Reviewerly/exHarmony huggingface dataset:
Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices

Data generated at the network edge can be processed locally by leveraging the emerging technology of Federated Learning (FL). However, non-IID local data will lead to degradation of model accuracy and the heterogeneity of edge nodes inevitably slows down model training efficiency. Moreover, to avoid the potential communication bottleneck in the parameter-server-based FL, we concentrate on the Decentralized Federated Learning (DFL) that performs distributed model training in Peer-to-Peer (P2P) manner. To address these challenges, we propose an asynchronous DFL system by incorporating neighbor selection and gradient push, termed AsyNG. Specifically, we require each edge node to push gradients only to a subset of neighbors for resource efficiency. Herein, we first give a theoretical convergence analysis of AsyNG under the complicated non-IID and heterogeneous scenario, and further design a priority-based algorithm to dynamically select neighbors for each edge node so as to achieve the trade-off between communication cost and model performance. We evaluate the performance of AsyNG through extensive experiments on a physical platform. Evaluation results show that AsyNG can reduce the communication cost by 60% and the completion time by about 30% for achieving the same test accuracy, compared to the baselines.
Understanding the Research Challenges in Low-Resource Language and Linking Bilingual News Articles in Multilingual News Archive

The developed world has focused on Web preservation compared to the developing world, especially news preservation for future generations. However, the news published online is volatile because of constant changes in the technologies used to disseminate information and the formats used for publication. News preservation became more complicated and challenging when the archive began to contain articles from low-resourced and morphologically complex languages like Urdu and Arabic, along with English news articles. The digital news story preservation framework is enriched with eighteen sources for Urdu, Arabic, and English news sources. This study presents challenges in low-resource languages (LRLs), research challenges, and details of how the framework is enhanced. In this paper, we introduce a multilingual news archive and discuss the digital news story extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, i.e., English, and low-resource languages, i.e., Urdu and Arabic. LRLs encountered a high error rate during preservation compared to high-resource languages (HRLs), corresponding to 10% and 03%, respectively. The extraction results show that few news sources are not regularly updated and release few new news stories online. LRLs require more detailed study for accurate news content extraction and archiving for future access. LRLs and HRLs enrich the digital news story preservation (DNSP) framework. The Digital News Stories Archive (DNSA) preserves a huge number of news articles from multiple news sources in LRLs and HRLs. This paper presents research challenges encountered during the preservation of Urdu and Arabic-language news articles to create a multilingual news archive. The second part of the paper compares two bilingual linking mechanisms for Urdu-to-English-language news articles in the DNSA: the common ratio measure for dual language (CRMDL) and the similarity measure based on transliteration words (SMTW) with the cosine similarity measure (CSM) baseline technique. The experimental results show that the SMTW is more effective than the CRMDL and CSM for linking Urdu-to-English news articles. The precision improved from 46% and 50% to 60%, and the recall improved from 64% and 67% to 82% for CSM, CRMDL, and SMTW, respectively, with improved impact of common terms as well.
Workload Characterization and Classification: A Step Towards Better Resource Utilization in a Cloud Data Center

Advancements in virtualization technology have led to better utilization of existing infrastructure. It allows numerous virtual machines with different workloads to coexist on the same physical server, resulting in a pool of server resources. It is critical to understand enterprise workloads to correctly create and configure existing and future support in such pools. Managing resources in a cloud data center is one of the most difficult tasks. The dynamic nature of the cloud environment, as well as the high level of uncertainty, has created these challenges. These applications’ diverse Quality of Service (QoS) requirements make data center management difficult. Accurate forecasting of future resource demand is required to meet QoS needs and ensure better resource utilization. Consequently, data center workload modeling and categorization are needed to meet software quality solutions cost-effectively. This paper uses traces of Bitbrain’s data to characterize and categorize workload. Clustering (K Means and Gaussian mixture model) and Classification strategies (K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machine) characterize and model the workload traces. K Means shows better results as compared to GMM when compared to the Calinski Harabasz index and Davies-Bouldin score. The results showed that the Decision Tree achieves the maximum accuracy of 99.18%, followed by K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) Logistic Regression (LR), Multi-Layer Perceptron (MLP), and Back Propagation Neural Networks.
Turkish Adaptation of the Chatbot System, Information and Service Quality Scale

This study aims to adapt the "Chatbot System, Information and Service Quality" scale developed by Nguyen, Chiu, and Le (2021) into Turkish. Validity and reliability studies of the scale were conducted. The original scale consists of 7-point Likert scale and 18 items. The aim of the scale is to examine the perception of system, information, and service quality for the sustainability of the use of chatbots. This research was conducted with university students. All of the scale adaptation stages were followed in the research. In the analysis of the data, various analyses were included for the validity and reliability studies of the scale. As a result of the research, a valid and reliable scale that can be used to determine the quality of chatbot systems based on information, service and system has been introduced to the literature.
A Tensor-based Markov Chain Model for Heterogeneous Information Network Collective Classification : Extended abstract

Heterogeneous Information Network(HIN) collective classification aims to classify one type of node, which is associated with multiple types of nodes through multiple types of relations. Previous studies have revealed that exploiting the relative importance of relation types is quite useful for improving node classification performance. We propose a Tensor-based Markov chain (T-Mark) model to improve the nodes classification accuracy by predicting the labels for unlabeled nodes and the importance ranking of relationship types automatically and simultaneously. Specifically, we build two tensor equations according to the HIN structure and content similarities among nodes of both labeled and unlabeled data. Consequently, We solve the semi-supervised T-Mark model by using an iterative process until obtaining two stationary distributions for labels and relation types. Experimental results on several real-world datasets demonstrate the effectiveness of T-Mark.
RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events (facts) based on historical information, and has attracted considerable attention due to its great practical significance. Accurate representations (embeddings) of entities and relations form the basis of TKG extrapolation. Recent work has been devoted to improving the rationality of entity representations. However, on the one hand, ignoring relation modeling results in incomplete relation representations; therefore, some approaches aggregate only immediately adjacent entities of relations, but this can lead to the "message islands" problem of relation modeling. On the other hand, ignoring the association constraints between relations and entities can make the embeddings of both relations and entities prone to overfitting. To address the abovementioned challenges, we propose an advanced method, namely, RETIA. For the former issue, we generate twin hyperrelation subgraphs for each historical subgraph and then aggregate both the adjacent entities and relations in the hyperrelation subgraphs through a graph convolutional network (GCN). About the latter concern, we propose a twin-interact module (TIM), which provides communication channels for relation aggregation and entity aggregation during the evolution of the historical sequence. Experiments conducted on five public datasets show that RETIA has made great improvements across several evaluation metrics. Our released code is available at https://github.com/CGCL-codes/RETIA.
Research on UAV Path Planning Method Based on Improved HPO Algorithm in Multitask Environment

When unmanned aerial vehicles (UAVs) are carrying out missions, they are faced with complex task environments. To enhance the adaptability of UAV applications, it is required that they possess fast path-planning capabilities. This article takes the execution of complex tasks by multiple UAVs in a 3-D environment as background and transforms the path-planning problem into a multiconstraint optimization problem. Innovatively combining the hunter–prey optimizer (HPO) algorithm and task allocation mechanism, this study achieves collaborative path planning for multiple UAVs for complex tasks. In order to improve the optimization speed of the HPO algorithm, the following improvements have been made: first, introducing the chaotic mapping model to improve the performance of population initialization; second, adopting the golden sine strategy to change the population update strategy and accelerate the convergence speed of the algorithm. Then, the single-peak and multipeak benchmark functions are used to test the average, standard deviation, and optimal values of the algorithm. Finally, path planning experiments are carried out in a 3-D map with multiple task points and obstacles, and the results show that the adapt golden sine HOP (AGSHPO) algorithm improves the robustness and real-time performance of the UAVs when executing complex tasks to a certain extent.
Depth-Hand: 3D Hand Keypoint Detection With Dense Depth Estimation

Hand pose is important to various applications and depth information is crucial for a reliable 3D keypoint detection. However, scopes of methods that rely on active depth cameras are limited by the power, volume and illumination. To explore a wider application range, this letter proposes a multi-task method that can detect 3D hand keypoints while estimating dense depth maps from stereo infrared inputs. Based on the inherent encoding-decoding relation of the depth estimation and hand keypoint detection, the proposed network is built with shared intermediate features and separate task branches. To achieve an end-to-end estimation, the hand region is automatically cropped from the depth map. Due to a lack of datasets, a two-step fusion training approach is designed following the transfer learning theory with a self-supervision loss. The proposed method is evaluated under stereo depth datasets and 3D hand keypoints datasets respectively. Meanwhile, a small dataset is also built to test the the overall model performance. Experimental results prove that the proposed method is capable of providing satisfying depth maps along with convincing 3D hand keypoints.
Non-Blocking Raft for High Throughput IoT Data

The Raft consensus protocol naturally fits time series databases, owing to the resemblance between its continuous log and the time series data. While the serialization of appending entries reduces the state space for ease design and implementation, it blocks the subsequent requests and thus limits the parallelism and throughput of Raft. Intuitively, once an entry arrives the follower, we may notice the leader and the client to unblock the subsequent as early, rather than waiting for its appending and committing. In this way, more requests can be processed in parallel, and thus the throughput increases, essential for IoT applications often with vast sensors and fast data ingestion. Of course, with higher parallelism, the risk of persistence for in-processing entries increases. It is a worthwhile trade-off in the IoT scenario since tiny data loss during leader failure is more acceptable than shutting out most data due to a low throughput. Our Non-Blocking Raft (NB-Raft) is implemented as the consensus protocol of Apache IoTDB, a commodity time series database management system, supporting various applications in Alibaba Cloud. Extensive evaluation shows that the throughput is improved by about 30% using our NB-Raft compared to the original Raft, a considerable amount of further data saved.
“The algorithm will screw you”: Blame, social actors and the 2020 A Level results algorithm on Twitter

In August 2020, the UK government and regulation body Ofqual replaced school examinations with automatically computed A Level grades in England and Wales. This algorithm factored in school attainment in each subject over the previous three years. Government officials initially stated that the algorithm was used to combat grade inflation. After public outcry, teacher assessment grades used instead. Views concerning who was to blame for this scandal were expressed on the social media website Twitter. While previous work used NLP-based opinion mining computational linguistic tools to analyse this discourse, shortcomings included accuracy issues, difficulties in interpretation and limited conclusions on who authors blamed. Thus, we chose to complement this research by analysing 18,239 tweets relating to the A Level algorithm using Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), underpinned by social actor representation. We examined how blame was attributed to different entities who were presented as social actors or having social agency. Through analysing transitivity in this discourse, we found the algorithm itself, the UK government and Ofqual were all implicated as potentially responsible as social actors through active agency, agency metaphor possession and instances of passive constructions. According to our results, students were found to have limited blame through the same analysis. We discuss how this builds upon existing research where the algorithm is implicated and how such a wide range of constructions obscure blame. Methodologically, we demonstrated that CL and CDA complement existing NLP-based computational linguistic tools in researching the 2020 A Level algorithm; however, there is further scope for how these approaches can be used in an iterative manner.
QChecker: Detecting Bugs in Quantum Programs via Static Analysis

Static analysis is the process of analyzing software code without executing the software. It can help find bugs and potential problems in software that may only appear at runtime. Although many static analysis tools have been developed for classical software, due to the nature of quantum programs, these existing tools are unsuitable for analyzing quantum programs. This paper presents QChecker, a static analysis tool that supports finding bugs in quantum programs in Qiskit. QChecker consists of two main modules: a module for extracting program information based on abstract syntax tree (AST), and a module for detecting bugs based on patterns. We evaluate the performance of QChecker using the Bugs4Q benchmark. The evaluation results show that QChecker can effectively detect various bugs in quantum programs.
3-D Ego-Motion Estimation Using Multi-Channel FMCW Radar

The problem of estimating the 3D ego-motion velocity using multi-channel FMCW radar sensors has been studied. For the first time, the problem of ego-motion estimation is treated using radar raw signals. A robust algorithm using multi-channel FMCW radar sensors to instantly determine the complete 3D motion state of the ego-vehicle (i.e., translational speed and rotational speed) is proposed. The angle information of targets is extracted, and then their phase information from different times instances is used to determine vehicle ego-motion through an optimization process. Any pre-processing steps, such as clustering or clutter suppression, are not required. The performance of the algorithm is compared with the state-of-the-art algorithms based on real-world data, and superior performance has been demonstrated. The algorithm proposed can be easily integrated into radar signal processing pipelines for other tasks relevant to autonomous driving.
Revisiting Citation Prediction with Cluster-Aware Text-Enhanced Heterogeneous Graph Neural Networks

Numerous papers get published all the time. However, some papers are born to be well-cited while others are not. In this work, we revisit the important problem of citation prediction, by focusing on the important yet realistic prediction on the average number of citations a paper will attract per year. The task is nonetheless challenging because many correlated factors underlie the potential impact of a paper, such as the prestige of its authors, the authority of its publishing venue, and the significance of the problems/techniques/applications it studies. To jointly model these factors, we propose to construct a heterogeneous publication network of nodes including papers, authors, venues, and terms. Moreover, we devise a novel heterogeneous graph neural network (HGN) to jointly embed all types of nodes and links, towards the modeling of research impact and its propagation. Beyond graph heterogeneity, we find it also important to consider the latent research domains, because the same nodes can have different impacts within different communities. Therefore, we further devise a novel cluster-aware (CA) module, which models all nodes and their interactions under the proper contexts of research domains. Finally, to exploit the information-rich texts associated with papers, we devise a novel text-enhancing (TE) module for automatic quality term mining. With the real-world publication data of DBLP, we construct three different networks and conduct comprehensive experiments to evaluate our proposed CATE-HGN framework, against various state-of-the-art models. Rich quantitative results and qualitative case studies demonstrate the superiority of CATE-HGN in citation prediction on publication networks, and indicate its general advantages in various relevant downstream tasks on text-rich heterogeneous networks.
Modeling Product’s Visual and Functional Characteristics for Recommender Systems (Extended Abstract)

Recommender systems aim at helping users to discover interesting items and assisting business owners to obtain more profits. Nonetheless, traditional recommendations fail to explore the varying importance of product characteristics for different product domains. In light of this, we propose a novel probabilistic model for recommendation, which could learn products’ characteristics in a fine-grained manner. Specifically, a user’s preference for a given product is modeled as a combination of visual and functional aspects. To make our method practical in large-scale industrial scenarios, we devise a computationally efficient learning algorithm to optimize VFPMF’s parameters. Experiments on four real-world datasets demonstrate the effectiveness and efficiency of our solution, compared with several state-of-the-art methods.
On the Pitfalls of Security Evaluation of Robust Federated Learning

Prior literature has demonstrated that Federated learning (FL) is vulnerable to poisoning attacks that aim to jeopardize FL performance, and consequently, has introduced numerous defenses and demonstrated their robustness in various FL settings. In this work, we closely investigate a largely over-looked aspect in the robust FL literature, i.e., the experimental setup used to evaluate the robustness of FL poisoning defenses. We thoroughly review 50 defense works and highlight several questionable trends in the experimental setup of FL poisoning defense papers; we discuss the potential repercussions of such experimental setups on the key conclusions made by these works about the robustness of the proposed defenses. As a representative case study, we also evaluate a recent poisoning recovery paper from IEEE S&P'23, called FedRecover. Our case study demonstrates the importance of the experimental setup decisions (e.g., selecting representative and challenging datasets) in the validity of the robustness claims; For instance, while FedRecover performs well for MNIST and FashionMNIST (used in the original paper), in our experiments it performed poorly for FEMNIST and CIFAR10.
Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction

Objectives: To improve the recognition accuracy of radar signals under a low signal-to-noise ratio (SNR). Technology or Method: We propose a novel radar signal recognition method based on a dual-channel model with the histogram of oriented gradients (HOG) feature extraction. Specifically, multisynchrosqueezing transform (MSST) and Choi-Williams distribution (CWD) transform are adopted individually to obtain the time-frequency distribution images of radar signals, and HOG feature extraction is performed on the preprocessed time-frequency images of each channel respectively. Then, the features of the two channels are fused and dimensionally reduced by the principal component analysis (PCA). Finally, the compact feature parameters are fed to the support vector machine (SVM) classifier to identify radar signals. Clinical or Biological Impact: The experimental results demonstrate that the proposed model achieves a high recognition performance with a small computational complexity, especially in low SNR. When the SNR is -12 dB, the recognition accuracy can reach more than 92%, which is over 6% higher than that of single-channel models and related CNN-based models.
Illumination Adaptive Multi-Scale Water Surface Object Detection with Intrinsic Decomposition Augmentation

Visual object detection is an essential task for the intelligent navigation of an Unmanned Surface Vehicle (USV), which can sense the obstacles while navigating. However, the harsh illumination conditions and large scale variation of the objects significantly harm the performance of object detection methods. To address the above problems, we propose a robust water surface object detection method named multi-scale feature fusion network with intrinsic decomposition generative adversarial network data augmentation (MFFDet-IDGAN). We introduce intrinsic decomposition as data augmentation for the object detection to achieve illumination adapting. And an intrinsic decomposition generative adversarial network (IDGAN) is proposed to achieve unsupervised intrinsic decomposition. Moreover, the multi-scale feature fusion network (MFFDet) adopts an improved bidirectional feature pyramid network (BiFPN) and spatial pyramid pooling (SPP) blocks to fuse features of different resolution for better multi-scale detection. And an improved weighted stochastic weight averaging (SWA) is proposed and applied in the training process to improve the generalization performance. We conduct extensive experiments on the Water Surface Object Detection Dataset (WSODD), and the results show that the proposed method can achieve 44% improvement over the baseline. And we further test our method on a real USV in the sailing process, the results show that our method can exceeding the baseline by 4.5%.