Reviewerly/exHarmony huggingface dataset:
Spectral Root Features for Replay Spoof Detection in Voice Assistants

In this paper, authors propose spectral root cepstral coefficients (SRCC) feature set to develop the effective countermeasure system for replay attacks on voice assistants (VAs). Experiments are performed on ReMASC dataset, which is specifically designed for the replay attack detection task. Logarithm operation in MFCC extraction is replaced by power-law nonlinearity (i.e. \((\cdot )^\gamma \)) to derive SRCC feature set. The proper choice of the \(\gamma \) helps to capture the system information of the speech signal, with a minimum number of cepstral coefficients. We investigated two approaches for proper choice of \(\gamma \)-value, in particular, by estimating the energy concentration in cepstral coefficients and by visualizing the spectrogram w.r.t. \(\gamma \)-value. This system representation of the speech signal, is the discriminative cue for the replay spoof speech detection (SSD) task as replay speech signal consists of additional transmission channel effects convolved with the genuine signal. The performance of the proposed feature set is validated using Gaussian Mixture Model (GMM), and Light Convolutional Neural Network (LCNN). Our primary system shows relative improvement of 47.49% over the baseline system (Constant-Q Cepstral Coefficients (CQCC)-GMM) on the evaluation set. The EER is further reduced to 11.84% on evaluation set by classifier-level fusion.
The Effects of Ecsotic Program (Education Information Technology-Based Online System) in Increasing the Knowledge, Attitude, and Practices Of Community in Dealing with the Covid-19 Pandemic

The Covid-19 pandemic has had an impact on society in the fields of policy, religion, social, culture, economy, technology and education. This activity aimed to increase the knowledge, attitude and behavior of the community regarding the COVID-19 pandemic through education information technology-based online system. This study was a quasi-experiment with one group pre-test and post-test design. The activity was carried out four months on three groups, namely children, adolescents, and adults. The planned program was about the dangers of Covid-19, increasing reading literacy and knowledge through posters, memes, and videos about Covid-19, overcoming and preventing Covid-19, providing entrepreneurial skills during the Covid-19 pandemic. The implementation method followed a number of steps: identifying potential and analyzing problems in the community, namely by conducting FGDs with related parties, program design, library research to reference material used during service, making program materials and implementing them through online and offline media by visiting the community. The findings showed that that there was an increase in the level of community knowledge, attitude, and behavior regarding the COVID-19 pandemics. This study found that the community should be approached in different ways based on their developmental stage relating to their ability to receive information.
Datasets

When software and hardware processes cause delay between an input and an output, consequences can be both harmful and annoying. In games, this type of delay can cause actions to lag behind a player's inputs. Studies on delayed game actions show negative impact on both player performance and quality of experience. Further research could expand upon this knowledge with more robust data modeling and analysis, as well as a wider range of actions. This paper describes publicly available datasets from four user studies on the effects of delay on the fundamental game action of selecting a moving target with a pointing device. They include performance data, such as time to selection, and demographic data, such as age and gaming experience. Example analysis illustrating the use of the datasets is shown, including comparison of pointing devices, demographic analysis with performance, and analytic modeling.
Chomsky Was (Almost) Right: Ontology-Based Parsing of Texts of a Narrow Domain

The common approach to the analysis of natural texts implies that semantic analysis should following the stage of parsing. However, medical texts are known as very complicated and written in a very specific language. Traditional parsers are demonstrating relatively small productivity here. In this article, we are demonstrating an opposite approach: ontology-based entailing of words in combination with simple shallow parsing rules. It allows us to increase UAS metrics from 0.82 for SpaCy to 0.834 for our approach.
An Anatomization of FPGA-Based Neural Networks

Ongoing advancements in the improvement of multilayer convolutional neural organizations have brought about upgrades in the precision of important recognition jobs, for example, huge category picture classification and cutting-edge automated recognition of speech. Custom hardware accelerators are crucial in improving their performance, given the large computational demands of Convolution Neural Networks (CNN). The Field-Programmable Gate Arrays (FPGAs) reconfigurability, computational abilities, and high energy efficacy makes it a propitious CNN hardware acceleration tool. CNN have demonstrated their value in picture identification and recognition applications; nonetheless, they require high CPU use and memory transmission capacity tasks that cause general CPUs to neglect to accomplish wanted execution levels. Consequently, to increase the throughput of CNNs, hardware accelerators using Application-Specific Integrated Circuits (ASICs), FPGAs, and Graphic Processing Units (GPUs) have been employed to improve CNN performance. To bring out their synonymity and dissimilarity, we group the works into many groups. Thus, it is anticipated that this review will lead to the upcoming development of successful hardware accelerators and be beneficial to researchers in deep learning.
Approximate geodesic distance tree filter for cost aggregation

The computation of the disparity for the pixels in the weak texture area has always been a difficult task in stereo vision. The non-local method based on a minimum spanning tree (MST) provides a solution to construct content-adaptive support regions to perform cost aggregation. However, it always introduces error disparity in slanted surfaces and is sensitive to noise and highly textural regions. The window-based methods are not effective for information dissemination. To overcome the problem mentioned above, this paper proposes an approximate geodesic distance tree filter, which utilizes geodesic distance as a pixels similarity metric and recursive techniques to perform the filtering process. The filtering process is performed recursively in four directions (namely from top-left, top-right, and vice versa), which make our filter with linear complexity. Our filter has advantages in the sense that: (1) the pixel similarity metric is approximated geodesic distance; (2) the computational complexity is linear to the image pixel. Due to these reasons, the proposed method can properly cope with cost aggregation in the textureless regions and preserve the boundary of disparity maps. We demonstrate the strength of our filter in several applications.
Comparative analysis of brand management strategies

The article presents the results of the analysis of three main strategies included in the brand management system. Brand management involves the development of a long-term plan that describes in detail the formation of strategic images and brand image, the expected dynamics of its brand development, and the response to changes in the external environment, market and consumer perceptions. Management strategies are used to achieve key brand development goals. First of all, branded products must be unique and differ favorably from competitors' products, which today is quite difficult in the context of globalization. Strategic brand management allows you to solve this problem by analyzing the market situation and using unusual methods to attract the attention of consumers. In order to increase the value of the product, it is necessary to build an emotional and trusting connection with the contact audience. A finely built emotional connection allows you to find a client who is not only suitable for the product, but also who will recommend it to his closest environment. When branding creates an emotional connection with customers, it allows you to increase market share, get more value for money, and grow your brand. In the face of tough competition in today's markets, it is very difficult for new brands to occupy a profitable niche. But it is worth remembering that the promoted brands gained popularity thanks to certain strategies that will be very effective today. Brand management includes three, interconnected in a single complex, strategies: brand promotion strategy, brand advertising strategy and brand positioning strategy. Many businesses and organizations, while recognizing the value and positive impact of a strong brand on sales, are rather vague about their brand strategy and future development. It is worth remembering that strategic brand management is necessary not only when a new brand is being created, but also in those cases when it is necessary to revive or renew an existing trademark.
Privacy Preserving Publication of Educational Data

The need for securing data privacy has increased as the online publication of data has grown by leaps and bounds in recent years. This situation puts a lot of people at risk of a security breach. K-anonymity, which was the first published method of providing data anonymity is no longer as effective to keep up with the demands of various types of dynamic data, like-medical, transactional, trajectory, educational etc. Research into data privacy has gained more focus to keep up with the demands of time, but educational data is still relatively unexplored. As more of the schooling system is moving online, it caused an increase of research into student’s results. This published academic information can cause a breach of privacy for the students. In our paper, we compare educational data with transactional data and modify the anony method to suit the safe publication of educational data.
Fast Multi-output Regression Based on Piecewise Linear Approximation

In this paper a fast Multi-output Regression algorithm is implemented based on piecewise linear approximation (PLA). Maximum distance point splitting is used to fit lines piecewisely. Then the best knot set of linear segmentation is obtained. The independent variable X is divided into some linear intervals, which make it possible that linear regression is implemented in fast and accurate way. The knot set with the smallest error in all dimensions of dependent variable is used as the node partition scheme. The matrix of regression model is derived by the least square. The Experiment results prove that the algorithm achieves the performance of the state-of-the-art with predicting time below millisecond.
The Construction of Case Event Logic Graph for Judgment Documents

The construction of a case event logic graph for the judgment document can more intuitively retrospect the development of the case. This paper proposes a joint model of event extraction and relationship recognition for judgment documents. By extracting the case information in the judgment document, a case event logic graph was constructed. The development process of the case was shown, and a reference was provided for the analysis of the context of the case. The experimental results show that the proposed method can extract events and identify the relationship between events, and the F1 value reaches 0.809. The case event logic graph reveals the development context of the case accurately and vividly.
Hierarchical Ensemble for Multi-view Clustering

Multi-view clustering is a challenging task due to the distinct feature distributions among different views. To permit complementarity while exploiting consistency among views, some multi-layer models have been developed. These models usually enforce consistent representation on the top layer for clustering purpose while allowing the other layers to represent various attributes of the multi-view data. However, a single consistent layer is often insufficient especially for complicated real-world tasks. In addition, the existing models often represent different views using the same number of layers without taking the various levels of complexity of different views into account. Furthermore, different views are often considered to be equal in the clustering process, which does not necessarily hold in many applications. To address these issues, in this paper, we present a hierarchical ensemble framework for multi-view clustering (HEMVC). It is superior to the existing methods in three facets. Firstly, HEMVC allows for different views to share more than one consistent layers and implement ensemble clustering on all shared layers. Secondly, it facilitates an adaptive clustering scheme by automatically quantifying the contribution of each layer and each view in the ensemble learning process. Thirdly, it represents different views using different numbers of layers to compensate various complexities of different views. To realize HEMVC, a two-stage algorithm has been derived and implemented. The experimental results on five benchmark datasets illustrate its performance by comparing with the state-of-the-art methods.
Noisy Mammogram Classification Method Based on New Weighted Fusion Framework

Convolutional neural network (CNN) has made outstanding performance in the classification of natural light images. However, images in many fields have the characteristics of high noise, low resolution, no color information and small data set, such as mammogram, which will affect the accuracy and robustness of the model. In order to improve the classification accuracy and the noise robustness of convolution network for mammogram images, we design a novel classification model based on the new weighted fusion convolution framework. This method has been improved from the following aspects: firstly, we take the place of traditional max-pooling layer with convolution layer with increased step, which achieves the purpose of down-sampling and extracts features more rationally through back-propagation. Secondly, we fuse multi-level feature maps to make full use of the information contained in the shallow levels and deep levels. At the same time, we design a new fusion method to effectively fuse the feature maps from different layers with different sizes. Finally, our model is tested on the mammographic image analysis society (MIAS), which is a mammographic medical image dataset. The experimental results show that the average accuracy of the model is as high as 97.6%, and the convolution layer with increased step has better robustness than the traditional max-pooling layer.
Research on Projection Filtering Method Based on Projection Symmetric Interval and Its Application in Underwater Navigation

For non-linear systems (NLSs), the state estimation problem is an essential and important problem. This paper deals with the nonlinear state estimation problems in nonlinear and non-Gaussian systems. Recently, the Bayesian filter designer based on the Bayesian principle has been widely applied to the state estimation problem in NLSs. However, we assume that the state estimation models are nonlinear and non-Gaussian, applying traditional, typical nonlinear filtering methods, and there is no precise result for the system state estimation problem. Therefore, the larger the estimation error, the lower the estimation accuracy. To perfect the imperfections, a projection filtering method (PFM) based on the Bayesian estimation approach is applied to estimate the state. First, this paper constructs its projection symmetric interval to select the basis function. Second, the prior probability density of NLSs can be projected into the basis function space, and the prior probability density solution can be solved by using the Fokker–Planck Equation (FPE). According to the Bayes formula, the proposed estimator utilizes the basis function in projected space to iteratively calculate the posterior probability density; thus, it avoids calculating the partial differential equation. By taking two illustrative examples, it is also compared with the traditional UKF and PF algorithm, and the numerical experiment results show the feasibility and effectiveness of the novel nonlinear state estimation filter algorithm.
Multi-Aspect Controlled Response Generation in a Multimodal Dialogue System using Hierarchical Transformer Network

Multimodality in dialogues has become crucial for a thorough understanding of the intent of the user to provide better responses to fulfill user's demands. Existing dialogue systems suffer from the issue of inconsistency and dull responses. Many goal-oriented conversational systems lack the different aspect information of the products or services to present an informative and exciting response. Aspects such as the price, color, pattern, rating are essential for deciding whether to purchase/order. To alleviate the issues in the existing systems, we propose the novel task of multi-aspect guided dialogue generation. This task is introduced to focus on making the responses focused and consistent with the different aspects mentioned in the current dialogue. In our present work, we design a hierarchical transformer network to capture the dialogue context for generating the responses. For creating the responses with multiple aspects, we explicitly give the aspect vectors at the time of decoding for generation. The information of the aspects specified to the decoder controls the overall generation process. We evaluate our proposed hierarchical framework on the newly created multi-domain multi-modal dialogue (MDMMD) dataset consisting of both text and images. Experimental results show that the proposed system outperforms all the existing and baseline approaches. The aspect controlled responses are immensely consistent with the ongoing dialogue and highly diverse resolving the issues of the current systems.
Energy-Efficient Hardware Implementation of K-means Clustering Algorithm

Clustering analysis is considered as one of the most vital analytical techniques for unsupervised machine learning and data mining. The algorithm technique influences the clustering results directly. Clustering has been a widely explored in various application domains such as neural networks, artificial intelligence (AI), statistics, image and video segmentation, and many more. This paper discusses the standard K-means clustering algorithm with its three respective modules: random initialization, training and testing. The work discussed in this paper implements the clustering algorithm in MATLAB tool and then verifies the theoretical results. It also modifies some parts of the algorithm to improve the time and space complexity. Further the algorithm is realized in Verilog and verified in Xilinx ISE tool.
Beyond instrumentality

Avijit Pathak makes a passionate plea for evolving an educational system which seeks to strike a balance between creative and critical pursuit of knowledge and acquisition of the necessary skills which would make learners useful members of society. But what is happening is the systematic neglect of critical aspects of education that would enhance our ability to think and the reduction of education to a mere skill-acquiring enterprise. Unless the empowering and emancipatory potential of education is properly recognized, education will lose its relevance. The very spirit of higher education will be lost. One needs to be alert to attempts which seek to sabotage the higher principles of education. The growth of the consumer industry has only succeeded in commodifying education.
PENGARUH PENGGUNA GAME DARING TERHADAP MOTIVASI BELAJAR BAHASA INDONESIA SISWA SMK

In the era of globalization and the rapid development of technology, online games or entertainment in the form of online games and in the form of multimedia, have become games that are fun and in demand by many people. Online games have their own charm that is addictive or addictive. The tendency to play online games is what will have a negative impact on users, especially among students. This phenomenon is very influential on the academic value of a student. The amount of time that is wasted so that you can play online games makes study schedules and school assignments that should be done by students at home to be neglected. This research method uses quantitative research methods and questionnaires as a means of collecting data. The purpose of this study was to determine the effect of online games on learning motivation of class XII students of SMK Plus Darusurur. The results of this study indicate that many students in class XII have online games and their academic scores are below the average score.
CNNapsule: A Lightweight Network with Fusion Features for Monocular Depth Estimation

Depth estimation from 2D images is a fundamental task for many applications, for example, robotics and 3D reconstruction. Because of the weak ability to perspective transformation, the existing CNN methods have limited generalization performance and large number of parameters. To solve these problems, we propose CNNapsule network for monocular depth estimation. Firstly, we extract CNN and Matrix Capsule features. Next, we propose a Fusion Block to combine the CNN with Matrix Capsule features. Then the skip connections are used to transmit the extracted and fused features. Moreover, we design the loss function with the consideration of long-tailed distribution, gradient and structural similarity. At last, we compare our method with the existing methods on NYU Depth V2 dataset. The experiment shows that our method has higher accuracy than the traditional methods and similar networks without pre-trained. Compared with the state-of-the-art, the trainable parameters of our method decrease by 65%. In the test experiment of images collected in the Internet and real images collected by mobile phone, the generalization performance of our method is further verified.
Study on the Evaluation Method of Aircraft Ergonomics Defect Consequence During Flight Test

Human error is an important cause of unsafe incidents during flight test, and one of the important reasons for error is that there are various defects in aircraft ergonomic design. Traditionally, more attention has been paid to the evaluation of fault consequence which exposed in aircraft, but there is a lack of relevant research on the defect consequence evaluation of aircraft ergonomics. In this paper, the possible consequences caused by the aircraft ergonomic defects are analyzed in combination with the practice of flight test, and an evaluation method based on extenics is proposed to evaluate the consequences of the aircraft ergonomic defects and the rating of the defect consequences and the suggested treatment methods are given. Finally, an example is given to verify the effectiveness of the method based on the braking system maneuverability.
UAV trajectory optimization for Precision Agriculture

Monitoring large-scale crops using an autonomous UAV demands optimal path planning methods to increase remote sensing autonomy. This paper presents a comprehensive optimization model to minimize the UAV battery consumption during crop monitoring tasks.