Reviewerly/exHarmony huggingface dataset:
Non-Autoregressive Line-Level Code Completion

Authors:
raw_author_name: Fang Liu
raw_affiliation_string: School of Computer Science and Engineering, State Key Laboratory of Complex &amp; Critical Software Environment, Beihang University, Beijing, China
raw_author_name: Zhiyi Fu
raw_affiliation_string: School of Computer Science, Peking University, Beijing, China
raw_author_name: Ge Li
raw_affiliation_string: School of Computer Science, Peking University, Beijing, China
raw_author_name: Zhi Jin
raw_affiliation_string: School of Computer Science, Peking University, Beijing, China
raw_author_name: Hui Liu
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Yiyang Hao
raw_affiliation_string: Silicon Heart Tech Co., Beijing, China
raw_author_name: Li Zhang
raw_affiliation_string: School of Computer Science and Engineering, State Key Laboratory of Complex &amp; Critical Software Environment, Beihang University, Beijing, China

Abstract: Software developers frequently use code completion tools to accelerate software development by suggesting the following code elements. Researchers usually employ AutoRegressive (AR) decoders to complete code sequences in a left-to-right, token-by-token fashion. To improve the accuracy and efficiency of code completion, we argue that tokens within a code statement have the potential to be predicted concurrently. In this paper, we first conduct an empirical study to analyze the dependency among the target tokens in line-level code completion. The results suggest that it is potentially practical to generate all statement tokens in parallel. To this end, we introduce SANAR, a simple and effective syntax-aware non-autoregressive model for line-level code completion. To further improve the quality of the generated code, we propose an adaptive and syntax-aware sampling strategy to boost the model’s performance. The experimental results obtained from two widely used datasets indicate that our model outperforms state-of-the-art code completion approaches of similar model size by a considerable margin, and is faster than these models with up to 9 × speed-up. Moreover, the extensive results additionally demonstrate that the enhancements achieved by SANAR become even more pronounced with larger model sizes, highlighting their significance.
Deep Learning Estimating of Epicentral Distance for Earthquake Early Warning Systems

Authors:
raw_author_name: Shunta Noda
raw_affiliation_string: 1Center for Railway Earthquake Engineering Research, Railway Technical Research Institute, Tokyo, Japan

Abstract: ABSTRACT To enhance the performance of earthquake early warning (EEW) systems that aim to issue alerts as quickly as possible, it is crucial to improve the accuracy of the epicentral distance Δ estimated via the single-station method. Although the conventional method estimates Δ from the slope of the initial P-wave envelope, this study applies deep learning techniques that can extract a variety of information from the waveform data. By analyzing ∼20,000 records observed at Kyoshin Network stations in Japan, the convolutional neural network (CNN) method achieved higher accuracy than the conventional method. Increasing the data length or the number of iterations of convolution, activation, and pooling layers in the typical CNN model did not significantly improve the accuracy of Δ estimation. An automatic structure search (AutoSS) technique, in which model structure and hyperparameters are randomly varied, was employed to identify models that yield higher accuracy. A typical CNN model was used as the initial structure. The models obtained through this technique showed improved accuracy with increased data length or computational cost. The models that delivered the highest accuracy among those generated using the AutoSS technique outperformed the typical CNN model in terms of accuracy, although their computational costs were comparable. The AutoSS technique offers a significant advantage in that it allows the selection of a model that matches the computational capabilities of the hardware used for its implementation, thereby ensuring optimal computational efficiency. This exhibits that deep learning technologies can be used to improve the performance of EEW systems.
A Fully Test-Time Training Framework for Semi-Supervised Node Classification on Out-of-Distribution Graphs

Authors:
raw_author_name: Jiaxin Zhang
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Yiqi Wang
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Xihong Yang
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: En Zhu
raw_affiliation_string: National University of Defense Technology, ChangSha, China

Abstract: Graph neural networks (GNNs) have shown great potential in representation learning for various graph tasks. However, the distribution shift between the training and test sets poses a challenge to the efficiency of GNNs. To address this challenge, HomoTTT propose a fully test-time training (FTTT) framework for GNNs to enhance the model’s generalization capabilities for node classification tasks. Specifically, our proposed HomoTTT designs a homophily-based and parameter-free graph contrastive learning task with adaptive augmentation to guide the model’s adaptation during the test time training, allowing the model to adapt for specific target data. In the inference stage, HomoTTT proposes to integrate the original GNN model and the adapted model after TTT using a homophily-based model selection method, which prevents potential performance degradation caused by unconstrained model adaptation. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of our proposed framework. Additionally, the exploratory study further validates the rationality of the homophily-based graph contrastive learning task with adaptive augmentation and the homophily-based model selection designed in HomoTTT .
Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?

Authors:
raw_author_name: Chirag Shah
raw_affiliation_string: University of Washington, Seattle, USA
raw_author_name: Emily M. Bender
raw_affiliation_string: University of Washington, Seattle, USA

Abstract: We observe a recent trend towards applying large language models (LLMs) in search and positioning them as effective information access systems. While the interfaces may look appealing and the apparent breadth of applicability is exciting, we are concerned that the field is rushing ahead with a technology without sufficient study of the uses it is meant to serve, how it would be used, and what its use would mean. We argue that it is important to reassert the central research focus of the field of information retrieval, because information access is not merely an application to be solved by the so-called ‘AI’ techniques du jour. Rather, it is a key human activity, with impacts on both individuals and society. As information scientists, we should be asking what do people and society want and need from information access systems and how do we design and build systems to meet those needs? With that goal, in this conceptual paper we investigate fundamental questions concerning information access from user and societal viewpoints. We revisit foundational work related to information behavior, information seeking, information retrieval, information filtering, and information access to resurface what we know about these fundamental questions and what may be missing. We then provide our conceptual framing about how we could fill this gap, focusing on methods as well as experimental and evaluation frameworks. We consider the Web as an information ecosystem and explore the ways in which synthetic media, produced by LLMs and otherwise, endangers that ecosystem. The primary goal of this conceptual paper is to shed light on what we still do not know about the potential impacts of LLM-based information access systems, how to advance our understanding of user behaviors, and where the next generations of students, scholars, and developers could fruitfully invest their energies.
A Hyperintensional Logic of Non-prime Evidence

Authors:
raw_author_name: Pietro Vigiani
raw_affiliation_string: Department of Philosophy, Scuola Normale Superiore, Pisa, Italy

Abstract: Abstract We present a logic of evidence that reduces agents’ epistemic idealisations by combining classical propositional logic with substructural modal logic for formulas in the scope of epistemic modalities. To this aim, we provide a neighborhood semantics of evidence, which provides a modal extension of Fine’s semantics for relevant propositional logic. Possible worlds semantics for classical propositional logic is then obtained by defining the set of possible worlds as a special subset of information states in Fine’s semantics. Finally, we prove that evidence is a hyperintensional and non-prime notion in our logic, and provide a sound and complete axiomatisation of our evidence logic.
An Escape Time Formulation for Subgraph Detection and Partitioning of Directed Graphs

Authors:
raw_author_name: Zachary M. Boyd
raw_affiliation_string: Department of Mathematics, Brigham Young University, Provo, UT 84602 USA.
raw_author_name: Braxton Osting
raw_affiliation_string: Department of Mathematics, University of Utah, Salt Lake City, UT 84112 USA.

Abstract: .We provide a rearrangement based algorithm for detection of subgraphs of k vertices with long escape times for directed or undirected networks that is not combinatorially complex to compute. Complementing other notions of densest subgraphs and graph cuts, our method is based on the mean hitting time required for a random walker to leave a designated set and hit the complement. We provide a new relaxation of this notion of hitting time on a given subgraph and use that relaxation to construct a subgraph detection algorithm that can be computed easily and a generalization to K-partitioning schemes. Using a modification of the subgraph detector on each component, we propose a graph partitioner that identifies regions where random walks live for comparably large times. Importantly, our method implicitly respects the directed nature of the data for directed graphs while also being applicable to undirected graphs. We apply the partitioning method for community detection to a large class of models and real-world data sets.Keywordsescape timepartitioningbang-bangMSC codes68R10
QR codes and automated decision-making in the COVID-19 pandemic

Authors:
raw_author_name: Gerard Goggin
raw_affiliation_string: The University of Sydney, Australia
raw_author_name: Rowan Wilken
raw_affiliation_string: RMIT University, Australia

Abstract: In this article, we explore Quick Response (QR) codes (machine-readable optical labels that link to information) and how, after a period of having fallen out of favor, they have been reactivated and have come to underpin COVID-19 automation and contact-tracing efforts. During the pandemic, they were used especially for “safe entry” and other kinds of check-in to locations to facilitate contact tracing. In this context, QR codes facilitate automated decision-making in relation to infectious disease surveillance and disease outbreak control. However, the use of QR codes for contact-tracing purposes has enjoyed mixed success and its implementation has encountered several challenges, as we illustrate through a case study that explores QR codes and COVID contact tracing in Singapore and Australia.
Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation

Authors:
raw_author_name: Beichen Lu
raw_affiliation_string: School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou 325000, People's Republic of China.
raw_author_name: Jialin Shi
raw_affiliation_string: School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou 325000, People's Republic of China.
raw_author_name: Ziyi Wang
raw_affiliation_string: School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou 325000, People's Republic of China.
raw_author_name: Zhifang Pan
raw_affiliation_string: School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou 325000, People's Republic of China.
raw_author_name: Yezhi Lin
raw_affiliation_string: School of the 1st Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou 325000, People's Republic of China.

Abstract: MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality.
TOMDS (Topic-Oriented Multi-Document Summarization): Enabling Personalized Customization of Multi-Document Summaries

Authors:
raw_author_name: Xin Zhang
raw_affiliation_string: School of Computer and Cyber Sciences, Communication University of China, Beijing 100024, China
raw_author_name: Qiyi Wei
raw_affiliation_string: School of Computer and Cyber Sciences, Communication University of China, Beijing 100024, China
raw_author_name: Qing Song
raw_affiliation_string: Convergence Media Center, Communication University of China, Beijing 100024, China
raw_author_name: Pengzhou Zhang
raw_affiliation_string: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China

Abstract: In a multi-document summarization task, if the user can decide on the summary topic, the generated summary can better align with the reader’s specific needs and preferences. This paper addresses the issue of overly general content generation by common multi-document summarization models and proposes a topic-oriented multi-document summarization (TOMDS) approach. The method is divided into two stages: extraction and abstraction. During the extractive stage, it primarily identifies and retrieves paragraphs relevant to the designated topic, subsequently sorting them based on their relevance to the topic and forming an initial subset of documents. In the abstractive stage, building upon the transformer architecture, the process includes two parts: encoding and decoding. In the encoding part, we integrated an external discourse parsing module that focuses on both micro-level within-paragraph semantic relationships and macro-level inter-paragraph connections, effectively combining these with the implicit relationships in the source document to produce more enriched semantic features. In the decoding part, we incorporated a topic-aware attention mechanism that dynamically zeroes in on information pertinent to the chosen topic, thus guiding the summary generation process more effectively. The proposed model was primarily evaluated using the standard text summary dataset WikiSum. The experimental results show that our model significantly enhanced the thematic relevance and flexibility of the summaries and improved the accuracy of grammatical and semantic comprehension in the generated summaries.
Supraharmonic Detection Algorithm Based on Interpolation of Self-Convolutional Window All-Phase Compressive Sampling Matching Pursuit

Authors:
raw_author_name: Yu Ji
raw_affiliation_string: School of Internet of Things Engineering, Jiangnan University, Wuxi 214222, China
raw_author_name: Wenxu Yan
raw_affiliation_string: School of Internet of Things Engineering, Jiangnan University, Wuxi 214222, China

Abstract: With the increase in the use of high-frequency power electronic devices, the harmonics injected into the power grid show a trend of high-frequency development. The continuous rise of the supraharmonic emission level in the distribution network has become one of the power quality problems that needs to be solved urgently in the power grid. In this paper, an algorithm based on the Interpolation of the Self-convolutional Window All-phase Compressive Sampling Matching Pursuit (ISWApCoSaMP) is proposed. Firstly, the self-convolution operation is used for the maximum sidelobe decay (MSD) window, and then the compressed sampling matching pursuit model based on the All-phase is constructed, leading to the All-phase Compressive Sampling Matching Pursuit (ApCoSaMP). Finally, the four-spectrum-line interpolation is combined to utilize spectrum line information to improve the accuracy of signal parameter detection in the frequency domain. The introduced All-phase greatly improves the phase measurement accuracy because the initial phase of the supraharmonic signal is selected for phase estimation. In addition, the self-convolutional window and four-spectrum-line interpolation make full use of the information in the time and frequency domains, thus optimizing the measurement results of amplitude and frequency. The algorithm achieves high accuracy in the measurement results of simulated signals and accurately measures supraharmonics.
Organizational Cybersecurity Systems and Sustainable Business Performance of Small and Medium Enterprises (SMEs) in Saudi Arabia: The Mediating and Moderating Role of Cybersecurity Resilience and Organizational Culture

Authors:
raw_author_name: Sabah Abdullah Al-Somali
raw_affiliation_string: Faculty of Economics and Administration, Management Information System Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia; The Management of Digital Transformation and Innovation Systems in Organization Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Abstract: Cybersecurity challenges in Saudi Arabia’s service and manufacturing sectors are escalating due to increased digital adoption, highlighting the need for robust security measures and awareness in SMEs. Therefore, this research is significant due to the increasing reliance on digital technologies and the unique cybersecurity challenges faced by SMEs in these vital economic sectors. With rapid technological advancements, IT capabilities and cybersecurity have become paramount, particularly in the post-COVID-19 era. The service and manufacturing sectors in Saudi Arabia have seen significant shifts towards digital operations. This study aimed to explore the impact of organizational cybersecurity systems on organizational resilience and sustainable business performance in Saudi Arabia’s service and manufacturing sectors, examining the mediating and moderating effects of organizational resilience and culture. A quantitative research method was employed, combining a thorough literature review with empirical data from a sample of 394 respondents in Saudi Arabia, split evenly between the service and manufacturing sectors. Smart PLS 3.3.3 was used to test the proposed hypotheses. The findings suggested a positive effect of the factors of organizational cybersecurity systems on organizational resilience. Organizational cybersecurity systems also significantly influenced sustainable business performance; however, organizational resilience and culture did not play mediating and moderating roles. This study is one of the first to offer a nuanced analysis of IT capabilities and cybersecurity within Saudi Arabia’s service and manufacturing sectors, especially in a post-COVID-19 context. The insights gleaned contribute to the academic discourse and have pivotal managerial implications for organizations navigating the digital era in Saudi Arabia.
Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence

Authors:
raw_author_name: Rubén Vázquez-Medina
raw_affiliation_string: Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Santiago de Querétaro 76090, Mexico

Abstract: The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes.
Synthetic Corpus Generation for Deep Learning-Based Translation of Spanish Sign Language

Authors:
raw_author_name: Miguel Ángel Martínez-del-Amor
raw_affiliation_string: Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, 41012 Sevilla, Spain; SCORE Lab, I3US, Universidad de Sevilla, 41012 Sevilla, Spain
raw_author_name: Juan Antonio Álvarez-García
raw_affiliation_string: Department of Languages and Computer Systems, Universidad de Sevilla, 41012 Sevilla, Spain
raw_author_name: Luis Miguel Soria-Morillo
raw_affiliation_string: Department of Languages and Computer Systems, Universidad de Sevilla, 41012 Sevilla, Spain

Abstract: Sign language serves as the primary mode of communication for the deaf community. With technological advancements, it is crucial to develop systems capable of enhancing communication between deaf and hearing individuals. This paper reviews recent state-of-the-art methods in sign language recognition, translation, and production. Additionally, we introduce a rule-based system, called ruLSE, for generating synthetic datasets in Spanish Sign Language. To check the usefulness of these datasets, we conduct experiments with two state-of-the-art models based on Transformers, MarianMT and Transformer-STMC. In general, we observe that the former achieves better results (+3.7 points in the BLEU-4 metric) although the latter is up to four times faster. Furthermore, the use of pre-trained word embeddings in Spanish enhances results. The rule-based system demonstrates superior performance and efficiency compared to Transformer models in Sign Language Production tasks. Lastly, we contribute to the state of the art by releasing the generated synthetic dataset in Spanish named synLSE.