Miracle-dz/UniFAR-FTUs huggingface dataset:
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment

The #MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment. The aggregation and analysis of such disclosures pave the way to development of technology-based prevention of sexual harassment. We contend that the lack of specificity in generic sentence classification models may not be the best way to tackle text subtleties that intrinsically prevail in a classification task as complex as identifying disclosures of sexual harassment. We propose the Disclosure Language Model, a three part ULMFiT architecture, consisting of a Language model, a Medium-Specific (Twitter) model and a Task-Specific classifier to tackle this problem and create a manually annotated real-world dataset to test our technique on this, to show that using a Discourse Language Model often yields better classification performance over (i) Generic deep learning based sentence classification models (ii) existing models that rely on handcrafted stylistic features. An extensive comparison with state-of-the-art generic and specific models along with a detailed error analysis presents the case for our proposed methodology.
ReMinds-CMT: An Interactive Tool Supporting Constraint Mining for Requirements Monitoring

[Context and Motivation] Existing approaches for specification mining and process mining allow to automatically identify requirementslevel system properties, which can be used for describing, verifying, or monitoring systems. We have developed an approach that can mine constraint candidates from event logs to support requirements monitoring. [Question/Problem] However, the usefulness of mining approaches is currently limited because of (i) weak support for adjusting the algorithms and settings to the current problem, (ii) the high number of properties mined from complex systems, and (iii) the typically high false positive rate. [Principal Ideas/Results] In this paper, we present ReMinds-CMT, a tool that guides domain experts throughout the mining process. [Contributions] The tool allows users to experiment with different thresholds and configurations of our mining, grouping, filtering, and ranking algorithms to ease the selection of useful constraints. We demonstrate the tool’s features using constraints mined from event logs of a complex cyber-physical system controlling unmanned aerial vehicles.
Two clear different wool phenotypes in White Majorcan ovine breed

This study characterised two different phenotypes of the White Majorcan ovine breed, a local indigenous breed restricted to Balearic Islands. For this purpose, 20 different adult animals from phenotype "rasa" (short wooled) and "ble" (long wooled) were studied. Each sample was analysed for wool yield, fibre diameter (finesse), fibre curvature, % medullation, % kemp, F30 (percentage of fibres with a diameter < 30 µ), length of short and long fibres, and crimp, as well as wool weight, body weight and wool yield. Two phenotypes were clearly differentiated and significantly different, except for body weight, which would correspond to the "lowland" and "highland" strains described by some authors. According to the bibliography, the long wooled phenotype, which is probably the highland strain, could be considered as the oldest.
Satisfiability Checking and Conjunctive Query Answering in Description Logics with Global and Local Cardinality Constraints

We introduce and investigate the expressive description logic (DL) ALCSCC, in which the global and local cardinality constraints introduced in previous papers can be mixed. On the one hand, we prove that this does not increase the complexity of satisfiability checking and other standard inference problems. On the other hand, the satisfiability problem becomes undecidable if inverse roles are added to the languages. In addition, even without inverse roles, conjunctive query entailment in this DL turns out to be undecidable. We prove that decidability of querying can be regained if global and local constraints are not mixed and the global constraints are appropriately restricted. The latter result is based on a locally-acyclic model construction, and it reduces query entailment to ABox consistency in the restricted setting, i.e., to ABox consistency w.r. t. restricted cardinality constraints in ALCSCC, for which we can show an ExpTime upper bound.
Open Celled Material Structural Properties Measurement : From Morphology To Transport Properties

Metallic foams are highly porous materials which present complex structure of three-dimensional open cells. The effective transport properties determination is essential for these widely used new materials. The aim of this work is to develop morphology analysis tools to study the impact of foams structure on physical transport properties. The reconstruction of the solid-pore interface allows the visualization of the 3D data and determination of specific surface and porosity. We present an original method to measure the geometrical tortuosity of a porous media for the two phases. A centerline extraction method allows us to model the solid matrix as a network of linear connected segments. The thermal conductivity of metallic foams is determined by solving energy equation over the solid phase skeleton. Results obtained on a set of nickel foams covering a wide range of pore size are discussed.
Arnold

Arnold is a physically based renderer for feature-length animation and visual effects. Conceived in an era of complex multi-pass rasterization-based workflows struggling to keep up with growing demands for complexity and realism, Arnold was created to take on the challenge of making the simple and elegant approach of brute-force Monte Carlo path tracing practical for production rendering. Achieving this required building a robust piece of ray-tracing software that can ingest large amounts of geometry with detailed shading and lighting and produce images with high fidelity, while scaling well with the available memory and processing power. Arnold’s guiding principles are to expose as few controls as possible, provide rapid feedback to artists, and adapt to various production workflows. In this article, we describe its architecture with a focus on the design and implementation choices made during its evolutionary development to meet the aforementioned requirements and goals. Arnold’s workhorse is a unidirectional path tracer that avoids the use of hard-to-manage and artifact-prone caching and sits on top of a ray-tracing engine optimized to shoot and shade billions of spatially incoherent rays throughout a scene. A comprehensive API provides the means to configure and extend the system’s functionality, to describe a scene, render it, and save the results.
Understanding IT-Enabled Social Features in Online Peer-to-Peer Businesses for Cultural Goods

Although the use of IT-enabled social features is gaining prominence on online peer-to-peer platforms, the use of these features is not well understood in the context of e-commerce marketplaces. In this study, we explain the effects of using IT-enabled social features for sellers by using data from Etsy.com, which is an online peer-to-peer marketplace for cultural goods that provides social features to its participants. Using the theory of fields of cultural production, we propose hypotheses regarding the direct and indirect impact of IT-enabled social features on sales. We find that sellers’ use of IT-enabled social features for community participation (e.g., following members) and content curation (e.g., sharing favorite items) is positively associated with their online status, which in turn is positively associated with their sales. However, sellers’ use of IT-enabled social features is directly negatively associated with sales. Overall, we find that the indirect positive association is large enough to offset the negative direct association. These results have important implications for sellers on online peer-to-peer platforms and for platform design.
Production of the Marker free transgenic plants – An update Review

Selectable marker genes are pivotal for the development of plant transformation technologies because marker genes allow researchers to identify or isolate the cells that are expressing the cloned DNA, to monitor and select the transformed progeny. As only a very small portion of cells are transformed in most experiments, the chances of recovering transgenic lines without selection are usually low. Since the selectable marker gene is expected to function in a range of cell types it is usually constructed as a chimeric gene using regulatory sequences that ensure constitutive expression throughout the plant. Transgenic crop or plants are very decisive for the growing population to maintain their necessary food, medicine, protect the environment and other things. But the transgenic crop or plants have some risk or controversy in human health, agriculture, and environment. Selectable marker genes are widely used for the efficient transformation of crop plants. In most cases, selection is based on antibiotic or herbicide resistance due mainly to consumer concerns, a suite of strategies (site-specific recombination, homologous recombination, transposition and co-transformation) have been developed to eliminate the marker gene from the nuclear or chloroplast genome after selection. Recently, scientists are producing several transgenic plants without selectable marker gene. This review article describes the developments made in the recent past on plant transformation systems using different selection methods adding a note on their importance as marker genes in transgenic crop plants.
Decidability and Complexity of ALCOIF with Transitive Closure (and More)

We prove that satisfiability and finite satisfiability in the description logic ALCOIFreg are NExpTime-complete when every regular role expression of the form α∗ contains either no functional role or only a single role name (and possibly its inverse). Notably, this encompasses the extension of ALCOIF with transitive closure of roles and the modal logic of linear orders and successor, with converse.
Heterotoki: non-structured and heterogeneous terminology alignment for Digital Humanities data producers

In this paper, we present an online communication-driven decision support system to align terms from a dataset with terms of another dataset (standardized controlled vocabulary or not). Heterotoki differs from existing proposals in that it takes place at the interface with humans,inviting the experts to commit on their definitions, so as to either agree to validate the mapping or to propose some enrichment to the terminologies. More precisely, differently to most of existing proposals that support terminology alignment, Heterotokisustains the negotiation of meaning thanks to semantic coordination support within its interface design. This negotiation involves domain experts having produced multiple datasets.
Making Decisions over Contextual Ontologies

Various probabilistic description logics (DLs) have been proposed for dealing with the uncertainty endemic to many domain knowledge representation scenarios. A particular class of such formalisms focuses on representing knowledge that is certain, but holds only in some uncertain contexts. In this paper, we consider an extension of those formalisms that allows an agent to influence the choice of the context and minimise its subjective cost. This is achieved through a combination of the light-weight DL EL and influence diagrams, a graphical model for representing decision situations, and their potential costs, under uncertainty.
Primary diffuse large B cell lymphoma of the cervix presenting as bilateral hydronephrosis

Introduction: Non-Hodgkin lymphoma (NHL) arises most frequently in lymph nodes or other lymphatic tissue. Primary lymphomas of the female reproductive tract are extremely rare, less than 1% of extra nodal NHL and <0.5% of gynecologic malignancies. Case Report: We present a case of a 66-year-old postmenopausal female presented with a one month history of fatigue, dysuria and hematuria. A non-contrast computed tomography scan of abdomen and pelvis revealed a large ~7–9 cm mass involving the lower uterine segment/cervix displacing the rectosigmoid colon. Biopsy revealed it to be a CD 20 positive diffuse large B cell type non-Hodgkin lymphoma (NHL). She was diagnosed as stage IE and started on R-CHOP chemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone). Conclusion: Primary diffuse large B cell lymphoma of the female genital tract is a rare diagnosis and must be diagnosed early due to the aggressiveness of the tumor. There is no standard treatment regimen. Patients are often treated with chemotherapy as the first line of treatment, which has provided promising results.
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media

Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research. While it has been established that these users share a common set of properties, the current state-of-the-art approaches utilize only text-based (stylistic and semantic) cues. We contend that the use of information from networks in the form of condensed social graph embeddings and author profiling using features from historical data can be combined with an existing set of features to improve the performance. To that end, we experiment on a manually annotated dataset of tweets created using a three-phase strategy and propose SNAP-BATNET, a deep learning based model to extract text-based features and a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings that outperform the current state-of-the-art. We conduct a comprehensive quantitative analysis with baselines, both generic and specific, that presents the case for SNAP-BATNET, along with an error analysis that highlights the limitations and challenges faced paving the way to the future of AI-based suicide ideation detection.
The Coupled Action Potential Pulse (Appulse)-Neural Network Efficiency from a Synchronised Oscillating Lipid Pulse Hodgkin Huxley Action Potential

Assumptions of almost instantaneous activation of progressive ion channels to produce the Hodgkin Huxley Action Potential are based upon the belief that electrostatic charge can travel from one channel to the next at the speed of the Action Potential: empirical evidence from channel spacing, ionic radii and diffusion coefficients demonstrate this is not the case. Evidence exists from ion channel studies and entropy measurements of a Synchronous Oscillating Hodgkin Huxley Action Potential Lipid Pulse Structure that is both efficient and fulfils entropy measurement results. Depending upon the spatial arrangement of ion channels and other membrane components: the pulse may be HH or Lipid ‘soliton’ or both. This is a more consistent model for membrane pulse transmission than either HH or a soliton: it is consistent with observed studies in both unmyelinated and myelinated axon and the principle may be applied both to axons and muscle, including cardiac muscle.
Parametrized spectra, a low-tech approach.

We give an alternative treatment of the foundations of parametrized spectra, with an eye toward applications in fixed-point theory. This treatment includes most of the central results from the book of May and Sigurdsson, sometimes with weaker hypotheses, and a new construction of the bicategory $\mathcal Ex$ of parametrized spectra. We also give a careful account of coherence results at the level of homotopy categories. The potential audience for this work may extend outside the boundaries of modern homotopy theory, so our treatment is structured to use as little technology as possible. In particular, many of the results are stated without using model categories. We also illustrate some applications to fixed-point theory.
Decremental Optimization of Dominating Sets Under Reachability Constraints

Given a dominating set, how much smaller a dominating set can we find through elementary operations? Here, we proceed by iterative vertex addition and removal while maintaining the property that the set forms a dominating set of bounded size. This can be seen as the optimization variant of the dominating set reconfiguration problem, where two dominating sets are given and the question is merely whether they can be reached one from another through elementary operations. We show that this problem is PSPACE-complete, even if the input graph is a bipartite graph, a split graph, or has bounded pathwidth. On the positive side, we give linear-time algorithms for cographs, trees and interval graphs. We also study the parameterized complexity of this problem. More precisely, we show that the problem is W[2]-hard when parameterized by the upper bound on the size of an intermediary dominating set. On the other hand, we give fixed-parameter algorithms with respect to the minimum size of a vertex cover, or $d+s$ where $d$ is the degeneracy and $s$ is the upper bound of output solution.
Silicon wafer defect detection using high frequency guided waves

In the photovoltaic industry monocrystalline silicon wafers are employed for the manufacture of solar panels with high conversion efficiency. The cutting process induces micro-cracks on the thin wafer surface. High frequency guided ultrasonic waves are considered for the structural monitoring of the wafers and the nondestructive characterization of the micro-cracks. The material anisotropy of the monocrystalline silicon leads to variations of the wave characteristics depending on the propagation direction relative to the crystal orientation. In non-principal directions of the crystal, wave beam skewing occurs. Selective excitation of the fundamental Lamb wave modes was achieved using a custom-made angle beam transducer and holder to achieve a controlled contact pressure. The out-of-plane component of the guided wave propagation was measured using a noncontact laser interferometer. Artificial defects were introduced in the wafers using a micro indenter with varying loads. The defects were characterized from microscopy images to measure the indent size and combined crack length. The scattering of the A0 Lamb wave mode was measured experimentally and the characteristics of the scattered wave field were correlated to the defect size. The detection sensitivity is discussed.
Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work

—The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection of Mild Cognitive Impairment (MCI) in the elderly, which enables early medical interventions to be provided. We focus on the system design and feature extraction of the sensor system, to validate our hypothesis of the use of sensor systems for early detection of MCI. Lessons learned from deploying the sensor system is presented, together with the solutions that are implemented to improve system reliability.
DCEF: Deep Collaborative Encoder Framework for Unsupervised Clustering

Collaborative representation is a popular feature learning approach, which encoding process is assisted by variety types of information. In this paper, we propose a collaborative representation restricted Boltzmann Machine (CRRBM) for modeling binary data and a collaborative representation Gaussian restricted Boltzmann Machine (CRGRBM) for modeling realvalued data by applying a collaborative representation strategy in the encoding procedure. We utilize Locality Sensitive Hashing (LSH) to generate similar sample subsets of the instance and observed feature set simultaneously from input data. Hence, we can obtain some mini blocks, which come from the intersection of instance and observed feature subsets. Then we integrate Contrastive Divergence and Bregman Divergence methods with mini blocks to optimize our CRRBM and CRGRBM models. In their training process, the complex collaborative relationships between multiple instances and features are fused into the hidden layer encoding. Hence, these encodings have dual characteristics of concealment and cooperation. Here, we develop two deep collaborative encoder frameworks (DCEF) based on the CRRBM and CRGRBM models: one is a DCEF with Gaussian linear visible units (GDCEF) for modeling real-valued data, and the other is a DCEF with binary visible units (BDCEF) for modeling binary data. We explore the collaborative representation capability of the hidden features in every layer of the GDCEF and BDCEF framework, especially in the deepest hidden layer. The experimental results show that the GDCEF and BDCEF frameworks have more outstanding performances than the classic Autoencoder framework for unsupervised clustering task on the MSRA-MM2.0 and UCI datasets, respectively.
APPLYING ECONOMIC MEASURES TO LAPSE RISK MANAGEMENT WITH MACHINE LEARNING APPROACHES

Abstract Modeling policyholders’ lapse behaviors is important to a life insurer, since lapses affect pricing, reserving, profitability, liquidity, risk management, and the solvency of the insurer. In this paper, we apply two machine learning methods to lapse modeling. Then, we evaluate the performance of these two methods along with two popular statistical methods by means of statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new to lapse risk management. We apply the aforementioned four methods to a large real-world insurance dataset. The results show that Extreme Gradient Boosting (XGBoost) and support vector machine outperform logistic regression (LR) and classification and regression tree with respect to statistic accuracy, while LR performs as well as XGBoost in terms of retention gains. This highlights the importance of a proper validation metric when comparing different methods. The optimization after the transformation brings out significant and consistent increases in economic gains. Therefore, the insurer should conduct optimization on its economic objective to achieve optimal lapse management.