Reviewerly/exHarmony huggingface dataset:
Searching Online for Art and Culture: User Behavior Analysis

With the constant expansion of the Web, search engines became part of people’s daily routines. How users behave during the search process depends on a variety factors, one of which is the topic of their search interest. This study focused on the behavior of users searching the Web for content related to art and cultural heritage. A proprietary, publicly available, federated search engine, in the form of a web and mobile app, was developed for this research’s purposes. This platform was used to monitor actual user behavior during a six-month period. Quantitative data related to the platform’s usage were collected and analyzed in order to provide a detailed picture of the way interested parties engaged with it. This information pertained not only to the search queries and results viewed, but also to the various characteristics of the search sessions themselves. The study presented an analysis of these data, with emphasis on query and result characteristics, usage devices, login preferences and session duration, and drew conclusions. The study’s findings showed, among other things, that art searchers showed a preference for shorter queries, a tendency for higher query repeatability, and showed interest in a wider number of results than general purpose searchers. Additionally, they were more keen to use desktop devices instead of mobile ones and displayed higher engagement metrics during longer search sessions or when logged in. These findings outlined an art searcher who was interested in concepts and people often revisited searches and results, showed interest for more than the first few hits, was attracted by rich content, and understood the art search process as a task which requires focus. They also pointed out a duality in the art search process itself which can be long and involved or short and purposeful.
High-Precision Satellite Video Stabilization Method Based on ED-RANSAC Operator

Video image stabilization technology is a crucial foundation for applications such as video image target identification, monitoring, and tracking. Satellite video covers a wide range of areas with complex and similar types of objects on the ground and diverse video types. However, currently, there is a lack of a general high-precision satellite video stabilization method (VSM) that can be applied to different land cover types and imaging modes. This paper proposes a high-precision VSM based on the ED-RANSAC, an error elimination operator constrained by Euclidean distance. Furthermore, a set of accuracy evaluation methods to ensure the reliability of video stabilization are sorted out. This paper conducted video stabilization experiments using optical video data from the Jilin-01 satellite and airborne SAR video data. Under the precision evaluation criteria proposed in this paper, the optical satellite video achieved inter-frame stabilization accuracy of better than 0.15 pixels in different test areas. The overall stabilization accuracy was better than 0.15 pixels. Similarly, the SAR video achieved inter-frame stabilization accuracy better than 0.3 pixels, and the overall stabilization accuracy was better than 0.3 pixels. These experimental results demonstrate the reliability and effectiveness of the proposed method for multi-modal satellite video stabilization.
Error‐correcting cryptographic S‐boxes with multiple error detection and correction

Summary In this paper, the error‐correcting 4‐bit S‐boxes for cryptography applications with multiple error detection and correction are presented. Three 4‐bit S‐boxes which are used in the lightweight block ciphers PRESENT, Midori, and KLEIN are considered for implementation of the error‐correcting method. The proposed method does not require two redundant S‐boxes for repairing the S‐box. The main circuit of the S‐box is implemented concurrently with the low‐cost error‐correcting part of the structure. This reduces the overall area consumption and delay of the proposed error‐correcting method (the area consists of the 4‐bit S‐box and self‐checking/error‐correcting circuits) . In the proposed error‐correcting method, the outputs of the S‐box are tested individually and it can detect and repair transient and permanent faults simultaneously. Therefore, the structure can detect and repair the single, double, triple, and quadruple faults at a time. The area and timing results, in 180 nm CMOS technology, show the proposed structures are acceptable in terms of area and delay overheads than those of the other methods.
IAT/ML: A Domain-Specific Approach for Discourse Analysis and Processing

Language technologies are gaining momentum as textual information saturates social networks and media outlets, compounded by the growing role of fake news and disinformation. In this context, approaches to represent and analyse discourses are becoming crucial. Although there is a large body of literature on text-based machine learning, it tends to focus on lexical and syntactical issues rather than semantic or pragmatic. These advances cannot tackle the complex and highly context-dependent problems of discourse evaluation that society demands. In this paper, we present IAT/ML, a modelling approach to represent and analyse discourses. IAT/ML focus on semantic and pragmatic issues, thus tackling a little researched area in language technologies. It does so by combining three analysis approaches: ontological, which focuses on what the discourse talks about, argumentation, which deals with how the text justifies what it says, and critical, which provides insights into the speakers’ beliefs and intentions, and is still being implemented. Together, these three modelling and analysis approaches make IAT/ML a comprehensive solution to represent and analyse complex discourses towards their evaluation and fact checking.
An Extractive Text Summarization Model for Generating Extended Abstracts of Medical Papers in Turkish

The rapid growth of technology has led to an increase in the amount of data available in the digital environment. This situation makes it difficult for users to find the information they are looking for within this vast dataset, making it time-consuming. To alleviate this difficulty, automatic text summarization systems have been developed as a more efficient way to access relevant information in texts compared to traditional summarization techniques. This study aims to extract extended summaries of Turkish medical papers written about COVID-19. Although scientific papers already have abstracts, more comprehensive summaries are still needed. To the best of our knowledge, automatic summarization of academic studies related to COVID-19 in the Turkish language has not been done before. A dataset was created by collecting 84 Turkish papers from DergiPark. Extended summaries of 2455 and 1708 characters were obtained using widely used extractive methods such as Term Frequency and LexRank algorithms, respectively. The performance of the text summarization model was evaluated based on Recall, Precision, and F-score criteria, and the algorithms were shown to be effective for Turkish. The results of the study showed similar accuracy rates to previous studies in the literature.
Analyzing synthetic datasets through the training and inference domain gap

Synthetic data has been shown to be incredibly useful for improving the performance of perception algorithms; however, it is still unclear how to identify the right techniques to generate and to train perception models with those synthetic datasets. In this work, we show how creating a digital twin of a real-world dataset allows us to have a more principled evaluation of the synthetic-to-real domain gaps for both training and inference, and use this information to test and evaluate synthetic datasets themselves, rather than the models trained on them. Furthermore, we show how this framework allows for a measure of the inference domain gap—a measure that tells us whether testing a perception model on synthetic data is representative of testing in the real world. We use these measures to generate a synthetic dataset from the nuScenes autonomous driving dataset, targeted to maximally improve performance on specific rare classes. We optimize the synthetic data generation parameters for this dataset in order to reduce the inference and training domain gaps. We show performance improvements of over 18% on the bicycle class. Our training results provide a way of measuring the training domain gap to analyze synthetic datasets.
AdverSAR: adversarial imagery examples generation for synthetic aperture radar dataset augmentation

In this research work, by using the comprehensive IRIS simulated SAR dataset that includes ground, aerial, and marine vehicles, we explored and exploited different GAN-based techniques to increase the efficiency and effectiveness of the DL-based SAR image classifiers pre-trained based on synthetically generated SAR imagery datasets. Particularly, in this paper, we present three adversarial attach techniques on the DL classifiers. Then, we propose a streamlined generative model for properly training of SAR classifiers with less susceptibility to newly introduced adversarial examples. Lastly, we discuss the merits of our proposed methodologies and offer our future research directions for the further improvement of the proposed SAR-GAN-CNN model and summarize our future research contributions.
Ignorance is bliss: flawed assumptions in simulated ground truth

Current generation artificial intelligence (AI) is heavily reliant on data and supervised learning (SL). However, dense and accurate truth for SL is often a bottleneck and any imperfections can negatively impact performance and/or result in biases. As a result, several corrective lines of research are being explored, including simulation (SIM). In this article, we discuss fundamental limitations in obtaining truth, both in the physical universe and SIM, and different truth uncertainty modeling strategies are explored. A case study from data-driven monocular vision is provided. These experiments demonstrate performance variability with respect to different truth uncertainty strategies in training and evaluating AI algorithms.
A phenomenological study of hotel workers' work-study experience: Focusing on doctoral courses for hotel workers

The purpose of this study is to understand and explore the meaning of experience, the motivation to participate in learning, and the difficulties experienced by hotel workers who are pursuing a doctoral course as both work and study. To this end, in-depth interviews were conducted with 6 hotel workers working in a 5-star hotel, phenomenological research methods were conducted, and Moustakas' Stevick-Colaizzi-Keen method was applied for data analysis. The results of this study were derived from a total of 15 theme, 54 meaning units, and 398 significant statements. The motivation for participation in learning includes ‘the energetic time together’, ‘learning, as a thirsty’, ‘my own goal of leading me’, ‘leap to a true expert’, ‘getting older, getting more anxious’, ‘support is the source of my strength’ six themes were derived. Experienced difficulties were derived from ‘struggling for compatibility’, ‘frustrated and unfortunate graduate classes’, ‘a stiff brain, hard time about writing thesis’, ‘myself of longterm investments’, and ‘walk on eggshells around someone’. As a result of the analysis, the four themes of experiences ‘a new me’, ‘the power of my life’, ‘I am an Influencer’, ‘my own path to take a step and a step by step’ were derived. This study is meaningful in that it analyzes in-depth the experience of hotel workers' doctoral course experience, motivation to participate in learning, and experience of difficulties, and suggests improvement plans in the hotel industry based on the results.
TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database

With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (e.g., chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose the Top-k Edge-Diversified Patterns Discovery problem to retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called Ted which achieves a guaranteed approximation ratio to the optimal result. Two optimization strategies are further developed to improve the performance. Experimental studies on real-world datasets demonstrate the superiority of Ted to traditional techniques.
Discovering Top-k Rules using Subjective and Objective Criteria

This paper studies two questions about rule discovery. Can we characterize the usefulness of rules using quantitative criteria? How can we discover rules using those criteria? As a testbed, we consider entity enhancing rules (REEs), which subsume common association rules and data quality rules as special cases. We characterize REEs using a bi-criteria model, with both objective measures such as support and confidence, and subjective measures for the user's needs; we learn the subjective measure and the weight vectors via active learning. Based on the bi-criteria model, we develop a top-k algorithm to discover top-ranked REEs, and an any-time algorithm for successive discovery via lazy evaluation. We parallelize these algorithms such that they guarantee to reduce runtime when more processors are used. Using real-life and synthetic datasets, we show that the algorithms are able to find top-ranked rules and speed up conventional rule-discovery methods by 134X on average.
Toward Efficient Homomorphic Encryption for Outsourced Databases through Parallel Caching

Many applications deployed to public clouds are concerned about the confidentiality of their outsourced data, such as financial services and electronic patient records. A plausible solution to this problem is homomorphic encryption (HE), which supports certain algebraic operations directly over the ciphertexts. The downside of HE schemes is their significant, if not prohibitive, performance overhead for data-intensive workloads that are very common for outsourced databases, or database-as-a-serve in cloud computing. The objective of this work is to mitigate the performance overhead incurred by the HE module in outsourced databases. To that end, this paper proposes a radix-based parallel caching optimization for accelerating the performance of homomorphic encryption (HE) of outsourced databases in cloud computing. The key insight of the proposed optimization is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. We design the radix HE algorithm and apply it to both batch- and incremental-HE schemes; we demonstrate the security of those radix-based HE schemes by showing that the problem of breaking them can be reduced to the problem of breaking their base HE schemes that are known IND-CPA (i.e. Indistinguishability under Chosen-Plaintext Attack). We implement the radix-based schemes as middleware of a 10-node Cassandra cluster on CloudLab; experiments on six workloads show that the proposed caching can boost state-of-the-art HE schemes, such as Paillier and Symmetria, by up to five orders of magnitude.
An Effective and Differentially Private Protocol for Secure Distributed Cardinality Estimation

Counting the number of distinct elements distributed over multiple data holders is a fundamental problem with many real-world applications ranging from crowd counting to network monitoring. Although a number of space and computationally efficient sketch methods (e.g., the Flajolet-Martin sketch and the HyperLogLog sketch) for cardinality estimation have been proposed to solve the above problem, these sketch methods are insecure when considering privacy concerns related to the use of each data holder's personal dataset. Despite a recently proposed protocol that successfully implements the well-known Flajolet-Martin (FM) sketch on a secret-sharing based multiparty computation (MPC) framework for solving the problem of private distributed cardinality estimation (PDCE), we observe that this MPC-FM protocol is not differentially private. In addition, the MPC-FM protocol is computationally expensive, which limits its applications to data holders with limited computation resources. To address the above issues, in this paper we propose a novel protocol DP-DICE, which is computationally efficient and differentially private for solving the problem of PDCE. Experimental results show that our DP-DICE achieves orders of magnitude speedup and reduces the estimation error by several times in comparison with state-of-the-arts under the same security requirements.
GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark.
Incremental Tabular Learning on Heterogeneous Feature Space

Recently, incremental learning has attracted a lot of interest in both research communities and industries. Generally, given a series of data sets sequentially, it tries to achieve good performance on the new data set while maintaining not bad performance on the old ones. Despite the recent success of incremental learning, existing works mainly assume that the coming data set is from the feature space of old ones, i.e., homogeneous feature space. And they adopt one feature extractor to forcibly project different feature spaces into one space. However, this assumption is hard to hold in real-world scenarios. Especially, the attributes of tables may sequentially increase in tabular learning. Thus, classic incremental learning models may hinder their effectiveness. In this paper, we propose a new method, incremental tabular learning on heterogeneous feature space (ILEAHE) to solve this issue. We first propose the ideas that feature extractors should be decomposed into shared and specific extractors to process the shared and specific features across different data sets respectively. Then, we propose a novel measurement named discriminative ability to measure specific extractors. Thus, two kinds of extractors can be discriminated and the specific extractor will more focus on those domain-specific features. We further demonstrate the effectiveness of ILEAHE through empirical studies.
Efficient Approximate Nearest Neighbor Search in Multi-dimensional Databases

Approximate nearest neighbor (ANN) search is a fundamental search in multi-dimensional databases, which has numerous real-world applications, such as image retrieval, recommendation, entity resolution, and sequence matching. Proximity graph (PG) has been the state-of-the-art index for ANN search. However, the search on existing PGs either suffers from a high time complexity or has no performance guarantee on the search result. In this paper, we propose a novel τ-monotonic graph (τ- MG) to address the limitations. The novelty of τ-MG lies in a τ-monotonic property. Based on this property, we prove that if the distance between a query q and its nearest neighbor is less than a constant τ, the search on τ-MG guarantees to find the exact nearest neighbor of q and the time complexity of the search is smaller than all existing PG-based methods. For index construction efficiency, we propose an approximate variant of τ-MG, namely τ-monotonic neighborhood graph (τ- MNG), which only requires the neighborhood of each node to be τ-monotonic. We further propose an optimization to reduce the number of distance computations in search. Our extensive experiments show that our techniques outperform all existing methods on well-known real-world datasets.
I/O-Efficient Butterfly Counting at Scale

Butterfly (a cyclic graph motif) counting is a fundamental task with many applications in graph analysis, which aims at computing the number of butterflies in a large graph. With the rapid growth of graph data, it is more and more challenging to do butterfly counting due to the super-linear time complexity and large memory consumption. In this paper, we study I/O-efficient algorithms for doing butterfly counting on hierarchical memory. Existing algorithms of the kind cannot guarantee I/O optimality. Observing that in order to count butterflies, it suffices to "witness" a subgraph instead of the whole structure, a new class of algorithms called semi-witnessing algorithm is proposed. We prove that a semi-witnessing algorithm is not restricted by the lower bound Ømega(|E|2/MB) of a witnessing algorithm, and give a new bound of Ømega(min(|E|2/MB, |E|/|V| √M B)). We further develop the IOBufs algorithm that manages to approach the I/O lower bound, and thus claim its optimality. Finally, we make efforts to parallelize IOBufs to further improve the performance and scalability. We show in the experiment that IOBufs significantly outperforms the state-of-the-art algorithms EMRC and BFC-EM. In addition, IOBufs can scale to conducting butterfly counting on the Clueweb graph with 37 billion edges and quintillions (10^18 ) of butterflies.
RLS Side Channels: Investigating Leakage of Row-Level Security Protected Data Through Query Execution Time

Many modern use cases of relational databases involve multi-tenancy. To allow a tenant to only access its data, relational database systems (RDBMSs) introduced row-level security (RLS). RLS enables specifying per-row access controls, which the database enforces by rewriting tenant queries to add an RLS policy filter that filters out rows the tenant is not allowed to view. Unfortunately, while RLS blocks queries from returning unauthorized data, side-effects of query execution can form a side-channel that leaks information about such secret data. This paper investigates how RLS query execution time can leak information about rows that the querying tenant is restricted from viewing. We show that in PostgreSQL and SQL Server, an attacker can craft index-using queries to learn whether a value they are not authorized to view exists in an RLS-protected table, and in some cases, how many times such a value exists in the table. Our attack succeeds in a realistic cloud setting: we successfully attack managed PostgreSQL and SQL Server database instances on AWS from virtual machines in the same and different data centers. To block the RLS time side-channel, we design a data-oblivious query scheme for the case of unique keys. We also analyze the trade-offs created by the data-oblivious approach for non-unique keys. To facilitate the evaluation of RLS attacks and defenses, we introduce a benchmark that supports multi-tenancy and RLS, which are not supported by established benchmarks such as YCSB. We implement our solution in PostgreSQL and show that it achieves security with minimal performance impact.