Reviewerly/exHarmony huggingface dataset:
CSDA-Net: Seeking reliable correspondences by channel-Spatial difference augment network

Establishing reliable correspondences is a fundamental task in computer vision, and it requires rich contextual information. In this paper, we propose a Channel-Spatial Difference Augment Network (CSDA-Net), by selectively aggregating information from spatial and channel aspects, to seek reliable correspondences for feature matching. Specifically, we firstly introduce the spatial and channel attention mechanism to construct a simple yet effective block for discriminately extracting the global context. After that, we design a Overlay Attention block by further exploiting the spatial and channel attention mechanism with different squeeze operations, to gather more comprehensive contextual information. Finally, the proposed CSDA-Net is able to achieve feature maps with a strong representative ability for feature matching due to the integration of the two novel blocks. Extensive experiments on outlier rejection and relative pose estimation have shown better performance improvements of our CSDA-Net over current state-of-the-art methods on both outdoor and indoor datasets.
On Exploring Attention-based Explanation for Transformer Models in Text Classification

The Transformer models have achieved unprecedented breakthroughs in text classification, and have become the foundation of most state-of-the-art NLP systems. The core function that drives the success is the attention mechanism, which provides the ability to dynamically focus on different parts of the input sequence when producing the predictions. Several previous works have investigated the usage of attention weights to explain the model predictions, because intuitively, attention weights reflect the importance of the input positions in the output. Specifically, the objective for explanation is to compute a relevance score for each input token, such that the key input words that are most important to the prediction can be identified. However, previous efforts produced mixed results. We find that the key reason why attention weights cannot be directly used as effective relevance indications is because they do not contain the directional information for relevance (i.e., whether the input tokens contribute towards or against the prediction). We then propose two novel explanation techniques, namely AGrad and RePAGrad, that produce directional relevance scores based on attention weights. To evaluate the explanation performance, we propose three properties that an effective explanation method should satisfy (i.e., faithfulness, resilience, and consistency), and design the corresponding test to quantify each property. Through extensive evaluations with Transformer models and pre-trained BERT models on multiple public text classification datasets, we show that AGrad and RePAGrad significantly outperform existing state-of-the-art explanation methods in faithfulness and consistency, at the cost of nominal degradation on resilience compared to attention weights. In addition, we reveal that elements of a model architecture can play an important role towards explainability.
Flower pollination algorithm with pollinator attraction

The Flower Pollination Algorithm (FPA) is a highly efficient optimization algorithm that is inspired by the evolution process of flowering plants. In the present study, a modified version of FPA is proposed accounting for an additional feature of flower pollination in nature that is the so-called pollinator attraction. Pollinator attraction represents the natural tendency of flower species to evolve in order to attract pollinators by using their colour, shape and scent as well as nutritious rewards. To reflect this evolution mechanism, the proposed FPA variant with Pollinator Attraction (FPAPA) provides fitter flowers of the population with higher probabilities of achieving pollen transfer via biotic pollination than other flowers. FPAPA is tested against a set of 28 benchmark mathematical functions, defined in IEEE-CEC'13 for real-parameter single-objective optimization problems, as well as structural optimization problems. Numerical experiments show that the modified FPA represents a statistically significant improvement upon the original FPA and that it can outperform other state-of-the-art optimization algorithms offering better and more robust optimal solutions. Additional research is suggested to combine FPAPA with other modified and hybridized versions of FPA to further increase its performance in challenging optimization problems.
Using FIWARE and Blockchain in Post Pandemic Vaccination Scenario

The emergence of the COVID-19 pandemic, caused by the SARS-Cov-2 virus, directly affected the displacement of people around the world. Some countries have restricted travel from certain countries where the pandemic has not been controlled. It is expected that with the emergence of different types of vaccines to contain the spread of this virus, international travel will resume both for business, tourism, and vacation. However, how to guarantee and verify that someone was vaccinated? Therefore, it is necessary to integrate most vaccination systems worldwide to offer digital proof of vaccination. One way to do this is to use FIWARE, a curated framework from an open-source platform for smart solutions, which utilizes NGSI Standards, enabling the integration of components and providing the basis for the interoperability and portability of smart solutions. However, data exchanged between systems need to be reliable and secure. One way is to overcome these challenges is using Blockchain with NGSI as an immutable decentralized transaction ledger to integrate systems. This paper proposes a new Generic Enabler (GE) named Canis Major to be integrated into the FIWARE ecosystem solutions to provide secure interoperability between systems used to integrate vaccination systems worldwide in a post-pandemic vaccination scenario. With this solution, any integrated vaccination system can reliably verify when and what type of vaccine a person has been vaccinated to contribute to the end of the pandemic.
RealisticHands: A Hybrid Model for 3D Hand Reconstruction

Estimating 3D hand meshes from RGB images robustly is a highly desirable task, made challenging due to the numerous degrees of freedom, and issues such as self-similarity and occlusions. Previous methods generally either use parametric 3D hand models or follow a model-free approach. While the former can be considered more robust, e.g. to occlusions, they are less expressive. We propose a hybrid approach, utilizing a deep neural network and differential rendering based optimization to demonstrably achieve the best of both worlds. In addition, we explore Virtual Reality (VR) as an application. Most VR headsets are nowadays equipped with multiple cameras, which we can leverage by extending our method to the egocentric stereo domain. This extension proves to be more resilient to the above mentioned issues. Finally, as a use-case, we show that the improved image-model alignment can be used to acquire the user’s hand texture, which leads to a more realistic virtual hand representation.
3D Geo-Indistinguishability for Indoor Location-Based Services

Indoor location-based services (LBS) are widely used in large-scale indoor buildings, such as high-rise hospitals and multi-story shopping malls. At the same time, location privacy protection in such three-dimensional (3D) space has recently attracted considerable attention. Currently, most existing location privacy protection schemes focus on two-dimensional (2D) location protection and fail to prevent location inference attacks when the user’s location data include height dimension, i.e., 3D geolocation. Enlightened by the concept of differential privacy, in this paper we first study the impact factors of the degree of indistinguishability of 3D geolocations. Then, we quantify location privacy for LBS applications in the 3D space with geo-indistinguishability (3D-GI) rigorously and provably. We develop a mechanism of three-variates Laplacian to generate perturbed locations considering the locations’ X, Y, and Z-coordinates simultaneously, guaranteeing geo-indistinguishability. Furthermore, the discretization noise-adding mechanism is studied to satisfy geo-indistinguishability in the 3D space under the finite precision of hardware/devices. Considering the discretized mechanism can only satisfy geo-indistinguishability in finite 3D space and users visit the limited regions, we further study the truncation of the Laplacian mechanism to limit the generated perturbed locations within a specific region. Simulation results demonstrate that the proposed 3D-GI outperforms the benchmarks while guaranteeing privacy regardless of the adversary’s prior knowledge.
A DQN-based Recommender System for Item-list Recommendation

Recommender systems are widely used to help users locate information by suggesting goods that best match users’ needs and preferences. Most existing recommender systems are built by implementing supervised learning-based algorithms. Unfortunately, the large search space with lots of local optimums makes it challenging for the existing recommender systems to apply in more and more new upcoming recommendation scenarios, such as the novel item-list recommendation scenario proposed by FUXI AI Lab, Netease. It requires to recommend an item list in each step, where the item list is one of the thousands of combinations of items. To tackle these challenges, recent researchers resort to adopting reinforcement learning (RL) for recommendations. It is natural to formalize item recommendation problem as a multi-step decision-making problem and use exploration and exploitation of RL to address the problem of large search space. In this paper, we propose a DQN-based recommender system for item-list recommendation that achieves second place in track II of the RL-based RecSys and it only performs slightly worse than the first place. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Smart Sensors for Augmented Electrical Experiments

With the recent increase in the use of augmented reality (AR) in educational laboratory settings, there is a need for new intelligent sensor systems capturing all aspects of the real environment. We present a smart sensor system meeting these requirements for STEM (science, technology, engineering, and mathematics) experiments in electrical circuits. The system consists of custom experiment boxes and cables combined with an application for the Microsoft HoloLens 2, which creates an AR experiment environment. The boxes combine sensors for measuring the electrical voltage and current at the integrated electrical components as well as a reconstruction of the currently constructed electrical circuit and the position of the sensor box on a table. Combing these data, the AR application visualizes the measurement data spatially and temporally coherent to the real experiment boxes, thus fulfilling demands derived from traditional multimedia learning theory. Following an evaluation of the accuracy and precision of the presented sensors, the usability of the system was evaluated with n=20 pupils in a German high school. In this evaluation, the usability of the system was rated with a system usability score of 94 out of 100.
Hierarchical Graph Representation Learning with Local Capsule Pooling

Hierarchical graph pooling has shown great potential for capturing high-quality graph representations through the node cluster selection mechanism. However, the current node cluster selection methods have inadequate clustering issues, and their scoring methods rely too much on the node representation, resulting in excessive graph structure information loss during pooling. In this paper, a local capsule pooling network (LCPN) is proposed to alleviate the above issues. Specifically, (i) a local capsule pooling (LCP) is proposed to alleviate the issue of insufficient clustering; (ii) a task-aware readout (TAR) mechanism is proposed to obtain a more expressive graph representation; (iii) a pooling information loss (PIL) term is proposed to further alleviate the information loss caused by pooling during training. Experimental results on the graph classification task, the graph reconstruction task, and the pooled graph adjacency visualization task show the superior performance of the proposed LCPN and demonstrate its effectiveness and efficiency.
The Open-Response Chemistry Cognitive Assistance Tutor System: Development and Implementation

This report showcases a new type of online homework system that provides students with a free-form interface and dynamic feedback. The ORCCA Tutor (Open-Response Chemistry Cognitive Assistance Tutor) is a production rules-based online tutoring system utilizing the Cognitive Tutoring Authoring Tools (CTAT) developed by Carnegie Mellon University. In this report, we discuss the interface design and the production rules that allow for a multitude of chemistry calculations to be solved in a wide variety of ways by students. We discuss improvements of the software, already implemented or planned for the future, based on think aloud/interviews with students.
Multi-Sign Language Glove based Hand Talking System

Abstract Many people around the world suffer from losing the ability to talk and hear with different levels of disabilities, caused by either a car or a work accident or some diseases. After losing communication, these people cannot do normal functions of normal life. Along with the aforementioned disabilities, those people may also have psychological effects. This paper introduces a technique to realize multiple sign language translation using a sensors-based glove and an Android smartphone for speech impaired people to communicate normally with people. The design of the hand talking system (HTS) was implemented with a minimum possible number of sensors and a capable sewing controller (Lilypad). The proposed HTS includes flex sensors, Arduino, smartphone, and accelerometer. The HTS uses an Android application programmed to store multi-language in the SQLite database and enables the user to interact with the system. The system provides talking with a letter formed words, or using the most frequently used words in daily communication by hand gesture. The HTS has achieved high accuracy obtained for American Sign Language and Arabic Sing Language which are about 98.26% and 99.33% respectively with an average accuracy of 98.795 for both Sign Languages.
RRT-Rope: A deterministic shortening approach for fast near-optimal path planning in large-scale uncluttered 3D environments

Many path planning algorithms have been introduced so far, but most are costly, in path cost and in processing time, in large-scale uncluttered 3D environments such as underground mining stopes explored by an unmanned aerial vehicle (UAV). Rapidly-exploring Random Tree (RRT) algorithms are popular because of their probabilistic completeness and rapidity in finding a feasible path in single-query problems. Many of the algorithms (e.g. Informed RRT*, RRT#) developed to improve RRT need considerable time to converge in large environments. Shortcutting an RRT is an old idea that has been proven to outperform RRT variants. This paper introduces a new method, RRT-Rope, that aims at finding a near-optimal solution in a drastically shorter amount of time. The proposed approach benefits from fast computation of a feasible path with an altered version of RRT-connect, and post-processes it quickly with a deterministic shortcutting technique, taking advantage of intermediate nodes added to each branch of the tree. This paper presents simulations and statistics carried out to show the efficiency of RRT-Rope, which gives better results in terms of path cost and computation time than other popular RRT variations and shortening techniques in all our simulation environments, and is up to 70% faster than the next best algorithm in a representative stope.
Towards Engineering Digital Twins by Active Behaviour Mining

In the context of Confirm, the Irish Research Centre on Smart Manufacturing, field demonstrators are used to show new techniques to industrial partners, various kinds of students, and the general public alike. Considering the robotics demonstrator for the Digital Thread concept used in Confirm, which is a small cyberphysical system based on the UR3 cobot and a web controller for it, we apply Active Automata Learning in order to obtain a Digital Twin for it. Behavior mining done in this fashion is nowadays uncommon, but it has various advantages over, e.g., models obtained with popular AI techniques in that the AAL models are accurate deterministic behavioural explanations for the system behaviour at the chosen level of abstraction, and they may be further amenable to formal verification, e.g., by model checking, in order to establish properties of interest.This extension has the effect of showcasing the Digital Twin concept, the AAL technique, the use of model checking, and the importance of working with formal models that are amenable to these technologies. We then reflect on the nature of the models and their uses and meaning, from the point of view of the comments and questions we receive in the demonstrations. We also consider the use of a feature-based approach to modelling the systems and their interactions, which is a further aspect for which the demonstrator could be used, with a special attention to the aspects of this work, like AAL and the feature based and feature interaction research, that connect directly with the collaboration with and the research of Bengt Jonsson.
Differentially Private Linkage Analysis with TDT — the case of two affected children per family

Statistical analyses of datasets containing genomic information is essential for personalized medicine. However, when the statistics are released as they are, there is a risk of identifying individuals. In this study, we propose efficient a nd practical privacy-preserving methods using the concept of differential privacy for linkage analysis with a transmission disequilibrium test (TDT). We focus on the case of two affected children in one family, and present differentially private data sharing methods based on three statistics, which are the TDT statistic, haplotype-based statistic, and combined statistic of these two. First, we show the sensitivities of each statistic and present the algorithm using the Laplace mechanism. Then, for the exponential mechanism, we adopt the shortest Hamming distance score as the score function and propose exact and approximation algorithms to find the scores. In our experiments, we measure the run time of each algorithm to show that it is feasible even on a large dataset containing 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> SNPs. Supplementary materials are available at https://github.com/ay0408/DP-linkage-analysis-TDT.
General digital competences of beginning trainees in commercial vocational education and training

Abstract Against the background of digital transformation processes that are currently changing the world of work, this paper examines general digital competences of beginning trainees in commercial vocational education and training (VET) programs. We are particularly interested in factors influencing digital competence profiles. From survey data including N = 480 trainees in one federal state in Germany, we were able to identify three different competence profiles (based on the trainees’ self-assessment of their general digital competence). Initial descriptive analysis reveals differences between competence profiles of different training professions (industrial clerks and retail salespersons reach higher competence levels than salespersons). However, regression results indicate that these differences can be explained by differences in school leaving certificates. Contrary to prior empirical evidence, we find no significant effect of trainees’ gender. Finally, the frequency of certain private digital activities (e.g. using office programs, conducting internet searches) affects digital competence profiles. Implications for both VET programs and further research are discussed.
Toward Realization of Scalable Packaging and Wiring for Large-Scale Superconducting Quantum Computers

In this paper, we review the basic components of superconducting quantum computers. We mainly focus on the packaging and wiring technologies required to realize large-scalable superconducting quantum computers.
Machine learning based 3D object detection for navigation in unstructured environments

In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
Deep learning model based on cascaded autoencoders and one‐class learning for detection and localization of anomalies from surveillance videos

Due to the need for increased security measures for monitoring and safeguarding the activities, video anomaly detection is considered as one of the significant research aspects in the domain of computer vision. Assigning human personnel to continuously check the surveillance videos for finding suspicious activities such as violence, robbery, wrong U-turns, to mention a few, is a laborious and error-prone task. It gives rise to the need for devising automated video surveillance systems ensuring security. Motivated by the same, this paper addresses the problem of detection and localization of anomalies from surveillance videos using pipelined deep autoencoders and one-class learning. Specifically, we used a convolutional autoencoder and a sequence-to-sequence long short-term memory autoencoder in a pipelined fashion for spatial and temporal learning of the videos, respectively. The authors followed the principle of one-class classification for training the model on normal data and testing it on anomalous testing data. The authors achieved a reasonably significant performance in terms of an equal error rate and the time required for anomaly detection and localization comparable to standard benchmarked approaches, thus, qualifies to work in a near-real-time manner for anomaly detection and localization.