Reviewerly/exHarmony huggingface dataset:
FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection

Carrier signal detection is a complicated and essential task in many domains because it demands a quick response to the existence of several carriers in the wideband, while also precisely predicting each carrier signal’s frequency centers and bandwidths, including single-carrier and multi-carrier modulation signals. Multi-carrier modulation signals, such as FSK and OFDM, could be incorrectly recognized as several single-carrier signals by using the spectrum center net (SCN) or FCN-based method. This paper designed a deep convolutional neural network (CNN) framework for multi-carrier signal detection by fusing the features of multiple consecutive frames of the broadband power spectra and estimating the information of each single-carrier or multi-carrier modulation signal in the broadband, called frame fusion spectrum center net (FFSCN), including FFSCN-R, FFSCN-MN, and FFSCN-FMN. FFSCN includes three base parts, the deep CNN-based backbone, the feature pyramid network (FPN) neck, and the regression network (RegNet) head. FFSCN-R and FFSCN-MN fusing the FPN out features, which use the Residual and MobileNetV3 backbone, respectively, and FFSCN-MN cost less inference time. To further reduce the complexity of FFSCN-MN, the designed FFSCN-FMN modifies the MobileNet blocks and fuses the features at each block of the backbone. The multiple consecutive frames of broadband power spectra not only preserve the high-resolution ratio of the broadband frequency, but also add the features of the signal changes in the time dimension. Extensive experimental results demonstrate that the proposed FFSCN can effectively detect multi-carrier and single-carrier modulation signals in the broadband power spectrum and outperform SCN in accuracy and efficiency.
Blockchain‐based object detection scheme using federated learning

Abstract The rapid development of computing devices and automation in various fields drastically increased the growth of data, which promotes the usage of machine learning (ML) techniques to get insights from the generated data. However, data processed by ML algorithms lead to several privacy issues, including leakage of users' biometric data while sharing it through the network to train the object detection model. Therefore, federated learning (FL) was introduced, in which the models are trained locally; only model parameters are shared between central authority (CA) and end nodes. They will eventually maintain a common model for all the participating devices. However, many problems are associated with FL, such as the difference in data consumption rate, training capabilities, geographical challenges, and storage capacity. These problems might lead to differences in the common global model and thus an inefficient FL approach. Moreover, the presence of a CA results in a single point of failure and is vulnerable to various attacks. Motivated by the aforementioned discussion, in this article, we propose a blockchain‐based object detection scheme using FL that eliminates the CA by using distributed InterPlanetary File System (IPFS). Global models can be aggregated periodically when several local model parameters are uploaded on the IPFS. Nodes can fetch the global model from the IPFS. The global aggregated object detection model has been evaluated for various scenarios such as human face detection, animal detection, unsafe content detection, noteworthy vehicle detection, and performance evaluation parameters such as accuracy, precision, recall, and end‐to‐end latency. Compared to traditional models, the proposed model achieved an average accuracy of 92.75% on the object detection scenarios mentioned above.
Foveated 3D range geometry compression via loss-tolerant variable precision depth encoding

The capacity of three-dimensional (3D) range geometry acquisition methods to capture high-precision scans at high frame rates increases every year. These improvements have influenced a broadening range of disciplines to implement 3D range geometry capture systems, including telepresence, medicine, the visual arts, and many others. However, its increased popularity, precision, and capture rates have caused mounting pressure on the storage and transmission of 3D range geometry, thus straining their capacities. Compression techniques seek to alleviate this pressure by offering reduced file sizes, while maintaining the levels of precision needed for particular applications. Several such compression methods use sinusoidal modulation approaches to encode floating-point 3D data into conventional 2D red, green, and blue (RGB) images. In some applications, such as telepresence, high precision may only be required in a particular region within a depth scan, thus allowing less important data to be compressed more aggressively. This paper proposes a feature-driven compression method that provides a way to encode regions of interest at higher levels of precision while encoding the remaining data less precisely to reduce file sizes. This method supports both lossless and lossy compression, enabling even greater file-size savings. For example, in the case of a depth scan of a bust, an algorithmically extracted bounding box of the face was used to create a foveated encoding distribution so that the facial region was encoded at higher precisions. When using JPEG 80, the RMS reconstruction error of this novel, to the best of our knowledge, encoding was 0.56 mm in the region of interest, compared to a globally fixed higher precision encoding where the error was 0.54 mm in the same region. However, the proposed encoding achieved a 26% reduction in overall compressed file size compared to the fixed, higher-precision encoding.
Constructing and Reconstructing Characters Using Gaussian Process Regression

In this paper, we consider the problem of constructing characters from a set of measured data using some electronic devices - such as pen tablet and electronic white board, etc. For such a problem, the Gaussian Process Regression is employed, where the Gaussian kernel is used as kernel function. Then, the character is then obtained as the average of posterior distribution based on the Gaussian process model. In particular, it is shown that transforming the typeface into a running style is available by adjusting some hyper-parameter of the kernel function. The design examples are included.
Channel synchronization based on deep learning

Abstract Aiming at the difficulty of the deep neural network (DNN) adapting to channel changes in communication systems, a channel synchronization deep neural network (CSDNN) based on deep learning (DL) is designed for realizing carrier synchronization, bit timing synchronization, and automatic gain control (AGC). By introducing a frequency‐domain cyclic convolution (FDCC) layer, the network transformed the time‐domain triangle activation into frequency domain linear activation taking FFT and IFFT matrixes as the activation function, solved the reverse gradient transmission‐blocking problems in training the time‐domain carrier synchronization neural network, effectively overcome the FFT inherent “fence” effect, and accurately compensated carrier frequency offset; By introducing a time‐domain cyclic convolution (TDCC) layer and the special frame structure design containing repetitive training sequence, the network training was completed to realize bit timing synchronization under the condition of the uncertain corresponding relationship between training data and labels. Combining phase inverse rotation dense (PIRD) layer, the network can be trained with very little training data to complete fast carrier synchronization and timing synchronization, at the same time adjust the received signal gain and suppress the jamming, which makes it is possible to train the channel synchronization deep neural network online under jamming environment, and provide a feasible way of realizing the intelligent communication system.
Quantum phase estimation with a general binary-outcome measurement

As the simplest phase estimation protocol, the inversion estimator has been widely adopted in the quantum metrology, since its performance can be simply quantified by the error-propagation formula. For a general binary-outcome measurement, we show that both the inversion estimator and the maximum likelihood estimator are the same and can asymptotically saturate the Cramér-Rao lower bound. These observations are applied to the parity detection and the zero-nonzero photon counting that have achieved super-resolved phase measurement near the shot-noise phase sensitivity in a coherent-state light Mach–Zehnder interferometer. We numerically show that the phase uncertainty almost follows the theoretical prediction of the lower bound. At the optimal working point, the best sensitivities also reach their associated analytic results for the two binary-outcome measurement schemes.
A text matching model based on dynamic multi‐mask and augmented adversarial

Abstract The text matching is a basic task of NLP and is important for tasks such as text retrieval, question answering, and so forth. The development of pre‐trained language models has promoted the progress of text matching tasks. However, due to the natural particularity of Chinese characters and expressions, the Chinese text matching tasks still have problems such as word segmentation difficulty, serious semantic loss, and model instability. In this paper, we propose the DAINet model, which includes DMM, AA and IO modules. We use the Dynamic Multi‐Mask module (DMM) to enhance the completeness of word segmentation. Then we use the Augmented Adversarial module (AA) to further extraction of semantic information. Finally, we use the Integrated Output module (IO) for a more stable output. We conducted experiments on LCQMC, BQ and Xiaobu datasets and compared the results with seven strong baseline models. The results showed that DAINet model made great improvement, including improving ACC value of BQ dataset to , AUC value to , ACC value of LCQMC dataset to and AUC value to . The ACC value of Xiaobu dataset was improved to and the AUC value was improved to . Further ablation experiment results show that the proposed DMM, AA and IO modules have good adaptability and improvement over existing models.
The 2019-20 Israeli electoral digital campaigns: Algorithmic campaigns’ slippery slope

This article focuses on the three Israeli digital campaigns during 2019–20 elections through their most prominent negative trends, namely: fake news, accounts and identities; politicians’ chat bots; influence and interruption networks, illegal information accumulation on Israeli citizens via social media and voting apps; anonymous and fake spam, and a complete lack of compliance with the statutory and regulatory framework. With technology’s speedy advancement, legal tools’ lagging, and political actors’ exploitation of the situation to their benefit, it seems that these campaigns, setting forth a fifth phase of political communication – Algorithmic Campaigns – constitute a rather bleak and grim future, as far as elections are concerned, thereby calling for rethinking.
Technologies for Entrepreneurship at Basic Education: the UKids project lesson for the after COVID-19

Since the computer and robotics revolution at the end of the 20th century, it is assumed that the way of working, or the time dedicated to leisure would change. And changes took place, albeit sequentially and following globalization and the respective capitalist demands. However, workers now have email and other platforms in their electronic systems that make them contactable 24 hours a day. Thus, instead of the expected increase in time dedicated to leisure and family, it decreases. Recently, this situation has undergone the first reversal, although forced, when the world is invaded, by surprise, with a pandemic called COVID-19. In two days, the globe economy is forced to slow down and parents in telecommuting, start to accompany more their children at school. Teaching undergoes an evolution from 19th century methodologies to 21st century methodologies. In other words, from work in the classroom they move to synchronous and asynchronous lessons. Also citizenship topics such as sustainable development together with Social and Educational Entrepreneurship are promoted in School programs. Simultaneously, two documents stand out and guide human behaviors until 2030 and 2063, respectively the UN Sustainable Development Goals and the 2063 Agenda for Africa.&amp;nbsp;The present work presents the new teaching strategies followed and the support technologies used in the implementation of the UKIDS pilot project. To implement it a 3rd year classroom from a primary school nearby Porto was used as the case study and the entrepreneurship culture dimension/”trash value” challenge were selected. Reusing things, helping their parents to separate the garbage, keeping all egg cartoon and make objects from them was compulsory. At the end, a questionnaire was implemented, and the 3rd year children reported to have learned more about the Development Goals with the Ukids project then in the classroom.
<i>How Hard Is Cyber-risk Management in IT/OT Systems?</i> A Theory to Classify and Conquer Hardness of Insuring ICSs

Third-party residual cyber-risk management (RCRM) services (e.g., insurance, re-insurance) are getting increasingly popular (currently, a multi-billion-dollar annual market) with C-suites managing industrial control systems (ICSs) based upon IoT-driven cyber-physical IT and OT technology. Apart from mitigating and diversifying losses from (major) cyber-threats RCRM services positively contribute to improved cyber-security as an added societal benefit. However, it is also well known that RCRM markets (RCRM for ICSs being a mere subset) are relatively nascent and sparse. There is a huge (approximately 10-fold) supply-demand gap in an environment where (a) annual cyber-losses range in trillions of USD, and (b) CRM markets (residual or otherwise) are annually worth only up to 0.25 trillion USD. The main reason for this wide gap is the age-old information asymmetry (IA) bottleneck between the demand and supply sides of the third-party RCRM market, which is significantly amplified in modern cyber-space settings. This setting primarily comprises interdependent and intra-networked ICSs (and/or traditional IT systems) from diverse application sectors inter-networked with each other in a service supply-chain environment. In this article, we are the first to prove that optimal cyber-risk diversification (integral to RCRM) under IA is computationally intractable, i.e., NP-hard, for such (systemic) inter-networked societies. Here, the term “optimal diversification” implies the best way a residual and profit-minded cyber-risk manager can form a portfolio of client coverage contracts. We follow this up with the design and analysis of a computational policy that alleviates this intractability challenge for the social good. Here, the social good can be ensured through denser RCRM markets that in principle improve cyber-security. Our work formally establishes (a) the reason why it has been very difficult in practice (without suitable policy intervention) to densify IA-affected RCRM markets despite their high demand in modern CPS/ICS/IoT societies; and (b) the efficacy of our computational policy to mitigate IA issues between the supply and demand sides of an RCRM market in such societies.
The relationship between the development level of American public libraries and the output of science and engineering publications

This paper explores the exact relationship between the development level of American public libraries and the output of science and engineering publications. We use the sample data from 1996 to 2019 to build a time series model, and analyze the long-term equilibrium, short-term correlation and Granger causality relationships between them with stationarity test, cointegration test, Granger causality test, and impulse response function. The results suggest that there are a long-term equilibrium relationship and a bidirectional Granger causality between them. The two interact and promote each other. The impact of the development level of American public libraries on the output of science and engineering publications can be seen in fewer time periods, while the impact of the output of science and engineering publications on the development level of American public libraries take more time periods to show.
Key performance indicators used to measure the adherence to the iterative software delivery model and policies

Abstract This paper analyzes the Agile metrics embedded in the widely adopted issue tracking tools and used by agile teams to deliver software products. Organizations face challenges when they are required to measure the performance of all the teams operating within the company to provide a holistic view of the software delivery performance at a higher level. A new set of key performance indicators (KPIs) is introduced that can help software companies standardize their delivery across the organization and provide an overview of the software delivery performance that can serve as an insight for the top management teams. The new metrics are applied to a project as part of a case study. The conclusions highlight that the metrics provided by the issue tracking systems do not provide insights into the non-functional requirements of the software products, the methodology applied during the delivery, and the quality of each increment produced. The proposed set of KPIs addresses these shortcomings.
Application of big data analysis technology based on Hadoop framework in agricultural soil improvement

Agricultural big data refers to an expert system of soil data mass storage platform based on Hadoop, which is formed after combining the characteristics of agricultural rationality and seasonality. Using big data technology software, aiming at the problems of massive growth and low sharing of current agricultural soil chemical characteristic data, and combined with Hadoop framework. In order to realize the platform, HBase + HDFS is used to build a cloud storage platform; Aiming at the problem of server load balancing, partition strategy and dynamic load balancing strategy are proposed; Parallel clustering is introduced to realize the classification and mining of soil chemical characteristic data; Finally, the storage platform is verified. The results show that the platform shows excellent performance in performance and clustering result scheme, which has great reference value for the application of big data analysis technology in agricultural production. In building a new countryside, the application prospect of big data technology is broad.
An improved multi-head-attention and its application for feature extraction and classification as regards the fishery science and technology literature

With the information age coming, the explosion of data is creating opportunities just in the management of the text-documents data in and of itself. Although several information technology is applied in web crawl and information category widely, some vital characteristics of such text classification and natural language processing are not able to obtain directly in specific fields. Therefore, this technical paper propose a multi-head-attention method to classify for Fishery Science and Technology Literature, which aims to help scholars or intelligence agent to obtain the classification results of prepared literature from the database directly. There are mainly three contributions in this paper: (i) the multi-head-attention method and improved training mechanisms is first developed for the text classification research; (ii) a novel compression scheme, referred as PP-MiniLM, based on task-agnostic distillation, pruning and quantization is presented to build the fast, small, accurate classification model; (iii) Furthermore, the experimental results indicate that the prediction accuracy is greatly improved based on the pruning and pooling.
Support subsets estimation for support vector machines retraining

• A new retraining methodology for SVMs is proposed • The new concept of Support Subsets is introduced • Methodology proposal reduces complexity computation for SVM training • Imbalance datasets produce balanced Support Subsets • The proposal compares successfully to well-known retraining techniques The availability of new data in previously trained Machine Learning ( ML ) models usually requires retraining and adjustment of the model. Support Vector Machine s ( SVM s) are widely used in ML because of their strong mathematical foundations and flexibility. However, SVM training is computationally expensive, both in time and memory. Hence, the training phase might be a limitation in problems where the model is updated regularly. As a solution, new methods for training and updating SVM s have been proposed in the past. In this paper, we introduce the concept of Support Subset and a new retraining methodology for SVM s. A Support Subset is a subset of the training set, such that retraining a ML model with this subset and the new data is equivalent to training with all the data. The performance of the proposal is evaluated in a variety of experiments on simulated and real datasets in terms of time, quality of the solution, resultant support vectors, and amount of employed data. The promising results provide a new research line for improving the effectiveness and adaptability of the proposed technique, including its generalization to other ML models.
Anomalous Trajectory Detection Using Masked Autoregressive Flow Considering Route Choice Probability

Taxis play a critical role in public traffic systems, and they deliver myriad travelers with convenient service due to temporal-spatial availability. However, anomalous trajectories such as trip fraud often occur due to greedy drivers. In this study, we propose an anomalous trajectory detection method that incorporates Route Choice analysis into Masked Autoregressive Flow, named MAFRC-ATD. The MAFRC-ATD integrates data-driven and model-based methods. First, we divide the urban traffic network into small grids and represent subtrajectories with a sequence of grids. Second, based on the subtrajectories, we employ the MAFRC-ATD model to calculate the anomaly score of each trajectory. Third, according to the anomaly score, we can identify the anomalous trajectories and distinguish between intentionally and unintentionally anomalous. Finally, we evaluate our method with a real-world dataset in Porto, Portugal. The experiment demonstrates that the MAFRC-ATD can effectively discover anomalous trajectories and can identify the unintentional detours due to traffic congestion.
Transformer‐based code search for software Q&amp;A sites

Abstract In software Q&amp;A sites, there are many code‐solving examples of individual program problems, and these codes with explanatory natural language descriptions are easy to understand and reuse. Code search in software Q&amp;A sites increases the productivity of developers. However, previous approaches to code search fail to capture structural code information and the interactivity between source codes and natural queries. In other words, most of them focus on specific code structures only. This paper proposes TCS (Transformer‐based code search), a novel neural network, to catch structural information for searching valid source codes from the query, which is vital for code search. The multi‐head attention mechanism in Transformer helps TCS learn enough information about the underlying semantic vector representation of codes and queries. An aligned attention matrix is also employed to catch relationships between codes and queries. Experimental results show that the proposed TCS can learn more structural information and has better performance than existing models.