Reviewerly/exHarmony huggingface dataset:
Automatic Modulation Recognition Based on Hybrid Neural Network

Recognizing signals is critical for understanding the increasingly crowded wireless spectrum space in noncooperative communications. Traditional threshold or pattern recognition-based solutions are labor-intensive and error-prone. Therefore, practitioners start to apply deep learning to automatic modulation classification (AMC). However, the recognition accuracy and robustness of recently presented neural network-based proposals are still unsatisfactory, especially when the signal-to-noise ratio (SNR) is low. In this backdrop, this paper presents a hybrid neural network model, called MCBL, which combines convolutional neural network, bidirectional long-short time memory, and attention mechanism to exploit their respective capability to extract the spatial, temporal, and salient features embedded in the signal samples. After formulating the AMC problem, the three modules of our hybrid dynamic neural network are detailed. To evaluate the performance of our proposal, 10 state-of-the-art neural networks (including two latest models) are chosen as benchmarks for the comparison experiments conducted on an open radio frequency (RF) dataset. Results have shown that the recognition accuracy of MCBL can reach 93% which is the highest among the tested DNN models. At the same time, the computation efficiency and robustness of MCBL are better than existing proposals.
Labeling of Radio Signal by Jointly Using Feature Engineering and Unsupervised Learning

For the purpose of radio transmitter identification with deep learning under non-cooperative conditions, this paper proposes a radio frequency(RF) signal sample labeling method based on unsupervised learning and radio frequency fingerprints. Firstly, extracted the carrier component fingerprint information of the radio signal to form a characteristic vector of representative individual information. Then utilized an unsupervised learning method which is improved by canopy algorithm to realize sample labeling of individual transmitter. The experimental process is based on the ACARS signal, used six types of ACARS signals with different IDs to verify the performance of the method. The results revealed that the accuracy of the method for the overall identification and labeling of radio frequency signals was over 96%.
Transfer Learning for Handwritten Character Recognition

Inunganbi, Sanasam Katariya, Robin SinghDeep learning is increasing rapidly and is becoming a leading mechanism for various applications in machine learning and artificial intelligence. Among the many research areas where deep learning shines, one crucial area is image classification. Handwritten character recognition is a fundamental research area in image classification. It has the ability to flourish in numerous utilizations such as postal automation, banking, form filling. Still, establishing such a system with high accuracy is a challenging effort with the diversified writing fashions, variable size, or different strokes for the same character and resemblance of diverse characters. Further, shapes and the inconclusive writing style of several individuals complicate the problem. The stated problem can be solved by an intelligent, and appropriate extraction of features is required. In this paper, a recognition system is presented using transfer learning. The technique is popular for building accurate designs in computer vision. Transfer learning helps to begin from patterns that have the experience to solve a different problem instead of starting from the basics. The performance of the proposed method has experimented on a self-acquired handwritten Meitei Mayek (Manipuri script) database contributed by diverse people holding different education backgrounds and ages. A total of 14,700 sample images are used for learning various pre-trained models. The highest average accuracy achieved among the models is 98.41%.
A data mining approach for automatic classification of rock permeability

Reliable estimates of porosity can be obtained from different types of geophysical well logs. However, obtaining in situ permeability estimates is still a major challenge in the geosciences. This work aims to evaluate the application of data mining techniques to NMR logs for rock permeability classification, thus far tested only on laboratory data. For this study, we used a petrophysical database from two Brazilian Pre-salt wells located in the Santos Basin, a formation notoriously difficult to characterize, mainly due to its diversity and complexity. Six classification algorithms were evaluated (k-NN, NB, C4.5, RF, SMO, and MLP) according to their ability to estimate the permeability of rocks in four distinct classes (low: <1 mD, intermediate: 1–10 mD, high: 10–100 mD, and excellent: >100 mD). The predictive performance of the algorithms was compared to the behavior of two traditional permeability estimators. With an accuracy of 66%, the Naïve Bayes algorithm, combined with two preprocessing steps – unsupervised discretization and attribute selection – achieved the highest predictive performance. That mark surpassed the accuracy obtained by Kenyon and Timur-Coates estimators by 154% and 106%, respectively, providing evidence for the superiority of the data mining technique to recognize permeability classes based on NMR logs. Classification experiments employing NMR logs in conjunction with conventional logs were also conducted, but this log combination was not able to best the predictive result based solely on the NMR log data.
Word-Embedding Model for Evaluating Text Generation of Imbalanced Spam Reviews

Generating minor class data of spam texts is expected to solve the imbalanced problem in spam detection of product reviews. There could be semantic differences between the generated texts and the original ones. Thus, by including the semantically differed texts in the spam dataset used for training is like a noise addition. For evaluating the generated texts, some manual preparations of ground-truth data are necessary. This work has evaluated the generated texts with some approaches to ensure their context and sequence similarities compared to the original texts for better performance of a spam detection. The employed approaches are expected to eliminate the manual tasks. Our research proposes an evaluation model that consists of word-embedding pre-trained and LSTM Siamese to evaluate text generation in imbalance review. The use of a combination of pre-trained word embedding and LSTM Siamese trained model will capture the semantic aspect of the text.
A simple illumination map estimation based on Retinex model for low-light image enhancement

This paper proposes a effective illumination map estimation based on Retinex theory for low illuminance image enhancement. Firstly, initial illumination map is calculated by finding the largest element value in the b, g and r channels. Secondly, we adopt anisotropic filter operations to process initial illumination map. Then, we propose an adaptive gamma correction to process it to make the illuminance map more accurate. Finally, we adopt unsharp masking to enhance details to get our result. Objective and subjective evaluation illustrate the superiority of our proposed algorithm.
Signal-to-noise ratio enhancement of temporal phase unwrapping using video grating projection

• The video grating substitutes for the traditional static grating, which can obtain the 766-gray-scale range deformed pattern. • The broader gray-scale range deformed patternis realistic and has better sinusoidal property. • Both intensity modulation and signal-to-noise ratio have been efficiently improved by broadening gray-scale range. • The reliable measurement range of the temporal phase unwrapping can also be efficiently broadened. Temporal phase unwrapping (TPU) has important application in fringe projection phase-shifting profilometry to measure multi-isolate objects and discontinuities surfaces. Due to the gray-scale standard of the digital projector, the gray-scale of the deformed pattern is of 256 at most. Suppose the frequency of the high-frequency patterns is enlarged for improving accuracy. In that case, it may lead to the vulnerability of intensity noise, particularly in terms of unit-frequency and low-frequency patterns. To some extent, it may challenge the reliable measurement range of the TPU method. To this end, the signal-to-noise ratio (SNR) enhancement of temporal phase unwrapping (SETPU) using video grating projection is proposed. By projecting the specially designed video grating onto the measured object instead of the traditional static grating, adequately controlling the exposure time and the gain of camera allows for the broader gray-scale patterns to be captured. The further analysis shows that the intensity modulation with the SETPU method is more than 3.7 times that with the traditional TPU method, and the SNR with the SETPU method is stronger than that with the traditional TPU method due to the broader gray-scale range so as to efficiently broaden the reliable measurement range of TPU method. The experimental results verify the validity and practicability of the proposed method.
Self-embedding watermarking method for G-code used in 3D printing

3D printing is faced with a lot of security issues, such as malicious tampering, intellectual property theft and so on. This work aims to protect the G-code file which controls the 3D printing process by proposing a self-embedding watermarking method for G-code file. This method groups the G-code lines into code blocks and achieves a random mapping relationship for each code block. Each code block is divided into two parts, carrying the authentication and recovery bits, respectively. The tampered regions are detected by leveraging the authentication bits in each code block. Meanwhile, the G-code files are restored based on the recovery bits and the geometric information of the neighboring code blocks. Experimental results indicate that the proposed method can effectively detect the tampered region and restore the G-code file to a large extent, while limiting the distortion caused to the 3D printed object by the watermarking.
Modeling of a System for Studying of Biological Tissues with the Use of Augmented Reality

Trends in medical technologies are novel techniques and methods for study of biological tissue and and identify the existing of a tissue anomaly. An attempt in this direction is based on augmented reality surgical systems and devices that allow doctors to visualize data from diagnostic and surgical procedures, and improve work efficiency. The primary aim of this work is to study biological tissue using Augmented Reality surgical systems and to model the behavior of an instrument-organ in an environment close to its real one. To solve these problems the following is done: i) an original tactile model of an instrument for robot-assisted surgery is designed and produced ii) Unified Modeling Language (UML) design of augmented reality surgical system, and iii) an extension program MOSAR ICTPro 2.0 with an augmented reality upgrade is design. This work is a continuation of previous research in the field of surgical robotics.
Multiple Authors Identification from Source Code Using Deep Learning Model

Authorship identification is an essential technique of identifying contributors of source code to facilitate the work of different security-related applications. It has been widely used in different code analysis applications and security-related tasks such as detecting ghostwriting, resolving copyright disputes, etc. For several decades it has become an emerging research topic in the field of plagiarism detection. Most of the identification methods consider code samples of a single author. However, the development of real-world software needs a team effort. As a result, multiple authors identification of source code is still a great research challenge. To overcome the challenge, we propose a Deep Learning approach to identify multiple authors of source codes by analyzing the coding style of authors. In this method, we use several machine learning and deep learning-based algorithm i.e. Deep Neural Network(DNN), Support Vector Machine(SVM), and Long short-term memory(LSTM) for detecting multiple authors from source code. The code2seq model over Abstract Syntax Tree is used to identify the structural representation of source code which helps to achieve better performance of the algorithms. The evaluation result of our model shows that it gains remarkable accuracy in identifying multiple contributors of source codes. The deep neural network model outperforms the other algorithms by achieving 96.7 % classification accuracy.
The Preliminary Study for Increasing the Productivity of Unit Tamban Lowland Irrigation Area based on Fact Findings of Observations

Abstract Unit Tamban is a fork system of lowland irrigation areas in Kapuas District. It has three primary canals, and each of them has a settling pond at the end. Most of the 3,506 ha planting area still apply once a year cropping patterns. A quick assessment based on field observations is carried out to determine the existing problems, including water management, water quality, and soil quality. The problem in the water management system is silting in the primary and secondary canals. The settling pond at the primary canal was no longer functioning, and it makes the silting worse because the water flow became uncontrolled. Water quality is measured with pH and TDS, the average pH at canal water was 4,22, and TDS was 71 mg/l. The soil type is mainly acid sulphate with pyritic materials, mostly 70 cm below the surface. Increasing cropping patterns twice a year requires improving the water circulations and increasing the soil pH higher than five. The problem in the secondary canal is expected to solve by changing the flow pattern from two way to 1-way flow is needed to avoid dead spots of water so that the leaching processes can work well.
Research and Application of Renewable Energy Power Consumption Certificate Based on Blockchain

With China's steady progress in the development and construction of renewable energy, the proportion of renewable energy in China is increasing, and the willingness of market players to purchase renewable energy power is increasing. However, the existing technical means have single tracking indicators, low reliability, difficult supervision, and can not track the consumption of users safely, transparently and efficiently. The paper introduces blockchain technology into renewable energy consumption voucher transaction. Firstly, it introduces the problems faced by renewable energy power consumption vouchers; Secondly, it studies the key technologies of blockchain to meet the power consumption demand of renewable energy; Finally, a renewable energy power consumption voucher management method based on blockchain technology is designed.
CONCILIATORY REASONING, SELF-DEFEAT, AND ABSTRACT ARGUMENTATION

Abstract According to conciliatory views on the significance of disagreement, it’s rational for you to become less confident in your take on an issue in case your epistemic peer’s take on it is different. These views are intuitively appealing, but they also face a powerful objection: in scenarios that involve disagreements over their own correctness, conciliatory views appear to self-defeat and, thereby, issue inconsistent recommendations. This paper provides a response to this objection. Drawing on the work from defeasible logics paradigm and abstract argumentation, it develops a formal model of conciliatory reasoning and explores its behavior in the troubling scenarios. The model suggests that the recommendations that conciliatory views issue in such scenarios are perfectly reasonable—even if outwardly they may look odd.
P-Wave detection using deep learning in time and frequency domain for imbalanced dataset

Early tsunami and earthquake warning systems need a good Automatic First Arrival Picking (AFAP) subsystem to determine the earthquake arrival time. This subsystem has a time-domain earthquake signal as the input and the arrival time of the primary earthquake wave (P-Wave) as the output. There are several methods of AFAP that are widely used nowadays, one of them is Short Term Average/Long Term Average (STA/LTA) fused with the AR-AIC method. Even though this method is real-time, its performance is still relatively low. With similar characteristics between the seismic signals and image data, utilising deep learning on AFAP can further increase its performance. The seismogram channels can be seen as the image height, and the signal at a certain window can be seen as the image width. Unfortunately, these image data will be considered an imbalanced dataset. In this research, deep learning with time domain and frequency domain are proposed as inputs with the Synthetic Minority Oversampling Technique (SMOTE) method. Deep learning is used because of its ability to generalise well on a huge dataset, while SMOTE is used to overcome the imbalanced dataset problem. With this proposed system, the accuracy is 99.3%, the Root Mean Square Error (RMSE) is 0.202 seconds, and the maximum execution time is 0.17 seconds with a periodic time of 0.4 seconds. With these results, the AFAP system has good results for estimating the first arrival earthquake time.
Blockchain Technology Research in Business, Management and Accounting Field: A Bibliometric Analysis

Undoubtable blockchain technology is a leading technology that has impacted the business world on a very high scale after the internet and has attracted the attention of researchers in different domains. Due to the increased interest of research scholars in business world, this study aims to examine the research on blockchain technology that has been conducted in the field of Business, Management and Accounting (BMA). Therefore, a bibliometric analysis was performed to look for the research trends and themes in this area by performing analysing on metadata. The study attempts to explore a graphical representation of bibliometric information of blockchain technology in the area of BMA using R Package bibliometric. Thus, it performed the following analysis: annual publications trends, most productive countries, most productive authors, most cited papers, most frequent journals, most productive affiliations, most frequent keywords and network analysis. The results reveal that research on blockchain technology in BMA started in 2016 and increased more than threefold in the year 2019 and the growth continued respectively. Furthermore, it's evident that research on blockchain technology in BMA is increasing significantly but is still in its early stage and is expected to grow significantly. This research may be repeated over time as the numbers will keep on changing and the scope of this research was only to Scopus database.
Recovery System using SDN Technology for Cyber Attack Solution

Corporate organizations, governments and public services seek to protect their valuable assets such as databases from cyberattacks. Attacks on security systems allow breaches, theft, and manipulation of data. Therefore, a system is needed to reduce vulnerabilities and threats. The security model implemented is the implementation of laws that guarantee data protection, blockchain, and authentication. This study offers designers a recovery system prototype that uses network management based on Software-Defined Networking (SDN) technology. The SDN technology applied is Ryu Controller, which allows access to specific databases in a specially programmed network. This research phase begins with needs analysis, virtual environment, examination, analysis, and cyber-attack testing. The results obtained in this study successfully implemented a recovery system with a total time for backing up four servers in one period was ±90 minutes, and the recovery time was ±90 minutes. Based on this, this research is fit for the purpose and can be used to reduce cyberattacks.
Developing a System of Indicators for Clustering Financial Cybercrime

This study aims to propose a system of indicators for dividing financial cybercrime into groups. The results of this study indicate absence of taxonomic indicators regarding clustering of financial cybercrimes and wide differences in approaches to the development of such indicators in relation to cybercrimes. The proposed system of indicators for clustering of financial cybercrime is designed to help create a unified, consistent and holistic classification of cybercrime. The results of the study can be applied in the criminal legal classification of unlawful acts corresponding to the general concept of financial cybercrime.
An Edge Computing Architecture and Application Oriented to Distributed Microgrid

With the rapid development of edge computing technology, the application of edge computing in smart grids has become more and more extensive. But edge computing has not yet been applied to the operation control of distributed power generation microgrid systems. This article proposes a microgrid-oriented edge computing architecture. First, we introduce the main functions of edge-cloud collaboration. Then we explain the construction plan of the architecture, including the realization of data processing, network communication and security mechanisms. Finally, we introduce the architecture application practice in a rural community in Central China.
An Embedding Method for Sentiment Classification across Multiple Languages

Embedding methods are feature representations of words, which are able to capture both semantic and syntactic information from contexts. However, existing embedding methods for learning context are typically unable to capturing sufficient sentiment information. In this study, we conduct an investigation on how to improve the performance of sentiment classification using sentiment embedding approach. Particularly, we first present a new word embedding method based on a supervised method to capture the semantic sentiment information. Then, Deep Learning models, by combining different architecture such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and attention mechanisms are developed for improving the performance of the sentiment classification. The evaluation on well-known bench-mark datasets with different languages (i.e., English and Vietnamese) indicates the promising results of our method.
IntStream: An Intent-driven Streaming Network Telemetry Framework

Due to the complexity of the network structure and the high growth of the transmission speed, the measurement and management of the network are facing serious challenges. The traditional bottom-up network telemetry methods are no longer applicable to complex network scenarios. To bridge this gap, we propose IntStream, an intent-driven streaming network telemetry framework to allow network operators to measure and analyze network traffic. However, there are three key challenges to building an intent-based telemetry system: (1) The diversity of network data sources. (2) The complexity of the measurement tasks. (3) The low overhead requirements of the telemetry system. IntStream introduces a lightweight component to extract and parse data from various types of data sources to form a data stream and divides the data stream conversation process into local and global stages. IntStream provides a set of rich expressive primitives to support users to write telemetry tasks based on intent. By performing part of the telemetry task on the local stage, the transmission overhead of intermediate data can be effectively reduced. The evaluation results conducted on a large campus network show that IntStream can support a wide range of telemetry tasks while reducing the intermediate data transmission overhead by 99.64% on average.