Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Logic Reasoning and Graph Neural Networks for Ontology-Mediated Query Answering With a Covering Axiom

The problem of query answering over incomplete attributed graph data is a challenging field of database management systems. When there are rules on data structure expressed in the form of the ontology, the theoretical complexity of finding exact solution satisfying ontology constraints increases. Logic-based methods use theoretical constructions to obtain efficient rewritings of the original queries with respect to ontology and find an answer to the rewriting query over incomplete data. However, there is an opportunity to use faster machine learning methods to label all the data and query over the “most probable” data model without taking into account the ontology. This research paper investigates the effectiveness of both mentioned approaches for answering ontology-mediated queries on graph databases that integrate an ontology with a covering axiom, which states that every node belongs to either of two classes. The first approach involves finding precise answers through logical reasoning and rewriting the problem into a datalog program, while the second approach employs a trained graph neural network to label data in a binary classification problem and leverages SQL for query answering. We conduct an in-depth analysis of the time performance of these approaches and evaluate the impact of training set selection on their ability of correct query answering. By comparing these approaches across various experiments, we provide insights into their strengths and limitations for answering ontology-mediated queries containing a Boolean conjunctive query. In particular, we showed the importance of logic-based approaches for ontology with a covering axiom and the inability of machine learning methods to find answers for ontology-mediated queries in large networks.
Orchid: An Online Learning Based Resource Partitioning Framework for Job Colocation With Multiple Objectives

Colocating multiple throughput-oriented jobs on the same server is a commonly used approach for improving system throughput in modern datacenters. The shared resources of the server are usually partitioned among the colocated jobs in order to prevent performance interference caused by resource contention. However, how to properly partition the shared resources among the colocated jobs is nontrival, because it usually has to trade off between two conflict objectives, as datacenter manager wants to maximize server throughput while job owners hope to experience a fair slowdown. Moreover, a desirable resource partitioning strategy should also be efficient, autonomous and adaptive. So far, this problem is not well-addressed in the literature due to several critical challenges. In this paper, we propose an online learning based framework, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Orchid</i> , to address the multi-objective resource partitioning problem. Orchid leverages contextual multi-armed bandit (CMAB) to model the resource partitioning problem and uses a light-weight online learning algorithm to learn the optimal partitioning configuration according to some easy-to-collect runtime system status. Orchid has two distinguished properties compared to the existing solutions: first, it has the ability to trade off between the two objectives flexibly according to the aspiration of decision maker; second, it has the awareness about runtime system status, which can help to improve the efficiency of finding the optimal partitioning configuration and the adaptivity to dynamic environment changes. Moreover, Orchid does not require any prior knowledge of jobs, and incurs a very small computational overhead. Our evaluations show that Orchid achieves all the desired properties of a good resource partitioning strategy, which outperforms the state-of-the-art baselines with significant margins. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Orchid</i> is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/OpenSourceOrchid/Orchid</uri> .
Auction-Based Storage Resource Allocation for Blockchain

The blockchain establishes trust by maintaining a distributed appending-only ledger, which is widely applied to the nodes lacking trust in the edge environment. However, the full-replication storage mode of blockchain is a big challenge for resource-constrained edge devices. What is worse, system performance is also affected by the large storage overhead. Existing solutions to reduce blockchain storage overhead often require additional security assumptions, lack incentives, or fail to account for resource heterogeneity. To overcome these limitations, we design an auction-based storage resource allocation scheme. Winners are selected to store blocks, taking into account the block preferences of nodes, and the fairness of the system. Nodes are incentivized by implementing fairness and equity in distributed auctions and data transactions through smart contracts. Finally, extensive experiments show 65%–81% savings in storage overhead compared to fully replicated storage.
Multitarget Occlusion Tracking With 3-D Spatio-Temporal Context Graph Model

In complex tracking scenarios of multitarget occlusion, especially when the target is seriously occluded, feature loss can lead to tracking failure. In this article, we develop a multitarget occlusion tracking algorithm based on a 3-D spatio-temporal context graph model, which can fully utilize the targets’ RGB-D information. Specifically, we first extract target’s 3-D features containing appearance, motion, spatial, and temporal features. Then, the spa’Io-temporal context features between different targets are also be acquired. Furthermore, the attribute relational graph (ARG) model with target’s 3-D spatio-temporal context features is constructed, which combines spatial features in the current frame and temporal features between consecutive frames. Moreover, if the target is determined to be occluded according to the matching results in the current frame, the probability relaxation method is used to assign the optimal tracking label to clustering blocks in occlusion area by matching these ARG models, which can address the multitarget cross occlusion problem. Extensive experiments on the Princeton Tracking Benchmark Datasets clearly demonstrate that our method can achieve superior performance against the typical algorithms proposed in recent years.
Neural Augmentation-Based Saturation Restoration for LDR Images of HDR Scenes

There are shadow and highlight regions in a low dynamic range (LDR) image which is captured from a high dynamic range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this paper, the saturated regions of the LDR image are restored by fusing model-based and data-driven approaches. With such a neural augmentation, two synthetic LDR images are first generated from the underlying LDR image via the new model-based approach. It relaxes the requirement of mapping integers to integers and improves the modeling accuracy. One is brighter than the input image to restore the shadow regions and the other is darker than the input image to restore the high-light regions. Both synthetic images are then refined via one single exposedness aware saturation restoration network (EASRN). Finally, the two synthetic images and the input image are combined together via an HDR synthesis algorithm or a multi-scale exposure fusion algorithm. Experimental results indicate that the proposed algorithm outperforms existing algorithms in terms of HDR-VDP-3. The proposed algorithm can be embedded in any smart phones or digital cameras to produce an information-enriched LDR image.
LAFD: Local-Differentially Private and Asynchronous Federated Learning With Direct Feedback Alignment

Federated learning is a promising approach for training machine learning models using distributed data from multiple mobile devices. However, privacy concerns arise when sensitive data are used for training. In this paper, we discuss the challenges of applying local differential privacy to federated learning, which is compounded by limited resources of mobile clients and the asynchronicity of federated learning. To address these challenges, we propose a framework called LAFD, that stands for Local-differentially Private and Asynchronous Federated Learning with Direct Feedback Alignment. LAFD consists of two parts: (a) LFL-DFALS: Local differentially private Federated Learning with Direct Feedback Alignment and Layer Sampling, and (b) AFL-LMTGR: Asynchronous Federated Learning with Local Model Training and Gradient Rebalancing. LFL-DFALS effectively reduces the computation and communication costs via direct feedback alignment and layer sampling. AFL-LMTGR handles the problem of stragglers via local model training, and gradient rebalancing. We demonstrate the performance of LFL-DFALS and AFL-LMTGR through experiments.
Dynamic Ensemble Algorithm Post-Selection Using Hardness-Aware Oracle

Dynamic Ensemble Selection (DES) algorithms have obtained better performance in many tasks compared to monolithic classifiers and static ensembles.However, it is reasonable to assume that no DES algorithm is the optimal solution in different scenarios since diversity plays an important role.Thus, this paper addresses this research gap by proposing a novel approach called Hardness-aware Oracle with Dynamic Ensemble Selection (HaO-DES) that operates as a post-selection strategy, evaluating and selecting the best DES techniques per instance.Each DES technique ensemble is evaluated using a new measure called Hardness-aware Oracle (HaO).HaO extends the traditional Oracle concept by assessing a DES technique based on how the classifiers in the selected ensemble work together, contrasting with the individual classifier evaluation in the traditional assessment.We performed experiments over 30 databases, using three base classifiers (Perceptron, Logistic Regression, and Naive Bayes) in homogeneous and heterogenous pools' configurations, to assess HaO-DES with four DES approaches (KNORA-U, KNOP, DES-P, and META-DES).We use three performance metrics to evaluate the experiments: accuracy, F-score, and Matthews Correlation Coefficient (MCC).The results show that our approach outperforms or obtains similar results against the four individual DES approaches, mainly when considering heterogeneous pool settings.We also demonstrated the HaO-DES efficiency in choosing suitable DES techniques in different situations.
Magnetic Anomaly Detection Network With Adaptive Time–Frequency Feature Expression

Magnetic anomaly detection (MAD) is a method that uses magnetometers to find hidden ferromagnetic objects based on variations in magnetic field signals. Many methods use time–frequency fusion features to detect target signals, but these features are extracted using unlearnable methods in the preprocessing. This article proposes a network with adaptive time–frequency feature expression to detect magnetic anomalies. The network adaptively learns the time–frequency features of magnetic anomaly signal through optimization, and in the feature fusion step, it adaptively selects key features for classification. In experiments, we compared our proposed method with the state-of-the-art (SOTA) time–frequency fusion detection method in simulation, semireal, and real datasets. Results show that our method has better detection performance, with high accuracy (ACC), detection rate (DR), and area under receiver operating characteristic curve (AUC). In real datasets, the proposed method outperforms other methods in low signal-to-noise ratio (SNR) cases and has a low false alarm rate (FAR), demonstrating its good comprehensive detection ability and robustness.
Deep Reinforcement Learning Processor Design for Mobile Applications

The deep neural network (DNN) has shown impressive performance in various applications such as computer vision and natural language processing. However, such high performance of DNN comes at a high computational cost. For calculating DNN, a large size of weight and feature map should be stored in memory and multiplied by each other after being loaded from memory. Recently, the portion of memory access for the total time and total power consumption has been increased in the DNN accelerators, due to the stagnant improvement of memory, which is known as the “memory wall” problem. Especially in the case of deep reinforcement learning, memory optimization is essential because it has mainly utilized multiple numbers of fully connected layers simultaneously. In this chapter, we propose a deep reinforcement learning accelerator that optimizes memory bandwidth and memory power consumption. The proposed accelerator optimizes memory bandwidth by dual-mode weight compression and optimizes memory power consumption with floating-point in-memory computing architecture.
Double Graph Attention Network Reasoning Method Based on Filtering and Program-Like Evidence for Table-Based Fact Verification

Table-based fact verification requests parsing table and statement structure and performing numerical and logical reasoning. Previous methods may select erroneous programs and ignore the interpretability of table-based fact verification. Thus, we propose a double graph attention network reasoning method based on filtering and program-like evidence (DGMFP). In detail, we initially obtain the filtering evidence based on tables and the program-like evidence based on logical forms to incorporate the semantic and symbolic information of evidence. Then, we construct an evidence graph with statement–evidence pairs as nodes and use the kernel in graph neural network to conduct more fine-grained joint reasoning and improve the interpretability of table-based fact verification. We also construct a connected graph with all entities and functions in the program-like evidence as nodes and use the graph attention network (GAT) to capture more fine-grained relationships within the program-like evidence. Finally, we connect the outputs of two GAT models and BERT model to predict labels. Experimental results on TABFACT show that DGMFP outperforms all baselines with 76.1% accuracy. Ablation studies further indicate that constructed two graphs, filtering evidence, and program-like evidence play an important role in better understanding the semi-structured table.
The Adaptability of a Transformer-Based OCR Model for Historical Documents

We tested the capabilities of Transformer-based text recognition technology when dealing with (multilingual) real-world datasets. This is a crucial aspect for libraries and archives that must digitise various sources. The digitisation process cannot rely solely on manual transcription due to the complexity and diversity of historical materials. Therefore, text recognition models must be able to adapt to various printed texts and manuscripts, especially regarding different handwriting styles. Our findings demonstrate that Transformer-based models can recognise text from printed and handwritten documents, even in multilingual environments. These models require minimal training data and are a suitable solution for digitising libraries and archives. However, it is essential to note that the quality of the recognised text can be affected by the handwriting style.
Stylistic Similarities in Greek Papyri Based on Letter Shapes: A Deep Learning Approach

This paper addresses the issue of clustering historical handwritings according to similarity in absence of metadata on date or style. While releasing a new dataset called AlphEpMu, it proposes to use SimSiam deep neural network model to evaluate similarity between images of individual characters from three Greek letter categories (alpha, epsilon and mu). Two specimens of a given letter category are defined as similar if they come from the same piece of manuscript (penned by a single writer). Similarity is then computed between pairs of images of the same letter category from different manuscripts. Last, scores of the three letter categories are merged to express similarity between the manuscripts. Applied to Greek Literary papyri, this approach is proved useful to paleographers since it allows organizing a complex group of 72 manuscripts (AlphEpMu-72) into a meaningful network but also spotting micro phenomena of similarity which can help explaining the general evolution of handwritings.
WISDOM-DX: An Automatic DX Assessment System Using a QA System Based on Web Data

DX is a must for Japanese companies to cope with Japan's 2025 Digital Cliff. To promote DX initiatives, self-assessment is necessary to make their position recognized. However, conventional manual assessments have high costs, hindering their DX promotion. We noticed that progress in corporate DX can be reflected in the amount of related information on the Web, and there is no reported method of utilizing this relation for DX assessment. This paper proposes WISDOM-DX, a system that accumulates information on corporate activities on the Web using a question-answering system, and ranks companies regarding DX initiatives. We compared WISDOM-DX with two baselines Prize and Google. The precision of WISDOM-DX, Prize, and Google were 56.3%, 45.8%, and 22.9%, respectively. The rate of DX-related award winners or certified companies obtained by WISDOM-DX and Google were 91.7% and 64.6%, respectively. The Area Under the Precision-Recall curve (AUPR) of WISDOM-DX, Prize, and Google were 0.540, 0.359, and 0.181, respectively. An opinion survey showed 60.7% positive and 32.1% neutral responses regarding the agreeability of WISDOM-DX's rankings, and 46.4% positive and 39.3% neutral responses regarding the usefulness of WISDOM-DX. These results showed WISDOM-DX's promising performance and the prospect of automating large-scale assessment regarding corporate DX initiatives.
Text Extraction for Handwritten Circuit Diagram Images

Paper-based handwritten electrical circuit diagrams still exist in educational scenarios and historical contexts. In order to check them or to derive their functional principles, they can be digitized for further analysis and simulation. This digitization effectively performs an electrical graph extraction and can be achieved by straight-forward instance segmentation, in which electrical symbols become the nodes and the interconnecting lines become the edges of the graph. For an accurate simulation however, the texts for describing the graph’s items properties have to be extracted as well and associated accordingly. The paper at hand describes a dataset as well as approaches based on optical character recognition, regular expressions and geometric matching. The source code of the described approach as well as the ground truth is integrated into a publicly available dataset and processing software.
Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction

Relation extraction task is a crucial and challenging aspect of Natural Language Processing. Several methods have surfaced as of late, exhibiting notable performance in addressing the task; however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. Our objective is to showcase that by leveraging the hierarchical structure and relational distribution of entities within a corpus without introducing external knowledge, a relation extraction model can achieve significantly enhanced performance. We therefore proposed a relation extraction approach based on the incorporation of pretrained knowledge graph embeddings at the corpus scale into the sentence-level contextual representation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach. The obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.
Typefaces and Ligatures in Printed Arabic Text: A Deep Learning-Based OCR Perspective

Arabic script is complex, with multiple shapes for the same characters in different positions. Another challenge of the script, in the context of recognition, is ligatures. A combination of a specific two or more character sequence takes a different shape than what those characters normally look like when they appear in a similar position. Deep learning-based systems are widely used for text recognition these days. In this work, we investigate the performance of deep learning systems for two alternative modeling choices: using characters as modeling units and using character shapes as modeling units. Moreover, we also investigate the impact on text recognition with mixed typefaces, where the training and test sets have samples from multiple typefaces, and discuss the effect of font families on recognition performance. We extend this by studying the effectiveness of the text recognition system in recognizing text from unseen typefaces, i.e., text in the test set is from a typeface not available in training. Finally, we present a methodology to automatically detect ligatures in printed Arabic text. We conducted experiments on the publicly available APTI dataset of printed Arabic text and report the findings and discuss the results.
Long Method Detection Using Graph Convolutional Networks

Long Method is a code smell that frequently happens in software development, which refers to the complex method with multiple functions. Detecting and refactoring such problems has been a popular topic in software refactoring, and many detection approaches have been proposed. In past years, the approaches based on metrics or rules have been the leading way in long method detection. However, the approach based on deep learning has also attracted extensive attention in recent studies. In this paper, we propose a graph-based deep learning approach to detect Long Method. The key point of our approach is that we extended the PDG (Program Dependency Graph) into a Directed-Heterogeneous Graph as the input graph and used the GCN (Graph Convolutional Network) to build a graph neural network for Long Method detection. Moreover, to get substantial data samples for the deep learning task, we propose a novel semi-automatic approach to generate a large number of data samples. Finally, to prove the validity of our approach, we compared our approach with the existing approaches based on five groups of datasets manually reviewed. The evaluation result shows that our approach achieved a good performance in Long Method detection.
Performance Evaluation of Parallel Sortings on the Supercomputer Fugaku

Sorting is one of the most basic algorithms, and developing highly parallel sorting programs is becoming increasingly important in high-performance computing because the number of CPU cores per node in modern supercomputers tends to increase. In this study, we have implemented two multi-threaded sorting algorithms based on samplesort and compared their performance on the supercomputer Fugaku. The first algorithm divides an input sequence into multiple blocks, sorts each block, and then selects pivots by sampling from each block at regular intervals. Each block is then partitioned using the pivots, and partitions in different blocks are merged into a single sorted sequence. The second algorithm differs from the first one in only selecting pivots, where the binary search is used to select pivots such that the number of elements in each partition is equal. We compare the performance of the two algorithms with different sequential sorting and multiway merging algorithms. We demonstrate that the second algorithm with BlockQuicksort (a quicksort accelerated by reducing conditional branches) for sequential sorting and the selection tree for merging shows consistently high speed and high parallel efficiency for various input data types and data sizes.