OpenRaiser/Intern-Atlas huggingface dataset:
Socially-Minded Intelligence: How Individuals, Groups, and AI Systems Can Make Each-Other Smarter (or Not)
Venue: arXiv cs.HC 2024
Year: 2024

A core part of human intelligence is the ability to work flexibly with others to achieve both individual and collective goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically focus either on the individual or the collective level of analysis. At the individual level, intelligence is seen as an individual-difference trait that exists independently of the social environment. At the collective level intelligence is conceptualized as a property of groups, but not in a way that can be used to understand how groups can make group members smarter or how group members acting as individuals might make the group itself more intelligent. In the present paper we argue that by focusing either on individual or collective intelligence without considering their interaction, existing conceptualizations of intelligence limit the potential of people and machines. To address this impasse, we identify and explore a new kind of intelligence - socially-minded intelligence - that can be applied to both individuals (in a social context) and collectives (of individual minds). From a socially-minded intelligence perspective, the potential intelligence of individuals is unlocked in groups, while the potential intelligence of groups is maximized by the flexible, context-sensitive commitment of individual group members. We propose ways in which socially-minded intelligence might be measured and cultivated within people, as well as how it might be modelled in AI systems. Finally, we discuss ways in which socially-minded intelligence might be used to improve human-AI teaming.
WISDOM: An AI-powered framework for emerging research detection using weak signal analysis and advanced topic modeling
Venue: arXiv cs.IR 2024
Year: 2024

The landscape of science and technology is characterized by its dynamic and evolving nature, constantly reshaped by new discoveries, innovations, and paradigm shifts. Moreover, science is undergoing a remarkable shift towards increasing interdisciplinary collaboration, where the convergence of diverse fields fosters innovative solutions to complex problems. Detecting emerging scientific topics is paramount as it enables industries, policymakers, and innovators to adapt their strategies, investments, and regulations proactively. As the common approach for detecting emerging technologies, despite being useful, bibliometric analyses may suffer from oversimplification and/or misinterpretation of complex interdisciplinary trends. In addition, relying solely on domain experts to pinpoint emerging technologies from science and technology trends might restrict the ability to systematically analyze extensive information and introduce subjective judgments into the interpretations. To overcome these drawbacks, in this work, we present an automated artificial intelligence-enabled framework, called WISDOM, for detecting emerging research themes using advanced topic modeling and weak signal analysis. The proposed approach can assist strategic planners and domain experts in more effectively recognizing and tracking trends related to emerging topics by swiftly processing and analyzing vast volumes of data, uncovering hidden cross-disciplinary patterns, and offering unbiased insights, thereby enhancing the efficiency and objectivity of the detection process. As the case technology, we assess WISDOM's performance in identifying emerging research as well as their trends, in the field of underwater sensing technologies using scientific papers published between 2004 and 2021.
Advertiser Content Understanding via LLMs for Google Ads Safety
Venue: arXiv cs.IR 2024
Year: 2024

Ads Content Safety at Google requires classifying billions of ads for Google Ads content policies. Consistent and accurate policy enforcement is important for advertiser experience and user safety and it is a challenging problem, so there is a lot of value for improving it for advertisers and users. Inconsistent policy enforcement causes increased policy friction and poor experience with good advertisers, and bad advertisers exploit the inconsistency by creating multiple similar ads in the hope that some will get through our defenses. This study proposes a method to understand advertiser's intent for content policy violations, using Large Language Models (LLMs). We focus on identifying good advertisers to reduce content over-flagging and improve advertiser experience, though the approach can easily be extended to classify bad advertisers too. We generate advertiser's content profile based on multiple signals from their ads, domains, targeting info, etc. We then use LLMs to classify the advertiser content profile, along with relying on any knowledge the LLM has of the advertiser, their products or brand, to understand whether they are likely to violate a certain policy or not. After minimal prompt tuning our method was able to reach 95\% accuracy on a small test set.
An Interactive Web Application for School-Based Physical Fitness Testing in California: Geospatial Analysis and Custom Mapping
Venue: arXiv cs.CY 2024
Year: 2024

Physical activity is essential for children's healthy growth and development. In the US, most states, including California, adhere to physical education standards and have implemented the mandated School-based Physical Fitness Testing (SB-PFT) for over two decades. Despite extensive data collection, research utilization of SB-PFT has been limited due to the absence of accessible analytical tools. We developed a web application using GeoServer, ArcGIS, and AWS to visualize SB-PFT data. This user-friendly platform enables education administrators and policymakers to analyze trends in children's physical fitness, identify successful programs at schools and districts, and evaluate new physical education initiatives. The application also features a custom mapping tool for comparing external datasets with SB-PFT data. We conclude that this platform, by integrating advanced analytical capabilities in an informatics-based tool, significantly enhances engagement in promoting children's physical fitness.
Multitask Mayhem: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning
Venue: arXiv cs.CL 2024
Year: 2024

Recent breakthroughs in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment analysis, etc. Red teaming/Safety alignment efforts show that fine-tuning models on benign (non-harmful) data could compromise safety. However, it remains unclear to what extent this phenomenon is influenced by different variables, including fine-tuning task, model calibrations, etc. This paper explores the task-wise safety degradation due to fine-tuning on downstream tasks such as summarization, code generation, translation, and classification across various calibration. Our results reveal that: 1) Fine-tuning LLMs for code generation and translation leads to the highest degradation in safety guardrails. 2) LLMs generally have weaker guardrails for translation and classification, with 73-92% of harmful prompts answered, across baseline and other calibrations, falling into one of two concern categories. 3) Current solutions, including guards and safety tuning datasets, lack cross-task robustness. To address these issues, we developed a new multitask safety dataset effectively reducing attack success rates across a range of tasks without compromising the model's overall helpfulness. Our work underscores the need for generalized alignment measures to ensure safer and more robust models.
Novel Saliency Analysis for the Forward Forward Algorithm
Venue: arXiv cs.LG 2024
Year: 2024

Incorporating the Forward Forward algorithm into neural network training represents a transformative shift from traditional methods, introducing a dual forward mechanism that streamlines the learning process by bypassing the complexities of derivative propagation. This method is noted for its simplicity and efficiency and involves executing two forward passes the first with actual data to promote positive reinforcement, and the second with synthetically generated negative data to enable discriminative learning. Our experiments confirm that the Forward Forward algorithm is not merely an experimental novelty but a viable training strategy that competes robustly with conventional multi layer perceptron (MLP) architectures. To overcome the limitations inherent in traditional saliency techniques, which predominantly rely on gradient based methods, we developed a bespoke saliency algorithm specifically tailored for the Forward Forward framework. This innovative algorithm enhances the intuitive understanding of feature importance and network decision-making, providing clear visualizations of the data features most influential in model predictions. By leveraging this specialized saliency method, we gain deeper insights into the internal workings of the model, significantly enhancing our interpretative capabilities beyond those offered by standard approaches. Our evaluations, utilizing the MNIST and Fashion MNIST datasets, demonstrate that our method performs comparably to traditional MLP-based models.
MedCodER: A Generative AI Assistant for Medical Coding
Venue: arXiv cs.CL 2024
Year: 2024

Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy text inputs, and the absence of supporting evidence annotations that justify code selection. Recent advancements in Generative Artificial Intelligence (AI) offer promising solutions to these challenges. In this work, we introduce MedCodER, a Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. MedCodER achieves a micro-F1 score of 0.60 on International Classification of Diseases (ICD) code prediction, significantly outperforming state-of-the-art methods. Additionally, we present a new dataset containing medical records annotated with disease diagnoses, ICD codes, and supporting evidence texts (https://doi.org/10.5281/zenodo.13308316). Ablation tests confirm that MedCodER's performance depends on the integration of each of its aforementioned components, as performance declines when these components are evaluated in isolation.
DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention
Venue: arXiv cs.NE 2024
Year: 2024

Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transformers that thrive in ultra-low power operations on neuromorphic hardware, however, without fully unlocking the potential of spiking neural networks. We introduce DS2TA, a Denoising Spiking transformer with attenuated SpatioTemporal Attention, designed specifically for vision applications. DS2TA introduces a new spiking attenuated spatiotemporal attention mechanism that considers input firing correlations occurring in both time and space, thereby fully harnessing the computational power of spiking neurons at the core of the transformer architecture. Importantly, DS2TA facilitates parameter-efficient spatiotemporal attention computation without introducing extra weights. DS2TA employs efficient hashmap-based nonlinear spiking attention denoisers to enhance the robustness and expressive power of spiking attention maps. DS2TA demonstrates state-of-the-art performances on several widely adopted static image and dynamic neuromorphic datasets. Operated over 4 time steps, DS2TA achieves 94.92% top-1 accuracy on CIFAR10 and 77.47% top-1 accuracy on CIFAR100, as well as 79.1% and 94.44% on CIFAR10-DVS and DVS-Gesture using 10 time steps.
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation
Venue: arXiv cs.CL 2024
Year: 2024

Recent studies show that text-to-image (T2I) models are vulnerable to adversarial attacks, especially with noun perturbations in text prompts. In this study, we investigate the impact of adversarial attacks on different POS tags within text prompts on the images generated by T2I models. We create a high-quality dataset for realistic POS tag token swapping and perform gradient-based attacks to find adversarial suffixes that mislead T2I models into generating images with altered tokens. Our empirical results show that the attack success rate (ASR) varies significantly among different POS tag categories, with nouns, proper nouns, and adjectives being the easiest to attack. We explore the mechanism behind the steering effect of adversarial suffixes, finding that the number of critical tokens and content fusion vary among POS tags, while features like suffix transferability are consistent across categories. We have made our implementation publicly available at - https://github.com/shahariar-shibli/Adversarial-Attack-on-POS-Tags.
Parallel Graph Drawing Algorithm for Bipartite Planar Graphs
Venue: arXiv cs.CG 2024
Year: 2024

We give a parallel $O(\log(n))$-time algorithm on a CRCW PRAM to assign vertical and horizontal segments to the vertices of any planar bipartite graph $G$ in the following manner: i) Two segments cannot share an interior point ii) Two segments intersect if and only if the corresponding vertices are adjacent, which uses a polynomial number of processors. In other words, represent vertices of a planar bipartite graph as parallel segments, and edges as intersection points between these segments. Note that two segments are not allowed to cross. Our method is based on a parallel algorithm for st-numbering which uses an ear decomposition search.
Preventing Rowhammer Exploits via Low-Cost Domain-Aware Memory Allocation
Venue: arXiv cs.CR 2024
Year: 2024

Rowhammer is a hardware security vulnerability at the heart of every system with modern DRAM-based memory. Despite its discovery a decade ago, comprehensive defenses remain elusive, while the probability of successful attacks grows with DRAM density. Hardware-based defenses have been ineffective, due to considerable cost, delays in commercial adoption, and attackers' repeated ability to circumvent them. Meanwhile, more flexible software-based solutions either incur substantial performance and memory capacity overheads, or offer limited forms of protection. Citadel is a new memory allocator design that prevents Rowhammer-initiated security exploits by addressing the vulnerability's root cause: physical adjacency of DRAM rows. Citadel enables creation of flexible security domains and isolates different domains in physically disjoint memory regions, guaranteeing security by design. On a server system, Citadel supports thousands of security domains at a modest 7.4% average memory overhead and no performance loss. In contrast, recent domain isolation schemes fail to support many workload scenarios due to excessive overheads, and incur 4--6x higher overheads for supported scenarios. As a software solution, Citadel offers readily deployable Rowhammer-aware isolation on legacy, current, and future systems.
VLMine: Long-Tail Data Mining with Vision Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Ensuring robust performance on long-tail examples is an important problem for many real-world applications of machine learning, such as autonomous driving. This work focuses on the problem of identifying rare examples within a corpus of unlabeled data. We propose a simple and scalable data mining approach that leverages the knowledge contained within a large vision language model (VLM). Our approach utilizes a VLM to summarize the content of an image into a set of keywords, and we identify rare examples based on keyword frequency. We find that the VLM offers a distinct signal for identifying long-tail examples when compared to conventional methods based on model uncertainty. Therefore, we propose a simple and general approach for integrating signals from multiple mining algorithms. We evaluate the proposed method on two diverse tasks: 2D image classification, in which inter-class variation is the primary source of data diversity, and on 3D object detection, where intra-class variation is the main concern. Furthermore, through the detection task, we demonstrate that the knowledge extracted from 2D images is transferable to the 3D domain. Our experiments consistently show large improvements (between 10\% and 50\%) over the baseline techniques on several representative benchmarks: ImageNet-LT, Places-LT, and the Waymo Open Dataset.
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA
Venue: arXiv cs.CL 2024
Year: 2024

Augmenting Large Language Models (LLMs) with information retrieval capabilities (i.e., Retrieval-Augmented Generation (RAG)) has proven beneficial for knowledge-intensive tasks. However, understanding users' contextual search intent when generating responses is an understudied topic for conversational question answering (QA). This conversational extension leads to additional concerns when compared to single-turn QA as it is more challenging for systems to comprehend conversational context and manage retrieved passages over multiple turns. In this work, we propose a method for enabling LLMs to decide when to retrieve in RAG settings given a conversational context. When retrieval is deemed necessary, the LLM then rewrites the conversation for passage retrieval and judges the relevance of returned passages before response generation. Operationally, we build on the single-turn SELF-RAG framework (Asai et al., 2023) and propose SELF-multi-RAG for conversational settings. SELF-multi-RAG demonstrates improved capabilities over single-turn variants with respect to retrieving relevant passages (by using summarized conversational context) and assessing the quality of generated responses. Experiments on three conversational QA datasets validate the enhanced response generation capabilities of SELF-multi-RAG, with improvements of ~13% measured by human annotation.
Ditto: Elastic Confidential VMs with Secure and Dynamic CPU Scaling
Venue: arXiv cs.CR 2024
Year: 2024

Confidential Virtual Machines (CVMs) are a type of VMbased Trusted Execution Environments (TEEs) designed to enhance the security of cloud-based VMs, safeguarding them even from malicious hypervisors. Although CVMs have been widely adopted by major cloud service providers, current CVM designs face significant challenges in runtime resource management due to their fixed capacities and lack of transparency. These limitations hamper efficient cloud resource management, leading to increased operational costs and reduced agility in responding to fluctuating workloads. This paper introduces a dynamic CPU resource management approach, featuring the novel concept of "Elastic CVM. This approach allows for hypervisor-assisted runtime adjustment of CPU resources using a specialized vCPU type, termed Worker vCPU. This new approach enhances CPU resource adaptability and operational efficiency without compromising security. Additionally, we introduce a Worker vCPU Abstraction Layer to simplify Worker vCPU deployment and management. To demonstrate the effectiveness of our approach, we have designed and implemented a serverless computing prototype platform, called Ditto. We show that Ditto significantly improves performance and efficiency through finergrain resource management. The concept of "Elastic CVM" and the Worker vCPU design not only optimize cloud resource utilization but also pave the way for more flexible and cost-effective confidential computing environments.
Analyzing Privacy Implications of Data Collection in Android Automotive OS
Venue: arXiv cs.CR 2024
Year: 2024

Modern vehicles have become sophisticated computation and sensor systems, as evidenced by advanced driver assistance systems, in-car infotainment, and autonomous driving capabilities. They collect and process vast amounts of data through various embedded subsystems. One significant player in this landscape is Android Automotive OS (AAOS), which has been integrated into over 100M vehicles and has become a dominant force in the in-vehicle infotainment market. With this extensive data collection, privacy has become increasingly crucial. The volume of data gathered by these systems raises questions about how this information is stored, used, and protected, making privacy a critical issue for manufacturers and consumers. However, very little has been done on vehicle data privacy. This paper focuses on the privacy implications of AAOS, examining the exact nature and scope of data collection and the corresponding privacy policies from the original equipment manufacturers (OEMs). We develop a novel automotive privacy analysis tool called PriDrive which employs three methodological approaches: network traffic inspection, and both static and dynamic analyses of Android images using rooted emulators from various OEMs. These methodologies are followed by an assessment of whether the collected data types were properly disclosed in OEMs and 3rd party apps' privacy policies (to identify any discrepancies or violations). Our evaluation on three different OEM platforms reveals that vehicle speed is collected at a sampling rate of roughly 25 Hz. Other properties such as model info, climate & AC, and seat data are collected in a batch 30 seconds into vehicle startup. In addition, several vehicle property types were collected without disclosure in their respective privacy policies. For example, OEM A's policies only covers 110 vehicle properties or 13.02% of the properties found in our static analysis.
Assessment of Submillimeter Precision via Structure from Motion Technique in Close-Range Capture Environments
Venue: arXiv cs.CV 2024
Year: 2024

Creating 3D models through the Structure from Motion technique is a recognized, efficient, cost-effective structural monitoring strategy. This technique is applied in several engineering fields, particularly for creating models of large structures from photographs taken a few tens of meters away. However, discussions about its usability and the procedures for conducting laboratory analysis, such as structural tests, are rarely addressed. This study investigates the potential of the SfM method to create submillimeter-quality models for structural tests, with short-distance captures. A series of experiments was carried out, with photographic captures at a 1-meter distance, using different quality settings: camera calibration model, Scale Bars dispersion, overlapping rates, and the use of vertical and oblique images. Employing a calibration model with images taken over a test board and a set of Scale Bars (SB) appropriately distributed over the test area, an overlap rate of 80 percent, and the integration of vertical and oblique images, RMSE values of approximately 0.1 mm were obtained. This result indicates the potential application of the technique for 3D modeling with submillimeter positional quality, as required for structural tests in laboratory environments.