Miracle-dz/UniFAR-FTUs huggingface dataset:
On structural models for ionic polymer metal composites (SPIE Best Student Paper Finalist)

Ionic polymer metal composites (IPMCs) are a class of soft electroactive polymers. IPMCs comprise a soft ionic polymer core, on which two stiff metal electrodes are plated. These active materials exhibit large bend- ing upon the application of a small driving voltage across their electrodes, in air or in aqueous environments. In a recent work, we presented compelling theoretical and numerical evidence suggesting that ionic polymer membranes exhibit complex multiaxial deformations neglected by reduced-order structural models. Where most beam theories (including Euler-Bernoulli, Timoshenko, and most higher-order shear deformation models) would suggest vanishing through-the-thickness deformation, we discover the onset of localized deformation that rever- berates into axial stretching. Building upon this effort, here we investigate the role of the electrodes and shear on multiaxial deformations of IPMCs. We establish a novel structural theory for IPMCs, based on the Euler- Bernoulli kinematics enriched with the through-the-thickness deformation in the ionic polymer, computed from a Saint-Venant-like problem for uniform bending. While considering boundary conditions that elicit non-uniform bending, we compare the results of this model against classical Euler-Bernoulli beam theory without enrichment and finite element simulations, encapsulating the nonlinear response of the material. We demonstrate that our theory can predict the macroscopic displacement of the IPMC, along with the localized deformation in the ionic polymer at the interface with the electrodes, which are not captured by the classical Euler-Bernoulli beam theory. This work paves the way to the development of more sophisticated structural theories for IPMCs and analogous active materials, affording an accurate description of deformations at a limited computational cost.
Mining Expressive Rules in Knowledge Graphs

We describe RuDiK, an algorithm and a system for mining declarative rules over RDF knowledge graphs (KGs). RuDiK can discover rules expressing both positive relationships between KG elements, e.g., “if two persons share at least one parent, they are likely to be siblings,” and negative patterns identifying data contradictions, e.g., “if two persons are married, one cannot be the child of the other” or “the birth year for a person cannot be bigger than her graduation year. ” While the first kind of rules identify new facts in the KG, the second kind enables the detection of incorrect triples and the generation of (training) negative examples for learning algorithms. High-quality rules are also critical for any reasoning task involving the KGs. Our approach increases the expressive power of the supported rule language w.r. t. the existing systems. RuDiK discovers rules containing (i) comparisons among literal values and (ii) selection conditions with constants. Richer rules increase the accuracy and the coverage over the facts in the KG for the task at hand. This is achieved with aggressive pruning of the search space and with disk-based algorithms, which enable the execution of the system in commodity machines. Also, RuDiK is robust to errors and missing data in the input graph. It discovers approximate rules with a measure of support that is aware of the quality issues. Our experimental evaluation with real-world KGs shows that RuDiK does better than existing solutions in terms of scalability and that it can identify effective rules for different target applications.
Machine learning for discovery analytics to support criminal investigations

Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the “traditional” criminal activities, ignoring political boundaries and legal jurisdictions. Building upon the possibilities of technologies like Big Data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support LEAs in their critical need to exploit all available resources and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions for information fusion and classification tools intended to support LEA’s investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.
Detection of change by L1-norm principal-component analysis

We consider the problem of detecting a change in an arbitrary vector process by examining the evolution of calculated data subspaces. In our developments, both the data subspaces and the change identification criterion are novel and founded in the theory of L1-norm principal-component analysis (PCA). The outcome is highly accurate, rapid detection of change in streaming data that vastly outperforms conventional eigenvector subspace methods (L2-norm PCA). In this paper, illustrations are offered in the context of artificial data and real electroencephalography (EEG) and electromyography (EMG) data sequences.
A YOLOv3-based Deep Learning Application Research for Condition Monitoring of Rail Thermite Welded Joints

Thermite welded joints are commonly applied in running rails and any failures related to it will result in delay of time schedule or safety issues. The detection and monitoring of these joints are essential for pre-emptive maintenance and therefore to improve the structural integrity of the railway system. This study aims to produce an application prototype to detect and monitor the rail thermite welded joints based on YOLOv3 deep learning algorithm. By using YOLOv3 (You Only Look Once) as the detector, the classification and location boundary box can be determined for each image of thermite welded joint. Experiment on the training and validation of this algorithm has achieved a good result and this application prototype, to be integrated with the proposed camera monitoring system, is capable of detecting and monitoring crucial components of rail track system.
Using Gene-level to Generalize Transcript-level Classification Performance on Multiple Colorectal Cancer Microarray Studies

Several classification algorithms have been applied into microarray studies for colorectal cancer identification. Algorithms such as naïve bayes, random forest, logistic regression, support vector machine, and deep learning have been successfully used in previous studies. The accuracy of these algorithms shown promising result through n-fold validation. However, most of studies are limited to transcript-level that will implicate to biased interpretation of classification result due to different microarray platform entanglement. Therefore, we applied gene-level classification to generalize transcript-level classification result on multiple colorectal cancer microarray studies through different classification algorithms including: naïve Bayes, random forest, logistic regression, support vector machine, and deep learning. We evaluated classification performance using several parameters including: accuracy, area under ROC curve, recall and precision. As the result, we found biased classification result in transcript-level from multiple microarray studies can be solved through gene-level classification by applying annotation and merging. In addition, applying batch effect removal method can make gene-level classification performance slightly improved. Furthermore, annotation and merging also can be used to solve another biased result of feature selection in transcript-level.
Analysing the Compatibility of Identifying Emotions by Facial Expressions and Text Analytics when Using Mobile Devices

Affective computing is an imperative topic for Human-Computer Interaction, where user emotions and emotional communication can be utilized to improve the usability of a system. Several strategies are available to detect user emotions but it is questionable when identifying the most suitable and compatible strategy which can be used to detect emotions when using mobile devices. Multimodal emotion recognition paves the path to detect emotions by combining two or more strategies in order to identify the most meaningful emotion. Emotion identification through facial expressions and text analytics has given high accuracies but combining them and practically applying them in the context of a mobile environment should be done. Three prototypes were developed using evolutionary prototyping which can detect emotions from facial expressions and text data, using state of the art APIs and SDKs where the base of the prototypes was a keyboard known as "Emotional Keyboard" which is compatible with Android devices. Evaluations of Prototype 1 and 2 have been performed based on participatory design and reviewed the compatibility of emotion identification through facial expressions and text data in the mobile context. Evaluation of Prototype 3 should be done in the future and a confusion matrix should be built to verify the accuracies by cross-checking with training and validation accuracies that have been obtained when developing the neural network.
Applying IPMC to soft robots

Soft robots have a great potential for realizing human-friendly robots with adaptability and gentleness. An actuator is a crucial element for realizing them; various types of soft actuators have been developed, such as pneumatic actuators and polymer actuators. We have focused on ionic polymer metal composites (IPMC), and applied them to soft robots. In this paper, new design and fabrication processes are presented with several examples of soft actuators/robots developed in our lab. Two topics are shown in this paper. The first one is a hose-free pneumatic actuator driven by gas/liquid chemical reaction through the IPMC membrane. The pneumatic or gas-driven actuators are one of the most promising actuators in soft robotics. However, they need airsupply hoses, valves, and gas sources, which makes their work as mobile or portable actuators difficult. We have applied the IPMC membrane to control water electrolysis/synthesis, where IPMC works as an ion-exchange membrane. It controls the gas pressure and drives air-driven actuators without a hose. Two examples are shown in this paper— electrically driven thin McKibben muscles and tether-less pneumatic robots. The second topic is on IPMC actuators/robots with various 3D shapes. Most of the previous robots driven by IPMC actuators have been made by cutting and assembling the IPMC sheets, or they are very simple robots for the demonstration of IPMC actuators. Two design and fabrication processes developed in our lab to realize soft robots with arbitrary 3D structures are discussed in this paper, which are multi-layer casting and paper/fabric assisted IPMC actuators or PF-IPMC process.
A novel 3-D radar imaging method based on sparse optimization

Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs a large memory space to store the cubic data matrix. These challenges have hindered 3-D radar imaging development and limited its applications. Owing to the sparsity of the radar image of typical targets, the data size could be significantly reduced by exploiting compressive sensing and sparse reconstruction techniques. These techniques prove important to mitigate the sidelobe levels that arise from successive Fourier-based processing. Towards this end, we first perform polar reformatting to the 2-D data matrix in azimuth and range. Then, a series of images over different values of the vertical variable z are generated by using different focusing filters. Sparse optimization is afterwards applied to the 3-D data cube to produce high-resolution, significantly reduced sidelobe 3-D image. Compared with the conventional 3-D radar imaging methods, the proposed method, in addition to high fidelity images, requires fewer data measurements and offers computationally efficient processing. Numerical simulations are provided to evaluate the effectiveness of the proposed method.
A new pedagogy to address the unacknowledged failure of American secondary CS education

THE NEW STATUS QUO As the new decade begins, by most accounts, it would appear that CS education in American public high schools is doing well. Visit classrooms and you’ll find students working with robotic sensors, writing games and animations in Scratch, interfacing with Arduino microcontrollers, constructing websites, and building apps with MIT App Inventor. Groups like CSTA (Computer Science Teachers Association) and CSforAll, whose missions are to support and expand high-quality, rigorous and demographically equitable secondary CS education, proudly announce that more students, particularly young women, have taken AP Computer Science1 than ever before, ascribing the uptick to the new AP CS Principles course, e.g., “Recent Good News on Participation and Opportunities for Young Women Studying Computer Science. ” [24]
Assessing Information Quality and Distinguishing Feature Subsets for Molecular Classification

A feature reduction scheme is developed in this study. The proposed scheme is a hybrid of unsupervised and supervised methods. The unsupervised process is designed to exclude the irrelevant and useless information before executing feature selection. Fuzzy clustering algorithm will categorize all features into different groups. Then, the supervised process using the target class information will purify the relevant and informative factors from the approved feature clusters. This purifying procedure is particularly suitable for high dimensional datasets with erroneous information. This proposed strategy is to retain the advantage of simplicity and lower selecting time consumption in filter methods while the disadvantage of high computational cost due to the exponential quantity of feature subsets in wrapper methods is completely avoided.
99mTc-MIBI Scintigraphy for the Preoperative Assessment of Histological Response to Neoadjuvant Chemotherapy in Patients With Osteosarcoma: A Systematic Review and a Bivariate Meta-Analysis

Purpose: There have been many attempts to preoperatively evaluate the chemotherapy response of osteosarcoma patients using 99mTc-MIBI scintigraphy. However, the evaluations were lacking in consistency. We performed this systematic review and meta-analysis to systematically evaluate the ability of 99mTc-MIBI scintigraphy in preoperatively assessing the response of osteosarcoma patients to neoadjuvant chemotherapy. Methods: For this systematic review and meta-analysis, PubMed, Web of Science, OVID, the Cochrane Library, and CNKI were searched. Eligible studies were included based on the defined criteria. The index test was 99mTc-MIBI scintigraphy, the reference standard was tumor necrosis rate. Quality Assessment of Diagnostic Accuracy Studies-2 was adopted for quality assessment of included studies. The statistical pooling analysis, meta-regression analysis, subgroup analysis, sensitivity analysis, and publication bias of our research were performed using STATA 15. Results: Eight articles with 189 osteosarcoma patients were included in this systematic review and meta-analysis. Our results demonstrated that the threshold effect of our meta-analysis was significant. The uptake change ratio of 99mTc-MIBI scintigraphy had a pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and the area under curve of 0.98 (0.58–1.00), 0.68 (0.47–0.84), 3.1 (1.7–5.5), 0.03 (0.00–0.90), 103 (4–3,003), and 0.91 (0.88–0.93) in preoperative assessment of response of osteosarcoma patients to neoadjuvant chemotherapy. Meta-regression analysis and subgroup analysis indicated the factors of method and cut off value may introduce the heterogeneity. The pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and the area under curve of washout rate of 99mTc-MIBI were 0.87 (0.69–0.95), 0.91 (0.75–0.97), 9.3 (3.2–27.0), 0.15 (0.06–0.37), 64 (14–301), and 0.89 (0.86–0.92), respectively. Sensitivity analysis and publication bias demonstrated our meta-analysis was reliable. Conclusion: Both the ΔUR and WR derived from 99mTc-MIBI scintigraphy were valuable in preoperatively assessing the response of osteosarcoma patients to neoadjuvant chemotherapy, and ΔUR may possess a more outstanding diagnostic accuracy than WR.
Self-powered thin-film impedance spectroscopy sensor system to measure corrosion presence in aerogenerators or aircrafts

The Structural Health Monitoring (SHM) is a key technology to detect and prevent critical damages in different kind of structures. Corrosion is one of the main issues affecting metallic structures exposed to wet environments or affected by high levels of moisture, such as in offshore aerogenerators or aircrafts, and, therefore, is a critical parameter to be monitored. In this work, an on-line SHM sensor is presented, based on impedance spectroscopy techniques, which can be used to continuously measure corrosion in the surface of different kind of structures, and which could be implemented in airframe and wind energy generation structures. The impedance of an interdigitated sensor is measured in a frequency range between 16mHz and 200kHz, with resulting impedance values up to 100GOhm. The impedance sensor aims at being a completely wireless and autonomous solution: the sensor integrates wireless data communication and is self-powered by two independent energy harvesting systems. An RF antenna is used to harvest energy from a RF emitter in static and low range applications, while a flexible PV panel is installed to directly harvest energy from the environment, also in moving applications such as wind generator blades. Moreover, the use of thin-film and flexible electronics facilitates the integration of the sensor into any surface with very little aerodynamic impact. All the interdigitated sensor, the associated control electronics and the energy harvesting system can be implemented in polymeric thin flexible substrates, combining printed and hybrid electronics and allowing curved and complex geometries for the sensor device.
Soft silicone elastomers with no chemical cross-linking and unprecedented softness and stability

For silicone elastomers used as actuators, softness is key for enabling actuation at low voltages. Recently, an extremely soft (Young’s modulus < 50 kPa) silicone elastomer without cross-links has been reported by Goff et al. Besides its extreme softness, the elastomer was reported to almost completely recover (82%) from a 10-cycle elongation of more than 5000%. This observation challenges conventional elasticity theory of cross-linked elastomers because a network without covalent crosslinks should not be able to strain-recover to such extent. In this work, the elastomer is hypothesized to be formed from concatenated rings through heterodifunctional uni-molecular ring closure. It is found that the elastic properties of this uncross-linked elastomer can be described by the dynamics of concatenated rings, which act as pseudo-crosslinks and pseudo-entanglements. Isolated rings and dangling rings function as external solvents and internal solvents respectively, thereby contributing to the unprecedented softness. The ability to precisely control the ratio between concatenated and dangling rings is expected to lead to even softer dielectric elastomers paving the way forward for ultra-soft robotics without significant mechanical losses.
Deep Audio-Visual Saliency: Baseline Model and Data

This paper introduces a conceptually simple and effective Deep Audio-Visual Embedding for dynamic saliency prediction dubbed “DAVE” in conjunction with our efforts towards building an Audio-Visual Eye-tracking corpus named “AVE”. Despite existing a strong relation between auditory and visual cues for guiding gaze during perception, video saliency models only consider visual cues and neglect the auditory information that is ubiquitous in dynamic scenes. Here, we propose a baseline deep audio-visual saliency model for multi-modal saliency prediction in the wild. Thus the proposed model is intentionally designed to be simple. A video baseline model is also developed on the same architecture to assess effectiveness of the audio-visual models on a fair basis. We demonstrate that audio-visual saliency model outperforms the video saliency models. The data and code are available at https://hrtavakoli.github. io/AVE/ and https://github.com/hrtavakoli/DAVE
Neural networks for optical vector and eye ball parameter estimation

In this work we evaluate neural networks, support vector machines and decision trees for the regression of the center of the eyeball and the optical vector based on the pupil ellipse. In the evaluation we analyze single ellipses as well as window-based approaches as input. Comparisons are made regarding accuracy and runtime. The evaluation gives an overview of the general expected accuracy with different models and amounts of input ellipses. A simulator was implemented for the generation of the training and evaluation data. For a visual evaluation and to push the state of the art in optical vector estimation, the best model was applied to real data. This real data came from public data sets in which the ellipse is already annotated by an algorithm. The optical vectors on real data and the generator are made publicly available. Link to the generator and models.
When you ignore what you see: How to study proof-readers’ error in pseudocode reading

When reading algorithms, expert programmers are often able to predict what the code should contain. On occasion, however, this ability may result in so-called proof-readers’ errors, where the visual input is ignored and programmers process the code based on their own predictions. The goal of this study is to gain initial understanding of how proof-readers’ errors are reflected in eye-movement parameters of an experienced programmer, and to search for parameters that may be indicative for proof-readers’ errors in pseudocode reading. We applied a case-study approach to test the hypothesis that cognitive processing of notation, when read both with and without proof-readers’ errors, results in similarities in terms of selected eye-movement measures. However, our experienced programmer turned out to become a critical case falsifying this hypothesis. In general, case studies with expert programmers are a rather novel approach for eye-tracking studies of programming, even though single cases of experts’ eye movements are actively applied for the development of eye movement modelling examples. This study therefore also points to the importance of regarding expert examples not just as representatives as ”expert reading” in general, but also as unique cases worth a closer investigation.