recently published research papers in computer science

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Computer science articles within Scientific Reports

Article 25 October 2024 | Open Access

Personalized movie recommendation in IoT-enhanced systems using graph convolutional network and multi-layer perceptron

• , Qian Huang
•  &  Haibin Xia

Optimal approximations for the free boundary problems of the space-time fractional Black-Scholes equations using a combined physics-informed neural network

• , Yousheng Tan
•  &  Jingjing Zheng

Article 24 October 2024 | Open Access

Precision game engineering through reshaping strategic payoffs

•  &  Ali R. Zomorrodi

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

• Naif Almakayeel

Grounded situation recognition under data scarcity

• , Zhiqiang Liu
•  &  Qiang Lu

Accurate quantification of dislocation loops in complex functional alloys enabled by deep learning image analysis

• Thomas Bilyk
• , Alexandra. M. Goryaeva
•  &  Estelle Meslin

Phishing URL detection with neural networks: an empirical study

• Hayk Ghalechyan
• , Elina Israyelyan
•  &  Arman Davtyan

Relative pose estimation from panoramic images using a hybrid neural network architecture

• Lars Offermann

Multi-label dental disorder diagnosis based on MobileNetV2 and swin transformer using bagging ensemble classifier

• Yasmin M. Alsakar
• , Naira Elazab
•  &  Mohammed Elmogy

Article 23 October 2024 | Open Access

Survivor detection approach for post earthquake search and rescue missions based on deep learning inspired algorithms

• Rajendrasinh Jadeja
• , Tapankumar Trivedi
•  &  Jaymit Surve

Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification

• Yogesh Kumar
• , Supriya Shrivastav
•  &  Muhammad Fazal Ijaz

Optimal control of discrete event systems under uncertain environment based on supervisory control theory and reinforcement learning

• Yingjun Liu
•  &  Fuchun Liu

STM-based symbolic regression for strength prediction of RC deep beams and corbels

• Khaled Megahed

Article 22 October 2024 | Open Access

Imbalanced graph learning via mixed entropy minimization

• , Huaguang Zhu
•  &  Jiali Chen

Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior

• , Miaole Hou
•  &  Tao Zhang

A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets

• Md Abrar Jahin
• , Md Sakib Hossain Shovon
•  &  Yutaka Watanobe

Research on hybrid strategy Particle Swarm Optimization algorithm and its applications

• Jicheng Yao
• , Xiaonan Luo
•  &  Hongtai Luo

Article 21 October 2024 | Open Access

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis

• Qiuyu Xiang
• , Dongfen Li
•  &  Xiaoyu Hua

An adaptive robust watermarking scheme based on chaotic mapping

•  &  Chenhao Yin

Design and implementation of the dual-center programming platform for ternary optical computer and electronic computer

• Sulan Zhang
•  &  Chunhua Zhang

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments

• Deafallah Alsadie
•  &  Musleh Alsulami

A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis

• Syed Abdullah Shah
• , Imran Taj
•  &  Shehzad Khalid

A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution

• , Zeqiao Zhang
•  &  Qiyi He

Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors

• Asma Ahmad Farhan
• , Amna Basharat
•  &  Summrina Kanwal

Sparse cross-transformer network for surface defect detection

• Xiaohua Huang
•  &  Xiaochun Zhu

Real-time spatiotemporal action localization algorithm using improved CNNs architecture

• Hengshuai Liu
• , Jianjun Li
•  &  Ming Zhang

Solving a real-world package delivery routing problem using quantum annealers

• Eneko Osaba
• , Esther Villar-Rodriguez
•  &  Antón Asla

Article 19 October 2024 | Open Access

SwinUNeCCt: bidirectional hash-based agent transformer for cervical cancer MRI image multi-task learning

• Chongshuang Yang
• , Zhuoyi Tan
•  &  Xiangyang Fu

Application of spiral enhanced whale optimization algorithm in solving optimization problems

• ShiZheng Qu
•  &  Zhuoshi Li

Joint encryption and error correction for secure quantum communication

• , Abhishek Parakh
•  &  Mahadevan Subramaniam

A hybrid sparrow optimization Kriging model and its application in geological modeling

• Xiaonan Shi
• , Yumo Wang
•  &  Aoqian Wang

Algorithm for detecting surface defects in wind turbines based on a lightweight YOLO model

• , Yulu Zhang
•  &  Gang Wang

EIDU-Net: edge-preserved inception DenseGCN U-Net for LiDAR point cloud segmentation

• , Jingyu Wang
•  &  Xin Cao

SAPPNet: students’ academic performance prediction during COVID-19 using neural network

• Naveed Ur Rehman Junejo
• , Qingsheng Huang
•  &  Gengzhong Zheng

Q-learning improved golden jackal optimization algorithm and its application to reliability optimization of hydraulic system

• Dongning Chen
• , Haowen Wang
•  &  Bingyu Wu

Article 18 October 2024 | Open Access

Modular YOLOv8 optimization for real-time UAV maritime rescue object detection

• Beigeng Zhao
•  &  Jiren Liu

A hybrid deep learning network for automatic diagnosis of cardiac arrhythmia based on 12-lead ECG

• Xiangyun Bai
• , Xinglong Dong
•  &  Henggui Zhang

Article 17 October 2024 | Open Access

Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset

• Ivan Izonin
• , Illia Nesterenko
•  &  Stergios Aristoteles Mitoulis

Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach

• Ahmad Almadhor
• , Ali Altalbe
•  &  Natalia Kryvinska

Article 16 October 2024 | Open Access

Image segmentation of tunnel water leakage defects in complex environments using an improved Unet model

• Pinglun Wang
•  &  Guigang Shi

Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation

• Laura Pfaff
• , Omar Darwish
•  &  Andreas Maier

Early diagnosis of Alzheimer’s disease using a group self-calibrated coordinate attention network based on multimodal MRI

• , Jingyuan Liu
•  &  Zhilin Zhang

Exploring neural markers of dereification in meditation based on EEG and personalized models of electrophysiological brain states

Geoinference of author affiliations using nlp-based text classification.

• , John S. Brownstein
•  &  Isaac S. Kohane

AnEEG: leveraging deep learning for effective artifact removal in EEG data

• Bhabesh Kalita
• , Nabamita Deb
•  &  Daisy Das

Article 15 October 2024 | Open Access

Efficient memristor accelerator for transformer self-attention functionality

• Meriem Bettayeb
• , Yasmin Halawani
•  &  Baker Mohammad

Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition

•  &  Jin Zhang

Detection of body shape changes in obesity monitoring using image processing techniques

• Uçman Ergün
• , Elif Aktepe
•  &  Yavuz Bahadır Koca

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology

• Ayan Chatterjee
• , Michael A. Riegler
•  &  Ram Bajpai

A deep learning approach for line-level Amharic Braille image recognition

• Nega Agmas Asfaw
• , Birhanu Hailu Belay
•  &  Kassawmar Mandefro Alemu

Browse broader subjects

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Join the community, trending research, triangulating python performance issues with scalene.

Scalene's CPU and memory profilers help Python programmers direct their optimization efforts by distinguishing between inefficient Python and efficient native execution time and memory usage.

Programming Languages Performance

Imprompter: Tricking LLM Agents into Improper Tool Use

We contribute to the security foundations of agent-based systems and surface a new class of automatically computed obfuscated adversarial prompt attacks that violate the confidentiality and integrity of user resources connected to an LLM agent.

Cryptography and Security

C++ Design Patterns for Low-latency Applications Including High-frequency Trading

0burak/imperial_hft • 8 Sep 2023

The research culminates in three main contributions: the creation of a Low-Latency Programming Repository, the optimisation of a market-neutral statistical arbitrage pairs trading strategy, and the implementation of the Disruptor pattern in C++.

Performance Trading and Market Microstructure

FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications

Then, we propose a language-model-based foundation TTS system.

Sound Audio and Speech Processing

LCB-net: Long-Context Biasing for Audio-Visual Speech Recognition

The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides.

Sound Multimedia Audio and Speech Processing

Scenario Execution for Robotics: A generic, backend-agnostic library for running reproducible robotics experiments and tests

IntelLabs/scenario_execution • 11 Sep 2024

Testing and evaluation of robotics systems is a difficult and oftentimes tedious task due to the systems' complexity and a lack of tools to conduct reproducible robotics experiments.

Robotics I.2.9

Voxel-SLAM: A Complete, Accurate, and Versatile LiDAR-Inertial SLAM System

hku-mars/Voxel-SLAM • 11 Oct 2024

The system consists of five modules: initialization, odometry, local mapping, loop closure, and global mapping, all employing the same map representation, an adaptive voxel map.

Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs

Large Language Models (LLMs) have demonstrated great potential in robotic applications by providing essential general knowledge.

Unikraft: Fast, Specialized Unikernels the Easy Way

unikraft/unikraft • 26 Apr 2021

Unikernels are famous for providing excellent performance in terms of boot times, throughput and memory consumption, to name a few metrics.

Operating Systems

ViPlanner: Visual Semantic Imperative Learning for Local Navigation

This optimization uses a differentiable formulation of a semantic costmap, which enables the planner to distinguish between the traversability of different terrains and accurately identify obstacles.

Computer Science News

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Title: instantid: zero-shot identity-preserving generation in seconds.

Abstract: There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by high storage demands, lengthy fine-tuning processes, and the need for multiple reference images. Conversely, existing ID embedding-based methods, while requiring only a single forward inference, face challenges: they either necessitate extensive fine-tuning across numerous model parameters, lack compatibility with community pre-trained models, or fail to maintain high face fidelity. Addressing these limitations, we introduce InstantID, a powerful diffusion model-based solution. Our plug-and-play module adeptly handles image personalization in various styles using just a single facial image, while ensuring high fidelity. To achieve this, we design a novel IdentityNet by imposing strong semantic and weak spatial conditions, integrating facial and landmark images with textual prompts to steer the image generation. InstantID demonstrates exceptional performance and efficiency, proving highly beneficial in real-world applications where identity preservation is paramount. Moreover, our work seamlessly integrates with popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving as an adaptable plugin. Our codes and pre-trained checkpoints will be available at this https URL .

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Nine CSE Research Papers in Computer Architecture Included in ISCA’s 25-Year Retrospective

By Kimberley Clementi

In commemoration of its 50th anniversary, the International Symposium on Computer Architecture (ISCA) has compiled a volume of memorable papers – a 25-Year Retrospective: 1996-2020 – to demonstrate the progression of research within computer architecture. The compilation of 98 research papers, culled from a pool of over a thousand submissions, included nine papers from UC San Diego’s Department of Computer Science and Engineering (CSE).

That means CSE faculty were involved in 10 percent of the selected papers.

Hadi Esmaeilzadeh had four research papers included in ISCA’s retrospective, as did CSE Professor Dean Tullsen. Jishen Zhao, an associate professor in the department, had one paper selected.

For more than two decades, their collective research has impacted the trajectory of computer architecture – spanning ground-breaking discoveries related to dark silicon, processors, simultaneous multithreading, and memory systems, enabling applications in machine learning, virtual reality, augmented reality, and artificial intelligence.

Papers for ISCA’s second commemorative volume were chosen by a team of referees to reflect the exciting evolution of computer architecture and share areas of potential growth. ISCA’s first volume, its 25 th anniversary retrospective, was published in 1998.

CSE faculty research included in ISCA@50 25-Year Retrospective: 1996-2020 :

Hadi Esmaeilzadeh – Bridging Computer Architecture and New Applications

Esmaeilzadeh, the inaugural holder of the Halıcıoğlu Chair in Computer Architecture at UC San Diego, is building the computer architecture that will enable AI and ML technologies of the future. He and his colleagues have focused on bridging the gap between the microarchitecture of processors and the increasing levels of performance required by computation-hungry applications. His highly-cited 2011 paper outlined this dilemma, while his more recent research proposes an architectural framework – dubbed Bit Fusion – developed to preserve accuracy while enabling ultra-power efficiency for deep learning.

Dark silicon and the end of multicore scaling

A reconfigurable fabric for accelerating large-scale datacenter services

General-purpose code acceleration with limited-precision analog computation

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network

Dean Tullsen – Standing the Test of Time

Widely considered one of computer architecture’s most influential researchers, CSE Professor Dean Tullsen has amassed five Test of Time Awards. This includes one for novel research on heterogenous multi-core architectures , which led to the design of many modern processors such as Qualcomm’s Snapdragon and Apple’s M1Pro and M1Max. Tullsen’s earlier contributions – including the introduction of simultaneous multithreading – were also deeply grounded in the issues of real, industry-leading processor design.

Exploiting choice: instruction fetch and issue on an implementable simultaneous multithreading processor

Selective value prediction

Single-ISA Heterogeneous Multi-Core Architectures for Multithreaded Workload Performance

Interconnections in Multi-Core Architectures: Understanding Mechanisms, Overheads and Scaling

Jishen Zhao – Creating Memorable Work in PIM

Zhao’s research stretches the boundary between computer architecture and system software, particularly in memory systems. Her 2016 ISCA paper showcases the possibilities that arise from combining emerging applications such as AI with technologies like ReRam, playing a crucial role in advancing research in Processing-in-Memory (PIM).

PRIME: a novel processing-in-memory architecture for neural network computation in ReRAM-based main memory

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Project-based learning in human–computer interaction: a service‐dominant logic approach

Purpose This study aims to propose a service-dominant logic (S-DL)-informed framework for teaching innovation in the context of human–computer interaction (HCI) education involving large industrial projects. Design/methodology/approach This study combines S-DL from the field of marketing with experiential and constructivist learning to enable value co-creation as the primary method of connecting diverse actors within the service ecology. The approach aligns with the current conceptualization of central university activities as a triad of research, education and innovation. Findings The teaching framework based on the S-DL enabled ongoing improvements to the course (a project-based, bachelor’s-level HCI course in the computer science department), easier management of stakeholders and learning experiences through students’ participation in real-life projects. The framework also helped to provide an understanding of how value co-creation works and brought a new dimension to HCI education. Practical implications The proposed framework and the authors’ experience described herein, along with examples of projects, can be helpful to educators designing and improving project-based HCI courses. It can also be useful for partner companies and organizations to realize the potential benefits of collaboration with universities. Decision-makers in industry and academia can benefit from these findings when discussing approaches to addressing sustainability issues. Originality/value While HCI has successfully contributed to innovation, HCI education has made only moderate efforts to include innovation as part of the curriculum. The proposed framework considers multiple service ecosystem actors and covers a broader set of co-created values for the involved partners and society than just learning benefits.

Recommender Systems: Past, Present, Future

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

Research on the Construction of Human-Computer Interaction System Based on a Machine Learning Algorithm

In this paper, we use machine learning algorithms to conduct in-depth research and analysis on the construction of human-computer interaction systems and propose a simple and effective method for extracting salient features based on contextual information. The method can retain the dynamic and static information of gestures intact, which results in a richer and more robust feature representation. Secondly, this paper proposes a dynamic planning algorithm based on feature matching, which uses the consistency and accuracy of feature matching to measure the similarity of two frames and then uses a dynamic planning algorithm to find the optimal matching distance between two gesture sequences. The algorithm ensures the continuity and accuracy of the gesture description and makes full use of the spatiotemporal location information of the features. The features and limitations of common motion target detection methods in motion gesture detection and common machine learning tracking methods in gesture tracking are first analyzed, and then, the kernel correlation filter method is improved by designing a confidence model and introducing a scale filter, and finally, comparison experiments are conducted on a self-built gesture dataset to verify the effectiveness of the improved method. During the training and validation of the model by the corpus, the complementary feature extraction methods are ablated and learned, and the corresponding results obtained are compared with the three baseline methods. But due to this feature, GMMs are not suitable when users want to model the time structure. It has been widely used in classification tasks. By using the kernel function, the support vector machine can transform the original input set into a high-dimensional feature space. After experiments, the speech emotion recognition method proposed in this paper outperforms the baseline methods, proving the effectiveness of complementary feature extraction and the superiority of the deep learning model. The speech is used as the input of the system, and the emotion recognition is performed on the input speech, and the corresponding emotion obtained is successfully applied to the human-computer dialogue system in combination with the online speech recognition method, which proves that the speech emotion recognition applied to the human-computer dialogue system has application research value.

Human–Computer Interaction-Oriented African Literature and African Philosophy Appreciation

African literature has played a major role in changing and shaping perceptions about African people and their way of life for the longest time. Unlike western cultures that are associated with advanced forms of writing, African literature is oral in nature, meaning it has to be recited and even performed. Although Africa has an old tribal culture, African philosophy is a new and strange idea among us. Although the problem of “universality” of African philosophy actually refers to the question of whether Africa has heckling of philosophy in the Western sense, obviously, the philosophy bred by Africa’s native culture must be acknowledged. Therefore, the human–computer interaction-oriented (HCI-oriented) method is proposed to appreciate African literature and African philosophy. To begin with, a physical object of tablet-aid is designed, and a depth camera is used to track the user’s hand and tablet-aid and then map them to the virtual scene, respectively. Then, a tactile redirection method is proposed to meet the user’s requirement of tactile consistency in head-mounted display virtual reality environment. Finally, electroencephalogram (EEG) emotion recognition, based on multiscale convolution kernel convolutional neural networks, is proposed to appreciate the reflection of African philosophy in African literature. The experimental results show that the proposed method has a strong immersion and a good interactive experience in navigation, selection, and manipulation. The proposed HCI method is not only easy to use, but also improves the interaction efficiency and accuracy during appreciation. In addition, the simulation of EEG emotion recognition reveals that the accuracy of emotion classification in 33-channel is 90.63%, almost close to the accuracy of the whole channel, and the proposed algorithm outperforms three baselines with respect to classification accuracy.

Wearable devices in diving: A systematic review (Preprint)

BACKGROUND Wearable devices have grown enormously in importance in recent years. While wearables have generally been well studied, they have not yet been discussed in the underwater environment. OBJECTIVE The reason for this systematic review was to systematically search for the wearables for underwater operation used in the scientific literature, to make a comprehensive map of their capabilities and features, and to discuss the general direction of development. METHODS In September 2021, we conducted an extensively search of existing literature in the largest databases using keywords. For this purpose, only articles were used that contained a wearable or device that can be used in diving. Only articles in English were considered, as well as peer-reviewed articles. RESULTS In the 36 relevant studies that were found, four device categories could be identified: safety devices, underwater communication devices, head-up displays and underwater human-computer interaction devices. CONCLUSIONS The possibilities and challenges of the respective technologies were considered and evaluated separately. Underwater communication has the most significant influence on future developments. Another topic that has not received enough attention is human-computer interaction.

Analyzing the mental states of the sports student based on augmentative communication with human–computer interaction

Recognition of facial expressions and its application to human computer interaction, physical education system and training framework based on human–computer interaction for augmentative and alternative communication, enhancing the human-computer interaction through the application of artificial intelligence, machine learning, and data mining, applications of human-computer interaction for improving erp usability in education systems, export citation format, share document.