OpenRaiser/Intern-Atlas huggingface dataset:
Protecting Africa's Future: Cybersecurity Strategies for Child Safety, Learning, and Skill Acquisition in Tanzania
Venue: arXiv cs.CY 2024
Year: 2024

Today, children across Africa are at a growing risk from the Internet. Dangers include harmful content, violence, exploitation, abuse, and neglect. All these have increased due to increased mobile and Internet technology use, which not only places their safety at risk but also affects their ability to learn essential skills for their future. This paper provides an overview of the unique challenges faced by third-world African countries in ensuring the online safety of children while also supporting their developmental needs. It highlights effective practices and policies adopted by other nations to safeguard children from online threats and enhance their digital literacy. We are focusing on sharing the best practices and policies other countries have used to protect children from abuse and help them succeed in the digital world. The study emphasizes the online safety strategies, legal frameworks, and recommendations specific to the United Republic of Tanzania, along with the significance of international collaborations with organizations like UNICEF and the UN. The goal is to provide African policymakers, educators, and cybersecurity professionals with practical guidance and recommendations to strengthen child online safety initiatives both within and beyond the continent.
$\textit{SKIntern}$: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Small Language Models (SLMs) are attracting attention due to the high computational demands and privacy concerns of Large Language Models (LLMs). Some studies fine-tune SLMs using Chains of Thought (CoT) data distilled from LLMs, aiming to enhance their reasoning ability. Furthermore, Some CoT distillation methods introduce external symbolic knowledge into the generation process to improve the limited knowledge memory, reasoning ability and out-of-domain (OOD) generalization of SLMs. However, the introduction of symbolic knowledge increases computational overhead and introduces potential noise. In this paper, we introduce $\textit{SKIntern}$, an innovative approach that empowers SLMs to internalize symbolic knowledge and few-shot examples gradually through a progressive fine-tuning process, guided by a predefined linear decay schedule under curriculum learning. By efficiently internalizing knowledge, $\textit{SKIntern}$ reduces computational overhead and speeds up the reasoning process by focusing solely on the question during inference. It outperforms state-of-the-art baselines by over 5\%, while reducing inference costs (measured in FLOPs) by up to $4\times$ across a wide range of SLMs in both in-domain (ID) and out-of-domain (OOD) tasks. Our code will be available at \url{https://github.com/Xnhyacinth/SKIntern}.
Exploring Scaling Laws for Local SGD in Large Language Model Training
Venue: arXiv cs.CL 2024
Year: 2024

This paper investigates scaling laws for local SGD in LLM training, a distributed optimization algorithm that facilitates training on loosely connected devices. Through extensive experiments, we show that local SGD achieves competitive results compared to conventional methods, given equivalent model parameters, datasets, and computational resources. Furthermore, we explore the application of local SGD in various practical scenarios, including multi-cluster setups and edge computing environments. Our findings elucidate the necessary conditions for effective multi-cluster LLM training and examine the potential and limitations of leveraging edge computing resources in the LLM training process. This demonstrates its viability as an alternative to single large-cluster training.
Constrained Two-Line Center Problems
Venue: arXiv cs.CG 2024
Year: 2024

Given a set P of n points in the plane, the two-line center problem asks to find two lines that minimize the maximum distance from each point in P to its closer one of the two resulting lines. The currently best algorithm for the problem takes $O(n^2\log^2n)$ time by Jaromczyk and Kowaluk in 1995. In this paper, we present faster algorithms for three variants of the two-line center problem in which the orientations of the resulting lines are constrained. Specifically, our algorithms solve the problem in $O(n \log n)$ time when the orientations of both lines are fixed; in $O(n \log^3 n)$ time when the orientation of one line is fixed; and in $O(n^2 α(n) \log n)$ time when the angle between the two lines is fixed, where $α(n)$ denotes the inverse Ackermann function.
Skill-Adpative Imitation Learning for UI Test Reuse
Venue: arXiv cs.SE 2024
Year: 2024

To alleviate the substantial cost of manually crafting user interface (UI) test cases, UI test migration aims to automatically generate test cases for a target mobile application (app) by adapting those from a source app that shares similar functionalities. Traditionally, this process has been approached as a sequential UI-event-mapping problem, where events in the source app are mapped to those in the target one based on their textual descriptions. Prior research has extensively focused on enhancing the event-mapping accuracy of NLP models. Although the advent of large language models (LLMs) with impressive NLP capabilities suggests the potential for near-perfect event-mapping, our study demonstrates that even the highly accurate event-mapping of LLMs is insufficient to address the implementation discrepancies between the source and the target apps, reducing the overall effectiveness of LLM-driven solutions for UI test migration. To address this challenge, in this paper, we propose SAIL, a skill-adaptive imitation learning framework designed to enhance the effectiveness of UI test migration through two key designs. First, SAIL leverages the source test cases as demonstrations and employs a multi-level abstraction of test cases' underlying skills, so as to extract the testing information from source test cases as the knowledge base for the subsequent test generation on the target app. Second, SAIL selectively reuses a subset of the learned skills to guide the generation of test cases for the target app with its novel context- and history-aware skill adaptation. While SAIL can be instantiated with any imitation learning techniques, we utilize the in-context learning capabilities of LLMs to instantiate SAIL. Evaluations results show that SAIL substantially improves the effectiveness of UI test migration, with 149\% higher success rate than state-of-the-art approaches.
Knowledge-Based Ultra-Low-Latency Semantic Communications for Robotic Edge Intelligence
Venue: arXiv cs.IT 2024
Year: 2024

The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scale knowledge graph (KG) is operated at an edge server as a "remote brain" to guide remote robots on environmental exploration or task execution. In this paper, we present a new air-interface framework targeting the said systems, called knowledge-based robotic semantic communications (SemCom), which consists of a protocol and relevant transmission techniques. First, the proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task. They include identification of all task-relevant knowledge paths (KPs) on the KG, semantic matching between KG and object classifier, and uploading of robot's observations for objects recognition and feasible KP identification. Next, to support ultra-low-latency feature transmission (ULL-FT), we propose a novel transmission approach that exploits classifier's robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture model, we derive the relation between BEP and classification margin, which sheds light on system requirements to support ULL-FT. Furthermore, for the case where the classification margin is insufficient for coping with channel distortion, we enhance the ULL-FT approach by studying retransmission and multi-view classification for enlarging the margin and further quantifying corresponding requirements. Finally, experiments using DNNs as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible KP identification.
Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel Dataset
Venue: arXiv cs.CV 2024
Year: 2024

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results.
"I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development
Venue: arXiv cs.SE 2024
Year: 2024

AI-empowered tools have emerged as a transformative force, fundamentally reshaping the software development industry and promising far-reaching impacts across diverse sectors. This study investigates the adoption, impact, and security considerations of AI-empowered tools in the software development process. Through semi-structured interviews with 19 software practitioners from diverse backgrounds, we explore three key aspects: the utility of AI tools, developers' attitudes towards them, and security and privacy responsibilities. Our findings reveal widespread adoption of AI tools across various stages of software development. Developers generally express positive attitudes towards AI, viewing it as an efficiency-enhancing assistant rather than a job replacement threat. However, they also recognized limitations in AI's ability to handle complex, unfamiliar, or highly specialized tasks in software development. Regarding security and privacy, we found varying levels of risk awareness among developers, with larger companies implementing more comprehensive risk management strategies. Our study provides insights into the current state of AI adoption in software development and offers recommendations for practitioners, organizations, AI providers, and regulatory bodies to effectively navigate the integration of AI in the software industry.
Parameterized Local Search for Max $c$-Cut
Venue: arXiv cs.CC 2024
Year: 2024

In the NP-hard Max $c$-Cut problem, one is given an undirected edge-weighted graph $G$ and aims to color the vertices of $G$ with $c$ colors such that the total weight of edges with distinctly colored endpoints is maximal. The case with $c=2$ is the famous Max Cut problem. To deal with the NP-hardness of this problem, we study parameterized local search algorithms. More precisely, we study LS Max $c$-Cut where we are also given a vertex coloring and an integer $k$ and the task is to find a better coloring that changes the color of at most $k$ vertices, if such a coloring exists; otherwise, the given coloring is $k$-optimal. We show that, for all $c\ge 2$, LS Max $c$-Cut presumably cannot be solved in $f(k)\cdot n^{\mathcal{O}(1)}$ time even on bipartite graphs. We then present an algorithm for LS Max $c$-Cut with running time $\mathcal{O}((3eΔ)^k\cdot c\cdot k^3\cdotΔ\cdot n)$, where $Δ$ is the maximum degree of the input graph. Finally, we evaluate the practical performance of this algorithm in a hill-climbing approach as a post-processing for a state-of-the-art heuristic for Max $c$-Cut. We show that using parameterized local search, the results of this state-of-the-art heuristic can be further improved on a set of standard benchmark instances.
Credit Card Fraud Detection: A Deep Learning Approach
Venue: arXiv cs.LG 2024
Year: 2024

Credit card is one of the most extensive methods of instalment for both online and offline mode of payment for electronic transactions in recent times. credit cards invention has provided significant ease in electronic transactions. However, it has also provided new fraud opportunities for criminals, which results in increased fraud rates. Substantial amount of money has been lost by many institutions and individuals due to fraudulent credit card transactions. Adapting improved and dynamic fraud recognition frameworks thus became essential for all credit card distributing banks to mitigate their losses. In fact, the problem of fraudulent credit card transactions implicates a number of relevant real-time challenges, namely: Concept drift, Class imbalance, and Verification latency. However, the vast majority of current systems are based on artificial intelligence (AI), Fuzzy logic, Machine Learning, Data mining, Genetic Algorithms, and so on, rely on assumptions that hardly address all the relevant challenges of fraud-detection system (FDS). This paper aims to understand & implement Deep Learning algorithms in order to obtain a high fraud coverage with very low false positive rate. Also, it aims to implement an auto-encoder as an unsupervised (semi-supervised) method of learning common patterns. Keywords: Credit card fraud, Fraud-detection system (FDS), Electronic transactions, Concept drift, Class imbalance, Verification latency, Machine Learning, Deep Learning
Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model
Venue: arXiv cs.CV 2024
Year: 2024

Large Multimodal Models (LMMs) have achieved significant progress by extending large language models. Building on this progress, the latest developments in LMMs demonstrate the ability to generate dense pixel-wise segmentation through the integration of segmentation models.Despite the innovations, the textual responses and segmentation masks of existing works remain at the instance level, showing limited ability to perform fine-grained understanding and segmentation even provided with detailed textual cues.To overcome this limitation, we introduce a Multi-Granularity Large Multimodal Model (MGLMM), which is capable of seamlessly adjusting the granularity of Segmentation and Captioning (SegCap) following user instructions, from panoptic SegCap to fine-grained SegCap. We name such a new task Multi-Granularity Segmentation and Captioning (MGSC). Observing the lack of a benchmark for model training and evaluation over the MGSC task, we establish a benchmark with aligned masks and captions in multi-granularity using our customized automated annotation pipeline. This benchmark comprises 10K images and more than 30K image-question pairs. We will release our dataset along with the implementation of our automated dataset annotation pipeline for further research.Besides, we propose a novel unified SegCap data format to unify heterogeneous segmentation datasets; it effectively facilitates learning to associate object concepts with visual features during multi-task training. Extensive experiments demonstrate that our MGLMM excels at tackling more than eight downstream tasks and achieves state-of-the-art performance in MGSC, GCG, image captioning, referring segmentation, multiple and empty segmentation, and reasoning segmentation tasks. The great performance and versatility of MGLMM underscore its potential impact on advancing multimodal research.
Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environments
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous operations of robots in unknown environments are challenging due to the lack of knowledge of the dynamics of the interactions, such as the objects' movability. This work introduces a novel Causal Reinforcement Learning approach to enhancing robotics operations and applies it to an urban search and rescue (SAR) scenario. Our proposed machine learning architecture enables robots to learn the causal relationships between the visual characteristics of the objects, such as texture and shape, and the objects' dynamics upon interaction, such as their movability, significantly improving their decision-making processes. We conducted causal discovery and RL experiments demonstrating the Causal RL's superior performance, showing a notable reduction in learning times by over 24.5% in complex situations, compared to non-causal models.
Sportoonizer: Augmenting Sports Highlights' Narration and Visual Impact via Automatic Manga B-Roll Generation
Venue: arXiv cs.HC 2024
Year: 2024

Sports highlights are becoming increasingly popular on video-sharing platforms. Yet, crafting sport highlight videos is challenging, which requires producing engaging narratives from different angles, and conforming to different platform affordances with constantly changing audiences. Many content creators therefore create derivative work of the original sports video through manga styles to enhance its expressiveness. But manually creating and inserting tailored manga-style content can still be time-consuming. We introduce Sportoonizer, a system embedding the pipeline for automatic generation of manga-style animations for highlights in sports videos and insertion into original videos. It seamlessly merges dynamic manga sequences with live-action footage, enriching the visual tapestry and deepening narrative scope. By leveraging genAIs, Sportoonizer crafts compelling storylines encapsulating the intensity of sports moments and athletes' personal journeys. Our evaluation study demonstrates that integrating manga B-rolls significantly enhances viewer engagement, visual interest, and emotional connection towards athletes' stories in the viewing experience.
Constrained Reasoning Chains for Enhancing Theory-of-Mind in Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Theory-of-Mind (ToM) ability possessed by Large Language Models (LLMs) has been shown to be limited. Most existing methods for improving ToM in LLMs adopt zero-shot prompting, and they face challenges including poor performance in complex ToM reasoning tasks and an inability to handle non-narrative contexts. We propose a zero-shot prompting method named Constrained Chain-of-ToM (CCoToM) that leverages domain knowledge and the causal relations between ToM dimensions to address these limitations. Specifically, CCoToM guides LLMs to construct explicit reasoning chains by first prompting LLMs to infer related ToM dimensions (e.g., belief). Afterward, CCoToM prompts LLMs to infer the queried ToM dimension based on the generated related ToM dimensions and corresponding causal relations. Additionally, CCoToM adaptively imposes constraints on prompts to introduce inductive biases and improve consistency between ToM dimensions. Besides narratives, CCoToM can also handle non-narrative contexts like conversations. Extensive experiments show that CCoToM consistently outperforms previous state-of-the-art methods by large margins across all LLMs and datasets used. We also conduct in-depth analyses to gain deeper insights into CCoToM. We have made our code publicly available.
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
Venue: arXiv cs.DC 2024
Year: 2024

Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model convergence. Fortunately, the advancement of low-Earth orbit (LEO) satellite networks offers promising new communication avenues to augment traditional terrestrial FL. Despite this potential, the limited satellite-ground communication bandwidth and the heterogeneous operating environments of ground devices-including variations in data, bandwidth, and computing power-pose substantial challenges for effective and robust satellite-assisted FL. To address these challenges, we propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework. SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth, ensuring the transmission of the most critical models. Additionally, a multigraph is constructed to capture real-time heterogeneous relationships between devices, including data distribution, terrestrial bandwidth, and computing capability. This multigraph enables SatFed to aggregate satellite-transmitted models into peer guidance, enhancing local training in heterogeneous environments. Extensive experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources
Venue: arXiv cs.CL 2024
Year: 2024

Recent developments in large language models (LLMs) have led to significant improvements in intelligent dialogue systems'ability to handle complex inquiries. However, current LLMs still exhibit limitations in specialized domain knowledge, particularly in technical fields such as agriculture. To address this problem, we propose ShizishanGPT, an intelligent question answering system for agriculture based on the Retrieval Augmented Generation (RAG) framework and agent architecture. ShizishanGPT consists of five key modules: including a generic GPT-4 based module for answering general questions; a search engine module that compensates for the problem that the large language model's own knowledge cannot be updated in a timely manner; an agricultural knowledge graph module for providing domain facts; a retrieval module which uses RAG to supplement domain knowledge; and an agricultural agent module, which invokes specialized models for crop phenotype prediction, gene expression analysis, and so on. We evaluated the ShizishanGPT using a dataset containing 100 agricultural questions specially designed for this study. The experimental results show that the tool significantly outperforms general LLMs as it provides more accurate and detailed answers due to its modular design and integration of different domain knowledge sources. Our source code, dataset, and model weights are publicly available at https://github.com/Zaiwen/CropGPT.