Reviewerly/exHarmony huggingface dataset:
Modulation Mode Recognition Method of Non-Cooperative Underwater Acoustic Communication Signal Based on Spectral Peak Feature Extraction and Random Forest

The modulation mode recognition of non-cooperative underwater acoustic (UWA) communication signal faces great challenges due to the influence of the UWA channel and the demand for efficient recognition. This work proposes a recognition method for UWA orthogonal frequency division multiplexing (OFDM), binary frequency shift keying (2FSK), four-frequency shift keying (4FSK), and eight-frequency shift keying (8FSK) by using spectral peak feature extraction combined with random forest (RF). First, a new spectral peak feature extraction method is proposed. In this method, pre-processing, waveform optimization, and feature extraction are used to ensure that the extracted feature maintains high robustness in the UWA channel. Then, we designed an RF classifier that can meet the demand for high-efficiency recognition and good performance. Finally, simulation and experimental results verified the feasibility of the recognition method.
The Legendre Symbol and the Modulo-2 Operator in Symmetric Schemes over Fnp

Motivated by modern cryptographic use cases such as multi-party computation (MPC), homomorphic encryption (HE), and zero-knowledge (ZK) protocols, several symmetric schemes that are efficient in these scenarios have recently been proposed in the literature. Some of these schemes are instantiated with low-degree nonlinear functions, for example low-degree power maps (e.g., MiMC, HadesMiMC, Poseidon) or the Toffoli gate (e.g., Ciminion). Others (e.g., Rescue, Vision, Grendel) are instead instantiated via high-degree functions which are easy to evaluate in the target application. A recent example for the latter case is the hash function Grendel, whose nonlinear layer is constructed using the Legendre symbol. In this paper, we analyze high-degree functions such as the Legendre symbol or the modulo-2 operation as building blocks for the nonlinear layer of a cryptographic scheme over Fnp.Our focus regards the security analysis rather than the efficiency in the mentioned use cases. For this purpose, we present several new invertible functions that make use of the Legendre symbol or of the modulo-2 operation.Even though these functions often provide strong statistical properties and ensure a high degree after a few rounds, the main problem regards their small number of possible outputs, that is, only three for the Legendre symbol and only two for the modulo-2 operation. By fixing them, it is possible to reduce the overall degree of the function significantly. We exploit this behavior by describing the first preimage attack on full Grendel, and we verify it in practice.
Self-testing of multipartite Greenberger-Horne-Zeilinger states of arbitrary local dimension with arbitrary number of measurements per party

Device-independent certification schemes have gained a lot of interest lately, not only for their applications in quantum information tasks, but also their implications towards foundations of quantum theory. The strongest form of device-independent certification, known as self-testing, often requires for a Bell inequality to be maximally violated by specific quantum states and measurements. In this work, using the techniques developed recently [S. Sarkar et al., npj Quantum Inf. 7, 151 (2021)], we provide a self-testing scheme for the multipartite Greenberger-Horne-Zeilinger states of arbitrary local dimension that does not rely on self-testing results for qubit states and that exploits the minimal number of two measurements per party. This makes our results interesting as far as practical implementation of device-independent certification methods is concerned. Our self-testing statement relies on maximal violation of a Bell inequality proposed recently [R. Augusiak et al., New J. Phys. 21, 113001 (2019)].
Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition Using Cross-Modality Discriminator Network and Unit-Class Loss

Heterogeneous face recognition (HFR) aims to match face images across different imaging domains such as visible-to-infrared and visible-to-thermal. Recently, the increasing utility of nonvisible imaging has increased the application prospects of HFR in areas such as biometrics, security, and surveillance. HFR is a challenging variate of face recognition due to the differences between different imaging domains. While the current research has proposed image preprocessing, feature extraction, or common subspace projection for HFR, the optimization of these multi-stage methods is a challenging task as each step needs to be optimized separately and the performance error accumulates over each stage. In this paper, we propose a unified end-to-end Cross-Modality Discriminator Network (CMDN) for HFR. The proposed network uses a Deep Relational Discriminator module to learn deep feature relations for cross-domain face matching. Simultaneously, the CMDN is used to extract modality-independent embedding vectors for face images. The CMDN parameters are optimized using a novel Unit-Class Loss that shows higher stability and accuracy over other popular metric-learning loss functions. The experimental results on five popular HFR datasets demonstrate that the proposed method achieves significant improvement over the existing state-of-the-art methods.
Fingerprinting the spatial sources of fine-grained sediment deposited in the bed of the Mehran River, southern Iran

Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: (1) traditional range test (TRT), Kruskal-Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT + KW + DFA); (2) traditional range test using mean values (RTM) and two additional tests (RTM + KW + DFA); (3) TRT + KW + PCA (principle component analysis), and; 4) RTM + KW + PCA, were used to the spatial sediment source discrimination. Tracer bi-plots were used as an additional step to assess the tracers selected in the different final composite signatures for source discrimination. The predictions of spatial source contributions generated by GLUE were assessed using statistical tests and virtual sample mixtures. On this basis, TRT + KW + DFA and RTM + KW + DFA yielded the best source discrimination and the tracers in these composite signatures were shown by the biplots to be broadly conservative during transportation from source to sink. Using these final two composite signatures, the estimated mean contributions for the western, central and eastern sub-basins, respectively, ranged between 10-60% (overall mean contribution 36%), 0.3-16% (overall mean contribution 6%) and 38-77% (overall mean contribution 58%). In comparison, the final tracers selected using TRT + KW + PCA generated respective corresponding contributions of 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%) compared with 17-69% (overall mean 41%), 0.2-12% (overall mean 5%) and 29-76% (overall mean 54%) using the final tracers selected by RTM + KW + PCA. Based on the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), GLUE with the final tracers selected using TRT + KW + PCA performed slightly better than GLUE with the final signatures selected by the three other combinations of statistical tests. Based on the virtual mixture tests, however, predictions provided by GLUE with the final tracers selected using TRT + KW + DFA and RTM + KW + DFA (mean MAE = 11% and mean RMSE = 13%) performed marginally better than GLUE with RTM + KW + PCA (mean MAE = 14% and mean RMSE = 16%) and GLUE with TRT + KW + PCA (mean MAE = 17% and mean RMSE = 19%). The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources.
Neural Networks for Automatic Posture Recognition in Ambient-Assisted Living

Human Action Recognition (HAR) is a rapidly evolving field impacting numerous domains, among which is Ambient Assisted Living (AAL). In such a context, the aim of HAR is meeting the needs of frail individuals, whether elderly and/or disabled and promoting autonomous, safe and secure living. To this goal, we propose a monitoring system detecting dangerous situations by classifying human postures through Artificial Intelligence (AI) solutions. The developed algorithm works on a set of features computed from the skeleton data provided by four Kinect One systems simultaneously recording the scene from different angles and identifying the posture of the subject in an ecological context within each recorded frame. Here, we compare the recognition abilities of Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) Sequence networks. Starting from the set of previously selected features we performed a further feature selection based on an SVM algorithm for the optimization of the MLP network and used a genetic algorithm for selecting the features for the LSTM sequence model. We then optimized the architecture and hyperparameters of both models before comparing their performances. The best MLP model (3 hidden layers and a Softmax output layer) achieved 78.4%, while the best LSTM (2 bidirectional LSTM layers, 2 dropout and a fully connected layer) reached 85.7%. The analysis of the performances on individual classes highlights the better suitability of the LSTM approach.
The Interplay between Digitalization, Education and Financial Development: A European Case Study

The paper explores the relationship between education, digitalization, and financial development between 1996 and 2019 with the aim of showcasing the differences between developed and emerging economies in Europe. We use a Bayesian VAR framework that includes variables related to education, digitalization, and financial development, as well as several endogenous variables to control for differences between countries in terms of nominal GDP growth, unemployment rate, and trade openness. Our findings clearly demonstrate the dynamic interdependence between financial development—including its two main components, financial institutions, and financial markets, digitalization, and education. Furthermore, we find that education is a leading variable in the financial development–education–digitalization nexus, whereas financial development and digitalization are laggard variables. These findings open possibilities for influencing joint policies on digitalization, education, and financial development, particularly in emerging European countries.
Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid

The standard of manual operation in smart grid, which require accurate manipulation, is high, especially in experimental, practice, and training systems based on virtual reality (VR). In the VR training system, data gloves are often used to obtain the accurate dataset of hand movements. Previous works rarely considered the multi-sensor datasets, which collected from the data gloves, to complete the action evaluation of VR training systems. In this paper, a vectorized graph convolutional deep learning model is proposed to evaluate the accuracy of test actions. First, the kernel of vectorized spatio-temporal graph convolutional of the data glove is constructed with different weights for different finger joints, and the data dimensionality reduction is also achieved. Then, different evaluation strategies are proposed for different actions. Finally, a convolution deep learning network for vectorized spatio-temporal graph is built to obtain the similarity between test actions and standard ones. The evaluation results of the proposed algorithm are compared with the subjective ones labeled by experts. The experimental results verify that the proposed action evaluation method based on the vectorized spatio-temporal graph convolutional is efficient for the manual operation accuracy evaluation in VR training systems of smart grids.
Decentralized Identity Management for E-Health Applications: State-of-the-Art and Guidance for Future Work

Background: The increasing usage of various online services requires an efficient digital identity management approach. Unfortunately, the original Internet protocols were not designed with built-in identity management, which creates challenges related to privacy, security, and usability. Thereis an increasing concern regarding the management of these sensitive data in the society, who have access to it and where it is stored. Blockchain technology can, potentially offer a secure solution to address this problem, in a decentralized manner without any centralized authority. This is of importance for e-health services where the patient and the healthcare provider often are required toprove their identity. Blockchain technology can be utilized for creating digital identities and make themanagement of those identities easier, giving a higher degree of control to the user than what current solutions offer. It can be used for creating a digital identity on the blockchain, making it easier to manage for individuals and entities, giving them greater control over who has their personal in-formation and how they handle it, and it could be utilized to create a higher degree of trust andsecurity to e-health applications.&#x0D; Objective The aim of this research work is to review the state-of-the- art regarding blockchain-based decentralized identity management for healthcare applications. Based on this summary, we provide a viewpoint on how blockchain-based decentralized identity frameworks couldbe utilized for virtualized healthcare applications.&#x0D; Method This research has applied a scoping, semi-systematic review approach to summarize thestate-of-the-art. Included identity management systems were evaluated based on seven criteria:Autonomy, Authority, Availability, Approval, Confidentiality, Tenacity and Interoperability ResultsSeven blockchain-based identity management systems were included and evaluated in this work:These include solutions built with Ethereum, Hyperledger Indy, Hyperledger Fabric, Hedera andSovrin blockchains.&#x0D; Conclusion Digital identity management is crucial for virtual healthcare. Decentralized identity management for healthcare purposes is currently being explored, both in academia and the private sector. More work is needed with the aim to improve the efficiency of current DIM solutions and to fully understand what technical frameworks are best suited for e-health applications.
Instructional design according to the ADDIE model in initial teacher training

This essay analyzes the instructional design from model ADDIE in the educational initial formation, in a course of seventh semester of the Degree in Special Education during 2020- 2021 at the Benemérita Escuela Normal Veracruzana (BENV). The qualitative methodology used was the teacher’s research-action (Ebbutt y Elliot, 2000) with triangulation strategies of method and subjects (Santos-Guerra, 1990). It articulates the description of the instructional design with the configuration of virtual learning environments, understood like spaces of education-learning and evaluation, an expansion of space and temporary possibilities from the intercommunication and interconnection mediated by the use of technology (Bustos &amp; Coll, 2010). The five phases that structure the ADDIE model are described and analyzed from various angles such as disciplinary and pedagogical dimension of Special Education, cross-institutional dimension, technological dimension, which allows assessing the links between participants. Learning focused on learning to learn and motivational learning were linked to broad and complex instructional processes for teacher training schools as Analysis Design, Development, Implementation and Evaluation.
Developing Cross-Platform Library Using Flutter

Third-party libraries are frequently utilized to save implementation time when developing new software. The significance of libraries in the creation of mobile applications cannot be overstated. Others can use the programmer's library created and shared with the rest of the world in their own projects as a result of your efforts. The purpose of this work is to create a taxi service library for developers using both Android and iOS, using Dart Object-Oriented Programming, Dio, and Retrofit. The programmer's creation of an interface for accessing platform-specific functionality from the library and creating Android and iOS apps from its projects needs to speed up software development. Therefore, the best solution is for the programmer to use it. Flutter is an open-source SDK for developing high-performance and more reliable mobile applications for operating systems like iOS and Android, from a single code base. Moreover, Flutter targets the top mobile operating systems like Android and iOS. When developing a Dart open source project, the common conclusion the programmer always ends up with is to share the produced outcomes with the developer community. In the dart world, the latter should be the least objective. This will quickly enable building an app without having to develop everything from scratch. It provides a solution for GPU rendering and UI, powered by native ARM code.
Technical Design and Development of a Self-Sovereign Identity Management Platform for Patient-Centric Healthcare Using Blockchain Technology

Clinical data in the United States are highly fragmented, stored in numerous different databases, and are defined by service providers or clinical specialties rather than by individuals or their families. As a result, linking or aggregating a complete record for a patient is a major technological, legal, and operational challenge. One of the factors that has made clinical data integration so difficult to achieve is the lack of a universal ID for everyone. This leads to other related problems of having to prove identity at each interaction with the health system and repeatedly providing basic information on demographics, insurance, payment, and medical conditions. Traditional solutions that require complex governance, expensive technology, and risks to privacy and security of the data have failed adequately to solve this interoperability problem. We describe the technical design decisions of a patient-centric decentralized health identity management system using the blockchain technology, called MediLinker, to address some of these challenges.Our multidisciplinary research group developed and implemented an identity wallet, which uses the blockchain technology to manage verifiable credentials issued by healthcare clinics, banks, and insurance companies. To manage patient's self-sovereign identity, we leveraged the Hyperledger Indy blockchain framework to store patient's decentralized identifiers (DIDs) and the schemas or format for each credential type. In contrast, the credentials containing patient data are stored 'off-ledger' in each person's wallet and accessible via a computer or smartphone. We used Hyperledger Aries as a middleware layer (API: Application Programming Interface) to connect Hyperledger Indy with the front-end, which was developed using a JavaScript framework, ReactJS (Web Application) and React Native (iOS Application).MediLinker allows users to store their personal data on digital wallets, which they control. It uses a decentralized trusted identity using Hyperledger Indy and Hyperledger Aries. Patients use MediLinker to register and share their information securely and in a trusted system with healthcare and other service providers. Each MediLinker wallet can have six credential types: health ID with patient demographics, insurance, medication list including COVID-19 vaccination status, credit card, medical power of attorney (MPOA) for guardians of pediatric or geriatric patients, and research consent. The system allows for in-person and remote granting and revoking of such permissions for care, research, or other purposes without repeatedly requiring physical identity documents or enrollment information.We successfully developed and tested a blockchain-based technical architecture, described in this article, as an identity management system that may be operationalized and scaled for future implementation to improve patient experience and control over their personal information.
Investigation of the Effect of Normalization Methods on ANFIS Success: Forestfire and Diabets Datasets

Machine learning and artificial intelligence techniques are more and more in our lives and studies in this field are increasing day by day. Data is vital for these studies. In order to draw meaningful conclusions from the available data, new methods are proposed and successful results are obtained. The preparation of the obtained data is very important in the studies to be carried out. Data preprocessing is very important in the preparation of data. The most critical stage of the data preprocessing process is the scaling or normalization of the data. Machine learning libraries such as scikit-learn and programming languages such as R provide the necessary libraries to scale data. However, it is not known exactly which normalization method will be applied and which will yield more successful results. The success of these normalization methods has been investigated on many different methods, but such a study has not been done on the adaptive neural fuzzy inference system (ANFIS). The aim of this study is to examine the success of normalization methods on ANFIS in terms of both classification and regression problems. So, for studies using the Anfis method, guidance will be provided on which normalization process will give better results in the data preprocessing stage. Four different normalization methods in the scikit-learn library were applied on the Diabets and Forestfire datasets in the UCI database. The results are presented separately for both classification and regression. It has been determined that min-max normalization in classification problems and working with original data in regression problems are more successful.
Anomaly detection for ICS based on deep learning: a use case for aeronautical radar data

Industrial control systems (ICS) are no longer restricted to industrial production. They are also at the heart of safety critical systems and carry out key information that require strong need in terms of availability and integrity. Furthermore, they are gradually connected with the Internet. In the context of Air Traffic Management, safety critical data are generally time series which contain periodic events. Anomalies can hardly be detected as we only have a little knowledge of the traffic characteristic and the kind of anomalies we might encounter. Consequently, detecting them is challenging as it requires high detection accuracy currently unfeasible with traditional methods based on anomaly signatures or predictions. To cope with this issue, we introduce an anomaly detection method for ICS based on Long Short Term Memory (LSTM) that outperforms the accuracy of traditional ones. We experiment and develop our method with one major dataset containing French civil radar aviation data. We then evaluate our scheme with different datasets containing ICS monitoring data (publicly available predictable time series data) and show that our autoencoder can detect anomalies from predictable times series and present a higher detection rate on average than traditional detection methods.