Reviewerly/exHarmony huggingface dataset:
Multimodal salient object detection via adversarial learning with collaborative generator

Multimodal salient object detection(MSOD), which utilizes multimodal information (e.g., RGB image and thermal infrared or depth image) to detect common salient objects, has received much attention recently. Different modalities reflect different appearance properties of salient objects, some of which could contribute to improving the precision and/or recall of MSOD. To greatly improve both Precision and Recall by fully exploring multimodal data, in this work, we propose an effective adversarial learning framework based on a novel collaborative generator for accurate multimodal salient object detection. In particular, the collaborative generator consists of three generators (generator1, generator2 and generator3), which aim at decreasing the false positive and false negative of the generated saliency maps and improving F-measure of the final saliency maps respectively. Generator1 and generator2 contain two encoder–decoder networks for multimodal inputs, and we propose a new co-attention model to perform adaptive interactions between different modalities. Furthermore, we apply generator3 to integrate feature maps from generator1 and generator2 in a complementary way. Through adversarially learning the collaborative generator and discriminator, both Precision and Recall of the predicted maps are boosted with the complementary benefits of multimodal data. Extensive experiments on three RGBT datasets and six RGBD datasets show that our method performs quite well against state-of-the-art MSOD methods.
Recent Progress in Quantum Key Distribution Network Deployments and Standards

Abstract Quantum key distribution (QKD) provides in principle unconditional security of key sharing based on the laws of physics only. In the last decade, several experimental and commercial QKD networks have been built and operated worldwide. Demonstrational applications of QKD in financial institutions, government networks, and critical infrastructures such as the power grid have been initially explored. However, large-scale deployment and full-scale commercialization of QKD networks still faces some technological and standardisation challenges. In this paper, recent developments and in-field deployments of QKD networks are reviewed and advancements in QKD standardisation are also discussed.
Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments.
Refining fine-tuned transformers with hand-crafted features for gender screening on question-answering communities

Machine learning and demographic analysis are a cornerstone for making community Question Answering (cQA) platforms more egalitarian and vibrant, safer as well. For instance, the two cooperate on successfully detecting suspicious/malicious activity and on stirring up the interest of community fellows to learn by exploring new topics. In this sense, both research fields play a vital role in reducing gender disparity across categories, when promoting unresolved questions to potential answerers. Current state-of-the-art artificial intelligence architectures, such as pre-trained transformers, train complex goals and million of parameters as a means of inferring and encoding knowledge from massive corpora. Fine-tuning is the process that allows later to transfer this encrypted information to a downstream task (e.g., gender classification). Needless to say, these pre-trained encoders also suffer from multiple disadvantages. To give an example, they are sensitive to irrelevant and misleading words, bringing about overfitting, usually on small datasets. This work offers a fresh look at this kind of technique by introducing PTM-SFFS, a novel approach that effectively pairs frontier transformers with linguistic properties via the use of traditional classifiers. Based on a feature wrapper (SFFS), PTM-SFFS refines the scores produced by a fine-tuned model via seeking for an array of mostly linguistic features to build a conventional statistical classifier (e.g., Bayes and MaxEnt). And as a result, this new discriminant function enhances the overall prediction rate by optimizing the synergy between both sorts of strategies. When applied to automatic gender recognition on cQA sites, PTM-SFFS increased the accuracy of seven fine-tuned state-of-the-art encoders up to 10% (XLNet). Thanks to its interpretability, we discover that it capitalizes on dependency parsing and metadata for improving the transference of lexicalized information to the target domain.
Dynamic analysis of fractional-order six-order discrete chaotic mapping and its application in information security

In this paper, we first generalize the six-order discrete chaotic mapping (SODCM) into fractional one. Then, we observe the chaotic behavior of fractional SODCM and analyze its maximum Lyapunov exponent, trajectory and bifurcation diagram. Next, the new digit image encryption method using fractional SODCM is developed for information security. Additionally, we verify the image encryption effect in several aspects, such as key space, statistics attack analysis, differential attack analysis, known-plaintext attack analysis and chosen-plaintext attack analysis, randomness test and aspect of hardware realization. Finally, it is shown that the performance of the proposed algorithm in this paper is better than several existing algorithms in almost all fields after comparison as a conclusion.
A varied density-based clustering algorithm

Discovering clusters of different sizes, shapes, and densities is a challenging duty. DBSCAN can find clusters of different shapes and sizes. But it has trouble finding clusters of different densities because it depends on a global value for its parameter Eps. Several methods have been proposed to tackle this problem, each method has its drawbacks. This paper introduces a new stand-alone method to discover clusters of different densities. The proposed method depends on the k-nearest neighbors to compute the local density of each object as the sum of distances to its k1-nearest neighbors, where 0 < k1 < k, it starts from any object. This object is called a cluster initiator. Any object that is reachable from a cluster initiator and has a local density similar to the local density of the cluster initiator is assigned the same cluster. So, the method requires a threshold for similarity, which will be called SR (Similarity Ratio). The proposed method discovers clusters of different densities, shapes, and sizes. The experimental results show the superior ability of the proposed method to detect clusters of different densities even with no discernible separations between them.
Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang–Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article.
Delineating preliminary prospective areas of ion-adsorption rare earth deposits with stream sediments geochemical mapping in South China

Ion-adsorption rare earth deposits (IAREDs) have received widespread attention as their indispensable supply of heavy rare earth elements (HREE), which have naturally paved the way for IAREDs exploration. However, studies on regional prospecting methods, especially geochemical methods, on IAREDs are very limited. Here, we aimed to study how to identify and delineate the potential prospecting areas of IAREDs based on 5244 sets of stream sediment geochemical data from the 76-element Geochemical Mapping (76-GEM) project in South China at the scale of 1:2,000,000. The results showed that stream sediments in South China had a higher average concentration of REE (212 mg/kg) than Chinese catchment sediments (187 mg/kg) and Chinese soils (190 mg/kg), which indicated a great prospecting potentiality of IAREDs. Geochemical and geological maps revealed that the positive REE anomalies were mainly associated with three types of parent rocks: granitic, mafic igneous and carbonate rocks. Almost all IAREDs were spatially distributed in the positive REE anomalies and more than 95 % of the IAREDs were located in granitic REE anomalies. The distinct geochemical characteristics of the parent rocks, such as δEu, δCe, Eu, Si, Ca, K, Ni, Cr and Ti content, could help to distinguish REE anomalies of granitic rocks from those of mafic and carbonate rocks. Accordingly, a two-step method was proposed to prospect for IAREDs with stream sediment geochemical mapping. Overall, this study corroborated the finding that low-sampling density stream sediment geochemical mapping is an efficient method of delineating potential target areas of IARED on a national scale. But it was difficult to distinguish between LREE deposits and HREE deposits. This method showed great potential in IARED prospecting, especially in areas with a low geological degree. To the future exploration of IAREDs, the targeted multi-scale approaches for different exploration stages are essential to be developed.
Erms: Efficient Resource Management for Shared Microservices with SLA Guarantees

A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to-end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements.
CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms

Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum Algorithms (VQAs). VQAs rely upon the iterative optimization of a parameterized unitary circuit (ansatz) with respect to an objective function. Since quantum machines are noisy and expensive resources, it is imperative to classically choose the VQA ansatz initial parameters to be as close to optimal as possible to improve VQA accuracy and accelerate their convergence on today's devices.
Factors affecting blockchain adoption in Italian companies: the moderating role of firm size.

Although Blockchain technology has shown its usefulness, there exist limited studies and industrial applications. Presented research investigates factors influencing blockchain adoption. It reflects on how the perceived benefit of blockchain may influence the adoption and how organisational size may affect this process. This study integrates the framework of the Technology Acceptance Model (TAM) with the Theory of Planned Behaviour (TPB) and extends it with the perceived benefits of blockchain adoption while including firm size as moderator. Data collected were analysed with a PLS-SEM. Our results showed that a significant predictor of intention to use blockchain is perceived behavioural control. Moreover, it was found that perceived benefit significantly influenced perceived usefulness. Finally, the study demonstrated that SMEs are more efficient in adopting blockchain compared to Large firms.
Modelling and predicting enterprise-level cyber risks in the context of sparse data availability

Abstract Despite growing attention to cyber risks in research and practice, quantitative cyber risk assessments remain limited, mainly due to a lack of reliable data. This analysis leverages sparse historical data to quantify the financial impact of cyber incidents at the enterprise level. For this purpose, an operational risk database—which has not been previously used in cyber research—was examined to model and predict the likelihood, severity and time dependence of a company’s cyber risk exposure. The proposed model can predict a negative time correlation, indicating that individual cyber exposure is increasing if no cyber loss has been reported in previous years, and vice versa. The results suggest that the probability of a cyber incident correlates with the subindustry, with the insurance sector being particularly exposed. The predicted financial losses from a cyber incident are less extreme than cited in recent investigations. The study confirms that cyber risks are heavy-tailed, jeopardising business operations and profitability.
Quantifiers satisfying semantic universals have shorter minimal description length

Despite wide variation among natural languages, there are linguistic properties thought to be universal to all or nearly all languages. Here, we consider universals at the semantic level, in the domain of quantifiers, which are given by the properties of monotonicity, quantity, and conservativity, and we investigate whether these universals might be explained by differences in complexity. First, we use a minimal pair methodology and compare the complexities of individual quantifiers using approximate Kolmogorov complexity. Second, we use a simple yet expressive grammar to generate a large collection of quantifiers and we investigate their complexities at an aggregate level in terms of both their minimal description lengths and their approximate Kolmogorov complexities. For minimal description length we find that quantifiers satisfying semantic universals are simpler: they have a shorter minimal description length. For approximate Kolmogorov complexity we find that monotone quantifiers have a lower Kolmogorov complexity than non-monotone quantifiers and for quantity and conservativity we find that approximate Kolmogorov complexity does not scale robustly. These results suggest that the simplicity of quantifier meanings, in terms of their minimal description length, partially explains the presence of semantic universals in the domain of quantifiers.
Untrained Graph Neural Networks for Denoising

A fundamental problem in signal processing is to denoise a signal. While there are many well-performing methods for denoising signals defined on regular supports, such as images defined on two-dimensional grids of pixels, many important classes of signals are defined over irregular domains such as graphs. This paper introduces two untrained graph neural network architectures for graph signal denoising, provides theoretical guarantees for their denoising capabilities in a simple setup, and numerically validates the theoretical results in more general scenarios. The two architectures differ on how they incorporate the information encoded in the graph, with one relying on graph convolutions and the other employing graph upsampling operators based on hierarchical clustering. Each architecture implements a different prior over the targeted signals. To numerically illustrate the validity of the theoretical results and to compare the performance of the proposed architectures with other denoising alternatives, we present several experimental results with real and synthetic datasets.
Aergia

Federated Learning (FL) is a popular deep learning approach that prevents centralizing large amounts of data, and instead relies on clients that update a global model using their local datasets. Classical FL algorithms use a central federator that, for each training round, waits for all clients to send their model updates before aggregating them. In practical deployments, clients might have different computing powers and network capabilities, which might lead slow clients to become performance bottlenecks. Previous works have suggested to use a deadline for each learning round so that the federator ignores the late updates of slow clients, or so that clients send partially trained models before the deadline. To speed up the training process, we instead propose Aergia, a novel approach where slow clients (i) freeze the part of their model that is the most computationally intensive to train; (ii) train the unfrozen part of their model; and (iii) offload the training of the frozen part of their model to a faster client that trains it using its own dataset. The offloading decisions are orchestrated by the federator based on the training speed that clients report and on the similarities between their datasets, which are privately evaluated thanks to a trusted execution environment. We show through extensive experiments that Aergia maintains high accuracy and significantly reduces the training time under heterogeneous settings by up to 27% and 53% compared to FedAvg and TiFL, respectively.
Reversible computing from a programming language perspective

Software plays a central role in all aspects of reversible computing systems, and a variety of reversible programming languages have been developed. This presentation highlights the principles and main ideas of reversible computing viewed from a programming language perspective with a focus on clean reversible languages. They are the building material for software that can reap the benefits of reversible hardware and interesting in their own right. Reversible computing is situated within programming languages in general, and the relevant concepts are elaborated, including computability, injectivization and reversibilization. Features representative for many reversible languages are presented, such as reversible updates, reversible iterations, and access to a program's inverse semantics. Metaprogramming methods of particular importance to reversible programming, are introduced, including program inversion and inverse interpretation. Our presentation is independent of a particular language, although primarily the reversible language, Janus, will be used in examples.
Pyo, a Chatbot Assistant for Introductory Programming Students

As a result of the technological advancements in our society, the number of students enrolling in Computer Science and related courses continues to rise. Programming is regarded as a difficult subject to learn, and as the size of classes and the number of students enrolled in online courses continues to increase, providing individualized support to determine where the difficulties of a student lie may appear impossible, resulting in frustration, lack of motivation, and an increase in dropout rates. Chatbots can mitigate this problem as it can provide simultaneous support to multiple students 24 hours a day, seven days a week, and by allowing introverted students to express questions more comfortably. This paper proposes the implementation of a chatbot denominated Pyo aiming to provide assistance to novice programmers. It was evaluated how students enrolled in an online introductory programming course interacted with Pyo in order to draw conclusions about the effectiveness of the chatbot’s features. The results indicate that Pyo facilitates student learning; however, more straightforward explanations of its functionalities were requested.
SNPU: Always-on 63.2μW Face Recognition Spike Domain Convolutional Neural Network Processor with Spike Train Decomposition and Shift-and-Accumulation Unit

Recently, always-on face recognition (FR) and action recognition chips are widely developed in battery-limited mobile devices for event detection [1]. CNNs with high accuracy are unable to realize <100μW ultralow-power inference because many fixed numbers of operations should always process the 2D inputs [2]. Neural networks with low precision weight such as BNN and XOR-Net were proposed to realize low power operation but their accuracy is too low to compare with CNN [3], [4]. Replacement of multiplication with simple operations such as ShiftNet and AdderNet were proposed to achieve low power but they required very high input/weight precisions [5], [6].