Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying and prioritisation entrepreneurial behaviour factors using fuzzy AHP approach

The purpose of this research is to realise how organisations have been sustaining their growth through applying entrepreneurial behaviour factors. Regarding the thematic nature of research model, experts' opinion in oil industry, companies in oil industry have been examined in the current study included: 1) oil pipeline and telecommunication company; 2) oil products distribution company; 3) National Gas Company in Semnan and Khorasan province have been brought for this research as statistic population. The number of experts participating in the study is 30 persons who were interested in improving discussion. The main tools used for gathering the data in this study were company records and questionnaire. In this study, structural factors, underlying factors, behaviour factors sub-criteria were ranked regarding to criteria related to different levels of entrepreneurial behaviour sub-criteria of oil industry by using fuzzy analytic hierarchy process (FAHP). The results obtained from fuzzy AHP method according to the entrepreneurial behaviour factors indicate that structural factors are more important than underlying factors and behaviour factors. According to the structural factors scale, it is concluded that entrepreneur organisation structure is more important than other factors.
Metadata on Biodiversity: Definition and Implementation

SINP (Information system on nature and landscape) and ECOSCOPE (Observation for research on biodiversity data hub) are two distinct scientific infrastructures on biodiversity relying on different data sources and producers. Their main objective is to document and share information on biodiversity in France. INPN (https://inpn.mnhn.fr) is the reference information system for data related to nature. It manages and disseminates the reference data of the "geodiversity and biodiversity" part of the SINP, and deliver the metadata and data to GBIF (Global Biodiversity Information Facility). For SINP and Ecoscope projects, working groups composed of scientific organisations have defined two compliant metadata profiles, also compliant with INSPIRE Directive, to describe data on this thematic. These profiles are implemented using existing metadata standards: ISO 19115/19139 (for geographic metadata) for SINP and EML (Ecological Metadata Language) and ISO 19115/19139 for ECOSCOPE. A mapping has also been processed between the two profiles, as well as several thesaurus for keywords and a classification system for taxonomic identification are used, so as to ensure interoperability between systems. The profiles are implemented in web applications for editing and managing data (GeoSource/GeoNetwork for SINP and an ad hoc application for ECOSCOPE). These applications allow the harvesting of metadata using OGC/CSW (Catalog Service for the Web) standard. Next steps will support increased metadata visibility through the automatization of web-services.
Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances

With the widespread use of intelligent systems, such as smart speakers, addressee recognition has become a concern in human-computer interaction, as more and more people expect such systems to understand complicated social scenes, including those outdoors, in cafeterias, and hospitals. Because previous studies typically focused only on pre-specified tasks with limited conversational situations such as controlling smart homes, we created a mock dataset called Addressee Recognition in Visual Scenes with Utterances (ARVSU) that contains a vast body of image variations in visual scenes with an annotated utterance and a corresponding addressee for each scenario. We also propose a multi-modal deep-learning-based model that takes different human cues, specifically eye gazes and transcripts of an utterance corpus, into account to predict the conversational addressee from a specific speaker's view in various real-life conversational scenarios. To the best of our knowledge, we are the first to introduce an end-to-end deep learning model that combines vision and transcripts of utterance for addressee recognition. As a result, our study suggests that future addressee recognition can reach the ability to understand human intention in many social situations previously unexplored, and our modality dataset is a first step in promoting research in this field.
PredCNN: Predictive Learning with Cascade Convolutions

Predicting future frames in videos remains an unsolved but challenging problem. Mainstream recurrent models suffer from huge memory usage and computation cost, while convolutional models are unable to effectively capture the temporal dependencies between consecutive video frames. To tackle this problem, we introduce an entirely CNN-based architecture, PredCNN, that models the dependencies between the next frame and the sequential video inputs. Inspired by the core idea of recurrent models that previous states have more transition operations than future states, we design a cascade multiplicative unit (CMU) that provides relatively more operations for previous video frames. This newly proposed unit enables PredCNN to predict future spatiotemporal data without any recurrent chain structures, which eases gradient propagation and enables a fully paralleled optimization. We show that PredCNN outperforms the state-of-the-art recurrent models for video prediction on the standard Moving MNIST dataset and two challenging crowd flow prediction datasets, and achieves a faster training speed and lower memory footprint.
Automata-Theoretic Foundations of FOND Planning for LTLf and LDLf Goals

We study planning for LTLf and LDLf temporally extended goals in nondeterministic fully observable domains (FOND). We consider both strong and strong cyclic plans, and develop foundational automata-based techniques to deal with both cases. Using these techniques we provide the computational characterization of both problems, separating the complexity in the size of the domain specification from that in the size of the formula. Specifically we establish them to be EXPTIME-complete and 2EXPTIME-complete, respectively, for both problems. In doing so, we also show 2EXPTIME-hardness for strong cyclic plans, which was open.
Boosting Combinatorial Problem Modeling with Machine Learning

In this work we present two general techniques to deal with multi-stage optimization problems under uncertainty, featuring off-line and on-line decisions. The methods are applicable when: 1) the uncertainty is exogenous; 2) there exists a heuristic for the on-line phase that can be modeled as a parametric convex optimization problem. The first technique replaces the on-line heuristics with an anticipatory solver, obtained through a systematic procedure. The second technique consists in making the off-line solver aware of the on-line heuristic, and capable of controlling its parameters so as to steer its behavior. We instantiate our approaches on two case studies: an energy management system with uncertain renewable generation and load demand, and a vehicle routing problem with uncertain travel times. We show how both techniques achieve high solution quality w.r. t. an oracle operating under perfect information, by obtaining different trade-offs in terms of computation time.
Towards Human-Engaged AI

Engagement, the key construct that describes the synergy between human (users) and technology (computing systems), is gaining increasing attention in academia and industry. Human-Engaged AI (HEAI) is an emerging research paradigm that aims to jointly advance the capability and capacity of human and AI technology. In this paper, we first review the key concepts in HEAI and its driving force from the integration of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). Then we present an HEAI framework developed from our own work.
Hypoxia adaptation in termites: hypoxic conditions enhance survival and reproductive activity in royals

Termite royals (queen and king) exhibit extraordinary longevity without sacrificing reproductive performance, unlike most animals, in whom lifespan is generally negatively associated with reproduction. Therefore, the regulatory mechanisms underlying longevity have attracted much attention. Although the ageing process is influenced by environmental factors in many insects during their life cycle, it remains unclear whether any factors have an effect on the extended survival and high reproductive capacity of termite royals. Here, we show that hypoxia, possibly an important environmental factor in the nests, enhances survival and reproductive activity in incipient royals of the subterranean termite Reticulitermes speratus compared with those in control conditions. Quantitative real‐time PCR analysis revealed that the expression levels of the vitellogenin gene in queens are maintained to a greater extent under hypoxic conditions than under control conditions. The expression levels of the antioxidant enzyme genes RsCAT1 and RsPHGPX are also significantly promoted by hypoxia in queens and kings respectively. These results suggest that hypoxic exposure can contribute in part to achieving high reproductive output by altering gene expression after founding of colonies in the royals. Our study provides novel insights into the effect of a nest environment on the reproductive characteristics in termite royals.
Consumer Responses to the CPA WEBTRUST™ Assurance

With the advent of WEBTRUST™, accountants are providing assurances about online transactions to consumers who may otherwise hesitate to engage in electronic commerce. This study explores the influence of assurance services on consumers' online transaction expectations and intent to purchase online. In short, the study supports the notion that many of the marketing efforts associated with WEBTRUST™ can have desirable effects on consumer behavior. Results suggest that consumers who pay more attention to the WEBTRUST™ seal and disclosures at the web site or who have been exposed to WEBTRUST™ advertising have higher online transaction expectations and stronger intent to purchase online than their counterparts. Similarly, consumers' knowledge about CPAs positively influences their online transaction expectations, but not intent to purchase online. However, the study also indicates areas of concern for practitioners and researchers. Only 56 percent of subjects noticed the seal, 27 percent clicked on the seal, a.
Recruiting for a multicentre DoD and VA longitudinal study: lessons learned

ABSTRACT Primary Objective: The primary objective of the study is to identify and delineate effective recruitment practises in a large, multi-site, longitudinal, observational study employing both military service members and Veterans. Setting: Four Chronic Effects of Neurotrauma Consortium sites. Design: A descriptive study. Results: Overall and cohort-specific recruitment increased with the addition of focused recruitment strategies and a military/Veteran-centric recruitment director. Conclusion: Use of site-specific strategies aligned with local Institutional Review Board procedures and emphasizing awareness of service member organizational allegiances was the key to effective recruiting. Adding a recruitment director with background similar to study participants coincided with significantly improved overall participant numbers and specific subpopulations of research subjects, thus adding to the value of the study.
Patent Litigation Prediction: A Convolutional Tensor Factorization Approach

Patent litigation is an expensive legal process faced by many companies. To reduce the cost of patent litigation, one effective approach is proactive management based on predictive analysis. However, automatic prediction of patent litigation is still an open problem due to the complexity of lawsuits. In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies. Specifically, CTF is a hybrid modeling approach, where the content features from the patents are represented by the Network embedding-combined Convolutional Neural Network (NCNN) and the lawsuit records of companies are summarized in a tensor, respectively. Then, CTF integrates NCNN and tensor factorization to systematically exploit both content information and collaborative information from large amount of data. Finally, the risky patents will be returned by a learning to rank strategy. Extensive experimental results on real-world data demonstrate the effectiveness of our framework.
Enhance quality care performance: Determination of the variables for establishing a common database in French paediatric critical care units

Abstract Selected variables for the French Paediatric Intensive Care registry. Rationale, aims, and objectives Providing quality care requires follow‐up in regard to clinical and economic activities. Over the past decade, medical databases and patient registries have expanded considerably, particularly in paediatric critical care medicine (eg, the Paediatric Intensive Care Audit Network (PICANet) in the UK, the Australian and New Zealand Paediatric Intensive Care (ANZPIC) Registry in Australia and New Zealand, and the Virtual Paediatric Intensive Care Unit Performance System (VPS) in the USA). Such a registry is not yet available in France. The aim of this study was to determine variables that ought to be included in a French paediatric critical care registry. Methods Variables, items, and subitems from 3 foreign registries and 2 French local databases were used. Items described each variable, and subitems described items. The Delphi method was used to evaluate and rate 65 variables, 90 items, and 17 subitems taking into account importance or relevance based on input from 28 French physicians affiliated with the French Paediatric Critical Care Group. Two ratings were used between January and May 2013. Results Fifteen files from 10 paediatric intensive care units were included. Out of 65 potential variables, 48 (74%) were considered to be indispensable, 16 (25%) were considered to be optional, and 1 (2%) was considered to be irrelevant. Out of 90 potential items, 62 (69%) were considered to be relevant, 23 (26%) were considered to be of little relevance, and 5 (6%) were considered to be irrelevant. Out of 17 potential subitems, 9 (53%) were considered to be relevant, 6 (35%) were considered to be of little relevance, and 2 (12%) were considered to be irrelevant. Conclusions The necessary variables that ought to be included in a French paediatric critical care registry were identified. The challenge now is to develop the French registry for paediatric intensive care units.
Sexual well-being after menopause: An International Menopause Society White Paper

Abstract Sexual well-being frequently declines following the menopause transition and can be associated with significant personal and relationship distress. This distress is the hallmark of female sexual dysfunction (FSD). FSD is highly prevalent in postmenopausal women. The prevalence of sexual problems increases with age, but conversely this is associated with decreasing distress with advancing age. This pattern has been seen across multiple international populations with varied cultural norms. While the etiology of FSD is multifactorial, the physiological changes of sex hormone insufficiency and postmenopausal symptoms, such as dyspareunia, are primary factors contributing to FSD at midlife. The International Menopause Society is working to increase awareness of FSD and to provide a framework for practitioners to address sexual medicine concerns. This White Paper aims to review the process of care for female sexual well-being following menopause, from initially approaching the discussion of FSD, to identifying clinical signs and symptoms, and ultimately determining the best available biopsychosocial therapies. As with most processes of care, the first step is often the most difficult. Health-care practitioners need to broach the topic of sexuality in the clinical setting. Lack of information on, comfort with, and biases about the topic of sexuality after menopause are significant hurdles that the International Menopause Society addresses in this document. Each member of the Writing Group remains committed to continued advocacy for the validity of FSD as a diagnosis, the need for therapies for women to be both available and included in health insurance coverage, and continued therapeutic research to provide evidence-based solutions.
Matrix completion with Preference Ranking for Top-N Recommendation

Matrix completion has become a popular method for top-N recommendation due to the low rank nature of sparse rating matrices. However, many existing methods produce top-N recommendations by recovering a user-item matrix solely based on a low rank function or its relaxations, while ignoring other important intrinsic characteristics of the top-N recommendation tasks such as preference ranking over the items. In this paper, we propose a novel matrix completion method that integrates the low rank and preference ranking characteristics of recommendation matrix under a self-recovery model for top-N recommendation. The proposed method is formulated as a joint minimization problem and solved using an ADMM algorithm. We conduct experiments on E-commerce datasets. The experimental results show the proposed approach outperforms several state-of-the-art methods.
Complexity of Approximate Query Answering under Inconsistency in Datalog+/-

Several semantics have been proposed to query inconsistent ontological knowledge bases, including the intersection of repairs and the intersection of closed repairs as two approximate inconsistency-tolerant semantics. In this paper, we analyze the complexity of conjunctive query answering under these two semantics for a wide range of Datalog+/- languages. We consider both the standard setting, where errors may only be in the database, and the generalized setting, where also the rules of a Datalog+/- knowledge base may be erroneous.
Microbiological quality of organic chicken meat during refrigerated storage in air and modified atmospheres

ABSTRACT 1. The aim of this study was to assess the incidence of pathogens and the development of spoilage microflora in organic chicken meat originating from a small poultry slaughterhouse and stored for 14 days at 2°C aerobically (control) or in one of two modified atmosphere packaging systems (MAP1: 80% O2, 20% CO2 and MAP2: 70% N2, 30% CO2). 2. Campylobacter jejuni survived well during storage; and was found on the skin in 95% of samples (262/276). 3. In general, both the skin and meat samples showed a good initial microbiological quality with total viable counts of less than 3 log cfu/g in meat and approximately 5 log cfu/g on skin. 4. No difference was found between breast and thigh samples during the experiment. 5. Shelf life was limited mainly by the development of mesophilic and psychrotrophic microflora on skin which were found at 7-day storage for the control and MAP1 and 10 days for MAP2.
Efficient Pruning of Large Knowledge Graphs

In this paper we present an efficient and highly accurate algorithm to prune noisy or over-ambiguous knowledge graphs given as input an extensional definition of a domain of interest, namely as a set of instances or concepts. Our method climbs the graph in a bottom-up fashion, iteratively layering the graph and pruning nodes and edges in each layer while not compromising the connectivity of the set of input nodes. Iterative layering and protection of pre-defined nodes allow to extract semantically coherent DAG structures from noisy or over-ambiguous cyclic graphs, without loss of information and without incurring in computational bottlenecks, which are the main problem of state-of-the-art methods for cleaning large, i.e., Web-scale, knowledge graphs. We apply our algorithm to the tasks of pruning automatically acquired taxonomies using benchmarking data from a SemEval evaluation exercise, as well as the extraction of a domain-adapted taxonomy from the Wikipedia category hierarchy. The results show the superiority of our approach over state-of-art algorithms in terms of both output quality and computational efficiency.
Aberrant methylation and silencing of the SPINT2 gene in high‐grade gliomas

Hepatocyte growth factor activator inhibitor type 2 (HAI‐2), encoded by the SPINT2 gene, is a membrane‐anchored protein that inhibits proteases involved in the activation of hepatocyte growth factor (HGF), a ligand of MET receptor. Epigenetic silencing of the SPINT2 gene has been reported in a human glioblastoma cell line (U87) and glioblastoma‐derived cancer stem cells. However, the incidence of SPINT2 methylation in tumor tissues obtained from glioma patients is unknown. In this study, we analyzed the methylation status of the SPINT2 gene of eight human glioblastoma cell lines and surgically resected glioma tissues of different grades (II, III, and IV) by bisulfite sequence analysis and methylation‐specific PCR. Most glioblastoma lines (7/8) showed methylation of the SPINT2 gene with a significantly reduced level of SPINT2mRNA compared to cultured astrocytes and normal brain tissues. However, all glioblastoma lines expressed mRNA for HGF activator (HGFAC), a target protease of HAI‐2/SPINT2. Forced expression of SPINT2 reduced MET phosphorylation of U87 glioblastoma cells both in vitro and in intracranial xenografts in nude mice. Methylation‐specific PCR analysis of the resected glioma tissues indicated notable methylation of the SPINT2 gene in 33.3% (2/6), 71.4% (10/14), and 74.3% (26/35) of grade II, III, and IV gliomas, respectively. Analysis of RNA sequencing data in a public database indicated an increased HGFAC/SPINT2 expression ratio in high‐grade compared to low‐grade gliomas (P = .01). In summary, aberrant methylation of the SPINT2 gene is frequently observed in high‐grade gliomas and might confer MET signaling in the glioma cells.
Back to nature

Could committing half of Earth's surface to conservation stave off the next mass extinction? Imagine a chain of uninterrupted wild landscapes spanning the savannas and forests of southern and central Africa, the woodlands of Poland and Belarus, and the tropical forests of Central and South America. By committing half of the planet's terrestrial and marine surface to nature, Edward O. Wilson believes that we can save millions of species, as well as humanity. This is Wilson's audacious proposal to stave off the coming biological apocalypse, the sixth mass extinction event, which he outlines in Half-Earth: Our Planet's Fight for Life.
Completeness-Preserving Dominance Techniques for Satisficing Planning

Dominance pruning methods have recently been introduced for optimal planning. They compare states based on their goal distance to prune those that can be proven to be worse than others. In this paper, we introduce dominance techniques for satisficing planning. We extend the definition of dominance, showing that being closer to the goal is not a prerequisite for dominance in the satisficing setting. We develop a new method to automatically find dominance relations in which a state dominates another if it has achieved more serializable sub-goals. We take advantage of dominance relations in different ways; while in optimal planning their usage focused on dominance pruning and action selection, we also use it to guide enforced hill-climbing search, resulting in a complete algorithm.