Reviewerly/exHarmony huggingface dataset:
SecBerg: Secure and Practical Iceberg Queries in Cloud

Secure queries are fundamental to data security, particularly in cloud databases. In data analytics, one of the common and practical queries is the iceberg query that can find aggregate values above a specified threshold. However, existing secure aggregate query schemes: 1) are unable to support secure iceberg queries equipped with the HAVING clause; 2) only consider additive aggregate functions; and 3) suffer from performance issues due to the use of homomorphic encryption to encrypt databases. In this article, we present a secure iceberg query scheme, SecBerg, to support both addition-based and comparison-based aggregate functions and ensure high efficiency and security simultaneously. To make it possible, we propose a secure bitmap index system to encode database values and pioneer the use of the arithmetic secret sharing technique to protect databases in the cloud environment. Furthermore, we carefully design efficient and secure protocols over arithmetic secret sharing to construct our SecBerg. Extensive evaluations are conducted, and the results indicate that SecBerg is significantly more efficient than the state-of-the-art relevant scheme in computational overhead and can attain orders of magnitude performance improvement at best.
Transaction Prediction by Using Graph Neural Network and Textual Industry Information

Transaction data owned by financial institutions can be alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict macroeconomic indicators as well as to sophisticate credit management, customer relationship management, and etc. However, it needs attention when a financial institution uses transaction data for aforementioned applications since occurrence of transactions depends on miscellaneous factors including customer loyalty, implying missing potential transactions. To solve this issue, we can predict occurrence of transactions by formulating the problem as a link prediction task in a transaction network with bank accounts as nodes and transaction volume as edges. With the recent advances in deep learning on graphs, we can expect better link prediction. We introduce an approach to predict transaction occurrence by using graph neural network with a special attention mechanism and textual industry information, analyzing the effectiveness of the proposed method, attention mechanism and node feature design as well as demonstrating its usage as an industry importance explainer.
A Hierarchical Decoder Architecture for Multilevel Fine-Grained Disaster Detection

As a cutting-edge challenge in the field of disaster evaluation, the detection of disasters in remote sensing images is crucial. However, most existing approaches to disaster detection simply solve the problem as a naive multi-class change detection, lacking accurate damage-level classification. In this paper, we propose a new approach to disaster detection called multi-level disaster detection (MLDD) that focuses on fine-grained damage-level classification. Our proposed approach tackles MLDD through hierarchical-correlation modeling and presents a universal disaster detection architecture. Specifically, we summarize two existing applicative methods, one-step training and pre-training, which are compatible with our proposed architecture. In addition, we propose two novel hierarchical approaches, namely the multi-task (MT) based and graph-encoding (GE) based approaches. The MT approach resolves MLDD through layer-wise learning in a progressive manner, building explicit multi-stage and implicit joint models to probe into the coarse-to-fine correlation for damage-level evaluation. The GE approach enhances hierarchical relationships by encoding multifold messaging directions and probabilities using a graph neural network. Furthermore, all four hierarchical paradigms can be embedded in our hierarchical MLDD architecture, which outperforms state-of-the-art methods on the xBD dataset, particularly in fine-grained damage-level classification. Overall, our proposed approach represents a significant improvement over existing disaster detection methods and has the potential to advance the field of disaster evaluation.
High-Throughput Polynomial Multiplier for Accelerating Saber on FPGA

Saber was once one of the most promising candidates for the post-quantum cryptography standardization, which relies on lattice-based hard mathematical problems. Polynomial multiplication is time-consuming within the Saber architecture and there is still a lack of designs targeting the high throughput applications whose parameters support Schoolbook polynomial multiplier. In this brief, we propose a high-performance Schoolbook polynomial multiplier with a balanced hardware efficiency. The Schoolbook algorithm is transformed into a Toeplitz matrix-vector product, and its symmetry is exploited to reconstruct the Schoolbook multiplier to satisfy the need for high parallelism. Combined with compact data loading structure and a centralizing multiplication, the multiplier achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.33\times $ </tex-math></inline-formula> higher throughput and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.58\times $ </tex-math></inline-formula> higher throughput-per-slice compared with the state-of-the-art implementation of polynomial multiplier for Saber on Xilinx FPGA. The experimental results also demonstrate that the proposed structure provides a better trade-off between performance and area.
Eye Movements and Vestibulo-Ocular Reflex as User Response in Virtual Reality

Virtual reality (VR) is increasingly used in gaming, training, and health-related applications. However, the level of immersion and presence generated by VR can significantly influence the user and the achievement of desired outcomes. Therefore, methods to assess levels of immersion and presence need to be established. In this paper, we investigate the vestibulo-ocular reflex (VOR) and saccadic eye movements in multiple subjects exposed to VR to determine if VR triggers these responses as in real life. Using an HTC VIVE Pro Eye headset and Tobii Pro Lab software, we record head and eye movements in response to a 3D video of a roller coaster ride. Our results show that critical scenes in the video triggered VOR and saccadic eye movements in most subjects, indicating a strong correlation between VOR and saccadic eye movements during VR immersion. These findings may have implications for the study of human behavior and suggest the use of eye movement measures as feedback mechanisms during VR experiences.
Counteracting Side Channels in Cross-User Client-Side Deduplicated Cloud Storage

Client-side data deduplication enables cloud storage services to reduce storage space and bandwidth consumption, resulting in reduced operating cost and a high level of user satisfaction. However, duplicate checks (i.e., the corresponding message exchange) generate a side channel, exposing the file existence status to attackers. In particular, the binary response from a duplicate check reveals file existence information. This can be exploited to launch further attacks, such as learning sensitive file content and establishing a covert channel. As current solutions provide only weaker privacy or rely on unreasonable assumptions, we propose random response (RARE) to achieve stronger privacy. The underlying principle is that the uploading user simultaneously sends a duplication request for two chunks. The cloud receiving the request returns a carefully designed randomized duplication response to preserve the deduplication gain and minimize privacy leakage. By proposing multiple chunk uploading and chunk rearrangement strategies, we optimize the design of RARE to significantly reduce the communication burden without compromising privacy. We also study the impact of different implementations of multiple chunk uploading on the deduplication gain, further reducing communication cost. The analytical results confirm that formal privacy is ensured, whereas experiment results demonstrate that RARE preserves both privacy and the deduplication benefit.
CocoSketch: High-Performance Sketch-Based Measurement Over Arbitrary Partial Key Query

Sketch-based measurement has emerged as a promising solutions due to its high accuracy and resource efficiency. Prior sketches focus on measuring single flow keys and cannot support measurement on multiple keys. This work takes a significant step towards supporting arbitrary partial key queries, which aims to provide information for any key in the predefined range of possible flow keys. The designed system, CocoSketch, casts arbitrary partial key queries to the subset sum estimation problem and makes the theoretical tools for subset sum estimation practical. CocoSketch utilizes two techniques: (1) stochastic variance minimization to significantly reduce per-packet update delay, and (2) removing circular dependencies in the per-packet update logic to make the implementation hardware-friendly. This paper extends the conference version by discussing how CocoSketch adapts to new measurement requirements, including: (1) collecting the exact information of specified flow keys, and (2) distributed measurement. CocoSketch is implemented on five popular platforms (CPU, Open vSwitch, Redis, P4, and FPGA). Experiment results show that compared to baselines that use traditional single-key sketches, CocoSketch improves average packet processing throughput by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27.2\times $ </tex-math></inline-formula> and accuracy by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10.4\times $ </tex-math></inline-formula> when measuring six flow keys.
Problematic Unordered Queries in Temporal Moment Measurement by Using Natural Language

This study examines the difficulty in measuring temporal moments by using natural language (TMMNL) in the untrimmed video. The purpose of TMMNL is to use natural language query to find a specific moment within a lengthy video. It’s a challenging task since other, closely related activities may divert attention from the target temporal moment. This issue has been addressed by existing research employing computer vision techniques like reinforcement, anchor, and ranking. In this research, we not only propose a TMMNL solution and show how to use natural language query to find the required moment, but we also identify a novel issue: if the given natural language query is unordered (without proper subject, verb and object), the system will have trouble understanding and the network may perform poorly. Previous methods perform poorer when a query is unordered and cannot able to retrieve the relevant moment as a result overall performance degradation. We introduce novel the-visual, the-action, the-object, and the-connecting words concept to address the problem of unordered query in TMMNL. Graph Convolutions with Latent variable for Visual-Textual Network (GCL-VTN) is our suggested network, which has three components: (1) visual-graph-convolution (visual GC), (2) textual-graph-convolution (textual GC), and (3) compatible method for learning embedding’s (CMLE). Visual-nodes in the visual GC detect regional attributes, object, and actor information in the same way as textual-nodes in the textual GC maintain word sequence using grammar-based query rules. A compatible method for learning embedding (CMLE) is also proposed, which integrates different modalities (moment, query) and trained grammar-based words into the same embedding space. In order to align and keep the query sequence, we also incorporate a stochastic latent variable in the CMLE that has prior and posterior distributions. The posterior distribution deals with both visual-textual data and works when the query is in the correct sequence or based on grammar rules. The prior distribution only deals with textual data and is effective when the query is unordered or not based on grammar rules. TACoS, Charades-STA, and activityNet-Captions are state-of-the-art; our GCL-VTN exceeds them all.
FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit

Federated learning (FL) emerges as a new distributed machine learning (ML) paradigm that enables thousands of mobile devices to collaboratively train ML models using local data without compromising user privacy. However, the FL learning quality highly relies on the data contribution from the distributed mobile devices. Therefore, a well-designed incentive mechanism with effectiveness, fairness, and reciprocity is in urgent need to guarantee the stable participation of users. In this article, we propose federated auction bandit ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAB</monospace> ), an incentive and client selection strategy based on a novel multiattribute reverse auction mechanism and a combinatorial multi-armed bandit (CMAB) algorithm. First, we develop a local contribution evaluation method based on importance sampling in the FL context. We then design a novel payment mechanism that is able to preserve individual rationality and incentive compatibility (truthfulness). At last, we design a UCB-based winner selection algorithm that is proven to achieve the server’s utility maximization with fairness and reciprocity. We have conducted extensive experiments on real data sets. The results demonstrate the superiority of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAB</monospace> , with a 10%–50% improvement in total reward, final accuracy, and convergence speed compared to state-of-the-art solutions.
Distributional Prototypical Methods for Reliable Explanation Space Construction

As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas. The prototype-based explanation is a method that provides an explanation of the model’s prediction using the distance between an input and learned prototypes to effectively perform case-based reasoning. However, existing methods are less reliable because distance is not always consistent with human perception. In this study, we construct a latent space which we call an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explanation space</i> with distributional embedding and latent space regularization. This explanation space ensures that similar (in terms of human-interpretable features) images share similar latent representations, and therefore provides a reliable explanation for the consistency between distance-based explanation and human perception. The explanation space also provides additional explanation by transition, allowing the user to understand the factors that affect the distance. Throughout extensive experiments including human evaluation, we have shown that the explanation space provides a more human-understandable explanation.
A Reliable Application of MPC for Securing the Tri-Training Algorithm

Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacy-preserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclosure of sensitive information from data owner. However, the previous PPDDM mostly works on a limited amount of labeled data. In contrast to the real world, unlabeled data is abundance and labeled data is scarce. The objectives of this paper are to study and to analyze privacy-preserving properties of semi-supervised learning (SSL) algorithm with the combination of labeled and unlabeled data, where data is distributed among multiple data owners. In this paper we propose a Privacy-preserving Distributed Data Mining (PPDDM) method by designing a reliable application of secure MPC to semi-supervised tri-training algorithms. We simulate the original tri-training algorithm and tri-training algorithm with secure MPC using a different types of classifiers and datasets. The simulation results show that tri-training in secure MPC has almost same accuracy compared to original tri-training algorithm. We also compare execution time in addition to performance evaluation of tri-training in secure and the original tri-training algorithms.
Meta-Learning Based Tasks Similarity Representation for Cross Domain Lifelong Learning

Deep neural networks perform better in most specific single tasks than humans, but it is hard to handle a sequence of new tasks from different domains.The deep learning-based models always need to remember the parameters of the learned tasks to perform well in the new tasks and forfeit the ability to generalize from previous data, which is inconsistent with human learning.We propose a novel lifelong learning framework that can guide the model to learn new knowledge without forgetting the old knowledge through learning the similarity representation based on meta-learning.Specifically, we employ a cross-domain triplets network (CDTN) by minimizing the maximum mean discrepancy (MMD) between the current task and the knowledge base to learn the domain invariant similarity representation between tasks in different domains.Furthermore, we add a self-attention module to enhance the extraction of similarity features.Secondly, a spatial attention network (SAN) which can assign different weights according to the learned similarity representation of tasks, is proposed in addition.The experimental results show that our method effectively reduces catastrophic forgetting compared with the state-of-the-art methods when learning many tasks.Moreover, we show that the proposed method can hardly forget the old knowledge while continuously enhancing the performance of the old tasks, which is more in line with the human way of learning.
Multi-Duplicated Characterization of Graph Structures Using Information Gain Ratio for Graph Neural Networks

Various graph neural networks (GNNs) have been proposed to solve node classification tasks in machine learning for graph data. GNNs use the structural information of graph data by aggregating the feature vectors of neighboring nodes. However, they fail to directly characterize and leverage the structural information. In this paper, we propose a multi-duplicated characterization of graph structures using information gain ratio (IGR) for GNNs (MSI-GNN), which enhances the performance of node classification by using an i-hop adjacency matrix as the structural information of the graph data. In MSI-GNN, the i-hop adjacency matrix is adaptively adjusted by two methods: (i) structural features in the matrix are selected based on the information gain ratio and occurrence filter, and (ii) the selected features in (i) for each node are duplicated and combined flexibly. In an experiment, we show that our MSI-GNN outperforms GCN, H2GCN, and GCNII in terms of average accuracies in benchmark graph datasets.
Counter Speech: Practices of Contradiction on Hate Speech and their Effects

Hate speech has physical and psychological consequences for the targeted individual, intimidates people to participate equally and fearlessly in public life, triggers mistrust and hostility between social groups, discourages participation in political deliberation and can incite, legitimize and/or coordinate open violence. Contradiction plays a pivotal role in the hate speech debate, because “speaking back” has been treated as an alternative to tackling the problem, assuming that “the best remedy for bad speech is more speech” and that “the best argument wins”. Based on the speech act theory and on the theory of communicative action, this paper addresses practices of hate and counter speech in the media. Such practices are illustrated in the case of the controversy surrounding the writer Oriana Fallaci and her islamophobic piece “The Rage and The Pride”, published a couple of weeks after the terror attack of the Twin Towers at the World Trade Center in New York City in 2001. For the analysis, all articles published in the Italian press about the case were submitted to a content analysis (n = 74). Outcomes show not only different practices of contradiction, but also the limitations of counter-argumentation by tackling the problem, leading rather to polarisation than to refutation of hate speech.
Some Questions Raised by the Practice of Online Analysis<sup>1</sup>

In this paper the author explores some of the issues raised by the practice of online analysis. In particular, he emphasizes the difference between the setting and the frame and proposes a theoretical approach of the latter as emerging within the transferential dynamic.Dans cet article, l'auteur explore certaines des questions soulevées par la pratique de l'analyse en ligne. En particulier, il souligne la différence entre le dispositif et le cadre et propose une approche théorique de ce dernier comme émergeant au sein de la dynamique transférentielle.In diesem Beitrag untersucht der Autor einige der Probleme, die durch die Praxis der Onlineanalyse aufgeworfen werden. Insbesondere betont er den Unterschied zwischen dem Setting und dem Rahmen und schlägt einen theoretischen Ansatz für letzteren vor, der innerhalb der Übertragungsdynamik entsteht.In questo articolo l’Autore esplora alcune delle questioni sollevate dalla pratica dell’analisi online. In particolare sottolinea la differenza tra l’ambiente e la cornice e propone un approccio teorico di quest’ultimo come emergente all’interno della dinamica transferale.В этой статье автор рассматривает некоторые вопросы в связи с практикой онлайн-анализа. В частности, он подчеркивает различие между сеттингом и рамкой и предлагает теоретический подход, рассматривающий последнее как явление, возникающее в динамике переноса.En este artículo, el autor explora algunas de las cuestiones que plantea la práctica del análisis virtual. En particular, hace hincapié en la diferencia entre el encuadre y el marco y propone un enfoque teórico de este último como emergente dentro de la dinámica transferencial.在线分析的实践所遇到的一些问题 在本文中, 作者探讨了在线分析的实践所遇到的一些问题。特别是, 他强调了设置和架构之间的区别, 并提出了一种理论方法, 即后者是在移情动力中的涌现。.
Blind quality assessment of multi-exposure fused images considering the detail, structure and color characteristics

In the process of multi-exposure image fusion (MEF), the appearance of various distortions will inevitably cause the deterioration of visual quality. It is essential to predict the visual quality of MEF images. In this work, a novel blind image quality assessment (IQA) method is proposed for MEF images considering the detail, structure, and color characteristics. Specifically, to better perceive the detail and structure distortion, based on the joint bilateral filtering, the MEF image is decomposed into two layers (i.e., the energy layer and the structure layer). Obviously, this is a symmetric process that the two decomposition results can independently and almost completely describe the information of MEF images. As the former layer contains rich intensity information and the latter captures some image structures, some energy-related and structure-related features are extracted from these two layers to perceive the detail and structure distortion phenomena. Besides, some color-related features are also obtained to present the color degradation which are combined with the above energy-related and structure-related features for quality regression. Experimental results on the public MEF image database demonstrate that the proposed method achieves higher performance than the state-of-the-art quality assessment ones.