Reviewerly/exHarmony huggingface dataset:
The Descriptive Complexity of Graph Neural Networks

We analyse the power of graph neural networks (GNNs) in terms of Boolean circuit complexity and descriptive complexity.We prove that the graph queries that can be computed by a polynomial-size bounded-depth family of GNNs are exactly those definable in the guarded fragment GFO+C of first-order logic with counting and with built-in relations. This puts GNNs in the circuit complexity class TC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> . Remarkably, the GNN families may use arbitrary real weights and a wide class of activation functions that includes the standard ReLU, logistic "sigmoid", and hyperbolic tangent functions. If the GNNs are allowed to use random initialisation and global readout (both standard features of GNNs widely used in practice), they can compute exactly the same queries as bounded depth Boolean circuits with threshold gates, that is, exactly the queries in TC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> .Moreover, we show that queries computable by a single GNN with piecewise linear activations and rational weights are definable in GFO+C without built-in relations. Therefore, they are contained in uniform TC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> .
Analysis of Tweets for Cyberbullying Detection

Cyberbullying takes place online on gadgets like smartphones and computers. Cyberbullying can occur through social media platforms. This paper presents a real-time cyber-bullying detection system for Twitter using Natural Language Processing (NLP) and Machine Learning (ML). The system is trained on a dataset of cyberbullying tweets using several ML algorithms and their performance is compared. Random Forest was found to provide the best results after tuning. To achieve real-time analysis, Selenium was used to scrape tweets from a given Twitter account and store the timestamp of the already checked tweets. Additionally, an image captioning model was employed to generate descriptions for images posted on the account and compare them with user-written captions to filter out spam tweets. The proposed work aims to prevent cyberbullying and provides a valuable tool for online platforms to detect and remove harmful content. The results of this study have shown that the selection of appropriate ML algorithms and preprocessing techniques significantly impact the performance of cyberbullying detection on Twitter. Our model sheds light on the appropriateness of different ML algorithms for the detection of cyberbullying.
Learning Graph-based Code Representations for Source-level Functional Similarity Detection

Detecting code functional similarity forms the basis of various software engineering tasks. However, the detection is challenging as functionally similar code fragments can be implemented differently, e.g., with irrelevant syntax. Recent studies incorporate program dependencies as semantics to identify syntactically different yet semantically similar programs, but they often focus only on local neighborhoods (e.g., one-hop dependencies), limiting the expressiveness of program semantics in modeling functionalities. In this paper, we present Tailor that explicitly exploits deep graph-structured code features for functional similarity detection. Given source-level programs, Tailor first represents them into code property graphs (CPGs) - which combine abstract syntax trees, control flow graphs, and data flow graphs - to collectively reason about program syntax and semantics. Then, Tailor learns representations of CPGs by applying a CPG-based neural network (CPGNN) to iteratively propagate information on them. It improves over prior work on code representation learning through a new graph neural network (GNN) tailored to CPG structures instead of the off-the-shelf GNNs used previously. We systematically evaluate Tailor on C and Java programs using two public benchmarks. Experimental results show that Tailor outperforms the state-of-the-art approaches, achieving 99.8% and 99.9% F-scores in code clone detection and 98.3% accuracy in source code classification.
The Smelly Eight: An Empirical Study on the Prevalence of Code Smells in Quantum Computing

Quantum Computing (QC) is a fast-growing field that has enhanced the emergence of new programming languages and frameworks. Furthermore, the increased availability of computational resources has also contributed to an influx in the development of quantum programs. Given that classical and QC are significantly different due to the intrinsic nature of quantum programs, several aspects of QC (e.g., performance, bugs) have been investigated, and novel approaches have been proposed. However, from a purely quantum perspective, maintenance, one of the major steps in a software development life-cycle, has not been considered by researchers yet. In this paper, we fill this gap and investigate the prevalence of code smells in quantum programs as an indicator of maintenance issues. We defined eight quantum-specific smells and validated them through a survey with 35 quantum developers. Since no tool specifically aims to detect quantum smells, we developed a tool called QSmell that supports the proposed quantum-specific smells. Finally, we conducted an empirical investigation to analyze the prevalence of quantum-specific smells in 15 open-source quantum programs. Our results showed that 11 programs (73.33%) contain at least one smell and, on average, a program has three smells. Furthermore, the long circuit is the most prevalent smell present in 53.33% of the programs.
A Comprehensive Study of Real-World Bugs in Machine Learning Model Optimization

Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Things (IoT) devices, pre-trained models are often processed to enhance their efficiency and compactness, using optimization techniques such as pruning and quantization. Similar to the optimization process in other complex systems, e.g., program compilers and databases, optimizations for ML models can contain bugs, leading to severe consequences such as system crashes and financial loss. While bugs in training, compiling and deployment stages have been extensively studied, there is still a lack of systematic understanding and characterization of model optimization bugs (MOBs). In this work, we conduct the first empirical study to identify and characterize MOBs. We collect a comprehensive dataset containing 371 MOBs from TensorFlow and PyTorch, the most extensively used open-source ML frameworks, covering the entire development time span of their optimizers (May 2019 to August 2022). We then investigate the collected bugs from various perspectives, including their symptoms, root causes, life cycles, detection and fixes. Our work unveils the status quo of MOBs in the wild, and reveals their features on which future detection techniques can be based. Our findings also serve as a warning to the developers and the users of ML frameworks, and an appeal to our research community to enact dedicated countermeasures.
Autonomy Is An Acquired Taste: Exploring Developer Preferences for GitHub Bots

Software bots fulfill an important role in collective software development, and their adoption by developers promises increased productivity. Past research has identified that bots that communicate too often can irritate developers, which affects the utility of the bot. However, it is not clear what other properties of human-bot collaboration affect developers' preferences, or what impact these properties might have. The main idea of this paper is to explore characteristics affecting developer preferences for interactions between humans and bots, in the context of GitHub pull requests. We carried out an exploratory sequential study with interviews and a subsequent vignette-based survey. We find developers generally prefer bots that are personable but show little autonomy, however, more experienced developers tend to prefer more autonomous bots. Based on this empirical evidence, we recommend bot developers increase configuration options for bots so that individual developers and projects can configure bots to best align with their own preferences and project cultures.
One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization

As pre-trained models automate many code intel-ligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance degradation on recent models UniXcoder and CodeT5. To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it. Updating only 0.6% of the overall parameters compared to full-model fine-tuning for each programming language, adapter tuning yields consistent improvements on code search and sum-marization tasks, achieving state-of-the-art results. In addition, we experimentally show its effectiveness in cross-lingual and low-resource scenarios. Multilingual fine-tuning with 200 samples per programming language approaches the results fine-tuned with the entire dataset on code summarization. Our experiments on three probing tasks show that adapter tuning significantly outperforms full-model fine-tuning and effectively overcomes catastrophic forgetting.
Source Code Recommender Systems: The Practitioners' Perspective

The automatic generation of source code is one of the long-lasting dreams in software engineering research. Several techniques have been proposed to speed up the writing of new code. For example, code completion techniques can recommend to developers the next few tokens they are likely to type, while retrieval-based approaches can suggest code snippets relevant for the task at hand. Also, deep learning has been used to automatically generate code statements starting from a natural language description. While research in this field is very active, there is no study investigating what the users of code recommender systems (i.e., software practitioners) actually need from these tools. We present a study involving 80 software developers to investigate the characteristics of code recommender systems they consider important. The output of our study is a taxonomy of 70 “requirements” that should be considered when designing code recommender systems. For example, developers would like the recommended code to use the same coding style of the code under development. Also, code recommenders being “aware” of the developers' knowledge (e.g., what are the framework/libraries they already used in the past) and able to customize the recommendations based on this knowledge would be appreciated by practitioners. The taxonomy output of our study points to a wide set of future research directions for code recommenders.
VULGEN: Realistic Vulnerability Generation Via Pattern Mining and Deep Learning

Building new, powerful data-driven defenses against prevalent software vulnerabilities needs sizable, quality vulnerability datasets, so does large-scale benchmarking of existing defense solutions. Automatic data generation would promisingly meet the need, yet there is little work aimed to generate much-needed quality vulnerable samples. Meanwhile, existing similar and adaptable techniques suffer critical limitations for that purpose. In this paper, we present VULGEN, the first injection-based vulnerability-generation technique that is not limited to a particular class of vulnerabilities. VULGEN combines the strengths of deterministic (pattern-based) and probabilistic (deep-learning/DL-based) program transformation approaches while mutually overcoming respective weaknesses. This is achieved through close collaborations between pattern mining/application and DL-based injection localization, which separates the concerns with how and where to inject. By leveraging large, pretrained programming language modeling and only learning locations, VULGEN mitigates its own needs for quality vulnerability data (for training the localization model). Extensive evaluations show that VULGEN significantly outperforms a state-of-the-art (SOTA) pattern-based peer technique as well as both Transformer- and GNN-based approaches in terms of the percentages of generated samples that are vulnerable and those also exactly matching the ground truth (by 38.0-430.1% and 16.3-158.2%, respectively). The VULGEN-generated samples led to substantial performance improvements for two SOTA DL-based vulnerability detectors (by up to 31.8% higher in F1), close to those brought by the ground-truth real-world samples and much higher than those by the same numbers of existing synthetic samples.
CC: Causality-Aware Coverage Criterion for Deep Neural Networks

Deep neural network (DNN) testing approaches have grown fast in recent years to test the correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently used to evaluate the quality of a test suite, and a number of coverage criteria based on neuron-wise, layer-wise, and path-/trace-wise coverage patterns have been published to date. However, we see that existing criteria are insufficient to represent how one neuron would influence subsequent neurons; hence, we lack a concept of how neurons, when functioning as causes and effects, might jointly make a DNN prediction. Given recent advances in interpreting DNN internals using causal inference, we present the first causality-aware DNN coverage criterion, which evaluates a test suite by quantifying the extent to which the suite provides new causal relations for testing DNNs. Performing standard causal inference on DNNs presents both theoretical and practical hurdles. We introduce CC (causal coverage), a practical and efficient coverage criterion that integrates a set of optimizations using DNN domain-specific knowledge. We illustrate the efficacy of CC using diverse, real-world inputs and adversarial inputs, such as adversarial examples (AEs) and backdoor inputs. We demonstrate that CC outperforms previous DNN criteria under various settings with moderate cost.
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations

Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, and popularity lift. However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing. Additionally, it is unclear if particular genres contribute to the emergence of inconsistency in recommendation performance across user groups. In this paper, we present an analysis of these three aspects of five well-known recommendation algorithms for user groups that differ in their preference for popular content. Additionally, we study how different genres affect the inconsistency of recommendation performance, and how this is aligned with the popularity of the genres. Using data from Last.fm, MovieLens, and MyAnimeList, we present two key findings. First, we find that users with little interest in popular content receive the worst recommendation accuracy, and that this is aligned with miscalibration and popularity lift. Second, our experiments show that particular genres contribute to a different extent to the inconsistency of recommendation performance, especially in terms of miscalibration in the case of the MyAnimeList dataset.
Melody Slot Machine HD

Melody Slot Machine HD is an application that allows users to experience generating melodies using musical structures while playing slot machines. To make it possible, we use the Generative Theory of Tonal Music (GTTM) to analyze the melody structure and generate variations of the melody. All variation melodies created on the basis of GTTM have a similar musical structure, so even if the melody switches to another variation in the middle, the overall structure will not collapse. It is possible to see the player on the display playing a new combination of a melody generated interactively by the user’s manipulation.
Criteria-based visualization design for hazard maps

Abstract. Probabilistic seismic hazard estimates are a key ingredient of earthquake risk mitigation strategies and are often communicated through seismic hazard maps. Though the literature suggests that visual design properties are key for effective communication using such maps, guidelines on how to optimally design hazard maps are missing from the literature. Current maps use color palettes and data classification schemes which have well-documented limitations that may inadvertently miscommunicate seismic hazard. We surveyed the literature on color and classification schemes to identify design criteria that have empirical support for communicating hazard information. These criteria were then applied to redesign the seismic hazard map for Germany. We established several communication goals for this map, including essential properties about moderate-hazard seismic regions and a critical hazard threshold related to the German seismic building codes. We elucidate our redesign process and the selection of new colors and classification schemes that satisfy the evidence-based criteria. In a mixed-methods survey, we evaluate the original and redesigned seismic hazard maps, finding that the redesign satisfies all the communication goals and improves users’ awareness about the spatial spread of seismic hazard relative to the original. We consider practical implications for the design of hazard maps across the natural hazards.
Quantum Speed Limit under Brachistochrone Evolution

According to the Heisenberg uncertainty principle between time and energy fluctuation, a concept of the quantum speed limit (QSL) has been established to determine the minimum evolutionary time between quantum states. Considerable theoretical and experimental efforts are invested in obtaining the QSL time bounds in various scenarios. However, it remains a long-standing goal to derive a meaningful QSL bound for a general quantum problem. Here, we propose a geometrical approach to derive a QSL bound for closed and open quantum systems. By solving a quantum brachistochrone problem in the framework of the Riemannian metric, we show that the QSL between a given initial state and a final state is determined not only by the entire dynamics of the system but also by the individual dynamics of a critical parameter. We exemplify the utility of the bound in three representative scenarios, demonstrating a pronounced advantage in finding a tight and meaningful QSL bound of a general quantum evolution problem.
Camera calibration method based on circular array calibration board

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Moving Learning: A Systematic Review of Mobile Learning Applications for Online Higher Education

Technological revolutionary changes have boosted mobile learning’s evolution from supplementary material for teaching to a flexible, strategic, and convenient resource, driving new paths in higher education. With global increases in wireless internet access and the advent of highly functional smartphones and tablets, which have impacted the rise in mobile device ownership, mobile learning has expanded its applications as a direct way to implement tailored learning settings. Notably, during the COVID-19 pandemic, together with other educational technologies, it became a solicited tool in remote education. In this systematic review, we will explore how educators and researchers have been documenting the development and impact of mobile learning tools in the teaching and learning process since the COVID-19 outbreak. Results show that, embedded with online higher education programs, mobile learning has empowered interaction in content creation, communication, and collaboration between learners and instructors, significantly impacting learning effectiveness. Moreover, although this technology is well established in higher education, it remains attractive for educators who actively use it because of its pedagogic potential.
Image quality enhancement of 4D light field microscopy via reference impge propagation-based one-shot learning

Abstract Four-dimensional (4D) light-field (LF) microscopes can acquire 3D information about target objects using a microlens array (MLA). However, the resolution and quality of sub-images in the LF images are reduced because of the spatial multiplexing of rays by the element lenses of the MLA. To overcome these limitations, this study proposes an LF one-shot learning technique that can convert LF sub-images into high-quality images similar to the 2D images of conventional optical microscopes obtained without any external training datasets for image enhancement. The proposed convolutional neural network model was trained using only one training dataset comprising a high-resolution reference image captured without an MLA as the ground truth. Further, its input was the central view of the LF image. After LF one-shot learning, the trained model should be able to convert well the other LF sub-images of various directional views that were not used in the main training process. Therefore, novel learning techniques were designed for LF one-shot learning. These novel techniques include an autoencoder-based model initialization method, a feature map-based learning algorithm to prevent the overfitting of the model, and cut loss to prevent saturation. The experimental results verified that the proposed technique effectively enhances the LF image quality and resolution using a reference image. Moreover, this method enhances the resolution by up to 13 times, decreases the noise amplification effect, and restores the lost details of microscopic objects. The proposed technique is stable and yields superior experimental results compared with those of the existing resolution-enhancing methods.
Disentangling Two Fundamental Paradigms in Human-Machine Communication Research: Media Equation and Media Evocation

In this theoretical paper, we delineate two fundamental paradigms in how scholars conceptualize the nature of machines in human-machine communication (HMC). In addition to the well-known Media Equation paradigm, we distinguish the Media Evocation paradigm. The Media Equation paradigm entails that people respond to machines as if they are humans, whereas the Media Evocation paradigm conceptualizes machines as objects that can evoke reflections about ontological categories. For each paradigm, we present the main propositions, research methodologies, and current challenges. We conclude with theoretical implications on how to integrate the two paradigms, and with a call for mixedmethod research that includes innovative data analyses and that takes ontological classifications into account when explaining social responses to machines.