Reviewerly/exHarmony huggingface dataset:
DoreBer: Document-Level Relation Extraction Method Based on BernNet

Document-level relation extraction (RE) task aims to predict predefined relation types of every entity pair in a given document. Compared with the sentence-level counterpart, document-level relation extraction task requires reasoning in a more complex environment, where exist much longer text and much larger amount of entities, making it a more challenging task. However, previous methods suffers from over-smoothing problem when account of GNN layers is high enough, leading high frequency signals on graph could not pass through filter, and then resulting in an insufficient approximation of the real function and finally causing a defective performance in tasks. To solve this problem, we propose a novel model, called DoreBer, for document-level RE task, which can obtain a higher quality of graph representation. Specifically, DoreBer performs estimation of filter over the normalized Laplacian spectrum of a graph by leveraging an order-K Bernstein polynomial approximation, and designs its spectral property by setting the coefficients of the Bernstein basis. Therefore, DoreBer can alleviate the over-smoothing problem to enhance learning ability of model. In addition, DoreBer has a higher interpretability for learned parameters of graph filter. We evaluate DoreBer on the DocRED public document-level RE dataset. Online experimental results demonstrate that DoreBer achieves significant performance improvements (2.72 and 2.76 higher on Ign <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> respectively), over the previous state-of-the-art on sequence-based method baseline. DoreBer reveals the potential of BernNet method in document-level relation extraction tasks and sheds light on a path to learn potential representation in high-dimensional data.The source code of this paper can be obtained from https://github.com/factor77/DoreBer/.
Streamlining 5G Spectrum Leasing

The growing demand for wireless communications drives the development of new networking technologies, but also makes efficient usage of the limited frequency spectrum increasingly important. Leasing unused spectrum on a short-term basis would help increase utilisation, but the complicated lease negotiations usually make it financially unviable. To address this, the whole leasing process including the negotiations needs to be streamlined. This paper proposes an automated spectrum marketplace for leasing that allows frequency bands to be put for anonymous auction between qualified (sub)leasers. The solution utilizes decentralized identifiers and verifiable credentials for the entities’ privacy and distributed ledgers for automation and auditing of contract agreements. The prototype implementation demonstrates that the solution significantly streamlines the spectrum leasing process compared to the existing solution.
A Triple Unlocking Mechanism Model Against Forging Signature Attack Based on Multivariate Polynomial Public Key Cryptosystem

Since the original signature model did not account for the possible threat of the forging signature attack, the majority of existing multivariate polynomial public key based signature schemes are at risk of forging signatures by equivalent key attacks. In this research, an enhanced signature model, Triple Unlocking Mechanism (TUM), is provided to resist all the possibility that the signature generated by the equivalent keys. The additional public keys are generated in the sake of enhancing the security by verifying internal unit information in the scheme after three designed unlocking operations. As a result, the signature can only be generated by the user who has the real legal key and the threat of the key recovery attack can be eliminated. The security of TUM signature based on random oracle model is proved. Experimental results demonstrate that, using an MI system coupled with TUM as an example, the advantages of the enhanced signature model are more secure than the original one at the expense of taking a little more time for signing. Moreover, influences of different parameters in multivariate polynomial public key signatures are investigated. Based on the original state-of-art model, the presented model is generic construction and applicable for existing multivariate signature scheme’s construction.
The State of the Art for Cross-Modal Retrieval: A Survey

Cross-modal retrieval, which aims to search for semantically relevant data across different modalities, has received increasing attention in recent years. Deep learning, with its ability to extract high-level representations from multimodal data, has become a popular approach for cross-modal retrieval. In this paper, we present a comprehensive survey of deep learning techniques for cross-modal retrieval including 35 papers published in recent years. The review is organized into four main sections, covering traditional subspace learning methods, deep learning, and machine learning-based approaches, techniques based on large multi-modal models, and an analysis of datasets used in the field of cross-modal retrieval. We compare and analyze the performance of different deep learning methods on benchmark datasets, the result shows that although a large number of innovative methods have been proposed, there are still some problems that need to be solved, such as multi-modal feature alignment, multi-modal feature fusion, and subspace learning, as well as specialized datasets.
A Survey on Cyber-Physical Security of Autonomous Vehicles Using a Context Awareness Method

Autonomous vehicles face challenges in ensuring cyber-physical security due to their reliance on image data from cameras processed by machine learning. These algorithms, however, are vulnerable to anomalies in the imagery, leading to decreased recognition accuracy and presenting security concerns. Current machine learning models struggle to predict unexpected vehicular situations, particularly with unpredictable objects and unexpected anomalies. To combat this, scholars are focusing on active inference, a method that can adapt models based on human cognition. This paper aims to incorporate active inference into autonomous vehicle systems. Multiple studies have delved into this approach, showing its potential to address security gaps in this field. Specifically, these frameworks have proven effective in handling unforeseen vehicular anomalies.
Blockchain-based Trust Management for Virtual Entities in the Metaverse: A Model for Avatar and Virtual Organization Interactions

As blockchain technology and decentralized systems evolve, the security of these infrastructures faces challenges from increasingly sophisticated threats. This research introduces a methodology designed to strengthen the security parameters of distributed systems, with a specific focus on its applicability within the Metaverse. Our probabilistic trust model dynamically allocates weights to system nodes based on their observed behaviour and the reputation of associated entities. This mechanism effectively counters a range of security threats, including the Sybil, Good/Bad mouthing, and On/Off attacks. By integrating blockchain technology, we establish a robust trust foundation within the Metaverse, ensuring enhanced security for digital interactions. To further combat deceptive activities and reduce superfluous intermediaries, our model incorporates smart contracts. Beyond their transactional utility, these contracts function as trust regulators for interactions among Metaverse avatars. Our trust model efficiently differentiates various virtual entities, assigning trust scores that resonate with their specific classifications. We also introduce a decentralized dispute resolution framework, where virtual entities act as impartial arbiters, promoting transparency and fairness in conflict resolution. We have implemented our proposed solution on real-time blockchain platform in comparison with the existing appraoches i.e., BTCGS and MSBC-CTrust. The evident enhancements in threat detection capabilities and the agility in neutralizing these threats validate our model’s resilience and adaptability.
Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation

Objective.Training neural networks for pixel-wise or voxel-wise image segmentation is a challenging task that requires a considerable amount of training samples with highly accurate and densely delineated ground truth maps. This challenge becomes especially prominent in the medical imaging domain, where obtaining reliable annotations for training samples is a difficult, time-consuming, and expert-dependent process. Therefore, developing models that can perform well under the conditions of limited annotated training data is desirable.Approach.In this study, we propose an innovative framework called the extremely sparse annotation neural network (ESA-Net) that learns with only the single central slice label for 3D volumetric segmentation which explores both intra-slice pixel dependencies and inter-slice image correlations with uncertainty estimation. Specifically, ESA-Net consists of four specially designed distinct components: (1) an intra-slice pixel dependency-guided pseudo-label generation module that exploits uncertainty in network predictions while generating pseudo-labels for unlabeled slices with temporal ensembling; (2) an inter-slice image correlation-constrained pseudo-label propagation module which propagates labels from the labeled central slice to unlabeled slices by self-supervised registration with rotation ensembling; (3) a pseudo-label fusion module that fuses the two sets of generated pseudo-labels with voxel-wise uncertainty guidance; and (4) a final segmentation network optimization module to make final predictions with scoring-based label quantification.Main results.Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image segmentation tasks and compared to five state-of-the-art methods.Significance.Results demonstrate that our proposed ESA-Net can consistently achieve better segmentation performances even under the extremely sparse annotation setting, highlighting its effectiveness in exploiting information from unlabeled data.
Surfing the Landscape of Physical Computing Platforms to Develop Electroacoustic Musical Instruments

There has been an increasing number, development, and availability of platforms choices to prototype electroacoustic instruments in the last decade. This demonstrates a growing interest in physical computing among the “technological-music” community. Bela and Daisy are nowadays among the most popular and widespread platforms specifically developed for out-of-the-box music technology purposes. Each of these platforms has its own unique features, capabilities, and community, providing options for different preferences and requirements and exhibiting an increasing adaptability in terms of computer music languages. This raises the question: which one is the ideal to develop an electroacoustic instrument? In this article, we present an overview of these platforms, among others that have been used since 2000, from the viewpoint of the performer-luthier at the development stage for an electroacoustic instrument to perform music in the wilderness. In addition, based on a specific project the authors are developing, we propose some design guidelines which considers the musical and technological possibilities and implications of these platforms.
Deeper Notions of Correctness in Image-Based DNNs: Lifting Properties from Pixel to Entities

Deep Neural Networks (DNNs) that process images are being widely used for many safety-critical tasks, from autonomous vehicles to medical diagnosis. Currently, DNN correctness properties are defined at the pixel level over the entire input. Such properties are useful to expose system failures related to sensor noise or adversarial attacks, but they cannot capture features that are relevant to domain-specific entities and reflect richer types of behaviors. To overcome this limitation, we envision the specification of properties based on the entities that may be present in image input, capturing their semantics and how they change. Creating such properties today is difficult as it requires determining where the entities appear in images, defining how each entity can change, and writing a specification that is compatible with each particular V&V client. We introduce an initial framework structured around those challenges to assist in the generation of Domain-specific Entity-based properties automatically by leveraging object detection models to identify entities in images and creating properties based on entity features. Our feasibility study provides initial evidence that the new properties can uncover interesting system failures, such as changes in skin color can modify the output of a gender classification network. We conclude by analyzing the framework potential to implement the vision and by outlining directions for future work.
Modeling the Centrality of Developer Output with Software Supply Chains

Raw developer output, as measured by the number of changes a developer makes to the system, is simplistic and potentially misleading measure of productivity as new developers tend to work on peripheral and experienced developers on more central parts of the system. In this work, we use Software Supply Chain (SSC) networks and Katz centrality and PageRank on these networks to suggest a more nuanced measure of developer productivity. Our SSC is a network that represents the relationships between developers and artifacts that make up a system. We combine author-to-file, co-changing files, call hierarchies, and reporting structure into a single SSC and calculate the centrality of each node. The measures of centrality can be used to better understand variations in the impact of developer output at Meta. We start by partially replicating prior work and show that the raw number of developer commits plateaus over a project-specific period. However, the centrality of developer work grows for the entire period of study, but the growth slows after one year. This implies that while raw output might plateau, more experienced developers work on more central parts of the system. Finally, we investigate the incremental contribution of SSC attributes in modeling developer output. We find that local attributes such as the number of reports and the specific project do not explain much variation (𝑅2 = 5.8%). In contrast, adding Katz centrality or PageRank produces a model with an 𝑅2 above 30%. SSCs and their centrality provide valuable insights into the centrality and importance of a developer’s work.
EvaCRC: Evaluating Code Review Comments

In code reviews, developers examine code changes authored by peers and provide feedback through comments. Despite the importance of these comments, no accepted approach currently exists for assessing their quality. Therefore, this study has two main objectives: (1) to devise a conceptual model for an explainable evaluation of review comment quality, and (2) to develop models for the automated evaluation of comments according to the conceptual model. To do so, we conduct mixed-method studies and propose a new approach: EvaCRC (Evaluating Code Review Comments). To achieve the first goal, we collect and synthesize quality attributes of review comments, by triangulating data from both authoritative documentation on code review standards and academic literature. We then validate these attributes using real-world instances. Finally, we establish mappings between quality attributes and grades by inquiring domain experts, thus defining our final explainable conceptual model. To achieve the second goal, EvaCRC leverages multi-label learning. To evaluate and refine EvaCRC, we conduct an industrial case study with a global ICT enterprise. The results indicate that EvaCRC can effectively evaluate review comments while offering reasons for the grades. Data and materials: https://doi.org/10.5281/zenodo.8297481
A Multidimensional Analysis of Bug Density in SAP HANA

Researchers and practitioners have been studying correlations between software metrics and defects for decades. The typical approach is to postulate a hypothesis that a certain metric correlates with the number of defects. A statistical test then utilizes historical data to accept or reject the hypothesis. Although this methodology has been widely adopted, our own experience is that such correlations are often limited in their practical relevance, particularly for large industrial projects: Interpreting and arguing about them is challenging and cumbersome; the difference between correlation and causation might not be clear; and the practical impact of a correlation is often questioned due to misconceptions between a statistical conclusion and the impact on singular events. Instead of discussing correlations, we found that the analysis for binary testedness results in more fruitful discussions. Binary testedness, as proposed by prior work, utilizes a metric to divide the source code into two parts and verifies whether more (or less) defects appear in each part than expected. In our work, we leverage the binary testedness approach and analyze several software metrics for a large industrial project to illustrate the concept. We furthermore introduce dynamic thresholds as a novel and more practical approach for source code classification compared to the static binary classification of previous works. Our results show that some studied metrics have a significant correlation with bug distribution, but effect sizes differ by several magnitudes across metrics. Overall, our approach moves away from “metric X correlates with defects” to a more fruitful “source code with attribute X has more (or less) bugs than expected”, reframing the discussion from questioning statistics and methods towards an evidence-based root cause analysis.
Commit-Level, Neural Vulnerability Detection and Assessment

Software Vulnerabilities (SVs) are security flaws that are exploitable in cyber-attacks. Delay in the detection and assessment of SVs might cause serious consequences due to the unknown impacts on the attacked systems. The state-of-the-art approaches have been proposed to work directly on the committed code changes for early detection. However, none of them could provide both commit-level vulnerability detection and assessment at once. Moreover, the assessment approaches still suffer low accuracy due to limited representations for code changes and surrounding contexts.
Understanding the Methodological Value of Mapping Alternative Fiction Scenarios in the Context of Domestic Violence

Smart homes have revolutionized the way we live, with new technologies connecting users’ devices with everyday objects. However, these advancements have also led to the emergence of unexpected dangers relating to technology-facilitated abuse. Domestic abusers can now maintain control beyond previous spatial boundaries, making it impossible for victims to recognize that they suffer from abuse. This paper is part of an ongoing MA project that aims to prevent smart products from being used as tools for domestic violence. The paper demonstrates the methodological value of mapping speculative scenarios during the ideation phase in the design process. The scenarios aim to encourage reflection, speculation, and debate about the potential consequences and ethical implications of emerging technologies. The paper proposes potential solutions that reimagine how applications can support healthy relationships by emphasizing transparency, consent, and trust-building. It highlights the need for awareness, empathy, and ethical design practices to ensure that smart homes promote the well-being and privacy of their occupants.
Analysis of Cyberaggression in Social Networks Involving Students and University Environments

Online aggressions have been widely reported worldwide and referenced as great harm since they bring immeasurable negative repercussions to those involved. Although social media websites’ official policy prohibits hate speech, the literature shows that aggressive content is still noticeable among the comments and disseminated publications in many online social networks. Meanwhile, the aggressive posts’ contents have yet to be explored in detail, including those associated with Portuguese social media groups. This paper aims to provide information on how aggression translates and is expressed online, considering two sources. The first sample is taken from eight transcribed interviews with Portuguese university students who have already been involved in aggressive online situations and shared the exact text content of the attacks. The second source was a series of posts from 151 Portuguese university student open groups on Facebook and Instagram that presented aggressive comments. A content analysis was performed with both samples separated for comparison, seeking similarities and patterns in both aggressive text analyses. Moreover, a thematic analysis was conducted to gather knowledge regarding the most frequent subjects among the aggressive content posted. While communications reported in interviews focus on image and body, death, and gender identity, themes such as immigrants, politics, lack of university support, and academic traditions emerged in the social network. The findings enhance knowledge regarding linguistics on cyberaggression and provide parameters for future research on detecting aggression situations online.
Anticipation as a Tool for Designing the Future

Anticipation is a concept that allows us to predict and plan for future events. As a tool, it enables us to forecast peoples’ desires or intentions and influences their behaviors and learning regarding digital experiences. This paper delves into the concept of anticipation and its relevance in designing for the future, particularly in the fields of User Experience (UX) and Human-Computer Interaction (HCI). It explores and defines anticipation concepts, definitions, and manifestations, and explores the various types and applications in various fields and their relationship with intelligent systems. This paper provides an extensive overview of how anticipation can shape the design field. This translates to leveraging Artificial Intelligence (AI) and Machine Learning (ML) to construct digital experiences that not only resonate with users but also proactively address their evolving needs. By examining the connection between anticipation and designing for the future, this paper offers a valuable resource for scholars and professionals interested in exploring the concept of anticipation and its potential to shape future outcomes.