Miracle-dz/UniFAR-FTUs huggingface dataset:
Rank and Select on Degenerate Strings

A 'degenerate string' is a sequence of subsets of some alphabet; it represents any string obtainable by selecting one character from each set from left to right. Recently, Alanko et al. generalized the rank-select problem to degenerate strings, where given a character $c$ and position $i$ the goal is to find either the $i$th set containing $c$ or the number of occurrences of $c$ in the first $i$ sets [SEA 2023]. The problem has applications to pangenomics; in another work by Alanko et al. they use it as the basis for a compact representation of 'de Bruijn Graphs' that supports fast membership queries. In this paper we revisit the rank-select problem on degenerate strings, introducing a new, natural parameter and reanalyzing existing reductions to rank-select on regular strings. Plugging in standard data structures, the time bounds for queries are improved exponentially while essentially matching, or improving, the space bounds. Furthermore, we provide a lower bound on space that shows that the reductions lead to succinct data structures in a wide range of cases. Finally, we provide implementations; our most compact structure matches the space of the most compact structure of Alanko et al. while answering queries twice as fast. We also provide an implementation using modern vector processing features; it uses less than one percent more space than the most compact structure of Alanko et al. while supporting queries four to seven times faster, and has competitive query time with all the remaining structures.
Mixed coordinate Node link Visualization for Co_authorship Hypergraph Networks

We present an algorithmic technique for visualizing the co-authorship networks and other networks modeled with hypergraphs (set systems). As more than two researchers can co-author a paper, a direct representation of the interaction of researchers through their joint works cannot be adequately modeled with direct links between the author-nodes. A hypergraph representation of a co-authorship network treats researchers/authors as nodes and papers as hyperedges (sets of authors). The visualization algorithm that we propose is based on one of the well-studied approaches representing both authors and papers as nodes of different classes. Our approach resembles some known ones like anchored maps but introduces some special techniques for optimizing the vertex positioning. The algorithm involves both continuous (force-directed) optimization and discrete optimization for determining the node coordinates. Moreover, one of the novelties of this work is classifying nodes and links using different colors. This usage has a meaningful purpose that helps the viewer to obtain valuable information from the visualization and increases the readability of the layout. The algorithm is tuned to enable the viewer to answer questions specific to co-authorship network studies.
Applications of Machine Learning in Predicting the Bridge Deck Surface Temperature

Recently, the application of statistical methods and artificial intelligence (AI) techniques such as machine learning and deep learning have gained notable interest in various fields, especially engineering. These data-driven methods can play a crucial role in making predictions faster with higher accuracy. In this study, several machine learning methods were implemented on real engineering data. This paper aims to build a robust model to predict the bridge deck surface temperature by adopting meteorological data obtained from a bridge de-icing project in Texas. Multiple Linear Regression (MLR), Random Forest (RF), k Nearest Neighbors (kNN), Support Vector Regression (SVR), and Extreme Gradient Boosting Decision Tree (XGBDT) were used, and the predicted results were compared and validated by available field data. All machine learning algorithms predicted the surface temperature with high accuracy, demonstrating the applicability of machine learning techniques in temperature prediction tasks. However, the best performance was achieved by XGBDT with R2 = 0.99.
Association between survivin-31 C / G polymorphism and the risk of urinary system cancer : a meta-analysis

As an inhibitor of apoptosis protein, survivin was abundantly expressed in many human malignancies. Although many studies have demonstrated the relationship between the survivin-31C/G (rs9904341) polymorphism and urinary system cancer susceptibility, the conclusions remained controversial. In order to clarify the effects of this polymorphism on the risk of urinary system cancer, a comprehensive meta-analysis was performed. Six databases were searched to identify the eligible studies. Pooled odds ratios (ORs) with 95% confidenceintervals (CIs) were calculated under the allelic, dominant, homozygous, heterozygous and recessive models. The data were analyzed by using the Stata 12.0. Nine case-control studies were included with a total of 2307 cases and 2722 controls. The results indicated that Survivin-31C/G (rs9904341) polymorphism was associated with increased risk of urinary system cancer (OR=1.28 95%CI=1.01-1.62, P=0.039). Stratified analysis by ethnicity (Asian and Caucasian) indicated thatSurvivin-31C/G variants were associated with a significantly increased risk of urinary system cancer in Asian population (OR=1.53 95%CI=1.27-1.85, P<0.001), but associated with a reduced risk of urinary tract cancer in Caucasian (OR=0.29 95%CI=0.13-0.64, P=0.002). This meta-analysis suggested that the Survivin-31C/G (rs9904341) variants increased the urinary system cancer predisposition in Asian population, and reduced the urinary system cancer predisposition in Caucasian.
Renaturization Plan for a River Valley Subject to High Human Impact - Hydrological Aspects

This article presents hydrological aspects of a renaturization plan for a river valley that has been significantly transformed as a result of human impact. A section of the Szarlejka River valley located at the border of the cities of Bytom and Radzionkow was used for this purpose. The hydrographic conditions of the area under consideration have been significantly altered due to human action. These transformations are the result of systematic residential and industrial infrastructure development, which has been accompanied by industrial waste disposal. Underground coal mining in the area has caused subsidence and discharge of saline mining waters to rivers. The Szarlejka is considered one of the most polluted rivers in Silesia Province (Polish: wojewodztwo śląskie) and all of Poland. The primary objective of the renaturization plan is to improve abiotic conditions and water status. This plan is based on the analysis of all accessible data, including archival maps and field work. Hydrological and hydrotechnical renaturization actions are proposed for the Szarlejka River valley that cover the elements, structures, and facilities, of which the mere presence brings the waters closer to their natural state (barrages inhibiting erosion, connections allowing for the circulation of aquatic organisms). The proposal also includes building astatic reservoirs, creating oxbow tanks in retained sections of the old river bed, and building a pond with a wetland and an island. The restored structures will affect abiotic and biotic conditions, consequently influencing the water recovery process with the aim of bringing it closer to its natural state. For the purposes of the planned renaturization, a geographic information system (GIS) was created, enabling digital map generation, database creation, and calculations.
RecSys Challenge 2018 : playlist continuation based on shared neighborhood-methods , matrix factorization and audio-feature classification

As part of the ACM RecSysChallenge 2018 in cooperation with Spotify, we created a recommendation system for music. We predict songs for different metadata and number of seed tracks using a combination of different approaches: Neighborhood-exploration, in combination with a latent feature representation for larger numbers of seed tracks and ranking of tracks based on an audio feature neural network. The neighborhood approach is an efficient way to generate good results, which can be improved inexpensively by matrix factorization and matching of audio features. With our approach, we reached a top 20% placement at the competition.
REAL-TIME VISUALIZATION OF MASSIVE TERRAIN DATASET

A framework based on tile-pyramid model and linear quadtree tile-index is put forward for the real-time visualization of massive terrain dataset. The real-time loading of terrain dataset is achieved with the view frustum culling technology and the target-tile searching algorithm based on resolution-testing. The dynamic bintree triangulation is used for the real-time rendering of the terrain tiles.
Experimental Demonstration of Soft-ROADMs With Dual-Arm Drop Elements for Future Optical-Wireless Converged Access Networks

Digital signal processing (DSP)-enabled soft reconfigurable optical add/drop multiplexers (Soft-ROADMs) offer flexible add/drop optical switching at wavelength, sub-wavelength and spectrally-overlapped orthogonal (I and Q) sub-band (SB) levels, which makes them highly desirable for enabling flexible reconfigurable optical-wireless converged access networks where both fixed and wireless access services are consolidated in a shared network, enabling network resource efficient and cost-effective connectivity solutions. However, the performance of the targeted sub-band (TSB) extracted by the soft-ROADM drop element is extremely sensitive to drop RF signal phase offset, which is a major limitation impacting the technical feasibility of soft-ROADMs. To overcome this challenge, in this paper, a phase-offset-insensitive soft-ROADM dual-arm IQ drop operation is proposed and experimentally demonstrated. A DSP implemented multi-input multi-output (MIMO)-based I/Q crosstalk mitigation technique is employed to achieve the insensitivity to drop RF signal phase offset. It is shown that, the traditional single-arm I/Q soft-ROADM drop element has a limited drop RF signal phase offset dynamic range of $\sim \pm \,\pi /4$($ < \pm \,0.05\pi $) in the presence of I/Q (I&Q) SBs, thus requiring accurate phase control, however, the proposed dual-arm IQ drop element demonstrates the ability to tolerate any arbitrary phase offset, thus completely eliminating the need to dynamically control the drop RF signal's phase. This paper also experimentally demonstrates the effectiveness of the new dual-arm drop element to mitigate any I/Q crosstalk effects caused by, various effects, including the fiber transmission channel-induced frequency response roll-off, symbol timing offset, and propagation delay drifting effects. Importantly, it is shown that the traditional single-arm I/Q drop element offers poor performance stability as the dropped TSB's BER performance degrades within a few minutes, whereas, the new dual-arm drop element achieves a highly stable BER performance. Soft-ROADMs incorporating the newly proposed dual-arm IQ drop element are thus experimentally shown to offer significantly reduced implementation complexity, thus radically increasing their suitability for application in optical-wireless converged access networks.
Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis

Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness, damping, friction, coupling effects, and defects. Consequently, their vibration signals display nonlinear characteristics. Entropy techniques prove to be effective in detecting these nonlinear dynamic characteristics. Recently, an approach called fuzzy dispersion entropy (DE–FDE) was introduced to quantify the uncertainty of time series. FDE, rooted in dispersion patterns and fuzzy set theory, addresses the sensitivity of DE to its parameters. However, FDE does not adequately account for the presence of multiple time scales inherent in signals. To address this limitation, the concept of multiscale fuzzy dispersion entropy (MFDE) was developed to capture the dynamical variability of time series across various scales of complexity. Compared to multiscale DE (MDE), MFDE exhibits reduced sensitivity to noise and higher stability. In order to enhance the stability of MFDE, we propose a refined composite MFDE (RCMFDE). In comparison with MFDE, MDE, and RCMDE, RCMFDE’s performance is assessed using synthetic signals and three real bearing datasets. The results consistently demonstrate the superiority of RCMFDE in detecting various patterns within synthetic and real bearing fault data. Importantly, classifiers built upon RCMFDE achieve notably high accuracy values for bearing fault diagnosis applications, outperforming classifiers based on refined composite multiscale dispersion and sample entropy methods.
Abnormal Event Behavior Detection For Home Surveillance Cameras System

Home surveillance cameras have a crucial role in ensuring the security and safety of residential premises. However, analyzing the vast amount of video data generated by these cameras manually is a time-consuming and challenging task. This study designed a camera work system consisting of three main modules namely object detection, face recognition, and anomaly behavior to detecting abnormal event behavior. Object detection used the Histogram Oriented Gradient and Support Vector Machine (HOG-SVM) combination. Facial recognition used Convolutional Neural Network (CNN) with the Visual Geometry Group-16 (VGG16) architecture that utilized transfer learning. Anomaly detection used spatiotemporal autoencoder based on threshold. Modules are tested using the standard metrics such as accuracy, precision, recall, and f1score. The three modules are integrated using a state machine into one system that can detect the abnormal event behavior. The overall accuracy of the system reached 84%, the precision value was 84.615%, the recall value was 94.286%, and the f1score was 89.189%.
Earth pressure and water pressure on retaining structure

The calculation methods of earth pressure and water pressure on retaining structure in the conditions of hydrostatic pressure, steady seepage, excess pore water pressure, have been analyzed and summarized. Some examples have been given to explain the difference between the methods of separately or integrally estimating water and earth pressures on retaining wall and the effects of seepage and excess pore water pressure on total pressure on retaining structure. Also the critical state analysis of stability of retaining structure has been emphasized
Investigation of PZT Materials for Reliable Piezostack Deformable Mirror with Modular Design

This article presents a study of the electrophysical properties of a piezoceramic material for use in adaptive optics. The key characteristics that may be important for the manufacturing of piezoelectric deformable mirrors are the following: piezoelectric constants (d31, d33, d15), capacitance, elastic compliance values s for different crystal directions, and the dielectric loss tangent (tgδ). Based on PZT ceramics, the PKP-12 material was developed with high values of the dielectric constant, piezoelectric modulus, and electromechanical coupling coefficients. The deformable mirror control elements are made from the resulting material—piezoceramic combs with five individual actuators in a row. In this case, the stroke of the actuator is in the range of 4.1–4.3 microns and the capacitance of the actuator is about 12 nF.
rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition

Although many deep learning (DL) algorithms have been proposed for the IMU-based HAR domain, traditional machine learning that utilizes handcrafted time series features (TSFs) still often performs well. It is not rare that combinations among DL and TSFs show better accuracy than DL-only approaches. However, there is a problem with time series features in IMU-based HAR. The amount of derived features can vary greatly depending on the method used to select the 3D basis. Fortunately, DL's strengths include capturing the features of input data and adaptively deriving parameters. Thus, as a new DNN model for IMU-based human activity recognition (HAR), this paper proposes rTsfNet, a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction. rTsfNet automatically selects 3D bases from which features should be derived by extracting 3D rotation parameters within the DNN. Then, time series features (TSFs), based on many researchers' wisdom, are derived to achieve HAR using MLP. Although rTsfNet is a model that does not use CNN, it achieved higher accuracy than existing models under well-managed benchmark conditions and multiple datasets: UCI HAR, PAMAP2, Daphnet, and OPPORTUNITY, all of which target different activities.
Research on path planning for mobile robots based on improved A-star algorithm

A-star algorithm, as A-star commonly used in the traditional path planning algorithm, has a good performance in the static map. The idea of optimization is mainly around the enlightening function, search neighborhood, curve smoothing and using two-way A-star algorithm to improve the computational efficiency of the algorithm. In simulation experiments using the modified A-star algorithm, the experimental results show that the modified A-star algorithm is better than the traditional A-star algorithm in the number of search points, path length and running time, and the planned path is smoother. Finally, the improved A-star algorithm is applied to the wheeled robot in the real indoor environment to verify the feasibility of the improved A-star algorithm.
FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures

Forecasting motion and spatial positions of objects is of fundamental importance, especially in safety-critical settings such as autonomous driving. In this work, we address the issue by forecasting two different modalities that carry complementary information, namely optical flow and depth. To this end we propose FLODCAST a flow and depth forecasting model that leverages a multitask recurrent architecture, trained to jointly forecast both modalities at once. We stress the importance of training using flows and depth maps together, demonstrating that both tasks improve when the model is informed of the other modality. We train the proposed model to also perform predictions for several timesteps in the future. This provides better supervision and leads to more precise predictions, retaining the capability of the model to yield outputs autoregressively for any future time horizon. We test our model on the challenging Cityscapes dataset, obtaining state of the art results for both flow and depth forecasting. Thanks to the high quality of the generated flows, we also report benefits on the downstream task of segmentation forecasting, injecting our predictions in a flow-based mask-warping framework.
Adverse selection and moral hazard in group-based lending: evidence from Eritrea

Krediet krijgen bij de bank is voor de bevolking van ontwikkelingslanden moeilijk. Een erkend probleem. Banken zijn er nu eenmaal niet om veel risico te nemen en solide garanties ontbreken daar doorgaans. Microkredietinstellingen moeten dan een oplossing bieden, vaak in de vorm van groepsleningen. Die geven de grootste zekerheid, want uit onderzoek blijkt dat de groepsleden (sociale) druk op elkaar uitoefenen als weigerachtige terugbetalers niet over de brug willen komen. Een volgende lening kan anders mogelijk in gevaar komen. Het fenomeen groepslening en vragen als 'hoe komen de groepen tot stand' en 'wat is de interactie' vormen het onderwerp van het onderzoek van Habteab Mehrteab. Twee initiatieven in Eritrea, het Southern Zone Saving and Credit Scheme en het Saving and Micro Credit Program worden grondig onder de loep genomen. Bij het verminderen van het risico voor de bank blijkt vooral de rol van de groepsleiders cruciaal.
Indonesian students’ reading literacy ability in the cooperative integrated reading and composition learning: A meta-analysis

A few of meta-analysis studies regarding the intervention of cooperative learning have been conducted. However, it has not been performed on the studies related to the intervention of cooperative integrated reading and composition (CIRC) learning for Indonesian students’ reading literacy. This study aims to approximate and examine the effect of CIRC learning on students’ reading literacy ability across group size of intervention, educational level, and geographical location. A meta-analysis was performed to conduct this study. The search of document using the database of Google Scholar found 97 documents. The selection of document established 10 documents published from 2015–2021 consisting of one conference paper and nine journal articles. Hedges’ equation was employed to measure the effect size. The data analysis used the Z test and Q Cochrane test. The results revealed that the CIRC learning had a strong positive effect (g=2.041; p<0.05) on students’ reading literacy ability. It indicates that the CIRC learning is effective for enhancing students’ reading literacy ability. Furthermore, some moderating factors such as group size of intervention, educational level, and geographical location did not affect students’ heterogeneous reading literacy ability. This study suggests language teachers to use the CIRC learning in enhancing students’ reading literacy ability.
A Greedy Task Allocation Mechanism for Time-Dependent Mobile Crowdsensing Systems

Task allocation is the process that facilitates the assignment of sensing activities or tasks to participants of a Mobile Crowdsensing (MCS) campaign. In traditional MCS environments, the purpose of task allocation is to maximize the profit of the organizing platform. However, the task allocation approach in these systems fail to deal with the specifics of time-dependent nature of the tasks and the issues that accompany time-dependent task allocation. Hence, we propose a novel mechanism called Time Dependent Task Allocation (TDTA) to allocate tasks in order to achieve maximum profit of the platform in a truly time-dependent mobile crowdsensing environment. In order to realize our goal, we also propose an algorithm for handling time-dependent task allocation called Interval Partition based Greedy Task Allocation (IPGTA). We then compare our proposed IPGTA algorithm with a baseline method and analyze their comparative performances. The simulation results indicate that IPGTA performs positively based on the parameters measured.