Reviewerly/exHarmony huggingface dataset:
Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network

Speech anti-spoofing is a crucial aspect of speaker recognition systems and has received a great deal of attention in recent years. Deep neural networks have achieved satisfactory results in datasets with similar training and testing data distributions, but their generalization ability is limited in datasets with different distributions. In this paper, we proposed a novel dual-input hierarchical fusion network (HFN) to improve the generalization ability of our model. The network had two inputs (the original speech signal and the time-reversed signal), which increased the volume and diversity of the training data. The hierarchical fusion model (HFM) enabled more thorough fusion of information from different input levels and improved model performance by fusing the two inputs after speech feature extraction. We finally evaluated the results using the ASVspoof 2021 PA (Physical Access) dataset, and the proposed system achieved an Equal Error Rate (EER) of 24.46% and a minimum tandem Detection Cost Function (min t-DCF) of 0.6708 in the test set. Compared with the four baseline systems in the ASVspoof 2021 competition, the proposed system min t-DCF values were decreased by 28.9%, 31.0%, 32.6%, and 32.9%, and the EERs were decreased by 35.7%, 38.1%, 45.4%, and 49.7%, respectively.
Developing the Intercultural Communicative Competence in Modern Conditions: Neuro-Didactic Approach, Neural Networks, and Social Networks

The article focuses on the link between neuro-didactics and the intercultural communicative competence. As an interdisciplinary field, neuro-didactics combines brain research and didactics in teaching Russian as a foreign language. The review covers relevant publications on modern scientific approaches to teaching a foreign language with the help of neuro-didactics. The author examined various issues connected with teaching Russian to foreigners to describe the most effective neuro-didactic methods of developing the intercultural communicative competence, e.g., the effect of certain hormones on learning, etc. Neuroscience data, social networks, messengers, and mass media provide a more effective academic process, facilitate language learning, and increase the effectiveness of teaching methods. Thus, basic neuro-didactics should be taught to future foreign language teachers. By understanding the mechanisms and nature of neural plasticity, they may develop new effective teaching and learning methods.
Social Distance as a Determiner of Business Internet Communication Variability in Email Correspondence

Social distance determines the variability of digital business communication. This research tested business email messages using a complex methodology related to the theoretical description, analysis, and systematization of linguistic material. The specifics of business Internet communication manifested themselves as the choice of certain language formulae, spelling, structural elements (greeting, signature, etc.), graphic means, abbreviations, etc. The social distance between colleagues proved to be one of the leading factors that affected the variability of Internet communication.
Genre Parameters of Online Marketing Fraud Text

The article raises the problem of identifying the online marketing fraud text as a virtual genre. This type of Internet communication remains linguistically understudied. The research objective was to determine the specifics of the genre of online marketing fraud texts in line with modern genre studies. The research relied on the method developed by T. V. Shmeleva, who identified the following genre parameters: dictum-modus content, purpose, author, and addressee. The study featured 30 accounts of popular bloggers known in Russia as infogypsy or online marketing fraudsters. Online marketing fraud, or infogypsyism, is a special type of manipulative Internet communication aimed at obtaining commercial benefits by selling an information product, which, as a rule, possesses neither intellectual nor practical value for the consumer. The speech genre model projected onto the obtained material showed a high epistemological significance. The article introduces the definition and genre-forming features of the online marketing fraud text. Further research is needed to identify the dominant strategies and tactics for promoting information products by bloggers in social networks.
DOC-BLOCK: A Blockchain-Based Authentication System for Digital Documents

The main aim of this project is to solve the problem of counterfeiting certificates. We are proposing a digital certificate system based on blockchain technology to verify the traveler’s identity using live a camera, which allows faster convergence and more generalizable representations. With our project, we need not carry the documents for verification instead of that we can make the documents in digital format for verification. This system saves on paper, cuts management costs, prevents document forgery, provides accurate reliable information on digital certificates and compare user live face with verified document face.
Counterfeit Detection using Deep Learning

Detecting counterfeit currency is a critical issue that has gained increasing attention in recent years. Deep learning techniques have shown significant promise in a variety of image processing tasks, including counterfeit currency detection. In this project, we propose a deep learning-based counterfeit currency detection system that uses convolutional neural networks (CNNs) for feature extraction and classification. The proposed system consists of two main phases: training and testing. In the training phase, a dataset of genuine and counterfeit banknotes is used to train the CNN model to distinguish between genuine and counterfeit banknotes. A CNN model extracts features from an input image using convolutional layers and then applies fully connected layers for classification. Experimental results show that the proposed deep learning-based counterfeit currency detection system achieves high accuracy. The system outperforms existing state-of-the-art techniques in terms of detection accuracy, robustness, and real-time performance. In conclusion, it can be said that the proposed system could be a useful tool for preventing the circulation of counterfeit currency and minimizing economic losses. The system can be integrated into various automated teller machines (ATMs) and vending machines to detect counterfeit money in real time
Comparison of Topologies Generated by Evolutionary Neural Architecture Search

Neural Architecture Search (NAS) has been widely applied across various fields, revealing intriguing patterns in the resulting optimized topologies. In this paper, we compare the topologies generated by NAS across two different experiments: linguistic grammaticality judgment and the MNIST task. Our analysis reveals a distinctive fork-like structure that consistently emerges in both experiments. Interestingly, this structure is highly effective despite not being typically designed by human experts. The emergence of this fork-like structure sheds new light on the potential of NAS to provide alternative designs that go beyond incremental improvements. Our paper offers a fresh perspective on automated architecture design, highlighting the potential of NAS to enable innovative approaches that can be applied across multiple domains.
A study on the classification of stylistic and formal features in English based on corpus data testing

The traditional statistical and rule combination algorithm lacks the determination of the inner cohesion of words, and the N-gram algorithm does not limit the length of N, which will produce a large number of invalid word strings, consume time and reduce the efficiency of the experiment. Therefore, this article first constructs a Chinese neologism corpus, adopts improved multi-PMI, and sets a double threshold to filter new words. Branch entropy is used to calculate the probabilities between words. Finally, the N-gram algorithm is used to segment the preprocessed corpus. We use multi-word mutual information and a double mutual information threshold to identify new words and improve their recognition accuracy. Experimental results show that the algorithm proposed in this article has been improved in accuracy, recall and F measures value by 7%, 3% and 5% respectively, which can promote the sharing of language information resources so that people can intuitively and accurately obtain language information services from the internet.
Occlusion Handling for Mobile AR Applications in Indoor and Outdoor Scenarios

When producing an engaging augmented reality (AR) user experience, it is crucial to create AR content that mimics real-life objects' behavior to the greatest extent possible. A critical aspect to achieve this is ensuring that the digital objects conform to line-of-sight rules and are either partially or completely occluded, just like real-world objects would be. The study explores the concept of utilizing a pre-existing 3D representation of the physical environment as an occlusion mask that governs the rendering of each pixel. Specifically, the research aligns a Level of Detail (LOD) 1 building model and a 3D mesh model with their real-world counterparts and evaluates the effectiveness of occlusion between the two models in an outdoor setting. Despite the mesh model containing more detailed information, the overall results do not show improvement. In an indoor scenario, the researchers leverage the scanning capability of HoloLens 2.0 to create a pre-scanned representation, which helps overcome the limited range and delay of the mesh reconstruction.
Quantum Related-Key Attack Based on Simon’s Algorithm and Its Applications

With the development of quantum technology, quantum computing has an increasingly significant impact on cryptanalysis. Several quantum algorithms, such as Simon’s algorithm, Grover’s algorithm, the Bernstein–Vazirani algorithm, Shor’s algorithm, and the Grover-meets-Simon algorithm, have been proposed successively. However, almost all cryptanalysis is based on the quantum chosen-plaintext attack (qCPA) model. This paper focuses on a powerful cryptanalytic model, quantum related-key attack (qRKA), and proposes a strategy of qRKAs against symmetric ciphers using Simon’s algorithm. We construct a periodic function to efficiently recover the secret key of symmetric ciphers if the attacked symmetric ciphers satisfy Simon’s promise, and present the complexity analysis on specific symmetric ciphers. Then, we apply qRKA to the Even–Mansour cipher and SoEM construction, recover their secret keys, and show their complexity comparison in the distinct attack models. This work is of great significance for the qRKA cryptanalysis of existing provably secure cryptographic schemes and the design of future quantum secure cryptographic schemes.
A Generic Approach towards Enhancing Utility and Privacy in Person-Specific Data Publishing Based on Attribute Usefulness and Uncertainty

This paper proposes a generic anonymization approach for person-specific data, which retains more information for data mining and analytical purposes while providing considerable privacy. The proposed approach takes into account the usefulness and uncertainty of attributes while anonymizing the data to significantly enhance data utility. We devised a method for determining the usefulness weight for each attribute item in a dataset, rather than manually deciding (or assuming based on domain knowledge) that a certain attribute might be more useful than another. We employed an information theory concept for measuring the uncertainty regarding sensitive attribute’s value in equivalence classes to prevent unnecessary generalization of data. A flexible generalization scheme that simultaneously considers both attribute usefulness and uncertainty is suggested to anonymize person-specific data. The proposed methodology involves six steps: primitive analysis of the dataset, such as analyzing attribute availability in the data, arranging the attributes into relevant categories, and sophisticated pre-processing, computing usefulness weights of attributes, ranking users based on similarities, computing uncertainty in sensitive attributes (SAs), and flexible data generalization. Our methodology offers the advantage of retaining higher truthfulness in data without losing guarantees of privacy. Experimental analysis on two real-life benchmark datasets with varying scales, and comparisons with prior state-of-the-art methods, demonstrate the potency of our anonymization approach. Specifically, our approach yielded better performance on three metrics, namely accuracy, information loss, and disclosure risk. The accuracy and information loss were improved by restraining heavier anonymization of data, and disclosure risk was improved by preserving higher uncertainty in the SA column. Lastly, our approach is generic and can be applied to any real-world person-specific tabular datasets encompassing both demographics and SAs of individuals.
Efficiency and impact factors of anatomical intelligence for breast and hand‐held ultrasound in lesion detection

To investigate the efficiency and impact factors of anatomical intelligence for breast (AI-Breast) and hand-held ultrasound (HHUS) in lesion detection.A total of 172 outpatient women were randomly selected, underwent AI-Breast ultrasound (Group AI) once and HHUS twice. HHUS was performed by breast imaging radiologists (Group A) and general radiologists (Group B). For the AI-Breast examination, a trained technician performed the whole-breast scan and data acquisition, while other general radiologists performed image interpretation. The examination time and lesion detection rate were recorded. The impact factors for breast lesion detection, including breast cup size, number of lesions, and benign or malignant lesions were analyzed.The detection rates of Group AI, A, and B were 92.8 ± 17.0%, 95.0 ± 13.6%, and 85.0 ± 22.9%, respectively. Comparable lesion detection rates were observed in Group AI and Group A (P > 0.05), but a significantly lower lesion detection rate was observed in Group B compared to the other two (both P < 0.05). Regarding missed diagnosis rates of malignant lesions, comparable performance was observed in Group AI, Group A, and Group B (8% vs. 4% vs. 14%, all P > 0.05). Scan times of Groups AI, A, and B were 262.15 ± 40.4 s, 237.5 ± 110.3 s, 281.2 ± 86.1 s, respectively. The scan time of Group AI was significantly higher than Group A (P < 0.01), but was slightly lower than Group B (P > 0.05). We found a strong linear correlation between scan time and cup size in Group AI (r = 0.745). No impacts of cup size and number of lesions were found on the lesion detection rate in Group AI (P > 0.05).With the assist of AI-Breast system, the lesion detection rate of AI-Breast ultrasound was comparable to that of a breast imaging radiologist and superior to that of the general radiologist. AI-Breast ultrasound may be used as a potential approach for breast lesions surveillance.
Entanglement based six-state quantum cryptography protocol: exact proof of security

Abstract A novel quantum key distribution (QKD) protocol is proposed, which uses six states of three states in two bases. The protocol provides a greater range of key distribution compared to known QKD protocols. The protocol has an accurate proof of secrecy based on fundamental entropy uncertainty relations, the use of which is possible with the entanglement version of the protocol. The implementation of the protocol does not require adjustment of the polarization state at the output of the communication line, since the receiving side does not use active polarization-sensitive elements—phase modulators, which virtually excludes active probing of the active elements of the system.
Review on Doctor’s Handwriting Recognition using Deep Learning

Specialists ordinarily compose in incomprehensible handwriting, making it troublesome for both the common public and a few drug specialists to get the drugs they have endorsed. It isn'tperfect for them to compose the prescription discreetly and deliberately since they will be dealing with handfuls of patients each day and will be swamped with work. As a result, their penmanship is illegible. This may result in reports or prescriptions consisting of brief shapes and cursive composing that a typical person or drug specialist won't be able to examine appropriately, which will cause endorsed drugs to be incorrectly spelled. However, some people usually to composing medicines in regional dialects since we all live in a range with a diversity of territorial dialects. It makes examining the content much more challenging. So, in this paper, we'll utilize a recognition framework to construct an apparatus that can interpret the handwriting of doctors into any dialect. This system will be made into an application that's completely independent and functioning. As the client transfers the medicine picture the program will pre-process the picture by performing image-processing, and word segmentations at first before processing the picture for preparation. CRNN which isutilized to train the model. We get the Yield within the shape of a pdf.
Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems

The symbiotic organisms search (SOS) algorithm is a current effective meta-heuristic algorithm, which is been applied to solve various types of optimization problems. However, the SOS can easily lead to overexploration in the parasitism phase, and it is difficult to balance between exploration and exploitation capabilities. In the present work, two extended versions of the SOS are proposed. Two different weight strategies (i.e., random-weight and adaptive-weight) are utilized to generate the weighted mutual vector, respectively. Meanwhile, the best organism is employed to produce the modified artificial parasite vector. The performance of the two improved algorithms is evaluated on 35 test functions. The results demonstrate that the proposed algorithms are able to provide very promising results. Furthermore, five real-world problems are solved by the two newly proposed methods. Experimental results demonstrate that the presented algorithms are more efficient than the compared algorithms. All the obtained results further indicate that the two proposed algorithms are competitive and provide better results when compared to a wide range of algorithms, including SOS and its five modified versions, as well as ten other meta-heuristic algorithms.
Future Teachers’ Spiritual Worldview Formation Factors

It is necessary to define factors that affect the process of shaping future teachers’ spiritual worldview. The knowledge factors influencing the formation of a spiritual worldview allow us to elicit the most effective ways and means to shape future teachers’ professional self-awareness. Therefore, this paper is devoted to studying factors that play an important role in future teachers’ spiritual worldview formation. Reflection determines the prerequisites and consequences of the future teachers’ spiritual worldview. It is noted that only a spiritually rich teacher can solve the problems of modern education. Eighteen factors for forming future teachers’ spiritual worldviews are revealed. The authors briefly describe the contributions of various scientific disciplines to this topic, which are widely used in modern science, especially pedagogical and psychological ones. The authors do not rely on any religious experience, aspects, or approaches. There can always be an influence of subjectivity and the prevalence of religious approaches over scientific concepts in such an approach. In conclusion, findings regarding the factors involved in forming future teachers’ spiritual worldviews are presented.
Characterizing the Bitcoin network topology with Node‐Probe

Abstract In blockchain networks, topology discovery is a prerequisite when investigating the network characteristics (e.g., performance and robustness), which can provide a deeper comprehension of the behavior of the nodes and topology dynamicity. In this paper, we conduct a longitudinal study on the Bitcoin topology by collecting network snapshots from 2018 to 2022 with Node‐Probe, our topology discovery technique that uses recursive scanning to find all reachable nodes in the Bitcoin network. Using Node‐Probe, we have collected 5‐week‐long snapshots (36‐day‐long snapshots) of the Bitcoin main network and analyzed the network properties, community structure, and topology dynamicity. We confirm that our approach achieves a precision of 99% with a recall of 98% in inferring the topology. Analytical results on community structure show that the Bitcoin network has more communities than what should be expected from a random network. Meanwhile, analytical results on dynamicity indicate that the topology stands firmly on heavy and long‐running nodes. Improving the propagation mechanism using master nodes could improve the propagation delay by proximity compared with the Bitcoin default protocol. Considering a K‐anonymity attack, any transaction from one of the autonomous systems containing only a single Bitcoin node can easily be linked to real users' IP information.
What should a generic object be?

Abstract Jacobs has proposed definitions for (weak, strong, split) generic objects for a fibered category; building on his definition of (split) generic objects, Jacobs develops a menagerie of important fibrational structures with applications to categorical logic and computer science, including higher order fibrations , polymorphic fibrations , $\lambda2$ - fibrations , triposes , and others. We observe that a split generic object need not in particular be a generic object under the given definitions, and that the definitions of polymorphic fibrations, triposes, etc. are strict enough to rule out some fundamental examples: for instance, the fibered preorder induced by a partial combinatory algebra in realizability is not a tripos in this sense. We propose a new alignment of terminology that emphasizes the forms of generic object appearing most commonly in nature, i.e. in the study of internal categories, triposes, and the denotational semantics of polymorphism. In addition, we propose a new class of acyclic generic objects inspired by recent developments in higher category theory and the semantics of homotopy type theory, generalizing the realignment property of universes to the setting of an arbitrary fibration.
Distributed Subweb Specifications for Traversing the Web

Abstract Link traversal–based query processing ( ltqp ), in which a sparql query is evaluated over a web of documents rather than a single dataset, is often seen as a theoretically interesting yet impractical technique. However, in a time where the hypercentralization of data has increasingly come under scrutiny, a decentralized Web of Data with a simple document-based interface is appealing, as it enables data publishers to control their data and access rights. While ( ltqp allows evaluating complex queries over such webs, it suffers from performance issues (due to the high number of documents containing data) as well as information quality concerns (due to the many sources providing such documents). In existing ltqp approaches, the burden of finding sources to query is entirely in the hands of the data consumer . In this paper, we argue that to solve these issues, data publishers should also be able to suggest sources of interest and guide the data consumer toward relevant and trustworthy data. We introduce a theoretical framework that enables such guided link traversal and study its properties. We illustrate with a theoretic example that this can improve query results and reduce the number of network requests. We evaluate our proposal experimentally on a virtual linked web with specifications and indeed observe that not just the data quality but also the efficiency of querying improves.