Reviewerly/exHarmony huggingface dataset:
RedPAC: A Simple Evolving Neuro-Fuzzy-based Intelligent Control Framework for Quadcopter

In this work, a simple evolving neuro-fuzzy system with less learning parameters is utilized to develop an intelligent controller namely Reduced Parsimonious Controller (RedPAC). The proposed RedPAC is a simplified version of one of the recently developed intelligent controller called Parsimonious Controller (PAC). In RedPAC, the network parameters are reduced into two steps. Firstly, unlike the conventional fuzzy logic or neuro-fuzzy-based intelligent controller, it has no premise parameters. Secondly, in contrast with PAC, the number of consequent parameters have further reduced to one parameter per rule in RedPAC. The sliding mode control (SMC) technique is utilized to adapt consequent parameters of RedPAC, where the SMC-based auxiliary robustifying control term has guaranteed the uniform asymptotic convergence of tracking error to zero. The proposed controller's performance has been evaluated by implementing it to control a quadcopter unmanned aerial vehicle (UAV) simulator namely Dronekit. In addition, trajectory tracking performance of the quadcopter is compared with three different benchmark controllers namely a linear PID, a nonlinear SMC, and an intelligent controller called PAC. RedPAC outperforms PID and SMC techniques. The results of tracking trajectories are also comparable to PAC; however, RedPAC needs comparatively less learning parameters to obtain a similar or better tracking accuracy.
Reclaiming human space at IoT

Computing has been incorporated into "things" (objects, clothes, etc.) which, using Internet protocols, are able to send and receive information, realizing ideas of ubiquity that were launched in the 1990s. Transforming a "thing" into a network node of a system that makes sense in people's environment has been a challenge for the Human-Computer Interaction (IHC) discipline. In this paper we investigate and propose a Socially Aware Design (SAwD) based process for building scenarios of ubiquitous and pervasive interaction with contemporary technology, including Internet of Things (IoT) scenarios. The starting point for SAwD is understanding the problem from stakeholders' engagement to evolve a design solution together. As part of the process, a solution proposal was created, implemented and iteratively tested with some of the stakeholders under the eyes of researchers. Three iterative cycles were held in workshops with children in a hospital setting that included the observation of researchers, discussions and proposals for the evolution of the solution. At each iteration in the process the importance of people in the technological environment was highlighted, emphasized by the intertwining of technology in the activities developed in each scenario built. The evolution of the system shows that the application of SAwD from its basic premise, which is to understand the problem with the stakeholders that act as the solution's co-designers, was fundamental in the process. Study results show the effectiveness of the proposed process and suggest recommendations for the design of Internet of Human Things (IoHT).
Understanding Deep Convolutional Networks for Biomedical Imaging: A Practical Tutorial

Medical imaging seeks to unveil the internal structures hidden by the skin and bones to assist disease diagnosis and also treatment optimisation. In the past, processing medical images used to be a laborious task. However, the development of artificial intelligence has allowed the machine to gain a high level of understanding to perceive and extract information from biomedical images. Deep learning models, in particular, the convolutional neural networks (CNNs), were developed and implemented successfully for various biomedical applications. Therefore, it is of paramount importance for healthcare practitioners to understand the mechanisms behind the implemented CNNs to accurately interpret their outcomes. This tutorial summarises the key steps to train a functional CNNs. CNNs are usually constructed in the order of a convolution operation, ReLU, spatial pooling and followed by the fully connected layers. In addition, we have also introduced a number of preprocessing methods that target the image augmentation to combat the sparse data problem. We further explored a generative model as an augmentation method known as the generative adversarial networks (GANs), where GANs may yield new useful information to the dataset as compared to the classical augmentation.
A Fuzzy Density-based Clustering Algorithm for Streaming Data

The exploitation of data streams, nowadays provided nonstop by a myriad of diverse applications, asks for specific analysis methods. In this paper, we propose SF-DBSCAN, a fuzzy version of the DBSCAN algorithm, aimed to perform unsupervised analysis of streaming data. Fuzziness is introduced by fuzzy borders of density-based clusters. We describe and discuss the proposed algorithm, which evolves the clusters at each occurrence of a new object. Three synthetic datasets are used to show the ability of SF-DBSCAN to successfully track changes of data distribution, thus properly addressing concept drift. SF-DBSCAN is compared with a basic, crisp streaming version of DBSCAN with regard to modelling effectiveness.
SENFIS - Selected Ensemble of Fuzzy Inference Systems

Fuzzy Inference Systems (SIF) for classification are machine learning models that employ linguistic rules to describe real problems in an interpretable way. However, when dealing with high dimensional input spaces, these systems tend to suffer from scalability problems and computational cost. This work presents the Selected Ensemble of Fuzzy Inference Systems (SENFIS), an automatic SIF model based on previously developed algorithms (AutoFIS-Class and RandomFIS) and composed of ensemble and subsampling strategies to deal with big datasets. Its performance is compared to those of its predecessors and of others similar fuzzy systems, making use of normal and large benchmarks databases for classification. In terms of accuracy, SENFIS performs as well as its predecessors, but at a lower computational cost. In addition, the resulting rule bases are smaller, especially for high dimensional problems.
Designing immersive virtual training environments for experiential learning

Virtual reality (VR) is among the key and most promising emerging technologies in the field of education. The current paper aims to present an innovative VR based approach for teacher education. The development of the VR application followed a full design cycle, with active involvement of education experts during the whole development process. The evaluation results indicate a positive impact of the VR intervention on the cultivation of empathy skills. Moreover, the results are statistically significant related to other parameters under investigation, including the sense of presence, embodiment and user's emotional experiences.
Online Atlasing Using an Iterative Centroid

Online atlasing, i.e. incrementing an atlas with new images as they are acquired, is key when performing studies on databases very large or still being gathered. We propose to this end a new diffeomorphic online atlasing method without having to perform again the atlasing process from scratch. New subjects are integrated following an iterative procedure gradually shifting the centroid of the images to its final position, making it computationally cheap to update regularly an atlas as new images are acquired (only needing a number of registrations equal to the number of new subjects). We evaluate this iterative centroid approach through the analysis of the sharpness and variance of the resulting atlases, and the transformations of images, comparing their deviations from a conventional method. We demonstrate that the transformations divergence between the two approaches is small and stable and that both atlases reach equivalent levels of image quality.
Adversarial auto‐encoder for unsupervised deep domain adaptation

Unsupervised visual domain adaptation aims to train a classifier that works well on a target domain given labelled source samples and unlabelled target samples. The key issue in unsupervised visual domain adaptation is how to do the feature alignment between source and target domains. Inspired by the adversarial learning in generative adversarial networks, this study proposes a novel adversarial auto-encoder for unsupervised deep domain adaptation. This method incorporates the auto-encoder with the adversarial learning so that the domain similarity and reconstruction information from the decoder can be exploited to facilitate the adversarial domain adaptation in the encoder. Extensive experiments on various visual recognition tasks show that the proposed method performs favourably against or better than competitive state-of-the-art methods.
A Study of Applications Based on Elliptic Curve Cryptography

Elliptical curve cryptography (ECC) is based on a public key cryptosystem that is on elliptic curve theory. ECC needs smaller keys in comparison to non-EC cryptography to provide equivalent security. Elliptic curves have multiple applications in pseudo-random generators, digital signatures, and key agreement. Here, we are presenting a survey of applications of ECC in smart grid communication, health IOT, anonymous key distribution scheme, multimedia and text encryption and in sensor networks. We discuss the merits and demerits of each application and measure their performance in respect to other comparable implementations.
High Resolution Medical Image Segmentation Using Data-Swapping Method

Deep neural network models used for medical image segmentation are large because they are trained with high-resolution three-dimensional (3D) images. Graphics processing units (GPUs) are widely used to accelerate training. However, the memory on a GPU is not large enough to train the models. A popular approach to tackling this problem is the patch-based method, which divides a large image into small patches and trains the models with these small patches. However, this method degrades the segmentation quality if a target object spans multiple patches. In this paper, we propose a novel approach for 3D medical image segmentation that utilizes the data-swapping method, which swaps out intermediate data from GPU memory to CPU memory to enlarge the effective GPU memory size for training high-resolution 3D medical images without patching. We enhanced the existing data-swapping method by introducing swapping inside forward propagation and selective swapping of analysis path in order to train 3D U-Net effectively. We applied this approach to train 3D U-Net with full-size images of 192 × 192 × 192 voxels for a brain tumor dataset. Compared with the patch-based method for patches of 128 × 128 × 128 voxels, our approach improved the mean Dice score by 3.9 percentage points and 4.1 percentage points when detecting a whole tumor sub-region and a tumor core sub-region, respectively. The total training time was reduced from 164 h to 47 h, resulting in an acceleration of 3.53 times.
What Business Simulations Teach: The Effect of Debriefing

This is the initial report of a multi-university study of students participating in business simulations. The study’s purpose is an attempt to determine what skills students learn while taking part in business gaming. This study defined a set of 16 primary learning skills that had been previously suggested by a variety of authors. University students were surveyed immediately upon completion of a business simulation that was included in their coursework. The results presented here are limited to US students enrolled in US universities. Future analysis will explore whether the data suggests international students learn the skills differently than US nationals. This study found that debriefing had a substantial and measurable impact on the student’s learning; however, debriefing alone was not the primary variable in learning. It was important only after the student considered the debriefing to be important and their instructor had the ability to facilitate the debriefing.
Hierarchical Reinforcement Learning with Clustering Abstract Machines

Hierarchical reinforcement learning (HRL) is another step towards the convergence of learning and planning methods. The resulting reusable abstract plans facilitate both the applicability of transfer learning and increasing of resilience in difficult environments with delayed rewards. However, on the way of the practical application of HRL, especially in robotics, there are a number of difficulties, among which the key is a semi-manual task of the creation of the hierarchy of actions, which the agent uses as a pre-trained scheme. In this paper, we present a new approach for simultaneous constructing and applying the hierarchy of actions and sub-goals. In contrast to prior efforts in this direction, the method is based on a united loop of clustering of the environment's states observed by the agent and allocation of sub-targets by the modified bottleneck method for constructing of abstract machines hierarchy. The general machine is built using the so-called programmable schemes, which are quite universal for the organization of transfer learning for a wide class of tasks. A particular abstract machine is assigned for each set of clustered states. The goal of each machine is to reach one of the found bottleneck states and then get into another cluster. We evaluate our approach using a standard suite of experiments on a challenging planning problem domain and show that our approach facilitates learning without prior knowledge.
Supporting feature model evolution by suggesting constraints from code-level dependency analyses

Feature models are a de facto standard for representing the commonalities and variability of product lines and configurable software systems. Requirements-level features are commonly implemented in multiple source code artifacts, which results in complex dependencies at the code level. As developers change and evolve features frequently, it is challenging to keep feature models consistent with their implementation. We thus present an approach combining feature-to-code mappings and code dependency analyses to inform engineers about possible inconsistencies. Our focus is on code-level changes requiring updates in feature dependencies and constraints. Our approach uses static code analysis and a variation control system to lift complex code-level dependencies to feature models. We present the suggested dependencies to the engineer in two ways: directly as links between features in a feature model and as a heatmap visualizing the dependency changes of all features in a model. We present results of an evaluation on the Pick-and-Place Unit system, which demonstrates the utility and performance of our approach and the quality of the suggestions.
Bioinspired Algorithm for Acquiring New Knowledge based on Information Resource Classification

The paper is devoted to solving the task of information resources classification to improve the effectiveness of the information processes. The paper is relevant due to significant growth of generated, transferred and processed information in science and business and the social development in general. The paper considers the main problems of information resources usage and information processing. It is suggested to solve the classification task in terms of information resources represented as ontological structures. Classification in implemented in accordance with two criteria of semantic similarity between the ontological elements: equivalence and hierarchy. The paper presents a bioinspired algorithm based on the model of firefly behavior in nature, encoding schemes to represent the solutions in the developed algorithm, and fitness function to evaluate the quality of the obtained solutions. To estimate the developed approach, we carried out the experiments on the basis of different numbers of the objects of the information resources. The research has shown that the proposed algorithm can give the effective solutions with polynomial time complexity O(tn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
Evolving Perfect Hash Families: A Combinatorial Viewpoint of Evolving Secret Sharing

Shamir's threshold secret sharing scheme gives an efficient way to share a secret among n participants such that any k or more of them can reconstruct the secret. For implementation, Shamir's scheme requires a finite field. Desmedt et al. (AsiaCrypt '94) proposed a multiplicative secret sharing scheme over non-abelian groups. In this paper, we extend (non-abelian) multiplicative secret sharing to accommodate unbounded number of participants. We introduce a new combinatorial concept of "evolving" Perfect Hash Families and present a secret sharing scheme as a consequence.
DWMAcc

PIM (processing-in-memory) based hardware accelerators have shown great potentials in addressing the computation and memory access intensity of modern CNNs (convolutional neural networks). While adopting NVM (non-volatile memory) helps to further mitigate the storage and energy consumption overhead, adopting quantization, e.g., shift-based quantization, helps to tradeoff the computation overhead and the accuracy loss, integrating both NVM and quantization in hardware accelerators leads to sub-optimal acceleration. In this paper, we exploit the natural shift property of DWM (domain wall memory) to devise DWMAcc, a DWM-based accelerator with asymmetrical storage of weight and input data, to speed up the inference phase of shift-based CNNs. DWMAcc supports flexible shift operations to enable fast processing with low performance and area overhead. We then optimize it with zero-sharing , input-reuse , and weight-share schemes. Our experimental results show that, on average, DWMAcc achieves 16.6× performance improvement and 85.6× energy consumption reduction over a state-of-the-art SRAM based design.
Understanding Face Age Estimation: humans and machine

Face age estimation is an important part of many disciplines, including dermatology, cosmetology, and computer vision. Traditional age estimation studies focus on certain parts of the face to analyse its surface topology. With the advances of deep learning, many Convolutional Neural Networks (CNNs) now outperform traditional methods in the age estimation task. However, it is still not clear what type of features these networks learn when estimating age. This study aims to investigate which facial features are important, for humans and for CNNs, on the age estimation of women in five age groups. We then compare the heat-maps from the human eye gaze with those of the CNN. We consider two main research questions: (1) Which facial regions do humans look at when performing age estimation? (2) Do humans and machine focus on the same facial regions when estimating the age? We answered these questions by conducting two experiments. In the first experiment, we used an eye-tracking software to detect where on the face the gaze of human participants focused the most when they were asked to assign the person in the image to an age class. In the second experiment, we used transfer learning on the network pre-trained on ImageNet, then fine-tuned the network on a benchmark face age dataset to classify the same images shown to the participants. The heat-maps of the VGG16 network were then visualised using Gradient-based Class Activation Map (Grad-CAM). The results showed how our model was almost as accurate as humans in predicting the age of a person from a single image of their face (CNN: 60%; humans: 61%). The results also showed that people mainly look at the eyes and nose when predicting a person's age, while the features learned by the CNN included the eyes, the mouth, and the skin surface.
Intelligent decision support system for the control of complex technical systems

This article discusses the design of the control system of complex technical objects. A model of the system for intelligent decision support in the performance of control tasks, characterized by multi-criteria and the presence of various kinds of uncertainties in the source information and expertise. The description of criteria, models and algorithms allowing to expand functionality of hardware of process of parametric admission control of a condition of technical systems is given.
Valkyrie Project: Flying Immersion in Virtual Reality

The aim of the paper was to explore a new approach for flight simulation in virtual reality and to study user's perception of virtual avatar embodiment. The exploratory research started with (i) the perception of wings on the body, and the natural movement that people do when flying. The results were used to (ii) create a model and a virtual environment in order to explore the extent of the virtual presence and virtual body ownership of the users. To get appropriate user feedback a questionnaire with 76 international participants was used. The results indicated that 78.6 % imagine having wings on their shoulders and on their back. In addition to that, 57.3 % move their arms as intuitive action to fly. Based on these results the model has to follow the flapping flight simulation in a way that wings would be attached to the shoulder. An angelic avatar was designed and a game-based story was used to justify the connection of moving hands and having wings on the shoulder. This provides an insight into the perception of wings and action of flying as felt by the user. The last experiment used questionnaires to assess the extent of immersion and presence of the users in the virtual environment. The results indicate that such a setting provides an immersive effect for flying in virtual reality.
The Consensus Games for Consensus Economics Under the Framework of Blockchain in Fintech

The goal of this paper is to introduce a new notion called “Consensus Game (CG)” with motivation from the mechanism design of blockchain economy under the consensus incentives from Bitcoin ecosystems in financial technology (Fintech), we then establish the general existence results for consensus equilibria of consensus games in terms of corresponding interpretation based on the viewpoint of Blockchain consensus in Fintech by applying the concept of hybrid solutions in game theory. As applications, our discussion in this paper for the illustration of some issues and problems on the stability of mining pool-games for miners by applying consensus games shows that the concept of consensus equilibria could be used as a fundamental tool for the study of consensus economics under the framework of Blockchain economy in Fintech.
Challenges for data collecting of teacher and student' behavior in different types of class using video and wearable device

Monitoring the behavior of both teachers and students using video data or Internet of Things (IoT) devices is necessary to achieve effective interactions and improve the environment in places of education. For the future, automatic data detection is required to be implemented, but prior to that learning data needs to be stored. Therefore, as a protocol, this research proposes a protocol for collecting video and vital data. Our proposed protocol has two types of behavior detection; (1) behavior tagging by an observer, and (2) heartbeat measurement as one of vital data using wearable devices. We conducted an experiment to test proposed protocol with participants who teachers and students, by holding two types of classes, a lecture type and a workshop type, and we used the results to determine the applicability of proposed protocol.