cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/25608513
title: Unlocking the Power of Content Addressable Memory for Memory-Intensive Applications

description: Due to the high cost of data movement in the traditional von Neumann architecture, particularly in many data-intensive workloads, in-memory computing (IMC), by integrating computation into memory, has emerged as a highly attractive alternative. Within the realm of IMC, Content-Addressable Memory (CAM) stands out as a specialized unit capable of executing in-memory search operations in a massively parallel fashion. CAM offers distinct advantages, particularly for applications that heavily rely on large-scale match or search operations. However, realizing the full potential of CAM-based accelerators necessitates cross-layer efforts.

This thesis aims to advance the development of IMC hardware, with focus on enhancing the capabilities of CAM-based hardware and applicability of such to memory-intensive applications. To enhance CAM's capabilities, this thesis delves into optimizing CAM-based hardware from both application-driven and circuit-driven perspectives. Specifically, to achieve end-to-end performance improvement for emerging ML workloads, such as recommendation systems and reinforcement learning, we explore the utilization of associative search and devise innovative CAM-based architecture which moves computation into memory. At the circuit-level, novel programming methods are developed to expand CAM's hardware capability by enabling more distance functions on CAM cells.

To broaden the applicability of CAM technology and facilitate collaboration between software and hardware experts, this thesis introduces an extensive CAM toolchain, comprising the CAMASim function and performance simulator, supporting accuracy prediction and performance evaluation. These contributions effectively tackle critical challenges in CAM-based hardware design for memory-intensive applications. Furthermore, they lay the foundation for extensive exploration in the broader realm of CAM-based accelerators.
DOI: 10.7274/25608513.v1
title: Unlocking the Power of Content Addressable Memory for Memory-Intensive Applications

description: Due to the high cost of data movement in the traditional von Neumann architecture, particularly in many data-intensive workloads, in-memory computing (IMC), by integrating computation into memory, has emerged as a highly attractive alternative. Within the realm of IMC, Content-Addressable Memory (CAM) stands out as a specialized unit capable of executing in-memory search operations in a massively parallel fashion. CAM offers distinct advantages, particularly for applications that heavily rely on large-scale match or search operations. However, realizing the full potential of CAM-based accelerators necessitates cross-layer efforts.

This thesis aims to advance the development of IMC hardware, with focus on enhancing the capabilities of CAM-based hardware and applicability of such to memory-intensive applications. To enhance CAM's capabilities, this thesis delves into optimizing CAM-based hardware from both application-driven and circuit-driven perspectives. Specifically, to achieve end-to-end performance improvement for emerging ML workloads, such as recommendation systems and reinforcement learning, we explore the utilization of associative search and devise innovative CAM-based architecture which moves computation into memory. At the circuit-level, novel programming methods are developed to expand CAM's hardware capability by enabling more distance functions on CAM cells.

To broaden the applicability of CAM technology and facilitate collaboration between software and hardware experts, this thesis introduces an extensive CAM toolchain, comprising the CAMASim function and performance simulator, supporting accuracy prediction and performance evaluation. These contributions effectively tackle critical challenges in CAM-based hardware design for memory-intensive applications. Furthermore, they lay the foundation for extensive exploration in the broader realm of CAM-based accelerators.
DOI: 10.7274/25611231
title: Graph Representation Learning Techniques for the Combat against Online Abusive Activity

description: With the growing prevalence of the Internet, abusive behaviors on online platforms have surged in recent decades. These online abusers take advantage of the popularity and conveniences of online platforms to engage in abusive activities toward online users, drawing attention from researchers on effective methods to combat various abusive activities on online platforms. However, traditional machine learning methods, focusing on modeling abusive behavior patterns, struggle to effectively detect and mitigate such behaviors due to their inability to account for the intricate relationships among abusers on online platforms. In response, this dissertation proposes to leverage graphs to depict the complex relationships among abusive users and employ Graph Representation Learning (GRL) methods to detect these online abusive activities (i.e., drug trafficking detection and malicious repository detection). Despite the notable success that existing GRL methods have gained on benchmark graph datasets like social, academic, and molecule graphs, they still face challenges for abuse detection in real-world scenarios: (i) Many real-world abuse detection tasks lack sufficient labeled data for model training, as obtaining a large amount of labeled data is always time-consuming and resource-intensive; (ii) Online abuse graph datasets frequently exhibit class imbalance and topology imbalance issues. To handle the first challenge, this dissertation presents advanced techniques, including graph meta-learning and graph self-supervised learning, to combat online abusive activities (i.e., drug trafficker identification and malicious repository detection). Specifically, to make optimal use of the limited labeled data, two novel GRL models (called Meta-AHIN and Meta-HG) that incorporate GRL and meta-learning are first designed to detect malicious repositories on social coding platforms and drug sellers on social media, respectively. In the second part, this dissertation fully exploits the benefits of handy unlabeled data. It proposes two graph self-supervised learning methods (i.e., Rep2Vec and HyGCL-DC) to detect malicious repositories and drug trafficking communities, respectively. Furthermore, to handle the second challenge about class and topology imbalance issues in graphs, this dissertation designs two novel models called CM-GCL and AD-GSMOTE to alleviate the imbalance issues in the self-supervised learning setting (CM-GCL) and the supervised learning setting (AD-GSMOTE) for various detection tasks. All designed models in this dissertation are evaluated on benchmark or real-world datasets, showcasing the effectiveness of GRL-based methods in real-world online abuse detection. Furthermore, this dissertation contributes to research fields by publicly releasing two newly collected datasets (i.e., Twitter-Drug and Twitter-HyDrug), providing valuable resources and assets for researchers in the areas of online abuse detection and GRL.
DOI: 10.7274/25611231.v1
title: Graph Representation Learning Techniques for the Combat against Online Abusive Activity

description: With the growing prevalence of the Internet, abusive behaviors on online platforms have surged in recent decades. These online abusers take advantage of the popularity and conveniences of online platforms to engage in abusive activities toward online users, drawing attention from researchers on effective methods to combat various abusive activities on online platforms. However, traditional machine learning methods, focusing on modeling abusive behavior patterns, struggle to effectively detect and mitigate such behaviors due to their inability to account for the intricate relationships among abusers on online platforms. In response, this dissertation proposes to leverage graphs to depict the complex relationships among abusive users and employ Graph Representation Learning (GRL) methods to detect these online abusive activities (i.e., drug trafficking detection and malicious repository detection). Despite the notable success that existing GRL methods have gained on benchmark graph datasets like social, academic, and molecule graphs, they still face challenges for abuse detection in real-world scenarios: (i) Many real-world abuse detection tasks lack sufficient labeled data for model training, as obtaining a large amount of labeled data is always time-consuming and resource-intensive; (ii) Online abuse graph datasets frequently exhibit class imbalance and topology imbalance issues. To handle the first challenge, this dissertation presents advanced techniques, including graph meta-learning and graph self-supervised learning, to combat online abusive activities (i.e., drug trafficker identification and malicious repository detection). Specifically, to make optimal use of the limited labeled data, two novel GRL models (called Meta-AHIN and Meta-HG) that incorporate GRL and meta-learning are first designed to detect malicious repositories on social coding platforms and drug sellers on social media, respectively. In the second part, this dissertation fully exploits the benefits of handy unlabeled data. It proposes two graph self-supervised learning methods (i.e., Rep2Vec and HyGCL-DC) to detect malicious repositories and drug trafficking communities, respectively. Furthermore, to handle the second challenge about class and topology imbalance issues in graphs, this dissertation designs two novel models called CM-GCL and AD-GSMOTE to alleviate the imbalance issues in the self-supervised learning setting (CM-GCL) and the supervised learning setting (AD-GSMOTE) for various detection tasks. All designed models in this dissertation are evaluated on benchmark or real-world datasets, showcasing the effectiveness of GRL-based methods in real-world online abuse detection. Furthermore, this dissertation contributes to research fields by publicly releasing two newly collected datasets (i.e., Twitter-Drug and Twitter-HyDrug), providing valuable resources and assets for researchers in the areas of online abuse detection and GRL.
DOI: 10.7274/26011648
title: Remote Physiological Monitoring: Domain Shifts and Their Effects on Model Behavior

description: Remote physiological monitoring is a category of computer vision problems in which physiological features are inferred from video data. This dissertation focuses on the challenging task of remote photoplethysmography (rPPG), a technique for inferring a subject's heart rate and its blood volume pulse waveform from video. In particular, rPPG leverages the minute color fluctuations on the subject's skin due to the light absorption of hemoglobin to arrive at the blood volume pulse waveform. Using rPPG as our focal problem, we explore generalization under domain shifts by releasing unique and challenging datasets, developing model training regimes that promote generalization for the rPPG problem, proposing techniques for explaining why models behave as they do even under domain shifted applications, and providing tools that could detect domain shifted data in an in-the-wild application.
DOI: 10.7274/26011648.v1
title: Remote Physiological Monitoring: Domain Shifts and Their Effects on Model Behavior

description: Remote physiological monitoring is a category of computer vision problems in which physiological features are inferred from video data. This dissertation focuses on the challenging task of remote photoplethysmography (rPPG), a technique for inferring a subject's heart rate and its blood volume pulse waveform from video. In particular, rPPG leverages the minute color fluctuations on the subject's skin due to the light absorption of hemoglobin to arrive at the blood volume pulse waveform. Using rPPG as our focal problem, we explore generalization under domain shifts by releasing unique and challenging datasets, developing model training regimes that promote generalization for the rPPG problem, proposing techniques for explaining why models behave as they do even under domain shifted applications, and providing tools that could detect domain shifted data in an in-the-wild application.
DOI: 10.7274/26170384
title: Exploring Trustworthy Concerns in Computer Vision: From Deterministic to Generative Domains

description: Computer vision (CV) research designs AI algorithms to approximate the human visual system across two primary aspects: deterministic and generative domains. Numerous CV applications have been proposed to enhance real-world implementations, such as face recognition and fauxtography detection with deterministic models, as well as text guided image generation with foundational generative models. While contemporary research has been largely concentrating on enhancing the overall accuracy of CV models, it is also crucial to explore the pivotal trustworthy concerns from the application users who prioritize their trust on AI models over the model outputs. Identifying such concerns in the CV applications and refining the CV models to better serve the users is a long-standing challenging task, as novel and advanced CV models are emerging in an endless stream. In this dissertation, we explore the trustworthy concerns of CV models in both the deterministic and generative domains. In particular, we focus on three trustworthy concerns that prevalently spreads in major CV applications: i) the privacy issue in face recognition, ii) the explainability issue in multimodal misinformation detection and iii) the faithfulness issue in text guided image generation. Along these lines, the dissertation consists of three specific aims: A1) we aim to protect the user privacy when their sensitive information is utilized as inputs by deterministic CV models, e.g., safeguarding identity privacy of users in facial recognition applications with improved accuracy and fairness; A2) we aim to enable explainability for the decisions making process of deterministic models, e.g., accurately detecting online multimodal misinformation and providing reasonable explanations for the detection results; and A3) we aim to explore and enhance the faithfulness property of image generative models, e.g., revealing the vulnerabilities of the diffusion model, a widely-used text-driven image generation model, where slight alterations to the input text can cause the model to generate incorrect images. Moreover, when given relevant context like storylines, improving the consistency of the character appearance in a series of generated images by regulating the generation process is also required.
DOI: 10.7274/26170384.v1
title: Exploring Trustworthy Concerns in Computer Vision: From Deterministic to Generative Domains

description: Computer vision (CV) research designs AI algorithms to approximate the human visual system across two primary aspects: deterministic and generative domains. Numerous CV applications have been proposed to enhance real-world implementations, such as face recognition and fauxtography detection with deterministic models, as well as text guided image generation with foundational generative models. While contemporary research has been largely concentrating on enhancing the overall accuracy of CV models, it is also crucial to explore the pivotal trustworthy concerns from the application users who prioritize their trust on AI models over the model outputs. Identifying such concerns in the CV applications and refining the CV models to better serve the users is a long-standing challenging task, as novel and advanced CV models are emerging in an endless stream. In this dissertation, we explore the trustworthy concerns of CV models in both the deterministic and generative domains. In particular, we focus on three trustworthy concerns that prevalently spreads in major CV applications: i) the privacy issue in face recognition, ii) the explainability issue in multimodal misinformation detection and iii) the faithfulness issue in text guided image generation. Along these lines, the dissertation consists of three specific aims: A1) we aim to protect the user privacy when their sensitive information is utilized as inputs by deterministic CV models, e.g., safeguarding identity privacy of users in facial recognition applications with improved accuracy and fairness; A2) we aim to enable explainability for the decisions making process of deterministic models, e.g., accurately detecting online multimodal misinformation and providing reasonable explanations for the detection results; and A3) we aim to explore and enhance the faithfulness property of image generative models, e.g., revealing the vulnerabilities of the diffusion model, a widely-used text-driven image generation model, where slight alterations to the input text can cause the model to generate incorrect images. Moreover, when given relevant context like storylines, improving the consistency of the character appearance in a series of generated images by regulating the generation process is also required.
DOI: 10.7274/26981713
title: Application-Driven Cross-Layer Design and Benchmarking with Emerging Devices

description: Data movement and processing have become significant bottlenecks for conventional Von Neumann architectures, especially as dataset sizes continue to grow. This issue is particularly concerning for deep learning deployment in Internet-of-Things (IoT) devices, which typically require real-time processing and power efficiency while being constrained by limited power and computational resources. Researchers have extensively explored solutions to reduce data movement and address the memory wall problem using complementary-metal-oxide-semiconductor (CMOS) technology. However, the limitations of CMOS technology in designing fast and dense on-chip memories, coupled with the intrinsic penalty of moving large volumes of data between memory and processor in Von Neumann architectures, underscore the need for new solutions. Computing in memory (CiM) is an emerging technique that can help alleviate the memory wall by eliminating data transfer (from memory to processor) and performing computation directly within the memory.

Emerging technologies are of particular interest for realizing CiM architectures due to their non-volatility, unique behaviors that enable the design of customized circuits, and compatibility with CMOS technology. These emerging devices can be leveraged in various ways, including CiM cores for few-shot learning and hyper-dimensional computing, embedded memories for deep neural networks (DNNs) and graph analytics, and preventing side-channel attacks (SCA) in IoT devices. Furthermore, it is crucial to consider cross-layer solutions to explore the design space across different layers of the device hierarchy, i.e., device, circuit, architecture, and application. Each layer must be carefully examined through both bottom-up and top-down approaches. In this dissertation, I adopt a cross-layer approach to propose and evaluate CiM solutions for various applications. I further quantify the benefits of each design by benchmarking the developed circuits and architectures.
DOI: 10.7274/26981713.v1
title: Application-Driven Cross-Layer Design and Benchmarking with Emerging Devices

description: Data movement and processing have become significant bottlenecks for conventional Von Neumann architectures, especially as dataset sizes continue to grow. This issue is particularly concerning for deep learning deployment in Internet-of-Things (IoT) devices, which typically require real-time processing and power efficiency while being constrained by limited power and computational resources. Researchers have extensively explored solutions to reduce data movement and address the memory wall problem using complementary-metal-oxide-semiconductor (CMOS) technology. However, the limitations of CMOS technology in designing fast and dense on-chip memories, coupled with the intrinsic penalty of moving large volumes of data between memory and processor in Von Neumann architectures, underscore the need for new solutions. Computing in memory (CiM) is an emerging technique that can help alleviate the memory wall by eliminating data transfer (from memory to processor) and performing computation directly within the memory.

Emerging technologies are of particular interest for realizing CiM architectures due to their non-volatility, unique behaviors that enable the design of customized circuits, and compatibility with CMOS technology. These emerging devices can be leveraged in various ways, including CiM cores for few-shot learning and hyper-dimensional computing, embedded memories for deep neural networks (DNNs) and graph analytics, and preventing side-channel attacks (SCA) in IoT devices. Furthermore, it is crucial to consider cross-layer solutions to explore the design space across different layers of the device hierarchy, i.e., device, circuit, architecture, and application. Each layer must be carefully examined through both bottom-up and top-down approaches. In this dissertation, I adopt a cross-layer approach to propose and evaluate CiM solutions for various applications. I further quantify the benefits of each design by benchmarking the developed circuits and architectures.
DOI: 10.15468/dl.gr4267
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pelophylax esculentus (Linnaeus, 1758)",
 "Year is 48",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008123-241107131044228/datasets/export for details.
DOI: 10.5281/zenodo.13901065
title: Dataset do artigo "O Que os estudos sobre desinformação na Ciência da Informação brasileira têm a nos dizer?

description: Artigo publicado em http://dx.doi.org/10.1590/1981-5344/51376
DOI: 10.5281/zenodo.13901066
title: Dataset do artigo "O Que os estudos sobre desinformação na Ciência da Informação brasileira têm a nos dizer?

description: Acesse o registro da versão 2 para fazer o download do dataset: https://zenodo.org/records/14075341
DOI: 10.15468/dl.awkp96
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lissotriton vulgaris (Linnaeus, 1758)",
 "Year is 48",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008125-241107131044228/datasets/export for details.
DOI: 10.15468/dl.ja4xtm
title: Occurrence Download

description: A dataset containing 21487 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Sweden",
 "DatasetKey is Xeno-canto - Bird sounds from around the world"
 ]
}

 The dataset includes 21487 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008124-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14075770
title: Apartamentos en venta en la delegación Benito Juárez, Ciudad de México

description: Este dataset contiene información sobre apartamentos en venta en la delegación Benito Juárez en Ciudad de México en el año 2024. Incluye datos como el precio, la ubicación (estado y ciudad), el tipo de propiedad (casa, departamento, terreno, oficina, etc.), y otras características, como superficie y número de habitaciones. 

Este conjunto de datos es ideal para comprender el comportamiento del mercado inmobiliario y las variaciones de precios en diferentes localidades mexicanas.