Reviewerly/exHarmony huggingface dataset:
Cluster-aware multiplex InfoMax for unsupervised graph representation learning

Unsupervised graph learning aims to learn an encoder that embeds high-dimensional nodes into compact continuous vectors and preserves the topological and semantic features simultaneously without using any label information. Recently, contrastive learning (CL) on graph learning revives the traditional InfoMax principle and generates two views of the input graph randomly and then maximizes the agreements between them. However, the stochastic augmentation of a graph leads to two problems that need to be solved. Firstly, it ignores the role of some essential nodes and discriminating feature dimensions on the graph and may decrease the informativeness of the generated view by removing these crucial edges. Secondly, there are multi-level substructures of a graph that can be exploited and utilized for the network encoder's topological learning. This paper proposes Cluster-Aware Multiplex InfoMax (CAMI) for unsupervised graph representation learning. We apply an adaptive graph augmentation scheme on both topological and feature dimensions to generate graph views without damaging the vital graph structure. To encourage the network encoder to capture more underlying node interactions, we additionally increase a mutual information maximization constraint between the node's representation and multi-level graph summaries. Extensive experimental results on seven realistic datasets with different tasks prove the CAMI framework's effectiveness.
Co-TS: Design and Implementation of a 2-UAV Cooperative Transportation System

UAV application research has attracted more and more attention of researchers. However, in the field of UAV transportation, especially in the case of multi-UAV cooperation, there is little research or design on the overall system of multi-UAV cooperative transportation due to the complexity of the task and many factors need to be considered. In this paper, we first introduce a 2-UAV cooperative transportation task scenario and we call it Co-TS. Then according to the scenario, we design a task planning system, which describes the entire process of 2-UAV performing transportation tasks; in order to solve the design complexity of this UAV upper-layer software, we propose a model architecture of 2-UAV transportation application, design the functions of each module and analyze the relationship between modules in the model architecture; in addition, to realize the correct execution of the 2-UAV cooperative transportation task, the task execution is divided into four states which include preparation, rising, forwarding, and landing states, and the corresponding cooperative transportation control flow algorithm is designed for four states. Finally, we develop this system and conduct experiments. The quantitative and qualitative results demonstrate the effectiveness of our method.
Advanced DAG-Based Ranking (ADR) Protocol for Blockchain Scalability

In the past decade, blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention. However, current blockchain systems face the problems of limited throughput, poor scalability, and high latency. Due to the failure of consensus algorithms in managing nodes’identities, blockchain technology is considered inappropriate for many applications, e.g., in IoT environments, because of poor scalability. This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking (ADR) protocol to improve blockchain scalability and throughput. The ADR protocol uses the directed acyclic graph ledger, where nodes are placed according to their ranking positions in the graph. It allows honest nodes to use the Direct Acyclic Graph (DAG) topology to write blocks and verify transactions instead of a chain of blocks. By using a three-step strategy, this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability. The first step involves the safe entry of nodes into the system by verifying their private and public keys. The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions. In the third step, a ranking algorithm is developed to separate the nodes created by attackers. After eliminating attacker nodes, the nodes are ranked according to their performance in the system, and true nodes are arranged in blocks in topological order. As a result, the ADR protocol is suitable for applications in the Internet of Things (IoT). We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes. Based on the simulation results, this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications (IOTA) and ByteBall.
Cooperative offensive decision-making for soccer robots based on bi-channel Q-value evaluation MADDPG

Applications of discrete–continuous hybrid action decision-making are more common in real life. However, there are fewer studies on multi-robot deep reinforcement learning based on parameterized action spaces. Cooperative decision-making for soccer robots is the representative task for studying it. In this paper, the reward function is desired to guide the learning of cooperative offensive for soccer robots. Hence, the shooting angle reward is designed to improve the scoring rate based on the basic reward function. Moreover, a MADDPG network structure based on bi-channel Q-value estimation (BI-MAPDDPG) is proposed. Two channels of Critic network with the discrete action weight deal with coupling between the discrete action and continuous action parameters well. Finally, simulation results show that soccer robots’ cooperative offensive decision-making based on BI-MAPDDPG is robust and scalable.
DSP

Jointly utilizing multiple GPUs to train graph neural networks (GNNs) is crucial for handling large graphs and achieving high efficiency. However, we find that existing systems suffer from high communication costs and low GPU utilization due to improper data layout and training procedures. Thus, we propose a system dubbed Distributed Sampling and Pipelining (DSP) for multi-GPU GNN training. DSP adopts a tailored data layout to utilize the fast NVLink connections among the GPUs, which stores the graph topology and popular node features in GPU memory. For efficient graph sampling with multiple GPUs, we introduce a collective sampling primitive (CSP), which pushes the sampling tasks to data to reduce communication. We also design a producer-consumer-based pipeline, which allows tasks from different mini-batches to run congruently to improve GPU utilization. We compare DSP with state-of-the-art GNN training frameworks, and the results show that DSP consistently outperforms the baselines under different datasets, GNN models and GPU counts. The speedup of DSP can be up to 26x and is over 2x in most cases.
iQAN

Vector search has drawn a rapid increase of interest in the research community due to its application in novel AI applications. Maximizing its performance is essential for many tasks but remains preliminary understood. In this work, we investigate the root causes of the scalability bottleneck of using intra-query parallelism to speedup the state-of-the-art graph-based vector search systems on multi-core architectures. Our in-depth analysis reveals several scalability challenges from both system and algorithm perspectives. Based on the insights, we propose iQAN, a parallel search algorithm with a set of optimizations that boost convergence, avoid redundant computations, and mitigate synchronization overhead. Our evaluation results on a wide range of real-world datasets show that iQAN achieves up to 37.7× and 76.6× lower latency than state-of-the-art sequential baselines on datasets ranging from a million to a hundred million datasets. We also show that iQAN achieves outstanding scalability as the graph size or the accuracy target increases, allowing it to outperform the state-of-the-art baseline on two billion-scale datasets by up to 16.0× with up to 64 cores.
Elastic Averaging for Efficient Pipelined DNN Training

Nowadays, the size of DNN models has grown rapidly. To train a large model, pipeline parallelism-based frameworks partition the model across GPUs and slice each batch of data into multiple micro-batches. However, pipeline parallelism suffers from a bubble issue and low peak utilization of GPUs. Recent work tries to address the two issues, but fails to exploit the benefit of vanilla pipeline parallelism, i.e., overlapping communication with computation. In this work, we employ an elastic averaging-based framework which explores elastic averaging to add multiple parallel pipelines. To help the framework exploit the advantage of pipeline parallelism while reducing the memory footprints, we propose a schedule, advance forward propagation. Moreover, since the numbers of parallel pipelines and micro-batches are essential to the framework performance, we propose a profiling-based tuning method to automatically determine the settings. We integrate those techniques into a prototype system, namely AvgPipe, based on PyTorch. Our experiments show that Avg-Pipe achieves a 1.7x speedups over state-of-the-art solutions of pipeline parallelism on average.
Feeling the Temperature of the Room

Thermal signals have been explored in HCI for emotion-elicitation and enhancing two-person communication, showing that temperature invokes social and emotional signals in individuals. Yet, extending these findings to group communication is missing. We investigated how thermal signals can be used to communicate group affective states in a hybrid meeting scenario to help people feel connected over a distance. We conducted a lab study (N=20 participants) and explored wrist-worn thermal feedback to communicate audience emotions. Our results show that thermal feedback is an effective method of conveying audience engagement without increasing workload and can help a presenter feel more in tune with the audience. We outline design implications for real-world wearable social thermal feedback systems for both virtual and in-person communication that support group affect communication and social connectedness. Thermal feedback has the potential to connect people across distances and facilitate more effective and dynamic communication in multiple contexts.
Quantum reinforcement learning

Abstract In this paper, we present implementations of an annealing-based and a gate-based quantum computing approach for finding the optimal policy to traverse a grid and compare them to a classical deep reinforcement learning approach. We extended these three approaches by allowing for stochastic actions instead of deterministic actions and by introducing a new learning technique called curriculum learning. With curriculum learning, we gradually increase the complexity of the environment and we find that it has a positive effect on the expected reward of a traversal. We see that the number of training steps needed for the two quantum approaches is lower than that needed for the classical approach.
AGREE: Aligning Cross-Modal Entities for Image-Text Retrieval Upon Vision-Language Pre-trained Models

Image-text retrieval is a challenging cross-modal task that arouses much attention. While the traditional methods cannot break down the barriers between different modalities, Vision-Language Pre-trained (VLP) models greatly improve image-text retrieval performance based on massive image-text pairs. Nonetheless, the VLP-based methods are still prone to produce retrieval results that cannot be cross-modal aligned with entities. Recent efforts try to fix this problem at the pre-training stage, which is not only expensive but also unpractical due to the unavailable of full datasets. In this paper, we novelly propose a lightweight and practical approach to align cross-modal entities for image-text retrieval upon VLP models only at the fine-tuning and re-ranking stages. We employ external knowledge and tools to construct extra fine-grained image-text pairs, and then emphasize cross-modal entity alignment through contrastive learning and entity-level mask modeling in fine-tuning. Besides, two re-ranking strategies are proposed, including one specially designed for zero-shot scenarios. Extensive experiments with several VLP models on multiple Chinese and English datasets show that our approach achieves state-of-the-art results in nearly all settings.
A novel methodology for writer (hand) identification: establishing Rigas Feraios wrote two important Greek documents discovered in Romania

Abstract The main goal of the present work is to determine the hand that has written two newly discovered documents in Romania. For giving the proper answer, the authors introduced the notion of “Ideal Representative”, namely of an object that very well represents the corresponding ideal alphabet symbol that a writer had in his/her mind when writing a document by hand. Moreover, the authors have introduced a novel method, which leads to the optimal evaluation of the Ideal Representative of any alphabet symbol in association with any handwritten document. Furthermore, the authors have introduced methods for comparing these Ideal Representatives, so as a final decision about the hand that has written a document may be obtained with a highly considerable likelihood. The related analysis manifests that the two documents discovered in Romania in 1998, belong to the great personality of Rigas Feraios. The presented method of automatic handwriting Identification seems to be of general applicability.
Achieving High Performance the Functional Way

research-article Share on Achieving High Performance the Functional Way: Expressing High-Performance Optimizations as Rewrite Strategies Authors: Bastian Hagedorn NVIDIA, Würselen, Germany NVIDIA, Würselen, GermanyView Profile , Johannes Lenfers University of Münster, Germany University of Münster, GermanyView Profile , Thomas Kœhler University of Glasgow, U.K University of Glasgow, U.KView Profile , Xueying Qin The University of Edinburgh, U.K The University of Edinburgh, U.KView Profile , Sergei Gorlatch University of Münster, Germany University of Münster, GermanyView Profile , Michel Steuwer The University of Edinburgh, U.K The University of Edinburgh, U.KView Profile Authors Info & Claims Communications of the ACMVolume 66Issue 3March 2023pp 89–97https://doi.org/10.1145/3580371Published:22 February 2023Publication History 0citation312DownloadsMetricsTotal Citations0Total Downloads312Last 12 Months312Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Level-Based Learning Swarm Optimizer with Stochastic Fractal Search for Parameters Identification of Solar Photovoltaic Models

As the most popular renewable energy, solar energy could be converted into electricity by photovoltaic (PV) systems directly. To maximize the effectiveness of the conversion, it is critical to find the precise and accurate parameters of the PV model. In this paper, we propose a level-based learning swarm optimizer with stochastic fractal search (LLSOF) to tackle the parameter estimation of several kinds of solar PV models. The population is separated into multiple levels according to their fitness at first. The individuals at the lower levels evolve through learning from the individuals at the higher levels. Benefiting from the interactive learning among levels, the population could approach the multiple optimal regions rapidly. To enhance the local search ability, stochastic fractal search is introduced to locate the optima accurately. Combination of both, the proposed LLSOF could achieve a good balance on both exploration and exploitation. To evaluate the performance of LLSOF, it is used to obtain the parameters of three PV models and compared with nine well-established algorithms. Comparative results validate the excellent performance of LLSOF. Moreover, the application manufactory’s data sheets report the superior efficiency and effectiveness of LLSOF for the parameter estimation of PV systems.
Radio Frequency Fingerprint Identification With Hybrid Time-Varying Distortions

Radio frequency fingerprint identification (RFFI) is a promising physical layer security technique that employs the hardware-introduced features extracted from the received signals for device identification.In this paper, we consider an RFFI problem in the presence of hybrid time-varying distortions (HTVDs) induced by multipath fading channel, carrier frequency offset (CFO), and phase offset.To solve this problem, an HTVDsrobust RFFI framework is proposed.Firstly, we derive that the residual HTVDs after CFO correction can be approximated as multiplicative interference in the frequency domain.Secondly, we define a novel signal analysis dimension named spectral quotient (SQ) representation and then present the spectral circular shift division (SCSD) method to generate the HTVDsrobust SQ signals, where the multiplicative interference can be suppressed.Thereafter, the statistics including root mean square (RMS), variance (VAR), skewness (SKE), and kurtosis (KUR) are extracted from the real and imaginary components of the SQ signals, respectively.Finally, the statistical features are used for the training and testing of the support vector machine (SVM) classifiers.To further enhance the performance of the proposed RFFI scheme, we also present the spectral circular multi-shift division (SCMSD) method, which increases the flexibility in the generation of the HTVDs-robust SQ signals.Given what we knew, this is the first time attempting to mitigate the HTVDs by leveraging the strong frequency correlation at the neighboring subcarriers in the multivariate hypothesis tasks.Compared to several handcraft feature-based RFFI methods, the proposed method exhibits superior identification accuracy and strong robustness.Experimental results show that the proposed RFFI scheme can achieve the accuracy of 91.3%with five devices and 86.4% with sixteen devices when the classifiers are trained with the additive white Gaussian noise but are tested with the Rayleigh channel.
Automatic One-Shot LiDAR Alignment Inspection System Using NIR Camera

In this study, we propose an accurate automatic light detection and ranging (LiDAR) alignment inspection system designed for manufacturing environments. The proposed inspection system is a single-shot, static method that uses a target system composed of a planar target with an near infrared (NIR) camera. Each laser beam spot projected on the target surface is segmented and located by analyzing the NIR images. We also propose a method to extract and match the corresponding beam feature points from the 3-D point cloud and NIR image. The correspondence points are used to estimate an extrinsic LiDAR pose related to the target frame, which is then converted into a pose relative to the vehicle’s body. We validated the performance of the proposed system with experiments using a low-resolution LiDAR, and it exhibited an accuracy as high as 0.02° and 0.4 mm for rotation and translation alignment, respectively.
Mapping the Privacy Landscape for Central Bank Digital Currencies

Now is the time to shape what future payment flows will reveal about you.
Positive–negative equal contrastive loss for semantic segmentation

The contextual information is critical for various computer vision tasks, previous works commonly design plug-and-play modules and structural losses to effectively extract and aggregate the global context. These methods utilize fine-label to optimize the model but ignore that fine-trained features are also precious training resources, which can introduce preferable distribution to hard pixels (i.e., misclassified pixels). Inspired by contrastive learning in unsupervised paradigm, we apply the contrastive loss in a supervised manner and re-design the loss function to cast off the stereotype of unsupervised learning (e.g., imbalance of positives and negatives, confusion of anchors computing). To this end, we propose Positive-Negative Equal contrastive loss (PNE loss), which increases the latent impact of positive embedding on the anchor and treats the positive as well as negative sample pairs equally. The PNE loss can be directly plugged right into existing semantic segmentation frameworks and leads to excellent performance with neglectable extra computational costs. We utilize a number of classic segmentation methods (e.g., DeepLabV3, HRNetV2, OCRNet, UperNet) and backbone (e.g., ResNet, HRNet, Swin Transformer) to conduct comprehensive experiments and achieve state-of-the-art performance on three benchmark datasets (e.g., Cityscapes, COCO-Stuff and ADE20K). Our code will be publicly available at https://github.com/jingw193/PNE_Loss.
VFLR: An Efficient and Privacy-Preserving Vertical Federated Framework for Logistic Regression

With the explosive growth of data volume and computing capability, federated learning, which involves constructing global models over multiple data islands, has demonstrated its advantages and vast prospects in the field of machine learning. However, due to commonly vertically partitioned data, coupled with privacy concerns about data leakage, there are still some challenging issues in traditional federated learning. To tackle these challenges, in this paper, we propose an efficient and privacy-preserving vertical federated learning framework for logistic regression, named VFLR, where multiple participants can collaboratively perform global model training and query over their vertically partitioned data. Specifically, we first design a data aggregation matrix construction algorithm, with which the vertically partitioned data can be aggregated for high-accuracy global model training. Then, by utilizing a novel symmetric homomorphic encryption, our framework can ensure that the whole training and query processes do not leak any private information. Moreover, based on the data aggregation matrix, multi-round interactions are not required in VFLR, improving training efficiency significantly. Detailed security analysis shows that VFLR can well protect data and model information from inference attacks. In addition, extensive experiments demonstrate that VFLR has high training and query accuracy and low computation and communication overhead.
Robust Multi-Model Estimation for Reliable Relative Navigation Based on Observability and Abnormity Analysis

High-precision relative positioning and navigation is a fundamental requirement for many applications such as flight formation, spacecraft docking and collision avoidance. The main purpose of this paper is to develop a robust multi-model estimation algorithm for reliable navigation when there are abnormities of measurement and motion. In order to deal with these abnormities, we propose a quantitative evaluation method of relative navigation system by introducing the degree of observability (DoO) and the degree of abnormity (DoA). In addition, we design a feedforward information fusion and a feedback information allocation method based on DoO and DoA, and thus form a multi-model robust estimation algorithm. In order to testify the effectiveness and robustness of the proposed algorithm, a practical experiment with real data sets gathered in urban areas has been carried out. The results showed that the maximum relative positioning RMSE reduction ratio can reach 75%, and the maximum relative velocity RMSE reduction ratio can reach 51% compared with EKF. Therefore, the proposed method can guarantee the accuracy and robustness of relative navigation under abnormal conditions.