Reviewerly/exHarmony huggingface dataset:
A novel reference model for ambient assisted living systems' architectures

AAL (Ambient Assisted Living) is lacking a RM (Reference Model) to serve as an abstraction of the domain. Therefore, to help implement new architectures established on the prior experiences of the designer’s expertise and former competences, in this paper, we propose a novel approach of an AAL RM. Our objective is to handle the resolution of conflicts that appear between the developers, and give an overview of the basis for implementing concrete software architectures for different families of AAL applications. Our proposed RM is a standardized clarification for developers to apply in the process of the design and implementation. We illuminate the AAL domain fundamental dimensions and we established a formal RM with an infrastructure that could survive the domain’s progression. To achieve integrated system-of-systems composed of systems, subsystems and components, our RM describes a standard and regulated structure to be established. High-level elements, essential properties and characteristics that must appear in the application’s design are described and illustrated in this work.
Coherence, Explanation, and Hypothesis Selection

This article provides a new approach to inference to the best explanation (IBE) based on a new coherence measure for comparing how well hypotheses explain the evidence. It addresses a number of criticisms of the use of probabilistic measures in this context by Glymour ([2015]), including limitations of earlier work on IBE (Glass [2012]). Computer experiments are used to show that the new approach finds the truth with a high degree of accuracy in hypothesis selection tasks and that in some cases its accuracy is greater than hypothesis selection based on maximizing posterior probability. Hence, by overcoming some of the problems with the previous approach, this work provides a more adequate defence of IBE and suggests that IBE not only tracks truth but also has practical advantages over the previous approach. Applications of the new approach to parameter estimation and model selection are also explored.
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions

In-depth scene descriptions and question answering tasks have greatly increased the scope of today’s definition of scene understanding. While such tasks are in principle open ended, current formulations primarily focus on describing only the current state of the scenes under consideration. In contrast, in this paper, we focus on the future states of the scenes which are also conditioned on actions. We posit this as a question answering task, where an answer has to be given about a future scene state, given observations of the current scene, and a question that includes a hypothetical action. Our solution is a hybrid model which integrates a physics engine into a question answering architecture in order to anticipate future scene states resulting from object-object interactions caused by an action. We demonstrate first results on this challenging new problem and compare to baselines, where we outperform fully data-driven end-to-end learning approaches.
Feedback-based metric learning for activity recognition

Mobile activity recognition is an effective approach to understanding human context in real time. Existing methods based on supervised learning that require a large amount of training samples for building activity recognition models. The collection of labeled training samples is a boring process and most users are reluctant to get involved. Crowdsourcing is a simple and potential approach to collecting the training samples and building accurate activity recognizers. Since different people usually have different physical features and behavior patterns, an accurate activity recognition model cannot be constructed directly from the training samples collected by crowdsourcing. In this paper, we have proposed a Mixture Expert Model for Activity Recognition (MEMAR) based on feedback and crowdsourcing samples. The proposed model can continuously discover the difference between user activity and crowdsourcing samples. Then we update activity recognition models with the discovered differences. A mobile can correctly utilize crowdsourcing samples for recognition model construction with MEMAR and can also track and recognize mobile users’ behavior dynamics. The experiments based on a smartphone dataset verify the validity of MEMAR. We believe MEMAR provides a basis for context-aware mobile applications.
Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership

A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.
The approximations of a function belonging Hölder class Hα[0,1) by second kind Chebyshev wavelet method and applications in solutions of differential equation

In this paper, two new estimates [Formula: see text] and [Formula: see text] of a function [Formula: see text] belonging to [Formula: see text] are obtained by Chebyshev wavelet method. These estimators are new, sharper and best possible in Wavelet Analysis. Using this method, the solutions of four differential equations are obtained. These solutions are approximately the same as exact solutions.
A scalable wideband speech codec using the wavelet packet transform based on the internet low bitrate codec

Most recent speech codecs employ code excited linear prediction (CELP) and transmit side information to improve speech quality under packet loss. Another approach to achieve high robustness to packet loss is to use the frame independent coding scheme based on the internet low bitrate codec (iLBC). The scalable wideband speech codec based on the iLBC was previously presented and outperformed G.729.1 at most bit rates according to the objective quality. This paper presents improvements to the previous work. Specifically, we employ the wavelet packet transform (WPT) instead of the modified discrete cosine transform (MDCT) to enhance the quality, and evaluate the proposed codec based on both the objective and subjective quality measures. The objective quality evaluation results show that clear improvement is achieved and that the proposed codec outperforms G.729.1 at the bit rate of 18 kbps or higher under clean channel conditions and has higher robustness to packet loss than G.729.1. The informal subjective test results also show similar trends.
<i>KIMA: The Wheel</i>–Voice Turned into Vision: A Participatory, Immersive Visual Soundscape Installation

Over the last five years, KIMA, an art and research project on sound and vision, has investigated visual properties of sound. Previous iterations of KIMA focused on digital representations of cymatics—physical sound patterns—as media for performance. The most recent development incorporated neural networks and machine learning strategies to explore visual expressions of sound in participatory music creation. The project, displayed on a 360-degree canvas at the London Roundhouse, prompted the audience to explore their own voice as intelligent, real-time visual representation. Machine learning algorithms played a key role in meaningful interpretation of sound as visual form. The resulting immersive performance turned the audience into cocreators of the piece.
Wavelet based edge feature enhancement for convolutional neural networks

Convolutional neural networks are able to perform a hierarchical learning process starting with local features. However, a limited attention is paid to enhancing such elementary level features like edges. We propose and evaluate two wavelet-based edge-feature enhancement methods to preprocess the input images to convolutional neural networks. The first method develops representations by decomposing the input images using wavelet transform and limited reconstructing subsequently. The second method develops such feature-enhanced inputs to the network using local modulus maxima of wavelet coefficients. For each method, we have developed a new preprocessing layer by implementing each proposed method and have appended to the network architecture. Our empirical evaluations demonstrate that the proposed methods are outperforming the baselines and previously published work.
Blind image deblurring based on multi-resolution ringing removal

In this paper, a blind deblurring algorithm that is well converged towards the latent sharp image and the blur PSF is proposed. The proposed algorithm employs our recently proposed ringing suppression regularizer in a solution to the blind deblurring problem. Latent sharp image and the blur PSF are estimated jointly via a multi-functional optimization problem addressed by a first-order primal-dual solver. This approach generates a multi-resolution pyramid of the input blurred image and propagates the estimation results of the lower resolution levels to the higher resolution ones. By considering the PSF initiation as an important step in the blind deblurring process, we introduce a PSF initiation method that benefits from the salient structure obtained via an l0-gradient smoothing of blurred image. A first-order primal-dual solution to the l0-gradient smoothing problem is introduced in this paper. Experimental results show that the proposed deblurring algorithm significantly enhances the results favorably against other state-of- the-art image deblurring methods.
spot: Open Source framework for scientific data repository and interactive visualization

spot is an open source and free visual data analytics tool for multi-dimensional data-sets. Its web-based interface enables user to do a quick and interactive analysis of complex data. Various operations on data are implemented such as aggregation and filtering. The interface supports OpenGL acceleration, which makes the generated charts very responsive. In order to have scalability, the software also supports PostgreSQL as its database. It follows FAIR principles to allow reuse and comparison of the published data-sets.
The structure of situation models as revealed by anaphor resolution

This paper investigates the structure of situation models as revealed by the process of anaphor resolution. The situation model for a text combines the meanings of its individual sentences with inferences generated from prior knowledge into an overall understanding of the text. We conceptualize situation models as implemented by collections of traces in episodic long-term memory that are retrieved based on their similarity to cues in working memory. Traces encode both contextual (i.e., spatial and temporal) and semantic information, providing structure to situation models. Four predictions were derived regarding the retrieval of antecedent traces during anaphor resolution. Experiment 1 evaluated the contextual structure of situation models, finding evidence for temporal gradients but not spatial gradients. Experiment 2 evaluated their semantic structure, finding an interaction between (a) the semantic similarity of anaphors and antecedents and (b) the interference caused by non-antecedent referents. These findings have implications for the representation of space and time in situation models, the continuity of working memory and long-term memory, and the conceptualization of situation models.
Cognitive Effort and Efficiency in Translation Revision

Empirical studies of revision are often based on either think aloud protocols, interviews, or observational methods. Eye tracking and keylogging methods are rarely applied to the study of revision behavior. The authors employ established methods from translation process research (TPR) to study the eye movement and typing behavior during self-revision (i.e., the phase in the translation process that follows a first complete draft). The authors measure the effect of behavior during the drafting phase on the relative revision duration. Relative revision duration is the time translators spend revising the first complete draft of the source text. They find that the most efficient process involves a large degree of concurrent reading and writing and few deletions during the drafting phase. The efficiency gains in terms of relative revision duration achieved by avoiding discontinuous typing, by making a larger number of deletions, pausing for longer amounts of time, and engaging in less concurrent reading and writing are outweighed by the gains in total task time by doing the exact opposite.
Embryonic array configuration optimization method based on reliability and hardware consumption

Embryonic Array (EA) with different configuration methods will directly affect its reliability and hardware consumption. At present, EA configuration design is lack of quantitative analysis method. In order to reasonably optimize EA configuration design, an EA configuration optimization design method is proposed, which is based on the constraints of EA hardware consumption and reliability. Through the analysis of EA working process and composition, quantitative analysis of EA reliability and hardware consumption are completed. Based on the constraints of EA hardware consumption and reliability, the mathematical model of EA configuration optimization design is established, which transfers EA configuration optimization design into an integer nonlinear programming model problem. According to the difference of the fitness value of individual waiting for mutation in population, adaptive mutation operator and crossover operator are selected, and a novel Modified Adaptive Differential Evolution (MADE) algorithm is proposed, which is used to solve EA configuration optimization design problem. Simulation experiments and analysis indicate that the MADE is able to effectively improve the speed, accuracy and stability of algorithm. Moreover, the proposed EA configuration optimization design method can select the most reasonable EA configuration design, and play an important guiding role in EA optimization design.
Bayesian Semiparametric ROC surface estimation under verification bias

The Receiver Operating Characteristic (ROC) surface is a generalization of the ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories. Verification bias occurs when not all subjects have their labels observed. This is a common problem in disease diagnosis since the gold standard test to get labels, i.e., the true disease status, can be invasive and expensive. The same situation happens in the evaluation of semi-supervised learning, where the unlabeled data are incorporated. A Bayesian approach for estimating the ROC surface is proposed based on continuous data under a semi-parametric trinormality assumption. The proposed method is then extended to situations in the presence of verification bias. The posterior distribution is computed under the trinormality assumption using a rank-based likelihood. The consistency of the posterior under a mild condition is also established. The proposed method is compared with existing methods for estimating an ROC surface. Simulation results show that it performs well in terms of accuracy. The method is applied to evaluate the performance of CA125 and HE4 in the diagnosis of epithelial ovarian cancer (EOC) as a demonstration.
Entangled nonlinear displaced number states

Entangled nonlinear displaced number states are defined as the superposition of separable two-mode nonlinear displaced number states. In this paper, the nonlinear functions related to the harmonious states and trapped ion motion in center of mass system are considered. Considering the importance of non-classical states in quantum information theory, the non-classical properties of these states, such as photon statistics and entanglement, will be studied. Photon statistics of the entangled nonlinear displaced number states is evaluated by calculating the Mandel parameter and second order coherence function; the entanglement of the states is also examined by calculating concurrence. According to calculations, all of the introduced states have sub-Poissonian photon statistics in some range of coherent amplitude where this range becomes larger by increasing the number of the photons, n. It was also observed that the quantum second-order coherence function of the maximally entangled states represent photon antibunching effect in all values of the related parameters.
Assessment of Complex Adaptive System Changeability Using a Learning Classifier System

Although many books and conference reports have considered the changeability of a complex adaptive system (CAS), no in-depth study has assessed how changes impact a CAS or the relationships among the CAS changeability parameters of agility, flexibility, robustness, and adaptability. This study analyzes the impact of CAS changeability when an external agent requires a change and analyzes how such a change affects the evolution of the CAS. We start by reviewing the general concepts of changeability and CAS, followed by an analysis of their relationship. A model using an extension of learning classifier system (XCSF) is presented and evaluated to meet the objectives of this research to: 1) accurately assess the impact of changeability on telecommunication-based CAS components and their evolution; and 2) gain insight into the impact of changes on CAS for the purpose of improving the engineering of such systems in the future. The relationship between changeability and fitness, which is an important CAS performance measure, is included. CAS simulations using the XCSF model are compared with data from a telecommunication company over the past few years; the comparison suggests that the XCSF approach may be useful for improving CAS engineering.
Learning correlation filters in independent feature channels for robust visual tracking

Although existing correlation filter-based tracking algorithms have shown competitive performance, most of them suffer from defects of insufficient learning, inauthentic combination and inflexible update when it comes to multiple feature channels. To tackle these problems, a specific correlation filter is learned for each feature channel and then the final response map is generated according to the confidence of channels. The confidence is decided by both spatial and temporal distribution of response maps. Under such circumstances, correlation filters are allowed to be updated independently with their own learning rates which can vary with the diversification of feature space in corresponding channels. We evaluate our work on OTB-2013, OTB-2015 and VOT-2017. Our approach outperforms the baseline fDSST by 6.1% in Mean Overlap Precision (OP) especially on OTB-2015 and shows competitive performance compared to state-of-the-art trackers with hand-crafted features while running at 70 FPS.
Generating High-Resolution Image and Depth Map Using a Camera Array With Mixed Focal Lengths

Producing high-resolution images and depth maps of a scene is critical to many applications. Although some imaging systems today are equipped with multiple cameras, the output image resolution is usually only a small fraction of the total number of sensor pixels. To significantly increase the output pixel ratio, we propose an imaging system that consists of an array of telescopic cameras and a wide-angle camera. By minimizing the overlap between the telescopic cameras and maximizing the overlap between the low-resolution wide-angle camera and the telescopic cameras, a camera system with nonparallel optical axes is created. The heterogeneous images of different resolutions are fused into a high-resolution wide-angle image, and its corresponding depth map is generated by pairwise heterogeneous matching. The performance of the proposed imaging system is evaluated using both synthetic and real data.
Reversible steganographic method based on interpolation by bivariate linear box-spline on the three directional mesh

Data hiding is the discipline of hiding information into digital media. A reversible steganographic method allows to extract data from the stego file as well as to retrieve the cover file. This paper proposes a reversible steganographic method based on interpolation by linear box-splines on the three directional mesh. Secret data is hidden in the error between the cover and interpolated pixels. To have better imperceptibility, we search for the nearest value to the cover pixel that can dissimulate data; this was achieved with two techniques: Optimal Pixel Adjustment Procedure and Message Adaptive Error. The proposed method is tested through experiments on a variety of images and compared to prior works. Results indicate a large capacity of hiding and good values of PSNR. The proposed work was also tested by steganalysis attacks; results show a low detectability rate. We find in the comparison that the proposed work surpasses the literature in the capacity of hiding with an equivalent level of imperceptibility.
Modelling and prediction of resources and services state evolvement for efficient runtime adaptations

Regardless of their implementation aspects and distribution elements, i.e. centralized or distributed, service-based environments such as cloud computing and edge/fog infrastructures, enable the provisioning of services addressing a wide range of application domains. The key requirement for users and consumers of such services refers to the corresponding levels of quality, which is affected both by the real-world dynamics – given the non-deterministic use of services, and by the underlying resources state – given the typically virtualized sharing nature of the resources. In this paper, an approach is presented that aims at estimating the evolvement of services and resources state in order to provide insights for runtime adaptations, as required to ensure services quality. The state refers to different metrics/parameters such as memory, number of users, throughput, etc, and can be extended and applied to different ones. The proposed approach exploits polynomial regression and prediction to identify the aforementioned state evolvement by mapping the two first monitoring data points for each metric/parameter to the corresponding function that depicts their evolvement. The latter provides added value in different cases, including among others the adaptation of monitoring time intervals, the estimation of the potential breach of quality thresholds, and the prediction of the time for runtime adaptations and scaling decisions. The effectiveness of the implemented approach is demonstrated and evaluated through a set of different scenarios.
Solving ECDLP via List Decoding

We provide a new approach to the elliptic curve discrete logarithm problem (ECDLP). First, we construct Elliptic Codes (EC codes) from the ECDLP. Then we propose an algorithm of finding the minimum weight codewords for algebraic geometry codes, especially for the elliptic code, via list decoding. Finally, with the minimum weight codewords, we show how to solve ECDLP. This work may provide a potential approach to speeding up the computation of ECDLP.