Reviewerly/exHarmony huggingface dataset:
An intellectual optimization of k-anonymity model for efficient privacy preservation in cloud platform

Concerns of security as well as privacy are the chief obstacles which have prevented the public cloud’s extensive adoption in Intel IT as well as across the industry. Generally, IT organizations are quite reluctant to store sensitive as well as valuable data in infrastructures which are out of their control. The technique of anonymization is employed by enterprises to raise the security of the public cloud’s data whilst facilitating the data’s analysis as well as application. The procedure of data anonymization will modify how the data is either employed or published in such a way that it will prevent the key information’s identification. The privacy issues are addressed using k-anonymity. However, the issue of selecting the variables for anonymization and suppression of variables without the loss of knowledge is an optimization problem. To address the selection of variables for anonymization and suppression, metaheuristic algorithms are used. Diverse research groups have successfully utilized the River Formation Dynamics (RFD) metaheuristic to handle numerous problems of discrete combinatorial optimization. Even so, this metaheuristic has never been adapted for use in domains of continuous optimization. To mitigate the local minima problem, hybridization of the algorithms is proposed. In this work, a modified K-Anonymity technique’s proposal has been given by using the Modified Hill Climbing (MHC) optimization, the RFD-MHC optimization, the RFD-PSO optimization, the RFD-MHC suppression as well as the RFD-PSO suppression. Furthermore, proposal for a suppression technique has also been given in this work. Experiments demonstrated that the RFD-PSO optimization has higher classification accuracy in the range of 6.73% to 8.55% when compared to manual K-anonymization. The work has also given better trade off for security analysis and data utility effectiveness.
Hidden target recognition method for high-speed network security threats based on attack graph theory

Instantaneous traffic changes in high-speed networks will interfere with abnormal traffic characteristics, making it difficult to accurately identify hidden targets of security threats. This paper designs a high-speed network security threat hidden target recognition method based on attack graph theory. Using the high-speed network traffic reduction method, under the condition that the network topology remains unchanged, the instantaneous input traffic is reduced according to a certain proportion, and after compressing the flow data scale, the abnormal traffic of the high-speed network is identified through the convolutional recurrent neural network, and the information entropy is used to describe the high-speed network. The abnormal traffic characteristics of the network are used as constraints to design an attack graph of hidden targets of high-speed network security threats, and an attack path discovery method based on multi-heuristic information fusion is designed to extract attack paths of high-speed networks, locate attacking hosts, and identify hidden threat targets. In the experiment, the method can accurately identify the hidden targets of high-speed network security threats, and has better identification ability.
Attribute-Based Verifiable Conditional Proxy Re-Encryption Scheme

There are mostly semi-honest agents in cloud computing, so agents may perform unreliable calculations during the actual execution process. In this paper, an attribute-based verifiable conditional proxy re-encryption (AB-VCPRE) scheme using a homomorphic signature is proposed to solve the problem that the current attribute-based conditional proxy re-encryption (AB-CPRE) algorithm cannot detect the illegal behavior of the agent. The scheme implements robustness, that is the re-encryption ciphertext, can be verified by the verification server, showing that the received ciphertext is correctly converted by the agent from the original ciphertext, thus, meaning that illegal activities of agents can be effectively detected. In addition, the article demonstrates the reliability of the constructed AB-VCPRE scheme validation in the standard model, and proves that the scheme satisfies CPA security in the selective security model based on the learning with errors (LWE) assumption.
Grover search inspired alternating operator ansatz of quantum approximate optimization algorithm for search problems

Abstract We use the mapping between two computation frameworks, Adiabatic Grover Search (AGS) and Adiabatic Quantum Computing (AQC), to translate the Grover search algorithm into the AQC regime. We then apply Trotterization on the schedule-dependent Hamiltonian of AGS to obtain the values of variational parameters in the Quantum Approximate Optimization Algorithm (QAOA) framework. The goal is to carry the optimal behavior of Grover search algorithm into the QAOA framework without the iterative machine learning processes.
TTANAD: Test-Time Augmentation for Network Anomaly Detection

Machine learning-based Network Intrusion Detection Systems (NIDS) are designed to protect networks by identifying anomalous behaviors or improper uses. In recent years, advanced attacks, such as those mimicking legitimate traffic, have been developed to avoid alerting such systems. Previous works mainly focused on improving the anomaly detector itself, whereas in this paper, we introduce a novel method, Test-Time Augmentation for Network Anomaly Detection (TTANAD), which utilizes test-time augmentation to enhance anomaly detection from the data side. TTANAD leverages the temporal characteristics of traffic data and produces temporal test-time augmentations on the monitored traffic data. This method aims to create additional points of view when examining network traffic during inference, making it suitable for a variety of anomaly detector algorithms. Our experimental results demonstrate that TTANAD outperforms the baseline in all benchmark datasets and with all examined anomaly detection algorithms, according to the Area Under the Receiver Operating Characteristic (AUC) metric.
SiamCMN: Jointing cycle memory and correlation network for Siamese object tracking

In heterogeneous scenes with latent non-deterministic states, template features determine the performance of the Siamese network-based trackers, however, background noise is easily introduced in the search area matching process, which causes the trackers lack sufficient discrimination. To solve this problem, this paper proposes an object tracking algorithm based on the cyclic memory module. Firstly, the extracted primary features are mapped to the cyclic spatial memory network, the spatial memory unit is used to store the interference components. On this basis, the cyclic structure is used to filter out the interference components and refine the template features. Then a channel correlation network is introduced into the proposed method, which can mine semantic complementary information and capture global contextual relevance by modeling the positive and the negative correlation between the channels. The experimental results on tracking benchmarks such as OTB50, OTB100, UAV123, VOT2018, GOT10k, and LaSOT have shown that the proposed method outperforms recent representative algorithms in tracking accuracy in challenging scenarios.
Using Principal Component Analysis (PCA) Combined with Multivariate Change-Point Analysis to Identify Brine Layers Based on the Geochemistry of the Core Sediment

The underground brine in Southern Laizhou Bay is characterized by its large scale and high concentration, which can affect the distribution and migration of geochemical elements in sediments. Most studies on the brine are based on hydrochemical analysis, with little consideration being given from a geochemical perspective. Principal component analysis (PCA) is a powerful tool for discovering relationships among many elements and grouping samples in large geochemical datasets. However, even after reducing the dimensions through PCA, researchers still need to make judgments about the meaning represented by each principal component. Change-point analysis can effectively identify the points at which the statistical properties change in a dataset. PCA and change-point analysis have their respective advantages in the study of large sets of geochemical data. Based on the geochemical data of the LZ908 core, by combining these two methods, this study identified four elements (U, MgO, Br, and Na2O) related to the action of seawater through PCA; then, multivariate change point analysis was conducted on these elements to detect the depths of different brine layers. The results of the analysis are basically consistent with those of other studies based on the water content, salinity, and other data, thus proving the effectiveness of this method. The combination of these two methods may also lead to novel approaches for related research.
Causality Extraction Cascade Model Based on Dual Labeling

Causal relation extraction is a crucial task in natural language processing. Current extraction methods have problems, including low accuracy of causal-event division and incorrect extraction of important semantic features. This study uses the bidirectional long short-term memory (BiLSTM) and attentive convolutional neural network (ACNN) models to construct a cascaded causal relationship extraction model to improve the precision of the extraction. The model uses two kinds of labels and then divides the causal event boundary after determining the relationship between the front and rear causal events. It automatically learns semantic features from sentences, reducing the dependence on external knowledge and improving the precision of extraction. The experimental results demonstrate that the precision of causality extraction can reach 81.67% and the F1 value can reach 83.2%.
A survey on federated learning for security and privacy in healthcare applications

Technological advances in smart devices and applications targeting the Internet of Healthcare Things provide a perfect environment for using Machine Learning-based techniques. However, traditional ML solutions operate on centralized data collection and processing. Federated Learning (FL) is a promising solution to train ML models on multiple decentralized devices without effectively sharing private data. Therefore, FL offers a secure architecture to handle highly sensitive data in the IoHT context. This survey comprehensively reviews emerging data security and privacy applications for FL in IoHT networks. First, we present a background overview of the basic concepts of FL applied in IoHT. In particular, we rigorously investigate and evaluate the main solutions to IoHT data security and privacy issues. In addition, we categorize the most relevant publications related to IoHT data security, whether due to advances in the architecture of FL or data protection. Next, we list several IoHT network datasets for model training. Finally, we highlight the essential lessons from this review, highlighting current challenges and possible directions for future research in data security and privacy in IoHT networks using FL.
Study of Blockchain for Supply Chain Management

Abstract: The market has seen significant change in recent years and has become more demanding and dynamic, creating a competitive atmosphere. The supply chain, which is heavily dependent on cooperation, integration, and flexibility, plays a significant part in helping the organization adapt to the changing environment. several business owners are interested in supply chain applications, and several specialized applications are used to better control the flow of the supply chain. Block chain technology has become one of the most significant new applications in the supply chain and has attracted the attention of many business owners due to its ability to be quickly adjusted to changing business environments and market conditions.One upon reading this will get to know about the effect of block chain technology utilization on this field.According to the study's findings, businesses should invest in block chain technology to make their supply chains more open, adaptable, and safe. There is no denying that block chain technology has a significant impact on building trust with supply chain stakeholders. The research paper also discussed several potential uses for the blockchain in the fields of collaboration and integration, as well as some positive implications.
Devising novel performance measures for assessing the behavior of multilayer perceptrons trained on regression tasks

This methodological article is mainly aimed at establishing a bridge between classification and regression tasks, in a frame shaped by performance evaluation. More specifically, a general procedure for calculating performance measures is proposed, which can be applied to both classification and regression models. To this end, a notable change in the policy used to evaluate the confusion matrix is made, with the goal of reporting information about regression performance therein. This policy, called generalized token sharing, allows to a) assess models trained on both classification and regression tasks, b) evaluate the importance of input features, and c) inspect the behavior of multilayer perceptrons by looking at their hidden layers. The occurrence of success and failure patterns at the hidden layers of multilayer perceptrons trained and tested on selected regression problems, together with the effectiveness of layer-wise training, is also discussed.
MWS and FWS codes for coordinate-wise weight functions

A combinatorial problem concerning the maximum size of the (Hamming) weight set of an $$[n,k]_q$$ linear code was recently introduced. Codes attaining the established upper bound are the Maximum Weight Spectrum (MWS) codes. Those $$[n,k]_q$$ codes with the same weight set as $$\mathbb {F}_q^n$$ are called Full Weight Spectrum (FWS) codes. FWS codes are necessarily “short”, whereas MWS codes are necessarily “long”. For fixed k, q the values of n for which an $$[n,k]_q$$ -FWS code exists are completely determined, but the determination of the minimum length M(H, k, q) of an $$[n,k]_q$$ -MWS code remains an open problem. The current work broadens discussion first to general coordinate-wise weight functions, and then specifically to the Lee weight and a Manhattan like weight. In the general case we provide bounds on n for which an FWS code exists, and bounds on n for which an MWS code exists. When specializing to the Lee or to the Manhattan setting we are able to completely determine the parameters of FWS codes. As with the Hamming case, we are able to provide an upper bound on $$M({\mathscr {L}},k,q)$$ (the minimum length of Lee MWS codes), and pose the determination of $$M({\mathscr {L}},k,q)$$ as an open problem. On the other hand, with respect to the Manhattan weight we completely determine the parameters of MWS codes.
Additive multi-task learning models and task diagnostics

This paper develops a model for multi-task machine learning that incorporates per-task parametric and nonparametric effects in an additive way. This allows a practitioner the flexibility of modeling the tasks in a customized manner, increasing model performance compared to other modern multi-task methods, while maintaining a high degree of model explainability. We also introduce novel methods for task diagnostics, which are based on the statistical influence of tasks on the model’s performance, and propose testing methods and remedial measures for outlier tasks. Additive multi-task learning model with task diagnostics is examined on a well-known real-world multi-task benchmark dataset and shows a significant performance improvement over other modern multi-task methods.
Bidirectional meta-Kronecker factored optimizer and Hausdorff distance loss for few-shot medical image segmentation

To increase the accuracy of medical image analysis using supervised learning-based AI technology, a large amount of accurately labeled training data is required. However, the supervised learning approach may not be applicable to real-world medical imaging due to the lack of labeled data, the privacy of patients, and the cost of specialized knowledge. To handle these issues, we utilized Kronecker-factored decomposition, which enhances both computational efficiency and stability of the learning process. We combined this approach with a model-agnostic meta-learning framework for the parameter optimization. Based on this method, we present a bidirectional meta-Kronecker factored optimizer (BM-KFO) framework to quickly optimize semantic segmentation tasks using just a few magnetic resonance imaging (MRI) images as input. This model-agnostic approach can be implemented without altering network components and is capable of learning the learning process and meta-initial points while training on previously unseen data. We also incorporated a combination of average Hausdorff distance loss (AHD-loss) and cross-entropy loss into our objective function to specifically target the morphology of organs or lesions in medical images. Through evaluation of the proposed method on the abdominal MRI dataset, we obtained an average performance of 78.07% in setting 1 and 79.85% in setting 2. Our experiments demonstrate that BM-KFO with AHD-loss is suitable for general medical image segmentation applications and achieves superior performance compared to the baseline method in few-shot learning tasks. In order to replicate the proposed method, we have shared our code on GitHub. The corresponding URL can be found: https://github.com/YeongjoonKim/BMKFO.git .
TLWSR: Weakly supervised real‐world scene text image super‐resolution using text label

Abstract Scene text image super‐resolution (STISR) has recently received considerable attention. Existing STISR methods are applicable to the situation that all the LR‐HR pairs are available. However, in real‐world scenarios, it is difficult and expensive to collect ground‐truth HR labels and align them with LR images, and thus it is essential to find a way to implement weakly supervised learning. We investigate the STISR problem in the situation that only a subset of HR labels is available and design a weak supervision framework using coarse‐grained text labels named TLWSR, which combines incomplete supervision and inexact supervision. Specifically, a lightweight text recognition network and connectionist temporal classification loss are used to guide the super‐resolution of text images during training. Extensive experiments on the benchmark TextZoom demonstrate that TLWSR generates distinguishable text images and exceeds the fully supervised baseline TSRN in boosting text recognition accuracywith only 50% HR labels available. Meanwhile, TLWSR can be applied to different super‐resolution backbones and significantly improves their performance. Furthermore, TLWSR shows good generalization capability to low‐quality images on scene text recognition benchmarks, which verifies the effectiveness of this framework. To the authors' knowledge, this is the first work exploring the problem of STISR in weakly supervised scenarios.
A Survey on Cyberbullying Classification and Detection

Social media platforms have seen an increase in the prevalence of cyberbullying. Protecting social media platforms against cyberbullying is essential as social media is extensively used among people of all ages. Events of cyberbullying have been rising, especially among young individuals who spend most of their time switching between various social media sites. This study gives an overview of the existing research on the categorization and detection of cyberbullying using several methods from the Deep learning and Machine learning field like Convolutional Neural Network, Recurrent Neural Network, Long Short -Term Memory, Gated Recurrent Unit, Bi-GRU-Attention-CapsNet, Support Vector Machine, Random Forest, Naive Bayes, and k-Nearest Neighbor, along with the study that examines the effects of various feature extraction techniques like Term frequency and Inverse Document Frequency, Information Gain, Dolphin Echolocation Algorithm, and Improved Dolphin Echolocation Algorithm.
Feature semantic alignment and information supplement for Text-based person search

The goal of person text-image matching is to retrieve images of specific pedestrians using natural language. Although a lot of research results have been achieved in persona text-image matching, existing methods still face two challenges. First,due to the ambiguous semantic information in the features, aligning the textual features with their corresponding image features is always tricky. Second, the absence of semantic information in each local feature of pedestrians poses a significant challenge to the network in extracting robust features that match both modalities. To address these issues, we propose a model for explicit semantic feature extraction and effective information supplement. On the one hand, by attaching the textual and image features with consistent and clear semantic information, the course-grained alignment between the textual and corresponding image features is achieved. On the other hand, an information supplement network is proposed, which captures the relationships between local features of each modality and supplements them to obtain more complete local features with semantic information. In the end, the local features are then concatenated to a comprehensive global feature, which capable of precise alignment of the textual and described image features. We did extensive experiments on CUHK-PEDES dataset and RSTPReid dataset, the experimental results show that our method has better performance. Additionally, the ablation experiment also proved the effectiveness of each module designed in this paper.
Graph neural networks for preference social recommendation

Social recommendation aims to improve the performance of recommendation systems with additional social network information. In the state of art, there are two major problems in applying graph neural networks (GNNs) to social recommendation: (i) Social network is connected through social relationships, not item preferences, i.e., there may be connected users with completely different preferences, and (ii) the user representation of current graph neural network layer of social network and user-item interaction network is the output of the mixed user representation of the previous layer, which causes information redundancy. To address the above problems, we propose graph neural networks for preference social recommendation. First, a friend influence indicator is proposed to transform social networks into a new view for describing the similarity of friend preferences. We name the new view the Social Preference Network. Next, we use different GNNs to capture the respective information of the social preference network and the user-item interaction network, which effectively avoids information redundancy. Finally, we use two losses to penalize the unobserved user-item interaction and the unit space vector angle, respectively, to preserve the original connection relationship and widen the distance between positive and negative samples. Experiment results show that the proposed PSR is effective and lightweight for recommendation tasks, especially in dealing with cold-start problems.