OpenRaiser/Intern-Atlas huggingface dataset:
What Students Can Learn About Artificial Intelligence -- Recommendations for K-12 Computing Education
Venue: arXiv cs.CY 2023
Year: 2023

Technological advances in the context of digital transformation are the basis for rapid developments in the field of artificial intelligence (AI). Although AI is not a new topic in computer science (CS), recent developments are having an immense impact on everyday life and society. In consequence, everyone needs competencies to be able to adequately and competently analyze, discuss and help shape the impact, opportunities, and limits of artificial intelligence on their personal lives and our society. As a result, an increasing number of CS curricula are being extended to include the topic of AI. However, in order to integrate AI into existing CS curricula, what students can and should learn in the context of AI needs to be clarified. This has proven to be particularly difficult, considering that so far CS education research on central concepts and principles of AI lacks sufficient elaboration. Therefore, in this paper, we present a curriculum of learning objectives that addresses digital literacy and the societal perspective in particular. The learning objectives can be used to comprehensively design curricula, but also allow for analyzing current curricula and teaching materials and provide insights into the central concepts and corresponding competencies of AI.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Venue: arXiv cs.IR 2023
Year: 2023

Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emerging and still unclear research question. Traditionally, Collaborative Filtering (CF) has been the most effective method for these tasks, predominantly relying on the extensive volume of rating data. In contrast, LLMs typically demand considerably less data while maintaining an exhaustive world knowledge about each item, such as movies or products. In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings. We investigate various LLMs in different sizes, ranging from 250M to 540B parameters and evaluate their performance in zero-shot, few-shot, and fine-tuning scenarios. We conduct comprehensive analysis to compare between LLMs and strong CF methods, and find that zero-shot LLMs lag behind traditional recommender models that have the access to user interaction data, indicating the importance of user interaction data. However, through fine-tuning, LLMs achieve comparable or even better performance with only a small fraction of the training data, demonstrating their potential through data efficiency.
Adaptive Molecular Communication Receivers with Tunable Ligand-Receptor Interactions
Venue: arXiv cs.ET 2023
Year: 2023

Molecular Communications (MC) underpins signaling in biological systems, enabling information transfer through biochemical molecules. The prospect of engineering this natural communication mechanism has inspired the Internet of Bio-Nano Things (IoBNT) applications, which rely on heterogeneous collaborative networks of natural and engineered biological devices, as well as artificial micro/nanomachines. A key attribute of natural MC systems is their adaptability, ensuring accurate information transmission in dynamic, time-varying biochemical environments. Therefore, integrating biological adaptation techniques into artificial MC networks, which are expected to operate in various biochemical environments, such as inside human body, is essential for robust and biocompatible IoBNT applications. This study explores the design of bio-inspired adaptive MC receivers capable of tuning their response functions for maintaining optimal detection performance in scenarios with time-varying received signals. The proposed receiver architectures are based on ligand-receptor interactions, with adaptivity achieved by modifying the sigmoidal-shaped ligand-receptor response curve in response to fluctuations in received signal statistics. The performance of these adaptive receivers is evaluated across a range of MC scenarios, including those with stochastic background interference, inter-symbol interference (ISI), and degrading enzymes, which involve time-varying scaling or shifting of received signals. Numerical results demonstrate the significant improvement in detection performance provided by adaptive receivers in dynamic MC scenarios.
Multimodal Contextualized Plan Prediction for Embodied Task Completion
Venue: arXiv cs.RO 2023
Year: 2023

Task planning is an important component of traditional robotics systems enabling robots to compose fine grained skills to perform more complex tasks. Recent work building systems for translating natural language to executable actions for task completion in simulated embodied agents is focused on directly predicting low level action sequences that would be expected to be directly executable by a physical robot. In this work, we instead focus on predicting a higher level plan representation for one such embodied task completion dataset - TEACh, under the assumption that techniques for high-level plan prediction from natural language are expected to be more transferable to physical robot systems. We demonstrate that better plans can be predicted using multimodal context, and that plan prediction and plan execution modules are likely dependent on each other and hence it may not be ideal to fully decouple them. Further, we benchmark execution of oracle plans to quantify the scope for improvement in plan prediction models.
SMART: Self-Morphing Adaptive Replanning Tree
Venue: arXiv cs.RO 2023
Year: 2023

The paper presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments. SMART performs risk based tree-pruning if the current path is obstructed by nearby moving obstacle(s), resulting in multiple disjoint subtrees. Then, for speedy recovery, it exploits these subtrees and performs informed tree-repair at hot-spots that lie at the intersection of subtrees to find a new path. The performance of SMART is comparatively evaluated with eight existing algorithms through extensive simulations. Two scenarios are considered with: 1) dynamic obstacles and 2) both static and dynamic obstacles. The results show that SMART yields significant improvements in replanning time, success rate and travel time. Finally, the performance of SMART is validated by a real laboratory experiment.
A tight upper bound on the number of non-zero weights of a constacyclic code
Venue: arXiv cs.IT 2023
Year: 2023

For a simple-root $λ$-constacyclic code $\mathcal{C}$ over $\mathbb{F}_q$, let $\langleρ\rangle$ and $\langleρ,M\rangle$ be the subgroups of the automorphism group of $\mathcal{C}$ generated by the cyclic shift $ρ$, and by the cyclic shift $ρ$ and the scalar multiplication $M$, respectively. Let $N_G(\mathcal{C}^\ast)$ be the number of orbits of a subgroup $G$ of automorphism group of $\mathcal{C}$ acting on $\mathcal{C}^\ast=\mathcal{C}\backslash\{0\}$. In this paper, we establish explicit formulas for $N_{\langleρ\rangle}(\mathcal{C}^\ast)$ and $N_{\langleρ,M\rangle}(\mathcal{C}^\ast)$. Consequently, we derive a upper bound on the number of nonzero weights of $\mathcal{C}$. We present some irreducible and reducible $λ$-constacyclic codes, which show that the upper bound is tight. A sufficient condition to guarantee $N_{\langleρ\rangle}(\mathcal{C}^\ast)=N_{\langleρ,M\rangle}(\mathcal{C}^\ast)$ is presented.
Dimensions of some LCD BCH codes
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we investigate the first few largest coset leaders modulo $\frac{q^m+1}λ$ where $λ\mid q+1$ and $q$ is an odd prime power, and give the dimensions of some LCD BCH codes of length $\frac{q^m+1}λ$ with large designed distances.We also determine the dimensions of some LCD BCH codes of length $n=\frac{(q^m+1)}λ$ with designed distances $2\leq δ\leq \frac{ q^{\lfloor(m+1)/2\rfloor}}λ+1$, where $ λ\mid q+1$ and $1<λ<q+1$. The LCD BCH codes presented in this paper have a sharper lower bound on the minimum distance than the BCH bound.
How Good are Commercial Large Language Models on African Languages?
Venue: arXiv cs.CL 2023
Year: 2023

Recent advancements in Natural Language Processing (NLP) has led to the proliferation of large pretrained language models. These models have been shown to yield good performance, using in-context learning, even on unseen tasks and languages. They have also been exposed as commercial APIs as a form of language-model-as-a-service, with great adoption. However, their performance on African languages is largely unknown. We present a preliminary analysis of commercial large language models on two tasks (machine translation and text classification) across eight African languages, spanning different language families and geographical areas. Our results suggest that commercial language models produce below-par performance on African languages. We also find that they perform better on text classification than machine translation. In general, our findings present a call-to-action to ensure African languages are well represented in commercial large language models, given their growing popularity.
Reliable Throughput of Generalized Collision Channel without Synchronization
Venue: arXiv cs.IT 2023
Year: 2023

We consider a generalized collision channel model for general multi-user communication systems, an extension of Massey and Mathys' collision channel without feedback for multiple access communications. In our model, there are multiple transmitters and receivers sharing the same communication channel. The transmitters are not synchronized and arbitrary time offsets between transmitters and receivers are assumed. A ``collision" occurs if two or more packets from different transmitters partially or completely overlap at a receiver. Our model includes the original collision channel as a special case. This paper focuses on reliable throughputs that are approachable for arbitrary time offsets. We consider both slot-synchronized and non-synchronized cases and characterize their reliable throughput regions for the generalized collision channel model. These two regions are proven to coincide. Moreover, it is shown that the protocol sequences constructed for multiple access communication remain ``throughput optimal" in the generalized collision channel model. We also identify the protocol sequences that can approach the outer boundary of the reliable throughput region.
An Asynchronous Massive Access Scheme with Dynamic Range Considerations
Venue: arXiv cs.IT 2023
Year: 2023

This paper studies the performance of a transmission and reception scheme for massive access under some practical challenges. One challenge is the near-far problem, i.e., an access point often receives signals from different transmitting devices at vastly different signal strengths. Another challenge is that the signals from different devices may be subject to arbitrary, analog, and heterogeneous delays. This paper considers a fully asynchronous model which is more realistic than the frame or symbol level synchrony needed in most existing work. A main theorem characterizes the asymptotic scaling of the codelength with the number of devices, a device delay upper bound, and the dynamic range of received signal strengths across devices. The scaling result suggests potential advantages of grouping devices with similar received signal strengths and letting the groups use time sharing. The performance of the proposed scheme is evaluated using simulations with and without grouping.
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Venue: arXiv cs.IR 2023
Year: 2023

Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services. However, existing recommenders face significant challenges in understanding the content of items. Large language models (LLMs), which possess deep semantic comprehension and extensive knowledge from pretraining, have proven to be effective in various natural language processing tasks. In this study, we explore the potential of leveraging both open- and closed-source LLMs to enhance content-based recommendation. With open-source LLMs, we utilize their deep layers as content encoders, enriching the representation of content at the embedding level. For closed-source LLMs, we employ prompting techniques to enrich the training data at the token level. Through comprehensive experiments, we demonstrate the high effectiveness of both types of LLMs and show the synergistic relationship between them. Notably, we observed a significant relative improvement of up to 19.32% compared to existing state-of-the-art recommendation models. These findings highlight the immense potential of both open- and closed-source of LLMs in enhancing content-based recommendation systems. We will make our code and LLM-generated data available for other researchers to reproduce our results.
Decentralized Governance for Virtual Community(DeGov4VC): Optimal Policy Design of Human-plant Symbiosis Co-creation
Venue: arXiv cs.HC 2023
Year: 2023

Does the decentralized nature of user behavior in interactive virtual communities help create rules promoting user engagement? Through scenarios like planting, this framework suggests a new paradigm for mutual influence that allows users to impact communities' political decisions. Sixteen participants in the first round of interviews were involved in the framework's creation. Then we developed and implemented our framework in the community with the help of other stakeholders. This proof-of-concept creates user groups using information from users' daily activities as input and grows the green plants in a virtual environment. Finally, we involved AI agents and stakeholders in the framework test and iterations. Our study's user evaluation of a few key stakeholders demonstrates how our strategy enhances user viscosity and experience. Via human-planting ecosystems in a virtual community, this research gives a fresh viewpoint on decentralized governance and an engaging method for co-creating interactive ecological communities.
Null-text Guidance in Diffusion Models is Secretly a Cartoon-style Creator
Venue: arXiv cs.CV 2023
Year: 2023

Classifier-free guidance is an effective sampling technique in diffusion models that has been widely adopted. The main idea is to extrapolate the model in the direction of text guidance and away from null-text guidance. In this paper, we demonstrate that null-text guidance in diffusion models is secretly a cartoon-style creator, i.e., the generated images can be efficiently transformed into cartoons by simply perturbing the null-text guidance. Specifically, we proposed two disturbance methods, i.e., Rollback disturbance (Back-D) and Image disturbance (Image-D), to construct misalignment between the noisy images used for predicting null-text guidance and text guidance (subsequently referred to as \textbf{null-text noisy image} and \textbf{text noisy image} respectively) in the sampling process. Back-D achieves cartoonization by altering the noise level of null-text noisy image via replacing $x_t$ with $x_{t+Δt}$. Image-D, alternatively, produces high-fidelity, diverse cartoons by defining $x_t$ as a clean input image, which further improves the incorporation of finer image details. Through comprehensive experiments, we delved into the principle of noise disturbing for null-text and uncovered that the efficacy of disturbance depends on the correlation between the null-text noisy image and the source image. Moreover, our proposed techniques, which can generate cartoon images and cartoonize specific ones, are training-free and easily integrated as a plug-and-play component in any classifier-free guided diffusion model. Project page is available at \url{https://nulltextforcartoon.github.io/}.
Integer points in the degree-sequence polytope
Venue: arXiv cs.DM 2023
Year: 2023

An integer vector $b \in \mathbb{Z}^d$ is a degree sequence if there exists a hypergraph with vertices $\{1,\dots,d\}$ such that each $b_i$ is the number of hyperedges containing $i$. The degree-sequence polytope $\mathscr{Z}^d$ is the convex hull of all degree sequences. We show that all but a $2^{-Ω(d)}$ fraction of integer vectors in the degree sequence polytope are degree sequences. Furthermore, the corresponding hypergraph of these points can be computed in time $2^{O(d)}$ via linear programming techniques. This is substantially faster than the $2^{O(d^2)}$ running time of the current-best algorithm for the degree-sequence problem. We also show that for $d\geq 98$, the degree-sequence polytope $\mathscr{Z}^d$ contains integer points that are not degree sequences. Furthermore, we prove that the linear optimization problem over $\mathscr{Z}^d$ is $\mathrm{NP}$-hard. This complements a recent result of Deza et al. (2018) who provide an algorithm that is polynomial in $d$ and the number of hyperedges.
Investigating the generative dynamics of energy-based neural networks
Venue: arXiv cs.NE 2023
Year: 2023

Generative neural networks can produce data samples according to the statistical properties of their training distribution. This feature can be used to test modern computational neuroscience hypotheses suggesting that spontaneous brain activity is partially supported by top-down generative processing. A widely studied class of generative models is that of Restricted Boltzmann Machines (RBMs), which can be used as building blocks for unsupervised deep learning architectures. In this work, we systematically explore the generative dynamics of RBMs, characterizing the number of states visited during top-down sampling and investigating whether the heterogeneity of visited attractors could be increased by starting the generation process from biased hidden states. By considering an RBM trained on a classic dataset of handwritten digits, we show that the capacity to produce diverse data prototypes can be increased by initiating top-down sampling from chimera states, which encode high-level visual features of multiple digits. We also found that the model is not capable of transitioning between all possible digit states within a single generation trajectory, suggesting that the top-down dynamics is heavily constrained by the shape of the energy function.
Traceability and Reuse Mechanisms, the most important Properties of Model Transformation Languages
Venue: arXiv cs.SE 2023
Year: 2023

Dedicated model transformation languages are claimed to provide many benefits over the use of general purpose languages for developing model transformations. However, the actual advantages associated with the use of MTLs are poorly understood empirically. There is little knowledge and empirical assessment about what advantages and disadvantages hold and where they originate from. In a prior interview study, we elicited expert opinions on what advantages result from what factors and a number of factors that moderate the influence. We aim to quantitatively asses the interview results to confirm or reject the effects posed by different factors. We intend to gain insights into how valuable different factors are so that future studies can draw on these data for designing targeted and relevant studies. We gather data on the factors and quality attributes using an online survey. To analyse the data, we use universal structure modelling based on a structure model. We use significance values and path coefficients produced bz USM for each hypothesised interdependence to confirm or reject correlation and to weigh the strength of influence present. We analyzed 113 responses. The results show that the Tracing and Reuse Mechanisms are most important overall. Though the observed effects were generally 10 times lower than anticipated. Additionally, we found that a more nuanced view of moderation effects is warranted. Their moderating influence differed significantly between the different influences, with the strongest effects being 1000 times higher than the weakest. The empirical assessment of MTLs is a complex topic that cannot be solved by looking at a single stand-alone factor. Our results provide clear indication that evaluation should consider transformations of different sizes and use-cases. Language development should focus on providing transformation specific reuse mechanisms .
THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment
Venue: arXiv cs.CL 2023
Year: 2023

This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task. This task requires the participant to identify a specific paragraph from a given supporting case that entails the decision for the query case. We try traditional lexical matching methods and pre-trained language models with different sizes. Furthermore, learning-to-rank methods are employed to further improve performance. However, learning-to-rank is not very robust on this task. which suggests that answer passages cannot simply be determined with information retrieval techniques. Experimental results show that more parameters and legal knowledge contribute to the legal case entailment task. Finally, we get the third place in COLIEE 2023. The implementation of our method can be found at https://github.com/CSHaitao/THUIR-COLIEE2023.
MISO: Legacy-compatible Privacy-preserving Single Sign-on using Trusted Execution Environments
Venue: arXiv cs.CR 2023
Year: 2023

Single sign-on (SSO) allows users to authenticate to third-party applications through a central identity provider. Despite their wide adoption, deployed SSO systems suffer from privacy problems such as user tracking by the identity provider. While numerous solutions have been proposed by academic papers, none were adopted because they require modifying identity providers, a significant adoption barrier in practice. Solutions do get deployed, however, fail to eliminate major privacy issues. Leveraging Trusted Execution Environments (TEEs), we propose MISO, the first privacy-preserving SSO system that is completely compatible with existing identity providers (such as Google and Facebook). This means MISO can be easily integrated into existing SSO ecosystem today and benefit end users. MISO also enables new functionality that standard SSO cannot offer: MISO allows users to leverage multiple identity providers in a single SSO workflow, potentially in a threshold fashion, to better protect user accounts. We fully implemented MISO based on Intel SGX. Our evaluation shows that MISO can handle high user concurrency with practical performance.