Reviewerly/exHarmony huggingface dataset:
A Fast Learned Key-Value Store for Concurrent and Distributed Systems

Efficient key-value (KV) store becomes important for concurrent and distributed systems to deliver high performance. The promising learned indexes leverage deep-learning models to complement existing KV stores and obtain significant performance improvements. However, existing schemes show limited scalability in concurrent systems due to containing high dependency among data. The practical system performance decreases when inserting a large amount of new data due to triggering frequent and inefficient retraining operations. Moreover, existing learned indexes become inefficient in distributed systems, since different machines incur high overheads to guarantee the data consistency when the index structures dynamically change. To address these problems in concurrent and distributed systems, we propose a fine-grained learned index scheme with high scalability, called FineStore, which constructs independent models with a flattened data structure under the trained data array to concurrently process the requests with low overheads. FineStore processes the new requests in-place with the support of non-blocking retraining, hence adapting to the new distributions without blocking the systems. In the distributed systems, different machines efficiently leverage the extended RCU barrier to guarantee the data consistency. We evaluate FineStore via YCSB and real-world datasets, and extensive experimental results demonstrate that FineStore improves the performance respectively by up to 1.8× and 2.5× than state-of-the-art XIndex and Masstree. We have released the open-source codes of FineStore for public use in GitHub.
Dupo: A Mixed-Initiative Authoring Tool for Responsive Visualization

Designing responsive visualizations for various screen types can be tedious as authors must manage multiple chart versions across design iterations. Automated approaches for responsive visualization must take into account the user's need for agency in exploring possible design ideas and applying customizations based on their own goals. We design and implement Dupo, a mixedinitiative approach to creating responsive visualizations that combines the agency afforded by a manual interface with automation provided by a recommender system. Given an initial design, users can browse automated design suggestions for a different screen type and make edits to a chosen design, thereby supporting quick prototyping and customizability. Dupo employs a two-step recommender pipeline that first suggests significant design changes (Exploration) followed by more subtle changes (Alteration). We evaluated Dupo with six expert responsive visualization authors. While creating responsive versions of a source design in Dupo, participants could reason about different design suggestions without having to manually prototype them, and thus avoid prematurely fixating on a particular design. This process led participants to create designs that they were satisfied with but which they had previously overlooked.
Ensemble Learning With Tournament Selected Glowworm Swarm Optimization Algorithm for Cyberbullying Detection on Social Media

Online social network (OSN) plays a crucial role to facilitate social connections; but, this social networking media increases antisocial behaviors, like trolling, cyberbullying, and hate speech.Cyberbullying has often resulted in serious physical and mental distress, especially for children and women, and even sometimes forces them to commit suicide.Conventional techniques for detecting cyberbullying, such as relying on users to report the instance of bullying, are not always effective.Deep learning (DL) and Machine learning (ML) techniques are trained to automatically recognize and flag potential cyberbullying content, along with identifying behavior patterns that are indicative of cyberbullying.Therefore, this study concentrates on the design and development of ensemble deep learning with tournament-selected glowworm swarm optimization (EDL-TSGSO) algorithm for cyberbullying detection and classification on Twitter data.The goal of the study is to examine social media data through the use of natural language processing (NLP) and ensemble learning process.This EDL-TSGSO technique preprocesses the raw tweets and then employs the Glove word embedding technique.In addition, the presented EDL-TSGSO technique utilizes ensemble long short-term memory with Adaboost (ELSTM-AB) model for effective cyberbullying detection and classification.The ensemble ELSTM-AB classifier integrates the prediction of LSTM and Adaboost models to enhance the overall classification performance.To further develop the cyberbullying detection performance of the EDL-TSGSO algorithm, the TSGSO algorithm is applied as a hyperparameter optimizer.The experimental validation of the EDL-TSGSO algorithm on the Twitter dataset demonstrates its promising performance over other state of art approaches in terms of different measures.
SimilarNet: Pairwise Similarity Comparator Layer for Versatile Comparison

In recent years, deep learning has attracted considerable attention owing to its ability to address complex problems in various fields.One notable problem is metric space learning, which is aimed at learning feature embeddings through the calculation of the metric space similarity of feature vectors in the embedding space by training embedding models.However, research on metric space similarity has been limited, and the existing methods, such as those based on the cosine similarity or concatenate layer, exhibit drawbacks in terms of flexibility and performance.For example, to apply the cosine similarity, the shapes of the two vectors must be identical, and thus, an advanced comparator cannot be used.Moreover, the concatenate layer cannot reflect the positions of the elements in the two vectors, leading to deteriorated performance and learning success rates.To address these limitations, this paper proposes a specialized artificial neural network layer named SimilarNet, designed to compare two feature vectors while considering the positions of their elements to produce an output in a vector format.By leveraging the advantages of the cosine similarity and concatenation layer, SimilarNet can effectively compare two vectors, enabling the construction of trained comparison models using multidimensional activation functions.In addition, SimilarNet can realize 1:1 comparisons of data with different shapes, unlike cosine similarity.The results of experiments conducted on various datasets indicate that models employing SimilarNet outperform those with the concatenate layer in terms of the comparison accuracy by 4.3% to 26.5% and learning success rate by 5% to 75%.
MULS-Net: A Multilevel Supervised Network for Ship Tracking From Low-Resolution Remote-Sensing Image Sequences

Ship detection and tracking from remote sensing image sequences has become an increasingly important research point. However, there are still many challenges for ship tracking from low-resolution remote sensing image sequences: 1) the dim and small objects contain only a few shape and texture features, making it difficult to detect and track ships; 2) broken clouds often resemble ships, resulting in false tracking; 3) the ship may be occluded by clouds leading to missed tracking. To address these challenges, we proposed a novel multi-level supervision network for ship tracking from low-resolution remote sensing image sequences. First, we designed a gradient difference-guided object clarification network component to significantly improve the object saliency, which is also implemented based on the multi-frame correlation enhancement images to improve the feature strength of small targets in the input data. Second, to reduce the difficulty of completing complex tasks, a multi-level supervised network framework with multiple components was presented to achieve improving the target clarity, detecting targets and tracking targets step-by-step. Finally, to improve the trajectory integrity and tracking accuracy, a joint tracking method based on a low frame rate tracking criterion was proposed to control the state of target tracking module. The method was validated on a self-assembled dataset from the GaoFen-4 satellite. The experiment results show the stronger competitive and accuracy of the proposed method than other state-of-the-art object tracker.
SGFNet: Segmentation Guided Fusion Network for 3D Object Detection

The self-driving application requires accurate 3D object detection as it is essential in several tasks, such as path and motion planning. However, up until this point, fusion-based detectors with cameras and LiDAR sensors have always been inferior to LiDAR-only detectors. This can be attributed to the dual scene representation problem caused by the differentiated modality of LiDAR points and images. Moreover, the projection of the image pixels is not guaranteed to reach its point cloud counterparts due to the sparsity of the points, losing image content in the fusion process. Bearing these in mind, we propose Segmentation Guided Fusion Network (SGFNet), an efficient multi-sensor fusion-based 3D object detector. It first separates feature extractions of images and points with unified high-dimensional feature representation through the novel-proposed auxiliary foreground segmentation head, and then projects hierarchical feature maps instead of the raw image pixels onto points to obtain the unified feature map, achieving consistent data modality. Such image feature maps are with several spatial resolutions to keep more image content during the projection process. Finally, the unified feature map is fed into a fusion-based region proposal module and bounding box regression head to generate accurate 3D bounding boxes. Extensive experiments conducted on KITTI and nuScenes datasets demonstrate that SGFNet achieves competitive performance on fusion-based 3D object detection tasks and reports a novel state-of-the-art in terms of 3D average precision metric.
TransKS: An Anomaly Detection Method for Telecommunication Networks Based on Deep Learning

As the scale of telecommunication networks continues to grow, the structure of network elements becomes increasingly complex. Consequently, the data service has experienced a significant boom, leading to a substantial increase in network failures and equipment alarms. Effectively detecting hidden anomalies within massive network time series presents a significant challenge for network operators during their operation and maintenance tasks. This paper proposes a multi-dimensional sequence anomaly detection method named TransKS that integrates Transform and K-SIGMA on dynamic variable thresholds, and applies it on the VoLTE and 5G core network. By designing the Transform multi-dimensional time series predictor, the method carries out parallel modeling and forecasting of the index sequence. It may thoroughly investigate the time dimension correlation relationship of the indicators as well as the linking effect of the indicators. And then we designed an adaptive sliding window approach on validation score by dynamically updating the value K and the threshold interval to detect context abnormalities and evaluate the abnormality of network element indicators quantitatively and reasonably. Experiments show that compared the performance with other algorithms, the TransKS has a higher accuracy rate of anomaly detection. When MAPE=0.01 and k is 4, the accuracy rate is the highest, up to 96.92%, which is at least 2.10% higher than other combined models, and its false positive rate and false positive rate have decreased, only 3.06% and 3.25%.
Blockchain-Based Approach for Crop Index Insurance in Agricultural Supply Chain

Crop insurance serves as a crucial risk mitigation strategy for farmers facing uncertainties stemming from unpredictable weather conditions and vulnerabilities within the agricultural production process.Unfortunately, the prevailing traditional methods of crop insurance are laden with complexities, high costs, and, more critically, a lack of trust, which has deterred farmers from safeguarding their crops.Addressing these challenges, we present an innovative blockchain-based crop index insurance solution aimed at delivering multiple benefits.Our solution leverages blockchain technology to ensure unprecedented transparency throughout the insurance ecosystem.Every transaction and data exchange among farmers, insurers, and weather data providers is recorded on an immutable ledger, fostering trust, accountability, and confidence among all stakeholders.Moreover, blockchain's inherent immutability and the use of smart contracts mitigate the risk of fraudulent activities in the crop insurance domain.Claims are processed autonomously, significantly reducing the possibility of fraudulent claims and enhancing the integrity of the system.One of the primary concerns of farmers, delayed claim settlements, is addressed by our solution.Smart contracts enable automated and prompt claim processing, ensuring that farmers receive timely payouts.Furthermore, our solution substantially reduces costs by eliminating intermediaries and streamlining administrative processes.This affordability democratizes crop insurance, making it accessible to a broader spectrum of farmers, including those in low-income countries.Our comprehensive approach integrates advanced algorithms for secure information sharing and interaction among stakeholders.We have rigorously tested the smart contract code under diverse scenarios using the Remix IDE, with the code's availability on GitHub as a reference.Security vulnerabilities have been meticulously assessed and addressed.Thus, our paper presents an effective, low-cost, and trustworthy solution utilizing blockchainbased smart contracts to facilitate timely crop insurance for farmers.By enhancing transparency, reducing fraud, expediting claim processing, and increasing affordability, our solution aims to revolutionize crop insurance, making it more accessible and reliable for farmers worldwide.
FFTransMOT: Feature-Fused Transformer for Enhanced Multi-Object Tracking

Multi-object tracking (MOT) is a key task in the field of computer vision, involving the identification, tracking, and classification of multiple objects in videos, connecting their trajectories to form a complete sequence. MOT consists of two fundamental components: object detection and data association, which require detecting objects in each frame, determining the objects to be tracked, performing data association in the next frame, and predicting the future trajectory of the objects. In this paper, we propose a Feature-Fused Transformer for Enhanced Multi-Object Tracking (FFMOT), where the Feature Fusion module is introduced to aid the model in determining object trajectories between frames and predicting their positions in future frames. We use a model to fuse the features extracted by the encoder at frame <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>t</i>-1</sub> with the featurest, resulting in new features, and the decoder performs data association matching between frame <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>t</i></sub> and the fused features. We also employ the self-attention mechanism to capture dependencies between input features, enhancing the accuracy and stability of object detection. We evaluated the proposed model rigorously on four datasets (MOT16, MOT17, DanceTrack, BDD 100k) and the experimental results demonstrate that our proposed FFMOT model outperforms other trackers in terms of tracking accuracy and robustness in MOT.
Automatic Compilation of CNN on Reconfigurable Array Processor Based on Scalable Instruction Group Library

Although reconfigurable architecture is an inevitable choice for dealing with high-intensive applications, the opacity of hardware structure programming and the fuzziness or imprecision of many dependencies in applications make it difficult to develop applications and take full advantage of reconfigurable architecture. In order to address these challenges, this paper proposes an automatic compilation framework based on a scalable instruction group library, using convolutional neural network (CNN) applications as an example. The proposed framework first identifies functions such as data extraction, data distribution, data summary, and data processing, and divides the reconfigurable processors into multiple logical types. Next, the calculation modes are extracted by analyzing the CNN calculation process. Based on these calculation modes, efficient assembly instruction groups are designed, constituting the scalable instruction group library. Using this library, efficient mapping for CNN applications written in high-level languages can be easily realized on reconfigurable array processors. The experimental results demonstrate that the proposed method reduces the difficulty of programming and achieves better speedup performance compared with OpenMP and LLVM parallel compilation models. Moreover, the scalability of the instruction group library provides helpful guidance for the reconfigurable implementation of domain-oriented applications.
Quantization-Based Adaptive Deep Image Compression Using Semantic Information

Deep image coding (DIC) for hybrid application contexts has recently attracted significant research interest because of its potential to support both human and machine visual tasks. Since the regions of interest (ROI) are different for different application contexts, it is important to design an adaptive image coding mechanism in practical DIC. In this paper, we propose the first quantization-based adaptive DIC framework for hybrid contexts of image reconstruction and classification. This framework can be applied to upgrade existing fixed-rate DIC models into adaptive DIC for hybrid contexts. It consists of two key modules: a semantics-based ROI mask generation module, and a module for generating ROI gain and inverse gain matrices. These matrices are used to control the quantization accuracy of different latent vector elements, thereby achieving encoding at different rates while prioritizing the reconstruction quality of the ROI. Moreover, we propose a five-stage training method for the quantization-based adaptive DIC model to optimize the rate-distortion-classification-perception (RDCP) tradeoff. Experiments over a wide rate range show that our method achieves superior RDCP tradeoff performance. Compared to the benchmark scheme BM-CHENG, the proposed algorithm improves the classification accuracy by an average of 15%. The average relative improvements on various metrics, such as natural image quality evaluator (NIQE), learned perceptual image patch similarity (LPIPS), and feature similarity index measure (FSIM) are about 22%, 47%, and 1%, respectively. The proposed algorithm is a promising candidate for fast adaptive coding with low-complexity constraints.
Domain-Specific Entity Recognition as Token-Pair Relation Classification

Named Entity Recognition (NER) is a fundamental but crucial task in natural language understanding, aiming at identifying entity mentions from free text. Current methods mainly use sequence-labeling and span-based models, where the former ignores the importance of token interaction, and the latter pays little attention to the global inter-dependency among entity tokens. In this work, we propose a novel NER model that consists of two branches: a Token-Pair Interaction Module (TPIM) and a U-shaped Network. The TPIM models head-tail relations between token pairs while capturing intrinsic token connectivity within entity boundaries. The U-shaped Network is employed to capture the contextual dependency in the token-pair relation matrix. Furthermore, we build a typical domain-specific entity dataset CCAEE based on real-world applications in the chemical accident domain. The experimental results on CCAEE and CLUENER datasets demonstrate that the proposed model can achieve competitive performance compared with popular NER methods.
Discriminative Prototype Learning for Few-Shot Object Detection in Remote-Sensing Images

Few-shot object detection (FSOD) in remote sensing images, which aims to detect never-seen objects with few training samples, has attracted wide attention. Some recent works leverage meta-learning to tackle this challenging task and achieve promising performance. However, information attenuation during feature extraction and simple prototype representation hamper further improvement in detecting novel classes. In this article, we propose a novel meta-learning-based FSOD approach named DPL-Net. Specially, within the meta-learning-based framework, performing role-specific feature extraction in query and support branches, DPL-Net adopts a Fine-grained Information Fusion (FIF) module to capture scale-aware information within query regions of interest (RoIs) and a Multi-frequency Information Enhancement (MIE) module to retain the spectral information of support samples, respectively. Moreover, considering the variability of remote sensing objects, a Discriminative Prototype Learning (DPL) strategy is developed to rectify the ambiguous distribution of support samples for more representative class-aware prototypes. Experiments on two benchmark datasets (NWPU VHR-10 and DIOR) demonstrate that our method effectively improves the performance of meta-learning in detecting remote sensing images with limited training data.
Control Inference Neural Network for Motion Planning With Dynamical Systems

This letter proposes an approach that combines the use of sampling-based motion planning with machine learning algorithms. In particular, the asymptotically optimal planner <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SST*</i> is used to generate asymptotically minimum time trajectories, considering systems with motion constraints (nonholonomic systems) and different orders in the dynamics of the models. The intermediate states that form the trajectories obtained by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SST*</i> planner, together with the controls that generated them, constitute the inputs and outputs, respectively, of the training set for a new variant of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MPNet</i> network. This contrasts with other works in which neural networks are proposed that infer states that serve as a guide for another complementary method to generate controls that respect the dynamics of the system. The proposed method uses a neural network to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly infer</i> the controls of the system (plus an application time of those controls), accelerating the generation time of trajectories up to three orders of magnitude in comparison to other methods based on learning. The proposed approach is evaluated with different dynamical systems and environments, including maps with concave obstacles and narrow passages.