Reviewerly/exHarmony huggingface dataset:
Improvement of Radio Frequency Fingerprint Portability for Wi-Fi Adaptor Identification

As quantum computing technology approaches maturity, the security of wireless communication using data encryption algorithms is facing great challenges. On the other hand, physical layer security technologies using the unique physical characteristic properties of one transmitter like radio frequency/ radiometric fingerprint, localization information, or channel state information are more resistant to attacks using quantum computing technology. Consequently, the research of physical layer security has attracted significant attentions recently. However, the RF fingerprint portability is an inevitable problem in practical applications. In this work, a joint RF feature calibration and weighted feature classification scheme is proposed to overcome the RF fingerprint portability problem caused by channel fading and receiver differentiation. Experimental results show that the average successful identification rate increases significantly using the proposed scheme.
Disentangling Prototype and Variation for Single Sample Face Recognition

Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. State-of-the-art SSPP FR methods are based on the prototype plus variation (i.e., P+V) model. However, the classic P+V model has two major limitations: 1) It is a linear model and cannot generalize many non-linear variations; 2) It can be severely impaired once the enrolment face images are contaminated with variations. To this end, we propose a novel disentangled prototype plus variation model, dubbed DisP+V, to tackle such limitations. DisP+V consists of an encoder-decoder structural generator and two discriminators. The generator and discriminators play two adversarial games such that the generator nonlinearly encodes the images into a latent semantic space, where the more discriminative prototype feature and the less discriminative variation feature are disentangled. Meanwhile, the prototype and variation features in the latent space can guide the generator to generate an identity-preserved prototype and the corresponding variation, respectively. Experiments on various real-world face datasets demonstrate the superiority of our DisP+V model over the classic P+V model for SSPP FR. Furthermore, DisP+V demonstrates its unique characteristics in the challenging prototype recovery task.
Adaptive Wavelet Methods for Earth Systems Modelling

This paper reviews how dynamically adaptive wavelet methods can be designed to simulate atmosphere and ocean dynamics in both flat and spherical geometries. We highlight the special features that these models must have in order to be valid for climate modelling applications. These include exact mass conservation and various mimetic properties that ensure the solutions remain physically realistic, even in the under-resolved conditions typical of climate models. Particular attention is paid to the implementation of complex topography in adaptive models. Using wavetrisk as an example, we explain in detail how to build a semi-realistic global atmosphere or ocean model of interest to the geophysical community. We end with a discussion of the challenges that remain to developing a realistic dynamically adaptive atmosphere or ocean climate models. These include scale-aware subgrid scale parameterizations of physical processes, such as clouds. Although we focus on adaptive wavelet methods, many of the topics we discuss are relevant for adaptive mesh refinement (AMR).
Computing Views of OWL Ontologies for the Semantic Web

This paper tackles the problem of computing views of OWL ontologies using a forgetting-based approach. In traditional relational databases, a view is a subset of a database, whereas in ontologies, a view is more than a subset; it contains not only axioms contained in the original ontology, but may also contain newly-derived axioms entailed by the original ontology (implicitly contained in the original ontology). Specifically, given an ontology , the signature of is the set of all the names in , and a view of is a new ontology obtained from using only part of ’s signature, namely the target signature, while preserving all logical entailments up to the target signature. Computing views of OWL ontologies is useful for Semantic Web applications such as ontology-based query answering, in a way that the view can be used as a substitute of the original ontology to answer queries formulated with the target signature, and information hiding, in the sense that it restricts users from viewing certain information of an ontology.
RDRF-Net: A pyramid architecture network with residual-based dynamic receptive fields for unsupervised depth estimation

Image depth estimation is a challenging problem in computer vision, especially considering both high accuracy and low run time. To save run time and maintain high accuracy, we present a new light-weight model in this paper, i.e., a Residual-based Dynamic Receptive Field Network (RDRF-Net). This model can automatically select the receptive fields suitable for different image scales to generate the depth maps with higher fitting degrees. Residual design and bottleneck layers are used to compress the network for reducing run time. Three groups of experiments are performed on the KITTI dataset to test the accuracy, computation time, and the impact of dynamic receptive fields. Experimental results show that RDRF-Net has comparable accuracy with Godard's model and significantly outperforms it in terms of run time. In addition, it performs closely to Pyd-Net in terms of run time and beats Pyd-Net's accuracy. Experiments also demonstrate the beneficial impact of dynamic receptive fields on improving depth estimation accuracy.
An Evolving Ontology for Vehicle Signals

The variety of vehicle data has motivated contributors from the automotive industry to develop and maintain the so-called Vehicle Signal Specification. As the semantics of the specification are limited to a tree-like hierarchy and data types, it has been considered for the foundation of a more expressive model in an ontology form. Since the first definition of the Vehicle Signal Specification Ontology, things have evolved, and assumptions have changed. This paper gives a structural overview of those changes. It introduces an overview of use-cases, which should be addressed by the ontology, and it presents improvements that facilitate the understanding and use of the ontology. We argue that the proposed changes are key to the standardization of a relevant and useful vehicle data model. Our approach defines a core ontology from the main concepts of the Vehicle Signal Specification. The proposed core ontology is accompanied by tools to automate the mapping process and keep a consistent alignment of the ontology with the continuously evolving Vehicle Signal Specification. We evaluate the core ontology against competency questions to show that the model satisfies them with less complexity than the existing solution.
Intermittent Private Information Retrieval With Application to Location Privacy

We study the problem of intermittent private information retrieval with multiple servers, in which a user consecutively requests one of <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> messages from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> replicated databases such that part of requests need to be protected while others do not need privacy. Motivated by the location privacy application, the correlation between requests is modeled by a Markov chain. We propose an intermittent private information retrieval scheme that concatenates an obfuscation scheme and a private information retrieval scheme for the time period when privacy is not needed, to prevent leakage incurred by the correlation over time. In the end, we illustrate how the proposed scheme for the problem of intermittent private information retrieval with Markov structure correlation can be applied to design a location privacy protection mechanism in the location privacy problem.
WITHDRAWN: Detection for enzymatic activity for bacteria isolated from the soil in polyethylene bags biodegradation

The study included investigating the bacteria growing in the soil that have the ability to produce enzymes that can be used to break down plastic wastes. Where 50 soil samples were collected from different areas in Al-Diwaniyah Governorate, at a rate of 1 gm per sample. After diluting each sample separately, the dilutions were cultured on different culture media such as mannitol, nutrient and MacConkey agar media. After an incubation period, the bacteria were purified and its ability to produce specific enzymes such as lipase, esterase and oxygenase were examined using proper procedure. The study has find that most isolated species are able to degrade plastic wastes but the genera Pseudomonas alcaligens and Bacillus have gave a highest ratio of degradation at the same conditions.
Mitigation of critical current fluctuation of Josephson junctions in superconducting quantum circuits

In superconducting quantum circuits, Josephson junctions are a key component to provide nonlinearity and enable superconducting qubits. Fabricating junctions with precise critical currents is crucial to defining qubit frequency. Here we suppress the critical current variation of Josephson junctions by varying the critical current density Jc and junction areas. The relative standard deviation of the critical current Ic is controlled to be below 0.8%. This uniform fabrication is promising for realizing scalable superconducting quantum computation.
On Invariant Subspaces in the Lai–Massey Scheme and a Primitivity Reduction

Abstract In symmetric cryptography, the round functions used as building blocks for iterated block ciphers are obtained as the composition of different layers acting as a sequence of bijective transformations providing global increasing complexity. The study of the conditions on such layers which make the group generated by the round functions of a block cipher a primitive group has been addressed in the past years, both in the case of Substitution Permutation Networks and Feistel Networks, giving to block cipher designers the recipe to avoid the imprimitivity attack, which exploits the invariance of some subspaces during the encryption. In the case of Lai–Massey schemes, where both Substitution Permutation Network and Feistel Network features are combined, the resistance against imprimitivity attacks has been a long-standing open problem. In this paper we consider a generalization of such a scheme and we prove its resistance against the imprimitivity attack. Our solution is obtained as a consequence of a more general result in which the problem of proving the primitivity of a generalized Lai–Massey scheme is reduced to the simpler one of proving the primitivity of the group generated by the round functions of a strictly related Substitution Permutation Network. We prove how this implies a reduction in the computational cost of invariant-subspace search.
An approach for document retrieval using cluster-based inverted indexing

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.
Realization of invariant-based shortcuts to population inversion with a superconducting circuit

Shortcuts to adiabaticity have been proved an effective routine for precise quantum state manipulation. Here, we experimentally demonstrate invariant-based shortcuts to adiabaticity to speed up the population transfer in a superconducting circuit. Through inverse engineering of the Hamiltonian, we realize this protocol in a single-qubit and a two-qubit system. The Lewis–Risenfeld phase is characterized experimentally. Furthermore, we investigate the robustness of the scheme against amplitude and frequency errors.
Triplet Attention

The Transformer model has benefited various real-world applications, where the self-attention mechanism with dot-products shows superior alignment ability on building long dependency. However, the pair-wisely attended self-attention limits further performance improvement on challenging tasks. To the extent of our knowledge, this is the first work to define the Triplet Attention (A3) for Transformer, which introduces triplet connections as the complementary dependency. Specifically, we define the triplet attention based on the scalar triplet product, which may be interchangeably used with the canonical one within the multi-head attention. It allows the self-attention mechanism to attend to diverse triplets and capture complex dependency. Then, we utilize the permuted formulation and kernel tricks to establish a linear approximation to A3. The proposed architecture could be smoothly integrated into the pre-training by modifying head configurations. Extensive experiments show that our methods achieve significant performance improvement on various tasks and two benchmarks.
A Features Decoupling Method for Multiple Manipulations Identification in Image Operation Chains

Recently, many forensic techniques have been developed to detect the use of a certain processing operation. When utilizing several manipulations to alter an image, artifacts left by manipulations that have been applied later can potentially disguise traces left by manipulations that were applied earlier. Therefore, the detection of manipulations become difficult. In this paper, we focus on identifying the manipulations in an image operation chain composed of multiple manipulations in a certain order. To address this issue, we analyze the relationship between manipulations identification and blind signal separation. Then, we propose a features decoupling method based on blind signal separation, which decouples the coupled features due to the superimposed processing artifacts and exploits the decoupled features to identify multiple operations. The experiments carried out on two image operation chains confirm the effectiveness of the proposed method.
Biomedical Image Processing with Improved SPIHT Algorithm and optimized Curvelet Transform Technique

The prime idea is to propose an effective compression technique obtaining improved PSNR and better compression ratio (CR) with Curvelet transformation technique and encoding the multi-wavelet parameters using Set Partitioning In Hierarchical Trees (SPIHT) algorithm. Also a comparative study is presented which highlights a dynamic image encoding algorithm against the conventional algorithms such as SVM, EZW & SPECK. Both quantitative and qualitative parameters of algorithm are analyzed for bio-medical images. Analysis inferences represents that the proposed technique treats structures and patterns considerably superior than conventional techniques.
More-Similar-Less-Important: Filter Pruning VIA Kmeans Clustering

Recent works indicate that traditional norm-based filter pruning methods are sensitive to the distribution of filters across different layers. Therefore, an additional sensitivity analysis is necessary to determine the pruning ratio of each layer, which is time-consuming. To solve this problem, many works proposed complicated pruning algorithms to search for the optimal pruned structures automatically under given FLOPs, which also training and time-consuming. In this paper, we propose a novel predefined filter pruning method for convolutional neural networks, called Filter Pruning via KMeans Clustering (FPKM), to identify the redundant filters based on the similarity of filters, regardless of the distribution of filters. FPKM eliminates the analysis of sensitivity to pruning and the searching process for pruned structure while ensuring accuracy. Extensive experiments on CIFAR10 and ImageNet datasets show that our FPKM method achieves significant improvement compared with state-of-the-art filter pruning methods.
Social Neural Hybrid Recommendation with Deep Representation Learning

Deep learning techniques are proved to be very effective in various domains, such as computer vision, pattern recognition, and natural language processing. With the rapid development of this technology, more and more deep models have been used in the recommendation system. However, only few attempts have been made in social-based recommender systems. This paper focuses on this issue and explores the use of deep neural networks for learning the interaction function from data. A novel recommendation model called SNHF (Social Neural Hybrid Filtering) is proposed. It combines collaborative and content-based filtering with social information in an architecture based on both models: (1) Generalized Matrix Factorization (GMF); and (2) Hybrid Multilayer Perceptron (HybMLP). Extensive experiments on two real-world datasets show that SNHF significantly outperforms state-of-the-art baselines and related work, especially in the cold start situation.
Face Detection for Color Image Based on MATLAB

Abstract Amazing results in many works in the field of deep convolutional neural networks using them in many fields of machine learning such as image classification control of atrial play, and image annotation. Mathematical analysis of deep convolutional neural networks (CNN) has a broad role in many applications including image classification applications. Face recognition technology has occupied a wide field in research that is still continuing in this work. Face recognition technology is being developed depending on the MATLAB program to develop the subjecting of the color image to the neural convolution network and the work of a program to reduce the time the network deals with the image to reduce the number of layers to only two layers and access the algorithm Suggested in this work. Computer vision of a color image in a face detection study. In this work, the researcher created a fast algorithm to reach the research goal based on new standards in the field of face detection. A new face detection program was designed using MATLAB, CascadeObjectDetector and trainCascadeObjectDetector the face detection devices were developed. In this work, a technique for panning and color image using the convolutional neural network for deep learning is proposed, the method used to reach the goal of this work.
A BERT-Bi-LSTM-Based Knowledge Graph Question Answering Method

With the development of knowledge graph, the research of question answering methods based on knowledge graph has gradually become a hot spot. However, in the current mainstream question answering methods, there is insufficient mining of the semantic information of question sentences, resulting in poor entity recognition and relation recognition effects, and many question answering methods rely on predefined rules, which have low transferability and high labor costs. To solve this problem, this paper proposes a knowledge graph question answering method based on BERT word vector, which mainly includes entity recognition and relation recognition. In the entity recognition part, the BERT-Bi-LSTM-CRF model is built, which can fully mine the semantic information contained in the question and improve the accuracy. In the part of relation recognition, the traditional method of semantic relation between multiple entities in the question is transformed into a text classification problem, which can simplify the model complexity and improve the accuracy. Finally, experiments were performed on entity recognition and relation recognition using related data sets. The results show that compared with traditional question answering methods, this method can achieve higher accuracy in entity recognition and relation recognition.
Blockchain Based Data Protection Framework for IoT in Untrusted Storage

With the continuous growth of the number of Internet of things(IoT) devices, more and more data are generated by IoT devices. IoT terminal devices need to transfer the data to the edge server for storage, so protecting the security of IoT data has become a huge challenge. As a new technology, blockchain has the characteristics of distributed, tamper-resistant. Smart contract running on the blockchain can automatically perform tasks. Because of these characteristics, blockchain can solve the data security problem of the IoT. In this paper, a blockchain-based data protection framework for the IoT in untrusted storage is proposed. Lightweight streaming authenticated data structures are also used to reduce the storage burden of the blockchain system and improve the efficiency of the framework. Finally, simulation results show that our work can reduce the storage burden of blockchain.
Convolutional Neural Network Hyper-Parameter Optimization Using Particle Swarm Optimization

CNN has recently gained popularity in the field of image processing. It has proven its niche in the field of machine learning. Computational models which use biological computation have been seen to use CNN. In this paper, we are going to optimize mainly one of these CNN hyper-parameters called convolution size. The optimization of parameter is actually the selection of the parameter by which the model will increase its performance. We are going to use evolutionary algorithm techniques. One of this techniques is called as particle swarm optimization(PSO). It is a community ground methods which select its population based on their fitness of the members where at the final selection the least fit members tend to stay at the population. We have various accuracy level by using only CNN and using CNN and PSO together.
Heatmap-Aware Pyramid Face Hallucination

Recent deep-learning-based face hallucination methods have achieved great success. Due to the parameter sharing characteristics of convolutional neural network, most existing deep-learning-based methods essentially use the same kernel for different regions of the entire face image in a convolution layer. This scheme of treating the face image as a whole will lead to the neglect of important facial details. To address this problem, we design a novel heatmap-aware convolution with spatially variant kernels rather than a spatially sharing kernel in the standard convolution to recover different regions. Based on this, we propose a heatmap-aware pyramid face super-resolution network (HaPSR) that embeds our heatmap-aware convolution into a two-branch network for both face super-resolution and facial heatmap estimation. The facial heatmap estimation branch can not only be used as an auxiliary to regularize face super-resolution reconstruction, but also provide an important basis for spatially variant kernels. Quantitative and qualitative experimental results demonstrate that our method outperforms state-of-the-arts.