Miracle-dz/UniFAR-FTUs huggingface dataset:
ANÁLISE ELASTOPLÁSTICA COM NÃO-LINEARIDADE GEOMÉTRICA DE ESTRUTURAS ATRAVÉS DE ELEMENTOS HEXAÉDRICOS TRI-LINEARES COM UM PONTO DE INTEGRAÇÃO

SCHMIDT, D. Análise Elastoplástica com Não-Linearidade Geométrica de Estruturas Através de Elementos Hexaédricos Tri-Lineares com Um Ponto de Integração. 2006. Dissertação (Mestrado em Engenharia Civil) – Programa de PósGraduação em Engenharia Civil, UFRGS, Porto Alegre. The main purposes of this work are the formulation and application of a computational algorithm for the elastoplastic static analysis of structures, including finite displacements and rotations. An hexahedrical isoparametric finite element with eight nodes and one-point quadrature is used. Mechanisms to avoid hourglass modes as well as volumetric and shear locking are introduced. A corotational formulation is employed to deal with the geometrically nonlinear analysis, whereas an explicit algorithm (based in Euler’s scheme) is implemented for the elastoplastic analysis. The applied constitutive models include the Mohr-Coulomb as well as the von Mises yield criterion with isotropic hardening. Numerical examples with highly nonlinear behavior are presented to demonstrate the range of applicability of the formulation.
Mineral composition, principal polyphenolic components, and evaluation of the anti-inflammatory, analgesic, and antioxidant properties of Cytisus villosus Pourr leaf extracts

Abstract Cytisus villosus Pourr. (C. villosus) is a medicinal plant belonging to the Fabaceae family, which grows in the Mediterranean area. It is used in traditional medicine against diseases related to inflammation. The objective of the present study was to identify the mineral and polyphenolic composition as well as to evaluate some biological properties including antioxidant, anti-inflammatory, and analgesic activities of C. villosus leaf aqueous extract. The chemical constituents were identified and quantified using ultra performance liquid chromatography-electrospray ionization tandem mass spectrometry (UPLC-ESI-MS/MS) methods. The antioxidant properties of C. villosus leaves were tested using reducing power (RP), 2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS), and 2,2′-diphenyl-1-picrylhydrazyl (DPPH) assays. The anti-inflammatory potency was evaluated in vitro and in vivo using the albumin denaturation test and the carrageenan test, respectively. Furthermore, the analgesic effect was performed in vivo using tail flick, acetic acid-induced contortion, and plantar tests. Mineralogical analysis revealed that potassium and calcium were the most abundant minerals. The analysis and quantification of the phytochemical composition using UPLC-ESI-MS/MS showed that quinic acid (57.478 ± 1.72 mg/kg) was the major compound of the aqueous extract, followed by salicylic acid (17.38 ± 0.2 mg/kg), isoquercetin (16.895 ± 1.01 mg/kg), and gallic acid (15.914 ± 1.51 mg/kg). The extracts showed potent antioxidant activity for all tests used. The highest antioxidant activity was recorded for the DPPH, ABTS and RP methods, with an IC50 of 3.94 ± 0.09, 2.88 ± 0.07, and 1.94 ± 0.10 μg/mL, respectively. Additionally, using the most frequent analgesic assays, the aqueous extract at a dose of 500 mg/kg exhibited a potent analgesic activity. Notably, an interesting inhibition of albumin denaturation was recorded with an IC50 of 383.94 μg/mL, corroborating the in vivo test. Overall, the results presented here may represent a scientific basis for the traditional use of C. villosus in the treatment of inflammation-related diseases.
ROBUST SLIDING-MODE CONTROL FOR NONLINEAR FLEXIBLE ARM USING NEURAL NETWORK

Abstract – This study addresses the design and properties of a sliding-mode neural-network control (SMNNC) system for a nonlinear flexible arm that is driven by a permanent magnet (PM) synchronous servo motor. First, the dynamic model of a flexible arm system with a tip mass is introduced. When the tip mass of the flexible arm is a rigid body, not only bending vibration but also torsional vibration occurr. In this study, the vibration states of the nonlinear system are assumed to be unmeasurable, i.e., only the actuator position can be acquired to feed into a suitable control system for stabilizing the vibration states indirectly. Then, a SMNNC scheme without the feedback of the vibration measure is proposed to control the motor-mechanism coupling system for periodic motion. All adaptive learning algorithms in the SMNNC system are derived in the sense of Lyapunov stability analysis, so that the system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed control scheme is verified by both numerical simulation and experimental results.
pimDB: From Main-Memory DBMS to Processing-In-Memory DBMS-Engines on Intelligent Memories

The performance and scalability of modern data-intensive systems are limited by massive data movement of growing datasets across the whole memory hierarchy to the CPUs. Such traditional processor-centric DBMS architectures are bandwidth- and latency-bound. Processing-in-Memory (PIM) designs seek to overcome these limitations by integrating memory and processing functionality on the same chip. PIM targets near- or in-memory data processing, leveraging the greater in-situ parallelism and bandwidth. In this paper, we introduce pimDB and provide an initial comparison of processor-centric and PIM-DBMS approaches under different aspects, such as scalability and parallelism, cache-awareness, or PIM-specific compute/bandwidth tradeoffs. The evaluation is performed end-to-end on a real PIM hardware system from UPMEM.
S.P.O.T: A Game-Based Application for Fostering Critical Machine Learning Literacy Among Children

This paper describes S.P.O.T. , a game-based application for promoting children's practical understanding of ML concepts and applications. Current tools for teaching ML in K-12 engage students in playful exploration of ML mechanisms and teach ML from a cognitive perspective. However, in S.P.O.T, learners interact with ML within real-life sociopolitical contexts and examine how ML predictions impact their daily lives and communities. Through the immersion of stories that mirror children's lived experiences, S.P.O.T. provides elementary school aged children with opportunities to learn how machine learning applications function and develop children's abilities to critically examine, question, and reimagine the consequences of ML decisions in the real world.
Thread-Level Attack-Surface Reduction

Existing debloating techniques designed to prevent buffer-overflow exploits through return-oriented programming do not differentiate roles within a process or binary, allowing all threads access to the full program functionality. For example, a worker thread that handles client connections (highest attack exposure) still has access to all the code that the management thread needs (highest potential fallout). We introduce thread-level attack-surface reduction (TLASR), a dynamic, context-aware approach that eliminates unused code on a thread level. For this, we (permanently or temporarily) eliminate parts of the text segment (both in shared libraries and the main binary) and use the mmview Linux extension to support multiple text-segment views in a single process. We reduce the executable code visible from a single thread in MariaDB, Memcached, OpenSSH, and Bash by 84 to 98.4 percent. As a result, the number of ROP gadgets decreases significantly (78–97%), with TLASR rendering an auto-ROP utility ineffective in all investigated benchmarks and eliminating all CVE-related functions ever reported for glibc in 97 percent of the cases.
Optimistic No-regret Algorithms for Discrete Caching

We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle. The successive file requests are assumed to be generated by an adversary, and no assumption is made on the accuracy of the oracle. We provide a universal lower bound for prediction-assisted online caching and proceed to design a suite of policies with a range of performance-complexity trade-offs. All proposed policies offer sublinear regret bounds commensurate with the accuracy of the oracle. In this pursuit, we design, to the best of our knowledge, the first optimistic Follow-the-Perturbed leader policy, which generalizes beyond the caching problem. We also study the problem of caching files with different sizes and the bipartite network caching problem.
“This is So Cool”: Exploring Identity Development and Roles for Middle School Girls of Color Within a STE(A)M-focused Summer Camp

This pilot study analyzes the ways in which students' self-perceptions and roles impact how they interact within a STE(A)M maker workshop. It is centered on a case study of two teenage girls of color, who participated in a 1-week summer enrichment opportunity focused around digital and physical coding, making, arts and design in 2019. The students that participated were rising ninth graders enrolled in an inaugural Upward Bound summer extension program aimed at providing STEM learning opportunities for younger learners. We utilized interaction analysis to understand learners’ interactions and self-expressions as they worked separately with other students on some of the initial learning activities, and then later together as a team to design a project of their own ideation and negotiation. Findings support key changes in their self-perceptions as well as their own sense of identity around STEM and computing. We discuss these findings in light of relevant theories and frameworks in the field, and explore the potential for future directions with this work.
Digital Drum Circles: Relational CS Education through Music Making

This demo presents a “digital drum circle”, a collection of bucket drums augmented with sensors that form a collective digital musical instrument. Through the drum circle prototype, we are exploring an alternative approach to computer science (CS) education that draws from cultural practices of collaborative music-making to create more engaging and meaningful learning experiences for young people. Within this context, we are interested in understanding how CS education might be remade through more equitable and culturally sustaining pedagogy approaches grounded in music and the performing arts.
Robust Adaptive Pseudo Affine Projection Algorithm for Censored Regression

Currently censored observation systems have widely used in various business applications related to adaptive signal processing, but the traditional adaptive processing algorithm has poor convergence performance in identifying censored regression systems. The least mean square algorithm of censored regression (CR-LMS) has been proposed to solve the above problem. However, the CR-LMS algorithm will not achieve good convergence under colored inputs. Therefore, a robust pseudo affine projection algorithm based on censored regression (CR-R-PAP) is presented for the parameter estimation problem of censored regression systems under colored inputs. Compared with the pseudo affine projection algorithm based on the censored regression (CR-PAP) algorithm, the CR-R-PAP algorithm can exclude adverse effects and improve convergence performance in environments containing impulse noise. The computer simulation results show that the performance of the proposed algorithm is better than the existing algorithm in the pulse environment with different background noise.
The SmartGame: mixing digital and tangible to foster math education and social interaction

In the current Work in Progress we present the SmartGame, a gamified web-app designed to support children in learning math and connected to an already existing tangible IOT device called SMARTER. Through introducing a cooperative modality and the union of digital and tangible advancements in technology, we also aim to foster interpersonal relationships in children from primary school, who suffered a lack of social interactions in person due to the recent pandemic. In the current project, we will study different possibilities of cooperation within the SmartGame, along with the ways in which teachers can personalize the activities and the gamification design based on students’ needs. Finally, we will test the web-app usability, user experience, effectiveness in supporting students’ education, and outcomes in fostering interpersonal relationships.
Developing Machine Learning Agency Among Youth: Investigating Youth Critical Use, Examination, and Production of Machine Learning Applications

Abstract. Young people are surrounded by machine learning (ML) devices and their lived experiences are increasingly shaped by the ML technologies that are ever-present in their lives. As innovations in machine learning technologies continue to shape society, it raises important implications for what young people learn, their career trajectories, and the required literacies they need to thrive in this changing occupational environment. Youth are particularly vulnerable to the impact of ML and very little has been done to empower them to critically engage in the discourse surrounding the next generation of technologies that have a marked potential to shape their lives for better or worse. My dissertation work seeks to develop youth autonomy and agency around ML by designing an intervention that supports youth critical use, examination, and production of ML applications in the context of promoting self-expression and social good. I will conduct a qualitative single case study research and collect multiple forms of data using interviews, story completions, digital artifacts, observations, and focus group discussions. These data sources will allow me to conduct an intensive analysis and investigation of how youth populations can be supported to develop the skills, practices and critical consciousness needed to effectively engage with machine learning technologies. Through my research, I also hope to advance the literature on how young people creatively collaborate with ML and use ML for self-expression.
Iterative Algorithm for Interference Detection based on Removing Targets for Automotive Radar

Nowadays most of automotive radars use frequency modulated continuous wave (FMCW) waveform for autonomous driving. However, they cause interference with each other if their operating bands overlap. It is important to propose an efficient algorithm for interference detection. Common algorithms can hardly detect the interference with similar power to or a little bit smaller power than power of target return signal. To solve the problem, the paper proposes an iterative algorithm for interference detection based on removing targets. Doppler-Range Processing (DRP) is used to estimate the parameters of targets accurately which helps remove targets because DRP is suited for not only low-speed moving targets but also high-speed moving targets. The sidelobe of peak of target appears in the Rang-Velocity image because data is missed which is caused by nulling interference. The paper uses the complex amplitude, the range and the velocity of the target estimated by DRP in the iterative algorithm to restore the missing data.
Designing Together, Miles Apart: A Longitudinal Tabletop Telepresence Adventure in Online Co-Design with Children

Children’s online co-design has become prevalent since COVID-19. However, related research focuses on insights gained across several shorter-term projects, rather than longitudinal investigations. To explore longitudinal co-design online, we engaged in participatory design with children (ages 8 - 12) for 20 sessions in two years on a single project: an online collaboration platform with tabletop telepresence robots. We found that (1) the online technology space required children to play a role as technology managers and troubleshooters, (2) the home setting shaped online social dynamics, and (3) providing children the ability to choose their design techniques prevented gridlock from situational uncertainties. We discuss how each finding resulted from interplay between our long-term technology design and online co-design processes. We then present insights about the future of online co-design, a conceptual model for longitudinal co-design online, and describe opportunities for further longitudinal online co-design research to generate new methods, techniques, and theories.
Scalar dispersion by coherent structures in uniformly sheared flow generated in a water tunnel

ABSTRACT In order to investigate the role of coherent structures as mechanisms of scalar dispersion, we studied measurements of a passive scalar plume released in a uniformly sheared turbulent flow generated in a water tunnel. The flow had homogeneous turbulence properties in the measurement domain and contained hairpin vortices similar to those in boundary layers, and so was an ideal test bed to study the effects of coherent structures on turbulent dispersion, free from the effects of inhomogeneities or boundaries. Measurements of the velocity and concentration fields were acquired simultaneously using stereo particle image velocimetry and planar laser-induced fluorescence. We found that dye was preferentially located far away from vortices and was less likely to appear in close proximity to vortices, which is attributed to the high dissipation at the periphery of the vortices. However, we also found that dye was not directly correlated with the uniform momentum zones in the flow, suggesting a more complex relationship exists between these zones, the locations of vortices, and dye transport. Considering scalar flux events rather than simply the presence of dye as our condition of interest, a conditional eddy analysis demonstrated that hairpin vortices are responsible for the large scalar flux events as well as the large Reynolds stress events in the flow. The fact that the Reynolds stress was correlated with the scalar flux further confirmed that coherent structures are dominant mechanisms for scalar transport. Furthermore, we found that the scalar flux vector was preferentially inclined by 155° and −25° with respect to the streamwise direction, and was thus approximately orthogonal to the planes of the legs of the most common upright and inverted hairpin structures in the flow. These findings demonstrate that coherent structures play an important and intricate role in turbulent diffusion.
Query Processing on Gaming Consoles

ACM Reference Format: Wei Cui, Qianxi Zhang, Spyros Blanas, Jesus Camacho-Rodriguez, Brandon Haynes,, Yinan Li, Ravi Ramamurthy, Peng Cheng, Rathijit Sen, Matteo Interlandi. 2023. Query Processing onGaming Consoles. In 19th International Workshop on Data Management on New Hardware (DaMoN ’23), June 18–23, 2023, Seattle, WA, USA. ACM, New York, NY, USA, 3 pages. https://doi.org/ 10.1145/3592980.3595313
Detecting and Measuring Aggressive Location Harvesting in Mobile Apps via Data-flow Path Embedding

Today, location-based services have become prevalent in the mobile platform, where mobile apps provide specific services to a user based on his or her location. Unfortunately, mobile apps can aggressively harvest location data with much higher accuracy and frequency than they need because the coarse-grained access control mechanism currently implemented in mobile operating systems (e.g., Android) cannot regulate such behavior. This unnecessary data collection violates the data minimization policy, yet no previous studies have investigated privacy violations from this perspective, and existing techniques are insufficient to address this violation. To fill this knowledge gap, we take the first step toward detecting and measuring this privacy risk in mobile apps at scale. Particularly, we annotate and release thefirst dataset to characterize those aggressive location harvesting apps and understand the challenges of automatic detection and classification. Next, we present a novel system, LocationScope, to address these challenges by(i) uncovering how an app collects locations and how to use such data through a fine-tuned value set analysis technique,(ii) recognizing the fine-grained location-based services an app provides via embedding data-flow paths, which is a combination of program analysis and machine learning techniques, extracted from its location data usages, and(iii) identifying aggressive apps with an outlier detection technique achieving a precision of 97% in aggressive app detection. Our technique has further been applied to millions of free Android apps from Google Play as of 2019 and 2021. Highlights of our measurements on detected aggressive apps include their growing trend from 2019 to 2021 and the app generators' significant contribution of aggressive location harvesting apps.
LUNAR: A Native Table Engine for Embedded Devices

Embedded systems have evolved tremendously in recent years. We perform a study on SQLite and find that the multiple layers of abstraction drastically reduce bandwidth utilization. To minimize the bandwidth loss in the I/O path, we propose Lunar, a novel native table storage engine. Lunar performs a cross-layer design across the database and file system to avoid the pitfalls of multi-layer abstraction while providing SQL-compatible APIs. It employs a type-aware storage layout that considers the access patterns of different data types. Then, Lunar designs a variable-size allocator to reduce fragmentation and optimize RAM and I/O bandwidth usage. Further, considering the limited resources on embedded devices, Lunar employs a modular architecture that enables selecting modules on demand. It also offers optional consistency modes to make a trade-off between resource consumption and consistency. Experiments show that Lunar achieves higher bandwidth utilization, outperforming state-of-the-art approaches while consuming fewer resources.
Towards Data-Based Cache Optimization of B+-Trees

The rise of in-memory databases and systems with considerably large memories and cache sizes requires the rethinking of the proper implementation of index structures like B+-trees in such systems. While disk block-sized nodes and binary search were considered as good in the past, smaller node sizes and cache-friendly linear search within nodes can be noticeably more performant nowadays. Considering the probabilistic distribution of lookup values to the B+-tree as part of a memory-friendly and cache-aware layout is a consequent next step, which is studied in this paper. Favoring frequently visited nodes and paths in the regard of cache hits can improve the overall performance of the tree and, thus, of the entire database system. We provide such an optimized B+-tree layout, which takes the probabilistic distribution of the lookup values as a basis. Experimental evaluation shows that choosing rather small node sizes in combination with our optimization algorithm can improve the performance by up to in comparison to a default baseline.