Reviewerly/exHarmony huggingface dataset:
Implementation of the Technological Competence of Future Specialists

The article deals with the problems of developing the technological competence of future teachers.It contains definitions of the concepts of competence and technological competence, provides an overview of the history of entry into education and the current level of relevance.Providing close cooperation between vocational education and production, improving the quality of vocational education, and continuous improvement of the educational process is an urgent task for reforming the system of professional education, training personnel capable of actively and effectively participating in the process of its sustainable growth and modernization.In the lesson of preparation for the profession, students acquire the skills, abilities, competencies, and the competence of interdependence.From a pedagogical point of view, competence is interpreted as a combination of the knowledge, skills and experience of individuals with specific social and professional status to the extent of the complexity of the tasks and problems they perform.Suitable for the competence approach we are considering is the "competence" and "jurisdiction", which differ in each other.The article describes the objectives of continuous improvement, effectiveness, and adaptation of the content of professional education, as well as the components of practical professional education.
A Network Security Risk Assessment Method Based on a B_NAG Model

Computer networks face a variety of cyberattacks. Most network attacks are contagious and destructive, and these types of attacks can be harmful to society and computer network security. Security evaluation is an effective method to solve network security problems. For accurate assessment of the vulnerabilities of computer networks, this paper proposes a network security risk assessment method based on a Bayesian network attack graph (B_NAG) model. First, a new resource attack graph (RAG) and the algorithm E-Loop, which is applied to eliminate loops in the B_NAG, are proposed. Second, to distinguish the confusing relationships between nodes of the attack graph in the conversion process, a related algorithm is proposed to generate the B_NAG model. Finally, to analyze the reachability of paths in B_NAG, the measuring indexs such as node attack complexity and node state transition are defined, and an iterative algorithm for obtaining the probability of reaching the target node is presented. On this basis, the posterior probability of related nodes can be calculated. A simulation environment is set up to evaluate the effectiveness of the B_NAG model. The experimental results indicate that the B_NAG model is realistic and effective in evaluating vulnerabilities of computer networks and can accurately highlight the degree of vulnerability in a chaotic relationship.
Logical abstractions for noisy variational Quantum algorithm simulation

Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms, which are among the leading candidates for useful quantum computation. Existing quantum circuit simulators do not address the common traits of variational algorithms, namely: 1) their ability to work with noisy qubits and operations, 2) their repeated execution of the same circuits but with different parameters, and 3) the fact that they sample from circuit final wavefunctions to drive a classical optimization routine. We present a quantum circuit simulation toolchain based on logical abstractions targeted for simulating variational algorithms. Our proposed toolchain encodes quantum amplitudes and noise probabilities in a probabilistic graphical model, and it compiles the circuits to logical formulas that support efficient repeated simulation of and sampling from quantum circuits for different parameters. Compared to state-of-the-art state vector and density matrix quantum circuit simulators, our simulation approach offers greater performance when sampling from noisy circuits with at least eight to 20 qubits and with around 12 operations on each qubit, making the approach ideal for simulating near-term variational quantum algorithms. And for simulating noise-free shallow quantum circuits with 32 qubits, our simulation approach offers a 66× reduction in sampling cost versus quantum circuit simulation techniques based on tensor network contraction.
Scene parsing for very high resolution remote sensing images using on attention-residual block-embedded adversarial networks

A novel deep learning architecture called Attention-Residual block-Embedded Adversarial Networks (AREANs) is proposed in this letter, which can give the robust pixel-wise scene understanding in remote sensing images without any post-processing and additional data. The generator of AREANs, a novel designed encoder-decoder structure network, takes full advantage of Attention-Residual block to learn local-to-global contextual information through semantic and position information enhanced aggregation. To further improve the performance, a patchGAN-based discriminator is applied to train the generator. This training method can not only promote to mine the distinguishable and inherent features from data, but also boost the feature extraction performance of the generator through fine-tuning its parameters. Moreover, the multipath composite loss is proposed as an auxiliary loss in the generator training stage to cope with the class imbalance problem. The comparative experimental results demonstrate that our proposed AREANs can achieve better performance on both Vaihingen and Potsdam datasets.
User Authentication Method Using Active Acoustic Sensing

Personal authentication of the wearable device is important because it has become personalized. Personal identification number (PIN) is generally used to identify the user. However, the user has to remember the PIN and it is easily attacked by shoulder hacking. Therefore, in this study, we propose an authentication method using active acoustic sensing to the user's body. Concretely, the speaker transmits the ultrasonic signal to the user's body and the microphone receives the reflected signal from the body. Each user has a different body composition, and it causes a different frequency response. The system authenticates the user utilizing the difference. Moreover, to improve authentication performance, we add four hand poses to authenticate. Since the proposed method requires one hand for authentication, it is suitable for the wearable computing environment. We implemented the prototype and evaluated the proposed method with nine participants. The evaluation results confirmed that the accuracy of user identification was 91.2% and the equal error rate of user authentication was 2.94% using four hand poses.
The Emperor’s New Autofill Framework:A Security Analysis of Autofill on iOS and Android

Password managers help users more effectively manage their passwords, encouraging them to adopt stronger passwords across their many accounts. In contrast to desktop systems where password managers receive no system-level support, mobile operating systems provide autofill frameworks designed to integrate with password managers to provide secure and usable autofill for browsers and other apps installed on mobile devices. In this paper, we evaluate mobile autofill frameworks on iOS and Android, examining whether they achieve substantive benefits over the ad-hoc desktop environment or become a problematic single point of failure. Our results find that while the frameworks address several common issues, they also enforce insecure behavior and fail to provide password managers sufficient information to override the frameworks' insecure behavior, resulting in mobile managers being less secure than their desktop counterparts overall. We also demonstrate how these frameworks act as a confused deputy in manager-assisted credential phishing attacks. Our results demonstrate the need for significant improvements to mobile autofill frameworks. We conclude the paper with recommendations for the design and implementation of secure autofill frameworks.
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation

We propose combining memory saving techniques with traditional U-Net architectures to increase the complexity of the models on the Brain Tumor Segmentation (BraTS) challenge. The BraTS challenge consists of a 3D segmentation of a 240x240x155x4 input image into a set of tumor classes. Because of the large volume and need for 3D convolutional layers, this task is very memory intensive. To address this, prior approaches use smaller cropped images while constraining the model's depth and width. Our 3D U-Net uses a reversible version of the mobile inverted bottleneck block defined in MobileNetV2, MnasNet and the more recent EfficientNet architectures to save activation memory during training. Using reversible layers enables the model to recompute input activations given the outputs of that layer, saving memory by eliminating the need to store activations during the forward pass. The inverted residual bottleneck block uses lightweight depthwise separable convolutions to reduce computation by decomposing convolutions into a pointwise convolution and a depthwise convolution. Further, this block inverts traditional bottleneck blocks by placing an intermediate expansion layer between the input and output linear 1x1 convolution, reducing the total number of channels. Given a fixed memory budget, with these memory saving techniques, we are able to train image volumes up to 3x larger, models with 25% more depth, or models with up to 2x the number of channels than a corresponding non-reversible network.
Intelligent Recognition of Chirp Radar Deceptive Jamming Based on Multi-Pulse Information Fusion

The perception of jamming types is very important for protecting our radar in complex electromagnetic environments. Radar active deceptive jamming based on digital radio frequency memory (DRFM) has a high coherence with the target echo, which confuses the information of the target echo and achieves the effect of hiding the real target. Traditional deceptive jamming recognition methods need to extract complex features and artificially set classification thresholds, which is inefficient. The existing neural network-based jamming identification methods still follow the pattern of signal modulation-type identification, so there are fewer types of jamming that can be identified, and the identification accuracy is low in the case of low jamming-to-noise ratios (JNR). This paper studies the input of jamming recognition networks and proposes an improved intelligent identification method for chirp radar deceptive jamming. This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). Using a CNN to classify the time–frequency image has realized the recognition of a variety of common deceptive jamming techniques. Similarly, by changing the network input, the original signal is used to replace the echo signal, which improves the accuracy of the jamming recognition in the case of a low JNR.
Measuring the Impact of Automatic Speech Recognition on Number Rendition in Simultaneous Interpreting

Automatic speech recognition (ASR) has been proposed as a tool to augment interpreters’ performance in simultaneous interpretation. In particular, ASR could offer support during the translation of several problem triggers identified in literature, such as numbers, terminology, and named entities. Because of the novelty of this technology, little is known about its real potential in the booth. The effect on the interpreters’ performance of manual information lookup with computer-assisted interpreting tools and of ASR simulated systems recently have been in the focus of empirical research. The present experimental study adds to this body of evidence by analysing the impact of a real-life ASR tool on the simultaneous interpretation of numbers. The results show a significant reduction of error rate, which drops from 39.8% without any support to 14.8% with the use of the ASR support.
On the Security of Verifiable Searchable Encryption Schemes

With cloud services, data users can retrieve encrypted data while preserving data confidentiality. However, this new paradigm suffers from many security concerns. A major concern is how to avoid insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. To address this issue, recently, two verifiable searchable encryption schemes (published in IEEE Transactions on Cloud Computing, doi: 10.1109/TCC.2020.2989296) in cloud storage were proposed which enjoys many desirable features. In this letter, we demonstrate that the schemes are insecure against insider keyword-guessing attack. Specifically, we show that the adversary can derive the keywords in an off-line manner.
A proposed reading event analysis model (REAM) for determining likely reading format preferences

Purpose This conceptual paper relates disparate evidence on the factors influencing reading format choice and preference, whether print or digital, in order to inform educational practice and scholarship. Design/methodology/approach The authors propose a reading event analysis model (REAM) to help guide practitioners and scholars through a consideration of relevant factors, as evidenced by empirical research, to predicting whether print or digital formats will best support the reading and/or learning objectives of a reader in a given reading event in the current technological era. Findings The evidence synthesized and communicated by the REAM model reflects complex interactions between reader characteristics, task characteristics and text characteristics that influence the effects and outcomes of reading in print or digital format. Research limitations/implications This model serves to guide scholars in the design of future empirical studies that account for critical performance variables related to reading comprehension and user preference. Practical implications In examining the effects of reading format on learning and the relationship of learning to overall reader format preferences, this model will help educators, educational administrators, industry practitioners, technologists and interface developers transfer current findings to practice, make decisions and determine developmental priorities to meet the needs of readers and learners across a variety of contexts and support the pursuit of equity in education. Originality/value This model is necessary and contributes important original synthesis and to an area of scholarship that in recent years has yielded results that at times appear contradictory. The model provides possible resolutions to these apparent contradictions in a construct that lends translational value for practice.
Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors

Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions: normal motion, transition, and falling. The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.
Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network

Most existing aspect-term level sentiment analysis (ATSA) approaches combined neural networks with attention mechanisms built upon given aspect to generate refined sentence representation for better predictions. In these methods, aspect terms are always provided in both training and testing process which may degrade aspect-level analysis into sentence-level prediction. However, the annotated aspect term might be unavailable in real-world scenarios which may challenge the applicability of the existing methods. In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown. We access this goal by proposing a capsule network based model named CAPSAR. In CAPSAR, sentiment categories are denoted by capsules and aspect term information is injected into sentiment capsules through a sentiment-aspect reconstruction procedure during the training. As a result, coherent patterns between aspects and sentimental expressions are encapsulated by these sentiment capsules. Experiments on three widely used benchmarks demonstrate these patterns have potential in exploring aspect terms from test sentence when only feeding the sentence to the model. Meanwhile, the proposed CAPSAR can clearly outperform SOTA methods in standard ATSA tasks.
Analysis and Impact of COVID-19 on Economy and Organization

COVID-19 has shown us the new dimension of the world where everything has changed.Due to new innovations in the digital world, organizations have tried their best not to get affected by covid-19 in terms of productivity and reachability to their customers.The organizations have changed their marketing strategies and style of marketing during the pandemic and have found the new way to reach to their customers.In this manuscript, the impact of COVID-19 on economy of the organizations as well world has been analyzed.
Quantum private query with authentication

The quantum privacy query (QPQ) is the quantum version of private query, which refers to that a user (Alice) to purchase data from a database owner (Bob) under the condition of ensuring the privacy of both parties. Specifically, Alice should not access data other than those she bought, and Bob cannot know what data Alice has obtained. The existing QPQ protocols focus on privacy protection and ignore other attacks from outside attackers, such as impersonation, man-in-the-middle and tampering. Undoubtedly, these attacks can destroy the QPQ process. In this paper, we present a secure QPQ protocol that can resist external active attacks. By designing an identity authentication mechanism and integrating it into the existing quantum key distribution-based QPQ protocol, the proposed protocol implements mutual identity authentication in the oblivious key agreement phase to resist impersonation and “man-in-the-middle” attacks. Besides, the mutual authentication process also generates a shared key between Alice and Bob to achieve the data source authentication and integrity protection against tampering attack in the data retrieval phase. The security analysis demonstrates that the proposed protocol not only retains the privacy protection strength of the original QPQ protocol, but also has strong resistance to external attacks.
Identification of Topics from Scientific Papers through Topic Modeling

Topic modeling is a probabilistic model that identifies topics covered in text(s).In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA).This analysis was performed in a corpus of 1000 academic papers written in English, obtained from PLOS ONE website, in the areas of Biology, Medicine, Physics and Social Sciences.The objective is to verify if the four academic fields were represented in the four topics obtained by topic modeling.The four topics obtained from Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) did not represent the four academic fields.
The Proteus Effect: How Avatars Influence Their Users’ Self-perception and Behaviour

The Proteus Effect, introduced by Yee and Bailenson (), suggests that users of a virtual environment adapt their behaviour to the characteristics of their respective avatars. Because numerous studies and experiments concerning the Proteus Effect have been published since 2007, we herein provide a literature review and evaluate the theoretical framework. Based on the comparison of their findings and conclusions about the theoretical framework of the effect and its explanatory approaches such as self-perception theory and priming, we classify these studies with regard to self-similarity, wishful identification, and embodied presence. This allows for revealing parallels to the processes of self-identification.
Elevation Angle Measurement of Sea-Skimming Targets for Fine Tracking in Multipath Environments of Sea Surfaces

Accurately measuring elevation angles of sea-skimming targets via monopulse processing is difficult owing to the multipath signals of sea surfaces. For tracking and shooting down sea-skimming anti-ship missiles, this study presents a fine measurement method for elevation angles of targets under constrained conditions in terms of antenna beam widths of the close-in weapon system (CIWS) radar. This method is based on Eckersten’s implementation of the “complex angle”. We design a specific application and extension of Eckersten’s method, based on its limitations and scope for improvement, to the CIWS radar for estimating elevation angles of sea-skimming targets accurately and effectively. We also evaluate the performance of the proposed method through simulations under various antenna beam widths and transmission frequencies.
LightPaintAR: Assist Light Painting Photography with Augmented Reality

Light painting photos are created by moving light sources in mid-air while taking a long exposure photo. However, it is challenging for novice users to leave accurate light traces without any spatial guidance. Therefore, we present LightPaintAR, a novel interface that leverages augmented reality (AR) traces as a spatial reference to enable precise movement of the light sources. LightPaintAR allows users to draft, edit, and adjust virtual light traces in AR, and move light sources along the AR traces to generate accurate light traces on photos. With LightPaintAR, users can light paint complex patterns with multiple strokes and colors. We evaluate the effectiveness and the usability of our system with a user study and showcase multiple light paintings created by the users. Further, we discuss future improvements of LightPaintAR.