Reviewerly/exHarmony huggingface dataset:
Holistic and analytic assessment of functional adequacy

Abstract This study looks at the correlation between functional adequacy (FA), holistically assessed, and analytic linguistic measures, in a corpus of texts written by Italian monolingual and multilingual primary school pupils. Texts were first evaluated using the FA rating scales by Kuiken and Vedder ( 2017 , 2018 ), plus one for Coherence &amp; Cohesion from the CEFR ( Council of Europe, 2001′ ). They were then coded for a number of features directly bearing on FA and its subdimensions. Results show correlations between holistic scores and analytic measures, such as those between Content and the number of words or secondary idea units ( r = .59 / .65). Others were less strong, yet going in the expected direction, e.g. more ambiguous referential expressions were negatively correlated to Comprehensibility. Correlations were generally stronger for monolingual than for multilingual children.
Subspace cross representation measure for robust face recognition with few samples

Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition.
Blockchain Technology and Future Banking

In recent years, the blockchain emerged as a trending technology, and the innovations introduced by blockchain technology have influenced financial services as well as other sectors. The banks are currently using several blockchain applications, but they are not mature and still not widely adopted. Despite the numerous benefits, blockchain has many challenges to be solved, such as lack of regulation and governance; energy and other costs of development and implementation; interoperability, technical, security, and privacy problems; and user-related challenges. Therefore, this chapter aims to overview blockchain technology and discuss the opportunities and challenges for the future banking sector concerning the extant literature.
Watch Me Write: Exploring the Effects of Revealing Creative Writing Process through Writing Replay

The written word is an asynchronous form of communication through which static texts are exchanged. However, the act of writing is a dynamic cognitive process in which evolving ideas are organized into a coherent narrative. This process is not shared with readers because they only see the final product. With the emerging culture of live streaming and sharing creative practice with online audiences, writing replays can be a new expressive medium and content for writers and another way to engage with reading for the audience. In this paper, we explore the benefits of watching a real-time writing replay. We recorded the writing processes of professional writers (n = 13) and interviewed them about their and others' replays. These writers found replays engaging. Replays facilitated self-reflection and helped writers empathize with each other. In addition, we conducted an online survey (n = 78) to compare readers' comprehension and perception of writing replays with those of traditional text. We found that writing replays enhanced user engagement and the perceived quality of the text for some writing styles. Participants from both studies considered using writing replays regularly.
Moving Target Tracking Algorithm Based on Improved Resampling Particle Filter in UWB Environment

In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.
Optimization of Imbalanced and Multidimensional Learning Under Bayes Minimum Risk and Savings Measure

The full potential of data analysis is crippled by imbalanced and high-dimensional data, which makes these topics significantly important. Consequently, substantial research efforts have been directed to obtain dimension reduction and resolve data imbalance, especially in the context of fraud detection analysis. This work aims to investigate the effectiveness of hybrid learning methods for alleviating the class imbalance and integrating dimensionality reduction techniques. In this regard, the current study examines different classification combinations to achieve optimal savings and improve classification performance. Against this background, several well-known machine learning models are selected such as logistic regression, random forest, CatBoost (CB), and XGBoost. These models are constructed and optimized based on Bayes minimum risk (BMR) associated with the oversampling method synthetic minority oversampling technique (SMOTE) and different feature selection (FS) techniques, both univariate and multivariate. To investigate the performance of the proposed approach, different possible scenarios are analyzed both with and without balancing, with and without FS, and optimization using BMR. With a major insight about the best method to use, BMR shows a good optimization when used with SMOTE, symmetrical uncertainty for FS, and CB as a boosted classifier, principally in terms of F1 score and savings metrics.
Stream Iterative Distributed Coded Computing for Learning Applications in Heterogeneous Systems

To improve the utility of learning applications and render machine learning solutions feasible for complex applications, a substantial amount of heavy computations is needed. Thus, it is essential to delegate the computations among several workers, which brings up the major challenge of coping with delays and failures caused by the system’s heterogeneity and uncertainties. In particular, minimizing the end-to-end job in-order execution delay, from arrival to delivery, is of great importance for real-world delay-sensitive applications. In this paper, for computation of each job iteration in a stochastic heterogeneous distributed system where the workers vary in their computing and communicating powers, we present a novel joint scheduling-coding framework that optimally split the coded computational load among the workers. This closes the gap between the workers’ response time, and is critical to maximize the resource utilization. To further reduce the in-order execution delay, we also incorporate redundant computations in each iteration of a distributed computational job. Our simulation results demonstrate that the delay obtained using the proposed solution is dramatically lower than the uniform split which is oblivious to the system’s heterogeneity and, in fact, is very close to an ideal lower bound just by introducing a small percentage of redundant computations.
Lexical semantics enhanced neural word embeddings

Current breakthroughs in natural language processing have benefited dramatically from neural language models, through which distributional semantics can leverage neural data representations to facilitate downstream applications. Since neural embeddings use context prediction on word co-occurrences to yield dense vectors, they are inevitably prone to capture more semantic association than semantic similarity. To improve vector space models in deriving semantic similarity, we post-process neural word embeddings through deep metric learning, through which we can inject lexical-semantic relations, including syn/antonymy and hypo/hypernymy, into a distributional space. We introduce hierarchy-fitting, a novel semantic specialization approach to modelling semantic similarity nuances inherently stored in the IS-A hierarchies. Hierarchy-fitting attains state-of-the-art results on the common- and rare-word benchmark datasets for deriving semantic similarity from neural word embeddings. It also incorporates an asymmetric distance function to specialize hypernymy's directionality explicitly, through which it significantly improves vanilla embeddings in multiple evaluation tasks of detecting hypernymy and directionality without negative impacts on semantic similarity judgement. The results demonstrate the efficacy of hierarchy-fitting in specializing neural embeddings with semantic relations in late fusion, potentially expanding its applicability to aggregating heterogeneous data and various knowledge resources for learning multimodal semantic spaces.
Towards Real-Time Safety Analysis of Small Unmanned Aerial Systems in the National Airspace

The rapid growth in the number of small Unmanned Aerial Systems (sUAS) and the related increase in reported airspace violations and incidents, makes it imperative to improve sUAS safety. In this paper, we describe our vision and preliminary results for deploying data analytic solutions onboard sUAS in order to detect, diagnose, and potentially mitigate emergent anomalies. We take a multi-prong approach to support both run-time and post-mortem analysis of flight anomalies. We use search-based techniques to systematically explore the vast sUAS configuration space in order to identify unsafe flight controller configurations, and inspect formal documentation and online forum discussions to identify 15 common types of sUAS flight failures. As a proof of concept, we train two deep-learning techniques (LSTM and ANN) to detect problems related to vibration, excessive attitude fluctuations, and compass interference, and demonstrate their capabilities for detecting runtime anomalies. We then perform a preliminary exploration of the impact various configuration parameters have upon vibration, and show how simple runtime adaptations of the configuration space, can potentially reduce vibration when it occurs. Finally, we position our approach within an architecture for the larger airspace ecosystem.
Switching Gaussian Mixture Variational RNN for Anomaly Detection of Diverse CDN Websites

To conduct service quality management of industry devices or Internet infrastructures, various deep learning approaches have been used for extracting the normal patterns of multivariate Key Performance Indicators (KPIs) for unsupervised anomaly detection. However, in the scenario of Content Delivery Networks (CDN), KPIs that belong to diverse websites usually exhibit various structures at different timesteps and show the non-stationary sequential relationship between them, which is extremely difficult for the existing deep learning approaches to characterize and identify anomalies. To address this issue, we propose a switching Gaussian mixture variational recurrent neural network (SGmVRNN) suitable for multivariate CDN KPIs. Specifically, SGmVRNN introduces the variational recurrent structure and assigns its latent variables into a mixture Gaussian distribution to model complex KPI time series and capture the diversely structural and dynamical characteristics within them, while in the next step it incorporates a switching mechanism to characterize these diversities, thus learning richer representations of KPIs. For efficient inference, we develop an upward-downward autoencoding inference method which combines the bottom-up likelihood and up-bottom prior information of the parameters for accurate posterior approximation. Extensive experiments on real-world data show that SGmVRNN significantly outperforms the state-of-the-art approaches according to F1-score on CDN KPIs from diverse websites.
Ontology Construction of City Hotline Service for Urban Grassroots Governance

In order to satisfy the current needs of urban grassroots governance, a novel city hotline service ontology is constructed to improve the efficiency and effectiveness of city hotline service in this paper. Firstly, the TOVE method is applied to construct government hotline service ontology for urban grassroots governance based on the cases of city hotline service. Secondly, the necessary concepts are defined, including the hierarchical relationships, attributes, and constraints among the seven modules of the city, personnel, organization, government, hotline appeal, hotline complaint object, and hotline processing results. Finally, the city hotline service ontology is built with the protégé ontology development tool and then tested in hotline scenarios of different urban grassroots governance. The testing results verify the validity and practicability of the proposed ontology so that the needs of hotline service are met in urban grassroots governance.
NMMF-Stream: A Fast and Accurate Stream-Processing Scheme for Network Monitoring Data Recovery

Recovery of missing network monitoring data is of great significance for network operation and maintenance tasks such as anomaly detection and traffic prediction. To exploit historical data for more accurate missing data recovery, some recent studies combine the data together as a tensor to learn more features. However, the need of performing high cost data decomposition compromises their speed and accuracy, which makes them difficult to track dynamic features from streaming monitoring data. To ensure fast and accurate recovery of network monitoring data, this paper proposes NMMF-Stream, a stream-processing scheme with a context extraction module and a generation module. To achieve fast feature extraction and missing data filling with a low sampling rate, we propose several novel techniques, including the context extraction based on both positive and negative monitoring data, context validation via measuring the Pointwise Mutual Information, GRU-based temporal feature learning and memorization, and a new composite loss function to guide the fast and accurate data filling. We have done extensive experiments using two real network traffic monitoring data sets and one network latency data set. The experimental results demonstrate that, compared with three baselines, NMMF-Stream can fill the newly arrived monitoring data very quickly with much higher accuracy.
Adversarial Attacks on Deep Neural Networks Based Modulation Recognition

Modulation recognition models based on deep neural network (DNN) have the advantages of automatic feature extraction, fast recognition and high accuracy. However, due to the interpretability defects, DNN models are vulnerable to adversarial examples designed by attackers. Most existing researches focus on the accuracy of modulation recognition models, while ignoring the huge threat of adversarial examples to the safety and reliability of the models. In the field of modulation recognition, many existing attack methods have good attack performance for simple neural networks, but poor performance for more complicated DNNs. Therefore, this paper proposes an adversarial attack method based on dynamic iterative. The proposed method uses a dynamic iterative step that changes with iteration instead of being fixed. Simulation results show that the proposed attack method has better attack performance when the disturbance is specified than the traditional attack methods.
Evaluation of deep learning model in the field of electromagnetic signal recognition

With the rapid growth of spatial electromagnetic devices, electromagnetic signals are becoming more and more dense and complex, traditional methods are increasingly difficult to cope with, so deep learning technology is gradually applied to this field. The deep learning network is deployed in the signal recognition carrier with form of model which can learn the essential characteristics of datasets. The performance of model quality largely determines the final effect of electromagnetic signal recognition. However, there are many problems in the process of deployment, and significant risks in both robustness and security. Therefore, this paper proposes a complete model quality evaluation index system in the field of electromagnetic signal recognition, and evaluates model performance based on business demand to ensure the working quality, break through the quality bottleneck and optimize the deployment of model in practical application.
Development of mobile app through React Native hybrid framework

Mobile apps often include methods and functions that collect and process sensitive user data. Anyway, when utilizing them, we usually encounter certain drawbacks. Therefore, the aim of this paper is to present a native app, as well as emphasize the necessary reasons for developing such application, it's main purpose, the technologies used to develop this application, the limitations and benefits, system capabilities and necessary conditions for manipulating this application. The Stellar Mobile App will be available to every user of mobile devices, regardless of the operating system in which it operates, or regardless of the device.
REGRETS: A New Corpus of Regrettable (Self-)Disclosures on Social Media

In the past few years, researchers have shown a growing interest in techniques for automated detection of regrettable disclosures (things people wish they had not shared) on social media. Most of these proposals formulate the task of automatically detecting potentially regrettable disclosures as a supervised classification problem. In such a setting, the underlying classification model is trained and validate on a dataset labeled accordingly. However, despite growing efforts, existing approaches remain limited, partly due to the lack of high-quality corpus of regrettable messages and comments shared on social media. Previous work tend to confuse regrettable disclosure with related concepts such as hate speech, profanity and offensive language, ignoring empirical findings on the reasons, the types of contents, and disclosure contexts that often lead to regrets. Moreover, corpora used in prior work are typically limited in size and w.r.t. their source domains (i.e., social media platforms) and scope (i.e., range of regret-related topical content used as labels). The goal of this paper is to contribute towards lowering the barrier for developing effective systems for automated detection of regret-related posts. We propose a novel methodology for large-scale data collection and semi-automated annotation. We introduce REGRETS, a new large-scale corpus of 4,7 million regrettable text-only posts and comments with high-quality annotations. Further, we propose regret-specific embeddings models pre-trained on our corpus of user-generated social media texts which were extracted from various popular social media ecosystems. Lastly, we report on analyses that demonstrate the feasibility of partly automating the annotation of social media texts, and the richness of the resulting corpus. We release our findings as resources to facilitate further interdisciplinary research: https://bit.ly/3fO36Ex.
Analyzing Credit Card Fraud Detection based on Machine Learning Models

Credit card use is not always the best way to use for payments, but the most demonstrable payment mode is through the credit card for both offline as well as for online payments, which can result in deficit of funds. As the online shopping is booming it helps in rendering the cashless payment modes. It can be used at shopping’s, paying rent, paying utilities bill, internet bill, travel and transportation, entertainment, food. Using for all these things there is a chance of fraud transactions for a credit card, hence there is more risk. There are many types of fraudulent detections most of the banks and institutions are preferring fraud detection applications.it has become very hard to find out the fraud detections, After the transaction is done there is a chance of detecting fraudulent transactions in the manual business processing system. In real time the bunco transactions are done with real transactions, but it seems not to be sufficient for detecting [1]. Machine learning and data science both are playing a very important role in identifying the fraud detections. This study uses data science and machine learning for detecting the fraud detection to demonstrate various modellings. The problem enables the transactions of the previously done transaction data.
Incentive and Knowledge Distillation Based Federated Learning for Cross-Silo Applications

Big data are usually characterized by heterogeneity in real-world cross-silo applications, such as healthcare, finance, and smart cities, leaving federated learning a big challenge. Further, many existing federated learning schemes fail to fully consider the diverse willingness and contributions of data providers in participation. In this paper, to address these challenges, we are motivated to propose an incentive and knowledge distillation based federated learning scheme for crosssilo applications. Specifically, we first develop a new federated learning framework, to support cooperative learning among diverse heterogeneous client models. Second, we devise an incentive mechanism, which not only stimulates workers to provide more high-quality data, but also improves clients’ enthusiasm for participating in federated learning. Third, a novel knowledge distillation algorithm is designed to deal with data heterogeneity. Extensive experiments on MNISTfflEMNIST and CIFAR10/100 datasets with both IID and Non-IID settings, demonstrate the high effectiveness of the proposed scheme, compared with stateof-the-art studies.