Reviewerly/exHarmony huggingface dataset:
Optimal readout of superconducting qubits exploiting high-level states

High-fidelity initialization, manipulation, and measurement of qubits are important in quantum computing. For the Google’s Sycamore processor, the gate fidelity of single- and two-qubit logic operations has improved to>99.6%, whereas single-shot measurement fidelity remains at the level of 97%, which severely limits the application of the superconducting approach to large-scale quantum computing. The current measurement scheme relies on the dispersive interaction between the qubit and the readout resonator, which was proposed back in 2004. However, the measurement fidelity is limited by the trade-off between the state separation and relaxation time of the two-level system. Recently, an exciting phenomenon was observed experimentally, wherein the separation-decay limit could be alleviated by exploiting the cascade decay nature of the higher levels; however, the mechanism and effectiveness of this phenomenon are still unclear. Herein, we present a theoretical tool to extract different types of errors in high-level states encoding dispersive measurement. For the realistic parameters of Google’s Sycamore processor, the use of state |2〉 is sufficient to suppress 92% of the decay readout error on average, where the total readout error is dominated by the background thermal excitation. We also show counter-intuitively that, the assistance of high-level states is effective in the measurement of logic 0, where there is no decay process.
Single Image Dehazing Via Region Adaptive Two-Shot Network

Single image dehazing is the key to enhancing image visibility in outdoor scenes, which facilitates human observation and computer recognition. The existing approaches generally utilize a one-shot strategy that indiscriminately applies the same filters to all local regions. However, due to neglecting inhomogeneous illumination and detail distortion, their dehazed results easily suffer from underfiltering or overfiltering across different regions. To tackle this issue, we propose a region-adaptive two-shot network (RATNet) that follows a coarse-to-fine framework. First, a lightweight subnetwork is applied to execute regular global filtering and obtain an initially restored image. Then, a two-branch subnetwork is put forward whose branches separately refine its illumination and detail. Eventually, we derive the final prediction by adaptively aggregating the results after illumination modification and detail restoration, whose region-variant weights are jointly optimized by maximizing the similarity between our fused result and haze-free counterpart. Extensive experiments validate the superiority of our proposed algorithm.
Design of novel SMS4-BSK encryption transmission system

In our day-to-day life, securing the confidential data and communicating the same to the authorized receiver are the biggest challenges. In Defense, Medical and Banking domains, the security over wireless medium should be high. SMS4-BSK is designed to provide a faster encryption with the required security over Wireless Local Area Network (WLAN). A novel SMS4-BSK architecture is proposed in this paper. The new design is implemented in Kintex 7 FPGA and it is proved that the new architecture can perform encryption and decryption faster than the existing SMS4 architectures. The proposed cryptosystem is capable of resisting cryptanalysis over Ciphertext-only attack, Known-plaintext attack, Chosen-plaintext attack, and Chosen-ciphertext attack. Finally, the possible ways of changes that can be made for improving speed are discussed.
Real time drone detection by moving camera using COROLA and CNN algorithm

Widespread and careless use of unmanned aerial vehicles, such as drones, often raises serious privacy and security issues. To some extent, timely and accurate detection of drones enables observers to counteract unwarranted intrusion and other forms of their misuse. This is not an easy task, however, drones, on one hand, being small in size, are difficult to spot in general. On the other hand, they fly at low altitudes and against a background containing several look-alike objects. In this work, a hybrid approach for the detection of drones by the moving spy drone camera is presented which combines Contiguous Outlier Representation via Online Low-rank Approximation (COROLA) and Convolutional Neural Network (CNN). The COROLA technique is used for detecting a small moving object present in a scene and the CNN algorithm is employed for accurate drone recognition in a wide array of complex backgrounds. This hybrid technique is robust and time-efficient as it obviates full processing of the entire image sequence. To demonstrate the effectiveness of our proposed approach, we have compared its performance with R-STIC (Regression on Spatial-Temporal Image Cube), a state-of-the-art detection method, under different real-life scenarios. The obtained results show that the proposed hybrid approach is better than R-STIC, in terms of computational efficiency, accuracy, and robustness.
Mapping Experience Ecosystems as Emergent Actor-Created Spaces

The paper introduces a conceptualization of experience ecosystems as semantic blended spaces instantiated by the activities carried out by independent actors moving freely and at will between different products, services, devices, people, and locations in pursuit of individual goals. This conceptualization is anchored to three distinct cultural and socio-technical shifts that characterize the current postdigital condition: the displacement of postmodernism as the cultural dominant; the embodiment of digitality and the emergence of a blended space of action; the occurrence of a postdigital society. It contributes to ongoing conversations on ecosystem-level and systemic design from the point of view of information architecture and user experience in five distinct ways: by centering the discourse on the actor-driven individual experience made possible by the postdigital condition; by framing the problem space from an embodied, spatial and architectural perspective; by considering the environment systemically as a blend of digital and physical non-contiguous spaces; by recasting the object of design to be the semantic and spatial relationships that exist or could exist between the elements of the actor-centered ecosystem; by introducing a mapping methodology that can be used to capture and spatially describe the relational complexity of said ecosystems for further intervention.
Laplacian state transfer in edge complemented coronas

In this paper, we investigate the existence of Laplacian perfect state transfer and Laplacian pretty good state transfer in edge complemented coronas. We give sufficient conditions for edge complemented coronas to not have Laplacian perfect state transfer. In contrast, we also give sufficient conditions for edge complemented coronas to have Laplacian pretty good state transfer.
Systematic Literature Review of Security Pattern Research

Security patterns encompass security-related issues in secure software system development and operations that often appear in certain contexts. Since the late 1990s, about 500 security patterns have been proposed. Although the technical components are well investigated, the direction, overall picture, and barriers to implementation are not. Here, a systematic literature review of 240 papers is used to devise a taxonomy for security pattern research. Our taxonomy and the survey results should improve communications among practitioners and researchers, standardize the terminology, and increase the effectiveness of security patterns.
Enhanced Bee Colony Approach for reducing the energy consumption during VM migration in cloud computing environment

Abstract To achieve virtualization in a cloud environment, resource utilization and energy need to be handled carefully. For this one should have to manage the workload, by distributing the load equally among the node. So that, the resources should be distributed equally among the cloud user and access data anytime from anywhere with minimum energy. In this paper, an enhanced Artificial Bee Colony (E-ABC) approach is presented to minimize overall energy consumption with minimum number of migrations. E-ABC approach migrates the VM from the overloaded host to underloaded hosts and hence save energy. The enhancement of the proposed work is exhibited by showing comparison with the Enhanced Cuckoo Search (E-CS) approach and Ant Colony Optimization technique using MATLAB simulator. Enhancement in the reduction of energy consumption of about 15.45 %, and 17.03 % is observed against E-CS, and existing work.
Autonomous Operation Control of IoT Blockchain Networks

Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with sufficient computing power, a blockchain node with low computing power and memory, such as an IoT sensor/actuator, cannot operate in a blockchain as a fully functional node. A lightweight blockchain provides practical blockchain availability over IoT networks. We propose essential operational advances to develop a lightweight blockchain over IoT networks. A dynamic network configuration enforced by deep clustering provides ad-hoc flexibility for IoT network environments. The proposed graph neural network technique enhances the efficiency of dApp (distributed application) spreading across IoT networks. In addition, the proposed blockchain technology is highly implementable in software because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed methods.
Recurrent Neural Network

Recurrent neural networks (RNN) are very powerful types of neural networks and are the most promising algorithm because they are the only ones with an internal memory (Boca Raton Mhaskar et al. Learning functions: when is deep better than shallow. arXiv:1603.00988, 2016). RNN is the most preferred algorithm for sequential data that includes speech, text, financial data, audio, video, weather, and much more as it can provide a deeper understanding of sequence and its meaning compared to other algorithms (Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras. [Authors: RajalingappaaShanmugamani]). It is generally used to obtain predictive results in sequential data.
TTPP: Temporal Transformer with Progressive Prediction for efficient action anticipation

Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states, and predict future actions from the hidden representations. It is well known that the recurrent pipeline is inefficient in capturing long-term information which may limit its performance in predication task. To address this problem, this paper proposes a simple yet efficient Temporal Transformer with Progressive Prediction (TTPP) framework, which repurposes a Transformer-style architecture to aggregate observed features, and then leverages a light-weight network to progressively predict future features and actions. Specifically, predicted features along with predicted probabilities are accumulated into the inputs of subsequent prediction. We evaluate our approach on three action datasets, namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also conduct a comprehensive study for several popular aggregation and prediction strategies. Extensive results show that TTPP not only outperforms the state-of-the-art methods but also more efficient.
A Reusable Generalized Voronoi Diagram-Based Feature Tree for Fast Robot Motion Planning in Trapped Environments

The sampling-based partial motion planning algorithm has been widely applied in real-time mobile robot navigation for its computational savings and its flexibility in avoiding obstacles. However, in some complex environments, partial planning algorithms are prone to fall into traps, resulting in the failure of motion planning. This paper proposes a feature tree algorithm based on Generalized Voronoi Diagram (GVD) to generate heuristic paths to guide partial motion planning. A GVD feature extraction algorithm is proposed to reduce the redundancy in the representation of obstacle-free regions and improve the searching efficiency in heuristic planning process. The feature node set guarantees that any node from obstacle-free regions can be connected to at least one feature node without any collision. For one map, the feature nodes only need to be extracted once and then can be reused in different scenarios on the same map. Thus, the feature extraction can be executed off-line. Based on GVD feature nodes, a feature tree is reported to generate a heuristic path and the nodes on the heuristic path are utilized sequentially as sub-goals to guide the partial motion planning. When the target changes, the feature tree can quickly replan a new heuristic path. The experimental studies reveal that our proposed method can significantly improve the robot motion planning efficiency and the navigation success rate in trapped environments.
A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method

Meteorological visibility is an important meteorological observation indicator to measure the weather transparency which is important for the transport safety. It is a challenging problem to estimate the visibilities accurately from the image characteristics. This paper proposes a transfer learning method for the meteorological visibility estimation based on image feature fusion. Different from the existing methods, the proposed method estimates the visibility based on the data processing and features’ extraction in the selected subregions of the whole image and therefore it had less computation load and higher efficiency. All the database images were gray-averaged firstly for the selection of effective subregions and features extraction. Effective subregions are extracted for static landmark objects which can provide useful information for visibility estimation. Four different feature extraction methods (Densest, ResNet50, Vgg16, and Vgg19) were used for the feature extraction of the subregions. The features extracted by the neural network were then imported into the proposed support vector regression (SVR) regression model, which derives the estimated visibilities of the subregions. Finally, based on the weight fusion of the visibility estimates from the subregion models, an overall comprehensive visibility was estimated for the whole image. Experimental results show that the visibility estimation accuracy is more than 90%. This method can estimate the visibility of the image, with high robustness and effectiveness.
A conditional Triplet loss for few-shot learning and its application to image co-segmentation

Few-shot learning tries to solve the problems that suffer the limited number of samples. In this paper we present a novel conditional Triplet loss for solving few-shot problems using deep metric learning. While the conventional Triplet loss suffers the limitation of random sampling of triplets which leads to slow convergence in training process, our proposed network tries to distinguish between samples so that it improves the training speed. Our main contributions are two-fold. (i) We propose a conditional Triplet loss to train a deep Triplet network for deep metric embedding. The proposed Triplet loss employs a penalty–reward technique to enhance the convergence of standard Triplet loss. (ii) We improve the performance of the existing image co-segmentation model by replacing the conventional loss function by our proposed conditional Triplet loss. To demonstrate the performance of the proposed network, experiments carry out on MNIST and CIFAR. Simulation results are evaluated by AUC and Recall (sensitivity) and indicate that the proposed conditional Triplet network achieves higher accuracy in comparison to state-of-the-arts.
Microscopic full field strain measurement of unidirectionally fiber reinforced plastics with the Kriging-digital image correlation and region splitting method

Microscopic strain measurements of unidirectional glass fiber reinforced plastic under transverse tensile loading were conducted. The microscopic full field strain measurement incorporated with digital image correlation (DIC) accompanied with an empirical semivariogram-based Kriging method. Since there are many factors causing inaccurate measurement results, a noise-resistant and region splitting approach was employed to improve the accuracy and validity of the strain measurement of composite material at the micro-level. Specimens with a few unidirectional fibers were fabricated, and the microscopic strain fields under tensile load were investigated using K-DIC. A detailed finite element model based on the specimen geometry was generated and its strain fields were numerically analyzed. The experimental and numerical results obtained by a standard approach like the moving least square method-based method were compared to discuss both the accuracy and effectiveness of the proposed approach.
Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks

Driving is a dynamic activity, which requires quick reflexes and decision making in order to respond to sudden changes in traffic conditions.Alcohol consumption impairs motor and cognitive skills, and causes many driving-related accidents annually.Passive methods of proactively detecting drivers who are too drunk to drive in order to notify them and prevent accidents, have recently been proposed.The effects of alcohol on a drinker's gait (walk) is a reliable indicator of their intoxication level.In this paper, we investigate detecting drinkers' intoxication levels from their gait by using neural networks to analyze sensor data gathered from their smartphone.Using data gathered from a large controlled alcohol study, we perform regression analysis using a Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architectures to predict a person's Blood Alcohol Concentration (BAC) from their smartphone's accelerometer and gyroscope data.We innovatively proposed a comprehensive suite of preprocessing techniques and model-specific extensions to vanilla CNN and bi-LSTM models, which are well thought out and adapted specifically for BAC estimation.Our Bi-LSTM architecture achieves an RMSE of 0.0167 and the CNN architecture achieves an RMSE of 0.0168, outperforming state-of-the-art intoxication detection models using Bayesian Regularized Multilayer Perceptrons (MLP) (RMSE of 0.017) and the Random Forest (RF), with hand-crafted features.Moreover, our models learn features from raw sensor data, obviating the need for hand-crafted features, which is time consuming.Moreover, they achieve lower variance across folds and are hence more generalizable.
Recognition of error correcting codes based on CNN with block mechanism and embedding

An error correcting code type recognition technique based on a deep learning approach is proposed in this paper. This problem could be addressed in the context of non-cooperative communications or adaptive coding and modulation. Inspired by text classification, we proposed a convolutional neural network (CNN) model improved by embedding and block mechanism to classify the linear block code, convolutional code, and turbo code with the only knowledge of the noisy information streams. It achieves higher recognition performance than the algorithms which are based on traditional deep learning and rank calculation. Further results show that the performance is greatly affected by block length and the dimension of the embedding layer. In a nutshell, the CNN with block mechanism and embedding is a promising feature extraction and classification technique, and it is suitable for the recognition of different kinds of communication signals.
Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions. When modeling complex sequences, attention mechanisms are regarded as the established approach to support deep neural networks with intrinsic interpretability. This paper focuses on the emerging trend of specifically designing diagnostic datasets for understanding the inner workings of attention mechanism based deep learning models for multivariate forecasting tasks. We design a novel benchmark of synthetically designed datasets with the transparent underlying generating process of multiple time series interactions with increasing complexity. The benchmark enables empirical evaluation of the performance of attention based deep neural networks in three different aspects: (i) prediction performance score, (ii) interpretability correctness, (iii) sensitivity analysis. Our analysis shows that although most models have satisfying and stable prediction performance results, they often fail to give correct interpretability. The only model with both a satisfying performance score and correct interpretability is IMV-LSTM, capturing both autocorrelations and crosscorrelations between multiple time series. Interestingly, while evaluating IMV-LSTM on simulated data from statistical and mechanistic models, the correctness of interpretability increases with more complex datasets.
A Non-Linear Convolution Network for Image Processing

This paper proposes a new neural network structure for image processing whose convolutional layers, instead of using kernels with fixed coefficients, use space-variant coefficients. The adoption of this strategy allows the system to adapt its behavior according to the spatial characteristics of the input data. This type of layers performs, as we demonstrate, a non-linear transfer function. The features generated by these layers, compared to the ones generated by canonical CNN layers, are more complex and more suitable to fit to the local characteristics of the images. Networks composed by these non-linear layers offer performance comparable with or superior to the ones which use canonical Convolutional Networks, using fewer layers and a significantly lower number of features. Several applications of these newly conceived networks to classical image-processing problems are analyzed. In particular, we consider: Single-Image Super-Resolution (SISR), Edge-Preserving Smoothing (EPS), Noise Removal (NR), and JPEG artifacts removal (JAR).