Reviewerly/exHarmony huggingface dataset:
Data Augmentation for Copy-Mechanism in Dialogue State Tracking

Traditional dialogue state tracking (DST) approaches need a predefined ontology to provide candidate values for each slot. To handle unseen slot values, the copy-mechanism has been widely used in DST models recently, which copies slot values from user utterance directly. Even though the state-of-the-art approaches have shown a promising performance on several benchmarks, there is still a significant gap between seen slot values (values that occur in both training set and test set) and unseen ones (values that only occur in the test set). In this paper, we aim to find out the factors that influence the generalization capability of the copy-mechanism based DST model. Our key observations include two points: 1) performance on unseen values is positively related to the diversity of slot values in the training set; 2) randomly generated strings can enhance the diversity of slot values as well as real values. Based on these observations, an interactive data augmentation algorithm is proposed to train copy-mechanism models, which augments the input dataset by duplicating user utterances and replacing the real slot values with randomly generated strings. Experimental results on three widely used datasets: WoZ 2.0, DSTC2 and Multi-WoZ demonstrate the effectiveness of our approach.
Lightweight Authenticated Encryption Mode with Enhancing Security Guarantees

Under the lightweight setting, the security requirements and the leaking security bits for devices or systems are stricter. This paper focuses on the security of permutation-based lightweight authenticated encryption (LAE) modes, presents a new syntax of LAE, and, on the basis of OPP. proposes an enhanced LAE mode called OPP-plus which supports beyond-birthday-bound (BBB) security and integrity security under the releasing unverified plaintext (INT-RUP). For achieving BBB security, OPP-plus is set up by two-round Even-Mansour ciphers with distinct keys. For ensuring INT-RUP security, the authentication part of OPP-plus utilizes an extra intermediated checksum technique to generate the authentication tag. Then, by using the hybrid argument, we prove that OPP-plus meets our stronger security requirements in the nonce-respecting scenario if the underlying permutation is an ideal random permutation. Finally, we discuss the properties of OPP-plus. OPP-plus is based on a pure-permutation, and supports adaptively block-size and smaller security losses. As OPP-plus does not perform the key expansion algorithm and its parts of combinatorial circuits can be replaced directly by lookup tables, the cost of keys' update is relatively small and the implementation efficiency is relatively high in practice.
Technological effectiveness of modern education: features, traditions, innovations

The purpose of the study is to analyze the experience in organizing the learning process at a university using digital technologies, aimed at realizing the content of pedagogical education in the context of its structural components and indicators of students’ readiness to work in an educational institution. The positive and negative aspects of the use of IT- technologies in the formation of the experience of professional and pedagogical activities of students in the direction of “Pedagogical education” are noted. The relevance of using digital technologies in the educational process is dictated by the prevailing socio-cultural conditions, the development of information and communication means of transmission and information. At the same time, the problem is the unwillingness of the faculty, students, to their use for several reasons: news, illiteracy, lack of equipment in detail-spatial environment of technical means, the quality of Internet connection. And technologies which provide ability to save dialogicality pedagogical culture, allowing to establish a dialogue in the systems “teacher-student”, “student-teacher” and the polylogue “student-teacher-students” are particularly in demand. The use of the technology “Immersion in professional and pedagogical activity through culture with the support of the teacher” allows the teacher to conduct a dialogue with students and purposefully form their readiness for pedagogical activity in modern conditions based on the acceptance of the values of pedagogical culture and identification in her, personal and semantic attitude to labor functions for the provision of educational services for the main educational programs of educational organizations of different levels.
Practice and Reflection on the Application of Blockchain Technology in the Guangdong-Hong Kong-Macao Greater Bay Area

Blockchain technology has now entered the 3.0 era with solutions of“Blockchain Plus Industry”. As a technological innovation and industry-leading region, the Guangdong-Hong Kong-Macao Greater Bay area has inherent advantages in the development of blockchain technology. How blockchain can lead the development of industry ecology in the future is a question worthy of our in-depth discussion. By analyzing the policy support and basic status of the Guangdong-Hong Kong-Macao Greater Bay Area, this paper sorted out the structure of the four-level characteristic development mode of blockchain technology, summarized the impossible triangle of technology, incompatibility issues, standard construction, supervision and other issues, and putforward relevant suggestions for the development of the blockchain in the Guangdong-Hong Kong-Macao Greater Bay area.
Image Noise Estimation Algorithm Based on Super-pixel Segmentation and Scale Invariance of Kurtosis

Noise estimation methods are widely utilized in many areas of image processing and detection, such as image deblurring, mosaic detection . Recently, many researchers tried to design the corresponding estimation algorithm from the three directions of image spatial domain, transform domain and image statistical properties. Based on the scale invariance of kurtosis and piecewise stationarity of natural image in band-pass domain, this paper proposes a noise estimation algorithm based on Super-pixel segmentation and K-means clustering considering the influence of dominant edge and texture on the statistical characteristics in the image space domain. In this algorithm, we use PCA filters to transform the noisy image to transform domain. Furthermore, we can transform the noise estimation problem into a nonconvex optimization problem by using scale invariance of kurtosis. Finally, a large number of experiments verify the reliability of the algorithm, and the results show that the effect of the algorithm acts as a counterweight to the recent state-of-the-art algorithms.
Features of distance learning of university students in the context of digitalization

This article is devoted to special aspects of giving classes in the discipline “Applied Physical Education and Sports” at the Department of Physical Culture at Siberian Federal University. These aspects are considered an example of development and introduction of online course of the swimming specialization in educational process. It is connected with the transition to distance learning caused by the spread of COVID-19 infection in the world. The transition to this format of study was an effective way of interacting with full-time students in the applied discipline. It made it possible to avoid the eventual interruption of educational activities, as feedback from the “student – university teacher” was organized. This allowed the students to receive the full information by providing interim assessment for the spring semester of the 2019-2020 academic year. The article reflects the personal experience of university teachers in the development and implementation of the electronic course, its main resources, content, and features of functioning. This allowed asynchronously teach students, posting interactive lectures, offer videos on the topic of the course, issue practical tasks and training programs, check the assimilation of material, as well as monitor the performance of tasks, maintain motor activity and health of students.
A Study of Targeted Telephone Scams Involving Live Attackers

We present the results of a research study in which participants were subjected to social engineering attacks via telephone, telephone scams, in order to determine the features of scams which people are most susceptible to. The study has involved 186 university participants who were attacked with one of 27 different attack scripts which span different independent variables including the pretext used and the method of elicitation. In order to ensure informed consent, each participant was warned that they would receive a scam phone call within 3 months. One independent variable used is the time between the warning and launching the scam. In spite of this warning, a large fraction of participants were still deceived by the scam. A limitation to research in the study of telephone scams is the lack of a dataset of real phone scams for examination. Each phone call in our study was recorded and we present the dataset of these recordings, and their transcripts. To our knowledge, there is no similar publicly-available dataset or phone scams. We hope that our dataset will support future research in phone scams and their detection.
Under Co-construction: Toward the Social Design of Explainable AI Systems

Technological advancements in machine learning affecting humans' lives on the one hand and also regulatory initiatives fostering transparency in algorithmic decision making on the other hand drive a recent surge of interest in explainable AI (XAI). Explainability is discussed as a solution to sociotechnical challenges such as intelligent software providing incomprehensible decisions or big data enabling fast learning but becoming too complex to fully comprehend and judge its achievements. With explainable AI, more insights into the functions, decisions, and usefulness of algorithms are expected.
An Adaptive 3D U-Net for White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from 3D-Brain MRI

Many methods for Alzheimer's disease detection in brain magnetic resonance imaging (MRI) is related to white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) regions. Therefore, in many neurological applications, the segmentation of these tissues in magnetic resonance imaging (MRI) plays an important role in the analysis. In the trend of deep learning, an application using 3 dimensional (3D) Convolution Neural Network will help doctors get the best result segmentation. In this research, we proposed an effective approach to segment automatically these tissues in Brain MRI 3D by using an adaptive 3D U-Net. In the experiments, a real MRI database, The Internet Brain Segmentation Repository (IBSR) 18, is evaluated with the proposed method and gives the promising Dice with 0.92, 0.87, 0.81 for WM, GM, and CSF segmentation.
A Medical Image Segmentation Method Based on Pixel-to-Pixel Threshold

This paper analyzes the characteristics of typical medical images. Because digital images are composed of pixels, according to the gray information of medical images, owing to Fourier image analysis theory, based on the local average value of $$3 \times 3$$ blocks in the image, the threshold value of each pixel is calculated, and each pixel of the image is segmented. At the same time, this paper discusses the asymmetry of image boundary impact of this method. In this paper, we first denoise the image, use $$3 \times 3$$ template to convolute the image, find the threshold base value of each pixel, then add a constant a to get the threshold value of each pixel, binarize each image, and finally process the image. This improves the speed and accuracy of segmentation. Compared with the previous algorithm, it has obvious advantages in segmentation accuracy, segmentation effect, running time and so on, which has certain practical and clinical significance.
Channel Attention Module and Weighted Local Feature Person Re-ID Network

Person re-identification, as a new technology emerging in the field of intelligent detection and analysis in recent years, has received more and more attention from researchers. Cross-camera is one of the important features in the field of person re-identification, which makes the work very challenging. At present, most pedestrian recognition methods only use global features or local features. However, the performance boost that comes from combining the two features is ignored. Therefore, we propose a network that integrates global and local features. The added channel attention module makes the network pay more attention to the similar semantic information between channels and increase the discrimination ability. In addition, the local features containing discriminative information occupy a larger weight. Finally, extensive experiments have verified the effectiveness of our method, and our method has shown state-of-the-art results in three main datasets: Market-1501, DukeMTMC-reID, and CUHK03.
Learning Performance Prediction with Imbalanced Virtual Learning Environment Students’ Interactions Data

One of the critical aspects in completing study in a virtual learning environment (VLE) is the student behavior when interacting with the system. However, in real cases, most of the student behavior data have imbalanced label distribution. This imbalanced dataset affects the model performance of machine learning algorithms significantly. This study attempts to examine several resampling methods such as random undersampling (RUS), oversampling with synthetic minority oversampling technique (SMOTE), and hybrid sampling (SMOTEENN) to resolve the imbalanced data issue. Several machine learning (ML) classifiers are employed to evaluate the efficiency of the resampling methods, including Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF). The experiment results indicate that the performance of classifiers is improved utilizing more balanced dataset. Furthermore, the Random Forest classifier has accomplished the best result among all other models while using SMOTEENN as a resampling approach.
On CNN Applied to Speech-to-Text – Comparative Analysis of Different Gradient Based Optimizers

In this paper the authors have developed a Convolutional Neural Network architecture adapted to Speech-to-Text research field. This type of network has been chosen due to its capacity to extract the relevant features and its popularity in classification problems. A particular model for a Speech-to-Text application has been designed. The parameters of the model (i.e. the size of filters and kernels), and the number of the layers have been chosen by conducting appropriate experiments, and the model that ensured the highest accuracy has been selected. The model takes raw waveforms of spoken digits as input, and outputs a text with the predicted digit. The network is capable of providing the right digit no matter the gender or age of the speaker. The overfitting has been avoided by using Dropout layers and early stopping function. In order to select the best model, the authors have taken into account two basic criteria: the accuracy of the model, and the execution time, respectively. Considering the computational time, the first order cost function has been chosen. By testing different gradient descent optimization algorithms, the best optimizer has been selected. The application has been developed using Python programming language.
Are Blockchains Trustless?

It is often touted that the innovation that the blockchain brought is its trustlessness, in that it does not require a trusted third party to achieve “true records of transactions.” However, digging a little deeper, it becomes apparent that what is meant by “trustless” in such a claim is rather vague. What does “trust” really mean in the context of an information system? Here lies an essential key for the application of blockchain to the real world. In this chapter, we look closer into the meaning of the claim, “Blockchains are trustless.”
Research on Physical Phenomenological Innovation Technology in Virtual Reality Movies

Abstract From the perspective of physical phenomenology, the experience of the experiencer in the virtual reality space is not only achieved by external technical means. The key to the experience of the experience is the operation of the “body field“. This process involves three important roles: body space, body intentionality, and body habits. Based on phenomenological theory, virtual reality technology has been widely used in genres of science fiction, magic and other themes, making movies more appealing. Based on body phenomenology, this article discusses the application of virtual reality technology in the design mode of movie scenes.
An Intelligent System of Oral English Assistant Teaching Based on Tag Matching

This paper introduces an online oral English teaching aid system based on tag matching. Data is first collected by the multi-parameter objective scoring system of Reading Aloud in English. Feedback of students’ oral English learning and learning resource recommendation then are given to students based on formative assessment theory. This system serves both teachers and students. Students can learn autonomously, monitor their study and obtain personalized learning resource recommendation. Meanwhile, teachers can have a visual interface of students’ learning results, which helps them to track students’ individual learning process as well as obtain an overall view of the whole class through the visual data analysis chart function. The application of this system can improve students’ oral English learning efficiency and help teachers complete their predetermined teaching objectives.
How the PC was born and why it hasn't died yet

Abstract Tablets and smartphones might be cool but one class of computer has reshaped the working world more than any other: the personal computer. Martin Cooper MBCS charts the PC's birth, life and ongoing success.
Classification-based self-supervised learning for anomaly detection

Anomaly detection aims to find different patterns from those seen previously. It is usually regarded as a oneclassification problem where abnormal classes are often scarce or not well defined, while the target class (training objects) are sufficient. Recently, several methods have achieved excellent performance through an auxiliary multi-class task(such as rotation predict) used in self-supervised learning. However, these classification-based approaches which adopt the crossentropy loss have inherent defects in anomaly detection. Specifically, the relative measure of cross-entropy may result that a normal sample suffered from a low score would be misclassified as an abnormality. In order to solve this problem, we propose an Absolute Measurement Anomaly Detection (AMAD), to constrain the distribution of activations of each input in the classification network. In details, this technique encourages the output of the ground truth class to be higher, vice versa for unrelated classes. Furtherly, different from the previous evaluation methods that counting the log-softmax activation of the model as normality score, we ignore the log-softmax since the score would be affected heavily provided that more misclassification occurs. We present experiments both in image datasets(CIFAR-10, Fashion-MNIST) and tabular datasets(KDDCUP et al.), which show that our technique achieves better performance in terms of AUROC and F1 score when compared with previous similar methods.
Recognizing Named Entity in Electric Power Documents

In this paper, we address the task of named entity recognition in the electric power domain. We make the following two main contributions. First, we propose an annotation approach and built a public dataset. In our approach, the distant supervision with manual annotation is adopted to obtain the pseudo-annotated domain corpus. Second, we compare several popular methods on our electric power dataset. Experiments demonstrate that the pre-trained neural model with a softmax classifier achieves the best performance with an F-measure score 83.50%.
Approaches to the choice of tools for constructing a methodology for learning to work with Big Data

The article actualizes the need for teaching students to work with Big Data technologies. Big Data is a promising and fundamental industry that requires a large number of qualified specialists in various fields. The aim of the work is to describe the concept of determining a set of hardware, software, algorithmic and methodological tools (taking into account the contingent of students and the capabilities of the educational institution) for building a methodology for teaching a discipline related to the study of Big Data processing methods. There are two main sectors of stakeholders who need specialists in the field of Big Data. A detailed comparative analysis of software solutions that support Big Data processing is carried out. The article describes the methodology for constructing a course for teaching students technologies for processing and analyzing Big Data. A plan for organizing a lecture course and laboratory practice with consideration of subtasks is proposed for students to perform during training. The composition and methodology of independent work of students in the discipline related to the study of Big Data, using a learning management system such as Moodle, are discussed. An example of implementing data processing by means of the RapidMiner Studio package using a multi-layer neural network training algorithm using the error back propagation method is presented.
Research on Intelligent Acquisition and Efficient Processing Method of Key Data of Power Consumption Based on Blockchain

Under the background of the continuous expansion of the scale and capacity of power grid and the sharp increase of information measurement points, the amount of data that needs to be analyzed and processed is growing geometrically. It is urgent to carry out the research of massive information intelligent collection and efficient computing processing scheme and storage scheme. In this paper, combined with the actual demand of monitoring data access and lossless compression method in engineering practice, we build Hadoop cloud cluster platform and hive distributed data warehouse, take analog measurement data set as cluster compression object, realize the effective compression and storage of measurement information, which has important theoretical significance and practical value for solving power grid large data processing.
Investigation of Heat Transfer in 3D Packaging for Practical-Scale Quantum Annealing Machines

A Qubit-chip/Interposer/Package substrate (QUIP) structure based on the 3D packaging technology is proposed to realize scalable quantum annealing machines. We investigate the thermal conduction of the QUIP structure with finite element method. The effect of the TSV diameter and number, the thickness of interposer and substrate, and the pitch of bumps on thermal conduction is discussed. We reveal the reduction of the maximum temperature of the qubit chips from 40.2 mK to 18.8 mK by optimizing the heat transfer to the heat sink. A cryogenic environment at 20 mK level is successfully achieved for qubit chip operation in QUIP structure.
Simulating Quantum Radar with Brownian Processes

We present a set of stochastic differential equations, defined through Brownian processes, that are solved in the complex field. The corresponding Kolmogorov-Chapman equation of such processes is a Schrödinger-like equation and then, we can represent quantum noise corresponding to a specific system by solving stochastic differential equations through ordinary numerical algorithms, making their implementation easier. These numerical algorithms can be considered as a definition of the corresponding processes used to define them. An application to basic quantum radar is attempted with the scope to outline the calculation of ROC (Receiver Operating Characteristic).