Reviewerly/exHarmony huggingface dataset:
An oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy

Hybrid data lead to overfitting in machine learning models, which may reduce the accuracy of classification. Feature selection can not only reduce the computational cost of processing hybrid data but also improve the accuracy of classification. The particle swarm optimization (PSO) algorithm has clear advantages in feature selection. This paper presents an oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy. First, a new distance function on the object set of a hybrid information system (HIS) is built, which yields a tolerance relation on this object set. Then, mutual information entropy is presented to measure the uncertainty of the HIS. On this basis, the maximum-relevance and minimal-redundancy model (MRMR model) for the HIS is proposed. Based on the MRMR model, a feature selection algorithm (denoted as MRMR) for the HIS is naturally designed. As the integration of the MRMR model and PSO can effectively explore all possible feature subsets, an oscillatory particle swarm optimization algorithm based on the MRMR model (denoted as OPSO-MRMR) for the HIS is also designed. Moreover, the MRMR model is utilized to define a fitness function that evaluates the quality of particles. The particle position update process is modified by means of a two-order oscillatory equation. Finally, an experimental analysis is conducted to compare the two designed algorithms with five other algorithms. The statistical analysis of classification accuracy and F1 score shows that OPSO-MRMR improves precision by 5.8% and 10.7% compared to the other six algorithms, respectively.
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment

Deep learning models are trained with certain assumptions about the data during the development stage and then used for prediction in the deployment stage. It is important to reason about the trustworthiness of the model's predictions with unseen data during deployment. Existing methods for specifying and verifying traditional software are insufficient for this task, as they cannot handle the complexity of DNN model architecture and expected outcomes. In this work, we propose a novel technique that uses rules derived from neural network computations to infer data preconditions for a DNN model to determine the trustworthiness of its predictions. Our approach, DeepInfer involves introducing a novel abstraction for a trained DNN model that enables weakest precondition reasoning using Dijkstra's Predicate Transformer Semantics. By deriving rules over the inductive type of neural network abstract representation, we can overcome the matrix dimensionality issues that arise from the backward non-linear computation from the output layer to the input layer. We utilize the weakest precondition computation using rules of each kind of activation function to compute layer-wise precondition from the given postcondition on the final output of a deep neural network. We extensively evaluated DeepInfer on 29 real-world DNN models using four different datasets collected from five different sources and demonstrated the utility, effectiveness, and performance improvement over closely related work. DeepInfer efficiently detects correct and incorrect predictions of high-accuracy models with high recall (0.98) and high F-1 score (0.84) and has significantly improved over the prior technique, SelfChecker. The average runtime overhead of DeepInfer is low, 0.22 sec for all the unseen datasets. We also compared runtime overhead using the same hardware settings and found that DeepInfer is 3.27 times faster than SelfChecker, the state-of-the-art in this area.
On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past

A big part of software developers' time is spent finding answers to their coding-task-related questions. To answer their questions, developers usually perform web searches, ask questions on Q&A websites, or, more recently, in chat communities. Yet, many of these questions have frequently already been answered in previous chat conversations or other online communities. Automatically identifying and then suggesting these previous answers to the askers could, thus, save time and effort. In an empirical analysis, we first explored the frequency of repeating questions on the Discord chat platform and assessed our approach to identify them automatically. The approach was then evaluated with real-world developers in a field experiment, through which we received 142 ratings on the helpfulness of the suggestions we provided to help answer 277 questions that developers posted in four Discord communities. We further collected qualitative feedback through 53 surveys and 10 follow-up interviews. We found that the suggestions were considered helpful in 40% of the cases, that suggesting Stack Overflow posts is more often considered helpful than past Discord conversations, and that developers have difficulties describing their problems as search queries and, thus, prefer describing them as natural language questions in online communities.
LibvDiff: Library Version Difference Guided OSS Version Identification in Binaries

Open-source software (OSS) has been extensively employed to expedite software development, inevitably exposing downstream software to the peril of potential vulnerabilities. Precisely identifying the version of OSS not only facilitates the detection of vulnerabilities associated with it but also enables timely alerts upon the release of 1-day vulnerabilities. However, current methods for identifying OSS versions rely heavily on version strings or constant features, which may not be present in compiled OSS binaries or may not be representative when only function code changes are made. As a result, these methods are often imprecise in identifying the version of OSS binaries being used.
Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles

AI-powered systems have gained widespread popularity in various domains, including Autonomous Vehicles (AVs). However, ensuring their reliability and safety is challenging due to their complex nature. Conventional test adequacy metrics, designed to evaluate the effectiveness of traditional software testing, are often insufficient or impractical for these systems. White-box metrics, which are specifically designed for these systems, leverage neuron coverage information. These coverage metrics necessitate access to the underlying AI model and training data, which may not always be available. Furthermore, the existing adequacy metrics exhibit weak correlations with the ability to detect faults in the generated test suite, creating a gap that we aim to bridge in this study.
Characterizing Software Maintenance Meetings: Information Shared, Discussion Outcomes, and Information Captured

A type of meeting that has been understudied in the software engineering literature to date is what we term the software maintenance meeting: a regularly scheduled team meeting in which emergent issues are addressed that are usually out of scope of the daily stand-up but not necessarily challenging enough to warrant an entirely separate meeting. These meetings tend to discuss a wide variety of topics and are crucial in keeping software development projects going, but little is known about these meetings and how they proceed. In this paper, we report on a single exploratory case study in which we analyzed ten consecutive maintenance meetings from a major healthcare software provider. We analyzed what kind of information is brought into the discussions held in these meetings and how, what outcomes arose from the discussions, and what information was captured for downstream use. Our findings are varied, giving rise to both practical considerations for those conducting these kinds of meetings and new research directions toward further understanding and supporting them.
VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks

Neural network repair aims to fix the 'bugs'1 of neural networks by modifying the model's architecture or parameters. However, due to the data-driven nature of neural networks, it is difficult to explain the relationship between the internal neurons and erroneous behaviors, making further repair challenging. While several work exists to identify responsible neurons based on gradient or causality analysis, their effectiveness heavily rely on the quality of available 'bugged' data and multiple heuristics in layer or neuron selection. In this work, we address the issue utilizing the power of formal verification (in particular for neural networks). Specifically, we propose VeRe, a verification-guided neural network repair framework that performs fault localization based on linear relaxation to symbolically calculate the repair significance of neurons and furthermore optimize the parameters of problematic neurons to repair erroneous behaviors. We evaluated VeRe on various repair tasks, and our experimental results show that VeRe can efficiently and effectively repair all neural networks without degrading the model's performance. For the task of removing backdoors, VeRe successfully reduces attack success rate from 98.47% to 0.38% on average, while causing an average performance drop of 0.9%. For the task of repairing safety properties, VeRe successfully repairs all the 36 tasks and achieves 99.87% generalization on average.
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges

The software engineering community recently has witnessed widespread deployment of AI programming assistants, such as GitHub Copilot. However, in practice, developers do not accept AI programming assistants' initial suggestions at a high frequency. This leaves a number of open questions related to the usability of these tools. To understand developers' practices while using these tools and the important usability challenges they face, we administered a survey to a large population of developers and received responses from a diverse set of 410 developers. Through a mix of qualitative and quantitative analyses, we found that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax, but resonate less with using them to help brainstorm potential solutions. We also found the most important reasons why developers do not use these tools are because these tools do not output code that addresses certain functional or non-functional requirements and because developers have trouble controlling the tool to generate the desired output. Our findings have implications for both creators and users of AI programming assistants, such as designing minimal cognitive effort interactions with these tools to reduce distractions for users while they are programming.
Dynamic and typological explanations of planning in complex problem-solving

Planning is an important component of complex problem-solving (CPS) skills, exerting a profound impact on student learning and development. Previous studies have primarily employed variable-centered methods to explore the influence of planning on CPS skills, thereby neglecting the dynamic role and mechanisms of planning as a metacognitive strategy in CPS processes. Through the use of sequence analysis of process data drawn from the finite state automata measurement framework within the PISA 2012 assessment, our study underscores the importance of planning in the inception of goal-directed behaviors, while also revealing typological differences in the use of planning strategy. By examining the role, mechanisms, and typologies of planning strategy, this study offers considerable pedagogical insights for enhancing CPS instruction. The study demonstrates that planning is instrumental in initiating goal-directed behaviors and enhencing interactions quality in CPS process. It sheds light on distinct typological differences in the use of planning strategies. Moreover, the use of planning is closely intertwined with motivation, cognitive load and personality traits. The findings advocate for instruction that is tailored to individual students' planning typologies, incorporating tailored scaffolding to foster CPS skills. Further research into personalized planning interventions has the potential to translate these insights into enhanced practical applications.
Inference serving with end-to-end latency SLOs over dynamic edge networks

Abstract While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not been carefully studied especially when the request has to be served over a dynamic wireless network at the edge. In this paper, we propose Jellyfish—a novel edge DL inference serving system that achieves soft guarantees for end-to-end inference latency service-level objectives (SLO). Jellyfish handles the network variability by utilizing both data and deep neural network (DNN) adaptation to conduct tradeoffs between accuracy and latency. Jellyfish features a new design that enables collective adaptation policies where the decisions for data and DNN adaptations are aligned and coordinated among multiple users with varying network conditions. We propose efficient algorithms to continuously map users and adapt DNNs at runtime, so that we fulfill latency SLOs while maximizing the overall inference accuracy. We further investigate dynamic DNNs, i.e., DNNs that encompass multiple architecture variants, and demonstrate their potential benefit through preliminary experiments. Our experiments based on a prototype implementation and real-world WiFi and LTE network traces show that Jellyfish can meet latency SLOs at around the 99th percentile while maintaining high accuracy.
ArZiGo: A recommendation system for scientific articles

The large number of scientific publications around the world is increasing at a rate of approximately 4%–5% per year. This fact has resulted in the need for tools that deal with relevant and high-quality publications. To address this necessity, search and reference management tools that include some recommendation algorithms have been developed. However, many of these solutions are proprietary tools and the full potential of recommender systems is rarely exploited. There are some solutions which provide recommendations for specific domains, by using ad-hoc resources. Furthermore, some other systems do not consider any personalization strategy to generate the recommendations. This paper presents ArZiGo, a web-based full prototype system for the search, management, and recommendation of scientific articles, which feeds on the Semantic Scholar Open Research Corpus, a corpus that is growing continually with more than 190M papers from all fields of science so far. ArZiGo combines different recommendation approaches within a hybrid system, in a configurable way, to recommend those papers that best suit the preferences of the users. A group of 30 human experts has participated in the evaluation of 500 recommendations in 10 research areas, 7 of which belong to the area of Computer Science and 3 to the area of Medicine, obtaining quite satisfactory results. Besides the appropriateness of the articles recommended, the execution time of the implemented algorithms has also been analyzed.
Analysing persuasion strategies in online advertising for third-hand cars in Tanzania: translanguaging in the virtual linguistic landscape

The main argument of this paper is that the Virtual Linguistic Landscape (VLL) of advertising allows the utilisation of persuasion strategies that transcend the traditional separation of named languages to produce the maximum effect on potential customers. The paper challenges the segregational view of language, which assumes that communication involves selecting linguistic resources from pre-determined sets of possibilities in a separate linguistic system from other communicative possibilities (Pennycook, A. (2017). Translanguaging and semiotic assemblages. International Journal of Multilingualism, 14(3), 269–282, p. 8). It used the Snipping Tool to collect 79 online car adverts from X, Facebook, Instagram, Kupatana, Jiji Tanzania, and Jamii Forums. Thematic and socio-semiotic analyses indicate that advertisers for used cars integrate icons, indices, and symbols in the VLL to communicate messages effectively. They also utilise repetitions, typography, nonce borrowings, nicknames, colloquialisms, endorsements, and testimonials to influence their audiences' attitudes and behaviours. The paper also shows that communication in the VLL is shaped by the relations between users, semiotic possibilities, and referents in making meaning, lending support to translanguaging.
Prioritization and exchange chains in privacy-preserving kidney exchange

The Kidney Exchange Problem (KEP) aims at finding an optimal set of exchanges among pairs of patients and their medically incompatible living kidney donors as well as altruistic donors who are not associated with any particular patient but want to donate a kidney to any person in need. Existing platforms that offer the finding of such exchanges for patient-donor pairs and altruistic donors are organized in a centralized fashion and operated by a single platform operator. This makes them susceptible to manipulation and corruption. Recent research has targeted these security issues by proposing decentralized Secure Multi-Party Computation (SMPC) protocols for solving the KEP. However, these protocols fail to meet two important requirements for kidney exchange in practice. First, they do not allow for altruistic donors. While such donors are not legally allowed in all countries, they have been shown to have a positive effect on the number of transplants that can be found. Second, the existing SMPC protocols do not support prioritization, which is used in existing platforms to give priority to certain exchanges or patient-donor pairs, e.g., to patients who are hard to match due to their medical characteristics. In this paper, we introduce a generic gate for implementing prioritization in kidney exchange. We extend two existing SMPC protocols for solving the KEP such that they allow for altruistic donors and prioritization and present one novel SMPC protocol for solving the KEP with altruistic donors and prioritization based on dynamic programming. We prove the security of all protocols and analyze their complexity. We implement all protocols and evaluate their performance for the setting where altruistic donors are legally allowed and for the setting where they are not. Thereby, we determine the performance impact of the inclusion of altruistic donors and obtain those approaches that perform best for each setting.
A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods

Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative analysis and treatment. The use of convolutional neural networks (CNNs) in image processing and deep learning research have led to super-resolution reconstruction methods based on deep learning. The study offers a G-guided generative multilevel network for training 3D neural networks with poorly sampled MR input data. The author suggest using super-resolution reconstruction (SRR) and modified sparse sampling to address these issues. Image-based Wasserstein GANs retain k-space data sparsity. Wasserstein Generative Adversarial Networks (WGANs) store and represent picture space knowledge. The method obtains null-valued k-space data and repairs fill gaps in the dataset to preserve data integrity. The proposed reconstruction method processes raw data samples and is able to perform subspace synchronization, deblurring, denoising, motion estimation, and super-resolution image production. The suggested algorithm uses different preprocessing methods to deblur and denoise datasets. Preliminary trials contextualize and speed up assessments. Results indicate that reconstructed pictures have better high-frequency features than sophisticated multi-frame techniques. This is supported by rising PSNR, MAE, and IEM measurements. A k-space correction block improves GAN network refinement learning in the suggested method. This block improves the network’s ability to avoid unnecessary data, speeding reconstruction. A k-space correction module can limit the generator’s output to critical lines, allowing the reconstruction of only missing lines. This improves convergence and speeds rebuilding. This study shows that this strategy reduces aliasing artifacts better than contemporaneous and noniterative methods.
A Survey on Comprehensive Exploration of Modern Perception System

This review paper offers an in-depth analysis of recent advancements in image-based intelligent transportation systems. It provides insights into various approaches employed for detecting false images, license plate recognition, distributing emergency messages, as well as detecting and classifying vehicle accidents and road accidents. The research critically evaluates the pros and cons of deep learning techniques, including position-based routing, enhanced YOLOv8, optimal K-means with a convolutional neural network, and gated hierarchical multi-task learning. The investigation of an Internet of Things-based vehicle accident detection and classification system highlights the benefits of sensor fusion for precise accident detection; nonetheless, the evaluation raises questions regarding the scalability and reliability of smartphone sensors. The utility of a deep learning system for road accident identification utilizing artificially created multi-perspective movies is emphasized, along with warnings about possible limits in capturing real-world circumstances
Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model

The popularization of the internet and the widespread use of smartphones have led to a rapid growth in the number of social media users. While information technology has brought convenience to people, it has also given rise to cyberbullying, which has a serious negative impact. The identity of online users is hidden, and due to the lack of supervision and the imperfections of relevant laws and policies, cyberbullying occurs from time to time, bringing serious mental harm and psychological trauma to the victims. The pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) has achieved good results in the field of natural language processing, which can be used for cyberbullying detection. In this research, we construct a variety of traditional machine learning, deep learning and Chinese pre-trained language models as a baseline, and propose a hybrid model based on a variant of BERT: XLNet, and deep Bi-LSTM for Chinese cyberbullying detection. In addition, real cyber bullying remarks are collected to expand the Chinese offensive language dataset COLDATASET. The performance of the proposed model outperforms all baseline models on this dataset, improving 4.29% compared to SVM—the best performing method in traditional machine learning, 1.49% compared to GRU—the best performing method in deep learning, and 1.13% compared to BERT.