Reviewerly/exHarmony huggingface dataset:
E-Textile Design through the Lens of Affordance

E-textiles design integrates materials not usually native to textile design e.g. conductive yarns and optical fibers. E-textiles themselves are soft systems; a computational composite made up of fluid and rigid materials, each component essential to the functionality of the e-textile. The making process represents another complex system. The process of integrating electronics and textiles requires that the designer negotiates and unifies the properties of tools, materials, and manual and machine enabled processes. E-textile designers leverage the affordances of unconventional materials to enable new functional and aesthetic potential while working within the constraints of different aspects of the e-textiles system; the tools, materials, and the requirements of electronics. This paper presents a discussion on affordance in e-textile design, drawing from literature detailing e-textile design processes and the author's practice. Affordance offers a new perspective in understanding the relationship between aspects of the e-textile design process. This paper focuses on the affordances of textile tools whilst also considering new affordances provided by e-textiles materials, and affordances and constraints in material manipulation. In the analysis of textiles tools, four key affordances that impact on e-textiles design were identified: design complexity, manual intervention, automation and tactile feedback. These exist to a greater or lesser degree, depending on the tool. Manual intervention, tactile feedback and design complexity are particularly beneficial for novel e-textile design, while automation can be problematic in e-textiles development since it can prevent the designer from enacting new techniques. Although the beneficial affordances are seldomly found together, there are examples of textiles tools that possess these affordances. Nevertheless, there remains a need for more tools that possess these affordances to allow for novel e-textiles development in the future.
Aggressive Language Detection Using VGCN-BERT for Spanish Texts

The increasing influence from users in social media has made that aggressive content disseminates over the internet. To tackle this problem, recent advances in Aggressive Language Detection have demonstrated a good performance of Deep Learning techniques. Recently Transformer based architectures such as Bidirectional Encoder Representations from Transformer (BERT) outperformed previous aggressive text detection baselines. However, most of the Transformers-based approaches are unable to properly capture global information such as language vocabulary. Thus, in this work, we focus on aggressive content detection using the combination of Vocabulary Graph Convolutional Network (VGCN) to capture global information and BERT to model local information. This combined approach called VGCN-BERT allows us to improve the feature level representation in Spanish aggressive language detection. Our experiments were performed on a benchmark called MEX-A3T aggressiveness dataset which is composed of aggressive and non-aggressive Tweets written in the Mexican Spanish variant. We report 86.46% in terms of F1-score using this VGCN-BERT approach which allows us to obtain comparable results with the current state-of-the-art, ensemble BERT, so as to detect aggressive content regarding the track MEX-A3T 2020.
Development of MIMO technique for 3D Auralization

The Sipario project has been created with the aim to virtually reconstruct 3D audio-video of artistical performances. To preserve the memory of the Opera and live venues, several historical Italian theatres have been assessed under an acoustic perspective based on Multiple Input Multiple Output (MIMO) technique. A prototype of loudspeaker and a multichannel microphone spherical array have been employed as the main instrumentation to determine the acoustic characteristics of these cultural heritage. The prototype of loudspeaker has been used by feeding the Exponential Sine Sweep (ESS) signal to all the 32 transducers while the microphone characterized by a 32channel spherical probe recorded the room impulse response (RIR). The utilization of this new generation of technology allows the assessment of a dynamic directivity pattern of any type of sound source, particularly referred to actors and singers moving onto the stage of the performing arts places. This paper introduces some mathematical framework for extracting any directivity of virtual microphones from real microphone array and of virtual sources from real loudspeaker.
A novel SMC-PHD filter for multi-target tracking without clustering

The probability hypothesis density (PHD) filter has been regarded as a practicable multi-target tracking algorithm which can alleviate the computational difficulty of the multi-target Bayes filter due to the multiple high-dimensional space integrals. As one of the major implementations of the PHD filter, sequential Monte Carlo PHD (SMC-PHD) is suitable for highly nonlinear systems. However, in scenarios with missing detections, the false-estimation problem occurs which leads to estimation performance degradation. To reduce the negative effects of missing detections, this paper develops a compensatory measurements generating mechanism and presents a novel measurement compensation based SMC-PHD filter, which can avoid the unreliable clustering. Comparative results verify the proposed filter, indicating good application prospects.
Challenges and Workarounds of Conducting Augmented Reality Usability Tests Remotely a Case Study

It has been demonstrated that virtual prototypes can be used productively in usability testing. Virtual prototypes, Augmented Reality (AR) in particular, has received some focus as it combines both the real and virtual worlds and because platforms capable of supporting AR have become more ubiquitous.This paper aims to explore the challenges of using AR to conduct usability testing in a remote delivery format. It presents a use case examination of the use of an AR tool to gather usability assessment data of a design prototype in a remote (distanced) format. An interview was conducted with those involved in conducting the test to understand issues related to the utilization of hardware provided by the usability test taker; the use of screen sharing to communicate instructions; challenges to efficiently gathering accurate observational data; and issues related to variability in hardware setup, consistency of the AR experience, and other environmental factors.The results suggest that an approach for utilizing AR for remote usability testing was feasible, but many challenges and frustrations exist. Some of these related to communication with the participants, lack of motivation, difficulties extrapolating the prototype to a real setting, and technical issues. Usability test participants were often more interested in simply completing assigned tasks rather than exploring the tool or product in more detail that often happens in person. Other observations included some limitations in AR technology itself such as level of immersion (ability to accurately visualize the product). Challenges due to internet connectivity between the facilitator and tester at time affected the ability to effectively direct the testing session. Future work should investigate new tools and approaches for mitigating current challenges and experimentally compare in person versus remote testing outcomes.
Ideology of monolingualism: How ignoring bilingualism makes society less inclusive

The computing community is struggling to diversify its workforce and academic programs. We argue that more attention to bilingualism is needed to make Hispanics/Latinx feel welcome in the field. In this panel we make visible how the ideology of monolingualism influences the design and implementation of user interfaces in ways that negatively affect usability for bilingual users. We end by presenting some of the impact that a lack of support of bilingualism can have in society and discussing issues of work safety and workplace communication.
New Approach for Analyzing Marine Ecosystem Structure Using Bayesian Networks

Aquatic ecosystems of the Black Sea are complex multiparametric systems with a hierarchical structure. Thus, the main goal of our research was to investigate possibilities of using Bayesian networks to study the structure fo the natural systems in the Black Sea. We used CMEMS Black Sea environmental dataset, which consists of 7 different variables, that, in our opinion, can describe structural relations in the Black Sea ecosystem -- sea surface temperature and salinity, concentrations of nitrates and phosphates, amount of chlorophyll-a and net primary production and also dissolved oxygen concentration. We think, that these variables can generally define interactions in water environment of the Black Sea, organisms, that live there and human activity. As a modelling result, we receive a structure of environmental variables interactions. At the top of this structure is a dissolved oxygen, as a final result of the ecosystem functioning. Further, we think it's more appropriate to use Dynamic Bayesian networks for investigation of spatio-temporal changes to distinguish main drivers of changes and provide more balanced management of natural territories.
Research on Video Classification Based on Deep Learning

At present, the video classification is a challenging computer vision problem, and there are few researches in this field. For ordinary image video which contains many uncertain factors such as complex background and illumination changes. However, human skeleton image video has the advantages of simple content and less redundant information. But for such a simple video, classification is also difficult. With the development of deep learning, this paper combines the method of deep learning to detect and classify human skeleton behavior. The purpose of this study is to help researchers to label large-scale bone behavior data, reduce the pressure of follow-up research, and it can expand to the field of short video classification in the future. After video preprocessing operations such as frame and image down-sampling, the processed data is input into the model for convolution. The next is to extract frame sequence features through LSTM layer. Finally, the full connection is performed to output the probability of classification labels. This method has high accuracy in classifying human skeleton behavior.
The Construction and Application of Virtual Reality Technology in the New Ecotourism Model

The use of virtual reality technology can provide tourists with a variety of services, including visually intuitive eco-tourism attractions information release and eco-tourism route planning. The eco-tourism information service using virtual reality technology can dynamically present the eco-tourism landscape in front of people, allowing participants to have personal experiences and experiences as if they were swimming in a real tourist area. This allows travel enthusiasts to explore the beautiful landscapes and cultural landscapes without leaving home. It also gives tourists the enjoyment of beauty, playing the role of pre-publicity, expanding influence and attracting tourists. Therefore, this research mainly discusses the application of virtual reality technology in the Lugu Lake eco-tourism exhibition, and realizes the display system of the three-dimensional scenes of the Lugu Lake eco-tourism key attractions. In this research, we first conduct field inspections, collect relevant materials, and then perform 3DSMax modeling. Use PS to process the textures, bake, and then perform VRP post-production to achieve real effects. Communicate with Unity3D through the Web page to realize an interactive virtual platform. This system is a virtual system that can make people feel the authenticity of the scene, so that people can see the entire environmental scenery of the eco-tourism scenery belt without leaving home. This will bring people a lot of convenience, and at the same time can help Lugu Lake eco-tourism Scenic Area to promote.
Hybrid Planning with Receding Horizon: A Case for Meta-self-awareness

The trade-off between the quality and timeliness of adaptation is a multi-faceted challenge in engineering self-adaptive systems. Obtaining adaptation plans that fulfill system objectives with high utility and in a timely manner is the holy grail, however, as recent research revealed, it is not trivial. Hybrid planning is concerned with resolving the time and quality tradeoff via dynamically combining multiple planners that individually aim to perform either timely or with high quality. The choice of the most fitting planner is steered based on assessments of runtime information. A hybrid planner for a self-adaptive system requires (i) a decision-making mechanism that utilizes (ii) system-level as well as (iii) feedback control-level information at runtime. In this paper, we present Hypezon, a hybrid planner for self-adaptive systems. Inspired by model predictive control, Hypezon leverages receding horizon control to utilize runtime information during its decision-making. Moreover, we propose to engineer Hypezon for self-adaptive systems via two alternative designs that conform to meta-self-aware architectures. Meta-self-awareness allows for obtaining knowledge and reasoning about own awareness via adding a higher-level reasoning entity. Hypezon aims to address the problem of hybrid planning by considering it as a case for meta-self-awareness.
Research on the Overall Architecture Design of Project Management Information System Based on SOA

Engineering project management system is an indispensable part of enterprises, and it is a key issue for the survival and development of construction enterprises. In this paper, the overall structure of the system is analyzed and designed in detail on the basis of following SOA architecture and its layered thought, and the design of each level is analyzed and explained in detail. Different from traditional management information systems, parallel management information systems interconnect virtual systems with real information systems, and integrate social factors such as human behavior and management system into overall consideration. The system has the characteristics of openness and flexible reconfiguration. To ensure the effective implementation of project construction, in order to comprehensively improve the project management ability.
Industry-Academic-Education Integration: Innovation and Practice of Training Mode of Applied Statistics Talents in Big Data Era —Taking Zhejiang University of Science and Technology as an Example

In the era of big data, new requirements are put forward for Applied Statistical Talents.Industry-academic-education integration is an effective way to cultivate applied talents.This topic aims at the existing problems, combined with the practice of Zhejiang University of Science and Technology, carried out the innovation and practice of applied statistics talent training mode.
Example-guided 3D Human Face Reconstruction from Sparse Landmarks

This paper presents an example-guided facial reconstruction method for creating a 3D refined human face model from sparse landmarks, with a given example dataset. It is challenging to rapidly and accurately generate a high-precision 3D face model from raw scan data with a simple setup and processing. The main roadblock is that existing 3D face databases are far from adequate to describe the full variability of faces. To address these problems, we analyse the characteristics of examples from a relatively small sample set for more reliable reconstruction knowledge, as well as a new landmark marking method to simplify the description of human face shape. Principal component analysis is used to extract the feature patterns from samples and simplify data representation. Then the 3D face model and the landmarks are correlated via a mapping matrix. An effective mapping algorithm is devised to learn the transformation relation from landmarks to 3D face shapes. Compared with existing methods, the proposed method can generate a high-precision 3D face model from sparse landmarks more accurately. The application and extensive experimental evaluations on the Chinese craniofacial database and FaceWarehouse database show that our method can achieve high accuracy, effectiveness and robustness in 3D face reconstruction.
Development of two-phase logic-oriented fuzzy AND/OR network

The architecture of AND/OR fuzzy neural networks exhibits outstanding learning abilities and significant interpretation capabilities. However, AND/OR networks suffer from structure-related problems namely low efficiency and slow convergence of learning due to several reasons such as high dimensionality and gradient-based learning algorithms which lead to a visible computing overhead. In this paper, we present a two-phase fuzzy logic-oriented network design that is composed of AND/OR neurons. This design takes advantages of Randomized Neural Network (RNN) to achieve higher convergence while exhibiting good nonlinear approximation capabilities. A gradient–based learning algorithm is implemented in the second phase of the design to further reduce values of performance index. The quality of the proposed design and resulting architecture is quantified through the use of numeric data along with fuzzy sets (information granules). Experimental results meet the research’s objectives and the proposed design methodology opens up new future directions for proceeding with more improvements.
Knowledge Graph-Based Approaches for Related Entities Recommendation

Major Web search engines have recently enriched regular Web search results by presenting related entity recommendations for a user’s query. In actual Web search engines, entity recommendation try to improve users’ experience by assisting them in finding related entities of an initial interest for further exploration. This concept has become an indispensable feature today. With the advent of knowledge bases it was very intuitive to use them for getting the related entities. The principle is to obtain the explicitly linked entities to a given entity in knowledge bases. Many studies and works had been done to present this new concept. They propose to extract named entities from the queries and linking them to knowledge bases and then ranking the related entity suggestions. These approaches have been shown to increase the users’ satisfaction. We collect recently published papers in this field and summarize them from different perspectives. To the best of our knowledge, this is the first review on related entities recommender systems using knowledge graphs.
Duality for Continuous Graphical Models

The dual normal factor graph and the factor graph duality theorem have been considered for discrete graphical models. In this paper, we show an application of the factor graph duality theorem to continuous graphical models. Specifically, we propose a method to solve exactly the Gaussian graphical models defined on the ladder graph if certain conditions on the local covariance matrices are satisfied. Unlike the conventional approaches, the efficiency of the method depends on the position of the zeros in the local covariance matrices. The method and details of the dualization are illustrated on two toy examples.
Towards a System-of-Systems Architecture Definition enabling Cross-Domain Embedded Vehicle Engineering

Engineering of vehicular embedded systems is a difficult task, as the ongoing integration of Cyber-physical Systems (CPS) or automation potentials during vehicle development leads to increasing complexity. In particular, to develop current or future Electric Vehicles (EV) is challenging due to different domains to consider while engineering the sub-components of the vehicle itself. Thus, in order to enable a mutual development of vehicular embedded systems on multiple abstraction levels, the Software Platform Embedded Systems (SPES) has been introduced. To cope with the complexity, this framework introduces viewpoints and hierarchy layers in shape of a matrix. However, while additionally multiple domains have to be considered when developing an EV, the SPES methodology is also missing specifications, impeding its application in actual industrial scenarios. To deal with both of the mentioned issues, this paper introduces an approach for Model-based Systems Engineering of electric vehicle systems based on SPES. By doing so, this framework is further refined by an architecture definition based on the ISO 42010 and a corresponding development process. By utilizing the EV use case, the outcome is thereby validated towards its industrial feasibility, which will enhance the applicability of SPES on the one hand and contribute to the Automotive area by dealing with the increasing complexity while engineering vehicular embedded systems on the other hand.
Illumination-aware Digital Image Compositing for Full-length Human Figures

In this paper, a novel compositor to realize illumination-aware digital image compositing is proposed. Digital image compositing is a technique to re-synthesize a single image from multiple visual elements. Assembling separated visual elements leads to highly efficient content creation at risk of losing visual coherence. Visual coherence has two types: positional and illumination. Compositing while maintaining illumination coherence is considered a challenging task, because light transport information is needed to reproduce global illumination effects—color bleeding, soft shadows, caustics, and so on. The pipeline of the proposed compositor incorporates shape reconstruction, as well as global illumination simulation and relighting; thus, the compositor can reproduce background-to-foreground color bleeding and soft shadows. It is empirically evaluated that the proposed compositor compares favorably with rendering both in terms of quality and speed. Because it is in principle impossible to render the foreground and background elements together, the proposed compositor is inevitably selected to generate images with the two types of visual coherence. This leads to efficient construction of photorealistic cyberworlds.
Autonomous buildings

Modern buildings aim to provide safe, comfortable, and energy-efficient living and working spaces which can adapt to the changing needs of the users and the dynamic nature of the building environment. But current approaches in design, engineering, operation, and maintenance of buildings is based on a rather static scenario of usage and thus rely on manual engineering to deal with changes. My proposed research seeks to bring together the formal semantic descriptions of requirements, and features and processes of a building and use them to create and dynamically manage an environment of autonomous multi-agent systems (MAS). The novelty of this research lies in using such semantic descriptions to drive the creation of agent organizations and their operational policies, and further, the derivation of agents' behaviour therefrom.