Miracle-dz/UniFAR-FTUs huggingface dataset:
Double Diffusive Convection Heat and Moisture Transfer Inside a Planted Roof Building Under Hot Humid Climate: Case of Lomé City in West Africa

: Planted roofs have been investigated as a passive cooling technology for energy efficiency purposes in buildings. More quantitative data on this topic are required to solve a lack of information for many specific regions. This study is focused on a numerical investigation of the thermal comfort inside a green roof rectangular ventilated cavity in a hot and humid climate like the one of Lomé in west Africa. The left vertical is heated and partly saturated with water to provide humidification of the indoor air. Transfer dimensionless equations are solved using an implicit ﬁnite difference scheme, the Thomas algorithm, and the Gauss-Seidel iterative method. We analyze the effects of inlet airflow on the thermal process inside the ventilated and planted enclosure have been investigated. The comfort temperature range deduced from the data is 25°C < T c < 27°, and that of the indoor air humidity is 49% < Hr <60%. The different ranges obtained are significant and lead to improving inside thermal comfort. The solar flux of 350 W.m -2 , the average value in the case of Lomé city, was used to establish a heat transfer correlation to predict heat transfer through the roof with a relative error not exceeding 4%. This model can be very useful for engineers in the design and optimization stage of a green roof in practical buildings.
A Learning to Rank method for Cross-market Recommendation

The cross-market recommendation aims at recommending relevant products to users in a target market by leveraging data from similar high-resource markets. Although recommendation has been widely studied in the literature, the biases of the individual markets limit the generalization of model learned on individual market. Based on the experimental data provided by the WSDM Cup challenge, we study data on various markets and propose a learning to rank model by combining the individual similarity as well as the structural feature. More specifically, We first benchmark several collaborative filtering methods using a single target market data, including a lot of the classic approaches as well as the advanced approaches. Then we fit some of these approaches to a cross-market use. Finally, we use learn2rank method to fuse all the methods mentioned to boost the performance. We get 0.6746 on the leaderboard which is the third place of the challenge. The source codes have been released at https://github.com/miziha-zp/BiuG-XMRec-WSDMCup22 .
An Effective Ensemble Framework with Multichannel Time Series for User Retention Prediction The first-place entry for Retention Score Prediction at WSDM CUP 2022

User retention is an important indicator that can help companies to understand user loyalty. Efficient solutions to predict user retention is essential for Internet companies like iQIYI to attract and keep more users. However, it is challenging to predict user retention due to the diversity of user behaviors and the complexity of multichannel time series. In this paper, we proposed an ensemble framework to address the user retention score prediction challenge in ACM WS-DM Cup 2022. In our solution, we extracted several useful features based on multichannel time series and assembled four tree-based models to predict the retention score. Besides, we developed post-processing methods to adjust the predictions which are crucial to improve the performance. Fortunately, our team QDU won 1st place with an evaluation score of 86.4486 on the private leader board. The source code is available at https://github.com/hansu1017/WSDM2022-Retention-Score-Prediction.
Disc Brake Wear Debris Generation and Collection

This paper describes a reproducible and practical dynamometer-based procedure for generating and collecting disc brake wear debris. The procedure is intended to provide friction material producers, brake system suppliers, and other interested researchers a standardized protocol for generating and collecting brake wear debris. A standardized procedure for generating and collecting wear debris is the starting point for characterizing a disc brake material and assessing its impact on the environment.
HESS Opinions: Participatory Digital Earth Twin Hydrology systems (DARTHs) for everyone: a blueprint for hydrologists

Abstract. The Digital Earth (DE) metaphor is very useful for both end users and for hydrological modellers (i.e., the coders). However, in literature it can promote the erroneous view of models as a commodity, that is a basic good used in commerce that is interchangeable with other goods of the same type, without any warning about the fact that some models work better than others, some models just work while others can be simply wrong. These distinctions are at the core of doing good science. This opinion contribution, on the one hand, tries to accept the challenge of adopting models as commodities but, on the other, it wants to show that this acceptance comes with some consequences as to how models must be structured. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate infrastructure that provides some basic services for connection to input data and allows for a modelling-by-components strategy, which, we argue, is the right one for accomplishing the requirements of the DE. The urgency for DARTHs to be Open Source and well written pieces of codes is discussed here in light of the Open Science movement and its ideas. The need to tie predictions to an estimated confidence interval is also supported. Finally, it is argued that DARTHs must promote a new participatory way of doing hydrological science, where researchers can contribute cooperatively to characterize and control model outcomes in various territories. Furthermore, this has consequences for the engineering of the systems.
How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model

Abstract. Pesticide transfers in agricultural catchments are responsible for diffuse but major risks to water quality. Spatialized pesticide transfer models are useful tools to assess the impact of the structure of the landscape on water quality. Before considering using these tools in operational contexts, quantifying their uncertainties is a preliminary necessary step. In this study, we explored how global sensitivity analysis can be applied to the recent PESHMELBA pesticide transfer model to quantify uncertainties on transfer simulations. We set up a virtual catchment based on a real one and we compared different approaches for sensitivity analysis that could handle the specificities of the model: high number of input parameters, limited size of sample due to computational cost and spatialized output. We compared Sobol' indices obtained from Polynomial Chaos Expansion, HSIC dependence measures and feature importance measures obtained from Random Forest surrogate model. Results showed the consistency of the different methods and they highlighted the relevance of Sobol' indices to capture interactions between parameters. Sensitivity indices were first computed for each landscape element (site sensitivity indices). Second, we proposed to aggregate them at the hillslope and the catchment scale in order to get a summary of the model sensitivity and a valuable insight into the model hydrodynamical behaviour. The methodology proposed in this paper may be extended to other modular and distributed hydrological models as there has been a growing interest in these methods in recent years.
Simulation of the operation of the solar panel of the pilot plant in the Ansys software package

The article presents the results of the calculation of the thermal parameters of the solar battery of the pilot plant, performed using the ANSYS program. To analyze the influence of various parameters on the efficiency of the solar battery, when setting data in the preprocessor, such parameters as density, thermal conductivity coefficients, and element sizes were varied. Environmental factors were also analyzed, namely, ambient temperature and heat flow. For the developed installation, the choice of training a neural network without a teacher is justified. The results of calculations are presented in the form of graphs and tables, as well as illustrations of the fields of temperature distribution and heat flow.
Improving the energy efficiency of production processes at industrial enterprises of the Russian Federation

The conclusion of the generating equipment for repair, in the optimal way, is the main one when planning repairs. But to consider this issue, it is necessary to understand the essence of the problem and find ways to solve the identified problem. It is necessary to start with the concept of "reliability", namely the reliability of electrical power systems. The article deals with power systems in general, properties of power systems, elements of power systems. The ratio of the functions and properties of energy systems is analyzed, where the main functions of the objects under consideration are: a function associated with the purpose of production equipment, a function associated with the fact of its creation. The meaning of the assignment function is that the object has some task that is performed. In turn, the fulfillment or non-fulfillment of the task is directly related to reliability. Reliability is the property of an object to perform a specified function related to its purpose, subject to specified constraints.
An improved posture evaluation method for cylindrical intersecting holes on large aerospace components based on monocular vision

Cylindrical intersecting holes (CIHs) are common connection and location reference features in the assembly of large aerospace structures such as missiles and rocket cabins. The posture accuracy of assembly holes significantly impacts the relative position accuracy of joined parts and the fatigue strength of the finished product. At present, monocular vision measurement is widely used in the automatic drilling of assembly holes due to its integration simplicity and lower cost, but in most research, only the front face edge of the hole is used in the measurement model, and the hole end surface is usually assumed to be plane, which inevitably leads to precision loss. In this research, a novel posture measurement method for CIHs is proposed. Firstly, by introducing an ambiguity removal strategy, a coarse posture estimation method based on the plane hypothesis of the two end surfaces of CIHs is suggested. Secondly, considering that there is no simple explicit expression for a CIH’s edge, it is difficult to adopt the conventional model projection-based pose optimization method. In view of this, the 3D points corresponding to the edge pixels of the CIHs’ image are derived, and the pose optimization model is established by minimizing the deviations between the distance from the points to the CIHs’ axis and the hole radius. Moreover, to better control the direction parameters of CIHs during the global optimization process, the approximately perpendicular and intersection constraints between the CIHs’ axis and cylindrical component axis are involved in the solution. The effectiveness of the posture measurement method is verified by comparative experiments with current methods and coordinate measuring machine, which demonstrates improvements in both measurement accuracy and robustness.
Differentially Private Online Aggregation

Database operations are often performed in batch mode, i.e. the analyst issuing the query must wait till the database has been processed in its entirety before getting feedback. Batch mode is inadequate for large databases since queries can take several hours to process and often an analyst is satisfied with an approximation. Online aggregation greatly improves user experience and saves resources by providing continuous feedback through running confidence intervals. Further, it provides an interface for users to terminate early and allocate resources elsewhere once a sufficient accuracy level has been achieved. Until now, online aggregation has not been studied in a differentially private setting. In this work, we formulate differentially private online aggregation such that it captures the tradeoffs between privacy, accuracy, and usability. Further, we develop a family of differentially private mechanisms, which includes our optimal Gap mechanisms, for answering AVG, COUNT, and SUM queries with WHERE conditions. Also, we develop various optimizations to improve the accuracy of the Gap mechanism and empirically confirm that the Gap mechanisms preform the best overall.
A REVIEW OF VIDEO-BASED AND MACHINE LEARNING APPROACHES TO HUMAN EYE BLINK DETECTION IN VIDEO

Automation of detection of human eye blink in video has a broad array of applications, including detection of disease, anti-spoofing software, and helping individuals with physical disabilities interact with computers. The present work provides a review of several papers within the past two decades which propose methods for automated blink detection, highlighting the evolution of the field alongside developments in machine learning techniques. Then, the strengths and shortcomings of several popular approaches are evaluated in the context of eye blink detection. Namely, I focus on appearance-based and motion-based computation, support vector machines, convolutional neural networks, and long short-term memory networks. Finally, I report the beginnings of a reproduction of the methods outlined in one of the papers reviewed.
Sparse PCA: a Geometric Approach

We consider the problem of maximizing the variance explained from a data matrix using orthogonal sparse principal components that have a support of fixed cardinality. While most existing methods focus on building principal components (PCs) iteratively through deflation, we propose GeoSPCA, a novel algorithm to build all PCs at once while satisfying the orthogonality constraints which brings substantial benefits over deflation. This novel approach is based on the left eigenvalues of the covariance matrix which helps circumvent the non-convexity of the problem by approximating the optimal solution using a binary linear optimization problem that can find the optimal solution. The resulting approximation can be used to tackle different versions of the sparse PCA problem including the case in which the principal components share the same support or have disjoint supports and the Structured Sparse PCA problem. We also propose optimality bounds and illustrate the benefits of GeoSPCA in selected real world problems both in terms of explained variance, sparsity and tractability. Improvements vs. the greedy algorithm, which is often at par with state-of-the-art techniques, reaches up to 24% in terms of variance while solving real world problems with 10,000s of variables and support cardinality of 100s in minutes. We also apply GeoSPCA in a face recognition problem yielding more than 10% improvement vs. other PCA based technique such as structured sparse PCA.
Influence on the Derived Ionospheric Convection

17 The Super Dual Auroral Radar Network (SuperDARN) was built to study ionospheric 18 convection and has in recent years been expanded geographically. Alongside software de19 velopments, this has resulted in many different versions of the convection maps dataset 20 being available. Using data from 2012 to 2018, we produce five different versions of the 21 widely used convection maps, using limited backscatter ranges, background models and 22 the exclusion/inclusion of data from specific radar groups such as the StormDARN radars. 23 This enables us to simulate how much information was missing from older SuperDARN 24 research. We study changes in the Heppner-Maynard boundary (HMB), the cross po25 lar cap potential (CPCP), the number of backscatter echoes (n) and the χ/n statistic 26 which is a measure of the global agreement between the measured and fitted velocities. 27 We find that the CPCP is reduced when the PolarDARN radars are introduced, but then 28 increases again when the StormDARN radars are added. When the background model 29 is changed from the RG96 model, to the most recent TS18 model, the CPCP tends to 30 decrease for lower values, but tends to increase for higher values. When comparing to 31 geomagnetic indices, we find that there is on average a linear relationship between the 32 HMB and the geomagnetic indices, as well as n, which breaks when the HMB is located 33 at latitudes below ∼50◦ due to the low observational density. Whilst n is important in 34 constraining the maps (maps with n >400 data points are unlikely to differ), it is in35 sufficient as the sole measure of quality. 36 Plain Language Summary 37 The ionosphere, where space begins and the atmosphere ends, moves as a result 38 of the Earth’s magnetic field coupling with the Sun. The Super Dual Auroral Radar Net39 work (SuperDARN) was built around the Earth’s magnetic poles to study this phenomenon, 40 known as ionospheric convection. Combining many line-of-sight convection measurements, 41 we are able to build global maps of ionospheric convection from SuperDARN data. This 42 encapsulates dynamics which are central to space weather phenomena. SuperDARN, which 43 has been gathering data for decades, has over time undergone numerous transformations, 44 including the development of new processing software and more radars being added to 45 the network. Using data from the years 2012 to 2018, we perform a statistical analysis 46 on processed SuperDARN convection maps for the entire dataset and assess systemat47 ically how the dataset has changed over the years. We consider how the addition of more 48
The improvements into the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv12)

South China Sea Operational Oceanography Forecasting System (SCSOFS) had been constructed and operated in National Marine Environmental Forecasting Center of China to provide daily 15 updated hydrodynamic forecasting in SCS for the nextfuture 5 days since 2013. This paper presents recent comprehensive updates of the configurations of the physical model and data assimilation scheme in order to improve SCSOFS forecasting skills. The paperIt highlights three of the most sensitive updates, including sea surface atmospheric forcing method, tracers advection discrete scheme, and modification of data assimilation scheme. Inter-comparison and accuracy assessment among five versions during the 20 whole upgrading processes are performed by employing OceanPredict Inter-comparison and Validation Task Team Class4 metrics. The results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version known as SCSOFSv1. Domain averaged monthly mean root mean square errors decrease from 1.21 °C to 0.52 °C for sea surface temperature, from 21.6cm to 8.5cm for sea level anomaly, respectively. 25
A Review of IoT Models

IoT is seen almost everywhere and the choices are numerous. At the same time if we want to think of an ecosystem the goal should be such that independent IoT models can be taken under one umbrella. Various models can communicate with each other, that should be the goal of an IoT model. Any new IoT model should be able to attach itself with an existing model so that the ultimate purpose of advancement of IoT, which is to connect everything, can be made possible. Communication protocols play an important role in the area of IoT. These communication protocols may vary with respect to the application area and domain. This survey discusses a variety of IoT Models and their approach for the implementation. The paper discusses the differences between the models and proposes the scope of scalability and interoperability in the various Iot models. Analysis of those models with the target of finding the factors stopping IoT models to communicate with each other is presented then there is a discussion presented for scope of scalability and interoperability in the existing models.
Polarimetric Change Analysis in Forest using PolInSAR Ground and Volume separation techniques

This work employs a Polarimetric SAR Interferometry (PolInSAR) two-layer model in order to separate the polarimetric response from ground and volume contributions in vegetated scenarios. The full rank covariance matrix for the ground and volume components may be extracted. The polarimetric properties of the extracted components will be evaluated under different acquisition conditions, in order to better understand the potentials of the PolInSAR separation technique for bio/geo-physical parameters extraction.
The web as platform: Data flows in social media

This dissertation looks into the history of Web 2.0 as "the web as platform" (O’Reilly 2004) and traces the transition of social network sites into social media platforms to examine how social media has transformed the web. In order to understand this process from an infrastructural perspective, I develop the concept of "platformization". This notion refers to the rise and consequences of the platform as the dominant infrastructural and economic model of the social web. Platformization, I argue, rests on a dual logic of social media platforms’ extension into the rest of the web and, simultaneously, their drive to make external web data "platform ready". I draw on digital methods to study the effects of social media on the web’s infrastructure and formulate a platform critique by examining the platform-specific objects that have been introduced by social media platforms such as social buttons and short URLs. Doing so, the thesis offers a contribution the emerging fields of software studies and platform studies. As part of this undertaking, I put forward new methods that I frame as digital methods for platform studies which utilize medium-specific features to explore the dynamics of platformization. I propose a new branch of platform studies that I call platform infrastructure studies, which analyzes the ecosystem of software platforms with platform-specific digital methods.
General admissibly ordered interval-valued overlap functions

Overlap functions are a class of aggregation functions that measure the overlapping degree between two values. They have been successfully applied in several problems in which associativity is not required, such as classification and image processing. Some generalizations of overlap functions were proposed for applications in problems with more than two classes, such as 𝑛 -dimensional and general overlap functions. To measure the overlapping of interval data, interval-valued overlap functions were defined, and, later, they were also generalized in the form of 𝑛 -dimensional and general iv-overlap functions. In order to apply some of those concepts in problems with interval data considering the use of admissible orders, which are total orders that refine the most used partial order for intervals, 𝑛 -dimensional admissibly ordered iv-overlap functions were recently introduced, proving to be suitable to be applied in classification problems. However, the sole construction method presented for this kind of function do not allow the use of the well known lexicographical orders. So, in this work we combine previous developments to introduce general admissibly ordered iv-overlap functions, present different construction methods for them and how to combine such methods, showcasing the flexibility of this approach, while also being compatible with the lexicographical orders.