Popular Programming Research Articles

Finding Cross-Rule Optimization Bugs in Datalog Engines

Datalog is a popular and widely-used declarative logic programming language. Datalog engines apply many cross-rule optimizations; bugs in them can cause incorrect results. To detect such optimization bugs, we propose an automated testing approach called Incremental Rule Evaluation (IRE), which synergistically tackles the test oracle and test case generation problem. The core idea behind the test oracle is to compare the results of an optimized program and a program without cross-rule optimization; any difference indicates a bug in the Datalog engine. Our core insight is that, for an optimized, incrementally-generated Datalog program, we can evaluate all rules individually by constructing a reference program to disable the optimizations that are performed among multiple rules. Incrementally generating test cases not only allows us to apply the test oracle for every new rule generated—we also can ensure that every newly added rule generates a non-empty result with a given probability and eschew recomputing already-known facts. We implemented IRE as a tool named Deopt, and evaluated Deopt on four mature Datalog engines, namely Soufflé, CozoDB, μZ, and DDlog, and discovered a total of 30 bugs. Of these, 13 were logic bugs, while the remaining were crash and error bugs. Deopt can detect all bugs found by queryFuzz, a state-of-the-art approach. Out of the bugs identified by Deopt, queryFuzz might be unable to detect 5. Our incremental test case generation approach is efficient; for example, for test cases containing 60 rules, our incremental approach can produce 1.17× (for DDlog) to 31.02× (for Soufflé) as many valid test cases with non-empty results as the naive random method. We believe that the simplicity and the generality of the approach will lead to its wide adoption in practice.

Cocoon: Static Information Flow Control in Rust

Information flow control (IFC) provides confidentiality by enforcing noninterference, which ensures that high-secrecy values cannot affect low-secrecy values. Prior work introduces fine-grained IFC approaches that modify the programming language and use non-standard compilation tools, impose run-time overhead, or report false secrecy leaks—all of which hinder adoption. This paper presents Cocoon, a Rust library for static type-based IFC that uses the unmodified Rust language and compiler. The key insight of Cocoon lies in leveraging Rust’s type system and procedural macros to establish an effect system that enforces noninterference. A performance evaluation shows that using Cocoon increases compile time but has no impact on application performance. To demonstrate Cocoon’s utility, we retrofitted two popular Rust programs, the Spotify TUI client and Mozilla’s Servo browser engine, to use Cocoon to enforce limited confidentiality policies

A class of models for large zero-inflated spatial data

AbstractSpatially correlated data with an excess of zeros, usually referred to as zero-inflated spatial data, arise in many disciplines. Examples include count data, for instance, abundance (or lack thereof) of animal species and disease counts, as well as semi-continuous data like observed precipitation. Spatial two-part models are a flexible class of models for such data. Fitting two-part models can be computationally expensive for large data due to high-dimensional dependent latent variables, costly matrix operations, and slow mixing Markov chains. We describe a flexible, computationally efficient approach for modeling large zero-inflated spatial data using the projection-based intrinsic conditional autoregression (PICAR) framework. We study our approach, which we call PICAR-Z, through extensive simulation studies and two environmental data sets. Our results suggest that PICAR-Z provides accurate predictions while remaining computationally efficient. An important goal of our work is to allow researchers who are not experts in computation to easily build computationally efficient extensions to zero-inflated spatial models; this also allows for a more thorough exploration of modeling choices in two-part models than was previously possible. We show that PICAR-Z is easy to implement and extend in popular probabilistic programming languages such as and .

Distributions for Compositionally Differentiating Parametric Discontinuities

Computations in physical simulation, computer graphics, and probabilistic inference often require the differentiation of discontinuous processes due to contact, occlusion, and changes at a point in time. Popular differentiable programming languages, such as PyTorch and JAX, ignore discontinuities during differentiation. This is incorrect for parametric discontinuities —conditionals containing at least one real-valued parameter and at least one variable of integration. We introduce Potto, the first differentiable first-order programming language to soundly differentiate parametric discontinuities. We present a denotational semantics for programs and program derivatives and show the two accord. We describe the implementation of Potto, which enables separate compilation of programs. Our prototype implementation overcomes previous compile-time bottlenecks achieving an 88.1x and 441.2x speed up in compile time and a 2.5x and 7.9x speed up in runtime, respectively, on two increasingly large image stylization benchmarks. We showcase Potto by implementing a prototype differentiable renderer with separately compiled shaders.

Analysis of Interaction Mechanisms between Java and JavaScript Programming Languages: from LiveConnect to Graal.js

The article is devoted to the study of the mechanisms of interaction between the popular programming languages Java and JavaScript using a retrospective approach. The author consistently examines the development of tools of this kind, starting from LiveConnect technology and Java applets of the mid-1990s and ending with the interpreters of the last decade. Particular attention is paid to comparing the performance of the currently relevant tools Nashorn and Graal.js. The latter is an implementation of the JavaScript language based on the Truffle framework and Context API, which is a Polyglot context for executing all supported guest languages with the Graal JIT compiler, ensuring their optimal performance. The author gives a definition to the term "warm-up", which is recommended by the GraalVM developers to achieve the highest performance. This is a kind of pre-compilation mode, the process of repeated preliminary execution of JavaScript code by the interpreter before its actual usage in order to obtain information about the most frequently utilized fragments of the program, its functions, types and data structures, alongside with their subsequent caching and optimization. The results of testing based on a project from Oracle and the authors application made it possible to verify the results declared by the GraalVM developers and can be used to optimize and improve the performance of multilingual projects using a unified technology stack, as well as when migrating the server code base from Java to JavaScript.

Modified RTK-GNSS for Challenging Environments

Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) is currently the premier technique for achieving centimeter-level accuracy quickly and easily. However, the robustness of RTK-GNSS diminishes in challenging environments due to severe multipath effects and a limited number of available GNSS signals. This is a pressing issue, especially for GNSS users in the navigation industry. This paper proposes and evaluates several methodologies designed to overcome these issues by enhancing the availability and reliability of RTK-GNSS solutions in urban environments. Our novel approach involves the integration of conventional methods with a new technique that leverages surplus satellites—those not initially used for positioning—to more reliably detect incorrect fix solutions. We conducted three tests in densely built-up areas within the Tokyo region. The results demonstrate that our approach not only surpasses the fix rate of the latest commercial receivers and a popular open-source RTK-GNSS program but also improves positional reliability to levels comparable to or exceeding those of the aforementioned commercial technology.

On the use of statistical machine translation for suggesting variable names for decompiled code: The Pharo case

Adequately selecting variable names is a difficult activity for practitioners. In 2018, Jaffe et al. proposed the use of statistical machine translation (SMT) to suggest descriptive variable names for decompiled code. A large corpus of decompiled C code was used to train the SMT model. Our paper presents the results of a partial replication of Jaffe’s experiment. We apply the same technique and methodology to a dataset made of code written in the Pharo programming language. We selected Pharo since its syntax is simple – it fits on half of a postcard – and because the optimizations performed by the compiler are limited to method scope. Our results indicate that SMT may recover between 8.9% and 69.88% of the variable names depending on the training set. Our replication concludes that: (i) the accuracy depends on the code similarity between the training and testing sets; (ii) the simplicity of the Pharo syntax and the satisfactory decompiled code alignment have a positive impact on predicting variable names; and (iii) a relatively small code corpus is sufficient to train the SMT model, which shows the applicability of the approach to less popular programming languages. Additionally, to assess SMT’s potential in improving original variable names, ten Pharo developers reviewed 400 SMT name suggestions, with four reviews per variable. Only 15 suggestions (3.75%) were unanimously viewed as improvements, while 45 (11.25%) were perceived as improvements by at least two reviewers, highlighting SMT’s limitations in providing suitable alternatives.

From grassroots literacy to transliteracies in the educational context of the Moldavian Csángó

ABSTRACT This paper investigates literacies in the Moldavian region of Romania, where bilingual speakers produce writings in a contested language they call the ‘Csángó mode of speaking’. The speakers sometimes define their language as collateral both with Standard Hungarian and Romanian. Our research question is the following: How are locally constructed collateral literacy practices transformed or neglected in standardising educational contexts? The theoretical question we raise in this article is whether Blommaert’s concept of grassroots literacy or the newer transliteracies framework is better suited to describe the process of a collateral language becoming literate. The collateral language, the Csángó mode of speaking, is no longer passed onto children by the parents, who still might use it among themselves, thus the children still might acquire a passive knowledge of it. Since 2001, a relatively popular educational programme teaching Standard Hungarian literacy has evolved. As our data, we use texts produced by c. 100 participants of the Moldavian Csángó Hungarian Educational Programme from eight villages. Our contribution is to investigate how transliteracies can constitute a resource not only for local ways of speaking, but also for the development of general literacy skills instead of skills bound to a single language.

Accurately clustering biological sequences in linear time by relatedness sorting

Clustering biological sequences into similar groups is an increasingly important task as the number of available sequences continues to grow exponentially. Search-based approaches to clustering scale super-linearly with the number of input sequences, making it impractical to cluster very large sets of sequences. Approaches to clustering sequences in linear time currently lack the accuracy of super-linear approaches. Here, I set out to develop and characterize a strategy for clustering with linear time complexity that retains the accuracy of less scalable approaches. The resulting algorithm, named Clusterize, sorts sequences by relatedness to linearize the clustering problem. Clusterize produces clusters with accuracy rivaling popular programs (CD-HIT, MMseqs2, and UCLUST) but exhibits linear asymptotic scalability. Clusterize generates higher accuracy and oftentimes much larger clusters than Linclust, a fast linear time clustering algorithm. I demonstrate the utility of Clusterize by accurately solving different clustering problems involving millions of nucleotide or protein sequences.

“Speak For Yourself”: Fox Sports 1, Reframing Sporting Conservatism, and “Sticking to Sports” in the Age of Trump

This article analyzes the Fox Sports 1 sports debate program Speak for Yourself. The article analyzes the thematic frames the show utilizes to espouse and reinforce sport as a site of conservative value-making and male bonding. In using an interracial male pairing of Colin Cowherd and Jason Whitlock, the show mimics similarly popular debate programs on rival network ESPN, such as Pardon the Interruption and First Take. However, Whitlock in particular advances a unique brand of Black conservatism, and his clashes with progressive activists and other black guests represent intraracial tensions among Black sport media members. This paper argues Speak for Yourself brings Newscorp’s conservative messaging to a nominally apolitical space in sport media.

Digital education community TV existence in post analog switch off

Background: Based on Law (UU) Number 11 of 2020 regarding Cipta Kerja or the Omnibus Law, broadcasting institutions should switch from terrestrial or analog broadcasts to digital broadcasting via Analog Switch Off (ASO), which provides opportunities for community TV to develop. UGTV is Indonesia’s first digital education community TV to broadcast cultural and education-based programs. Purpose: To analyze the existence of UGTV as the first digital education community TV in Indonesia. Methods: Researchers distributed questionnaires to 144 respondents in Depok and 115 in Bekasi to determine the most popular programs. Researchers then conducted interviews with UGTV staff and leaders and compared the quality of UGTV programs based on KPI standards. Results: Based on the research results, UGTV as Indonesia’s first digital educational community television has met all KPI standards by offering some programs such as Team Teaching and Info Kampus. The types of broadcast programs in the form of tourism and cultural programs are still in demand and influence the audience’s viewing interest. Based on qualitative analysis as a follow-up analysis, the public response in these three areas is currently low because people have many new media choices that can be accessed easily at lower costs. Conclusion: The public feels that television lacks “interesting” shows so the existence of UGTV and other community channels can become a choice for the audience. Implications: KPI and KPID must actively monitor and develop broadcast content by collaborating with community broadcasting institutions so that broadcast production can be more varied.

Toward trustworthy COVID-19 interventions: Building vaccine trust through community-university partnerships.

Prior research identifies trust as critical to increase vaccine acceptance and uptake. However, few intervention studies have sought to develop or test strategies for bolstering vaccine-related trust. To address this gap, this exploratory study identifies features of COVID-19 vaccine hesitancy interventions that can promote or undermine trust across three interconnected domains: institutional, interpersonal, and product (the vaccine itself). We draw on focus groups (N = 27 participants) with community and university partners involved with hosting COVID-19 testing and vaccine events in underserved Oklahoma communities. Focus groups explored participants' experiences serving community health needs and elicited feedback on proposed vaccine hesitancy interventions. Proposed interventions included two technology-based strategies (text message reminders and tablet-based testimonials and education) and one dialogue-based strategy (anti-body test interpretation). We find that community partners perceived local universities as trustworthy institutions because of their association with popular sports programs, academic credentials, and proximity, creating opportunities to address vaccine-related distrust through community-university partnerships. The most promising intervention strategies for building interpersonal trust included engaging in one-on-one dialogue and using autonomy enhancing approaches. Finally, interventions that successfully encouraged vaccine trust did so by incorporating personalized health information about individuals' potential level of protection and susceptibility to the COVID-19 virus. These findings can inform future public health efforts to create trustworthy vaccine hesitancy interventions.

An Eager Satisfiability Modulo Theories Solver for Algebraic Datatypes

Algebraic data types (ADTs) are a construct classically found in functional programming languages that capture data structures like enumerated types, lists, and trees. In recent years, interest in ADTs has increased. For example, popular programming languages, like Python, have added support for ADTs. Automated reasoning about ADTs can be done using satisfiability modulo theories (SMT) solving, an extension of the Boolean satisfiability problem with first-order logic and associated background theories. Unfortunately, SMT solvers that support ADTs do not scale as state-of-the-art approaches all use variations of the same lazy approach. In this paper, we present an SMT solver that takes a fundamentally different approach, an eager approach. Specifically, our solver reduces ADT queries to a simpler logical theory, uninterpreted functions (UF), and then uses an existing solver on the reduced query. We prove the soundness and completeness of our approach and demonstrate that it outperforms the state of the art on existing benchmarks, as well as a new, more challenging benchmark set from the planning domain.

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection

Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods are generally limited to only a few popular programming languages due to insufficient annotated data as well as their own model design constraints. To address these issues, we present AdaCCD, a novel cross-lingual adaptation method that can detect cloned codes in a new language without annotations in that language. AdaCCD leverages language-agnostic code representations from pre-trained programming language models and propose an Adaptively Refined Contrastive Learning framework to transfer knowledge from resource-rich languages to resource-poor languages. We evaluate the cross-lingual adaptation results of AdaCCD by constructing a multilingual code clone detection benchmark consisting of 5 programming languages. AdaCCD achieves significant improvements over other baselines, and achieve comparable performance to supervised fine-tuning.

Abstract 1291: ASPIRE: Advocates and Scientists Partner in Research Education

Abstract UNC Lineberger Comprehensive Cancer Center (LCCC) has a robust and growing cancer patient advocacy community. UNC Lineberger is the flagship publicly funded comprehensive cancer center in North Carolina. We are constantly looking for ways to educate patient and community advocates on recent advances in cancer biology research and clinical trials. Our advocates are also valuable partners for our basic and clinical researchers that may be early in their career. We developed and implemented an inaugural research education program for new advocates. We model this program partly on the highly successful and popular AACR Scientist Survivor Program that recently celebrated it’s 25th anniversary. Some of the UNC LCCC advocates participate in the SSP and were motivated to develop our own local program. We will showcase our pilot 2024 ASPIRE education program that brings together cancer patient and community advocates and cancer researchers to learn from each other. The basic itinerary of the ASPIRE program is: 1.Three separate one-hour online presentations by UNC Lineberger researchers with a patient advocate moderator. The topics covered are: Cancer 101 : cancer biology, diagnosis, staging, treatment. Clinical Trials 101: clinical trials, phases, patient protection, progress with for example accrual of representative populations. Research 101: bench research, bioinformatic, AI, how research works and how it may lead to new treatments. To showcase a tissue donation program being developed by UNC Patient Advocates that will benefit researchers. 2.The lecture series is followed by a breakfast roundtable discussion for all the participants to discuss their experiences. There will be short presentations and discussions in small groups of researchers and cancer patient advocates. We will summarize our ASPIRE pilot experience, how our participation in the SSP program motivated this local program, and how we plan to incorporate feedback from participants to inform future programs. We hope this will lead to more informed patient advocates that can then be even more powerful in research advocacy. Citation Format: Laurie Betts, Jennifer Potter, Veronica Carlisle, Patricia A. Spears. ASPIRE: Advocates and Scientists Partner in Research Education [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1291.

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport in plane-parallel geometry on three GPU cards: NVIDIA Tesla A100, Tesla V100, and GeForce RTX3080. We compared Numba and CuPy to each other and our CUDA C implementation. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks.

Has Secondary Science Program Become an Elite Urban Education Product in the Former Colonized Nation?

A distinct education has become a heretical right for urban elites despite educational equality is an official agenda. This has not only widened the gaps between urban and rural counterparts but also developed discriminations amongst different classes of student within the urban schools. To validate this tenet, the facilities offered by various schools were compared. Yet, whether a particular secondary education program has become an exclusive right essentially for urban elites to deprive others; is not widely explored—an investigation of this study. Educational background and some selective socioeconomic status (SES) of 925,617 students; completed their secondary school certificate from 3,315 schools were collected to make comparison through descriptive analysis of secondary data. Findings note that secondary science education has become a popular program amongst the urban elites. Subsequently, they also perform well in the public examination. Furthermore, discussions suggest that science being an international education demands both formal and informal higher budgets. Hence, a greater success of urban elites in secondary science provision helps them to succeed successively in procuring higher education and thereby in job-market. Thus, a policy intervention is required to ensure an effective implementation of educational equity concept.

Licensed to Rock (or so they say). How Popular Music Programmes at Higher Music Education Institutions Create Professional Musicians

Due to technological innovations, the music industries have become more accessible for outsiders over the past two decades. Yet, over the same period we have seen an increasing number of popular music programmes at higher music education institutions (HPME programmes). Drawing from interviews with teachers, focus groups with students and a content analysis of policy documents at three Dutch HPME programmes, we investigate whether and in which ways students and teachers perceive such programmes to contribute to the career development of their students. Results indicate that the main benefits that these programmes are perceived to offer concern the development of a set of necessary competences, the establishment of industry relationships and the acquisition of symbolic resources. Second, we consider whether these benefits are understood to contribute to a form of professionalism. In line with the ‘normative value’ perspective on professionalism we find that a norm of expertise is promoted, and in line with the ‘power struggle’ perspective we find that these symbolic resources help to foster a professional identity, both of which are believed to help students to stand out from musicians without forms of formal education entering the market.

APLIKASI PENJUALAN OXYGEN PADA PT. SINAR PURNAMA SAKTI SEMARANG


 Sinar Purnama Sakti Semarang is an agent with a trading business that is engaged in the sale of Gas Oxygen (O2), Acyteline (C2H2), Carbondioxide (CO2) and Argon (Ar) experiencing problems, namely in the process of data processing sales of goods are still completely manual. This results in a good accumulation of transaction data, so that the efficiency and completeness of the data is not guaranteed. The purpose of this paper is to create an application that can help process data quickly and efficiently at PT. Sinar Purnama Sakti Semarang. This sales application is very helpful for users. With the new system in the sales application, it will make it easier to process data and make sales reports. In addition, PT. Sinar Purnama Sakti Semarang can also provide proof of payment in the form of a note. Performance of PT. Sinar Purnama Sakti Semarang becomes faster and more efficient after the sales application. So to simplify the sales information system is done with a computerized system. There are several programs that can be used in processing sales applications, one of which is the Visual Basic 6.0 program. Microsoft Visual Basic 6.0 is one of the most popular programs used in programming. Many people have used it and many people already know it. As for storage, the author uses Microsoft Office Access. Based on the description above, the authors take the title in accordance with the need for data processing information in PT. Sinar Purnama Sakti Semarang.

Task-Level Checkpointing and Localized Recovery to Tolerate Permanent Node Failures for Nested Fork–Join Programs in Clusters

Exascale supercomputers consist of millions of processing units, and this number is still growing. Therefore, hardware failures, such as permanent node failures, become increasingly frequent. They can be tolerated with system-level Checkpoint/Restart, which saves the whole application state transparently and, if needed, restarts the application from the saved state; or with application-level checkpointing, which saves only relevant data via explicit calls in the program. The former approach requires no additional programming expense, whereas the latter is more efficient and allows to continue program execution after failures on the intact resources (localized shrinking recovery). An increasingly popular programming paradigm is asynchronous many-task (AMT) programming. Here, programmers identify parallel tasks, and a runtime system assigns the tasks to worker threads. Since tasks have clearly defined interfaces, the runtime system can automatically extract and save their interface data. This approach, called task-level checkpointing (TC), combines the respective strengths of system-level and application-level checkpointing. AMTs come in many variants, and so far, TC has only been applied to a few, rather simple variants. This paper considers TC for a different AMT variant: nested fork–join (NFJ) programs that run on clusters of multicore nodes under work stealing. We present the first TC scheme for this setting. It performs a localized shrinking recovery and can handle multiple node failures. In experiments with four benchmarks, we observed execution time overheads of around 44 % at 1536 workers, and negligible recovery costs. Additionally, we developed and experimentally validated a prediction model for the running times of the scheme.