Reviewerly/exHarmony huggingface dataset:
LibvDiff: Library Version Difference Guided OSS Version Identification in Binaries

Authors:
raw_author_name: Chaopeng Dong
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, -Select-, China
raw_author_name: Yang Xiao
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Yongpan Wang
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Hong Li
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhi Li
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Limin Sun
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

Abstract: 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

Authors:
raw_author_name: Aldeida Aleti
raw_affiliation_string: Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia

Abstract: 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

Authors:
raw_author_name: Adriana Meza Soria
raw_affiliation_string: Department of Informatics, University of California, Irvine, California, USA
raw_author_name: Janet Burge
raw_affiliation_string: Department of Mathematics and Computer Science, Colorado College, Colorado, Colorado, USA
raw_author_name: André Van der Hoek
raw_affiliation_string: Department of Informatics, University of California, Irvine, Irvine, California, USA

Abstract: 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

Authors:
raw_author_name: Jianan Ma
raw_affiliation_string: Hangzhou Dianzi University, Hangzhou, China Zhejiang University, Hangzhou, China
raw_author_name: Jingyi Wang
raw_affiliation_string: ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China; Zhejiang University, Hangzhou, China ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China
raw_author_name: Youcheng Sun
raw_affiliation_string: University of Manchester, Manchester, United Kingdom
raw_author_name: Cheng-Chao Huang
raw_affiliation_string: Nanjing Institute of Software Technology, ISCAS, Nanjing, China
raw_author_name: Zhen Wang
raw_affiliation_string: Hangzhou Dianzi University, Hangzhou, China

Abstract: 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

Authors:
raw_author_name: Jenny T. Liang
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, PA, USA
raw_author_name: Chenyang Yang
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, PA, USA
raw_author_name: Brad A. Myers
raw_affiliation_string: Carnegie Mellon University, Pittsburgh, PA, USA

Abstract: 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

Authors:
raw_author_name: Xiu-Qing Zhang
raw_affiliation_string: Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing 100875, China
raw_author_name: Qin-Hui Gao
raw_affiliation_string: Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing 100875, China
raw_author_name: Wei Tian
raw_affiliation_string: Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing 100875, China
raw_author_name: Tao Xin
raw_affiliation_string: Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing 100875, China

Abstract: 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

Authors:
raw_author_name: Vinod Nigade
raw_affiliation_string: Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
raw_author_name: Pablo Bauszat
raw_affiliation_string: Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
raw_author_name: Henri Bal
raw_affiliation_string: Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
raw_author_name: Lin Wang
raw_affiliation_string: Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Abstract: 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

Authors:
raw_author_name: Mikel Larrañaga
raw_affiliation_string: Languages and Information Systems, University of the Basque Country UPV/EHU, Manuel Lardizabal Ibilbidea, 1, Donostia, 20018, Basque Country, Spain
raw_author_name: Ana Arruarte
raw_affiliation_string: Languages and Information Systems, University of the Basque Country UPV/EHU, Manuel Lardizabal Ibilbidea, 1, Donostia, 20018, Basque Country, Spain

Abstract: 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.
Prioritization and exchange chains in privacy-preserving kidney exchange

Authors:
raw_author_name: Malte Breuer
raw_affiliation_string: Department of Computer Science, RWTH Aachen University, Germany
raw_author_name: Urike Meyer
raw_affiliation_string: Department of Computer Science, RWTH Aachen University, Germany
raw_author_name: Susanne Wetzel
raw_affiliation_string: Department of Computer Science, Stevens Institute of Technology, NJ, USA

Abstract: 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.
Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model

Authors:
raw_author_name: Shifeng Chen
raw_affiliation_string: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Jialin Wang
raw_affiliation_string: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Ketai He
raw_affiliation_string: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract: 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.
High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

Authors:
raw_author_name: Zhihui Gao
raw_affiliation_string: School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China
raw_author_name: Meng Han
raw_affiliation_string: School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China
raw_author_name: Shujuan Liu
raw_affiliation_string: School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China
raw_author_name: Ang Li
raw_affiliation_string: School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China
raw_author_name: Dongliang Mu
raw_affiliation_string: School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China

Abstract: The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.
Application of facial expression recognition technology based on feature fusion in teaching

Authors:
raw_author_name: Xiangyu Deng
raw_affiliation_string: College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu Province China
raw_author_name: Yiman Hu
raw_affiliation_string: College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu Province China
raw_author_name: Yahan Yang
raw_affiliation_string: Radiation Oncology Department, Gansu Provincial Hospital, Lanzhou, Gansu Province China

Abstract: With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved