Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Networks Architectures for Signals Supported on Graphs

We describe two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs. The selection graph neural network (GNN) replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a possibly nonlinear subsampling stage where nearby nodes pool their information in a set of preselected sample nodes. A key component of the architecture is to remember the position of sampled nodes to permit computation of convolutional features at deeper layers. The aggregation GNN diffuses the signal through the graph and stores the sequence of diffused components observed by a designated node. This procedure effectively aggregates all components into a stream of information having temporal structure to which the convolution and pooling stages of regular CNNs can be applied. A multinode version of aggregation GNNs is further introduced for operation in large scale graphs. An important property of selection and aggregation GNNs is that they reduce to conventional CNNs when particularized to time signals reinterpreted as graph signals in a circulant graph. Comparative numerical analyses are performed in a synthetic source localization application. Performance is evaluated for a text category classification problem using word proximity networks. Multinode aggregation GNNs are consistently the best performing GNN architecture.
Image moments: generic descriptors for decoupled image-based visual servo

Moments are generic and (usually) intuitive descriptors that can be computed from several kinds of objects defined either from closed contours (continuous object) or a set of points (discrete object). In this paper, we propose to use moments to design a decoupled image-based visual servo. The analytical form of the interaction matrix related to the moments computed for a discrete object is derived, and we show that it is different of the form obtained for a continuous object. Six visual features are selected to design a decoupled control scheme when the object is parallel to the image plane. This nice property is then generalized to the case where the desired object position is not parallel to the image plane. Finally, experimental results are presented to illustrate the validity of our approach and its robustness with respect to modeling errors.
Stealth Key Exchange and Confined Access to the Record Protocol Data in TLS 1.3

We show how to embed a covert key exchange sub protocol within a regular TLS 1.3 execution, generating a stealth key in addition to the regular session keys. The idea, which has appeared in the literature before, is to use the exchanged nonces to transport another key value. Our contribution is to give a rigorous model and analysis of the security of such embedded key exchanges, requiring that the stealth key remains secure even if the regular key is under adversarial control. Specifically for our stealth version of the TLS 1.3 protocol we show that this extra key is secure in this setting under the common assumptions about the TLS protocol. As an application of stealth key exchange we discuss sanitizable channel protocols, where a designated party can partly access and modify payload data in a channel protocol. This may be, for instance, an intrusion detection system monitoring the incoming traffic for malicious content and putting suspicious parts in quarantine. The noteworthy feature, inherited from the stealth key exchange part, is that the sender and receiver can use the extra key to still communicate securely and covertly within the sanitizable channel, e.g., by pre-encrypting confidential parts and making only dedicated parts available to the sanitizer. We discuss how such sanitizable channels can be implemented with authenticated encryption schemes like GCM or ChaChaPoly. In combination with our stealth key exchange protocol, we thus derive a full-fledged sanitizable connection protocol, including key establishment, which perfectly complies with regular TLS 1.3 traffic on the network level. We also assess the potential effectiveness of the approach for the intrusion detection system Snort.
CiTIUS at the TREC 2020 Health Misinformation Track

The TREC Health Misinformation track focuses on discerning reliable from unreliable information and correct from incorrect information. This problem is very common in Web Search results and it is especially critical when it is related to health content [1]. This year’s task focuses on COVID-19 and SARS-CoV-2 misinformation. In our experiments, we applied a BM25 retrieval baseline as a first step. Afterwards, we used a reliability classifier recently developed by our team [2]. Finally, we also experimented with BERT-based variants that attempt to estimate similarity between sentences
Feature Unlearning for Generative Models via Implicit Feedback

We tackle the problem of feature unlearning from a pre-trained image generative model. Unlike a common unlearning task where an unlearning target is a subset of the training set, we aim to unlearn a speciﬁc feature, such as hairstyle from facial images, from the pretrained generative models. As the target feature is only presented in a local region of an image, unlearning the entire image from the pretrained model may result in losing other details in the remaining region of the image. To specify which features to unlearn, we develop an implicit feedback mechanism where a user can select images containing the target feature. From the implicit feedback, we identify a latent representation corresponding to the target feature and then use the representation to un-learn the generative model. Our framework is generalizable for the two well-known families of generative models: GANs and VAEs. Through experiments on MNIST and CelebA datasets, we show that target features are successfully removed while keeping the ﬁdelity of the original models.
kτ, ε-anonymity: Towards Privacy-Preserving Publishing of Spatiotemporal Trajectory Data

Mobile network operators can track subscribers via passive or active monitoring of device locations. The recorded trajectories offer an unprecedented outlook on the activities of large user populations, which enables developing new networking solutions and services, and scaling up studies across research disciplines. Yet, the disclosure of individual trajectories raises significant privacy concerns: thus, these data are often protected by restrictive non-disclosure agreements that limit their availability and impede potential usages. In this paper, we contribute to the development of technical solutions to the problem of privacy-preserving publishing of spatiotemporal trajectories of mobile subscribers. We propose an algorithm that generalizes the data so that they satisfy $k^{\tau,\epsilon}$-anonymity, an original privacy criterion that thwarts attacks on trajectories. Evaluations with real-world datasets demonstrate that our algorithm attains its objective while retaining a substantial level of accuracy in the data. Our work is a step forward in the direction of open, privacy-preserving datasets of spatiotemporal trajectories.
Observing the yield curve of compacted pack ice

[1] A method for observing the yield curve of compacted pack ice is developed based on the characteristic analysis of the stress field within the pack ice. The analysis shows that the slope of the yield curve is associated with the angle between intersecting linear kinematic features; thus by measuring the intersection angles we can inversely estimate the yield curve. Applying this method to the observed angles generates a curved diamond yield curve, which possesses almost all the advantages identified by the other methods. However, due to the limited data available, more observations are favored to refine this yield curve.
Tweet Disambiguate Entities Retrieve Folksonomy SubTree Step 1 : Discover Categories Generate Topic Profile from SubTrees Step 2 : Discover Profile Topic Profile : “ English Football ” “ World Cup ”

Twitter, a micro-blogging service, provides users with a framework for writing brief, often-noisy postings about their lives. These posts are called “Tweets. ” In this paper we present early results on discovering Twitter users’ topics of interest by examining the entities they mention in their Tweets. Our approach leverages a knowledge base to disambiguate and categorize the entities in the Tweets. We then develop a “topic profile,” which characterizes users’ topics of interest, by discerning which categories appear frequently and cover the entities. We demonstrate that even in this early work we are able to successfully discover the main topics of interest for the users in our study.
Seabed classification of the South Tasman Rise from SIMRAD EM12 backscatter data using artificial neural networks

We have developed an automated method for sea‐floor classification for the South Tasman Rise, based on a SIMRAD EM12‐backscatter (13 kHz) mosaic and 47 sea‐floor samples. The samples have been divided into 3 distinct groups representing: (i) thick blankets of foraminiferal ooze; (ii) mixed sediments comprising sand/silt/mud (turbidites/chalk); and (iii) outcrops of metamorphic basement and volcanic rocks. A total of 515 sub‐areas, each measuring 32 × 32 pixels (∼4 km2) and representing the different seabed types, were extracted from the image from areas 128×128 pixels large, centred on the sample locations. The texture of the sub‐images was analysed by calculating grey‐level run‐length features, spatial grey‐level dependence matrices, and grey‐level difference vectors in four directions. A total of 100 samples for each class and 18 feature statistics were chosen to train an artificial neural network to recognise the textural attributes and their variability for each class. The performance of the network w.
Learning from Small Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

—Motivated by the problem of learning when the number of training samples is small, this paper shows how to incorporate into support-vector machines (SVMs) those properties that have made convolutional neural networks (CNNs) successful. Particularly important is the ability to incorporate domain knowledge of invariances, e.g., translational invariance of images. Kernels based on the minimum distance over a group of transformations, which corresponds to deﬁning similarity as the best over the possible transformations, are not generally positive deﬁnite. Perhaps it is for this reason that they have neither previously been experimentally tested for their performance nor studied theoretically. Instead, previous attempts have employed kernels based on the average distance over a group of transformations, which are trivially positive deﬁnite, but which generally yield both poor margins as well as poor performance, as we show. We address this lacuna and show that positive deﬁniteness indeed holds with high probability for kernels based on the minimum distance in the small training sample set regime of interest, and that they do yield the best results in that regime. Another important property of CNNs is their ability to incorporate local features at multiple spatial scales, e.g., through max pooling. A third important property is their ability to provide the beneﬁts of composition through the architecture of multiple layers. We show how these additional properties can also be embedded into SVMs. We verify through experiments on widely available image sets that the resulting SVMs do provide superior accuracy in comparison to well-established deep neural network (DNN) benchmarks for small sample sizes.
Market Signaling with Grades *

We consider a market signaling model in which receivers observe both the sender's costly signal as well as a stochastic grade that is correlated with the sender's type. In equilibrium, the sender resolves the trade-off between using the costly signal versus relying on the noisy grade to distinguish himself. When grades are sufficiently informative , separating equilibria do not survive common refinements. The prediction depends on the prior—it involves full pooling when the prior puts sufficient weight on the high type and partial pooling otherwise. The equilibrium converges to the full-information outcome as the fraction of high types goes to one—resolving a long-standing paradox within the signaling literature.
Resource-aware Federated Learning using Knowledge Extraction and Multi-model Fusion

With increasing concern about user data privacy, feder- ated learning (FL) has been developed as a unique training paradigm for training machine learning models on edge de- vices without access to sensitive data. Traditional FL and existing methods directly employ aggregation methods on all edges of the same models and training devices for a cloud server. Although these methods protect data privacy, they are not capable of model heterogeneity, even ignore the heterogeneous computing power, and incur steep communica- tion costs. In this paper, we purpose a resource-aware FL to aggregate an ensemble of local knowledge extracted from edge models, instead of aggregating the weights of each local model, which is then distilled into a robust global knowledge as the server model through knowledge distillation. The local model and the global knowledge are extracted into a tiny size knowledge network by deep mutual learning. Such knowledge extraction allows the edge client to deploy a resource- aware model and perform multi-model knowledge fusion while maintaining communication efﬁciency and model het- erogeneity. Empirical results show that our approach has signiﬁcantly improved over existing FL algorithms in terms of communication cost and generalization performance in heterogeneous data and models. Our approach reduces the com- munication cost of VGG-11 by up to 102 × and ResNet-32 by up to 30 × when training ResNet-20 as the knowledge net- work.
Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification

Computer-aided diagnosis (CAD) is becoming a prominent approach to assist clinicians spanning across multiple fields. These automated systems take advantage of various computer vision (CV) procedures, as well as artificial intelligence (AI) techniques, so that a diagnosis of a given image (e.g., computed tomography and ultrasound) can be formulated. Advances in both areas (CV and AI) are enabling ever increasing performances of CAD systems, which can ultimately avoid performing invasive procedures such as fine-needle aspiration. In this study, we focus on thyroid ultrasonography to present a novel knowledge-driven classification framework. The proposed system leverages cues provided by an ensemble of experts, in order to guide the learning phase of a densely connected convolutional network (DenseNet). The ensemble is composed by various networks pretrained on ImageNet, including AlexNet, ResNet, VGG, and others, so that previously computed feature parameters could be used to create ultrasonography domain experts via transfer learning, decreasing, moreover, the number of samples required for training. To validate the proposed method, extensive experiments were performed, providing detailed performances for both the experts ensemble and the knowledge-driven DenseNet. The obtained results, show how the the proposed system can become a great asset when formulating a diagnosis, by leveraging previous knowledge derived from a consult.
Towards HCCI Control by Ozone Seeding

Nowadays, the main objectives in the automobile engine field are to reduce fuel consumption and pollutant emissions. HCCI engines can be a good solution to meet pollutant emission requirements and to achieve high combustion efficiency. However, before an HCCI engine is used as a conventional engine, several problems must be overcome, in particular control of the progression of combustion. Many studies have been conducted into possible control methods. A new strategy consists in using oxidizing chemical species such as ozone to seed the intake of a HCCI engine. As increasingly smaller ozonizers are now being designed, this kind of device could be integrated on a vehicle and on a HCCI engine, in order to control combustion phasing and promote the future use of this engine as a conventional engine. In the present study, experiments on a HCCI engine fuelled with iso-octane were carried out with ozone seeding in the intake. Results showed that when assisted by the addition of ozone, combustion can be enhanced and moved forward. Consequently, the use of oxidizing chemical species can be a means to control HCCI combustion. Depending on the inlet temperature, the control of combustion phasing may be more or less easy due to sensitivity to the ozone concentration. The present results also show the existence of a cool flame in the case of iso-octane combustion, indicating that ozone seeding can also be used in order to study iso-octane cool flame in a HCCI engine.
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime

While the decision-theoretic optimality of the Bayesian formalism under correct model specification is well-known (Berger 2013), the Bayesian case becomes less clear under model misspecification (Grunwald 2017; Ramamoorthi 2015; Fushiki 2005). To formally understand the consequences of Bayesian misspecification, this work examines the relationship between posterior predictive risk and its sensitivity to correct model assumptions, i.e., choice of likelihood and prior. We present the multisample PAC$^m$-Bayes risk. This risk is justified by theoretical analysis based on PAC-Bayes as well as empirical study on a number of toy problems. The PAC$^m$-Bayes risk is appealing in that it entails direct minimization of the Monte-Carlo approximated posterior predictive risk yet recovers both the Bayesian formalism as well as the MLE in its limits. Our work is heavily influenced by Masegosa (2019); our contributions are to align training and generalization risks while offering a tighter bound which empirically performs at least as well and sometimes much better.
Synergistic Processing in Cell's Multicore Architecture

Eight synergistic processor units enable the Cell Broadband Engine's breakthrough performance. The SPU architecture implements a novel, pervasively data-parallel architecture combining scalar and SIMD processing on a wide data path. A large number of SPUs per chip provide high thread-level parallelism. The streamlined architecture provides an efficient multithreaded execution environment for both scalar and SIMD threads and represents a reaffirmation of the RISC principles of combining leading edge architecture and compiler optimizations. These design decisions have enabled the Cell BE to deliver unprecedented supercomputer-class compute power for consumer applications
Wavelet denoising of epicardial fluorescence imaging

High-resolution optical mapping is an emerging technique to record the activation and propagation of transmembrane potential on the surface of cardiac tissues. Important electrodynamic information previously not available from extracellular electric recording could be extracted from these detailed optical recordings. The noise contamination in the images is a major obstacle that prohibits higher level of information extraction. Because the patterns of interest contain sharp wavefronts and structures that we wish to detect and track in a series of frames, we seek to perform denoising based on wavelet decomposition approaches. Among the wavelet denoise methods that were tested in this preliminary study, the wavelet packet produced the best results that could be extended to denoise the entire image sequence for multi-dimensional information processing.
On Active Video Summarization: Customized Summaries via On-Line Interaction with the User

Automatic video summarization is a key enabler to facilitate the browsing and re-viewing of our digitally stored video memories. In the case of egocentric and consumer videos, due to their personal nature, it is desirable to personalize the summaries. Current state-of-the-art methods excel at generating automatic video summaries by being tuned to ﬁt some general user’s preferences, or using preferences obtained prior to the summarization process. However, in many cases after seeing the generated summary, it would be desirable to reﬁne it to the user’s particular preferences. In this paper, we review Active Video Summarization (AVS), an interactive approach to gather the user’s preferences while creating the summary, that we introduced in (del Molino et al. 2017). AVS asks questions about the summary to update it on-line until the user is satisﬁed. To minimize the interaction, the best segment to inquire next is inferred from the previous feedback. AVS was evaluated in the commonly used UTEgo dataset and in a new dataset for customized video summarization (CSumm) recorded with a Google Glass, which was introduced in (del Molino et al. 2017). The results showed that AVS achieves an excellent compromise between usability and quality. In over 41% of the videos, AVS was considered the best over all tested baselines, including summaries manually generated. Also, when looking for speciﬁc events in the video, AVS provided an average level of satisfaction higher than those of all other baselines after only six questions to the user.
Brain: biomedical knowledge manipulation

Summary: Brain is a Java software library facilitating the manipulation and creation of ontologies and knowledge bases represented with the Web Ontology Language (OWL). Availability and implementation: The Java source code and the library are freely available at https://github.com/loopasam/Brain and on the Maven Central repository (GroupId: uk.ac.ebi.brain). The documentation is available at https://github.com/loopasam/Brain/wiki. Contact: croset@ebi.ac. uk Supplementary information: Supplementary data are available at Bioinformatics online.
On the spatial variability of the Martian Elastic Lithosphere Thickness: Evidence for Mantle Plumes?

[1] The present day elastic lithosphere thickness at the Martian north pole has recently been constrained to De > 300 km and this is a factor of 3–4 larger than elastic thickness estimates for other Amazonian surface features like the Tharsis volcanoes. Here we present a model for the Martian elastic lithosphere thickness which takes the locally varying crustal thickness, the local concentration of heat-producing elements, as well as variations of strain rate into account. The model predicts De = 196 km at the north pole today, whereas elastic thicknesses at the Tharsis volcanoes are best compatible with middle to late Amazonian loading ages. Therefore, although a large degree of spatial heterogeneity can be explained by the presented model, large elastic thicknesses in excess of 300 km cannot be reproduced. In order to fit all elastic thickness estimates derived from observations, mantle heat flux at the north pole needs to be reduced by 35%. However, this can only be reconciled with a bulk chondritic concentration of heat-producing elements in the Martian interior if the excess heat is deposited elsewhere. Therefore, this argues for the presence of recently active mantle plumes, possibly underneath Tharsis. The size and strength of such a plume can be constrained by the elastic thickness at the Tharsis Montes and maximum average heat flux between 8 and 24 mW m-2, corresponding to a central peak heat flux of 40 to 120 mW m−2, is consistent with the observations. Such a plume would leave a clear signature in the surface heat flux and should be readily detectable by in situ heat flux measurements.