Reviewerly/exHarmony huggingface dataset:
Post-selection Based Generalized Kennedy Receiver for Discriminating Binary Coherent States

The generalized Kennedy receiver can be used to improve the secret key rate of continuous variable quantum key distribution protocol and thus attracts much attention recently. In this letter, we analytically study the performance of the post-selection based generalized Kennedy receiver in the presence of thermal noise. To improve the performance of the generalized Kennedy receiver, we optimize the displacement operation for different post-selection parameter and different thermal noises. Numerical results show that the thermal noise can greatly degrade the performance of the post-selection based generalized Kennedy receiver. Besides, the resistance of the generalized Kennedy receiver to the thermal noise can be improved by choosing an appropriate post-selection parameter.
Communication-Aware RRT*: Path Planning for Robotic Communication Operation in Obstacle Environments

In this paper, we are interested in path optimization for robotic communication operations in obstacle environments. Consider a robot that needs to perform a given communication task (e.g., data uploading, broadcasting, or relaying) while navigating from a start position to a designated final position, avoiding obstacles, and minimizing its total motion and communication costs. Our goal is to develop a general path planning algorithm applicable to various robotic communication scenarios in which a robot operates in realistic channel fading environments and in the presence of obstacles. We show how we can adapt the traditional Rapidly-Exploring Random Tree Search Star (RRT*) path planning algorithm to jointly consider both communication and motion objectives in realistic channel environments that contain obstacles. We further show that our proposed approach can provide theoretical optimality guarantees while being computationally efficient. We extensively evaluate our proposed approach in realistic wireless channel environments for various transmission settings and communication tasks. The results demonstrate the efficacy of our proposed approach.
Road Garbage Segmentation With Deep Supervision and High Fusion Network for Cleaning Vehicles

An intelligent cleaning vehicle improves the efficiency of road cleaning to a great extent. In this case, road garbage recognition is fundamental and crucial. Usually, the pebbles are too small, and the sand has an inconspicuous boundary and unfixed shape on the roads. These issues make the stones and sand too hard to be detected by a garbage recognition system. Hence, we designed a novel semantic segmentation network that acquires the areas and categories of road garbage. We designed a Deep Supervision and High Fusion (DSHF) block to improve the road garbage segmentation accuracy. The designed block with a backbone network of HFCN and UNet++ is comparable with a model that only has either a deep supervision block or a high fusion block. According to the collected road garbage segmentation dataset comprising four categories (stones, leaves, sand and bottles), the model we designed has improved the metric value of MPA by 3% over the state-of-art methods and achieved the highest MIoU (Mean Intersection over Union) with the value of 77.92%. The results of our experiments show that the semantic segmentation of road garbage is feasible and that the proposed DSHF block is practical for improving the segmentation effect of road garbage.
Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks

For football players who participate in sports, the word "health" is extremely important. Athletes cannot create their own value in competitive competitions without a strong foundation. Scholars have paid a lot of attention to athlete health this year, and many analysis methods have been proposed, but there have been few studies using neural networks. As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. To begin, this article employs wearable sensors to collect health data from football players. The time step data are then fed into a recurrent neural network to extract deep features, followed by the health prediction results. The collected football player health dataset is used in this paper to conduct experiments. The simulation results prove the reliability and superiority of the proposed algorithm. Furthermore, the algorithm presented in this paper can serve as a foundation for the football team's and coaches' scientific training plans.
DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation

Mobile captured images can be aligned using their gyroscope sensors. Optical image stabilizer (OIS) terminates this possibility by adjusting the images during the capturing. In this work, we propose a deep network that compensates for the motions caused by the OIS, such that the gyroscopes can be used for image alignment on the OIS cameras. To achieve this, we first record both videos and gyroscope readings with an OIS camera as training data. Then, we convert gyroscope readings into motion fields. Second, we propose an Essential Mixtures motion model for rolling shutter cameras, where an array of rotations within a frame are extracted as the ground-truth guidance. Third, we train a convolutional neural network with gyroscope motions as input to compensate for the OIS motion. Once finished, the compensation network can be applied for other scenes, where the image alignment is purely based on gyroscopes with no need for images contents, delivering strong robustness. Experiments show that our results are comparable with that of non-OIS cameras, and outperform image-based alignment results with a relatively large margin. Code and dataset is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lhaippp/DeepOIS</uri> .
Group authentication for cloud-to-things computing: Review and improvement

Group authentication enables a set of users to mutually authenticate each other without the help of a coordinating manager, which has proven effective for the new framework of cloud-to-things computing. Recently many group authentication schemes have been proposed using threshold secret sharing technique. In this article we review these schemes and show that some of group authentication schemes based on secret sharing are insecure. Specifically we develop an impersonation attack against such schemes. This attack allows an outsider with no credential to successfully authenticate herself to a group of users. The idea behind our attack is quite simple: in the threshold setting, when the size of the group is larger than the threshold an outsider can compute a linear combination of honest users’ tokens which validates her identity though she is actually not a registered member in the group. Finally we propose three types of security enhancement to fix the vulnerability. Our improved group authentication schemes avoid the above attack by breaking the linearity of threshold secret sharing, using the technique of signature, splitting communication and commitment scheme.
Federated user activity analysis via network traffic and deep neural network in mobile wireless networks

User activity analysis (UAA) is a promising technology for network management and network security via network traffic. Recently, deep learning (DL) has been applied into network traffic analysis for outstanding performance. These previously proposed network traffic analysis methods generally requires huge amounts of data from network users. In detail, the common methods are to collect these traffic data collected from users into cloud server for processing and analysis, which generally have great performance and are denoted as centralized methods. However, one of the biggest drawbacks of these methods is the risk of data privacy disclosure. Thus, we proposed a federated learning-based UAA method (which is named as FedeUAA) for reducing the risk of data leakage in mobile wireless networks. FedeUAA method has no requirement to upload data to cloud server, while it directly trains the DL models in local devices, and only needs to upload the knowledge (model weight or model gradient) rather than data. Simulation results demonstrated that the FedeUAA method can effectively reduce the risk of data privacy disclosure with slight performance loss.
ChartNavigator: An Interactive Pattern Identification and Annotation Framework for Charts

Patterns in charts refer to interesting visual features or forms. Identifying patterns not only helps analysts understand the ‘shape’ of the data but also supports better and faster decision-making. Existing solutions for identifying patterns in charts require a large number of labeled data instances, making it intractable without user supervision. In this paper, we propose ChartNavigator, an interactive pattern identification and annotation framework for unlabeled visualization charts. ChartNavigator leverages a novel chart-sensitive deep factor model to map patterns into a low-dimensional factor representation space, and facilitates rich analysis with the derived representations. We design and implement a visual interface to support efficient identification and annotation of potential patterns in charts. Evaluations with multiple datasets show that our approach outperforms the baseline models in identifying and annotating patterns
Dynamic Texture Synthesis By Incorporating Long-range Spatial and Temporal Correlations

The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos. The major drawback of existing dynamic texture synthesis models comes from poor treatment of the long-range texture correlation and motion information. To address this problem, we incorporate a new loss term, called the Shifted Gram loss, to capture the structural and long-range correlation of the reference texture video. Furthermore, we introduce a frame sampling strategy to exploit long-period motion across multiple frames. With these two new techniques, the application scope of existing texture synthesis models can be extended. That is, they can synthesize not only homogeneous but also structured dynamic texture patterns. Thorough experimental results are provided to demonstrate that our proposed dynamic texture synthesis model offers state-of-the-art visual performance.
BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization

This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner. In this paper, we tackle the problem of information heterogeneity arising in multi-agent networks where the placement of informative agents plays a crucial role in the learning dynamics. Specifically, we propose BayGo, a novel fully decentralized joint Bayesian learning and graph optimization framework with proven fast convergence over a sparse graph. Under our framework, agents are able to learn and communicate with the most informative agent to their own learning. Unlike prior works, our framework assumes no prior knowledge of the data distribution across agents nor does it assume any knowledge of the true parameter of the system. The proposed alternating minimization based framework ensures global connectivity in a fully decentralized way while minimizing the number of communication links. We theoretically show that by optimizing the proposed objective function, the estimation error of the posterior probability distribution decreases exponentially at each iteration. Via extensive simulations, we show that our framework achieves faster convergence and higher accuracy compared to fully-connected and star topology graphs.
Sample-based Federated Learning via Mini-batch SSCA

In this paper, we investigate unconstrained and constrained sample-based federated optimization, respectively. For each problem, we propose a privacy preserving algorithm using stochastic successive convex approximation (SSCA) techniques, and show that it can converge to a Karush-Kuhn-Tucker (KKT) point. To the best of our knowledge, SSCA has not been used for solving federated optimization, and federated optimization with nonconvex constraints has not been investigated. Next, we customize the two proposed SSCA-based algorithms to two application examples, and provide closed-form solutions for the respective approximate convex problems at each iteration of SSCA. Finally, numerical experiments demonstrate inherent advantages of the proposed algorithms in terms of convergence speed, communication cost and model specification.
Local Path Planning Method Considering Blind Spots Based on Cost Map for Wheeled Mobile Robot

This paper proposes a local path planning method considering blind spots based on the cost map for a wheeled mobile robot. In this research, a laser range finder (LRF) is utilized to recognize environmental information in real time. Blind spots occur when corners or obstacles are present in the environment. If humans or objects move from the blind spots toward the robot, collisions might occur. To avoid this collision, this paper describes the path planning that takes into account the blind spots. The position of a human in the blind spots is estimated from environment information measured by LRF. It is assumed that a human can move in all directions from these estimated positions. Therefore, in the cost map, the cost of the estimated positions of the human is represented as a circle. This cost is used for the calculation of path planning by dynamic window approach. Using the proposed method, the path plan of the robot is achieved by considering its blind spots. The effectiveness of the proposed method is further demonstrated through simulations and experiments.
Learning decision thresholds for risk stratification models from aggregate clinician behavior

Abstract Using a risk stratification model to guide clinical practice often requires the choice of a cutoff—called the decision threshold—on the model’s output to trigger a subsequent action such as an electronic alert. Choosing this cutoff is not always straightforward. We propose a flexible approach that leverages the collective information in treatment decisions made in real life to learn reference decision thresholds from physician practice. Using the example of prescribing a statin for primary prevention of cardiovascular disease based on 10-year risk calculated by the 2013 pooled cohort equations, we demonstrate the feasibility of using real-world data to learn the implicit decision threshold that reflects existing physician behavior. Learning a decision threshold in this manner allows for evaluation of a proposed operating point against the threshold reflective of the community standard of care. Furthermore, this approach can be used to monitor and audit model-guided clinical decision making following model deployment.
An ontology-based approach for preprocessing in machine learning

Increasing pressure on internationally operating companies leads to the application of novel technologies, e.g., Machine Learning models. However, Machine Learning algorithms require preprocessing, i.e., data cleaning, which is time consuming and requires domain-specific knowledge. Formalized knowledge bases capture such domain-specific knowledge in a computer-interpretable way and have the potential to reduce manual efforts for this process. This paper presents a framework for semantic preprocessing, which is evaluated at the example of an industrial use case from the semiconductor industry.
SSIM-based sparse image restoration

In this paper, we provide a sparse image restoration algorithm with a SSIM-based objective function. The proposed technique is a modification to the SSIM-inspired OMP (iOMP) and, and it has two parallel sparse restoration paths. One of them is L2-sense OMP and the other is SSIM-sense OMP (iOMP). Both paths intersects only at the starting point and gives different quality levels after each iteration. This distinction enables us to select the coefficients of the best quality and to avoid the uncertainty issue of iOMP. From the point of view of the SSIM levels, the conducted experiments proved that, the proposed methodology works better than iOMP and OMP. Also, the performance of this method is checked for significance by the t-test, and the obtained results proved that the method works well especially for large images and the data-independent based dictionary.
Reversible 2:4 Decoder Using Universal Fredkin Gate

The technologies like quantum computing, Nanotechnology is emerging due to the greater research in reversible logic. It has the power reduction capability the same mechanism is also having in the quantum computing. Designing a quantum circuit among various combinational circuits the decoder has capability to change binary information of input lines to distinct output lines and also there are many applications where decoder is the main basic component. In this paper a comprehensive introduction of basic gates used to design n to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> Universal Quantum decoder, here n represents the no of inputs are mentioned. The proposed 2:4 decoder circuit is designed using a universal reversible Fredkin gates and Feynman gate. The proposed work is simulated using IBM Quantum Composer. In comparison to existing quantum decoders the proposed decoder design has optimum values in all the parameters considered.
Word Spotting of Handwritten Hindi Scripts by Circular Histogram of Oriented Displacement (CHOD) Features

This paper presents a segmentation-based word spotting technique for handwritten Hindi scripts using newly proposed shape descriptor called circular histogram of oriented displacement (CHOD). A word spotting model is developed by training multi-layer perceptron (MLP) with CHOD features. Metrics of evaluation used are k-precision (kPr) and mean average precision (MAP). The proposed technique has been evaluated on two datasets consisting of segmented handwritten Hindi (Devanagari) word images and has posted very good performance. This is an indication of CHOD features having good discriminative powers. The proposed technique has been compared with other techniques for the same datasets and found to compare very well.
Structural learning and estimation of joint causal effects among network-dependent variables

Abstract Bayesian networks in the form of Directed Acyclic Graphs (DAGs) represent an effective tool for modeling and inferring dependence relations among variables, a process known as structural learning . In addition, when equipped with the notion of intervention , a causal DAG model can be adopted to quantify the causal effect on a response due to a hypothetical intervention on some variable. Observational data cannot distinguish between DAGs encoding the same set of conditional independencies (Markov equivalent DAGs), which however can be different from a causal perspective. In addition, because causal effects depend on the underlying network structure, uncertainty around the DAG generating model crucially affects the causal estimation results. We propose a Bayesian methodology which combines structural learning of Gaussian DAG models and inference of causal effects as arising from simultaneous interventions on any given set of variables in the system. Our approach fully accounts for the uncertainty around both the network structure and causal relationships through a joint posterior distribution over DAGs, DAG parameters and then causal effects.
Text2PyCode: Machine Translation of Natural Language Intent to Python Source Code

Natural Language Processing has improved tremendously with the success of Deep Learning. Neural Machine Translation (NMT) has arisen as the most powerful with the power of Deep Learning. The same idea has been recently applied to source code. Code Generation (CG) is the task of generating source code from natural language input. This paper introduces a Python parallel corpus of natural language intent and source code pairs. It also proposes a Code Generation model based on Transformer architecture used for NMT by using code tokenization and code embeddings on the custom parallel corpus. The proposed architecture achieved a good BLEU score of 32.4 and Rouge-L of 82.1, which is on par with natural language translation.
Talking to Autonomous Drones: Command and Control Based on Hierarchical Task Decomposition

With increasing onboard intelligence and planning capabilities, autonomous unmanned aircraft system (UAS) can execute a wide spectrum of tasks ranging from low-level flight tasks to complex abstract mission tasks. Regardless of their onboard intelligence, most state-of-the-art UAS are commanded with waypoint-based command and control (C2) interfaces. However, waypoint-based interfaces are not well suited to represent mission tasks at different levels of abstraction. In this work, we address the problem of designing a C2 interface for autonomous UAS at a conceptual level. We propose a C2 interface that allows operating UAS at different levels of autonomy using task decomposition and a hierarchical data format for transmission. This interface is designed to enable transparent and consistent communication of mission tasks between the ground control station and the onboard systems. We demonstrate the application of the interface for two different UAS mission scenarios in detail. The proposed interface is designed to supersede waypoint-based C2 interfaces for the next generation of UAS providing high-level autonomy through onboard intelligence.
Evolutionary Convolutional Neural Network Optimization with Cross-Tasks Transfer Strategy

Convolutional neural networks (CNNs) have shown great success in a variety of real-world applications and the outstanding performance of the state-of-the-art CNNs is primarily driven by the elaborate architecture. Evolutionary convolutional neural network (ECNN) is a promising approach to design the optimal CNN architecture automatically. Nevertheless, most of the existing ECNN methods only focus on improving the performance of the discovered CNN architectures without considering the relevance between different classification tasks. Transfer learning is a human-like learning approach and has been introduced to solve complex problems in the domain of evolutionary algorithms (EAs). In this paper, an effective ECNN optimization method with cross-tasks transfer strategy (CTS) is proposed to facilitate the evolution process. The proposed method is then evaluated on benchmark image classification datasets as a case study. The experimental results show that the proposed method can not only speed up the evolutionary process significantly but also achieve competitive classification accuracy. To be specific, our proposed method can reach the same accuracy at least 40 iterations early and an improvement of accuracy for 0.88% and 3.12% on MNIST-FASHION and CIFAR10 datasets compared with ECNN, respectively.