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arXiv 2023年

作者： Goodenough, Kenneth De Bone, Sébastian Addala, Vaishnavi Krastanov, Stefan Jansen, Sarah Gijswijt, Dion Elkouss, David QuTech Delft University of Technology Netherlands College of Information and Computer Science University of Massachusetts Amherst United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Delft Institute of Applied Mathematics Delft University of Technology Netherlands Korteweg-de Vries Institute for Mathematics University of Amsterdam Netherlands Networked Quantum Devices Unit Okinawa Institute of Science and Technology Graduate University Japan

Noisy hardware forms one of the main hurdles to the realization of a near-term quantum internet. Distillation protocols allows one to overcome this noise at the cost of an increased overhead. We consider here an experimentally relevant class of distillation protocols, which distill n to k end-to-end entangled pairs using bilocal Clifford operations, a single round of communication and a possible final local operation depending on the observed measurement outcomes. In the case of permutationally invariant depolarizing noise on the input states, we find a correspondence between these distillation protocols and graph codes. We leverage this correspondence to find provably optimal distillation protocols in this class for several tasks important for the quantum internet. This correspondence allows us to investigate use cases for so-called non-trivial measurement syndromes. Furthermore, we detail a recipe to construct the circuit used for the distillation protocol given a graph code. We use this to find circuits of short depth and small number of two-qubit gates. Additionally, we develop a black-box circuit optimization algorithm, and find that both approaches yield comparable circuits. Finally, we investigate the teleportation of encoded states and find protocols which jointly improve the rate and fidelities with respect to prior art. © 2023, CC BY.

关键词： Quantum entanglement

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LEMS: Optimized Large Model Framework for Edge-AI in Consumer Internet of Things Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Rehman, Abdul Mahmood, Khalid Hassan, Mahmood Ul Javed, Muhammad Wasim Aurangzeb, Khursheed Anwar, Muhammad Shahid University of Haripur Department of Information Technology Hattar Road Khyber Pakhtunkhwa Haripur22620 Pakistan Applied College Muhayil Engineering and Technical Specializations Unit King Khalid University Aseer Saudi Arabia IIC University of Technology Department of Computer Science Phnom Penh Cambodia King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia Department of Ai and Software Gachon University Seongnam-si13120 Korea Republic of

Edge computing plays a critical role in enabling real-time deployment of artificial intelligence (AI) on resource-constrained consumer IoT devices. These devices face significant challenges in balancing energy efficiency, latency, accuracy, and security. To address these issues, an Optimized Lightweight Large Model Framework (LEMS) is proposed that enhances advanced model compression techniques, including pruning and quantization, to reduce computational and memory demands. Additionally, LEMS employs a hybrid edge-cloud processing architecture that optimizes resource utilization by offloading complex tasks to the cloud while maintaining low-latency performance at the edge. To ensure data security, the framework integrates lightweight cryptographic protocols, ensuring privacy without overwhelming the constrained devices. The LEMS evaluated on various IoT platforms, including Raspberry Pi 4 and ESP32 microcontrollers, using real-world dataset: MIMIC-III. Results show that LEMS reduces model size by up to 40% and cuts energy consumption by 15%, while preserving 91% inference accuracy. Moreover, the hybrid processing reduced latency by 60%, and the security mechanisms incurred less than 5% computational overhead. © 2025 IEEE.

关键词： Computation offloading

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Predictive Distributed Learning based on Stability and Complexity Measures

Predictive Distributed Learning based on Stability and Compl...

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science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Efosa Charles Igodan Olumide O. Obe A-F Thompson Otasowie Owolafe Linda Osarumen Usiosefe Peter Katyo Dept. of Computer Science Faculty of Physical Sciences University of Benin Benin City Nigeria Dept. of Computer Science School of Computing Federal University of Technology Akure Akure Nigeria Dept. of Cyber Security School of Computing University of Technology Akure Akure Nigeria ICT - Modernization Department Risk and Management Unit Nigerian Custom Service Abuja Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Due to the inherent properties of high dimensional datasets, the majority of feature selection methods already in use do not scale well and eventually become inapplicable. As a result of this limitation, the performance of most classifiers are degraded. The objective of this paper is to propose an improved distributed feature selection technique by sample while maintaining class distribution towards obtaining optimal subsets using data complexity-stability measures. Whilst the Kuncheva and Jaccard were applied in obtaining stable subsets, the Fisher’s Discriminant Ratio was used to discriminate good candidate features from bad candidate features. The performance of the implementation framework was demonstrated using Brain Tumor Microarray datasets (using the original and the Synthetic Minority Oversampling Technique - induced datasets). The experiments contrasted the accuracy of three machine learning classifiers (decision tree, support vector machines and naive Bayes), trained with subsets of features generated by three selectors (information gain, gain ratio, and Chi-Squared). We observed that the highest accuracy percentages (100%, 100%, and 99.5%, respectively) were obtained by SVM, decision tree and naive Bayes respectively when the distributed learning techniques combined with data-complexity-stability measures were applied compared to existing techniques. Additionally, the stability of the feature subsets generated showed significant reduction both in the runtime complexities (2.97sec, 3.94sec, and 4.54sec), and the percentage of the number of selected features (0.52%, 1.39%, and 1.43%) with an alpha value of 0.75 respectively. These results showed that our proposed method achieved maximum performance and has future prospects.

关键词： Support vector machines Accuracy Distance learning Scalability Stability criteria Length measurement Feature extraction

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A New Lightweight Attention-based Model for Emotion Recognition on Distorted Social Media Face Images

TechRxiv

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TechRxiv 2023年

作者： Roy, Ayush Shivakumara, Palaiahnakote Pal, Umapada Gornale, Shivanand S. Liu, Cheng-Lin Jadavpur University India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science Rani Channamma University Belagavi India Institute of Automation Chinese Academy of Sciences China

The recognition of human emotions remains a challenging task for social media images. This is due to distortions created by different social media conflict with the minute changes in facial expression. This study presents a new model called the Global Spectral-Spatial Attention Network (GSSAN), which leverages both local and global information simultaneously. The proposed model comprises a shallow Convolutional Neural Network (CNN) with an MBResNext block, which integrates the features extracted from MobileNet, ResNet, and DenseNet for extracting local features. In addition, to strengthen the discriminating power of the features, GSSAN incorporates Fourier features, which provide essential cues for minute changes in the face images. To test the proposed model for emotion recognition using social media images, we conduct experiments on two widely-used datasets: FER-2013 and AffectNet. The same benchmark datasets are uploaded and downloaded to create a distorted social media image dataset to test the proposed model. Experiments on distorted social media images dataset show that the model surpasses the accuracy of SOTA models by 0.69% for FER-2013 and 0.51% for AffectNet social mediad datasets. The same inference can be drawn from the experiments on standard datasets. © 2023, CC BY.

关键词： Face recognition

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COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions

arXiv

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arXiv 2024年

作者： Bishal, Mahathir Mohammad Chowdory, Md. Rakibul Hassan Das, Anik Kabir, Muhammad Ashad Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh Department of Computer Science St. Francis Xavier University AntigonishNSB2G 2W5 Canada Data Science Research Unit School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia

The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary objective is to develop a machine learning-based web application for automatically classifying COVID-19-related discussions on social media. To achieve this, we label COVID-19-related Twitter data, provide benchmark classification results, and develop a web application. We collected data using the Twitter API and labeled a total of 6,667 tweets into five different classes: health risks, prevention, symptoms, transmission, and treatment. We extracted features using various feature extraction methods and applied them to seven different traditional machine learning algorithms, including Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbour, Logistic Regression, and Linear SVC. Additionally, we used four deep learning algorithms: LSTM, CNN, RNN, and BERT, for classification. Overall, we achieved a maximum F1 score of 90.43% with the CNN algorithm in deep learning. The Linear SVC algorithm exhibited the highest F1 score at 86.13%, surpassing other traditional machine learning approaches. Our study not only contributes to the field of health-related data analysis but also provides a valuable resource in the form of a web-based tool for efficient data classification, which can aid in addressing public health challenges and increasing awareness during pandemics. We made the dataset and application publicly available, which can be downloaded from this link https://***/Bishal16/COVID19-Health-RelatedData-Classification-Website. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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A Generative Adversarial Network-Based Approach for Reflective-Metasurface unit-Cell Synthesis in mmWave Bands

A Generative Adversarial Network-Based Approach for Reflecti...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Panithan La-aiddee Paramin Sangwongngam Lunchakorn Wuttisittikulkij Pisit Vanichchanunt Department of Electrical Engineering Wireless Communication Ecosystem Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

In recent years, data-driven design approaches based on generative machine learning (ML) have been applied in the field of metasurface design. Even though generative machine learning has demonstrated remarkable capabilities in metasurface synthesis, training procedures to obtain an appropriate model remain a challenge. In this work, we present the deployment of conditional generative adversarial networks (cGAN) to design reflective standard unit cells for the use of 5G millimeter wave (mm Wave) passive metasurface reflectors. A total of 48 different configurations of cGAN were trained over a range of hyperparameters selected under proper guidelines used in the literature. The design performance of each cGAN configuration is measured by a mean absolute error (MAE) between a simulated reflection phase of a generated unit cell and a target reflection phase. The test results show that cGAN s trained by updating the discriminator one step for each single generator update tend to offer lower MAEs compared to cGANs trained by updating the discriminator three iterations for one generator update. The optimal cGAN among 48 hyperparameter configurations can generate unit cells with reflection phase resembling the desired target one.

关键词： Training Structural rings Phase measurement 5G mobile communication Machine learning Metasurfaces Reflection

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Weighted Sum of Segmented Correlation: an Efficient Method for Spectra Matching in Hyperspectral Images

Weighted Sum of Segmented Correlation: an Efficient Method f...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sampriti Soor Priyanka Kumari B. S. Daya Sagar Amba Shetty School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Water Resources and Ocean Engineering NIT Karnataka India Systems Science and Informatics Unit Indian Statistical Institute Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Matching a target spectrum with known spectra in a spectral library is a common method for material identification in hyperspectral imaging research. Hyperspectral spectra exhibit precise absorption features across different wavelength segments, and the unique shapes and positions of these absorptions create distinct spectral signatures for each material, aiding in their identification. Therefore, only the specific positions can be considered for material identification. This study introduces the Weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum, and derives a matching index, favoring positive correlations and penalizing negative correlations using assigned weights. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces.

关键词： Image segmentation Correlation Absorption Shape Geoscience and remote sensing Libraries Iron

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Graphically Residual Attentive Network for tackling aerial image occlusion

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computers and Electrical Engineering 2025年 125卷

作者： Pradhan, Praveen Kumar Purkayastha, Kunal Sharma, Aaditya Lochan Baruah, Udayan Sen, Biswaraj Ghosal, Palash Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Sikkim Rangpo737136 India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Sikkim Rangpo737136 India Centre for Computers and Communication Technology Chisopani Sikkim South Sikkim737136 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal Kolkata700108 India Birangana Sati Sadhani Rajyik Vishwavidyalaya Assam Golaghat785621 India

Deep learning has rapidly advanced, enabling new applications such as object detection, text recognition, and occlusion handling. However, challenges remain in the detection of objects in complex environments such as aerial images where things like motion blur, low light, and significant occlusion occur. This paper addresses a similar challenge by introducing a novel supervised framework, the Graphically Residual Attentive Network (GRESIDAN). In the same model, GRESIDAN integrates three synergistic pipelines for object detection, occlusion detection, and occlusion removal. GRESIDAN uses a residually attentive block combining ResNet-18 and a multi-headed attention mechanism to improve feature extraction and detection accuracy in low-quality, occluded aerial images. A graphically attentive occlusion detection pipeline is implemented to handle occlusion, segment better, and mask out the occluder in the aerial image. The GRESIDAN model is validated on the COCO-2017 dataset and a custom private aerial object detection dataset, outperforming the state-of-the-art methods in handling occlusion and detecting objects. Our contributions provide a robust solution to the problem of detecting and handling occluded objects in aerial imagery, pushing the boundaries of automated visual recognition in challenging real-world scenarios. The code for public use in training and testing is available on GitHub. © 2025 Elsevier Ltd

关键词： Image segmentation

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Lost in Translation: Researchers’ Reflections on Writing in English for CHI

Lost in Translation: Researchers’ Reflections on Writing in...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Sharma, Sumita Norouzi, Behnaz White, Edward Peter Greenwood Gazulla, Eva Durall Yousufi, Mohsin Y.K. Iivari, Netta Howell, Noura Klemettilä, Pauli Shahid, Suleman Sarcar, Sayan Li, Xingyu Mishra, Wricha INTERACT Research Unit University of Oulu Oulu Finland Kuopio Finland INTERACT University of Oulu Oulu Finland Georgia Institute of Technology Atlanta GA United States INTERACT Research Unit University of Oulu Oulu Finland University of Oulu Oulu Finland Computer Science Department Lahore University of Management Sciences Punjab Lahore Pakistan School of Computing and Digital Technology Birmingham City University Birmingham United Kingdom School of Literature Media and Communication Georgia Institute of Technology Atlanta GA United States MIT Institute of Design Pune India

ISBN: (纸本)9798400713958

What would linguistic diversity and inclusion look like for Human-computer Interaction (HCI) research and researchers? As neurotypical and neurodiverse non-native- and native-English researchers, we conduct research in a variety of languages and contexts with diverse user groups and communities, and face several challenges when that research is written, read, reviewed, and published in English. In this paper, we present our first-person reflections on writing for research in English. We have two main goals: (1) to showcase the rich linguistic diversity of HCI research and researchers, and (2) to document challenges of translating socio-culturally nuanced findings and insights into English from other languages. This paper presents our vision of linguistic diversity and inclusion in HCI research at CHI - a mélange of thoughts utilizing diverse vocabularies and languages. © 2025 Copyright held by the owner/author(s).

关键词： Diversity Inclusion Writing for Research

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