Reviewerly/exHarmony huggingface dataset:
Flight With Limited Field of View: A Parallel and Gradient-Free Strategy for Micro Aerial Vehicle

In this article, a parallel and gradient-free strategy is presented for autonomous navigation of a quadrotor with a limited field of view (FOV). The FOV constraint is nonlinear and noncontinuous in general and involves higher order dynamics of the vehicle. Planning problems with such constraints are difficult to solve, especially with the real-time requirement. The proposed method first constructs an environment map by integrating the measurement of a limited-FOV sensor in a parallel process. Based on the map, it then selects a feasible local goal from a global path and generates a constraint-free nominal control sequence. Finally, a stochastic optimal control problem is solved to satisfy the limited FOV constraint, the dynamic constraint, and the collision-free conditions at the same time. The objective functions are nonlinear and noncontinuous and involves dynamic of MAV. A parallel-gradient-free algorithm is used to address these problems. Both the sensing and planning problems are solved parallelly on a lightweight graphics processing unit. The effectiveness of the proposed strategy is demonstrated through both simulation and real-flight experiments.
Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery

Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.
SPEULER: Semantics-preserving Euler Diagrams

Creating comprehensible visualizations of highly overlapping set-typed data is a challenging task due to its complexity. To facilitate insights into set connectivity and to leverage semantic relations between intersections, we propose a fast two-step layout technique for Euler diagrams that are both well-matched and well-formed. Our method conforms to established form guidelines for Euler diagrams regarding semantics, aesthetics, and readability. First, we establish an initial ordering of the data, which we then use to incrementally create a planar, connected, and monotone dual graph representation. In the next step, the graph is transformed into a circular layout that maintains the semantics and yields simple Euler diagrams with smooth curves. When the data cannot be represented by simple diagrams, our algorithm always falls back to a solution that is not well-formed but still well-matched, whereas previous methods often fail to produce expected results. We show the usefulness of our method for visualizing set-typed data using examples from text analysis and infographics. Furthermore, we discuss the characteristics of our approach and evaluate our method against state-of-the-art methods.
Quantum Machine Learning Algorithm for Knowledge Graphs

Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs. Since most tensor problems are NP-hard [18], it is challenging to devise quantum algorithms to support the inference task. We simplify the modeling task by making the plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves speedup with a polylogarithmic runtime in the dimension of knowledge graph tensor.
Classification Framework for Fraud Detection Using Hidden Markov Model

Machine learning is described as a computer program that learns from experience E with regard to some task T and some performance measure P, if its performance on T improves with E as measured by P. Suppose we have a credit card fraud detection which watches which transactions we mark as fraud or not, and on the basis, it knows how to filter better fraudulent transactions then, E is watching your transactions is fraud or not, T is classifying your transactions as fraud or not, P is number of transactions correctly differentiated as spam or not spam. Machine learning has two types: supervised learning and unsupervised learning. Supervised learning is the type of machine learning where machine is provided with input mapped with its output, and these inputs and outputs are used to make a machine learn a particular function from the trained dataset. There are two branches of supervised learning, i.e., classification and regression. In unsupervised learning, we do not supervise model instead we allow machine to work on its own to discover information. Clustering is type of unsupervised learning.
A Real-Time Automatic Translation of Text to Sign Language

Communication is a basic need of every human being; by this, they can learn, express their feelings and exchange their ideas, but deaf people cannot listen and speak. For communication, they use various hands gestures, also known as Sign Language (SL), which they learn from special schools. As normal people have not taken SL classes; therefore, they are unable to perform signs of daily routine sentences (e.g., what are the specifications of this mobile phone?). A technological solution can facilitate in overcoming this communication gap by which normal people can communicate with deaf people. This paper presents an architecture for an application named Sign4PSL that translates the sentences to Pakistan Sign Language (PSL) for deaf people with visual representation using virtual signing character. This research aims to develop a generic independent application that is lightweight and reusable on any platform, including web and mobile, with an ability to perform offline text translation. The Sign4PSL relies on a knowledge base that stores both corpus of PSL Words and their coded form in the notation system. Sign4PSL takes English language text as an input, performs the translation to PSL through sign language notation and displays gestures to the user using virtual character. The system is tested on deaf students at a special school. The results have shown that the students were able to understand the story presented to them appropriately.
Evaluating GANs in Medical Imaging

Generative Adversarial Networks (GANs) have recently gained large interest in computer vision being used in many tasks, but their evaluation is still an open issue. This is especially true in medical imaging where GAN application is at its infancy, and where the use of scores based on models trained on datasets far away from the medical domain, e.g. the Inception score, can lead to misleading results. To overcome such limitations we propose a framework to evaluate images generated by GANs in terms of fidelity and structural similarity with the real ones. On the one hand, we measure the distance between the probability densities of the real and generated samples by exploiting feature representations given by a Convolutional Neural Network (CNN) trained as a discriminator. On the other hand, we compute domain-independent metrics catching the image high-level quality. We also introduce a visual layer explaining the CNN. We extensively evaluate the proposed approach with 4 state-of-the-art GANs over a real-world medical dataset of CT lung images.
A Method for Designing and Analyzing Automotive Software Architecture: A Case Study for an Autonomous Electric Vehicle

Software complexity is increased in automotive systems because many software functions are required for autonomous driving, electrified vehicles, and connected cars. In addition, autonomous driving requires centralized software that generally decreases evolvability with many connections. Thus, the automotive industry adopted the microservice architecture within the service-oriented architecture (SOA), which was already being used in distributed computing environments in the information and communication technology (ICT) industry. However, the software characteristics of an automotive system are different from those of an ICT system. Automotive software generally fulfills safety and real-time requirements that are not required in ICT software. Another challenge is integrating electric control units (ECUs) because software platforms supporting SOA require relatively high computational power and network bandwidth, which increases ECU cost. Thus, the deployment of software functions must be considered before integrating ECUs to find an optimal design solution for evolvability, dependability, real-time performance, cost, etc. However, many OEMs integrate ECUs based on deploying vehicular features without software architecture. It causes optimality problems during integrating ECUs. We propose component-based sensor-process-actuator architectural style for high-level architecture to handle quality attributes. Software architecture for an autonomous electrified vehicle will be presented with the proposed architectural style. The architecture is used to deploy software components and integrated ECUs with empirical quantitative analysis. Four design patterns for dependability with the architectural style will also be introduced.
Deep MRI Reconstruction with Generative Vision Transformers

Supervised training of deep network models for MRI reconstruction requires access to large databases of fully-sampled MRI acquisitions. To alleviate dependency on costly databases, unsupervised learning strategies have received interest. A powerful framework that eliminates the need for training data altogether is the deep image prior (DIP). To do this, DIP inverts randomly-initialized models to infer network parameters most consistent with the undersampled test data. However, existing DIP methods leverage convolutional backbones, suffering from limited sensitivity to long-range spatial dependencies and thereby poor model invertibility. To address these limitations, here we propose an unsupervised MRI reconstruction based on a novel generative vision transformer (GVTrans). GVTrans progressively maps low-dimensional noise and latent variables onto MR images via cascaded blocks of cross-attention vision transformers. Cross-attention mechanism between latents and image features serve to enhance representational learning of local and global context. Meanwhile, latent and noise injections at each network layer permit fine control of generated image features, improving model invertibility. Demonstrations are performed for scan-specific reconstruction of brain MRI data at multiple contrasts and acceleration factors. GVTrans yields superior performance to state-of-the-art generative models based on convolutional neural networks (CNNs).
Sentiment Analysis of COVID-19 Vaccine Perception Using NLP

This paper analyzes the sentiments of the people residing in India with regards to the COVID-19 vaccine. The COVID-19 pandemic has also coincided with the social media companies experiencing increased traffic. This is due to people having limited options/platforms for expressing their opinion. Taking advantage of this, the paper uses three different models to perform sentiment analysis on English tweets posted by users in India and chooses a deep learning model after comparing their results.
Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution

High quality imaging usually requires bulky and expensive lenses to compensate geometric and chromatic aberrations. This poses high constraints on the optical hash or low cost applications. Although one can utilize algorithmic reconstruction to remove the artifacts of low-end lenses, the degeneration from optical aberrations is spatially varying and the computation has to trade off efficiency for performance. For example, we need to conduct patch-wise optimization or train a large set of local deep neural networks to achieve high reconstruction performance across the whole image. In this paper, we propose a PSF aware deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating deep priors, thus leading to a universal and flexible optical aberration correction method. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters, which largely alleviates the time and memory consumption of model learning. The approach is of high efficiency in both training and testing stages. Extensive results verify the promising applications of our proposed approach for compact low-end cameras. The code is available at https://github.com/leehsiu/UABC
Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images

Deep learning methods such as convolutional neural networks (CNNs) are difficult to directly utilize to analyze whole slide images (WSIs) due to the large image dimensions. We overcome this limitation by proposing a novel two-stage approach. First, we extract a set of representative patches (called mosaic) from a WSI. Each patch of a mosaic is encoded to a feature vector using a deep network. The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i.e., anatomic site and primary diagnosis. In the second stage, a set of encoded patch-level features from a WSI is used to compute the primary diagnosis probability through the proposed Pay Attention with Focus scheme, an attention-weighted averaging of predicted probabilities for all patches of a mosaic modulated by a trainable focal factor. Experimental results show that the proposed model can be robust, and effective for the classification of WSIs.
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection

Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth estimation and normal estimation. These different tasks provide rich information for the whole robotic perception system. All tasks have their own characteristics while sharing some latent correlations. However, some of the task predictions may suffer from the unreliability dealing with complex scenes and anomalies. We propose an attention-based failure detection approach by exploiting the correlations among multiple tasks. The proposed framework infers task failures by evaluating the individual prediction, across multiple visual perception tasks for different regions in an image. The formulation of the evaluations is based on an attention network supervised by multi-task uncertainty estimation and their corresponding prediction errors. Our proposed framework <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code link https://github.com/ethz-asl/uncertainty_with_multiple_tasks. generates more accurate estimations of the prediction error for the different task's predictions.
k-NDDP: An Efficient Anonymization Model for Social Network Data Release

With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than 0.8%, also added dummy links are 60% less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data.
Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance

Even though CCTV cameras are widely deployed for traffic surveillance and have therefore the potential of becoming cheap automated sensors for traffic speed analysis, their large-scale usage toward this goal has not been reported yet. A key difficulty lies in fact in the camera calibration phase. Existing state-of-the-art methods perform the calibration using image processing or keypoint detection techniques that require high-quality video streams, yet typical CCTV footage is low-resolution and noisy. As a result, these methods largely fail in real-world conditions. In contrast, we propose two novel calibration techniques whose only inputs come from an off-the-shelf object detector. Both methods consider multiple detections jointly, leveraging the fact that cars have similar and well-known 3D shapes with normalized dimensions. The first one is based on minimizing an energy function corresponding to a 3D reprojection error, the second one instead learns from synthetic training data to predict the scene geometry directly. Noticing the lack of speed estimation benchmarks faithfully reflecting the actual quality of surveillance cameras, we introduce a novel dataset collected from public CCTV streams. Experimental results conducted on three diverse benchmarks demonstrate excellent speed estimation accuracy that could enable the wide use of CCTV cameras for traffic analysis, even in challenging conditions where state-of-the-art methods completely fail. Additional information can be found on our project web page: https://rebrand.ly/nle-cctv
Books (Are Not Like People): A Postdigital Fable

Abstract What are books? In 2054, where reading and writing have been banned, a scholar in a dystopian academy known as University V might legitimately pose such a question. This article uses speculative fiction as a form of narrative enquiry to explore the socio-materiality of the iconic educational artefact of the textbook. It gives an empirical account of socio-material practices of textbook use (and non-use) gathered from a series of interviews with online distance education students. We analyse these interviews via speculative fiction. We engage in a sense-making activity of the student testimony by narrating their story, via a scholar looking back at our times from a post-literature future. We seek to contribute to a relative dearth of future studies that use real student data. We give an example of how speculative fiction may be used as a form of research method to analyse and interpret such data. In so doing, we seek to cast a light on current educational practices, to show how books and people are entangled. As people, objects and spaces of education intertwine, they call our attention to the interplay of form and function. They decentre the human actor. We attempt to show how form legitimates certain types of knowledge, certain people, indeed people themselves from other non-human actors. We conclude that knowledge is not disembodied, is not stable and is not locked up in books. In our final analysis, we conclude what may seem obviously true, that books are not like people.
Real-time Covid-19 Face Mask Detection with YOLOv4

COVID-19 pandemic has caused widespread political and financial instability across the world. The WHO has released several recommendations for coronavirus control. One of the best preventive measures is wearing a face mask in public places. According to research, wearing a face mask significantly decreases the chance of infection. As a result, a face mask detection deep learning model can be used to detect either individuals are wearing a mask or not, allowing the system to reduce the number of enforcing agents on the ground. Using the YOLOv4 architecture, the system is trained to capture facial mask features in images and real-time streams. After training 4000 epochs, the YOLOv4 object detection algorithm achieved remarkable results in real-time scenarios, with an average FPS of 49.5 and a mAP of 98%.
Qmin – A machine learning-based application for processing and analysis of mineral chemistry data

Mineral chemistry analysis is a valuable tool with many applications in mineralogy and mineral prospecting and beneficiation studies. This type of analysis can point out relevant information, such as the concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Therefore, considerable amounts of data have been generated, especially with the use of electron probe microanalyzers (EPMA), either for academic research or for prospecting and applied mineralogical work in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, investigated the possibility that such research might benefit from the versatility of machine learning algorithms. We present Qmin, an application that assists in increasing the efficiency of processing and analysis of mineral chemistry data through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forests algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, which is maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 22,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ∼99% for the minerals known by Qmin. This outcome emphasizes the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data.