bluuebunny/crossref_metadata_2025 huggingface dataset:
Blood pressure trends following birth in infants born under 25 weeks’ gestational age: a retrospective cohort study
Authors: Persad Emma , Brindefalk Björn , Rakow Alexander
Published: March 2024

Objective

The aim of our study was to describe postnatal blood pressure (BP) trends and evaluate relevant dynamics and outcomes for a subgroup of extremely preterm (EPT) infants.

Design

Retrospective observational cohort study.

Setting

Patients admitted to Karolinska University Hospital Stockholm.

Patients

EPT infants born between 22+0 and 24+6 weeks’ gestational age (GA) undergoing invasive, continuous BP monitoring through an umbilical arterial catheter.

Main outcome measures

Physiological BP trends, the influence of cardiovascular active interventions and fluid boluses on BP, and relevant adverse outcomes, including intraventricular haemorrhage (IVH), necrotising enterocolitis (NEC) and death, were mapped over the first week of life.

Results

We included 125 infants between January 2009 and November 2021. Mean BP values were 31 mm Hg, 32 mm Hg and 35 mm Hg, at 3 hours, 24 hours and 48 hours, respectively. A pronounced BP dip and nadir were observed around 20 hours, with a mean BP value of 32 mm Hg. 84% received fluid boluses within the first week of life; however, we could not observe any noteworthy change in BP following administration. Only 8% of patients received cardiovascular active drugs, which were too few to infer drug-specific effects. Overall, 48% developed IVH, 15% developed NEC and 25% died.

Conclusions

Approximating clinically acceptable mean BP values using GA gives underestimations in these infants. The postnatal BP dip should be regarded as a physiological phenomenon and not automatic grounds for interventions which may momentarily stabilise BP but have no appreciable short-term or long-term effects. Further studies are warranted for improved understanding of clinically relevant trends and outcomes.
Guselkumab provides durable improvement across psoriatic arthritis disease domains: post hoc analysis of a phase 3, randomised, double-blind, placebo-controlled study
Authors: Coates Laura C , Gossec Laure , Zimmermann Miriam , Shawi May , Rampakakis Emmanouil , Shiff Natalie J , Kollmeier Alexa P , Xu Xie L , Nash Peter , Mease Philip J , Helliwell Philip S
Published: March 2024

Objective

Evaluate long-term guselkumab effectiveness across Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-recognised domains/related conditions of psoriatic arthritis (PsA).

Methods

Post hoc analyses used data from DISCOVER-2 (NCT03158285) biologic/Janus-kinase inhibitor-naïve participants with active PsA (≥5 swollen/≥5 tender joints, C-reactive protein ≥0.6 mg/dL), randomised (1:1:1) to guselkumab every 4 or 8 weeks (Q4W/Q8W) or placebo with crossover to guselkumab. Outcomes aligned with key GRAPPA-recognised domains of overall disease activity, peripheral arthritis, axial disease, enthesitis/dactylitis and skin psoriasis (nail psoriasis was not evaluated). PsA-related conditions (inflammatory bowel disease (IBD)/uveitis) were assessed via adverse events through W112. Least squares mean changes from baseline through W100 in continuous outcomes employed repeated measures mixed-effects models adjusting for baseline scores. Binary measure response rates were determined with non-responder imputation for missing data.

Results

442/493 (90%) of guselkumab-randomised patients completed treatment through W100. Following early reductions in disease activity with guselkumab, durable improvements were observed across key PsA domains (swollen/tender joints, psoriasis, spinal pain, enthesitis/dactylitis) through W100. Response rates of therapeutically relevant targets generally increased through W100 with guselkumab Q4W/Q8W: Disease Activity Index for PsA low disease activity (LDA) 62%/59%, enthesitis resolution 61%/70%, dactylitis resolution 72%/83%, 100% improvement in Psoriasis Area and Severity Index 59%/53%, Psoriatic Arthritis Disease Activity Score LDA 51%/49% and minimal disease activity 38%/40%. Through W112, no cases of IBD developed among guselkumab-randomised patients and one case of uveitis was reported.

Conclusion

In biologic-naïve patients with active PsA, guselkumab provided early and durable improvements in key GRAPPA-recognised domains through 2 years, with substantial proportions achieving important treatment targets.
Grafite: Taming Adversarial Queries with Optimal Range Filters
Authors: Costa Marco , Ferragina Paolo , Vinciguerra Giorgio
Published: March 2024

Range filters allow checking whether a query range intersects a given set of keys with a chance of returning a false positive answer, thus generalising the functionality of Bloom filters from point to range queries. Existing practical range filters have addressed this problem heuristically, resulting in high false positive rates and query times when dealing with adversarial inputs, such as in the common scenario where queries are correlated with the keys.

We introduce Grafite, a novel range filter that solves these issues with a simple design and clear theoretical guarantees that hold regardless of the input data and query distribution: given a fixed space budget of B bits per key, the query time is O(1), and the false positive probability is upper bounded by l/2B-2, where l is the query range size. Our experimental evaluation shows that Grafite is the only range filter to date to achieve robust and predictable false positive rates across all combinations of datasets, query workloads, and range sizes, while providing faster queries and construction times, and dominating all competitors in the case of correlated queries.

As a further contribution, we introduce a very simple heuristic range filter whose performance on uncorrelated queries is very close to or better than the one achieved by the best heuristic range filters proposed in the literature so far.
NOC-NOC: Towards Performance-optimal Distributed Transactions
Authors: Liu Si , Multazzu Luca , Wei Hengfeng , Basin David A.
Published: March 2024

Substantial research efforts have been devoted to studying the performance optimality problem for distributed database transactions. However, they focus just on optimizing transactional reads, and thus overlook crucial factors, such as the efficiency of writes, which also impact the overall system performance. Motivated by a recent study on Twitter's workloads showing the prominence of write-heavy workloads in practice, we make a substantial step towards performance-optimal distributed transactions by also aiming to optimize writes, a fundamentally new dimension to this problem. We propose a new design objective and establish impossibility results with respect to the achievable isolation levels. Guided by these results, we present two new transaction algorithms with different isolation guarantees that fulfill this design objective. Our evaluation demonstrates that these algorithms outperform the state of the art.
Sub-optimal Join Order Identification with L1-error
Authors: Izenov Yesdaulet , Datta Asoke , Tsan Brian , Rusu Florin
Published: March 2024

Q-error -- the standard metric for quantifying the error of individual cardinality estimates -- has been widely adopted as a surrogate for query plan optimality in recent work on learning-based cardinality estimation. However, the only result connecting Q-error with plan optimality is an upper-bound on the cost of the worst possible query plan computed from a set of cardinality estimates---there is no connection between Q-error and the real plans generated by standard query optimizers. Therefore, in order to identify sub-optimal query plans, we propose a learning-based method having as its main feature a novel measure called L1-error. Similar to Q-error, L1-error requires complete knowledge of true cardinalities and estimates for all the sub-plans of a query plan. Unlike Q-error, which considers the estimates independently, L1-error is defined as a permutation distance between true cardinalities and estimates for all the sub-plans having the same number of joins. Moreover, L1-error takes into account errors relative to the magnitude of their cardinalities and gives larger weight to small multi-way joins. Our experimental results confirm that, when L1-error is integrated into a standard decision tree classifier, it leads to the accurate identification of sub-optimal plans across four different benchmarks. This accuracy can be further improved by combining L1-error with Q-error into a composite feature that can be computed without overhead from the same data.
Optimizing Dataflow Systems for Scalable Interactive Visualization
Authors: Yang Junran , Joo Hyekang Kevin , Yerramreddy Sai , Moritz Dominik , Battle Leilani
Published: March 2024

Supporting the interactive exploration of large datasets is a popular and challenging use case for data management systems. Traditionally, the interface and the back-end system are built and optimized separately, and interface design and system optimization require different skill sets that are difficult for one person to master. To enable analysts to focus on visualization design, we contribute VegaPlus, a system that automatically optimizes interactive dashboards to support large datasets. To achieve this, VegaPlus leverages two core ideas. First, we introduce an optimizer that can reason about execution plans in Vega, a back-end DBMS, or a mix of both environments. The optimizer also considers how user interactions may alter execution plan performance, and can partially or fully rewrite the plans when needed. Through a series of benchmark experiments on seven different dashboard designs, our results show that VegaPlus provides superior performance and versatility compared to standard dashboard optimization techniques.
Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory
Authors: Li Xiaochen , Liu Weiran , Lou Jian , Hong Yuan , Zhang Lei , Qin Zhan , Ren Kui
Published: March 2024

Top-k frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-k items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy.

In this paper, we present a novel framework HG-LDP to achieve accurate Top-k item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior "accuracy-privacy-memory efficiency" tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior "accuracy-privacy-memory efficiency" tradeoff, saving 2300× memory over baseline methods when the item domain size is 41,270. Our code is anonymously open-sourced via the link.
Privacy Amplification by Sampling under User-level Differential Privacy
Authors: Fang Juanru , Yi Ke
Published: March 2024

Random sampling is an effective tool for reducing the computational costs of query processing in large databases. It has also been used frequently for private data analysis, in particular, under differential privacy (DP). An interesting phenomenon that the literature has identified, is that sampling can amplify the privacy guarantee of a mechanism, which in turn leads to reduced noise scales that have to be injected.

All existing privacy amplification results only hold in the standard, record-level DP model. Recently, user-level differential privacy (user-DP) has gained a lot of attention as it protects all data records contributed by any particular user, thus offering stronger privacy protection. Sampling-based mechanisms under user-DP have not been explored so far, except naively running the mechanism on a sample without privacy amplification, which results in large DP noises. In fact, sampling is in even more demand under user-DP, since all state-of-the-art user-DP mechanisms have high computational costs due to the complex relationships between users and records. In this paper, we take the first step towards the study of privacy amplification by sampling under user-DP, and give the amplification results for two common user-DP sampling strategies: simple sampling and sample-and-explore. The experimental results show that these sampling-based mechanisms can be a useful tool to obtain some quick and reasonably accurate estimates on large private datasets.
Modeling Shifting Workloads for Learned Database Systems
Authors: Wu Peizhi , Ives Zachary G.
Published: March 2024

Learned database systems address several weaknesses of traditional cost estimation techniques in query optimization: they learn a model of a database instance, e.g., as queries are executed. However, when the database instance has skew and correlation, it is nontrivial to create an effective training set that anticipates workload shifts, where query structure changes and/or different regions of the data contribute to query answers. Our predictive model may perform poorly with these out-of-distribution inputs. In this paper, we study how the notion of a replay buffer can be managed through online algorithms to build a concise yet representative model of the workload distribution --- allowing for rapid adaptation and effective prediction of cardinalities and costs. We experimentally validate our methods over several data domains.
SWIX: A Memory-efficient Sliding Window Learned Index
Authors: Liang Liang , Yang Guang , Hadian Ali , Croquevielle Luis Alberto , Heinis Thomas
Published: March 2024

Data stream processing systems enable querying over sliding windows of streams of data. Efficient index structures for the streaming window are a crucial building block to enable querying the sliding window for operations such as aggregation and joins. This paper proposes SWIX, a novel memory-efficient learned index for sliding windows. Unlike conventional learned indexes that rely on tree structures to achieve logarithmic query cost, SWIX has a flat structure that uses substantially less memory and enables efficient query execution while having a low cost for index maintenance when inserting (and retraining). SWIX dynamically adapts itself to the real-time distribution shifts of data streams.

SWIX outperforms existing indexes in terms of query execution time and memory footprint for workloads characterized by very frequent updates. Our results show that SWIX has a significantly smaller memory footprint than conventional, streaming, and learned indexes, using only 22% to 42% of the size compared to state-of-the-art approaches, yet outperforming them by up 1.2× to 1.6× on average (and up to 52×) in terms of query time, making it a space- and time-efficient method for indexing data streams. For concurrent learned indexes, Parallel SWIX can achieve up to 3.45× throughput with only 34% of memory consumption.
Determining the Largest Overlap between Tables
Authors: Zecchini Luca , Bleifuß Tobias , Simonini Giovanni , Bergamaschi Sonia , Naumann Felix
Published: March 2024

Both on the Web and in data lakes, it is possible to detect much redundant data in the form of largely overlapping pairs of tables. In many cases, this overlap is not accidental and provides significant information about the relatedness of the tables. Unfortunately, efficiently quantifying the overlap between two tables is not trivial. In particular, detecting their largest overlap, i.e., their largest common subtable, is a computationally challenging problem. As the information overlap may not occur in contiguous portions of the tables, only the ability to permute columns and rows can reveal it.

The detection of the largest overlap can help us in relevant tasks such as the discovery of multiple coexisting versions of the same table, which can present differences in the completeness and correctness of the conveyed information. Automatically detecting these highly similar, matching tables would allow us to guarantee their consistency through data cleaning or change propagation, but also to eliminate redundancy to free up storage space or to save additional work for the editors.

We present the first formal definition of this problem, and with it Sloth, our solution to efficiently detect the largest overlap between two tables. We experimentally demonstrate on real-world datasets its efficacy in solving this task, analyzing its performance and showing its impact on multiple use cases.
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models
Authors: Zhang Hailin , Liu Zirui , Chen Boxuan , Zhao Yikai , Zhao Tong , Yang Tong , Cui Bin
Published: March 2024

Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding compression solutions cannot simultaneously meet three key design requirements: memory efficiency, low latency, and adaptability to dynamic data distribution. This paper presents CAFE, a Compact, Adaptive, and Fast Embedding compression framework that addresses the above requirements. The design philosophy of CAFE is to dynamically allocate more memory resources to important features (called hot features), and allocate less memory to unimportant ones. In CAFE, we propose a fast and lightweight sketch data structure, named HotSketch, to capture feature importance and report hot features in real time. For each reported hot feature, we assign it a unique embedding. For the non-hot features, we allow multiple features to share one embedding by using hash embedding technique. Guided by our design philosophy, we further propose a multi-level hash embedding framework to optimize the embedding tables of non-hot features. We theoretically analyze the accuracy of HotSketch, and analyze the model convergence against deviation. Extensive experiments show that CAFE significantly outperforms existing embedding compression methods, yielding 3.92% and 3.68% superior testing AUC on Criteo Kaggle dataset and CriteoTB dataset at a compression ratio of 10000x. The source codes of CAFE are available at GitHub.
LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries
Authors: Aytimur Mehmet , Reiner Silvan , Wörteler Leonard , Chondrogiannis Theodoros , Grossniklaus Michael
Published: March 2024

Cardinality estimation is an important step in cost-based database query optimization. The accuracy of the estimates directly affects the ability of an optimizer to identify the most efficient query execution plan correctly. In this paper, we study cardinality estimation of LIKE-queries, i.e., queries that use the LIKE-operator to match a pattern with wildcards against string-valued attributes. While both traditional and machine-learning-based approaches have been proposed to tackle this problem, we argue that they all suffer from drawbacks. Most importantly, many state-of-the-art approaches are not designed for patterns that contain wildcards in-between characters. Based on past research on neural language models, we introduce the LIKE-Pattern Language Model (LPLM) that uses a new language and a novel probability distribution function to capture the semantics of general LIKE-patterns. We also propose a method to generate training data for our model. We demonstrate that our method outperforms state-of-the-art approaches in terms of precision (Q-error), while offering comparable runtime performance and memory requirements.
Fast Shapley Value Computation in Data Assemblage Tasks as Cooperative Simple Games
Authors: Luo Xuan , Pei Jian , Xu Cheng , Zhang Wenjie , Xu Jianliang
Published: March 2024

In this paper, we tackle the challenging problem of Shapley value computation in data markets in a novel setting of data assemblage tasks with binary utility functions among data owners. By modeling these scenarios as cooperative simple games, we leverage pivotal probabilities to transform the computation into a problem of counting beneficiaries. Moreover, we make an insightful observation that the Shapley values can be computed using subsets of minimal syntheses within the inclusion-exclusion framework in combinatorics. Based on this insight, we develop a game decomposition approach and utilize techniques in Boolean function decomposition into disjunctive normal form. One interesting property of our method is that the time complexity depends only on the data owners participating in those minimal syntheses, rather than all the data owners. Extensive experiments with real data sets demonstrate a significant efficiency improvement for computing the Shapley values in data assemblage tasks modeled as simple games.
Cabin: A Compressed Adaptive Binned Scan Index
Authors: Chen Yiyuan , Chen Shimin
Published: March 2024

Scan is a fundamental operation widely used in main-memory analytical database systems. To accelerate scans, previous studies build either record-order or sort-order structures known as scan indices. While achieving good performance, scan indices often incur significant space overhead, limiting their use in main-memory databases. For example, the most recent and best performing scan index, BinDex, consists of a sort-order position array, which is an array of rowIDs in the value order, and a set of record-order bit vectors, representing records in pre-defined value intervals. The structures can be much larger than the base data column size.

In this paper, we propose a novel scan index, Cabin, that exploits the following three techniques for better time-space tradeoff. 1) filter sketches that represent every 2^w-2 value intervals with a w-bit sketched vector, thereby exponentially reducing the space for the bit vectors; 2) selective position array that removes the rowID array for a fraction of intervals in order to lower the space overhead for the position array; and 3) data-aware intervals that judiciously select interval boundaries based on the data characteristics to better support popular values in skewed data distributions or categorical attributes. Experimental results show that compared with state-of-the-art scan solutions, Cabin achieves better time-space tradeoff, and attains 1.70 -- 4.48x improvement for average scan performance given the same space budget.
Summarized Causal Explanations For Aggregate Views
Authors: Youngmann Brit , Cafarella Michael , Gilad Amir , Roy Sudeepa
Published: March 2024

SQL queries with group-by and average are frequently used and plotted as bar charts in several data analysis applications. Understanding the reasons behind the results in such an aggregate view may be a highly nontrivial and time-consuming task, especially for large datasets with multiple attributes. Hence, generating automated explanations for aggregate views can allow users to gain better insights into the results while saving time in data analysis. When providing explanations for such views, it is paramount to ensure that they are succinct yet comprehensive, reveal different types of insights that hold for different aggregate answers in the view, and, most importantly, they reflect reality and arm users to make informed data-driven decisions, i.e., the explanations do not only consider correlations but are causal. In this paper, we present CauSumX, a framework for generating summarized causal explanations for the entire aggregate view. Using background knowledge captured in a causal DAG, CauSumX finds the most effective causal treatments for different groups in the view. We formally define the framework and the optimization problem, study its complexity, and devise an efficient algorithm using the Apriori algorithm, LP rounding, and several optimizations. We experimentally show that our system generates useful summarized causal explanations compared to prior work and scales well for large high-dimensional data.
Reflecting on Research Practices
Authors: Pochat Victor Le
Published: March 2024

Encouraging the security research community to engage in meta-research.
From Conventional to Programmable Matter Systems: A Review of Design, Materials, and Technologies
Authors: Chafik Ahmed Amine , Gaber Jaafar , Tayane Souad , Ennaji Mohamed , Bourgeois Julien , Ghazawi Tarek El
Published: March 2024

Programmable matter represents a system of elements whose interactions can be programmed for a certain behavior to emerge (e.g., color, shape) upon suitable commands (e.g., instruction, stimuli) by altering its physical characteristics. Even though its appellation may refer to a morphable physical material, programmable matter has been represented through several approaches from different perspectives (e.g., robots, smart materials) that seek the same objective: controllable behavior such as smart shape alteration. Researchers, engineers, and artists have expressed interest in the development of smart modeling clay as a novel alternative to conventional matter and classical means of prototyping. Henceforth, users will be able to do/undo/redo forms based on computed data (CAD) or interactions (sensors), which will help them unlock more features and increase the usefulness of their products. However, with such a promising technology, many challenges need to be addressed, as programmable matter relies on energy consumption, data transmission, stimuli control, and shape formation mechanisms. Furthermore, numerous devices and technologies are created under the name of programmable matter, which may pose ambiguity to the control strategies. In this study, we determine the basic operations required to form a shape, then review different realizations using the shape shifting ability of programmable matter and their fitting classifications, and finally discuss potential challenges.
Federated Momentum Contrastive Clustering
Authors: Miao Runxuan , Koyuncu Erdem
Published: March 2024

Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a central entity, considering data privacy requirements and computational resources. Federated Learning (FL) has been developed successfully to aggregate a global model while training on distributed local data, respecting the data privacy of edge devices. However, most FL research effort focuses on supervised learning algorithms. A fully unsupervised federated clustering scheme has not been considered in the existing literature. We present federated momentum contrastive clustering (FedMCC), a generic federated clustering framework that can not only cluster data automatically but also extract discriminative representations training from distributed local data over multiple users. In FedMCC, we demonstrate a two-stage federated learning paradigm where the first stage aims to learn differentiable instance embeddings and the second stage accounts for clustering data automatically. The experimental results show that FedMCC not only achieves superior clustering performance but also outperforms several existing federated self-supervised methods for linear evaluation and semi-supervised learning tasks. Additionally, FedMCC can easily be adapted to ordinary centralized clustering through what we call momentum contrastive clustering (MCC). We show that MCC achieves state-of-the-art clustering accuracy results in certain datasets such as STL-10 and ImageNet-10. We also present a method to reduce the memory footprint of our clustering schemes.
DiPri
: Distance-Based Seed Prioritization for Greybox Fuzzing
Authors: Qian Ruixiang , Zhang Quanjun , Fang Chunrong , Yang Ding , Li Shun , Li Binyu , Chen Zhenyu
Published: March 2024

Greybox fuzzing is a powerful testing technique. Given a set of initial seeds, greybox fuzzing continuously generates new test inputs to execute the program under test and drives executions with code coverage as feedback. Seed prioritization is an important step of greybox fuzzing that helps greybox fuzzing choose promising seeds for input generation in priority. However, mainstream greybox fuzzers like AFL++ and Zest tend to neglect the importance of seed prioritization. They may pick seeds plainly according to the sequential order of the seeds being queued or an order produced with a random-based approach, which may consequently degrade their performance in exploring code and exposing bugs. In the meantime, existing state-of-the-art techniques like Alphuzz and K-Scheduler adopt complex strategies to schedule seeds. Although powerful, such strategies also inevitably incur great overhead and will reduce the scalability of the proposed technique.

In this article, we propose a novel distance-based seed prioritization approach named
DiPri
to facilitate greybox fuzzing. Specifically,
DiPri
evaluates the queued seeds according to seed distances and chooses the outlier ones, which are the farthest from the others, in priority to improve the probabilities of discovering previously unexplored code regions. To make a profound evaluation of
DiPri
, we prototype
DiPri
on AFL++ and conduct large-scale experiments with four baselines and 24 C/C++ fuzz targets, where eight are from widely adopted real-world projects, eight are from the coverage-based benchmark FuzzBench, and eight are from the bug-based benchmark Magma. The results obtained through a fuzzing exceeding 50,000 CPU hours suggest that
DiPri
can (1) insignificantly influence the host fuzzer’s capability of code coverage by slightly improving the branch coverage on the eight targets from real-world projects and slightly reducing the branch coverage on the eight targets from FuzzBench, and (2) improve the host fuzzer’s capability of finding bugs by triggering five more Magma bugs. Besides the evaluation with the three C/C++ benchmarks, we integrate
DiPri
into the Java fuzzer Zest and conduct experiments on a Java benchmark composed of five real-world programs for more than 8,000 CPU hours to empirically study the scalability of
DiPri
. The results with the Java benchmark demonstrate that
DiPri
is pretty scalable and can help the host fuzzer find bugs more consistently.
On Estimating the Feasible Solution Space of Multi-objective Testing Resource Allocation
Authors: Zhang Guofu , Li Lei , Su Zhaopin , Yue Feng , Chen Yang , Li Miqing , Yao Xin
Published: March 2024

The multi-objective testing resource allocation problem (MOTRAP) is concerned on how to reasonably plan the testing time of software testers to save the cost and improve the reliability as much as possible. The feasible solution space of a MOTRAP is determined by its variables (i.e., the time invested in each component) and constraints (e.g., the pre-specified reliability, cost, or time). Although a variety of state-of-the-art constrained multi-objective optimisers can be used to find individual solutions in this space, their search remains inefficient and expensive due to the fact that this space is very tiny compared to the large search space. The decision maker may often suffer a prolonged but unsuccessful search that fails to return a feasible solution. In this work, we first formulate a heavily constrained MOTRAP on the basis of an architecture-based model, in which reliability, cost, and time are optimised under the pre-specified multiple constraints on reliability, cost, and time. Then, to estimate the feasible solution space of this specific MOTRAP, we develop theoretical and algorithmic approaches to deduce new tighter lower and upper bounds on variables from constraints. Importantly, our approach can help the decision maker identify whether their constraint settings are practicable, and meanwhile, the derived bounds can just enclose the tiny feasible solution space and help off-the-shelf constrained multi-objective optimisers make the search within the feasible solution space as much as possible. Additionally, to further make good use of these bounds, we propose a generalised bound constraint handling method that can be readily employed by constrained multi-objective optimisers to pull infeasible solutions back into the estimated space with theoretical guarantee. Finally, we evaluate our approach on application and empirical cases. Experimental results reveal that our approach significantly enhances the efficiency, effectiveness, and robustness of off-the-shelf constrained multi-objective optimisers and state-of-the-art bound constraint handling methods at finding high-quality solutions for the decision maker. These improvements may help the decision maker take the stress out of setting constraints and selecting constrained multi-objective optimisers and facilitate the testing planning more efficiently and effectively.
The Epidemiology of Web Accessibility: Improving Internet Health by Identifying and Tackling the Root Cause of Web Accessibility Issues
Authors: Carvalho Luis P.
Published: March 2024

The epidemic of inaccessible websites presents a major challenge to equitable information access and continues to threaten our vision of an inclusive digital society. Despite decades of research, education, legislation, and policy reform globally, little progress has been made to ensure the accessibility of websites. Today's web accessibility strategies and tools are predicated on the belief that web accessibility issues are introduced through code created by the website developer and therefore focus on that website's code in isolation. However, as demonstrated in modern web security research, vulnerabilities can be introduced via dependencies to external libraries and frameworks outside of their development control. This PhD research explores an epidemiology-inspired approach to web accessibility to accurately model internet health, with the objective of identifying the root causes of accessibility barriers, how they propagate in the web ecosystem, and how to help web developers prevent and mitigate the propagation of accessibility barriers.
SIGACCESS Travel Award Report
Authors: Angelini Robin
Published: March 2024

My name is Robin Angelini and I am a Deaf graduate student enrolled at TU Wien (Vienna University of Technology), where I am in the final semester of my master's program in Informatics with a major focus on Media and Human-Centered Computing. I had the privilege of receiving the SIGACCESS Travel Award scholarship, which allowed me to attend the 25th International ACM SIGACCESS Conference on Computers and Accessibility. In this report, I share my experiences and insights from the conference.
Phenolic Content, Antioxidant Potential, and Antimicrobial Activity of Uvaria chamae (Annonaceae), a Food Plant from Burkina Faso
Authors: Kaboré Kayaba , Dibala Crépin Ibingou , Sama Hemayoro , Diao Mamounata , Somda Marius K. , Dicko Mamoudou H.
Published: March 2024

The study aimed to evaluate phenolic content and antioxidant and antibacterial potentials of the fractions of the hydroethanolic extract of Uvaria chamae leaves, a food plant from Burkina Faso. Thus, the hexane, dichloromethane, ethyl acetate, and butanol fractions of the hydroalcoholic extract after drying were used to determine phenolic compound content, antioxidant activity, and antimicrobial potential on strains of pathogenic bacteria responsible for food contamination. Phytochemical analyses were performed according to standardized methods, while antioxidant activity was evaluated by DPPH and FRAP methods. The antibacterial activity of the fractions was determined by diffusion and microdilution methods on the agar medium with gentamicin as a reference antibiotic. All the six strains, namely, Salmonella typhi ATCC 19430, Escherichia coli ATCC 8739, Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 9027, Bacillus cereus ATCC 13061, and Listeria monocytogenes ATCC 7644, were sensitive to the fractions tested. Minimum inhibitory concentrations ranged from 37 µg·mL−1 to 1.67 mg·mL−1, respectively, gentamicin and butanolic fractions, while minimum bactericidal concentrations of the fractions ranged from 0.037 to 2.500 mg·mL−1 depending on the bacterial strain. Antioxidant activity varied significantly between fractions. For DPPH free radical scavenging activity, the butanol fraction was the most active, with an IC50 of 280 μg/mL, while the lowest activity (705 μg/mL) was recorded by the hexane fraction. Those of trolox and ascorbic acid used as standards were 80 and 100 μg/mL, respectively. Ferric reducing power (FRAP) ranged from 0.34 to 0.40 mmol EAA/g extract for the hexanic and ethyl acetate fractions, respectively. Phenolic compound contents also varied significantly between fractions. Butanoic and ethyl acetate presented the best contents of total phenolics and flavonoids, respectively. Significant and positive correlations were also recorded between phenolics and antioxidant activities. The antibacterial and antioxidant activities of the active fractions would be related to their richness in bioactive compounds, including phenolic, which are powerful natural antioxidants. U. chamae leaf extracts could therefore be used as dietary supplements to boost the immune system and prevent bacterial infections.
Attenuating Atherosclerosis through Inhibition of the NF‐κB/NLRP3/IL‐1β Pathway‐Mediated Pyroptosis in Vascular Smooth Muscle Cells (VSMCs)
Authors: Li Shihuan , Li Qingjie , Zhou Qiaofeng , Li Suqin , Wang Siqi , Yao Qing , Ouyang Changhan , Liu Chao , Li Mincai
Published: March 2024

Objective. We investigated the effects of resveratrol (Res) and MCC950 on the pyroptosis of vascular smooth muscle cells (VSMCs) and the potential pathway. Methods and Results. Compared with the control (Con) group, the atherosclerosis (AS) group showed calcified nodules, which suggested that the calcification medium induced the calcification of VSMCs. VSMCs showed proliferative activity and significantly attenuated calcification under treatment with 10 μmol/L Res. The calcium salt was detected by alizarin red S staining. Res and MCC950 downregulated the calcification, inflammatory, pyroptosis, and transcription factor‐related indicators all decreased by RT‐qPCR with Western blot and immunofluorescence. The results showed that Res and MCC950 refrained the calcification of VSMCs and that Res has a better effect than MCC950. Plaques and calcium salt deposits were present in the carotid region in the control group. More calcium salt deposits were evident in the plaques of the Par group by HE staining and alizarin red S staining. The calcification indexes BMP2, Runx2, and related indexes declined by immunofluorescence, which showed parthenolide‐inhibited AS. The related protein expressions were consistent with the expression of the cell experiments. Conclusion. Our data demonstrated that inflammatory response and pyroptosis exacerbate AS and unravel the link between VSMCs and the progression of AS lesions. Res and MCC950 inhibited the calcification of VSMCs by regulating NF‐κB/NLRP3/IL‐1β signaling axis. P53 can exacerbate the AS lesions by acting on NLRP3 inflammasome and pyroptosis. Our findings supported the clinical applications of Res and MCC950 in VSMCs individuals to counteract pyroptosis and AS, and P53 inhibitors also can be a potential treatment for AS.
Structure–Activity Relationship and Stimuli-Responsiveness Behavior of Metallosupramolecular Polymers
Authors: Coulibaly Souleymane , Tchambaga Etienne Camara , Koné Aboudramane , Kouassi Adingra Francesco , Siomenan Coulibali
Published: March 2024

The widespread discourse on the circular economy has fueled a growing demand for polymeric materials characterized by mechanical robustness, sustainability, renewability, and the ability to mend defects. Such materials can be crafted using dynamic covalent bonds, albeit rarely or more efficiently through noncovalent interactions. Metal–ligand interactions, commonly employed by living organisms to adapt to environmental changes, play a pivotal role in this endeavor. Metallosupramolecular polymers (MSPs), formed through the incorporation of metal–ligand interactions, present a versatile platform for tailoring physicochemical properties. This review explores recent advancements in MSPs achieved through the assembly of (macro)monomers via reversible metal–ligand interactions. Various strategies and pathways for synthesizing these materials are discussed, along with their resulting properties. The review delves into the stimuli-responsive behavior of coordination metal–ligand polymers, shedding light on the impact of the core employed in MSPs. Additionally, it examines the influence of parameters such as solvent choice and counter-ions on the supramolecular assemblies. The ability of these materials to adapt their properties in response to changing environmental conditions challenges the traditional goal of creating stable materials, marking a paradigm shift in material design.
Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction Using Machine Learning Technique
Authors: Ibrahim Muhammad , Abbas Sagheer , Fatima Areej , Ghazal Taher M. , Saleem Muhammad , Alharbi Meshal , Alotaibi Fahad Mazaed , Adnan Khan Muhammad , Waqas Muhammad , Elmitwally Nouh
Published: March 2024

The abnormality of haemoglobin in the human body is the fundamental cause of thalassemia disease. Thalassemia is considered a common genetic blood condition that has received extensive investigation in medical research globally. Likely, inherited disorders will be passed down to children from their parents. If both parents are beta Thalassemia carriers, 25% of their children will have intermediate or major beta thalassemia, which is fatal. An efficient method of beta thalassemia is prenatal screening after couples have received counselling. Identifying Thalassemia carriers involves a costly, time-consuming, and specialized test using quantifiable blood features. However, cost-effective and speedy screening methods must be developed to address this issue. The demise rate due to thalassemia development is outstandingly high around the globe. The passing rate due to thalassemia development can be reduced by following the proper procedure early; otherwise, it significantly impacts the body. A machine learning-based late fusion model proposes the detection of beta-thalassemia carriers by analyzing red blood cells. This study applied the late fusion technique to employ four machine learning algorithms. For identifying the beta-thalassemia carriers, logistics regression, Naïve Bayes, decision tree, and neural network have achieved an accuracy of 94.01%, 93.15%, 97.93%, and 98.07%, respectively, by using the features-based dataset. The late fusion-based ML model achieved an overall accuracy of 96% for detecting beta-thalassemia carriers. The proposed late fusion model performs better than previously published approaches regarding efficiency, reliability, and precision.
Efficacy of the Combined Use of Bed Sill and Sacrificial Piles to Control Local Scour around Circular Bridge Piers
Authors: Javidi Vahdati Vahid , Ahmadi Ahmad , Abedini Amirabbas , Heidarpour Manouchehr
Published: March 2024

In this study, the results are presented and discussed from laboratory test campaigns specifically designed to investigate the behavior of sacrificial piles as a countermeasure against local scouring at a circular bridge pier and clear‐water conditions with flow intensity slightly below the threshold of sediment motion. Sacrificial piles are assessed on the upstream side of the pier in two transverse and triangular arrangements. Piles can reduce scouring by deflecting the flow and creating a low‐velocity wake region behind them. The efficiency of the piles against local scouring depends on the diameter of the piles, the number of piles, and the angle of the wedge. The investigation was aimed at evaluating the effectiveness of the sacrificial piles as a function of different dimensionless groups. It was found that the triangular arrangement of sacrificial piles has better results than the transverse arrangement. The results showed that the triangular sacrificial piles reduced the maximum local scour depth at the pier to 37.2% in the best configuration. Combined countermeasures were tested, which were composed of sacrificial piles and a bed sill downstream of the pier; in the best configuration, the scour depth reduction in front of the pier reached 51.1%. The increased efficiency of the combination of bed sill and sacrificial piles (BSSP) is an advantage that can reduce the risk of pier failure when the duration of the flood is short. This last result shows that a combination of BSSP may be a very effective countermeasure against local scouring at bridge piers. Finally, the coherent turbulent flow structure around the best combination of BSSP was investigated, and its effect on the bed scouring pattern was studied. A 3D analysis of the bursting process was used. Turbulence characteristics, as well as the occurrence and transition probabilities of bursting events, were calculated. The obtained results confirmed the quite effective effect of the combination of these two countermeasures in reducing the scour depth.
Metal‐Organic Framework‐Derived Solid Catalyst for Biodiesel Production from Jatropha curcas Oil: Kinetic Study and Process Optimization Using Response Surface Methodology
Authors: Zohmingliana Hlawncheu , Ruatpuia Joseph V. L. , Anal Jasha Momo H. , Chai Feng , Halder Gopinath , Dhakshinamoorthy Amarajothi , Rokhum Samuel Lalthazuala
Published: March 2024

Jatropha curcas oil (JCO) is a promising source for the manufacturing of biodiesel and has gained a lot of attention due to its environmental friendliness and availability in many parts of the world as a result of the rising need for energy. In this study, JCO was converted to biodiesel using a heterogeneous CaO‐ZrO2 catalyst made from biomass and MOFs. The central composite design of response surface technique was used to change the transesterification parameters to enhance JCO conversion. After optimization using RSM, the reaction parameters were set to a catalyst loading of 6.34 wt%, a reaction time of 68 minutes, a temperature of 92.9°C, and a methanol‐to‐oil molar ratio of 18 : 1; then, the yield of biodiesel was found to be 97.12 ± 0.4%. Using various analytical techniques, the chemical composition, texture, and its morphology have been examined by FT‐IR, SEM‐EDS, XRD, TGA, and BET. Moreover, 1H NMR, 13C NMR, and GC‐MS have all been used to describe the biodiesel that has been created. While the catalyst’s activity reduced, it was found that, after being washed with hexane and dried by calcination, it could still be used up to the fifth cycle.