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SUSTEM: An Improved Rule-based Sundanese Stemmer

ACM Transactions on Asian and Low-Resource Language Information Processing 2024年第6期23卷 1-29页

作者： Setiawan, Irwan Kao, Hung-Yu Department of Computer and Informatics Engineering Politeknik Negeri Bandung Bandung Indonesia Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University No. 1 Daxue Rd. East Dist. Tainan City701 Taiwan

Current Sundanese stemmers either ignore reduplication words or define rules to handle only affixes. There is a significant amount of reduplication words in the Sundanese language. Because of that, it is impossible to achieve superior stemming precision in the Sundanese language without addressing reduplication words. This article presents an improved stemmer for the Sundanese language, which handles affixed and reduplicated words. With a Sundanese root word list, we use a rules-based stemming technique. In our approach, all stems produced by the affixes removal or normalization processes are added to the stem list. Using a stem list can help increase stemmer accuracy by reducing stemming errors caused by affix removal sequence errors or morphological issues. The current Sundanese language stemmer, RBSS, was used as a comparison. Two datasets with 8,218 unique affixed words and reduplication words were evaluated. The results show that our stemmer’s strength and accuracy have improved noticeably. The use of stem list and word reduplication rules improved our stemmer’s affixed type recognition and allowed us to achieve up to 99.30% accuracy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sundanese language reduplication word affixed word stemming

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HoloStream: a GPU-powered high-speed user interface for holographic microscopy imaging

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Optics Express 2025年第7期33卷 15648-15660页

作者： Morales, Johan Obando-Vásquez, Sofia Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering EAFIT University Medellin Colombia Optical Imaging Research Lab Electrical and Computer Engineering Department University of Massachusetts Dartmouth DartmouthMA United States

This paper presents HoloStream, a GPU-powered high-speed user interface designed for holographic microscopy imaging. The platform reconstructs quantitative phase images rapidly for off-axis digital holographic microscopy (DHM) systems operating in the telecentric regime. By optimizing critical computational procedures, the HoloStream app reconstructs phase maps of 1280 × 960 pixels up to 11 frames per second (FPS) in live visualization and 90 fps for pre-recorded holographic videos. This real-time phase compensation is performed by upgrading the semi-heuristic phase compensation (SHPC) algorithm through GPU acceleration via PyCUDA. The interface also offers a user-friendly experience with flexible control over acquisition modes, visualization formats, and 3D tracking functionalities. Experimental validation demonstrates the platform’s capability to achieve fast and accurate phase imaging for live microscopy applications. The HoloStream app advances DHM technology by combining high-speed processing with ease of use, making it suitable for a wide range of DHM imaging applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： computer graphics equipment

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White balancing based improved nighttime image dehazing

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Multimedia Tools and Applications 2025年第1期84卷 445-462页

作者： Ullah, Sami Hassan, Najmul Bhatti, Naeem Zia, Muhammad Shin, Jungpil COMSIP LAB Department of Electronics Quaid-i-Azam University Islamabad45320 Pakistan School of Computer Science and Engineering The University of Aizu Fukushima Aizuwakamatsu965-8580 Japan

The nighttime captured images mostly suffer from low contrast, color distortion, and glow effects due to the presence of haze (fog or smog), illumination variations, and artificial lighting. In this paper, we present an image enhancement framework, which successfully deals with the aforementioned effects faced while imaging at night time. At first, the proposed approach achieves glow decomposition by performing white balancing of the image. Next, we enhance the low-contrast areas by performing contrast-limited adaptive histogram equalization (CLAHE) which achieves image enhancement at the cost of introducing noise and edge distortion. In order to eliminate the introduced noise and preserve the edges we perform bilateral filtering. The proposed framework capitalizes on the individual strength of each method while encountering the artifacts produced during processing at each stage. We perform suitable tuning of the parameters of the methods involved for their performance coherence. The quantitative and qualitative comparison with the existing approaches demonstrate the increased effectiveness of the proposed approach in dehazing the nighttime images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood

Detecting Subtle Differences between Human and Model Languag...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xu, Yang Wang, Yu An, Hao Liu, Zhichen Li, Yongyuan Department of Computer Science and Engineering Southern University of Science and Technology China Digital Linguistics Lab Department of Linguistics Bielefeld University Germany

ISBN: (纸本)9798891761643

Human and model-generated texts can be distinguished by examining the magnitude of likelihood in language. However, it is becoming increasingly difficult as language model's capabilities of generating human-like texts keep evolving. This study provides a new perspective by using the relative likelihood values instead of absolute ones, and extracting useful features from the spectrum-view of likelihood for the human-model text detection task. We propose a detection procedure with two classification methods, supervised and heuristic-based, respectively, which results in competitive performances with previous zero-shot detection methods and a new state-of-the-art on short-text detection. Our method can also reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. Our code is available at https://***/CLCS-SUSTech/FourierGPT. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Optimizing Data Movement in Heterogeneous Computing: A LASSA-based Approach for Efficient Nucleation List Precomputation

Optimizing Data Movement in Heterogeneous Computing: A LASSA...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Biswajit Bhowmik Girish K K Himanshu Pandey Piyus Prabhanjans Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods for managing data transfers—particularly in GPU offloading scenarios—suffer from significant inefficiencies. These inefficiencies are particularly evident in nucleation list precomputation for non-equilibrium solidification models, where redundant data movements and complex dynamic work-sharing in OpenMP lead to significant performance overhead. To tackle this issue, this paper proposes a novel solution that integrates the Location-Aware Heap Static Single Assignment (LASSA) algorithm into the compilation process. This approach identifies and eliminates redundant memory copy operations, optimizing data transfers and reducing overhead. The findings reveal a dramatic performance boost, with up to a 9.6-fold increase in efficiency. By addressing the specific challenges of nucleation list precomputation, this work provides valuable insights into optimizing data movement in heterogeneous computing environments, paving the way for enhanced performance in parallel programming models.

关键词： Solid modeling Technological innovation Parallel programming Computational modeling High performance computing System performance Graphics processing units Parallel processing Heterogeneous networks Data models

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Efficient Parallel Algorithm for Detecting Longest Flow Paths in Flow Direction Grids

Efficient Parallel Algorithm for Detecting Longest Flow Path...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Karunamuni Jayarukshi Shivali Agarwal Girish K K Santosh Goudar Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

High-performance computing (HPC) has transformed the capacity to address complex computational tasks across various scientific fields by enabling the efficient processing of large datasets and intricate simulations. In hydrological modeling, a critical task is identifying the longest flow channel within a catchment, which is essential for understanding water flow patterns and managing resources. However, existing geographic information system (GIS) algorithms for flow path identification often suffer from inefficiencies and inaccuracies. To address these challenges, this paper introduces innovative parallel methods utilizing Open Multi-Processing (OpenMP), a widely-used API that supports multi-platform shared-memory parallel programming. This approach optimizes the analysis of flow direction data, resulting in faster and more accurate identification of flow channels. The results demonstrate that the proposed method outperforms current approaches, offering substantial improvements in both performance and precision. These advancements have the potential to significantly enhance hydrological modeling practices and water resource management.

关键词： Accuracy Parallel programming Computational modeling Scalability Redundancy Computational efficiency Reliability Parallel algorithms Water resources Geographic information systems

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Exploitation or Exploration Next? User Behavior Decoupling and Emerging Intent Modeling for Next-Item Recommendation 24

Exploitation or Exploration Next? User Behavior Decoupling a...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhu, Nengjun Sun, Lingdan Luo, Xiangfeng Cao, Jian Zhang, Qi Lu, Xinjiang School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Tongji University Department of Computer Science Shanghai China Baidu Research Business Intelligence Lab Beijing China

ISBN: (纸本)9798331506681

Recent trends in next-item recommendation systems have focused on modeling user intents. Traditional methods often extract users' inherent intents from the most representative items in a session, overlooking 'unexpected items' that deviate from the majority in various contextual aspects. These unexpected items, frequently present, can be crucial indicators of a user's inclination towards exploring new options, signaling emerging intents that warrant significant attention. In response, we introduce DbMei, a novel approach that decouples user behaviors and emphasizes the modeling of emerging intents. DbMei distinguishes between two user behavior types: 'focused shopping', which aligns with users' inherent intents, and'wandering shopping', which aligns with emerging intents. Focused shopping is analyzed using topic modeling and hypergraph learning while wandering shopping is explored through session neighbor retrieval. An exploitation-exploration mechanism is employed to determine the behavioral probability distribution for upcoming items. This integrated modeling of focused and wandering shopping behaviors drives our recommendation process. Extensive empirical studies on two real-world datasets, Amazon-KDD and Beauty, showcase DbMei's superiority over leading methods regarding Recall and MRR metrics. Our code is publicly available at https://***/sunlingdan-123/DbMei. © 2024 IEEE.

关键词： Behavior decoupling Hypergraph learning Intent modeling Session-based recommendation

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PA-RDFKNet: Unifying Plant Age Estimation through RGB-Depth Fusion and Knowledge Distillation

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 226-235页

作者： Bansal, Shreya Singh, Malya Barda, Seema Goel, Neeraj Saini, Mukesh Indian Institute of Technology Ropar Department of Computer Science and Engineering Punjab Rupnagar140001 India MBZUAI Metaverse Lab United Arab Emirates

Agriculture is facing bigger challenges in the 21st century due to the scarcity of resources. Artificial intelligence is being integrated with agriculture to cater to people's needs, unlocking fresh avenues for sustainability and innovation. One of the crucial agricultural practices is plant growth monitoring to detect plant stress at an early stage. In the past, there have been preliminary attempts at plant growth monitoring using red-green-blue (RGB) and depth images. The major challenge of this approach is the unavailability of the depth camera at the farmers' end. In this work, we have developed a transformer-based plant age RGB-depth fusion knowledge distillation network (PA-RDFKNet), a multi-to-single modal teacher-student network, that exploits the combined knowledge of RGB-depth pairs at the training time to infer the growth using RGB images alone during test time. The model uses a distillation loss that combines response-based, feature-based, and relation-based knowledge distillation techniques in the offline scheme. The proposed knowledge distillation improves the mean squared error for RGB images from 2 to 0.14 weeks. The results are validated on three different datasets. © 2024 IEEE.

关键词： Teaching

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