Miracle-dz/UniFAR-FTUs huggingface dataset:
Motif-Based Visual Analysis of Dynamic Networks

Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks remains challenging due to the underlying large-scale data containing elusive topological changes. We propose two complementary pixel-based visualizations, which reflect occurrences of selected sub-networks (motifs) and provide a time-scalable overview of dynamic networks: a network-level census (motif significance profiles) linked with a node-level sub-network metric (graphlet degree vectors) views to reveal structural changes, trends, states, and outliers. The network census captures significantly occurring motifs compared to their expected occurrences in random networks and exposes structural changes in a dynamic network. The sub-network metrics display the local topological neighborhood of a node in a single network belonging to the dynamic network. The linked pixel-based visualizations allow exploring motifs in different-sized networks to analyze the changing structures within and across dynamic networks, for instance, to visually analyze the shape and rate of changes in the network topology. We describe the identification of visual patterns, also considering different reordering strategies to emphasize visual patterns. We demonstrate the approach’s usefulness by a use case analysis based on real-world large-scale dynamic networks, such as the evolving social networks of Reddit or Facebook.
Catastrophic Cooling in Superwinds. III. Nonequilibrium Photoionization

Observations of some starburst-driven galactic superwinds suggest that strong radiative cooling could play a key role in the nature of feedback and the formation of stars and molecular gas in star-forming galaxies. These catastrophically cooling superwinds are not adequately described by adiabatic fluid models, but they can be reproduced by incorporating nonequilibrium radiative cooling functions into the fluid model. In this work, we have employed the atomic and cooling module maihem implemented in the framework of the flash hydrodynamics code to simulate the formation of radiatively cooling superwinds as well as their corresponding nonequilibrium ionization (NEI) states for various outflow parameters, gas metallicities, and ambient densities. We employ the photoionization program cloudy to predict radiation- and density-bounded photoionization for these radiatively cooling superwinds, and we predict UV and optical line emission. Our nonequilibrium photoionization models built with the NEI states demonstrate the enhancement of C iv, especially in metal-rich, catastrophically cooling outflows, and O vi in metal-poor ones.
Calibrated Selective Classification

Selective classification allows models to abstain from making predictions (e.g., say"I don't know") when in doubt in order to obtain better effective accuracy. While typical selective models can be effective at producing more accurate predictions on average, they may still allow for wrong predictions that have high confidence, or skip correct predictions that have low confidence. Providing calibrated uncertainty estimates alongside predictions -- probabilities that correspond to true frequencies -- can be as important as having predictions that are simply accurate on average. However, uncertainty estimates can be unreliable for certain inputs. In this paper, we develop a new approach to selective classification in which we propose a method for rejecting examples with"uncertain"uncertainties. By doing so, we aim to make predictions with {well-calibrated} uncertainty estimates over the distribution of accepted examples, a property we call selective calibration. We present a framework for learning selectively calibrated models, where a separate selector network is trained to improve the selective calibration error of a given base model. In particular, our work focuses on achieving robust calibration, where the model is intentionally designed to be tested on out-of-domain data. We achieve this through a training strategy inspired by distributionally robust optimization, in which we apply simulated input perturbations to the known, in-domain training data. We demonstrate the empirical effectiveness of our approach on multiple image classification and lung cancer risk assessment tasks.
Distributed Spatio-Temporal Information Based Cooperative 3D Positioning in GNSS-Denied Environments

A distributed spatio-temporal information based cooperative positioning (STICP) algorithm is proposed for wireless networks that require three-dimensional (3D) coordinates and operate in the global navigation satellite system (GNSS) denied environments. Our algorithm supports any type of ranging measurements that can determine the distance between nodes. We first utilize a finite symmetric sampling based scaled unscented transform (SUT) method for approximating the nonlinear terms of the messages passing on the associated factor graph (FG) with high precision, despite relying on a small number of samples. Then, we propose an enhanced anchor upgrading mechanism to avoid any redundant iterations. Our simulation results and analysis show that the proposed STICP has a lower computational complexity than the state-of-the-art belief propagation based localizer, despite achieving an even more competitive positioning performance.
Low-Level Physiological Implications of End-to-End Learning of Speech Recognition

Current speech recognition architectures perform very well from the point of view of machine learning, hence user interaction. This suggests that they are emulating the human biological system well. We investigate whether the inference can be inverted to provide insights into that biological system; in particular the hearing mechanism. Using SincNet, we confirm that end-to-end systems do learn well known filterbank structures. However, we also show that wider band-width filters are important in the learned structure. Whilst some benefits can be gained by initialising both narrow and wide-band filters, physiological constraints suggest that such filters arise in mid-brain rather than the cochlea. We show that standard machine learning architectures must be modified to allow this process to be emulated neurally.
MultiView : a methodology for supporting multiple view schemata in object-oriented

It has been widely recognized that object-oriented database (OODB) technology needs to be extended to provide a mechanism similar to views in relational database systems. We define an object-oriented view to be an arbitrarily complex virtual sche:r;na graph with possibly restructured generalization and decomposition hierarchies - rather than just one virtual class as has been pro posed in the literature. In this paper, we propose a methodology, called Multi View, for supporting multiple such view schemata. MultiView breaks the schema design task into the following indepen dent and well-defined subtasks: (1) the customization of type descriptions and object sets of existing classes by deriving virtual classes, (2) the integration of all derived classes into one consistent global schema graph, and (3) the definition of arbitrarily complex view schemata on this augmented global schema. For the first task of MultiView, we define a set of object algebra operators that can be used by the view definer for class customization. For the second task of MultiView, we propose an algorithm that automatically integrates these newly derived virtual classes into the global schema. We solve the third task of MultiView by first letting the view definer explicitly select the desired view classes from the global schema using a view definition language and then by automatically generating a view class hierarchy for these selected classes. In addition, we present algorithms ·that verify the closure property of a view and, if found to be incomplete, transform it into a closed, yet minimal, view. In this paper, we introduce the fundamental concept of view independence and show Multi View to be view independent. We also outline implementation techniques for realizing Multi View with existing OODB technology.
Nonlocal Bounded Variations with Applications

Motivated by problems where jumps across lower dimensional subsets and sharp transitions across interfaces are of interest, this paper studies the properties of fractional bounded variation ($BV$)-type spaces. Two different natural fractional analogs of classical $BV$ are considered: $BV^\alpha$, a space induced from the Riesz-fractional gradient that has been recently studied by Comi-Stefani; and $bv^\alpha$, induced by the Gagliardo-type fractional gradient often used in Dirichlet forms and Peridynamics - this one is naturally related to the Caffarelli-Roquejoffre-Savin fractional perimeter. Our main theoretical result is that the latter $bv^\alpha$ actually corresponds to the Gagliardo-Slobodeckij space $W^{\alpha,1}$. As an application, using the properties of these spaces, novel image denoising models are introduced and their corresponding Fenchel pre-dual formulations are derived. The latter requires density of smooth functions with compact support. We establish this density property for convex domains.
Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Indigenous African languages are categorized as under-served in Natural Language Processing. They therefore experience poor digital inclusivity and information access. The processing challenge with such languages has been how to use machine learning and deep learning models without the requisite data. The Kencorpus project intends to bridge this gap by collecting and storing text and speech data that is good enough for data-driven solutions in applications such as machine translation, question answering and transcription in multilingual communities. The Kencorpus dataset is a text and speech corpus for three languages predominantly spoken in Kenya: Swahili, Dholuo and Luhya. Data collection was done by researchers from communities, schools, media, and publishers. The Kencorpus' dataset has a collection of 5,594 items - 4,442 texts (5.6M words) and 1,152 speech files (177hrs). Based on this data, Part of Speech tagging sets for Dholuo and Luhya (50,000 and 93,000 words respectively) were developed. We developed 7,537 Question-Answer pairs for Swahili and created a text translation set of 13,400 sentences from Dholuo and Luhya into Swahili. The datasets are useful for downstream machine learning tasks such as model training and translation. We also developed two proof of concept systems: for Kiswahili speech-to-text and machine learning system for Question Answering task, with results of 18.87% word error rate and 80% Exact Match (EM) respectively. These initial results give great promise to the usability of Kencorpus to the machine learning community. Kencorpus is one of few public domain corpora for these three low resource languages and forms a basis of learning and sharing experiences for similar works especially for low resource languages.
Walk on the Low Side: LOFAR Explores the Low-frequency Radio Emission of GASP Jellyfish Galaxies

Jellyfish galaxies, characterized by long filaments of stripped interstellar medium extending from their disks, are the prime laboratories to study the outcomes of ram pressure stripping. At radio wavelengths, they often show unilateral emission extending beyond the stellar disk, and an excess of radio luminosity with respect to that expected from their current star formation rate. We present new 144 MHz images provided by the LOFAR Two-metre Sky Survey for a sample of six galaxies from the GASP survey. These galaxies are characterized by a high global luminosity at 144 MHz (6−27 × 1022 W Hz−1), in excess compared to their ongoing star formation rate. The comparison of radio and Hα images smoothed with a Gaussian beam corresponding to ∼10 kpc reveals a sublinear spatial correlation between the two emissions with an average slope of k = 0.50. In their stellar disk we measure k = 0.77, which is close to the radio-to-star formation linear relation. We speculate that, as a consequence of the ram pressure, in these jellyfish galaxies cosmic ray transport is more efficient than in normal galaxies. Radio tails typically have higher radio-to-Hα ratios than the disks, thus we suggest that the radio emission is boosted by electrons stripped from the disks. In all galaxies, the star formation rate has decreased by a factor ≤10 within the last ∼108 yr. The observed radio emission is consistent with the past star formation, so we propose that this recent decline may be the cause of their radio luminosity-to-star formation rate excess.
Quantum Multiple-Access One-Time Pad

We introduce and analyze an information theoretical task that we call the quantum multiple-access one-time pad. Here, a number of senders initially share a correlated quantum state with a receiver and an eavesdropper. Each sender performs a local operation to encode a classical message and sends their system to the receiver, who subsequently performs a measurement to decode the messages. The receiver will be able to decode the messages almost perfectly, while the eavesdropper must not be able to extract information about the messages even if they have access to the quantum systems transmitted. We consider a “conditional” scenario in which a portion of the receiver’s side information is also accessible to the eavesdropper. We investigate the maximum amount of classical information that can be encoded by each of the senders. We derive a single-letter characterization for the achievable rate region in an asymptotic limit of infinitely many copies and vanishingly small error.
Towards Benchmarking Explainable Artificial Intelligence Methods

The currently dominating artificial intelligence and machine learning technology, neural networks, builds on inductive statistical learning. Neural networks of today are information processing systems void of understanding and reasoning capabilities, consequently, they cannot explain promoted decisions in a humanly valid form. In this work, we revisit and use fundamental philosophy of science theories as an analytical lens with the goal of revealing, what can be expected, and more importantly, not expected, from methods that aim to explain decisions promoted by a neural network. By conducting a case study we investigate a selection of explainability method's performance over two mundane domains, animals and headgear. Through our study, we lay bare that the usefulness of these methods relies on human domain knowledge and our ability to understand, generalise and reason. The explainability methods can be useful when the goal is to gain further insights into a trained neural network's strengths and weaknesses. If our aim instead is to use these explainability methods to promote actionable decisions or build trust in ML-models they need to be less ambiguous than they are today. In this work, we conclude from our study, that benchmarking explainability methods, is a central quest towards trustworthy artificial intelligence and machine learning.
EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging. The remote experiment during the pandemic yields several challenges, and we discuss the possible solutions. This paper explores machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. The results show that Random Forest and RBF SVM perform well for EEG classification tasks. Furthermore, we investigate how to conduct such BCI experiments using affordable consumer-grade devices to collect EEG-based BCI data. In addition, we have developed the data collection protocol, EEG4Students, that grants non-experts who are interested in a guideline for such data collection. Our code and data can be found at https://github.com/GuangyaoDou/EEG4Students.
Ctrl-VIO: Continuous-Time Visual-Inertial Odometry for Rolling Shutter Cameras

In this letter, we propose a probabilistic continuous-time visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates the fusion of asynchronized high-frequency IMU data and motion-distorted rolling shutter images. To prevent intractable computation load, the proposed VIO is sliding-window and keyframe-based. We propose to probabilistically marginalize the control points to keep the constant number of keyframes in the sliding window. Furthermore, the line exposure time difference (line delay) of the rolling shutter camera can be online calibrated in our continuous-time VIO. To extensively examine the performance of our continuous-time VIO, experiments are conducted on publicly-available WHU-RSVI, TUM-RSVI, and SenseTime-RSVI rolling shutter datasets. The results demonstrate the proposed continuous-time VIO significantly outperforms the existing state-of-the-art VIO methods.
The WKY-Haq Oscillator as Power Source of Inductive Power Transfer

This paper is concerned with the wireless power transmission system based on inductance known as inductive power transfer (IPT), We have introduced a new oscillator called WKY-Haq, with an approximate mathematical relationship to adjust its frequency that was obtained experimentally in the electronics lab in physics department in the university of Benghazi. The WKY-Haq oscillator is a strong oscillator for operating the IPT system at low frequency 77.66 kHz with an excellent efficiency using the series-series (SS) Topology. Only the presence of a larger number of turns in the receiver can greatly improve the efficiency.
Image Based Food Energy Estimation With Depth Domain Adaptation

Assessment of dietary intake has primarily relied on self-report instruments, which are prone to measurement errors. Dietary assessment methods have increasingly incorporated tech-nological advances particularly mobile, image based approaches to address some of these limitations and further automation. Mobile, image-based methods can reduce user burden and bias by automatically estimating dietary intake from eating occasion images that are captured by mobile devices. In this paper, we propose an “Energy Density Map” which is a pixel-to-pixel mapping from the RGB image to the energy density of the food. We then incorporate the “Energy Density Map” with an associated depth map that is captured by a depth sensor to estimate the food energy. The proposed method is evaluated on the N utrition5k dataset. Experimental results show improved results compared to baseline methods with an average error of 13.29 kCal and average percentage error of 13.57 % between the ground-truth and the estimated energy of the food.
Skin Lesion Analysis: A State-of-the-Art Survey, Systematic Review, and Future Trends

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists in order to reduce the challenges that are raised by manual inspection. The purpose of this article is to provide a comprehensive literature review of cutting-edge CAD techniques published between 2011 and 2020. The Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) method was used to identify a total of 365 publications, 221 for skin lesion segmentation and 144 for skin lesion classification. These articles are analyzed and summarized in a number of different ways so that we can contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (datasets utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria (metrics). We also intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. In addition, in this survey, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. In conclusion, enlightening findings, recommendations, and trends are discussed for the pur1 ar X iv :2 20 8. 12 23 2v 1 [ ee ss . I V ] 2 5 A ug 2 02 2 pose of future research surveillance in related fields of interest. It is anticipated that it will guide researchers of all levels, from beginners to experts, in the process of developing an automated and robust CAD system for skin lesion analysis.
Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection

—Environmental perception with multi-modal fusion of radar and camera is crucial in autonomous driving to increase the accuracy, completeness, and robustness. This paper focuses on how to utilize millimeter-wave (MMW) radar and camera sensor fusion for 3D object detection. A novel method which realizes the feature-level fusion under bird-eye view (BEV) for a better feature representation is proposed. Firstly, radar features are augmented with temporal accumulation and sent to a temporal-spatial encoder for radar feature extraction. Meanwhile, multi-scale image 2D features which adapt to various spatial scales are obtained by image backbone and neck model. Then, image features are transformed to BEV with the designed view trans-former. In addition, this work fuses the multi-modal features with a two-stage fusion model called point fusion and ROI fusion, respectively. Finally, a detection head regresses objects category and 3D locations. Experimental results demonstrate that the proposed method realizes the state-of-the-art (SOTA) performance under the most important detection metrics——mean average precision (mAP) and nuScenes detection score (NDS) on the challenging nuScenes dataset.
XDRI Attacks - and - How to Enhance Resilience of Residential Routers

We explore the security of residential routers and ﬁnd a range of critical vulnerabilities. Our evaluations show that 10 out of 36 popular routers are vulnerable to injections of fake records via misinterpretation of special characters. We also ﬁnd that in 15 of the 36 routers the mechanisms, that are meant to prevent cache poisoning attacks, can be circumvented. In our Internet-wide study with an advertisement network, we identiﬁed and analyzed 976 residential routers used by web clients, out of which more than 95% were found vulnerable to our attacks. Overall, vulnerable routers are prevalent and are distributed among 177 countries and 4830 networks. To understand the core factors causing the vulnerabilities we perform black- and white-box analyses of the routers. We ﬁnd that many problems can be attributed to incorrect assumptions on the protocols’ behaviour and the Internet, misunderstanding of the standard recommendations, bugs, and simpliﬁed DNS software implementations. We provide recommendations to mitigate our attacks. We also set up a tool to enable everyone to evaluate the security of their routers at https://xdi-attack.net/ .