OpenRaiser/Intern-Atlas huggingface dataset:
A Realistic Protocol for Evaluation of Weakly Supervised Object Localization
Venue: arXiv cs.CV 2024
Year: 2024

Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-level labels. The absence of bounding box (bbox) supervision during training raises challenges in the literature for hyper-parameter tuning, model selection, and evaluation. WSOL methods rely on a validation set with bbox annotations for model selection, and a test set with bbox annotations for threshold estimation for producing bboxes from localization maps. This approach, however, is not aligned with the WSOL setting as these annotations are typically unavailable in real-world scenarios. Our initial empirical analysis shows a significant decline in LOC performance when model selection and threshold estimation rely solely on class labels and the image itself, respectively, compared to using manual bbox annotations. This highlights the importance of incorporating bbox labels for optimal model performance. In this paper, a new WSOL evaluation protocol is proposed that provides LOC information without the need for manual bbox annotations. In particular, we generated noisy pseudo-boxes from a pretrained off-the-shelf region proposal method such as Selective Search, CLIP, and RPN for model selection. These bboxes are also employed to estimate the threshold from LOC maps, circumventing the need for test-set bbox annotations. Our experiments with several WSOL methods on ILSVRC and CUB datasets show that using the proposed pseudo-bboxes for validation facilitates the model selection and threshold estimation, with LOC performance comparable to those selected using GT bboxes on the validation set and threshold estimation on the test set. It also outperforms models selected using class-level labels, and then dynamically thresholded based solely on LOC maps.
AIGeN: An Adversarial Approach for Instruction Generation in VLN
Venue: arXiv cs.CV 2024
Year: 2024

In the last few years, the research interest in Vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance by exploiting synthetic training data. In this work, we propose AIGeN, a novel architecture inspired by Generative Adversarial Networks (GANs) that produces meaningful and well-formed synthetic instructions to improve navigation agents' performance. The model is composed of a Transformer decoder (GPT-2) and a Transformer encoder (BERT). During the training phase, the decoder generates sentences for a sequence of images describing the agent's path to a particular point while the encoder discriminates between real and fake instructions. Experimentally, we evaluate the quality of the generated instructions and perform extensive ablation studies. Additionally, we generate synthetic instructions for 217K trajectories using AIGeN on Habitat-Matterport 3D Dataset (HM3D) and show an improvement in the performance of an off-the-shelf VLN method. The validation analysis of our proposal is conducted on REVERIE and R2R and highlights the promising aspects of our proposal, achieving state-of-the-art performance.
Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions
Venue: arXiv cs.AR 2024
Year: 2024

Deep learning (DL) is becoming the cornerstone of numerous applications both in datacenters and at the edge. Specialized hardware is often necessary to meet the performance requirements of state-of-the-art DL models, but the rapid pace of change in DL models and the wide variety of systems integrating DL make it impossible to create custom computer chips for all but the largest markets. Field-programmable gate arrays (FPGAs) present a unique blend of reprogrammability and direct hardware execution that make them suitable for accelerating DL inference. They offer the ability to customize processing pipelines and memory hierarchies to achieve lower latency and higher energy efficiency compared to general-purpose CPUs and GPUs, at a fraction of the development time and cost of custom chips. Their diverse high-speed IOs also enable directly interfacing the FPGA to the network and/or a variety of external sensors, making them suitable for both datacenter and edge use cases. As DL has become an ever more important workload, FPGA architectures are evolving to enable higher DL performance. In this article, we survey both academic and industrial FPGA architecture enhancements for DL. First, we give a brief introduction on the basics of FPGA architecture and how its components lead to strengths and weaknesses for DL applications. Next, we discuss different styles of DL inference accelerators on FPGA, ranging from model-specific dataflow styles to software-programmable overlay styles. We survey DL-specific enhancements to traditional FPGA building blocks such as logic blocks, arithmetic circuitry, and on-chip memories, as well as new in-fabric DL-specialized blocks for accelerating tensor computations. Finally, we discuss hybrid devices that combine processors and coarse-grained accelerator blocks with FPGA-like interconnect and networks-on-chip, and highlight promising future research directions.
Design and Implementation of a Java-Based Client-Server Application
Venue: arXiv cs.DC 2024
Year: 2024

This report details the development of a networked distributed system named Group Communication System (GCS), implemented in Java to exemplify socket programming and communication protocols. GCS facilitates group-based client-server communication through a command-line interface (CLI), enabling seamless group interaction and management. The project emphasizes fault tolerance, design patterns, and version control system (VCS) utilization. The report offers insights into system architecture, implementation, and practical considerations, providing a comprehensive understanding of distributed systems' technical background and operational aspects.
GeoAI Reproducibility and Replicability: a computational and spatial perspective
Venue: arXiv cs.CV 2024
Year: 2024

GeoAI has emerged as an exciting interdisciplinary research area that combines spatial theories and data with cutting-edge AI models to address geospatial problems in a novel, data-driven manner. While GeoAI research has flourished in the GIScience literature, its reproducibility and replicability (R&R), fundamental principles that determine the reusability, reliability, and scientific rigor of research findings, have rarely been discussed. This paper aims to provide an in-depth analysis of this topic from both computational and spatial perspectives. We first categorize the major goals for reproducing GeoAI research, namely, validation (repeatability), learning and adapting the method for solving a similar or new problem (reproducibility), and examining the generalizability of the research findings (replicability). Each of these goals requires different levels of understanding of GeoAI, as well as different methods to ensure its success. We then discuss the factors that may cause the lack of R&R in GeoAI research, with an emphasis on (1) the selection and use of training data; (2) the uncertainty that resides in the GeoAI model design, training, deployment, and inference processes; and more importantly (3) the inherent spatial heterogeneity of geospatial data and processes. We use a deep learning-based image analysis task as an example to demonstrate the results' uncertainty and spatial variance caused by different factors. The findings reiterate the importance of knowledge sharing, as well as the generation of a "replicability map" that incorporates spatial autocorrelation and spatial heterogeneity into consideration in quantifying the spatial replicability of GeoAI research.
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning
Venue: arXiv cs.LG 2024
Year: 2024

E-health allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by Artificial Intelligence (AI) technologies to help doctors make diagnosis. By allowing multiple devices to train models collaboratively, federated learning is a promising solution to address the communication and privacy issues in e-health. However, applying federated learning in e-health faces many challenges. First, medical data is both horizontally and vertically partitioned. Since single Horizontal Federated Learning (HFL) or Vertical Federated Learning (VFL) techniques cannot deal with both types of data partitioning, directly applying them may consume excessive communication cost due to transmitting a part of raw data when requiring high modeling accuracy. Second, a naive combination of HFL and VFL has limitations including low training efficiency, unsound convergence analysis, and lack of parameter tuning strategies. In this paper, we provide a thorough study on an effective integration of HFL and VFL, to achieve communication efficiency and overcome the above limitations when data is both horizontally and vertically partitioned. Specifically, we propose a hybrid federated learning framework with one intermediate result exchange and two aggregation phases. Based on this framework, we develop a Hybrid Stochastic Gradient Descent (HSGD) algorithm to train models. Then, we theoretically analyze the convergence upper bound of the proposed algorithm. Using the convergence results, we design adaptive strategies to adjust the training parameters and shrink the size of transmitted data. Experimental results validate that the proposed HSGD algorithm can achieve the desired accuracy while reducing communication cost, and they also verify the effectiveness of the adaptive strategies.
Shaping Realities: Enhancing 3D Generative AI with Fabrication Constraints
Venue: arXiv cs.HC 2024
Year: 2024

Generative AI tools are becoming more prevalent in 3D modeling, enabling users to manipulate or create new models with text or images as inputs. This makes it easier for users to rapidly customize and iterate on their 3D designs and explore new creative ideas. These methods focus on the aesthetic quality of the 3D models, refining them to look similar to the prompts provided by the user. However, when creating 3D models intended for fabrication, designers need to trade-off the aesthetic qualities of a 3D model with their intended physical properties. To be functional post-fabrication, 3D models have to satisfy structural constraints informed by physical principles. Currently, such requirements are not enforced by generative AI tools. This leads to the development of aesthetically appealing, but potentially non-functional 3D geometry, that would be hard to fabricate and use in the real world. This workshop paper highlights the limitations of generative AI tools in translating digital creations into the physical world and proposes new augmentations to generative AI tools for creating physically viable 3D models. We advocate for the development of tools that manipulate or generate 3D models by considering not only the aesthetic appearance but also using physical properties as constraints. This exploration seeks to bridge the gap between digital creativity and real-world applicability, extending the creative potential of generative AI into the tangible domain.
Epigraphics: Message-Driven Infographics Authoring
Venue: arXiv cs.HC 2024
Year: 2024

The message a designer wants to convey plays a pivotal role in directing the design of an infographic, yet most authoring workflows start with creating the visualizations or graphics first without gauging whether they fit the message. To address this gap, we propose Epigraphics, a web-based authoring system that treats an "epigraph" as the first-class object, and uses it to guide infographic asset creation, editing, and syncing. The system uses the text-based message to recommend visualizations, graphics, data filters, color palettes, and animations. It further supports between-asset interactions and fine-tuning such as recoloring, highlighting, and animation syncing that enhance the aesthetic cohesiveness of the assets. A gallery and case studies show that our system can produce infographics inspired by existing popular ones, and a task-based usability study with 10 designers show that a text-sourced workflow can standardize content, empower users to think more about the big picture, and facilitate rapid prototyping.
PD-L1 Classification of Weakly-Labeled Whole Slide Images of Breast Cancer
Venue: arXiv cs.CE 2024
Year: 2024

Specific and effective breast cancer therapy relies on the accurate quantification of PD-L1 positivity in tumors, which appears in the form of brown stainings in high resolution whole slide images (WSIs). However, the retrieval and extensive labeling of PD-L1 stained WSIs is a time-consuming and challenging task for pathologists, resulting in low reproducibility, especially for borderline images. This study aims to develop and compare models able to classify PD-L1 positivity of breast cancer samples based on WSI analysis, relying only on WSI-level labels. The task consists of two phases: identifying regions of interest (ROI) and classifying tumors as PD-L1 positive or negative. For the latter, two model categories were developed, with different feature extraction methodologies. The first encodes images based on the colour distance from a base color. The second uses a convolutional autoencoder to obtain embeddings of WSI tiles, and aggregates them into a WSI-level embedding. For both model types, features are fed into downstream ML classifiers. Two datasets from different clinical centers were used in two different training configurations: (1) training on one dataset and testing on the other; (2) combining the datasets. We also tested the performance with or without human preprocessing to remove brown artefacts Colour distance based models achieve the best performances on testing configuration (1) with artefact removal, while autoencoder-based models are superior in the remaining cases, which are prone to greater data variability.
SoK (or SoLK?): On the Quantitative Study of Sociodemographic Factors and Computer Security Behaviors
Venue: arXiv cs.CR 2024
Year: 2024

Researchers are increasingly exploring how gender, culture, and other sociodemographic factors correlate with user computer security and privacy behaviors. To more holistically understand relationships between these factors and behaviors, we make two contributions. First, we broadly survey existing scholarship on sociodemographics and secure behavior (151 papers) before conducting a focused literature review of 47 papers to synthesize what is currently known and identify open questions for future research. Second, by incorporating contemporary social and critical theories, we establish guidelines for future studies of sociodemographic factors and security behaviors that address how to overcome common pitfalls. We present a case study to demonstrate our guidelines in action, at-scale, that conduct a measurement study of the relationships between sociodemographics and de-identified, aggregated log data of security and privacy behaviors among 16,829 users on Facebook across 16 countries. Through these contributions, we position our work as a systemization of a lack of knowledge (SoLK). Overall, we find contradictory results and vast unknowns about how identity shapes security behavior. Through our guidelines and discussion, we chart new directions to more deeply examine how and why sociodemographic factors affect security behaviors.
Smart Pilot Assignment for IoT in Massive MIMO Systems: A Path Towards Scalable IoT Infrastructure
Venue: arXiv cs.NI 2024
Year: 2024

5G sets the foundation for an era of creativity with its faster speeds, increased data throughput, reduced latency, and enhanced IoT connectivity, all enabled by Massive MIMO (M-MIMO) technology. M-MIMO boosts network efficiency and enhances user experience by employing intelligent user scheduling. This paper presents a user scheduling scheme and pilot assignment strategy designed for IoT devices, emphasizing mitigating pilot contamination, a key obstacle to improving spectral efficiency (SE) and system scalability in M-MIMO networks. We utilize a user clustering-based pilot allocation scheme to boost IoT device scalability in M-MIMO systems. Additionally, our smart pilot allocation minimizes interference and enhances SE by treating pilot assignment as a graph coloring problem, optimizing it through integer linear programming (ILP). Recognizing the computational complexity of ILP, we introduced a binary search-based heuristic predicated on interference threshold to expedite the computation, while maintaining a near-optimal solution. The simulation results show a significant decrease in the required pilot overhead (about 17%), and substantial enhancement in SE (about 8-14%).
GaitPoint+: A Gait Recognition Network Incorporating Point Cloud Analysis and Recycling
Venue: arXiv cs.CV 2024
Year: 2024

Gait is a behavioral biometric modality that can be used to recognize individuals by the way they walk from a far distance. Most existing gait recognition approaches rely on either silhouettes or skeletons, while their joint use is underexplored. Features from silhouettes and skeletons can provide complementary information for more robust recognition against appearance changes or pose estimation errors. To exploit the benefits of both silhouette and skeleton features, we propose a new gait recognition network, referred to as the GaitPoint+. Our approach models skeleton key points as a 3D point cloud, and employs a computational complexity-conscious 3D point processing approach to extract skeleton features, which are then combined with silhouette features for improved accuracy. Since silhouette- or CNN-based methods already require considerable amount of computational resources, it is preferable that the key point learning module is faster and more lightweight. We present a detailed analysis of the utilization of every human key point after the use of traditional max-pooling, and show that while elbow and ankle points are used most commonly, many useful points are discarded by max-pooling. Thus, we present a method to recycle some of the discarded points by a Recycling Max-Pooling module, during processing of skeleton point clouds, and achieve further performance improvement. We provide a comprehensive set of experimental results showing that (i) incorporating skeleton features obtained by a point-based 3D point cloud processing approach boosts the performance of three different state-of-the-art silhouette- and CNN-based baselines; (ii) recycling the discarded points increases the accuracy further. Ablation studies are also provided to show the effectiveness and contribution of different components of our approach.
Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
Venue: arXiv cs.LG 2024
Year: 2024

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023.
Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain
Venue: arXiv cs.CV 2024
Year: 2024

Few-shot segmentation is a task to segment objects or regions of novel classes within an image given only a few annotated examples. In the generalized setting, the task extends to segment both the base and the novel classes. The main challenge is how to train the model such that the addition of novel classes does not hurt the base classes performance, also known as catastrophic forgetting. To mitigate this issue, we use SegGPT as our base model and train it on the base classes. Then, we use separate learnable prompts to handle predictions for each novel class. To handle various object sizes which typically present in remote sensing domain, we perform patch-based prediction. To address the discontinuities along patch boundaries, we propose a patch-and-stitch technique by re-framing the problem as an image inpainting task. During inference, we also utilize image similarity search over image embeddings for prompt selection and novel class filtering to reduce false positive predictions. Based on our experiments, our proposed method boosts the weighted mIoU of a simple fine-tuned SegGPT from 15.96 to 35.08 on the validation set of few-shot OpenEarthMap dataset given in the challenge.
Cluster-based Graph Collaborative Filtering
Venue: arXiv cs.IR 2024
Year: 2024

Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-order neighboring nodes. However, most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. Thus, the noisy information from unreliable neighbor nodes (e.g., users with dissimilar interests) negatively impacts the representation learning of the target node. Additionally, conducting graph convolution operations without differentiating high-order neighbors suffers the over-smoothing issue when stacking more layers, resulting in performance degradation. In this paper, we aim to capture more valuable information from high-order neighboring nodes while avoiding noise for better representation learning of the target node. To achieve this goal, we propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF). This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them. Specifically, we design an unsupervised and optimizable soft node clustering approach to classify user and item nodes into multiple clusters. Based on the soft node clustering results and the topology of the user-item interaction graph, we assign the nodes with probabilities for different clusters to construct the cluster-specific graphs. To evaluate the effectiveness of ClusterGCF, we conducted extensive experiments on four publicly available datasets. Experimental results demonstrate that our model can significantly improve recommendation performance.