Reviewerly/exHarmony huggingface dataset:
Visualising User Experiences: Analysing Embodiment of UX in Autonomous Vehicle Concepts

Abstract The prospect of autonomous vehicles and associated technologies has disrupted traditional modes of vehicle operation and ownership. This requires automotive designers to shift their focus from designing vehicle form to consider the design of transport experiences. As such, there is a need to explore how best to support automotive designers in communicating user experiences (UX) alongside the physical design of vehicles. This paper presents an industry case study conducted with Ford Design Asia Pacific to assess the embodiment of UX in early concepts. Attributes of generalised model for UX are mapped to designers' storyboard illustration for the experience of an advanced concept for an autonomous vehicle interior. Results show how a mix of captions, sketches of users and contextual features illustrate different attributes of user experience. From findings we conclude firstly, the need to develop a toolkit to help designers communicate descriptions of as yet designed interactions. We also conclude that sketching contextual features of experience can provide a starting point to develop aspects of UX that can be used to differentiate and identify the Ford brand.
Nonlinear and Time Varying System Identification Using a Novel Adaptive Fully Connected Recurrent Wavelet Network

This paper presents a novel Adaptive Fully Connected Recurrent Wavelet Network (AFCRWN) for online identification of nonlinear dynamic and time varying systems. The AFCRWN inherits the architecture of fully connected recurrent neural network proposed by Williams & Zipser. Since the AFCRWN incorporates translated and dilated versions of scaling function and wavelet instead of global functions as activation functions of hidden neurons, this would lead to a significant improvement of network performance. An adaptive gradient based algorithm is used to adjust the shapes and weights of scaling functions and wavelets. Simulation results for modeling of different dynamic nonlinear and dynamic nonlinear and time varying systems are presented. Comparisons with a network of neurons with wavelets and a network of neurons with sigmoid functions are provided. Computer simulation results have successfully validated the superior performance of AFCRWN.
Malicious Loop Detection Using Support Vector Machine

Existing Side-channel attack techniques, such as meltdown attacks, show that attackers can exploit the microarchitecture and OS vulnerabilities to achieve their goals. In this paper, we present the development of our real-time system for detecting side-channel attacks. Unlike previous works, our proposed detection system does not rely on synchronisation between the attackers and victims. Instead, it uses processors' performance indicators to capture malicious Flush+ Reload activities with an accuracy of up to 99%. Moreover, the detection activities can be achieved with minimum time delay in both native and cloud systems with a low overhead performance of approximately less than 1% in the host system.
Planting the Seed of Positive Human-IoT Interaction

We present a visual interface for communicating the internal state of a coffee machine via a tree metaphor. Nature-inspired representations have a positive impact on human well-being. We also hypothesize that representing the coffee machine as a tree stimulates emotional connection to it, which leads to better maintenance performance.The first study assessed the understandability of the tree representation, comparing it with icon-based and chart-based representations. An online survey with 25 participants indicated no significant mean error difference between representations.A two-week field study assessed the maintenance performance of 12 participants, comparing the tree representation with the icon-based representation. Based on 240 interactions with the coffee machine, we concluded that participants understood the machine states significantly better in the tree representation. Their comments and behavior indicated that the tree representation encouraged an emotional engagement with the machine. Moreover, the participants performed significantly more optional maintenance tasks with the tree representation.
Constructing a corpus-informed list of Arabic formulaic sequences (ArFSs) for language pedagogy and technology

Abstract This study aims to construct a corpus-informed list of Arabic Formulaic Sequences (ArFSs) for use in language pedagogy (LP) and Natural Language Processing (NLP) applications. A hybrid mixed methods model was adopted for extracting ArFSs from a corpus, that combined automatic and manual extracting methods, based on well-established quantitative and qualitative criteria that are relevant from the perspective of LP and NLP. The pedagogical implications of this list are examined to facilitate the inclusion of ArFSs in the process of learning and teaching Arabic, particularly for non-native speakers. The computational implications of the ArFSs list are related to the key role of the ArFSs as a novel language resource in the improvement of various Arabic NLP tasks.
The Sonipy Framework: Getting Started

Having established the design criteria for a comprehensive heterogeneous data sonification software framework (DSDF) in the previous chapter, this chapter introduces two pillars of such a framework, the Python and Csound programming languages, as integrated through a Python-Csound Application Programming Interface (API). The result is as a mature, stable, flexible, and comprehensive combination of tools suitable for real and non-realtime sonification, some of the features of which are illustrated in the examples of subsequent chapters.
Distributed Differential Utility/Cost Analysis for Privacy Protection

In the era of Big Data, as vast amounts of data are collected and shared among collaborators or uploaded to the Internet, the attacks on data privacy become more and more serious. Compressive privacy (CP) is a kind of privacy-preserving dimension-reduced projection schemes such that the projected data can be well used for the intended utility task but not for malicious applications. Nevertheless, most of existing CP approaches belong to centralized processing, which are not applicable to the cases that data is dispersedly collected/stored at distributed nodes and cannot be centralized to one node for processing due to various reasons. To tackle this problem, we propose a distributed differential utility/cost analysis (dDUCA), in which each node in the network is only allowed to exchange and combine the compressive-and-lossy projection matrix with its one-hop neighbors. Using the projection matrix, the classification of the projected data is performed. Experiments on several datasets confirm the effectiveness of the proposed method in terms of both privacy protection and utility retention.
Exploring the Application of Serious Game Based on Augmented Reality: A Case Study on Tsingtao Beer Museum

Abstract This work aims to explore the feasibility and significance of serious game based on augmented reality in museums, and approaches its effect on the experience of visitors. Depending on the status of museums and the characteristics and advantages of AR serious game, the article demonstrates that the experience and the follow-up purchase intention of visitors can be enhanced by improving the interestingness, learning and interaction of visiting museums, and setting up personalized mechanisms. Taking Tsingtao Beer Museum as an example, we designed a game concept prototype and described the method of game development. Finally, the article proposes the prospect and significance of further study on the application of AR in museums.
A New Video Tracking Algorithm Based on Multi-Complementary Features Fusion

In order to make full use of the diversity of sample information in the tracking process and improve the generalization ability of the tracker, this paper integrates the object model prediction results on the basis of the Staple algorithm, and applies weighted bands to the simple linearity of different predictive response results in the algorithm. To the uncertainties, a new adaptive response factor graph fusion method with weight coefficients is proposed, which effectively improves the reliability of the video target tracking algorithm. Theoretical analysis and experimental simulation show that the proposed algorithm is more accurate and robust than the classical Staple algorithm, and it maintains high real-time performance.
Self-Adaptation for Heterogeneous Client-Server Online Games

Online games have been widely spread due to the increase in Internet bandwidth. Yamagata,Satoru In the development of an online game, we must consider the time lag problem caused by heterogeneous environments. Nakagawa,Hiroyuki In this paper, we apply the MAPE loop model, which consists of four key activities (monitoring, analysis, planning, and execution) for adaptation, to reduce this problem in a heterogeneous online game environment. Sei,Yuichi In particular, we propose a MAPE loop control pattern for a heterogeneous client/server model. Tahara,Yasuyuki We experimentally embedded the pattern in an online game application. Ohsuga,Akihiko The results demonstrate that our MAPE loop control pattern helps to reduce the time lag problem in an online game application.
An Improved PSO Algorithm with Migration Behavior and Asynchronous Varying Acceleration Coefficient

Particle swarm optimization (PSO) has been developing at a fast pace and has a wide range of applications since it is easy to understand and implement. However, it is greatly limited by the problem of being trapped in local optimum. Inspired by the migration behavior of animals, MBPSO is proposed. In MBPSO, the whole evolutionary cycle is divided into several increasing sub-evolutionary cycles. As the particles complete each sub-evolutionary cycle, they move around from their original position. Moreover, in order to ensure convergence and improve con-vergence speed, acceleration coefficient and inertia weight change asynchronously with time. Standard PSO, SAPSO and SecPSO are selected for comparison with MBPSO. Then the performance of the four algorithms in six functions is tested. It is ultimately proved that the proposed MBPSO algorithm is more effective than the other three PSO algorithms.
Susereum: Towards a Reward Structure for Sustainable Scientific Research Software

Research software has opened up new pathways of discovery in many and diverse disciplines. This research software is developed under unique budgetary and schedule constraints. Its development is driven by knowledge discovery goals often without documented requirements. As a result, the software code quality is impacted which often hinders its sustainability beyond the immediate research goals. More importantly, the prevalent reward structures favor contributions in terms of research articles and systematically undervalues research codes contributions. As a result, researchers and funding agencies do not allocate appropriate efforts or resources to the development, sustenance, and dissemination of research codebases. This paper presents Susereum, a Blockchain based platform that aims at achieving two goals. First, restructuring prevalent incentives by awarding permanent immutable credit to research code authors similar to the credit awarded to the authors of scientific articles. Second, distributing sovereignty by empowering peers through a consensus process to define code sustainability and impact metrics.
An Exploratory Tool for Analyzing Computational Jobs on XSEDE/HPC Resources

Supercomputers are an important tool for researchers. These systems are needed by scientists to conduct experiments that would not be possible by normal desktop systems. Researchers across varying disciplines utilize these resources, from materials research, astronomical sciences, physics, biochemistry, and more. It is beneficial to the individuals who manage these High-Performance Computing(HPC) resources to have a sense of what types of research is being done on their systems.
Structure Study of Multiple Traveling Salesman Problem using Genetic Algorithm

In the multi-traveling-salesman problem, each salesman must traverse a certain number of cities. The set of these cities is viewed as a unit in this paper. Owing to the shortcomings of the unit structure research in the typical multi-traveling-salesman model, this paper proposes an improved model with quadratic partitioning, which can be applied for the optimization of complex topology structure. Among these complex optimization problems, the structure arrangement of thermoelectric modules is taken as an example. In detail, the equilibrium between terminal voltage and internal resistance is evaluated as the local optimization criterion, and the genetic algorithm is used to solve the problem. The result finally proves that the improved model has excellent applicability by obtaining the high energy conversion of thermoelectric modules.
Semantic Situation Extraction from Satellite Image Based on Neural Networks

Satellite Image Situation Awareness (SISA) is a task that generates semantic situations from satellite images automatically. It requires not only the position and basic attributions (color, size, etc.) of targets but also the relationships (counting, relative position, existence, comparison, etc.) among them and realization of the situation analysis rules. We propose a novel framework which consists of the Background Process, Visual Question Answering (VQA), and Association Rules Set (ARS), in which, the Background Process deals with the situational map, the VQA and ARS identifies the relationships through answering a set of questions on SISA. To verify the performance of our method, we build the evaluation dataset based on CLEVR. Experiments demonstrate that our approach outperforms the traditional SISA systems on accuracy and automaticity. To the best of our knowledge, we are the first to solve SA problem using VQA method. The meaning of our research are: (1) We provide the possibility that SISA can be accomplished through VQA (without precise scene graph). (2) We broaden the application of VQA.
The sound of lambda

<blockquote>Abstract: Can lambda calculus be transformed to an artistic expression and if so, what could it sound like? This paper discusses the CodeKlavier’s Ckalcuλator: an arithmetic calculator for the piano following lambda calculus principles. The CodeKlavier is aspiring to become a performative programming language for the piano and the Ckalcuλator is the fourth sub-system in its development. As a well understood formalisation of computation, lambda calculus is utilised as the foundation of the Ckalcuλator in order to help us achieve a transition from a coding system to a computer programming language. Performing lambda calculus with the piano adds a conceptual, creative and performative dimension to otherwise simple arithmetic operations. This paper gives a brief introduction to the project, discusses the motivation, the system, and its artistic application before reflecting on the project’s future. </blockquote>
Optimizer Comparison with Dropout for Neural Sequence Labeling in Myanmar Stemmer

In Myanmar language, texts typically contain many different forms of a basic word. Morphological variants are generally the most common problem in mis-spellings, wrong translation and irrelevant retrieval query. The effectiveness of searching is obviously related to the stemming process. Moreover, there is no space separation in Myanmar language. Therefore, the tasks of segmenting the initial texts to words sequence is fully related to the stemming process. In present-day, deep learning approaches have become very good performance in variety of tasks, such as natural language processing, speech recognition, image recognizing. Among different types of neural networks, CNN networks have been most extensively used in text processing to extract morphological information (prefix and suffix of a word). This paper proposes the optimization process in Neural Architecture, how loss functions fit into the equation and finding the best optimizer. This paper also classified the efficiency of dropout under each optimizer to improve CNN-based model which jointly learns stemming and segmentation boundaries in parallel. It has obtained significant improvements on model performance after using dropout and the highest F-score is dropout probability 0.2. According to the experimental results, the SGD and Adam optimizer have a vast effect on the performance. And then, RMSProp optimizer performs better than other optimizers even though there is less dropout nodes.
The Kolmozero deposit: a unique Li source in the European Arctic of Russia

Abstract Tens to hundreds of pegmatite veins can accumulate within a pegmatite field. However, only a limited number of pegmatite veins can be practically valued. Hence, there is a problem of identifying rare metal pegmatites at the stage of geological prospecting and appraisal. Geochemical indicators are most impartial, reproducible and highly informative for these purposes. The Kolmozero lithium deposit is located in the Neoarchaean metagabbro-anorthozites of the Patchemvareksky massif in the junction zone of the two major regional structures of the Archaean age, i.e. the Murmansk block and the Kolmozero-Voronya Greenstone Belt of the Kola Peninsula. It is shown that pegmatites of the Kolmozero deposit with industrially valued rare metal mineralization (Be, Ta, Nb, Li) are poor in large-ion lithophile elements (LILE) (Sr and Ba) and in high charged high field strength elements (HFSE) (REE, Th, Zr). They also have low values of fractionation indexes – Mg/Li and Zr/Hf. The defined geochemical indicators have been applied to estimate feldspar pegmatites and muscovite-feldspar pegmatites that are spatially associated with rare metal pegmatites of the Kolmozero lithium deposit. The feldspar (beryllium-bearing) and muscovite-feldspar (beryllium-niobium-tantalum) pegmatites have rare metal mineralization and, like albite-spodumene pegmatites, are rich in Li, Nb, Ta, Be and depleted by Sr, Ba, Zr, Y, REE. Contents of ore elements (Li, Nb, Ta, Be) content increases from feldspar to muscovite-feldspar and albite-spodumen pegmatites. It is accompanied by a decrease in values of fractionation indexes of Zr/Hf and Mg/Li, as well as a decrease in contents of light, medium and heavy lantanoids Zr, Ba and Sr.
Image Edge Based Efficient Stereo Matching

In this paper, we present the image-edge based stereo matching method-an extension of the ELAS algorithm- which increases the robustness of stereo matching. The image region is divided into triangular regions by triangulation on support points. Based on image Canny edge information, our approach divides triangular regions into three groups according to whether piecewise disparity smooth is valid or not. The Bayesian method is used for piecewise disparity smooth areas, and the local stereo method is used for disparity discontinuity areas. For the disparity smooth areas, we further strengthen the influence of disparity prior by making use of color prior. Based on experimental results, our method is comparable in accuracy and efficiency.
A Modular Hybrid Exoskeletal-Soft Glove for High Degree of Freedom Monitoring Capability

In this paper, we present a hybrid exoskeletal-soft glove for the application of on-axis angle sensors that can be placed close to the center of rotation of the digit joints. 3D printed exoskeletal digit segments that run medially on most digits connect to low friction bearings. Exoskeletal segments and bearings provide rigid fixation points for a variety of traditional angle sensors, while a combination of textile and rigid structure fixate exoskeletal digits to the digits and hand. Exoskeletal digits are designed modularly so that only required digits are used and to reduce difficulty in donning and doffing. On-axis measurement may prove useful in control or assessment tasks in rehabilitation. The articulation of the digits while wearing the glove is demonstrated, albeit without sensors, showing little restriction at an early stage of the design process. Exoskeletal metacarpophalangeal joints of the 3rd and 4th digits require more work as the flexion/extension joint axis is inaccessible and moves when he digits are articulated. The proposed device must be customized for an individual and will facilitate an alternative approach to existing hand posture monitoring techniques.
Facial Representation for Automatic Facial Action Unit Analysis System

Facial Action Coding System (FACS) is the most influential sign judgment method for facial behavior, and it is a comprehensive and anatomical system which could encode various facial movements by the combination of basic AUs (Action Units). However, automatic facial AUs recognition remains challenging due to several open questions such as rigid and non-rigid facial motions, multiple AUs detection, intensity estimation and naturalistic context application. This paper introduces recent advances in automatic facial action unit recognition, focusing on the fundamental components of face detection and registration, facial action representation, feature selection and classification. We discussed the role of each component, main techniques with their characteristics, challenges and potential directions of facial action unit analysis.
Application of thermocouple sensors on hot stone for voltage generating in renewable energy

This study is conducted for the search of new energy to accommodate today’s renewable energy shortage in various aspects such as the high cost needed to make renewable energy plantations, requiring a large amount of space, revealing pollution in the early farms of renewable energy and renewable energy limited in each country itself. To solve this problem, a renewable source of renewable energy is reviewed based on hot stone and data collection to strengthen this new discovery. By placing the thermocouple in the rock, it will display the current temperature in the rock and, in practice, the temperature inside the rock is higher than its surrounding temperature. In this study the temperature heat in the rock will be converted into a voltage value and the results will be displayed on the LCD. To charge 12V battery, it will take 12 to 16 hours completely and also depends on sensor sensitivity used in the thermocouple and the weather at that time. With this study, it has proven that hot stone can be one of the more effective and efficient renewable new energy to reduce space, pollution and low cost to be implemented.
Towards Information Theoretic Measurement of Fidelity and Diversity in Handwriting Synthesis

One of the basic goals in handwriting synthesis is to produce samples with both sufficient fidelity (legibility) and diversity. It is not easy in general to automatically evaluate these two qualities, however. In this paper, we show how a relatively simple neural network-based generative model can be used to synthesize online handwritten digits that look quite natural. This autoregressive model contains a parameter which directly controls the fidelity and diversity of generated samples, and must be tuned in order to make a tradeoff between them. We show that optimizing this parameter on the basis of likelihood generally leads to poor results. Then, we propose another objective function for fidelity-diversity tradeoff which is derived from an information theoretic measure, and is approximated via sampling. The proposed approach has been experimented on English and Farsi digits, and as it was observed, the obtained handwriting samples are mostly legible and fairly diverse (representing multiple styles). Also, the plausibility of the proposed performance measure has been compared with log-likelihood and Maximum Mean Discrepancy.