Reviewerly/exHarmony huggingface dataset:
Analytic and directional wavelet packets in the space of periodic signals

The paper presents a versatile library of analytic and quasi-analytic complex-valued wavelet packets (WPs) which originate from discrete splines of arbitrary orders. The real parts of the quasi-analytic WPs are the regular spline-based orthonormal WPs designed in [4]. The imaginary parts are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Tensor products of 1D quasi-analytic WPs provide a diversity of 2D WPs oriented in multiple directions. The designed computational scheme in the paper enables us to get fast and easy implementation of the WP transforms. The presented WPs proved to be efficient in signal/image processing applications such as restoration of images degraded by either additive noise or missing of up to 90% of their pixels.
Improving OCR of historical newspapers and journals published in Finland

This paper presents experiments on Optical character recognition (OCR) of historical newspapers and journals published in Finland. The corpus has two main languages: Finnish and Swedish and is written in both Blackletter and Antiqua fonts. Here we experiment with how much training data is enough to train high accuracy models, and try to train a joint model for both languages and all fonts. So far we have not been successful in getting one best model for all, but it is promising that with the mixed model we get the best results on the Finnish test set with 95 % CAR, which clearly surpasses previous results on this data set.
Some notes on optimal fuzzy reasoning methods

The optimal fuzzy reasoning (OFR) method proposed by Zhang and Cai (2004) is treated as a control problem and it is dramatically different from other existing fuzzy reasoning methods. The aim of this paper is to make some notes on OFR methods. We give a modification of an OFR method presented by Zhang and Cai. Some advantages and limitations of OFR methods are examined. We introduce several new OFR methods based on the Łukasiewicz implication, the Kleene-Dienes implication and the Zadeh implication. The reasoning consistency of the proposed methods is studied. The relationship between the OFR method and other fuzzy reasoning methods (e.g., the compositional rule of inference method and the pointwise optimizing fuzzy inference method) is examined.
On Emotions as Features for Speech Overlaps Classification

Although being a frequently occurring phenomenon in spoken communication, speech overlaps did not obtain the deserved attention in research so far&#x2014;in both Human-Human Interaction (HHI) and Human-Computer Interaction (HCI). It is common knowledge that overlaps can figure as a competitive, rude interruption as well as a cooperative, convenient feedback signal giving important insight on the course of the interaction&#x2014;but how are they related to the internal state of the overlapping speaker or the overlapped speaker? In this paper, we investigate dyadic human-human interactions and focus on the relations between the emotional changes occurring around overlaps in both interaction participants. Further to an in-depth statistical analysis of the changes in control and valence levels with respect to the nature of the overlap, we also present a classification approach based on features derived from such emotional changes surrounding an overlap and compare the classification performance of these features to classic acoustic features. We show that the automatic classification of competitive and cooperative overlaps using the changes in valence and control levels of the overlapping speaker outperforms common approaches employing acoustic and linguistic features.
Designing a Neural Network Model for Time Series Forecasting

Artificial neural networks are highly flexible and efficient tools in the approximation of time series patterns. In recent years, more than 5,000 studies oriented to the use of neural networks in time series forecasting have been evidenced in the extant literature. However, the methodology used for its specification and construction still involves a lot of trial and error or is inherited from econometric and statistical procedures that do not fit perfectly to the characteristics of the time series. This is especially true when they present non-linear behavior; moreover, it is not designed for working with neural networks. The objective of this chapter is to present a five-step guide for the specification, design, and validation of a neural network model for forecasting time series.
Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition

In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network (RNN) model for the classification of human daily life activities by using the accelerometer and gyroscope data of a smartphone. The proposed model was trained by using the open-source TensorFlow library, optimised and deployed on an Android smartphone. Hardware resource requirements for the implementation are empirically investigated and the effect of data quantization on the accuracy of the implementation is discussed. In addition, we profile the power budget for running the proposed model on smartphone. Results of this work will be of use for deep learning implemented on edge computing devices, which leverages the user privacy as the raw data never leaves the person.
The Role of the Input in Natural Language Video Description

Natural language video description (NLVD) has recently received strong interest in the computer vision, natural language processing (NLP), multimedia, and autonomous robotics communities. The state-of-the-art (SotA) approaches obtained remarkable results when tested on the benchmark datasets. However, those approaches poorly generalize to new datasets. In addition, none of the existing works focus on the processing of the input to the NLVD systems, which is both visual and textual. In this paper, an extensive study is presented to deal with the role of the visual input, evaluated with respect to the overall NLP performance. This is achieved by performing data augmentation of the visual component, applying common transformations to model camera distortions, noise, lighting, and camera positioning that are typical in real-world operative scenarios. A t-SNE-based analysis is proposed to evaluate the effects of the considered transformations on the overall visual data distribution. For this study, the English subset of the Microsoft Research Video Description (MSVD) dataset is considered, which is used commonly for NLVD. It was observed that this dataset contains a relevant amount of syntactic and semantic errors. These errors have been amended manually, and the new version of the dataset (called MSVD-v2) is used in the experimentation. The MSVD-v2 dataset is released to help to gain insight into the NLVD problem.
Discovering intersectionality part I

In this forum we explore different perspectives for how to apply intersectionality as a critical framework for design across multiple contexts. --- Yolanda A. Rankin and Jakita O. Thomas, Editors
Freudian Slip? The Changing Cultural Fortunes of Psychoanalytic Concepts

It is often argued that psychoanalysis has declined in prominence since its ascendance in the mid-20th century. To assess this claim we examined the trajectory of psychoanalytic concepts from 1900 to 2008 in the massive Google Books database. The changing relative frequency of a sample of English-language psychoanalytic terms was explored and compared to a sample of terms in French. The frequency of the English terms was further explored from 2008 to 2017 using the Corpus of Contemporary American English (COCA). The English terms rose steeply from the 1940s and declined steeply from the early 1990s. In contrast, the French terms rose steeply from the 1960s and plateaued from the 1970s. In addition, psychoanalytic terms were markedly more prominent in French since the 1960s. The findings are discussed in the context of historical trends in the reception of psychoanalysis in the Anglophone and Francophone worlds.
Parameter Interval Estimation of Semiparametric Spline Truncated Regression Model for Longitudinal Data

Abstract Regression analysis is one method in statistics that is used to determine the pattern of functional relationships between response variables with predictor variables. Semiparametric regression approach is a combination of parametric regression and nonparametric regression. The most popular estimator for nonparametric regression or semiparametric regression is spline truncated estimator. Spline is the estimation method that is most often used because it has excellent statistical interpretation and visual interpretation compared to other methods. Regression modelling using longitudinal data is often found in everyday life, where observations are carried out for each subject over a period of time. Interval estimation is often examined by nonparametric regression and semiparametric regression; this estimation aims to determine predictor variables that have a significant influence on the response variable. One indicator used in poverty analysis is the poverty line. Based on Indonesia’s macro poverty analysis calculations, in the period March 2016 to March 2017, the poverty line increased by 5.67 percent, with increases in urban and rural areas at 5.79 percent and 5.19 percent respectively. Modelling using semiparametric spline truncated regression for longitudinal data on data on the percentage of poor people in Indonesia produces the best model using W 1 weighting and one point knot. Based on the results of the study with a significance level of 0.05, it was found that the percentage of poor people was influenced by the human development index (HDI) and the unemployment rate. This semiparametric regression model has a minimum GCV value of 1.677, MSE of 5.477 × 10 −2 and R 2 value of 98.67%.
Evaluating Temporal Predictive Features for Virtual Patients Feedbacks

In the intelligent virtual agent domain, several machine learning models have been proposed to automatically determine the feedbacks of virtual agents during an interaction, using human-human interaction datasets as training corpora and most commonly based on verbal and prosodic features \citeMorency2010, Truong2010a. These approaches suppose an accurate system to automatically recognize speech and prosody. That makes the overall model's performance dependent on the individual performances of speech and prosody recognizers. As a consequence, one challenge remains to identify features that could be easily and accurately recognized during a human-machine interaction for predicting virtual agents' feedbacks in real time.
Ethics and Consent in the (Sociotechnical) Wild

When we speak of ethics, we refer to the articulation of moral principles intended to promote societal and individual good. Derived of moral philosophy, they describe the codified process by which we determine how and why specific human conduct might be deemed right or wrong, good or bad. This is especially critical in the context of human-subjects research, where ill-considered interventions may otherwise result in harm to participants. Socio-technical studies conducted in naturalistic settings, what HCI terms ‘in the wild’ research, present some tensions with our current approaches to ethical practice. In particular, the ways in which we inform, secure and support participant consent. This chapter explores these emerging tensions and, through the voices of interviewed experts, highlights some of the issues arising around user consent and sociotechnical systems.
Visual Analytics Toward Prediction of Employee Erosion Through Data Science Tools

Effective data visualization in business intelligence is an indispensable unit. The exercise of visual analytics furnishes the interactive platform to facilitate sterling user experience. With a suitable predictive algorithm to predict the outcome and visual analytics, we can generate consequential reports coalescing and combining end users’ knowledge and reasoning capability. The primary tools that are used for analytics need to serve the purpose on two ends, i.e., efficient data analysis and effective data visualization. R has gained colossal popularity when it comes to its excellent data visualization potential. Moreover, with data visualization tools like Microsoft Power BI and Tableau, compelling visuals with dashboard and excellent report can be created by integration with R and incorporating R scripts and R plugins. The paper aims to scrutinize the power of visualization in predictive analytics employing rich R visuals assisted by powerful visualization tools.
Non-chaotic and chaotic propagation of stationary and dynamic images through MVKS turbulence

Anisoplanatic electromagnetic (EM) propagation across a turbulent atmosphere has been recently examined for an unmodulated carrier propagating over an image-bearing transparency through optical lensing, and for the embedded information inside a carrier recovered using heterodyning and digital demodulation. Carrier modulation yielded better recovery than simple lens-based imaging. A possible mitigation strategy is proposed whereby the image information is encrypted on an RF chaotic carrier, thereafter secondarily embedded onto an optical carrier. Results based on the modified von Karman (MVKS) and the Hufnagel-Valley (H-V) models showed that the signal/image recovery under turbulence is improved compared with non-chaotic propagation. The case of time-varying/dynamic images is also taken up; it is demonstrated via cross-correlation products that turbulence is mitigated by the use of chaotic carrier encryption. Overall, transmission via chaos offers mitigation against distortions due to turbulence along with the security feature inherent via the chaos keys which prevent signal recovery without key-matching.
Near-Duplicate Video Cleansing Method Based on Locality Sensitive Hashing and the Sorted Neighborhood Method

With the wide utilization of intelligent video surveillance technology, increasing amounts of near-duplicate video has been generated, which seriously affects the data quality of the video data set. Cleaning this dirty data automatically from the video data set has become an important issue that needs to be urgently resolved. In this chapter, a near-duplicate video cleansing method based on locality sensitive hashing (LSH) and the sorted neighborhood method (SNM) is presented in an attempt to solve the above problem. First, the speeded-up robust feature is extracted from the video and then the sorted candidate set is built by using LSH; on this basis, the near-duplicate videos are cleaned by using the SNM. Finally, the simulation experiments are implemented to show that the presented method in this chapter is effective, which can be used to clean near-duplicate videos automatically and improve video data quality.
A Non-contact Human Body Height and Weight Measurement Approach Using Ultrasonic Sensor

Human body height and weight are the key factors for personal health monitoring and many works have been conducted to accurately and conveniently measure these two physiology parameters. However, in many conditions such as in the outer space, measuring human body weight becomes impossible for traditional approaches such as using weight scale. In this paper, we present a medical instrument, called the Ultrasonic Measurer to measure human body height and weight using a non-contact approach. This is a low-cost, efficient device to estimate human body sizes using an ultrasonic sensor, the SRF04. This sensor provides good measurements in close distance over a range up to 300 cm. Moreover, we present a convenient approach to estimate human body weight from the measured sizes using the close relationship between a human body’s weight and its sizes, despite the variation in shapes. The measurements and estimations were implemented on a set of 50 men and women with different body shapes. The proposed method proved to give good results as the sizes are estimated with the average error of less than 0.3% and weight is estimated with the average error of less than 2.8%. The module is currently designed to be used as a standalone device. In the future, the module is equipped with the Internet of Things (IoT) for various remote health monitoring applications.
Lung Tumor Classification on Human Chest X-Ray Using Statistical Modelling Approach

Abstract Lung tumor is a group of abnormal cells that are formed from the process of excessive and uncoordinated cell division in the lung or known as a neoplasia. Neoplasia refers to the growth of new cells that are different from the growth of cells around it. The Tumor can formed to be benign tumors that not cause cancer and malignant tumors that can cause cancer. Chest X-ray is the most technique that used for detecting a lung tumor. Image processing is done by mean for distinguishing the classification lung tumor. Based on previous research the most used method is the mathematical method, but the result obtained are not maximal. Therefore, in this study we propose methods to classify lung tumor by using statistical modelling approach with logit link function based on parametric model, and nonparametric model using penalized spline estimator. Based on the proposed method, we get the classification accuracy of 80% for parametric model approach and 85% for nonparametric model approach, it means that the nonparametric model approach is better than the parametric model approach.
INCOMPLETE ACQUISITION FROM A USAGE-BASED PERSPECTIVE

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Dynamic multifoveated structure for real-time vision tasks in robotic systems

Foveation is a technique that allows real-time image processing by drastically reducing the amount of visual data without loosing essential information around some focused area. When a robot needs to pay attention at two or more regions of the image at the same time, e.g., for tracking two or more objects, multifoveation is necessary. In this case, computing features twice in the intersections between the different foveated structures, which could linearly increase the processing time, must be avoided. To solve this redundancy removal problem, we propose two algorithms. The first one is based on the previous calculation of redundant blocks and the second one is based on a pixel-by-pixel processing at execution time. Experimental results show a gain in processing time for the block-based model in comparison with the pixel-by-pixel and also of both in comparison with other approaches that sequentially calculate various single foveated images. Robotics vision and other tasks related to dynamic visual attention, as recognition, real-time surveillance, video transmission, and image rendering, are examples of applications that can rely on and strongly benefit from such model.
15 Years of Consequence-Based Reasoning

Description logics (DLs) are a family of formal languages for knowledge representation with numerous applications. Consequence-based reasoning is a promising approach to DL reasoning which can be traced back to the work of Franz Baader and his group on efficient subsumption algorithms for the $$\mathcal {EL}$$ family of DLs circa 2004. Consequence-based reasoning combines ideas from hypertableaux and resolution in a way that has proved very effective in practice, and it still remains an active field of research. In this paper, we review the evolution of the field in the last 15 years and discuss the various consequence-based calculi that have been developed for different DLs, from the lightweight $$\mathcal {EL}$$ to the expressive $$\mathcal {SROIQ}$$ . We thus provide a comprehensive and up-to-date analysis that highlights the common characteristics of these calculi and discusses their implementation.
Negotiating collaboration and commodification: the social media logic of co-design on Pinterest

This study presents an empirical case focused on the co-design activities of professional designers and client-stakeholders in the context of the social media platform Pinterest. Overall, this study finds that as Pinterest moves from a tool being used behind the scenes by designers as part of the creative process to a tool for co-designing with stakeholders, tensions emerge around the publicness and informal nature of communication through social media, the types of participation by stakeholders in the design process, and the complexities of quantified collaboration. In-depth interviews with professional designers and a multi-stakeholder team reveal a continuum of designer-stakeholder participation. These co-design practices are marked by social media logic, reconfigured roles of participation; and perceptions of platform design affordances. As such, these platform-specific co-design practices suggest new materialities and temporalities for collaborative design processes within a platform economy. Rather than focusing on a participatory design event, this study draws attention to the routine, everyday ways co-designing is occurring on Pinterest as part of design projects and the role this practice has in re-positioning co-design in the digital age.
Low-Cost Heterogeneous Unmanned Ground Vehicle (UGV) Testbed for Systems of Autonomous Vehicles Research

The following topics are dealt with: systems engineering; Internet of Things; mobile robots; security of data; control engineering computing; learning (artificial intelligence); road vehicles; data visualisation; transportation; health care.
Improved Hybrid Approach of Filtering Using Classified Library Resources in Recommender System

The goal of planned library recommender system is to provide needful library resources quickly. The important phases required to perform are build and update user profiles and search the proper library resources. This proposed system uses a hybrid approach for filtering available books of different subjects, research journal articles, and other resources. Content-based filtering evaluates user profile with available library resources. The results generated are satisfying the users need. The system can generate satisfactory recommendations, since in dataset most of the entries for books and research journal articles are rich with keywords. This richness is possible by referring to abstract and TOC (table of contents) while adding records of research journals articles and books, respectively. Collaborative filtering computes recommendations by searching users with similar interests. Finally, to the active user, recommendations are provided which are generated with the hybrid approach. To make it simpler and develop the outcome of the recommendation process, categorization of available records is made into distinct classes. The distinct classes are defined in ACM CCS 2012. The classifier is the output of relevant machine learning methods. The paper discusses the improvement in results by hybrid approach due to the use of classified library resources.
Multiple flow‐based knowledge transfer via adversarial networks

The authors propose a new knowledge transfer method coupled with a generative adversarial network (GAN) when multiple-flow-based knowledge is considered in a teacher–student framework using a residual network (ResNet). In this method, several independent discriminators adapting multilayer-perceptron-based structures were designed for flow-based knowledge transfer. The proposed GAN-based optimisation alternatively updates the multiple discriminators and a student ResNet such that the flow-based features of the student ResNet are generated as closely as possible to the real features of a teacher ResNet. The experiments demonstrate that the student ResNet trained using the proposed method more accurately captures the distribution of the flow-based teacher knowledge than the -distance-based training method. In addition, the proposed method provided better classification accuracy than the existing GAN-based knowledge transfer method.