Reviewerly/exHarmony huggingface dataset:
Wavelet Transform based Multiple Image Watermarking Technique

Abstract In satellite communication the information’s like frequency, Polarization, Data format, Timing information are need to be protected for secure communication. Digital watermarking is the technique which provides secure way of communication in which the secret information or watermark can be transmitted safely. Wavelet Transform is playing an increasingly important role in watermarking due to its good spatial-frequency characteristics, and its wide applications in the image and video coding standards. In this work a novel Hybrid Steerable Pyramid with Discrete Wavelet Transform (SP+DWT) based multiple watermarking is attempted for colour images. In order to increase the robustness of the algorithm Arnold transform and Fibonacci transform based image scrambler is applied to watermarks before embedding in to cover image. The performance of the proposed scheme is validated for various attacks, such as Gaussian noise, Salt &amp; Pepper Noise, Rotation, Translation and Speckle Noise. The proposed method is withstood for all the noise attacks and the performance of the proposed algorithm is measured using Peak Signal to Noise Ratio (PSNR), Normalized Correlation (NC) and Similarity Measure (SM). Pearson Independent component Analysis (ICA) is applied at the receiving end to extract watermarks from the input image. To analyse the inclusion of image scrambler the results of proposed algorithm is compared with same algorithm without image scrambler applied. The performance of proposed technique is also compared with other wavelet transforms like Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT) and Integer Wavelet Transform (IWT). Simulation results such as PSNR,NC,SM shows that hybrid SP+DWT based watermarking technique and extraction using Pearson ICA results in high imperceptible, better security and robustness, when compared to DWT, WPT and IWT based watermarking and extraction using Pearson ICA.
Function-Level Dynamic Monitoring and Analysis System for Smart Contract

The close integration of blockchain and smart contract technology has become an important foundation for current trusted applications. High-quality, high-efficiency and high-security codes have become basic requirements for smart contract applications because they are not easy to be modified after being deployed on blockchain. This paper proposes a function-level dynamic monitoring and analysis method for smart contract, and implements a prototype system. The method adds a “shadow stack” and related data structures to virtual machine of testing blockchain platform by analyzing the principle of function management with original stack, then monitors the bytecode after code instrumentation, records the function calling relationships as well as the relevant metrics of time, instruction number and gas consumption. The prototype system identifies contract inefficient behaviors using visualization and intelligent analysis methods, then forms a smart contract optimization closed loop through iterative improvement. Finally, the paper verified the high feasibility and applicability of the monitoring and analyzing method as well as prototype system's performance through experiments.
Inquisitive Dynamic Epistemic Logic in a Non-classical Setting

This paper studies the operations of public announcement of statements and public utterance of questions in the context of substructutral inquisitive epistemic logic. It was shown elsewhere that the logical laws governing the modalities of knowing and entertaining from standard inquisitive epistemic logic generalize smoothly to substructural logics. In this paper we show that the situation is different with the reduction axioms that in the standard setting govern the modality of public announcement/utterance. The standard reduction axioms depend on some features of classical logic that are not preserved in substructural logics. Using an additional auxiliary modality, we show how to overcome this obstacle and formulate an alternative set of reduction axioms for the public announcement/utterance modality that can be used even in the context of our general non-classical setting.
Extraction of Information from Handwriting using Optical Character recognition and Neural Networks

Image Processing is a vital tool when one is dealing with several images and wishes to perform several complex actions on the same. With advances in technologies, one can now compress, manipulate, extract required information, etc. from any image one wants to. One such application of Image processing is detecting handwritten text and converting it to a digital text format. The main objective is to bridge the gap between the actual bit of paper and the digital world and in doing so, one can operate on the digital data much faster as compared to the actual data. Hence, in this paper, we aim to implement the detection of handwritten text via Optical Character Recognition (OCR). The entire paper will be implemented on TensorFlow. This research work has also analyzed various results and taken appropriate dataset to train the model. Further, the importance of this paper lies in the fact that it can facilitate and open various unexplored avenues. The key novelty of the paper lies in the fact that the data-set used is comprehensive which helps us to produce better result. In addition, the paper successfully analyzes handwritten scripts and extracts it in digital form. Analyzing the text can help combat forgery, understand certain temperaments of the person writing the text, and so on. Coupled with this, this paper has successfully implemented an improved version as compared to the pre-existing solutions by using the convergence of convoluted neural networks (CNN) and the Recurrent Neural Network (RNN).
An Efficient Accelerator of the Squaring for the Verifiable Delay Function Over a Class Group

Nowadays, the verifiable delay function (VDF) is widely regarded as the core function for the next-generation blockchain system because it is slow to evaluate but easy to verify. In general, the squaring operation takes a significant proportion of VDF computation. Moreover, the squaring over a class group, including large-number extended greatest common divisor (GCD) computations, divisions, and multiplications, is extremely hard to be accelerated in hardware. In this paper, for the first time, we propose an efficient architecture for squaring by utilizing many algorithmic transformations and architectural optimizations to reduce the critical path and calculation cycles. Firstly, the squaring algorithm is modified to achieve partial parallel computing, and a very hardware-efficient extended GCD algorithm is selected to reduce the whole computation cycles. Secondly, highly-parallelized architectures for large-number division and multiplication are devised respectively. Finally, the proposed architecture is coded using hardware description language (HDL) and synthesized under the TSMC 28-nm CMOS technology. The synthesis results show that the proposed design with the input width of 2048 bits averagely takes 6.319us per squaring at a frequency of 500 MHz. Compared to the original squaring with the same setting running over an Intel(R) Core(TM) i7-6850K 3.60GHz CPU, our design achieves about 2x speedup.
SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool

In this paper, we present SoDa, an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) output power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant's 30-min power output, based on the configuration of the panel. The working hypothesis to generate high-resolution (e.g. 1 second) solar data is that the conditional distribution of the time series of solar power output given the cloud density is the same for different locations. We therefore propose a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, and train our stochastic model parameters for the cloudy states on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU). In the paper we introduce the stochastic model, and the methodology used for the training of its parameters. The numerical results show that our tool creates synthetic solar time series at high resolutions that are statistically representative of the measured solar power and illustrate how to make use of the tool to create synthetic data for arbitrary sites in the footprint covered by the NSRDB.
RETRACTED: Computer aided intelligent medical system and nursing of breast surgery infection

Breast surgery infection is one of the most common malignancies in women around the world. Breast cellulitis is a severe bacterial infection that affects the skin of the breast. This condition can occur from the broken skin, resulting from surgery and cancer treatment's most common complications. Breast implant infection is related to cosmetic and reconstructive surgery of significant complications. The existing method prevents and detects infectious diseases in general intensive, time-consuming, and use resources (primarily human). The proposed Random Decision Tree (RDT) algorithm is to detect breast surgery infection to resolve this problem. The determination of the embedded medical system is based on the increasingly quantitative data. The medical system's contact from the current patient's contact will record their entire medical history electronically. Whether the patient or ward is in the operating room, technological advances, and convenient stool and medical-related health indicators can be reliably measured to guide all efforts' safety and provide the best clinical outcome. The proposed RDT algorithm classifies breast surgery infection or not infection, and the proposed algorithm offers high performance compared to other existing methods.
Deep Radar Sensor Models for Accurate and Robust Object Tracking

Object tracking is one of the key challenges for perception systems of autonomous vehicles. Recursive Bayesian state estimation can be used to obtain object tracks. Both the measurement association and the object update within such Bayesian filters rely on sensor measurement models. These models offer an approximation of the expected sensor values that can be error-prone due to a mismatch between model and reality. The discrepancy is caused by the limited descriptive capacity of measurement models since sensor measurements are highly object and situation dependent. We address this problem in a data-driven approach by using Deep Neural Networks (DNNs) to learn situation dependent sensor measurement models. In detail, the DNN acts as a virtual sensor that uses current sensor measurements to regress necessary corrections of predicted object states. It can be directly plugged into existing tracking frameworks, substituting the previously hand-modeled association and update steps during Bayesian Filtering. We apply the proposed DNN-based measurement models to the problem of vehicle tracking using radar data in an Extended Kalman Filter setup and compare it to a classical closest reflex and an L-shape measurement update model. Extensive evaluation on a real-world dataset shows that our model improves performances significantly compared to state of the art methods.
Scalable Graph Convolutional Network based Link Prediction on a Distributed Graph Database Server

Graph Convolutional Networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices and edges with rich types of attributes is a significant issue to be addressed due to the storage and computation limitations of the machines. In this paper we present a scalable link prediction approach which conducts GCN training and link prediction on top of a distributed graph database server called JasmineGraph. We partition graph data and persist them in multiple workers. We implement parallel graph node embedding generation using GraphSAGE algorithm in multiple workers. Our approach avoids facing performance bottlenecks in GCN training using an intelligent scheduling algorithm. We show our approach scales well with an increasing number of partitions (2,4,8, and 16) using four real world data sets called Twitter, Amazon, Reddit, and DBLP-V11. JasmineGraph was able to train a GCN from the largest dataset DBLP-V11 (> 9.3GB) in 11 hours and 40 minutes time using 16 workers on a single server while the original GraphSAGE implementation could not process it at all. The original GraphSAGE implementation processed the second largest dataset Reddit in 238 minutes while JasmineGraph took only 100 minutes on the same hardware with 16 workers leading to 2.4 times improved performance.
Anomaly Detection of Environmental Sensor Data using Recurrent Neural Network at the Edge Device

Advance in sensor technology brings numerous challenges with it in the context of data collection, storage and processing. Edge-enabled AI processing of sensor data is a large part of the sensor data processing. Sensor data about environments have natural errors and incompleteness in the collection process and need to be processed in real-time. Due to large volumes of data, monitoring and reporting of analyzed results need to be processed at the edge side of generated data. This paper proposes a time series data anomaly detection method that is based on neural network. Our models are evaluated using synthesis data generated from time series with trend, seasonal and noise component. In the case of zero-based dataset, GRU model with hidden cells (240 cells) and using only input values without additional features, applied with 0.3 dropout, produced 100% recall and 99.7% accuracy. In the case of non-zero-based dataset, LTSM model with hidden cells (240 cells) and using only input values without additional features, produced 86.7% recall and 99.5% accuracy. We also suggest the edge monitoring system with anomaly detection function of each environmental sensor using our pretrained detection model. Users can recognize an environmental status of the workplace using the prediction method with previous sensor outputs in real-time.
Protecting the Health and Longevity of the Peer-Review Process in the Software Engineering Community

Peer review is the evaluation of scientific, academic, and professional work by other experts in the same field. The main purpose of the peerreview process is to maintain the integrity of the scientific process and increase the quality of the work product by providing timely, professional, and unbiased feedback. The most substantive scientific activity that relies on the success and integrity of the peer-review process is, by all means, scientific publications. All software engineering conferences and workshops as well as all journals and magazines, including IEEE Software's focus and feature articles, select which articles to publish after a peer-review process.
Polyhedra of Finite State Machines and Their Use in the Identification Problem

The possible sets of joint distribution of the word occurrence frequencies in the finite state machine input and output sequences are considered. A geometric description of such sets as convex polyhedra in a real unit cube of suitable dimension is proposed. A method has been developed for comparison of unknown and reference automata by the observed input and output sequence fragments. The method does not require installation to the fixed initial state.
Evaluating SEU Resilience of CNNs with Fault Injection

Convolutional neural networks (CNNs) are quickly growing as a solution for advanced image processing in many mission-critical high-performance and embedded computing systems ranging from supercomputers and data centers to aircraft and spacecraft. However, the systems running CNNs are increasingly susceptible to single-event upsets (SEUs) which are bit flips that result from charged particle strikes. To better understand how to mitigate the effects of SEUs on CNNs, the behavior of CNNs when exposed to SEUs must be better understood. Software fault-injection tools allow us to emulate SEUs to analyze the effects of various CNN architectures and input data features on overall resilience. Fault injection on three combinations of CNNs and datasets yielded insights into their behavior. When focusing on a threshold of 1% error in classification accuracy, more complex CNNs tended to be less resilient to SEUs, and easier classification tasks on well-clustered input data were more resilient to SEUs. Overall, the number of bits flipped to reach this threshold ranged from 20 to 3,790 bits. Results demonstrate that CNNs are highly resilient to SEUs, but the complexity of the CNN and difficulty of the classification task will decrease that resilience.
Automatic Modulation Recognition Based on Multi-Dimensional Feature Extraction

As an intermediate step between signal detection and demodulation, automatic modulation recognition (AMR) is commonly used in cognitive radio networks to identify different types of communication modulation. A new automatic modulation scheme is proposed, based on decision tree theory, which is a general method for different types of band-limited Gaussian noise modulation types. In particular, by combining the instantaneous statistic feature and high-order cumulants feature, the key features are extracted to realize the blind recognition of analog and digital signals. In addition, a new characteristic parameter AT is proposed to improve the performance of modulation recognition under low signal-to-noise ratio (SNR). The simulation results show that, for all the analyzed signals, when the SNR reaches 3dB, the recognition success rate can reach more than 95%, reflecting the superiority of the method proposed in this paper.
Identifying Reflected Images From Object Detector in Indoor Environment Utilizing Depth Information

We observed that mirror reflection severely degrades person detection performance in an indoor environment, which is an essential task for service robots. To address this problem, we propose a new real-time method to identify reflected virtual images in an indoor environment utilizing 3D depth information. Images reflected by the mirror are similar to real objects, so it is a non-trivial task to differentiate them. Conventional object detectors, which do not deal with this problem, obviously recognize reflected images as real objects. The proposed method compares the geometric relationship between the 3D spatial information of the detected object and its surrounding environment where the object locates. It analyzes the layout of surrounding indoor space utilizing semantic segmentation and plane detection method. With the estimated layout of indoor space, detected object candidates are examined whether they are real or reflected images utilizing 3D depth information. To verify the proposed method, a large indoor dataset was newly acquired and examined in a dedicated <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Living-lab</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> environment. The performance of the algorithm is verified by comparing conventional detectors with the proposed method in the acquired <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Living-lab dataset</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> .
Securing Multiple Information Using Bio-Chaotic Keys

To advancement in security applications, for the first time, a bio-chaotic key generated from the emerging biometric trait "finger knuckle print (FKP)" has been proposed for data security and authentication.In the system, the feasibility to generate multiple keys from the single FKP image has been exploited to secure multiple information, which results in significant enhancement of the security strength.The encryption and decryption processes keep the user-specificity and perform better in reducing space for key storage and transmission.The research contribution is to facilitate a simplified optical implementation.Simulation results show the validity and effectiveness of the proposed scheme.
Trend Analysis on Korea’s National R&amp;amp;D in Logistics

This study examined how national research and development (R&D) in the domain of logistics has changed recently in the Republic of Korea. We conducted basic statistical analysis and social network analysis on 5,327 logistics-related R&D projects undertaken during 2005â2019. Data for performing these analyses were collected from the R&D database of the National Science and Technology Information Service (NTIS). By constructing a co-occurrence matrix with keywords, we conducted degree and betweenness centrality analysis and visualized the network matrix to display a cluster map. This study presents our observations related to the following findings: (1) the chronical trends of logistics R&D, (2) focused fields of logistics R&D, (3) the relations among keywords, and (4) the characteristics of logistics R&D. Finally, we suggest policy implications to boost and diversify logistics R&D.
On the transaction dynamics of the Ethereum-based cryptocurrency

Abstract Distributed blockchain-based consensus platforms have witnessed steady growth in recent years. In special, cryptocurrency is one of the main applications of the blockchain technology. Despite the recent interest in blockchain, we still lack in-depth analysis of systems that use such a technology. In fact, most of the existing works focus on Bitcoin. Moreover, blockchain-based cryptocurrency systems are highly dynamic. Their internal mechanisms and consensus algorithms evolve over time. Users also change their interests in a given platform, which in turn, reflect their behaviour. In this article, we model the Ethereum-based cryptocurrency transaction network, a more recent blockchain platform that is gaining a significant share in the cryptocurrency market. We model the transactions of Ethereum as a complex system, representing this complex system as a time-varying graph. Our model and the analysis we conduct rely on a 3-year dataset of Ethereum-based cryptocurrency transactions, comprising more than 38 million users (i.e. unique wallet addresses) and almost 300 million transactions. We analyse the evolution of users and transactions over time. Our study also highlights the centralization tendency of the transaction network on both user and time aspects. Finally, we also analyse the formation of communities and the evolution of connected components considering the dynamics of the Ethereum-based cryptocurrency transaction network.