Reviewerly/exHarmony huggingface dataset:
A Security-Evaluation Framework for Mobile Cross-Border e-Government Solutions

Security evaluation is crucial for any security-critical system. In this context, a system can mean technical systems, organizations, or any other entity with certain security requirements. The major challenge in doing risk analysis is the trade-off between completeness and complexity. When done on a more abstract level, certain risks are potentially overlooked. When done on a very detailed level, risk analyses quickly become complex and exceed available resources. To tackle this challenge, various norms and standards propose different security evaluation methodologies. These methodologies vary depending on their target scope. Also, these standards typically remain on a rather abstract level to ensure broad applicability to different systems. In practice, this often complicates the application of these standards to concrete technical systems. In this paper, we tackle this issue by proposing a customized security-evaluation framework tailored to the special characteristics of cross-border e-government services. The proposed framework does not re-invent the wheel but combines aspects and approaches of established norms and standards to cherry-pick from each standard those aspects most beneficial for the given context. We evaluated the proposed framework by applying it to a set of software building blocks, which have been developed in the Horizon-2020 project mGov4EU and leverage mobile cross-border e-government services in Europe. The conducted evaluation shows that the proposed framework facilitates the practical application of security evaluations in the targeted domain and supports evaluators in handling the trade-off between completeness and complexity.
Frequency-Independent Centroid Frequency Shift Method for Signal Attenuation Estimation

Signal attenuation estimation is a critical task in signal processing and is essential for analyzing media characteristics and compensating for energy loss. Current centroid frequency shift-based methods for estimating attenuation are mostly based on the assumptions of full-band analysis and frequency-dependent or independent quality factor (Q). In this paper, we propose a novel frequency-independent centroid frequency shift (FiCFS) method for signal attenuation estimation with higher adaptability. It is based on arbitrary frequency bands instead of the full-band spectrum defined by the conventional centroid frequency shift (CFS) method, and accordingly, the derivation is performed by incomplete gamma functions instead of ordinary gamma functions. Through rigorous mathematical derivations, the first moment (centroid frequency) and the second moment (variance) are proved to be frequency insensitive for arbitrary frequency bands, and then the arbitrary frequency band-based CFS method, i.e., the FiCFS method, is derived with the frequency-weighted exponential spectrum assumption. The matching ability of the frequency-weighted exponential spectrum to other signal spectra is verified, demonstrating the method’s adaptability to most attenuated signals. Experimental results using synthetic and field data sets demonstrate that the proposed method is adaptive, noise-immune, and reliable.
Dialect Translation of English Language to Telangana: Mexin Project

Generally Telangana dialect is frequently spoken in vocal daily interactions. Official Telugu is the language used in books, newspapers, academic journals, and other types of literature. Telangana only produces a small quantity of literature and written material in documentary series form. Despite numerous attempts, the Telangana language’s range is still confined to vocal forms. We are attempting to build a dataset of Telangana words, that are obtained from various documents, novels, essays, plays, and everyday interactions of native speakers, to mitigate this barrier and enable the electronic profusion of Telangana dialect. The first phase of the work consisted of extracting some research papers relevant to the topic and gaining some more insight into the objective focused. We then moved on to collect words in the Telangana language as a second phase, i.e., making a dataset. Then using other methods such as tokenization we began with the third phase of our project to implement the proposed work where finally conversion of Telangana dialects is translated to English..
GCG-Net: Graph Classification Geolocation Network

Large-scale visual geolocation is a meaningful task that involves locating a query image by comparing it with images in a database and predicting the most similar image. However, the widely-used training framework based on contrastive learning cannot fully utilize all data and is difficult to adapt to larger scales. At the same time, the traditional Convolutional Neural Networks (CNNs) and Vector of Locally Aggregated Descriptors (VLAD) using aggregated features cannot fully reflect the relationship between the local features of the image. Therefore, a Graph Neural Network (GNN) is designed as the feature extraction network, and then a training framework based on image classification is constructed. Specifically, a data grouping strategy and special loss function are designed for better training results. After training, we adopt an image retrieval strategy based on kNN for position. In addition, considering that existing datasets cannot be adapted to our requirements, two datasets are constructed for experiments, that one contains large-scale satellite images and the other fuses satellite and Unmanned Aerial Vehicle (UAV) images. Results demonstrate that our method outperforms other common methods on both the two datasets. The results demonstrate the effectiveness of our approach for UAV visual geolocation and provide ideas for future research in this field.
Density Coverage-Based Exemplar Selection for Incremental SAR Automatic Target Recognition

The traditional Synthetic Aperture Radar Automatic Target Recognition (SAR/ATR) algorithm can train a sufficient number of known class samples and classify the samples in the test set. However, if the old model is trained only with the new class samples, the old class samples’ knowledge is easily forgotten by the new model, which is called catastrophic forgetting. The reason is that the model only fits the distribution of current training samples, so training the whole data set is necessary. Due to the limitation of storage resources, it is often not feasible to retain the whole data set. In order to avoid this phenomenon, a small number of old class samples can be kept to train with the new class samples. Therefore, how to select the old class samples becomes the key point. In this paper, the Density Coverage-Based Exemplar Selection (DCBES) is proposed to choose the key samples of the old class. DCBES selects samples based on the metric learning theory and the set covering theory. First, the metric learning theory is used to measure the similarity between samples and to obtain the density range of samples. Then the exemplar selection problem is considered a set covering problem, to select a fixed number of exemplars to achieve the maximum coverage of the class density range. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our method is superior to other exemplar selection methods and achieves the best results.
Evaluation of Depression Detection in Sentiment Analysis Through Machine Learning Model

Nowadays, the significant increase in the usage of social media platforms has led to many informative insights into the individual’s personality. Twitter is used as a platform to share emotions and views in terms of tweets. These labels and tweets can be used for sentiment analysis. Modern machine learning models can be used for data analysis for a large dataset. In this study, machine learning models are used to classify the dataset into various groups depending on the sentiment score for the tweets showing some depression. Identifying depression is done by creating a bag of words about depression. A score is provided on the polarization. Polarization is performed by giving a value for positive, negative, and neutral rated on a Likert scale of 0–4. The study aims to detect depression in Twitter users depending on the data shared on social media. In this study, machine learning models were implemented to detect depression among people. The models used in the study are Logistic Regression, Bernoulli Naive Bayes, LinearSVC, Voting Classifier, XGBoost, AdaBoost, and GradientBoost. The accuracy score for Logistic Regression is 86%, Bernoulli Naive Bayes is 84%, LinearSVC is 85%, Voting Classifier has a score of 86%, XGBoost is 82%, AdaBoost is 79%, and GradientBoost is 79%. From the result analysis, it can be seen in the study that Logistic Regression, LinearSVC, and Voting Classifiers have performed well in comparison with other models used.
Feature Selection Methods Comparison: Logistic Regression-Based Algorithm and Neural Network Tools

Features selection of high-dimensional data is desirable, mainly when extensive data is used and generated more often. Currently considered research problems are related to the appropriate feature selection in a multidimensional space allowing the selection of only those relevant to the analyzed problem. The implemented and applied machine learning approach made it possible to recognize feature profiles to distinguish two classes of observations. Regarding methods based on logistic regression, 21 features were selected, and 10 features related to the examined problem were identified for neural networks. This made it possible to significantly reduce the dimensionality of the data from as many as 406 original dimensions. Moreover, the feature selection approaches allowed for consistent results; as many as eight features were common to both utilized methods. The application of the recognized profiles also made it possible to obtain very high classification quality metrics, which in the case of logistic regression both for feature selection and classification, amounted to almost 94% of the weighted classification accuracy while maintaining the F1score metric at a very high level of 93%. The achieved results indicate the high efficiency of the proposed feature selection solution. The presented results support the potential of the implemented logistic regression-based approach for solving feature selection and classification for two-class problems.
Object Detection by Tiny-YOLO on TurtleBot3 as an Educational Robot

By the unceasing efforts of many scientists, machine learning algorithms are increasing quickly with an enhanced object detection performance for the explicit applications on specific devices. This paper investigates the operation of TurtleBot3 as an educational wheeled autonomous robot which Tiny-YOLO algorithm has been applied as a novel application on it. At first, the required knowledge about the kinematics and dynamics of a differential drive robot with constraints is studied, and a graph where the robot is working under some specific conditions is plotted. The robot operating system (ROS) and simultaneous localization and mapping (SLAM) of the TurtleBot3 have been explained. The robot is equipped with a LiDAR sensor and a camera embedded onto it as a sensor fusion SLAM for a more precise type of mapping. Finally, Tiny-YOLO as a machine learning algorithm for object detection is implemented based on the fact that Raspberry Pi could not handle the full YOLO due to its memory capacity limitation, and the results are discussed.
Pre-training Classification and Clustering Models for Vietnamese Automatic Text Summarization

Our investigation aimed to propose a new single-document extractive text summarization model, which consists of a classifier and a summary component based on pre-trained clustering models. First, we train the classifier on the training data set, then we train the clustering models on the subtraining data sets, and split from the entire training data set. In the summary process, the summary model uses the classifier to predict the input text label and then uses this label to choose the corresponding clustering model for summarizing. The model’s numerical test results on the Vietnamese data set based on ROUGE-1, ROUGE-2, and ROUGE-L are $$51.50 \%$$ , $$16.26 \%$$ , and $$29.25 \%$$ , respectively. In addition, our model can perform well on cost-effective resources like an ARM CPU to summarize large amounts of documents.
American State Government Cybersecurity Policies

With the exponential rise in the cybersecurity incidents in state and local governments in the post-Covid 19 era, American state governments have had to adopt cybersecurity policies governing not only state government employees, but also to provide guidelines to local governments. In this paper, we comparatively assess the scope of the cyber-security policies across American state governments. We examined the cybersecurity policies considered by states to date. About one-third of the policies that were considered in the state legislatures were enacted. Almost every state has at least one cybersecurity policy that was newly enforced. We coded these policies in terms of their scope, their mandate requirements, funding, and management. Our initial analysis shows that there is convergence in terms of their scope. The policies have quickly evolved in the context of the Covid-19 pandemic, with initial concerns on broader range of cybersecurity incidents to that of ransomware related policies.
A Unified and Accurate Subpixel Detector of Control Points for Camera Calibration

Camera calibration is a necessary step in 3D computer vision, and the accuracy of calibration is largely dependent on the localization precision of the control points of the calibration patterns. However, the sub-pixel localization methods for the commonly used two types of control points, corners of the squares and centers of the circles, are markedly different from each other. In this work, we propose a unified approach to sub-pixel localization for the two types of control points. The approach is derived from theoretical analysis and consists of a closed-form solution, followed by algorithms for practical implementation. Both synthetic dataset and real calibration experiments have been used to test the proposed approach and good results have been obtained. Compared with state-of-the-art techniques, the proposed approach provides a unified and high-precision sub-pixel localization method of the control points for the commonly used calibration patterns.
Spatiotemporal Multiscale Correlation Embedding With Process Variable Reorder for Industrial Soft Sensing

Soft sensor techniques have been extensively utilised for predicting key variables in the process industry. To extract fine-grained and holistic features for efficient prediction, proper modeling strategies for multiscale spatial and temporal correlations are heavily desired. In this paper, we summarize the multiscale correlations as short and long temporal dependency, local and global spatial relevance, and spatiotemporal coupled correlations. Such correlations should be explicitly and comprehensively characterized as different key variables often require diverse or multiscale spatiotemporal features. To this end, a spatio-temporal multi-scale correlation embedding (STMCE) framework is proposed for soft sensing with characterization and refining of multiscale spatial and temporal features. On the one hand, we design a reorder data patch embedding strategy (RDPE) to extract local spatiotemporal coupled correlations by reordering the permutation of process variables and embedding local correlations into high-dimensional vectors. On the other hand, we propose a global feature fusion and refining network (GFFRNet) via self-attention mechanism and feature refining technique, which enables the model to effectively aggregate and privilege predictive features for key variable prediction. Superior to existing methods that partially focus on spatial or temporal dependencies, our approach explicitly models both local and global spatiotemporal coupled relevance under multiscale perspective, encouraging the model to explore more fine-grained and comprehensive information from process data and further improve prediction accuracy. In comparison with ten baseline models, our STMCE empirically presents the state-of-the-art results on both benchmark and real-world industrial datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., obtaining a relative MAE reduction of 13.32% compared with the second best method in the benchmark multiphase flow dataset.
Artificial Intelligence for Knowledge Visualization: An Overview

Artificial Intelligence (AI) has transformed the way we interact with data and information. The growing volume of data and knowledge has made knowledge visualization (KV) important tool for understanding and analyzing complex datasets. AI has the potential to significantly improve KV by automating tasks such as data processing, pattern recognition, and visualization design. This paper provides a comprehensive overview of the current state-of-the-art on the application of AI for KV, as well as the challenges related to visualizing complex datasets. Different KV approaches, such as information visualization, ontologies and concept mapping are examined. We explore various AI techniques used to enhance these methods, including natural language processing (NLP), machine learning (ML), and deep learning (DL). Finally, we discuss the limitations and promising areas for future research in this field.
Transfer Learning-Based End-to-End Indian English Recognition System

ASR or Automatic Speech Recognition systems has been a focus of recent study. ASR is a computer science and linguistics based multidisciplinary system or technology that allows a computing system to deduce the transcribed form of text from the spoken speech waveform. DeepSpeech, a cutting-edge speech recognition system created by means of the end-to-end (E2E) deep learning approach, is one of the most modern ASR architectures. The deep learning approach basically is a highly enhanced Recurrent Neural Network (RNN) training algorithm that makes use of many Graphical Processing Units (GPUs). The majority of the training is conducted on foreign English speech resources, resulting in limited generalisation for Indian English. We developed an E2E-based ASR system for Indian English adopting a transfer learning strategy and the DeepSpeech model. We also employed fine-tuning as well as data argumentation techniques so as to optimise and refine DeepSpeech ASR in an Indian English scenario. Transfer learning as well as fine-tuning of the already trained DeepSpeech model is carried out using IndicTTS database of Indian English. The untrained model, the model trained by us, and other available ASR systems are analysed and compared.
Exploring the Usability of Phone-Based Virtual Reality in Management-Communication Training

Abstract Phone-based virtual reality (VR) is a potentially important delivery medium for management communication training, particularly as hybrid work environments create the need for new ways to deliver training experiences where students can role-play and practice privately with virtual employees. However, few studies have focused on the viability of delivering phone-based VR, particularly in terms of understandability and usability. In this preliminary case study, we utilize a proxy group of business school students and study their ability to assemble, navigate, and complete a training module using a phone-based VR system that focuses on management communication training. Overall, the system obtained an excellent mean rating on a common standardized instrument, the System Usability Scale (SUS), and other measures. We also detail concerns of a small but important participant group who gave the system less favorable ratings and delineate modifications that may be appropriate for the intended audience, Texas Department of Transportation (TxDOT) employees.
QfaR: Location-Guided Scanning of Visual Codes from Long Distances

Visual codes such as QR codes provide a low-cost and convenient communication channel between physical objects and mobile devices, but typically operate when the code and the device are in close physical proximity. We propose a system, called QfaR, which enables mobile devices to scan visual codes across long distances even where the image resolution of the visual codes is extremely low. QfaR is based on location-guided code scanning, where we utilize a crowd-sourced database of physical locations of codes. Our key observation is that if the approximate location of the codes and the user is known, the space of possible codes can be dramatically pruned down. Then, even if every "single bit" from the low-resolution code cannot be recovered, QfaR can still identify the visual code from the pruned list with high probability. By applying computer vision techniques, QfaR is also robust against challenging imaging conditions, such as tilt, motion blur, etc. Experimental results with common iOS and Android devices show that QfaR can significantly enhance distances at which codes can be scanned, e.g., 3.6cm-sized codes can be scanned at a distance of 7.5 meters, and 0.5m-sized codes at about 100 meters. QfaR has many potential applications, and beyond our diverse experiments, we also conduct a simple case study on its use for efficiently scanning QR code-based badges to estimate event attendance.
Quantitative and Qualitative Exploration of the Effect of a Wearable Item on Non-Organic Virtual Limb Embodiment and User Behavior in Immersive Environments

Abstract This experiment was designed to investigate the potential contribution to the sense of embodiment of a wristband worn by the participants in both real and virtual environments. In addition, two virtual limb models were compared following a mixed between-within subjects design: an organic hand and a non-organic prosthesis matching the proposed task. Quantitative results revealed no significant effect of the wristband, while post-experiment semistructured interviews revealed that the wristband fostered the identification with the virtual limbs for several participants, but that it might be conditioned by interindividual differences. Ownership scores were significantly higher with the virtual hand. However, participants experienced a very high sense of agency with both conditions despite the lack of finger tracking when controlling the prosthesis. Agency was positively correlated with participants' perceived change in their body schema when embodying the prosthesis. Subjective and objective measures demonstrated that participants were less hesitant and that more collisions were recorded at higher speeds against potentially threatening objects with the non-organic prosthesis.