OpenRaiser/Intern-Atlas huggingface dataset:
Transformation of the discrete logarithm problem over $\mathbb F_{2^n}$ to the QUBO problem using normal bases
Venue: arXiv cs.CR 2024
Year: 2024

Quantum computations are very important branch of modern cryptology. According to the number of working physical qubits available in general-purpose quantum computers and in quantum annealers, there is no coincidence, that nowadays quantum annealers allow to solve larger problems. In this paper we focus on solving discrete logarithm problem (DLP) over binary fields using quantum annealing. It is worth to note, that however solving DLP over prime fields using quantum annealing has been considered before, no author, until now, has considered DLP over binary fields using quantum annealing. Therefore, in this paper, we aim to bridge this gap. We present a polynomial transformation of the discrete logarithm problem over binary fields to the Quadratic Unconstrained Binary Optimization (QUBO) problem, using approximately $3n^2$ logical variables for the binary field $\mathbb{F}_{2^n}$. In our estimations, we assume the existence of an optimal normal base of II type in the given fields. Such a QUBO instance can then be solved using quantum annealing.
Research on Predicting Public Opinion Event Heat Levels Based on Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, with the rapid development of large language models, serval models such as GPT-4o have demonstrated extraordinary capabilities, surpassing human performance in various language tasks. As a result, many researchers have begun exploring their potential applications in the field of public opinion analysis. This study proposes a novel large-language-models-based method for public opinion event heat level prediction. First, we preprocessed and classified 62,836 Chinese hot event data collected between July 2022 and December 2023. Then, based on each event's online dissemination heat index, we used the MiniBatchKMeans algorithm to automatically cluster the events and categorize them into four heat levels (ranging from low heat to very high heat). Next, we randomly selected 250 events from each heat level, totalling 1,000 events, to build the evaluation dataset. During the evaluation process, we employed various large language models to assess their accuracy in predicting event heat levels in two scenarios: without reference cases and with similar case references. The results showed that GPT-4o and DeepseekV2 performed the best in the latter case, achieving prediction accuracies of 41.4% and 41.5%, respectively. Although the overall prediction accuracy remains relatively low, it is worth noting that for low-heat (Level 1) events, the prediction accuracies of these two models reached 73.6% and 70.4%, respectively. Additionally, the prediction accuracy showed a downward trend from Level 1 to Level 4, which correlates with the uneven distribution of data across the heat levels in the actual dataset. This suggests that with the more robust dataset, public opinion event heat level prediction based on large language models will have significant research potential for the future.
Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we propose a framework to enhance the robustness of the neural models by mitigating the effects of process-induced and aging-related variations of analog computing components on the accuracy of the analog neural networks. We model these variations as the noise affecting the precision of the activations and introduce a denoising block inserted between selected layers of a pre-trained model. We demonstrate that training the denoising block significantly increases the model's robustness against various noise levels. To minimize the overhead associated with adding these blocks, we present an exploration algorithm to identify optimal insertion points for the denoising blocks. Additionally, we propose a specialized architecture to efficiently execute the denoising blocks, which can be integrated into mixed-signal accelerators. We evaluate the effectiveness of our approach using Deep Neural Network (DNN) models trained on the ImageNet and CIFAR-10 datasets. The results show that on average, by accepting 2.03% parameter count overhead, the accuracy drop due to the variations reduces from 31.7% to 1.15%.
Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecture
Venue: arXiv cs.AI 2024
Year: 2024

Integrating machine learning (ML) into customer service chatbots enhances their ability to understand and respond to user queries, ultimately improving service performance. However, they may appear artificial to some users and affecting customer experience. Hence, meticulous evaluation of ML models for each pipeline component is crucial for optimizing performance, though differences in functionalities can lead to unfair comparisons. In this paper, we present a tailored experimental evaluation approach for goal-oriented customer service chatbots with pipeline architecture, focusing on three key components: Natural Language Understanding (NLU), dialogue management (DM), and Natural Language Generation (NLG). Our methodology emphasizes individual assessment to determine optimal ML models. Specifically, we focus on optimizing hyperparameters and evaluating candidate models for NLU (utilizing BERT and LSTM), DM (employing DQN and DDQN), and NLG (leveraging GPT-2 and DialoGPT). The results show that for the NLU component, BERT excelled in intent detection whereas LSTM was superior for slot filling. For the DM component, the DDQN model outperformed DQN by achieving fewer turns, higher rewards, as well as greater success rates. For NLG, the large language model GPT-2 surpassed DialoGPT in BLEU, METEOR, and ROUGE metrics. These findings aim to provide a benchmark for future research in developing and optimizing customer service chatbots, offering valuable insights into model performance and optimal hyperparameters.
Optimal Tree-Based Mechanisms for Differentially Private Approximate CDFs
Venue: arXiv cs.IT 2024
Year: 2024

This paper considers the $\varepsilon$-differentially private (DP) release of an approximate cumulative distribution function (CDF) of the samples in a dataset. We assume that the true (approximate) CDF is obtained after lumping the data samples into a fixed number $K$ of bins. In this work, we extend the well-known binary tree mechanism to the class of \emph{level-uniform tree-based} mechanisms and identify $\varepsilon$-DP mechanisms that have a small $\ell_2$-error. We identify optimal or close-to-optimal tree structures when either of the parameters, which are the branching factors or the privacy budgets at each tree level, are given, and when the algorithm designer is free to choose both sets of parameters. Interestingly, when we allow the branching factors to take on real values, under certain mild restrictions, the optimal level-uniform tree-based mechanism is obtained by choosing equal branching factors \emph{independent} of $K$, and equal privacy budgets at all levels. Furthermore, for selected $K$ values, we explicitly identify the optimal \emph{integer} branching factors and tree height, assuming equal privacy budgets at all levels. Finally, we describe general strategies for improving the private CDF estimates further, by combining multiple noisy estimates and by post-processing the estimates for consistency.
Corpus-informed Retrieval Augmented Generation of Clarifying Questions
Venue: arXiv cs.IR 2024
Year: 2024

This study aims to develop models that generate corpus informed clarifying questions for web search, in a way that ensures the questions align with the available information in the retrieval corpus. We demonstrate the effectiveness of Retrieval Augmented Language Models (RAG) in this process, emphasising their ability to (i) jointly model the user query and retrieval corpus to pinpoint the uncertainty and ask for clarifications end-to-end and (ii) model more evidence documents, which can be used towards increasing the breadth of the questions asked. However, we observe that in current datasets search intents are largely unsupported by the corpus, which is problematic both for training and evaluation. This causes question generation models to ``hallucinate'', ie. suggest intents that are not in the corpus, which can have detrimental effects in performance. To address this, we propose dataset augmentation methods that align the ground truth clarifications with the retrieval corpus. Additionally, we explore techniques to enhance the relevance of the evidence pool during inference, but find that identifying ground truth intents within the corpus remains challenging. Our analysis suggests that this challenge is partly due to the bias of current datasets towards clarification taxonomies and calls for data that can support generating corpus-informed clarifications.
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Ensembling various LLMs to unlock their complementary potential and leverage their individual strengths is highly valuable. Previous studies typically focus on two main paradigms: sample-level and token-level ensembles. Sample-level ensemble methods either select or blend fully generated outputs, which hinders dynamic correction and enhancement of outputs during the generation process. On the other hand, token-level ensemble methods enable real-time correction through fine-grained ensemble at each generation step. However, the information carried by an individual token is quite limited, leading to suboptimal decisions at each step. To address these issues, we propose SweetSpan, a span-level ensemble method that effectively balances the need for real-time adjustments and the information required for accurate ensemble decisions. Our approach involves two key steps: First, we have each candidate model independently generate candidate spans based on the shared prefix. Second, we calculate perplexity scores to facilitate mutual evaluation among the candidate models and achieve robust span selection by filtering out unfaithful scores. To comprehensively evaluate ensemble methods, we propose a new challenging setting (ensemble models with significant performance gaps) in addition to the standard setting (ensemble the best-performing models) to assess the performance of model ensembles in more realistic scenarios. Experimental results in both standard and challenging settings across various language generation tasks demonstrate the effectiveness, robustness, and versatility of our approach compared with previous ensemble methods.
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction
Venue: arXiv cs.LG 2024
Year: 2024

Recent advancements in feature representation and dimension reduction have highlighted their crucial role in enhancing the efficacy of predictive modeling. This work introduces TemporalPaD, a novel end-to-end deep learning framework designed for temporal pattern datasets. TemporalPaD integrates reinforcement learning (RL) with neural networks to achieve concurrent feature representation and feature reduction. The framework consists of three cooperative modules: a Policy Module, a Representation Module, and a Classification Module, structured based on the Actor-Critic (AC) framework. The Policy Module, responsible for dimensionality reduction through RL, functions as the actor, while the Representation Module for feature extraction and the Classification Module collectively serve as the critic. We comprehensively evaluate TemporalPaD using 29 UCI datasets, a well-known benchmark for validating feature reduction algorithms, through 10 independent tests and 10-fold cross-validation. Additionally, given that TemporalPaD is specifically designed for time series data, we apply it to a real-world DNA classification problem involving enhancer category and enhancer strength. The results demonstrate that TemporalPaD is an efficient and effective framework for achieving feature reduction, applicable to both structured data and sequence datasets. The source code of the proposed TemporalPaD is freely available as supplementary material to this article and at http://www.healthinformaticslab.org/supp/.
Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques
Venue: arXiv cs.RO 2024
Year: 2024

Developing sophisticated control architectures has endowed robots, particularly humanoid robots, with numerous capabilities. However, tuning these architectures remains a challenging and time-consuming task that requires expert intervention. In this work, we propose a methodology to automatically tune the gains of all layers of a hierarchical control architecture for walking humanoids. We tested our methodology by employing different gradient-free optimization methods: Genetic Algorithm (GA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Evolution Strategy (ES), and Differential Evolution (DE). We validated the parameter found both in simulation and on the real ergoCub humanoid robot. Our results show that GA achieves the fastest convergence (10 x 10^3 function evaluations vs 25 x 10^3 needed by the other algorithms) and 100% success rate in completing the task both in simulation and when transferred on the real robotic platform. These findings highlight the potential of our proposed method to automate the tuning process, reducing the need for manual intervention.
Image-guided topic modeling for interpretable privacy classification
Venue: arXiv cs.CV 2024
Year: 2024

Predicting and explaining the private information contained in an image in human-understandable terms is a complex and contextual task. This task is challenging even for large language models. To facilitate the understanding of privacy decisions, we propose to predict image privacy based on a set of natural language content descriptors. These content descriptors are associated with privacy scores that reflect how people perceive image content. We generate descriptors with our novel Image-guided Topic Modeling (ITM) approach. ITM leverages, via multimodality alignment, both vision information and image textual descriptions from a vision language model. We use the ITM-generated descriptors to learn a privacy predictor, Priv$\times$ITM, whose decisions are interpretable by design. Our Priv$\times$ITM classifier outperforms the reference interpretable method by 5 percentage points in accuracy and performs comparably to the current non-interpretable state-of-the-art model.
Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender Systems
Venue: arXiv cs.IR 2024
Year: 2024

Recommender systems play an important role in supporting the achievement of the United Nations sustainable development goals (SDGs). In recommender systems, explanations can support different goals, such as increasing a user's trust in a recommendation, persuading a user to purchase specific items, or increasing the understanding of the reasons behind a recommendation. In this paper, we discuss the concept of "sustainability-aware persuasive explanations" which we regard as a major concept to support the achievement of the mentioned SDGs. Such explanations are orthogonal to most existing explanation approaches since they focus on a "less is more" principle, which per se is not included in existing e-commerce platforms. Based on a user study in three item domains, we analyze the potential impacts of sustainability-aware persuasive explanations. The study results are promising regarding user acceptance and the potential impacts of such explanations.
Semantic Model Component Implementation for Model-driven Semantic Communications
Venue: arXiv cs.AI 2024
Year: 2024

The key feature of model-driven semantic communication is the propagation of the model. The semantic model component (SMC) is designed to drive the intelligent model to transmit in the physical channel, allowing the intelligence to flow through the networks. According to the characteristics of neural networks with common and individual model parameters, this paper designs the cross-source-domain and cross-task semantic component model. Considering that the basic model is deployed on the edge node, the large server node updates the edge node by transmitting only the semantic component model to the edge node so that the edge node can handle different sources and different tasks. In addition, this paper also discusses how channel noise affects the performance of the model and proposes methods of injection noise and regularization to improve the noise resistance of the model. Experiments show that SMCs use smaller model parameters to achieve cross-source, cross-task functionality while maintaining performance and improving the model's tolerance to noise. Finally, a component transfer-based unmanned vehicle tracking prototype was implemented to verify the feasibility of model components in practical applications.
Interaction Equivalence
Venue: arXiv cs.LO 2024
Year: 2024

Contextual equivalence is the de facto standard notion of program equivalence. A key theorem is that contextual equivalence is an equational theory. Making contextual equivalence more intensional, for example taking into account the time cost of the computation, seems a natural refinement. Such a change, however, does not induce an equational theory, for an apparently essential reason: cost is not invariant under reduction. In the paradigmatic case of the untyped $λ$-calculus, we introduce interaction equivalence. Inspired by game semantics, we observe the number of interaction steps between terms and contexts but -- crucially -- ignore their own internal steps. We prove that interaction equivalence is an equational theory and we characterize it as $B$, the well-known theory induced by Böhm tree equality. Ours is the first observational characterization of $B$ obtained without enriching the discriminating power of contexts with extra features such as non-determinism. To prove our results, we develop interaction-based refinements of the Böhm-out technique and of intersection types.
Optimum Configuration for Hovering n-Quadrotors carrying a Slung Payload
Venue: arXiv cs.RO 2024
Year: 2024

This work proposes a strategy for organising quadrotors around a payload to enable hovering without external stimuli, together with a MATLAB software for modelling the dynamics of a quadrotor-payload system. Based on geometric concepts, the proposed design keeps the payload and system centre of mass aligned. Hovering tests that are successful confirm the method's efficiency. Moreover, the algorithm is improved to take thrust capacities and propeller distances into account, calculating the minimum number of quadrotors needed for hovering. The algorithm's effectiveness is demonstrated by numerical examples, which reveal that larger quadrotors may require fewer units while smaller ones give greater flexibility. Our code can be found at: \href{https://github.com/Hosnooo/Swarm-Slung-Payload}{https://github.com/Hosnooo/Swarm-Slung-Payload}
Moldable Development Patterns
Venue: arXiv cs.SE 2024
Year: 2024

Moldable development supports decision-making by making software systems explainable. This is done by making it cheap to add numerous custom tools to your software, turning it into a live, explorable domain model. Based on several years of experience of applying moldable development to both open-source and industrial systems, we have identified several mutually supporting patterns to explain how moldable development works in practice. This paper targets (i) readers curious to learn about moldable development, (ii) current users of the Glamorous Toolkit moldable IDE wanting to learn best practices, and (iii) developers interested in applying moldable development using other platforms and technology.
Classification and regression of trajectories rendered as images via 2D Convolutional Neural Networks
Venue: arXiv cs.CV 2024
Year: 2024

Trajectories can be regarded as time-series of coordinates, typically arising from motile objects. Methods for trajectory classification are particularly important to detect different movement patterns, while methods for regression to compute motility metrics and forecasting. Recent advances in computer vision have facilitated the processing of trajectories rendered as images via artificial neural networks with 2d convolutional layers (CNNs). This approach leverages the capability of CNNs to learn spatial hierarchies of features from images, necessary to recognize complex shapes. Moreover, it overcomes the limitation of other machine learning methods that require input trajectories with a fixed number of points. However, rendering trajectories as images can introduce poorly investigated artifacts such as information loss due to the plotting of coordinates on a discrete grid, and spectral changes due to line thickness and aliasing. In this study, we investigate the effectiveness of CNNs for solving classification and regression problems from synthetic trajectories that have been rendered as images using different modalities. The parameters considered in this study include line thickness, image resolution, usage of motion history (color-coding of the temporal component) and anti-aliasing. Results highlight the importance of choosing an appropriate image resolution according to model depth and motion history in applications where movement direction is critical.
Emu3: Next-Token Prediction is All You Need
Venue: arXiv cs.CV 2024
Year: 2024

While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e.g., Stable Diffusion) and compositional approaches (e.g., CLIP combined with LLMs). In this paper, we introduce Emu3, a new suite of state-of-the-art multimodal models trained solely with next-token prediction. By tokenizing images, text, and videos into a discrete space, we train a single transformer from scratch on a mixture of multimodal sequences. Emu3 outperforms several well-established task-specific models in both generation and perception tasks, surpassing flagship models such as SDXL and LLaVA-1.6, while eliminating the need for diffusion or compositional architectures. Emu3 is also capable of generating high-fidelity video via predicting the next token in a video sequence. We simplify complex multimodal model designs by converging on a singular focus: tokens, unlocking great potential for scaling both during training and inference. Our results demonstrate that next-token prediction is a promising path towards building general multimodal intelligence beyond language. We open-source key techniques and models to support further research in this direction.
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. One of the primary approaches to anomaly detection are methods based on forecasting. Nevertheless, extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing performance overestimation of anomaly detection algorithms. This manuscript addresses this gap by introducing a dataset comprising time series data of network entities' behavior, collected from the CESNET3 network. The dataset was created from 40 weeks of network traffic of 275 thousand active IP addresses. The ISP origin of the presented data ensures a high level of variability among network entities, which forms a unique and authentic challenge for forecasting and anomaly detection models. It provides valuable insights into the practical deployment of forecast-based anomaly detection approaches.