Reviewerly/exHarmony huggingface dataset:
Detecting of Audio Stegosystems with Information Embedding Based on Modulated Reverberation

Audio signal with embedding of information based on modulated reverberation is considered. The goal of such stegosystem commonly is to provide a copy right protection by digital watermarking but sometimes may be interesting also to detect a fact of hidden signal presence by means of steganalysis. Detecting of such stegosystems occurs impossibly when it is executed either by a listening of audio signal or by a observation of signal wave form if deph of embedding is very small. Therefore we propose a new detection algorithm based on properties of modulated reverberation. A performance evaluation of such algorithm is investigated by simulation in terms of the probability of missimg and the probability of false alarm.
Library Guides: Linking to Library Resources: EBSCOhost

This guide will show you how to link to resources in the DiPietro Library How to link to articles and EBSCOhost eBooks.
A Survey of Hand Crafted and Deep Learning Methods for Image Aesthetic Assessment.

Automatic image aesthetics assessment is a computer vision problem that deals with the categorization of images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adhere to the key principles of photography (balance, rhythm, harmony, contrast, unity, look, feel, tone and texture). Owing to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This paper presents a literature review of the recent techniques of automatic image aesthetics assessment. A large number of traditional hand crafted and deep learning based approaches are reviewed. Key problem aspects are discussed such as why some features or models perform better than others and what are the limitations. A comparison of the quantitative results of different methods is also provided at the end.
Stretchable electronics for ubiquitous physiological monitoring

While great advances in medicine has been made in the past century, the overall infrastructure of the healthcare system has not progressed. Patients (who are not feeling well) are still expected to travel to a centralized location for discrete, reactionary-based care where the healthcare provider only has a brief window to assess the patient’s health. Unless the symptoms are overt at the time of examination, the subjective evaluation relies heavily on the self-reporting of symptoms from the patient. This often results in delayed or improper diagnoses. In contrast, we know that physiological signals precede clinical deterioration. We have developed a suite of low-cost, unobtrusive, Band-Aid©-like physiological sensors to continuously monitor patients’ cardiovascular and pulmonary functions. We seek to continuously quantify subtle physiological changes to predict, and eventually prevent, the onset of acute clinical events.
The Use of Mobile Phone as Teaching Aid On EFL Students’ Speaking Performance and Perception

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This paper presents the effect of Mobile Phone on EFL Students’ speaking performance and perception. While mobile phone has been utilized beyond its primary use as a communication tool, there are only a limited number of studies in pedagogy which highlights the importance of mobile phone in teaching-learning activities. The objective of this paper is to show the effect of Mobile Phone as teaching aid on EFL Students’ speaking performance and perception. The result of this study shows students who taught using mobile phone outperformed students who taught by conventional teaching.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Penelitian ini berisi tentang pengaruh dari &lt;em&gt;mobile phone&lt;/em&gt; terhadap kemampuan bahasa Inggris dan persepsi dari siswa. Banyak kegunaan &lt;em&gt;mobile phone&lt;/em&gt; selain sebagai alat komunikasi, namun hanya sedikit yang membahas kegunaan &lt;em&gt;mobile&lt;/em&gt; &lt;em&gt;phone&lt;/em&gt; di dalam kegiatan belajar mengajar. Penelitian ini bertujuan untuk melihat pengaruh dari &lt;em&gt;mobile phone&lt;/em&gt; sebagai bahan bantu pengajaran terhadap kemampuan berbicara siswa. Hasil dari penelitian ini menunjukkan siswa yang belajar dengan menggunakan &lt;em&gt;mobile Phone&lt;/em&gt; memiliki pencapain yang lebih baik daripada siswa yang belajar menggunakan cara konvensional.
Score Fusion Method by Neural Network Using GPS and Wi-Fi Log Data

Recently, personal authentication has become more important than ever because of authentication vulnerability problems. In cases where extremely high confidentiality is required, multi-factor authentication is used. In this study, we focus on score fusion method, which merges authentication scores of each factor in multi-factor authentication. In conventional score fusion methods, the weight of each factor is fixed. These methods work well when all users have a similar tendency for each factor. However, behavioral authentication is highly dependent on each user's characteristics, and the tendency of which factors have high accuracy may differ greatly among users. There is no score fusion method suitable for such a case where the tendency of each factor is different. We propose a user dependent weighting score fusion method using a neural network. Since our proposed method constructs a simple binary classification network for each user, the weight is personally adjusted for each user. The comparison experiment results show that True Acceptance Rate (TAR) of our proposed method is higher than conventional methods.
Connecting two types of representations of a permutation of $\F_q$

In this paper, we connect two types of representations of a permutation $\sigma$ of the finite field $\F_q$. One type is algebraic, in which the permutation is represented as the composition of degree-one polynomials and $k$ copies of $x^{q-2}$, for some prescribed value of $k$. The other type is combinatorial, in which the permutation is represented as the composition of a degree-one rational function followed by the product of $k$ $2$-cycles on $\bP^1(\F_q):=\F_q\cup\{\infty\}$, where each $2$-cycle moves $\infty$. We show that, after modding out by obvious equivalences amongst the algebraic representations, then for each $k$ there is a bijection between the algebraic representations of $\sigma$ and the combinatorial representations of $\sigma$. We also prove analogous results for permutations of $\bP^1(\F_q)$. One consequence is a new characterization of the notion of Carlitz rank of a permutation on $\F_q$, which we use elsewhere to provide an explicit formula for the Carlitz rank. Another consequence involves a classical theorem of Carlitz, which says that if $q>2$ then the group of permutations of $\F_q$ is generated by the permutations induced by degree-one polynomials and $x^{q-2}$. Our bijection provides a new perspective from which the two proofs of this result in the literature can be seen to arise naturally, without requiring the clever tricks that previously appeared to be needed in order to discover those proofs.
accVizR: using tidy data approach and layered grammar of graphics for visualizing accounting data

This paper introduces accVizR, a developing R software package that provides a toolset for visualizing accounting data and transforming it to visual performance reports. From the earliest days of the profession, Luca Pacioli highlighted the importance of data, math and visual support in the accounting. Evolution of technical means put pressures on the profession to evolve and analyze large amount of data into actionable insights. The developed R package provides a toolset for accountants to work with accounting data in a "tidy approach" (Grolemund and Wickham, 2016) while offering data viz capabilities to facilitate decision making. The paper highlights key functionalities in the package as well as provide the results of the tests on accounting dataset.
Compressing, Expanding, and Ending Scenes

Chapter 9 describes and illustrates several shooting and editing techniques for compressing or expanding time within a scene, as well as techniques for ending scenes. A movie usually tells a story that takes place over days, months, or years. Compressing a story into an hour or two of screen time requires techniques that move time ahead in ways that seem natural to viewers. Most of a film’s acceleration of time occurs between scenes, but time within a scene is often compressed even though it seems continuous. Some of the time manipulation techniques that are examined are transitions, jump cuts, cutaways, cross-cutting scenes with parallel action, repeating action, and slow motion.
Optimizing Chinese Story Generation Based on Multi-channel Word Embedding and Frequent Pattern Tree Structure

Abstract Much space remains for generating stories in the Chinese language. In this paper, we propose a novel approach to address this issue by multi-channel word embedding and effective control of the part-of-speech structure while producing sentences to imitate the writing style. The proposed approach consists of four parts: We first preprocess the sentences to label all sentences in the data set according to the format of &lt;SOS&gt; &lt;MOS&gt; &lt;EOS&gt;, where &lt;SOS&gt; &lt;EOS&gt;, and &lt;MOS&gt; represent the beginning, the end of the sentence, and the separation between sentences. We then propose a multi-channel method to embed words by integrating traditional vectorization methods including Word2vec, Fasttext, LexVec, and Glove to improve the information in the input data. We next optimize the model architecture to effectively control the process of sentence generation based on the BERT (Bidirectional encoder representations from) model. Finally, we perform some optimization on performance. For example, Softmax function in the model was optimized to reduce the search time during training. In addition, the GAN (generative adversarial network) architecture for the data set was revised to improve the training performance of the model. All sentences in the data set are built into a tree structure and the part-of-speech structure of the next sentence was generated through model generation based on FP-tree. The experimental results show that the proposed method can effectively control the generation of Chinese stories.
Learning Class Unique Features in Fine-Grained Visual Classification

A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the details. Conventional cross entropy trained Convolutional Neural Network (CNN) fails this challenge as it may suffer from producing inter-class invariant features in FGVC. In this work, we innovatively propose to regularize the training of CNN by enforcing the uniqueness of the features to each category from an information theoretic perspective. To achieve this goal, we formulate a minimax loss based on a game theoretic framework, where a Nash equilibria is proved to be consistent with this regularization objective. Besides, to prevent from a feasible solution of minimax loss that may produce redundant features, we present a Feature Redundancy Loss (FRL) based on normalized inner product between each selected feature map pair to complement the proposed minimax loss. Superior experimental results on several influential benchmarks along with visualization show that our method gives full play to the performance of the baseline model without additional computation and achieves comparable results with state-of-the-art models.
Karl Popper’s Theory of Cognition and Education Problems

The article deals with the peculiarities of K. Popper’s theory of scientific knowledge, and shows the influence of the epistemological principles which were substantiated by the scientist on the approaches to the improvement of education and upbringing. The fundamental differences between the evolutionary epistemology of the outstanding philosopher and the traditional empirical and sensualistic theory of cognition, according to which knowledge flows into us from the outside through the senses, have been clarified. K. Popper argues that man is extremely active in acquiring knowledge, and information does not flow into him from the environment, but, on the contrary, man explores the environment and actively “sucks” information from it. The emphasis is placed on the fact that a person is not only active but also critical towards the information he receives. The results of K. Popper’s critical analysis of education and the shortcomings of the educational process in schools (inconsistency with innate human qualities, passivity of students in the learning process) are presented and the ways of overcoming them are suggested. The position of the philosopher on the peculiarities of state control over education, his idea of the goals of educational activities, the necessity to involve students in the construction of learning content, the change in the nature of relations between participants in the educational process, generalizations about the transformation of learning into solving problems, development of students’ critical thinking have been revealed.
Grey-box Adversarial Attack And Defence For Sentiment Classification

We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our results show that once trained, the attacking model is capable of generating high-quality adversarial examples substantially faster (one order of magnitude less in time) than state-of-the-art attacking methods. These examples also preserve the original sentiment according to human evaluation. Additionally, our framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. Code is available at: https://github.com/ibm-aur-nlp/adv-def-text-dist.
대학교육 서비스품질의 중요도-만족도 분석 연구

Purpose: To increase the competitiveness of the university and improve the quality of education by providing education services that meet the student''s satisfaction needs based on the education service quality survey of university students.Methods: A questionnaire consisting of 6 elements and 38 questions was prepared and students were surveyed. The importance and satisfaction ISA analysis was conducted.Results: The results of this study are as follows; there was a difference in the perception of importance and satisfaction by student characteristics regarding the results of educational service quality measurement. Working students need an extra effort in the educational environment and foreign students in student services.Conclusion: The importance of educational service quality-satisfaction survey should be used to identify the factors of educational service quality by student characteristics to improve student satisfaction, improve the quality of education, and support educational services with a strategic approach.
Implementation of the IMAKOM Organization Presence (Computer Student Association) Panca Budi Medan Development University Using QR Code-Based ID Cards: Implementation of the IMAKOM Organization Presence (Computer Student Association) Panca Budi Medan Development University Using QR Code-Based ID Cards

Progress in the field of information systems today has helped a lot in processing data that is very appropriate in supporting the processing of data into information. One of the technologies in question is the development of software applications, especially for mobile devices. One of the fastest mobile devices is a Smartphone. In writing this thesis, the writer raises the title of the thesis of Attendance in the Organization of IMAKOM (Computer Student Association) Medan Unpab Based on Id Card Using Qr due to the still manual validation process at imakom organization so with this application later it will attendance online and the data generated online real-time. This application is made using Android smartphone technology because of the many members of imakom using this smartphone because it has many advantages such as the availability of social media and can be used anytime and anywhere, in learning java programs that can be run on this smartphone, in this application later it will do a barcode performance in the form of QR code which will later be compiled to the database automatically and generate reports in real-time.
A Multivariate Time-Series Based Approach for Quality Modeling in Wireless Networks

This work presents a method for estimating key quality indicators (KQIs) from measurements gathered at the nodes of a wireless network. The procedure employs multivariate adaptive filtering and a clustering algorithm to produce a KQI time-series suitable for post-processing by the network management system. The framework design, aimed to be applied to 5G and 6G systems, can cope with a nonstationary environment, allow fast and online training, and provide flexibility for its implementation. The concept’s feasibility was evaluated using measurements collected from a live heterogeneous network, and initial results were compared to other linear regression techniques. Suggestions for modifications in the algorithms are also described, as well as directions for future research.
Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition.

The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions, especially in scenarios like education, care-giving, and rehabilitation. Faces and voices constitute two important sources of information to enable artificial systems to reliably recognize individuals. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. However, when those networks are applied to different and unprecedented scenarios not included in the training set, they can suffer a drop in performance. For example, with robotic platforms in ever-changing and realistic environments, where always new sensory evidence is acquired, the performance of those models degrades. One solution is to make robots learn from their first-hand sensory data with self-supervision. This allows coping with the inherent variability of the data gathered in realistic and interactive contexts. To this aim, we propose a cognitive architecture integrating low-level perceptual processes with a spatial working memory mechanism. The architecture autonomously organizes the robot's sensory experience into a structured dataset suitable for human recognition. Our results demonstrate the effectiveness of our architecture and show that it is a promising solution in the quest of making robots more autonomous in their learning process.
Generic Perceptual Loss for Modeling Structured Output Dependencies

The perceptual loss has been widely used as an effective loss term in image synthesis tasks including image super-resolution, and style transfer. It was believed that the success lies in the high-level perceptual feature representations extracted from CNNs pretrained with a large set of images. Here we reveal that, what matters is the network structure instead of the trained weights. Without any learning, the structure of a deep network is sufficient to capture the dependencies between multiple levels of variable statistics using multiple layers of CNNs. This insight removes the requirements of pre-training and a particular network structure (commonly, VGG) that are previously assumed for the perceptual loss, thus enabling a significantly wider range of applications. To this end, we demonstrate that a randomly-weighted deep CNN can be used to model the structured dependencies of outputs. On a few dense per-pixel prediction tasks such as semantic segmentation, depth estimation and instance segmentation, we show improved results of using the extended randomized perceptual loss, compared to the baselines using pixel-wise loss alone. We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.
Penggunaan Media dalam Pembelajaran Tema 7 Indahnya Keragaman di Negeriku Ditinjau dari Gaya Belajar Siswa di Kelas IV F SD Ta’mirul Islam Laweyan Surakarta Tahun Ajaran 2019/2020

This research to analyze the used of learning media and conformity between the learning media used in Theme 7 with the learning style of students in grade IV Elementary School. The research method used was descriptive qualitative. The reseacrh subjects were teachers and students of grade IV F Ta’mirul Islam Elementary School. The learning styles distinguished in visual, audiotorial and kinesthetic. The results of this research show that the media used in learning Theme 7 was suitable and in accordance with the principles and considerations of learning media selection. From 24 students, 12 students with visual learning style, 7 students with audiotorial learning style, and 5 students with kinesthetic learning style. The conclusion of this research was learning media used in Theme 7 facilitates more visual learning styles, but there are also learning media that facilitate audiotorial learning styles and kinesthetic learning styles. The implication of this research can increase knowledge about the use of learning media that is reviewed from learning style and can encaurage teachers to pay attention to consideration in the use of learning media especially learning style of students . Keywords: Learning Media, Thematic Learning, Theme 7 Indahnya Keragaman di Negeriku, Elementary School,  and Learning Style of Students.
Problems and Reform Ideas in Law Theory Teaching in Universities Based on Big Data Analysis

As an important part of the development of science and technology, big data analysis has been applied in many fields. While big data analysis has brought revolutionary influence to the legal industry, universities, as the primary source of legal talents, will inevitably bring invisible pressure to the traditional law teaching. In order to change this situation, we can reform the legal theory education from the aspects of law teaching mode, teaching system and talent training to adapt to the new mode of law education in the big data environment. The teaching research of law theory in universities under big data analysis not only increases the mode of law education, but also expands the development space of law theory teaching. Big data analysis will play an important role in the improvement of legal education, which will make it possible to realize the fairness of education and cultivate more excellent legal talents needed in the new era. According to the survey results, the learning effect of big data analysis on legal professional knowledge has been slightly improved by 31.13%, and the number of people who use websites to query relevant knowledge is as high as 85.71%. Aiming at the problems in law teaching process, this paper explores the reform of law theory teaching mode under the background of big data analysis.