Reviewerly/exHarmony huggingface dataset:
Moving on from the Software Engineers' Gambit: An Approach to Support the Defense of Software Effort Estimates

Pressure for higher productivity and faster delivery is increasingly pervading software organizations. This can lead software engineers to act like chess players playing a gambit—making sacrifices of their technically sound estimates, thus submitting their teams to time pressure. In turn, time pressure can have varied detrimental effects, such as poor product quality and emotional distress, decreasing productivity, which leads to more time pressure and delays: a hard-to-stop vicious cycle. This reveals a need for moving on from the more passive strategy of yielding to pressure to a more active one of defending software estimates. Therefore, we propose an approach to support software estimators in acquiring knowledge on how to carry out such defense, by introducing negotiation principles encapsulated in a set of defense lenses, presented through a digital simulation. We evaluated the proposed approach through a controlled experiment with software practitioners from different companies. We collected data on participants' attitudes, subjective norms, perceived behavioral control, and intentions to perform the defense of their estimates in light of the Theory of Planned Behavior. We employed a frequentist and a bayesian approach to data analysis. Results show improved scores among experimental group participants after engaging with the digital simulation and learning about the lenses. They were also more inclined to choose a defense action when facing pressure scenarios than a control group exposed to questions to reflect on the reasons and outcomes of pressure over estimates. Qualitative evidence reveals that practitioners perceived the set of lenses as useful in their current work environments. Collectively, these results show the effectiveness of the proposed approach and its perceived relevance for the industry, despite the low amount of time required to engage with it.
Fuzzing Automatic Differentiation in Deep-Learning Libraries

Deep learning (DL) has attracted wide attention and has been widely deployed in recent years. As a result, more and more research efforts have been dedicated to testing DL libraries and frameworks. However, existing work largely overlooked one crucial component of any DL system, automatic differentiation (AD), which is the basis for the recent development of DL. To this end, we propose ∇Fuzz, the first general and practical approach specifically targeting the critical AD component in DL libraries. Our key insight is that each DL library API can be abstracted into a function processing tensors/vectors, which can be differentially tested under various execution scenarios (for computing outputs/gradients with different implementations). We have implemented <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\nabla \text{Fuzz}$</tex> as a fully automated API-level fuzzer targeting AD in DL libraries, which utilizes differential testing on different execution scenarios to test both first-order and high-order gradients, and also includes automated filtering strategies to remove false positives caused by numerical instability. We have performed an extensive study on four of the most popular and actively-maintained DL libraries, PyTorch, TensorFlow, JAX, and OneFlow. The result shows that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\nabla \text{Fuzz}$</tex> substantially outperforms state-of-the-art fuzzers in terms of both code coverage and bug detection. To date, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\nabla \text{Fuzz}$</tex> has detected 173 bugs for the studied DL libraries, with 144 already confirmed by developers (117 of which are previously unknown bugs and 107 are related to AD). Remarkably, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\nabla \text{Fuzz}$</tex> contributed 58.3% (7/12) of all high-priority AD bugs for PyTorch and JAX during a two-month period. None of the confirmed AD bugs were detected by existing fuzzers.
Understanding the Threats of Upstream Vulnerabilities to Downstream Projects in the Maven Ecosystem

Modern software systems are increasingly relying on dependencies from the ecosystem. A recent estimation shows that around 35% of an open-source project's code come from its depended libraries. Unfortunately, open-source libraries are often threatened by various vulnerability issues, and the number of disclosed vulnerabilities is increasing steadily over the years. Such vulnerabilities can pose significant security threats to the whole ecosystem, not only to the vulnerable libraries themselves, but also to the corresponding downstream projects. Many Software Composition Analysis (SCA) tools have been proposed, aiming to detect vulnerable libraries or components referring to existing vulnerability databases. However, recent studies report that such tools often generate a large number of false alerts. Particularly, up to 73.3% of the projects depending on vulnerable libraries are actually safe. Aiming to devise more precise tools, understanding the threats of vulnerabilities holistically in the ecosystem is significant, as already performed by a number of existing studies. However, previous researches either analyze at a very coarse granularity (e.g., without analyzing the source code) or are limited by the study scales. This study aims to bridge such gaps. In particular, we collect 44,450 instances of (CVE, upstream, downstream) relations and analyze around 50 million invocations made from downstream to upstream projects to understand the potential threats of upstream vulnerabilities to downstream projects in the Maven ecosystem. Our investigation makes interesting yet significant findings with respect to multiple aspects, including the reach-ability of vulnerabilities, the complexities of the reachable paths as well as how downstream projects and developers perceive upstream vulnerabilities. We believe such findings can not only provide a holistic understanding towards the threats of upstream vulnerabilities in the Maven ecosystem, but also can guide future researches in this field.
FedDebug: Systematic Debugging for Federated Learning Applications

In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and collaborative training make FL appealing for applications with data-privacy concerns, such as medical imaging. However, these FL characteristics pose unprecedented challenges for debugging. When a global model's performance deteriorates, identifying the responsible rounds and clients is a major pain point. Developers resort to trial-and-error debugging with subsets of clients, hoping to increase the global model's accuracy or let future FL rounds retune the model, which are time-consuming and costly. We design a systematic fault localization framework, Fedde-bug,that advances the FL debugging on two novel fronts. First, Feddebug enables interactive debugging of realtime collaborative training in FL by leveraging record and replay techniques to construct a simulation that mirrors live FL. Feddebug'sbreakpoint can help inspect an FL state (round, client, and global model) and move between rounds and clients' models seam-lessly, enabling a fine-grained step-by-step inspection. Second, Feddebug automatically identifies the client(s) responsible for lowering the global model's performance without any testing data and labels-both are essential for existing debugging techniques. Feddebug's strengths come from adapting differential testing in conjunction with neuron activations to determine the client(s) deviating from normal behavior. Feddebug achieves 100% accuracy in finding a single faulty client and 90.3% accuracy in finding multiple faulty clients. Feddebug's interactive de-bugging incurs 1.2% overhead during training, while it localizes a faulty client in only 2.1% of a round's training time. With FedDebug,we bring effective debugging practices to federated learning, improving the quality and productivity of FL application developers.
Metamorphic Shader Fusion for Testing Graphics Shader Compilers

Computer graphics are powered by graphics APIs (e.g., OpenGL, Direct3D) and their associated shader compilers, which render high-quality images by compiling and optimizing user-written high-level shader programs into GPU machine code. Graphics rendering is extensively used in production scenarios like virtual reality (VR), gaming, autonomous driving, and robotics. Despite the development by industrial manufacturers such as Intel, Nvidia, and AMD, shader compilers - like traditional software - may produce ill-rendered outputs. In turn, these errors may result in negative results, from poor user experience in entertainment to accidents in driving assistance systems. This paper introduces FSHADER, a metamorphic testing (MT) framework designed specifically for shader compilers to uncover erroneous compilations and optimizations. FSHADER tests shader compilers by mutating input shader programs via four carefully-designed metamorphic relations (MRs). In particular, FSHADER fuses two shader programs via an MR and checks the visual consistency between the image rendered from the fused shader program with the output of fusing individually rendered images. Our study of 12 shader compilers covers five mainstream GPU vendors, including Intel, AMD, Nvidia, ARM, and Apple. We successfully uncover over 16K error-triggering inputs that generate incorrect rendering outputs. We manually locate and characterize buggy optimization places, and developers have confirmed representative bugs.
Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs

Concurrent programs suffer from data races. To prevent data races, programmers use locks. However, programs can eliminate data races only when they acquire and release correct locks at correct timing. The lock API of C, in which people have developed a large portion of legacy system programs, does not validate the correct use of locks. On the other hand, Rust, a recently developed system programming language, provides a lock API that guarantees the correct use of locks via type checking. This makes rewriting legacy system programs in Rust a promising way to retrofit safety into them. Unfortunately, manual C-to-Rust translation is extremely laborious due to the discrepancies between their lock APIs. Even the state-of-the-art automatic C-to-Rust translator retains the C lock API, expecting developers to replace them with the Rust lock API. In this work, we propose an automatic tool to replace the C lock API with the Rust lock API. It facilitates C-to-Rust translation of concurrent programs with less human effort than the current practice. Our tool consists of a Rust code transformer that takes a lock summary as an input and a static analyzer that efficiently generates precise lock summaries. We show that the transformer is scalable and widely applicable while preserving the semantics; it transforms 66 KLOC in 2.6 seconds and successfully handles 74% of real-world programs. We also show that the analyzer is scalable and precise; it analyzes 66 KLOC in 4.3 seconds.
Dealing with New User Problem Using Content-Based Deep Matrix Factorization

Recommender systems (RS) are very necessary and important in digital life. Especially, the RS can support users to select appropriate products/items in online systems such as shopping, entertainment, education and other domains. However, techniques in RS are facing with new user problem which means that the RS has no history data to learn and recommend for the users who have not rated the items. This work proposes an approach which is called Content-Based Deep Matrix Factorization (CBDMF) for RS, especially for the new user problem. In this approach, the item information (e.g., item descriptions and other item meta-data) is pre-processed and converted to Term Frequency-Inverse Document Frequency (TF-IDF) vector, then, this vector is integrated with the user and item latent factor vectors before inputting to a deep neuron networks for predictions. We provide architecture of the CBDMF as well as evaluated on several scenarios of new user problems. Experimental results on published Movie and Book data sets show that the CBDMF can work well for recommendations in case of new user problem.
Assessing the impact of quality of education on the knowledge economy: evidence from Algeria

This research was focused on Algeria and looked at how education quality affected the knowledge economy. Two indicators of the knowledge economy—the number of fixed line subscribers and the number of internet subscribers—were examined for their effects on Algeria’s educational system. The theoretical anchor was the endogenous or new growth hypothesis. The study used an ex-post facto research design. Secondary sources were used to get panel-structured data that spanned 48 provinces and 22 years (1999–2020). The equations were estimated using the fixed-random effect model and Hausman test. The Kao (Engle Ganger-based) cointegration test proved that the series did indeed have a long-run connection. The findings indicated that the knowledge economy was boosted by a rise in students’ Baccalaureate and Intermediate test success rates. This is due to the fact that a rise in exam success rates indicates that individuals at various educational levels are now receiving education of a higher caliber, and the economy depends on this caliber of education. The rise in students and professors is a sign that there are more knowledgeable people and specialized educators accessible to fuel the knowledge economy. As more educated citizens pass their exams and graduate from college, they are hired from the labor market into industries as significant production factors whose skills, values, and knowledge acquired through specialized training are expected to promote machine handling and coordination of other human and nonhuman production factors to fuel the knowledge economy. Teachers should be provided with ongoing professional development opportunities to help them provide instruction at all educational levels, according to a recommendation. This would improve their ability to educate and inspire students and help them do better on their intermediate and baccalaureate exams. To make it easy for instructors and academics to participate, the training should be offered as sandwich courses, seminars, conferences, workshops, and even full-time training programs.
Major 3 Satisfiability logic in Discrete Hopfield Neural Network integrated with multi-objective Election Algorithm

&lt;abstract&gt; &lt;p&gt;Discrete Hopfield Neural Network is widely used in solving various optimization problems and logic mining. Boolean algebras are used to govern the Discrete Hopfield Neural Network to produce final neuron states that possess a global minimum energy solution. Non-systematic satisfiability logic is popular due to the flexibility that it provides to the logical structure compared to systematic satisfiability. Hence, this study proposed a non-systematic majority logic named Major 3 Satisfiability logic that will be embedded in the Discrete Hopfield Neural Network. The model will be integrated with an evolutionary algorithm which is the multi-objective Election Algorithm in the training phase to increase the optimality of the learning process of the model. Higher content addressable memory is proposed rather than one to extend the measure of this work capability. The model will be compared with different order logical combinations $ k = \mathrm{3, 2} $, $ k = \mathrm{3, 2}, 1 $ and $ k = \mathrm{3, 1} $. The performance of those logical combinations will be measured by Mean Absolute Error, Global Minimum Energy, Total Neuron Variation, Jaccard Similarity Index and Gower and Legendre Similarity Index. The results show that $ k = \mathrm{3, 2} $ has the best overall performance due to its advantage of having the highest chances for the clauses to be satisfied and the absence of the first-order logic. Since it is also a non-systematic logical structure, it gains the highest diversity value during the learning phase.&lt;/p&gt; &lt;/abstract&gt;
Postdigital Artificial Intelligence

Artificial intelligences (AI) are progressively evolving multidisciplinary technologies that cannot be easily defined. In simplest terms, AI imitate human cognition and make decisions like a human. The conceptual roots of AI have a long history, yet it was the invention of computers that led to tangible, working examples of AI technologies. AI offer many opportunities, such as improved efficiency and productivity, enhanced decision-making, personalization and customization, improved efficiency, and solving complex problems. However, there are also potential drawbacks and challenges to using AI, such as privacy and security, ethical and societal implications, and so on, that require approaching AI technologies with caution.
Improved Related-Key Rectangle Attacks On <tt>GIFT</tt>

Abstract GIFT is a lightweight cipher proposed by Banik et al. at CHES’17, motivated by the design strategy of PRESENT. GIFT-64[2021] is a variant of GIFT proposed by Sun et al. at EUROCRYPT’22 to achieve better resistance against differential attack while maintaining a similar security level against linear attack. At EUROCRYPT’22, Dong et al. proposed a new rectangle framework considering the key guessing strategies for linear key-schedule ciphers, and established a uniform automatic search model for the whole rectangle attack. In this paper, we extend it to be applicable to bit-oriented ciphers, and construct an automatic search model involved in the distinguisher and key-recovery phase for GIFT. Moreover, we utilize the key relations of the linear key-schedule to the model, and find some new distinguishers both for GIFT-64 and GIFT-64[2021]. To evaluate the probability more accurately, we propose a method to calculate the probability of the 2-round middle part which connects the boomerang distinguisher for GIFT, and apply it with the SAT method to evaluate the probability of the whole distinguishers. As a result, we search out a new 20-round related-key boomerang distinguisher for GIFT-64, and achieve a 26-round attack with better time complexity than the best previous attack. For GIFT-64[2021], we find a 20-round boomerang distinguisher and give the first 26-round rectangle attack under related-key scenario.
Digital Forensic Advanced Evidence Collection and Analysis of Web Browser Activity

Web browsers are the applications that the majority of computer users utilize the most. Users carry out a wide range of tasks, including accessing the internet, downloading files, and using social media programs, using a web browser to access email accounts. Many crimes committed using digital resources need to be investigated by looking at online browser history. Such information must be included in the reports of the examiners that will generate one of the data gathered, particularly about crimes involving entering the URL, downloaded files, access times, search phrases browser type, and times. Different methods are used by web browsers to store user data. Additionally, the locations where data is stored vary depending on the operating system being used. The analysis of web browsers on digital resources that are subject to criminal activity, data from various browsers on various operating systems, storage types, and data types that can be retrieved are all demonstrated in this study. Also, we demonstrated the capabilities and tools used in the web browser to review the records.
Consensus on community guidelines: an experimental study on the legitimacy of content removal in social media

Abstract The popularization of social media has led to a considerable increase in the importance of discursive expressions of violence, especially when directed at vulnerable communities. While social media platforms have created rules to regulate such expressions, little information is available on the perception of the legitimacy of these rules in the general population, regardless of the importance of the former for the latter. It is therefore the objective of this study to analyze the perception of the seriousness of such content and the degree to which the population has established a consensus on the withdrawal of restricted discursive behaviour on three major social media platforms (Facebook, Instagram and Twitter). For this purpose, 918 participants were immersed in an experimental paradigm in three different groups ( n 1 = 302; n 2 = 301; n 3 = 315). Each was presented with stimuli containing discursive behaviour that is banned by community guidelines. The stimuli were presented differently to each group (i.e., description of the banned behaviour, description and accompanying example, example only). Our experimental data reveals that the degree of consensus on the need to remove content is quite high, regardless of the style of presentation. It furthermore suggests that the behaviour in question is perceived as very serious, due to the harm that our participants presume it to cause. These results have important implications for the debate on freedom of expression on the Internet and its regulation by private actors.
Metaphors in Voice User Interfaces: A Slippery Fish

We explore a range of different metaphors used for Voice User Interfaces (VUIs) by designers, end-users, manufacturers, and researchers using a novel framework derived from semi-structured interviews and a literature review. We focus less on the well-established idea of metaphors as a way for interface designers to help novice users learn how to interact with novel technology, and more on other ways metaphors can be used. We find that metaphors people use are contextually fluid, can change with the mode of conversation, and can reveal differences in how people perceive VUIs compared to other devices. Not all metaphors are helpful, and some may be offensive. Analyzing this broader class of metaphors can help understand, perhaps even predict problems. Metaphor analysis can be a low-cost tool to inspire design creativity and facilitate complex discussions about sociotechnical issues, enabling us to spot potential opportunities and problems in the situated use of technologies.
Aircraft segmentation in remote sensing images based on multi-scale residual U-Net with attention

Abstract Aircraft segmentation in remote sensing images (RSIs) is an important but challenging problem for both civil and military applications. U-Net and its variants are widely used in RSI detection, but they are not suitable for multi-scale aircraft segmentation in RSIs, due to the aircrafts in RSIs are relatively small with various orientations, different sizes, fuzzy illumination and shadow, obscure boundary and irregular background. To overcome this problem, a multi-scale residual U-Net with attention (MSRAU-Net) model is constructed for multi-scale aircraft segmentation in RSIs. A multi-scale convolutional module, two modified Respaths and two kinds of attention modules are designed and introduced into MSRAU-Net to extract the multi-scale feature and make the feature fusion between the contraction path and the expansion path more efficient. Different from U-Net, MSRAU-Net replaces the convolutional block of U-Net with the Inception residual block to help the U-Net architecture coordinate the features learned from aircrafts with different scales, and the residual module and attention module are introduced into the modified Respath to deepen the network layers and solve the gradient disappearing problem while extracting the more effective feature from RSIs. The experiments on the RSI dataset validate that MSRAU-Net outperforms the other networks, in particular for detecting the small aircrafts. Compared with attention U-Net and MultiMixUNet, the precision of MSRAU-Net is improved by 9.25 and 3.36, respectively.