OpenRaiser/Intern-Atlas huggingface dataset:
Color Maker: a Mixed-Initiative Approach to Creating Accessible Color Maps
Venue: arXiv cs.HC 2024
Year: 2024

Quantitative data is frequently represented using color, yet designing effective color mappings is a challenging task, requiring one to balance perceptual standards with personal color preference. Current design tools either overwhelm novices with complexity or offer limited customization options. We present ColorMaker, a mixed-initiative approach for creating colormaps. ColorMaker combines fluid user interaction with real-time optimization to generate smooth, continuous color ramps. Users specify their loose color preferences while leaving the algorithm to generate precise color sequences, meeting both designer needs and established guidelines. ColorMaker can create new colormaps, including designs accessible for people with color-vision deficiencies, starting from scratch or with only partial input, thus supporting ideation and iterative refinement. We show that our approach can generate designs with similar or superior perceptual characteristics to standard colormaps. A user study demonstrates how designers of varying skill levels can use this tool to create custom, high-quality colormaps. ColorMaker is available at https://colormaker.org
Explicit Subcodes of Reed-Solomon Codes that Efficiently Achieve List Decoding Capacity
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we introduce a novel explicit family of subcodes of Reed-Solomon (RS) codes that efficiently achieve list decoding capacity with a constant output list size. Our approach builds upon the idea of large linear subcodes of RS codes evaluated on a subfield, similar to the method employed by Guruswami and Xing (STOC 2013). However, our approach diverges by leveraging the idea of {\it permuted product codes}, thereby simplifying the construction by avoiding the need of {\it subspace designs}. Specifically, the codes are constructed by initially forming the tensor product of two RS codes with carefully selected evaluation sets, followed by specific cyclic shifts to the codeword rows. This process results in each codeword column being treated as an individual coordinate, reminiscent of prior capacity-achieving codes, such as folded RS codes and univariate multiplicity codes. This construction is easily shown to be a subcode of an interleaved RS code, equivalently, an RS code evaluated on a subfield. Alternatively, the codes can be constructed by the evaluation of bivariate polynomials over orbits generated by \emph{two} affine transformations with coprime orders, extending the earlier use of a single affine transformation in folded RS codes and the recent affine folded RS codes introduced by Bhandari {\it et al.} (IEEE T-IT, Feb.~2024). While our codes require large, yet constant characteristic, the two affine transformations facilitate achieving code length equal to the field size, without the restriction of the field being prime, contrasting with univariate multiplicity codes.
Chaotic Encryption Applied to Optical Ethernet in Industrial Control Systems
Venue: arXiv cs.CR 2024
Year: 2024

In the past decades, Ethernet has become an alternative technology for the field buses traditionally used in industrial control systems and distributed measurement systems. Among different transmission media in Ethernet standards, optical fiber provides the best bandwidth, excellent immunity to electromagnetic interference, and less signal loses than other wired media. Due to the absence of a standard that provides security at the physical layer of optical Ethernet links, the main motivation of this paper is to propose and implement the necessary modifications to introduce encryption in Ethernet 1000Base-X standard. This has consisted of symmetric streaming encryption of the 8b10b symbols flow at physical coding sublayer level, thanks to a keystream generator based on chaotic algorithm. The overall system has been implemented and tested in an field programmable gate array and Ethernet traffic has been encrypted and transmitted over an optical link. The experimental results show that it is possible to cipher traffic at this level and hide the complete Ethernet traffic pattern from passive eavesdroppers. In addition, no space overhead is introduced in data frames during encryption, achieving the maximum throughput.
Accelerating Material Property Prediction using Generically Complete Isometry Invariants
Venue: arXiv cs.LG 2024
Year: 2024

Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these algorithms is the representation used for a periodic crystal. While similar objects like molecules and proteins have a finite number of atoms and their representation can be built based upon a finite point cloud interpretation, periodic crystals are unbounded in size, making their representation more challenging. In the present work, we adapt the Pointwise Distance Distribution (PDD), a continuous and generically complete isometry invariant for periodic point sets, as a representation for our learning algorithm. The PDD distinguished all (more than 660 thousand) periodic crystals in the Cambridge Structural Database as purely periodic sets of points without atomic types. We develop a transformer model with a modified self-attention mechanism that combines PDD with compositional information via a spatial encoding method. This model is tested on the crystals of the Materials Project and Jarvis-DFT databases and shown to produce accuracy on par with state-of-the-art methods while being several times faster in both training and prediction time.
Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game
Venue: arXiv cs.LG 2024
Year: 2024

Players in pricing games with complex structures are increasingly adopting artificial intelligence (AI) aided learning algorithms to make pricing decisions for maximizing profits. This is raising concern for the antitrust agencies as the practice of using AI may promote tacit algorithmic collusion among otherwise independent players. Recent studies of games in canonical forms have shown contrasting claims ranging from none to a high level of tacit collusion among AI-guided players. In this paper, we examine the concern for tacit collusion by considering a practical game where EV charging hubs compete by dynamically varying their prices. Such a game is likely to be commonplace in the near future as EV adoption grows in all sectors of transportation. The hubs source power from the day-ahead (DA) and real-time (RT) electricity markets as well as from in-house battery storage systems. Their goal is to maximize profits via pricing and efficiently managing the cost of power usage. To aid our examination, we develop a two-step data-driven methodology. The first step obtains the DA commitment by solving a stochastic model. The second step generates the pricing strategies by solving a competitive Markov decision process model using a multi-agent deep reinforcement learning (MADRL) framework. We evaluate the resulting pricing strategies using an index for the level of tacit algorithmic collusion. An index value of zero indicates no collusion (perfect competition) and one indicates full collusion (monopolistic behavior). Results from our numerical case study yield collusion index values between 0.14 and 0.45, suggesting a low to moderate level of collusion.
Sensor-Based Data Acquisition via Ubiquitous Device to Detect Muscle Strength Training Activities
Venue: arXiv cs.HC 2024
Year: 2024

Maintaining a high quality of life through physical activities (PA) to prevent health decline is crucial. However, the relationship between individuals health status, PA preferences, and motion factors is complex. PA discussions consistently show a positive correlation with healthy aging experiences, but no explicit relation to specific types of musculoskeletal exercises. Taking advantage of the increasingly widespread existence of smartphones, especially in Indonesia, this research utilizes embedded sensors for Human Activity Recognition (HAR). Based on 25 participants data, performing nine types of selected motion, this study has successfully identified important sensor attributes that play important roles in the right and left hands for muscle strength motions as the basis for developing machine learning models with the LSTM algorithm.
Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness
Venue: arXiv cs.CR 2024
Year: 2024

Knowledge sharing about emerging threats is crucial in the rapidly advancing field of cybersecurity and forms the foundation of Cyber Threat Intelligence (CTI). In this context, Large Language Models are becoming increasingly significant in the field of cybersecurity, presenting a wide range of opportunities. This study surveys the performance of ChatGPT, GPT4all, Dolly, Stanford Alpaca, Alpaca-LoRA, Falcon, and Vicuna chatbots in binary classification and Named Entity Recognition (NER) tasks performed using Open Source INTelligence (OSINT). We utilize well-established data collected in previous research from Twitter to assess the competitiveness of these chatbots when compared to specialized models trained for those tasks. In binary classification experiments, Chatbot GPT-4 as a commercial model achieved an acceptable F1 score of 0.94, and the open-source GPT4all model achieved an F1 score of 0.90. However, concerning cybersecurity entity recognition, all evaluated chatbots have limitations and are less effective. This study demonstrates the capability of chatbots for OSINT binary classification and shows that they require further improvement in NER to effectively replace specially trained models. Our results shed light on the limitations of the LLM chatbots when compared to specialized models, and can help researchers improve chatbots technology with the objective to reduce the required effort to integrate machine learning in OSINT-based CTI tools.
Chaotic Encryption for 10-Gb Ethernet Optical Links
Venue: arXiv cs.CR 2024
Year: 2024

In this paper, a new physical layer encryption method for optical 10-Gb Ethernet links is proposed. Necessary modifications to introduce encryption in Ethernet 10GBase-R standard have been considered. This security enhancement has consisted of a symmetric streaming encryption of the 64b/66b data flow at physical coding sublayer level thanks to two keystream generators based on a chaotic algorithm. The overall system has been implemented and tested in a field programmable gate array. Ethernet traffic has been encrypted, transmitted, and decrypted over a multimode optical link. Experimental results are analyzed concluding that it is possible to cipher traffic at this level and hide the complete Ethernet traffic pattern from any passive eavesdropper. In addition, no overhead is introduced during encryption, getting no losses in the total throughput.
AMuSE: Adaptive Multimodal Analysis for Speaker Emotion Recognition in Group Conversations
Venue: arXiv cs.SD 2024
Year: 2024

Analyzing individual emotions during group conversation is crucial in developing intelligent agents capable of natural human-machine interaction. While reliable emotion recognition techniques depend on different modalities (text, audio, video), the inherent heterogeneity between these modalities and the dynamic cross-modal interactions influenced by an individual's unique behavioral patterns make the task of emotion recognition very challenging. This difficulty is compounded in group settings, where the emotion and its temporal evolution are not only influenced by the individual but also by external contexts like audience reaction and context of the ongoing conversation. To meet this challenge, we propose a Multimodal Attention Network that captures cross-modal interactions at various levels of spatial abstraction by jointly learning its interactive bunch of mode-specific Peripheral and Central networks. The proposed MAN injects cross-modal attention via its Peripheral key-value pairs within each layer of a mode-specific Central query network. The resulting cross-attended mode-specific descriptors are then combined using an Adaptive Fusion technique that enables the model to integrate the discriminative and complementary mode-specific data patterns within an instance-specific multimodal descriptor. Given a dialogue represented by a sequence of utterances, the proposed AMuSE model condenses both spatial and temporal features into two dense descriptors: speaker-level and utterance-level. This helps not only in delivering better classification performance (3-5% improvement in Weighted-F1 and 5-7% improvement in Accuracy) in large-scale public datasets but also helps the users in understanding the reasoning behind each emotion prediction made by the model via its Multimodal Explainability Visualization module.
SAM-based instance segmentation models for the automation of structural damage detection
Venue: arXiv cs.CV 2024
Year: 2024

Automating visual inspection for capturing defects based on civil structures appearance is crucial due to its currently labour-intensive and time-consuming nature. An important aspect of automated inspection is image acquisition, which is rapid and cost-effective considering the pervasive developments in both software and hardware computing in recent years. Previous studies largely focused on concrete and asphalt, with less attention to masonry cracks. The latter also lacks publicly available datasets. In this paper, we first present a corresponding data set for instance segmentation with 1,300 annotated images (640 pixels x 640 pixels), named as MCrack1300, covering bricks, broken bricks, and cracks. We then test several leading algorithms for benchmarking, including the latest large-scale model, the prompt-based Segment Anything Model (SAM). We fine-tune the encoder using Low-Rank Adaptation (LoRA) and proposed two novel methods for automation of SAM execution. The first method involves abandoning the prompt encoder and connecting the SAM encoder to other decoders, while the second method introduces a learnable self-generating prompter. In order to ensure the seamless integration of the two proposed methods with SAM encoder section, we redesign the feature extractor. Both proposed methods exceed state-of-the-art performance, surpassing the best benchmark by approximately 3% for all classes and around 6% for cracks specifically. Based on successful detection, we propose a method based on a monocular camera and the Hough Line Transform to automatically transform images into orthographic projection maps. By incorporating known real sizes of brick units, we accurately estimate crack dimensions, with the results differing by less than 10% from those obtained by laser scanning. Overall, we address important research gaps in automated masonry crack detection and size estimation.
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Recent proprietary large language models (LLMs), such as GPT-4, have achieved a milestone in tackling diverse challenges in the biomedical domain, ranging from multiple-choice questions to long-form generations. To address challenges that still cannot be handled with the encoded knowledge of LLMs, various retrieval-augmented generation (RAG) methods have been developed by searching documents from the knowledge corpus and appending them unconditionally or selectively to the input of LLMs for generation. However, when applying existing methods to different domain-specific problems, poor generalization becomes apparent, leading to fetching incorrect documents or making inaccurate judgments. In this paper, we introduce Self-BioRAG, a framework reliable for biomedical text that specializes in generating explanations, retrieving domain-specific documents, and self-reflecting generated responses. We utilize 84k filtered biomedical instruction sets to train Self-BioRAG that can assess its generated explanations with customized reflective tokens. Our work proves that domain-specific components, such as a retriever, domain-related document corpus, and instruction sets are necessary for adhering to domain-related instructions. Using three major medical question-answering benchmark datasets, experimental results of Self-BioRAG demonstrate significant performance gains by achieving a 7.2% absolute improvement on average over the state-of-the-art open-foundation model with a parameter size of 7B or less. Overall, we analyze that Self-BioRAG finds the clues in the question, retrieves relevant documents if needed, and understands how to answer with information from retrieved documents and encoded knowledge as a medical expert does. We release our data and code for training our framework components and model weights (7B and 13B) to enhance capabilities in biomedical and clinical domains.
FabHacks: Transform Everyday Objects into Functional Fixtures
Venue: arXiv cs.GR 2024
Year: 2024

Storage, organizing, and decorating are an important part of home design. While one can buy commercial items for many of these tasks, this can be costly, and re-use is more sustainable. An alternative is a "home hack", a functional assembly that can be constructed from existing household items. However, coming up with such hacks requires combining objects to make a physically valid design, which might be difficult to test if they are large, require nailing or screwing something to the wall, or the designer has mobility limitations. In this work, we present a design and visualization system for creating workable functional assemblies, FabHacks, which is based on a solver-aided domain-specific language (S-DSL) FabHaL. By analyzing existing home hacks shared online, we create a design abstraction for connecting household items using predefined types of connections. We provide a UI for FabHaL that can be used to design assemblies that fulfill a given specification. Our system leverages a physics-based solver that takes an assembly design and finds its expected physical configuration. Our validation includes a user study showing that users can create assemblies successfully using our UI and explore a range of designs.
Optimal Sparse Survival Trees
Venue: arXiv cs.LG 2024
Year: 2024

Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for survival analysis due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds.
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
Venue: arXiv cs.CR 2024
Year: 2024

In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security. Decision-based attacks, which only require feedback on the decision of a model rather than detailed probabilities or scores, are particularly insidious and difficult to defend against. This work introduces L-AutoDA (Large Language Model-based Automated Decision-based Adversarial Attacks), a novel approach leveraging the generative capabilities of Large Language Models (LLMs) to automate the design of these attacks. By iteratively interacting with LLMs in an evolutionary framework, L-AutoDA automatically designs competitive attack algorithms efficiently without much human effort. We demonstrate the efficacy of L-AutoDA on CIFAR-10 dataset, showing significant improvements over baseline methods in both success rate and computational efficiency. Our findings underscore the potential of language models as tools for adversarial attack generation and highlight new avenues for the development of robust AI systems.
A Comprehensive Survey of Compression Algorithms for Language Models
Venue: arXiv cs.CL 2024
Year: 2024

How can we compress language models without sacrificing accuracy? The number of compression algorithms for language models is rapidly growing to benefit from remarkable advances of recent language models without side effects due to the gigantic size of language models, such as increased carbon emissions and expensive maintenance fees. While numerous compression algorithms have shown remarkable progress in compressing language models, it ironically becomes challenging to capture emerging trends and identify the fundamental concepts underlying them due to the excessive number of algorithms. In this paper, we survey and summarize diverse compression algorithms including pruning, quantization, knowledge distillation, low-rank approximation, parameter sharing, and efficient architecture design. We not only summarize the overall trend of diverse compression algorithms but also select representative algorithms and provide in-depth analyses of them. We discuss the value of each category of compression algorithms, and the desired properties of low-cost compression algorithms which have a significant impact due to the emergence of large language models. Finally, we introduce promising future research topics based on our survey results.
The Capacity of the Weighted Read Channel
Venue: arXiv cs.IT 2024
Year: 2024

One of the primary sequencing methods gaining prominence in DNA storage is nanopore sequencing, attributed to various factors. In this work, we consider a simplified model of the sequencer, characterized as a channel. This channel takes a sequence and processes it using a sliding window of length $\ell$, shifting the window by $δ$ characters each time. The output of this channel, which we refer to as the read vector, is a vector containing the sums of the entries in each of the windows. The capacity of the channel is defined as the maximal information rate of the channel. Previous works have already revealed capacity values for certain parameters $\ell$ and $δ$. In this work, we show that when $δ< \ell < 2δ$, the capacity value is given by $\frac{1}δ\log_2 \frac{1}{2}(\ell+1+ \sqrt{(\ell+1)^2 - 4(\ell - δ)(\ell-δ+1)})$. Additionally, we construct an upper bound when $2δ< \ell$. Finally, we extend the model to the two-dimensional case and present several results on its capacity.
Indexing Portuguese NLP Resources with PT-Pump-Up
Venue: arXiv cs.CL 2024
Year: 2024

The recent advances in natural language processing (NLP) are linked to training processes that require vast amounts of corpora. Access to this data is commonly not a trivial process due to resource dispersion and the need to maintain these infrastructures online and up-to-date. New developments in NLP are often compromised due to the scarcity of data or lack of a shared repository that works as an entry point to the community. This is especially true in low and mid-resource languages, such as Portuguese, which lack data and proper resource management infrastructures. In this work, we propose PT-Pump-Up, a set of tools that aim to reduce resource dispersion and improve the accessibility to Portuguese NLP resources. Our proposal is divided into four software components: a) a web platform to list the available resources; b) a client-side Python package to simplify the loading of Portuguese NLP resources; c) an administrative Python package to manage the platform and d) a public GitHub repository to foster future collaboration and contributions. All four components are accessible using: https://linktr.ee/pt_pump_up