Reviewerly/exHarmony huggingface dataset:
A Performance Comparison of HPC Workloads on Traditional and Cloud-Based HPC Clusters

Cloud Computing allows users to access large computing infrastructures quickly. In the High Performance Computing (HPC) context, public cloud resources emerge as an economical alternative, allowing institutions and research groups to use highly parallel infrastructures in the cloud. However, parallel runtime systems and software optimizations proposed over the years to improve the performance and scalability of HPC applications targeted traditional on-premise HPC clusters, where developers have direct access to the underlying hardware without any kind of virtualization. In this paper, we analyze the performance and scalability of HPC applications from the NAS Parallel Benchmarks suite when running on a virtualized HPC cluster built on top of Amazon Web Services (AWS), contrasting them with the results obtained with the same applications running on a traditional on-premise HPC cluster from Grid'5000. Our results show that CPU-bound applications achieve similar results in both platforms, whereas communication-bound applications may be impacted by the limited network bandwidth in the cloud. Cloud infrastructure demonstrated better performance under workloads with moderate communication and medium-sized messages.
Improving Text-to-Speech Systems Through Preprocessing and Postprocessing Applications

Text-to-Speech (TTS) systems are significant technologies that provide speech synthesis that is both natural and human-like by converting text to speech signals. TTS systems need every letter, word, and sentence in the text to be correctly pronounced. Therefore, advanced algorithms and artificial intelligence techniques are used to control speech attributes. However, the use of complex models and multilayer deep neural networks alone is not sufficient to synthesize natural speech. In order to enhance the user experience on TTS systems, additional processing is necessary. In addition to steps such as text cleaning, correction and editing, natural language processing (NLP) applications should be integrated into TTS systems. The preprocessing stage improves the quality of the texts and eliminates grammatical errors, resulting in the production of speech expressions with the correct stress and intonation. Moreover, the post-processing of audio signals makes the sound more fluent and emotionally rich. In addition, editing and controlling the attributes employed in speech synthesis will result in a speech experience that is more realistic and user-friendly. This study investigated the effects of various pre-processing and post-processing techniques on Turkish TTS systems for this reason. In the experiments carried out, it was observed how the correction of text input and post-processing of synthesized sounds affect the performance of the TTS system. Pre- and post-processing has been demonstrated to enhance the accuracy, fluency, and emotional expression of TTS systems in experiments. In order to improve the quality of TTS technologies and the user experience, it is suggested that future research should focus on the integration of pre- and post- processing.
Cell2Doc: ML Pipeline for Generating Documentation in Computational Notebooks

Computational notebooks have become the go-to way for solving data-science problems. While they are designed to combine code and documentation, prior work shows that documentation is largely ignored by the developers because of the manual effort. Automated documentation generation can help, but existing techniques fail to capture algorithmic details and developers often end up editing the generated text to provide more explanation and sub-steps. This paper proposes a novel machine-learning pipeline, Cell2Doc, for code cell documentation in Python data science notebooks. Our approach works by identifying different logical contexts within a code cell, generating documentation for them separately, and finally combining them to arrive at the documentation for the entire code cell. Cell2Doc takes advantage of the capabilities of existing pre-trained language models and improves their efficiency for code cell documentation. We also provide a new benchmark dataset for this task, along with a data-preprocessing pipeline that can be used to create new datasets. We also investigate an appropriate input representation for this task. Our automated evaluation suggests that our best input representation improves the pre-trained model's performance by 2.5x on average. Further, Cell2Doc achieves 1.33x improvement during human evaluation in terms of correctness, informativeness, and readability against the corresponding standalone pretrained model.
Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain

API recommendation methods have evolved from literal and semantic keyword matching to query expansion and query clarification. The latest query clarification method is knowledge graph (KG)-based, but limitations include out-of-vocabulary (OOV) failures and rigid question templates. To address these limitations, we propose a novel knowledge-guided query clarification approach for API recommendation that leverages a large language model (LLM) guided by KG. We utilize the LLM as a neural knowledge base to overcome OOV failures, generating fluent and appropriate clarification questions and options. We also leverage the structured API knowledge and entity relationships stored in the KG to filter out noise, and transfer the optimal clarification path from KG to the LLM, increasing the efficiency of the clarification process. Our approach is designed as an AI chain that consists of five steps, each handled by a separate LLM call, to improve accuracy, efficiency, and fluency for query clarification in API recommendation. We verify the usefulness of each unit in our AI chain, which all received high scores close to a perfect 5. When compared to the baselines, our approach shows a significant improvement in MRR, with a maximum increase of 63.9% higher when the query statement is covered in KG and 37.2% when it is not. Ablation experiments reveal that the guidance of knowledge in the KG and the knowledge-guided pathfinding strategy are crucial for our approach's performance, resulting in a 19.0% and 22.2% increase in MAP, respectively. Our approach demonstrates a way to bridge the gap between KG and LLM, effectively compensating for the strengths and weaknesses of both.
Personalized Guidelines for Design, Implementation and Evaluation of Anti-Phishing Interventions

Background: Current anti-phishing interventions, which typically involve one-size-fits-all solutions, suffer from limitations such as inadequate usability and poor implementation. Human-centric challenges in anti-phishing technologies remain little understood. Research shows a deficiency in the comprehension of end-user preferences, mental states, and cognitive requirements by developers and practitioners involved in the design, implementation, and evaluation of anti-phishing interventions. Aims: This study addresses the current lack of resources and guidelines for the design, implementation and evaluation of anti-phishing interventions, by presenting personalized guidelines to the developers and practitioners. Method: Through an analysis of 53 academic studies and 16 items of grey literature studies, we systematically identified the challenges and recommendations within the anti-phishing interventions, across different practitioner groups and intervention types. Results: We identified 22 dominant factors at the individual, technical, and organizational levels, that affected the effectiveness of anti-phishing interventions and, accordingly, reported 41 guidelines based on the suggestions and recommendations provided in the studies to improve the outcome of anti-phishing interventions. Conclusions: Our dominant factors can help developers and practitioners enhance their understanding of human-centric, technical and organizational issues in anti-phishing interventions. Our customized guidelines can empower developers and practitioners to counteract phishing attacks.
Identifying Flakiness in Quantum Programs

In recent years, software engineers have explored ways to assist quantum software programmers.Our goal in this paper is to continue this exploration and see if quantum software programmers deal with some problems plaguing classical programs.Specifically, we examine whether intermittently failing tests, i.e., flaky tests, affect quantum software development.To explore flakiness, we conduct a preliminary analysis of 14 quantum software repositories.Then, we identify flaky tests and categorize their causes and methods of fixing them.We find flaky tests in 12 out of 14 quantum software repositories.In these 12 repositories, the lower boundary of the percentage of issues related to flaky tests ranges between 0.26% and 1.85% per repository.We identify 46 distinct flaky test reports with 8 groups of causes and 7 common solutions.Further, we notice that quantum programmers are not using some of the recent flaky test countermeasures developed by software engineers.This work may interest practitioners, as it provides useful insight into the resolution of flaky tests in quantum programs.Researchers may also find the paper helpful as it offers quantitative data on flaky tests in quantum software and points to new research opportunities.
Evaluation Model and Performance Analysis of NIC Aggregations in Containerized Private Clouds

The availability of computational resources changed significantly due to cloud computing. In addition, we have witnessed efforts to execute High-Performance Computing (HPC) applications in the cloud attracted by the advantages of cost savings and scalable/elastic resource allocation. Allocating more powerful hardware and exclusivity allocating resources such as memory, storage, and CPU can improve performance in the cloud. For network interconnection, significant noise, and other inferences are generated by several simultaneous instances (multitenants) communicating using the same network. As increasing the network bandwidth may be an alternative, we designed an evaluation model, and performance analysis of NIC aggregation approaches in containerized private clouds. The experiments using NAS Parallel Benchmarks revealed that NIC aggregation approach outperforms the baseline up to ≈98% of the executions with applications characterized by intensive network use. Also, the Balance Round-Robin aggregation mode performed better than the 802.3ad aggregation mode in most assessments.
Analisis Usability Aplikasi Mobile Kampung Merah Putih Menggunakan Metode Heuristics Evaluation

The Kampung Merah Putih mobile application is one of the works of the nation's children that deserves respect. For some developed countries, smart applications may be familiar, but for Indonesians who tend to want practicality in everything, it is a very interesting thing. The Kampung Merah Putih application is an application created to facilitate environmental management. The satisfaction of Kampung Merah Putih application users needs to be evaluated using the Heuristic Evaluation method with the hope that application users can always be satisfied with its features. Data will be analyzed using quantitative methods with descriptive statistics and inferential statistics. Collecting data by distributing questionnaires online with the sample used is active users for at least one month of use. The result shows the tendency of respondents to agree with the items asked in the Heuristic Evaluation of &gt; 80% of the total answers. However, there are many regression modeling results on the T-test that need attention for further evaluation, namely the dimensions of Visibility of System Status, User Control and Freedom, Consistency and Standard, and Recognition Rather Than Recall Help and Documentation.
ATOM: Automated Black-Box Testing of Multi-Label Image Classification Systems

Multi-label Image Classification Systems (MICSs) developed based on Deep Neural Networks (DNNs) are extensively used in people's daily life. Currently, although there are a variety of approaches to test DNN-based systems, they typically rely on the internals of DNNs to design test cases, and do not take the core specification of MICS (i.e., correctly recognizing multiple objects in a given image) into account. In this paper, we propose ATOM, an automated and systematic black-box testing framework for testing MICS. Specifically, ATOM exploits the label combination as the testing adequacy criteria, hoping to systematically examine the impact of correlations between a fixed number of labels on the classification ability of MICS. Then, ATOM leverages image search engine and natural language processing to find test images that are not only common to the real-world, but also relevant to target label combinations. Finally, ATOM combines metamorphic testing and label information to realize test oracle identification, based on which the ability of MICS in classifying different label combinations is evaluated. To evaluate the effectiveness of ATOM, we have performed experiments on two popular datasets of MICS, VOC and COCO (each with five state-of-the-art DNN models), and one real-world photo tagging application from our industrial partner. The experimental results reveal that the performance of current DNN-based MICSs remains less satisfactory even in recognizing correlations between only two labels, as ATOM triggers a total number of 6,049 such label combination related errors for all MICSs studied. In particular, ATOM reports 587 error-revealing images for the industrial MICS, in which 92% of them are confirmed by the developers.
Modeling Programmer Attention as Scanpath Prediction

This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people intake when performing programming tasks. Models of programmer attention refer to machine prediction of what information is important to people. Models of programmer attention are important because they help researchers build better interfaces, assistive technologies, and more human-like AI. For many years, researchers in SE have built these models based on features such as mouse clicks, key logging, and IDE interactions. Yet the holy grail in this area is scanpath prediction - the prediction of the sequence of eye fixations a person would take over a visual stimulus. A person's eye movements are considered the most concrete evidence that a person is taking in a piece of information. Scanpath prediction is a notoriously difficult problem, but we believe that the emergence of lower-cost, higheraccuracy eye tracking equipment and better large language models of source code brings a solution within grasp. We present an eye tracking experiment with 27 programmers and a prototype scanpath predictor to present preliminary results and obtain early community feedback.
How to Train Your Neural Bug Detector: Artificial vs Real Bugs

Real bug fixes found in open source repositories seem to be the perfect source for learning to localize and repair real bugs. Yet, the scale of existing bug fix collections is typically too small for training data-intensive neural approaches. Neural bug detectors are hence almost exclusively trained on artificial bugs, produced by mutating existing source code and thus easily obtainable at large scales. However, neural bug detectors trained on artificial bugs usually underperform when faced with real bugs. To address this shortcoming, we set out to explore the impact of training on real bug fixes at scale. Our systematic study compares neural bug detectors trained on real bug fixes, artificial bugs and mixtures of real and artificial bugs at various dataset scales and with varying training techniques. Based on our insights gained from training on a novel dataset of 33k real bug fixes, we were able to identify a training setting capable of significantly improving the performance of existing neural bug detectors by up to 170% on simple bugs in Python. In addition, our evaluation shows that further gains can be expected by increasing the size of the real bug fix dataset or the code dataset used for generating artificial bugs. To facilitate future research on neural bug detection, we release our real bug fix dataset, trained models and code.
QuraTest: Integrating Quantum Specific Features in Quantum Program Testing

The recent fast development of quantum computers breaks several computation limitations that are difficult for conventional computers. Up to the present, although many approaches and tools have been proposed to test quantum programs, the fundamental features of quantum programs, i.e., magnitude, phase, and entanglement, have been largely overlooked, leading to limited fault detection capability and reduced testing effectiveness. To address this problem, we propose an automated testing framework named QURATEST, equipped with three test case generators (including two newly proposed techniques, UCNOT and IQFT in this paper, as well as one based on Random techniques) to test quantum programs. Overall, the proposed generators enable the generation of diverse test inputs by considering the quantum features of quantum programs. In the experiments, we perform an in-depth evaluation of QURATEST from three aspects: generated test case diversity, output coverage of the program under test, and fault detection capability. The results demonstrate the potential of our newly proposed techniques in that IQFT can generate the most diverse test cases regarding magnitude, phase, and entanglement, with 66% cell coverage. Comparatively, the Random approach only has 10% cell coverage. Regarding the evaluations of the output coverage, IQFT can achieve the highest output coverage in 70.2% (33 out of 47) of all quantum programs. In terms of fault detection, UCNOT outperforms the other two techniques. Specifically, the test cases generated by UCNOT have the best mutation score in 88.4% (23 out of 26) quantum programs.
From Commit Message Generation to History-Aware Commit Message Completion

Commit messages are crucial to software development, allowing developers to track changes and collaborate effectively. Despite their utility, most commit messages lack important information since writing high-quality commit messages is tedious and time-consuming. The active research on commit message generation (CMG) has not yet led to wide adoption in practice. We argue that if we could shift the focus from commit message generation to commit message completion and use previous commit history as additional context, we could significantly improve the quality and the personal nature of the resulting commit messages. In this paper, we propose and evaluate both of these novel ideas. Since the existing datasets lack historical data, we collect and share a novel dataset called CommitChronicle, containing 10.7M commits across 20 programming languages. We use this dataset to evaluate the completion setting and the usefulness of the historical context for state-of-the-art CMG models and GPT-3.5-turbo. Our results show that in some contexts, commit message completion shows better results than generation, and that while in general GPT-3.5-turbo performs worse, it shows potential for long and detailed messages. As for the history, the results show that historical information improves the performance of CMG models in the generation task, and the performance of GPT-3.5-turbo in both generation and completion.
Multispecies Ensemble Architecture For Texts Authorship Classification

This paper deals with the problem of text authorship identification. This issue is now one of the most demanded tasks in the field of natural language processing. A review of popular authorship analysis techniques is conducted. The implementation of a hybrid classifier built on the ensemble approach is suggested in the paper. By balancing a variety of methodologies and choosing the best outcomes from them, this is anticipated to produce more accurate authorship identification quality measures. This classifier offers a consistently high level of author identification. The development of an ensemble approach with a quantum-inspired component is offered as a classifier architecture. A number of experiments have verified the method's benefits. The results of computer experiments are presented, which confirmed the hypotheses about the effectiveness of the ensemble approach to the task of text classification. The proposed classifier can be effectively applied to identify borrowing cases. For example, in an educational environment, to better detect student plagiarism. At the moment, the applied implementation of the classifier is being tested in the form of a support module for the University Academic Integrity System.
Analyzing Application Response Time Lag: in Mobile Edge and Fog Computing Environments

The 21st-century surge in computing infrastructure across mobile, centralized, and decentralized realms has ushered in a new era of diverse applications. However, this progress comes hand in hand with amplified security risks. Essential security measures, including resource-intensive static and dynamic scans, strain these infrastructures, leading to a noticeable lag in user experiences. This study delves into the investigation of lag in the response time user encounters when various infrastructure elements are being scanned. The infrastructure elements undergoes both static and dynamic security scans. By leveraging Prisma Cloud <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> by PaloAlto <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> for security analysis, the research narrows its focus to the analysis of response time delays. Employing tools like Twistlock <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , a representative application hosted on Google Cloud® Platform undergoes rigorous scanning. User response times are meticulously recorded through in-depth network traffic analysis. Techniques such as capturing TCP/IP 3-way handshake data are employed to precisely measure the extent of lag in response times. Through data analytic techniques, the study identifies distinct patterns and anomalies within response times, ultimately striving to statistically evaluate the impact of security scans on user responses. The findings drawn from these analyses pave the way for well-rounded conclusions and valuable recommendations.