Reviewerly/exHarmony huggingface dataset:
Adaptive basis functions for prototype-based classification of functional data

Abstract We present a framework for distance-based classification of functional data. We consider the analysis of labeled spectral data and time series by means of generalized matrix relevance learning vector quantization (GMLVQ) as an example. To take advantage of the functional nature, a functional expansion of the input data is considered. Instead of using a predefined set of basis functions for the expansion, a more flexible scheme of an adaptive functional basis is employed. GMLVQ is applied on the resulting functional parameters to solve the classification task. For comparison of the classification, a GMLVQ system is also applied to the raw input data, as well as on data expanded by a different predefined functional basis. Computer experiments show that the methods offer potential to improve classification performance significantly. Furthermore, the analysis of the adapted set of basis functions give further insights into the data structure and yields an option for a drastic reduction of dimensionality.
The opinions and attitudes of students – future IT teachers – on the use of VR and AR in teaching

Recent technological advances in the field of Virtual Reality (VR) have resulted in market release of multiple head mounted displays such as Oculus Rift, Sony Gear VR and HTC Vive. This new technology brings great possibilities in education in terms of new methods of learning, and its applications in teaching have already been studied in several studies worldwide. Augmented Reality (AR), as an emerging technology, is predicted to have even more potential usages than VR, both in education and other areas. The advantages of AR are increased freedom, lack of simulator sickness, and broader application range. The aim of this paper was to investigate the opinions and attitudes of students who will, presumably after their graduation, teach ICT in primary education schools. The research showed that even though a majority of students have positive opinions and expectations of these technologies, such as immersive learning and pupils' motivation, only a few of them feel competent enough to use them in classroom. They also have some concerns regarding health issues of using AR/VR technologies, as well as students losing their concentration in class.
Your WiFi Knows You Fall: A Channel Data-Driven Device-Free Fall Sensing System

Falls are the second leading cause of injury deaths worldwide, inducing over 0.6 million accidental deaths per year. Among various prevention strategies, fall-related research has been prioritized. However, conventional fall detection solutions rely on computer vision or wearable sensors embody several inherent limitations such as scalability, coverage, and privacy issues. To this end, we present FallSense, a transparent and real-time fall sensing system driven by wireless channel data. FallSense is built on a Dynamic Template Matching (DTM) algorithm, which can start with a light training set and keep updating on usage. FallSense has been realized on commodity WiFi devices and evaluated in real environments. Experimental results show that FallSense outperforms another state-of-the-art approach WiFall in terms of detection precision, false alarm rate and complexity.
Increasing Self-Adaptation in a Hybrid Decision-Making and Planning System with Reinforcement Learning

Task-level decision-making and AI planning are used to control autonomous robots from a high-level, mission-oriented perspective. The dynamic selection of most suitable actions allows the system to adapt to changes in the environment as well as its own state. Nevertheless, decision-making and AI planning often require a priori definitions of capabilities, rules, decision models, or world knowledge. Due to the challenge of handling the uncertainty of robot applications in dynamic and uncontrolled environments such definitions or descriptions are always incomplete, hence the possible adaptation capabilities are limited. In this paper, we present how the self-adaptation of a robot planning and decision-making system can be improved by incorporating reinforcement learning. Particularly, we show our approach of integrating deep reinforcement learning into the ROS Hybrid Behaviour Planner (RHBP).
A global sparse gradient based coupled system for image denoising

We consider a coupled system for image denoising, which consists of an anisotropic diffusion equation and a global sparse gradient model (GSG). The global sparse gradient model is used to estimate the edge map from the noisy image accurately and robustly. The anisotropic diffusion process is guided by the edge map. Furthermore, we prove the existence and uniqueness of the solutions of the coupled system with Dirichlet initial–boundary value problem. Finally, comparing with the edge indicators based on local methods, experimental results demonstrate that our proposed method has the state-of-the-art performance among the PDE-based anisotropic diffusion approaches both in objective measurement and visual evaluation.
Robust and effective automatic parameter choice for medical image filtering.

The analysis of medical image data currently requires the interpretation of a trained and experienced user. The technological advances in imaging machinery and the understanding of disease onset, as well as medical planning, all favour the need for ever more automatic and robust methods for evaluating the health state of a subject. Here, we concentrate on methods for processing medical image data, as currently provided by existing imaging technologies, in particular the effectiveness of automatic image filtering in order to remove noise and improve the sharpness of distinct objects. The filtering approach is based on a partial differential equation, namely the Perona–Malik anisotropic diffusion equation. The approach adopted for terminating the iterative filtering procedure is based on image quality descriptors. In specific, we observe the rate of change of these to infer the transient effects of the filtering process. The entire pipeline is demonstrated to work effectively on different sets of medical image data, including MRI, CTA and CT, both in individual 2-D images in a stack, as well as treating the complete 3D volumetric dataset.
A Comparison of MFCC and LPCC with Deep Learning for Speaker Recognition

The biological information includes a fingerprint, an iris, a face, a vein, a voice. Among them, since the voice is not touched directly, the psychological burden on the user at the time of input is small as compared with other biological information. In addition, it is possible to input voice easily. Speaker recognition refers to automatically determining whose voice by the characteristics of individuality included in the voice. In order to perform individual authentication, it is necessary to extract the characteristics of the speaker from the voice data. One of the methods is Mel frequency cepstral coefficient (MFCC) and other is linear prediction cepstral coefficients (LPCC). In this paper, the speaker recognition is performed using MFCC and LPCC features by using a neural network with deep learning, we will evaluate features of MFCC and LPCC on speaker recognition.
Discovering dependencies among mined association rules with population-based metaheuristics

Stochastic population-based nature-inspired metaheuristics have been proven as a robust tool for mining association rules. These algorithms are very scalable, as well as very fast compared with some deterministic ones that search for solutions exhaustively. Typically, algorithms for association rule mining identify a lot of rules depending, on the transaction database and number of attributes. Therefore, evaluating these rules is very complex. On the other hand, establishing the relationships between discovered association rules can be considered as a very hard problem that cannot easily be solved manually. In this paper, we propose a new algorithm based on stochastic population-based nature-inspired metaheuristics for discovering dependencies among association rules.
Accurate Ellipse Extraction in Low-Quality Images

We propose an efficient method for extracting parameters of elliptic regions in digital images. Only one 2-dimensional accumulator array A(ρ, θ) is used for Hough voting, defined by voting angle θ and voting distance p. For each voting angle θ, the voting distance p is considered to be a stochastic variable, where voting values A(ρ, θ) define the probabilistic weights. We state how the statistical variance is related to the major axis, minor axis, and direction of an elliptic region, and also how the statistical mean is related to the centre; we provide two relationship functions in image space. After voting, a linear function and a quadratic function are fitted in the parameters space. The major axis, minor axis and direction are computed based on the coefficients of the fitted quadratic function. The centre is determined by using the coefficients of the fitted linear function. The proposed method is tested on synthetic images and real-world images. Experimental results show that the method extracts accurately parameters of elliptic regions, even in noisy and low-resolution images.
Dynamic User Authentication Protocol for Industrial IoT without Timestamping

Internet of Things (IoT) technology has drawn the attention of the industry, where it has been able to, and still can, solve many industrial intractable issues. However, the emerging technology suffers from severe security shortcomings. Authentication is a cornerstone of IoT security, as it presents the measures of checking the legitimacy of communication entities. The Industrial IoT (IIoT) technology has special conditions, resulting from a lack of resources and a shortage of security skills. As far as we can tell, from the literature, IIoT user authentication has not been studied extensively. In 2017 Tai et al. presented an authenticated key agreement for IoT networks. Here we prove that Tai et al. scheme is susceptible to sever security weaknesses, such as; i. unknown key share attacks, ii. node capturing attacks, iii node information secrecy. In this research article, we offer an innovative IIoT user authentication protocol that can achieve secure remote user authentication without timestamping requiring precise synchronization, our protocol only needs Hashing and Xor-ing. We examine the efficiency of the presented protocol using Tmote Sky node over an MSP430 microcontroller using a COOJA simulator. we also show its correctness using the Scyther verification tool.
SSFD<sup>+</sup>: A Robust Two-Stage Face Detector

Face detectors based on deep learning have demonstrated great progress in detecting multi-scale faces by using multi-scale feature maps and input pyramids. However, using input pyramids and multi-scale feature maps increases the training difficulty and complexity of the network. In this paper, we focus on achieving comparable performance and simplifying the network architecture for detecting multi-scale faces. To enable network learning of multi-scale facial features from a single-scale feature map and a single-scale input image: 1) we conducted a comparative study to investigate which layer contributes more to detecting multi-scale faces and 2) we designed and implemented a simple network structure to improve the performance of detecting multi-scale faces by incorporating additional contextual information. SSFD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> achieves mAPs of (91.3%, 90.3%, 83.1%) and (92.4%, 90.9%, 83.7%) on the (easy, medium, and hard) subsets of the WIDER FACE validation and testing datasets, respectively, and promising results on the FDDB, PASCAL Faces, and AFW datasets.
MAGIC: A Fundamental Framework for Gesture Representation, Comparison and Assessment

Gestures play a fundamental role in instructional processes between agents. However, effectively transferring this non-verbal information becomes complex when the agents are not physically co-located. Recently, remote collaboration systems that transfer gestural information have been developed. Nonetheless, these systems relegate gestures to an illustrative role: only a representation of the gesture is transmitted. We argue that further comparisons between the gestures can provide information of how well the tasks are being understood and performed. While gesture comparison frameworks exist, they only rely on gesture's appearance, leaving semantics and pragmatical aspects aside. This work introduces the Multi-Agent Gestural Instructions Comparer (MAGIC), an architecture that represents and compares gestures at the morphological, semantical and pragmatical levels. MAGIC abstracts gestures via a three-stage pipeline based on a taxonomy classification, a dynamic semantics framework and a constituency parsing; and utilizes a comparison scheme based on subtrees intersections to describe gesture similarity. This work shows the feasibility of the framework by assessing MAGIC's gesture matching accuracy against other gesture comparison frameworks during a mentor-mentee remote collaborative physical task scenario.
A scalable Echo State Networks hardware generator for embedded systems using high-level synthesis

Reservoir computing (RC) features with the rich computational dynamics is a kind of powerful machine learning paradigm that is well suited for non-linear time-series prediction and classification problems. However, this impressive performance comes with a cost of complex arithmetic operations and high memory usage that make it significantly challenging to deploy on embedded systems. Solutions based on CPU and/or GPU-based designs, provides flexibility but suffers from a lack of efficiency in terms of power, performance, and area (PPA). Although hardware-accelerated solutions can improve efficiency, it takes longer design cycles and is time-consuming. Furthermore, it may happen that design spec requires run change due to the fact that the network is retrained with the new data set to improve the performance. It leads to extra effort in the redesign of the hardware-accelerated solution. This preliminary work presents the design and implementation of a hardware generator for RC-ESNs (echo state networks) to tackle the problem. The proposed methodology is demonstrated by various offline-trained network parameters and topologies. Compared to existing solutions, the proposed framework provides scalability with the support of DSE in agile hardware design.
An Eye-Tracker-Based 3D Point-of-Gaze Estimation Method Using Head Movement

Eye trackers are currently used to sense the positions of both the centers of the pupils and the point-of-gaze (POG) position on a screen, in keeping with the original objective for which they were designed; however, it remains difficult to measure the positions of three-dimensional (3D) POGs. This paper proposes a method for 3D gaze estimation by using head movement, pupil position data, and POGs on a screen. The method assumes that a person, usually unintentionally, moves his or her head a short distance such that multiple straight lines can be drawn from the center point between the two pupils to the POG. When the person is continuously focusing on a given 3D POG while moving, these lines represent the lines of sight that intersect at a 3D POG. That 3D POG can, therefore, be found from the intersection of several lines of sight formed by head movements. To evaluate the performance of the proposed method, experimental equipment was constructed, and experiments with five male and five female participants were performed in which the participants looked at nine test points in a 3D space for approximately 20 s each. The experimental results reveal that the proposed method can measure 3D POGs with average distance errors of 13.36 cm, 7.58 cm, 5.72 cm, 3.97 cm, and 3.52 cm for head movement distances of 1 cm, 2 cm, 3 cm, 4 cm, and 5 cm, respectively.
Intelligent Tutoring Design Alternatives in a Serious Game

The adaptive learning potential in video games can be increased by incorporating intelligent tutoring system (ITS) approaches. However, very few studies have examined the ITS capability in a game. Across a four-year research project, we implemented a number of intelligent tutoring design alternatives and integrated them into the serious 3D video game. The game, Heuristica, was designed to improve critical thinking by exposing players to different cognitive biases through game play, and then adaptively giving players opportunities to practice avoiding or mitigating each bias. We describe the intelligent tutoring system, and how we structured the learner’s environment, provided learners with tailored feedback, supported spaced and massed practice, and provided opportunities for in-game learner reflection. We describe the design and functionality of the Student Modeler and interface features that support student feedback and interaction. We evaluate the tradeoffs made in the student model design and the impact they had on the game experience, focusing on the open student model summary screen, mixed-initiative opportunities and algorithms for selection of learning opportunities. This paper contributes to the intelligent tutoring literature by describing one of the first intelligent tutoring systems embedded in a 3D game for training and provides lessons learned from implementations in a single game.
Multi-GPU approach for big data mining

This paper identifies scalability bounds of the evolutionary induced decision trees (DT)s. In order to conquer the barriers concerning the large-scale data we propose a novel multi-GPU approach. It incorporates the knowledge of the global DT induction and EA parallelization. The search for a tree structure and tests is performed sequentially by a CPU, while the fitness calculations are delegated to GPUs, thus the core evolution is unchanged. The results show that the evolutionary induction is accelerated several thousand times by using up to 4 GPUs on datasets with up to 1 billion objects.
Neoliberalism, Technocracy and Higher Education: Editors’ Introduction

This special issue of Social Epistemology has its origin in two symposia organised by the Contemporary Philosophy of Technology Research Group at the University of Birmingham (UK). These were ‘The Digital University in a Neoliberal Age’ in November 2017 and ‘The Neoliberal Imagination’ in February 2018. The articles published here explore the relationship between neoliberalism, technocracy and technology, with a special but not exclusive focus on universities, which are open to contestation concerning their role in the public sphere and the politics of knowledge production. Where universities are discussed, English higher education is focused on, because this is undergoing an intense audit-driven neoliberal re-engineering. English higher education is an outlier in Europe to the extent to which it has undergone marketisation, with top-down reforms imposed by the supposedly rolled back, less-interventionist neoliberal state (Shattock, Times Higher, 3 April 2019). Central to these are state-imposed technocratic audit regimes which are used to provide ‘objective’ data on ‘performance’, which has, always already, to be ‘excellent’ (see Readings’ 1997 classic critique of the vacuity of the notion of ‘excellence’). Before briefly introducing each article, we will situate the concerns mentioned above in the context of a social epistemological critique of knowledge production.
An Encryption-Based Approach to Protect Fog Federations from Rogue Nodes

The cloud computing paradigm has revolutionized the concept of computing and has gained a huge attention since it provides computing resources as a service over the internet. As auspicious as this model is, it brings forth many challenges: everything from data security to time latency issues with data computation and delivery to end users. To manage these challenges with the cloud, a fog computing paradigm has emerged. In the context of the computing, fog computing involves placing mini clouds close to end users to solve time latency problems. However, as fog computing is an extension of cloud computing, it inherits the same security and privacy challenges encountered by traditional cloud computing. These challenges have accelerated the research community’s efforts to find effective solutions. In this paper, we propose a secure and fine-grained data access control scheme based on the ciphertext-policy attribute-based encryption (CP-ABE) algorithm to prevent fog nodes from violating end users’ confidentiality in situations where a compromised fog node has been ousted. In addition, to provide decreased time latency and low communication overhead between the Cloud Service Provider (CSP) and the fog nodes (FNs), our scheme classifies the fog nodes into fog federations (FF), by location and services provided, and each fog node divides the plaintext to be encrypted into multiple blocks to accelerate the time when retrieving ciphertext from the CSP. We demonstrate our scheme’s efficiency by carrying on a simulation and analyzing its security and performance.
Defining Joint Embedding Distortion for Adaptive MP3 Steganography

In this paper, a universal joint embedding distortion function (JED) is proposed to improve the undetectability and imperceptibility of MP3 steganography, which can be applied to Huffman codeword mapping (HCM) and sign bit flipping (SBF). Content-aware and statistical distortions are synthetically modeled to formulate the atom modification of the quantified modified discrete cosine transform (QMDCT) coefficients. On the one hand, to retain the hearing imperceptibility, the absolute threshold of hearing is employed to measure the auditory sensitivity of each QMDCT coefficient. On the other hand, considering most of the existing universal MP3 steganalysis features are designed based on correlations, the forward and backward transition probability are utilized to characterize the correlations between adjacent QMDCT coefficients. What's more, we present an implementation of JED in sign bits domain. Experimental results demonstrate that our method is able to achieve higher embedding capacity and better imperceptibility. The detection accuracy of the proposed scheme is about 75% with the bitrate of 320kbps and embedding rate of 11kbit/s, which is respectively decreased by 9.54% ~ 16.94% than existing MP3 steganographic methods.
LCS-based automatic configuration of approximate computing parameters for FPGA system designs

In application domains like data analysis or image processing, ever-increasing performance demands push the capabilities of computational systems to their limits. With technology scaling plateauing out, engineers are forced to rethink their approach to system design. The research field of approximate computing provides a new design paradigm which trades off accuracy against computational resources. In a complex system, multiple approximation methods can be combined to maximize the resulting benefits, but because of error propagation in the system, doing this in a controlled manner is challenging. To solve this problem, we propose to use concepts developed in the field of evolutionary machine learning to optimize approximation parameters, focusing on systems implemented on FPGA hardware. Our approach uses the rules of a learning classifier system to adjust approximation parameters. The resulting effects on both the application quality and resource usage are estimated on-the-fly and fed back to the rules with every fitness update, allowing the system to be carefully tuned to specific design goals. We illustrate the application of the proposed system to a real-world image processing problem and highlight some practical implications. As this is work in progress, we outline remaining open questions and future directions for our research.