Reviewerly/exHarmony huggingface dataset:
A New Method for Microscopy Image Segmentation Using Multi-scale Line Detection

Image segmentation plays a crucial role in many biomedical imaging applications by automating and facilitating delineating of anatomical structures and different regions of interest. The objective of microscopic image segmentation is to accurately identify the boundaries of cells, cell nuclei, or histological structures in stained tissue images with various markers. Over the last few years, numerous techniques for segmenting microscopy images have been developed. Despite the advancements in segmentation techniques for microscopy images, there are still significant challenges when dealing with variations, high levels of noise, and variations in image features. (e.g., nucleus shape, cell size, concavity points between nuclei) and complexity of method parameter space. This paper introduces a new method for distinguishing nuclei and other biological structures in microscopy images. The proposed method exploits the multi scales line detection method, which presents an effective method for automatically extracting blood vessels from retinal images to detect boundaries of cells, cell nuclei and the concave points that split the contours into segments. The effectiveness of the method has been evaluated both qualitatively and quantitatively and compared with four other segmentation algorithms - the Chan & Vese model and three thresholding techniques, namely Otsu, Kapur, and Kittler method. These evaluations were conducted using a variety of publicly available datasets from the Broad-Bioimage Benchmark. The comparison results were analyzed using the Jaccard similarity index (JI) and the Dice coefficient (DSC) measure set agreement.
Multicarrier Waveforms Classification with LDA and CNN for 5G

Accurate classification of multi-carrier waveforms is significant for ensuring quality signal reception, improved system throughput, and reduce the power consumption in future wireless generations as 5G. The aim of this paper is to improve the precision classification of various multicarrier waveforms. Here, we propose a novel representation of multicarrier signals in AWGN environment and use suitable networks for classification, which utilizes deep convolutional neural networks to classify OFDM-QAM, and FBMC-OQAM. LDA-based (Linear Discriminant Analysis) method is proposed in this paper to reduce the input dimensions of CNN. The results reveal that LDA-CNN is a promising candidate for wireless communication.
Basis Set Generation and Optimization in the NISQ Era with Quiqbox.jl

In the noisy intermediate-scale quantum era, ab initio computation of electronic structure problems has become one of the major benchmarks for identifying the boundary between classical and quantum computational power. Basis sets play a key role in the electronic structure methods implemented on both classical and quantum devices. To investigate the consequences of single-particle basis sets, we propose a framework for more customizable basis set generation and optimization. This framework allows composite basis sets to go beyond typical basis set frameworks, such as atomic basis sets, by introducing the concept of mixed-contracted Gaussian-type orbitals. These basis set generations set the stage for more flexible variational optimization of basis set parameters. To realize this framework, we have developed an open-source software package named "Quiqbox" in the Julia programming language. We demonstrate various examples of using Quiqbox for basis set optimization and generation, ranging from optimizing atomic basis sets on the Hartree-Fock level, preparing the initial state for variational quantum eigensolver computation, and constructing basis sets with completely delocalized orbitals. We also include various benchmarks of Quiqbox for basis set optimization and ab initial electronic structure computation.
Evaluating compliance for organizational information security and business continuity: three strata of ventriloqual agency

Purpose The purpose of this research is to study how compliance evaluation becomes performed in practice. Compliance evaluation is a common practice among organizations that need to evaluate their posture against a set of criteria (e.g. a standard, legislative framework and “best practices”). The results of these evaluations have significant importance for organizations, especially in the context of information security and continuity. The author argues that how these evaluations become performed is not merely a “social” activity but shaped by the materiality of the evaluation criteria Design/methodology/approach The authors adopt a sociomaterial practice-based view to study the compliance evaluation through in situ participant observations from compliance evaluation workshops to evaluate organizational compliance against a information security and business continuity criteria. The empirical material was analyzed to construct vignettes that serve to illustrate the practice of compliance evaluation. Findings The research analysis shows how the information security and business continuity criteria themselves partake in the compliance evaluations by operating through (ventriloqually) the evaluators on three strata: the material, the textual and the structural. The author also provides a conceptualization of a hybrid agency. Originality/value This research contributes to lack of studies on the organizational-level compliance. Further, the research is an original contribution to information security and business continuity management by focusing on the practices of compliance evaluation. Further, the research has theoretical novelty by adopting the ventriloqual agency as a hybrid agency to study the sociomateriality of a phenomenon.
Research on Image Segmentation Algorithm Based on Level Set

Digital image processing has garnered significant attention and extensive research in both civilian and military domains. Image segmentation serves as a fundamental component of image processing, directly influencing the outcomes of subsequent image processing steps. Hence, it holds immense research value to efficiently and accurately extract target regions from diverse and complex images. In recent years, numerous image segmentation algorithms have been proposed. However, traditional segmentation methods fall short in fully addressing challenges like topological changes in image contours, limiting their applicability across various image types. To enhance the adaptability and universality of image segmentation algorithms, this study employs the level set method to transform the curve evolution problem into a numerical solution involving partial differential equations. This paper initially surveys the research trends in level set-based image segmentation. It then elucidates the characteristics of several classical level set image segmentation algorithms, including CV, RSF, LGIF, LRCV, and RABFLR, while also providing recursive methodologies for the LGIF model, LRCV model, and RABFLR model. Subsequently, an enhanced LGIF model is proposed, which incorporates bilateral filtering for image preprocessing and effectively integrates global and local information. Experimental results demonstrate a significant improvement in segmentation effectiveness compared to existing algorithms.
Conspiracy Spoofing Orders Detection with Transformer-Based Deep Graph Learning

Spoofing behaviors profits from other participants via placing large amounts of deceptive orders to create misleading signals with the help of advanced electronic trading techniques, severely harming the healthy and sustainable development of modern financial markets. Existing works focus mostly on detecting individual spoofing issuers or orders, which ignore the capability of mining hidden conspiracy fraud patterns. Consequently, they face significant challenges in tracking organized spoofing behaviors. To this end, in this paper, we proposed a novel transformer-based graph neural network to learn the temporal inter-connected transaction representations for conspiracy spoofing behavior detection. In particular, we model user orders in the financial market by the temporal transaction graph layer first, which produces temporal and relational encodings of the user’s order behaviors. Then, we devise a transformer-based neural network to automatically learn the deeper sequential and inter-connected representations, which could perceive a boarder view of the user’s transaction patterns. Finally, the temporal and relational features are jointly optimized by the detection network in an end-to-end manner. We conduct extensive experiments on a real-world dataset from one of the largest financial exchange markets in East Asia. The result strongly proves the superior performance of our proposed method in fighting against conspiracy spoofing behaviors with higher accuracy without handcraft feature engineering, compared with ten state-of-the-art baselines. To the best of our knowledge, this is the first work that proposed a transformer-based deep graph learning approach for spoofing detection challenges and demonstrates its practical ability in real-world applications.
Supervised Discriminative Discrete Hashing for Cross-Modal Retrieval

With the growing interest in cross-modal retrieval technology, cross-modal hashing has become a mainstream trend for comparing and searching between different modalities. However, when faced with multi-label information, existing research has often neglected the unique features of each individual label, resulting in learned hash codes lacking discriminative power. In this article, we present a novel cross-modal hashing method called Supervised Discriminative Discrete Hashing ( $$\text {SD}^{2}\text {H}$$ ), which consists of two stages: hash code learning and hash function learning. Specifically, we extract each individual label from the multi-label information and introduce their one-to-one corresponding hash code. By mapping the original multi-label information to Hamming space through the single-label hash codes, we can obtain more discriminative to-be-learned hash codes. Furthermore, we establish a connection between label information and original feature vectors to explore the potential consistency. Additionally, we use a discrete optimization algorithm to reduce information loss and quantization error during the learning process. The superiority of our proposed $$\text {SD}^{2}\text {H}$$ method over several state-of-the-art hashing methods has been demonstrated through extensive experiments on two benchmark datasets.
Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College Students

Recommendation systems play a crucial role in helping college students find job opportunities. However, the sparsity of interactions in employment recommendation for college students poses a challenge for models based on historical user preferences. To address this issue, we propose a novel model called Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students (EERIS). The model comprises two components: a similarity information component that uses pooled users to determine the nearest neighbor in user similarity measurement, and a global interaction component that uses interaction vectors of user groups to enhance interactions. To evaluate the missing interactions, we propose a loss function called CellLoss. These components are combined based on ensemble learning to improve the model’s generalization and scalability. Our experiments on two real-world datasets demonstrate the superior performance of the EERIS model. Ablation experiments further confirm that each component positively contributes to the model’s performance. Additionally, we design a revised metric for better model testing. Overall, the proposed EERIS model effectively addresses the interaction sparsity in employment recommendation for college students and provides satisfactory recommendations to students.
LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning Capabilities for NLI

Natural Language Inference (NLI) is considered a representative task to test natural language understanding (NLU). In this work, we propose an extensible framework to collectively yet categorically test diverse Logical reasoning capabilities required for NLI (and, by extension, NLU). Motivated by behavioral testing, we create a semi-synthetic large test bench (363 templates, 363k examples) and an associated framework that offers the following utilities: (1) individually test and analyze reasoning capabilities along 17 reasoning dimensions (including pragmatic reasoning); (2) design experiments to study cross-capability information content (leave one out or bring one in); and (3) the synthetic nature enables us to control for artifacts and biases. We extend a publicly available framework of automated test case instantiation from free-form natural language templates (CheckList) and a well-defined taxonomy of capabilities to cover a wide range of increasingly harder test cases while varying the complexity of natural language. Through our analysis of state-of-the-art NLI systems, we observe that our benchmark is indeed hard (and non-trivial even with training on additional resources). Some capabilities stand out as harder. Further, fine-grained analysis and fine-tuning experiments reveal more insights about these capabilities and the models – supporting and extending previous observations; thus showing the utility of the proposed testbench.
Multi-head Similarity Feature Representation and Filtration for Image-Text Matching

The field of multimedia analysis has been increasingly focused on image-text retrieval, which aims to retrieve semantically relevant images or text through queries of the opposite modality. The key challenge is to learn the correspondence between images and text. Existing methods have focused on processing inter-modality information interaction but have not given sufficient attention to learning the correspondence between the two modalities during this process. However, these methods have a low accurate image-text matching due to they are not deal with the noise during the process of the visual and textual representations. To avoid the noise in the training process, we propose a novel Multi-head Similarity Feature Representation and Filtration (MSFRF) approach for image-text matching. The proposed MSFRF method captures the detailed associations of feature representations from different modalities and reduces the interference of noisy information in the extracted features for improving the performance of matching. Extensive experiments on two benchmark datasets show that the proposed MSFRF method outperforms the state-of-the-art image-text matching methods.
CNMBI: Determining the Number of Clusters Using Center Pairwise Matching and Boundary Filtering

One of the main challenges in data mining is choosing the optimal number of clusters without prior information. Notably, existing methods are usually in the philosophy of cluster validation and hence have underlying assumptions on data distribution, which prevents their application to complex data such as large-scale images and high-dimensional data from the real world. In this regard, we propose an approach named CNMBI. Leveraging the distribution information inherent in the data space, we map the target task as a dynamic comparison process between cluster centers regarding positional behavior, without relying on the complete clustering results and designing the complex validity index as before. Bipartite graph theory is then employed to efficiently model this process. Additionally, we find that different samples have different confidence levels and thereby actively remove low-confidence ones, which is, for the first time to our knowledge, considered in cluster number determination. CNMBI is robust and allows for more flexibility in the dimension and shape of the target data (e.g., CIFAR-10 and STL-10). Extensive comparisof-the-art competitors on various challenging datasets demonstrate the superiority of our method.
An Efficient Feature Selection Method for High Dimensional Data Based on Improved BOA in AIoT

Feature selection can eliminate irrelevant redundant information in high-dimensional data, reduce dimensions, improve classification performance, and relieve the computational burden of machine learning. In this paper, we propose a feature selection method based on improved binary butterfly op-timization algorithm (BCSBOA). BCSBOA uses Tent chaotic mapping to improve the population initialization method, which makes the distribution of population individuals more uniform and improves population diversity. The mutation perturbation strategy is adopted to improve the ability of the algorithm to escape from local optimum. Through the adaptive weight strategy, the optimization ability of the global and local search stages is further balanced. In addition, BCSBOA maps the solution to the binary space through the Sigmoid function, proposes a new fitness function, jointly optimizes the classification accuracy and the number of features, and finally improves the feature selection effect. Simulation results of the algorithm performance over 10 UCI data sets show that, the classification accuracy and data-feature subset size have been improved. These findings also support the feasibility of our algorithm in improving machine learning performance.
Efficient Blockchain Data Trusty Provenance Based on the W3C PROV Model

Data provenance can effectively ensure the correctness of data mining results. To realize efficient and trusty data provenance, we propose an efficient blockchain data trusty provenance architecture based on the W3C PROV model. First, we integrate the W3C PROV model into the blockchain system to offer a unified provision information standard so as to form convincing assessments about its quality, reliability, or trustworthiness. Second, we design multi-bucket indexes within blocks and a skip list index among blocks to improve the query efficiency of the provenance information on blockchain. Finally, we design hash-linked list indexes to improve the verification efficiency of the completeness and correctness of the provenance information and propose a verifiable data query algorithm based on the above index. Experiential results show that our proposed architecture performance is effective and efficient.
A Poisoning Attack Based on Variant Generative Adversarial Networks in Recommender Systems

The emergence of poisoning attacks brings significant security risks to recommender systems. Injecting a well-designed set of fake user profiles into these systems can severely impact the quality of recommendations. However, existing attack models against recommender systems struggle to balance the imperceptibility and harmfulness of the generated fake user profiles. In this paper, we propose a novel poisoning attack model based on variant GAN. Firstly, we construct a candidate item set and divide each user rating behavior sequence into multiple shorter sequences. By identifying high-impact short sequences and calculating the proportion of high-impact sequences in each user rating sequence, we can determine the template profiles. Secondly, we design two different generators to simulate the fake user profiles, and employ a discriminator to enhance the generation of higher-quality fake user profiles. Finally, experimental results on three real datasets demonstrate that the proposed method outperforms state-of-the-art attacks in terms of attack performance, and the generated fake profiles are more difficult to detect when compared to the baselines.
Separate-and-Aggregate: A Transformer-Based Patch Refinement Model for Knowledge Graph Completion

Knowledge graph completion (KGC) is the task of inferencing missing facts from any given knowledge graphs (KG). Previous KGC methods typically represent knowledge graph entities and relations as trainable continuous embeddings and fuse the embeddings of the entity h (or t) and relation r into hidden representations of query (h, r, ?) (or (?, r, t)) to approximate the missing entities. To achieve this, they either use shallow linear transformations or deep convolutional modules. However, the linear transformations suffer from the expressiveness issue while the deep convolutional modules introduce unnecessary inductive bias, which could potentially degrade the model performance. Thus, we propose a novel Transformer-based Patch Refinement Model (PatReFormer) for KGC. PatReFormer first segments the embedding into a sequence of patches and then employs cross-attention modules to allow bi-directional embedding feature interaction between the entities and relations, leading to a better understanding of the underlying KG. We conduct experiments on four popular KGC benchmarks, WN18RR, FB15k-237, YAGO37 and DB100K. The experimental results show significant performance improvement from existing KGC methods on standard KGC evaluation metrics, e.g., MRR and H@n. Our analysis first verifies the effectiveness of our model design choices in PatReFormer. We then find that PatReFormer can better capture KG information from a large relation embedding dimension. Finally, we demonstrate that the strength of PatReFormer is at complex relation types, compared to other KGC models.
PBCI-DS: A Benchmark Peripheral Blood Cell Image Dataset for Object Detection

Blood testing has always been one of the important methods for disease diagnosis, but currently, blood testing instruments face the problems of long time consumption, complex processes, and limited detection types. Therefore, a blood cell dataset for artificial intelligence is necessary. Peripheral Blood Cell Image Dataset (PBCI-DS) provides a total of 17092 images of 8 categories of peripheral blood cells and corresponding cell labeling files. The purpose of PBCI-DS is to serve as a training model for object detection. The experiment uses four YOLO series models (YOLO-v5s, YOLO-v5l, YOLO-v6, YOLO-v7) and SSD models of deep learning methods to train the database, and compares and evaluates the results to prove the effectiveness and usability of PBCI-DS.
Refined Node Type Graph Convolutional Network for Recommendation

Recently, because of the remarkable performance in alleviating the data sparseness problem in recommender systems, Graph Convolutional (Neural) Networks (GCNs) have drawn wide attention as an effective recommendation approach. By modeling the user-item interaction graph, GCN iteratively aggregates neighboring nodes into embeddings of different depths according to the importance of each node. However, the existing GCN-based methods face the common issues that, they do not consider the node information and graph structure during aggregating nodes, such that they cannot assign reasonable weights to the neighboring nodes. Additionally, they ignore the differences in node types in the user-item interaction graph and thus, cannot explore the complex relationship between users and items, resulting in a suboptimal result. To solve these problems, a novel GCN-based framework called RNT-GCN is proposed in this paper. RNT-GCN integrates the structure of the graph and node information to assign reasonable importance to different nodes. In addition, RNT-GCN refines the node types, such that the heterogeneous properties of the user-item interaction graph can be better preserved, and the collaborative information of users and items can be effectively extracted. Extensive experiments prove the RNT-GCN achieved significant performance compared to SOTA methods.