Reviewerly/exHarmony huggingface dataset:
A video-based assessment of likeability and usability in a play application of robots for children

Purpose The purpose of this paper is to conduct research with children, who have different abilities from adults, in terms of language understanding and level of attention, is a challenging task, especially concerning novel interactive systems such as social robots. Consequently, self-reporting methods are often replaced or supplemented by observational methods that are usually carried out taking advantage of video recordings. However, some limitations make this approach challenging for studies conducted with groups of children in real-world environments, whose relevance is being addressed more and more frequently in human-robot interaction (HRI) research. Thus, there is a growing need for rigorous observation approaches in unstructured test environments. Design/methodology/approach This paper presents an alternative analysis approach, in relation to an experimental child-robot interaction (CRI) application, which was developed at the Academy of Arts and Design, Tsinghua University, China. The proposed methodology is based on the analysis of video recordings of in-wild activities of children with a robot. The methodology has the aim of providing a framework to facilitate knowledge identification and structuring. It was implemented for experiment evaluation and validation purposes and to propose a reference structure for the organization of new experiments and the stimulation of new ideas and activities in the design process. Findings This methodology provides a logical structure, which can be used to identify the effectiveness or limits of design choices, pertaining to such aspects as the morphology or movement of robots or the choice of their specific role in education, all of which play crucial roles in the design process and could be improved to achieve better results. This structured identification is a practical implication for the design process, above all when it is oriented toward social robots and their interaction with children or elderly senile people. In this case, the outcomes were the identification of important elements of an experiment (psychological profiles of the involved children and possible problems or risks) and their impact on the design process. Originality/value The methodological approach, which structures and uses cognitive maps to elaborate multicriteria evaluation models, is not new to the operations research field (where it is defined as a multimethodology application of Soft OR), but it has not yet been applied in the field of HRI studies, to analyze children’s perception of a robot and to identify the factors that can affect a good CRI or to structure knowledge that can be shared to guide the design process of robots for the experience of children playing.
Precise measurement of known and unknown freeform surfaces using Experimental Ray Tracing

Lenses and mirrors with freeform surfaces are the latest step in the evolution of optical components. However, the measurement of these components still challenges metrology. We have developed a gradient-based measurement technique that is able to measure freeform specular surfaces either if their form is known or not. The measurement of freeform surfaces is a challenge for every measurement system. Especially if the form of the surface is not known in advance. Our measurement system can measure continuous freeform surfaces with up to 10° deviation from a plane surface even if the surface model is not known in advance. Therefore, a ray, represented by a narrow laser beam, is targeted on the surface under test (SUT) under a certain angle. Affected by its slope, the surface reflects the ray in a new direction. This direction is measured by using a variation of Experimental Ray Tracing (ERT). This includes the measurement of the position of the reflected ray in two parallel planes. Calculating the difference of the position on these planes, the direction of the ray in relation to them can be calculated. Having the direction of the reflected ray, as well as the direction of the incident ray, one can determine the surface normal at the point of reflection. By moving the SUT, the incident ray targets on a different point on the SUT. Therewith, various points are investigated. Using appropriate integration methods, the surface can be reconstructed. Although, with the introduction of the incident ray under a certain angle comes the issue, that the point of reflection changes with the sag of the SUT. This leads to an unequal distant measurement grid of points of reflection even if the SUT has been moved to equal distant sample points. This shift has to be considered for the reconstruction of the surface. This issue is solved in different ways for known or unknown surfaces. For an unknown surface, the investigated sample points are transferred into a coordinate system where they are equal distant. This is the coordinate system of the incident beam. Performing the integration here and transferring the reconstructed surface back into the coordinate system of the SUT leads to the expected shift of the sample points. For a known surface, the expected surface form is taken into account to determine the sample point shift. Therewith, the difference between the measured surface normals and the expected normals can be calculated and the integration can be performed only on the normal residuals. By adding the residuals to the model, the surface can be reconstructed. The measurement technique described above has been implemented in an experimental setup. To show the abilities of this technique, we will show the process of the measurement of a known and an unknown surface using the same sample. The results will be evaluated and compared.
Three-dimensional shape measurement of fine structure by detecting phase distribution of only zeroth order diffraction beam based on speckle interferometry

In the measurement of three-dimensional structure beyond the diffraction limit of the objective lens of the optical system using the speckle interferometry, it was reported that the shape of a measured object can be measured by detecting the change of phase distribution generated by a lateral shift instead of analyzing the focal image of the measured object. In this paper, it is investigated that the three-dimensional shape measurement can be realized by detecting the phase change of only the zeroth-order diffraction light by the lateral shift of the object using experimental results. As a result, it is confirmed that the shape information of the object is included in the zeroth-order diffraction light and that the three-dimensional shape can be measured by using only zeroth-order light without any higher order diffraction lights.
Splitting Merged Characters of Kannada Benchmark Dataset using Simplified Paired-Valleys and L-Cut

We reduce the computational complexity of the paired-valley algorithm for splitting merged characters, from Θ(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) down to Θ(N), where N is the number of symbols merged. We also propose an effective way (L-cut algorithm) to separate the merged half-consonants (known in Kannada as ottus) from the base symbols. We have created a benchmark dataset of 4033 sub-word images in Kannada, each comprising two or more merged characters. We test the recognition accuracy of Tesseract OCR on the created benchmark dataset, before and after applying our technique. The accuracy of Tesseract v3 OCR on the created dataset of 61.6% increases by 20% to a value of 81.7% after the splitting of the characters by our method. The algorithm's scalability to other scripts has been explored by limited experiments on Telugu and Tamil.
Cross Chain Bribery Contracts: Majority vs Mighty Minority

Bribery is a perilous issue in the real world, especially in an economical aspect. This fraudulence is unavoidable, and more importantly, it is more difficult to trace in case smart contracts are utilized for bribing on a distributed public blockchain. In our paper, we propose a new threat to the security of a blockchain system, cross-chain bribery using smart contracts. An arbitrary wealthy briber can utilize cross-chain smart contracts to manipulate a consensus mechanism on a victim's blockchain or to disgrace a victim's blockchain. To better understand this threat, our paper proposes a framework to analyze bribery using cross-chain smart contracts. We analyze the amount of incentive to bribe rational miners in a victim's blockchain and also a full cost of conducting a cross-chain bribery attack. The result is that such attacks can be carried out with a reasonable amount of money or cryptocurrencies.
The Social Implications of Wearable Information Technologies

Existing theories of information technology (IT) use focus on utilitarian benefits to a user but seldom capture the complex socio-emotional relationships people have with IT. Understanding how, why, and when technology users create particular identification with technology use has been critical as technology has gradually become a part of people's identity. This research examines the complex way people relate to IT by exploring how IT can be incorporated into an individual's identity. We extend a conceptual framework from IT identity (ITID) theory to explore the complex relationships people have with IT in a dynamic, social environment. This study also explores how ITID changes over time. The research questions are: What are the social implications of ITID? How do these social implications influence ITID change over time?
Does Choosing the Concept on Which to Solve Each Practice Problem in an Adaptive Tutor Affect Learning?

We conducted a controlled study to investigate whether having students choose the concept on which to solve each practice problem in an adaptive tutor helped improve learning. We analyzed data from an adaptive tutor used by introductory programming students over three semesters. The tutor presented code-tracing problems, used pretest-practice-post-test protocol, and presented line-by-line explanation of the correct solution as feedback. We found that choice did not increase the amount of learning or pace of learning. But, it resulted in greater improvement in score on learned concepts, and the effect size was medium.
Performance Prediction for Distributed Graph Computing

Performance estimation for executing graph algorithms on the distributed systems, especially with sacrificing the accuracy of results to improve the runtime performance, is a pre-requisite for optimizing system parameters to achieve an applicable tradeoff between the runtime and the inaccuracy of results. This paper presents an experimental approach that predicts the runtime and the inaccuracy of conducting graph algorithms on the BSP-based distributed graph computing systems to optimize system parameters by using an artificial neural network (ANN) model. It samples different scales of subgraphs from the complete input graphs and executes the underlying algorithm on each subgraph to capture its characteristics. Then it essentially predicts the performance of executing the underlying algorithm on the complete graph by learning the scalability that how the runtime and the inaccuracy of results vary with different scales of graphs with an ANN network, which is trained off-line based on the captured characteristics of the subgraphs. We conducted all experimental studies on the Amazon EC2 Cloud. The experimental results demonstrate that the prediction approach can effectively predict the runtime with a relative error rate under 8% averagely and the inaccuracy of results with a relative error rate under 25% averagely compared to the actual performance results.
Rassel: Robot Assistant for the Elderly

Assisting the elderly is today a necessity and a responsibility of our society. As they reach old age they need more assistance and companionship when they eventually live alone. In this context, technology, and cognitive assistants in particular, can help in the task of caring for our elderly. In this work, we have developed an assistant robot for the elderly, called Rassel, that helps to take care of senior people who is living alone, helps them to keep active by recommending custom activities and also helps with their health assistance by monitoring their vital signs and maintaining contact with relatives or emergency contacts if a medical urgency occurs. This robot has been developed and presented in the IBM Country Project ‘Cogs for Good’.
PROOF SYSTEMS FOR VARIOUS FDE-BASED MODAL LOGICS

Abstract We present novel proof systems for various FDE-based modal logics. Among the systems considered are a number of Belnapian modal logics introduced in Odintsov &amp; Wansing (2010) and Odintsov &amp; Wansing (2017), as well as the modal logic KN4 with strong implication introduced in Goble (2006). In particular, we provide a Hilbert-style axiom system for the logic $BK^{\square - } $ and characterize the logic BK as an axiomatic extension of the system $BK^{FS} $ . For KN4 we provide both an FDE-style axiom system and a decidable sequent calculus for which a contraction elimination and a cut elimination result are shown.
Image compression and encryption scheme with hyper-chaotic system and mean error control

Based on a hyper-chaotic system, an image compression and encryption scheme is presented by adopting mean error control to improve the quantization compression performance of the discrete cosine transform (DCT) coefficients. The transform coefficients of DCT forming a structure similar to the sub-band can be classified and encoded. Then the image is scanned with Zigzag operation in different directions to achieve sufficiently scrambled effects. The cyclic shift matrix for image encryption is generated by the hyper-chaotic system. The four initial inputs for generating the cyclic shift matrix serve as the key. Simulation results demonstrate the security and the effectiveness of the proposed image compression and encryption scheme.
UML2App

The development of app for mobile devices poses extra challenges to the ones already present in the development of the so-called traditional applications. The interaction capabilities differ from those found in desktop applications, which must be carefully considered to produce a natural way of interacting with mobile devices. Another relevant aspect in the development of such apps is fragmentation, given the variability in the operating systems they use, the guidelines that drive the development for each operating system and the hardware characteristics of the devices. In this work a model-driven approach for the generation of mobile apps is introduced aimed at contributing to address the issues previously discussed. This approach relies on a UML profile applied to a set of models, including models such as a task model. Currently, the implementation is focused in Android framework.
Design and Implementation of Cloud Service System Based on Face Recognition

Face recognition technology can be applied to many aspects in smart city, and the combination of face recognition and deep learning can bring new applications to the public security. The use of deep learning machine vision technology and video-based image retrieval technology can quickly and easily solve the current problem of quickly finding the missing children and arresting criminal suspects. The main purpose of this paper is to propose a novel face recognition method for population search and criminal pursuit in smart cities. In large and medium-sized security, the face pictures of the most similar face images can be accurately searched in tens of millions of photos. The storage requires a powerful information processing center for a variety of information storage and processing. To fundamentally support the safe operation of a large system, cloud-based network architecture is considered and a smart city cloud computing data center is built. In addition, this paper proposed a cloud server architecture for face recognition in smart city environments.
EKEGWO: Enhanced Kernel-Based Exponential Grey Wolf Optimizer for Bi-Objective Data Clustering

The widespread application of clustering in various fields leads to the discovery of different clustering techniques in order to partition multidimensional data into separable clusters. Although there are various clustering approaches used in literature, optimized clustering techniques with multi-objective consideration are rare. This paper proposes a novel data clustering algorithm, Enhanced Kernel-based Exponential Grey Wolf Optimization (EKEGWO), handling two objectives. EKEGWO, which is the extension of KEGWO, adopts weight exponential functions to improve the searching process of clustering. Moreover, the fitness function of the algorithm includes intra-cluster distance and the inter-cluster distance as an objective to provide an optimum selection of cluster centroids. The performance of the proposed technique is evaluated by comparing with the existing approaches PSC, mPSC, GWO, and EGWO for two datasets: banknote authentication and iris. Four metrics, Mean Square Error (MSE), F-measure, rand and jaccord coefficient, estimates the clustering efficiency of the algorithm. The proposed EKEGWO algorithm can attain an MSE of 837, F-measure of 0.9657, rand coefficient of 0.8472, jaccord coefficient of 0.7812, for the banknote dataset.
Overview of a human factors toolbox

This chapter introduces the contents of a human factors toolbox including: contextual inquiry methods, task analysis, applying human factors in design, heuristic evaluation, cognitive walkthrough, expert reviews, simulated use studies, use focused risk analysis and risk management, known use error and post market surveillance, as well as preparing a human factors report for agency submission. The purpose for each tool is described in order to assist in the planning of a robust human factors program for medical device design.
Effects of Weather on Drone to IoT QKD

The Internet of Things (IoT) is playing a growing role in society, and includes control over a range from household appliances to municipal power grids and nationwide assets. As a result, hackers from a national level down to low level criminals are looking to take advantage of important IoT infrastructures. These present an immediate target for cyber-terrorists as well as more mundane attacks by thieves and personal enemies, and it is imperative to devise defensive measures. In this work, we describe potential possible attacks on several IoT systems. As a result, vendors of the IoT infrastructure employ conventional encryption which is based on complexity to fight against hackers. However the emerging technology of quantum computing will make deciphering of the conventional encryption an easy task. This leads us to propose a scheme for use of QKD (quantum key distribution) which could be effective as a countermeasure. In this paper we will describe the feasibility study of weather effect design guidelines for a small, short range, mobile QKD system from drone to IoT on the ground.
Fractional Fourier ridges for demodulation of interferograms with quadratic phase

A method based on the fractional Fourier ridges for accurate phase demodulation of a single interferogram with quadratic phase is presented. The interferograms being analyzed may contain circular, elliptic or astigmatic fringes. In signal processing field, such interferograms can be called 2-D chirp-type signals. Since the fractional Fourier transform (FRFT) of a chirp signal is a function under the matched angle that is determined by chirp rates of the signal, so the method can be used to match the multiple chirp rates in chirp-type signals with multiple chirp components. In this work, the FRFT of all row (column) signals are firstly calculated, and the ridge of the FRFT amplitude of each row (column) signal in FRFT domain is recorded. Repeat the above process for each angle of a searching range. Then a ridge tracking approach is employed to determine the matched angle, which can be used to calculate the coefficient of the square term of row (column) coordinates. Moreover, under the matched angle, the ridge of the FRFT amplitude of each row (column) signal all lie on a straight line. The slope and constant term of the line can be used to calculate the coefficient of the linear term of row (column) coordinates and the coefficient of cross term, respectively. The same procedures are implemented to all column (row) signals to determine the coefficients of the square and liner term of column (row) coordinates. According to the obtained coefficients, the phase of the fringe pattern can be constructed without phase unwrapping operation. Furthermore, the present procedure is also capable of analysis of interferograms with or without circularly symmetry fringe distribution instead of using complex and time consuming algorithms for recovering phase from fringe patterns with closed fringes. Finally, the method is tested in simulated and real data.
Voting-based seismic data classification system using logistic regression models

Early detection of seismic events is fundamental to mitigate the damages produced by these natural phenomena, and mainly, save lives. In this paper, logistic regression models were applied to distinguish between seismic and non-seismic local events considering data segments from four seismological stations (epicenter in the Santander department, Colombia) of the National Seismological Network of Colombia. The classification outputs obtained by each individual classifier were then combined using voting functions. For each station, Degree of Polarization, Ratio of Vertical power to Total power, Skewness and Kurtosis of the three-component seismic data were extracted. Results showed that the best individual classifier achieved an accuracy of 95% and reaches an accuracy of 98% using voting functions. This classifier is the first step in the development of alert mechanisms for the early detection of seismic events in Colombia.
Publishing Uncertainty on the Semantic Web: Blurring the LOD Bubbles

The open nature of the Web exposes it to the many imperfections of our world. As a result, before we can use knowledge obtained from the Web, we need to represent that fuzzy, vague, ambiguous and uncertain information. Current standards of the Semantic Web and Linked Data do not support such a representation in a formal way and independently of any theory. We present a new vocabulary and a framework to capture and handle uncertainty in the Semantic Web. First, we define a vocabulary for uncertainty and explain how it allows the publishing of uncertainty information relying on different theories. In addition, we introduce an extension to represent and exchange calculations involved in the evaluation of uncertainty. Then we show how this model and its operational definitions support querying a data source containing different levels of uncertainty metadata. Finally, we discuss the perspectives with a view on supporting reasoning over uncertain linked data.
Mathematical models for the discordant size content replacement attack

The incidence of attacks on audio signal self-recovery schemes has been growing. One of the more severe attacks is the discordant size content replacement attack. This attack replaces a set of samples of an audio signal with another set of samples of different sizes. This paper proposes a mathematical model of a communication channel subject to the discordant size content replacement attack. The goal is obtain a mathematical description and input-output relation, to detect how the attack tampered with an audio signal, to analyse its behaviour, and to use the model to design new schemes for audio signal self-recovery that are robust against this attack. Models of content replacement attacks with equal, larger, and smaller sizes are presented. Tests show that these models function correctly.