Miracle-dz/UniFAR-FTUs huggingface dataset:
Harnessing the Potential of Native Microbial Communities for Bioremediation of Oil Spills in the Iberian Peninsula NW Coast

Oil spills are among the most catastrophic events to marine ecosystems and current remediation techniques are not suitable for ecological restoration. Bioremediation approaches can take advantage of the activity of microorganisms with biodegradation capacity thus helping to accelerate the recovery of contaminated environments. The use of native microorganisms can increase the bioremediation efficiency since they have higher potential to survive in the natural environment while preventing unpredictable ecological impacts associated with the introduction of non-native organisms. In order to know the geographical scale to which a native bioremediation consortium can be applied, we need to understand the spatial heterogeneity of the natural microbial communities with potential for hydrocarbon degradation. In the present study, we aim to describe the genetic diversity and the potential of native microbial communities to degrade petroleum hydrocarbons, at an early stage of bioremediation, along the NW Iberian Peninsula coast, an area particularly susceptible to oil spills. Seawater samples collected in 47 sites were exposed to crude oil for 2 weeks, in enrichment experiments. Seawater samples collected in situ, and samples collected after the enrichment with crude oil, were characterized for prokaryotic communities by using 16S rRNA gene amplicon sequencing and predictive functional profiling. Results showed a drastic decrease in richness and diversity of microbial communities after the enrichment with crude oil. Enriched microbial communities were mainly dominated by genera known to degrade hydrocarbons, namely Alcanivorax, Pseudomonas, Acinetobacter, Rhodococcus, Flavobacterium, Oleibacter, Marinobacter, and Thalassospira, without significant differences between geographic areas and locations. Predictive functional profiling of the enriched microbial consortia showed a high potential to degrade the aromatic compounds aminobenzoate, benzoate, chlorocyclohexane, chlorobenzene, ethylbenzene, naphthalene, polycyclic aromatic compounds, styrene, toluene, and xylene. Only a few genera contributed for more than 50% of this genetic potential for aromatic compounds degradation in the enriched communities, namely Alcanivorax, Thalassospira, and Pseudomonas spp. This work is a starting point for the future development of prototype consortia of hydrocarbon-degrading bacteria to mitigate oil spills in the Iberian NW coast.
Effects of Real-Ambient PM2.5 Exposure on Lung Damage Modulated by Nrf2−/−

Previous studies have shown that long-term exposure to fine particulate matter (PM2.5) increases the morbidity and mortality of pulmonary diseases such as asthma, chronic obstructive pulmonary disease and pulmonary emphysema. Oxidative stress and inflammation play key roles in pulmonary damage caused by PM2.5. Nuclear factor erythroid 2-related factor 2 (Nrf2) could regulate the expression of antioxidant and anti-inflammatory genes and is pivotal for protection against PM2.5-induced oxidative stress. In this study, a real-ambient exposure system was constructed with the outdoor ambient air in north China. Wild-type (WT) and Nrf2−/− (KO) mice were exposed to the real-ambient system for six weeks. After PM2.5 exposure, our data showed that the levels of inflammatory factors and malondialdehyde were significantly increased in WT and KO mice. Moreover, the lung function and pathological phenotype of the WT mice were altered but there was no obvious change in the Nrf2−/− mice. To further explore the potential molecular mechanisms, we performed RNA-sequencing. The RNA-sequence analysis results showed that the CYP450 pathway in the first ten pathways of KEGG was related to the metabolism of PM2.5. In WT and KO mice, the expression of CYP2E1 in the CYP450 pathway showed opposite trends after PM2.5 exposure. The data showed that the expression of the CYP2E1 gene in WT-PM mice increased while it decreased in KO-PM; the expression of the CYP2E1 protein showed a similar trend. CYP2E1 is primarily distributed in the endoplasmic reticulum (ER) where it could metabolize various exogenous substances attached to PM2.5 and produce highly toxic oxidation products closely related to ER stress. Consistently, the expression level of GRP94, a biomarker of ER stress, was increased in WT mice and reduced in KO mice under PM2.5 exposure. Persistent ER stress is a mechanism that causes lung damage under PM2.5 exposure. Nrf2 facilitates lung injury during PM2.5 exposure and CYP2E1 metabolism is involved in this process.
Deep Neural Networks for Depression Recognition Based on 2D and 3D Facial Expressions Under Emotional Stimulus Tasks

The proportion of individuals with depression has rapidly increased along with the growth of the global population. Depression has been the currently most prevalent mental health disorder. An effective depression recognition system is especially crucial for the early detection of potential depression risk. A depression-related dataset is also critical while evaluating the system for depression or potential depression risk detection. Due to the sensitive nature of clinical data, availability and scale of such datasets are scarce. To our knowledge, there are few extensively practical depression datasets for the Chinese population. In this study, we first create a large-scale dataset by asking subjects to perform five mood-elicitation tasks. After each task, subjects' audio and video are collected, including 3D information (depth information) of facial expressions via a Kinect. The constructed dataset is from a real environment, i.e., several psychiatric hospitals, and has a specific scale. Then we propose a novel approach for potential depression risk recognition based on two kinds of different deep belief network (DBN) models. One model extracts 2D appearance features from facial images collected by an optical camera, while the other model extracts 3D dynamic features from 3D facial points collected by a Kinect. The final decision result comes from the combination of the two models. Finally, we evaluate all proposed deep models on our built dataset. The experimental results demonstrate that (1) our proposed method is able to identify patients with potential depression risk; (2) the recognition performance of combined 2D and 3D features model outperforms using either 2D or 3D features model only; (3) the performance of depression recognition is higher in the positive and negative emotional stimulus, and females' recognition rate is generally higher than that for males. Meanwhile, we compare the performance with other methods on the same dataset. The experimental results show that our integrated 2D and 3D features DBN is more reasonable and universal than other methods, and the experimental paradigm designed for depression is reasonable and practical.
Recent Trends on Biosurfactants With Antimicrobial Activity Produced by Bacteria Associated With Human Health: Different Perspectives on Their Properties, Challenges, and Potential Applications

The attention towards the bacteria associated with human health is growing more and more, above all regarding the bacteria that inhabit the niches offered by the human body, i.e., the gastrointestinal tract, skin, vaginal environment, and lungs. Among the secondary metabolites released by microorganisms associated with human health, little consideration is given to the biosurfactants, molecules with both hydrophobic and hydrophilic nature. Their role in the complex human environment is not only the mere biosurfactant function, but they could also control the microbiota through the quorum sensing system and the antimicrobial activity. These functions protect them and, accordingly, the human body principally from microbial and fungal pathogens. Consequently, nowadays, biosurfactants are emerging as promising bioactive molecules due to their very different structures, biological functions, low toxicity, higher biodegradability, and versatility. Therefore, this review provides a comprehensive perspective of biosurfactants with antimicrobial activity produced by bacteria associated with the human body and related to everything human beings are in contact with, e.g., food, beverages, and food-waste dumping sites. For the first time, the role of an “-omic” approach is highlighted to predict gene products for biosurfactant production, and an overview of the available gene sequences is reported. Besides, antimicrobial biosurfactants’ features, challenges, and potential applications in the biomedical, food, and nutraceutical industries are discussed.
Research on Optimal Operation of Electricity Heat Hydrogen System Based on Evaluation of New Energy Consumption Potential

Under the background of the “double high” power system, the electricity heat hydrogen system (EHHS) plays a significant role in the process of energy decarbonization. In order to meet the different optimization objectives of the system under different new energy consumption states, a new energy consumption potential assessment and optimized operation method based on intuitionistic fuzzy rough set theory is proposed. By using the intuitionistic fuzzy rough set theory, the continuous attribute data is divided into different levels and the results of its membership and non-membership are gotten at different levels. The membership results of real-time consumption data are matched with the rule sets, and then the system consumption state judgment result is obtained. In this article, the system consumption situation is divided into five states, and compared with the traditional division method, so the system state can be described more comprehensively. At the same time, the fuzzy set is used to deal with the ambiguity of the boundary between each state. The intuition theory is used to solve the problem of the uncertainty of the consumption state, and then the accurate judgment can be realized. In response to different consumption states, an optimal scheduling model is established in which a hydrogen heat energy system (HHES) is involved to meet different requirements, and a hybrid particle swarm optimization algorithm is used to solve the model. Adopting the IEEE-30 bus system as the network structure of EHHS in the simulation, the analysis shows that the dynamic state division method based on intuitionistic fuzzy rough set theory can better be used to judge the system state according to real-time variable factors. The system optimization based on the consumption state division has the advantages of improving the operating economy and increasing the consumption of new energy.
Neural-Symbolic Integration for Fairness in AI

Deep learning has achieved state-of-the-art results in various application domains ranging from image recognition to language translation and game playing. However, it is now generally accepted that deep learning alone has not been able to satisfy the requirement of fairness and, ultimately, trust in Artificial Intelligence (AI). In this paper, we propose an interactive neural-symbolic approach for fairness in AI based on the Logic Tensor Network (LTN) framework. We show that the extraction of symbolic knowledge from LTN-based deep networks combined with fairness constraints offer a general method for instilling fairness into deep networks via continual learning. Explainable AI approaches which otherwise could identify but not fix fairness issues are shown to be enriched with an ability to improve fairness results. Experimental results on three real-world data sets used to predict income, credit risk and recidivism in financial applications show that our approach can satisfy fairness metrics while maintaining state-of-the-art classification performance.
A Review on Android Malware: Attacks, Countermeasures and Challenges Ahead

Smartphones usage have become ubiquitous in modern life serving as a double-edged sword with opportunities and challenges in it. Along with the benefits, smartphones also have high exposure to malware. Malware has progressively penetrated thereby causing more turbulence. Malware authors have become increasingly sophisticated and are able to evade detection by anti-malware engines. This has led to a constant arms race between malware authors and malware defenders. This survey converges on Android malware and covers a walkthrough of the various obfuscation attacks deployed during malware analysis phase along with the myriad of adversarial attacks operated at malware detection phase. The review also unscrambles the difficulties currently faced in deploying an on-device, lightweight malware detector. It sheds spotlight for researchers to perceive the current state of the art techniques available to fend off malware along with suggestions on possible future directions. Journal of Cyber Security and Mobility, Vol. 10_1, 177–230. doi: 10.13052/jcsm2245-1439.1017 © 2021 River Publishers 178 S. G. Selvaganapathy et al.
User Privacy and Data Flow Control for Android Apps: Systematic Literature Review

Android mobile apps gain access to numerous users’ private data. Users of different Android mobile apps have less control over their sensitive data during their installation and run-time. Too often, these apps consider data privacy less serious than users’ expectations. Many mobile apps misbehave and upload users’ data without permission which confirmed the possibility of privacy leakage through different network channels. The literature has proposed various approaches to protect user’s data and avoid privacy violations. In this paper, we provide a comprehensive overview of state-of-art research on Android user privacy, and data flow control. the aim is to highlight the main trends, pinpoint the main methodologies applied, and enumerate the privacy violations faced by Android users. We also shed some light on the directions where the researcher’s community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we surveyed 114 relevant research papers published in leading conferences and journals. Our thorough examination of the relevant literature has led to a critical analysis of the proposed solutions with a focus on user privacy extensions and mechanism for the Android mobile platform. Furthermore, possible solutions and research directions have been discussed.
Towards Unsupervised Knowledge Extraction

Integration of symbolic and sub-symbolic approaches is rapidly emerging as an Artificial Intelligence (AI) paradigm. This paper presents a proof-of-concept approach towards an unsupervised learning method, based on Restricted Boltzmann Machines (RBMs), for extracting semantic associations among prominent entities within data. Validation of the approach is performed in two datasets that connect language and vision, namely Visual Genome and GQA. A methodology to formally structure the extracted knowledge for subsequent use through reasoning engines is also offered.
Towards More Robust Fashion Recognition by Combining of Deep-Learning-Based Detection with Semantic Reasoning

The company FutureTV produces and distributes self-produced videos in the fashion domain. It creates revenue through the placement of relevant advertising. The placement of apposite ads, though, requires an understanding of the contents of the videos. Until now, this tagging is created manually in a labor-intensive process. We believe that image recognition technologies can significantly decrease the need for manual involvement in the tagging process. However, the tagging of videos comes with additional challenges: Preliminary, new deep-learning models need to be trained on vast amounts of data obtained in a labor-intensive data-collection process. We suggest a new approach for the combining of deep-learning-based recognition with a semantic reasoning engine. Through the explicit declaration of knowledge fitting to the fashion categories present in the training data of the recognition system, we argue that it is possible to refine the recognition results and win extra knowledge beyond what is found in the neural net.
Cover-aware Steganalysis

The increasing digitization offers new ways, possibilities and needs for a secure transmission of information. Steganography and its analysis constitute an essential part of IT-Security. In this work we show how methods of blind-steganalysis can be improved to work in cover-aware scenarios, we will call this non-blind steganalysis. The main objective was to examine how to take advantage of the knowledge of reference images to maximize the accuracy-rate of the analysis. Therefore we evaluated common stego-tools and their embedding algorithms and established a dataset of 353110 images. The images have been applied to test the potency of the improved methods of the non-blind steganalysis. The results show that the accuray can be significantly improved by using cover-images to produce reference images. Also the aggregation of the outcomes has shown to have a positive impact on the accuracy. Particularly noteworthy is the correlation between the qualities of the stego- and coverimages. Only by consindering both, the accuracy could strongly be improved. Interestingly the difference between both qualities also has a deep impact on the results.
StarryThoughts

Enabling the free and equal exchange of arguments on social issues in a respectful manner is an integral part of establishing the democratic ideal. However, the current manifestation of online spaces tends to facilitate the gathering of like-minded people, leading to the polarization of opinions. Such polarization inhibits the sharing of diverse opinions and deteriorates respect for disagreeing opinions. To tackle this issue, we present StarryThoughts, an online system that supports users to express and explore diverse perspectives on social issues. The system supports three types of exploration of the collected arguments online: navigating opinions based on the demographic identities of the posters, checking the the stereotypes users hold towards demographics in relation to given social issues, and engaging with opinions with semantically different point-of-views. By deploying the system to the public in co-operation with a nationwide broadcasting company, we collected 1,950 opinions with 144 free-form responses from 1,209 visitors as initial data and iterated on the design. Results from a user study with 56 participants showed that the system enables participants to explore a wide range of opinions on social issues, be more informed on the various arguments, and be more confident about their opinions. From our findings, we provide several design considerations for building online systems for supporting users to explore diverse opinions on social issues.
StreamSketch

Creative live streams, where artists or designers demonstrate their creative process, have emerged as a unique and popular genre of live streams due to the real-time interactivity they afford. However, streamer-viewer interactions on most live streaming platforms only enable users to utilize text and emojis to communicate, which limits what viewers can convey and share in real time. To investigate the design space of potential visual and non-textual modalities within creative live streams, we first analyzed existing Twitch extensions and conducted a formative study with streamers who share creative activities to uncover key challenges that these streamers face. We then designed and implemented a prototype system, StreamSketch, which enables viewers and streamers to interact during live streams using multiple modalities, including freeform sketches and text. The prototype was evaluated by two professional artist streamers and their viewers during six streaming sessions. Overall, streamers and viewers found that StreamSketch provided increased engagement and new affordances compared to the traditional text-only modality, and highlighted how efficiency, moderation, and tool integration were continued challenges.
CrowdFolio

Freelancers increasingly earn their livelihood through online marketplaces. To attract new clients, freelancers continuously curate their online portfolios to convey their unique skills and style. However, many lack access to rapid, regular, and inexpensive feedback needed to improve their portfolios. Existing crowd feedback systems, which collect feedback on individual creative projects (i.e., decomposed approach), could fill this need, but it is unclear how they might support feedback on multiple projects (i.e., holistic approach). In a between-subjects study with 30 freelancers, we compared decomposed and holistic feedback collection approaches using CrowdFolio, a crowd feedback system for portfolios. The holistic approach helped freelancers discover new ways to describe their work, while the decomposed approach provided detailed insight about the visual attractiveness of projects. This study contributes evidence that portfolio feedback systems, regardless of collection approach, can positively support professional development by impacting how freelancers portray themselves online and reflect on their identity.
FITNet: Identifying Fashion Influencers on Twitter

JINDA HAN, University of Illinois at Urbana-Champaign, USA QINGLIN CHEN, University of Illinois at Urbana-Champaign, USA XILUN JIN, University of Illinois at Urbana-Champaign, USA WEIKAI XU, University of Illinois at Urbana-Champaign, USA WANXIAN YANG, University of Illinois at Urbana-Champaign, USA SUHANSANU KUMAR, University of Illinois at Urbana-Champaign, USA LI ZHAO, University of Missouri, USA HARI SUNDARAM, University of Illinois at Urbana-Champaign, USA RANJITHA KUMAR, University of Illinois at Urbana-Champaign, USA
The effect of acid strength and postacidification reaction time on the determination of chlorophyll a in ethanol extracts of aquatic periphyton

Quantification of chlorophyll a (Chl a) is essential to the study of aquatic ecosystems, yet differences in methodology may introduce significant errors to its determination in ethanol extracts. Insufficient acidification slows the conversion of Chl a to pheophytin a leading to an underestimate of Chl a concentration. Furthermore, slight differences in the postacidification reaction time can introduce greater errors in calculated Chl a and impede our ability to make cross‐study comparisons. We used known concentrations of pure Chl a from the blue‐green algae Anacystis nidulans dissolved in 95% ethanol to evaluate the effect of acid strength and postacidification reaction time on the spectrophotometric determination of Chl a. Increasing acid strength resulted in more rapid stabilization of calculated Chl a concentration. At reaction times less than 120 s estimates of Chl a deviated from known concentrations by as much as 84.8%. The magnitude of error in the calculated Chl a values were dependent on acid strength and reaction time, which allowed us to develop predictive equations to correct Chl a measurements that were insufficiently acidified or read prior to reaction completion. We validated our predictive equations using benthic periphyton samples from northern Alaska and northwestern Vermont, U.S.A. Our results indicate that under‐acidified samples with known reaction times can be easily corrected so results from different methods can be standardized. For future analyses we recommend acidifying ethanol‐extracted algal samples to 0.008 mol HCl L−1 and allowing samples to react for 30–60 min to ensure accurate and consistent results.
Word Complexity is in the Eye of the Beholder

Lexical complexity is a highly subjective notion, yet this factor is often neglected in lexical simplification and readability systems which use a ”one-size-fits-all” approach. In this paper, we investigate which aspects contribute to the notion of lexical complexity in various groups of readers, focusing on native and non-native speakers of English, and how the notion of complexity changes depending on the proficiency level of a non-native reader. To facilitate reproducibility of our approach and foster further research into these aspects, we release a dataset of complex words annotated by readers with different backgrounds.