Reviewerly/exHarmony huggingface dataset:
Fine-Tuning BERT on Twitter and Reddit Data in Luganda and English

Deep learning techniques, driven by the Transformer architecture and models like BERT, find broad utility. While sentiment analysis in high-resource languages is well-established, it's largely unexplored in low-resource ones. Our focus is on Luganda, a prevalent Ugandan language, spoken by over 21 million people. We utilized three datasets, from social media, to train machine learning models as baseline models and used BERT for deep learning. Our findings enhance sentiment analysis in both Luganda and English. Our approach for data extraction aids domain-specific dataset construction. This research advances NLP and aligns with global deep-learning initiatives.
Leveraging Salience Analysis and Sparse Attention for Long Document Summarization

Extractive and abstractive summarization models have led to promising results in summarizing relatively short documents, but still face the challenge from longer-form documents (e.g., scientific papers). Specifically, extractive models produce inaccurate or redundant summaries due to their weak salience analysis, while transformer-based abstractive models suffer from the quadratic dependency on the sequence length for their full attention mechanism. To remedy this, we propose a novel hybrid model named LDSumm (Long Document Summarization), which is composed of an extractive module that enhances the salience analysis by leveraging hierarchical structure (especially section information) of a document, and an abstractive module that introduces sparse attention ideas to increase the input size of BART. We conduct extensive experiments on two scientific-paper datasets: arXiv and PubMed. Experimental results show that LDSumm outperforms the baseline BART and other comparison models and obtains greater gain on the longer-paper dataset arXiv.
Enhancing a User Matchmaking Algorithm using Personalized PageRank

With the increasing number of users in online communities and social networking platforms, it is becoming more difficult for users to meet and connect with individuals who share similar opinions or interests. The paper proposes a user matchmaking algorithm based on personalized PageRank to provide potential friends to individual users. A set of user profiles is transformed into a graph model for efficiently discovering meaningful connections and influential users. The semantic relationship between two user profiles is then estimated using word and sentence embeddings. By incorporating both embedding models and personalized graph analytics, the proposed algorithm can capture complex semantic information and high-order user relationships, making the matchmaking process more accurate. Experiments conducted on a simulated user profile dataset show that the proposed algorithm consistently outperforms existing state-of-the-art methods in terms of the F1 score and mean average precision metrics.
EssAI Checker: Automated Scoring of Student's Factual Essay Using Latent Semantic Analysis with Lexical Chaining and Synonym Conversion

The researchers conducted a study titled "EssAI Checker: Automated Scoring of Student's Factual Essay Using Latent Semantic Analysis with Lexical Chaining and Synonym Conversion" by which they developed a tool that will automatically score students' essay answers based on the essay answer key. This study aims to help with the problem that existed when meetings and assessments are switched virtually, and to be of help to people in the academe, especially teachers with automatically scoring essays. The solution that was presented is through developing an improved algorithm that performs better for checking factual essays using five main processes which are Pre-processing, Latent Semantic Analysis with Lexical Chaining, Synonym Conversion and Cosine Similarity. Three experimental tools were developed in total where LSA Only, LSA with Synonym Conversion, and LSA with Lexical Chaining and Synonym Conversion is implemented. The tools achieved 65.73%, 67.32%, and 70.44% accuracy, respectively. The tool that implements LSA with Lexical Chaining and Synonym Conversion obtained the highest accuracy of 70.44%.
A New Improved Multiple Behaviors Whale Optimization Algorithm

The Whale Optimization Algorithm (WOA) has demonstrated superior performance compared with other swarm intelligence algorithms, making it widely used and applied to practical problem-solving. In this paper, an enhanced WOA is proposed, referred to as the Improved Whale Optimization Algorithm (IWOA). The convergence factor of the traditional WOA is replaced with a random trigonometric function, which considers both the historical and current best positions of the whale population during the bubble-encircling search phase, thereby accelerating the global search speed of the whale population. In the random-encircling search phase, a combination of equiprobable random selection and roulette wheel selection is used to select random positions. Additionally, a random perturbation of individuals is introduced in the spiral search phase to prevent premature convergence to local optima. The effectiveness and robustness of the IWOA are evaluated using 4 benchmark function problems, and the results are compared with three other algorithms including particle swarm optimization (PSO). The experimental results demonstrate that the proposed IWOA outperforms other algorithms in terms of convergence speed and accuracy.
Spelling Check with Sparse Distributed Representations Learning

This study focused on enhancing learning sequences using a method inspired by the brain, following Hawkins's approach. Capable of not only recognizing existing sequences but also learning new ones and ensuring fault-tolerant operations, the learning method was evaluated through a spelling check. The evaluation utilized the standard TREC-5 Confusion Track dataset to automatically correct incorrect words. The new method was compared with other techniques, such as Levenshtein Distance, pyspellchecker, LSTM, and Elmosclstm (Semantically Conditioned LSTM and Elmo Transformer), which is the state-of-the-art. The results demonstrated that the highest accuracy at the word level was 79.35%%, surpassing Elmosclstm's 74.41%. Additionally, at the sentence level, the brain-inspired method achieved 90.75% accuracy, outperforming Elmosclstm's 72.18%.
CoroGraph: Bridging Cache Efficiency and Work Efficiency for Graph Algorithm Execution

Many systems are designed to run graph algorithms efficiently in memory but they achieve only cache efficiency or work efficiency. We tackle this fundamental trade-off in existing systems by designing CoroGraph, a system that attains both cache efficiency and work efficiency for in-memory graph processing. CoroGraph adopts a novel hybrid execution model , which generates update messages at vertex granularity to prioritize promising vertices for work efficiency, and commits updates at partition granularity to share data access for cache efficiency. To overlap the random memory access of graph algorithms with computation, CoroGraph extensively uses coroutine , i.e., a lightweight function in C++ that can yield and resume with low overhead, to prefetch the required data. A suite of designs are incorporated to reap the full benefits of coroutine, which include prefetch pipeline, cache-friendly graph format, and stop-free synchronization. We compare CoroGraph with five state-of-the-art graph algorithm systems via extensive experiments. The results show that CoroGraph yields shorter algorithm execution time than all baselines in 18 out of 20 cases, and its speedup over the best-performing baseline can be over 2x. Detailed profiling suggests that CoroGraph achieves both cache efficiency and work efficiency with a low memory stall and a small number of processed edges.
Efficient and Accurate SimRank-Based Similarity Joins: Experiments, Analysis, and Improvement

SimRank-based similarity joins, which mainly include threshold-based and top- k similarity joins, are important types of all-pair SimRank queries. Although a line of related algorithms have been proposed recently, they still fall short of providing approximation guarantee and suffer from scalability issues on medium and large graphs. Meanwhile, we also lack an extensive analysis of existing techniques in terms of accuracy and efficiency. Motivated by these challenges, we first conduct detailed analysis of state-of-the-art algorithms and provide additional theoretical results. Second, to address the limitations of existing techniques, we propose simple yet effective algorithm frameworks for both queries to theoretically guarantee the approximation bound, and present a more efficient all-pair algorithm inspired by randomized local push of Personalized PageRank. Next, we analyze the algorithmic complexity of threshold-based and top- k similarity joins by leveraging a reasonable assumption of SimRank distribution. Through extensive experiments, we find that our proposed methods far exceed existing ones with respect to query efficiency, approximation guarantee and practical accuracy, while our theoretical analysis nicely matches the empirical study.
Errata for "SpaceSaving±: An Optimal Algorithm for Frequency Estimation and Frequent Items in the Bounded-Deletion Model"

This errata article points out an implicit assumption in the work of four of us published in VLDB 2022. The SpaceSaving± algorithm in bounded deletion data stream presented in the paper implicitly assumed deletions happen after all insertions. When insertions and deletions are interleaved, that algorithm may severely underestimate item's frequency. We first illustrate this phenomenon by an example and then present a modified algorithm with minor changes to allow interleaving between insertions and deletions. We also include a pointer to a full analysis of the new algorithms.
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively. Unfortunately, existing approaches are often constrained by their limited detection capacity and slow adaptation to evolving data streams, inhibiting their efficacy and efficiency in handling concept drift , which is a major challenge in evolving data streams. In this paper, we introduce METER, a novel dynamic concept adaptation framework that introduces a new paradigm for OAD. METER addresses concept drift by first training a base detection model on historical data to capture recurring central concepts , and then learning to dynamically adapt to new concepts in data streams upon detecting concept drift. Particularly, METER employs a novel dynamic concept adaptation technique that leverages a hypernetwork to dynamically generate the parameter shift of the base detection model, providing a more effective and efficient solution than conventional retraining or fine-tuning approaches. Further, METER incorporates a lightweight drift detection controller, underpinned by evidential deep learning, to support robust and interpretable concept drift detection. We conduct an extensive experimental evaluation, and the results show that METER significantly outperforms existing OAD approaches in various application scenarios.
Mammoths are Slow: The Overlooked Transactions of Graph Data

This paper argues for better concurrency control to support mammoth transactions , which read and write to many items. While these requests are prevalent on graph data, few systems support them efficiently. Currently, developers must make the uncomfortable choice between accepting dismal performance or abandoning transactional semantics. Applications deserve better: we believe that inherent graph properties provide a path forward to designing efficient concurrency control that preserves strong isolation.
VeriDKG: A Verifiable SPARQL Query Engine for Decentralized Knowledge Graphs

The ability to decentralize knowledge graphs (KG) is important to exploit the full potential of the Semantic Web and realize the Web 3.0 vision. However, decentralization also renders KGs more prone to attacks with adverse effects on data integrity and query verifiability. While existing studies focus on ensuring data integrity, how to ensure query verifiability - thus guarding against incorrect, incomplete, or outdated query results - remains unsolved. We propose VeriDKG, the first SPARQL query engine for decentralized knowledge graphs (DKG) that offers both data integrity and query verifiability guarantees. The core of VeriDKG is the RGB-Trie, a new blockchain-maintained authenticated data structure (ADS) facilitating correctness proofs for SPARQL query results. VeriDKG enables verifiability of subqueries by gathering global index information on subgraphs using the RGB-Trie, which is implemented as a new variant of the Merkle prefix tree with an RGB color model. To enable verifiability of the final query result, the RGB-Trie is integrated with a cryptographic accumulator to support verifiable aggregation operations. A rigorous analysis of query verifiability in VeriDKG is presented, along with evidence from an extensive experimental study demonstrating its state-of-the-art query performance on the largeRDFbench benchmark.
Algorithmic Complexity Attacks on Dynamic Learned Indexes

Learned Index Structures (LIS) view a sorted index as a model that learns the data distribution, takes a data element key as input, and outputs the predicted position of the key. The original LIS can only handle lookup operations with no support for updates, rendering it impractical to use for typical workloads. To address this limitation, recent studies have focused on designing efficient dynamic learned indexes. ALEX, as the first and one of the representative dynamic learned index structures, enables dynamism by incorporating a series of design choices, including adaptive key space partitioning, dynamic model retraining, and sophisticated engineering and policies that prioritize read/write performance. While these design choices offer improved average-case performance, the emphasis on flexibility and performance increases the attack surface by allowing adversarial behaviors that maximize ALEX's memory space and time complexity in worst-case scenarios. In this work, we present the first systematic investigation of algorithmic complexity attacks (ACAs) targeting the worst-case scenarios of ALEX. We introduce new ACAs that fall into two categories, space ACAs and time ACAs, which target the memory space and time complexity, respectively. First, our space ACA on data nodes exploits ALEX's gapped array layout and uses Multiple-Choice Knapsack (MCK) to generate an optimal adversarial insertion plan for maximizing the memory consumption at the data node level. Second, our space ACA on internal nodes exploits ALEX's catastrophic cost mitigation mechanism, causing an out-of-memory (OOM) error with only a few hundred adversarial insertions. Third, our time ACA generates pathological insertions to increase the disparity between the actual key distribution and the linear models of data nodes, deteriorating the runtime performance by up to 1, 641× compared to ALEX operating under legitimate workloads.
Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs

Bipartite graphs characterize relationships between two different sets of entities, like actor-movie, user-item, and author-paper. The butterfly, a 4-vertices 4-edges (2,2)-biclique, is the simplest cohesive motif in a bipartite graph and is the fundamental component of higher-order substructures. Counting and enumerating the butterflies offer significant benefits across various applications, including fraud detection, graph embedding, and community search. While the corresponding motif, the triangle, in the unipartite graphs has been widely studied in both static and temporal settings, the extension of butterfly to temporal bipartite graphs remains unexplored. In this paper, we investigate the temporal butterfly counting and enumeration problem: count and enumerate the butterflies whose edges establish following a certain order within a given duration. Towards efficient computation, we devise a non-trivial baseline rooted in the state-of-the-art butterfly counting algorithm on static graphs, further, explore the intrinsic property of the temporal butterfly, and develop a new optimization framework with a compact data structure and effective priority strategy. The time complexity is proved to be significantly reduced without compromising on space efficiency. In addition, we generalize our algorithms to practical streaming settings and multi-core computing architectures. Our extensive experiments on 11 large-scale real-world datasets demonstrate the efficiency and scalability of our solutions.
ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems

Natural Language to SQL systems (NL-to-SQL) have recently shown improved accuracy (exceeding 80%) for natural language to SQL query translation due to the emergence of transformer-based language models, and the popularity of the Spider benchmark. However, Spider mainly contains simple databases with few tables, columns, and entries, which do not reflect a realistic setting. Moreover, complex real-world databases with domain-specific content have little to no training data available in the form of NL/SQL-pairs leading to poor performance of existing NL-to-SQL systems. In this paper, we introduce ScienceBenchmark , a new complex NL-to-SQL benchmark for three real-world, highly domain-specific databases. For this new benchmark, SQL experts and domain experts created high-quality NL/SQL-pairs for each domain. To garner more data, we extended the small amount of human-generated data with synthetic data generated using GPT-3. We show that our benchmark is highly challenging, as the top performing systems on Spider achieve a very low performance on our benchmark. Thus, the challenge is many-fold: creating NL-to-SQL systems for highly complex domains with a small amount of hand-made training data augmented with synthetic data. To our knowledge, ScienceBenchmark is the first NL-to-SQL benchmark designed with complex real-world scientific databases, containing challenging training and test data carefully validated by domain experts.
Testing Graph Database Systems via Graph-Aware Metamorphic Relations

Graph database systems (GDBs) have supported many important real-world applications such as social networks, logistics, and path planning. Meanwhile, logic bugs are also prevalent in GDBs, leading to incorrect results and severe consequences. However, the logic bugs largely cannot be revealed by prior solutions which are unaware of the graph native structures of the graph data. In this paper, we propose Gamera (Graph-aware metamorphic relations), a novel metamorphic testing approach to uncover unknown logic bugs in GDBs. We design three classes of novel graph-aware Metamorphic Relations (MRs) based on the graph native structures. Gamera would generate a set of queries according to the graph-aware MRs to test diverse and complex GDB operations, and check whether the GDB query results conform to the chosen MRs. We thoroughly evaluated the effectiveness of Gamera on seven widely-used GDBs such as Neo4j and OrientDB. Gamera was highly effective in detecting logic bugs in GDBs. In total, it detected 39 logic bugs, of which 15 bugs have been confirmed, and three bugs have been fixed. Our experiments also demonstrated that Gamera significantly outperformed prior solutions including Grand, GD-smith and GDBMeter. Gamera has been well-recognized by GDB developers and we open-source our prototype implementation to contribute to the community.
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning

In database systems, accurate cardinality estimation is a cornerstone of effective query optimization. In this context, estimators that use machine learning have shown significant promise. Despite their potential, the effectiveness of these learned estimators strongly depends on their ability to learn from small training sets. This paper presents a novel approach for learned cardinality estimation that addresses this issue by enhancing sample efficiency. We propose a neural network architecture informed by geometric deep learning principles that represents queries as join graphs. Furthermore, we introduce an innovative encoding for complex predicates, treating their encoding as a feature selection problem. Additionally, we devise a regularization term that employs equalities of the relational algebra and three-valued logic, augmenting the training process without requiring additional ground truth cardinalities. We rigorously evaluate our model across multiple benchmarks, examining q-errors, runtimes, and the impact of workload distribution shifts. Our results demonstrate that our model significantly improves the end-to-end runtimes of PostgreSQL, even with cardinalities gathered from as little as 100 query executions.
FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation

Data augmentation enhances the accuracy of DL models by diversifying training samples through a sequence of data transformations. While recent advancements in data augmentation have demonstrated remarkable efficacy, they often rely on computationally expensive and dynamic algorithms. Unfortunately, current system optimizations, primarily designed to leverage CPUs, cannot effectively support these methods due to costs and limited resource availability. To address these issues, we introduce FusionFlow, a system that cooperatively utilizes both CPUs and GPUs to accelerate the data preprocessing stage of DL training that runs the data augmentation algorithm. FusionFlow orchestrates data preprocessing tasks across CPUs and GPUs while minimizing interference with GPU-based model training. In doing so, it effectively mitigates the risk of GPU memory overflow by managing memory allocations of the tasks within the GPU-wide free space. Furthermore, FusionFlow provides a dynamic scheduling strategy for tasks with varying computational demands and reallocates compute resources on the fly to enhance training throughput for both single and multi-GPU DL jobs. Our evaluations show that FusionFlow outperforms existing CPU-based methods by 16--285% in single-machine scenarios and, to achieve similar training speeds, requires 50--60% fewer CPUs compared to utilizing scalable compute resources from external servers.
Digital competence and information literacy: clarifying concepts based on a literature review

This study aimed to collect input for the definition of a conceptual framework of digital competence for information literacy to be developed in the context of doctoral programs in Education. A systematic literature review methodology was adopted, and several steps were developed that included: preliminary readings and initial mapping, which allowed to define the search terms and expressions; the definition of inclusion and exclusion criteria; the research in databases and aggregators; the pre-selection of articles; and the selection of the corpus of analysis, which included seven articles published in scientific journals with peer review. The studies are mainly focused on information literacy. The two concepts are addressed simultaneously in only three articles. Digital competence is related to the mastery of digital tools, namely to search for information in databases or to define alert strategies. We infer that this competence seems to be closer to more operative concepts, such as digital skills. Information literacy requires the effective use of information involving information search, selection, evaluation and communication. We conclude that it is necessary to cross-reference and triangulate existing studies in order to define the digital competences for information literacy that doctoral students in Education should develop. This is the object of an ongoing doctoral project.