Reviewerly/exHarmony huggingface dataset:
Incorporation of Iterative Self-supervised Pre-training in the Creation of the ASR System for the Tatar Language

In this paper, we study the iterative self-supervised pretraining procedure for the Tatar language speech recognition system. The complete recipe includes the use of base pre-trained model (the multilingual XLSR model or the Librispeech (English) Wav2Vec 2.0 Base model), the next step was a “source” self-supervised pre-training on collected Tatar unlabeled data (mostly broadcast audio), then the resulting model was used for additional “target” self-supervised pretraining on the annotated corpus (target domain, without using labels), and the final step was to fine-tune the model on the annotated corpus with labels. To conduct the experiments we prepared a 328-h unlabeled and a 129-h annotated audio corpora. Experiments on three datasets (two proprietary and publicly available Common Voice as the third one) showed that the first “source” pretraining step allows ASR models to show on average 24.3% lower WER, and both source and target pretraining - 33.3% lower WER than a simple finetunes base model. The resulting accuracy for the Common Voice (read speech) test dataset is WER 5.37%, on the private TatarCorpus (read clean speech) is 4.65%, and for the spontaneous speech dataset collected from the TV shows is 22.6%, all of the results are the best-published results on these datasets. Additionally, we show that using a multilingual base model can be beneficial for the case of fine-tuning (10.5% less WER for this case), but applying self-supervised pretraining steps eliminates this difference.
Deep Learning-Based Side-Channel Analysis Against AES Inner Rounds

Side-channel attacks (SCA) focus on vulnerabilities caused by insecure implementations and exploit them to deduce useful information about the data being processed or the data itself through leakages obtained from the device. There have been many studies exploiting these leakages, and most of the state-of-the-art attacks have been shown to work on AES implementations. The methodology is usually based on exploiting leakages for the outer rounds, i.e., the first and the last round. In some cases, due to partial countermeasures or the nature of the device itself, it might not be possible to attack the outer rounds. In this case, the attacker needs to resort to attacking the inner rounds. This work provides a generalization for inner round side-channel attacks on AES and experimentally validates it with non-profiled and profiled attacks. We formulate the computation of the hypothesis values of any byte in the intermediate rounds. The more inner the AES round is, the higher is the attack complexity in terms of the number of bits to be guessed for the hypothesis. We discuss the main limitations for obtaining predictions in inner rounds and, in particular, we compare the performance of Correlation Power Analysis (CPA) against deep learning-based profiled side-channel attacks (DL-SCA). We show that because trained deep learning models require fewer traces in the attack phase, they also have fewer complexity limitations to attack inner AES rounds than non-profiled attacks such as CPA. This paper is the first to propose deep learning-based profiled attacks on inner rounds of AES to the best of our knowledge.
Constrained Bayesian doubly elastic net Lasso for linear quantile mixed models

In this article, we propose a novel constrained Bayesian elastic net approach for linear quantile mixed model shrinkage. A partially collapsed Gibbs sampling algorithm is developed for efficient posterior computation based on a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We demonstrate the proposed method based on simulated data and an experimental dataset from a longitudinal study of age-related macular degeneration trial. Both simulation studies and real data analysis indicate that the proposed constrained Bayesian elastic net approach is competitive with the existing methods under a variety of scenarios, such as presence of a large number of covariates and collinearity.
Integrating Augmented Reality and Virtual Reality Technology into Chinese Education: An Observation from Nine Cases

This study implemented a teacher training course with a five-phase model (discussion, hands-on activities, lesson planning, experimentation, and reflections) aimed at finding out the affordance of AR/VR technology in Chinese education, and highlighting the challenges intern teachers may encounter during implementation. The researcher conducted a longitudinal study in 2018 and 2019. In total, nineteen intern Chinese teachers participated in this study, forming nine groups that implemented nine AVR Chinese courses. The teachers' reports, instructional documents, videos, and participant-provided data were collected for data analysis. It has been concluded that most AVR was a helpful tool for learners of any age. AVR activities created by the teachers were associated with lower-level cognitive learning. The challenges were insufficient Chinese learning content on the market, time-consuming course preparation, technical issues, and a lack of pre-training guidelines. Finally, a well-developed training course is necessary to guide teachers in understanding the full potential of AVR technology in Chinese lessons. More discussions and suggestions were stated in the study.
Effects of an e-reader intervention on literacy, numeracy and non-verbal reasoning among adolescent girls in Zambia: evidence from a randomised controlled trial

This paper presents results from a three-arm randomized controlled trial in Zambia with a sample of nearly 1200 adolescent girls enrolled in Grade 7. Selected primary schools (N=36) were randomly assigned within each of three districts to one of three arms: 1) e-readers, which girls could take home, provided within a safe space group platform plus community engagement activities; 2) safe space groups plus community engagement activities; and 3) control. The intent-to-treat estimates indicate that girls in the e-reader arm scored significantly better on two basic literacy assessments as well as non-verbal reasoning compared with girls in the control arm.
Motion Object Tracking System Based on FPGA

The technology of moving object detection and tracking has been widely used in consumer electronics, industrial detection, security monitoring and intelligent transportation. This paper uses FPGA(Field Programmable Gate Array) as the platform, because its unique parallel processing mechanism and powerful computing ability can improve the processing speed and performance of the system. At the same time, a design and implementation of SOPC (system on programmable chip) system based on NIOSII is proposed. The system bus clock reaches 266.67MHz. Combined with simple and effective detection and tracking algorithm, the processing speed of motion tracking is greatly improved by using floating-point hardware acceleration. The system can process 320 * 240 true color images at a speed of 20 frames per second. The tracking accuracy is over 93%. The results show that the system has achieved the expected function, has good real-time performance and tracking accuracy, and has good portability, and has a wide application prospect.
Estimating Clustering Coefficient of Multiplex Graphs with Local Differential Privacy

Multiplex graph analysis occupies a prominent position in many real-world applications, such as commodity recommendation, marketing and pandemic tracking. Due to the existence of untrusted data curators, it is in urgent need to design decentralized privacy mechanisms for analyzing multiplex graphs. Local differential privacy(LDP) is an emerging technique for preserving decentralized private data, which has drawn a great deal of attention from academic and industrial fields. The potential of LDP has been proved in various graph analysis tasks in recent researches. However, existing LDP studies may result in insufficient privacy preservation and heavy computation burden for multiplex graphs. In this paper, we introduce an eclectic privacy definition for multiplex graphs. Under this definition, we propose a randomized mechanism, called RALL, to estimate clustering coefficient of multiplex graphs with lower computation cost and higher protection strength. Furthermore, we present a post-processing method to improve the estimation accuracy. The effectiveness and efficiency of RALL mechanism are validated through extensive experiments.
A Formula for Disaster: A Unified Approach to Elliptic Curve Special-Point-Based Attacks

The Refined Power Analysis, Zero-Value Point, and Exceptional Procedure attacks introduced side-channel techniques against specific cases of elliptic curve cryptography. The three attacks recover bits of a static ECDH key adaptively, collecting information on whether a certain multiple of the input point was computed. We unify and generalize these attacks in a common framework, and solve the corresponding problem for a broader class of inputs. We also introduce a version of the attack against windowed scalar multiplication methods, recovering the full scalar instead of just a part of it. Finally, we systematically analyze elliptic curve point addition formulas from the Explicit-Formulas Database, classify all non-trivial exceptional points, and find them in new formulas. These results indicate the usefulness of our tooling, which we released publicly, for unrolling formulas and finding special points, and potentially for independent future work.
Data mining in education: Children living or working on the street with lost data problem

Data mining in education predictions are made about other groups based on the big data of education.However, the extent to which training data can be accessed is relative.A group that can be described as missing data is children living or working on the street.For this reason this study aimed to deal with children working or living on the street.Children who have to live or work on the street before they can complete their development are faced with many dangers arising from the street and may be exposed to social exclusion.In this context, the aim of this study is to examine the social dimension of the problems faced by children living or working on the street and to analyze and evaluate the limited number of data on this issue.The research is a compilation study in line with the literature analysis of the sources about children living and working on the street.
Analog Privacy-Preserving Coded Computing

The state-of-the-art approaches to privacy-preserving coded computing rely on quantizing the data into a finite field, so that Shamir's secret sharing can be employed. Such coded computing solutions, however, are not properly scalable with the size of dataset, mainly due to computation overflows. To address such a critical issue, we propose a novel extension of certain coded computing schemes to the analog domain. This includes distributed polynomial evaluation and Lagrange coded computing (LCC) that are widely used in the literature. All the operations in the proposed protocols are done over the infinite fields of R/C but for practical implementations floating-point numbers are used. We characterize the privacy of data in our proposed protocols, against any subset of colluding servers up to a certain size, in terms of the distinguishing security (DS) and the mutual information security (MIS) metrics. Also, the accuracy of outcome is characterized in a practical setting assuming operations are performed using floating-point numbers. Consequently, fundamental trade-offs between the accuracy of the outcome and their privacy level are observed in the analog domain and are numerically evaluated. Moreover, we implement analog LCC (ALCC) to perform matrix-matrix multiplication over a batch of matrices. It is observed that ALCC is superior compared to LCC, implemented using fixed-point numbers, assuming both schemes use an equal number of bits to represent data symbols.
RoBERTa and Stacked Bidirectional GRU for Fine-grained Chinese Named Entity Recognition

research-article Share on RoBERTa and Stacked Bidirectional GRU for Fine-grained Chinese Named Entity Recognition Authors: Lizhou Li Beijing University of Technology, China Beijing University of Technology, ChinaView Profile , Shuang Zheng Beijing University of Technology, China Beijing University of Technology, ChinaView Profile , Quanmin Wang Beijing University of Technology, China Beijing University of Technology, ChinaView Profile Authors Info & Claims ICMAI 2021: 2021 6th International Conference on Mathematics and Artificial IntelligenceMarch 2021 Pages 95–100https://doi.org/10.1145/3460569.3460576Online:31 August 2021Publication History 0citation73DownloadsMetricsTotal Citations0Total Downloads73Last 12 Months73Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Circuit Fault Diagnosis Method Based on Spectrum Analyses and ELM

To overcome the difficulty of the circuit fault diagnosis in analog circuits, which is hard to analysis and judgment by traditional method, a novel method based on frequency domain analysis and ELM neural network is proposed. Since the circuit frequency response curve can reflect the operating state and characteristics of the circuit, when a fault occurs, the frequency response characteristic curve will also change. By analyzing the changes in the transfer spectrum characteristics to determine whether it is in a fault state, this method has clear physical meaning and good robustness. The training process can be simplified by using ELM neural network. Through simulation and experiment, the results show that the method is sensitive and feasible for the circuit fault detection.
The Range Automaton: An Efficient Approach to Text-Searching

String matching is one of the most extensively studied problems in computer science, mainly due to its direct applications to such diverse areas as text, image and signal processing, speech analysis and recognition, information retrieval, data compression, computational biology and chemistry. In the last few decades a myriad of alternative solutions have been proposed, based on very different techniques. However, automata have always played a very important role in the design of efficient string matching algorithms. In this paper we introduce the Range Automaton, a weak yet efficient variant of the non-deterministic suffix automaton of a string whose configuration can be encoded in a very simple form and which is particularly suitable to be used for solving text-searching problems. As a first example of its effectiveness we present an efficient string matching algorithm based on the Range Automaton, named Backward Range Automaton Matcher, which turns out to be very fast in many practical cases. Despite our algorithm has a quadratic worst-case time complexity, experimental results show that it obtains in most cases the best running times when compared against the most effective automata based algorithms. In the case of long patterns, the speed-up reaches \(250\%\). This makes our proposed solution one of the most flexible algorithms in practical cases.
Parameter Identification for Malicious Transaction Detection in Blockchain Protocols

As blockchain-based platforms become increasingly ubiquitous, malicious actors looking to either break the underlying platform or leverage it for nefarious purposes will also become more common. In order to combat these actors, robust mechanisms to detect and address illicit activities must be developed. Many of the current approaches to detecting abnormal activity in blockchain-based platforms are platform specific. In this paper we provide some generic parameters that should be valid for most permissionless blockchain platforms, particularly permissionless blockchain platforms that can be used for malicious transaction detection. We then analyze those parameters in the Ethereum cryptocurrency platform. These parameters include volumetric transaction rate and unique address activity.
Towards Data Ecosystem Based Winter Road Maintenance ERP System

Traditional Enterprise Resource Planning (ERP) systems are geared towards processing organizations' internal information. However, business processes supported by these ERP systems increasing rely on external data. In order to understand these data dependencies, it is proposed to use data ecosystem models instead of traditional ERP implementation reference models to deploy ERP systems for their users. The winter road maintenance case is explored as a representative case of ERP systems using variety of data sources and requiring advanced data processing features. The winter road maintenance data ecosystem model is developed by means of participative modeling and application of the ecosystem model to configure ERP systems for their users is outlined. The data ecosystem approach allows organizations to partners providing data and data processing services for implementation and operation of the system.
Automated Corpus Annotation for Cybersecurity Named Entity Recognition with Small Keyword Dictionary

In order to assist security analysts in obtaining information pertaining to the cybersecurity tailored to the security domain are needed. Since labeled text data is scarce and expensive, Named Entity Recognition (NER) is used to detect the relevant domain entities from the raw text. To train a new NER model for cybersecurity, traditional NER requires a training corpus annotated with cybersecurity entities. Our previous work proposed a Human-Machine Interaction method for semi-automatic labeling and corpus generation for cybersecurity entities. This method requires small dictionary that has the pairs of keywords and their categories, and text data. However, the semantic similarity measurement in the method to solve the ambiguous keywords requires the specific category names even if non cybersecurity related categories. In this work, we introduce another semantic similarity measurement using text category classifier which does not require to give the specific non cybersecurity related category name. We compare the performance of the two semantic similarity measurements, and the new measurement performs better. The experimental evaluation result shows that our method with the training data that is annotated by small dictionary performs almost same performance of the models that are trained with fully annotated data.
Scalable Undergraduate Cybersecurity Curriculum Through Auto-graded E-Learning Labs

The US and world economies need more trained cybersecurity workers. The demand for these workers has driven prominent private universities to create large reduced-cost programs for graduate students. Unfortunately, a large segment of the population does not have the prerequisite knowledge to enter into these graduate-level programs. In this paper, we look at how to develop an undergraduate cybersecurity online program through e-learning with automatically graded labs. We develop techniques to increase lab participation and integrity through a concept we call derivate labs. We provide preliminary evidence that these labs have, in fact, increased engagement and integrity in our online sections of courses in our undergraduate cybersecurity program.
Lifestyle Analysis via a Corpus of Disease-Fighting Weblogs

In recent years, the population of pre-diabetics in Japan has been increasing year by year. Type 2 diabetes is a type of lifestyle-related disease that can be prevented to a certain extent by correcting lifestyle habits. However, by the time we realize that there is a problem with our lifestyle, it may be too late. Therefore, early detection of risk factors for lifestyle-related diseases is important. In this study, we collected the blogs of lifestyle-related disease fighters, set up multiple keyword categories that are considered to be related to risk factors, and constructed a corpus of disease fighting blogs with labels for each category. The results of the evaluation experiments show that our proposed method can be applied to a wide range of topics. As a result of evaluation experiments, our proposed method achieves categorization of keywords and sentences with higher accuracy than the simple method.
Preparing Decoherence-Recoherence Times of a Qubit with Special Nonselective Measurement Schemes

The appearance of decoherence-recoherence events is analyzed in a dephasing model of a qubit which interacts with a bosonic environment. The initial condition is prepared from the thermal state of the whole system by performing a nonselective measurement on the qubit. Decoherence-recoherence events appear in the reduced dynamics of the qubit uniquely if special nonselective preparation measurements are adopted and the spectral density of the system fulfills determined conditions. General lower bounds are found for the number of decoherence-recoherence events in terms of the properties of the measurement scheme and of the spectral density. The number is finite for canonical spectral densities which are super-ohmic, perturbed super-ohmic, or ohmic at low frequencies and are arbitrarily tailored at high frequencies. The number is infinite in the sub-ohmic regime and for perturbations of the sub-ohmic or ohmic regime. Decoherence-recoherence times are evaluated, analytically and numerically, for ohmic-like spectral densities with exponential high-frequency cutoffs.
User Interface Matters: Analysing the Complexity of Mobile Applications from a Visual Perspective

Product-centric techniques to analyze mobile applications leverage traditional source code analysis, size, market success, complexity, and others. Many of these techniques converge in the strategy of evaluating metrics taken from the source code that delivers the functionality of the software product. However, when following the Model-View-Controller (MVC) architecture, mobile applications are typically constructed by a compound of at least two programming languages, one to deliver the functionality and the other to describe the visual aspects. The latter is commonly left out from source code analysis, even though critical parts of the application are present in the graphic User Interface (UI). In this paper, we identify an opportunity to strengthen the product-centric mobile app analysis by incorporating UI metrics. This approach aims to enhance the expressiveness of source code metrics and deliver a more comprehensive analysis of the complexity, maintainability, and effort estimation of a mobile app. To introduce the concept, we present a case study realized using a block-based programming language to create mobile apps, in which we describe and calculate functional and UI metrics, discover commonalities and differences, discuss traits, and open tracks for further research.
Adversarial Examples Generation And Attack On SAR Image Classification

It has been demonstrated that deep learning models are vulnerable to adversarial examples, and most existing algorithms can generate adversarial examples to attack image classification or recognition models trained from target datasets with visible image such as ImageNet, PASCAL VOC and COCO. In order to expand the image style of adversarial examples and decide whether the attacking specialty exists in SAR images, the MI-FGSM and AdvGAN algorithms are introduced to generate adversarial examples, and the attacks for SAR image classification are executed in this paper. The experimental results show that the adversarial example is also definitely aggressive to SAR image, and the attack success rate is affected by the structure of deep learning networks training the target models and the category of the image.