Reviewerly/exHarmony huggingface dataset:
PyExplore: Query Recommendations for Data Exploration without Query Logs

Helping users explore data becomes increasingly more important as databases get larger and more complex. In this demo, we present PyExplore, a data exploration tool aimed at helping end users formulate queries over new datasets. PyExplore takes as input an initial query from the user along with some parameters and provides interesting queries by leveraging data correlations and diversity.
Underwater Depth Estimation for Spherical Images

This paper proposes a method for monocular underwater depth estimation, which is an open problem in robotics and computer vision. To this end, we leverage publicly available in-air RGB-D image pairs for underwater depth estimation in the spherical domain with an unsupervised approach. For this, the in-air images are style-transferred to the underwater style as the first step. Given those synthetic underwater images and their ground truth depth, we then train a network to estimate the depth. This way, our learning model is designed to obtain the depth up to scale, without the need of corresponding ground truth underwater depth data, which is typically not available. We test our approach on style-transferred in-air images as well as on our own real underwater dataset, for which we computed sparse ground truth depths data via stereopsis. This dataset is provided for download. Experiments with this data against a state-of-the-art in-air network as well as different artificial inputs show that the style transfer as well as the depth estimation exhibit promising performance.
Analysis and Monitoring of Cyber-Physical Systems via Environmental Domain Knowledge &amp; Modeling

While verifying adherence to a specification (i.e., specification-based testing) is important, the results are only as valid as the specification itself. Problematically, verifying a system specification must be done within the context of changing or even unknown environmental domain knowledge that could render the specification ineffective or incorrect. This issue is even more apparent in the context of self-adaptive systems, where uncertainty in both the system configuration and environment can impact the validity of the system. This paper introduces a method to explicitly model domain knowledge of the environment as a secondary system to enable design-time verification against documented environmental assumptions (i.e., those elements external to the system). In addition, run-time monitors are used to detect scenarios in the actual environment not specified by the modeled environmental domain knowledge. Rather than simply identifying unexpected inputs, our approach is able to identify run-time violations of the environmental domain knowledge, even when inputs appear valid based on the domain assumptions embedded in the system specification. These violations can then be used to correspondingly update the system and environmental specifications via automated run-time adaptation or subsequent design-time revisions. We illustrate our approach by applying our method to a running example of a goal-based model of a baby monitor.
An Empirical Study of Image Captioning using Deep Learning

Image captioning is one of the most recent challenges that caught the interest of the computer vision community as well as the Natural Language Processing community. Recently, the tedious task of image captioning has attained quite notable progress by using numerous techniques. The primary goal of this paper is to study existing Deep Learning techniques for Image Captioning. We have discussed a convolutional neural network-based Image Caption generation model and the salient steps involved in it. We have also discussed dataset and evaluation metrics widely used in fundamental systems.
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour

EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks. Currently, EfficientNets can take on the order of days to train; for example, training an EfficientNet-B0 model takes 23 hours on a Cloud TPU v2-8 node [1]. In this paper, we explore techniques to scale up the training of EfficientNets on TPU-v3 Pods with 2048 cores, motivated by speedups that can be achieved when training at such scales. We discuss optimizations required to scale training to a batch size of 65536 on 1024 TPU-v3 cores, such as selecting large batch optimizers and learning rate schedules as well as utilizing distributed evaluation and batch normalization techniques. Additionally, we present timing and performance benchmarks for EfficientNet models trained on the ImageNet dataset in order to analyze the behavior of EfficientNets at scale. With our optimizations, we are able to train EfficientNet on ImageNet to an accuracy of 83% in 1 hour and 4 minutes.
A Recommendation Model Based on User Behaviors on Commercial Websites Using TF-IDF, KMeans, and Apriori Algorithms

In recent years, recommendation systems have been widely introduced in various domains. The three main types of recommendation system are content-based, collaborative and hybrid filtering. Nowadays, much research applies machine learning techniques to construct recommendation models. This research also implemented a recommendation model using three machine learning techniques: TF-IDF, KMeans, and Apriori algorithms. TF-IDF was applied to form word vectorization from webpage headings. KMeans was utilized for clustering webpage headings while the Apriori algorithm was employed to find the association of webpage clusters. The elbow method was utilized to obtain the optimal number of clusters. KNN, Decision Tree, and Multi-Layer Perceptron were employed to evaluate the prediction accuracy. The dataset analyzed in the research was collected from a specific commercial website. User behaviors on the website were considered as the dataset in the research. The recommendation lists were retrieved from webpages in the same cluster and associated clusters. The prediction accuracy of the proposed model was approximately 88.62%.
Graph matching survey for medical imaging: On the way to deep learning

The interest on graph matching has not stopped growing since the late seventies. The basic idea of graph matching consists of generating graph representations of different data or structures and compare those representations by searching correspondences between them. There are manifold techniques that have been developed to find those correspondences and the choice of one or another depends on the characteristics of the application of interest. These applications range from pattern recognition (e.g. biometric identification) to signal processing or artificial intelligence. One of the aspects that make graph matching so attractive is its ability to facilitate data analysis, and medical imaging is one of the fields that can benefit from this in a greater extent. The potential of graph matching to find similarities and differences between data acquired at different points in time shows its potential to improve diagnosis, follow-up of human diseases or any other of the clinical scenarios that require comparison between different datasets. In spite of the large amount of papers that were published in this field to the date there is no survey paper of graph matching for clinical applications. This survey aims to fill this gap.
A Book Recommendation System Using Decision Tree-based Fuzzy Logic for E-Commerce Sites

Recommendation systems are systems developed to advise users on the most accurate product, especially on e-commerce sites, movie viewing platforms, and music listening platforms. Nowadays, the increasing number of studies on data is led to applying different methods in recommendation systems. A fuzzy-logic-based product recommendation system is proposed for users who want to buy books on e-commerce sites in the study. Clustering is made using unsupervised learning with information from the “also bought-viewed” book data. A decision tree model is created with the data set. The rules of the Fuzzy model used in the study are created by using this decision tree. It is observed that successful results are obtained when tests are performed with actual data and decision trees, and fuzzy models are used together. Usually, in fuzzy models, data is not required. It is necessary to know the parameters and their effects during the design of the model. However, it will be complicated to determine rules in complex and challenging models like as in this study. As a result of the successful results obtained in this study, it has been shown that the rules are created quickly and accurately with the help of a method such as decision trees.
Parameter discrepancy hypothesis: adversarial attack for graph data

Graph neural networks are vulnerable to adversarial attacks. Therefore, this paper generalizes existing graph neural network adversarial attacks as a contradictory data hypothesis. Attack methods based on this hypothesis add perturbations to the training data, making it difficult to fit the training data and hence disturbing training. The contradictory data hypothesis cannot fit such cases well, causing the model to overfit the training set and be unable to generalize to the test set. This paper proposes a parameter discrepancy hypothesis for adversarial attacks against graph data, and shows that model training parameters differ significantly before and after the attack. A new attack model is consequently established using Cook distance. Extensive experiments verified the parameter discrepancy hypothesis rationality, Cook distance effectiveness, and the proposed attack method.
RAD: Realtime and Accurate 3D Object Detection on Embedded Systems

To our knowledge, the fastest 3D object detector on Li-DAR data works at 42.03 point clouds-per-second on high-end machines and 6.15 point clouds-per-second on embedded boards. We propose a deep 3D object detector with higher detection accuracy running at 84.46 point clouds-per-second on high-end machines and 10.91 point clouds-per-second on computing boards that is 2 and 1.77 times faster compared to fastest published network. We achieve considerably higher processing rate without reducing the complexity of the network but by designing a more efficient decoder. Our extensive and practical experiments reveal that the detection accuracy of our proposed network is comparable to the best-performed method using practical metrics but it is 3.36 times faster. Besides, we carefully analyze the model and indicate that negligible error in two regression outputs contributes to the reduction in the average precision. Overall, considering the accuracy and speed, our proposed network is highly practical to be executed on embedded boards for ADAS applications.
Target Localization and Circumnavigation With Integral Action in R²

Prior results on target localization and circumnavigation with bearing measurements in \mathbb R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are extended with integral action, resulting in a control system that is robust to bounded load disturbances on the control inputs. Such disturbances may arise in practice due to modeling errors and need to be considered to ensure small tracking errors. The control inputs are modeled as the system velocities in the bearing and orthogonal bearing directions, facilitating an implementation on both aerial and ground vehicles. This is demonstrated in a simulation example, followed by an experimental application with ground-based vehicle circumnavigation, highlighting the performance and importance of using the proposed controllers.
A Crowdsourcing-Based Framework for the Development and Validation of Machine Readable Parallel Corpus for Sign Languages

Sign languages are used by the deaf and mute community of the world. These are gesture based languages where the subjects use hands and facial expressions to perform different gestures. There are hundreds of different sign languages in the world. Furthermore, like natural languages, there exist different dialects for many sign languages. In order to facilitate the deaf community several different repositories of video gestures are available for many sign languages of the world. These video based repositories do not support the development of an automated language translation systems. This research aims to investigate the idea of engaging the deaf community for the development and validation of a parallel corpus for a sign language and its dialects. As a principal contribution, this research presents a framework for building a parallel corpus for sign languages by harnessing the powers of crowdsourcing with editorial manager, thus it engages a diversified set of stakeholders for building and validating a repository in a quality controlled manner. It further presents processes to develop a word-level parallel corpus for different dialects of a sign language; and a process to develop sentence-level translation corpus comprising of source and translated sentences. The proposed framework has been successfully implemented and involved different stakeholders to build corpus. As a result, a word-level parallel corpus comprising of the gestures of almost 700 words of Pakistan Sign Language (PSL) has been developed. While, a sentence-level translation corpus comprising of more than 8000 sentences for different tenses has also been developed for PSL. This sentence-level corpus can be used in developing and evaluating machine translation models for natural to sign language translation and vice-versa. While the machine-readable word level parallel corpus will help in generating avatar based videos for the translated sentences in different dialects of a sign language.
Vehicle Model Classification Using Deep Learning

One of the most significant issues in modern road safety and intelligent transportation systems is the automation of vehicle detection and identification. Many challenges have been solved in the advancement of image processing, pattern recognition, and deep learning technology in order to accomplish this goal. Vehicle Type Classification is a difficult task since the dataset has a large class imbalance, and several viewpoints for different cars can be identical. The proposed framework employs a shallow Convolutional Neural Networks (CNN) architecture to prevent overfitting and ensure that the correct features are learned, and an augmentation technique is utilized to produce synthetic images by using the image data generation model in Keras due to class imbalance. The shallow CNN is used to extract features from the generated images, and then Softmax activation is used to classify them. Finally, the proposed system will achieve the classification of vehicle type i.e. classify the different car models with efficiently by novel methodology. The findings of the experiments demonstrate that shallow CNN can do well in real-world situations.
Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution

Burst image super-resolution is an ill-posed problem that aims to restore a high-resolution (HR) image from a sequence of low-resolution (LR) burst images. To restore a photo-realistic HR image using their abundant information, it is essential to align each burst of frames containing random hand-held motion. Some kernel prediction networks (KPNs) that are operated without external motion compensation such as optical flow estimation have been applied to burst image processing as implicit image alignment modules. However, the existing methods do not consider the interdependencies among the kernels of different sizes that have a significant effect on each pixel. In this paper, we propose a novel weighted multi-kernel prediction network (WMKPN) that can learn the discriminative features on each pixel for burst image super-resolution. Our experimental results demonstrate that WMKPN improves the visual quality of super-resolved images. To the best of our knowledge, it outperforms the state-of-the-art within kernel prediction methods and multiple frame super-resolution (MFSR) on both the Zurich RAW to RGB and BurstSR datasets.
Ontological Modeling for Excavation Documentation and Virtual Reconstruction of an Ancient Egyptian Site

In this article we introduce our semantic modeling approach for data from over 50 years of excavations at Tell el-Daba in Egypt. The CIDOC CRM with some of its extensions is used as an ontological framework to provide the semantics for creating a knowledge graph containing material remains, excavated areas, and documentation resources. An objective of the project A Puzzle in 4D is to digitize the documentation and create metadata for analog and digital resources in order to provide the data to the research community and facilitate future work for this important archaeological site. Using an example of 3D reconstruction of a tomb, we show how the knowledge graph linked to digital resources can be exploited for a specific task to encounter available information that is essential for a virtual reconstruction. Moreover, we show an approach of modeling to represent the interpretations supporting reconstructions as well as relate them to the sources used, thus providing transparency for the model and provenance data. Modeling for excavation documentation as well as virtual reconstruction has been tailored to the large amount of data processed from the project. The goal is to propose a semantic modeling feasible even on a large scale while still preserving the basic underlying ontological structures.
Multi-Target Multi-Camera Vehicle Tracking for City-Scale Traffic Management

Multi-target multi-camera (MTMC) tracking is one of the important fields in computer vision, where multiple objects are tracked across multiple cameras. MTMC tracking can be applied to various tasks such as crowd analysis, city-scale traffic management, and transportation systems analysis for intelligent city planning. However, it is challenging due to the large variety of conditions of each camera, such as perspective and illumination. Furthermore, MTMC tracking for vehicles is more problematic because of the relatively large inter-class similarity and intra-class variability. In this paper, we tackle the MTMC tracking problem for vehicles by dividing it into three main steps: (i) vehicle detection and feature extraction, (ii) multi-target single-camera tracking using the appearance feature of each vehicle, and (iii) multi-camera association of local trajectories from each camera. Our method shows comparable results with other highly-ranked methods in AI City Challenge 2021 and outperforms a recent MTMC tracking method that ranked first place in AI City Challenge 2020.
A Simulation Design of Immersive Virtual Reality for Animal Handling Training to Biomedical Sciences Undergraduates

Background: One area of biomedical research concerns is applying new treatments to cure human diseases, moving bench-side research to the bedside practice. While using animal models is crucial in the research process, researchers should strictly adhere to the moral 4R framework to protect animal welfare—replacement, reduction, refinement, and responsibility. Virtual reality (VR) applies computer technology to create a simulated environment, allowing players to immerse and interact with animated 3D contexts. We developed a virtual animal-holding simulator (ViSi) using immersive virtual reality technology for students studying in the undergraduate biomedical sciences programme. The specific objectives of the paper are to 1) describe the development of the VR courseware for animal training and 2) describe the learning experience among students. Method and Result: An evaluation of the courseware was conducted among Year one and two biomedical sciences students. Students who participated in ViSi responded positively about their involvement in the virtual environment experience and their concentration on the assigned task. Discussion: ViSi is a reliable simulation technology that can train animal handling skills, which replaces real animals, while learners’ multi-cognition could still be enhanced with simulation training. Thus, the impact of immersive VR technology integrated into skills training is promising, although few technical problems are to be resolved.
The CoCe Design Space

In this paper, we map out the CoCe design space - a design space for co-located collaborative games that use multi-display composition. The design space grew out of the analysis of game instances based on the 4in1 concept. First, we did a horizontal analysis of 16 game instances with 31 corresponding gameplay design patterns (GDP), followed by a vertical analysis of 89 GDPs occurring in the description of the GDP Cooperation. Through inductive analysis, we have identified four perspectives with corresponding dimensions that span the CoCe design space. By applying the CoCe design space with game instances, we illustrate how it can be used both as an analytic tool for analysis of games and also as a generative tool in the design or re-design of cooperative games that use multi-display composition.
Eliciting New Perspectives in RtD Studies through Annotated Portfolios: A Case Study of Robotic Artefacts

In this paper, we investigate how to elicit new perspectives in research-through-design (RtD) studies through annotated portfolios. Situating the usage in human-robot interaction (HRI), we used two robotic artefacts as a case study: we first created our own annotated portfolio and subsequently ran online workshops during which we asked HRI experts to annotate our robotic artefacts. We report on the different aspects revealed about the value, use, and further improvements of the robotic artefacts through using the annotated portfolio technique ourselves versus using it with experts. We suggest that annotated portfolios – when performed by external experts – allow design researchers to obtain a form of creative and generative peer critique. Our paper offers methodological considerations for conducting expert annotation sessions. Further, we discuss the use of annotated portfolios to unveil designerly HRI knowledge in RtD studies.
Assessing Developer Expertise from the Statistical Distribution of Programming Syntax Patterns

Accurate assessment of developer expertise is crucial for the assignment of an individual to perform a task or, more generally, to be involved in a project that requires an adequate level of knowledge. Potential programmers can come from a large pool. Therefore, automatic means to provide such assessment of expertise from written programs would be highly valuable in such context.
Autonomous Planning and Intraoperative Augmented Reality Navigation for Neurosurgery

Neurosurgical interventions in the brain are challenging due to delicate anatomical structures. During surgery, precise navigation of surgical instruments supports surgeons and allows prevention of adverse events. Here, an augmented reality-based navigation aid with automated segmentation of risk structures and path planning is presented. Superimposed patient models are visualized during neurosurgical interventions on the example of the ventricular puncture. The proposed system is experimentally validated in a realistic operating room scenario with expert neurosurgeons to determine its quality of support as well as its potential for clinical translation. The automated segmentation reaches a F1-Score of 95-99%. Paths are planned correctly in 93.4%. The entire process enables navigation aid in under five minutes. Validation shows that the system allows for a puncture success rate of 81.7% with mean accuracy of 4.8 ± 2.5 mm. A control group who performed the standard-of-care procedure reached a rate of 71.7% with 6.5 ± 2.4 mm accuracy. Acceptability analysis shows that 85.7% of the participating surgeons approve of the system's convenience and 92.9% expect accuracy improvement. The presented navigation aid for ventricular puncture enables automated surgical planning and may improve accuracy and success rates of neurosurgical interventions.