Reviewerly/exHarmony huggingface dataset:
CalQNet - Detection of Calibration Quality for Life-Long Stereo Camera Setups

Many mobile robotic platforms rely on an accurate knowledge of the extrinsic calibration parameters, especially systems performing visual stereo matching. Although a number of accurate stereo camera calibration methods have been developed, which provide good initial “factory” calibrations, the determined parameters can lose their validity over time as the sensors are exposed to environmental conditions and external effects. Thus, on autonomous platforms on-board diagnostic methods for an early detection of the need to repeat calibration procedures have the potential to prevent critical failures of crucial systems, such as state estimation or obstacle detection. In this work, we present a novel data-driven method to estimate the quality of extrinsic calibration and detect discrepancies between the original calibration and the current system state for stereo camera systems. The framework consists of a novel dataset generation pipeline to train CalQNet, a deep convolutional neural network. CalQNet can estimate the extrinsic calibration quality using a new metric that approximates the degree of miscalibration in stereo setups. We show the framework's ability to predict the divergence of a state-of-the-art stereo-visual odometry system following a degraded calibration in two real-world experiments.
Jigsaw Puzzle Solving as a Consistent Labeling Problem

We explore the idea of abstracting the jigsaw puzzle problem as a consistent labeling problem, a classical concept introduced in the1980 s by Hummel and Zucker for which a solid theory and powerful algorithms are available. The problem amounts to maximizing a well-known quadratic function over a probability space which we solve using standard relaxation labeling algorithms endowed with matrix balancing mechanisms to enforce one-to-one correspondence constraints. Preliminary experimental results on publicly available datasets demonstrate the feasibility of the proposed approach.
Identification of Anomalous Behavioral Patterns in Crowd Scenes

Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade. The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures. Although, researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time. The proposed research work focuses on detection of local and global anomaly detection of crowd. Fusion of spatial-temporal features assist in differentiation of feature trained using Mask R-CNN with Resnet101 as a backbone architecture for feature extraction. The data from, BIWI Walking Pedestrian dataset and the Crowds-By-Examples (CBE) dataset and Self-Generated dataset has been used for experimentation. The data deals with different situations like one set of data deals with normal situations like people walking and acting individually, in a group or in a dense crowd. The other set of data contains images four unique anomalies like fight, accident, explosion and people behaving normally. The simulated results show that in terms of precision and recall, our system performs well with Self-Generated dataset. Moreover, our system uses an early stopping mechanism, which allows our system to outperform to make our model efficient. That is why, on 89th epoch our system starts generating finest results.
Adversarial Kernel Sampling on Class-imbalanced Data Streams

This paper investigates online active learning in the setting of class-imbalanced data streams, where labels are allowed to be queried of with limited budgets. In this setup, conventional learning would be biased towards majority classes and consequently harm the performance. To address this issue, imbalance learning technique adopts both asymmetric losses and asymmetric queries to tackle the imbalance. Although this approach is effective, it may not guarantee the performance in an adversarial setting where the actual labels are unknown, and they may be chosen by the adversary
Remaining Futureproof: Lasting Librarian Roles in Managing Digital Collections

From the students' reactions, trainers recognize the changing technological, social, and economic context of library and information science (LIS) and the students' competence expectations. They incorporate these experiences when updating the training program, and draw important conclusions therefrom regarding the future of libraries and LIS. This is crucial to providing students with a credible vision: where is LIS headed; what is the future library like; what roles will libraries have in the era of artificial intelligence, virtual reality, and data science? It is not enough to deny stereotyped predictions, for example, that robotics may take their jobs. In our study we rely on our experiences from courses on digital collections to refute incorrect assumptions and to introduce the four futureproof attributes of LIS: (1) human creativity remains essential; (2) the professional use of social media is crucial in accessing community knowledge; (3) collaboration competences have lasting value; (4) research-oriented attitude is increasingly important in the librarian profession.
Truth, Justice, and Sexual Violence: A Comparative Analysis of Op-Eds Surrounding the Hill-Thomas and Ford-Kavanaugh Hearings

Using discourse analysis to examine newspaper Op-Eds during the Hill-Thomas and Ford-Kavanaugh hearings, this study explores how media can function as an arbiter of public opinion during controversial events. This analysis highlights the importance of Op-Eds in defining whose voices matter in shaping national mediated discourses, examining the role media play in critical public debates surrounding issues of gender, race, power, and sexual violence. Against a backdrop of increasing numbers of women in the Senate chambers, the growing numbers of women and people of color across American newsrooms, and the impact of the #MeToo movement in the national conversation about sexual violence, this study examines changes in opinion authorship and media discourses surrounding these two hearings set 27 years apart. Findings show a shift in Op-Ed contributors, from mostly men in 1991 to predominantly women in 2018. Support for women and sexual assault accusers was largely absent in Op-Eds from 1991, replaced by vague debates about fairness and justice; while in 2018, Op-Ed authors offered tangible support for accusers. This study considers both the promise and limitations of these shifting media discourses to address structural and institutional power relations.
Real-time visualization reconstruction in a real-world environment using Augmented Reality

Even with the growth of digital data sources and support for creating visualizations, much information disseminated in chart format is still static, whether printed or digital. Some common problems of visualization components can be easily corrected on the chart design using a charting tool, but in the real world, mainly in the printed media, it is not a simple task. We investigate a method to reconstruct visualizations in real-time and in a real-world environment using Augmented Reality. We propose a prototype that interacts with extracted data from a bitmap chart in the real world, and we evaluated it with usability experts. The results show a high degree of satisfaction on many factors, mainly filter usage and valuable feedback about interaction.
VR Eye-Tracking using Deflectometry

We present a novel approach for accurate eye tracking as required, e.g., in VR/AR/MR headsets. Our method exploits the retrieved surface normals and dense 3D features extracted from deflectometry measurements to estimate the gazing direction.
ADC CF: Adaptive deep concatenation coder framework for visual question answering

Multimodal teaching activity faces significant problems in Visual Question Answering (VQA), which involves simultaneous comprehension with reduced performance fidelity. However, Conventional methods are employed for portrayal and queries in a defined manner, which fails to accomplish the required performance accuracy rate. For elucidating the excellent image and question representation, this paper suggests an Adaptive Deep Concatenated Coder Framework (ADCCF) that enrolls both the image and question attributes simultaneously with the optimized residual layer. The Coder Framework comprises of cascaded layers of Encoder-Decoder architecture, which captures rich, meaningful query characteristics and image details through the use of keywords employing significant object areas in the picture. ADCCF layer has an encoder segment that blueprints the self-recognition of queries in which questions are concatenated to limit the answers and decoder segment blueprints the commanded-recognition of images. The simulation results of ADCCF are tested with both the VQA datasets 1.0 and 2.0 and manifests an improved performance accuracy ratio of 72.45% for 1.0 dataset and 73.57% for 2.0 datasets, thus proving the reliability of the proposed framework.
Weakly supervised learning of multi-object 3D scene decompositions using deep shape priors

Representing scenes at the granularity of objects is a prerequisite for scene understanding and decision making. We propose PriSMONet, a novel approach based on Prior Shape knowledge for learning Multi-Object 3D scene decomposition and representations from single images. Our approach learns to decompose images of synthetic scenes with multiple objects on a planar surface into its constituent scene objects and to infer their 3D properties from a single view. A recurrent encoder regresses a latent representation of 3D shape, pose and texture of each object from an input RGB image. By differentiable rendering, we train our model to decompose scenes from RGB-D images in a self-supervised way. The 3D shapes are represented continuously in function-space as signed distance functions which we pre-train from example shapes in a supervised way. These shape priors provide weak supervision signals to better condition the challenging overall learning task. We evaluate the accuracy of our model in inferring 3D scene layout, demonstrate its generative capabilities, assess its generalization to real images, and point out benefits of the learned representation.
Probabilistic Causes in Markov Chains

The paper studies a probabilistic notion of causes in Markov chains that relies on the counterfactuality principle and the probability-raising property. This notion is motivated by the use of causes for monitoring purposes where the aim is to detect faulty or undesired behaviours before they actually occur. A cause is a set of finite executions of the system after which the probability of the effect exceeds a given threshold. We introduce multiple types of costs that capture the consump-tion of resources from different perspectives, and study the complexity of computing cost-minimal causes.
Performative Virtual Scenes: A Dynamic VR Environment Design Approach

This paper proposes a dynamic VR environment design approach to enhance the way a user experiences a computer generated character's movement. We transferred concepts and practices from performance studies to create a dynamic VR environment, in which design elements are interconnected and changing with the character's movement. Our focus was on space and light as two essential elements of both dance performances and virtual environments. We employed and tested our dynamic VR environment design approach by means of two studies (on space and on light) with 24 users in total. The results of these studies are discussed with regard to the enhancement of, and benefits for the user experience, followed by suggestions for the applicability of our approach to other fields in VR design.
IRPMID: Medical XRAY Image Impulse Noise Removal using Partition Aided Median, Interpolation and DWT

The use of dental radiography techniques like XRay has increased the diagnosis efficiency, but the images contain impulse noise. Therefore, denoising is a very important factor in any subjective evaluation of the quality of images. The proposed impulse noise removal method namely ‘Impulse noise removal using Partition aided Median, Interpolation and DWT techniques’ is successfully implemented in the Dental XRAY images. The abbreviation of this method is IRPMID. The techniques such as median filter, interpolation, and Discrete Wavelet Transform (DWT) empower the proposed method with the partition based approach in denoising the salt and pepper noise. This pre-eminent method can diminish the noise range to more than 90%. The dental XRay images can gain a significant improvement in image enhancement in terms of PSNR, IEF, SSIM, etc.
Development of 3D Physics Learning Media using Augmented Reality for First-year Junior High School Students

Abstract This study aims to produce 3D physics learning media in Indonesian first-year Junior High Schools (SMP/MTs) using Augmented Reality (AR). This research development uses the ADDIE (Analysis, Design, Development, Implementation and Evaluate) model. The development stage was carried out at the Physics Education Laboratory of the FKIP University of Riau and the implementation stage was carried out on 30 first-year students of SMPN 30 Pekanbaru. The 3D learning media validation sheet instrument by using AR consists of four assessment aspects, namely; (1) design aspects, (2) pedagogical aspects, (3) content aspects, and (4) technical aspects. Validation was carried out by six validators consisting of 2 material experts, 2 media experts, and 2 physics teachers. The results of six-validator assessment provide an average for each aspect as follows; (1) the design aspect has an average of 4.43 which is categorized as very valid, (2) the pedagogical aspect has an average of 4.42 very valid categories, (3) the content aspect has an average of 4.38 very valid categories, and (4) the technical aspect has an average of 4.51 which is also categorized as very valid. Overall the average validity for the four aspects of the assessment is 4.43 very valid categories. After the 3D physics learning media was developed, it was then declared feasible by the experts, the implementation phase of 3D physics learning media or small-scale trials on students was carried out to see the students' responses to 3D physics learning media. The student response test was carried out using a student response questionnaire containing 10 statements consisting of 6 aspects of assessment. The results of the students' responses at the implementation stage showed that 67% of the 30 students thought that 3D learning media using AR was very good for use in the learning process. Then 33% of 30 students think that 3D learning media using AR is good for use in the learning process. Certainly the 3D physics learning media product is suitable for use in the physics learning process in Junior High Schools.
Stria measurement using wave front phase imaging on an artificial plate

Wave Front Phase Imaging (WFPI) is used to measure the stria on an artificial, transparent plate made of Schott N-BK7&reg; glass material by accurately measuring the Optical Path Difference (OPD) map. WFPI is a new technique capable of reconstructing an accurate high resolution wave front phase map by capturing two intensity images at different propagation distances. An incoherent light source generated by a light emitting diode (LED) is collimated and transmitted through the sample. The resultant light beam carries the wave front information regarding the refraction index changes inside the sample<sup>1</sup>. Using this information, WFPI solves the Transport Intensity Equation (TIE) to obtain the wave front phase map. Topography of reflective surfaces can also be studied with a different arrangement where the collimated light beam is reflected and carrying the wave front phase, which again is proportional to the surface topography. Three Schott N-BK7&reg; glass block samples were measured, each marked in which location the wave front phase measurement will be performed<sup>2</sup>. Although WFPI output is an OPD map, knowing the value of refractive index of the material at the wavelength used in the measurements will lead to also knowing the thickness variations of the plate.
Automatic Text Summarization Using Deep Learning and Reinforcement Learning

Choosing relevant information from a giant source of data available online is a difficult and challenging task. Automatic summarization can address this challenge. Summarization is the task of condensing a chunk of text to a shorter version, which reduces the size of the initial text and simultaneously preserves the meaning of content. This model proposes automatic text summarization based on the reinforcement learning method and uses a deep learning network to estimate Q value. Here, we use rouge to analyze the performance of our model. ROUGE is used for evaluating automatic text summarization. The three phases of the project are text processing, text formation, and text evaluation. In text processing, select a set of sentences using latent semantic analysis and form a summary using reinforcement and deep Q network. And then, the summary is evaluated using rouge.
A Deep Learning framework for Interoperable Machine Learning

In this paper, we introduce an opinionated, extensible, Python framework that transpiles variety of classical Statistical and Machine Learning models onto Open Neural Network Exchange (ONNX) format via an underlying Deep Learning model. We achieve this by exploiting the compositionality of Deep Learning technology. By appropriately choosing the features, architecture, loss functions, and regularizers, among others, the fidelity between the source model and the target model can be specified. Depending on the model being transpiled, the fidelity can be exact or approximate. We present the design details, APIs of the framework, reference implementations, road map for development, and guidelines for contributions. A reference implementation is available for the popular scikit-learn APIs.
Extracting Vehicle Track Information from Unstabilized Drone Aerial Videos Using YOLOv4 Common Object Detector and Computer Vision

Several applications are common in providing vehicle detection and tracking in videos. Unfortunately, these applications are most effective in stationary surveillance situations where background subtraction yields phenomenal result for object detection. Machine learning models, such as Single Shot Detector like You Only Look Once (YOLO), do not depend on motion to detect objects of interest in images; however, they suffer from high misdetections due to varying sizes of objects particularly in drone footage of urban areas. In this paper, we present a method for extracting temporal vehicle track information from urban area drone surveillance videos that out-performs state of the art YOLO + deepSORT multi object tracker and achieves vehicle counts comparable to manual human counts.
DL-AMP and DBTO: An Automatic Merge Planning and Trajectory Optimization and its Application in Autonomous Driving

This paper presents an automatic merging algorithm for autonomous driving vehicles, which decouples the specific motion planning problem into a Dual-Layer Automatic Merge Planning (DL-AMP) and a Descent-Based Trajectory Optimization (DBTO). This work leads to great improvements in finding the best merge opportunity, lateral and longitudinal merge planning and control, trajectory postprocessing and driving comfort. Our algorithm's robustness, adaptiveness and efficiency have been tested and validated both in simulations and on-road tests.
Architectural framework for an interactive learning toolkit

At present, a significant demand has emerged for online educational tools that can be used as replacement for classroom education. Due to the ease of access, the preference of many users is focused on m-learning applications. This paper presents an architectural framework for an interactive mobile learning toolkit. This study explores different software design patterns and presents the implementation details of the prototype. As a case study, the application is applied for the primary education sector in Sri Lanka, as there is a lack of adaptive learning mobile toolkits that allow teachers and students to interact effectively. The study is concluded to be user-friendly, understandable, useful, and efficient through a System Usability Study.
Feature Selection with Class Hierarchy for Imbalance Problems

In this paper, we aim to improve the classification performance in imbalance data by mitigating the impact of the curse of dimensionality especially in minority classes of a few samples. We exploit a class hierarchy realized as a binary tree whose node has a subset of classes. We construct such a binary tree in a top-down way by taking into consideration the separability of classes and the size of the feature subset. It is expected that the generalization performance is improved, especially in minority classes having a small number of samples, and that the interpretability of the decision rule is enhanced by the smallness of the number of features. Experimental results showed a remarkable improvement is by the proposed method in large-scale problems with many classes, e.g. from 48% to 62% in the balanced accuracy. In addition, only one feature was chosen in every node of the class hierarchy in all the four datasets, bringing a high interpretability of the classification rules.
A Quantitative Study of Waterfall and Agile Methodologies With the Perspective of Project Management

Software engineering is used in order to develop larger and complex software products. As software product is needed in almost all the industries, software engineering becomes really important. Software development can be done through various software development life cycle (SDLC) models like waterfall model, agile model, spiral model, prototype model, etc. SDLC is a framework that defines the tasks that to be performed at each step in the development process. Authors are mainly focusing on two models (i.e., waterfall and agile model). Waterfall model is a serial model which follows a strict sequence. Agile methodology can be divided into scrum methodology and extreme programming. Scrum methodology mainly focuses on how to manage tasks in a team-based environment. Scrum consists of three main roles. They are scrum master, product owner, and scrum team. While comparing both the models, the main difference obtained is waterfall does not allow any customer involvement while agile does allow it.