Reviewerly/exHarmony huggingface dataset:
Theoretical Analysis of the Advantage of Deepening Neural Networks

We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in order to understand the advantage of deepening neural networks. Unless deep neural networks have enough expressivity, they cannot have good performance even though learning is successful. In this situation, the proposed criteria contribute to understanding the advantage of deepening neural networks since they can evaluate the expressivity independently from the efficiency of learning. The first criterion shows the approximation accuracy of deep neural networks to the target function. This criterion has the background that the goal of deep learning is approximating the target function by deep neural networks. The second criterion shows the property of linear regions of functions computable by deep neural networks. This criterion has the background that deep neural networks whose activation functions are piecewise linear are also piecewise linear. Furthermore, by the two criteria, we show that to increase layers is more effective than to increase units at each layer on improving the expressivity of deep neural networks.
SciModeler: A Metamodel and Graph Database for Consolidating Scientific Knowledge by Linking Empirical Data with Theoretical Constructs

An important purpose of science is building and advancing general theories from empirical data.This process is complicated by the immense volume of empirical data and scientific theories in some fields.Particularly, the systematic linking of empirical data with theoretical constructs is currently lacking.Within this article, we propose a prototypical solution (i.e., a metamodel and graph database) for consolidating scientific knowledge by linking theoretical constructs with empirical data.We conducted a case study within the field of health behavior change where the system is used to record three scientific theories and three empirical studies as well as their mutual links.Finally, we demonstrate how the system can be queried to accumulate knowledge.
A Provable Data Possession Protocol in Cloud Storage Systems with Fault Tolerance

To maintain the availability and durability of data, data owners (DOs) usually store their mass data in remote cloud, which is namely called cloud storage. Cloud storage is one of the most important applications of cloud computing. Through the technique of cloud storage, DOs can enjoy the services and benefits of cloud computing. However, while bringing the convenience and efficiency, there must be some security issues like data corrupted threatened in the process of using cloud storage services. Through many provable data possession (PDP) protocols has been presented to deal with these threats, most of them don not consider the problem that how should the protocol proceed when the corrupted data was found. In this paper, we propose a provable data possession protocol with fault tolerance including corrupted data locating and recovering utilizing Cuckoo Filter and Reed-Solomon codes respectively. Finally, we illustrate the security and performance of the proposed schemes, which shows the practicability of the proposed protocol.
Master′s Programs: Key Issues of Development

The article provides an overview of the Russian Master's program trends with an emphasis on the evolution of the curricula and the achievement of their new quality. Based on the secondary data analysis, the dynamics of the processes was assessed and the landscape of empirical studies of the second level of higher education was presented. A number of discussion issues related to the understanding of the present and future of Master's programs was outlined. The conclusion is made about the growth of interest in involvement in the Master`s programs. It was revealed that the development of different types and formats of curriculum with a focus on both foreign practices and Russian experience can be observed. It was proved that the most important decisions related to educational programs are focused on the formation of the Master's program product portfolio, the determination of strategic alternatives for its development, the choice of program management models and the development of external partnerships of universities to create new generation programs. It is becoming important for the academic community to find an answer to the question of new emphasis and approaches to the design of the educational process for the second level of higher education. The author comes to the conclusion that the efforts to develop Master's programs should be supported by «targeted» research, the results of which should form an information basis for making strategic and tactical decisions.
Research on pedestrian detection algorithm in driverless urban traffic environment

Pedestrian detection in urban traffic environment is an important field of driverless vehicle research. Due to the variability of traffic flow, target detection algorithm cannot extract complete feature information, which brings great challenges to driverless pedestrian detection. Target detection algorithm YOLOv4 has excellent detection performance in object detection, but it is not perfect in identifying semi-blocked pedestrians. In this paper, the Spatial Pyramid Pooling was added in front of the third yolo detection head module of YOLOv4 to optimize the extraction of deep network features. Then, on the basis of optimizing the network, pruning strategy was adopted to simplify the target detection algorithm, which was called TidyYOLOv4.TidyYOLOv4 and YOLOv4 (network set input image size is 864×864) were compared on the self-made human head data set. Total BFLOPS decreased by 95.04% and Inference time decreased by 82.82%. The above experimental results show that the optimized TidyYOLOv4 algorithm is more suitable for driverless pedestrian detection in urban traffic environment.
Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

Abstract In recent years, the demographic change in conjunction with a lack of professional caregivers led to retirement homes reaching capacity. The Alzheimer Disease International stated that over 50 million people suffered from dementia in 2019 worldwide and twice the amount will presumably be effected in 2030. The field of Ambient Assisted Living (AAL) tackles this problem by facilitating technical system-aided everyday life. AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives and physicians. Moreover, the system is capable of recognizing emergency situations like human falls. Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning

Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning. To take full advantage of global information, which exploits the states from all agents and the related environment for decomposing Q values into individual credits, we propose a general meta-learning-based Mixing Network with Meta Policy Gradient~(MNMPG) framework to distill the global hierarchy for delicate reward decomposition. The excitation signal for learning global hierarchy is deduced from the episode reward difference between before and after "exercise updates" through the utility network. Our method is generally applicable to the CTDE method using a monotonic mixing network. Experiments on the StarCraft II micromanagement benchmark demonstrate that our method just with a simple utility network is able to outperform the current state-of-the-art MARL algorithms on 4 of 5 super hard scenarios. Better performance can be further achieved when combined with a role-based utility network.
Peran Musik dalam Film Score

Scores are things that cannot be separated from the film. The score has an important role in the film. Apart from being a communication tool, scores are needed to convey visual messages. The rise of filmmaking lately especially those related to scores requires composers with a variety different disciplines. This paper discusses the score on the film. The method used is observation. The data studied is in the form of journals, books, pictures, videos, which have been described in written form.
Examination of signal separation/demodulation method of multi-value modulation sensor terminal signal using STFT in storage batch signal processing

In this paper, in order to establish the storage batch signal processing technology on the network, the method of separating the IoT/M2M terminal signal that collided or interfered and the method of demodulating the data after signal separation, which cannot be realized by the conventional receivers. We proposed a method of signal separation by feature quantity extraction using short-time Fourier transform and the method of using the obtained feature quantity for demodulation. In addition, the effectiveness of the proposed method for multi-valued modulated signals will be evaluated by computer simulation.
Multi stratagem analysis of sentiments on twitter data using partial phrase harmonizing

Abstract Sentiment analysis is constructive in the application environment for business intelligence and suggests systems because it is a very easy medium for the two ends of the availability to communicate. Numerous strategies and schemes have been worn inside the sentiment analysis, such as language processing, polarity lexicons, machine learning, and psychometric scales which establish diverse types of analyzing sentiments as assumptions ended, scheme reveals, and corroboration data set. Since the internet has to turn into a commanding resource of retrospect the sphere of sentiment is moreover referred to as Sentiment Analysis or Opinion Mining. It has seen an enormous boost in academia over the decades. Analyzing sentiment to extract sentiments in different levels like word, sentence, and document provides articles’ feeling polarities. While well identified consumers’ sentiments articulated in sentences by opinion. Customary machine learning schemes cannot virtuously mirror the views of writers. This paper proposes a scheme called multi-strategy sentiments with semantic resemblance to disentangle the topic with partial phrase matching. Additionally, the Naïve Bayes classification is applied to search for the probability of the distribution of knowledge in different categories of knowledge set.
A New Sentiment Analysis System of Climate Change for Smart City Governance Based on Deep Learning

With the world’s massive population growth, governments over the world are trying to move closer to their citizens, in order to improve the management and governance of their cities, and made it smarter than ever before. Smart city technologies are used to analyze and evaluate huge volumes of data to monitor city dwellers, for better governance. Moreover, social media can be a useful vehicle for governments to better understand their citizens. Twitter sentiment analysis is a great approach to provide deep insight into how citizens behave towards phenomena and thus has definite use for smart city governance and monitoring. Climate change is a critical phenomenon. In recent years, the existence of climate change or global warming became an increasingly public debate. In this paper we developed a deep learning model. Based on convolutional Neural Network (CNN) in order to identify believers and deniers of the climate change phenomenon. And we examined the temporal patterns of climate change discussions on Twitter and its driving factors. Results demonstrate that the developed CNN model successfully identified citizens behavior towards climate change with an overall accuracy of 97% denier and 91% believer. Our model provides improved understanding of factors affecting citizen attitudes on climate change, as an efficient tool for smart city monitoring and governance.
GANs with First-Order Greedy Discriminators

We propose a variant of the standard min-max framework for GANs to learn adistribution, where the discriminator can update its strategy in a greedymanner until it reaches a first-order stationary point. We give an algorithm totrain such a GAN and show that it provably converges from any initial point toan approximate local equilibrium for this framework. Our algorithm runs in timepolynomial in the smoothness parameters of the loss function and independent ofthe dimension, and allows for loss functions that can be nonconvex andnonconcave in the parameters of the generator and discriminator. Empirically,GANs trained using our algorithm consistently learn a greater number of modesthan gradient descent-ascent (GDA), optimistic mirror descent (OMD), andunrolled GANs when applied to a synthetic Gaussian mixture dataset. Moreover,they perform significantly better on CIFAR-10 than OMD and GDA when comparingthe mean and standard deviation of the Inception Score respectively.
Polish Web resources described in the "Polish World" directory (1997). Characteristics of domains and their conservation state

For the purposes of this study, the print version of the Polish World directory by Martin Miszczak (Helion, 1997) was used to create an index of historical URLs and verify their current availability and presence in Web archives. The quantitative analysis of the index was prepared to obtain the rank data on top-level domains (TLDs) and subdomains, while the language of pages published in domains other than .PL was also examined. This study uncovered a low current availability (21.77 per cent) of Polish World URIs with a 79.6 presence in Web archives (60.35 for addresses unreachable today). Forty-six per cent of the addresses from the directory were available on domains other than .PL, of which only 15.36 per cent had content in Polish. It would seem that in 1997, Polish Internet users were able to use Polish-centric resources, mostly already available through the Polish country domain. The 180 domain names with the .PL suffix uncovered during the study constitute around 20 per cent of .PL domain names active until at least the end of 1996 on the Web.
UFIR Filtering Under Uncertain One-Step Delayed and Missing Data

This paper develops the unbiased finite impulse response (UFIR) filter for wireless sensor network (WSN) systems whose measurements are affected by random delays and packet dropout due to inescapable failures in the transmission and sensors. The Bernoulli distribution is used to model delays in arrived measurement data with known transmission probability. The effectiveness of the UFIR filter is compared experimentally to the KF and game theory recursive H1 filter in terms of accuracy and robustness employing the GPS-measured vehicle coordinates transmitted with latency over WSN.
Improving Model Robustness with Latent Distribution Locally and Globally

In this work, we consider model robustness of deep neural networks against adversarial attacks from a global manifold perspective. Leveraging both the local and global latent information, we propose a novel adversarial training method through robust optimization, and a tractable way to generate Latent Manifold Adversarial Examples (LMAEs) via an adversarial game between a discriminator and a classifier. The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner. ATLD preserves the local and global information of latent manifold and promises improved robustness against adversarial attacks. To verify the effectiveness of our proposed method, we conduct extensive experiments over different datasets (e.g., CIFAR-10, CIFAR-100, SVHN) with different adversarial attacks (e.g., PGD, CW), and show that our method substantially outperforms the state-of-the-art (e.g., Feature Scattering) in adversarial robustness by a large accuracy margin. The source codes are available at https://github.com/LitterQ/ATLD-pytorch.
Research on the Application of Data Encryption Technology Based on Network Security Maintenance in Computer Network Security

Abstract The rapid development of computer technology and the extensive coverage of computer network have promoted the rise of Chinese computer industry in the 21st century. But while computer technology benefits mankind, it also carries great potential risks. For example, the loss or damage of key data, the disclosure or abuse of personal privacy, has undoubtedly brought serious losses to human society. Therefore, it is necessary to develop and make good use of data encryption technology, and to increase the defense line for computer network security, so as to maintain computer network security and reduce the possibility of information being tampered with or stolen. Based on the current situation of computer network security and the necessity of strengthening computer network security, this paper analyzes the factors affecting computer network security, points out the classification and value of data encryption technology, thus provides a scientific theoretical basis for the application of data encryption technology in computer network security.
TO THE QUESTION OF IMPROVING THE QUALITY OF THE EDUCATIONAL PROCESS BY APPLICATION OF MODERN DIGITAL AND INFOCOMMUNICATION TECHNOLOGIES

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Study on the Computer Database Technology in College English Teaching

Abstract With the rapid development of global economy, the use of English is becoming more and more important, which leads to more and more attention to college English teaching. The database technology has become an important part of current higher education, which can help students better improve the quality of English learning. For example, a corpus database is a collection of language examples, which can provide practical cases for English learning, which can help students to fit in with foreign language learning methods. Therefore, database technology provides language optimization teaching method, which provides students with a new way of knowledge acquisition and knowledge mining. Through the database technology, we optimize the language material, which allows learners to have a new cognitive process. Firstly, this paper analyzes the current English teaching database model and data mining technology. Then, this paper analyzes the role of database technology in English teaching.
AI based Computational Trust Model for Intelligent Virtual Assistant

The Intelligent virtual assistant (IVA) also called AI assistant or digital assistant is software developed as a product by organizations like Google, Apple, Microsoft and Amazon.Virtual assistant based on Artificial Intelligence which works and processes on natural language commands given by humans.It helps the user to work more efficiently and also saves time.It is human friendly as it works on natural language commands given by humans.Voice-controlled Intelligent Virtual Assistants (IVAs) have seen gigantic development as of late on cell phones and as independent gadgets in individuals' homes.The intelligent virtual assistant is very useful for illiterate and visually impaired people around the world.While research has analyzed the expected advantages and downsides of these gadgets for IVA clients, barely any investigations have exactly assessed the need of security and trust as a singular choice to use IVAs.In this proposed work, different IPA users and non-users (N=1000) are surveyed to understand and analyze the barriers and motivations to adopting IPAs and how users are concerned about data privacy and trust with respect to organizational compliances and social contract related to IPA data and how these concerns have affected the acceptance and use of IPAs.We have used Naïve Byes Classifier to compute trust in IVA devices and further evaluate probability of using different trusted IVA devices.
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective

The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), i.e. a human imperceptible perturbation fools the DNN. Moreover, a single perturbation, often called universal adversarial perturbation (UAP), can be generated to fool the DNN for most images. A similar misalignment phenomenon has recently also been observed in the deep steganography task, where a decoder network can retrieve a secret image back from a slightly perturbed cover image. We attempt explaining the success of both in a unified manner from the Fourier perspective. We perform task-specific and joint analysis and reveal that (a) frequency is a key factor that influences their performance based on the proposed entropy metric for quantifying the frequency distribution; (b) their success can be attributed to a DNN being highly sensitive to high-frequency content. We also perform feature layer analysis for providing deep insight on model generalization and robustness. Additionally, we propose two new variants of universal perturbations: (1) Universal Secret Adversarial Perturbation (USAP) that simultaneously achieves attack and hiding; (2) high-pass UAP (HP-UAP) that is less visible to the human eye.
From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention

Clinical coding is the task of assigning a set of alphanumeric codes, referred to as ICD (International Classification of Diseases), to a medical event based on the context captured in a clinical narrative. The latest version of ICD, ICD-10, includes more than 70,000 codes. As this is a labor-intensive and error-prone task, automatic ICD coding of medical reports using machine learning has gained significant interest in the last decade. Existing literature has modeled this problem as a multi-label task. Nevertheless, such multi-label approach is challenging due to the extremely large label set size. Furthermore, the interpretability of the predictions is essential for the endusers (e.g., healthcare providers and insurance companies). In this paper, we propose a novel approach for automatic ICD coding by reformulating the extreme multi-label problem into a simpler multi-class problem using a hierarchical solution. We made this approach viable through extensive data collection to acquire phrase-level human coder annotations to supervise our models on learning the specific relations between the input text and predicted ICD codes. Our approach employs two independently trained networks, the sentence tagger and the ICD classifier, stacked hierarchically to predict a codeset for a medical report. The sentence tagger identifies focus sentences containing a medical event or concept relevant to an ICD coding. Using a supervised attention mechanism, the ICD classifier then assigns each focus sentence with an ICD code. The proposed approach outperforms strong baselines by large margins of 23% in subset accuracy, 18% in micro-F1, and 15% in instance based F-1. With our proposed approach, interpretability is achieved not through implicitly learned attention scores but by attributing each prediction to a particular sentence and words selected by human coders.