Miracle-dz/UniFAR-FTUs huggingface dataset:
DiagConfig: Configuration Diagnosis of Performance Violations in Configurable Software Systems

Performance degradation due to misconfiguration in software systems that violates SLOs (service-level objectives) is commonplace. Diagnosing and explaining the root causes of such performance violations in configurable software systems is often challenging due to their increasing complexity. Although there are many tools and techniques for diagnosing performance violations, they provide limited evidence to attribute causes of observed performance violations to specific configurations. This is because the configuration is not originally considered in those tools. This paper proposes DiagConfig, specifically designed to conduct configuration diagnosis of performance violations. It leverages static code analysis to track configuration option propagation, identifies performance-sensitive options, detects performance violations, and constructs cause-effect chains that help stakeholders better understand the relationship between configuration and performance violations. Experimental evaluations with eight real-world software demonstrate that DiagConfig produces fewer false positives than a state-of-the-art documentation analysis-based tool (i.e., 5 vs 41) in the identification of performance-sensitive options, and outperforms a statistics-based debugging tool in the diagnosis of performance violations caused by configuration changes, offering more comprehensive results (recall: 0.892 vs 0.289). Moreover, we also show that DiagConfig can accelerate auto-tuning by compressing configuration space.
DENT: A Tool for Tagging Stack Overflow Posts with Deep Learning Energy Patterns

Energy efficiency has become an important consideration in deep learning systems. However, it remains a largely under-emphasized aspect during the development. Despite the emergence of energy-efficient deep learning patterns, their adoption remains a challenge due to limited awareness. To address this gap, we present DENT (Deep Learning Energy Pattern Tagger, a Chrome extension used to add "energy pattern tags" to the deep learning related questions from Stack Overflow. The idea of DENT is to hint to the developers about the possible energy-saving opportunities associated with the Stack Overflow post through energy pattern labels. We hope this will increase awareness about energy patterns in deep learning and improve their adoption. A preliminary evaluation of DENT achieved an average precision of 0.74, recall of 0.66, and an F1-score of 0.65 with an accuracy of 66%. The demonstration of the tool is available at https://youtu.be/S0Wf_w0xajw and the related artifacts are available at https://rishalab.github. io/DENT/
Research Online

The management of populations of threatened species requires the capacity to identify areas of high habitat value. We developed a high resolution species distribution model (SDM) for the endangered Pilbara northern quoll Dasyurus hallucatus , population using MaxEnt software and a combined suite of bioclimatic and landscape variables. Once common throughout much of northern Australia, this marsupial carnivore has recently declined throughout much of its former range and is listed as endangered by the IUCN. Other than the potential threats presented by climate change, and the invasive cane toad Rhinella marina (which has not yet arrived in the Pilbara). The Pilbara population is also impacted by introduced predators, pastoral and mining activities. To account for sample bias resulting from targeted surveys un-evenly spread through the region, a pseudo-absence bias layer was developed from presence records of other critical weight-range non-volant mammals. The resulting model was then tested using
GENDER DIFFERENTIALS IN POLITICAL PARTICIPATION IN THE SOUTH WEST REGION OF CAMEROON: THE IMPLICATIONS ON WOMEN DEVELOPMENT

In the wake of democratic governance across nations of the world, gender differentials in political participation have been a subject of debate. This study argues that although there are constitutional provisions and public policies which guarantee equal participation in the political process in Cameroon, gender differentials still persist The paper uses data from a survey conducted in the South West region in 2012. After examining basic patterns of differences in participation in political activities, it used the chi square test to determine the relationship between gender and political participation in the South Western region of the country. The findings show that there are gender differences in political participation in the region with more men being actively involved than women. It also fund that there is high tendency for more women to engage in politics related activities with improved political structure and processes in the region. The study recommend among others that government at all levels should apart from their women empowerment agenda; provide a level playing ground that could enthrone gender equity. Yet, the introduction of policy and programmes that would ensure active engagement of the women population in economic activities is imperative.
Attention Discriminant Sampling for Point Clouds

This paper describes an attention-driven approach to 3-D point cloud sampling. We establish our method based on a structure-aware attention discriminant analysis that explores geometric and semantic relations embodied among points and their clusters. The proposed attention discriminant sampling (ADS) starts by efficiently decomposing a given point cloud into clusters to implicitly encode its structural and geometric relatedness among points. By treating each cluster as a structural component, ADS then draws on evaluating two levels of self-attention: within-cluster and between-cluster. The former reflects the semantic complexity entailed by the learned features of points within each cluster, while the latter reveals the semantic similarity between clusters. Driven by structurally preserving the point distribution, these two aspects of self-attention help avoid sampling redundancy and decide the number of sampled points in each cluster. Extensive experiments demonstrate that ADS significantly improves classification performance to 95.1% on ModelNet40 and 87.5% on ScanObjectNN and achieves 86.9% mIoU on ShapeNet Part Segmentation. For scene segmentation, ADS yields 91.1% accuracy on S3DIS with higher mIoU to the state-of-the-art and 75.6% mIoU on ScanNetV2. Furthermore, ADS surpasses the state-of-the-art with 55.0% mAP50 on ScanNetV2 object detection.
Adapting Performance Analytic Techniques in a Real-World Database-Centric System: An Industrial Experience Report

Database-centric architectures have been widely adopted in large-scale software systems in various domains to deal with the ever-increasing amount and complexity of data. Prior studies have proposed a wide range of performance analytic techniques aimed at assisting developers in pinpointing software performance inefficiencies and diagnosing performance issues. However, directly applying these existing techniques to large-scale database-centric systems can be challenging and may not perform well due to the unique nature of such systems. In particular, compared to typical database-based systems like online shopping systems, in database-centric systems, a majority of the business logic and calculations reside in the database instead of the application. As the calculations in the database typically use domain-specific languages such as SQL, the performance issues of such systems and their diagnosis may be significantly different from the systems dominated by traditional programming languages such as Java. In this paper, we share our experience of adapting performance analytic techniques in a large-scale database-centric system from our industrial collaborator. Our adapted performance analysis pays special attention to the database and the interactions between the database and the application with minimal reliance on expert knowledge and manual effort. Moreover, we document our encountered challenges and how they are addressed during the development and adoption of our solution in the industrial setting as well as the corresponding lessons learned. We also discuss the real-world performance issues detected by applying our analysis to the target database-centric system. We anticipate that our solution and the reported experience can be helpful for practitioners and researchers who would like to ensure and improve the performance of database-centric systems.
Drug Name Correction of Medication Records from Aeromedical Certification Exams

Misspelled drug names have been introduced in Federal Aviation Administration medication records from aeromedical certification exams because of typographical errors and from transcriptions of hand-written medical prescriptions. The correction of misspelled drug names contributes to aviation safety by improving the accuracy of the records and database quality needed for aeromedical research and operational queries. This report describes development of an automatic spell correction system to correct misspelled drug names. This study compares candidate algorithms using linguistic and context metrics as well as reference dictionaries. A selection confidence index is introduced to rank the fuzzy matching results. The best correction rates are over 96% for the validation data set and approximately 93% for the much larger data set of records. To further improve the correction, more effort might be made in correcting cases in which one correct drug name or English word is misused as another drug name, refining the reference dictionary, interpreting abbreviations and strings with multiple words squeezed together, and using machine learning algorithms.
Text characteristics of English language university Web sites

The nature of the contents of academic Web sites is of direct relevance to the new field of scientific Web intelligence, and for search engine and topic-specific crawler designers. We analyze word frequencies in national academic Webs using the Web sites of three English-speaking nations: Australia, New Zealand, and the United Kingdom. Strong regularities were found in page size and word frequency distributions, but with significant anomalies. At least 26% of pages contain no words. High frequency words include university names and acronyms, Internet terminology, and computing product names: not always words in common usage away from the Web. A minority of low frequency words are spelling mistakes, with other common types including nonwords, proper names, foreign language terms or computer science variable names. Based upon these findings, recommendations for data cleansing and filtering are made, particularly for clustering applications.
Narrowband Internet of Things

Narrowband Internet of Things (NB-IoT) is a new cellular technology introduced in 3GPP Release 13 for providing wide-area coverage for the Internet of Things (IoT). This article provides an overview of the air interface of NB-IoT. It describes how NB-IoT addresses key IoT requirements such as deployment flexibility, low device complexity, long battery life time, support of massive number of devices in a cell, and significant coverage extension beyond existing cellular technologies. Keywords; NB-IoT, LPWA, LTE
Morphees+: Studying Everyday Reconfigurable Objects for the Design and Taxonomy of Reconfigurable UIs

Users interact with many reconfigurable objects in daily life. These objects embed reconfigurations and shape- changing features that users are familiar with. For this reason, everyday reconfigurable objects have informed the design and taxonomy of shape changing UI. However, they have never been explored systematically. In this paper, we present a data set of 82 everyday reconfigurable objects that we collected in a workshop. We discuss how they can inspire the design of reconfigurable interfaces. We particularly focus on taxonomies of reconfigurable interfaces. Taxonomies have been suggested to help design and communication among researchers, however despite their extensive use, taxonomies are rarely evaluated. This paper analyses two established taxonomies - Rasmussen's and Roudaut's - using daily reconfigurable objects. We show relationships between the taxonomies and area for improvements. We propose Morphees+, a refined taxonomy based on Roudaut's Shape Resolution Taxonomy.
Commit-Level, Neural Vulnerability Detection and Assessment

Software Vulnerabilities (SVs) are security flaws that are exploitable in cyber-attacks. Delay in the detection and assessment of SVs might cause serious consequences due to the unknown impacts on the attacked systems. The state-of-the-art approaches have been proposed to work directly on the committed code changes for early detection. However, none of them could provide both commit-level vulnerability detection and assessment at once. Moreover, the assessment approaches still suffer low accuracy due to limited representations for code changes and surrounding contexts. We propose a Context-aware, Graph-based, Commit-level Vulnerability Detection and Assessment Model, VDA, that evaluates a code change, detects any vulnerability and provides the CVSS assessment grades. To build VDA, we have key novel components. First, we design a novel context-aware, graph-based, representation learning model to learn the contextualized embeddings for the code changes that integrate program dependencies and the surrounding contexts of code changes, facilitating the automated vulnerability detection and assessment. Second, VDA considers the mutual impact of learning to detect vulnerability and learning to assess each vulnerability assessment type. To do so, it leverages multi-task learning among the vulnerability detection and vulnerability assessment tasks, improving all the tasks at the same time. Our empirical evaluation shows that on a C vulnerability dataset, VDA achieves 25.5% and 26.9% relatively higher than the baselines in vulnerability assessment regarding F-score and MCC, respectively. In a Java dataset, it achieves 31% and 33.3% relatively higher than the baselines in F-score and MCC, respectively. VDA also relatively improves the vulnerability detection over the baselines from 13.4–322% in F-score.
BFSig: Leveraging File Significance in Bus Factor Estimation

Software projects experience the departure of developers due to various reasons. As developers are one of the main sources of knowledge in software projects, their absence will inevitably result in a certain degree of knowledge depletion. Bus Factor (BF) is a metric to evaluate how this knowledge loss can affect the project’s continuity. Conventionally, BF is calculated as the smallest set of developers, removing over half the project knowledge upon departure. Current state-of-the-art approaches measure developers’ knowledge by the number of authored files, utilizing version control system (VCS) information. However, numerous studies have shown that files in software projects have different significance. In this study, we explore how weighting files according to their significance affects the performance of two prevailing BF estimators. We derive significance scores by computing five well-known graph metrics from the project’s dependency graph: PageRank, In-/Out-/All-Degree, and Betweenness Centralities. Furthermore, we introduce BFSig , a prototype of our approach. Finally, we present a new dataset comprising reported BF scores collected by surveying software practitioners from five prominent Github repositories. Our results indicate that BFSig outperforms the baselines by up to an 18% reduction in terms of Normalized Mean Absolute Error (NMAE). Moreover, BFSig yields 18% fewer False Negatives in identifying potential risks associated with low BF. Besides, our respondent confirmed BFSig versatility by showing its ability to assess the BF of the project’s subfolders. In conclusion, we believe to estimate BF from authorship, software components of higher importance should be assigned heavier weight. Currently, BFSig exclusively explores the topological characteristics of these components. Nevertheless, considering attributes such as code complexity and bug proneness could potentially enhance the performance of BFSig.
Analyzing Microservice Connectivity with Kubesonde

Modern cloud-based applications are composed of several microservices that interact over a network. They are complex distributed systems, to the point that developers may not even be aware of how microservices connect to each other and to the Internet. As a consequence, the security of these applications can be greatly compromised. This work explicitly targets this context by providing a methodology to assess microservice connectivity, a software tool that implements it, and findings from analyzing real cloud applications. Specifically, it introduces Kubesonde, a cloud-native software that instruments live applications running on a Kubernetes cluster to analyze microservice connectivity, with minimal impact on performance. An assessment of microservices in 200 popular cloud applications with Kubesonde revealed significant issues in terms of network isolation: more than 60% of them had discrepancies between their declared and actual connectivity, and none restricted outbound connections towards the Internet. Our analysis shows that Kubesonde offers valuable insights on the connectivity between microservices, beyond what is possible with existing tools.
Fast and Designated-veriﬁer Friendly zkSNARKs in the BPK Model

In ASIACRYPT 2016, Bellare et al. ﬁrst demonstrated that it is impossible to achieve subversion soundness and standard zero knowledge simultaneously. Subsequently, there have been lots of eﬀort to construct zero-knowledge succinct non-interactive arguments of knowledge protocols (zk-SNARKs) that satisfy subversion zero knowledge (S-ZK) and standard soundness from the zk-SNARK in the common reference string (CRS) model. The various constructions could be regarded secure in the bare public key (BPK) model because of the equivalence between S-ZK in the CRS model, and uniform non-black-box zero knowledge in the BPK model has been proved by Abdolmaleki et al. in PKC 2020. In this study, We proposed the ﬁrst publicly veriﬁable non-uniform ZK zk-SNARK scheme in the BPK model maintaining comparable eﬃ-ciency with its conventional counterpart, which can also be compatible with the well-known transformation proposed by Bitansky et al. in TCC 2013 to obtain an eﬃcient designated-veriﬁer zk-SNARK. We achieve this goal by only adding a constant number of elements into the CRS, and using an unconventional but natural method to transform Groth’s zk-SNARK in EUROCRYPT 2016. In addition, we propose a new speed-up technique that provides a trade-oﬀ. Speciﬁcally, if a logarithmic number of elements are added into the CRS, according to diﬀerent circuits, the CRS veriﬁcation time in our construction could be approximately 9% − 23% shorter than that in the conventional counterpart.
Metamong: Detecting Render-Update Bugs in Web Browsers through Fuzzing

A render-update bug arises when a web browser produces an erroneous rendering output due to incorrect rendering updates. Such render-update bugs seriously harm the usability and reliability of web browsers. However, we find that detecting render-update bugs is challenging because the render-update bug is a semantic bug - given a rendering result, it is difficult to determine if it is correct due to the complex rendering specification of DOM and CSS. Thus, unlike memory corruption bugs, the incorrect rendering output does not raise the violation or crash. In practice, render-update bug detection relies on the time-prohibitive manual analysis of domain experts to determine the bug. This paper proposes Metamong, an automated framework to detect render-update bugs without false positive issues via differential fuzz testing. Metamong features two key components: (i) page mutator, and (ii) render-update oracle. The page mutator generates render-update operations, which change the content of the web page, to trigger a render-update bug. The render-update oracle exploits an HTML standard rule, so-called yielding, to produce the correct rendering result of a given web page. Combining these components, Metamong creates two HTML files where each constructs the same web page, but only one of them induces the render-update. It then uses differential testing to compare their rendering outputs to determine a bug. We implemented a prototype of Metamong, which performs differential fuzz testing on popular browsers, Chrome and Firefox. By far, Metamong identified 19 new render-update bugs, 17 in Chrome and two in Firefox. All of those have been confirmed by each browser vendor and five are already fixed, demonstrating the practical effectiveness of Metamong in identifying render-update bugs.
API-Knowledge Aware Search-Based Software Testing: Where, What, and How

Search-based software testing (SBST) has proved its effectiveness in generating test cases to achieve its defined test goals, such as branch and data-dependency coverage. However, to detect more program faults in an effective way, pre-defined goals can hardly be adaptive in diversified projects. In this work, we propose KAT, a novel knowledge-aware SBST approach to generate on-demand assertions in the program under test (PUT) based on its used APIs. KAT constructs an API knowledge graph from the API documentation to derive the constraints that the client codes need to satisfy. Each constraint is instrumented into the PUT as a program branch, serving as a test goal to guide SBST to detect faults. We evaluate KAT with two baselines (i.e., EvoSuite and Catcher) with a close-world and an open-world experiment to detect API bugs. The close-world experiment shows that KAT outperforms the baselines in the F1-score (0.55 vs. 0.24 and 0.30) to detect API-related bugs. The open-world experiment shows that KAT can detect 59.64% and 9.05% more bugs than the baselines in practice.
Parallel Acyclic Joins: Optimal Algorithms and Cyclicity Separation

We study equi-join computation in the massively parallel computation (MPC) model. Currently, a main open question under this topic is whether it is possible to design an algorithm that can process any join with load \(O(N {\rm {polylog}} N / p^{1/\rho ^*}) \) — measured in the number of words communicated per machine — where N is the total number of tuples in the input relations, ρ* is the join’s fractional edge covering number, and p is the number of machines. We settle the question in the negative for the class of tuple-based algorithms (all the known MPC join algorithms fall in this class) by proving the existence of a join query with ρ* = 2 that requires a load of Ω(N/p1/3) to evaluate. Our lower bound provides solid evidence that the “AGM bound” alone is not sufficient for characterizing the hardness of join evaluation in MPC (a phenomenon that does not exist in RAM). The hard join instance identified in our argument is cyclic, which leaves the question of whether \(O(N {\rm {polylog}} N / p^{1/\rho ^*}) \) is still possible for acyclic joins. We answer this question in the affirmative by showing that any acyclic join can be evaluated with load \(O(N / p^{1/\rho ^*}) \) , which is asymptotically optimal (there are no polylogarithmic factors in our bound). The separation between cyclic and acyclic joins is yet another phenomenon that is absent in RAM. Our algorithm owes to the discovery of a new mathematical structure — we call “canonical edge cover” — of acyclic hypergraphs, which has numerous non-trivial properties and makes an elegant addition to database theory.