Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalizability of Hand Kinematic Synergies derived using Independent Component Analysis

In this paper, hand synergies were derived using independent component analysis (ICA) and compared against synergies derived from our previous methods using principal component analysis (PCA). For ICA, we used two algorithms — Infomax and entropy bound minimization (EBM). For all the methods, the synergies were extracted from rapid hand grasps. The extracted synergies were then tested for generalizability in reconstructing natural hand grasps and American Sign Language (ASL) postures that were different from rapid grasps. The results indicate that the synergies derived from ICA were able to generalize only marginally better when compared to those from PCA. Among the two ICA methods, Infomax performed slightly better in yielding lower reconstruction error while EBM performed better in sparse selection of synergies. The implications and future scope were discussed.
A method for determination of damping for edgewise blade vibrations

Violent edgewise blade vibrations have in recent years been a large problem for some stall-regulated wind turbines. Owing to the complexity of the phenomenon, it has been difficult to predict the risk of these vibrations with aeroelastic load prediction tools. One problem is the choice of parameters in the aeroelastic model, e.g. structural damping and aerodynamic aerofoil characteristics. In many cases a high degree of uncertainty in the predicted response exists and the need for experimental verification methods is obvious. In this work a new method to identify the effective damping for the edgewise blade mode shape for wind turbines has been developed. The method consists of an exciter mechanism which makes it possible to excite the edgewise blade mode shapes from the wind turbine nacelle. Furthermore, the method consists of an analysis method which enables a straightforward determination of the damping. The analysis method is based on a local blade whirl description of the edgewise blade vibrations. The method is verified on a Bonus wind turbine, and for this specific turbine the effective damping for edgewise blade vibrations has been determined. The results support the further development of aeroelastic models and show potential for fine-tuning of parameters of importance for the edgewise blade vibration problem. Furthermore, the method can be used for experimental investigation of the risk of edgewise blade vibrations for a specific turbine. Copyright © 2000 John Wiley & Sons, Ltd.
The Influence of Overlap Degree Research on Nozzle Governing Characteristic

To solve the problems existing in the flow characteristics of steam turbine unit, the influence of valve overlap degree on nozzle governing steam turbine had been studied. The combined flow characteristics of given valve overlap degree were obtained for a 600MW steam turbine unit by the method of theoretical calculation combined with simulation test, and the influence of valve overlap degree on governing stage efficiency and steam chest pressure had been also analyzed. This paper discussed the selection of rational overlap degree and introduced a new method of building model for governing stage efficiency of steam turbine in constant pressure operation condition, which provided theoretical guidance for optimization research on nozzle governing steam turbine operation.
Integrating Ethics & Engineering: A Graduate Option in Systems Engineering, Ethics, and Technology Studies

This paper describes an engineering graduate option in Systems Engineering designed to overcome some of the effects of specialization and compartmentalization by building a link between technical and ethical training. Students in this option produce case studies that emphasize ethical issues in the design process. The goal of the program is to turn out ethical professionals who are able to reflect on the moral implications of technology. The proposed approach uses realistic or real‐hypothetical hybrid case studies as a type of vicarious mentoring, and, when supplemented with readings in ethical theory and codes, may serve as a starting point for a deeper understanding of behavioral dilemmas. The developers of this approach are a multi‐disciplinary team from the Engineering School and the Darden Graduate School of Business Administration at the University of Virginia. The paper describes how the graduate option is structured and provides data on student outcomes.
Grey Relational Analysis and Augmented Reality to Construct a Recommendation System on Cultural Creative Products

This paper tries to select attributes of creative products which take Augmented Reality (AR) technology as user interaction with the media in order to obtain valuable information of the user experience, then, those data sets are summarized and analyzed to build a recommendation system on cultural creative products. Firstly, the external form of cultural creative products can be divided into shape, material, color and texture, user according to their own preference to select the most suitable goods of their own cognitive. Then, Augmented Reality (AR) technology and image recognition with 3D model allows users to select their favorite style of the product through an interactive manner and ranking of a series of prototype design is obtained according to the Grey Relational Analysis. Finally, a product recommendation system let the user choose to verify the research results for the culture creative products.
Golden Eagle Abundance in Alaska: Migration Counts and Movement Data Generate a Conservative Population Estimate

Abstract. Estimating species density and abundance is challenging but important for establishing conservation and management strategies. Significant progress has been made toward estimating Golden Eagle (Aquila chrysaetos) abundance in the conterminous United States of America (USA) but much less is known about eagle abundance in Alaska. Here, we paired migration count and GPS-tracking data collected near Gunsight Mountain, Alaska, in a Bayesian framework to estimate the number of Golden Eagles in south-central Alaska. We estimated 1204 (95% credible interval: 866, 1526) potentially breeding (≥4 yr old) Golden Eagles annually moved through the Gunsight Mountain migration corridor and summered over an area of 150,325 km2 in south-central Alaska, equating to a density of 0.80 potentially breeding eagles/100 km2. By extrapolating across the species' nesting range in Alaska (1,180,489 km2) and incorporating published productivity and age-specific survival rates for eagles <4 yr old into our hierarchical model, we estimated 12,717 (95% credible interval: 9043, 16,349) Golden Eagles of all ages occur in Alaska, annually. We propose this as a conservative statewide population estimate because we used methods that likely underestimated population size. Even so, our estimate is three to five times larger than previous estimates and likely represents about one quarter of the USA's population.
Design Education Oriented To Nature

Nature is a starting point to analyse and to conceive solutions for complex systems, and a reservoir of inspiration to provide innovative answers to the product development as well as to the interaction between man and nature: this is the first key idea that the authors would like to transmit to the new generations of engineers and designers. Furthermore, it is also important to embed the development of technology within a sustainable development frame-work: from this point of view the authors are encouraging students to develop a particular case-study.
Enabling Passive Backscatter Tag Localization Without Active Receivers

Backscattering tags transmit passively without an on-board active radio transmitter. Almost all present-day backscatter systems, however, rely on active radio receivers. This presents a significant scalability, power and cost challenge for backscatter systems. To overcome this barrier, recent research has empowered these passive tags with the ability to reliably receive backscatter signals from other tags. This forms the building block of passive networks wherein tags talk to each other without an active radio on either the transmit or receive side. For wider functionality, accurate localization of such tags is critical. All known backscatter tag localization techniques rely on active receivers for measuring and characterizing the received signal. As a result, they cannot be directly applied to passive tag-to-tag networks. This paper overcomes the gap by developing a localization technique for such passive networks based on a novel method for phase-based ranging in passive receivers. This method allows pairs of passive tags to collaboratively determine the inter-tag channel phase while effectively minimizing the effects of multipath and noise in the surrounding environment. Building on this, we develop a localization technique that benefits from large link diversity uniquely available in a passive tag-to-tag network. We evaluate the performance of our techniques with extensive micro-benchmarking experiments in an indoor environment using fabricated prototypes of tag hardware. We show that our phase-based ranging performs similar to active receivers, providing median 1D ranging error <1 cm and median localization error also <1 cm. Benefiting from the large-scale link diversity our localization technique outperforms several state-of-the-art techniques that use active receivers.
Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals

With the advance in automatic speech recognition, voice user interface has gained popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online communication due to its non-contact. Additionally, various ambient noise impedes the public applications of voice user interfaces due to the requirement of audio-only speech recognition methods for a high signal-to-noise ratio. In this paper, we present Wavoice, the first noise-resistant multi-modal speech recognition system that fuses two distinct voice sensing modalities, i.e., millimeter-wave (mmWave) signals and audio signals from a microphone, together. One key contribution is that we model the inherent correlation between mmWave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Furthermore, we elaborate on two novel modules into the neural attention mechanism for multi-modal signals fusion, and result in accurate speech recognition. Extensive experiments verify Wavoice's effectiveness under various conditions with the character recognition error rate below 1% in a range of 7 meters. Wavoice outperforms existing audio-only speech recognition methods with lower character error rate and word error rate. The evaluation in complex scenes validates the robustness of Wavoice.
Are CNN based Malware Detection Models Robust?: Developing Superior Models using Adversarial Attack and Defense

The tremendous increase of malicious applications in the android ecosystem has prompted researchers to explore deep learning based malware detection models. However, research in other domains suggests that deep learning models are adversarially vulnerable, and thus we aim to investigate the robustness of deep learning based malware detection models. We first developed two image-based E-CNN malware detection models based on android permission and intent. We then acted as an adversary and designed the ECO-FGSM evasion attack against the above models, which achieved more than 50% fooling rate with limited perturbations. The evasion attack converts maximum malware samples into adversarial samples while minimizing the perturbations and maintaining the sample's syntactical, functional, and behavioral integrity. Later, we used adversarial retraining to counter the evasion attack and develop adversarially superior malware detection models, which should be an essential step before any real-world deployment.
Adapting a Floor Sensor for Autonomous Robots in a Smart Building

We demonstrate a real time recognition of autonomous robots using a floor sensor. In these years, many types of autonomous mobility such as delivery robots and guiding robots are introduced to building facilities. To provide smooth operation of robots in a building, accurate position detection of robots by infrastructural sensors is required. By privacy concerns, there are many situations that camera recognition is not appropriate to install. Therefore, we adapted a flooring pressure sensor system of Fichvita to take positions of robots and people anonymously. This system had a function to recognize human footsteps originally. To enable robot recognition extending the function, we collected trajectory data of robots by the system installed in our building. By improving the software, now it recognizes a guiding robot and a delivery robot in addition to human footsteps in real time.
”CodeInnova”: A Unified Framework for Teaching Programming and Computational Thinking In Primary Schools

Teaching programming and computational thinking (CT) in primary schools have become more common in the last decade. However, there has been little international consensus on what teaching the topics encompass. We present CodeInnova, a framework developed jointly between four partnering countries for teaching programming and CT in K–9. In this paper, we present the curriculum for teaching CT and the accompanying teaching materials developed in CodeInnova. We also discuss preliminary results of testing the developed resources in classrooms.
SpiderWeb: Enabling Through-Screen Visible Light Communication

We are now witnessing a trend of realizing full-screen on electronic devices such as smartphones to maximize their screen-to-body ratio for a better user experience. Thus the bezel/narrow-bezel on today's devices to host various line-of-sight sensors would disappear. This trend not only is forcing sensors like the front cameras to be placed under the screen of devices, but also will challenge the deployment of the emerging Visible Light Communication (VLC) technology, a paradigm for the next-generation wireless communication. In this work, we propose the concept of through-screen VLC with photosensors placed under Organic Light-Emitting Diode (OLED) screen. Though being transparent, an OLED screen greatly attenuates the intensity of passing-through light, degrading the efficiency of intensity-based VLC systems. In this paper, we instead exploit the color domain to build SpiderWeb, a through-screen VLC system. For the first time, we observe that an OLED screen introduces a color-pulling effect at photosensors, affecting the decoding of color-based VLC signals. Motivated by this observation and by the structure of spider's web, we design the SWebCSK Color-Shift Keying modulation scheme and a slope-based demodulation method, which can eliminate the color-pulling effect. We prototype SpiderWeb with off-the-shelf hardware and evaluate its performance thoroughly under various scenarios. The results show that compared to existing solutions, our solutions can reduce the bit error rate by two orders of magnitude and can achieve a 3.4x data rate.
Better Never Than Late: Timely Edge Video Analytics Over the Air

Edge video analytics based on deep learning has become an important building block for many modern intelligent applications such as mobile augmented reality and autonomous driving. Various mechanisms have been developed to handle dynamic wireless networks, compute resource availability, and achieve high analytics accuracy via filtering, DNN compression, pruning, and adaptation. So far, limited attention has been paid to timeliness---providing strict service-level objectives (SLO) for edge video analytics pipelines, which is essential for the usability of user-interactive and mission-critical intelligent applications. In this paper, we analyze the challenges in achieving SLO for edge video analytics and present a system design for timely edge video analytics over the air leveraging a simple yet effective idea---feedback control. Our preliminary evaluation based on a system prototype and real-world network traces shows the potential of our design. We also discuss the limitations, calling for future work.
Exploring Co-dependency of IoT Data Quality and Model Robustness in Precision Cattle Farming

Low-cost sensors are extensively used in numerous Internet of Things (IoT) applications to measure relevant physical processes. Today, processing context data is increasingly done by proprietary algorithms tuned to a specific use-case, e.g., a sensor measuring activity intensity of a cow. Readings from these sensors may be subject to data distribution shifts, which challenge robustness of models using these sensor readings. In this paper, we propose a new sensor data processing framework, which leverages a co-dependency between data quality and model robustness to detect performance issues of data-driven predictive models in the field. We show how distribution shifts in the input data impact the quality of the model, which relies on application-specific sensors, and present indicators capable of detecting such shifts in the wild. The proposed framework used in the context of precision cattle farming allows improving the quality of cow lameness predictive models on the field data by up to 62%.
Using data cards for teaching data based decision trees in middle school

ACM Reference Format: Susanne Podworny, Yannik Fleischer, Sven Hüsing, Rolf Biehler, Daniel Frischemeier, Lukas Höper, and Carsten Schulte. 2021. Using data cards for teaching data based decision trees in middle school. In 21st Koli Calling International Conference on Computing Education Research (Koli Calling ’21), November 18–21, 2021, Joensuu, Finland. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3488042.3489966
Three +1 Perspectives on Computational Thinking

Computational Thinking (CT) is a highly contentious subject with many diverging meanings and definitions. This study presents a preliminary literature review of 71 peer-reviewed articles on CT. The papers indicate the existence of five main aspects that have historically been used in association with its definition: Algorithm, Abstraction, Modelling, Simulation, and Implementation. Based on this preliminary literature study, semi-structured interviews with eight CT scholars are conducted, in order to evaluate these aspects and to identify qualitatively different perspectives on CT, which integrate the mentioned aspects in different ways. From the interviews, three different perspectives emerged, focusing on: Reasoning, Simplification, and Automation. Furthermore, the goal of having computationally educated citizens is extrapolated from the interviews, indicating an additional perspective (+1) titled Empowerment, which appears as embedded within all the previous three perspectives. This paper proposes to put these three (+1) perspectives in dialogue, in an effort to support researchers and practitioners working with CT across different fields.
Human-AI interaction

user interaction with AI systems does not necessarily follow this rigid interaction pattern. Inspired by Kristina Höök and Yang et al. [1,2], we define human-AI interaction as the completion of a user’s task with the help of AI support, which may manifest itself in non-intermittent scenarios. By overlooking these other interaction paradigms, we neglect the opportunity to define and support alternative human-AI scenarios. In this article, we present and outline three types of human-AI interaction paradigms, which we refer to as intermittent, continuous, and proactive, highlighting a diverse set With the rise in artificial intelligence (AI)–driven interactive systems, both academics and practitioners within human-computer interaction (HCI) have a growing focus on human-AI interaction. This has resulted in, for example, system-design guidelines and reflections on the differences and challenges when designing for AI-driven interaction as opposed to moretraditional applications [1]. We argue that the current work on human-AI interaction is defined primarily by a focus on what we refer to as intermittent interaction scenarios, in which there is a clear line between the human initiator of an interaction and an almost W Insights → We have identified three types of human-AI interaction paradigms, which we refer to as intermittent, continuous, and proactive. → HCI researchers and practitioners need to systematically explore novel techniques to support end users in non-intermittent AI interaction scenarios. Human-AI Interaction: Intermittent, Continuous, and Proactive Niels van Berkel, Mikael B. Skov, and Jesper Kjeldskov, Aalborg University
A DISPATCHING ALGORITHM FOR MULTIPLE-LOAD AGVS USING A FUZZY DECISION-MAKING METHOD IN A JOB SHOP ENVIRONMENT

Abstract This paper proposes a dispatching algorithm for automated guided vehicles (AGV) with multiple-load capacity operating in a job shop environment. At each dispatching point, the algorithm prioritizes all move requests based on the fuzzy urgency which is determined by the fuzzy multi-criteria decision-making method. Then, the algorithm selects a set of requests under the restriction of target dispatching time interval. Through simulation, the performance of the proposed algorithm is compared with several dispatching algorithms in terms of system throughput in a hypothetical job shop environment.
Vortex dynamics and hydrodynamic performance enhancement mechanism in batoid fish oscillatory swimming

Abstract The effects of chordwise deformation and the half-amplitude asymmetry on the hydrodynamic performance and vortex dynamics of batoid fish have been numerically investigated, in which the two parameters were represented by the wavenumber ($W$) and the ratio of the half-amplitude above the longitudinal axis to that below ($HAR$). Fin kinematics were prescribed based on biological data. Simulations were conducted using the immersed boundary method. It was found that moderate chordwise deformation enhances the thrust, saves the power and increases the efficiency. A large $HAR$ can also increase thrust performance. By using the derivative-moment transformation theory at several subdomains to capture the local vortical structures and a force decomposition, it was shown that, at high Strouhal numbers ($St$), the tip vortex is the main source of thrust, whereas the leading-edge vortex (LEV) and trailing-edge vortex weaken the thrust generation. However, at lower $St$, the LEV would enhance the thrust. The least deformation ($W=0$) leads to the largest effective angle of attack, and thus the strongest vortices. However, moderate deformation ($W=0.4$) has an optimal balance between the performance enhancement and the opposite effect of different local structures. The performance enhancement of $HAR$ was also due to the increase of the vortical contributions. This work provides a new insight into the role of vortices and the force enhancement mechanism in aquatic swimming.