OpenRaiser/Intern-Atlas huggingface dataset:
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation
Venue: arXiv cs.LG 2023
Year: 2023

Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness, and robustness. Unfortunately, on the one hand, sound and complete certification algorithms of ReLU-based NNs do not scale to large-scale NNs. On the other hand, incomplete certification algorithms are easier to compute, but they result in loose bounds that deteriorate with the depth of NN, which diminishes their effectiveness. In this paper, we ask the following question; can we replace the ReLU activation function with one that opens the door to incomplete certification algorithms that are easy to compute but can produce tight bounds on the NN's outputs? We introduce DeepBern-Nets, a class of NNs with activation functions based on Bernstein polynomials instead of the commonly used ReLU activation. Bernstein polynomials are smooth and differentiable functions with desirable properties such as the so-called range enclosure and subdivision properties. We design a novel algorithm, called Bern-IBP, to efficiently compute tight bounds on DeepBern-Nets outputs. Our approach leverages the properties of Bernstein polynomials to improve the tractability of neural network certification tasks while maintaining the accuracy of the trained networks. We conduct comprehensive experiments in adversarial robustness and reachability analysis settings to assess the effectiveness of the proposed Bernstein polynomial activation in enhancing the certification process. Our proposed framework achieves high certified accuracy for adversarially-trained NNs, which is often a challenging task for certifiers of ReLU-based NNs. Moreover, using Bern-IBP bounds for certified training results in NNs with state-of-the-art certified accuracy compared to ReLU networks. This work establishes Bernstein polynomial activation as a promising alternative for improving NN certification tasks across various applications.
Understanding and Improving Optimization in Predictive Coding Networks
Venue: arXiv cs.NE 2023
Year: 2023

Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible. In contrast, the standard learning algorithm for predictive coding (PC) models in neuroscience, known as the inference learning algorithm (IL), is a promising, bio-plausible alternative. However, several challenges and questions hinder IL's application to real-world problems. For example, IL is computationally demanding, and without memory-intensive optimizers like Adam, IL may converge to poor local minima. Moreover, although IL can reduce loss more quickly than BP, the reasons for these speedups or their robustness remains unclear. In this paper, we tackle these challenges by 1) altering the standard implementation of PC circuits to substantially reduce computation, 2) developing a novel optimizer that improves the convergence of IL without increasing memory usage, and 3) establishing theoretical results that help elucidate the conditions under which IL is sensitive to second and higher-order information.
IdEALS: Idiomatic Expressions for Advancement of Language Skills
Venue: arXiv cs.CL 2023
Year: 2023

Although significant progress has been made in developing methods for Grammatical Error Correction (GEC), addressing word choice improvements has been notably lacking and enhancing sentence expressivity by replacing phrases with advanced expressions is an understudied aspect. In this paper, we focus on this area and present our investigation into the task of incorporating the usage of idiomatic expressions in student writing. To facilitate our study, we curate extensive training sets and expert-annotated testing sets using real-world data and evaluate various approaches and compare their performance against human experts.
Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification
Venue: arXiv cs.CV 2023
Year: 2023

Multi-spectral vehicle re-identification aims to address the challenge of identifying vehicles in complex lighting conditions by incorporating complementary visible and infrared information. However, in harsh environments, the discriminative cues in RGB and NIR modalities are often lost due to strong flares from vehicle lamps or sunlight, and existing multi-modal fusion methods are limited in their ability to recover these important cues. To address this problem, we propose a Flare-Aware Cross-modal Enhancement Network that adaptively restores flare-corrupted RGB and NIR features with guidance from the flare-immunized thermal infrared spectrum. First, to reduce the influence of locally degraded appearance due to intense flare, we propose a Mutual Flare Mask Prediction module to jointly obtain flare-corrupted masks in RGB and NIR modalities in a self-supervised manner. Second, to use the flare-immunized TI information to enhance the masked RGB and NIR, we propose a Flare-Aware Cross-modal Enhancement module that adaptively guides feature extraction of masked RGB and NIR spectra with prior flare-immunized knowledge from the TI spectrum. Third, to extract common informative semantic information from RGB and NIR, we propose an Inter-modality Consistency loss that enforces semantic consistency between the two modalities. Finally, to evaluate the proposed FACENet in handling intense flare, we introduce a new multi-spectral vehicle re-ID dataset, called WMVEID863, with additional challenges such as motion blur, significant background changes, and particularly intense flare degradation. Comprehensive experiments on both the newly collected dataset and public benchmark multi-spectral vehicle re-ID datasets demonstrate the superior performance of the proposed FACENet compared to state-of-the-art methods, especially in handling strong flares. The code and dataset will be released at this link.
Are you cloud-certified? Preparing Computing Undergraduates for Cloud Certification with Experiential Learning
Venue: arXiv cs.SE 2023
Year: 2023

Cloud Computing skills have been increasing in demand. Many software engineers are learning these skills and taking cloud certification examinations to be job competitive. Preparing undergraduates to be cloud-certified remains challenging as cloud computing is a relatively new topic in the computing curriculum, and many of these certifications require working experience. In this paper, we report our experiences designing a course with experiential learning to prepare our computing undergraduates to take the cloud certification. We adopt a university project-based experiential learning framework to engage industry partners who provide project requirements for students to develop cloud solutions and an experiential risk learning model to design the course contents. We prepare these students to take on the Amazon Web Services Solution Architect - Associate (AWS-SAA) while doing the course. We do this over 3 semester terms and report our findings before and after our design with experiential learning. We are motivated by the students' average 93\% passing rates over the terms. Even when the certification is taken out of the graded components, we still see an encouraging 89\% participation rate. The quantitative feedback shows increased ratings across the survey questions compared to before experiential learning. We acknowledge concerns about the students' heavy workload and increased administrative efforts for the faculty members. We summarise our approach with actionable weekly topics, activities and takeaways. We hope this experience report can help other educators design cloud computing content and certifications for computing students in software engineering.
iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous parking (AP) is an emering technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.
ChatGPT, Can You Generate Solutions for my Coding Exercises? An Evaluation on its Effectiveness in an undergraduate Java Programming Course
Venue: arXiv cs.SE 2023
Year: 2023

In this study, we assess the efficacy of employing the ChatGPT language model to generate solutions for coding exercises within an undergraduate Java programming course. ChatGPT, a large-scale, deep learning-driven natural language processing model, is capable of producing programming code based on textual input. Our evaluation involves analyzing ChatGPT-generated solutions for 80 diverse programming exercises and comparing them to the correct solutions. Our findings indicate that ChatGPT accurately generates Java programming solutions, which are characterized by high readability and well-structured organization. Additionally, the model can produce alternative, memory-efficient solutions. However, as a natural language processing model, ChatGPT struggles with coding exercises containing non-textual descriptions or class files, leading to invalid solutions. In conclusion, ChatGPT holds potential as a valuable tool for students seeking to overcome programming challenges and explore alternative approaches to solving coding problems. By understanding its limitations, educators can design coding exercises that minimize the potential for misuse as a cheating aid while maintaining their validity as assessment tools.
UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning
Venue: arXiv cs.CL 2023
Year: 2023

Vision-and-language (VL) pre-training, which aims to learn a general representation of image-text pairs that can be transferred to various vision-and-language tasks. Compared with modeling uni-modal data, the main challenge of the VL model is: how to learn the cross-modal interaction from multimodal data, especially the fine-grained interaction. Existing works have shown that fully transformer-based models that adopt attention mechanisms to learn in-layer cross-model interaction can demonstrate impressive performance on various cross-modal downstream tasks. However, they ignored that the semantic information of the different modals at the same layer was not uniform, which leads to the cross-modal interaction collapsing into a limited multi-modal semantic information interaction. In this work, we propose the UNIMO-3 model, which has the capacity to simultaneously learn the multimodal in-layer interaction and cross-layer interaction. UNIMO-3 model can establish effective connections between different layers in a cross-modal encoder, and adaptively capture the interaction between two modalities at different levels. The experimental results show that our model achieves state-of-the-art performance in various downstream tasks, and through ablation study can prove that effective cross-layer learning improves the model's ability of multimodal representation.
Achieving Maximum Efficiency in Schnorr-based Multi-signature and Applications in Blockchain
Venue: arXiv cs.CR 2023
Year: 2023

Multi-signature aggregates signatures from multiple users on the same message into a joint signature, which is widely applied in blockchain to reduce the percentage of signatures in blocks and improve the throughput of transactions. The $k$-sum attacks are one of the major challenges to design secure multi-signature schemes. In this work, we address $k$-sum attacks from a novel angle by defining a Public Third Party (PTP), which is an automatic process that can be verifiable by the public and restricts the signing phase from continuing until receiving commitments from all signers. Further, a two-round multi-signature scheme MEMS with PTP is proposed, which is secure based on discrete logarithm assumption in the random oracle model. As each signer communicates directly with the PTP instead of other co-signers, the total amount of communications is significantly reduced. In addition, as PTP participates in the computation of the aggregation and signing algorithms, the computation cost left for each signer and verifier remains the same as the basis Schnorr signature. To the best of our knowledge, this is the maximum efficiency that a Schnorr-based multi-signature scheme can achieve. Further, MEMS is applied in blockchain platform, e.g., Fabric, to improve the transaction efficiency.
FlowChroma -- A Deep Recurrent Neural Network for Video Colorization
Venue: arXiv cs.CV 2023
Year: 2023

We develop an automated video colorization framework that minimizes the flickering of colors across frames. If we apply image colorization techniques to successive frames of a video, they treat each frame as a separate colorization task. Thus, they do not necessarily maintain the colors of a scene consistently across subsequent frames. The proposed solution includes a novel deep recurrent encoder-decoder architecture which is capable of maintaining temporal and contextual coherence between consecutive frames of a video. We use a high-level semantic feature extractor to automatically identify the context of a scenario including objects, with a custom fusion layer that combines the spatial and temporal features of a frame sequence. We demonstrate experimental results, qualitatively showing that recurrent neural networks can be successfully used to improve color consistency in video colorization.
LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

We propose LLM-Eval, a unified multi-dimensional automatic evaluation method for open-domain conversations with large language models (LLMs). Existing evaluation methods often rely on human annotations, ground-truth responses, or multiple LLM prompts, which can be expensive and time-consuming. To address these issues, we design a single prompt-based evaluation method that leverages a unified evaluation schema to cover multiple dimensions of conversation quality in a single model call. We extensively evaluate the performance of LLM-Eval on various benchmark datasets, demonstrating its effectiveness, efficiency, and adaptability compared to state-of-the-art evaluation methods. Our analysis also highlights the importance of choosing suitable LLMs and decoding strategies for accurate evaluation results. LLM-Eval offers a versatile and robust solution for evaluating open-domain conversation systems, streamlining the evaluation process and providing consistent performance across diverse scenarios.
The Drivers of Global News Spreading Patterns
Venue: arXiv cs.SI 2023
Year: 2023

The web radically changed the dissemination of information and the global spread of news. In this study, we aim to reconstruct the connectivity patterns within nations shaping news propagation globally in 2022. We do this by analyzing a dataset of unprecedented size, containing 140 million news articles from 183 countries and related to 37,802 domains in the GDELT database. Unlike previous research, we focus on the sequential mention of events across various countries, thus incorporating a temporal dimension into the analysis of news dissemination networks. Our results show a significant imbalance in online news spreading. We identify news superspreaders forming a tightly interconnected rich club, exerting significant influence on the global news agenda. To further investigate the mechanisms underlying news dissemination and the shaping of global public opinion, we model countries' interactions using a gravity model, incorporating economic, geographical, and cultural factors. Consistent with previous studies, we find that countries' GDP is one of the main drivers to shape the worldwide news agenda.
Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles
Venue: arXiv cs.RO 2023
Year: 2023

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length on a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) Transform the high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q network (DQN) to find the desired path on the small-size map. (3) Build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. Simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency. With similar planned paths, the speed of the proposed method is more than 100 times faster than that of the A* method and 30 times faster than that of H3DM method. Also, simulation proves that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks.
A Critical Reexamination of Intra-List Distance and Dispersion
Venue: arXiv cs.IR 2023
Year: 2023

Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified recommendation is to define and optimize an appropriate diversity objective. In this study, we revisit the most popular diversity objective called intra-list distance (ILD), defined as the average pairwise distance between selected items, and a similar but lesser known objective called dispersion, which is the minimum pairwise distance. Owing to their simplicity and flexibility, ILD and dispersion have been used in a plethora of diversified recommendation research. Nevertheless, we do not actually know what kind of items are preferred by them. We present a critical reexamination of ILD and dispersion from theoretical and experimental perspectives. Our theoretical results reveal that these objectives have potential drawbacks: ILD may select duplicate items that are very close to each other, whereas dispersion may overlook distant item pairs. As a competitor to ILD and dispersion, we design a diversity objective called Gaussian ILD, which can interpolate between ILD and dispersion by tuning the bandwidth parameter. We verify our theoretical results by experimental results using real-world data and confirm the extreme behavior of ILD and dispersion in practice.
Differentiable Stripe Patterns for Inverse Design of Structured Surfaces
Venue: arXiv cs.GR 2023
Year: 2023

Stripe patterns are ubiquitous in nature and everyday life. While the synthesis of these patterns has been thoroughly studied in the literature, their potential to control the mechanics of structured materials remains largely unexplored. In this work, we introduce Differentiable Stripe Patterns -- a computational approach for automated design of physical surfaces structured with stripe-shaped bi-material distributions. Our method builds on the work by Knoppel and colleagues for generating globally-continuous and equally-spaced stripe patterns. To unlock the full potential of this design space, we propose a gradient-based optimization tool to automatically compute stripe patterns that best approximate macromechanical performance goals. Specifically, we propose a computational model that combines solid shell finite elements with XFEM for accurate and fully-differentiable modeling of elastic bi-material surfaces. To resolve non-uniqueness problems in the original method, we furthermore propose a robust formulation that yields unique and differentiable stripe patterns. %Finally, we introduce design space regularizers to avoid numerical singularities and improve stripe neatness We combine these components with equilibrium state derivatives into an end-to-end differentiable pipeline that enables inverse design of mechanical stripe patterns. We demonstrate our method on a diverse set of examples that illustrate the potential of stripe patterns as a design space for structured materials. Our simulation results are experimentally validated on physical prototypes.
The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey
Venue: arXiv cs.DC 2023
Year: 2023

Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. With the rapid growing size of real world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. In addition, we establish connections between GNN systems, graph processing systems and DL systems.
Walsh Meets OAM in Holographic MIMO
Venue: arXiv cs.IT 2023
Year: 2023

Holographic multiple-input multiple-output (MIMO) is deemed as a promising technique beyond massive MIMO, unleashing near-field communications, localization, and sensing in the next-generation wireless networks. Semi-continuous surface with densely packed elements brings new opportunities for increased spatial degrees of freedom (DoFs) and spectrum efficiency (SE) even in the line-of-sight (LoS) condition. In this paper, we analyze holographic MIMO performance with disk-shaped large intelligent surfaces (LISs) according to different precoding designs. Beyond the well-known technique of orbital angular momentum (OAM) of radio waves, we propose a new design based on polar Walsh functions. Furthermore, we characterize the performance gap between the proposed scheme and the optimal case with singular value decomposition (SVD) alongside perfect channel state information (CSI) as well as other benchmark schemes in terms of channel capacity. It is verified that the proposed scheme marginally underperforms the OAM-based approach, while offering potential perspectives for reducing implementation complexity and expenditure.