Reviewerly/exHarmony huggingface dataset:
Adaptive Focal Inverse Distance Transform Maps for Cell Recognition

The quantitative analysis of cells is crucial for clinical diagnosis, and effective analysis requires accurate detection and classification. Using point annotations for weakly supervised learning is a common approach for cell recognition, which significantly reduces the labeling workload. Cell recognition methods based on point annotations primarily rely on manually crafted smooth pseudo labels. However, the diversity of cell shapes can render the fixed encodings ineffective. In this paper, we propose a multi-task cell recognition framework. The framework utilizes a regression task to adaptively generate smooth pseudo labels with cell morphological features to guide the robust learning of probability branch and utilizes an additional branch for classification. Meanwhile, in order to address the issue of multiple high-response points in one cell, we introduce Non-Maximum Suppression (NMS) to avoid duplicate detection. On a bone marrow cell recognition dataset, our method is compared with five representative methods. Compared with the best performing method, our method achieves improvements of 2.0 F1 score and 3.6 F1 score in detection and classification, respectively.
Enhancing Heterogeneous Graph Contrastive Learning with Strongly Correlated Subgraphs

Graph contrastive learning maximizes the mutual information between the embedding representations of the same data instances in different augmented views of a graph, obtaining feature representations for graph data in an unsupervised manner without the need for manual labeling. Most existing node-level graph contrastive learning models only consider embeddings of the same node in different views as positive sample pairs, ignoring rich inherent neighboring relation and resulting in certain contrastive information loss. To address this issue, we propose a heterogeneous graph contrastive learning model that incorporates strongly correlated subgraph features. We design a contrastive learning framework suitable for heterogeneous graphs and introduce high-level neighborhood information during the contrasting process. Specifically, our model selects a strongly correlated subgraph for each target node in the heterogeneous graph based on both topological structure information and node attribute feature information. In the calculation of contrastive loss, we perform feature shifting operations on positive and negative samples based on subgraph encoding to enhance the model's ability to discriminate between approximate samples. We conduct node classification and ablation experiments on multiple public heterogeneous datasets and the results verify the effectiveness of the research contributions of our model.
Effective Guidance in Zero-Shot Multilingual Translation via Multiple Language Prototypes

In a multilingual neural machine translation model that fully shares parameters across all languages, a popular approach is to use an artificial language token to guide translation into the desired target language. However, recent studies have shown that language-specific signals in prepended language tokens are not adequate to guide the MNMT models to translate into right directions, especially on zero-shot translation (i.e., off-target translation issue). We argue that the representations of prepended language tokens are overly affected by its context information, resulting in potential information loss of language tokens and insufficient indicative ability. To address this issue, we introduce multiple language prototypes to guide translation into the desired target language. Specifically, we categorize sparse contextualized language representations into a few representative prototypes over training set, and inject their representations into each individual token to guide the models. Experiments on several multilingual datasets show that our method significantly alleviates the off-target translation issue and improves the translation quality on both zero-shot and supervised directions.
Exploring Non-isometric Alignment Inference for Representation Learning of Irregular Sequences

The development of Internet of Things (IoT) technology has led to increasingly diverse and complex data collection methods. This unstable sampling environment has resulted in the generation of a large number of irregular monitoring data streams, posing significant challenges for related data analysis tasks. We have observed that irregular sequence sampling densities are uneven, containing randomly occurring dense and sparse intervals. This data imbalance tendency often leads to overfitting in the dense regions and underfitting in the sparse regions, ultimately impeding the representation performance of models. Conversely, the irregularity at the data level has limited impact on the deep semantics of sequences. Based on this observation, we propose a novel Non-isometric Alignment Inference Architecture (NAIA), which utilizes a multi-level semantic continuous representation structure based on inter-interval segmentation to learn representations of irregular sequences. This architecture efficiently extracts the latent features of irregular sequences. We evaluate the performance of NAIA on multiple datasets for downstream tasks and compare it with recent benchmark methods, demonstrating NAIA's state-of-the-art performance results.
Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm

The risk of fraudulent activity has significantly increased with the rise in digital payments. To resolve this issue there is a need for reliable real-time fraud detection technologies. This research introduced an innovative method called stacked autoencoder kernel extreme learning machine optimized by the dandelion algorithm (S-AEKELM-DA) to detect fraudulent transactions. The primary objective was to enhance the kernel extreme learning machine (KELM) performance by integrating the dandelion technique into a stacked autoencoder kernel ELM architecture. This study aimed to improve the overall effectiveness of the proposed method in fraud detection by optimizing the regularization parameter (c) and the kernel parameter (σ). To evaluate the S-AEKELM-DA approach; simulations and experiments were conducted using four credit card datasets. The results demonstrated remarkable performance, with our method achieving high accuracy, recall, precision, and F1-score in real time for detecting fraudulent transactions. These findings highlight the effectiveness and reliability of the suggested approach. By incorporating the dandelion algorithm into the S-AEKELM framework, this research advances fraud detection capabilities, thus ensuring the security of digital transactions.
Differentiable Topics Guided New Paper Recommendation

There are a large number of scientific papers published each year. Since the progresses on scientific theories and technologies are quite different, it is challenging to recommend valuable new papers to the interested researchers. In this paper, we investigate the new paper recommendation task from the point of involved topics and use the concept of subspace to distinguish the academic contributions. We model the papers as topic distributions over subspaces through the neural topic model. The academic influences between papers are modeled as the topic propagation, which are learned by the asymmetric graph convolution on the academic network, reflecting the asymmetry of academic knowledge propagation. The experimental results on real datasets show that our model is better than the baselines on new paper recommendation. Specially, the introduced subspace concept can help find the differences between high quality papers and others, which are related to their innovations. Besides, we conduct the experiments from multiple aspects to verify the robustness of our model.
Double-Layer Blockchain-Based Decentralized Integrity Verification for Multi-chain Cross-Chain Data

With the development of blockchain technology, issues like storage, throughput, and latency emerge. Multi-chain solutions are devised to enable data sharing across blockchains, but in complex cross-chain scenarios, data integrity faces risks. Due to the decentralized nature of blockchain, centralized verification schemes are not feasible, making decentralized cross-chain data integrity verification a critical and challenging problem. In this paper, based on the ideas of "governing the chain by chain" and "double layer blockchain", we propose a double-layer blockchain-based decentralized integrity verification scheme. We construct a supervision-chain by selecting representative nodes from multiple blockchains, which is responsible for cross-chain data integrity verification and recording results. Specifically, our scheme relies on two consensus phases: integrity consensus for verification and block consensus for result recording. We also integrate a reputation system and an election algorithm within the supervision-chain. Through security analysis and performance evaluation, we demonstrate the security and effectiveness of our proposed scheme.
DRPDDet: Dynamic Rotated Proposals Decoder for Oriented Object Detection

Oriented object detection has gained popularity in diverse fields. However, in the domain of two-stage detection algorithms, the generation of high-quality proposals with a high recall rate remains a formidable challenge, especially in the context of remote sensing images where sparse and dense scenes coexist. To address this, we propose the DRPDDet method, which aims to improve the accuracy and recall of proposals for Oriented target detection. Our approach involves generating high-quality horizontal proposals and dynamically decoding them into rotated proposals to predict the final rotated bounding boxes. To achieve high-quality horizontal proposals, we introduce the innovative HarmonyRPN module. This module integrates foreground information from the RPN classification branch into the original feature map, creating a fused feature map that incorporates multi-scale foreground information. By doing so, the RPN generates horizontal proposals that focus more on foreground objects, which leads to improved regression performance. Additionally, we design a dynamic rotated proposals decoder that adaptively generates rotated proposals based on the constraints of the horizontal proposals, enabling accurate detection in complex scenes. We evaluate our proposed method on the DOTA and HRSC2016 remote sensing datasets, and the experimental results demonstrate its effectiveness in complex scenes. Our method improves the accuracy of proposals in various scenarios while maintaining a high recall rate.
Gated Bi-View Graph Structure Learning

Graph structure learning (GSL), which aims to optimize graph structure and learn suitable parameters of graph neural networks (GNNs) simultaneously, has shown great potential in boosting the performance of GNNs. As a branch of GSL, multi-view methods mainly learn an optimal graph structure (final view) from multiple information sources (basic views). However, basic views' structural information is insufficient, existing methods ignore the fact that different views can complement each other. Moreover, existing methods obtain the final view through simple combination, fail to constrain the noise, which inevitably brings irrelevant information. To tackle these problems, we propose a Gated Bi-View GSL architecture, named GBV-GSL, which interacts two basic views through a selection gating mechanism, so as to "turn off" noise as well as supplement insufficient structures. Specifically, two basic views that focus on different knowledge are extracted from original graph as two inputs of the model. Furthermore, we propose a novel view interaction technique based on selection gating mechanism to remove redundant structural information and supplement insufficient topology while retaining their focused knowledge. Finally, we design a view attention fusion mechanism to adaptively fuse two interacted views to generate the final view. In numerical experiments involving both clean and attacked conditions, GBV-GSL shows significant improvements in the effectiveness and robustness of structure learning and node representation learning. Code is available at https://github.com/Simba9257/GBV-GSL .
Enhancing Consensus Security and Privacy with Multichain Ring Signatures Based on HotStuff

The paper introduces a novel consensus algorithm named MRPBFT, which is derived from the HotStuff consensus protocol and improved upon to address security deficiencies in traditional consensus algorithms within the domain of digital asset transactions. MRPBFT aims to enhance security and privacy protection while pursuing higher consensus efficiency. It employs a multi-primary-node approach and a ring signature mechanism to reinforce security and privacy preservation features in the consensus system. This algorithm primarily focuses on two main improvements: Firstly, it proposes the ed25519LRS signature algorithm and discusses its anonymity for transaction participants and the non-forgeability of signature information in the identity verification and message verification processes within the consensus algorithm. Secondly, the paper introduces MPBFT asynchronous view changes and a multi-primary-node mechanism to enhance consensus efficiency, allowing for view switching in the absence of global consensus. With the introduction of the multi-primary-node mechanism, nodes can be flexibly added or removed, supporting parallel processing of multiple proposals and transactions. Finally, through comparative experiments, the paper demonstrates that the improved algorithm performs significantly better in terms of throughput and network latency.
Identify Vulnerability Types: A Cross-Project Multiclass Vulnerability Classification System Based on Deep Domain Adaptation

Software Vulnerability Detection(SVD) is a important means to ensure system security due to the ubiquity of software. Deep learning-based approaches achieve state-of-the-art performance in SVD but one of the most crucial issues is coping with the scarcity of labeled data in projects to be detected. One reliable solution is to employ transfer learning skills to leverage labeled data from other software projects. However, existing cross-project approaches only focused on detecting whether the function code is vulnerable or not. The requirement to identify vulnerability types is essential because it offers information to patch the vulnerabilities. Our aim in this paper is to propose the first system for cross-project multiclass vulnerability classification. We detect at the granularity of code snippet, which is finer-grained compare to function and effective to catch inter-procedure vulnerability patterns. After generating code snippets, we define several principles to extract snippet attentions and build a deep model to obtain the fused deep features; We then extend different domain adaptation approaches to reduce feature distributions of different projects. Experimental results indicate that our system outperforms other state-of-the-art systems.
An investigation into the impacts of deep learning‐based re‐sampling on specific emitter identification performance

Abstract Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that most IoT devices use weak or no encryption. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while reducing the hardware requirements of the IoT devices that collect them. DL‐driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network‐only approach.
An insider user authentication method based on improved temporal convolutional network

With the rapid development of information technology, information system security and insider threat detection have become important topics for organizational management. In the current network environment, user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence. Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction. They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data, and also do not adequately reflect the personalized usage characteristics of users, leading to bottlenecks in the performance of the authentication algorithm. In order to solve the above problems, this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism (ECA-TCN) to extract user mouse dynamics features and constructs an one-class Support Vector Machine (OCSVM) for each user for authentication. Experimental results show that compared with four existing deep learning algorithms, the method retains more adequate key information and improves the classification performance of the neural network. In the final authentication, the Area Under the Curve (AUC) can reach 96%.
Pelatihan Peningkatan ICT Dalam Pelaksanaan AKM Siswa SDN 1 Bojong Cilimus

The National Assessment is an evaluation program organized by the Ministry of Education and Culture to improve the quality of education by photographing the inputs, processes and outputs of learning in all educational units. Minimum Competency Assessment (AKM) is conducted to measure reading literacy and numeracy. Reading literacy is defined as the ability to understand, use, evaluate, reflect on various types of written texts to develop individual capacities as citizens of Indonesia and citizens of the world and to be able to contribute productively to society. Numeracy is the ability to think using mathematical concepts, procedures, facts, and tools to solve everyday problems in various types of contexts that are relevant to individuals as citizens of Indonesia and the world. Prior to the implementation of AKM at SDN 1 Bojong Cilimus, it was necessary to carry out a simulation for grade 5 students so that the implementation of AKM could run smoothly and students could work on literacy and numeracy questions properly according to the expectations of all parties.&#x0D; Keywords: national assessment, minimum competency assessment, literacy, numeracy, students
The technique of schematic visualization of the emotional sphere in group psychotherapy work with clients who have a low level of reflexivity

The article aims to describe the technique of schematic visualization of the client's emotional sphere, which is developed for use in group psychotherapy and is aimed at increasing the level of the client's reflexivity by increasing the quality of the process of reflection in this sphere. For productive work with the client in most psychotherapeutic methods, especially the humanistic paradigm, one of the important factors is such a property of the client's personality as the possibility of the existence of the process of self-knowledge, i.e. the client's reflexivity, but it is not always developed to a level sufficient for psychotherapeutic work (in this case, "reflexivity” is used as a broader concept than “reflection”). To solve this issue, it is necessary to develop new techniques, which is proposed by the author of the work. The use of this technique contributes to increasing the ability to manifest one's own emotions and feelings and to the client's visual demonstration and awareness of the features of the structure of his emotional sphere and formations in it, which are characteristic of persons with traumatic emotional experience. For example: "gluing" of emotions of asthenic and sthenic circles; destructive connotation of positive emotions with negative ones; inability to feel "pure" emotion; the phenomenon of potentiation of negative emotionality; loss of resourcefulness of certain emotions, which are contaminated by other emotions; the existence of "through" emotions, which become core characteristics of the emotional sphere of the individual. During the application of the technique, the work is structured in such a way as to maximally involve the dynamics of the group, emotional, cognitive and behavioral levels to increase the quality of reflection of the emotional sphere and the client's reflexivity as a whole. The classification of reflexivity is used for the analytical part in the application of the technique (for example, the comparison of emotional manifestations characteristic of the client in retrospective, prospective and situational vectors), and the idea of "surreality" at the stage of transformation and modeling. In this work, 9 stages of technique application are provided in detail and the role and tasks of the psychotherapist, client and group members at each of them are described.
The Drivers of Economic Bubbles in Cryptocurrencies That Affect Its Long-Term Sustainability

Cryptocurrencies have emerged as a viable alternative to traditional financial systems. However, the rapid rise of cryptocurrencies has led to concerns and debates about the sustainable characteristics of risky investments like cryptocurrencies. This research explores the drivers of economic bubbles in the cryptocurrency market, potentially affecting their long-term sustainability and implications. In the quantitative analysis, this research employs time series analysis to analyze data and identify patterns of historical cryptocurrency prices and trading volume to explore the relationship between bubble indicators and sustainability factors. The qualitative analysis reveals that most investors rely on social media, newspapers, and other trusted businesses to gather information about the cryptocurrencies they intend to invest in.
AN EVALUATION OF HEURISTIC METHODS FOR THE BANDWIDTH REDUCTION OF LARGE-SCALE GRAPHS

This paper studies the bandwidth reduction problem for large-scale sparse matrices in serial computations. A heuristic for bandwidth reduction reorders the rows and columns of a given sparse matrix, placing entries with a non-null value as close to the main diagonal as possible. Recently, a paper proposed the FNCHC+ heuristic. The heuristic method is a variant of the Fast Node Centroid Hill-Climbing algorithm. The FNCHC+ heuristic presented better results than the other existing heuristics in the literature when applied to reduce the bandwidth of large-scale graphs (of the underline matrices) with sizes up to 18.6 million vertices and up to 57.2 million edges. The present paper provides new experiments with even larger graphs. Specifically, the present study performs experiments with test problems containing up to 24 million vertices and 130 million edges. The results confirm that the FNCHC+ algorithm is the state-of-the-art metaheuristic algorithm for reducing the bandwidth of large-scale matrices.