Reviewerly/exHarmony huggingface dataset:
A C&amp;W-based Attack Algorithm for End-to-End Communication Systems

End-to-end communication systems based on deep neural networks are extremely vulnerable to adversarial attacks. Specifically, we elaborate on how an attacker can craft Carlini and Wagner attacks (C&W) against end-to-end communication systems. We analyze the effect of this algorithm on end-to-end systems from the perspectives of white-box attacks and black-box attacks. We show that the C&W algorithm is destructive to end-to-end communication systems. We also reveal that white-box attacks are more destructive than black-box attacks. Under blackbox attacks, the block error rate of the system can increase by three orders of magnitude.
The association between two categorical variables measured by chi-square statistic, w index, and contingency coefficient

This paper provides a brief overview of analysis of dependence between two categorical variables. It shows the calculation of the chi-square test, as well as its effect size using w index and the calculation of the measures of associations.DOI: https://doi.org/10.24040/sjss.2021.7.2.50-55
A Two-Step Method for Dynamics of Abstract Argumentation

This paper proposes a two-step method to handle the dynamics of argumentation. Firstly, we reach the conflict-free labellings of the updated argumentation framework by the intersection of expansions of the conflict-free labellings of argumentation frameworks before updating. Then we select the conflict-free labellings which have the least illegally labelled arguments when restricted to part of both argumentation frameworks in the update process. Finally, we prove the soundness and completeness of our method under the complete semantics. In other words, the complete labellings of the resulted argumentation framework after update is the same as that of our method.
Collecting Big Data in Cinemas to Improve Recommendation Systems - A Model with Three Types of Motion Sensors

With the advent of video-on-demand services on the Internet, research on recommendation systems has shifted to these services. In classic cinemas, recommendation systems are also used, but they are also most often associated with devices connected to the Internet (computers, smartphones). As a rule, these systems collect different data types that users consciously generate, and data that users unconsciously generate are rarely used. The category of unconsciously generated data includes ECG, EEG, GSR, pulse rate, blinking, unconscious body movements etc. The development of the Internet of Things device has enabled mass monitoring of some unconsciously generated data by users intending to improve the recommendation system's accuracy. This paper defines a model for monitoring cinema spectators unconscious body movements with the help of three different non-invasive sensors and based on a simple neural network. The above data can be used for better user profiling, provided that we know where the user is sitting in the cinema. Since various loyalty systems (cards, apps, etc.) are available in cinemas today, seating location information is often known for registered users. The aim of the paper is to define a simple model for monitoring cinema viewers’ unconscious body movements to increase the accuracy of recommendation systems.
Basic structure of neural networks

Neural networks are a subset of machine learning, and they are at the heart of deep learning algorithms. This chapter introduces neural networks, which have been among the most widely used and successful models for machine learning. Most of the modern deep learning models are also based on neural networks. These networks have a long history of development and great success in research communities.
Risk Assessment of Highway Engineering Investment Based on Broad Learning System

Highway projects are usually built in extremely complex natural and cultural environments, which is a continuous and dynamic management practice process. Therefore, it often has the characteristics of long construction period, large investment scale, many security risks and high technical requirements. How to effectively reduce these uncertain risk factors is the direction of continuous progress of relevant researchers.
Discrete Cosine Transform Method for Watermarking in Digital Image Processing

Today, image data is part of the development of big data whose volume, variety, and velocity are huge. The watermarking process is one of the simplest methods but effective and popular methods to protect copyright from images and insert other important information in an image. This study aims to carry out a watermarking process on an image by inserting another image. The method used is Discrete Cosine Transform (DCT) in the process of watermarking images. This research conducted 20 experiments for the image watermarking process with a combination of existing image data. The results of the experiment show that all of the image objects carried out by the watermarking process using DCT were successfully carried out with an accuracy value is 100%. However, the process of decryption or image separation after watermarking was not successful. In the image decryption (or image restoration) experiment using DCT, nothing works because the original image inserted by another image does not return to its original state. This result shows that DCT is only good to use during the watermarking process.
Improving Speaker Verification in Noisy Environment Using DNN Classifier

Speaker verification in noisy environments is still a challenging task. Previous studies have proposed speaker embeddings (x-vectors, ThinResNet) with classifier models (PLDA, cosine) to classify if an audio is spoken by a specific speaker. The verification process is defined in 3 steps: training an embedding extractor, enrollment and verification. Most studies were trying to mitigate the noisy issue by augmenting noises in the embedding extractor. This method helps the extractor to tolerate more types of noise during the inference process. However, the classification model is still sensitive in noisy environments. In this paper, we (1) evaluate the effectiveness of different speaker embedding models and classifiers in various conditions, and (2) propose a neural network classifier on top of embedding vectors and train it with data augmentation. Experimental results indicate that the proposed pipeline outperforms the traditional pipeline by 5% F1 on a clean test set and 9% F1 on noisy test sets.
Interlineation of Functions

This chapter is devoted to the construction and study of interlineation operators of functions of two or more variables with and without preservation of the class of differentiation. When constructing interlineation operators of functions, new information operators are used: traces of an approximate function and traces of some differential operators with partial derivatives up to the specified order on a system of parallel or intersecting lines. If information operators use only traces of functions, then we have an interlineation of the Lagrangian type. If the interlineation of functions uses traces of the approximate function and traces of some system of differential operators from the approximate function, then we have a Hermitian-type interlineation. If the approximate function has continuous derivatives only up to a certain order, then Hermitian interlineation operators with given traces on the system of lines give functions that do not have the same class of differentiation as the approximate function. For this case, the work presents a generalized D'Alembert formula (the classical D'Alembert formula is its partial case), which automatically preserves the same class of differentiation to which the approximate function belongs. The work presents examples of Hermitian interlineation operators with and without preservation of the class of differentiation on the system of parallel, mutually perpendicular, and intersecting lines.Here give integral representations of the residual terms of the approximation of differentiation functions of two variables with and without preservation of the class of differentiation. An example of Lagrangian interlineation on a system of mutually perpendicular lines with the optimal choice of these interlineation lines is given.
Analysis of an Automatic Early Warning System Based on Fog Architecture

Abstract In the process of analysing and processing terminal sensor information, a large number of terminal sensors are needed to collect front-end information. These front-end data collection, analysis and processing require high real-time, and need the support of location aware mobile computing services. Traditional cloud computing architecture is not the best choice for service scenarios with high real-time requirements. The fog computing architecture is to extend cloud computing services to the edge of the sensor network, coupled with appropriate fitness algorithms, can effectively improve the information analysis and early warning response speed of the geological disaster information early warning system.
Clustering Friendly Dictionary Learning

In this work we propose a dictionary learning based clustering approach. We regularize dictionary learning with a clustering loss; in particular, we have used sparse subspace clustering and K-means clustering. The basic idea is to use the coefficients from dictionary learning as inputs for clustering. Comparison with state-of-the-art deep learning based techniques shows that our proposed method improves upon them.
A Recognition Method for Multi-object Information Based on Multi-source Data Fusion

The accuracy of object recognition is difficult to be improved by single information acquisition, we propose a recognition method for multi-object information based on multi-source data Fusion. By analyzing the high-level semantic features of RGB images and Depth images, feature fusion module is added, then, the calculation of parameters of the model is reduced based on the idea of residual learning. Combined with the GRU recursive neural network, a tighter feature sequence is to generated, which improved the accuracy of RGB-D object recognition. Finally, improved method has been experimented on multiple public data sets, the results show that the object recognition method in this paper integrates depth information, Compared with single RGB image, the recognition accuracy is significantly improved; Compared with other RGB-D-oriented deep learning methods, the recognition accuracy of the method in the article has been significantly improved by at least 2.5% in 2D3D dataset.
Corpora in Applied Linguistics

Over the past forty years, corpora (singular, corpus) have played an increasingly important role in Applied Linguistics (AL).
An Anti-DPA Elliptic Curve Cryptography Scalar Multiplier with Randomized Base Point

Scalar multiplication is the core operation of Elliptic Curve Cryptography (ECC), and its circuit is susceptible to the leakage of side-channel information, so the private key can be obtained through power analysis attacks. In this paper, a dual-field ECC scalar multiplication with power analysis resistance is introduced. The paper presents an ECSM with introduction of random point which can resist Simple Power Attack (SPA) and Differential Power Attack (DPA). The paper designs the hardware structures based the proposed algorithm, test and verify the Anti-attack function of the design. Base on the SMIC 0.18μm CMOS process, use the Synopsys DC tool for comprehensive evaluation of the dual-field ECC scalar multiplication circuit against side channel attacks, and select the 256-bit in the prime field and 233-bit in the binary field respectively as representatives, the cost of the side channel attack defense scheme proposed in this paper is 14.77% in area and an average increase in computing time of about 74 % compared with the circuit without protection.
Towards Generalized Violence Detection; a Pose Estimation Approach

In recent years, there has been a growth in the development of automated systems which can identify human actions. Some of these automated systems are also being developed to detect violence of various forms. Deep learning has shown a lot of promise in detecting violence in videos, especially with the rise of Neural Networks. However, a trend in most of the papers is to report intra-dataset test results. In this paper we evaluate multiple models and demonstrate that they generalize poorly while performing inter-dataset testing. Furthermore, we propose several approaches to incorporate pose data to improve the initial inter-dataset testing results.Three different datasets have been used to carry out the proposed method. These are Hockey Fights Dataset, Movie Dataset, and a dataset based on South- Asian Context
Culturally Responsive Pedagogy, Universal Design for Learning, Ubiquitous Learning, and Seamless Learning

The purpose of this chapter is to develop a one-stop checklist that assists educators in providing online teaching grounded in the principles of culturally responsive pedagogy (CRP), Universal Design for Learning (UDL), ubiquitous learning (u-learning), and seamless learning. The authors explore how these paradigms inform the intentional design of learner-centered approaches in online learning environments and what an integrated approach could look like. This chapter will be relevant for faculty in higher education aiming to offer online curricula that emphasize active, collaborative, constructive, authentic, and goal-directed learning.
Application of Internet of Things Technology in Early Warning and Monitoring of Equipment Status

With the current iterative update of machinery and equipment in various factories in our country, the requirements for real-time condition monitoring of equipment are also increasing. Based on the Internet of Things technology, the real-time performance of equipment detection and early warning can be better and effectively improved. This article is based on the research of the Internet of Things technology. In the Internet of Things, a large number of sensors with different functions are used for network information. As the information source, the sensor has different types, and the collected information content and format are different. The information from this way is instantaneous, and the actual information is captured according to certain rules, and the data can be updated at any time. Based on the technology and concept of the Internet of Things, so this time through ZigBee wireless white networking communication, a device condition early warning monitoring system is designed. Analyze and process a large amount of data from sensors. After analysis, it can display the monitoring results that users need to meet the needs of various customers. This design uses the real-time data transmitted in the server to establish a remote web user monitoring interface in the system. The system designed this time can perform preventive monitoring on this point. First, set a normal working sound. If the sound changes, the system will detect it and automatically generate an alarm. Experimental studies have shown that this can effectively avoid the automatic shutdown of similar equipment. It can continuously monitor and reduce the number of manual inspections, thereby saving labor costs.
A Special Weight for Inverse Gaussian Mixing Distribution in Normal Variance Mean Mixture with Application

Normal Variance-Mean Mixture (NVMM) provides a general framework for deriving models with desirable properties for modelling financial market variables such as exchange rates, equity prices, and interest rates measured over short time intervals, i.e. daily or weekly.Such data sets are characterized by non-normality and are usually skewed, fat-tailed and exhibit excess kurtosis.The Generalised Hyperbolic distribution (GHD) introduced by Barndorff-Nielsen (1977) which act as Normal variance-mean mixtures with Generalised Inverse Gaussian (GIG) mixing distribution nest a number of special and limiting case distributions.The Normal Inverse Gaussian (NIG) distribution is obtained when the Inverse Gaussian is the mixing distribution, i.e., the index parameter of the GIG is 1 2 -.The NIG is very popular because of its analytical tractability.In the mixing mechanism, the mixing distribution characterizes the prior information of the random variable of the conditional distribution.Therefore, considering finite mixture models is one way of extending the work.The GIG is a three parameter distribution denoted by
Geometry-Aware Deep Video Deblurring via Recurrent Feature Refinement

Blurring in videos is a frequent phenomenon in real-world video data owing to camera shake or object movement at different scene depths. Hence, video deblurring is an ill-posed problem that requires understanding of geometric and temporal information. Traditional model-based optimization methods first define a degradation model and then solve an optimization problem to recover the latent frames with a variational model for additional external information, such as optical flow, segmentation, depth, or camera movement. Recent deep-learning-based approaches learn from numerous training pairs of blurred and clean latent frames, with the powerful representation ability of deep convolutional neural networks. Although deep models have achieved remarkable performances without the explicit model, existing deep methods do not utilize geometrical information as strong priors. Therefore, they cannot handle extreme blurring caused by large camera shake or scene depth variations. In this paper, we propose a geometry-aware deep video deblurring method via a recurrent feature refinement module that exploits optimization-based and deep-learning-based schemes. In addition to the off-the-shelf deep geometry estimation modules, we design an effective fusion module for geometrical information with deep video features. Specifically, similar to model-based optimization, our proposed module recurrently refines video features as well as geometrical information to restore more precise latent frames. To evaluate the effectiveness and generalization of our framework, we perform tests on eight baseline networks whose structures are motivated by the previous research. The experimental results show that our framework offers greater performances than the eight baselines and produces state-of-the-art performance on four video deblurring benchmark datasets.
Modeling and Evolution Analysis of Community Residents' Prevention and Control Blockchain System for Public Health Emergencies

In the process of prevention and control of public health emergencies, it is difficult to obtain and monitor the health behavior and status information of community residents in daily life and work environment, making community prevention management department face challenges in residents' epidemic risk prevention and control. To fill this gap, this research trys to provide support to help residents to form good health behavior norms and offer suggestions for community prevention management department. Combining the efficient and safe characteristics of the blockchain system, this research establishes an accurate and reliable community residents' epidemic prevention and control blockchain system dynamics model which includes community residents, epidemic prevention and control departments, residents' health behavior monitoring and incentive mechanisms. The simulation experiment of four different scenarios was carried out based on Vensim. The results show that the community supervision intensity is an indispensable and important factor in improving residents' health behavior norms. To help residents develop healthy behavior habits, it is necessary to vigorously improving supervision intensity of health behaviors while increase the collecting health behavior intensity and the incentive for health behaviors.
Extreme Learning Machine with Harmony Search for High Dimensional Data Classifications

Extreme learning machine (ELM) with harmony search (HS) method for solving high dimensional data pattern classifications (abbreviated to HDHS-ELM) is studied in this paper. Firstly, HDHS mechanism which can enhance the global search ability to make features in feature space more separable is used to adjust the hidden nodes' parameters of neural classifier to improve the classification performance and reduce the hidden neurons' quantity. For balancing and reducing empirical and structural risks, the output weights are then computed through regularization technique. Compared with classifiers trained with another three algorithms related to ELM, the simulation results of classifier trained with HDHS- ELM illustrate better performance.
Analyzing Morphological Changes in Zebrafish Embryos Exposed to Toxic Chemicals

Subtle morphological changes caused by toxic chemicals in different regions of organs in small zebrafish embryos can be difficult to detect by human eyes or even by traditional image processing tools. Without brute force testing arbitrary parameters and thresholds in various combinations of imaging algorithms, we present our systematic approach in which we first train convolutional neural networks (CNNs) to recognize which organs of which zebrafish embryos contain significant morphological differences so they can be reliably classified as being exposed to either the fresh water or the toxic water contaminated with chemicals. From the training results, the organ images in each identified image subset were then analyzed by the technique, occlusion sensitivity map, to allocate the exact locations and the scales of morphological changes caused by different chemicals. In the experiment section of this paper, we use multiple chemical exposure scenarios to demonstrate the morphological differences in organs that can be detected by our approach.
Terminal Aesthetics

Abstract While DAWs such as FL Studio and Live appealed to music producers because of the influx and accessibility of creative options presented by the software, other programs embodied minimalist aesthetics, fostering creativity through limitations in design. Max, for example, is rarely described as software at all but rather as a musical “environment.” As the visual and algorithmic nature of the program suggests, Max requires composers to embrace an alternative digital literacy when working with the software, one more akin to computer coding than to traditional music composition. This chapter aligns the creative practices of multimedia composition in Max with the technical skills and design aesthetics of computer programmers and software designers. Surveying concepts from software studies, computer science, and the individual aesthetics of software developers—including those of original Max developer Miller Puckette—the chapter presents insights into the nature of composition and instrumentality in the digital age.
Review Based on Attention Model

Attention Model is important in deep learning and has been widely used in different fields. Alignment functions and distribution functions, which are commonly used in basic attention model, are reviewed and taxonomy is conducted on basic attention mechanism. In addition, it was discussed whether attention is explanatory to neural networks. Finally, the potential research directions in future are provided.