Reviewerly/exHarmony huggingface dataset:
3D Multi-Object Tracking based on Two-Stage Data Association for Collaborative Perception Scenarios

This paper proposes a 3D multi-object tracker suitable for collaborative perception scenarios. Our tracker aims to associate detection candidates obtained from the ego-vehicle after performing collaborative perception over time. It considers the temporal asynchronous information exchanged among connected vehicles and focuses on dealing with objects that fail to track due to missed detection. To achieve this, we propose a two-stage data association module with a supplementary mechanism. It adapts the association strategy to track objects according to their state robustly. Specifically, the first stage works on most general objects. The second stage aims to associate spatiotemporal asynchronous detection candidates or tracked objects consecutively missed multiple times. A supplementary mechanism is applied to temporarily missed objects by the detector. We conduct experiments on the DAIR-V2X dataset and use the detection candidates generated by a collaborative detection module. Experimental results demonstrate that the proposed method outperforms baselines in tracking performance while achieving comparable speed.
Suggestion Bot: Analyzing the Impact of Automated Suggested Changes on Code Reviews

Peer code reviews are crucial for maintaining the quality of the code in software repositories. Developers have introduced a number of software bots to help with the code review process. Despite the benefits of automating code review tasks, many developers face challenges interacting with these bots due to non-comprehensive feedback and disruptive notifications. In this paper, we analyze how incorporating a bot in software development cycle will decrease turnaround time of pull request. We created a bot called “SUGGESTION BOT” to automatically review the code base using GitHub’s suggested changes functionality in order to solve this issue. A preliminary comparative empirical investigation between the utilization of this bot and manual review procedures was also conducted in this study. We evaluate SUGGESTION BOT concerning its impact on review time and also analyze whether the comments given by the bot are clear and useful for users. Our results provide implications for the design of future systems and improving human-bot interactions for code review.
Unified digital image correlation under meshfree framework

Abstract The implementation of digital image correlation (DIC) involves several different problems, such as image prefiltering, image intensity gradient calculation, image intensity interpolation at subpixel positions, shape function construction and strain calculation. This paper offers a unified insight into the nature of several key problems in DIC technique. We treat all the problems involved in the former mentioned key steps as fundamentally the same problem, that is, reconstructing an analytical description from a discrete and noisy sampling of a signal, such as discrete image intensity and displacement at some scattered nodes. From the reconstructed analytical description, the gradient and physical value at subpixel or integral pixel position can be analytically calculated without extra error. Here, we solve all these problems using the same mathematical tool, the meshfree method, leading to a unified DIC (U‐DIC) method. This method introduces errors solely during the steps involving the determination of the continuous description. However, it effectively avoids errors in the remaining steps. It holds the best balance between spatial resolution and measurement resolution for both displacement and strain measurements compared to 28 state‐of‐the‐art DIC algorithms based on the benchmark tests on DIC Challenge 2.0. It also holds all the unique advantages of the advanced meshfree DIC (MF‐DIC) compared to conventional local DIC and global DIC. The novel concept and excellent balance between spatial resolution and measurement resolution make U‐DIC an attractive replacement for conventional DIC methods. Additionally, the consistency of the implementation procedures in U‐DIC can simplify the parameter selection in DIC, which is of potential for the standardization of DIC technique.
MCRaft: synergistic collaboration of multi leaders for IoT cluster stability optimization

Raft is a consensus algorithm to coordinate the proper execution of protocols and rules within the system. Owing to its security and simplicity, it can be applied to the field of IoT with blockchains. When IoT device clusters run under the Raft consensus algorithm, as the number of devices increases, split voting is prone to arise, therefore the Raft algorithm cannot run efficiently under large-scale nodes. To improve the problem of split voting and delay in system recovery after a leader collapse (DISR), this paper proposes a high-fault-tolerant consensus algorithm based on synergistic collaboration of multi leaders within the IoT clusters—MCRaft. The concept of super node cluster is introduced. MCRaft elects three leaders to form a super node cluster mapping single Raft leader. The election of the MCRaft node is verified through the log hash stored by the node, and the voting overhead of the cluster is born by the node’s own computing power instead of relying on the voting mechanism, making the election of leaders more efficient and secure. According to the experiments, the synergistic collaboration of the super node clusters effectively prevents DISR issue hence achieve efficient transmission, improving the fault tolerance of the IoT cluster. MCRaft algorithm improves the election efficiency by about 84% compared with that of common Raft algorithm within 100 nodes. 200 crash experiments indicates that the MCRaft algorithm improves its stability by about 24% on average compared with that of common Raft.
A method for <scp>LPI</scp> radar signals recognition based on complex convolutional neural network

Abstract For the low probability of intercept (LPI) radar signal recognition problem, recognition algorithms based on deep learning usually use time‐frequency analysis to convert the signal into a two‐dimensional feature image for classification and recognition. However, these methods often have problems with large network size, high computational complexity, and huge memory consumption, making them difficult to apply on small devices with limited computational power and storage space. This paper proposes an LPI radar signal recognition method based on a lightweight complex convolutional neural network, named CV‐LPINet. It uses a complex convolutional module to complete data fusion of IQ sampled signals, uses a deep separable convolutional module to extract features and reduce dimensions, and introduces residual structures to improve network training. Experiments show that the average recognition accuracy of this method is 91.76% with a signal to noise ratio in the range of −6 to 10 dB, and its recognition accuracy is similar to that of typical algorithms. However, the network size is significantly reduced and the computational complexity is low, making it suitable for small intelligent devices.
A Study of Sentence-BERT Based Essay Off-topic Detection

Automated essay scoring systems are widely used in education, and essay off-topic detection is an integral part of this. Traditionally off-topic essay detection is based on text features represented as spatial vectors, however, this approach only addresses the structure of essay statements and requires the use of manual features. This paper proposed to use the Sentence-BERT model to detect off-topic essays, the method first obtains a large amount of high-quality data to build a corpus of off-topic essays, and two Siamese twin pre-trained models are used to embed sentences in the essay topic, and the body of the essay, generate semantically rich sentence vectors and then use cosine similarity to calculate the similarity between the topic and the body of the essay after averaging the pooled sentence vectors, and select the optimal threshold to determine off-topic essays through continuous training. The experimental results show that the proposed method improves the accuracy, recall, and F1 values by 9.5%, 11.2%, and 10.4% respectively over the C-BGRU (Convolutional-Bidirectional Gate Recurrent Unit) based Siamese twin network and also has an excellent performance in topics with different degrees of divergence.
Adaptive Learning through Artificial Intelligence

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SVD-Based Graph Fourier Transforms on Directed Product Graphs

Graph Fourier transform (GFT) is one of the fundamental tools in graph signal processing to decompose graph signals into different frequency components and to represent graph signals with strong correlation by different modes of variation in an effective way. The GFT on undirected graphs has been well studied and several approaches have been proposed to define GFTs on directed graphs. In this article, based on the singular value decompositions of some graph Laplacians, we propose two GFTs on the Cartesian product graph of two directed graphs. We show that the proposed GFTs could represent spatial-temporal data sets on directed graphs with strong correlation efficiently, and in the undirected graph setting they are essentially the joint GFT in the literature. In this article, we also consider the bandlimiting procedure in frequency domains of the proposed GFTs, and demonstrate their performances on denoising the hourly temperature data sets collected at 32 weather stations in the region of Brest (France) and at 218 locations in the United States.
FEDERATED LEARNING TECHNIQUES APPLIED TO CREDIT RISK MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW

The control of credit risk is a crucial task for financial institutions. Various subjective and quantitative indicators are used to forecast credit risks. Machine learning technology uses customer data to create accurate predictive models due to current AI/ML breakthroughs. Data from many institutions can be used to improve models. Nevertheless, exchanging data across numerous organizations incurs significant communication costs and compromises the privacy of client data. Credit institutions and financing solution providers benefit from expanded data sources via the arrival of technological developments (AI and Machine Learning, Deep learning). The possibilities to increase customer knowledge and improve their credit risk assessment and management system are increased. This research study explores federated learning techniques for credit risk management. The literature review covers research papers published between 2018 and 2022 and presents different federated learning techniques and their applications in credit risk assessment. The study shows that the choice of technique depends on the application’s specific requirements.
ExistenceMap-PointPillars: A Multi-Fusion Network for Stable 3D Object Detection with Pseudo 2D Maps

Recognition of surrounding traffic participants is important for safe driving of automated vehicles. There are approaches to recognize the surrounding environment that are 3D object detection using only a LiDAR sensor or fusion with a camera. However, when information from the other sensor is lost, stable object detection becomes difficult in the fusion method. In this paper, we propose ExistenceMap-PointPillars, which is an effective method for 3D object detection with information from other sensors by a simple modification to LiDAR base 3D object detection network. Pseudo 2D map which shows object existence regions can be generated from any information and can be easily fused with the 3D object detection network. The experimental results on our dataset show that ExistenceMap-PointPillars improves mean Average Precision (mAP) by +3.59% over PointPillars and outperforms other sensor fusion methods. ExistenceMap-PointPillars is effective in significantly reducing False Positives and enables stable 3D object detection even when information from other sensors is intercepted.
Future-aware and High-order representation learning for cold-start recommendation

The Cold-start problem is critical but challenging for dynamic recommender systems since new entities (users/items) are added dynamically without any purchasing behavior. Most existing methods solve the problem by building the relationship between cold-start entities and existing entities. However, due to several challenges, such approaches can not effectively handle the cold start in dynamic recommender systems. It is hard to learn and predict dynamically for constantly added entities without any historical interactions. Moreover, it is challenging to characterize cold-start entities precisely for indicating future purchasing with limited information. This paper formalizes the dynamic recommender systems as a time-evolving graph to handle the challenges of modeling the dynamic relations between users and items. Mainly, we design a unified learning framework that can learn future-aware representations for newly added entities. Additionally, we build a novel mapping function to model high-order interactions between attributes and further convey obtained expressive information to the high-order neighbors on the graph. Extensive experiments were conducted, and the results demonstrate the outstanding performance of the proposed method on MovieLens 1M and LastFM datasets, providing at least 16.69% and 11.11% relative performance gains, respectively.
Messages from Melbourne: Towards linking all the things

I'm doing some work with Nicole Kearney (@nicolekearney) at the Melbourne Museum on the general theme of "linking all the things". It's the end of the first full week we've had, so here's a quick update of what we've been up to. Brainstorming The things we want to do are being captured as a project on GitHub. This is where we come up with ideas, comment on then, then try to figure out which ones can be done. So far there are three things we've made a serious start...
Refined Objectification for Improving End-to-End Driving Model Explanation Persuasibility<sup>*</sup>

With the rapid development of deep learning, many end-to-end autonomous driving models with high prediction accuracy are developed. However, since autonomous driving technology is closely related to human life, users need to be convinced that end-to-end driving models (E2EDMs) not only have high prediction accuracy in known scenarios but also in practice for unknown scenarios. Therefore, engineers and end-users need to grasp the calculation methods of the E2EDMs based on the driving models’ explanations and ensure the explanations are satisfactory. However, few studies have focused on improving the explanation excellence.In this study, among many properties, we aim to improve the persuasibility of the explanation, we propose ROB (refined objectification branches), a structure that could be mounted to any type of existing E2EDMs. By persuasibility evaluation experiments, we demonstrate that one could improve the persuasibility of the explanations by mounting ROB to the E2EDM. As shown in Fig. 1, the focus area of the driving model accurately shrinks to the important and concise objects on account of ROB. We also perform an ablation study to further discuss each branch’s influence on persuasibility. In addition, we test ROB on multiple mainstream backbones and demonstrate that our structure could also improve the model’s prediction accuracy.
Multi-robot task assignment based on discrete firefly algorithm

In order to solve the multi-robot task assignment problem, a multi-robot task assignment model considering robot capability constraints and task relationship constraints is established, and the model is solved by using the firefly algorithm. A new firefly distance representation method is proposed for discrete problems with parallelism of feasible solutions in the same dimension, and the iteration rule and random search method of the discrete firefly algorithm are improved to obtain an improved discrete firefly algorithm, and an adaptation function based on two evaluation indexes of energy consumption and time penalty is established. Finally, a specific example of 4 robots with 20 tasks is used to verify the effectiveness of several improved methods, and the improved discrete firefly algorithm is compared with discrete firefly algorithm, genetic algorithm and particle swarm algorithm, and the results show the superiority of the improved discrete firefly algorithm.
A General Feature Extraction Framework of Pre-Trained Language Models for Aspect-Category Sentiment Analysis

Aspect-category sentiment analysis (ACSA), which aims to predict the sentiment polarity of different categories, is an aspect-level classification task. The main challenge in ACSA is that categories may not appear in the sentences. Recently, pretrained language models (PLMs) have shown their effectiveness on downstream tasks (e.g. sentence-level tasks). However, there is not much improvement in ACSA with the direct use of PLMs. Therefore, the question naturally arises whether PLMs have rich semantic information so that we can obtain good ACSA models only based on PLMs. In this paper, we propose a general feature extraction framework and fine-tune PLMs. Our key idea is to construct an adaptive input representation that converts the aspect-level task to a sentence-level task. In addition, we introduce the attention mechanism into the models to capture different sentiment information. Experimental results on three public datasets demonstrate the effectiveness of our methods.
High Fidelity Motion Adaptive Face Reenactment

Along with the development of virtual reality and augmented reality technology, the requirement for high fidelity face reenactment methods increases gradually. Recent face reenactment works either use landmarks or Action Units (AUs) based motion representation as the guided signal. However, it is difficult to represent the complex facial geometry and movement completely only with landmarks or AUs, which leads to the limited effect of these methods. To address this challenge, we propose a high fidelity face reenactment framework that combines landmarks and AUs together for facial motion representation. Specifically, we design a Motion Alignment Converter (MAC) module to align the landmarks and AUs when driving and source identities are different. After that, an Adaptive Motion Controller (AMC) module is proposed to inject motion information into the face reenactor. Moreover, to optimize output quality, the proposed method processes background and face regions separately. Qualitive and quantitative experiments are conducted on VoxCeleb1 and CelebV datasets, the results show that the proposed method can generate face images with more details and better background than existing methods.
Iterated Sub-window Weighted Guided Filter

This paper presents a new structure-preserving texture-removing filtering technique termed as Iterated Side Window Weighted Guided Filtering (ISWWGF). In the literature, the algorithms can not preserve the edges of the structures during removing textures. On one hand, these algorithms can not accurately discriminate structure from textures, hence the edges of structures are smoothed away as textures. On the other hand, even though the edges of the structures are discriminated correctly, they do not have the correct way to preserve them. In the proposed filter, we use the edge-aware weight and introduce a threshold to perfectly discriminate structures from textures. By multiple iterations, the textures are removed away and the structures are preserved by a technique named as side window. Through theoretical analysis of typical edge signals, we discover that traditional weighted guided filtering (WGF) operations violate its locally linear model condition and causes blurring in edges. Based on this crucial insight, the side window strategy is adopted to better preserve edges of structures. By experimental analysis, we demonstrate the technical correctness of the new method and extensive experimental results on a variety of image processing applications are presented to demonstrate the effectiveness of the new technique and its state of the art performances.
Lymphoma Ultrasound Image Classification with Causal Attention and Feature Fusion

Medical image classification aids in diagnosis eases the work of physicians. In this paper, we propose an image classification method for lymphoma ultrasound image classification. We make the model more focused on the lymphoma lesion region by fusing the features of the original image and the image of the lesion region obtained through semantic segmentation in parallel, which is used to solve the problem that the grayscale values of the lymphoma lesion region and the background region are similar and difficult to distinguish. The problem of generalization of different lymphoma ultrasound images obtained from different instrument scans is handled by adding a causal attention mechanism to the model. Finally, we use counterfactual interpretation to perturb both the original image and the lesion image to obtain the instance-level interpretation. It is demonstrated experimentally that the method effectively improves the performance of classification.
Recommendation System with Privacy Protection Based on Differential Privacy Aggregation

The recommender systems are widely used as an important tool to help users find useful information. While the personalized recommendation is often accompanied with the risk of privacy disclosure. How to achieve accurate recommendation on the premise of ensuring privacy is a very challenging research topic. Towards this end, we propose a framework named DPAGS(differential privacy aggregation based on GraphSAGE) which realizes better privacy protection characteristics with required recommending accuracy. Specifically, the system includes three modules: sub-model training based on graph embedding, multichannel voting with noise perturbations and recommendation. In the sub-model training module, we use GraphSAGE to get the sub-models for multiple channel voting. In the multichannel voting module, to protect the privacy of the original data, we introduce Laplace noise to achieve the purpose of perturbation; to expand unlabeled dataset, we use the classifiers constructed according to the sub-models to vote to obtain the expanded dataset, which is used for training the final graph embedding model. In the recommendation module, the cosine similarity of vectors between users and movies is calculated through the final graph embedding model obtained in the multiple channel voting module, and the final recommendation list is output according to the nearest neighbours method. Comparative experiments on MovieLens-1m dataset demonstrate our model has better performance under a given privacy budget.