consolidation algorithms can significantly outperform the commonly used multi-dimensional bin packing approaches for large-scale cloud data centers.
DISTANCE LEARNING IN DEVELOPING FUTURE MUSIC TEACHERS’ INSTRUMENTAL PERFORMANCE COMPETENCE

The scientific and methodological background of creation and development of the distance learning courses for the future music teachers is substantiated. The components and structure of future music teachers’ instrumental performance competence are defined; the content of the course is revealed. The materials are based on the authors’ teaching experience within the distance learning course “Basic Musical Instrument (Piano)”. The main blocks of the distance course design and development are considered among them to be theoretical, practical, individual work, and control blocks. The specificity of distance learning methods in the future music teachers’ instrumental and performance training is substantiated and three main methods are distinguished. The method of involving information and communication technologies, including multimedia; project method, and features of knowledge and skills controlling are elaborated. The results of implementation and experimental research of using distance learning courses for developing future music teachers’ instrumental performance competence are described. The influence of different methods use on students’ success is explored.
Towards Personalized Intensive Care Decision Support Using a Bayesian Network: A Multicenter Glycemic Control Study

Personalized treatment in glycemic control (GC) is a visibly promising research area that requires improved mechanisms providing patient-specific procedures to enable complicated decision support.Available per-patient data must be more than written records, and be fully integrated in this personalization process.This article presents a process for relating the intensive care unit patients' demographic and admission data to their GC performance.With this objective, a probabilistic Bayesian network was chosen to provide more personalized decisions.As a case study, average daily blood glucose measurements were chosen as the interest target node in order to weigh GC that provides a reduced nursing workload.To test the idea, data from 482 patients, with nine variables from four Malaysian intensive care units with different controls were exploited.The identified steps crucial in building a dependable model are variable selection, continuous state discretization, and unsupervised structure learning.Using a multi-target node evaluation, a network with 80% mean overall classification precision was obtained with a normalized equal distance discretization algorithm and a maximum weight spanning tree technique.Meanwhile, the interest target node scored 90.39% precision.The results from this study, which are complemented with an evaluation of missing data, are proposed as a benchmark for using Bayesian networks in this type of application.
Adding Organizational Reasoning to Agent-Based Simulations in GAMA

The GAMA platform supports simulation with a bottom-up design from an agent perspective using a BDI framework. This chapter proposes a design for implementing the AORTA framework for organizational reasoning in the GAMA platform to support combining a bottom-up BDI model with a top-down organizational model. In doing so also we contribute towards maturing organizational reasoning for engineering multi-agent systems. The contribution is twofold: an operational semantics of the BDI framework in the GAMA platform, and an extension of it with operational semantics of AORTA.