Reviewerly/exHarmony huggingface dataset:
Frequency hopping signal parameter estimation algorithm based on time-frequency point correlation

Aiming at the low accuracy of frequency hopping signal parameter estimation under the influence of complex noise and interference, a frequency-hopping signal parameter estimation algorithm based on time-frequency point correlation is proposed. First, we design the kernel function to obtain the time-frequency distribution of the frequency-hopping signal. Then, we use the correlation of the time-frequency points to establish the target transfer relationship model. Besides, the feature quantity combines density screening is used for hierarchical clustering, and ridge queuing methods filter out the interference ridge label. Finally, we perform parameter estimation on the frequency hopping ridge. The simulation results show that the parameter error estimated by the proposed algorithm continues to decrease with the improvement of the signal-to-noise ratio. In the case of −6dB, the error of each parameter is as low as about 0.1. This algorithm can completely extract the frequency hopping ridge under the condition of a low signal-to-noise ratio, achieve an excellent parameter estimation effect, and provide a reference for communication signal parameter estimation.
Wavelet denoising method based on improved threshold function

Voice signal is disturbed by environmental noise during recording, which reduces the clarity of voice signal and affects the acquisition of useful signals. The wavelet threshold de noising method is widely used in the field of signal de noising. However, the hard/soft thresholding function in traditional wavelet transform has some problems such as discontinuity and fixed deviation when filtering. Therefore, an improved wavelet thresholding function method is proposed and applied in speech signal denoising. Through theoretical analysis and simulation, the results show that the improved wavelet threshold denoising algorithm can not only improve the signal-to-noise ratio (SNR) of the voice signal, but also largely avoid the distortion of the voice signal.
The Decentralized Grid-Aware P2P Energy Market enabled by a Distributed Ledger Implementation

As power systems continuously become increasingly volatile and complex systems, conventional market designs turn inefficient, unnecessarily exclusive, and also agnostic towards grid and system capacities. While partial solutions to this already exist, the emerging distributed ledger technologies open up the field to holistic solutions. This work outlines the shortcomings of nowadays market designs, provides a conceptual design improvement along with its technical requirements and finally presents a high-level implementation based on IBM’s blockchain project Hyperledger Fabric.
LSTM Based Named Entity Chunking and Entity Extraction

Some Natural Language Processing (NLP) jobs require the automatic extraction of key information from a text document, which is why automatic extraction is required. With the rise of social media, digital journalism, and blogging, automatic extraction is becoming increasingly important. The amount of information available is enormous, and information extraction will aid in the management of such vast amounts of data. A important subtask of automatic information extraction is named entity recognition (NER), also known as entity identification, entity chunking, and entity extraction. NER is also known as entity chunking and entity extraction. In an unstructured text document, it locates and categorises the identified entities with unique significance by categorising them into pre-defined categories like person, organisation, location, and so on. In a large number of occasions, this contain the most important information about the document. There are numerous applications for this information. It can be used to improve the ordering and filtering of key terms in documents, or it can simply be used as an input to NLP activities such as text summarization, question answering, and machine translation, among other things..
Key Point Sensitive Loss for Long-tailed Visual Recognition

For long-tailed distributed data, existing classification models often learn overwhelmingly on the head classes while ignoring the tail classes, resulting in poor generalization capability. To address this problem, we thereby propose a new approach in this paper, in which a key point sensitive (KPS) loss is presented to regularize the key points strongly to improve the generalization performance of the classification model. Meanwhile, in order to improve the performance on tail classes, the proposed KPS loss also assigns relatively large margins on tail classes. Furthermore, we propose a gradient adjustment (GA) optimization strategy to re-balance the gradients of positive and negative samples for each class. By virtue of the gradient analysis of the loss function, it is found that the tail classes always receive negative signals during training, which misleads the tail prediction to be biased towards the head. The proposed GA strategy can circumvent excessive negative signals on tail classes and further improve the overall classification accuracy. Extensive experiments conducted on long-tailed benchmarks show that the proposed method is capable of significantly improving the classification accuracy of the model in tail classes while maintaining competent performance in head classes.
Anomaly detection as vision-based obstacle detection for vehicle automation in industrial environment

Nowadays, produced cars are equipped with mechatronical actuators as well as with a wide range of sensors in order to realize driver assistance functions. These components could enable cars’ automation at low speeds on company premises, although autonomous driving in public traffic is still facing technical and legal challenges. For automating vehicles in an industrial environment a reliable obstacle detection system is required. State-of-the-art solution for protective devices in Automated Guided Vehicles is the distance measuring laser scanner. Since laser scanners are not basic equipment of today’s cars in contrast to monocameras mounted behind the windscreen, we develop a computer vision algorithm that is able to detect obstacles in camera images reliably. Therefore, we make use of our well-known operational design domain by teaching an anomaly detection how the vehicle path should look like. The result is an anomaly detection algorithm that consists of a pre-trained feature extractor and a shallow classifier, modelling the probability of occurrence. We record a data set of a real industrial environment and show a robust classifier after training the algorithm with images of only one run. The performance as an obstacle detection is on par with a semantic segmentation, but requires a fraction of the training data and no labeling.
Multi-Task Learning Based on Stochastic Configuration Networks

When the human brain learns multiple related or continuous tasks, it will produce knowledge sharing and transfer. Thus, fast and effective task learning can be realized. This idea leads to multi-task learning. The key of multi-task learning is to find the correlation between tasks and establish a fast and effective model based on these relationship information. This paper proposes a multi-task learning framework based on stochastic configuration networks. It organically combines the idea of the classical parameter sharing multi-task learning with that of constraint sharing configuration in stochastic configuration networks. It organically combines the idea of the classical parameter sharing multi-task learning with that of constraint sharing configuration in stochastic configuration neural networks. Moreover, it provides an efficient multi-kernel function selection mechanism. The convergence of the proposed algorithm is proved theoretically. The experiment results on one simulation data set and four real life data sets verify the effectiveness of the proposed algorithm.
Deep Learning-Based Extraction of Concepts: A Comparative Study and Application on Medical Data

With the exponential increase of data on the web, the manual acquisition of ontology has become a time-consuming and tedious task. Thus, switching to ontology learning enables the ontologies’ acquisition automation. In this paper, we deal with the phase of concepts’ extraction. Our motivation is to capitalise on the confirmed advantages of deep learning (DL) models and word embedding techniques to automatically extract relevant concepts from large amounts of textual data. A four phases approach is proposed where different models and techniques are applied and a comparative study is achieved: the preprocessing phase, the classification phase, based on DL models, the terms filtering phase, where we experimented and compared three methods to extract the relevant terms, and the semantic enrichment phase experimenting and comparing word embedding techniques to semantically enrich the discovered concepts. The approach is implemented and evaluated on different medical datasets. The obtained results proved the suitability of the experimented models and techniques for the concepts’ extraction.
Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets

In preparation for the battlefields of the future, using unmanned aerial vehicles (UAV) loaded with multisensors to track dynamic targets has become the research focus in recent years. According to the air combat tracking scenarios and traditional multisensor weighted fusion algorithms, this paper contains designs of a new data fusion method using a global Kalman filter and LSTM prediction measurement variance, which uses an adaptive truncation mechanism to determine the optimal weights. The method considers the temporal correlation of the measured data and introduces a detection mechanism for maneuvering of targets. Numerical simulation results show the accuracy of the algorithm can be improved about 66% by training 871 flight data. Based on a mature refitted civil wing UAV platform, the field experiments verified the data fusion method for tracking dynamic target is effective, stable, and has generalization ability.
Secure aggregate signature scheme for smart city applications

With the recent advances in technology, smart cities are rapidly earning momentum. To provide better lives, smart cities should host several applications to increase the efficiency and accessibility of services. Since these services involve the conveyance of significant data among the smart cities' cloud and the dwellers over the Internet, security and privacy are critical. This paper proposes an authentication protocol with a full aggregation signature to secure the smart city applications. A certificate-less aggregate signature (CLAS) is used for smart city real applications such as secure routing and database outsourcing. The proposed CLAS achieves message authentication, user anonymity, constant signature size, unlinkability, and is resistant to replay attacks. Also, we have proved that the proposed scheme is resistant to all malicious adversaries. Finally, the performance evaluation results show that the proposed CLAS performs better than existing relevant schemes.
Hypergraph Lambek grammars

In the graph grammar theory, there is a natural generalization of the context-free grammar approach to graphs called hyperedge replacement grammar (HRG). The latter is based on the hyperedge replacement procedure, and it preserves the main principles and properties of context-free grammars (e.g. the pumping lemma). In the string case, there is an alternative approach to describing formal languages, which was introduced by J. Lambek; it is based on a substructural logic called the Lambek calculus. In this paper, we generalize this calculus and this grammar approach to hypergraphs, which results in defining the hypergraph Lambek calculus (HL) and hypergraph Lambek grammars (HL-grammars). We show how to embed the Lambek calculus in HL justifying that the presented generalization is appropriate. After that, we study several structural properties of HL and turn to the question of expressive power of its grammars. It appears that HL-grammars are more powerful than HRGs while having the same algorithmic complexity with them; in particular, we show that intersection of a language generated by an HRG and of a language generated by an HL-grammar can be generated by some HL-grammar as well. Consequently, HL-grammars might be considered as enhancement of HRGs.
Cyclic proofs for the first-order µ-calculus

Abstract We introduce a path-based cyclic proof system for first-order $\mu $-calculus, the extension of first-order logic by second-order quantifiers for least and greatest fixed points of definable monotone functions. We prove soundness of the system and demonstrate it to be as expressive as the known trace-based cyclic systems of Dam and Sprenger. Furthermore, we establish cut-free completeness of our system for the fragment corresponding to the modal $\mu $-calculus.
Adaptive weighted multiscale feature fusion for small drone object detection

Drone object detection in low-altitude airspace plays an essential role in many practical applications, such as security and airspace monitoring. Despite the remarkable progress made by many methods, drone object detection still remains challenging due to the complex background and huge differences in scales of drones. To address the above issues, an improved fully convolutional one-stage object detection (FCOS) model based on adaptive weighted feature fusion (AWFF) module is proposed for multiscale drone object detection in complex background. By learning the spatial relevance of feature maps at each scale and improving the scale invariance of features based on the channel attention mechanism, AWFF module could adaptively fuse the features of adjacent scale. In addition, a receptive field enhancement module is designed to reduce the information loss in the feature fusion process. Extensive experiments are conducted to evaluate the effectiveness of the proposed module and method on the constructed low-altitude drone dataset, which concludes that the mean average precision of the AWFF-FCOS is increased by 2.1% compared with the baseline method. And extensive ablation experiments further demonstrate that the proposed AWFF module and REF module could be integrated into the state-of-the-art method to improve the performance of drone object detection.
Gaussian soft control for controlled-Z gate on superconducting qubits with unilateral external driving

Abstract Soft quantum control is a valid technique for highly selective interactions recently illustrated in Haase et al (2018 Phys. Rev. Lett. 121 050402), holding efficient resonant couplings among target levels while largely suppressing unwanted off-resonant contributions. Here we present a model for implementing a controlled- Z (CZ) gate in superconducting circuit quantum electrodynamics (QED) with two qubits being coupled to a microwave cavity. An external classical field that drives only one qubit, combined with the strong single-mode quantized cavity field dressing both qubits, is employed to induce the CZ gate between two qubits, and is also further tailored as a Gaussian soft control (GSC) to improve gate performances in various aspects. By contrast, we show that, with the same gate time, the CZ gate based on GSC can hold a higher fidelity, greater resilience to parameter errors, and stronger robustness against decoherence of system than that based on a rectangular pulse.
E-FAIR-DB: Functional Dependencies to Discover Data Bias and Enhance Data Equity

Decisions based on algorithms and systems generated from data have become essential tools that pervade all aspects of our daily lives; for these advances to be reliable, the results should be accurate but should also respect all the facets of data equity [ 11 ]. In this context, the concepts of Fairness and Diversity have become relevant topics of discussion within the field of Data Science Ethics and, in general, in Data Science. Although data equity is desirable, reconciling this property with accurate decision-making is a critical tradeoff, because applying a repair procedure to restore equity might modify the original data in such a way that the final decision is inaccurate w.r.t. the ultimate objective of the analysis. In this work, we propose E-FAIR-DB, a novel solution that, exploiting the notion of Functional Dependency—a type of data constraint—aims at restoring data equity by discovering and solving discrimination in datasets. The proposed solution is implemented as a pipeline that, first, mines functional dependencies to detect and evaluate fairness and diversity in the input dataset, and then, based on these understandings and on the objective of the data analysis, mitigates data bias, minimizing the number of modifications. Our tool can identify, through the mined dependencies, the attributes of the database that encompass discrimination (e.g., gender, ethnicity, or religion); then, based on these dependencies, it determines the smallest amount of data that must be added and/or removed to mitigate such bias. We evaluate our proposal both through theoretical considerations and experiments on two real-world datasets.
Digital Trust and the Reconstruction of Trust in the Digital Society: An Integrated Model based on Trust Theory and Expectation Confirmation Theory

Digital trust is born with the evolution of digital society. It is an inescapable topic in the digital society and it is developed from traditional interpersonal trust and institutional trust and has been extensively used in the Internet space. At present, the research on digital trust is rare. Based on the Trust Theory and the Expectation Confirmation Theory, this paper puts forward an integration model with user satisfaction as the intermediary variable. Besides, this paper develops a set of scales for evaluating digital trust combined with maturity scales and points out that digital trust consists of digital cognitive trust and emotional trust. This paper assumed that user perception and user expectation indirectly affect digital trust through user satisfaction and used SPSS 23.0 to do reliability, validity test, and exploratory factor analysis. The results found that user satisfaction plays a mediating role by fitting, evaluating, and optimizing the structural equation model with AMOS23.0. User satisfaction is a partial intermediary between user perception and digital trust, and it is the complete intermediary between user expectation and digital trust. These results demonstrate two things. Firstly, in the digital society, the construction of users' digital trust is based on users' satisfaction. The government should provide diversified and high-quality e-government services as far as possible. Secondly, digital trust is directly or indirectly affected by user perception and user expectation. The government should build a safe, green, and harmonious digital environment for users and make e-government services consistent with users' expectation.
Multipath Routing for Multipartite State Distribution in Quantum Networks

Multipath routing for multipartite state distribution is proposed. Compared to shortest path routing, multipath routing achieved exponential rate improvement and an observed 6000 × speedup on error-prone grid networks.
A quantitative analysis of a generalized Hopfield model that stores and retrieves mismatched memory patterns

We study a class of Hopfield models where the memories are represented by a mixture of Gaussian and binary variables and the neurons are Ising spins. We study the properties of this family of models as the relative weight of the two kinds of variables in the patterns varies. We quantitatively determine how the retrieval phase squeezes towards zero as the memory patterns contain a larger fraction of mismatched variables. As the memory is purely Gaussian retrieval is lost for any positive storage capacity. It is shown that this comes about because of the spherical symmetry of the free energy in the Gaussian case. Introducing two different memory pattern overlaps between spin configurations and each contribution to the pattern from the two kinds of variables one can observe that the Gaussian parts of the patterns act as a noise, making retrieval more difficult. The basins of attraction of the states, the accuracy of the retrieval and the storage capacity are studied by means of Monte Carlo numerical simulations. We uncover that even in the limit where the network capacity shrinks to zero, the (few) retrieval states maintain a large basin of attraction and large overlaps with the mismatched patterns. So the network can be used for retrieval, but with a very small capacity.
Multilayer attention receptive fusion network for multiscale ship detection with complex background

Visual ship detection is a crucial component in recognition systems for detecting objects at different scales. However, there remains a challenge in detecting multiscale ships with the influence of various sizes of ships and complex backgrounds. We propose an efficient multilayer attention receptive fusion network (MARN) that is based on the well-established YOLOv4. MARN is a one-stage algorithm that essentially extracts semantic information from feature maps at different scales to highlight salient features of ships, thereby improving the detection performance of multiscale ships. Moreover, a multilayer information interactive fusion module (MIFM) and attention receptive field block (ARFB) are applied and combined reasonably to build a bidirectional fine-grained feature pyramid. Specifically, MIFM is used to fuse features at different scales, which not only concatenates high-level semantic features from deep layers but also reshapes richer features from shallower layers. Meanwhile, ARFB consists of a receptive field block, spatial attention, and channel attention, effectively emphasizing the most important features and suppressing unnecessary ones. Finally, experiments on the Singapore maritime dataset demonstrate that MARN can effectively solve the detection problem of multiscale ships in the complex marine environment.
Extending the Omniglot Challenge: Imitating Handwriting Styles on a New Sequential Data Set

With the increasing effectiveness of one-/few-shot learning techniques in the context of handwritten character generation and recognition, the call to extend the commonly associated Omniglot challenge is becoming more pressing. However, the sequential Omniglot data set represents unrealistically written characters. Therefore, we present new data, a new challenge, and a new model as follows: on the data side, we introduce DigiLeTs, a data set containing 23 870 new trajectories of Latin letters and Arabic numbers from 77 participants with high natural variance within character types. On the challenge side, we introduce the task to imitate handwriting styles in a one-shot manner. On the model side, we extend a generative, recurrent neural network model equipped with a one-shot inference mechanism that allows to reuse previously extracted compositional encodings and that has already been proven promising to solve the original Omniglot challenge. The new model is able to reassemble previously learned components into new characters of new styles in a one-shot manner. Most surprisingly, in a strictly forward manner, it often generates plausible, unknown characters in an already known style. With this work, we hope to inspire future research to investigate how compositional structures develop and are employed for rapid, concept-oriented learning, imitation, and understanding.