Reviewerly/exHarmony huggingface dataset:
The Call Graph Chronicles: Unleashing the Power Within

Call graph generation is critical for program understanding and analysis, but achieving both accuracy and precision is challenging. Existing methods trade off one for the other, particularly in dy- namic languages like JavaScript. This paper introduces "Graphia," an approach that combines structural and semantic information using a Graph Neural Network (GNN) to enhance call graph accu- racy. Graphia’s two-step process employs an initial call graph as training data for the GNN, which then uncovers true call edges in new programs. Experimental results show Graphia significantly improves true positive rates in vulnerability detection, achieving up to 95%. This approach advances call graph accuracy by effectively incorporating code structure and context, particularly in complex dynamic language scenarios.
A Practical Human Labeling Method for Online Just-in-Time Software Defect Prediction

Just-in-Time Software Defect Prediction (JIT-SDP) can be seen as an online learning problem where additional software changes produced over time may be labeled and used to create training examples. These training examples form a data stream that can be used to update JIT-SDP models in an attempt to avoid models becoming obsolete and poorly performing. However, labeling procedures adopted in existing online JIT-SDP studies implicitly assume that practitioners would not inspect software changes upon a defect-inducing prediction, delaying the production of training examples. This is inconsistent with a real-world scenario where practitioners would adopt JIT-SDP models and inspect certain software changes predicted as defect-inducing to check whether they really induce defects. Such inspection means that some software changes would be labeled much earlier than assumed in existing work, potentially leading to different JIT-SDP models and performance results. This paper aims at formulating a more practical human labeling procedure that takes into account the adoption of JIT-SDP models during the software development process. It then analyses whether and to what extent it would impact the predictive performance of JIT-SDP models. We also propose a new method to target the labeling of software changes with the aim of saving human inspection effort. Experiments based on 14 GitHub projects revealed that adopting a more realistic labeling procedure led to significantly higher predictive performance than when delaying the labeling process, meaning that existing work may have been underestimating the performance of JIT-SDP. In addition, our proposed method to target the labeling process was able to reduce human effort while maintaining predictive performance by recommending practitioners to inspect software changes that are more likely to induce defects. We encourage the adoption of more realistic human labeling methods in research studies to obtain an evaluation of JIT-SDP predictive performance that is closer to reality.
OOM-Guard: Towards Improving the Ergonomics of Rust OOM Handling via a Reservation-Based Approach

Out of memory (OOM) is an exceptional system state where any further memory allocation requests may fail. Such allocation failures would crash the process or system if not handled properly, and they may also lead to an inconsistent program state that cannot be recovered easily. Current mechanisms for preventing such hazards highly rely on the manual effort of the programmers themselves. This paper studies the OOM issues of Rust, which is an emerging system programming language that stresses the importance of memory safety but still lacks handy mechanisms to handle OOM well. Even worse, Rust employs an infallible mode of memory allocations by default. As a result, the program written by Rust would simply abort itself when OOM occurs. Such crashes would lead to critical robustness issues for services or modules of operating systems. We propose OOM-Guard, a handy approach for Rust programmers to handle OOM. OOM-Guard is by nature a reservation-based approach that aims to convert the handlings for many possible failed memory allocations into handlings for a smaller number of reservations. In order to achieve efficient reservation, OOM-Guard incorporates a subtle cost analysis algorithm based on static analysis and a proxy allocator. We then apply OOM-Guard to two well-known Rust projects, Bento and rCore. Results show that OOM-Guard can largely reduce developers' efforts for handling OOM and incurs trivial overhead in both memory space and execution time.
PEM: Representing Binary Program Semantics for Similarity Analysis via a Probabilistic Execution Model

Binary similarity analysis determines if two binary executables are from the same source program. Existing techniques leverage static and dynamic program features and may utilize advanced Deep Learning techniques. Although they have demonstrated great potential, the community believes that a more effective representation of program semantics can further improve similarity analysis. In this paper, we propose a new method to represent binary program semantics. It is based on a novel probabilistic execution engine that can effectively sample the input space and the program path space of subject binaries. More importantly, it ensures that the collected samples are comparable across binaries, addressing the substantial variations of input specifications. Our evaluation on 9 real-world projects with 35k functions, and comparison with 6 state-of-the-art techniques show that PEM can achieve a precision of 96% with common settings, outperforming the baselines by 10-20%.
KG4CraSolver: Recommending Crash Solutions via Knowledge Graph

Fixing crashes is challenging, and developers often discuss their encountered crashes and refer to similar crashes and solutions on online Q&A forums (e.g., Stack Overflow). However, a crash often involves very complex contexts, which includes different contextual elements, e.g., purposes, environments, code, and crash traces. Existing crash solution recommendation or general solution recommendation techniques only use an incomplete context or treat the entire context as pure texts to search relevant solutions for a given crash, resulting in inaccurate recommendation results. In this work, we propose a novel crash solution knowledge graph (KG) to summarize the complete crash context and its solution with a graph-structured representation. To construct the crash solution KG automatically, we propose to leverage prompt learning to construct the KG from SO threads with a small set of labeled data. Based on the constructed KG, we further propose a novel KG-based crash solution recommendation technique KG4CraSolver, which precisely finds the relevant SO thread for an encountered crash by finely analyzing and matching the complete crash context based on the crash solution KG. The evaluation results show that the constructed KG is of high quality and KG4CraSolver outperforms baselines in terms of all metrics (e.g., 13.4%-113.4% MRR improvements). Moreover, we perform a user study and find that KG4CraSolver helps participants find crash solutions 34.4% faster and 63.3% more accurately.
Towards Automated Detection of Unethical Behavior in Open-Source Software Projects

Given the rapid growth of Open-Source Software (OSS) projects, ethical considerations are becoming more important. Past studies focused on specific ethical issues (e.g., gender bias and fairness in OSS). There is little to no study on the different types of unethical behavior in OSS projects. We present the first study of unethical behavior in OSS projects from the stakeholders’ perspective. Our study of 316 GitHub issues provides a taxonomy of 15 types of unethical behavior guided by six ethical principles (e.g., autonomy). Examples of new unethical behavior include soft forking (copying a repository without forking) and self-promotion (promoting a repository without self-identifying as contributor to the repository). We also identify 18 types of software artifacts affected by the unethical behavior. The diverse types of unethical behavior identified in our study (1) call for attentions of developers and researchers when making contributions in GitHub, and (2) point to future research on automated detection of unethical behavior in OSS projects. From our study, we propose Etor, an approach that can automatically detect six types of unethical behavior by using ontological engineering and Semantic Web Rule Language (SWRL) rules to model GitHub attributes and software artifacts. Our evaluation on 195,621 GitHub issues (1,765 GitHub repositories) shows that Etor can automatically detect 548 unethical behavior with 74.8% average true positive rate (up to 100% true positive rate). This shows the feasibility of automated detection of unethical behavior in OSS projects.
DecompoVision: Reliability Analysis of Machine Vision Components through Decomposition and Reuse

Analyzing reliability of Machine Vision Components (MVC) against scene changes (such as rain or fog) in their operational environment is crucial for safety-critical applications. Safety analysis relies on the availability of precisely specified and, ideally, machine-verifiable requirements. The state-of-the-art reliability framework ICRAF developed machine-verifiable requirements obtained using human performance data. However, ICRAF is limited to analyzing reliability of MVCs solving simple vision tasks, such as image classification. Yet, many real-world safety-critical systems require solving more complex vision tasks, such as object detection and instance segmentation. Fortunately, many complex vision tasks (which we call “c-tasks”) can be represented as a sequence of simple vision subtasks. For instance, object detection can be decomposed as object localization followed by classification. Based on this fact, in this paper, we show that the analysis of c-tasks can also be decomposed as a sequential analysis of their simple subtasks, which allows us to apply existing techniques for analyzing simple vision tasks. Specifically, we propose a modular reliability framework, DecompoVision, that decomposes: (1) the problem of solving a c-task, (2) the reliability requirements, and (3) the reliability analysis, and, as a result, provides deeper insights into MVC reliability. DecompoVision extends ICRAF to handle complex vision tasks and enables reuse of existing artifacts across different c-tasks. We capture new reliability gaps by checking our requirements on 13 widely used object detection MVCs, and, for the first time, benchmark segmentation MVCs.
Recommending Analogical APIs via Knowledge Graph Embedding

Library migration, which replaces the current library with a different one to retain the same software behavior, is common in software evolution. An essential part of this is finding an analogous API for the desired functionality. However, due to the multitude of libraries/APIs, manually finding such an API is time-consuming and error-prone. Researchers created automated analogical API recommendation techniques, notably documentation-based methods. Despite potential, these methods have limitations, e.g., incomplete semantic understanding in documentation and scalability issues. In this study, we present KGE4AR, a novel documentation-based approach using knowledge graph (KG) embedding for recommending analogical APIs during library migration. KGE4AR introduces a unified API KG to comprehensively represent documentation knowledge, capturing high-level semantics. It further embeds this unified API KG into vectors for efficient, scalable similarity calculation. We assess KGE4AR with 35,773 Java libraries in two scenarios, with and without target libraries. KGE4AR notably outperforms state-of-the-art techniques (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements), showcasing scalability with growing library counts.
STraceBERT: Source Code Retrieval using Semantic Application Traces

Software reverse engineering is an essential task in software engineering and security, but it can be a challenging process, especially for adversarial artifacts. To address this challenge, we present STraceBERT, a novel approach that utilizes a Java dynamic analysis tool to record calls to core Java libraries, and pretrain a BERT-style model on the recorded application traces for effective method source code retrieval from a candidate set. Our experiments demonstrate the effectiveness of STraceBERT in retrieving the source code compared to existing approaches. Our proposed approach offers a promising solution to the problem of code retrieval in software reverse engineering and opens up new avenues for further research in this area.
Implementasi Proses Rekrutmen Peserta Didik Baru pada SMA Negeri 1 Kedamean Kota Gresik

The focus of this research is to uncover the implementation of online student registration management at SMA Negeri 1 Kedamean when applying the online PPDB system. This study adopts a qualitative method with the aim of gaining a comprehensive understanding of the data encountered or discovered by the researcher. The research findings reveal that in assessing the qualifications of potential new students at SMA Negeri 1 Kedamean, a student database is created and managed to facilitate the selection and admission process of students at SMA Negeri 1 Kedamean. The school's policy involves data analysis, using technology to analyze admission data such as student profiles, school preferences, and demographic data to improve the selection process. Based on technical guidance from the provincial government governing the implementation of online PPDB at SMA Negeri 1 Kedamean, it is decided that the implementation of online PPDB will take place in a meeting room selected as the venue for the online PPDB. The Online PPDB Body and Budget are sourced from the school's BOS fund, and in the New Student Admission System for the Online PPDB Academic Year 2023/2024, there are four pathways: Affirmation, Achievement, Special Selection, and Academic Path in the ongoing Online PPDB. Direct connections to the databases of the Social and Educational Services of the Ministry of Religion and KONI prevent fraud during the implementation of the Online PPDB. The process of admitting new students through online PPDB is now more transparent, making it easier for students and organizers to verify actual data and facilitating the accountability of the organizers because all data is created using this system.
Analyzing inexact hypergradients for bilevel learning

Abstract Estimating hyperparameters has been a long-standing problem in machine learning. We consider the case where the task at hand is modeled as the solution to an optimization problem. Here the exact gradient with respect to the hyperparameters cannot be feasibly computed and approximate strategies are required. We introduce a unified framework for computing hypergradients that generalizes existing methods based on the implicit function theorem and automatic differentiation/backpropagation, showing that these two seemingly disparate approaches are actually tightly connected. Our framework is extremely flexible, allowing its subproblems to be solved with any suitable method, to any degree of accuracy. We derive a priori and computable a posteriori error bounds for all our methods and numerically show that our a posteriori bounds are usually more accurate. Our numerical results also show that, surprisingly, for efficient bilevel optimization, the choice of hypergradient algorithm is at least as important as the choice of lower-level solver.
Discrete Time Fourier Transform (DTFT) and Discrete Fourier Transform (DFT)

This chapter shows how MATLAB’s Symbolic Math Toolbox® can be used to calculate the DTFT and DFT.
Expert System for Detection of Diseases in Layers Using Forward Chaining and Certainty Factor Methods

Inaccuracies in the process of diagnosing a type of disease result in errors in handling so that it will pose a risk of death. Accurate diagnostic process results require a high level of confidence so that the results are truly convincing. Current technological developments are making more and more mindsets for the development of information technology in the field of computerization born. One of them is an expert system. This expert system is often used to analyze disease in laying hens. The deficiency in previous research is that there is no degree of confidence so what happens is that the diagnosis often only uses the value of the expert. The role of the system user is only to select the available symptoms without giving the weighted value of the selected symptoms. This study aims to build an expert system capable of detecting symptoms in laying hens by assigning a degree of confidence to each symptom. The system is built with a combination of forward chaining techniques with a certainty factor, the weight value is based on a combination of the weight of symptoms from users and experts to anticipate conditions that are not ideal. Several stages in the research include data collection, knowledge base modeling, implementation into applications and testing. The conclusion that can be drawn from the trial results is that the system can show a maximum validity value of up to 100% when compared to manual calculations.
Speech Corpus for Medina Dialect

Automatic Speech Recognition (ASR) has standard rules which must be followed and considered carefully. Some difficulties that lead to less ASR performance is variations in pronunciation and small words misrecognition. Arabic ASR faces some challenges like difficulty in obtaining corpora for spoken dialects. Obtaining a wide range of diacritized text as well as the enormous number of word forms is considered a major challenge due to the Arabic language morphology richness and its’ letters capability to be written without diacritics. Although Arabic is one of the most popular languages, Arabic ASR systems are still rare compared with other languages. As ASR systems depend primarily on speech corpuses, Arabic ASR systems requires specific-dialect speech corpuses. Such speech corpuses are still deficient, costly, nor sometimes exists. In this research, we contribute to overcome the lack of speech recognition and misunderstanding for one of the most famous dialects in Saudi Arabia, Medina. We created a brand-new corpus “Haneen Corpus“, which consists of 70,364 tokens that have been uttered using Medina dialect, and constructed a dictionary using 64 phonemes to analyse the correct pronunciation of words. Our Medina Dialect ASR System exploited Hidden Markov Models (HMM) that achieved 92.09 % speech recognition accuracy.
FORMATION OF COMPETENCES TO SUPPORT SUSTAINABLE DEVELOPMENT IN NATURAL SCIENCES EDUCATION AT THE LOWER SECONDARY SCHOOLS IN BULGARIA

FORMATION OF COMPETENCES TO SUPPORT SUSTAINABLE DEVELOPMENT IN NATURAL SCIENCES EDUCATION AT THE LOWER SECONDARY SCHOOLS IN BULGARIA
Students' Response to The Development of U-GEO Media Trading Card Game (TCG) with Teams Games Tournament (TGT)

The results of observations in class XI Social Sciences, the learning media used were only blackboards and PowerPoint. The media can make students bored and unable to attract interest in learning. This study aims to determine students' responses to TGC learning media on the material of cultural diversity in Bangkalan. Research using descriptive method with a quantitative approach. Data collection tool used is a questionnaire sheet. The data analysis technique used in this study is data reduction, data presentation and conclusion. The results showed that the percentage of responses of students in class XI IPS SMAN 1 Bangkalan to TGC learning media on the cognitive dimension indicators of content understanding averaged 86.2%, indicators of clarity of learning instructions and information averaged 86% and indicators of suitability of the average card display average 87.8%. On the affective dimension of student responses on motivation indicators obtained an average percentage of 86.8%, attractiveness indicators an average of 89.8% and curiosity indicators an average of 85.6%. On the conative dimension, the response of students to the indicator asking an average of 86.6% and the indicator responding to questions is an average of 95.6%.
Applying Arduino and individually addressable LED strips to the development of interactive materials to promote computer science undergraduate course

Arduino is a hardware and software platform known for its ease of use and accessibility, and can be operated by people with different levels of technical knowledge. With such a powerful tool, the goal of this research was to develop interactive and engaging materials that combine Arduino and individually addressable LED strips to attract people to computer science course in exhibitions and showcases by demonstrating the capabilities and accessibility of the technology. This paper presents two demos created with addressable LED. The first is an LED race based on the Open LED Race project, and the second is a Connect-4 game using an LED matrix.
Implementasi Rest Api Pada Aplikasi E-Perpus (Studi Kasus: SMA Gama Yogyakarta)

The library information system is a software development application used to monitor transaction processes within a library. These transactions encompass book borrowing and returning. This library information system is capable of handling both the book borrowing and returning processes, as well as managing master data, thereby aiding in the organization of the library's book collection. It also facilitates book searches through a catalog and generates transaction reports.&#x0D; The research process included surveys and interviews with library personnel, revealing that the duration of service for transaction activities and difficulties in book management were the primary challenges faced by the GAMA Yogyakarta High School library. With the implementation of the library information system, it is anticipated that it will assist library staff in their work, enhancing the efficiency and effectiveness of library operations, and expediting the service provided by staff for book borrowing and returning transactions.
PERANCANGAN UI/UX WEBSITE DALAM MANAGEMENT APLIKASI GYM MENGGUNAKAN METODE DESIGN THINKING

In Indonesia, there are many services that provide fitness services. However, only a few have implemented technological sophistication such as the absence of mobile applications and manual admin management. This causes few business units in Indonesia to be able to retain customers due to the lack of management in the gym business unit. Management in gym services is the main focus to improve performance and quality in the business so that it can be properly designed with the right calculations as well, with the existence of technological sophistication can help these problems by developing a website for admin management in the gym business. The design uses the design thinking method which includes the empathize, define, ideate, prototype, test stages by conducting tests to find out how easy, efficient, easy to remember, and errors or security of the designed website. Testing is done by interviewing users directly. So that it can be immediately implemented in real terms and can continue to be evaluated to improve admin performance.
Implementasi Metode PCQ – Queue Tree Pada Router Mikrotik Untuk Meningkatkan Quality Of Service Jaringan Internet Di Desa Renah Semanek

Abstract_Bandwidth management is a network management technique in an effort to provide fair and satisfactory network performance, which regulates computer networks so that bandwidth is used optimally and evenly. The internet network located in Renah Semanek Village has problems such as unstable internet connection, unequal internet distribution, making it difficult for everyone to access the internet. This study aims to stabilize the internet connection by dividing the maximum possible bandwidth across all clients, so that users will get an equal amount of bandwidth. This implementation has been tested and proven to improve Quality of Service (QoS) on Rt Rw Net. The results of the implementation of the PCQ-Queue Tree method get an increase in QoS value seen from the upload and download parameter testing, initially the internet connection was not divided equally.&#x0D;