Reviewerly/exHarmony huggingface dataset:
Structure-aware contrastive hashing for unsupervised cross-modal retrieval

Cross-modal hashing has attracted a lot of attention and achieved remarkable success in large-scale cross-media similarity retrieval applications because of its superior computational performance and low storage overhead. However, constructing similarity relationships among samples in cross-modal unsupervised hashing is challenging because of the lack of manual annotation. Most existing unsupervised methods directly use the representations extracted from the backbone of their respective modality to construct instance similarity matrices, leading to inaccurate similarity matrices and resulting in suboptimal hash codes. To address this issue, a novel unsupervised hashing model, named Structure-aware Contrastive Hashing for Unsupervised Cross-modal Retrieval (SACH), is proposed in this paper. Specifically, we concurrently employ both high-dimensional representations and discriminative representations learned by the network to construct a more informative semantic correlative matrix for multiple modalities. Moreover, we design a Multimodal Structure-aware Alignment Network to minimize heterogeneity in the high-order semantic space of each modality, effectively reducing disparities within heterogeneous data sources and enhancing the consistency of semantic information across modalities. Extensive experimental results on two widely utilized datasets demonstrate the superiority of our proposed SACH method in cross-modal retrieval tasks over existing state-of-the-art methods.
High-performance eight-channel system with fractal superconducting nanowire single-photon detectors

Superconducting nanowire single-photon detectors (SNSPDs) have become a mainstream photon-counting technology that has been used in various applications. So far, most multi-channel SNSPD systems, either reported in literature or been commercially available, are polarization sensitive, that is, the system detection efficiency (SDE) of each channel is dependent on the state of polarization of the to-be-detected photons. Here, we report on an eight-channel system with fractal superconducting nanowire single-photon detectors working in the wavelength range of 930-940 nm that all feature low polarization sensitivity. In a close-cycled Gifford-McMahon cryocooler system with the base temperature of 2.2 K, we install and compare the performance of two types of devices: (1) SNSPD, composed of a single, continuous nanowire, and (2) superconducting nanowire avalanche photodetector (SNAP), composed of 16 cascaded units of two nanowires electrically connected in parallel. The highest system detection efficiency (SDE) among the eight channels reaches 96−5+4%, with polarization sensitivity of 1.02 and dark-count rate of 13 counts per second. The average SDE for eight channels for all states of polarization is estimated to be 90 ± 5%. We conclude that both the SNSPDs and the SNAPs can reach saturated, high SDE at the wavelength of interest, and the SNSPDs show lower dark- (false-) count rates while the SNAPs show better properties in the time domain. Using this system, we showcase the measurements of the second-order photon-correlation functions of light emission from a single-photon source based on a semiconductor quantum dot and from a pulsed laser. We believe that our work provides new choices of systems with single-photon detectors combining the merits of high SDE, low polarization sensitivity, and low noise that can be tailored for different applications.
TAILOR: inTer-feAture distinctIon fiLter fusiOn pRuning

Filter pruning is an effective method for reducing the size of convolutional neural networks without sacrificing performance. Most filter pruning methods prioritize filters with high information content, but fail to consider that filters with low information content might capture essential features. Moreover, we have discovered that the distinctions among feature maps generated by filters can identify crucial features. Based on this insight, we propose a novel pruning method called inTer-feAture distinctIon fiLter fusiOn pRuning (TAILOR), which fuses the feature distinctions between filters. TAILOR randomly selects multiple filter sets within a convolutional layer and calculates the output feature maps of the next convolutional layer of these sets. Subsequently, an intelligent distinction optimization scheme is proposed to obtain the optimal filter set for filter pruning, which supplants the original convolutional layer. Experimental results indicated that the inter-feature distinctions among filters significantly affect filter pruning. TAILOR outperforms state-of-the-art filter-pruning methods in terms of model prediction accuracy, floating-point operations, and parameter scale. For instance, with VGG-16, TAILOR achieves a 73.89% FLOPs reduction by removing 91.85% of the parameters, while improving accuracy by 0.36% on the CIFAR-10.
Towards blockchain-enabled decentralized and secure federated learning

The conventional machine learning process typically operates under the premise of centralized data aggregation, where all data is collected at a central location for model training. However, this raises substantial privacy concerns when the data contains private information. In this context, federated learning has emerged as a prominent solution for preserving privacy in machine learning. This innovative paradigm allows multiple data owners, or clients, to collaboratively train a machine learning model while keeping their local data unshared. A federated learning task is typically initiated by companies, often referred to as model owners, who do not possess enough training data and are willing to financially remunerate clients who contribute to the development of the federated learning model. This situation demands a trading platform that enables model owners to effectively select clients, while ensuring robustness against malicious clients who execute poisoning attacks for unfair financial gain. To address these issues, we design a contribution-based exploration-exploitation mechanism implemented as a smart contract. This mechanism cherry-picks clients with high data quality based on the Shapley value, which is calculated based on local models to evaluate the contribution of each client. Unlike other state-of-the-art security mechanisms, the proposed mechanism can adapt to various scenarios with heterogeneous data distribution and various attacks, while mitigating the effect of malicious behaviors without compromising training accuracy. To accelerate the time-consuming calculation of the Shapley value, we design a parallel computing algorithm that partitions blockchain nodes into multiple shards and distributes calculation tasks among them. The algorithm improves efficiency and tolerates potential false calculation results from malicious nodes.
An information fractal dimensional relative entropy

Shannon entropy is used to measure information uncertainty, while the information dimension is used to measure information complexity. Given two probability distributions, the difference can be measured by relative entropy. However, the existing relative entropy does not consider the effect of information dimension. To improve the existing entropy, a new relative entropy is presented in this paper. The information fractal dimension is considered in the proposed relative entropy. The new relative entropy is more generalized than the initial relative entropy. When dimension is not considered, it will degenerate to the initial relative entropy. Another interesting point is that the new relative entropy may have negative values when calculating. The physical meaning is still under exploration. Finally, some application examples are provided to exemplify the utilization of the proposed relative entropy.
MAInt: A multi-task learning model with automatic feature interaction learning for personalized recommendations

Nowadays, more and more recommender systems apply multi-task learning to provide users with more accurate personalized recommendations. However, most of the existing multi-task learning models ignore the importance of explicit feature interactions in different tasks, which are an important part of recommender system models. In addition, the knowledge sharing mechanism of the existing model is difficult to meet the needs of all tasks, resulting in the performance degradation of some tasks. To address these issues, we propose a Multi-task learning model based on Automatic feature Interaction learning (MAInt). The Tasks Interaction Layer in MAInt automatically learns explicit feature interactions in different tasks through self-attention neural networks. At the same time, this process allows each task to interact with each other, so that each task can adaptively extract helpful information from other task features. We conducted multiple experiments on two popular public datasets: Census-income Dataset and Ali-CCP Dataset, and the experimental results verified the effectiveness of our proposed model.
Sparse Identification of Fractional Chaotic Systems based on the time-domain data

The complexity inherent in defining and computing fractional derivatives renders the development of a data-driven modeling framework for fractional-order systems more formidable than for their integer-order counterparts. This study introduces a methodology for sparsely identifying fractional chaotic systems by leveraging time-domain data to ascertain the system’s governing equations. Initially, we establish a sparse identification framework specifically tailored for fractional order systems and introduce a joint iterative thresholding method designed to identify these systems’ fractional and integer order components concurrently. Moreover, this study conducts a comparative analysis of two error criteria for model selection. It introduces a strategy predicated on minimizing the distance to balance sparsity and model fitting error optimally. To validate the efficacy and precision of the proposed methodology, numerical simulations were conducted on fractional-order Lorenz and Chua’s circuits, affirming the robustness and accuracy of our approach.
Sentiment Visualization of Correlation of Loneliness Mapped through Social Intelligence Analysis

Loneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18-70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon. This paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed. This study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations. The analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue. This paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.
Graph-based social relation inference with multi-level conditional attention

Social relation inference intrinsically requires high-level semantic understanding. In order to accurately infer relations of persons in images, one needs not only to understand scenes and objects in images, but also to adaptively attend to important clues. Unlike prior works of classifying social relations using attention on detected objects, we propose a MUlti-level Conditional Attention (MUCA) mechanism for social relation inference, which attends to scenes, objects and human interactions based on each person pair. Then, we develop a transformer-style network to achieve the MUCA mechanism. The novel network named as Graph-based Relation Inference Transformer (i.e., GRIT) consists of two modules, i.e., a Conditional Query Module (CQM) and a Relation Attention Module (RAM). Specifically, we design a graph-based CQM to generate informative relation queries for all person pairs, which fuses local features and global context for each person pair. Moreover, we fully take advantage of transformer-style networks in RAM for multi-level attentions in classifying social relations. To our best knowledge, GRIT is the first for inferring social relations with multi-level conditional attention. GRIT is end-to-end trainable and significantly outperforms existing methods on two benchmark datasets, e.g., with performance improvement of 7.8% on PIPA and 9.6% on PISC.
A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms

As the geographically distributed cloud infrastructure continues to grow in scale and the intricacy of workflow applications increases, there is a growing threat to the operational efficiency of the system. This poses the risk of resource inefficiencies and elevated energy consumption. Load balancing becomes a critical concern for this category of applications, particularly when they encounter overwhelming demand surges and resource outages. Numerous comprehensive methods still overlook certain aspects, such as the inclusion of multiple objectives, addressing local minimum problems, and optimizing resource utilization to its fullest potential. Our research focus is on exploring cloud-deployed three-tier applications to address these challenges. This paper presents a new approach to load balancing, introducing a novel weight assignment algorithm. The algorithm effectively distributes tasks among virtual machines using three carefully optimized algorithms. To begin with, we convert the user tasks into the directed acyclic graph (DAG) structured workflow, organizing them according to their execution sequence. The iterative critical path search algorithm, implemented with a layer-based priority assigning technique, addresses the critical path challenge. This process involves finding a critical path, dividing tasks into distinct layers, classifying them based on their computational complexity and priorities, and distinguishing between critical and non-critical tasks. Following the classification process, the tasks are organized and prioritized using an improved Moth Flame Optimization approach based on multiple criteria, which consider criticality, priority, and makespan. This approach significantly reduces scheduling latency and delays. Finally, task allocation is proposed using the high convergence rock hyrax optimization algorithm with a divide-and-conquer strategy, enabling effective exploration and exploitation of overcrowded resources for improved load balancing. The CloudSim simulation tool is used to carry out the simulation, where it assesses various performance aspects such as latency, bandwidth, completion time, throughput, energy usage, instances of not meeting QoS violations, and overhead.
Improving small sample medical image segmentation using CBAM: Insights from two datasets

Abstract Medical image segmentation is one of the key tasks in the medical field and is crucial for accurate lesion detection and treatment planning. However, the small sample problem has been one of the challenges in medical image segmentation. In this study, the small sample medical image segmentation problem is evaluated experimentally based on two different datasets, the VOC dataset and the esophageal medical images. In the VOC dataset experiments, we used the code from the BAM project on GitHub as a benchmark and compared it. Additionally, we improved the benchmark code by adding the Attention Mechanism (CBAM), placing its position before the support set undergoes global average pooling. By comparing the experimental results, we found that the model with the added CBAM achieved better segmentation results under small sample conditions. In the esophageal medical image experiments, the code from the BAM project was also used as a benchmark and a similar experimental design was performed. We further added CBAM by placing it before the global average pooling of the support set. The experimental results show that the model with the addition of CBAM achieves a significant improvement in the small sample segmentation task of esophageal medical images. To summarize, this study validates the effectiveness of using the BAM model in the small-sample medical image segmentation task on two different datasets. By adding CBAM, our model exhibits better segmentation performance under small sample conditions. These findings provide useful insights for small-sample medical image segmentation and are important for future research and practice.
Double laser-faults based PFA on cryptographic circuits with algebraic analysis

Cryptographic algorithms have been employed in a variety of fields as the primary method to protect information security. The security of a cryptographic algorithm is closely related to its operating environment and physical devices. Algebraic Persistent Fault Analysis (APFA) is a new fault analysis method for block ciphers proposed in CHES 2022, which utilizes the fault that persists in encryptions and introduces algebraic analysis in the fault analysis step. In the fault injection step, as the transistors of the integrated circuit are getting smaller and tighter, even high-precision devices may cause more than one fault per injection. However, more faults may lead to a more efficient attack in the fault analysis step. In this paper, APFA for double faults is proposed, which can deal with the double faults model and reduce the number of required ciphertexts. The practicality of our fault injection is validated by laser fault injection experiments on the SRAM embedded in an ATmega163L microcontroller. The effectiveness of our fault analysis is proven by successfully recovering the key of PRESENT-128 and AES-128. The number of ciphertexts needed for key recovery is reduced by 46% compared to PFA with a single fault.
Robust CoDA balances and the role of the variance in complex riverine geochemical systems

This study introduces a robust method for analyzing the geochemical behavior of chemical species in river catchment water. It focuses on isometric log-ratio coordinates obtained from a sequential partition method that successively maximizes the explained variance in the data set. Robust orthonormal coordinates are created based on hierarchical clustering and robust estimation of the variation matrix. Applying this to the water chemistry of Italy's Arno and Tiber basins, the research reveals the associations of variables in data structure and processes across varying geological and climatic conditions. The method uncovers key contrasting geochemical processes and suggests that the behavior of simple balances characterized by lower variances (i.e., Ca2+/HCO3− and Na+/Cl−) are mainly influenced by random fluctuations with no differences between classical or robust methods. However, when balances describe more complex geochemical processes resulting in frequency distributions affected by the presence of bimodality or outliers, significant differences among the two approaches emerge, compromising the data interpretation. The proposed metodology offers more insights into the investigation of catchment geochemistry's resilience to hydroclimatic changes, marking a significant step in understanding large-scale environmental dynamics.
Biotite chemistry as an indicator of hydrothermal deposit types and fluid sources: Insights from big data compilation, multivariate statistical analysis, and machine learning

Identification of a deposit type in an early stage of exploration can save money and time. However, despite several studies, there is a lack of simple deposit classifiers based exclusively on the major and trace element chemistry of minerals, which is relatively easier to measure. Furthermore, the identification of fluid source is crucial for any ore-genetic study, as it exert a first order control on physicochemical characteristics of fluid, which ultimately control the alteration mineralogy, metal solubility and mobilization, and metal association in a deposit. The stable isotope composition (e.g., δ34S, δ13C, δ18O, δ11B, etc.) of rocks and minerals is conventionally used to constrain the source of ore-forming fluids, however, the potential of mineral chemistry for constraining fluid source is not well-explored. In this study, we compile the major element composition (wt% of oxides) of a large number (n = 1932) of hydrothermal biotite from various ore settings and execute multivariate statistical analysis and machine learning-based predictive modeling to explore the potential of using biotite chemistry as an indicator of deposit type and fluid sources. The atoms per formula units (i.e., apfu values), and calculated parameters (e.g., Xphl, chlorine intercept value, log(fH2O/fHCl)fluid) is used to perform principle component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The results show that among the different ore settings, iron-oxide copper‑gold, Archean gold, porphyry, and magmatic-hydrothermal rare earth metals deposits and among the fluid sources, (meta-)evaporitic, magmatic-hydrothermal, and metamorphic fluid sources can be reliably discriminated using biotite chemistry. We also show that skarn, hydrothermal platinum group elements, and carbonatite-related rare earth elements deposits may not be discriminated using biotite chemistry. Similarly, mafic rock-derived, carbonatite-derived, and meteoric fluid sources may not be discriminated by biotite chemistry. We propose multiple binary and ternary classifier diagrams, which can be used as discriminator of deposit types and fluid sources. Furthermore, we have developed four different PLS-DA predictive modeling, two for deposit types and two for fluid sources, which can predict deposit types and fluid sources with good accuracy. We provide a Windows© based program that can be used to discriminate deposit types using our PLS-DA predictive modeling. The binary andternary classifiers and the Windows© programs are tested with recently published biotite data, and it has been shown that these classifiers and programs can efficiently separate deposit types and fluid sources. We also discuss about the limitations of the classifiers.