OpenRaiser/Intern-Atlas huggingface dataset:
UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style
Venue: arXiv cs.IR 2024
Year: 2024

The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users within massive data has become a challenging and popular task. However, existing anime recommendation systems have focused on text features but still need to integrate image features. In addition, most multi-modal recommendation research is constrained by tightly coupled datasets, limiting its applicability to anime illustrations. We propose the User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps. In the feature extract phase, for image features, we are the first to combine image painting style features with semantic features to construct a dual-output image encoder for enhancing representation. For text features, we obtain text embeddings based on fine-tuning Sentence-Transformers by incorporating domain knowledge that composes a variety of domain text pairs from multilingual mappings, entity relationships, and term explanation perspectives, respectively. In the multi-modal fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism to weight multi-modal features dynamically according to user features at the interaction level and employ the DCN-V2 module to model bounded-degree multi-modal crosses effectively. UMAIR-FPS surpasses the stat-of-the-art baselines on large real-world datasets, demonstrating substantial performance enhancements.
Darwin Turing Dawkins: Building a General Theory of Evolution
Venue: arXiv cs.GL 2024
Year: 2024

Living things, computers, societies, and even books are part of a grand evolutionary struggle to survive. That struggle shapes nature, nations, religions, art, science, and you. What you think, feel, and do is determined by it. Darwinian evolution does not apply solely to the genes that are stored in DNA. Using the insights of Alan Turing and Richard Dawkins, we will see that it also applies to the memes we store in our brains and the information we store in our computers. The next time you run for president, fight a war, or just deal with the ordinary problems humans are heir to, perhaps this book will be of use. If you want to understand why and when you will die, or if you want to achieve greatness this book may help. If you are concerned about where the computer revolution is headed, this book may provide some answers.
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Instruction-tuning language models has become a crucial step in aligning them for general use. Typically, this process involves extensive training on large datasets, incurring high training costs. In this paper, we introduce a novel training data selection based on the learning percentage of the samples. We assert that current language models possess the capability to autonomously select high-quality training data, leading to comparable or improved performance compared to training on the entire dataset. Our experiments span different-sized models, revealing that this characteristic holds for models ranging from 1B (small) to 13B (large) in size. Moreover, we demonstrate an interesting finding that the data hardness transfers across model sizes, and a smaller 350M model can effectively curate high-quality training data with hard samples for a larger 13B model, resulting in an equally or superior instruction-tuned model compared to training on the complete dataset. Utilizing open-sourced OPT and Llama-2 models up to 13B in size, two publicly available instruction-tuning training datasets and evaluated by both automatic metrics & humans, our paper introduces a novel approach to training data selection, showcasing a more efficient alternative.
Incremental Sequence Labeling: A Tale of Two Shifts
Venue: arXiv cs.CL 2024
Year: 2024

The incremental sequence labeling task involves continuously learning new classes over time while retaining knowledge of the previous ones. Our investigation identifies two significant semantic shifts: E2O (where the model mislabels an old entity as a non-entity) and O2E (where the model labels a non-entity or old entity as a new entity). Previous research has predominantly focused on addressing the E2O problem, neglecting the O2E issue. This negligence results in a model bias towards classifying new data samples as belonging to the new class during the learning process. To address these challenges, we propose a novel framework, Incremental Sequential Labeling without Semantic Shifts (IS3). Motivated by the identified semantic shifts (E2O and O2E), IS3 aims to mitigate catastrophic forgetting in models. As for the E2O problem, we use knowledge distillation to maintain the model's discriminative ability for old entities. Simultaneously, to tackle the O2E problem, we alleviate the model's bias towards new entities through debiased loss and optimization levels. Our experimental evaluation, conducted on three datasets with various incremental settings, demonstrates the superior performance of IS3 compared to the previous state-of-the-art method by a significant margin.The data, code, and scripts are publicly available at https://github.com/zzz47zzz/codebase-for-incremental-learning-with-llm.
Steering Conversational Large Language Models for Long Emotional Support Conversations
Venue: arXiv cs.CL 2024
Year: 2024

In this study, we address the challenge of enabling large language models (LLMs) to consistently adhere to emotional support strategies in extended conversations. We focus on the steerability of the Llama-2 and Llama-3 suite of models, examining their ability to maintain these strategies throughout interactions. To assess this, we introduce the Strategy Relevant Attention (SRA) metric, which quantifies the model's adherence to the prompted strategy through attention maps. To facilitate our study, we create a strategy-conditioned synthetic conversational dataset derived from the ESConv dataset. We also propose various baselines informed by our proposed SRA metric to address the challenge and propose a fine-tuned model that significantly enhances the steerability of the base model in following the strategy throughout the conversation. The code and data are publicly available on our GitHub.
Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration
Venue: arXiv cs.CV 2024
Year: 2024

The rising global prevalence of skin conditions, some of which can escalate to life-threatening stages if not timely diagnosed and treated, presents a significant healthcare challenge. This issue is particularly acute in remote areas where limited access to healthcare often results in delayed treatment, allowing skin diseases to advance to more critical stages. One of the primary challenges in diagnosing skin diseases is their low inter-class variations, as many exhibit similar visual characteristics, making accurate classification challenging. This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. This approach mimics the diagnostic process employed by medical professionals. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. This component plays a crucial role in refining visual details and enhancing feature extraction, leading to improved differentiation between classes and, consequently, elevating the overall effectiveness of the model. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures. The results of these experiments not only demonstrate the effectiveness of the proposed method but also its potential applicability under-resourced healthcare environments.
Can Transformers Predict Vibrations?
Venue: arXiv cs.LG 2024
Year: 2024

Highly accurate time-series vibration prediction is an important research issue for electric vehicles (EVs). EVs often experience vibrations when driving on rough terrains, known as torsional resonance. This resonance, caused by the interaction between motor and tire vibrations, puts excessive loads on the vehicle's drive shaft. However, current damping technologies only detect resonance after the vibration amplitude of the drive shaft torque reaches a certain threshold, leading to significant loads on the shaft at the time of detection. In this study, we propose a novel approach to address this issue by introducing Resoformer, a transformer-based model for predicting torsional resonance. Resoformer utilizes time-series of the motor rotation speed as input and predicts the amplitude of torsional vibration at a specified quantile occurring in the shaft after the input series. By calculating the attention between recursive and convolutional features extracted from the measured data points, Resoformer improves the accuracy of vibration forecasting. To evaluate the model, we use a vibration dataset called VIBES (Dataset for Forecasting Vibration Transition in EVs), consisting of 2,600 simulator-generated vibration sequences. Our experiments, conducted on strong baselines built on the VIBES dataset, demonstrate that Resoformer achieves state-of-the-art results. In conclusion, our study answers the question "Can Transformers Forecast Vibrations?" While traditional transformer architectures show low performance in forecasting torsional resonance waves, our findings indicate that combining recurrent neural network and temporal convolutional network using the transformer architecture improves the accuracy of long-term vibration forecasting.
Strong hallucinations from negation and how to fix them
Venue: arXiv cs.CL 2024
Year: 2024

Despite great performance on many tasks, language models (LMs) still struggle with reasoning, sometimes providing responses that cannot possibly be true because they stem from logical incoherence. We call such responses \textit{strong hallucinations} and prove that they follow from an LM's computation of its internal representations for logical operators and outputs from those representations. Focusing on negation, we provide a novel solution in which negation is treated not as another element of a latent representation, but as \textit{an operation over an LM's latent representations that constrains how they may evolve}. We show that our approach improves model performance in cloze prompting and natural language inference tasks with negation without requiring training on sparse negative data.
Precise Hybrid-Actuation Robotic Fiber for Enhanced Cervical Disease Treatment
Venue: arXiv cs.RO 2024
Year: 2024

Treatment for high-grade precancerous cervical lesions and early-stage cancers, mainly affecting women of reproductive age, often involves fertility-sparing treatment methods. Commonly used local treatments for cervical precancers have shown the risk of leaving a positive cancer margin and engendering subsequent complications according to the precision and depth of excision. An intra-operative device that allows the careful excision of the disease while conserving healthy cervical tissue would potentially enhance such treatment. In this study, we developed a polymer-based robotic fiber measuring 150 mm in length and 1.7 mm in diameter, fabricated using a highly scalable fiber drawing technique. This robotic fiber utilizes a hybrid actuation mechanism, combining electrothermal and tendon-driven actuation mechanisms, thus enabling a maximum motion range of 46 mm from its origin with a sub-100 μm motion precision. We also developed control algorithms for the actuation methods of this robotic fiber, including predefined path control and telemanipulation, enabling coarse positioning of the fiber tip to the target area followed by a precise scan. The combination of a surgical laser fiber with the robotic fiber allows for high-precision surgical ablation. Additionally, we conducted experiments using a cervical phantom that demonstrated the robotic fiber's ability to access and perform high-precision scans, highlighting its potential for cervical disease treatments and improvement of oncological outcomes.
Deterministic Leader Election for Stationary Programmable Matter with Common Direction
Venue: arXiv cs.DC 2024
Year: 2024

Leader Election is an important primitive for programmable matter, since it is often an intermediate step for the solution of more complex problems. Although the leader election problem itself is well studied even in the specific context of programmable matter systems, research on fault tolerant approaches is more limited. We consider the problem in the previously studied Amoebot model on a triangular grid, when the configuration is connected but contains nodes the particles cannot move to (e.g., obstacles). We assume that particles agree on a common direction (i.e., the horizontal axis) but do not have chirality (i.e., they do not agree on the other two directions of the triangular grid). We begin by showing that an election algorithm with explicit termination is not possible in this case, but we provide an implicitly terminating algorithm that elects a unique leader without requiring any movement. These results are in contrast to those in the more common model with chirality but no agreement on directions, where explicit termination is always possible but the number of elected leaders depends on the symmetry of the initial configuration. Solving the problem under the assumption of one common direction allows for a unique leader to be elected in a stationary and deterministic way, which until now was only possible for simply connected configurations under a sequential scheduler.
Credential Control Balance: A Universal Blockchain Account Model Abstract From Bank to Bitcoin, Ethereum External Owned Account and Account Abstraction
Venue: arXiv cs.CR 2024
Year: 2024

Blockchain market value peaked at $3 trillion, fell to $1 trillion, then recovered to $1.5 trillion and is rising again. Blockchain accounts secure most on-chain assets in this huge market (Web-12). This paper initiates a universal blockchain account model from a comprehensive review of blockchain account development, encompassing both academic and industry perspectives. This paper uses a model analysis method to analysis the account progress and create high level new account model. And it uses systematic literature review method to search, filter, analysis and evaluate the papers about account models and analyzes related technology trade-offs. Searching with key words: blockchain, account, private key and security in WOS, Scopus and Bitcoin and Ethereum community repositories, this research provides in-depth insights into the design and evaluation of account models, from traditional bank accounts to Bitcoin, EVM-adaptable, and abstraction accounts. Through data-driven comparisons of account models (security, cost, adoption), this study also explores future directions and provides an overview of cross-model account theory, guiding further blockchain research. This paper leaves deeper dives into model change drivers, application technology advancements.
Fine Tuning Named Entity Extraction Models for the Fantasy Domain
Venue: arXiv cs.CL 2024
Year: 2024

Named Entity Recognition (NER) is a sequence classification Natural Language Processing task where entities are identified in the text and classified into predefined categories. It acts as a foundation for most information extraction systems. Dungeons and Dragons (D&D) is an open-ended tabletop fantasy game with its own diverse lore. DnD entities are domain-specific and are thus unrecognizable by even the state-of-the-art off-the-shelf NER systems as the NER systems are trained on general data for pre-defined categories such as: person (PERS), location (LOC), organization (ORG), and miscellaneous (MISC). For meaningful extraction of information from fantasy text, the entities need to be classified into domain-specific entity categories as well as the models be fine-tuned on a domain-relevant corpus. This work uses available lore of monsters in the D&D domain to fine-tune Trankit, which is a prolific NER framework that uses a pre-trained model for NER. Upon this training, the system acquires the ability to extract monster names from relevant domain documents under a novel NER tag. This work compares the accuracy of the monster name identification against; the zero-shot Trankit model and two FLAIR models. The fine-tuned Trankit model achieves an 87.86% F1 score surpassing all the other considered models.
Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning
Venue: arXiv cs.CL 2024
Year: 2024

Demonstration ordering, which is an important strategy for in-context learning (ICL), can significantly affects the performance of large language models (LLMs). However, most of the current approaches of ordering require high computational costs to introduce the priori knowledge. In this paper, inspired by the human learning process, we propose a simple but effective demonstration ordering method for ICL, named the few-shot In-Context Curriculum Learning (ICCL). The ICCL implies gradually increasing the complexity of prompt demonstrations during the inference process. The difficulty can be assessed by human experts or LLMs-driven metrics, such as perplexity. Then we design extensive experiments to discuss the effectiveness of the ICCL at both corpus-level and instance-level. Moreover, we also investigate the formation mechanism of LLM's ICCL capability. Experimental results demonstrate that ICCL, developed during the instruction-tuning stage, is effective for representative open-source LLMs. To facilitate further research and applications by other scholars, we make the code publicly available.
Fitness-based Linkage Learning and Maximum-Clique Conditional Linkage Modelling for Gray-box Optimization with RV-GOMEA
Venue: arXiv cs.NE 2024
Year: 2024

For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can be excellently leveraged by the Real-Valued GOMEA (RV-GOMEA) that uses a linkage model to capture dependencies between problem variables. Recently, conditional linkage models were introduced for RV-GOMEA, expanding its state-of-the-art performance even to problems with overlapping dependencies. However, that work assumed that the dependency structure is known a priori. Fitness-based linkage learning techniques have previously been used to detect dependencies during optimization, but only for non-conditional linkage models. In this work, we combine fitness-based linkage learning and conditional linkage modelling in RV-GOMEA. In addition, we propose a new way to model overlapping dependencies in conditional linkage models to maximize the joint sampling of fully interdependent groups of variables. We compare the resulting novel variant of RV-GOMEA to other variants of RV-GOMEA and VkD-CMA on 12 problems with varying degree of overlapping dependencies. We find that the new RV-GOMEA not only performs best on most problems, also the overhead of learning the conditional linkage models during optimization is often negligible.
Distillation Enhanced Generative Retrieval
Venue: arXiv cs.CL 2024
Year: 2024

Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generative language models, distinct from traditional sparse or dense retrieval methods. In this work, we identify a viable direction to further enhance generative retrieval via distillation and propose a feasible framework, named DGR. DGR utilizes sophisticated ranking models, such as the cross-encoder, in a teacher role to supply a passage rank list, which captures the varying relevance degrees of passages instead of binary hard labels; subsequently, DGR employs a specially designed distilled RankNet loss to optimize the generative retrieval model, considering the passage rank order provided by the teacher model as labels. This framework only requires an additional distillation step to enhance current generative retrieval systems and does not add any burden to the inference stage. We conduct experiments on four public datasets, and the results indicate that DGR achieves state-of-the-art performance among the generative retrieval methods. Additionally, DGR demonstrates exceptional robustness and generalizability with various teacher models and distillation losses.
On Permutation Selectors and their Applications in Ad-Hoc Radio Networks Protocols
Venue: arXiv cs.DS 2024
Year: 2024

Selective families of sets, or selectors, are combinatorial tools used to "isolate" individual members of sets from some set family. Given a set $X$ and an element $x\in X$, to isolate $x$ from $X$, at least one of the sets in the selector must intersect $X$ on exactly $x$. We study (k,N)-permutation selectors which have the property that they can isolate each element of each $k$-element subset of $\{0,1,...,N-1\}$ in each possible order. These selectors can be used in protocols for ad-hoc radio networks to more efficiently disseminate information along multiple hops. In 2004, Gasieniec, Radzik and Xin gave a construction of a (k,N)-permutation selector of size $O(k^2\log^3 N)$. This paper improves this by providing a probabilistic construction of a (k,N)-permutation selector of size $O(k^2\log N)$. Remarkably, this matches the asymptotic bound for standard strong (k,N)-selectors, that isolate each element of each set of size $k$, but with no restriction on the order. We then show that the use of our (k,N)-permutation selector improves the best running time for gossiping in ad-hoc radio networks by a poly-logarithmic factor.
Insights into mobile health application market via a content analysis of marketplace data with machine learning
Venue: arXiv cs.HC 2024
Year: 2024

Background Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come. Objective This study aims to investigate the current landscape of smartphone apps that focus on improving and sustaining health and wellbeing. Understanding the categories that popular apps focus on and the relevant features provided to users, which lead to higher user scores and downloads will offer insights to enable higher adoption in the general populace. This study on 1,000 mobile health applications aims to shed light on the reasons why particular apps are liked and adopted while many are not. Methods User-generated data (i.e. review scores) and company-generated data (i.e. app descriptions) were collected from app marketplaces and manually coded and categorized by two researchers. For analysis, Artificial Neural Networks, Random Forest and Naïve Bayes Artificial Intelligence algorithms were used. Results The analysis led to features that attracted more download behavior and higher user scores. The findings suggest that apps that mention a privacy policy or provide videos in description lead to higher user scores, whereas free apps with in-app purchase possibilities, social networking and sharing features and feedback mechanisms lead to higher number of downloads. Moreover, differences in user scores and the total number of downloads are detected in distinct subcategories of mobile health apps. Conclusion This study contributes to the current knowledge of m-health application use by reviewing mobile health applications using content analysis and machine learning algorithms. The content analysis adds significant value by providing classification, keywords and factors that influence download behavior and user scores in a m-health context.