Reviewerly/exHarmony huggingface dataset:
Printer Source Identification Based on Graph Model

Printer source identification is an important means of document inspection and plays an important role in forensic identification. In the research of printer source recognition, traditional methods basically rely on specific characters to recognize printed documents, but the recognition of Chinese printed documents is usually difficult because there are few or no specific characters. In view of this situation, this paper proposes a text-independent printer source identification method, which uses a graphical model to model the timing relationship of the printer, and then extracts the timing characteristics of the printer, which belong to the text-independent printer. Internal features, so that the method can be recognized without relying on specific characters, and has achieved good experimental results. Experimental data show that the proposed method is very useful for the traceability of printed documents.
LDRC: Long-tail Distantly Supervised Relation Extraction via Contrastive Learning

Long-tail problem is one of the major challenges in distantly supervised relation extraction. Some recent works on the long-tail problem attempt to transfer knowledge from data-rich and semantically similar head classes to data-poor tail classes using a relation hierarchical tree. These methods, however, are based on the assumption that long-tail and head relations have a strong correlation, which does not always hold true, and the model’s ability to learn long-tail relations is essentially not improved. In this paper, a novel joint learning framework that combines relation extraction and contrastive learning is proposed, allowing the model to directly learn the subtle differences between different categories to improve long-tail relation extraction. Experimental results show that our proposed model outperforms the current state-of-the-art (SOTA) model on various mainstream datasets.
Code Switching in Digital Communication

This research examines Codeswitching (CS) from Hebrew into Arabic as used by Arabs from Iksal and Um Al-Fahm, in Israel, in the context of Arabic used in Digital communication (WhatsApp and Viber), specifically the written colloquial rural Palestinian Arabic dialect of Arabs in Israel.The study focuses on topics that motivate uses of code switching, the syntactic characteristics of these code switches, phonological aspects, Categories of CS.The expectations were that switches by bilinguals are largely limited to greeting phrases and are restricted to clause boundaries with one-word switching into Hebrew.Moreover, there is an adaptation of L2 to L1 phonology.The results show that the most frequent topics evolve around education and employment.Moreover, code switches can still be code switches even if they have been adapted to Arabic phonology.The results also reinforce previous findings, such as Paolillo (2011) and Lee (2007), on how synchronicity affects the usage of CS.
A Comparative Study of Stream Reasoning Engines

The diverse research efforts in recent years in the area of stream reasoning (SR) led to a wide range of SR engines. However, the lack of standardization and the diverse choices in SR (e.g., tuple-driven vs. time-driven engines, streaming all results vs. newly derived ones, ...) mean that real comparability among the engines is hardly given. A first step towards achieving comparability and standardization is the RSP-QL model, implemented in the RSP4J framework, which allows for describing and formalizing the semantics of SR engines. To further advance the state of the art in comparative research of stream reasoning, we present the results of a survey to quantify the in-use importance of several key performance indicators (KPIs) and features and compare SR engines along these KPIs with the CityBench and the CSRBench oracle. Our analysis shows that the two RSP4J implementations C-SPARQL2.0 and YASPER outperform the well-known C-SPARQL implementation in terms of performance and configurability. Our comparison against a naive SR extension of the incremental reasoning engine RDFox shows that SR engines still have potential for improvement. To avoid a costly integration of engines into several different benchmarking environments, we finally present a unifying interface, already aligned with the CityBench and CSRBench, for benchmarking SR engines.
A Knowledge Graph of Contentious Terminology for Inclusive Representation of Cultural Heritage

Cultural heritage collections available as linked open data (LOD) may contain harmful stereotypes about people and cultures, for example, in outdated textual descriptions of objects. Galleries, libraries, archives, and museums (GLAM) have suggested various approaches to tackle potentially problematic content in digital collections. However, the domain expertise and discussions about words and phrases used in LOD-collections are scattered across different resources and detached from the collections themselves. In this paper, we capture domain expertise about English and Dutch contentious heritage terminology in a knowledge graph. Contentious terms in the resulting graph are then linked to entities from other LOD-resources used in the cultural domain and beyond, including Wikidata and WordNet. We make our design decisions explicit and report on the linking process. The developed knowledge graph makes expert knowledge interoperable, so it can be reused by the cultural heritage community and other LOD-developers to contribute to a more inclusive representation of cultural heritage on the Web.
Whyis 2: An Open Source Framework for Knowledge Graph Development and Research

Whyis is the first open source framework for creating custom provenance-driven knowledge graph applications, or KGApps, supporting three principal tasks: knowledge curation, inference, and interaction. It has been used in knowledge graph projects in materials science, health informatics, and radio spectrum policy. All knowledge in Whyis graphs are encapsulated in nanopublications, which simplifies and standardizes the production of qualified knowledge in knowledge graphs. The architecture of Whyis enables what we consider to be essential requirements for knowledge graph construction, maintenance, and use. These requirements include support for automated and manual curation of knowledge from diverse sources, provenance traces of all knowledge, domain-specific user interaction, and generalized distributed knowledge inference. We coin the term “Nano-scale knowledge graph” to refer to nanopublication-driven knowledge graphs. Knowledge graph developers can use Whyis to configure custom sets of knowledge curation pipelines using custom data importers and semantic extract, transform, and load scripts. The flexible, nanopublication-based architecture of Whyis lets knowledge graph developers integrate, extend, and publish knowledge from heterogeneous sources on the web. Whyis KGApps and are easily developed locally, managed using source control, and deployable via continuous integration, server deployment scripts, and as docker containers.
Software Defect Prediction Through a Hybrid Approach Comprising of a Statistical Tool and a Machine Learning Model

Traditional statistical learning algorithms perform poorly in case of learning from an imbalanced dataset. Software defect prediction (SDP) is a useful way to identify defects in the primary phases of the software development life cycle. This SDP methodology will help to remove software defects and induce to build a cost-effective and good quality of software products. Several statistical and machine learning models have been employed to predict defects in software modules. But the imbalanced nature of this type of datasets is one of the key characteristics, which needs to be exploited, for the successful development of a defect prediction model. Imbalanced software datasets contain non-uniform class distributions with most of the instances belonging to a specific class compared to that of the other class. We propose a novel hybrid model based on Hellinger distance-based decision tree (HDDT) and artificial neural network (ANN), which we call as hybrid HDDT-ANN model, for analysis of software defect prediction (SDP) data. This is a newly developed model which is found to be quite effective in predicting software bugs. A comparative study of several supervised machine learning models with our proposed model using different performance measures is also produced. Hybrid HDDT-ANN also takes care of the strength of a skew-insensitive distance measure, known as Hellinger distance, in handling class imbalance problems. A detailed experiment was performed over ten NASA SDP datasets to prove the superiority of the proposed method.
Knowledge augmentation-based soft constraints for semi-supervised clustering

Cluster analysis discovers natural structures from different perspectives of data objects and has become an effective method in data mining. The emergence of semi-supervised clustering techniques has improved the performance of unsupervised clustering algorithms. Clustering with guidance information is a clustering method variant that uses pairwise constraints based on background knowledge. This method increases the interpretability of the results through a knowledge-guided perspective but simultaneously suffers from the problem of constraint conflict. This paper designs knowledge augmentation-based soft constraints as a new pairwise constraint representation and proposes a Soft Constraints Kmeans (SCop-Kmeans) method to resolve constraint conflicts. By describing constraint knowledge from multiple perspectives, the association strength of pairwise constraints is calculated to obtain the assignment basis of objects. SCop-Kmeans can solve the sample allocation conflict problem caused by the contradiction between different constraints and improve the clustering stability. Finally, experiments are performed using UCI public standard datasets. The proposed method further improves the accuracy of clustering and performs well in experiments with different numbers of constraints, which shows that the proposed method has advantages in using constraint information to guide clustering.
Improving Efficiency and Accuracy in English Translation Learning: Investigating a Semantic Analysis Correction Algorithm

Translation serves as a vital link in connecting individuals from diverse cultural backgrounds, assuming greater significance in the context of globalization. With the continued growth of international communication, the importance of effective translation in fostering mutual understanding, exploring new horizons, and building relationships cannot be overstated. However, the persistent language barrier necessitates improvements in translation accuracy and efficiency. In recent years, information technology has facilitated the development of innovative algorithms for English translation. This study aims to investigate correction algorithms for English translation learning, proposing a method that encompasses semantic word similarity calculations, a log-linear model construction, and the selection of appropriate translations using a dependency tree-to-string approach. Our research examines the performance of this system through comprehensive testing, showcasing noteworthy enhancements in the efficacy and precision of the translations in English. By addressing the users’ needs for English translation correction, this work contributes to bridging language gaps and fostering effective cross-cultural communication.
Forming Optimality Criteria for Mechatronic Control Systems Using a Fuzzy Logic

At present, the problem of forming an optimality criterion in parametric synthesis of mechatronic systems state controllers is a highly urgent problem. Previously developed optimality criteria based on the principle of indirect quality indicators of automatic control systems (ACS) are largely oriented towards providing analytical solutions for the task of synthesis, rather than actual system properties. More complete and accurate taking into account of system requirements necessitates changing over from indirect optimality criteria to direct ones, ensuring a compromise between the main requirements, taking into account the specific features of the system. To that end, for optimization of electromechanical systems it is proposed to use a comprehensive optimality criterion, formed on the basis of a mathematical fuzzy logic instrument.
AI and international law

International law shapes and limits the prospects for AI governance at the international level. Despite developments over the past 50 years, in legal terms states remain the primary actor under international law, and unless they agree otherwise, have almost sole power over its territories and those within them, as well as exclusive authority to make binding international rules. At the same time, states use treaties to arrange their relations with one another and to create treaty regimes to govern matters of international interest and concern. As examples of how international law affects AI governance, existing treaty regimes shape the issues and conversations concerning international intellectual property protection for AI generated inventions and works. International trade and investment agreements, particularly more recent trade agreements, have placed constraints on states to govern cross-border, online services, as well as cross-border data transfers, necessary for the provision of services with artificial intelligence features and for training artificial intelligence models. Thus, international law forms part of the regulatory landscape for artificial intelligence systems and provides some of the language used by companies, states, and other actors to develop AI norms. At the same time, that states have such legal power under international law and that such law applies only indirectly to multinational enterprises speaks to the limitations of that law as a tool for AI governance.
How AI is re-shaping the financial industry: lights, shades and perspectives

While Artificial Intelligence (“AI”) is quickly spreading across all industries, the financial sector has been amongst the fastest adopters of AI algorithms. By leveraging big data, AI is fundamentally changing the operating models and competitive dynamics of financial industry by enhancing its predictive power and decision-making process. In particular, AI has the potential to reduce time and cost of service and to support the development of client-centric strategies. AI can indeed help in creating contextualized hyper-personalized strategies for clients, adopting “human-like virtual interaction” while preserving customer satisfaction, increasing clients’ protection from fraudulent activities. On the other hand, AI is exposing the financial system to unprecedent hazards and uncertainties to be tackled by regulators. AI can be subject to human biases, it can trigger new systemic risks as well as new undesired dynamics such as algorithmic collusion. At the same time, it is necessary to define the building principles of AI such as ethics, privacy, explainability, and competition. All these factors are required to be collectively considered to unlock the promise of AI to build a better financial ecosystem for all.
Accelerated dynamic data reduction using spatial and temporal properties

Due to improvements in high-performance computing (HPC) capabilities, many of today’s applications produce petabytes worth of data, causing bottlenecks within the system. Importance-based sampling methods, including our spatio-temporal hybrid data sampling method, are capable of resolving these bottlenecks. While our hybrid method has been shown to outperform existing methods, its effectiveness relies heavily on user parameters, such as histogram bins, error threshold, or number of regions. Moreover, the throughput it demonstrates must be higher to avoid becoming a bottleneck itself. In this article, we resolve both of these issues. First, we assess the effects of several user input parameters and detail techniques to help determine optimal parameters. Next, we detail and implement accelerated versions of our method using OpenMP and CUDA. Upon analyzing our implementations, we find 9.8× to 31.5× throughput improvements. Next, we demonstrate how our method can accept different base sampling algorithms and the effects these different algorithms have. Finally, we compare our sampling methods to the lossy compressor cuSZ in terms of data preservation and data movement.
ICAD-MI: Interdisciplinary concept association discovery from the perspective of metaphor interpretation

Interdisciplinary concept association discovery is a fundamental task in interdisciplinary knowledge organization. Unlike general concept association, interdisciplinary concept association mainly manifests in the correlation between fine-grained concept properties, which requires that interdisciplinary concept association discovery be explored through a fine-grained semantic association discovery tool. Existing concept association discovery methods are limited in their ability to identify interdisciplinary concept associations at fine-grained conceptual properties because they can only identify which two concepts are associated at the coarse level. To bridge this gap, we propose a method we called interdisciplinary concept association discovery based on metaphor interpretation (ICAD-MI). First, we explored the mechanism of interdisciplinary conceptual metaphor on both the cognitive and language layers, which provides a solid foundation for our method. Second, we introduced the four-step ICAD-MI method, which integrates deep learning techniques with word semantics and multidimensional contexts. We tested the ICAD-MI framework using a dataset comprising a total of 1,915 data points of interdisciplinary metaphorical expressions (IMEs) on a typical interdisciplinary conceptual metaphor Computer is a brain. Our model achieved a precision of 94.4%, a recall of 73.9%, and an F1 score of 82.9%, which outperforms the four baseline methods. Additionally, we conducted parameter analysis to further validate the effectiveness of our proposed method. The code and datasets are publicly available at: https://github.com/haihua0913/ICADMI.
Are the BERT family zero-shot learners? A study on their potential and limitations

Starting from the resurgence of deep learning, language models (LMs) have never been so popular. Through simply increasing model scale and data size, large LMs pre-trained with self-supervision objectives demonstrate awe-inspiring results on both task performance and generalization. At the early stage, supervised fine-tuning is indispensable in adapting pre-trained language models (PLMs) to downstream tasks. Later on, the sustained growth of model capacity and data size, as well as newly presented pre-training techniques, make the PLMs perform well under the few-shot setting, especially in the recent paradigm of prompt-based learning. After witnessing the success of PLMs for few-shot tasks, we propose to further study the potential and limitations of PLMs for the zero-shot setting. We utilize 3 models from the most popular BERT family to launch the empirical study on 20 different datasets. We are surprised to find that some simple strategies (without the need of human efforts or unsupervised data) can yield very promising results on a few widely-used datasets, e.g., 88.34%(±0.60) accuracy on the IMDB dataset, and 84.88%(±2.83) accuracy on the Amazon dataset, which outperforms manually created prompts without engineering in achieving much better and stable performance with the accuracy of 74.06%(±13.04), 75.54%(±11.77) for comparison. However, we also observe some limitations of PLMs under the zero-shot setting, particularly for the language understanding tasks (e.g., GLUE, SuperGLUE).2
Educational Opportunities

Journal AWWAVolume 115, Issue 5 p. 95-95 Departments Educational Opportunities First published: 30 May 2023 https://doi.org/10.1002/awwa.2116Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume115, Issue5June 2023Pages 95-95 RelatedInformation
Optimum: Runtime optimization for multiple mixed model deployment deep learning inference

GPUs used in data centers to perform deep learning inference tasks are underutilized. Previous systems tended to deploy a single model on a GPU to ensure that inference tasks met throughput and latency requirements. The rapid increase in one GPU’s resources, as well as the emergence of scenarios such as small models and small batches, have exacerbated the issue of low GPU utilization. In this case, a mixed model deployment-based solution can significantly improve GPU utilization while also providing greater flexibility to the inference system’s upper layer. The selection of model combinations and optimization strategies in mixed model deployment, however, remain unresolved issues. This paper proposes Optimum, the first model-combination planning and runtime optimization framework for mixed model deployment. Facing enormous search spaces, Optimum uses performance prediction for model combination selection with low search overhead. The predictor is based on a multilayer perceptron. Its input features are the profiling results of the model engine, and the output is the performance degradation. The runtime optimization strategies allow Optimum to perform performance optimization and fine-grained tradeoff. The Optimum prototype is based on CUDA multi-stream and TensorRT. The test results show that we have a flat 10.3% performance improvement over mainstream single-model deployments. We have a performance improvement of up to 7.09% over the state-of-the-art with an order of magnitude reduction in search overhead.
Compressed sampling of gearbox oil temperature signal based on an improved intrinsic mode function basis mixed-dictionary

The oil temperature signal of gearbox is always redundant in long-term use, which will occupy a lot of computer storage space. An improved intrinsic mode function basis mixed-dictionary K-singular value decomposition (IMDK-SVD) algorithm for compressed sampling of the aperiodic oil temperature signal is proposed. Firstly, an improved mixed-dictionary is designed for sparse representation of the aperiodic oil temperature signal, this mixed-dictionary is composed of a class-specific dictionary that extracts the commonness of the same group of data and a shared dictionary that extracts the difference of the different group of data. Secondly, the measurement matrix with variable-step sampling is proposed, and the sampling points is adaptively determined by the information entropy index of signal. Benefitting from these improvements, the advantages of high reconstruction accuracy and saving sampling points as possible are achieved, and the effectiveness of proposed method is verified based on the simulation signal and the experimental signal.
Development of Neural Network Algorithms for Early Detection of Drowning in Swimming Pools

The aquatic environment is a field of increased danger to human life and health. Statistics claim about 372,000 annual deaths due to drownings, which allows them to occupy the 3rd place among the causes of unintentional death of a person. This sad statistic also includes swimming pools. Creating a system that would be able to monitor the position of a person in the water and report potential drownings could significantly improve this statistic. Such a system can be implemented by means of computer vision and neural networks. For our task, it is necessary to create a neural network model that could process multiple continuous streams of frames in real time.