Miracle-dz/UniFAR-FTUs huggingface dataset:
Patch Replacement: A Transformation-based Method to Improve Robustness against Adversarial Attacks

Deep Neural Networks (DNNs) are robust against intra-class variability of images, pose variations and random noise, but vulnerable to imperceptible adversarial perturbations that are well-crafted precisely to mislead. While random noise even of relatively large magnitude can hardly affect predictions, adversarial perturbations of very small magnitude can make a classifier fail completely. To enhance robustness, we introduce a new adversarial defense called patch replacement, which transforms both the input images and their intermediate features at early layers to make adversarial perturbations behave similarly to random noise. We decompose images/features into small patches and quantize them according to a codebook learned from legitimate training images. This maintains the semantic information of legitimate images, while removing as much as possible the effect of adversarial perturbations. Experiments show that patch replacement improves robustness against both white-box and gray-box attacks, compared with other transformation-based defenses. It has a low computational cost since it does not need training or fine-tuning the network. Importantly, in the white-box scenario, it increases the robustness, while other transformation-based defenses do not.
SYNC: A Crowdsourcing Platform for News Co-editing

We present SYNC, a crowdsourcing platform that allows news audiences to read news curated, aggregated, organized, and edited by the crowd, and to participate in news aggregation and editing at anytime. Through crowdsourcing, SYNC brings together news information from diverse sources and from contributors with different perspectives, enabling news audiences to obtain a more complete context of specific news events, thereby synchronizing their knowledge of the event. SYNC employs ”blocks” and a timeline to help with editors structure and organize news information, and a news material panel to facilitate finding news material to help with aggregation and editing. Our user evaluation showed that participants were positive about the usefulness and societal impacts of SYNC, and found the user interface easy to follow. They also indicated improvements to make to better support news aggregation and editing.
LeadBoSki: A Smart Personal Assistant for Leadership Support in Video-Meetings

Leading teams effectively is an essential factor for team performance. Main events where leaders practice leadership are video-meetings which are core elements of remote teamwork. However, leading teams is challenging. In particular, in video-meetings, leadership is complex due to limitations such as aggravated understanding of social signals and team dynamics. Voice-based smart personal assistants (SPA) such as Amazon’s Alexa can provide real-time support and have the potential to maintain effective leadership in video-meetings. Therefore, we developed the SPA LeadBoSki for real-time leadership support in video-meetings. LeadBoSki detects leadership potentials in video-meetings and sends leadership potential advice to the leader. Pilot study results with two real-world teams (n=9) show a positive leader experience and stimulating behavior by the leader for the team members’ motivation. Our work contributes with the SPA LeadBoSki as the first SPA for leadership support in video-meetings of its kind and as a potential use-case for future workplaces.
3rd Solution to WAB Challenge: A Two Stage Model Using only Pixel-level Features for Video-Image Retrieval

In this article, we propose an effective product retrieval system pipeline robust to large-scale E-commerce platform product data. Our proposed method consists of three parts: detection, retrieval, and post-processing. We first perform the detection task of precisely retrieving the target product and then using the model based on metric learning to retrieve the corresponding item. To improve the search robustness against noise and misleading bounding boxes, we apply post-processing methods such as weighted box fusion and feature connection. Using the proposed method, we won third place in the Watch and Buy: Multimodal Product Identification Challenge
Interactive Intention Prediction Model for Humanoid Robot Based on Visual Features

It is necessary for humanoid robots to have the capability of human-like social environment perception, which could enable robots to interact with people intelligently. In order to improve the interaction capability of humanoid robots in complex and changeable social environments, an interactive intention prediction model(IIPM) is proposed, which can quantitatively predict the intensity of interactive intention by the visual features of face orientation, social distance and facial expression in the actual social environment. Based on this model, humanoid robots can make autonomous decisions and select interactive person to carry out interactive tasks reasonably. Finally, the prediction accuracy of the IIPM is proved by single-person and multi-person experiments, which provides an effective and accurate solution for natural human-robot interaction (HRI).
Scarecrow - Intelligent Annotation platform for Engine Health Management

Engine Health Management (EHM) in the context of applications such as gas turbines, power packs is dependent on massive amount of data captured by onboard sensors. The data streams are then processed to extract key points and trends capturing events related to failures and deterioration, which subsequently need to be enhanced by insights and judgements from Subject Matter Experts (SME). Data volumes and extremely demanding requirements, commercial and regulatory, cause human efforts to quickly emerge as bottleneck in EHM service delivery. We have developed an intelligent data annotation platform called Scarecrow which records SME inputs, generates machine learning models in near real-time which are then deployed into analytic pipelines for EHM diagnostics. Scarecrow enables seamless ML ops strategy through human assisted feature learning, model building and deployment
Designing for Data Awareness: Addressing Privacy and Security Concerns About “Smart” Technologies

The internet of things (IoT) and smart home technologies are pervasive in the U.S. and abroad. Devices like smart speakers, cameras, thermostats, and vacuums promise to save consumers time and energy and to make tasks easier. Many devices also provide significant benefits through accessibility features that offer hands-free options, voice commands, and management through smartphone apps. At the same time, however, researchers and the media have documented a number of vulnerabilities in these devices, which raises concerns about what and how much data is being collected, how that data is used, and who has access to the data. In this one-day workshop, participants will work together to brainstorm potential solutions for making smart device data more visible and interpretable for consumers. Through rotating breakout sessions and full-group discussions, participants will identify data-based threats in popular smart home technologies, select data flows that are most concerning, a nd generate design ideas for tools or other artifacts that can help consumers make more informed decisions about using these devices. Opportunities for networking and future collaborations will also be incorporated.
A propelled multiple fusion Deep Belief Network for weld defects detection

With the characteristics including fuzzy edges, high image noise, low pixels and contrast, X-ray images of weld defect are difficult to be effectively recognized. Various well-known deep network is used for improving image recognition performance, so that researchers pay more attention on weld defects detection by using deep network with stack structure. However, such stack structure shows some disadvantages, such as inaccuracy recognition on confusion feature, low uncertainty-handling efficiency, time-consuming and complex computation. In this paper, a propelled multiple fusion Deep Belief Network (PMF-DBN) structure with Fuzzy Classifiers (FC) is created for weld defect classification and recognition. The proposed PMF-DBN enjoy both the ability of DBN neural representation and the of capability of fuzzy representation in order to meet the requirements of variant image feature processing. Meanwhile, instead of time-consuming fine-tuning training, the outputs feature data of each layer is fused in a propelled way, by which effective feature extraction can be achieved. Experiments on weld defects multi-classification demonstrate effectiveness of the PMF-DBN. Compared with the DBN, PMF-DBN has higher recognition accuracy and better fitting performance.
MINDNOTES : A Mobile Platform to enable users to break stigma around mental health and connect with therapists

We describe a mobile app for helping people manage their mental health, keeping in mind the unique challenges offered by the Indian context. One of the biggest challenges in India is the stigma around mental health and consequently the concerns and misconceptions about reaching out for professional help. Therefore, the proposed app focuses on tackling the barriers that people experience when reaching out for help and provides a free-of-cost platform for interactions with mental health professionals to alleviate any concerns and obtain an informed view. All content is developed by clinical psychologists and rather than replacing professional help, the app provides an example of a technology intervention for mental health that supplements existing infrastructures of mental healthcare.
Unique Terminal Regions and Specific Deletions of the Segmented Double-Stranded RNA Genome of Alternaria Alternata Virus 1, in the Proposed Family Alternaviridae

Alternaria alternata virus 1 (AaV1) has been identified in the saprophytic fungus Alternaria alternata strain EGS 35–193. AaV1 has four genomic double-stranded (ds)RNA segments (dsRNA1–4) packaged in isometric particles. The 3' end of each coding strand is polyadenylated (36–50nt), but the presence of a cap structure at each 5' end has not previously been investigated. Here, we have characterized the AaV1 genome and found that it has unique features among the mycoviruses. We confirmed the existence of cap structures on the 5' ends of the AaV1 genomic dsRNAs using RNA dot blots with anti-cap antibodies and the oligo-capping method. Polyclonal antibodies against purified AaV1 particles specifically bound to an 82kDa protein, suggesting that this protein is the major capsid component. Subsequent Edman degradation indicated that the AaV1 dsRNA3 segment encodes the major coat protein. Two kinds of defective AaV1 dsRNA2, which is 2,794bp (844 aa) in length when intact, appeared in EGS 35–193 during subculturing, as confirmed by RT-PCR and northern hybridization. Sequence analysis revealed that one of the two defective dsRNA2s contained a 231bp deletion, while the other carried both the 231bp deletion and an additional 465bp deletion in the open reading frame. Both deletions occurred in-frame, resulting in predicted proteins of 767 aa and 612 aa. The fungal isolates carrying virions with the defective dsRNA2s showed impaired growth and abnormal pigmentation. To our best knowledge, AaV1 is the first dsRNA virus to be identified with both 5' cap and 3'poly(A) structures on its genomic segments, as well as the specific deletions of dsRNA2.
Identification of Gene-Set Signature in Early-Stage Hepatocellular Carcinoma and Relevant Immune Characteristics

Background The incidence of hepatocellular carcinoma (HCC) is rising worldwide, and there is limited therapeutic efficacy due to tumor microenvironment heterogeneity and difficulty in early-stage screening. This study aimed to develop and validate a gene set-based signature for early-stage HCC (eHCC) patients and further explored specific marker dysregulation mechanisms as well as immune characteristics. Methods We performed an integrated bioinformatics analysis of genomic, transcriptomic, and clinical data with three independent cohorts. We systematically reviewed the crosstalk between specific genes, tumor prognosis, immune characteristics, and biological function in the different pathological stage samples. Univariate and multivariate survival analyses were performed in The Cancer Genome Atlas (TCGA) patients with survival data. Diethylnitrosamine (DEN)-induced HCC in Wistar rats was employed to verify the reliability of the predictions. Results We identified a Cluster gene that potentially segregates patients with eHCC from non-tumor, through integrated analysis of expression, overall survival, immune cell characteristics, and biology function landscapes. Immune infiltration analysis showed that lower infiltration of specific immune cells may be responsible for significantly worse prognosis in HCC (hazard ratio, 1.691; 95% CI: 1.171–2.441; p = 0.012), such as CD8 Tem and cytotoxic T cells (CTLs) in eHCC. Our results identified that Cluster C1 signature presented a high accuracy in predicting CD8 Tem and CTL immune cells (receiver operating characteristic (ROC) = 0.647) and cancerization (ROC = 0.946) in liver. As a central member of Cluster C1, overexpressed PRKDC was associated with the higher genetic alteration in eHCC than advanced-stage HCC (aHCC), which was also connected to immune cell-related poor prognosis. Finally, the predictive outcome of Cluster C1 and PRKDC alteration in DEN-induced eHCC rats was also confirmed. Conclusions As a tumor prognosis-relevant gene set-based signature, Cluster C1 showed an effective approach to predict cancerization of eHCC and its related immune characteristics with considerable clinical value.
COVID-19 Era Effect on Pandemic and Post-pandemic Pediatric Telemedicine Use: A Survey of the European Academy of Pediatrics Research in Ambulatory Settings Network

Background: During the COVID-19 pandemic, telemedicine use has increased within community pediatrics. This trend runs counter to reluctance to adaptation of the new mode of healthcare that existed prior to the pandemic. Little is known about what we can expect after the pandemic: if physicians will opt for telemedicine modalities and if tele-pediatrics will continue to be a significant mode of community pediatric care. Objective: The goal of this study was to survey primary pediatric care providers as to their experiences and clinical decision making with telemedicine modalities prior to and during the COVID-19 pandemic, as well as their projected use after the pandemic ends. Material and methods: Using the EAPRASnet database we surveyed pediatricians throughout Europe, using a web-based questionnaire. The survey was performed during the COVID-19 pandemic (June–July 2020), assessed telemedicine use for several modalities, prior to and during the pandemic as well as predicted use after the pandemic will have resolved. Participants were also surveyed regarding clinical decision making in two hypothetical clinical scenarios managed by telemedicine. Results: A total of 710 physicians participated, 76% were pediatricians. The percentage of respondents who reported daily use for at least 50% of all encounters via telemedicine modalities increased during the pandemic: phone calls (4% prior to the pandemic to 52% during the pandemic), emails (2–9%), text messages (1–6%), social media (3–11%), cell-phone pictures/video (1–9%), and video conferencing (1–7%) (p < 0.005). The predicted post-pandemic use of these modalities partially declined to 19, 4, 3, 6, 9, and 4%, respectively (p < 0.005), yet demonstrating a prospectively sustained use of pictures/videos after the pandemic. Reported high likelihood of remotely treating suspected pneumonia and acute otitis media with antibiotics decreased from 8 to 16% during the pandemic to an assumed 2 and 4% after the pandemic, respectively (p < 0.005). Conclusions: This study demonstrates an increased utilization of telemedicine by pediatric providers during the COVID-19 pandemic, as well as a partially sustained effect that will promote telemedicine use as part of a hybrid care provision after the pandemic will have resolved.
Feature blindness: A challenge for understanding and modelling visual object recognition

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal that CNNs provide good mechanistic models of shape-bias and, more generally, human visual processing. However, it is also possible that humans and CNNs show a shape-bias for very different reasons, namely, shape-bias in humans may be a consequence of architectural and cognitive constraints whereas CNNs show a shape-bias as a consequence of learning the statistics of the environment. We investigated this question by exploring shape-bias in humans and CNNs when they learn in a novel environment. We observed that, in this new environment, humans (i) focused on shape and overlooked many non-shape features, even when non-shape features were more diagnostic, (ii) learned based on only one out of multiple predictive features, and (iii) failed to learn when global features, such as shape, were absent. This behaviour contrasted with the predictions of a statistical inference model with no priors, showing the strong role that shape-bias plays in human feature selection. It also contrasted with CNNs that (i) preferred to categorise objects based on non-shape features, and (ii) increased reliance on these non-shape features as they became more predictive. This was the case even when the CNN was pre-trained to have a shape-bias and the convolutional backbone was frozen. These results suggest that shape-bias has a different source in humans and CNNs: while learning in CNNs is driven by the statistical properties of the environment, humans are highly constrained by their previous biases, which suggests that cognitive constraints play a key role in how humans learn to recognise novel objects. Author summary Any object consists of hundreds of visual features that can be used to recognise it. How do humans select which feature to use? Do we always choose features that are best at predicting the object? In a series of experiments using carefully designed stimuli, we find that humans frequently ignore many features that are clearly visible and highly predictive. This behaviour is statistically inefficient and we show that it contrasts with statistical inference models such as state-of-the-art neural networks. Unlike humans, these models learn to rely on the most predictive feature when trained on the same data. We argue that the reason underlying human behaviour may be a bias to look for features that are less hungry for cognitive resources and generalise better to novel instances. Models that incorporate cognitive constraints may not only allow us to better understand human vision but also help us develop machine learning models that are more robust to changes in incidental features of objects.
Triazolopyrimidines Target Aerobic Respiration in Mycobacterium tuberculosis

We previously identified a series of triazolopyrimidines with antitubercular activity. We determined that Mycobacterium tuberculosis strains with mutations in QcrB, a subunit of the cytochrome bcc-aa3 supercomplex, were resistant. ABSTRACT We previously identified a series of triazolopyrimidines with antitubercular activity. We determined that Mycobacterium tuberculosis strains with mutations in QcrB, a subunit of the cytochrome bcc-aa3 supercomplex, were resistant. A cytochrome bd oxidase deletion strain was more sensitive to this series. We isolated resistant mutants with mutations in Rv1339. Compounds led to the depletion of intracellular ATP levels and were active against intracellular bacteria, but they did not inhibit human mitochondrial respiration. These data are consistent with triazolopyrimidines acting via inhibition of QcrB.
Escherichia coli strains from patients with inflammatory bowel diseases have disease-specific genomic adaptations

Objective Escherichia coli is over-abundant in the gut microbiome of patients with IBD, yet most studies have focused on the adherent-invasive E. coli pathotype. Here, we aimed to identify IBD-specific or phenotype-specific genomic functions of diverse E. coli lineages. Design We investigated E. coli from patients with UC, CD and a pouch and healthy subjects. The majority of E. coli genomes were reconstructed directly from metagenomic samples, including publicly available and newly sequenced fecal metagenomes. Clinical metadata and biomarkers were collected. Functional analysis at the gene and mutation level and genome replication rates of E. coli strains were performed, and correlated with IBD phenotypes and biomarkers. Results Overall, 530 E. coli genomes were analysed. A specific E. coli lineage (B2) was more prevalent in UC compared to other IBD phenotypes. Genomic metabolic capacities varied across E. coli lineages and IBD phenotypes. Specifically, sialidases involved in host mucin utilization, were exclusively present in a single lineage and were depleted in patients with a pouch. In contrast, enzymes that hydrolyze inulin were enriched in patients with a pouch. E. coli from patients with UC were twice as likely to encode the genotoxic molecule colibactin than strains from patients with CD or pouch. Strikingly, patients with a pouch showed the highest E. coli growth rates, even in the presence of antibiotics. Fecal calprotectin did not correlate with the relative abundance of E. coli. Finally, we identified multiple IBD-specific loss-of function mutations in E. coli genes encoding for bacterial cell envelope and secretion components. Conclusion This study presents E. coli as a commensal species better adapted to the overly-active mucosal immune milieu in IBD, that may benefit from intestinal inflammation, rather than causing it. The evidence given here suggests adaptive evolution toward attenuated virulence in some E. coli strains, coupled with a rapid growth despite the presence of antibiotics.
Nuclear import and chaperoning of monomeric histones H3 and H4 is mediated by Imp5, NASP and the HAT1 complex

Core histones package chromosomal DNA and regulate genomic transactions, with their import and deposition involving a dedicated repertoire of molecular chaperones. Histones H3 and H4 have been predominantly characterised as obligate heterodimers, however, recent findings have alluded to the existence of a significant pool of monomeric histone H3 in the nucleoplasm. Using a combination of in vitro and in vivo experiments, here we show that monomeric H3 and H4 use an Importin 5 (Imp5) dependent pathway for their nuclear import, distinct from Importin 4 (Imp4) previously described for H3-H4 dimers. Using mutants that disrupt the histone fold, we show monomeric H3 loses its interaction with Imp4, but retains interactions with Imp5 and the chaperone NASP. H4 monomeric mutants similarly bind Imp5 and not Imp4, however, they lose interaction with NASP, retaining their interaction with the HAT1-RBBP7 complex instead. In vitro experiments revealed that Imp5 and NASP are mutually exclusive in their binding, suggesting a facilitated hand-off mechanism. Furthermore, new H3 accumulates rapidly in a NASP-bound complex after nuclear translocation. NASP can assemble into three distinct co-chaperoning complexes, including a novel complex containing NASP, H3 and the putative ubiquitin ligase UBR7, a NASP-H3-H4-RBBP7 subcomplex and the previously characterised NASP-H3-H4-ASF1-HAT1-RBBP7 multi-chaperoning complex. Here we propose an alternative import pathway and folding mechanism for monomeric H3 and H4 that involves Imp5, rather than Imp4, and hands off to nuclear chaperones NASP, RBBP7 and HAT1.