Miracle-dz/UniFAR-FTUs huggingface dataset:
The Design and Observed Effects of Robot-performed Manual Gestures: A Systematic Review

Communication using manual (hand) gestures is considered a defining property of social robots, and their physical embodiment and presence, therefore, we see a need for a comprehensive overview of the state-of-the-art in social robots that use gestures. This systematic literature review aims to address this need by (1) describing the gesture production process of a social robot, including the design and planning steps, and (2) providing a survey of the effects of robot-performed gestures on human-robot interactions in a multitude of domains. We identify patterns and themes from the existing body of literature, resulting in nine outstanding questions for research on robot-performed gestures regarding: developments in sensor technology and AI, structuring the gesture design and evaluation process, the relationship between physical appearance and gestures, the effects of planning on the overall interaction, standardizing measurements of gesture “quality,” individual differences, gesture mirroring, whether human-likeness is desirable, and universal accessibility of robots. We also reflect on current methodological practices in studies of robot-performed gestures and suggest improvements regarding replicability, external validity, measurement instruments used, and connections with other disciplines. These outstanding questions and methodological suggestions can guide future work in this field of research.
Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity

The outstanding performance of deep neural networks has promoted deep learning applications in a broad set of domains. However, the potential risks caused by adversarial samples have hindered the large-scale deployment of deep learning. In these scenarios, adversarial perturbations, imperceptible to human eyes, significantly decrease the model’s final performance. Many papers have been published on adversarial attacks and their countermeasures in the realm of deep learning. Most focus on evasion attacks, where the adversarial examples are found at test time, as opposed to poisoning attacks where poisoned data is inserted into the training data. Further, it is difficult to evaluate the real threat of adversarial attacks or the robustness of a deep learning model, as there are no standard evaluation methods. Hence, with this article, we review the literature to date. Additionally, we attempt to offer the first analysis framework for a systematic understanding of adversarial attacks. The framework is built from the perspective of cybersecurity to provide a lifecycle for adversarial attacks and defenses.
Effects of vitamin D supplementation on glycemic control of children and adolescents with type 1 diabetes mellitus: a systematic review

Abstract Objectives To evaluate the effect of vitamin D supplementation on glycemic control in children and adolescents with T1DM. Content A systematic search was conducted of the Medline/PubMed, Web of Science, Embase, BVS/Lilacs, Cochrane Library, Scopus, Cinahl, Food Science, and FSTA databases. Two reviewers independently extracted article data and assessed quality. Summary A total of 1,613 eligible articles were retrieved, ten of which met the selection criteria: eight clinical trials, one retrospective cohort study, and one cross-sectional study. Regarding the cutoff points used to classify vitamin D status, most of the studies set deficiency at 25-hydroxyvitamin D <20 ng/mL, sufficiency at ≥30 ng/mL, and insufficiency as the interval between these values. Regarding intervention strategies, most used cholecalciferol for supplementation, but there was great variation in the dose and supplementation time. When evaluating the effect of vitamin D supplementation on HbA1c, a significant improvement in glycemic control was observed in 50% of the studies. However, only one of these studies was classified as being of positive methodological quality, with three having their quality classified as neutral and one as negative. Outlook There is yet no consistent evidence on the effect of vitamin D supplementation on glycemic control as an adjuvant in the treatment of children and adolescents with T1DM.
Universal Dependencies and Author Attribution of Short Texts with Syntax Alone

Improving methods of stylometrics and classification so that they give good results with small texts is the focus of much research in the digital humanities and in the NLP community more generally. Recent work [Gorman 2020] has suggested that an approach using combinations of shallow and deep morpho-syntactic information can be quite successful. But because the data in that study were taken from hand annotated dependency treebanks, the wider applicability of such an approach remains in question. The present paper seeks to answer this question by using machine-generated morphological and syntactic annotations as the basis for a closed-set classification experiment. Texts were parsed according to the Universal Dependency schema using the “udpipe” package for R. Experiments were carried out on data from several languages covering a range of morphological complexity. To limit confounders, consideration of vocabulary was excluded. Results were quite promising, and, not surprisingly, a more complex morphology correlates with better accuracy (e.g., 100-token texts in Polish: 88% correct; 100-token texts in English: 74%). The method presented here has particular advantages for stylometrics as practiced in literary analysis and other fields in the humanities. The Universal Dependency annotation categories are generally similar to those used in traditional grammars. Thus, the variables which serve to distinguish the style of a given author are relatively easier to interpret and understand than, for example, are character n-grams or function words. This fact, combined with the availability of easy-to-use dependency parsers, opens up the study of a syntax-centered stylometrics to persons with a wide range of expertise. Even students at the early stages of their studies can identify and investigate the morpho-syntactic “signature” of a particular author. Therefore, the characterization of texts based on computational annotation of this type deserves a place in classification studies because of its combination of good results and good interpretability.
Text Data Vectorization Model of Ukrainian-Language Internet Communication Content

This paper proposes a model for analyzing the content of the modern Ukrainian everyday language. The model is built by combining the known and significant, for meaning transfer, frequency dictionaries, which cumulatively cover spheres of activities and types of content. Studies established that the best separability, when classifying texts using the proposed model, is observed at a word vector length of 1500 units; it determines the model's optimal dimension. According to the results of the test classification of more than 400 texts, the Text Rank keyword search method has been established as the most suitable for work with the proposed model of the modern Ukrainian everyday language. To compare the effectiveness of different keyword search methods, it was used the method of visual analytics MDS, which is considered an effective and sufficient tool for visual verification of the results of classification of digital texts into different categories, including both thematic classes categories of emotionally charged texts. The conducted research provides opportunities of using the created model of modern Ukrainian everyday language to solve the text analysis issues and their classification by various grounds. It is essential to prevent suicidal tendencies, detect bullying on social networks, determine the negative emotional charged texts, and warn users about potentially harmful content.
Rediscussing the Political Struggle in the Light of Reform in Late 11th Century China under the View of Digital Humanities

In late 11th century, a reform carried out by Wang Anshi (1021–1086) brought about controversies and initiated a series of political struggles between factionalized reformers and anti-reformers. The origin and nature of these factionalized struggles have been discussed for a long time among scholars. In this paper, we discuss the issue based on the literary and political relationships among people in the era of the reform. First, two matrices are respectively constructed of the literary and political relations among these people based on the data collected from CBDB (China Biographical Database). Then a Poission-Gamma factorization model is adopted to obtain the key factors of the matrices, and the Louvain Modularity algorithm is used for community detection. The results show that people engaging in similar literary pursuits were more likely to share political interests and people belonging to the same literary groups were more likely to join in the same political groups, suggesting. Ensuing discussions illustrate that people’s differing academic views indeed played a shaping role in the formation and exacerbation of factionalized struggle, for which the mechanism unfolded herein of “literati politics” was highly responsible.
Characterizing the flrst Alexander Z(Z)-modules of surface-links and of virtual links

The casing of a multi-function key holder has a front casing portion which includes parallel top and bottom parts that extend forwardly and that are spaced apart vertically to define a lens receiving space therebetween, and a rear casing portion which extends rearwardly from the front casing portion and which confines an air chamber. The rear casing portion is formed with an air inlet adapted to permit blowing of air into the air chamber, and an air outlet adapted to permit releasing of the air that was blown into the air chamber, thereby permitting generation of a whistling sound output. A planar magnifying lens piece is disposed in the lens receiving space, and has an edge section mounted pivotally on the front casing portion. The lens piece is movable between a concealed position, where the lens piece is confined between the top and bottom parts of the front casing portion, and an extended position, where the lens piece is exposed from the top and bottom parts of the front casing portion. A key chain has a key holding end adapted for holding a bunch of keys thereon, and a mounting end opposite to the key holding end and mounted on the casing.
Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings.
Undergraduate Nursing Students' Research Activities and Utilization: A Turkish Sample

DOI: 10.5294/aqui.2014. 14.2. 11 Objective: The aim of this study was determine undergraduate nursing students’ research activities and utilization. Method: A descriptive design was used in the study. This study conducted with undergraduate nursing students at the two universities in Turkey during the spring term of 2012. The population consisted of 216 participants. The data were collected through a questionnaire developed by the author based on literature. The Percentage, Chi-square and Fisher's exact tests were used to evaluate the collected data. Results: Approximately half of the students (49,5%) knew how to reach scientific publications but none of the students followed or read scientific publications. Only a few students (4,2%) had participated in a scientific research activity. It was found that 87,0% of the students did not utilize the research findings in clinical practice. Of 216 students, 34,4% did not know how to utilize the research findings. Conclusions: The majority of the students did not utilize the research findings in clinical practice. Insufficient knowledge about research utilization was the most prominent reason for not utilizing research findings by students. It is suggested that students should be encouraged and supported to utilize research findings and conduct research. DOI: 10.5294/aqui.2014. 14.2. 11
Indigenous traditional games - planning resource

The activities outlined in this resource are a contribution towards the implementation of Indigenous Traditional Games across the education curriculum from Kindergarten level to Year 12 (K-12). This booklet, Indigenous Traditional Games – Planning Resource, is designed to provide information relevant to planning activities based on the extensive range of games outlined in the book, Yulunga: Indigenous Traditional Games. The Yulunga: Indigenous Traditional Games package was developed for the Australian Sports Commission (ASC) in 2009 by Ken Edwards with assistance from Indigenous co-researcher Troy Meston. The Yulunga program was designed as part of a consultancy to the ASC and has been available at no cost. Due to the ongoing interest and use of this program and the inclusion of Indigenous Games in the Australian Curriculum for HPE this resource, based on many requests, has been developed.
Two decades of digital photogrammetry: Revisiting Chandler’s 1999 paper on “Effective application of automated digital photogrammetry for geomorphological research” – a synthesis

Digital photogrammetry has experienced rapid development regarding the technology involved and its ease of use over the past two decades. We revisit the work of Jim Chandler who in 1999 published a technical communication seeking to familiarise novice users of photogrammetric methods with important theoretical concepts and practical considerations. In doing so, we assess considerations such as camera calibration and the need for photo-control and check points, as they apply to modern software and workflows, in particular for structure-from-motion (SfM) photogrammetry. We also highlight the implications of lightweight drones being the new platform of choice for many photogrammetry-based studies in the geosciences. Finally, we present three examples based on our own work, showing the opportunities that SfM photogrammetry offers at different scales and systems: at the micro-scale for monitoring geomorphological change, and at the meso-scale for hydrological modelling and the reconstruction of vegetation canopies. Our examples showcase developments and applications of photogrammetry which go beyond what was considered feasible 20 years ago and indicate future directions that applications may take. Nevertheless, we demonstrate that, in-line with Chandler’s recommendations, the pre-calibration of consumer-grade cameras, instead of relying entirely on self-calibration by software, can yield palpable benefits in micro-scale applications and that measurements of sufficient control points are still central to generating reproducible, high-accuracy products. With the unprecedented ease of use and wide areas of application, scientists applying photogrammetric methods would do well to remember basic considerations and seek methods for the validation of generated products.
On the difficulty of learning chaotic dynamics with RNNs

Recurrent neural networks (RNNs) are wide-spread machine learning tools for modeling sequential and time series data. They are notoriously hard to train because their loss gradients backpropagated in time tend to saturate or diverge during training. This is known as the exploding and vanishing gradient problem. Previous solutions to this issue either built on rather complicated, purpose-engineered architectures with gated memory buffers, or - more recently - imposed constraints that ensure convergence to a fixed point or restrict (the eigenspectrum of) the recurrence matrix. Such constraints, however, convey severe limitations on the expressivity of the RNN. Essential intrinsic dynamics such as multistability or chaos are disabled. This is inherently at disaccord with the chaotic nature of many, if not most, time series encountered in nature and society. It is particularly problematic in scientific applications where one aims to reconstruct the underlying dynamical system. Here we offer a comprehensive theoretical treatment of this problem by relating the loss gradients during RNN training to the Lyapunov spectrum of RNN-generated orbits. We mathematically prove that RNNs producing stable equilibrium or cyclic behavior have bounded gradients, whereas the gradients of RNNs with chaotic dynamics always diverge. Based on these analyses and insights we suggest ways of how to optimize the training process on chaotic data according to the system's Lyapunov spectrum, regardless of the employed RNN architecture.
Optimizing Out-of-Plane Stiffness for Soft Grippers

In this letter, we presented a data-driven framework to optimize the out-of-plane stiffness for soft grippers to achieve mechanical properties as hard-to-twist and easy-to-bend. The effectiveness of this method is demonstrated in the design of a soft pneumatic bending actuator (SPBA). First, a new objective function is defined to quantitatively evaluate the out-of-plane stiffness as well as the bending performance. Then, sensitivity analysis is conducted on the parametric model of an SPBA design to determine the optimized design parameters with the help of Finite Element Analysis (FEA). To enable the computation of numerical optimization, a data-driven approach is employed to learn a cost function that directly represents the out-of-plane stiffness as a differentiable function of the design variables. A gradient-based method is used to maximize the out-of-plane stiffness of the SPBA while ensuring specific bending performance. The effectiveness of our method has been demonstrated in physical experiments taken on 3D-printed grippers.
Adversarial Contrastive Learning via Asymmetric InfoNCE

Contrastive learning (CL) has recently been applied to adversarial learning tasks. Such practice considers adversarial samples as additional positive views of an instance, and by maximizing their agreements with each other, yields better adversarial robustness. However, this mechanism can be potentially flawed, since adversarial perturbations may cause instance-level identity confusion, which can impede CL performance by pulling together different instances with separate identities. To address this issue, we propose to treat adversarial samples unequally when contrasted, with an asymmetric InfoNCE objective ($A-InfoNCE$) that allows discriminating considerations of adversarial samples. Specifically, adversaries are viewed as inferior positives that induce weaker learning signals, or as hard negatives exhibiting higher contrast to other negative samples. In the asymmetric fashion, the adverse impacts of conflicting objectives between CL and adversarial learning can be effectively mitigated. Experiments show that our approach consistently outperforms existing Adversarial CL methods across different finetuning schemes without additional computational cost. The proposed A-InfoNCE is also a generic form that can be readily extended to other CL methods. Code is available at https://github.com/yqy2001/A-InfoNCE.
Editing Out-of-domain GAN Inversion via Differential Activations

Despite the demonstrated editing capacity in the latent space of a pretrained GAN model, inverting real-world images is stuck in a dilemma that the reconstruction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. In particular, during the phase of composition, we introduce a differential activation module for detecting semantic changes from a global perspective, \ie, the relative gap between the features of edited and unedited images. With the aid of the generated Diff-CAM mask, a coarse reconstruction can intuitively be composited by the paired original and edited images. In this way, the attribute-irrelevant regions can be survived in almost whole, while the quality of such an intermediate result is still limited by an unavoidable ghosting effect. Consequently, in the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction. Extensive experiments exhibit superiorities over the state-of-the-art methods, in terms of qualitative and quantitative evaluations. The robustness and flexibility of our method is also validated on both scenarios of single attribute and multi-attribute manipulations.
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

Research into Few-shot Semantic Segmentation (FSS) has attracted great attention, with the goal to segment target objects in a query image given only a few annotated support images of the target class. A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images. However, most existing approaches either compressed the support information into a few class-wise prototypes, or used partial support information (e.g., only foreground) at the pixel level, causing non-negligible information loss. In this paper, we propose Dense pixel-wise Cross-query-and-support Attention weighted Mask Aggregation (DCAMA), where both foreground and background support information are fully exploited via multi-level pixel-wise correlations between paired query and support features. Implemented with the scaled dot-product attention in the Transformer architecture, DCAMA treats every query pixel as a token, computes its similarities with all support pixels, and predicts its segmentation label as an additive aggregation of all the support pixels' labels -- weighted by the similarities. Based on the unique formulation of DCAMA, we further propose efficient and effective one-pass inference for n-shot segmentation, where pixels of all support images are collected for the mask aggregation at once. Experiments show that our DCAMA significantly advances the state of the art on standard FSS benchmarks of PASCAL-5i, COCO-20i, and FSS-1000, e.g., with 3.1%, 9.7%, and 3.6% absolute improvements in 1-shot mIoU over previous best records. Ablative studies also verify the design DCAMA.