OpenRaiser/Intern-Atlas huggingface dataset:
Containing Analog Data Deluge at Edge through Frequency-Domain Compression in Collaborative Compute-in-Memory Networks
Venue: arXiv cs.LG 2023
Year: 2023

Edge computing is a promising solution for handling high-dimensional, multispectral analog data from sensors and IoT devices for applications such as autonomous drones. However, edge devices' limited storage and computing resources make it challenging to perform complex predictive modeling at the edge. Compute-in-memory (CiM) has emerged as a principal paradigm to minimize energy for deep learning-based inference at the edge. Nevertheless, integrating storage and processing complicates memory cells and/or memory peripherals, essentially trading off area efficiency for energy efficiency. This paper proposes a novel solution to improve area efficiency in deep learning inference tasks. The proposed method employs two key strategies. Firstly, a Frequency domain learning approach uses binarized Walsh-Hadamard Transforms, reducing the necessary parameters for DNN (by 87% in MobileNetV2) and enabling compute-in-SRAM, which better utilizes parallelism during inference. Secondly, a memory-immersed collaborative digitization method is described among CiM arrays to reduce the area overheads of conventional ADCs. This facilitates more CiM arrays in limited footprint designs, leading to better parallelism and reduced external memory accesses. Different networking configurations are explored, where Flash, SA, and their hybrid digitization steps can be implemented using the memory-immersed scheme. The results are demonstrated using a 65 nm CMOS test chip, exhibiting significant area and energy savings compared to a 40 nm-node 5-bit SAR ADC and 5-bit Flash ADC. By processing analog data more efficiently, it is possible to selectively retain valuable data from sensors and alleviate the challenges posed by the analog data deluge.
Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection
Venue: arXiv cs.CR 2023
Year: 2023

The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy breaches. In cybersecurity, the sensitive data along with the contextual information and high-quality labeling in each enterprise organization play an essential role in constructing high-performance machine learning (ML) models for detecting cyber threats. Nonetheless, the risks coming from poisoning internal adversaries against FL systems have raised discussions about designing robust anti-poisoning frameworks. Whereas defensive mechanisms in the past were based on outlier detection, recent approaches tend to be more concerned with latent space representation. In this paper, we investigate a novel robust aggregation method for FL, namely Fed-LSAE, which takes advantage of latent space representation via the penultimate layer and Autoencoder to exclude malicious clients from the training process. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting-edge poisoning attacks for developing a robust FL-based threat detector in the context of IoT. More specifically, the FL evaluation witnesses an upward trend of approximately 98% across all metrics when integrating with our Fed-LSAE defense.
The social stratification of internal migration and daily mobility during the COVID-19 pandemic
Venue: arXiv cs.CY 2023
Year: 2023

This study leverages mobile phone data for 5.4 million users to unveil the complex dynamics of internal migration and daily mobility in Santiago de Chile during the global COVID-19 pandemic, with a focus on socioeconomic differentials. Major findings include an increase in daily mobility among lower-income brackets compared to higher ones in 2020. In contrast, long-term relocation patterns rose primarily among higher-income groups. These shifts indicate a nuanced response to the pandemic across socioeconomic strata. Unlike in 2017, economic factors in 2020 influenced a change not only in the decision to emigrate but also in the selection of destinations, suggesting a profound transformation in mobility behaviors. Contrary to expectations, there was no evidence supporting a preference for rural over urban destinations despite the surge in emigration from Santiago during the pandemic. The study enhances our understanding of how varying socioeconomic conditions intersect with mobility decisions during crises and provides valuable insights for policymakers aiming to enact fair, informed measures in rapidly changing circumstances.
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
Venue: arXiv cs.AI 2023
Year: 2023

As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques are emerging to address hallucination, it is equally crucial to delve into its underlying causes. Consequently, in this preliminary exploratory investigation, we examine how linguistic factors in prompts, specifically readability, formality, and concreteness, influence the occurrence of hallucinations. Our experimental results suggest that prompts characterized by greater formality and concreteness tend to result in reduced hallucination. However, the outcomes pertaining to readability are somewhat inconclusive, showing a mixed pattern.
From Unstable Contacts to Stable Control: A Deep Learning Paradigm for HD-sEMG in Neurorobotics
Venue: arXiv cs.RO 2023
Year: 2023

In the past decade, there has been significant advancement in designing wearable neural interfaces for controlling neurorobotic systems, particularly bionic limbs. These interfaces function by decoding signals captured non-invasively from the skin's surface. Portable high-density surface electromyography (HD-sEMG) modules combined with deep learning decoding have attracted interest by achieving excellent gesture prediction and myoelectric control of prosthetic systems and neurorobots. However, factors like pixel-shape electrode size and unstable skin contact make HD-sEMG susceptible to pixel electrode drops. The sparse electrode-skin disconnections rooted in issues such as low adhesion, sweating, hair blockage, and skin stretch challenge the reliability and scalability of these modules as the perception unit for neurorobotic systems. This paper proposes a novel deep-learning model providing resiliency for HD-sEMG modules, which can be used in the wearable interfaces of neurorobots. The proposed 3D Dilated Efficient CapsNet model trains on an augmented input space to computationally `force' the network to learn channel dropout variations and thus learn robustness to channel dropout. The proposed framework maintained high performance under a sensor dropout reliability study conducted. Results show conventional models' performance significantly degrades with dropout and is recovered using the proposed architecture and the training paradigm.
AttentionMix: Data augmentation method that relies on BERT attention mechanism
Venue: arXiv cs.CL 2023
Year: 2023

The Mixup method has proven to be a powerful data augmentation technique in Computer Vision, with many successors that perform image mixing in a guided manner. One of the interesting research directions is transferring the underlying Mixup idea to other domains, e.g. Natural Language Processing (NLP). Even though there already exist several methods that apply Mixup to textual data, there is still room for new, improved approaches. In this work, we introduce AttentionMix, a novel mixing method that relies on attention-based information. While the paper focuses on the BERT attention mechanism, the proposed approach can be applied to generally any attention-based model. AttentionMix is evaluated on 3 standard sentiment classification datasets and in all three cases outperforms two benchmark approaches that utilize Mixup mechanism, as well as the vanilla BERT method. The results confirm that the attention-based information can be effectively used for data augmentation in the NLP domain.
Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI.
ChatGPT-4 as a Tool for Reviewing Academic Books in Spanish
Venue: arXiv cs.AI 2023
Year: 2023

This study evaluates the potential of ChatGPT-4, an artificial intelligence language model developed by OpenAI, as an editing tool for Spanish literary and academic books. The need for efficient and accessible reviewing and editing processes in the publishing industry has driven the search for automated solutions. ChatGPT-4, being one of the most advanced language models, offers notable capabilities in text comprehension and generation. In this study, the features and capabilities of ChatGPT-4 are analyzed in terms of grammatical correction, stylistic coherence, and linguistic enrichment of texts in Spanish. Tests were conducted with 100 literary and academic texts, where the edits made by ChatGPT-4 were compared to those made by expert human reviewers and editors. The results show that while ChatGPT-4 is capable of making grammatical and orthographic corrections with high accuracy and in a very short time, it still faces challenges in areas such as context sensitivity, bibliometric analysis, deep contextual understanding, and interaction with visual content like graphs and tables. However, it is observed that collaboration between ChatGPT-4 and human reviewers and editors can be a promising strategy for improving efficiency without compromising quality. Furthermore, the authors consider that ChatGPT-4 represents a valuable tool in the editing process, but its use should be complementary to the work of human editors to ensure high-caliber editing in Spanish literary and academic books.
TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models
Venue: arXiv cs.GR 2023
Year: 2023

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.
Open-endedness induced through a predator-prey scenario using modular robots
Venue: arXiv cs.RO 2023
Year: 2023

This work investigates how a predator-prey scenario can induce the emergence of Open-Ended Evolution (OEE). We utilize modular robots of fixed morphologies whose controllers are subject to evolution. In both species, robots can send and receive signals and perceive the relative positions of other robots in the environment. Specifically, we introduce a feature we call a tagging system: it modifies how individuals can perceive each other and is expected to increase behavioral complexity. Our results show the emergence of adaptive strategies, demonstrating the viability of inducing OEE through predator-prey dynamics using modular robots. Such emergence, nevertheless, seemed to depend on conditioning reproduction to an explicit behavioral criterion.
A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing
Venue: arXiv cs.DC 2023
Year: 2023

Due to the recent wide use of computational resources in cloud computing, new resource provisioning challenges have been emerged. Resource provisioning techniques must keep total costs to a minimum while meeting the requirements of the requests. According to widely usage of cloud services, it seems more challenging to develop effective schemes for provisioning services cost-effectively; we have proposed a novel learning based resource provisioning approach that achieves cost-reduction guarantees of demands. The contributions of our optimized resource provisioning (ORP) approach are as follows. Firstly, it is designed to provide a cost-effective method to efficiently handle the provisioning of requested applications; while most of the existing models allow only workflows in general which cares about the dependencies of the tasks, ORP performs based on services of which applications comprised and cares about their efficient provisioning totally. Secondly, it is a learning automata-based approach which selects the most proper resources for hosting each service of the demanded application; our approach considers both cost and service requirements together for deploying applications. Thirdly, a comprehensive evaluation is performed for three typical workloads: data-intensive, process-intensive and normal applications. The experimental results show that our method adapts most of the requirements efficiently, and furthermore the resulting performance meets our design goals.
Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning
Venue: arXiv cs.LG 2023
Year: 2023

Catastrophic forgetting remains a critical challenge in the field of continual learning, where neural networks struggle to retain prior knowledge while assimilating new information. Most existing studies emphasize mitigating this issue only when encountering new tasks, overlooking the significance of the pre-task phase. Therefore, we shift the attention to the current task learning stage, presenting a novel framework, C&F (Create and Find Flatness), which builds a flat training space for each task in advance. Specifically, during the learning of the current task, our framework adaptively creates a flat region around the minimum in the loss landscape. Subsequently, it finds the parameters' importance to the current task based on their flatness degrees. When adapting the model to a new task, constraints are applied according to the flatness and a flat space is simultaneously prepared for the impending task. We theoretically demonstrate the consistency between the created and found flatness. In this manner, our framework not only accommodates ample parameter space for learning new tasks but also preserves the preceding knowledge of earlier tasks. Experimental results exhibit C&F's state-of-the-art performance as a standalone continual learning approach and its efficacy as a framework incorporating other methods. Our work is available at https://github.com/Eric8932/Create-and-Find-Flatness.
A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques
Venue: arXiv cs.GT 2023
Year: 2023

Cloud computing as a fairly new commercial paradigm, widely investigated by different researchers, already has a great range of challenges. Pricing is a major problem in Cloud computing marketplace; as providers are competing to attract more customers without knowing the pricing policies of each other. To overcome this lack of knowledge, we model their competition by an incomplete-information game. Considering the issue, this work proposes a pricing policy related to the regret minimization algorithm and applies it to the considered incomplete-information game. Based on the competition based marketplace of the Cloud, providers update the distribution of their strategies using the experienced regret. The idea of iteratively applying the algorithm for updating probabilities of strategies causes the regret get minimized faster. The experimental results show much more increase in profits of the providers in comparison with other pricing policies. Besides, the efficiency of a variety of regret minimization techniques in a simulated marketplace of Cloud are discussed which have not been observed in the studied literature. Moreover, return on investment of providers in considered organizations is studied and promising results appeared.
Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images
Venue: arXiv cs.CV 2023
Year: 2023

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pre-trained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.
Software Compartmentalization Trade-Offs with Hardware Capabilities
Venue: arXiv cs.CR 2023
Year: 2023

Compartmentalization is a form of defensive software design in which an application is broken down into isolated but communicating components. Retrofitting compartmentalization into existing applications is often thought to be expensive from the engineering effort and performance overhead points of view. Still, recent years have seen proposals of compartmentalization methods with promises of low engineering efforts and reduced performance impact. ARM Morello combines a modern ARM processor with an implementation of Capability Hardware Enhanced RISC Instructions (CHERI) aiming to provide efficient and secure compartmentalization. Past works exploring CHERI-based compartmentalization were restricted to emulated/FPGA prototypes. In this paper, we explore possible compartmentalization schemes with CHERI on the Morello chip. We propose two approaches representing different trade-offs in terms of engineering effort, security, scalability, and performance impact. We describe and implement these approaches on a prototype OS running bare metal on the Morello chip, compartmentalize two popular applications, and investigate the performance overheads. Furthermore, we show that compartmentalization can be achieved with an engineering cost that can be quite low if one is willing to trade off on scalability and security, and that performance overheads are similar to other intra-address space isolation mechanisms.
Studying Lobby Influence in the European Parliament
Venue: arXiv cs.CL 2023
Year: 2023

We present a method based on natural language processing (NLP), for studying the influence of interest groups (lobbies) in the law-making process in the European Parliament (EP). We collect and analyze novel datasets of lobbies' position papers and speeches made by members of the EP (MEPs). By comparing these texts on the basis of semantic similarity and entailment, we are able to discover interpretable links between MEPs and lobbies. In the absence of a ground-truth dataset of such links, we perform an indirect validation by comparing the discovered links with a dataset, which we curate, of retweet links between MEPs and lobbies, and with the publicly disclosed meetings of MEPs. Our best method achieves an AUC score of 0.77 and performs significantly better than several baselines. Moreover, an aggregate analysis of the discovered links, between groups of related lobbies and political groups of MEPs, correspond to the expectations from the ideology of the groups (e.g., center-left groups are associated with social causes). We believe that this work, which encompasses the methodology, datasets, and results, is a step towards enhancing the transparency of the intricate decision-making processes within democratic institutions.