Reviewerly/exHarmony huggingface dataset:
Lexical Portrait of an Oral Story: a Corpus Study of Speech of Two Generations of Native Speakers of Russian

The article is devoted to the quantitative and statistical analysis of the vocabulary of the oral stories corpus based on pictures. The subjects were 42 people, monolingual native speakers of the Russian (representatives of 15 families). The purpose of the study is to identify and describe the lexical parameters that distinguish the oral stories of representatives of different generations. An attempt is made to consider the connection of these parameters with the formation of native speakers’ ideas about the lexical structure of the narrative. The following results are obtained. The nature of the stimulus material ensured an approximately equal degree of complexity in the texts of both groups, although the volume of the parents’ active vocabulary significantly exceeded the volume of the children’s vocabulary. At the level of individual texts, both groups use equally diverse vocabulary per unit of text volume. In the children’s stories, the main “plot” vocabulary is quite heterogeneous, while the parents tend to lexical unification of the narrative. In the texts of both groups, local reference and direction of movement are expressed in the same way, which indicates their “basic” narrative function, which does not depend on the age of the subjects. The stories of the parents are also unified in terms of vocabulary, showing the logical structure of the narrative and different types of semantic relations in the text.
The Transformation and Application of Virtual and Reality in Creative Teaching: A New Interpretation of the Triadic Ballet

Virtual imaging technology has been widely used in entertainment, medicine, engineering and other fields, and the advancement of virtual imaging has also provided new opportunities for art performances and exhibitions. This research uses live dance, screen dance and virtual dance displays to conduct the audience’s experience of watching dance performances and compares the three forms. A total of 30 students participated in this research. According to the results of this study, the content of the dance works includes route and trajectory, movements and expressions, costumes, and overall atmosphere; there is no obvious difference under the three viewing conditions, and the spatial performance of dance works is best in live performances. According to the research results, the physical stage performance still has the advantage of space performance, virtual image has the advantage of solving the distance between the audience and the stage, and the screen image is helpful for the performance of the stage color. The results of this study show that the presentation of different technologies can improve the audience’s viewing experience of dance, but how to create an impressive spatial experience using the screen image and a virtual environment for a live performance still needs technical improvement.
Joint Extraction of Entities and Relations via Entity and Relation Heterogeneous Graph Attention Networks

Entity and relation extraction (ERE) is a core task in information extraction. This task has always faced the overlap problem. It was found that heterogeneous graph attention networks could enhance semantic analysis and fusion between entities and relations to improve the ERE performance in our previous work. In this paper, an entity and relation heterogeneous graph attention network (ERHGA) is proposed for joint ERE. A heterogeneous graph attention network with a gate mechanism was constructed containing word nodes, subject nodes, and relation nodes to learn and enhance the embedding of parts for relational triple extraction. The ERHGA was evaluated on the public relation extraction dataset named WebNLG. The experimental results demonstrate that the ERHGA, by taking subjects and relations as a priori information, can effectively handle the relational triple extraction problem and outperform all baselines to 93.3%, especially overlapping relational triples.
Large Field-Size Throughput/Area Accelerator for Elliptic-Curve Point Multiplication on FPGA

This article presents a throughput/area accelerator for elliptic-curve point multiplication over GF(2571). To optimize the throughput, we proposed an efficient hardware accelerator architecture for a fully recursive Karatsuba multiplier to perform polynomial multiplications in one clock cycle. To minimize the hardware resources, we have utilized the proposed Karatsuba multiplier for modular square implementations. Moreover, the Itoh-Tsujii algorithm for modular inverse computation is operated using multiplier resources. These strategies permit us to reduce the hardware resources of our implemented accelerator over a large field size of 571 bits. A controller is implemented to provide control functionalities. Our throughput/area accelerator is implemented in Verilog HDL using the Vivado IDE tool. The results after the place-and-route are given on Xilinx Virtex-6 and Virtex-7 devices. The utilized slices on Virtex-6 and Virtex-7 devices are 6107 and 5683, respectively. For the same FPGA devices, our accelerator can operate at a maximum of 319 MHz and 361 MHz. The latency values for Virtex-6 and Virtex-7 devices are 28.73 μs and 25.38 μs. The comparison to the state-of-the-art shows that the proposed architecture outperforms in throughput/area values. Thus, our accelerator architecture is suitable for cryptographic applications that demand a throughput and area simultaneously.
Intrinsically Safe Drone Propulsion System for Underground Coal Mining Applications: Computational and Experimental Studies

The mining industry has recently shown increased interest in drones for routine activities in underground and surface mines. Designing a drone for coal mines is extremely complicated since the Mine Safety and Health Administration (MSHA) has tight guidelines for any equipment that can be used in underground coal mines. Due to these criteria, designing a drone for underground coal mining is exceedingly difficult. This paper explores the challenges of creating an intrinsically safe drone propulsion system. To address the challenges of designing an intrinsically safe drone’s propulsion system for an underground coal mine, this work aims to investigate the potential approaches to enhance efficiency and mitigate the heat. The study begins with the drone’s sizing approach before moving on to the experimental setup that is utilized to test the drone’s propulsion system. Finally, answers to numerous issues arising during the inquiry are offered, and these solutions are empirically explored.
A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization

The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important.We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators.The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871.The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.
Design and implementation of trusted boot based on a new trusted computing dual-architecture

Trusted Platform Module (TPM) is considered to be one of the best technologies to ensure computer system security at present, but it has some defects. The TPM chip exposed on the motherboard is easily monitored or bypassed. As a slave device on the bus, it is under the control of the master device and has no higher privileges to access system resources than the CPU used only to compute in traditional computers. The current trusted computing architecture cannot ensure that the TPM chip starts to work prior to the CPU. To solve these problems, this paper propose a new trusted computing dual-architecture, which is made up of a trusted computing subsystem and a traditional computing subsystem. A new hardware root-of-trust and a kind of boot process in the trusted computing dual-architecture are implemented to provide a higher-level trust for the whole system. In a word, the new trusted computing dual-architecture ensures that the hardware root-of-trust is a trusted source due to its dual-architecture design and trusted boot process. It can effectively introduce and set up a trusted chain for the whole computer system. Active-defense capabilities obtained can ensure system security to avoid those defects in previous trusted computing.
Person Re-Identification Based on Contour Information Embedding

Person re-identification (Re-ID) plays an important role in the search for missing people and the tracking of suspects. Person re-identification based on deep learning has made great progress in recent years, and the application of the pedestrian contour feature has also received attention. In the study, we found that pedestrian contour feature is not enough in the representation of CNN. On this basis, in order to improve the recognition performance of Re-ID network, we propose a contour information extraction module (CIEM) and a contour information embedding method, so that the network can focus on more contour information. Our method is competitive in experimental data; the mAP of the dataset Market1501 reached 83.8% and Rank-1 reached 95.1%. The mAP of the DukeMTMC-reID dataset reached 73.5% and Rank-1 reached 86.8%. The experimental results show that adding contour information to the network can improve the recognition rate, and good contour features play an important role in Re-ID research.
AGGREGATION OPERATORS VISUALIZATION USING 3D COGNITIVE GRAPHICS

To solve social dilemmas in multi-agent reinforcement learning it is necessary to formalize the corresponding reward functions in the form of aggregation operators. Such formalization encounters difficulties associated with the lack of an intuitive vision of the aggregation process among many practitioners. Various visualization methods have been proposed to overcome these difficulties. The proposed methods have disadvantages that prevent their wide application. The method proposed in the article differs from the existing ones by the use of a three-dimensional cognitive image which corresponds to a physical object with a well-developed intuitive spatial and physical perception. This method is based on the use of 3D cognitive graphics. The properties of the aggregation operator are directly related to the properties of the physical object which has a well-developed intuitive representation. An absolutely solid plane fixed on the support line by springs acts as such an object. The result of aggregation corresponds to the angle of deviation of the plane from the horizon. By placing loads that meet the criteria and indices of their interaction on this plane it is possible to simulate the corresponding operator including by means of virtual reality. A preliminary asessment of the formal properties of the proposed method was carried out in comparison with existing visualization methods using the example of the Choquet integral of the 2nd order. Based on the results of this assessment conclusions were drawn about the advantages of a three-dimensional balance model over existing visualization methods and the feasibility of continuing work in this direction.
Blockchain Federated Learning Framework for Privacy-Preservation

Real-time data for machine learning introduced challenges, especially security and privacy issues, while sharing critical private data. The proposed unified federated learning system threaded with blockchain technology is a solution for personal communication over decentralized networks and challenges related to traditional machine learning. Federated Learning (FL) ensures the client's local data privacy. The blockchain part of the system helps the server to authenticate local models from the client and introduces a notion of trust. FL-based Logistic Regression, Support Vector Machine (SVM), and Artificial Neural Network (ANN) models have experimented on two different domains: network traffic and healthcare. To assess the system's validity, we have used three benchmark datasets- KDDCUP99, NSL-KDD, and Pima Indians Diabetes Database. Numerical results demonstrated that the proposed approach is efficient for reliable and private communication over a decentralized network. For KDDCUP99 and NSL-KDD dataset achieves 99% and for Pima Indians Diabetes achieves 65.88% accuracy using the Fl-based ANN model. Additionally, we evaluate the performance of the FL model integrated with blockchain for privacy and security.
Path Planning and Static Obstacle Avoidance for Unmanned Aerial Systems

The recent advent of computational intelligence and the field of deep learning has shown a significant application for the task of efficient navigation of automated and unmanned vehicles. The notion of a robot intelligently deciding a path based on minimalistic spatial knowledge and operating in a collision-free manner illustrates significant real-world importance. This paper offers a novel study for determining the best strategy for robust path planning in a simulated environment sufficed with static obstacles. The paradigm of behavior cloning and imitation learning is extensively explored, these techniques have depicted a better analogy to the human brain hence, justifying the experiments. This paper also conducts extensive tests on the existing technologies as baselines for an unbiased comparison, these algorithms include Rapidly- exploring Random Tree (RRT), A* search algorithm, and an improved rendition of the Ant Colony Optimization (ACO). The algorithms developed are centric to Unmanned Aerial Systems (UAS) however a correlation is also shown to unmanned ground systems and other automated robotics.
A Secure Mechanism for Safeguarding Cloud Infrastructure

Security is frequently viewed as the largest impediment to a cloud-based approach, but in actuality, it can be the majorenabler. Cloud security guarantees that your information and apps are easily accessible to authorized users. In this paper, we shall be putting forward, the cloud ecosystem's security concerns. The most crucial concerns for the popularity of cloud computing services are privacy and security. Here we try to depict a study of data that is hosted on the cloud and the issues in its security. The study will examine the particular data protection practices used globally to offer optimum data security while reducing threats and risks. Although many apps benefit by having access to data on the cloud, but doing so poses concerns since it makes data accessible to apps that could already contain security flaws. Analog to this, data may be at risk if a guest OS operates on top of the hypervisor, without consideration for dependability of the guest OS, resulting in a flaw in security. The paper ends with a case study where the request has been classified as safe or malicious. If the malicious request is identified, then these requests are to be discarded so that the cloud remains safe. The classification has been conducted using Machine Learning and Deep Learning concepts and an accuracy of 85% has been achieved.
Recent Trends in Modalities and Deep Learning Methods for Breast Cancer Detection

Automated and intelligent healthcare using deep learning has produced promising results in both diagnosis of diseases and precision medicine. Disease diagnosis without any human intervention has gained prominence among clinicians recently. Breast Cancer, the most prevalent type of cancer in women is the major cause of death among them. Therefore, its diagnosis is crucial in the early stage to lower the death rates. The use of medical imaging in the identification and diagnosis of breast cancer is highly recommended. The literature has offered several modalities, demonstrating a keen interest in several research areas. In pertinent medical domains, each modality has a significant role to play. This study has examined the benefits and drawbacks of various imaging techniques for diagnosing breast cancer, including mammography, ultrasound, and MRI. Using various imaging modalities, the techniques developed to make it easier to divide breast cancer into cancerous and non-cancerous classes. A quick insight into the modalities and subsequent machine learning and deep learning applications that have been widely used in the diagnosis of this disease is the major goal of this study.
CRFs for Digital Signature and NIZK Proof System in Web Services

Web services are service-oriented computing technology which allows computers running different operating domains to access and share each other's databases. Each web service is an application (like online business) which may require the private information of users. Thus, it will be important to preserve these web users' individual privacy. The traditional approaches to achieve this goal in web security is to use the cryptographic technologies, such as digital signature, NIZK proof system. Whereas, some recent research results indicate that these cryptographic technologies may suffer from the algorithm substitution attack (ASA). ASA means that the cryptographic technology would be embedded some backdoor in the process of its implementation by the attacker, and with the backdoor information the attacker can steal the user's private information. To address this problem, the concept of cryptographic reverse firewall (CRF) has been introduced, which could sanitize the messages inputting and outputting the user's computer. In this paper, we construct the CRFs for the efficient Pointcheval-Sanders (PS) signature as well as the NIZK proof system.
Urban-semantic computer vision: a framework for contextual understanding of people in urban spaces

Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common (Ridgeway 2018, Coifman et al. 1998, Sun et al. 2020). Despite the often-discussed biases in the algorithms' training and unequally borne benefits (Khosla et al. 2012), almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity in these practices that enables semantic knowledge or analysis within urban contexts, especially within the context of using and occupying urban space. This paper will critique existing uses of artificial intelligence and computer vision in urban practices to propose a new framework for understanding people, action, and public space. This paper revisits Geertz's (1973) use of thick descriptions in generating interpretive theories of culture and activity and uses this lens to establish a framework to approach evaluating the varied uses of computer vision technologies that weigh meaning. By discussing cases of implemented examples of urban computer vision—from LinkNYC and Numina's urban measurements to the Detroit Police's use of DataWorks Plus's facial recognition technology—it proposes a framework for evaluating the thickness of the algorithm's conclusions against the computational method's complexity required to produce that outcome. Further, we discuss how the framework's positioning may differ (and conflict) between different users of the technology, from engineer to urban planner and policymaker, to citizen. This paper also discusses how the current use and training of deep learning algorithms and how this process limits semantic learning and proposes three potential methodologies toward gaining a more contextually specific, urban-semantic, description of urban space relevant to urbanists. This paper contributes to the critical conversations regarding the proliferation of artificial intelligence by challenging the current applications of these technologies in the urban environment by highlighting their failures within this context while also proposing an evolution of these algorithms that may ultimately make them sensitive and useful within this spatial and cultural milieu.
Challenging Folk-Linguistics: Grammatical and Spelling Variation in Students’ Writing in Hebrew on WhatsApp and in Essays

Abstract With the increasing use of mobile phones among young people, there is growing public concern about possible detrimental effects of digital writing on learners’ literacy and language skills. We ask whether and to what extent nonstandard forms typical of spoken Hebrew and of digital communication are also found in the formal writing of high-school students. To this end, we compare between two corpora of Modern Hebrew: a naturalistic corpus of 7,120 WhatsApp messages (35,085 words) written by 80 students in three classroom WhatsApp groups and a corpus of 291 school essays (34,700 words) written by 291 students. The findings indicate that a rather clear distinction is maintained between adherence to traditional formal writing in school essays as opposed to a more lenient approach on WhatsApp. The findings thus provide empirical linguistic evidence challenging the predominant folk-linguistic public ideology.
Audio deepfakes: A survey

A deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. The key difference between manual editing and deepfakes is that deepfakes are AI generated or AI manipulated and closely resemble authentic artifacts. In some cases, deepfakes can be fabricated using AI-generated content in its entirety. Deepfakes have started to have a major impact on society with more generation mechanisms emerging everyday. This article makes a contribution in understanding the landscape of deepfakes, and their detection and generation methods. We evaluate various categories of deepfakes especially in audio. The purpose of this survey is to provide readers with a deeper understanding of (1) different deepfake categories; (2) how they could be created and detected; (3) more specifically, how audio deepfakes are created and detected in more detail, which is the main focus of this paper. We found that generative adversarial networks (GANs), convolutional neural networks (CNNs), and deep neural networks (DNNs) are common ways of creating and detecting deepfakes. In our evaluation of over 150 methods, we found that the majority of the focus is on video deepfakes, and, in particular, the generation of video deepfakes. We found that for text deepfakes, there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential heavy overlaps with human generation of fake content. Our study reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes. This survey has been conducted with a different perspective, compared to existing survey papers that mostly focus on just video and image deepfakes. This survey mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This article's most important contribution is to critically analyze and provide a unique source of audio deepfake research, mostly ranging from 2016 to 2021. To the best of our knowledge, this is the first survey focusing on audio deepfakes generation and detection in English.