Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Algorithms for Interaction-Sparse Recommendations: Neural Networks don't Always Win

In recent years, top-K recommender systems with implicit feed-back data gained interest in many real-world business scenarios. In particular, neural networks have shown promising results on these tasks. However, while traditional recommender systems are built on datasets with frequent user interactions, insurance recommenders often have access to a very limited amount of user interactions, as people only buy a few insurance products. In this paper, we shed new light on the problem of top-K recommendations for interaction-sparse recommender problems. In particular, we analyze six different recommender algorithms, namely a popularity-based baseline and compare it against two matrix factorization methods (SVD++, ALS), one neural network approach (JCA) and two combinations of neural network and factorization machine approaches (DeepFM, NeuFM). We evaluate these algorithms on six different interaction-sparse datasets and one dataset with a less sparse interaction pattern to elucidate the unique behavior of interaction-sparse datasets. In our experimental evaluation based on real-world insurance data, we demonstrate that DeepFM shows the best performance followed by JCA and SVD++, which indicates that neural network approaches are the dominant technologies. However, for the remaining five datasets we observe a different pattern. Overall, the matrix factorization method SVD++ is the winner. Surpris-ingly, the simple popularity-based approach comes out second followed by the neural network approach JCA.
A Sequential Black-Start Restoration Model for Resilient Active Distribution Networks

This letter presents a novel sequential black-start restoration model to improve the system resilience after a natural disaster in an active distribution network. Two challenges have been addressed: First, a new set of radiality constraints is designed as the network topology is changing along the restoration paths; Second, a black-start restoration model is proposed where multiple backup black-start units in the system are coordinated to find the best restoration paths. Case studies on two IEEE test systems verify the effectiveness of the proposed model.
Household Food Insecurity is a Predictor of Acute Malnutrition but Not Chronic Malnutrition among Children (6-59 Months Old): A Cross-Sectional in Tamale South Constituency

Background: The influence of household food insecurity on poor child growth has been widely studied. However, there is a lack of clarity in such relationships therefore, more studies are required. Hence, the present study evaluated household food insecurity and sciodemographic determinants of undernutrition in young children (6-59 months old) in Ghana. Materials and methods: This cross-sectional study was conducted in Tamale South using 240 mother-child pairs. Mothers and their children (6-59 months old) were recruited from child welfare clinics within the Constituency. Data were collected from face-to-face interviews using a structured questionnaire. Child anthropometric data were measured (weight and height/length) based on World Health Organization’s prescribed procedures. Child anthropometric Z-scores for the determination of acute and chronic malnutrition were generated using WHO Anthroplus 1.0. 4. Household food insecurity access scale (HFIAS) was used to estimate household food insecurity. Binary logistics regression models were run using Statistical Product and Service Solutions version 24.0 to estimate the odds of acute and chronic malnutrition following the independent variables (HFIAS and sciodemographic factors). Results: The prevalence of acute malnutrition (wasting) was 7.5%. Also, the prevalence of chronic malnutrition (stunting) was 29.2%. In multiple binary logistics regressions model, there was a significant relationship between higher scores of HFIAS and the odds of acute malnutrition [1.34(1.07-1.68); P=0.010]. In the study, children aged 6-23 months had lower odds for chronic malnutrition [0.36 (1.20-0.66); P=0.001] compared with their colleagues (24-59 months old). Conclusions: In the study, we found a medium and a high prevalence of acute and chronic malnutrition, respectively. Also, HFIAS was a significant predictor of acute malnutrition but not chronic malnutrition. Further, children less than 24 months old compared with those aged 24-59 months old had lower odds for chronic malnutrition. To reduce the odds for acute malnutrition, nutrition and health authorities in the Tamale South Constituency ought to intensify education on strategies to increase household food security status.
A flexible data generator for , privacy-preserving , data mining and record linkage

This manual describes prototype software for the flexible generation of data sets that contain randomly created personal information that is based on real data. Each of the records in such a data set is created individually. The generator allows the specification of different attribute types, including attributes based on frequency look-up table, functions, as well as several types of compound attributes where the values in one attribute depend upon the values in one or two other attributes. The manual describes the structure of the generator, its program modules and the classes and methods within these modules, how to use and customise the generator, the format of look-up tables used, how the generator can be installed, and how its functionality can be tested. The generator software is licensed under the Mozilla Public License Version 2, which is included in Appendix A.
Strong Indecomposability of the Profinite Grothendieck-Teichmüller Group

In the present paper, by applying anabelian Grothendieck Conjecturetype results, we prove that the profinite Grothendieck-Teichmüller group GT satisfies strong indecomposability [i.e., the property that every open subgroup has no nontrivial product decomposition]. This gives an affirmative answer to an open problem — which naturally arises in the context of a famous open problem concerning the comparison of Gal(Q/Q) and GT — posed in a first author’s previous work. 2020 Mathematics Subject Classification: Primary 14H30; Secondary 14H25.
Full-3-D tomography for crustal structure in Southern California based on the scattering-integral and the adjoint-wave ﬁ eld methods Solid iterations using two complementary methods for F3DT inversion: the ﬁ eld (AW) technique

We have successfully applied full-3-D tomography (F3DT) based on a combination of the scattering-integral method (SI-F3DT) and the adjoint-wave ﬁ eld method (AW-F3DT) to iteratively improve a 3-D starting model, the Southern California Earthquake Center (SCEC) Community Velocity Model version 4.0 (CVM-S4). In F3DT, the sensitivity (Fréchet) kernels are computed using numerical solutions of the 3-D elastodynamic equation and the nonlinearity of the structural inversion problem is accounted for through an iterative tomographic navigation process. More than half-a-million mis ﬁ t measurements made on about 38,000 earthquake seismograms and 12,000 ambient-noise correlagrams have been assimilated into our inversion. After 26 F3DT iterations, synthetic seismograms computed using our latest model, CVM-S4.26, show substantially better ﬁ t to observed seismograms at frequencies below 0.2 Hz than those computed using our 3-D starting model CVM-S4 and the other SCEC CVM, CVM-H11.9, which was improved through 16 iterations of AW-F3DT. CVM-S4.26 has revealed strong crustal heterogeneities throughout Southern California, some of which are completely missing in CVM-S4 and CVM-H11.9 but exist in models obtained from previous crustal-scale 2-D active-source refraction tomography models. At shallow depths, our model shows strong correlation with sedimentary basins and reveals velocity contrasts across major mapped strike-slip and dip-slip faults. At middle to lower crustal depths, structural features in our model may provide new insights into regional tectonics. When combined with physics-based seismic hazard analysis tools, we expect our model to provide more accurate estimates of seismic hazards in Southern California.
Optimal power scheduling of renewable energy systems in microgrids using distributed energy storage system

The rapid depletion of fossil fuel resources and increase in demand of electricity has renewed interest in micro-grids (MGs). Incorporating renewable energy sources (RESs) and storage devices into MG could play a vital role in enhancing the reliability of the system. However, due to the intermittent nature of RESs, voltage fluctuations may occur in the system. Therefore, storage devices can be the most viable option to handle the intermittent issues of RESs. Nowadays, handling a group of batteries with different ratings in the MG has been a major focus of research. The batteries with different ratings used for storage as well as grid support is termed as a distributed energy storage system. This work mainly emphasises on optimal power management of an MG by controlling the charging and discharging rates of individual battery. A droop-based controller is proposed for optimal power management of batteries. An aggregator has been suggested at the MG which distributes the power among the various charging stations (CSs) and each CS finally distributes the power among the individual batteries based on droop participation factor. Moreover, the charging and discharging rates of batteries are controlled to achieve the desired power flow between the MG and CSs. The simulation results show the efficacy of the controller to meet the desired power at the MG. The proposed system is compared with an existing system to prove the efficacy of the proposed controller. Moreover, all the critical cases have been considered, such as the sudden failure of any generating unit. It has been observed that due to sudden failure of any generating unit, CSs manages power of the MG by altering charging and discharging rate of the batteries.
Learning design som systematisk alternativ til one-hit wonders – implementering af educational it i de våde fag på Aarhus Universitet

Aarhus Universitet (AU) vedtog i 2011 en ambitios politik for educational it med en raekke indsatsomrader, hvoraf saerligt anskaffelsen og implementeringen af en faelles e-laeringsplatform, uddannelse af universitetets undervisere samt udvikling af undervisningen er af hojeste prioritet (Aarhus Universitet, 2011). Pga. begraensede midler har der imidlertid vaeret behov for en ekstraordinaer systematisk og effektiv made at handtere implementerings- og udviklingsarbejdet pa. Dette bevirkede, at Science and Technology (ST) i 2013 udviklede et learning design-framework, STREAM (Godsk, 2013), til at guide transformeringen af kurser og som et omdrejningspunkt for uddannelse af undervisere pa fakultetets adjunktpaedagogikum. I 2015 blev STREAM endvidere adopteret af Faculty of Health (HE) og her yderligere operationaliseret til et sakaldt toolkit vha. Dee Finks castle-top diagram for saledes at muliggore eksplicit design af sammenhaengende undervisningsforlob (Conole & Fill, 2005; Fink, 2013). En operationalisering, der betod, at learning design-tilgangen ogsa kunne benyttes til at paedagogisk kvalificere implementeringen og ibrugtagningen af universitetets nye e-laeringsplatform, Blackboard Learn. I artiklen beskriver vi vores learning design-framework, STREAM, de dertilhorende toolkits ved de vade fag (dvs. fag pa HE og ST ved AU), samt tre cases omkring hvorledes framework er anvendt til savel uddannelse af undervisere og transformering af kurser. Ydermere reflekteres der over learning design-tilgangen som metode til systematisk og effektiv implementering og udvikling af educational it ved et universitet.
Metal Defect Detection Based on Yolov5

Metal surface defect detection has been a challenge in the industrial field. The current metal surface defect algorithms target only at a few types of defects and fail to perform well on defects with different scales. In this paper, a large number of metal surface defects are studied based on GC10-DET data set. An improved yolov5 detection network is designed targeting defects of various scales, especially of small-scaled objects, using a specific data enhancement method to regularize and an effective loss function to address data imbalance caused by small-scaled object defects. Finally, the comparative experiment on GC10-DET data set proves the major improvements on accuracy superiority of the proposed method.
The Echo Model and Simulation Method of Impulse Radar Fuze Based on Attribute Scattering Center Model

This article focuses on solving the problem of performance evaluation and algorithm verification of ultra-bandwidth impulse radar fuze. A modeling and simulation method for the scattering echo of an impulse radar target is proposed. We distinguish the scattering center of the extended target and construct its echo based on the attribute scattering center model. Besides, the position variation characteristics of the scattering center, and the azimuth dependence of the scattering amplitude are fully considered. At the same time, the motion information of the target is modulated to obtain the target scattering echo of the impulse radar. We generate the scattering echo of the typical streamlined moving target at different observation angles by calculation. The changes of the echo position and amplitude of each scattering center are consistent with the scattering echo model. We use the generated echo data to image, and the imaging results are basically consistent with the shape of the target, which verifies the effectiveness of the proposed method.
A novel distributed secondary voltage control method for AC microgrids based on voltage observer

Since the islanded AC Microgrid is affected by the impedance of line, it is difficult to coordinate the voltage regulation and reactive load distribution. A distributed secondary voltage controller based on observer is proposed for isolated AC Microgrids, and it does not need more voltage communications. This method can guarantee the estimated average voltage equals to average of actual output voltages that can converge to the nominal values, and realize accurate proportional load sharing in a microgrid. Then, the convergence of the voltage observer is proved. Finally, the proposed control method is verified by time-domain simulation.
Relative straggle of sputtering parameters as a potential measure of power conversion efficiency of Perovskite materials

The growing demand for electricity necessitates the development of time-efficient computational methods in search of better solar energy-harvesting materials. One challenge has been ways to improve the power conversion efficiencies (PCEs) of potential harvesters.In this paper, an ion sputtering approach was used to study the problem of estimation of the power conversion efficiencies (PCEs) of perovskite solar device materials through their surface sputtering signatures. This rests on a hypothesis that the similarity between the phenomenon of photon excitation from light-sensitive absorbers, leading to electricity generation, and ejection of energetic particles from sputtered surfaces, could be exploited to estimate PCE. Hence, a Monte Carlo (MC) simulation of the sputtering of surfaces of perovskite materials with energetic ions was performed; and a number of quantities that could be used for the estimation of PCE were calculated. The results indicated that the relative straggle of surface sputtering parameters of the materials could be an effective measure of the theoretical maximum power conversion efficiencies of these materials. In particular, the relative straggle of the projected range of the ions in the respective materials was found to be close to reported experimental results of PCEs in the literature.
Hierarchical Attention Networks for Multimodal Machine Learning

The Visual Question Answering (VQA) task is to infer the correct answer to a free-form question based on the given image. This task is challenging because it requires model handling both visual and textual information. Most successful attempts on VQA task have been achieved by using attention mechanism which can capture inter-modal and intra-modal dependencies. In this paper, we raise a new attention-based model to solve VQA. We use question information to guide model concentrate on special regions and attribute and hierarchically reason the answer. We also propose multi-modal fusion strategy based on co-attention method to fuse both visual and textual information. Under the same experimental conditions, extensive experiments on VQA-v2.0 dataset illustrate our method performance exceeds the performance of some state-of-the-art methods of the same experimental conditions.
Angle Steel Tower Bolt Installation Detection Based on Simplified Attention Mechanism

Incorporating position and channel attention into the image semantic segmentation network is an important strategy to improve the segmentation effect. However, there is a huge computational overhead in counting the correlation between any two positions or channels. To address the issue, this paper proposes a simplified attention mechanism network integrating position attention and channel attention. In addition, to further optimize the segmentation results, this paper adopts a new data-dependent upsampling method to expand the high-level feature map to the input size. Compared with the traditional attention network, our method reduces 32.6% of parameter and calculation amount, and obtains an average cross-comparison result of 95.42% on the dataset of angle steel tower bolt.
Research on Super-resolution Location Technology of Leakage Source Based on Ultrasonic Array Sensor

Leakage in the pressure structure is a problem that must be paid attention to, especially in some special applications, such as spacecraft, which will have a serious impact on the entire system. This paper presents a new method of leak source location based on a single sensor array. The elastic wave excited by the leakage is collected by an array sensor placed on the wall, and then the frequency-space characteristics of these signals are analyzed to obtain the spatial point that meets these characteristics, that is, the spatial location of the leakage source. At the same time, combining the super-resolution technology and the randomness of the signal, integrating the distribution maps of the positioning results in different time periods, and finally obtaining a high-precision estimation of the location of the leakage source. The experimental results show that, under the conditions considered in this paper, the average value of the absolute positioning error on a square meter board is about 2 cm.
Dynamic response of transmission line conductors under downburst and synoptic winds

In the current study, dynamic and quasi-static analyses were performed to investigate the response of multiple-spanned and single-spanned transmission line conductors under both downburst and synoptic winds considering different wind velocities and different length spans. Two critical downburst configurations, recommended in the literature and expected to cause maximum conductor reactions, were considered in the analyses. The objective of the study was to assess the importance of including the dynamic effect when predicting the conductor`s reactions on the towers. This was achieved by calculating the mean, the background and the resonant reaction components, and evaluating the contribution of the resonant component to the peak reaction. The results show that the maximum contribution of the resonant component is generally low (in the order of 6%) for the multiple-spanned system at different wind velocities for both downburst and synoptic winds. For the single-spanned system, the result show a relatively high maximum contribution (in the order of 16%) at low wind velocity and a low maximum contribution (in the order of 6%) at high wind velocity for both downburst and synoptic winds. Such contributions may justify the usage of the quasi-static approach for analyzing transmission line conductors subjected to the high wind velocities typically used for the line design.
Cultural Differences in Systems Engineering: What They Are, What They Aren’t, and How to Measure Them Verifying Training Programs: Improving Integrated Product Development Teams

Despite frequent use of the term culture in contemporary engineering practice, there is currently no reliable way of understanding what constitutes true shared beliefs and meanings that impact how groups of Systems Engineers spread across multiple departments and countries work together. This paper develops a framework to address that gap and measure cultural variation in Systems Engineering contexts. Drawing from the field of cultural anthropology, we demonstrate methods to empirically measure cultural consensus and divergence related to fostering collaboration, understanding business success or failure, and identifying characteristics of effective cross-functional teams. The discussion identifies substantive content of what real organizational cultures are, where they are different, and where they overlap. That quantified content feeds culture as a rigorous element of organizational strategy into global alignment and process excellence or six sigma initiatives targeted at changing team behavior. The result is a framework for conducting rigorous, empirical investigations to truly understand the human dimensions that are critical to effective Systems Engineering practice. We discuss areas this approach applies to global process alignment and team effectiveness.
Improvement for Road Traffic Safety Gray Clustering Evaluation Method

As determining the gray values, the tra-ditional gray clustering evaluation method compares the evaluation results among the samples due to con-strains related to the sample data, which fails to tru-ly realize the evaluation purposes. In order to im-prove the traditional method, this paper develops a reasonable and effective evaluation standard through a review of the related evaluation standards and by improving the traditional method in determining the gray values, so as to obtain a more reasonable stan-dard which yields more accurate and reasonable re-sults. The improved method has a theoretical basis and its algorithm is simple, and easier to execute, with results that can better reflect the traffic safety situation on urban streets. Applications of this method to traffic safety evaluation in three cities have provided more promising outcomes.
Optimal Allocation of Aerial Target Detection and Attack in Cooperative Multi-fighter Air Combat

To solve the problem of cooperative guidance in information-based cooperative multi-fighter air combat,the model of detection and attack task allocation was built.The detection task allocation model was first built by the battlefield situation and the detection capability of radar.Then,the attack task allocation was built by the battlefield situation and the engagement capability of weapon.Finally,the multi objective decision problem was translated into single objective optimization.Particle swarm optimization algorithm was designed to solve the problem by building the mapping between particles.Simulation results indicate that the algorithm is feasible and effective.