Reviewerly/exHarmony huggingface dataset:
River Surface Velocimetry Based on Virtual River Dataset and Modulated-RAFT

Many image-based velocimetry methods, such as large-scale particle image velocimetry (LSPIV), space-time image velocimetry (STIV) and optical flow velocimetry (OFV), have been proposed to estimate river surface velocity (RSV) efficiently and precisely. Among these methods, OFV-based methods have drawn great attention due to its high field resolution and low requirement of tracers. As a powerful approach for estimate optical flow accurately and efficiently, deep optical flow estimation is utilized in OFV-based methods as well. However, these methods often use irrelevant datasets for training due to the immeasurability of optical flow. Models obtained by such approaches suffer from limited generalization because of domain drift. Besides, high similarity of river surfaces often leads to ambiguous correlation volume extracted by deep optical flow estimation model, which can cause mismatch. This work proposed a method for accurately and robustly RSV estimation under velocity range of 0-6.0 m/s. Specifically, we introduced a method for generating relative optical flow datasets and proposed MRAFT, a deep optical flow estimation model combining with correlation volume modulation. Experiment results demonstrate that the generated virtual river datasets effectively improve the generalization of the model and MRAFT remarkably alleviates the mismatch. Our work facilitates the application of deep optical flow on RSV estimation and provides other related researches with optical flow datasets for fine-tuning.
The Challenge of Remote Learning with the Elderly during the Covid-19 Pandemic

ABSTRACT This article aimed to discuss the challenges faced by teachers of Youth and Adult Education (YAE) in the municipality of João Pessoa/PB, to teach the elderly during the covid-19 pandemic. The qualitative method was adopted and information was collected with 10 teachers through a questionnaire in Google Forms virtual format. They were examined with the Critical Discourse Analysis (CDA). The results revealed the difficulties that teachers face in dealing with remote teaching, especially with the elderly, since this portion of the population has its own technological deficiencies and is not used to this teaching format.
The role of prosody for the expression of illocutionary types. The prosodic system of questions in spoken Italian and French according to Language into Act Theory

This article presents a corpus-based study of the correlations between prosodic contours and question speech acts in Italian and French from the perspective of the Language into Act Theory (L-AcT). A rich taxonomy of question illocutionary types is derived from two comparable corpora of informal speech taken from the C-ORAL-ROM collection and illustrated through prototypic examples. The number of questions in speech is evaluated as &amp;lt;10% of utterances. Despite their syntactic and accentual differences, the two languages share comparable pragmatic values conveyed by defined prosodic variations. Total questions, which can be answered with yes or no , are expected with canonical rising contours. Still, a good percentage shows the so-called declarative prosody (26% in FR and 36% in IT). They have been supposed to be biased by presupposing the proposition's truth and considered Requests for confirmations instead of genuinely Seeking for information acts . But presupposition, depending on intentional states, is hard to be detected, and no clear linguistic correlation was found. The corpus-based study of the prosody/pragmatics relation allows a better understanding of the system. Total questions should be framed within the larger category of Directives aimed at the addressee's linguistic behavior , which does not foresee seeking information as the only goal. When the speaker makes a hypothesis on what he asks—that is not an actual presupposition—he performs a Request for confirmation . This is true in most Total questions, and prosody complies with the canonical contour. In contrast, declarative contours correlate with corpus contexts in which the speaker does not make any hypothesis but puts pressure on the addressee ( Challenging Questions ). Partial questions genuinely seek information and comprise Open questions , which are verbless utterances where the addressee is requested to freely provide information on a given topic. Tag questions, Double questions , and Alternative questions correspond to Illocutionary patterns that, according to L-AcT, are composed of two pragmatic units framed within one prosodic strategy.
Concept of Electronic Evidence in Criminal Legal Procedure

Objective : elucidating the potential of digital transformation for elaborating the optimal means and methods of collecting evidences and introducing scientific organization of labor of the officials implementing criminal procedure. The scientific approach within the concept consists in minimizing the costs of collecting evidentiary information in criminal cases in electronic form and by electronic means, as well as storing the criminal case materials in electronic form. Methods : dialectic method occupies the leading position among the research methods, the issues of electronic documentation being considered in the interaction and interdependence with information-technological development of the society. The set of scientific cognition methods within the research creates prerequisites for objective and comprehensive approach to the problems under study. Results : the authors’ concept of electronic evidence is a system of information-technological and legal views on the criminal-procedural form, which is intended for optimizing the process of collecting, registering and preserving them in the criminal case materials. The concept development is aimed at elaborating new approaches to organizing the work of investigation agencies and courts, taking into account the achievements in the sphere of information technologies, providing new techniques of collecting criminal-relevant, criminal-procedural, criminological significant information when investigating and hearing of a criminal case. The proposed concept is also aimed at improving interaction and in-service communication of the officials of the preliminary investigation bodies with the officials of information-technological systems for the purposes of collecting evidentiary information in electronic form. Scientiﬁc novelty : the changes were systemically analyzed, which are taking place in the contemporary information society, through the prism of the emerging problems between the sectoral criminal-procedural evidentiary law and more modern technological means of collecting evidentiary information. The article demonstrates a new approach to creating technological interaction using digital technologies, on the scientific base of organization of proving activity, intended to optimize and rationalize the process of proving in criminal procedure. Practical signiﬁcance : the research materials can be used to prepare proposals on making changes and additions in the current legislation with a view of implementing the practice of already functioning models of criminal-procedural activity of foreign countries, an inexhaustible potential of information-technologies, software, and artificial intelligence to rationalize proving in criminal cases.
Swedish journalists' perceptions of legal protection against unlawful online harassment

This study examined journalists' perceptions regarding the legal system's ability to protect them against online harassment. By utilizing open-ended survey responses from respondents with varying levels of trust in the legal system, the findings suggested a need for increased technical proficiency, resources, and priority within the legal system to adequately address the issue. Additionally, a reciprocal relationship between the normalization of online harassment within the journalistic profession and the legal system's commitment to providing protection was identified. However, the study also found that when the legal system's mediated approach to online harassment is positive, it affects attitudes and norms relating to legal protection. Consequently, it reveals a unique insight into how journalists respond to the message conveyed by fair treatment and respect from the legal system. Notably, this result implies that when such messages are internalized, journalists feel more empowered to take measures against online harassment. As a result of this analysis, I propose that current laws should be implemented more effectively and that policy strategies should be developed to positively influence social norms and social control to bolster journalistic autonomy and freedom of speech in the digital age.
New public-centered child protection system in Korea

Key messageKorea's child abuse response system was transformed under policy change in April 2020, from what was previously operated on a private-centered basis to a focus on the public sector with expanded role of local governments.Promising outcomes are expected with new system as greater governmental intervention will effectively protect at-risk children with acceleration in institutional collaboration and expertise in information management and administration.
The Growth of Medical Knowledge and Data Sharing

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Underwater multi-source DOA tracking using uniform linear array based on improved GM-PHD filter

Abstract Uniform linear array (ULA) is commonly used for collecting underwater acoustic signals, and the direction-of-arrival (DOA) is obtained by array signal processing methods. The ULA-based multi-source DOA tracking suffers from the performance degradation caused by the presence of false tracks from port-starboard ambiguity. To address this problem, we propose a bearing ambiguity discrimination algorithm. Firstly, the sum of real bearing motion velocity between truth and ambiguous targets is established with the Gaussian distribution. Secondly, an improved Gaussian mixture probability hypothesis density filter, which can adaptively generate target birth intensity, is utilized to select truth and ambiguous bearings generated by the same target from the estimated posterior intensity. Thirdly, truth and ambiguous bearings are discriminated according to the fact that the change of array heading will lead to a significant change in ambiguous bearing. Simulation and real experimental results illustrate that the proposed algorithm can provide unambiguous target-generated tracks and accurately estimate the number of targets.
Recent Advances in Cybersecurity and Fraud Detection in Financial Services: A Survey

Cybersecurity issues have been gaining continuous attention from financial institutions and regulatory bodies and cyberattacks have escalated after the pandemic. Adversaries are often determined to exploit new vulnerabilities and the financial service industry is losing huge amounts in spite of strong layered defenses. In this paper, we analyze the current scenario of cybersecurity risks and provide a comprehensive overview of the recent approaches in evolving cybersecurity and fraud detection practices at scale. This paper reviews new challenges in effective cybersecurity measures and financial fraud detection. Finally some key potential directions are proposed to inspire intelligent solutions for defending and mitigating against cyberattacks.
Zone-based Federated Learning for Mobile Sensing Data

This paper proposes Zone-based Federated Learning (ZoneFL) to simultaneously achieve good model accuracy while adapting to user mobility behavior, scaling well as the number of users increases, and protecting user data privacy. ZoneFL divides the physical space into geographical zones mapped to a mobile-edge-cloud system architecture for good model accuracy and scalability. Each zone has a federated training model, called a zone model, which adapts well to data and behaviors of users in that zone. Benefiting from the FL design, the user data privacy is protected during the ZoneFL training. We propose two novel zone-based federated training algorithms to optimize zone models to user mobility behavior: Zone Merge and Split (ZMS) and Zone Gradient Diffusion (ZGD). ZMS optimizes zone models by adapting the zone geographical partitions through merging of neighboring zones or splitting of large zones into smaller ones. Different from ZMS, ZGD maintains fixed zones and optimizes a zone model by incorporating the gradients derived from neighboring zones' data. ZGD uses a self-attention mechanism to dynamically control the impact of one zone on its neighbors. Extensive analysis and experimental results demonstrate that ZoneFL significantly outperforms traditional FL in two models for heart rate prediction and human activity recognition. In addition, we developed a ZoneFL system using Android phones and AWS cloud. The system was used in a heart rate prediction field study with 63 users for 4 months, which demonstrated the feasibility of ZoneFL in real-life.
Hybrid Quantum-Classical Machine Learning for Near Real-time Space to Ground Communication of ISS Lightning Imaging Sensor Data

Near real-time sensor data is growing exponentially faster than our ability to sense and make sense of it as the current classical computing and supercomputing approaches require an enormous volume of computational resources, storage, and training time. Quantum computing is posed to exponentially outperforms today's high-performance computers and accelerate the evolution of information occurring in classical systems and sensor networks. The Lightning Imaging Sensor (LIS) mounted on the International Space Station (ISS) locates, senses, and detects lightning activities from low Earth orbit and measures radiant energy at millisecond timing over a broad regional spectrum. In this paper, we are introducing a space-to-ground hybrid quantum-classical machine learning architecture to demonstrate the application potential and the feasibility of Hybrid Quantum Neural Networks using the ISS LIS lightning dataset and a corresponding background dataset as inputs (1) to train a classical deep neural network model (2) where the feature extraction outputs are encoded as quantum states using multiple calibrated quantum encoding patterns (3) then used as part of a quantum feature mapping process, (4) allowing for enhanced hybrid QNN training, optimization, and auto-differentiation, setting the path towards polynomial advantage. The following is how the rest of the paper is structured: The introduction is covered in section I, and the motivation and our contribution are covered in section II. Section III presents related works. Section IV describes the NRT lightning imaging sensor. Section V describes the space- to-ground hybrid quantum architecture. Section VI. Space-to-ground hybrid quantum-classical machine learning architecture. Section VII describes quantum machine learning. Section VIII presents a performance analysis. Finally, section XI concludes the research paper.
Estimating the Output of Behind the Meter Solar Farms by Breaking Irradiance Data into its Diffuse and Direct Components

As more behind the meter solar farms are installed onto the power grid, the true load on the power grid becomes more hidden to the utility because the meters only read the net difference between the native load and the solar generation. This becomes problematic for grid planning since the grid needs to be ready to handle the full native load in the case of a hot and cloudy summer day when load is very high and solar generation is low. This study breaks down solar irradiance into the diffuse components from the ground and sky, and the direct (beam) irradiance. These components are then combined using the Liu-Jordan model to estimate the solar irradiance on a tilted surface. This method was then applied to data from a weather station in an area in ComEd’s service territory to estimate the solar panel output of a local 2MW metered solar farm. When comparing the predicted generation and measured generation from this solar farm, it was determined that their existed inconsistencies within the data set. After reducing the size of the data set to remove potentially poor data, this method estimated solar production with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.900 with an average absolute value error of 148kW. Based on these findings, this methodology had produced efficient results and can also be used to determine when a solar farm is not producing as expected.
Smart Supply Chain Management with Attribute-Based Encryption Access Control

The traditional supply chain management process is becoming more automated with the use of cutting-edge blockchain technologies. This helps in reducing the time taken for various processes and also helps in reducing the overall cost. In this paper, we developed a framework for tracking the progress of the supply chain process. The framework uses a variety of technologies to track the status and the progress of products in transit. Raspberry Pi 400 is used to track the progress of products at each chain level using Radio-frequency Identification (RFID) tags as well as other sensors. The tracking data collected by the Raspberry Pi is stored on the blockchain. A decentralized storage network (DSN) is used to store the encryption of Intellectual Property (IP) data (e.g., blueprints, design choices at each production stage, etc). Attribute-based encryption is used to allow access to the data only to authorized stakeholders who have the appropriate attributes. We leverage smart contracts and Node.js to develop a decentralized application (DApp) for monitoring the supply chain process. The DApp is hosted on the decentralized Ethereum platform, which provides security and transparency for all transactions. Our security analysis demonstrates that the system is secure against data theft and the results obtained from our experiments show that our proposed framework is scalable and efficient.
Ensemble of Gated Recurrent Unit and Convolutional Neural Network for Sarcasm Detection in Bangla

Sarcasm is a form of emotional expression in which someone speaks or writes something entirely contrary to what is truly intended with humor underlying. Detecting sarcasm is a difficult task as the actual meaning of a text is not consistent. In Bangla language, not enough work has been done in this field. Therefore, we have experimented with some of the state of the art NLP's techniques, machine learning and deep learning models in an existing dataset. In this paper, we have proposed a sarcasm detection AI for Bangla language based on deep learning architecture which is an ensemble model consists gated recurrent unit (GRU) and convolutional neural network (CNN). Our proposed model achieved 96% F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score and 96% of accuracy which surpasses the existing traditional machine learning models performance.
Transformation of Visual Information into Bangla Textual Representation

In the past several years, the interest in research like generating humanoid descriptions of scenarios by detecting and analyzing their components has been increased tremendously. Even though a significant amount of research has been put into automating the process of converting visual information into written representation, some languages, such as Bangla, which have a limited amount of resources, continue to be quite unfocused due to a lack of standard datasets. In order to resolve this issue, we have introduced a new dataset named “Biboron”, in which we manually gathered information in Bangla of images extracted from the widely available Flickr30k dataset that were then post-processed and examined for quality assurance. “Biboron” contains 1,58,915 distinct sentences describing 31,783 images which further specifies the versatile nature of the dataset. Furthermore, we have presented two models in order to enhance the automated extraction of visual information from images and represent in Bangla. The first model includes Local Attention, whilst the second model is based on Multi-Head Attention with Transformers. The image feature extractor of the models utilized VGG16, while bidirectional LSTM backed by CuDNN was used in the decoder network. The BLEU scores suggest that the second model appears to outperform the first one in terms of generating more relevant textual representations from images by achieving BLEU-1, BLEU -2, BLEU -3, BLEU -4 scores of 0.78, 0.53, 0.37, 0.21 respectively
mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving

Detecting dangerous driving has been of critical interest for the past few years. However, a practical yet minimally intrusive solution remains challenging as existing technologies heavily rely on visual features or physical proximity. With this motivation, we explore the feasibility of purely using mm Wave radars to detect dangerous driving behaviors. We first study characteristics of dangerous driving and find some unique patterns of range-doppler caused by 9 typical dangerous driving actions. We then develop a novel Fused-CNN model to detect dangerous driving instances from regular driving and classify 9 different dangerous driving actions. Through extensive experiments with 5 volunteer drivers in real driving environments, we observe that our system can distinguish dangerous driving actions with an average accuracy of 97(±2)%. We also compare our approach with existing state-of-the-art baselines to establish their significance,
Incorporating an Integrated Software System for Stroke Prediction using Machine Learning Algorithms and Artificial Neural Network

A stroke is a condition where there is no flow of blood in the brain, depriving it of oxygen and resulting in long-term brain damage and loss of function. Most typically, a clot in an artery delivering blood to the brain is the culprit. Hemorrhage, in which blood leaks into the brain due to a ruptured vessel, is another possible reason. A stroke can result in long-term harm, such as partial paralysis, speech impairment, cognitive impairment, and memory loss. The second leading cause of mortality is a stroke, which is also a significant contributor to disability worldwide. According to the World Stroke Organization (WSO), a stroke will occur in 1 out of every four adults over the age of 25. The number of stroke victims worldwide exceeds 110 million. The objective of the thesis is to incorporate an integrated software system that will predict stroke using artificial neural networks and machine learning algorithms like logistic regression, decision tree classification, random forest classification, K-nearest neighbors, support vector machines, and Nave Bayes classification. The rate of strokes is already alarming. Having an intuitive system to conveniently predict stroke could help reduce the rate of stroke by educating people, which is the focus of the research.