Miracle-dz/UniFAR-FTUs huggingface dataset:
NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images

Large-scale datasets are driving the rapid developments of deep convolutional neural networks for visual sentiment analysis. However, the annotation of large-scale datasets is expensive and time consuming. Instead, it is easy to obtain weakly labeled web images from the Internet. However, noisy labels still lead to seriously degraded performance when we use images directly from the web for training networks. To address this drawback, we propose an end-to-end weakly supervised learning network, which is robust to mislabeled web images. Specifically, the proposed attention module automatically eliminates the distraction of those samples with incorrect labels by reducing their attention scores in the training process. On the other hand, the special-class activation map module is designed to stimulate the network by focusing on the significant regions from the samples with correct labels in a weakly supervised learning approach. Besides the process of feature learning, applying regularization to the classifier is considered to minimize the distance of those samples within the same class and maximize the distance between different class centroids. Quantitative and qualitative evaluations on well- and mislabeled web image datasets demonstrate that the proposed algorithm outperforms the related methods.
Research of Automatic Generation for Engineering Geological Survey Reports Based on a Four-Dimensional Dynamic Template

Errors and inefficiency may be caused by manual processing of complex templates for the preparation and management of engineering survey reports. To address this problem, this paper analyzes the multidimensional variable features of professional field documents and proposes a generation model of standardized reports based on a four-dimensional dynamic template. This approach splits the standardized report into multiple parts to construct a hierarchical tree that represents the report structure according to the report rules, then stores the tree in a graph database, and finally generates the desired report dynamically by retrieving the relational tree for the template and obtaining the relevant data. The model has been applied to the engineering geological survey report system and is shown to improve the working efficiency and data accuracy of the report preparation. Results indicate that the model is feasible and effective.
Developing Amaia: A Conversational Agent for Helping Portuguese Entrepreneurs - An Extensive Exploration of Question-Matching Approaches for Portuguese

This paper describes how we tackled the development of Amaia, a conversational agent for Portuguese entrepreneurs. After introducing the domain corpus used as Amaia’s Knowledge Base (KB), we make an extensive comparison of approaches for automatically matching user requests with Frequently Asked Questions (FAQs) in the KB, covering Information Retrieval (IR), approaches based on static and contextual word embeddings, and a model of Semantic Textual Similarity (STS) trained for Portuguese, which achieved the best performance. We further describe how we decreased the model’s complexity and improved scalability, with minimal impact on performance. In the end, Amaia combines an IR library and an STS model with reduced features. Towards a more human-like behavior, Amaia can also answer out-of-domain questions, based on a second corpus integrated in the KB. Such interactions are identified with a text classifier, also described in the paper.
Numerical Model for Hydration-Lubricated Contact and Its Friction Behavior at Nanoscale

In this study, we propose a numerical model to investigate the contact and friction behaviors of hydration-lubricated contact in an aqueous solution confined in a gap of several molecular diameters. A force–distance curve was obtained under different surface forces, and friction behavior was determined using the modified Eyring thermal activation model. The results showed that the normal boundary contact pressure and contact gaps were obtained using the proposed numerical model. In addition, friction force increased with the logarithm of sliding velocity. Hence, the Eyring model could predict the friction behavior of hydration lubrication. Moreover, it could capture the underlying physical principles of the contact and friction behaviors of hydration lubrication.
Developing a Conversational Agent With a Globally Distributed Team: An Experience Report

In this experience report, we discuss the development of a solution that enables conflict-affected youth to discover and access relevant learning content. A team of individuals from a not-for-profit, a large multi-national technology company, and an academic institution, collaborated to develop that solution as a conversational agent named Hakeem. We provide a brief motivation and product description before outlining our design and development process including forming a distributed virtual team, engaging in user-centred design with conflict-affected youth in Lebanon, and using a minimum viable product approach while adapting Scrum for distributed development. We end this report with a reflection on the lessons learned thus far.
Application of Support Vector Machine Methods in Classification of Customer Communications Data

The paper used automatic information extraction technique, text classification by SVM (Support vector machine) method, combined with Vietnamese word separation technique and natural language processing. Application results of the research used in extracting information, collecting user feedback from e-commerce websites, social networking sites providing businesses with useful information from contributing users in order to build an effective business strategy. The main contributions of this paper are the following: • Apply the BiGAN, the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on Motorbike Generator task. • Improves learning for BiGAN outperforming several state-of-the-art GAN methods training on motorbike dataset by a few techniques such as preprocessing data, data augmentation and hyperparameter tuning.
Polynomial Evaluation on Superscalar Architecture, Applied to the Elementary Function ex

The evaluation of small degree polynomials is critical for the computation of elementary functions. It has been extensively studied and is well documented. In this article, we evaluate existing methods for polynomial evaluation on superscalar architecture. In addition, we have completed this work with a factorization method, which is surprisingly neglected in the literature. This work focuses on out-of-order Intel processors, amongst others, of which computational units are available. Moreover, we applied our work on the elementary function ex that requires, in the current implementation, an evaluation of a polynomial of degree 10 for a satisfying precision and performance. Our results show that the factorization scheme is the fastest in benchmarks, and that latency and throughput are intrinsically dependent on each other on superscalar architecture.
Maresin-1 and Resolvin E1 Promote Regenerative Properties of Periodontal Ligament Stem Cells Under Inflammatory Conditions

Maresin-1 (MaR1) and Resolvin E1 (RvE1) are specialized pro-resolving lipid mediators (SPMs) that regulate inflammatory processes. We have previously demonstrated the hard and soft tissue regenerative capacity of RvE1 in an in vivo model of the periodontal disease characterized by inflammatory tissue destruction. Regeneration of periodontal tissues requires a well-orchestrated process mediated by periodontal ligament stem cells. However, limited data are available on how SPMs can regulate the regenerative properties of human periodontal ligament stem cells (hPDLSCs) under inflammatory conditions. Thus, we measured the impact of MaR1 and RvE1 in an in vitro model of hPDLSC under stimulation with IL-1β and TNF-α by evaluating pluripotency, migration, viability/cell death, periodontal ligament markers (α-smooth muscle actin, tenomodulin, and periostin), cementogenic-osteogenic differentiation, and phosphoproteomic perturbations. The data showed that the pro-inflammatory milieu suppresses pluripotency, viability, and migration of hPDLSCs; MaR1 and RvE1 both restored regenerative capacity by increasing hPDLSC viability, accelerating wound healing/migration, and up-regulating periodontal ligament markers and cementogenic-osteogenic differentiation. Protein phosphorylation perturbations were associated with the SPM-induced regenerative capacity of hPDLSCs. Together, these results demonstrate that MaR1 and RvE1 restore or improve the regenerative properties of highly specialized stem cells when inflammation is present and offer opportunities for direct pharmacologic treatment of lost tissue integrity.
GIS-Based Emotional Computing: A Review of Quantitative Approaches to Measure the Emotion Layer of Human-Environment Relationships

In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human–environment relationship is proposed for enriching traditional methods of various related disciplines such as urban planning. This paper proposes the geographic information system (GIS)-based emotional computing concept, which is a novel framework for applying GIS methods to collective human emotion. The methodology presented in this paper consists of three key steps: (1) collecting georeferenced data containing emotion and environment information such as social media and official sites, (2) detecting emotions using AI-based emotional computing technics such as natural language processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and deeply explore the connection between people and their surroundings with the help of GIS methods. Generally, the framework provides a standard workflow to calculate and analyze the new information layer for researchers, in which a measured human-centric perspective onto the environment is possible.
Characterizing outdateness with technical lag: an exploratory study

Background: Nowadays, many applications are built reusing a large number of components, retrieved from software collections such as npm (JavaScript) or PyPi (Python). Those components are built in their corresponding upstream repositories, where they are being developed. This architecture of reusing causes some constraints on how much outdated is an application when it is deployed in production environments. Goal: To understand how outdateness of applications, and the components on which they depend, can be computed, so that different situations can be measured and assessed with the help of metrics. Based on this understanding, we also want to produce a model to characterize ecosystems (collections of reusable components). Method: Use the technical lag framework to analyze the flows from upstream repositories, to collection of components, to application building and later deployment. Using this framework, analyze lag in version availability in each of these stages, and constraints that set limits on how much outdated can be deployed applications. Results: We define a model which allows us to better understand the factors that influence outdateness of an application produced with reusable components from repositories of components. The model allows us to find the factors for defining metrics for measuring outdateness, and to explore the factors that influence outdateness for components in applications. We propose some of those factors as the basis to characterize ecosystems or collections of components with respect to their impact on the outdateness of applications built with them. Conclusions: Technical lag is an appropriate framework for studying lags in version propagation from upstream development to deployment.
On the Detection of Community Smells Using Genetic Programming-based Ensemble Classifier Chain

Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.CCS CONCEPTS• Software and its engineering → Software organization and properties.
A Complexity Theoretical Study of Fuzzy K-Means

The fuzzy K-means problem is a popular generalization of the well-known K-means problem to soft clusterings. In this article, we present the first algorithmic study of the problem going beyond heuristics. Our main result is that, assuming a constant number of clusters, there is a polynomial time approximation scheme for the fuzzy K-means problem. As a part of our analysis, we also prove the existence of small coresets for fuzzy K-means. At the heart of our proofs are two novel techniques developed to analyze the otherwise notoriously difficult fuzzy K-means objective function.
Reimagining (Women’s) Health

An ever-increasing body of work within HCI investigates questions of around “Women’s Health” with the aim to disrupt the status quo of defaulting to an implicit norm of cis-male bodies. This laudable and feminist project has the potential to drastically improve the inclusivity and availability of health care. To explore how this research attends to gender, embodiment and identity, we conducted a critical discourse analysis of 17 publications explicitly positioning themselves as works concerned with “Women’s Health”. We find essentialised articulations of embodiment and gender, though little discussion on the intersections of race, class, sexuality and cultural contexts. Through two speculative designs, we illustrate potential responses to our analysis: The Shadow Zine, a reflection of self and the Compass, a token for community care.1 Our work provides an opportunity to develop a broader frame of gender and health, one that centers (gendered) marginalised health by attending to the power structures of existing medical practices and norms.
Psychological first aid to support healthcare professionals

Psychological first aid (PFA) may be considered a form of acute psychological crisis intervention designed to foster human resilience. PFA has been universally endorsed as an important addition to the corpus of mental health-related support services in the wake of disasters. As support grows for its utilization beyond disasters alone, consideration should be given to the application of PFA to high-risk occupational setting. While PFA has begun to be used in the emergency services, it would also seem to be well suited for fostering resilience in healthcare settings where burnout and vicarious traumatization may approach the prevalence of silent epidemics. This paper describes PFA and argues for its utilization as a non-stigmatizing approach to foster the resilience of healthcare professionals and in doing so avoiding the “second victim” phenomenon.
Matrine Inhibits CNS Autoimmunity Through an IFN-β-Dependent Mechanism

Matrine (MAT), a quinolizidine alkaloid component derived from the root of Sophora flavescens, suppresses experimental autoimmune encephalomyelitis (EAE), the animal model of multiple sclerosis (MS), by inducing the production of immunomodulatory molecules, e.g., IL-10. In an effort to find the upstream pathway(s) of the mechanism underlying these effects, we have tested certain upregulated immunomodulatory molecules. Among them, we found increased levels of IL-27 and IFN-β, one of the first-line MS therapies. Indeed, while low levels of IFN-β production in sera and type I interferon receptor (IFNAR1) expression in spinal cord of saline-treated control EAE mice were detected, they were significantly increased after MAT treatment. Increased numbers of CD11b+IFN-β+ microglia/infiltrating macrophages were observed in the CNS of MAT-treated mice. The key role of IFN-β induction in the suppressive effect of MAT on EAE was further verified by administration of anti-IFN-β neutralizing antibody, which largely reversed the therapeutic effect of MAT. Further, we found that, while MAT treatment induced production of IL-27 and IL-10 by CNS microglia/macrophages, this effect was significantly reduced by IFN-β neutralizing antibody. Finally, the role of IFN-β in MAT-induced IL-27 and IL-10 production was further confirmed in human monocytes in vitro. Together, our study demonstrates that MAT exerts its therapeutic effect in EAE through an IFN-β/IL-27/IL-10 pathway, and is likely a novel, safe, low-cost, and effective therapy as an alternative to exogenous IFN-β for MS.
Automatic library categorization

Software ecosystems contain several types of artefacts such as libraries, documentation and source code files. Recent studies show that the Maven software ecosystem alone already contains over 2.8 million artefacts and over 70, 000 libraries. Given the size of the ecosystem, selecting a library represents a challenge to its users. The MVNRepository website offers a category-based search functionality as a solution. However, not all of the libraries have been categorised, which leads to incomplete search results. This work proposes an approach to the automatic categorisation of libraries through machine learning classifiers trained on class and method names. Our preliminary results show that the approach is accurate, suggesting that large-scale applications may be feasible.
Engaging with Tweets: The Missing Dataset On Social Media

Most social media websites make use of recommender systems to show the content of interest for their users and to keep them engaged with the platform. On Twitter users can share and engage with the content by tweets. The ACM RecSys challenge 2020 focuses on predicting tweet engagements by users. In this paper, we present our approach for modeling this task. We take a deep look into the dataset and provide interesting observations from the dataset. Based on these findings, we construct a set of features and use gradient boosting trees to classify different types of engagements. Our method ranked 28 in the final leaderboard with an overall score of 63.
MICROBIOLOGICAL QUALITY AND SAFETY ASSESSMENT OF CAMEL MILK ( CAMELUS DROMEDARIES) IN SAUDI ARABIA (QASSIM REGION)

The microbiological quality and safety of raw camel milk from different farms in Qassim region (middle Saudi Arabia) were examined. Milk samples (n=33) were aseptically collected from the milking bowls. Samples were analyzed for several microbial quality attributes including aerobic total plate count (ATPC), psychrotrophs (PC), aerobic mesophilic sporeforming bacteria (AMSC), Enterobacteriaceae , total coliforms, faecal coliforms and moulds and yeasts. Furthermore, the presence of selected pathogens such as Staphylococcus aureus and Salmonella was detected. The mean log counts per ml for ATPC, psychrotrophs, aerobic mesophilic spore former, Enterobacteriaceae , and moulds and yeasts were 5.0, 3.8, 2.1, 2.7, and 1.9, respectively. Coliform group was found in 45.5 % of samples while 12% were faecal coliform positive as revealed by MPN method. S. aureus was located in 70% of the samples and the mean count was 2.7 log cfu per ml. Meanwhile, salmonella was detected in 24% of the samples. Results indicate the potential health risk of consuming raw camel milk under the present production conditions.
Security as Culture A Systematic Literature Review of DevSecOps

DevOps goes beyond automation, continuous integration and delivery processes, since it also encompasses people. In fact, DevOps promotes the collaboration between the development team and the operations team. When security comes into DevOps routines, people play an even more relevant role involving the collaboration between those teams and security team. Moreover, security is especially relevant while developing critical systems where we need to manage goals, risks and evidences. After implementing security into the DevOps toolchain, work only starts. We also need to start with behavioral changes in order to create a security culture. Several authors underlined DevSecOps, as one of the proposals for solving or, at least, minimizing this challenge. However, to date, the characterization of such a culture remains unclear. In this paper, a Systematic Literature Review was carried out to provide a better understanding of this topic from the human factor´s perspective. However it raises the following question: Is DevSecOps going to become mainstream?
Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?: A Position Paper

For the past few years most published research on recommendation algorithms has been based on deep learning (DL) methods. Following common research practices in our field, these works usually demonstrate that a new DL method is outperforming other models not based on deep learning in offline experiments. This almost consistent success of DL based models is however not observed in recommendation-related machine learning competitions like the challenges that are held with the yearly ACM RecSys conference. Instead the winning solutions mostly consist of substantial feature engineering efforts and the use of gradient boosting or ensemble techniques. In this paper we investigate possible reasons for this surprising phenomenon. We consider multiple possible factors such as the characteristics and complexity of the problem settings, datasets, and DL methods; the background of the competition participants; or the particularities of the evaluation approach.