OpenRaiser/Intern-Atlas huggingface dataset:
$\textit{e-Uber}$: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing
Venue: arXiv cs.AI 2023
Year: 2023

The sharing-economy-based business model has recently seen success in the transportation and accommodation sectors with companies like Uber and Airbnb. There is growing interest in applying this model to energy systems, with modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). In this work, we exploit the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits spatial crowdsourcing, reinforcement learning, and reverse auction theory. Specifically, the platform uses reinforcement learning to understand the drivers' preferences towards different ride-sharing and energy-sharing tasks. Based on these preferences, a personalized list is recommended to each driver through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid on their preferred tasks in their list in a reverse auction fashion. Then e-Uber solves the task assignment optimization problem that minimizes cost and guarantees V2G energy requirement. We prove that this problem is NP-hard and introduce a bipartite matching-inspired heuristic, Bipartite Matching-based Winner selection (BMW), that has polynomial time complexity. Results from experiments using real data from NYC taxi trips and energy consumption show that e-Uber performs close to the optimum and finds better solutions compared to a state-of-the-art approach
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion
Venue: arXiv cs.CV 2023
Year: 2023

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility.
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Venue: arXiv cs.LG 2023
Year: 2023

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.
Stochastic Domain Wall-Magnetic Tunnel Junction Artificial Neurons for Noise-Resilient Spiking Neural Networks
Venue: arXiv cs.NE 2023
Year: 2023

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring advantages of scalability and energy efficiency. Domain wall (DW) based magnetic tunnel junction (MTJ) devices are well suited for probabilistic neural networks given their intrinsic integrate-and-fire behavior with tunable stochasticity. Here, we present a scaled DW-MTJ neuron with voltage-dependent firing probability. The measured behavior was used to simulate a SNN that attains accuracy during learning compared to an equivalent, but more complicated, multi-weight (MW) DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire (LIF) device. However, during inference, the binary DW-MTJ neuron outperformed the other devices after gaussian noise was introduced to the Fashion-MNIST classification task. This work shows that DW-MTJ devices can be used to construct noise-resilient networks suitable for neuromorphic computing on the edge.
Characteristics of LIS Research Articles Affecting Their Citation Impact
Venue: arXiv cs.DL 2023
Year: 2023

The paper analyses the citation impact of Library and Information Science, LIS for short, research articles published in 31 leading international LIS journals in 2015. The main research question is: to what degree do authors' disciplinary composition in association with other content characteristics of LIS articles affect their citation impact? The impact is analysed in terms of the number of citations received and their authority, using outlier normalization and subfield normalization. The article characteristics analysed using quantitative content analysis include topic, methodology, type of contribution, and the disciplinary composition of their author teams. The citations received by the articles are traced from 2015 to May 2021. Citing document authority is measured by the citations they had received up to May 2021. The overall finding was that authors' disciplinary composition is significantly associated with citation scores. The differences in citation scores between disciplinary compositions appeared typically within information retrieval and scientific communication. In both topics LIS and computer science jointly received significantly higher citation scores than many disciplines like LIS alone or humanities in information retrieval, or natural sciences, medicine, or social sciences alone in scientific communication. The paper is original in allowing joint analysis of content, authorship composition, and impact.
Measuring and Evading Turkmenistan's Internet Censorship: A Case Study in Large-Scale Measurements of a Low-Penetration Country
Venue: arXiv cs.CR 2023
Year: 2023

Since 2006, Turkmenistan has been listed as one of the few Internet enemies by Reporters without Borders due to its extensively censored Internet and strictly regulated information control policies. Existing reports of filtering in Turkmenistan rely on a small number of vantage points or test a small number of websites. Yet, the country's poor Internet adoption rates and small population can make more comprehensive measurement challenging. With a population of only six million people and an Internet penetration rate of only 38%, it is challenging to either recruit in-country volunteers or obtain vantage points to conduct remote network measurements at scale. We present the largest measurement study to date of Turkmenistan's Web censorship. To do so, we developed TMC, which tests the blocking status of millions of domains across the three foundational protocols of the Web (DNS, HTTP, and HTTPS). Importantly, TMC does not require access to vantage points in the country. We apply TMC to 15.5M domains, our results reveal that Turkmenistan censors more than 122K domains, using different blocklists for each protocol. We also reverse-engineer these censored domains, identifying 6K over-blocking rules causing incidental filtering of more than 5.4M domains. Finally, we use Geneva, an open-source censorship evasion tool, to discover five new censorship evasion strategies that can defeat Turkmenistan's censorship at both transport and application layers. We will publicly release both the data collected by TMC and the code for censorship evasion.
SNIPS: Succinct Proof of Storage for Efficient Data Synchronization in Decentralized Storage Systems
Venue: arXiv cs.DC 2023
Year: 2023

Data synchronization in decentralized storage systems is essential to guarantee sufficient redundancy to prevent data loss. We present SNIPS, the first succinct proof of storage algorithm for synchronizing storage peers. A peer constructs a proof for its stored chunks and sends it to verifier peers. A verifier queries the proof to identify and subsequently requests missing chunks. The proof is succinct, supports membership queries, and requires only a few bits per chunk. We evaluated our SNIPS algorithm on a cluster of 1000 peers running Ethereum Swarm. Our results show that SNIPS reduces the amount of synchronization data by three orders of magnitude compared to the state-of-the-art. Additionally, creating and verifying a proof is linear with the number of chunks and typically requires only tens of microseconds per chunk. These qualities are vital for our use case, as we envision running SNIPS frequently to maintain sufficient redundancy consistently.
Financial Time Series Forecasting using CNN and Transformer
Venue: arXiv cs.LG 2023
Year: 2023

Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult to model short-term and long-term temporal dependencies between data points. Convolutional Neural Networks (CNN) are good at capturing local patterns for modeling short-term dependencies. However, CNNs cannot learn long-term dependencies due to the limited receptive field. Transformers on the other hand are capable of learning global context and long-term dependencies. In this paper, we propose to harness the power of CNNs and Transformers to model both short-term and long-term dependencies within a time series, and forecast if the price would go up, down or remain the same (flat) in the future. In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.
Life Regression based Patch Slimming for Vision Transformers
Venue: arXiv cs.CV 2023
Year: 2023

Vision transformers have achieved remarkable success in computer vision tasks by using multi-head self-attention modules to capture long-range dependencies within images. However, the high inference computation cost poses a new challenge. Several methods have been proposed to address this problem, mainly by slimming patches. In the inference stage, these methods classify patches into two classes, one to keep and the other to discard in multiple layers. This approach results in additional computation at every layer where patches are discarded, which hinders inference acceleration. In this study, we tackle the patch slimming problem from a different perspective by proposing a life regression module that determines the lifespan of each image patch in one go. During inference, the patch is discarded once the current layer index exceeds its life. Our proposed method avoids additional computation and parameters in multiple layers to enhance inference speed while maintaining competitive performance. Additionally, our approach requires fewer training epochs than other patch slimming methods.
ClusterFusion: Real-time Relative Positioning and Dense Reconstruction for UAV Cluster
Venue: arXiv cs.RO 2023
Year: 2023

As robotics technology advances, dense point cloud maps are increasingly in demand. However, dense reconstruction using a single unmanned aerial vehicle (UAV) suffers from limitations in flight speed and battery power, resulting in slow reconstruction and low coverage. Cluster UAV systems offer greater flexibility and wider coverage for map building. Existing methods of cluster UAVs face challenges with accurate relative positioning, scale drift, and high-speed dense point cloud map generation. To address these issues, we propose a cluster framework for large-scale dense reconstruction and real-time collaborative localization. The front-end of the framework is an improved visual odometry which can effectively handle large-scale scenes. Collaborative localization between UAVs is enabled through a two-stage joint optimization algorithm and a relative pose optimization algorithm, effectively achieving accurate relative positioning of UAVs and mitigating scale drift. Estimated poses are used to achieve real-time dense reconstruction and fusion of point cloud maps. To evaluate the performance of our proposed method, we conduct qualitative and quantitative experiments on real-world data. The results demonstrate that our framework can effectively suppress scale drift and generate large-scale dense point cloud maps in real-time, with the reconstruction speed increasing as more UAVs are added to the system.
Multi-Graph Convolution Network for Pose Forecasting
Venue: arXiv cs.CV 2023
Year: 2023

Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose sequences. This task is complex due to modeling spatial and temporal relationships. The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks. However, RNNs have several drawbacks, such as vanishing or exploding gradients. Other researchers have attempted to solve the communication problem in the spatial dimension by integrating Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) models. These works deal with temporal and spatial information separately, which limits the effectiveness. To fix this problem, we propose a novel approach called the multi-graph convolution network (MGCN) for 3D human pose forecasting. This model simultaneously captures spatial and temporal information by introducing an augmented graph for pose sequences. Multiple frames give multiple parts, joined together in a single graph instance. Furthermore, we also explore the influence of natural structure and sequence-aware attention to our model. In our experimental evaluation of the large-scale benchmark datasets, Human3.6M, AMSS and 3DPW, MGCN outperforms the state-of-the-art in pose prediction.
A Data Set of Generalizable Python Code Change Patterns
Venue: arXiv cs.SE 2023
Year: 2023

Mining repetitive code changes from version control history is a common way of discovering unknown change patterns. Such change patterns can be used in code recommender systems or automated program repair techniques. While there are such tools and datasets exist for Java, there is little work on finding and recommending such changes in Python. In this paper, we present a data set of manually vetted generalizable Python repetitive code change patterns. We create a coding guideline to identify generalizable change patterns that can be used in automated tooling. We leverage the mined change patterns from recent work that mines repetitive changes in Python projects and use our coding guideline to manually review the patterns. For each change, we also record a description of the change and why it is applied along with other characteristics such as the number of projects it occurs in. This review process allows us to identify and share 72 Python change patterns that can be used to build and advance Python developer support tools.
Execution traces and reduction sequences
Venue: arXiv cs.LO 2023
Year: 2023

In this note, we defend that the notion of algorithm as a set of execution traces is somewhat independent of the notion of abstract state machine. It can be reformulated in the more general framework of small step operational semantics.
Real-Time Character Rise Motions
Venue: arXiv cs.RO 2023
Year: 2023

This paper presents an uncomplicated dynamic controller for generating physically-plausible three-dimensional full-body biped character rise motions on-the-fly at run-time. Our low-dimensional controller uses fundamental reference information (e.g., center-of-mass, hands, and feet locations) to produce balanced biped get-up poses by means of a real-time physically-based simulation. The key idea is to use a simple approximate model (i.e., similar to the inverted-pendulum stepping model) to create continuous reference trajectories that can be seamlessly tracked by an articulated biped character to create balanced rise-motions. Our approach does not use any key-framed data or any computationally expensive processing (e.g., offline-optimization or search algorithms). We demonstrate the effectiveness and ease of our technique through example (i.e., a biped character picking itself up from different laying positions).
A Novel Two-Layer DAG-based Reactive Protocol for IoT Data Reliability in Metaverse
Venue: arXiv cs.DC 2023
Year: 2023

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49\% of nodes are malicious.
APISENS- Sentiment Scoring Tool for APIs with Crowd-Knowledge
Venue: arXiv cs.SE 2023
Year: 2023

Utilizing pre-existing software artifacts, such as libraries and Application Programming Interfaces (APIs), is crucial for software development efficiency. However, the abundance of artifacts that provide similar functionality can lead to confusion among developers, resulting in a challenge for proper selection and implementation. Through our preliminary investigation, we found that utilizing the collective knowledge of a crowd can greatly assist developers in acquiring a thorough and complete understanding of the complexities involved in the software development process. Especially as emotions are an inseparable part of human nature, it influences developers' activities. In this regard, we attempt to build a tool that can retrieve sentiment information for software APIs so that developers can determine APIs to utilize for their tasks. We employ the dataset from the most popular platforms (i.e., Twitter and YouTube) to build our research prototype. The source code, tool, and demo video are available on GitHub at \url{https://github.com/FalconLK/APISens}.
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies have shown that private information can still be leaked through shared gradients. To further minimize the risk of privacy leakage, existing defenses usually require clients to locally modify their gradients (e.g., differential privacy) prior to sharing with the server. While these approaches are effective in certain cases, they regard the entire data as a single entity to protect, which usually comes at a large cost in model utility. In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses. Moreover, we observe that existing inference attacks often rely on a machine learning model to extract the private information (e.g., attributes). We thus formulate such a privacy defense as an adversarial learning problem, where RecUP-FL generates slight perturbations that can be added to the gradients before sharing to fool adversary models. To improve the transferability to un-queryable black-box adversary models, inspired by the idea of meta-learning, RecUP-FL forms a model zoo containing a set of substitute models and iteratively alternates between simulations of the white-box and the black-box adversarial attack scenarios to generate perturbations. Extensive experiments on four datasets under various adversarial settings (both attribute inference attack and data reconstruction attack) show that RecUP-FL can meet user-specified privacy constraints over the sensitive attributes while significantly improving the model utility compared with state-of-the-art privacy defenses.
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Venue: arXiv cs.CV 2023
Year: 2023

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
HPN: Personalized Federated Hyperparameter Optimization
Venue: arXiv cs.LG 2023
Year: 2023

Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and challenging problems. However, existing works predominantly focus on tailoring models. Yet, due to the heterogeneity of clients, they may each require different choices of hyperparameters, which have not been studied so far. We pinpoint two challenges of personalized federated hyperparameter optimization (pFedHPO): handling the exponentially increased search space and characterizing each client without compromising its data privacy. To overcome them, we propose learning a \textsc{H}yper\textsc{P}arameter \textsc{N}etwork (HPN) fed with client encoding to decide personalized hyperparameters. The client encoding is calculated with a random projection-based procedure to protect each client's privacy. Besides, we design a novel mechanism to debias the low-fidelity function evaluation samples for learning HPN. We conduct extensive experiments on FL tasks from various domains, demonstrating the superiority of HPN.