OpenRaiser/Intern-Atlas huggingface dataset:
Promoting Rural Entrepreneurship through Technology: A Case Study using Productivity Enhancing Technology Experience Kits (PETE-Kits)
Venue: arXiv cs.CY 2023
Year: 2023

Contribution: Case study of a rural-focused educational program with two components: 1) introducing high school students and teachers to Smart and Connected Technologies (SCTs) that can be used to solve local problems; 2) engaging the local community in supporting local technology-driven entrepreneurship. Background: Rural communities typically lag behind in terms of participation in the digital economy, and use of technology in general. Yet they often have the most to gain, due to high rates of self-employment and lower private-sector job opportunities. Research Questions: Can a broadly-scoped rural technology education program lead to improvements in 1) student and teacher SCT awareness, 2) SCT skills, 3) aspirations for future SCT use directed toward entrepreneurship and overall community wellbeing? Methodology: Our multidisciplinary team used a mixed-methods approach to engage a rural high school robotics team as well as the local community. Over the course of one year, students took part in hands-on-training with SCTs ("PETE-Kits" and associated curriculum) and brainstormed entrepreneurial projects via ideation events. Community members were involved at the beginning and end of the project, including judging a "shark-tank" style event where student business ideas using SCT were presented. Findings: Results from student pre / post activity assessments suggest that the program was effective at increasing comfort with technology and combining technical skills with entrepreneurial opportunities. Post surveys from community members, including teachers, demonstrated clear support for the program and an appreciation of how SCTs / digital skills could benefit the local economy and wellbeing.
Practical Parallel Algorithms for Near-Optimal Densest Subgraphs on Massive Graphs
Venue: arXiv cs.DS 2023
Year: 2023

The densest subgraph problem has received significant attention, both in theory and in practice, due to its applications in problems such as community detection, social network analysis, and spam detection. Due to the high cost of obtaining exact solutions, much attention has focused on designing approximate densest subgraph algorithms. However, existing approaches are not able to scale to massive graphs with billions of edges. In this paper, we introduce a new framework that combines approximate densest subgraph algorithms with a pruning optimization. We design new parallel variants of the state-of-the-art sequential Greedy++ algorithm, and plug it into our framework in conjunction with a parallel pruning technique based on $k$-core decomposition to obtain parallel $(1+\varepsilon)$-approximate densest subgraph algorithms. On a single thread, our algorithms achieve $2.6$--$34\times$ speedup over Greedy++, and obtain up to $22.37\times$ self relative parallel speedup on a 30-core machine with two-way hyper-threading. Compared with the state-of-the-art parallel algorithm by Harb et al. [NeurIPS'22], we achieve up to a $114\times$ speedup on the same machine. Finally, against the recent sequential algorithm of Xu et al. [PACMMOD'23], we achieve up to a $25.9\times$ speedup. The scalability of our algorithms enables us to obtain near-optimal density statistics on the hyperlink2012 (with roughly 113 billion edges) and clueweb (with roughly 37 billion edges) graphs for the first time in the literature.
Convex Methods for Constrained Linear Bandits
Venue: arXiv cs.LG 2023
Year: 2023

Recently, bandit optimization has received significant attention in real-world safety-critical systems that involve repeated interactions with humans. While there exist various algorithms with performance guarantees in the literature, practical implementation of the algorithms has not received as much attention. This work presents a comprehensive study on the computational aspects of safe bandit algorithms, specifically safe linear bandits, by introducing a framework that leverages convex programming tools to create computationally efficient policies. In particular, we first characterize the properties of the optimal policy for safe linear bandit problem and then propose an end-to-end pipeline of safe linear bandit algorithms that only involves solving convex problems. We also numerically evaluate the performance of our proposed methods.
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Venue: arXiv cs.CL 2023
Year: 2023

Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models help to trace evidence from an externally provided knowledge base allowing the model predictions to be better interpreted and verified. In this work, we critically evaluate these models in their ability to perform in scientific document reasoning tasks. To this end, we tuned multiple such model variants with science-focused instructions and evaluated them on a scientific document reasoning benchmark for the usefulness of the retrieved document passages. Our findings suggest that models justify predictions in science tasks with fabricated evidence and leveraging scientific corpus as pretraining data does not alleviate the risk of evidence fabrication.
A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders
Venue: arXiv cs.CV 2023
Year: 2023

In this research we propose a deep learning approach for detecting anomalies in videos using convolutional autoencoder and decoder neural networks on the UCSD dataset.Our method utilizes a convolutional autoencoder to learn the spatiotemporal patterns of normal videos and then compares each frame of a test video to this learned representation. We evaluated our approach on the UCSD dataset and achieved an overall accuracy of 99.35% on the Ped1 dataset and 99.77% on the Ped2 dataset, demonstrating the effectiveness of our method for detecting anomalies in surveillance videos. The results show that our method outperforms other state-of-the-art methods, and it can be used in real-world applications for video anomaly detection.
Evaluating multiple large language models in pediatric ophthalmology
Venue: arXiv cs.CL 2023
Year: 2023

IMPORTANCE The response effectiveness of different large language models (LLMs) and various individuals, including medical students, graduate students, and practicing physicians, in pediatric ophthalmology consultations, has not been clearly established yet. OBJECTIVE Design a 100-question exam based on pediatric ophthalmology to evaluate the performance of LLMs in highly specialized scenarios and compare them with the performance of medical students and physicians at different levels. DESIGN, SETTING, AND PARTICIPANTS This survey study assessed three LLMs, namely ChatGPT (GPT-3.5), GPT-4, and PaLM2, were assessed alongside three human cohorts: medical students, postgraduate students, and attending physicians, in their ability to answer questions related to pediatric ophthalmology. It was conducted by administering questionnaires in the form of test papers through the LLM network interface, with the valuable participation of volunteers. MAIN OUTCOMES AND MEASURES Mean scores of LLM and humans on 100 multiple-choice questions, as well as the answer stability, correlation, and response confidence of each LLM. RESULTS GPT-4 performed comparably to attending physicians, while ChatGPT (GPT-3.5) and PaLM2 outperformed medical students but slightly trailed behind postgraduate students. Furthermore, GPT-4 exhibited greater stability and confidence when responding to inquiries compared to ChatGPT (GPT-3.5) and PaLM2. CONCLUSIONS AND RELEVANCE Our results underscore the potential for LLMs to provide medical assistance in pediatric ophthalmology and suggest significant capacity to guide the education of medical students.
An Efficient Probabilistic Solution to Mapping Errors in LiDAR-Camera Fusion for Autonomous Vehicles
Venue: arXiv cs.RO 2023
Year: 2023

LiDAR-camera fusion is one of the core processes for the perception system of current automated driving systems. The typical sensor fusion process includes a list of coordinate transformation operations following system calibration. Although a significant amount of research has been done to improve the fusion accuracy, there are still inherent data mapping errors in practice related to system synchronization offsets, vehicle vibrations, the small size of the target, and fast relative moving speeds. Moreover, more and more complicated algorithms to improve fusion accuracy can overwhelm the onboard computational resources, limiting the actual implementation. This study proposes a novel and low-cost probabilistic LiDAR-Camera fusion method to alleviate these inherent mapping errors in scene reconstruction. By calculating shape similarity using KL-divergence and applying RANSAC-regression-based trajectory smoother, the effects of LiDAR-camera mapping errors are minimized in object localization and distance estimation. Designed experiments are conducted to prove the robustness and effectiveness of the proposed strategy.
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Venue: arXiv cs.CL 2023
Year: 2023

Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences. Employing graph neural networks to capture structural patterns from syntactic dependency parsing has been confirmed as an effective approach for boosting ABSA. In most works, the topology of dependency trees or dependency-based attention coefficients is often loosely regarded as edges between aspects and opinions, which can result in insufficient and ambiguous syntactic utilization. To address these problems, we propose a new reinforced dependency graph convolutional network (RDGCN) that improves the importance calculation of dependencies in both distance and type views. Initially, we propose an importance calculation criterion for the minimum distances over dependency trees. Under the criterion, we design a distance-importance function that leverages reinforcement learning for weight distribution search and dissimilarity control. Since dependency types often do not have explicit syntax like tree distances, we use global attention and mask mechanisms to design type-importance functions. Finally, we merge these weights and implement feature aggregation and classification. Comprehensive experiments on three popular datasets demonstrate the effectiveness of the criterion and importance functions. RDGCN outperforms state-of-the-art GNN-based baselines in all validations.
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities
Venue: arXiv cs.AI 2023
Year: 2023

Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation. However, despite significant advances in terms of performance, its lack of explainability and interpretability, inherent to neural networks in general since their inception, remains a major source of criticism. Hence it comes as no surprise that the expansion of deep learning methods in remote sensing is being accompanied by increasingly intensive efforts oriented towards addressing this drawback through the exploration of a wide spectrum of Explainable Artificial Intelligence techniques. This chapter, organized according to prominent Earth observation application fields, presents a panorama of the state-of-the-art in explainable remote sensing image analysis.
Near-Linear Scaling Data Parallel Training with Overlapping-Aware Gradient Compression
Venue: arXiv cs.DC 2023
Year: 2023

Existing Data Parallel (DP) trainings for deep neural networks (DNNs) often experience limited scalability in speedup due to substantial communication overheads. While Overlapping technique can mitigate such problem by paralleling communication and computation in DP, its effectiveness is constrained by the high communication-to-computation ratios (CCR) of DP training tasks. Gradient compression (GC) is a promising technique to obtain lower CCR by reducing communication volume directly. However, it is challenging to obtain real performance improvement by applying GC into Overlapping because of (1) severe performance penalties in traditional GCs caused by high compression overhead and (2) decline of Overlapping benefit owing to the possible data dependency in GC schemes. In this paper, we propose COVAP, a novel GC scheme designing a new coarse-grained filter, makes the compression overhead close to zero. COVAP ensures an almost complete overlap of communication and computation by employing adaptive compression ratios and tensor sharding tailored to specific training tasks. COVAP also adopts an improved error feedback mechanism to maintain training accuracy. Experiments are conducted on Alibaba Cloud ECS instances with different DNNs of real-world applications. The results illustrate that COVAP outperforms existent GC schemes in time-to-solution by 1.92x-15.39x and exhibits near-linear scaling. Furthermore, COVAP achieves best scalability under experiments on four different cluster sizes.
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
Venue: arXiv cs.IR 2023
Year: 2023

In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness. We introduce \emph{inferiority}, a novel (un)fairness measure quantifying a user's competitive disadvantage for their recommended items. Inferiority complements \emph{envy}, a fairness notion measuring preference for others' recommendations. We combine inferiority and envy with \emph{utility}, an accuracy-related measure of aggregated relevancy scores. Since these measures are non-differentiable, we reformulate them using a probabilistic interpretation of recommender systems, yielding differentiable versions. We combine these loss functions in a multi-objective optimization problem called \texttt{FEIR} (Fairness through Envy and Inferiority Reduction), applied as post-processing for standard recommender systems. Experiments on synthetic and real-world data demonstrate that our approach improves trade-offs between inferiority, envy, and utility compared to naive recommendations and the baseline methods.
On Characterizing the Evolution of Embedding Space of Neural Networks using Algebraic Topology
Venue: arXiv cs.LG 2023
Year: 2023

We study how the topology of feature embedding space changes as it passes through the layers of a well-trained deep neural network (DNN) through Betti numbers. Motivated by existing studies using simplicial complexes on shallow fully connected networks (FCN), we present an extended analysis using Cubical homology instead, with a variety of popular deep architectures and real image datasets. We demonstrate that as depth increases, a topologically complicated dataset is transformed into a simple one, resulting in Betti numbers attaining their lowest possible value. The rate of decay in topological complexity (as a metric) helps quantify the impact of architectural choices on the generalization ability. Interestingly from a representation learning perspective, we highlight several invariances such as topological invariance of (1) an architecture on similar datasets; (2) embedding space of a dataset for architectures of variable depth; (3) embedding space to input resolution/size, and (4) data sub-sampling. In order to further demonstrate the link between expressivity \& the generalization capability of a network, we consider the task of ranking pre-trained models for downstream classification task (transfer learning). Compared to existing approaches, the proposed metric has a better correlation to the actually achievable accuracy via fine-tuning the pre-trained model.
LuminanceL1Loss: A loss function which measures percieved brightness and colour differences
Venue: arXiv cs.CV 2023
Year: 2023

We introduce LuminanceL1Loss, a novel loss function designed to enhance the performance of image restoration tasks. We demonstrate its superiority over MSE when applied to the Retinexformer, BUIFD and DnCNN architectures. Our proposed LuminanceL1Loss leverages a unique approach by transforming images into grayscale and subsequently computing the MSE loss for both grayscale and color channels. Experimental results demonstrate that this innovative loss function consistently outperforms traditional methods, showcasing its potential in image denoising and other related tasks in image reconstruction. It demonstrates gains up to 4.7dB. The results presented in this study highlight the efficacy of LuminanceL1Loss for various image restoration tasks.
AIRIC: Orchestration of Virtualized Radio Access Networks with Noisy Neighbours
Venue: arXiv cs.NI 2023
Year: 2023

Radio Access Networks virtualization (vRAN) is on its way becoming a reality driven by the new requirements in mobile networks, such as scalability and cost reduction. Unfortunately, there is no free lunch but a high price to be paid in terms of computing overhead introduced by noisy neighbors problem when multiple virtualized base station instances share computing platforms. In this paper, first, we thoroughly dissect the multiple sources of computing overhead in a vRAN, quantifying their different contributions to the overall performance degradation. Second, we design an AI-driven Radio Intelligent Controller (AIRIC) to orchestrate vRAN computing resources. AIRIC relies upon a hybrid neural network architecture combining a relation network (RN) and a deep Q-Network (DQN) such that: (i) the demand of concurrent virtual base stations is satisfied considering the overhead posed by the noisy neighbors problem while the operating costs of the vRAN infrastructure is minimized; and (ii) dynamically changing contexts in terms of network demand, signal-to-noise ratio (SNR) and the number of base station instances are efficiently supported. Our results show that AIRIC performs very closely to an offline optimal oracle, attaining up to 30% resource savings, and substantially outperforms existing benchmarks in service guarantees.
Higher-Order Bayesian Networks, Exactly (Extended version)
Venue: arXiv cs.LO 2023
Year: 2023

Bayesian networks (BNs) are graphical \emph{first-order} probabilistic models that allow for a compact representation of large probability distributions, and for efficient inference, both exact and approximate. We introduce a \emph{higher-order} programming language -- in the idealized form of a $λ$-calculus -- which we prove \emph{sound and complete} w.r.t. BNs: each BN can be encoded as a term, and conversely each (possibly higher-order and recursive) program of ground type \emph{compiles} into a BN. The language allows for the specification of recursive probability models and hierarchical structures. Moreover, we provide a \emph{compositional} and \emph{cost-aware} semantics which is based on factors, the standard mathematical tool used in Bayesian inference. Our results rely on advanced techniques rooted into linear logic, intersection types, rewriting theory, and Girard's geometry of interaction, which are here combined in a novel way.
An Empirical Analysis of Just-in-Time Compilation in Modern Databases
Venue: arXiv cs.DB 2023
Year: 2023

JIT (Just-in-Time) technology has garnered significant attention for improving the efficiency of database execution. It offers higher performance by eliminating interpretation overhead compared to traditional execution engines. LLVM serves as the primary JIT architecture, which was implemented in PostgreSQL since version 11. However, recent advancements in WASM-based databases, such as Mutable, present an alternative JIT approach. This approach minimizes the extensive engineering efforts associated with the execution engine and focuses on optimizing supported operators for lower latency and higher throughput. In this paper, we perform comprehensive experiments on these two representative open-source databases to gain deeper insights into the effectiveness of different JIT architectures.
3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud
Venue: arXiv cs.CV 2023
Year: 2023

Greenhouse production of fruits and vegetables in developed countries is challenged by labor 12 scarcity and high labor costs. Robots offer a good solution for sustainable and cost-effective 13 production. Acquiring accurate spatial information about relevant plant parts is vital for 14 successful robot operation. Robot perception in greenhouses is challenging due to variations in 15 plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based 16 method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which 17 provides essential information to harvest the tomato bunches. 18 19 Specifically, this paper proposes a method that detects four anatomical landmarks in the color 20 image and then integrates 3D point-cloud information to determine the 3D pose. A 21 comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the 22 performance of different parts of the method. The results showed: (1) high accuracy in object 23 detection, achieving an Average Precision (AP) of AP@0.5=0.96; (2) an average Percentage of 24 Detected Joints (PDJ) of the keypoints of PhDJ@0.2=94.31%; and (3) 3D pose estimation 25 accuracy with mean absolute errors (MAE) of 11.38o and 9.93o for the relative upper and lower 26 angles between the peduncle and main stem, respectively. Furthermore, the capability to handle 27 variations in viewpoint was investigated, demonstrating the method was robust to view changes. 28 However, canonical and higher views resulted in slightly higher performance compared to other 29 views. Although tomato was selected as a use case, the proposed method is also applicable to 30 other greenhouse crops like pepper.
Negotiation Strategies in Ubiquitous Human-Computer Interaction: A Novel Storyboards Scale & Field Study
Venue: arXiv cs.HC 2023
Year: 2023

In today's connected society, self-tracking technologies (STTs), such as wearables and mobile fitness apps, empower humans to improve their health and well-being through ubiquitous physical activity monitoring, with several personal and societal benefits. Despite the advances in such technologies' hardware, low user engagement and decreased effectiveness limitations demand more informed and theoretically-founded Human-Computer Interaction designs. To address these challenges, we build upon the previously unexplored Leisure Constraints Negotiation Model and the Transtheoretical Model to systematically define and assess the effectiveness of STTs' features that acknowledge users' contextual constraints and establish human-negotiated STTs narratives. Specifically, we introduce and validate a human-centric scale, StoryWear, which exploits and explores eleven dimensions of negotiation strategies that humans utilize to overcome constraints regarding exercise participation, captured through an inclusive storyboards format. Based on our preliminary studies, StoryWear shows high reliability, rendering it suitable for future work in ubiquitous computing. Our results indicate that negotiation strategies vary in perceived effectiveness and have higher appeal for existing STTs' users, with self-motivation, commitment, and understanding of the negative impact of non-exercise placed at the top. Finally, we give actionable guidelines for real-world implementation and a commentary on the future of personalized training.