Reviewerly/exHarmony huggingface dataset:
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, current RSCTIR methods mainly focus on global features of RS images, which leads to the neglect of local features that reflect target relationships and saliency. In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels. MIDF leverages local information to correct global information, utilizes global information to supplement local information, and uses the dynamic addition of the two to generate prominent visual representation. To alleviate the pressure of the redundant targets on the graph convolution network (GCN) and to improve the model’s attention on salient instances during modeling local features, the denoised representation matrix and the enhanced adjacency matrix (DREA) are devised to assist GCN in producing superior local representations. DREA not only filters out redundant features with high similarity, but also obtains more powerful local features by enhancing the features of prominent objects. Finally, to make full use of the information in the similarity matrix during inference, we come up with a plug-and-play multivariate rerank (MR) algorithm. The algorithm utilizes the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbors of the retrieval results to perform a reverse search, and improves the performance by combining multiple components of bidirectional retrieval. Extensive experiments on public datasets strongly demonstrate the state-of-the-art performance of GaLR methods on the RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files have been made available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xiaoyuan1996/GaLR</uri> .
Hybrid Machine Learning Technique for Personality Classification from Online Text using HEXACO Model

Personality refers to a person's unique collection of traits that influence their habits, behaviors, attitudes, and thinking patterns. Text accessible on social networking sites may be used to automatically identify an individual's personality characteristics. In the trials, a publicly accessible benchmark dataset from Kaggle is utilized. The major problem with the previous study was the skewness of the dataset, which was reduced by using the Re-sampling method, essentially random over-sampling, which resulted in improved performance. Although the results produced by all classifiers across all personality characteristics are acceptable, the performance of the XGBoost classifier is exceptional, reaching more than 99 percent precision and accuracy for various qualities. Individual preferences, talents, social and human values, personality variations, and human will power must all be addressed while analyzing entrepreneurial activities, intents, and performance, especially with entrepreneurship playing such an important part in the contemporary dynamic. Using the HEXACO Personality Characteristics Model, this study demonstrates a link between personality traits and entrepreneurial success, indicating that personality traits have a significant and direct effect on entrepreneurial performance.
Decoupled Side Information Fusion for Sequential Recommendation

Side information fusion for sequential recommendation (SR) aims to effectively leverage various side information to enhance the performance of next-item prediction. Most state-of-the-art methods build on self-attention networks and focus on exploring various solutions to integrate the item embedding and side information embeddings before the attention layer. However, our analysis shows that the early integration of various types of embeddings limits the expressiveness of attention matrices due to a rank bottleneck and constrains the flexibility of gradients. Also, it involves mixed correlations among the different heterogeneous information resources, which brings extra disturbance to attention calculation. Motivated by this, we propose Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation. We theoretically and empirically show that the proposed solution allows higher-rank attention matrices and flexible gradients to enhance the modeling capacity of side information fusion. Also, auxiliary attribute predictors are proposed to further activate the beneficial interaction between side information and item representation learning. Extensive experiments on four real-world datasets demonstrate that our proposed solution stably outperforms state-of-the-art SR models. Further studies show that our proposed solution can be readily incorporated into current attention-based SR models and significantly boost performance. Our source code is available at https://github.com/AIM-SE/DIF-SR.
PromptChainer: Chaining Large Language Model Prompts through Visual Programming

While LLMs have made it possible to rapidly prototype new ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single run of an LLM. Recent work has found that chaining multiple LLM runs together (with the output of one step being the input to the next) can help users accomplish these more complex tasks, and in a way that is perceived to be more transparent and controllable. However, it remains unknown what users need when authoring their own LLM chains – a key step to lowering the barriers for non-AI-experts to prototype AI-infused applications. In this work, we explore the LLM chain authoring process. We find from pilot studies that users need support transforming data between steps of a chain, as well as debugging the chain at multiple granularities. To address these needs, we designed PromptChainer, an interactive interface for visually programming chains. Through case studies with four designers and developers, we show that PromptChainer supports building prototypes for a range of applications, and conclude with open questions on scaling chains to even more complex tasks, as well as supporting low-fi chain prototyping.
Efficient text document clustering approach using multi-search Arithmetic Optimization Algorithm

Text document clustering is to divide textual contents into clusters or groups. It received wide attention due to the vast amount of daily data from the Web. In the last decade, Meta-Heuristic (MH) techniques have been adopted to solve clustering problems. Motivated by that, the authors introduce a reliable version of the newly developed MH algorithm called Arithmetic Optimization Algorithm (AOA). Math arithmetic operators inspire the AOA: multiplication, subtraction adding, and division. The AOA showed good performance in several global problems; nonetheless, it suffers from entrapment in local optima in complicated and high dimensional problems. Therefore, this paper proposes an improved version of AOA for the text document clustering problem. The Improved AOA (IAOA) introduces an integration between Opposition-based learning (OBL) and Levy flight distribution (LFD) with AOA to tackle the limitations of the traditional AOA. The IAOA is examined with different UCI datasets for the text clustering problems and assessed with a set of CEC2019 benchmark functions as a global optimization algorithm with extensive comparison to existing optimization algorithms. Overall, experimental results show the superiority of the proposed IAOA compared to several optimization algorithms. Moreover, the proposed IAOA is compared with twenty-one state-of-the-art methods using thirty-one benchmark text datasets, and the results proved the superiority of the proposed IAOA.
Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning

Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website's content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies' detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.
Calibrating the adaptive learning rate to improve convergence of ADAM

Adaptive gradient methods (AGMs) have become popular in optimizing the nonconvex problems in deep learning area. We revisit AGMs and identify that the adaptive learning rate (A-LR) used by AGMs varies significantly across the dimensions of the problem over epochs (i.e., anisotropic scale), which may lead to issues in convergence and generalization. All existing modified AGMs actually represent efforts in revising the A-LR. Theoretically, we provide a new way to analyze the convergence of AGMs and prove that the convergence rate of Adam also depends on its hyper-parameter є, which has been overlooked previously. Based on these two facts, we propose a new AGM by calibrating the A-LR with an activation (softplus) function, resulting in the Sadam and SAMSGrad methods. We further prove that these algorithms enjoy better convergence speed under nonconvex, non-strongly convex, and Polyak-Łojasiewicz conditions compared with Adam. Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks.
Image Copyright Protection Based on Blockchain and Zero-Watermark

Generally, image protection is often accomplished by digital watermark, which is also widely used in the field of data protection and data ownership authentication. However, the existing digital watermark has inherent disadvantages, it needs completely trusted third parties to act as the arbitration parties, which may be difficult to achieve. Second, an image watermark algorithm will inevitably lead to the loss of image data when operating on the image data, while it is usually irreversible. The zero-watermark algorithm can solve the problem of data loss but relies more on the trusted third party than a traditional digital watermark, which makes its prospects limited. With the development of blockchain technology in recent years, its features of de-trusted third parties combine the fairness and process automation of smart contracts to make up for the shortcomings of the zero-watermarking algorithm. This paper studies image storage and authentication frameworks. This framework utilizes the advantages of zero-watermarking algorithm and blockchain, using the Inter-Planetary File System to solve the data expansion problem of blockchain, which well avoids the shortcomings of zero-watermarking algorithm and blockchain. Experiment and inference illustrate that the proposed framework is feasible.
A blockchain-based secure storage scheme for medical information

Abstract Medical data involves a large amount of personal information and is highly privacy sensitive. In the age of big data, the increasing informatization of healthcare makes it vital that medical information is stored securely and accurately. However, current medical information is subject to the risk of privacy leakage and difficult to share. To address these issues, this paper proposes a healthcare information security storage solution based on hyperledger fabric and the attribute-based access control framework. The scheme first utilizes attribute-based access control, which allows dynamic and fine-grained access to medical information, and then stores the medical information in the blockchain, which can be secured and tamper-proof by formulating corresponding smart contracts. In addition, this solution also incorporates IPFS technology to relieve the storage pressure of the blockchain. Experiments show that the proposed scheme combining access control of attributes and blockchain technology in this paper can not only ensure the secure storage and integrity of medical information but also has a high throughput when accessing medical information
Private Data Trading Towards Range Counting Queries in Internet of Things

The data collected in Internet of Thing (IoT) systems (IoT data) have stimulated dramatic extension to the boundary of commercialized data statistic analysis, owing to the pervasive availability of low-cost wireless network access and off-the-shelf mobile devices. In such cases, many data consumers post their queries for urban statistic analysis in the system, like the scales of traffics, and then data contributors in IoT networks upload their contents, which can be evaluated by data brokers and responded to data consumers. However, huge volumes of devices bring large scales of data, constituting heavy burdens for data exchange. Even worse, contents in IoT systems are also sensitive as they are usually linked to private physical status of data contributors. The previous studies for IoT data trading fail to provide comprehensive estimation and pricing towards these difficulties. Therefore, this paper proposes a novel framework for the range counting trading over IoT networks by jointly considering data utility, bandwidth consumption, and privacy preservation. The range counting accumulates the number of data items falling in a concerned range of value, providing important information on the underlying data distribution. This paper first proposes a novel sampling-based method with histogram sketching for range counting estimation. The estimator is proved to be unbiased and achieves advanced performance on variance. Then the framework adopts a perturbation mechanism that can further preserve the results under differential privacy. The theoretical analysis shows that the mechanism can guarantee the privacy preservation under a given size of samples and the accuracy requirement of results. Finally, two types of pricing strategies for range counting trading are introduced for different circumstances, providing holistic consideration on how the parameters given in the estimator should be used for data trading. The framework is evaluated by estimating the air pollution levels and the traffic levels with different ranges on the 2014 CityPulse Smart City datasets. The evaluation results demonstrate that our framework can provide more accurate and reliable statistical information, with reduced bandwidth consumption and strengthened privacy preservation.
Adaptive Learning Attention Network for Underwater Image Enhancement

Underwater images suffer from color casts and low illumination due to the scattering and absorption of light as it propagates in water. These problems can interfere with underwater vision tasks, such as recognition and detection. We propose an adaptive learning attention network for underwater image enhancement based on supervised learning, named LANet, to solve these degradation issues. First, a multiscale fusion module is proposed to combine different spatial information. Second, we design a novel parallel attention module(PAM) to focus on the illuminated features and more significant color information coupled with the pixel and channel attention. Then, an adaptive learning module(ALM) can retain the shallow information and adaptively learn important feature information. Further, we utilize a multinomial loss function that is formed by mean absolute error and perceptual loss. Finally, we introduce an asynchronous training mode to promote the network&#x2019;s performance of multinomial loss function. Qualitative analysis and quantitative evaluations show the excellent performance of our method on different underwater datasets. The code is available at: <uri>https://github.com/LiuShiBen/LANet</uri>.
Joint Global and Local Hierarchical Priors for Learned Image Compression

Recently, learned image compression methods have out-performed traditional hand-crafted ones including BPG. One of the keys to this success is learned entropy models that estimate the probability distribution of the quantized latent representation. Like other vision tasks, most recent learned entropy models are based on convolutional neural networks (CNNs). However, CNNs have a limitation in modeling long-range dependencies due to their nature of local connectivity, which can be a significant bottleneck in image compression where reducing spatial redundancy is a key point. To overcome this issue, we propose a novel entropy model called Information Transformer (Informer) that exploits both global and local information in a content-dependent manner using an attention mechanism. Our experiments show that Informer improves rate-distortion performance over the state-of-the-art methods on the Kodak and Tecnick datasets without the quadratic computational complexity problem. Our source code is available at https://github.com/naver-ai/informer.
Strict Hierarchy between Parallel, Sequential, and Indefinite-Causal-Order Strategies for Channel Discrimination

We present an instance of a task of minimum-error discrimination of two qubit-qubit quantum channels for which a sequential strategy outperforms any parallel strategy. We then establish two new classes of strategies for channel discrimination that involve indefinite causal order and show that there exists a strict hierarchy among the performance of all four strategies. Our proof technique employs a general method of computer-assisted proofs. We also provide a systematic method for finding pairs of channels that showcase this phenomenon, demonstrating that the hierarchy between the strategies is not exclusive to our main example.
Learning Based Energy Efficient Task Offloading for Vehicular Collaborative Edge Computing

Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles’ idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay.
Secure blockchain enabled Cyber- Physical health systems using ensemble convolution neural network classification

Breast cancer is the most widely recognized malignancy affecting women. The risk of death has been consistently associated with breast cancer. In addition, the cyber-physical system (CPS)is the processing and data transfer of physical processes. This study presents a safe, intrusive, blockchain-based data transfer using the CPS classification model in the health industry to overcome the problem. Considering the challenges in breast tumor classification, this paper accords a reasonablearrangement to examine the mammogram image to discover the detection and classification of various stages of cancer. The breast cancer detection images obtained from the mammogram were processed and experimentally evaluated for parameters such as a sensitivity of 90%, a specificity of 98%,andaclassification accuracy of 98%.The results of the ensemble convolution neural network (E-CNN) classifier, such as VGG-16 and Inception-v3, which separates ordinary and unusual cases from the applied advanced mammographic image, will be projected by comparing the two existing methods.
Introduction to Probability Theory

Any realistic model of a real-world phenomenon must take into account the possibility of randomness. That is, more often than not, the quantities we are interested in will not be predictable in advance but, rather, will exhibit an inherent variation that should be taken into account by the model. This is usually accomplished by allowing the model to be probabilistic in nature. Such a model is, naturally enough, referred to as a probability model.