Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate

Cross-domain recommender systems could potentially improve the recommendation performance by means of transferring abundant knowledge from the auxiliary domain to the target domain. They could help address some key challenges in recommender systems, such as data sparsity and cold start. However, most existing cross-domain recommendation approaches represent the user preferences based on a single kind of user’s feature or behavior and fail to explore the hidden interaction effects of different kinds of features or behaviors. In this article, we propose the Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate (SGCross) to transfer user representations from multiple perspectives. The SGCross model constructs a user profile by learning the personal preference from a personal view, the dynamic preference from a temporal view, as well as the collaborative preference from a collaborative view. Specifically, a Multi-view Hierarchical Gate (MHG) is designed to transfer the informative representations of user knowledge on different views from the auxiliary domain separately, aiming to enhance the user representations. Furthermore, a two-stage attentive fusion module is designed to integrate transferred information at two levels: the domain level and the view level. Extensive experiments on the Amazon dataset and the Douban dataset have demonstrated that SGCross effectively improves the accuracy of cross-domain recommendations and outperforms the state-of-the-art baseline models.
Efficient PHY Layer Abstraction for 5G NR Sidelink in ns-3

Physical (PHY) layer abstraction is an effective method to reduce the runtimes compared with link simulations but still accurately characterize the link performance. As a result, PHY layer abstraction for IEEE 802.11 WLAN and 3GPP LTE/5G has been widely configured in the network simulators such as ns-3, which achieve faster system-level simulations quantifying the network performance. Since the first publicly accessible 5G NR Sidelink (SL) link simulator has been recently developed, it provides a possibility of implementing the first PHY layer abstraction on 5G NR SL. This work deploys an efficient PHY layer abstraction method (i.e., EESM-log-SGN) for 5G NR SL based on the offline NR SL link simulation. The obtained layer abstraction which is further stored in ns-3 for use aims at the common 5G NR SL scenario of OFDM unicast single layer mapping in the context of Independent and Identically Distributed (i.i.d.) frequency-selective channels. We provide details about implementation, performance, and validation.
Model Counting Meets F0 Estimation

Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSP’s and computation of zeroth frequency moments (F0) for data streams. Our investigations lead us to observe a striking similarity in the core techniques employed in the algorithmic frameworks that have evolved separately for model counting and F0 computation. We design a recipe for translating algorithms developed for F0 estimation to model counting, resulting in new algorithms for model counting. We also provide a recipe for transforming sampling algorithm over streams to constraint sampling algorithms. We then observe that algorithms in the context of distributed streaming can be transformed into distributed algorithms for model counting. We next turn our attention to viewing streaming from the lens of counting and show that framing F0 estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of streaming problems, which had been subjected to case-specific analysis in prior works. In particular, our view yields an algorithm for multidimensional range efficient F0 estimation with a simpler analysis.
Developing a Multimodal Classroom Engagement Analysis Dashboard for Higher-Education

Developing learning analytics dashboards (LADs) is a growing research interest as online learning tools have become more accessible in K-12 and higher education settings. This paper reports our multimodal classroom engagement data analysis and dashboard design process and the resulting engagement dashboard. Our work stems from the importance of monitoring classroom engagement, which refers to students' active physical and cognitive involvement in learning that influences their motivation and success in a given course. To monitor this vital facade of learning, we developed an engagement dashboard using an iterative and user-centered process. We first created a multimodal machine learning model that utilizes face and pose features obtained from recent deep learning models. Then, we created a dashboard where users can view their engagement over time and discover their learning/teaching patterns. Finally, we conducted user studies with undergraduate and graduate-level participants to obtain feedback on our dashboard design. Our paper makes three contributions by (1) presenting a student-centric, open-source dashboard, (2) demonstrating a baseline architecture for engagement analysis using our open-access data, and (3) presenting user insights and design takeaways to inspire future LADs. We expect our research to guide the development of tools for novice teacher education, student self-evaluation, and engagement evaluation in crowded classrooms.
Deep Object Detection for Complex Architectural Floor Plans with Efficient Receptive Fields

Architectural floor plans play an important role in sharing the building information among engineers, designers, and clients. Automatic floor plan analysis can help in improving work efficiency and accuracy. Object detection and recognition are critical in understanding and analyzing a floor plan document. However, few research works have been conducted to date for automatic object detection in architectural floor plans. In this paper, a convolutional neural network, namely ArchNet, is proposed to detect various visual objects, such as door, window, and stairs. The ArchNet is a modified version of YOLO network, and consists of five modules: backbone, multiscale receptive fields, neck, head, and non-maximal suppression. In this paper, ArchNet is used to detect 13 object classes commonly found in architectural floor plans. Experimental results show that the proposed architecture can achieve a mean average precision of 75% which is superior compared to the state-of-the-art techniques.
Analysis of entropy generation during natural convection within entrapped porous triangular cavities during hot or cold fluid disposal

ABSTRACT Entropy generation analysis is carried out during natural convection within entrapped porous triangular cavities for two cases based on the heating or cooling of the inclined and horizontal walls (case 1: hot inclined walls with cold horizontal walls and case 2: cold inclined walls with hot horizontal walls). The results are plotted in terms of the isotherms (θ), streamlines (ψ), and entropy generation maps (Sθ and Sψ). The total entropy generation (Stotal), average Bejan number (Beav), and average Nusselt number ( or ) as a function of Darcy number (Dam) at a high Rayleigh number (Ram = 106) have been studied for both cases 1 and 2 and the optimal case is recommended based on least Stotal and largest or .
A New Weak Sinusoidal Signal Detection Method Based on Duffing Oscillators

It is found in numerical simulations that Duffing oscillator state transition from big-cycle motion to chaotic motion has more robust sensitivity to the amplitude of the forced periodic term than the transition from chaotic motion to big-cycle motion,as long as the reference signal initial phase and the Duffing oscillator initial value are matched.On this basis,a new weak sinusoidal signal detection scheme is advanced,whose feasibility is then analyzed.The advanced scheme shows higher detection capability by comparing with traditional methods.Besides,considering the characteristics of the Duffing oscillator phase space,a new Duffing oscillator motion state judging method is proposed,which features good real-time performance and low computational complexity,and is verified with simulations.
Optimal Quadrature Points for Approximating Integrals When Function Values are Observed with Error

A discussion of numerical integration in an introductory calculus course typically includes discussion of the trapezoidal rule and Simpson's rule. Attention is usually restricted to the case where the interval of integration is subdivided equally; this leads to simpler formulas but may also lead to very poor approximations to the definite integral when compared with other subdivision strategies. Consider approximation of the definite integral
Rankin-Selberg integrals in many complex variables and Rankin-Selberg integrals associated to non-unique models

In this thesis we explore Rankin-Selberg integrals in many complex variables and Rankin-Selberg integrals associated to non-unique models. In particular we try to bring to light a possible connection between them. First, we reinterpret the integral of Kohnen-Skoruppa for the Spin L-function on GSp(4) as an integral associated to a non-unique model, following Piatetski-Shapiro and Rallis. Then we give two integrals on the quasisplit unitary group GU(2,2) in four variables. The first integral is in two complex variables, and applies to generic cusp forms on GU(2,2); it represents the product of the standard (8 dimensional representation) and the exterior square (6 dimensional representation) of the dual group. The second integral on GU(2,2) represents the exterior square L-function and is associated to a non-unique model; this integral is a reinterpretation of an integral of Gritsenko who considered holomorphic Hermitian modular forms of genus two. We then give an integral representation for the Spin L-function on GSp(6), again associated to a non-unique model. As we explain below, these non-unique model integrals on GU(2,2) and GSp(6) may be considered analogues of the integral of Kohnen-Skoruppa. Some of the results in this thesis stem from work done jointly with Shrenik Shah.
Nesting Circles: An Interactive Visualization Paradigm for Network Intrusion Detection System Alerts

Intrusion detection systems (IDSs) are valuable tools for fighting against those who want to intrude on the network and steal sensitive information for any reason. These tools, however, have difficulties in their essence. The generated alerts are in textual format, and extracting the exact information from the textual files needs lots of time and scrutiny. Also, not all alerts are accurate, and these tools suffer a setback named false-positive alerts, meaning that although no attack occurs, they may log some alerts. It is almost impossible to detect the penetration according to the discussed conditions. Information visualization is a method that transforms information into a visual representation for a better and quicker understanding. Indeed, the more the visualization is representative and straightforward, the more information it can transfer and the more worthy it is. This paper proposes a new paradigm for visualizing IDS alerts named nesting circles. We keep simplicity by using circles as the primary mark and the size and color as the only used channels. This makes the visualization easy to read and intuitive to understand. Furthermore, nesting circles provide a complete visualization of explicit and implicit information to the admin, and the previous approaches lacked this vital feature. The efficiency of nesting circles is examined through the VAST challenge case study, and it is shown to be effective in finding hidden attacks in the logs.
Image Enhancement Based on the Contourlet Transform and Mathematical Morphology

Traditional methods for image enhancement cannot capture the geometric information of images and tend to amplify noise.To overcome the above enhancement issues,a new enhancement algorithm for image was proposed based on the Contourlet transform and mathematical morphology.The new algorithm decomposed image into approximation-coefficients and detail coefficients.Morphological dilation operations was used to distinguish noise form detail information according to dependencies of contourlet coefficients,then nonlinear mapping function was used to modify the contourlet coefficients.And the enhanced image were obtained by contourlet inverse transform.The experiments show that utilizing the proposed algorithm can enhances image detail and the signal to noise ratio.
Post-Discharge Problems in Cardiac Surgery Patients

Background: Cardiac surgeries have contributed greatly to resolve the problems relating to Coronary Artery Diseases (CAD) and valvular heart diseases. There are numerous physical and psychosocial problems that cardiac surgery patients experience once they were discharged from the hospital. Purpose: The aim of this study is to determine post-discharge problems and their frequencies in cardiac surgery patients at tertiary care hospital. Methods: Descriptive cross sectional study design was used to determine problems and their frequencies in CABG and valvular surgery in post discharge period. Data was collected from 96 patients through consecutive sampling technique at Cardiac Center of public tertiary care hospital during July to September 2018 on patient’s first follow-up after discharge. The questionnaire content validity index was checked by cardiac surgery experts and calculated 0.88. The data was analyzed on SPSS (23 version). descriptive statistical tests frequency, percentage used for categorical variables, and mean and standard deviation for continuous variables and Likert scale for post-discharge problems in cardiac surgery patients. Results: Male patients were high in percentage (71.9%) with mean age of 52 year had CAD due to stress and sedentary life. CABG was done for (80.3) and valvular surgeries done for (19.8%). Pain in the surgical wound site and different body parts, chest wound problems like discharge and swelling. Other problems were difficulty in breathing and pain with cough. Psychosocial problems were found in more than half of the patients like difficulty in sleeping, phobia of body movement and losing job, palpitation and apprehension, sadness and avoided social interaction with others. Conclusion: The study found different problems such as pain, difficulty in breathing, sleepiness, tiredness, difficulty in preforming Activities of daily livings and wound related issues. Significant number of participants reported about social isolation, anxiety, restlessness and fear. The findings of current study can be utilized to develop a discharge package for patients.
Estimates for Generalized Parabolic Marcinkiewicz Integrals with Rough Kernels on Product Domains

We prove Lp estimates of a class of generalized Marcinkiewicz integral operators with mixed homogeneity on product domains. By using these estimates along with an extrapolation argument, we obtain the boundedness of our operators under very weak conditions on the kernel functions. Our results in this paper improve and extend several known results on both generalized Marcinkiewicz integrals and parabolic Marcinkiewicz integrals on product domains.
The Evolution of Cataclysmic Variables

Abstract Using Eggletons code the evolution of cataclysmic variables (CVs) is investigated. CVs might suffer the loss of mass and angular momentum during their evolution, we present the models of CVs with mass loss and angular momentum loss (AML) due to gravitation wave radiation (GR) and/or magnetic braking (MB). It is found that the loss of mass and angular momentum has significant influence on the evolution of CVs, and that the change of the star structure or their atmosphere properties is a possible mechanism which underlies a sudden change in the rate of AML owing to MB.
DURATION OF LOAD UNDER LONG-TERM BENDING LOAD OF LAMINATED VENEER LUMBER AND WOODEN I-BEAM

A wide range of wooden materials have been developed and used in construction. Although wood-based composites are important structural materials, few studies focusing on their creep rupture properties have been reported. We conducted long-term bending tests and investigated long-term allowable loading levels of laminated veneer lumber (LVL) and wooden I-beams made of LVL flanges and oriented-strand-board (OSB) web. Creep rupture of wooden Ibeam specimens occurred in their LVL flanges. At the same time, LVL and I-beam specimens showed similar estimated long-term (50 years) allowable stress levels, which were equal or higher values than that of lumber. Survival analysis of the obtained data indicated no effect of the specimen type (LVL or I-beam) on survival distribution. The duration of load of wood-based composite materials could be estimated based on that of its component members.
Prescribers’ Compliance to Human Serum Albumin Usage Criteria and Its Implication on Cost Saving

Human serum albumin (HSA) is a commonly used product with high impact on healthcare expenditure. Little is known regarding the appropriateness of HSA use in healthcare system. This study aimed to assess prescriber’s compliance to the approved HSA criteria. Potential cost saving was also estimated. A cross-sectional study was performed in a tertiary care, university hospital in Bangkok in 2019. Prescriptions for HSA for all adult patients along with corresponding medical records were reviewed and assessed against the approved HSA criteria. Deviation from the criteria was used to estimate the potential cost saving. A total of 688 prescriptions were reviewed, 200 prescriptions met the inclusion criteria and were included in the analysis. Among six indications in the approved criteria for HSA, the rates of criteria compliance ranged from 48 to 90%. Hypoalbuminemia was found to be the main reason for inappropriate use. The estimated cost saving from inappropriate HSA use was €3,543 (€59 per prescription). Significant deviation from the approved criteria of HSA use was seen in a Thai hospital and significant cost saving can be made by increasing the compliance to the criteria. A practice improvement initiative to promote rational use of HSA is warranted.
Small Printed Tri-band Antenna with Reduced Ground-plane Effect

Abstract This paper presents a small printed tri-band antenna with a reduced ground-plane effect for WLAN/M-WIMAX applications. The radiator and ground plane of the antenna are etched on a printed circuit board with an overall size of 25×24.5×0.762 mm3. A rectangular notch and a circular slot are cut from the radiator while an asymmetrical strip is attached to the radiator. The simulation and measurement suggest that the printed antenna achieves three operating bandwidths of 2.4–2.484 GHz and 5.15–5.35 GHz for WLAN, 3.3–3.69 GHz for M-WIMAX for a 10-dB return loss, and high gains on Y-Z plane of 2.1, 3.3 and 4.8 dB at 2.4, 3.5 and 5.2 GHz, respectively. In particular, the ground-plane effect on impedance performance is significantly reduced by minimising the size of the ground plane and then by cutting a notch and a circular slot from the radiator because the electric currents on the ground plane are significantly suppressed at all the operating bandwidths. In addition, a study on the geometrical and electrical parameters of the proposed antenna may be a useful reference for antenna engineers.
Correction to "An Identification of Suzuki Groups with Groups of Generalized Lie Type"

I am indebted to R. Steinberg for pointing out that the Remark at the end of the paper is incorrect. The involutive map a is an automorphism. The mistake I made there is in using a result of Dieudonne which has been known to be wrong. The fact that the a is an automorphism can be seen, for example, by Steinberg's theory on coverings of algebraic groups (cf. Generateurs, relations et revetements de groupes algebriques, Bruxelles, 1962).
Residues in succession recurrent U-Net for segmentation of retinal blood vessels

The use of deep learning is particularly effective for biomedical applications involving semantic segmentation. In semantic segmentation, one of the most popular deep learning architectures is U-Net, which is specifically designed for feature cascading for pixel classification. There are several versions of U-Net, such as Residual U-Net (ResU-Net), Recurrent U-Net (RU-Net), and Recurrent Residual U-Net (R2U-Net), which have been proposed for improved performance. The recurrent connection in a layer of the neural network can create a cycle of transferring the output information of a layer back to itself as an input. Each layer's output responses can thus be thought of as additional input variables. The new model is based on Residues in Succession U-Net where the residues from successive layers extract reinforced information from the previous layers in addition to the recurrent feedback loop exhibiting several advantages. The improved learning and accumulation of the features in subsequent layers play a major part. The proposed model produces precise extraction and accumulation of features from each layer reinforcing the learning. The outputs of the combination of recurrent and residues in successive layers ensure better feature representation for segmentation tasks. We use a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE) for measuring the abilities of the Residues in Succession Recurrent U-Net (RSR U-Net) to segment blood vessels in retinal images. The testing and evaluation results show that the new model provides improved performance when compared to U-Net, R2U-Net and Residues in Succession U-Net in the same experimentation setup.
Solving the Unbalanced Assignment Problem: Simpler Is Better

Recently, Yadaiah and Haragopal published in the American Journal of Operations Research a new approach to solving the unbalanced assignment problem. They also provide a numerical example which they solve with their approach and get a cost of 1550 which they claim is optimum. This approach might be of interest; however, their approach does not guarantee the optimal solution. In this short paper, we will show that solving this same example from the Yadaiah and Haragopal paper by using a simple textbook formulation to balance the problem and then solve it with the classic Hungarian method of Kuhn yields the true optimal solution with a cost of 1520.
Secrecy Analysis of Uplink IM-OFDMA Systems in the Presence of IQ Imbalance

This article introduces the secrecy analysis of the index modulation-based orthogonal frequency division multiple access (IM-OFDMA) schemes by considering the in-phase and quadrature imbalance (IQI) presence at the users' RF front ends. In particular, by assuming the existence of an eavesdropper, the secrecy performance of the considered uplink multiple-access system is investigated through the strictly positive secrecy capacity and secrecy outage probability, and closed-form expressions are derived for both quantities. Moreover, the secrecy performance is conducted regarding IQI compensation based on a preamble-based estimation method. Further, analysis of the impact of the different parameters of IM-OFDMA, IQI, and estimation method is included via analytical and simulation-based results. The results reveal that IM-OFDMA is more reliable in secrecy performance than OFDMA, regardless of whether IQI is present. Also, IQI compensation can lead to a better secrecy performance compared to the case of no IQI.