Reviewerly/exHarmony huggingface dataset:
Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network

Forward error correction coding is the most common way of channel coding and the key point of error correction coding. Therefore, the recognition of which coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, the receiver cannot know the types of channel coding previously in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. In the paper, we come up with a neoteric method to identify the types of FEC codes based on Recurrent Neural Network (RNN) under the condition of non-cooperative communication. The algorithm classifies the input data into Bose-Chaudhuri-Hocquenghem (BCH) codes, Low-density Parity-check (LDPC) codes, Turbo codes and convolutional codes. So as to train the RNN model with better performance, the weight initialization method is optimized and the network performance is improved. The experimental result indicates that the average recognition rate of this model is 99% when the signal-to-noise ratio (SNR) ranges from 0 dB to 10 dB, which is in line with the requirements of engineering practice under the condition of non-cooperative communication. Moreover, the comparison of different parameters and models show the effectiveness and practicability of the algorithm proposed.
A Fish Retina-Inspired Single Image Dehazing Method

Outdoor images are significantly degraded by bad weather conditions, such as fog and dust, which seriously limits efficient information extraction by computer vision applications. Most existing weather-degraded hazy image enhancement methods ignore the wavelength dependence of the scattering coefficient and therefore cannot well handle the colorized haze in which the medium transmission varies in different color channels. In this work, we propose a fish retina-inspired method to solve this problem. Fish have special retinal mechanisms for extracting useful information from underwater environments where photons are scattered and absorbed spatially introducing wavelength-dependent degradation of visibility. Due to the similarity between suspensions in water and aerosols in the air, these mechanisms should also be suitable for processing hazy scenes. The proposed method imitates bipolar cells with scene-dependent center-surround receptive fields to eliminate redundant information, ganglion cells with nonlinear processing to boost contrast, and the centrifugal pathway from amacrine cells to the horizontal cells to adaptively protect visual information from excessive lateral suppression. Compared with state-of-the-art methods on various images degraded by fog, haze, or dust, the proposed method shows quite competitive performance both qualitatively and quantitatively.
Explainable AI (XAI) Models Applied to Planning in Financial Markets

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Single-Image Super Resolution Using Convolutional Neural Network

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. CubeSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation and research and have the potential to enhance satellite constellation resilience. However, the inherent size, weight and power limitations of CubeSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution. Low resolution limits the utility of CubeSat images for military planning purposes and national intelligence applications. This paper implements a super-resolution deep learning architecture and proposes potential applications to CubeSat imagery.
Black Widow Crawler for TOR network to search for criminal patterns

This work aims to develop the Black Widow crawler focused on the Tor network, which searches, analyzes, and indexes websites containing criminal patterns. Also, a comparison of the results of the Black Widow crawler with one of the best crawlers found during information gathering is carried out, to confirm the possibility of an improvement in the current crawlers of the Tor network. The experiments demonstrate that the current crawlers can be improved. Compared to the current crawler, the Black Widow crawler achieved a 240% improvement in the number of indexed secret services.
MODES: model-based optimization on distributed embedded systems

Abstract The predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math> that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math> -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math> -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math> by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ( $$\textit{MODES}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math> -B), run-time efficiency ( $$\textit{MODES}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math> -I), and statistical stability for both modes, $$\textit{MODES}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MODES</mml:mi></mml:math> outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.
Improved Structured Encryption for SQL Databases via Hybrid Indexing

We introduce a new technique for indexing joins in encrypted SQL databases called partially precomputed joins which achieves lower leakage and bandwidth than those used in prior constructions. These techniques are incorporated into state-of-the-art structured encryption schemes for SQL data, yielding a hybrid indexing scheme with both partially and fully precomputed join indexes. We then introduce the idea of leakage-aware query planning by giving a heuristic that helps the client decide, at query time, which index to use so as to minimize leakage and stay below a given bandwidth budget. We conclude by simulating our constructions on real datasets, showing that our heuristic is accurate and that partially-precomputed joins perform well in practice.
A provably secure and public auditing protocol based on the bell triangle for cloud data

With the rapid development of cloud computing, cloud storage has been accepted as a convenient service by an increasing number of organizations and individuals. However, when users lose control of their data, ensuring that the uploaded data are securely stored by the cloud becomes a critical requirement. A key research challenge associated with existing designs of data auditing protocols is the limitation of application scenarios due to low auditing efficiency. In order to address this problem, we propose a provably secure and public auditing protocol based on Bell triangle for cloud data with blockless verification and batch auditing. We introduce the associative property of Bell triangle; design an index conversion algorithm to convert the one-dimensional index to a two-dimensional index ensuring that the block signature satisfies the Bell triangle requirements, and develop a recursive association algorithm to ensure relevance between the signatures of adjacent file blocks. This protocol offers significant advantages over existing protocols in terms of auditing efficiency and security in a multi-user and high-volume environment. In addition, the security analysis and the results of the experiment indicate that the protocol is secure and efficient.
EM-based extended object tracking without a priori extension evolution model

This paper proposes a novel extended object tracking (EOT) problem, in which the three quantities of object extension (OE), including semi-axis lengths and orientation, are depicted more realistic, rather than assuming prior evolution models as in the traditional methods. By the fact that the semi-axis lengths are intrinsic parameters of object size and the orientation is a state-dependent parameter, OE quantities are treated as parameters to be identified. Such consideration is not only more consistent with the OE practical meaning, but also reduces the uncertainty induced by prior parameter settings. The resultant EOT problem brings out the new challenge: deep coupling between kinematic state and OE, which is difficult to directly derive an Bayesian solution. In the expectation maximization framework, an optimization scheme is developed for joint OE identification and kinematic state estimation. Simulation results show the superiority of the proposed method compared with the state-of-the-art ones.
Development of a Voice-controlled Intelligent Wheelchair System using Raspberry Pi

This paper proposed a voice recognition-based intelligent wheelchair system for physically disabled people who unable to control the wheelchair by their upper and lower limbs. This development employs voice command to controls the movement of the wheelchair in different directions. The android device is used as microphone to be connected to the Google Assistant prior to data processing by the Raspberry Pi. The Raspberry Pi will then command the servo motors to act accordingly. This system offers automatic obstacle detection via the use of an infrared sensor which aids the user to apply momentary brake button upon the obstacle detection. The proposed development has been evaluated in terms of movement accuracy and system responsiveness upon the commands given, in both control mode i.e., voice controller and joystick controller. For the movement accuracy, the results indicate that the minimum accuracies are 90% and 95% for the voice recognition controller and joystick controller, respectively. For the system responsiveness, the system response in less than 1.2 seconds for the voice recognition controller and less than 0.65 seconds for the joystick controller. With this noticeable performance, the intelligent wheelchair enables users with limited control over their limbs to maneuver the wheelchair using voice commands.
Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self-correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self-correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.
Secure Role and Rights Management for Automotive Access and Feature Activation

The trend towards fully autonomous vehicles changes the concept of car ownership drastically. Purchasing a personal car becomes obsolete. Thus, business models related to feature activation are gaining even higher importance for car manufacturers in order to retain their customers. Various recent security incidents demonstrated however that vehicles are a valuable attack goal ranging from illegal access to car features to the theft of the whole vehicles.
HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.
Accelerating numerical simulation of continuous-time Boolean satisfiability solver using discrete gradient

To explore the design of analog computing devices, modeling the problem-solving process as a continuous-time dynamical system is important. Ercsey-Ravasz and Toroczkai [Nature Physics 7, 966 (2011)] proposed such a model for solving the Boolean satisfiability (SAT) problem. This system consists of a gradient system that minimizes the potential function reduced from the SAT problem and a system for achieving a temporal variation of the potential function to avoid the problem of non-solution local minima. Although the ability of the system to find a solution to the SAT problem is demonstrated, its large simulation cost hinders theoretical research towards the physical realization and limits its utility on digital computers. This is due to the necessity of small time steps to maintain the numerical stability of the simulation. In this study, we propose a fast and stable numerical simulation algorithm for this solver using the discrete gradient method to allow a larger time step. We also propose an adaptive time step control method for this system. The proposed algorithm achieves a faster simulation by a factor of approximately 100, compared to conventional methods. Although taking a large time step degrades the accuracy, we found that it does not necessarily degrade the performance as a SAT solver; this indicates the new utility of the discrete gradient apart from the conventional studies of numerical simulation algorithms that pursue accuracy as well as efficiency.
Stability control for end effect of mobile manipulator in uneven terrain based on active disturbance rejection control

Purpose When the mobile manipulator is traveling on an unconstructed terrain, the external disturbance is generated. The load on the end of the mobile manipulator will be affected strictly by the disturbance. The purpose of this paper is to reject the disturbance and keep the end effector in a stable pose all the time, a control method is proposed for the onboard manipulator. Design/methodology/approach In this paper, the kinematics and dynamics models of the end pose stability control system for the tracked robot are built. Through the guidance of this model information, the control framework based on active disturbance rejection control (ADRC) is designed, which keeps the attitude of the end of the manipulator stable in the pitch, roll and yaw direction. Meanwhile, the control algorithm is operated with cloud computing because the research object, the rescue robot, aims to be lightweight and execute work with remote manipulation. Findings The challenging simulation experiments demonstrate that the methodology can achieve valid stability control performance in the challenging terrain road in terms of robustness and real-time. Originality/value This research facilitates the stable posture control of the end-effector of the mobile manipulator and maintains it in a suitable stable operating environment. The entire system can normally work even in dynamic disturbance scenarios and uncertain nonlinear modeling. Furthermore, an example is given to guide the parameter tuning of ADRC by using model information and estimate the unknown internal modeling uncertainty, which is difficult to be modeled or identified.
Dependency Parsing-based Entity Relation Extraction over Chinese Complex Text

Open Relation Extraction (ORE) plays a significant role in the field of Information Extraction. It breaks the limitation that traditional relation extraction must pre-define relational types in the annotated corpus and specific domains restrictions, to realize the goal of extracting entities and the relation between entities in the open domain. However, with the increase of sentence complexity, the precision and recall of Entity Relation Extraction will be significantly reduced. To solve this problem, we present an unsupervised Clause_CORE method based on Chinese grammar and dependency parsing features. Clause_CORE is used for complex sentences processing, including decomposing complex sentence and dynamically complementing sentence components, which can reduce sentences complexity and maintain the integrity of sentences at the same time. Then, we perform dependency parsing for complete sentences and implement open entity relation extraction based on the model constructed by Chinese grammar rules. The experimental results show that the performance of Clause_CORE method is better than that of other advanced Chinese ORE systems on Wikipedia and Sina news datasets, which proves the correctness and effectiveness of the method. The results on mixed datasets of news data and encyclopedia data prove the generalization and portability of the method.
A Tool for Moving Blockchain Computations Off-Chain

This paper proposes a new approach and tool for blockchain software developers and architects to use for determining which computations of a smart contract can be effectively done off-chain without loss of trust and how they can be moved off-chain automatically. Our approach uses Finite State Machines (FSMs) and Hierarchical State Machines (HSMs) modeling in order to create smart contract patterns using graphs and then use pattern properties to identify which parts of the smart contracts should be considered for moving off-chain. Once software execution transits into the pattern's entry state, off-chain execution continues until the execution transits into the pattern's exit state, when execution continues on-chain. This paper further describes a tool for (1) FSM input and visualization, its analysis, and automatic transformation into a smart contract, and (2) an interface to support the developers' decision to optimally off-chain computations and to (3) define the communications between on-chain and off-chain computations.
Towards Privacy-preserving Framework for Non-Intrusive Load Monitoring

The proliferation of smart meters has led to a vast collection of fine-grained energy data. While energy consumption data has many useful applications that can improve energy-efficiency, it also leaks private information, such as user behavior and occupancy. Such privacy concerns may prevent users from sharing raw energy data. In recent years, federated learning has emerged as a solution that mitigates the problem of sharing raw data by training models in a decentralized manner. In this framework, all data is local to the client, and a centralized server builds a model by exchanging model parameters. Unfortunately, studies have shown that federated models are also prone to privacy attacks. Thus, in this paper, we explore the use of differentially private federated learning (DPFL) and study its effectiveness in mitigating privacy risk for energy-efficiency applications. In particular, we study the effectiveness of the DPFL on Non-Intrusive Load Monitoring (NILM) models' accuracy and whether it can decrease privacy risks. In doing so, we develop an open-source framework that streamlines NILM models' training in a privacy-preserving federated manner. Further, we evaluate our approach on different datasets and compare them against the non-private federated approach as a baseline.
Depth distillation: unsupervised metric depth estimation for UAVs by finding consensus between kinematics, optical flow and deep learning

Estimating precise metric depth is an essential task for UAV navigation, which is very difficult to learn unsupervised without access to odometry. At the same time, depth recovery from kinematics and optical flow is mathematically precise, but less numerically stable and robust, especially in the focus of expansion areas. We propose a model that combines the analytical, vision-with-odometry approach, with deep unsupervised learning, into a single formulation for metric depth estimation, which is both fast and accurate. The two pathways – analytical and data-driven – form a robust ensemble, which provides supervisory signal to a single deep net that distills the consensus between scene geometry, pose, kinematics, camera intrinsics and the input RGB. The distilled net has low runtime and memory costs, being suitable for embedded devices. We validate our results against an off-the-shelf SfM-based solution. We also introduce a new real-world dataset of almost 20 minutes of continuous UAV flight, on which we demonstrate better accuracy and capabilities than the deep learning and analytical approaches.
Blockchain Application in Healthcare

Healthcare data can be created, copied and modified faster than ever before. Data is the fuel behind more efficient care. Unfortunately, healthcare today suffers from compartmentalized and fragmented data, delayed communications, and disparate workflow tools caused by the lack of interoperability. Blockchain technology maybe the vehicle to solve these problems, as besides it possesses key properties, such as immutability, decentralization, and transparency, which potentially address pressing issues in healthcare, such as incomplete records at point of care and difficult access to patients' own health information. An efficient and effective healthcare system should be interoperable, allowing software apps and technology platforms to communicate securely and seamlessly, exchange data, and use the exchanged data across health organizations and app vendors. In this paper, we address the aspect of storage of Electronic Medical Records using smart contracts. The implementation is done using Ethereum's blockchain network to solve problems faced by centralized platforms. The results were encouraging, and the implementation was also successful in reducing storage costs by storing large files off-chain (IPFS).