Reviewerly/exHarmony huggingface dataset:
Beyond position-awareness—Extending a self-adaptive fall detection system

Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. We propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Using the data of four publicly available datasets, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor’s position at runtime. In this paper, we extend our self-adaptive fall detection system with (i) additional algorithms for fall detection, (ii) an approach for cross-positional sensor fusion, (iii) a fall detection approach that relies on outlier detection, and (iv) a smart fall alert. Additionally, we present implementation and evaluation of these extensions.
Tiny People Pose

While recent progress in pose recognition has been impressive, there remains ample margin for improvement, particularly in challenging scenarios such as low resolution images. In this paper, we consider the problem of recognizing pose from tiny images of people, down to 24px high. This is relevant when interpreting people at a distance, which is important in applications such as autonomous driving and surveillance in crowds. Addressing this challenge, which has received little attention so far, can inspire modifications of traditional deep learning approaches that are likely to be applicable well beyond the case of pose recognition. Given the intrinsic ambiguity of recovering a person's pose from a small image, we propose to predict a posterior probability over pose configurations. In order to do so we: (1) define a new neural network architecture that explicitly expresses uncertainty; (2) train the network by explicitly minimizing a novel loss function based on the data log-likelihood; and (3) estimate posterior probability maps for all joints as a semi-dense sub-pixel Gaussian mixture model. We asses our method on downsampled versions of popular pose recognition benchmarks as well as on an additional newly-introduced testing dataset. Compared to state-of-the-art techniques, we show far superior performance at low resolution for both deterministic and probabilistic pose prediction.
Identification of Key Factors for a Development of Smart Organization

In the process of intellectualization, informatization, and digitalization of all types of activity, the problem situations started to form, the ones requiring the rigorous mathematical justification of the obtained conclusions’ effectiveness. Along with the search for answers to the question about how to evaluate the obtained result, how far the effectiveness indicators are from the planned values, what the analyzed activity risks and the reasons for their occurrence are, it remains vital to search for answer’s: “What was the obstacle to achieving the preplanned value of a controlled parameter?” or “How to estimate the value of the influence of the analyzed structure’s internal environment parameters on the formed external indicator of its activity?”. Answers to these and many other questions can be obtained in the process of formalization. However, various deviations from the initial model assumptions inevitably appear during the process of handling the applied problems; therefore, the use of the standard mathematical statistics methods can lead to the obtained conclusions’ distortion. In this regard, it becomes necessary to use new methods to process information, the ones that are robust to the possible deviations of the real data characteristics from the expected ones. The article shows a new approach to the development of the statistical data analysis technologies and the obtained conclusions quality evaluation methods in relation to the evaluation of a self-learning organization’s activity.
CoFluences

Social influences are key drivers of many human behaviors, and have been the focus of an abundance of discrete simulation models. In participatory modeling, the emphasis is on developing models in an intuitive and transparent manner. Fuzzy Cognitive Mapping (FCM) provides such an intuitive and transparent process, but it can only simulate the thinking of one entity rather than how entities influence each other. Hybrid architectures based on FCM and Agent Based Modeling (ABM) can bridge this gap, but current software implementing these architectures either restricted the models (e.g., limiting agent heterogeneity by requiring that they all follow the same rules) or required extensive coding (which participatory modeling avoids). In this paper, we contribute to software development by presenting CoFluences, and to the theory of modeling and simulation by better characterizing hybrid ABM/FCM architectures. CoFluences is the first software to develop and simulate hybrid ABM/FCM models in a participatory setting, and where agents can follow different rules. Although we take a User-Centered Design approach to develop CoFluences, a comprehensive usability study will be necessary to fully evaluate it in context. In addition, the growing interest in developing simulation software involving FCM will call for more standardization, and for a better understanding of how an FCM behaves in a hybrid simulation.
Hand- and gaze-control of telepresence robots

Mobile robotic telepresence systems are increasingly used to promote social interaction between geographically dispersed people. People with severe motor disabilities may use eye-gaze to control a telepresence robots. However, use of gaze control for navigation of robots needs to be explored. This paper presents an experimental comparison between gaze-controlled and hand-controlled telepresence robots with a head-mounted display. Participants (n = 16) had similar experience of presence and self-assessment, but gaze control was 31% slower than hand control. Gaze-controlled robots had more collisions and higher deviations from optimal paths. Moreover, with gaze control, participants reported a higher workload, a reduced feeling of dominance, and their situation awareness was significantly degraded. The accuracy of their post-trial reproduction of the maze layout and the trial duration were also significantly lower.
High‐throughput and flexible ASIC implementations of SIMON and SPECK lightweight block ciphers

Summary In this paper, high‐throughput and flexible hardware implementations of the SIMON and SPECK lightweight block ciphers are presented. The most complex block in the SPECK algorithm is addition modulo 2 n , where n is word size (half of the input data). In the proposed structure of modular adder, we used the Sklansky adder, which is an efficient parallel prefix adder with low critical path delay and suitable hardware resources. In the SIMON block cipher, to reduce critical path delay, we use a tree structure for implementation of XOR operations. In addition, we proposed flexible structures that can perform various configurations of the SIMON and SPECK ciphers to support variable key sizes (128, 144, 192, and 256 bits) and block sizes (64, 96, and 128 bits). Therefore, the flexible architectures provide versatile implementations with adaptive security level and the ability of encryption of longer messages based on variable key size and variable block size. Implementation results of the proposed structures in 180 nm CMOS technology for different key and block sizes are achieved. The results show that the proposed structures have better critical path delay compared with other's related works.
Web Mining and Minimization Framework Design on Sentimental Analysis for Social Tweets Using Machine Learning

Web expansion includes various objective parameters such as storage devices, descriptive machines and third party optimization algorithms, making the web bulky and stacked for dynamic search results. Thus, web search optimization technique research provides various solutions. In this paper, a sentimental tweets segregation and classification based on content is proposed under the objective of web minimization for optimized search results. The methodology includes K Nearest Neighbouring (KNN) approach for extraction of similar tweets strength using the pre-learnt logs threshold values. Thus, appended to proposed novel framework designed for pattern extraction with respect to tweets logs and producing a results with 97.82% accuracy via real-time tweeter logs. The proposed framework is expanded towards pre-processed datasets of major social networking platforms to retrieve higher order of accuracy. The resultant outcomes are processed under reinforcement based machine learning for web minimization with an incurred enhancement of 1.75 refreshing rate with 4G LTE bandwidth.
Data article: Distributed PV power data for three cities in Australia

We present a publicly available dataset containing photovoltaic (PV) system power measurements and metadata from 1287 residential installations across three states/territories in Australia—though mainly for the cities of Canberra, Perth, and Adelaide. The data are recorded between September 2016 and March 2017 at 10-min temporal resolution, and consist of real inverter reported power measurements from PV systems that are well distributed throughout each city. The dataset represents a considerably valuable resource, as public access to spatio-temporal PV power data is almost nonexistent; this dataset has already been used in numerous articles by the authors. The PV power data are free to download and are available in their raw, quality controlled (QC), and “tuned” formats. Each PV system is accompanied by individual metadata including geolocation, user reported metadata, and simulated parameterization. Data provenance, download, usage rights, and example usage are detailed within. Researchers are encouraged to leverage this rich spatio-temporal dataset of distributed PV power data in their research.
Video Processing Techniques for the Contactless Investigation of Large Oscillations

Abstract The experimental acquisition of large vibrations presents various technical difficulties. Especially in the case of geometric nonlinearities, dealing with very flexible, very light structures causes minimal variations in mass or stiffness to affect severely the dynamical response. Thus, sensors’ added masses change the behaviour of the structure with respect to the unloaded condition. Moreover, the most common tools regularly employed for acquisition in vibration analysis - that is to say, laser vibrometers and accelerometers - are often designed with small amplitudes in mind. Their recordings are known to lack accuracy when the investigated structure undergoes large or very large motions, due to geometrical reasons. Image-based measurement techniques offer a valid solution to this problem. Here, an ensemble of three video processing techniques are benchmarked against each other and tested as viable options for the non-contact dynamic characterisation of slender beam-like structures. The methods have been applied to the case study of an aluminium spar for a highly-flexible airwing prototype and compared to the measurements recorded by a laser velocimeter and several Raspberry PI Inertial Measurement Units (IMUs), which also proved to be minimally invasive.
State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Abstract Multi‐Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up‐to‐date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage

This paper addresses the problem of multiple models fitting in the general context where the sought structures can be described by a mixture of heterogeneous parametric models drawn from different classes. To this end, we conceive a multi-model selection framework that extend T-linkage to cope with different nested class of models. Our method, called MCT, compares favourably with the state-of-the-art on publicly available data-sets for various fitting problems: lines and conics, homographies and fundamental matrices, planes and cylinders.
Cognitive Software-Defined Networking Using Fuzzy Cognitive Maps

Future networks are expected to provide improved support for several different kinds of applications and services. All these services will have diverse characteristics and requirements to be satisfied. A potential technology to upgrade efficiently and effectively current generation networks is virtualization via network “softwarization.” This approach requires the combination of software-defined networking (SDN) and network function virtualization. Nevertheless, such a new complex network structure will raise further issues and challenges to be solved both reactively and proactively, without human intervention. In order to achieve that, academia and industry have identified the solution in the implementation and deployment of machine learning. Hence, very likely, 5G (and especially beyond 5G) networks will be cognitive virtualized networks. In that context, this paper proposes a cognitive SDN architecture based on fuzzy cognitive maps (FCMs). First, specific design modifications of FCMs are proposed to overcome some well-known issues of this learning paradigm. Second, the efficient integration with an SDN architecture is presented and analyzed. Finally, the emulation of a sample network scenario via Mininet is provided to validate the effectiveness and the potential of the new cognitive system and its capability to act and to adapt independently of human intervention.
An Efficient Energy-Aware Tasks Scheduling with Deadline-Constrained in Cloud Computing

Nowadays, Cloud Computing (CC) has emerged as a new paradigm for hosting and delivering services over the Internet. However, the wider deployment of Cloud and the rapid increase in the capacity, as well as the size of data centers, induces a tremendous rise in electricity consumption, escalating data center ownership costs and increasing carbon footprints. This expanding scale of data centers has made energy consumption an imperative issue. Besides, users’ requirements regarding execution time, deadline, QoS have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers, especially in a high-stress environment in which the tasks have very critical deadlines. To address these issues, this paper proposes an efficient Energy-Aware Tasks Scheduling with Deadline-constrained in Cloud Computing (EATSD). The main goal of the proposed solution is to reduce the energy consumption of the cloud resources, consider different users’ priorities and optimize the makespan under the deadlines constraints. Further, the proposed algorithm has been simulated using the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance by minimizing the makespan, reducing energy consumption and improving resource utilization while meeting deadline constraints.
Synthesizing Environment-Aware Activities via Activity Sketches

In order to learn to perform activities from demonstrations or descriptions, agents need to distill what the essence of the given activity is, and how it can be adapted to new environments. In this work, we address the problem: environment-aware program generation. Given a visual demonstration or a description of an activity, we generate program sketches representing the essential instructions and propose a model to flesh these into full programs representing the actions needed to perform the activity under the presented environmental constraints. To this end, we build upon VirtualHome, to create a new dataset VirtualHome-Env, where we collect program sketches to represent activities and match programs with environments that can afford them. Furthermore, we construct a knowledge base to sample realistic environments and another knowledge base to seek out the programs under the sampled environments. Finally, we propose RNN-ResActGraph, a network that generates a program from a given sketch and an environment graph and tracks the changes in the environment induced by the program.
Visual Segmentation for Information Extraction from Heterogeneous Visually Rich Documents

Physical and digital documents often contain visually rich information. With such information, there is no strict ordering or positioning in the document where the data values must appear. Along with textual cues, these documents often also rely on salient visual features to define distinct semantic boundaries and augment the information they disseminate. When performing information extraction (IE), traditional techniques fall short, as they use a text-only representation and do not consider the visual cues inherent to the layout of these documents. We propose VS2, a generalized approach for information extraction from heterogeneous visually rich documents. There are two major contributions of this work. First, we propose a robust segmentation algorithm that decomposes a visually rich document into a bag of visually isolated but semantically coherent areas, called logical blocks. Document type agnostic low-level visual and semantic features are used in this process. Our second contribution is a distantly supervised search-and-select method for identifying the named entities within these documents by utilizing the context boundaries defined by these logical blocks. Experimental results on three heterogeneous datasets suggest that the proposed approach significantly outperforms its text-only counterparts on all datasets. Comparing it against the state-of-the-art methods also reveal that VS2 performs comparably or better on all datasets.
From theory to practice: guidelines for enhancing information security management

Purpose This study aims to identify the implications of security behaviour determinants for security management to propose respective guidelines which can be integrated with current security management practices, including those following the widely adopted information security standards ISO 27001, 27002, 27003 and 27005. Design/methodology/approach Based on an exhaustive analysis of related literature, the authors identify critical factors influencing employee security behaviour and ISP compliance. The authors use these factors to perform a gap analysis of widely adopted information security standards ISO 27001, 27002, 27003 and 27005 and identify issues not covered or only partially addressed. Drawing on the implications of security behaviour determinants and the identified gaps, the authors provide guidelines which can enhance security management practices. Findings The authors uncover the factors shaping security behaviour barely or partly considered in the ISO information security standards ISO 27001, 27002, 27003 and 27005, including top management participation, accommodating individual characteristics, embracing the cultural context, encouraging employees to comply out of habit and considering the cost of compliance. Furthermore, the authors provide guidelines to security managers on enhancing their security management practices when implementing the above ISO Standards. Practical implications This study offers guidelines on how to create and design security management practices whilst implementing ISO standards (ISO 27001, ISO 27002, ISO 27003, ISO 27005) so as to enhance ISP compliance. Originality/value This study analyses the role and implications of security behaviour determinants, discusses discrepancies and conflicting findings in related literature, provides a gap analysis of commonly used information security standards (ISO 27001, 27002, 27003 and 27005) and proposes guidelines on enhancing security management practices towards improving ISP compliance.
Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning

Residual cancer burden (RCB) has been proposed to measure the postneoadjuvant breast cancer response. In the workflow of RCB assessment, estimation of cancer cellularity is a critical task, which is conventionally achieved by manually reviewing the hematoxylin and eosin- (H&E-) stained microscopic slides of cancer sections. In this work, we develop an automatic and direct method to estimate cellularity from histopathological image patches using deep feature representation, tree boosting, and support vector machine (SVM), avoiding the segmentation and classification of nuclei. Using a training set of 2394 patches and a test set of 185 patches, the estimations by our method show strong correlation to those by the human pathologists in terms of intraclass correlation (ICC) (0.94 with 95% CI of (0.93, 0.96)), Kendall's tau (0.83 with 95% CI of (0.79, 0.86)), and the prediction probability (0.93 with 95% CI of (0.91, 0.94)), compared to two other methods (ICC of 0.74 with 95% CI of (0.70, 0.77) and 0.83 with 95% CI of (0.79, 0.86)). Our method improves the accuracy and does not rely on annotations of individual nucleus.