Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluations of human emotional state & affective computing to support job-seekers

The act of re-engaging the unemployed into work is a crucial policy agenda and the main task of employment agencies. However, the means of accomplishing this task is challenging because the employment agencies have not helped much in given effective job search advice to job seekers in applying to the right vacancy. In recent times, behavioral economics research has revealed that people make systematic mistakes in decision-making. Most times job seekers fail in predictable ways not because they lack knowledge, practice, or willingness, but because they are limited in the way, they make decisions: "forces" that influence them (e.g., emotions, relativity, social norms, etc) trick the brains. However, this is not a helpless situation. Once the job seekers understand when and where they might make erroneous decisions in applying to the right vacancies. The goal of this research is to develop and compare different technological interventions and measure long-term their ability to provoke positive changes in the life of the users in applying for the right vacancies. The researchers intend to recruit 70 subjects (20-40 years) and to make use of a pre-assessment tool such as questionnaires, interact, and conduct surveys and interviews to administer to selected job seekers communities in order to measure their level of cognitive abilities and their link to human stress in the context of applying to the right vacancy. The researchers intend to collect three types of data from the selected subjects. The data include: (a) demographic data, (b) psychological data, which capture a subject's general susceptibility to stress and (c) self-report data, on the subject's stress and workload levels. Then, Ecological Momentarily Intervention (EMI) is use to examine how the users can be intervened with right context, encouragement, or support in the right moment in order to reduce their burden and increase intervention personalization in applying for the right vacancy.
Footprint of knowledge acquisition improvement in failure diagnosis analysis

Fault tree analysis (FTA) as an effective and efficient risk assessment tool are widely used to analyze the reliability of a complex system. In this context, FTA can properly improve the safety performance of the system by preventing an event which may lead to occurrence of a catastrophic accident. However, traditional FTA is still suffering from dynamic structure demonstration and importantly epistemic uncertainty processing. In this study, a novel methodology is introduced using Bayesian updating mechanism to deal with dynamic structure and 2‐tuple fuzzy set named as intuitionistic fuzzy numbers are employed to cope with subjectivity of uncertainty processing. Accordingly, the most critical system components which affect the system reliability are recognized by using an appropriate sensitivity analysis method. The proposed methodology is then applied on a real case study application (a brake fluid filling system) in order to examine the effectiveness and feasibility of the approach. The results illustrated that the new methodology can have enough benefits for diagnosing the systems' faults compared with listing approaches of safety and reliability analysis. In terms of empirical case study, “electromotor failure” was evaluated as the second most critical basic event in conventional‐based approaches, whereas in the novel methodology “high pressure liquefied material” was recognized as the second one.
Support vector regression‐based imputation in analogy‐based software development effort estimation

Missing data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort estimation (SDEE) techniques. To deal with this challenge, several imputation techniques have been investigated in SDEE and k‐nearest neighbors (KNN)‐based imputation is still the most frequently used. To the best of our knowledge, no study has used support vector regression (SVR)‐based imputation to construct accurate estimation techniques, in particular those based on analogy. This paper introduces a new imputation technique based on SVR for handling MD in two analogy‐based SDEE techniques: classical analogy and fuzzy analogy. More specifically, we investigate whether the use of SVR instead of KNN in imputing MD improves the predictive performance of these two analogy‐based techniques. A total of 1134 experiments were conducted involving seven datasets, SVR/KNN MD imputation techniques (KNN with Euclidean and Manhattan distances), three missingness mechanisms (missing completely at random, missing at random, non‐ignorable missing), and MD percentages from 10% to 90%. The results suggest that the use of SVR imputation, rather than KNN imputation, may improve the prediction performance of both analogy‐based techniques. Furthermore, we found that the impact of MD percentage upon effort prediction performance is reduced when using SVR rather than KNN. Moreover, fuzzy analogy generates better estimates in terms of the standardized accuracy measure than classical analogy regardless of the MD technique, the dataset used, the missingness mechanism, or the MD percentage.
Hybrid NER System for Multi-Source Offer Feeds

Data available across the web is largely unstructured. Offers published by multiple sources like banks, digital wallets, merchants, etc., are one of the most accessed advertising data in today's world. This data gets accessed by millions of people on a daily basis and is easily interpreted by humans, but since it is largely unstructured and diverse, using an algorithmic way to extract meaningful information out of these offers is hard. Identifying the essential offer entities (for instance, its amount, the product on which the offer is applicable, the merchant providing the offer, etc.) from these offers plays a vital role in targeting the right customers to improve sales. This work presents and evaluates various existing Named Entity Recognizer (NER) models which can identify the required entities from offer feeds. We also propose a novel Hybrid NER model constructed by two-level stacking of Conditional Random Field, Bidirectional LSTM and Spacy models at the first level and an SVM classifier at the second. The proposed hybrid model has been tested on offer feeds collected from multiple sources and has shown better performance in the offer domain when compared to the existing models.
Recovery of Structured Signals From Corrupted Non-Linear Measurements

This paper studies the problem of recovering a structured signal from a relatively small number of corrupted non-linear measurements. Assuming that signal and corruption are contained in some structure-promoted set, we suggest an extended Lasso to disentangle signal and corruption. We also provide conditions under which this recovery procedure can successfully reconstruct both signal and corruption.
Implementing an ERP System: The Paradigm of a Chemical Company

An application of a methodology present in the previous paper An ERP Implementation Method: Studying a Pharmaceutical Company, is under consideration for a chemical company. Half of the paper as far as the methodology used is similar and only the application is different. In this paper it is tried to resolve problems and drawbacks of the methodology and to prove that it is satisfied enough to add some knowledge in the area of ERP systems Implementation. The modelling tool is again Petri-nets and the effort is to analyze the problem using this mathematical element. Finally, the SAP implementation method is presented, representing all the knowledge captured from this case study.
Service-Oriented Software Architecture for Cloud Robotics

In this article, we present an overview of the use of service-oriented architecture and Web services in developing robotics applications and software integrated with the Internet and the Cloud. This is a recent trend that emerged since 2010 from the concept of cloud robotics, which leverages the use of cloud infrastructures for robotics applications following a service-oriented architecture approach. In particular, we distinguish two main categories: (\textit{i.}) virtualization of robotics systems and (\textit{ii.}) computation offloading from robots to cloud-based services. We discuss the main approaches proposed in the literature to design robotics systems through the Web and their integration to the cloud through a service-oriented computing framework.
Air Photography And Archaeology

Thank you very much for reading air photography and archaeology. As you may know, people have search hundreds times for their favorite books like this air photography and archaeology, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious bugs inside their desktop computer. air photography and archaeology is available in our book collection an online access to it is set as public so you can download it instantly. Our books collection spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the air photography and archaeology is universally compatible with any devices to read.
Learning Interpretable Models with Causal Guarantees

Machine learning has shown much promise in helping improve the quality of medical, legal, and economic decision-making. In these applications, machine learning models must satisfy two important criteria: (i) they must be causal, since the goal is typically to predict individual treatment effects, and (ii) they must be interpretable, so that human decision makers can validate and trust the model predictions. There has recently been much progress along each direction independently, yet the state-of-the-art approaches are fundamentally incompatible. We propose a framework for learning causal interpretable models---from observational data---that can be used to predict individual treatment effects. Our framework can be used with any algorithm for learning interpretable models. Furthermore, we prove an error bound on the treatment effects predicted by our model. Finally, in an experiment on real-world data, we show that the models trained using our framework significantly outperform a number of baselines.
Harnessing Macrophages for Controlled-Release Drug Delivery: Lessons From Microbes

With the effectiveness of therapeutic agents ever decreasing and the increased incidence of multi-drug resistant pathogens, there is a clear need for administration of more potent, potentially more toxic, drugs. Alternatively, biopharmaceuticals may hold potential but require specialized protection from premature in vivo degradation. Thus, a paralleled need for specialized drug delivery systems has arisen. Although cell-mediated drug delivery is not a completely novel concept, the few applications described to date are not yet ready for in vivo application, for various reasons such as drug-induced carrier cell death, limited control over the site and timing of drug release and/or drug degradation by the host immune system. Here, we present our hypothesis for a new drug delivery system, which aims to negate these limitations. We propose transport of nanoparticle-encapsulated drugs inside autologous macrophages polarized to M1 phenotype for high mobility and treated to induce transient phagosome maturation arrest. In addition, we propose a significant shift of existing paradigms in the study of host-microbe interactions, in order to study microbial host immune evasion and dissemination patterns for their therapeutic utilization in the context of drug delivery. We describe a system in which microbial strategies may be adopted to facilitate absolute control over drug delivery, and without sacrificing the host carrier cells. We provide a comprehensive summary of the lessons we can learn from microbes in the context of drug delivery and discuss their feasibility for in vivo therapeutic application. We then describe our proposed “synthetic microbe drug delivery system” in detail. In our opinion, this multidisciplinary approach may hold the solution to effective, controlled drug delivery.
Phenotype and Function of Activated Natural Killer Cells From Patients With Prostate Cancer: Patient-Dependent Responses to Priming and IL-2 Activation

Background: Although immunotherapy has emerged as the “next generation” of cancer treatments, it has not yet been shown to be successful in the treatment of patients with prostate cancer, for whom therapeutic options remain limited to radiotherapy and androgen (hormone) deprivation therapy. Previous studies have shown that priming natural killer (NK) cells isolated from healthy individuals via co-incubation with CTV-1 cells derived from an acute lymphoblastic leukemia (ALL) enhances their cytotoxicity against human DU145 (metastatic) prostate cancer cells, but it remains unknown to what extent NK cells from patients with prostate cancer can be triggered to kill. Herein, we explore the phenotype of peripheral blood NK cells in patients with prostate cancer and compare the capacity of CTV-1 cell-mediated priming and IL-2 stimulation to trigger NK cell-mediated killing of the human PC3 (metastatic) prostate cancer cell line. Methods: The phenotype of resting, primed (co-incubation with CTV-1 cells for 17 h) and IL-2 activated (100 IU/ml IL-2 for 17 h) NK cells isolated from frozen-thawed peripheral blood mononuclear cell (PBMC) preparations from patients with benign disease (n = 6) and prostate cancer (n = 18) and their cytotoxicity against PC3 and K562 cells was determined by flow cytometry. Relationship(s) between NK cell phenotypic features and cytotoxic potential were interrogated using Spearman Rank correlation matrices. Results and Conclusions: NK cell priming and IL-2 activation of patient-derived NK cells resulted in similar levels of cytotoxicity, but distinct NK cell phenotypes. Importantly, the capacity of priming and IL-2 stimulation to trigger cytotoxicity was patient-dependent and mutually exclusive, in that NK cells from ~50% of patients preferentially responded to priming whereas NK cells from the remaining patients preferentially responded to cytokine stimulation. In addition to providing more insight into the biology of primed and cytokine-stimulated NK cells, this study supports the use of autologous NK cell-based immunotherapies for the treatment of prostate cancer. However, our findings also indicate that patients will need to be stratified according to their potential responsiveness to individual therapeutic approaches.
β-Defensins: Farming the Microbiome for Homeostasis and Health

Diverse commensal populations are now regarded as key to physiological homeostasis and protection against disease. Although bacteria are the most abundant component of microbiomes, and the most intensively studied, the microbiome also consists of viral, fungal, archael, and protozoan communities, about which comparatively little is known. Host-defense peptides (HDPs), originally described as antimicrobial, now have renewed significance as curators of the pervasive microbial loads required to maintain homeostasis and manage microbiome diversity. Harnessing HDP biology to transition away from non-selective, antibiotic-mediated treatments for clearance of microbes is a new paradigm, particularly in veterinary medicine. One family of evolutionarily conserved HDPs, β-defensins which are produced in diverse combinations by epithelial and immune cell populations, are multifunctional cationic peptides which manage the cross-talk between host and microbes and maintain a healthy yet dynamic equilibrium across mucosal systems. They are therefore key gatekeepers to the oral, respiratory, reproductive and enteric tissues, preventing pathogen-associated inflammation and disease and maintaining physiological normality. Expansions in the number of genes encoding these natural antibiotics have been described in the genomes of some species, the functional significance of which has only recently being appreciated. β-defensin expression has been documented pre-birth and disruptions in their regulation may play a role in maladaptive neonatal immune programming, thereby contributing to subsequent disease susceptibility. Here we review recent evidence supporting a critical role for β-defensins as farmers of the pervasive and complex prokaryotic ecosystems that occupy all body surfaces and cavities. We also share some new perspectives on the role of β-defensins as sensors of homeostasis and the immune vanguard particularly at sites of immunological privilege where inflammation is attenuated.
Formation of Large Native Sulfur Deposits Does Not Require Molecular Oxygen

Large native (i.e., elemental) sulfur deposits can be part of caprock assemblages found on top of or in lateral position to salt diapirs and as stratabound mineralization in gypsum and anhydrite lithologies. Native sulfur is formed when hydrocarbons come in contact with sulfate minerals in presence of liquid water. The prevailing model for native sulfur formation in such settings is that sulfide produced by sulfate-reducing bacteria is oxidized to zero-valent sulfur in presence of molecular oxygen (O2). Although possible, such a scenario is problematic because: (1) exposure to oxygen would drastically decrease growth of microbial sulfate-reducing organisms, thereby slowing down sulfide production; (2) on geologic timescales, excess supply with oxygen would convert sulfide into sulfate rather than native sulfur; and (3) to produce large native sulfur deposits, enormous amounts of oxygenated water would need to be brought in close proximity to environments in which ample hydrocarbon supply sustains sulfate reduction. However, sulfur stable isotope data from native sulfur deposits emplaced at a stage after the formation of the host rocks indicate that the sulfur was formed in a setting with little solute exchange with the ambient environment and little supply of dissolved oxygen. We deduce that there must be a process for the formation of native sulfur in absence of an external oxidant for sulfide. We hypothesize that in systems with little solute exchange, sulfate-reducing organisms, possibly in cooperation with other anaerobic microbial partners, drive the formation of native sulfur deposits. In order to cope with sulfide stress, microbes may shift from harmful sulfide production to non-hazardous native sulfur production. We propose four possible mechanisms as a means to form native sulfur: (1) a modified sulfate reduction process that produces sulfur compounds with an intermediate oxidation state, (2) coupling of sulfide oxidation to methanogenesis that utilizes methylated compounds, acetate or carbon dioxide, (3) ammonium oxidation coupled to sulfate reduction, and (4) sulfur comproportionation of sulfate and sulfide. We show these reactions are thermodynamically favorable and especially useful in environments with multiple stressors, such as salt and dissolved sulfide, and provide evidence that microbial species functioning in such environments produce native sulfur. Integrating these insights, we argue that microbes may form large native sulfur deposits in absence of light and external oxidants such as O2, nitrate, and metal oxides. The existence of such a process would not only explain enigmatic occurrences of native sulfur in the geologic record, but also provide an explanation for cryptic sulfur and carbon cycling beneath the seabed.
Calcium Isotopes in Human Urine as a Diagnostic Tool for Bone Loss: Additional Evidence for Time Delays in Bone Response to Experimental Bed Rest

The calcium (Ca) isotopic composition in urine during bed rest has been demonstrated to be systematically light, indicating a negative bone mineral balance (i.e., bone loss). Here we present new Ca isotope data on urine during the “nutritional countermeasures” (NUC) bed rest study. We analyzed the Ca isotopic composition of 24 h pooled urine samples from seven healthy male subjects during baseline data collection (BDC), head-down-tilt bed rest and recovery. Additionally, we analyzed urine from two follow-up examinations after the regeneration phase. We observed a change in Ca isotopic composition during the bed rest phase, indicative of bone loss with a time delay of 10 to 21 days. We also observe that the Ca isotopic composition of urine is strongly dependent on the individual Ca metabolism and varies between subjects. We relate this individuality in Ca metabolism to differences in the amounts of Ca being recycled in the kidneys. Previous studies have shown that the more Ca is reabsorbed in the kidneys the more enriched the urine becomes in heavy isotopes of calcium. The Ca isotopic composition of urine is thus modified by more than one process and cannot be used in a straightforward manner to monitor net bone mineral balance. To overcome this problem, we propose a new baseline approach for using Ca isotopes, which effectively cancels out the effects of individual renal Ca reabsorption. This allows us to detect bone loss in patients without ambiguity by combining measurements of the Ca isotopic composition of urine and daily Ca excretion rate and comparing these to data collected on healthy individuals with a normal steady-state bone balance.
Plant ER-PM Contact Sites in Endocytosis and Autophagy: Does the Local Composition of Membrane Phospholipid Play a Role?

Publisher's copyright statement: Copyright c © 2019 Wang and Hussey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Comprehensive Analysis of the Association Between the rs1138272 Polymorphism of the GSTP1 Gene and Cancer Susceptibility

Background: We obtained conflicting results regarding the relationship between the genetic role of the rs1138272 C/T polymorphism of the GSTP1 (Glutathione S-Transferase pi) gene and the risk of various cancers. Methods: Using the presently available data, a meta-analysis was conducted to comprehensively evaluate the genetic relationship between the GSTP1 rs1138272 polymorphism and cancer susceptibility. Results: A total of 43 studies including 15,688 cases and 17,143 controls were recruited into our quantitative synthesis. In the overall population, we observed an increased risk of overall cancer cases, compared with unrelated controls, in the genetic models of allele T vs. allele C (P-association = 0.007, OR = 1.17), carrier T vs. carrier C (P-association = 0.035, OR = 1.11), TT vs. CC (P-association = 0.002, OR = 1.45), TT vs. CC+CT (P-association = 0.009, OR = 1.42), and CT+TT vs. CC (P-association = 0.027, OR = 1.13). We detected similar positive results within the Asian population. Additionally, there was a significant increase in the incidence of cancer for Africans under all genetic models (all P-association < 0.05, OR > 1). When targeting the Caucasian population, we detected a positive association with the TT vs. CC and TT vs. CC+CT models in the “Colorectal cancer” (P-association < 0.05, OR < 1) and “Head and neck cancer” (P-association < 0.05, OR > 1) subgroups. For the “Lung cancer” subgroup, we observed a slightly increased risk in Caucasians under the models of allele T vs. allele C, carrier T vs. carrier C, CT vs. CC, and CT+TT vs. CC (P-association < 0.05, OR > 1). Conclusion: The TT genotype of the GSTP1 rs1138272 polymorphism is likely related to the susceptibility to overall cancer in the Asian and African populations and, specifically, “Colorectal” and “Head and neck” cancers in the Caucasian population. In addition, the CT genotype of the GSTP1 rs1138272 polymorphism may be linked to the risk of lung cancer in Caucasians. Additional evidence is required to confirm this conclusion.