Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Intelligence Model to Predict the Virality of Press Articles

Currently, many people share news, links, or videos, without being aware of the impact they can have on people's decisions or ways of acting. A clear example, recently experienced in Colombia, corresponds to the national strike which happened at the time of this research. Due to these unexpected circumstances, colombians experienced the influence news have on decision making that can affect the country, not only economically but politically, and socially. It showed how news can generate fear in people, or even misinform, as is the case of fake news. For these reasons, it is key to determine the relevance a story can have. Predicting the impact, will allow us to pay more attention to those news that can affect people more, avoiding misinformation and fake news. However, the problem is that there is no way of predicting the impact that a press article can have. Therefore, the aim of this work is to implement a machine learning model that allows us to predict, with the best possible accuracy, the virality of online press articles (defining virality as the amount of clicks that an article receives when it is opened). In order to achieve this goal, we followed the CRISP-DM methodology, which focuses on machine learning projects. The best obtained result corresponds to the model where the core of the architecture was based on BERT, a pre-trained model, which, for a pair of press articles headlines, predicted whether the first headline would be more viral than the second one. On the other hand, the evaluation was carried out by comparing the amount of clicks for a pair of articles. For a practitioner point of view, digital marketers can use our results to select the best words for their online marketing campaign. For a theoretical point of view, our results present an innovative natural language processing approach based on one of the best breed of Neural network models (BERT).
An Enactment on Privacy Vulnerabilities of Encrypted IOT Traffic

- The growing marketplace for clever home IoTdevices guarantees new conveniences for customers at the same time as imparting new challenges for keeping privateness inside the home. Internet of Things (IoT) devices regularly performs unique features, which can lead them to prone to attackers looking for to research sensitive consumer facts. In precise, user pastime can be inferred simply by using looking for peaks in visitors dispatched via IoT devices. Because this kind of inference is based on the shape of the traffic in preference to the data being sent, encryption does not protect against it. Our aim is to arm the builders of IoT device software with a library that they can use to obfuscate the traffic that their devices ship to save you this sort of attack.
A review of proton therapy – Current status and future directions

The original rationale for proton therapy was its highly conformal depth‐dose distributions compared to photons, which allow greater sparing of normal tissues and escalation of tumor doses, thus potentially improving outcomes. Additionally, recent research has revealed previously unrecognized advantages of proton therapy. For instance, the higher relative biological effectiveness (RBE) near the end of the proton range can be exploited to increase the difference in biologically effective dose in tumors versus normal tissues. Moreover, the smaller “dose bath,” that is, the compact nature of proton dose distributions, has been found to reduce the exposure of circulating lymphocytes and the immune organs at risk. There is emerging evidence that the resulting sparing of the immune system has the potential to improve outcomes.
Severe and Pervasive? Consequences of Sexual Harassment for Graduate Students and their Title IX Report Outcomes

Sexual harassment of graduate students is prevalent, yet little is known about their experiences reporting sexual harassment to their university. We conducted interviews with 32 graduate students who reported sexual harassment to Title IX to understand how survivors’ experiences of harassment align with report outcomes. Nearly all participants experienced severe, education-limiting consequences of the harassment and reported to ensure safety and restore educational access. Most reports were deemed unactionable and findings of responsibility were rare, demonstrating a disconnect between survivors’ experiences and Title IX outcomes. Our analysis suggests that Title IX practitioners rely on notions of “severity” rather than harassment consequences.
Space Vector Modulation Methods for Common-Mode Voltage Reduction in Current-Source Drives

Various reduced common-mode voltage space vector modulation (RCMV SVM) methods have been proposed to reduce destructive common-mode voltage (CMV) in both voltage source converter (VSC) and current source converter (CSC) based motor drive systems. Most of them reduce the CMV by avoiding using zero-state vectors. Unlike CMV in VSCs which is related to constant dclink voltage, CMV in CSCs is related to AC side voltages which vary sinusoidally. Thus, zero-state vectors in CSCs can be selected, instead of being avoided, to
Duplicate Latent Representation Suppression for Multi-object Variational Autoencoders

Generative object-centric scene representation learning is crucial for structural visual scene understanding. Built upon variational autoencoders (VAEs) [11], current approaches infer a set of latent object representations to interpret a scene observation (e.g. an image) under the assumption that each part (e.g. a pixel) of a scene observation must be explained by one and only one object of the underlying scene. Despite the impressive performance these models achieved in unsupervised scene factorization and representation learning, we show empirically that they often produce duplicate scene object representations which directly harms the scene factorization performance. In this paper, we address the issue by introducing a differentiable prior that explicitly forces the inference to suppress duplicate latent object representations. The extension is evaluated by adding it to three different unsupervised scene factorization approaches. The results show that the models trained with the proposed method not only outperform the original models in scene factorization and have fewer duplicate representations, but also achieve better variational posterior approximations than the original models.
Label2im: Knowledge Graph Guided Image Generation from Labels

Most recent generation methods synthesize images from either complex textual descriptions or scene graphs. However, users need to elaborate attributes and relationships of objects in the scene, and scene graphs are more difﬁcult to obtain. To simplify the burden of users, in this work, we propose a Label2im model to generate images from object labels directly with the help of a Knowledge Graph (KG), e.g . Visual Genome. To acquire rational interactions between objects, we explore possible relationships from the KG. Considering that there is a large gap between the label domain and image domain, we propose to learn knowledge representations of the scene graph from the KG to ensure the semantic consistency. First, given several object labels, we design a Scene Graph Selection Module (SGSM) to explore interactions between objects in the KG and generate a set of scene graphs. Second, the structure representation and knowledge embedding of the scene graph are learned and integrated in the Scene Graph Representation Module (SGRM), which leads to rational scene layouts. Based on the scene layouts and KG, we employ the Cascaded Reﬁnement Network (CRN) to generate the ﬁnal image. To encode knowledge information in the generation process, we propose a Triplet Attention Module (TAM) which is embedded in the CRN. We verify the effectiveness of the proposed method on the Visual Genome dataset and demonstrate that our method is able to generate complex images with rich content and ﬁne details.
Latent-optimization based Disease-aware Image Editing for Medical Image Augmentation

Data augmentation addresses the critical challenge of limited data in medical imaging. While generative adversarial networks (GANs) have been a popular choice in synthesizing medical images, controlled generation targeting disease-speciﬁc semantic has been difﬁcult, partly due to the difﬁculty to disentangle local disease-speciﬁc semantic factors from global disease-irrelevant factors. In this work, we present a semantic image editing framework for medical image augmentation that is able to generate smooth variations along the desired direction of disease attributes in user-deﬁned regions of interest. This is achieved by discovering the optimal trajectory on the latent manifold of a pre-trained StyleGAN, guided by a mask of the region of interest and explicitly constrained by desired directions of semantic changes. We test the presented method on the public Chest X-ray dataset. To evaluate the quality of the generated medical images, we leverage both domain experts (pulmonologists) for qualitative assessments and present a novel metric to quantify the ability of the presented method to generate progression of disease severity in the synthesized images. We also show that data augmentation using the presented method improves downstream classiﬁcation tasks.
PDF-Distil: including Prediction Disagreements in Feature-based Distillation for object detection

Knowledge distillation aims at compressing deep models by transferring the learned knowledge from precise but cumbersome teacher models to compact student models. Due to the extreme imbalance between the foreground and the background of images, when traditional knowledge distillation methods are directly applied to the object detection task, there is a large performance gap between the teacher model and the student model. We tackle this imbalance problem from a sampling perspective, and we pro-pose to include the teacher-student prediction disagreements into a feature-based detection distillation framework. This is done with PDF-Distil by dynamically generating a weighting mask applied to the knowledge distillation loss, based on the disagreements between the predictions of both models. Extensive experiments on PASCAL VOC and MS COCO datasets demonstrate that, compared to state-of-the-art methods, PDF-Distil is able to better reduce the performance gap between the teacher and student models.
FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning

Deep neural networks (DNNs) have recently achieved a great success in computer vision and several related fields. Despite such progress, current neural architectures still suffer from catastrophic interference (a.k.a. forgetting) which obstructs DNNs to learn continually. While several state-of-the-art methods have been proposed to mitigate forgetting, these existing solutions are either highly rigid (as regularization) or time/memory demanding (as replay). An intermediate class of methods, based on dynamic networks, has been proposed in the literature and provides a reasonable balance between task memorization and computational footprint. In this paper, we devise a dynamic network architecture for continual learning based on a novel forgetting-free neural block (FFNB). Training FFNB features on new tasks is achieved using a novel procedure that constrains the underlying parameters in the null-space of the previous tasks, while training classifier parameters equates to Fisher discriminant analysis. The latter provides an effective incremental process which is also optimal from a Bayesian perspective. The trained features and classifiers are further enhanced using an incremental"end-to-end"fine-tuning. Extensive experiments, conducted on different challenging classification problems, show the high effectiveness of the proposed method.
FDM/OFDM with reduced peak to average power ratio

A method of reducing PAPR in an OFDM transmission, including at the transmitter, pre-adjusting each subcarrier phase by an amount determined by a quadratic function of the subcarrier index and a predetermined factor, and at the receiver, recovering the predetermined factor from a pilot subcarrier and reversing the preadjustments to recover the signal. The factor may be found at the transmitter by a search method.
Crosstalk analysis of multi‐microstrip coupled lines using transmission line modeling

This article presents a new analytical method to predict crosstalk of a homogeneous terminated two microstrip coupled lines over a ground plane using transmission line theory. The derived formula is frequency and location dependent, which can be used to quickly estimate the crosstalk of a coupled line. Also, the effect of the geometrical parameters of the lines and load are included in the derived formula. Presented method can be used for the other types of coupled lines including lossy or lossless lines. To verify the accuracy of the introduced method, a few microstrip coupled line structures with different geometrical parameters are considered numerically and experimentally. The results of crosstalk based on the proposed analytical methods, simulation study using high frequency structure simulator and those obtained by measurements are reported and compared with each other. It is shown that our proposed method accurately estimates the amount of crosstalk for a two microstrip coupled lines.
Does a GAN leave distinct model-specific fingerprints?

Generative Adversarial Networks (GANs) have been breaking their own records in terms of the quality of the synthesized images, which could be so high as to make it impossible to distinguish generated images from real ones by human eyes. This has raised threats to security and privacy-sensitive applications, and thus it is important to be able to tell if an image is generated by GANs, and better yet, by which GAN. The task is in a sense similar to digital image forensics for establishing image authenticity, but the literature has inconclusive reports as to whether GANs leave unique ﬁngerprints in the generated images. In this paper, we attempt to develop a comprehensive understanding towards answering this question. We propose a model to extract ﬁngerprints that can be viewed largely as GAN-speciﬁc. We further identify a few key components that contribute to deﬁning the ﬁngerprint of the generated images. Using experiments based on state-of-the-art GAN models and different datasets, we evaluate the performance of our model and verify the major conclusions of our analysis.
PRN: Psychology-Inspired Relation Network for Detecting Social Interaction Groups from Single Images

Detecting interaction groups is an essential task for understanding human behaviours and social activities. However, it is still challenging to identify social interactions and the resulting crowd groups using purely visual cues, especially from single images. Prior works either require additional statistics, such as interpersonal angles and kinaesthetic information, or simply deduce the group memberships with the similarity of individual actions. In this paper, we present the Psychology-inspired Relation Network (PRN) to comprehensively understand the static social scenes and effectively model the interaction relations between individuals. More concretely, stimulated by recent advances in social psychology, we ﬁrst predict the keypoint heatmap from an image with the human bounding boxes as the visual representations of the key factors determining interaction groups: distance, orientation and postural openness. We then incorporate the personal and mutual inﬂuences together to compute the interaction strength matrix via self-attention, and ﬁ-nally utilise a perception to convert this matrix into dyadic interaction probability. More-over, we devise two loss functions, the dyad loss to optimise the dyadic interaction probability and the group loss to enhance the distinguishability among different social groups. To evaluate the performance of PRN, we introduce a novel dataset containing various scenes with different crowd densities, by merging representative databases and relabeling the group labels. Our method achieves outstanding results on the proposed dataset.
Image-Text Alignment using Adaptive Cross-attention with Transformer Encoder for Scene Graphs

Neural image and text encoders have been proposed to align the abstract image and symbolic text representation. Global-local and local-local information integration be-tween two modalities are essential for an effective alignment. In this paper, we present RELation-aware Adaptive Cross-attention (RELAX) that achieves state-of-the-art performance in cross-modal retrieval tasks by incorporating several novel improvements. First, cross-attention methods integrate global-local information via weighted global feature of a modality (taken as value) for a local feature of the other modality (taken as query). We can make more accurate alignments if we could also consider the global weights of the query modality. To this end, we introduce adaptive embedding to consider the weights. Second, to enhance the usage of scene-graphs that can capture the high-level relation of local features, we introduce transformer encoders for textual scene graphs to align with visual scene graphs. Lastly, we use NT-XEnt loss that takes the weighted sum of the samples based on their importance. We show that our approach is effective in extensive experiments that outperform other state-of-the-art models.
A Large-Scale Longitudinal Multimodal Dataset of State-Backed Information Operations on Twitter

This paper proposes a large-scale and comprehensive dataset of 28 sub-datasets of state-backed tweets and accounts affiliated with 14 different countries, spanning more than 3 years, and a corresponding "negative" dataset of background tweets from the same time period and on similar topics. To our knowledge, this is the first dataset that contains both state-sponsored propaganda tweets and carefully collected corresponding negative tweet datasets for so many countries spanning such a long period of time.
Adaptive Tensor Networks Decomposition

Tensor Decomposition (TD) is a powerful technique in solving high-dimensional optimization problems and has been widely used in machine learning and data science. Many TD models aim to establish a trade-off between computational complexity and representation ability. However, they have the problem of tensor rank selection and latent factor arrangement, and the neglected internal correlation between different modes. In this paper, we propose a data-adaptive TD model established on a generalized tensor rank and name it adaptive tensor network (ATN) decomposition, which constructs an optimal topological structure for TD according to the intrinsic properties of the data. We exploit the generalized tensor rank to measure the correlation between two modes of the data and establish a multilinear connection between the corresponding latent factors with an adaptive rank. ATN possesses the merits of permutation invariance, strong robustness, and represents high-order data with fewer parameters. We veriﬁed ATN’s effectiveness and superiority on three typical tasks: tensor completion, image denoising, and neural network compression. Experimental results on synthetic data and real datasets demonstrate that the overall performance of ATN surpasses the state-of-the-art TD methods.
A Feasibility Study of Photoacoustic Detection of Hidden Dental Caries Using a Fiber-Based Imaging System

In this paper, the feasibility of an optical fiber-based photoacoustic imaging system for detecting caries lesions inside a tooth is examined. Models of hidden caries were prepared using a pigment with an absorption spectrum similar to that of real caries lesions, and the occlusal surface of the model teeth containing the pigment was irradiated with laser pulses with a wavelength of 532 nm. An examination of the frequency spectra of the emitted photoacoustic waves revealed that the spectra from simulated caries lesions included frequency components in the range of 0.5–1.2 MHz that were not seen in the spectra from healthy parts of the teeth. This indicates that hidden caries can be detected via a photoacoustic imaging technique. Accordingly, an imaging system for clinical applications was fabricated. It consists of a bundle of hollow-optical fibers for laser radiation and an acoustic probe that is attached to the tooth surface. Results of ex vivo imaging experiments using model teeth and an extracted tooth with hidden caries lesions show that relatively large caries lesions inside teeth that are not seen in visual inspections can be detected by focusing on the above frequency components of the photoacoustic waves.
Learning Exposure Correction Via Consistency Modeling

Existing works on exposure correction have exclusively focused on either under-exposure or over-exposure. Recent work targeting both under-, and over-exposure achieved state of the art. However, it tends to produce images with inconsistent correction and sometimes color artifacts. In this paper, we propose a novel neural network architecture for exposure correction. The proposed network targets both under-, and over-exposure. We introduce a deep feature matching loss that enables the network to learn exposure-invariant representation in the feature space, which guarantees image exposure consistency. Moreover, we leverage a global attention mechanism to allow long-range interactions between distant pixels for exposure correction. This results in consistently corrected images, free of localized color distortions. Through extensive quantitative and qualitative experiments, we demonstrate that the proposed network outperforms the existing state-of-the-art. Code: https://github.com/elientumba2019/Exposure-Correction-BMVC-2021
A user-centric approach to IT-security risk analysis for an identity management solution

: In order to build identity management (IdM) solutions that are secure in the practical application context, a holistic approach their IT-security risk analysis is required. This complements the indispensable technical, and crypto-focused analysis of risks and vulnerabilities with an approach that puts another important vector for security in the center: the users and their usage of the technology over the whole lifecycle. In our short paper we focus exclusively on the user-centric approach and present an IT-security risk analysis that is structured around the IdM lifecycle
Effect of Popularity Shocks on User Behaviour

Users often post on content-sharing platforms in the hope of attracting high engagement from viewers. Some posts receive unusual attention and go "viral", eliciting a significant response (likes, views, shares) to the creator in the form of popularity shocks. Past theories have suggested a sense of reputation as one of the key drivers of online activity and the tendency of users to repeat fruitful behaviors. Based on these, we theorize popularity shocks to be linked with changes in the behavior of users. In this paper, we propose a framework to study the changes in user activity in terms of frequency of posting and content posted around popularity shocks. Further, given the sudden nature of their occurrence, we look into the survival durations of effects associated with these shocks. We observe that popularity shocks lead to an increase in the posting frequency of users, and users alter their content to match with the one which resulted in the shock. Also, it is found that shocks are tough to maintain, with effects fading within a few days for most users. High response from viewers and diversification of content posted is found to be linked with longer survival durations of the shock effects. We believe our work fills the gap related to observing users' online behavior exposed to sudden popularity and has widespread implications for platforms, users, and brands involved in marketing on such platforms.