Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Inferential Risk Evaluation On Multiple IR Systems

Information retrieval (IR) ranking models in production systems continually evolve in response to user feedback, insights from research, and new developments. Rather than investing all engineering resources to produce a single challenger to the existing system, a commercial provider might choose to explore multiple new ranking models simultaneously. However, even small changes to a complex model can have unintended consequences. In particular, the per-topic effectiveness profile is likely to change, and even when an overall improvement is achieved, gains are rarely observed for every query, introducing the risk that some users or queries may be negatively impacted by the new model if deployed into production. Risk adjustments that re-weight losses relative to gains and mitigate such behavior are available when making one-to-one system comparisons, but not for one-to-many or many-to-one comparisons. Moreover, no IR evaluation methodology integrates priors from previous or alternative rankers in a homogeneous inferential framework. In this work, we propose a Bayesian approach where multiple challengers are compared to a single champion. We also show that risk can be incorporated, and demonstrate the benefits of doing so. Finally, the alternative scenario that is commonly encountered in academic research is also considered, when a single challenger is compared against several previous champions.
GMCM: Graph-based Micro-behavior Conversion Model for Post-click Conversion Rate Estimation

Purchase-related micro-behaviors, e.g., favorite, add to cart, read reviews, etc., provide implicit feedback of users' decision-making process. Such informative feedback can lead to fine-grained post-click conversion rate (CVR) modeling of the buying process. However, most existing works on CVR estimation either neglect these informative feedback, or model them as a sequential pattern with Recurrent Neural Networks. We argue such modeling could be inappropriate since different orders of micro-behaviors may represent similar user buying intention, and micro-behaviors often correlate with each other. To this end, we propose to represent user micro-behaviors as a Purchase-related Micro-behavior Graph (PMG). Specifically, each node stands for one micro-behavior, and edge weights denote the connection strength. Based on this graph representation, we frame CVR estimation as a graph classification problem over the PMG instances. We propose a novel CVR model, namely, Graph-based Micro-behavior Conversion Model (GMCM), that utilizes Graph Convolutional networks (GCN) to enhance the conventional CVR modeling. In addition, we adopt multi-task learning and inverse propensity weighting to tackle two well-recognized issues in CVR estimation: data sparsity and sample selection bias. Extensive experiments on six large-scale production datasets demonstrate that the proposed methods outperform the state-of-the-art CVR methods under industrial setting.
Mécanismes moléculaires de la réponse aux stress environnementaux chez la cyanobactérie marine Prochlorococcus

Prochlorococcus est le plus petit et le plus abondant organisme unicellulaire photosynthetique des oceans. Il est exceptionnel de par sa predominance, son adaptation aux environnements pauvres en nutriments, sa forte densite de la surface jusqu'a des profondeurs de 200m et sa composition pigmentaire unique. Afin de mieux comprendre comment cet organisme s'adapte et s'acclimate a de telles conditions, les mecanismes de reponse au stress ont ete etudies. Les systemes de transduction a deux composants, composes d'histidine kinases et de regulateurs de reponse, sont des elements importants permettant aux bacteries de detecter et de s'acclimater rapidement aux changements de leur environnement. L'analyse du genome de la souche Prochlorococcus MED4, adaptee aux conditions oceaniques de surface, a revele un tres petit nombre de genes codant ces systemes (5 kinases et 6 regulateurs) ainsi qu'une tres faible densite de ces systemes. Une etude par RT-PCR quantitative a revele que certains genes de regulateurs sont actives par un stress de forte lumiere alors que la plupart des genes de ces systemes sont reprimes par un choc thermique. Les cyanobacteries repondent egalement a un exces de lumiere en regulant l'expression des genes codant les proteines heat-shock. L'etude de l'expression de ces genes, realisee sur la souche d'eau douce Synechocystis sp. PPC 6803, la souche marine de surface Prochlorococcus MED4, et la souche marine de profondeur Prochlorococcus MIT9313, a montre une regulation dynamique de la reponse heat-shock refletant les differentes niches ecologiques dans lesquels ces souches evoluent. Enfin, chez certaines cyanobacteries, l'utilisation d'une antenne externe (composee de proteines IsiA) autour du photosysteme (PS) I permet de compenser la diminution des phycobilisomes et du niveau de PS I en reponse a une carence en fer. Le meme type d'arrangement autour de PS II chez Prochlorococcus SS120, semble etre une strategie qui maximise la capture de l'energie lumineuse en profondeur. L'etude de l'expression des genes pcb, codant les complexes collecteurs de lumiere, chez Prochlorococcus, a permis de mettre en evidence la presence de genes isiA-like induit par une carence en fer. Ces resultats suggerent que les contraintes environnementales ont du avoir un effet direct sur l'evolution des genes pcb chez Prochlorococcus. Cette etude a donc permis de mettre en evidence de nouvelles caracteristiques de la reponse au stress de Prochlorococcus qui pourront nous aider a mieux comprendre son abondance dans l'ocean mondial.
Advanced Lichen Sclerosus Resulting in Complete Labial Fusion and Urinary Retention in a Postmenopausal Patient

Lichen sclerosus is a benign, chronic dermatological condition of unknown etiology that primarily affects the genitalia. The mainstay of therapy is a topical high-potency corticosteroid. Although benign, it is progressive and, in advanced stage disease, can lead to severe functional impairment requiring surgical management. We present a case of advanced lichen sclerosus that resulted in complete labial fusion and obstructive urinary retention requiring surgical intervention. We also performed a review of the literature on similar lichen sclerosus cases. A 71-yearold patient was referred to gynecologic surgery from their urologist for evaluation of complete labial fusion with associated urinary retention. A vulvoperineoplasty was successfully performed. Postoperatively, she was started on estradiol cream daily to assist with initial wound healing. After 19 days, the vagina remained patent and was healing properly. Four months later, she was successfully titrated to twice weekly dosing of clobetasol without recurrence of symptoms. In addition to successful surgical intervention, this case illustrates the need for frequent follow-up and maintenance therapy to prevent relapse.
Segmenting Search Query Logs by Learning to Detect Search Task Boundaries

To fulfill their information needs, users submit sets of related queries to available search engines. Query logs record users' activities along with timestamps and additional search-related information. The analysis of those chronological query logs enables the modeling of search tasks from user interactions. Previous research works rely on clicked URLs and surrounding queries to determine if adjacent queries are part of the same search tasks to segment the query logs properly. However, waiting for clicked URLs or future adjacent queries could render the use of these methods unfeasible in user supporting applications that require model results on the fly. Therefore, we propose a model for sequential search log segmentation. The proposed model uses only query pairs and their time span, generating results suited for on the fly user supporting applications, with improved accuracy over existing search segmentation approaches. We also show the advantages of fine-tuning the proposed model for adjusting the architecture to a small annotated collection.
Comparison of structural and compositional characteristics in old-growth and mature, managed hardwood forests of Minnesota, U.S.A.

Extended rotation of managed temperate hardwood forests is sometimes presumed to provide the important compositional and structural features of old-growth hardwood forests. However, the features of temperate hardwood old-growth and managed stands of extended rotation age have not been fully quantified and compared. This study compared quantitative parameters (density and volume of logs and snags, coarse woody debris volume (volume of logs + volume of snags), the proportion of hollow logs, basal area and tree, sapling, large seedling and small seedling densities), distributional patterns (diameter class and rot class of live trees, decay class of logs), and vascular plant species composition and diversity in old-growth and mature, managed sugar maple (Acer saccharum L.) - basswood (Tilia americana L.) and northern red oak (Quercus rubra L.) stands. Old-growth forests had higher coarse woody debris volumes and higher proportions of hollow logs, of live trees in large diameter classes, of logs in decay class.
A Review on Access Control Policy and Key Generation in Data Sharing

— This review is based on the access control policy and data sharing mechanisms. We know that Cloud computing is the best & proficient manner on handle our information remotely. Data Confidentiality is one of the chief problem now a day’s. Security is also matters while data storing & sharing with others. Whenever we are using platform like cloud trust factor also considered. A lot of unauthorized community tries to access & steal the confidential data. In the current time cryptographic technologies are used to secure data. Sharing cloud data in between group of users at a best level is still a complicated issue, especially when dealing with dynamic users group . In this paper we present and discuss propose system which deals with dynamic user group problem like revocation and data privacy & make access control policy.
Cascade or Recency: Constructing Better Evaluation Metrics for Session Search

Recently session search evaluation has been paid more attention as a realistic search scenario usually involves multiple queries and interactions between users and systems. Evolved from model-based evaluation metrics for a single query, existing session-based metrics also follow a generic framework based on the cascade hypothesis. The cascade hypothesis assumes that lower-ranked search results and later-issued queries receive less attention from users and should therefore be assigned smaller weights when calculating evaluation metrics. This hypothesis gains much success in modeling search users' behavior and designing evaluation metrics, by explaining why users' attention decays on search engine result pages. However, recent studies have found that the recency effect also plays an important role in determining user satisfaction in search sessions. Especially, whether a user feels satisfied in the later-issued queries heavily influences his/her search satisfaction in the whole session. To take both the cascade hypothesis and the recency effect into the design of session search evaluation metrics, we propose Recency-aware Session-based Metrics (RSMs) to simultaneously characterize users' examination process with a browsing model and cognitive process with a utility accumulation model. With both self-constructed and public available user search behavior datasets, we show the effectiveness of proposed RSMs by comparing them with existing session-based metrics in the light of correlation with user satisfaction. We also find that the influence of the cascade and the recency effects varies dramatically among tasks with different difficulties and complexities, which suggests that we should use different model parameters for different types of search tasks. Our findings highlight the importance of investigating and utilizing cognitive effects besides examination hypotheses in search evaluation.
Assessing language comprehension in motor impaired children needing AAC: validity and reliability of the Norwegian version of the receptive language test C-BiLLT

Abstract Children with severe motor impairments who need augmentative and alternative communication (AAC) comprise a heterogeneous group with wide variability in cognitive functioning. Assessment of language comprehension will help find the best possible communication solution for each child, but there is a lack of appropriate instruments. This study investigates the reliability and validity of the Norwegian version of the spoken language comprehension test C-BiLLT (computer-based instrument for low motor language testing) – the C-BiLLT-Nor – and whether response modality influences test results. The participants were 238 children with typical development aged 1;2 to 7;10 (years/months) who were assessed with the C-BiLLT-Nor and tests of language comprehension and non-verbal reasoning. There was excellent internal consistency and good test–retest reliability. Tests of language comprehension and non-verbal reasoning correlated significantly with the C-BiLLT-Nor, indicating good construct validity. Factor analysis yielded a two-factor solution, suggesting it as a measure of receptive vocabulary, grammar, and overall language comprehension. No difference in results could be related to response mode, implying that gaze pointing is a viable option for children who cannot point with a finger. The C-BiLLT-Nor, with norms from 1;6–7;6 is a reliable measure of language comprehension.
Models Versus Satisfaction: Towards a Better Understanding of Evaluation Metrics

Evaluation metrics play an important role in the batch evaluation of IR systems. Based on a user model that describes how users interact with the rank list, an evaluation metric is defined to link the relevance scores of a list of documents to an estimation of system effectiveness and user satisfaction. Therefore, the validity of an evaluation metric has two facets: whether the underlying user model can accurately predict user behavior and whether the evaluation metric correlates well with user satisfaction. While a tremendous amount of work has been undertaken to design, evaluate, and compare different evaluation metrics, few studies have explored the consistency between these two facets of evaluation metrics. Specifically, we want to investigate whether the metrics that are well calibrated with user behavior data can perform as well in estimating user satisfaction. To shed light on this research question, we compare the performance of various metrics with the C/W/L Framework in estimating user satisfaction when they are optimized to fit observed user behavior. Experimental results on both self-collected and public available user search behavior datasets show that the metrics optimized to fit users' click behavior can perform as well as those calibrated with user satisfaction feedback. We also investigate the reliability in the calibration process of evaluation metrics to find out how much data is required for parameter tuning. Our findings provide empirical support for the consistency between user behavior modeling and satisfaction measurement, as well as guidance for tuning the parameters in evaluation metrics.
Make It a Chorus: Knowledge- and Time-aware Item Modeling for Sequential Recommendation

Traditional recommender systems mainly aim to model inherent and long-term user preference, while dynamic user demands are also of great importance. Typically, a historical consumption will have impacts on the user demands for its relational items. For instance, users tend to buy complementary items together (iPhone and Airpods) but not substitutive items (Powerbeats and Airpods), although substitutes of the bought one still cater to his/her preference. To better model the effects of history sequence, previous studies introduce the semantics of item relations to capture user demands for recommendation. However, we argue that the temporal evolution of the effects caused by different relations cannot be neglected. In the example above, user demands for headphones can be promoted after a long period when a new one is needed. To model dynamic meanings of an item in different sequence contexts, a novel method Chorus is proposed to take both item relations and corresponding temporal dynamics into consideration. Chorus aims to derive the embedding of target item in a knowledge-aware and time-aware way, where each item will get its basic representation and relation-related ones. Then, we devise temporal kernel functions to combine these representations dynamically, according to whether there are relational items in history sequence as well as the elapsed time. The enhanced target item embedding is flexible to work with various algorithms to calculate the ranking score and generate recommendations. According to extensive experiments in three real-world datasets, Chorus gains significant improvements compared to state-of-the-art baseline methods. Furthermore, the time-related parameters are highly interpretable and hence can strengthen the explainability of recommendation.
Deep learning based medical X-ray image recognition and classification

Analysis of radiology images are mostly being done by medical specialists, as it is a critical sector and people expect highest level of care and service regardless of cost. Though, it is quite limited due to its complexity and subjectivity of the images. Extensive variation exists across different interpreters and fatigue in terms of image interpretation by human experts. Our primary objective is to analyze medical X-ray images using deep learning and exploit images using Pandas, Keras, OpenCV, TensorFlow etc. to achieve classification of diseases like Atelectasis, Consolidation, Cardiomegaly, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pleural, Pneumonia, Pneumothorax, Thickening etc. We have used Convolutional Neural Networks (CNN) algorithm because CNN based deep learning classification approaches have ability to automatically extract the high level representations from big data using little pre-processing compared to other image classification algorithms. Ultimately, our simple and efficient model will lead clinicians towards better diagnostic decisions for patients to provide them solutions with good accuracy for medical imaging.
Finding the Best of Both Worlds: Faster and More Robust Top-k Document Retrieval

Many top-k document retrieval strategies have been proposed based on the WAND and MaxScore heuristics and yet, from recent work, it is surprisingly difficult to identify the "fastest" strategy. This becomes even more challenging when considering various retrieval criteria, like different ranking models and values of k. In this paper, we conduct the first extensive comparison between ten effective strategies, many of which were never compared before to our knowledge, examining their efficiency under five representative ranking models. Based on a careful analysis of the comparison, we propose LazyBM, a remarkably simple retrieval strategy that bridges the gap between the best performing WAND-based and MaxScore-based approaches. Empirically, LazyBM considerably outperforms all of the considered strategies across ranking models, values of k, and index configurations under both mean and tail query latency.
A Case Study of BIM Implementation in Rail Track Rehabilitation

Building Information Modeling (BIM) is an Industry 4.0 methodology that is increasingly used in the domain of Architecture, Engineering, and Construction (AEC). BIM emerges as a new methodology, one that is more collaborative and based on parametric three-Dimensional (3D) models, centralizing different types of information of a geometric, physical, and economic nature. The purpose of this paper is to analyze the application of the BIM methodology to a rail track rehabilitation case study using a geotextile and geogrid in the ballast layer base. The creation of the 3D and 4D BIM models was performed using various BIM-based tools, which made it possible to achieve the spatial and parametric representation of the rail track and the simulation of the main construction tasks. A new BIM object pertaining to the rail track was created. This paper describes the procedures applied in achieving the BIM models, the limitations involved, and the interoperability between the BIM tools. Additionally, the potential for information extraction with respect to the infrastructure design, construction, and operation, e.g., planning and scheduling, quantities, graphic outputs, and track geometry quality, was demonstrated. It was concluded that the BIM methodology was viable and could be implemented with benefits, despite certain difficulties and limitations, which emphasize the need for further developments.
DataMirror: Reflecting on One's Data Self: A Tool for Social Media Users to Explore Their Digital Footprints

Small pieces of data that are shared online, over time and across multiple social networks, have the potential to reveal more cumulatively than a person intends. This could result in harm, loss or detriment to them depending what information is revealed, who can access it, and how it is processed. But how aware are social network users of how much information they are actually disclosing? And if they could examine all their data, what cumulative revelations might be found that could potentially increase their risk of various online threats (social engineering, fraud, identify theft, loss of face, etc.)? In this paper, we present DataMirror, an initial prototype tool, that enables social network users to aggregate their online data so that they can search, browse and visualise what they have put online. The aim of the tool is to investigate and explore people's awareness of their data self that is projected online; not only in terms of the volume of information that they might share, but what it may mean when combined together, what pieces of sensitive information may be gleaned from their data, and what machine learning may infer about them given their data.
HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation

With the increasing popularity of location-aware social media services, next-Point-of-Interest (POI) recommendation has gained significant research interest. The key challenge of next-POI recommendation is to precisely learn users' sequential movements from sparse check-in data. To this end, various embedding methods have been proposed to learn the representations of check-in data in the Euclidean space. However, their ability to learn complex patterns, especially hierarchical structures, is limited by the dimensionality of the Euclidean space. To this end, we propose a new research direction that aims to learn the representations of check-in activities in a hyperbolic space, which yields two advantages. First, it can effectively capture the underlying hierarchical structures, which are implied by the power-law distributions of user movements. Second, it provides high representative strength and enables the check-in data to be effectively represented in a low-dimensional space. Specifically, to solve the next-POI recommendation task, we propose a novel hyperbolic metric embedding (HME) model, which projects the check-in data into a hyperbolic space. The HME jointly captures sequential transition, user preference, category and region information in a unified approach by learning embeddings in a shared hyperbolic space. To the best of our knowledge, this is the first study to explore a non-Euclidean embedding model for next-POI recommendation. We conduct extensive experiments on three check-in datasets to demonstrate the superiority of our hyperbolic embedding approach over the state-of-the-art next-POI recommendation algorithms. Moreover, we conduct experiments on another four online transaction datasets for next-item recommendation to further demonstrate the generality of our proposed model.