OpenRaiser/Intern-Atlas huggingface dataset:
Conformal Load Prediction with Transductive Graph Autoencoders
Venue: arXiv cs.LG 2024
Year: 2024

Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to calibrate the GNN outputs and produce valid prediction intervals. We handle data heteroscedasticity through error reweighting and Conformalized Quantile Regression (CQR). We compare the performance of our method against baseline techniques on real-world transportation datasets. Our approach has better coverage and efficiency than all baselines and showcases robustness and adaptability.
Highly Connected Graph Partitioning: Exact Formulation and Solution Methods
Venue: arXiv cs.DM 2024
Year: 2024

Graph partitioning (GP) and vertex connectivity have traditionally been two distinct fields of study. This paper introduces the highly connected graph partitioning (HCGP) problem, which partitions a graph into compact, size balanced, and $Q$-(vertex) connected parts for any $Q\geq 1$. This problem is valuable in applications that seek cohesion and fault-tolerance within their parts, such as community detection in social networks and resiliency-focused partitioning of power networks. Existing research in this fundamental interconnection primarily focuses on providing theoretical existence guarantees of highly connected partitions for a limited set of dense graphs, and do not include canonical GP considerations such as size balance and compactness. This paper's key contribution is providing a general modeling and algorithmic approach for HCGP, inspired by recent work in the political districting problem, a special case of HCGP with $Q=1$. This approach models $Q$-connectivity constraints as mixed integer programs for any $Q\geq 1$ and provides an efficient branch-and-cut method to solve HCGP. When solution time is a priority over optimality, this paper provides a heuristic method specifically designed for HCGP with $Q=2$. A computational analysis evaluates these methods using a test bed of instances from various real-world graphs. In this analysis, the branch-and-cut method finds an optimal solution within one hour in $82.8\%$ of the instances solved. For $Q=2$, small and sparse instances are challenging for the heuristic, whereas large and sparse instances are challenging for the exact method. Furthermore, this study quantifies the computational cost of ensuring higher connectivity using the branch-and-cut approach, compared to a baseline of ensuring $1$-connectivity. Overall, this work serves as an effective tool to partition a graph into resilient and cohesive parts.
A Survey of Pipeline Tools for Data Engineering
Venue: arXiv cs.LG 2024
Year: 2024

Currently, a variety of pipeline tools are available for use in data engineering. Data scientists can use these tools to resolve data wrangling issues associated with data and accomplish some data engineering tasks from data ingestion through data preparation to utilization as input for machine learning (ML). Some of these tools have essential built-in components or can be combined with other tools to perform desired data engineering operations. While some tools are wholly or partly commercial, several open-source tools are available to perform expert-level data engineering tasks. This survey examines the broad categories and examples of pipeline tools based on their design and data engineering intentions. These categories are Extract Transform Load/Extract Load Transform (ETL/ELT), pipelines for Data Integration, Ingestion, and Transformation, Data Pipeline Orchestration and Workflow Management, and Machine Learning Pipelines. The survey also provides a broad outline of the utilization with examples within these broad groups and finally, a discussion is presented with case studies indicating the usage of pipeline tools for data engineering. The studies present some first-user application experiences with sample data, some complexities of the applied pipeline, and a summary note of approaches to using these tools to prepare data for machine learning.
An open dataset of article processing charges from six large scholarly publishers (2019-2023)
Venue: arXiv cs.DL 2024
Year: 2024

This paper introduces a dataset of article processing charges (APCs) produced from the price lists of six large scholarly publishers - Elsevier, Frontiers, PLOS, MDPI, Springer Nature and Wiley - between 2019 and 2023. APC price lists were downloaded from publisher websites each year as well as via Wayback Machine snapshots to retrieve fees per journal per year. The dataset includes journal metadata, APC collection method, and annual APC price list information in several currencies (USD, EUR, GBP, CHF, JPY, CAD) for 8,712 unique journals and 36,618 journal-year combinations. The dataset was generated to allow for more precise analysis of APCs and can support library collection development and scientometric analysis estimating APCs paid in gold and hybrid OA journals.
From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition
Venue: arXiv cs.CV 2024
Year: 2024

People's social relationships are often manifested through their surroundings, with certain objects or interactions acting as symbols for specific relationships, e.g., wedding rings, roses, hugs, or holding hands. This brings unique challenges to recognizing social relationships, requiring understanding and capturing the essence of these contexts from visual appearances. However, current methods of social relationship understanding rely on the basic classification paradigm of detected persons and objects, which fails to understand the comprehensive context and often overlooks decisive social factors, especially subtle visual cues. To highlight the social-aware context and intricate details, we propose a novel approach that recognizes \textbf{Con}textual \textbf{So}cial \textbf{R}elationships (\textbf{ConSoR}) from a social cognitive perspective. Specifically, to incorporate social-aware semantics, we build a lightweight adapter upon the frozen CLIP to learn social concepts via our novel multi-modal side adapter tuning mechanism. Further, we construct social-aware descriptive language prompts (e.g., scene, activity, objects, emotions) with social relationships for each image, and then compel ConSoR to concentrate more intensively on the decisive visual social factors via visual-linguistic contrasting. Impressively, ConSoR outperforms previous methods with a 12.2\% gain on the People-in-Social-Context (PISC) dataset and a 9.8\% increase on the People-in-Photo-Album (PIPA) benchmark. Furthermore, we observe that ConSoR excels at finding critical visual evidence to reveal social relationships.
Teaching Literature Reviewing for Software Engineering Research
Venue: arXiv cs.SE 2024
Year: 2024

The goal of this chapter is to support teachers in holistically introducing graduate students to literature reviews, with a particular focus on secondary research. It provides an overview of the overall literature review process and the different types of literature review before diving into guidelines for selecting and conducting different types of literature review. The chapter also provides recommendations for evaluating the quality of existing literature reviews and concludes with a summary of our learning goals and how the chapter supports teachers in addressing them.
Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design
Venue: arXiv cs.IT 2024
Year: 2024

The evolution of fifth generation (5G) wireless communication networks has led to an increased need for wireless resource management solutions that provide higher data rates, wide coverage, low latency, and power efficiency. Yet, many of existing traditional approaches remain non-practical due to computational limitations, and unrealistic presumptions of static network conditions and algorithm initialization dependencies. This creates an important gap between theoretical analysis and real-time processing of algorithms. To bridge this gap, deep learning based techniques offer promising solutions with their representational capabilities for universal function approximation. We propose a novel unsupervised deep learning based joint power allocation and beamforming design for multi-user multiple-input single-output (MU-MISO) system. The objective is to enhance the spectral efficiency by maximizing the sum-rate with the proposed joint design framework, NNBF-P while also offering computationally efficient solution in contrast to conventional approaches. We conduct experiments for diverse settings to compare the performance of NNBF-P with zero-forcing beamforming (ZFBF), minimum mean square error (MMSE) beamforming, and NNBF, which is also our deep learning based beamforming design without joint power allocation scheme. Experiment results demonstrate the superiority of NNBF-P compared to ZFBF, and MMSE while NNBF can have lower performances than MMSE and ZFBF in some experiment settings. It can also demonstrate the effectiveness of joint design framework with respect to NNBF.
Banal Deception Human-AI Ecosystems: A Study of People's Perceptions of LLM-generated Deceptive Behaviour
Venue: arXiv cs.CY 2024
Year: 2024

Large language models (LLMs) can provide users with false, inaccurate, or misleading information, and we consider the output of this type of information as what Natale (2021) calls `banal' deceptive behaviour. Here, we investigate peoples' perceptions of ChatGPT-generated deceptive behaviour and how this affects peoples' own behaviour and trust. To do this, we use a mixed-methods approach comprising of (i) an online survey with 220 participants and (ii) semi-structured interviews with 12 participants. Our results show that (i) the most common types of deceptive information encountered were over-simplifications and outdated information; (ii) humans' perceptions of trust and `worthiness' of talking to ChatGPT are impacted by `banal' deceptive behaviour; (iii) the perceived responsibility for deception is influenced by education level and the frequency of deceptive information; and (iv) users become more cautious after encountering deceptive information, but they come to trust the technology more when they identify advantages of using it. Our findings contribute to the understanding of human-AI interaction dynamics in the context of \textit{Deceptive AI Ecosystems}, and highlight the importance of user-centric approaches to mitigating the potential harms of deceptive AI technologies.
Designing Child-Centered Content Exposure and Moderation
Venue: arXiv cs.HC 2024
Year: 2024

Research on children's online experience and computer interaction often overlooks the relationship children have with hidden algorithms that control the content they encounter. Furthermore, it is not only about how children interact with targeted content but also how their development and agency are largely affected by these. By engaging with the body of literature at the intersection of i) human-centered design approaches, ii) exclusion and discrimination in A.I., iii) privacy, transparency, and accountability, and iv) children's online citizenship, this article dives into the question of "How can we approach the design of a child-centered moderation process to (1) include aspects that families value for their children and (2) provide explanations for content appropriateness and removal so that we can scale (according to systems and human needs) the moderation process assisted by A.I.?". This article contributes a sociotechnical highlight of core challenges and opportunities of designing child-centered content control tools. The article concludes by grounding and characterizing design considerations for a child-centered, family-guided moderation system. We hope this work serves as a stepping stone for designers and researchers pursuing children's safety online with an eye on hidden agents controlling children's online experiences and, by extension, the values and opportunities children are exposed to.
State Soup: In-Context Skill Learning, Retrieval and Mixing
Venue: arXiv cs.LG 2024
Year: 2024

A new breed of gated-linear recurrent neural networks has reached state-of-the-art performance on a range of sequence modeling problems. Such models naturally handle long sequences efficiently, as the cost of processing a new input is independent of sequence length. Here, we explore another advantage of these stateful sequence models, inspired by the success of model merging through parameter interpolation. Building on parallels between fine-tuning and in-context learning, we investigate whether we can treat internal states as task vectors that can be stored, retrieved, and then linearly combined, exploiting the linearity of recurrence. We study this form of fast model merging on Mamba-2.8b, a pretrained recurrent model, and present preliminary evidence that simple linear state interpolation methods suffice to improve next-token perplexity as well as downstream in-context learning task performance.
ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery
Venue: arXiv cs.CV 2024
Year: 2024

Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitation, we propose ConceptHash, a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part, and these concepts are automatically discovered without human annotations. Specifically, we leverage a Vision Transformer architecture and introduce concept tokens as visual prompts, along with image patch tokens as model inputs. Each concept is then mapped to a specific sub-code at the model output, providing natural sub-code interpretability. To capture subtle visual differences among highly similar sub-categories (e.g., bird species), we incorporate language guidance to ensure that the learned hash codes are distinguishable within fine-grained object classes while maintaining semantic alignment. This approach allows us to develop hash codes that exhibit similarity within families of species while remaining distinct from species in other families. Extensive experiments on four fine-grained image retrieval benchmarks demonstrate that ConceptHash outperforms previous methods by a significant margin, offering unique sub-code interpretability as an additional benefit. Code at: https://github.com/kamwoh/concepthash.
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Venue: arXiv cs.CL 2024
Year: 2024

High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent existing open-source data creation methods from scaling effectively, potentially limiting the diversity and quality of public alignment datasets. Is it possible to synthesize high-quality instruction data at scale by extracting it directly from an aligned LLM? We present a self-synthesis method for generating large-scale alignment data named Magpie. Our key observation is that aligned LLMs like Llama-3-Instruct can generate a user query when we input only the left-side templates up to the position reserved for user messages, thanks to their auto-regressive nature. We use this method to prompt Llama-3-Instruct and generate 4 million instructions along with their corresponding responses. We perform a comprehensive analysis of the extracted data and select 300K high-quality instances. To compare Magpie data with other public instruction datasets, we fine-tune Llama-3-8B-Base with each dataset and evaluate the performance of the fine-tuned models. Our results indicate that in some tasks, models fine-tuned with Magpie perform comparably to the official Llama-3-8B-Instruct, despite the latter being enhanced with 10 million data points through supervised fine-tuning (SFT) and subsequent feedback learning. We also show that using Magpie solely for SFT can surpass the performance of previous public datasets utilized for both SFT and preference optimization, such as direct preference optimization with UltraFeedback. This advantage is evident on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.
Real3D: Scaling Up Large Reconstruction Models with Real-World Images
Venue: arXiv cs.CV 2024
Year: 2024

The default strategy for training single-view Large Reconstruction Models (LRMs) follows the fully supervised route using large-scale datasets of synthetic 3D assets or multi-view captures. Although these resources simplify the training procedure, they are hard to scale up beyond the existing datasets and they are not necessarily representative of the real distribution of object shapes. To address these limitations, in this paper, we introduce Real3D, the first LRM system that can be trained using single-view real-world images. Real3D introduces a novel self-training framework that can benefit from both the existing synthetic data and diverse single-view real images. We propose two unsupervised losses that allow us to supervise LRMs at the pixel- and semantic-level, even for training examples without ground-truth 3D or novel views. To further improve performance and scale up the image data, we develop an automatic data curation approach to collect high-quality examples from in-the-wild images. Our experiments show that Real3D consistently outperforms prior work in four diverse evaluation settings that include real and synthetic data, as well as both in-domain and out-of-domain shapes. Code and model can be found here: https://hwjiang1510.github.io/Real3D/
Machine Learning-Driven Open-Source Framework for Assessing QoE in Multimedia Networks
Venue: arXiv cs.NI 2024
Year: 2024

The Internet is integral to modern life, influencing communication, business, and lifestyles globally. As dependence on Internet services grows, the demand for high-quality service delivery increases. Service providers must maintain high standards of quality of service and quality of experience (QoE) to ensure user satisfaction. QoE, which reflects user satisfaction with service quality, is a key metric for multimedia services, yet it is challenging to measure due to its subjective nature and the complexities of real-time feedback. This paper introduces a machine learning-based framework for objectively assessing QoE in multimedia networks. The open-source framework complies with the ITU-T P.1203 standard. It automates data collection and user satisfaction prediction using key network parameters such as delay, jitter, packet loss, bitrate, and throughput. Using a dataset of over 20,000 records from various network conditions, the Random Forest model predicts the mean opinion score with 95.8% accuracy. Our framework addresses the limitations of existing QoE models by integrating real-time data collection, machine learning predictions, and adherence to international standards. This approach enhances QoE evaluation accuracy and allows dynamic network resource management, optimizing performance and cost-efficiency. Its open-source nature encourages adaptation and extension for various multimedia services. The findings significantly affect the telecommunications industry in managing and optimizing multimedia services. The network centric QoE prediction of the framework offers a scalable solution to improve user satisfaction without the need for content-specific data. Future enhancements could include advanced machine learning models and broader applicability to digital services. This research contributes a practical, standardized tool for QoE assessment across diverse networks and platforms.
A Sublinear Algorithm for Approximate Shortest Paths in Large Networks
Venue: arXiv cs.DS 2024
Year: 2024

Computing distances and finding shortest paths in massive real-world networks is a fundamental algorithmic task in network analysis. There are two main approaches to solving this task. On one hand are traversal-based algorithms like bidirectional breadth-first search (BiBFS) with no preprocessing step and slow individual distance inquiries. On the other hand are indexing-based approaches, which maintain a large index. This allows for answering individual inquiries very fast; however, index creation is prohibitively expensive. We seek to bridge these two extremes: quickly answer distance inquiries without the need for costly preprocessing. In this work, we propose a new algorithm and data structure, WormHole, for approximate shortest path computations. WormHole leverages structural properties of social networks to build a sublinearly sized index, drawing upon the explicit core-periphery decomposition of Ben-Eliezer et al. Empirically, the preprocessing time of WormHole improves upon index-based solutions by orders of magnitude, and individual inquiries are consistently much faster than in BiBFS. The acceleration comes at the cost of a minor accuracy trade-off. Nonetheless, our empirical evidence demonstrates that WormHole accurately answers essentially all inquiries within a maximum additive error of 2. We complement these empirical results with provable theoretical guarantees, showing that WormHole requires $n^{o(1)}$ node queries per distance inquiry in random power-law networks. In contrast, any approach without a preprocessing step requires $n^{Ω(1)}$ queries for the same task. WormHole does not require reading the whole graph. Unlike the vast majority of index-based algorithms, it returns paths, not just distances. For faster inquiry times, it can be combined effectively with other index-based solutions, by running them only on the sublinear core.
FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images
Venue: arXiv cs.CV 2024
Year: 2024

Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection tools. Recently, several techniques have been proposed to differentiate deepfakes from realistic images and videos. This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes Discrete Wavelet Transforms (DWT) to extract discriminative features from the self-blended images (SBI) to be used for training a convolutional network architecture model. The SBIs blend the image with itself by introducing several forgery artifacts in a copy of the image before blending it. This prevents the classifier from overfitting specific artifacts by learning more generic representations. These blended images are then fed into the frequency features extractor to detect artifacts that can not be detected easily in the time domain. The proposed approach has been evaluated on FF++ and Celeb-DF datasets and the obtained results outperformed the state-of-the-art techniques with the cross-dataset evaluation protocol.