Reviewerly/exHarmony huggingface dataset:
Unsupervised Neural Network approach for the Identification of Anomaly in Speech Signal from Spectrogram Images

Abstract In this paper, anomaly identification in speech signal is carried out using an unsupervised Neural Network approach. Input audio signal is divided into small segments of information. Initial segment of the samples are used in the training process. The later portion of the sample segments are used in the test process. The features (Spectral Roll-off, Spectral Centroid, Mel Frequency Cepstral Coefficient (MFFC), Pitch (PH) and Energy Density (ED)) are extracted from the input data. The extracted 1D features are converted into spectrogram images. Then, the images are fed as an input to Neural Network for the prediction of feature values. If three or more feature value does not exceed the threshold value of 75% then the input signal is considered to be free from anomaly. The proposed model results in an accuracy of 97.50% in the detection of anomaly in input speech signal.
Audio Attacks and Defenses against AED Systems - A Practical Study.

Audio Event Detection (AED) Systems capture audio from the environment and employ some deep learning algorithms for detecting the presence of a specific sound of interest. In this paper, we evaluate deep learning-based AED systems against evasion attacks through adversarial examples. We run multiple security critical AED tasks, implemented as CNNs classifiers, and then generate audio adversarial examples using two different types of noise, namely background and white noise, that can be used by the adversary to evade detection. We also examine the robustness of existing third-party AED capable devices, such as Nest devices manufactured by Google, which run their own black-box deep learning models. We show that an adversary can focus on audio adversarial inputs to cause AED systems to misclassify, similarly to what has been previously done by works focusing on adversarial examples from the image domain. We then, seek to improve classifiers' robustness through countermeasures to the attacks. We employ adversarial training and a custom denoising technique. We show that these countermeasures, when applied to audio input, can be successful, either in isolation or in combination, generating relevant increases of nearly fifty percent in the performance of the classifiers when these are under attack.
La configuración de espacios fractales en la música minimalista (Configuration of Fractal Spaces in Minimal Music)

Spanish Abstract: Teniendo en cuenta la importancia de la repeticion en el minimalismo y la recurrencia del uso reiterativo de motivos musicales en las formas clasicas, se pretende en este ensayo, en primera instancia, establecer una genealogia de la repeticion a lo largo de la historia de la musica, para luego ver de que forma las formas recursivas encuentran su mayor expresion en el minimalismo. Se realizara un analisis sobre la notacion, la caligrafia y la gramatica musical, y se indagara sobre la forma en que estas fueron el objeto de profundas transformaciones durante la Segunda Revolucion Industrial. Se vera tambien como estas transformaciones permitieron una reformulacion tanto en la notacion musical, como de la misma definicion de musica. En la ultima parte de este ensayo, a partir de la teoria de la narracion fractal de German A. Duarte, se propondra un analisis de las obras mas caracteristicas del minimalismo en la musica para de que manera la musica para minimalista configura espacios fractales.English Abstract: Taking into account the importance of repetition in minimalism and the frequency in the reiterative use of musical motifs in classical forms, this essay tries first to establish a genealogy of repetition throughout the history of music to later explore how recursive forms find their greatest expression in minimalism. We will carry out an analysis on notation, calligraphy and musical grammar, and study the way in which these elements deeply changed during the technological acceleration of the second industrial revolution. We will also see how these transformations allowed a reformulation of musical notation and the definition of music itself. In the last part of this essay, based on the theory of fractal narration by German A. Duarte, we will propose an analysis of the most characteristic works of minimalism in music and address how minimal music shapes fractal spaces.
Multidimensional Included and Excluded Sums

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21)Multidimensional Included and Excluded SumsHelen Xu, Sean Fraser, and Charles E. LeisersonHelen Xu, Sean Fraser, and Charles E. Leisersonpp.182 - 192Chapter DOI:https://doi.org/10.1137/1.9781611976830.17PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract This paper presents algorithms for the included-sums and excluded-sums problems used by scientific computing applications such as the fast multipole method. These problems are defined in terms of a d-dimensional array of N elements and a binary associative operator ⊕ on the elements. The included-sum problem requires that the elements within overlapping boxes cornered at each element within the array be reduced using ⊕. The excluded-sum problem reduces the elements outside each box. The weak versions of these problems assume that the operator ⊕ has an inverse ⊖, whereas the strong versions do not require this assumption. In addition to studying existing algorithms to solve these problems, we introduce three new algorithms. The bidirectional box-sum (BDBS) algorithm solves the strong included-sums problem in Θ(dN) time, asymptotically beating the classical summed-area table (SAT) algorithm, which runs in Θ(2dN) and which only solves the weak version of the problem. Empirically, the BDBS algorithm outperforms the SAT algorithm in higher dimensions by up to 17.1×. The box-complement algorithm solves the strong excluded-sums problem in Θ(dN) time, asymptotically beating the state-of-the-art corners algorithm by Demaine et al., which runs in Ω(2dN) time. The box-complement algorithm empirically outperforms the corners algorithm by about 1.4× given similar amounts of space in three dimensions. If the assumptions for the weak excluded-sums problem can be satisfied, the bidirectional box-sum complement (BDBSC) algorithm, which is a trivial extension of the BDBS algorithm, can beat box-complement by up to a factor of 4. Previous chapter Next chapter RelatedDetails Published:2021eISBN:978-1-61197-683-0 https://doi.org/10.1137/1.9781611976830Book Series Name:ProceedingsBook Code:PRACDA21Book Pages:1-239
Keynote: Indoor Networks with a 6th Sense

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Wireless connectivity will soon enable people to consume mixed reality content anywhere and cloud-connected mobile robots will perform complex tasks collaboratively. This connectivity will come to enterprises, factory floors and digital homes first, powered by indoor networks that offer much higher data rates, greater reliability and lower latency than today's networks. In addition to providing traditional communication capabilities, the future indoor network will have a "sixth sense" that enables it to provide sensory information and insights to people and machines. It will serve as the core infrastructure for smart buildings and help enterprises operate more efficiently by further enabling capabilities such as immersive virtual workplaces, indoor navigation and asset tracking. In this talk I will examine the enabling technologies of the future indoor network and present our latest research results and our vision for implementing them in commercial and residential environments.
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model

Commit message is a document that summarizes source code changes in natural language. A good commit message clearly shows the source code changes, so this enhances collaboration between developers. Therefore, our work is to develop a model that automatically writes the commit message. To this end, we release 345K datasets consisting of code modification and commit messages in six programming languages (Python, PHP, Go, Java, JavaScript, and Ruby). Similar to the neural machine translation (NMT) model, using our dataset, we feed the code modification to the encoder input and the commit message to the decoder input and measure the result of the generated commit message with BLEU-4. Also, we propose the following two training methods to improve the result of generating the commit message: (1) A method of preprocessing the input to feed the code modification to the encoder input. (2) A method that uses an initial weight suitable for the code domain to reduce the gap in contextual representation between programming language (PL) and natural language (NL). Training code, dataset, and pre-trained weights are available at this https URL
Applying Permutations and Cuckoo Search for Obtaining a New Steganography Approach in Spatial Domain

Video Steganography is an art and science of embedding secret information into a carrying video file in such a way that others cannot observe the embedded information. Cuckoo Search (CS) is a meta-heuristic algorithm which has been developed by Xin-She Yang and Suash Deb in 2009. CS is very effective in solving many optimization problems that have been found in previous literature. In this paper, a new efficient approach for embedding a secret image in a digital video is proposed. Generally, any colored image consists of three color components (Red, Green, and Blue). So, an image's pixel has three bytes; each of which belongs to one different color component. For security purposes, each secret image's color component is embedded separately into a selected cover video's frame. The proposed approach is based on the permutations on 3 sections of a secret byte, 3-3-2 bits. These three sections are permuted to obtain five different patterns of a specified secret byte. Then, the population of five different pairs is built; each pair consists of one different pattern repeated twice. Good pixels are so chosen via using CS algorithm to achieve the minimum distortion in carrier pixels due to embedding. The sum of absolute values of sectional differences is used as an objective function to compare all the distances between the 3-3-2 Least Significant Bit (LSB) values of a cover frame's pixel and the generated different patterns of a specified secret byte. Experimental results show that the efficiency of the suggested approach is successful since the Peak Signal to Noise Ratio (PSNR) is above 52 decibels.
The Process of Acquiring Declarative Sentence on Aphasia

The present study investigates the process of acquiring declarative sentences in aphasia language learners. This study seeks to address the question of how the process of an aphasia language learner to acquire declarative sentences. Using a case study, the process of interview was implemented to explore the subject's response toward this issue. The subject is an adult language learner who is 69 years old. The data were obtained from interviews and documents. The interview section was used to explore the process of acquiring declarative sentences, and the documents were used to check the validity of the interview. The data analysis of acquiring declarative sentences showed: (1) the process of acquiring the language on an adult is through behaviorist theory that consists of subject's routinizes in reading books, newspapers, and comics. (2) In adult language learners, the duration of acquiring the language is not too long and challenging. The subject takes a significant effort to deal with various activities, and it is the characteristic of an adult in acquiring the language. (3) In the construction of declarative sentences, the subjects could produce six sentences correctly and no grammar errors. He also could interact with others through those sentences. The challenge is on the way he produces, which is slowly mouth movement. Additionally, we open the gate of future research with pieces of evidence. It was recommended the future research on this study might be conducted in a quantitative method with more adult language learners involved.
Intelligent reminder technology of borrowing expiration in electronic library based on deep learning

Library, as an institution for collecting, sorting and collecting books and materials for people to read and reference, has spread all over major regions and universities. The "great circulation" of books makes the management mode of pure manual book number borrowing registration far from meeting people's borrowing needs. In order to realize the intelligent reminding of electronic library's borrowing expiration, this paper proposes the intelligent reminding technology of electronic library's borrowing expiration based on deep learning. The feature analysis model of edge template feature parameters of e-library borrowing information parameters is constructed, the structure of template feature parameters of e-library borrowing information parameters is reorganized by adopting feature space reorganization, personalized information fusion and feature map fusion processing of e-library borrowing information parameters are carried out by adopting multilevel semantic feature analysis method, and personalized feature extraction of e-library borrowing information parameters is realized by adopting shallow feature map combination control method. The extracted personalized features of electronic library borrowing information parameters are identified by deep learning algorithm, and a multi-level feature analysis model for intelligent reminding of electronic library borrowing expiration is constructed. Combined with the deep feature information distribution of electronic library borrowing information parameters, personalized intelligent reminding of electronic library borrowing expiration is carried out. The simulation results show that this method has a high level of intelligent reminding of electronic library's borrowing expiration, and the precision of intelligent reminding of electronic library's borrowing expiration is better, which improves the personalized fusion and automatic reminding ability of electronic library's borrowing information parameters.
Transformación digital de un centro educativo. Hacia la eficiencia digital en el ceip san josé

espanolEn el presente articulo pretendemos mostrar el camino seguido por el CEIP San Jose de Palenciana hacia la transformacion digital, para, por un lado, optimizar las tareas administrativas del centro, la comunicacion interna y externa y la coordinacion docente y, por otro, para la integracion de las TIC y su conversion en TEP bajo enfoques metodologicos activos en que el alumnado asuma roles protagonistas en la construccion de su aprendizaje. El camino iniciado hace ya siete cursos escolares nos esta afianzando en nuestro convencimiento de que otra educacion, no solo es posible, sino necesaria, puesto que los niveles competenciales del alumnado estan creciendo de forma significativa. Igualmente, el liderazgo pedagogico y distribuido en que hemos basado nuestro cambio nos ha llevado al establecimiento de estructuras docentes cooperativas con altos grados de satisfaccion entre el claustro de profesorado. EnglishIn this article we intend to show the path followed by CEIP San Jose de Palenciana towards digital transformation, in order, on the one hand, to optimize the administrative tasks of the center, internal and external communication and teaching coordination and, on the other, for the integration of ICT and its conversion into PTSD under active methodological approaches in which students assume leading roles in the construction of their learning. The path started seven school years ago is strengthening us in our conviction that another education is not only possible, but necessary, since the skill levels of the students are growing significantly. Likewise, the pedagogical and distributed leadership on which we have based our change has led us to establish cooperative teaching structures with high levels of satisfaction among the teaching staff.
A Study on Instructional Design for Occupational Health Nursing Education Using Flipped Learning

Purpose: The purpose of this study is to develop instructional design for occupational health nursing education using flipped learning. Methods: The instructional design for occupational health nursing developed in this study combined the ADDIE (Analysis, Design, Development, Implementation, Evaluation) and PARTNER (Preparation, Assessment, Relevance, Team activity, Nub lecture, Evaluation, Reflection) models. Results: In the analysis step, course analysis, learner analysis, and environmental analysis are performed. In the design step, chapter lesson-plans, chapter lesson-material development plans, and session-specific lesson plans are included. In the development step, pre-learning materials, lesson materials, and evaluation materials are developed for each session. In implementation, teaching and learning activities are performed for each session of pre-class (preparation, assessment), in-class (relevance, team activity, nub lecture, evaluation), and post-class (reflection). Finally, in the evaluation step, learner evaluation and instructor self-evaluation are conducted. Conclusion: This study provides guidelines and reference materials for occupational health nursing education using flipped learning, as well as for the long-term, future development of nursing education. Therefore, the application and effect of flipped learning in occupational health nursing education should be studied.
Task-Related c-Fos Expression in the Posterior Parietal Cortex During the “Rubber Tail Task” Is Diminished in Ca2+-Dependent Activator Protein for Secretion 2 (Caps2)-Knockout Mice

Rubber hand illusion (RHI), a kind of body ownership illusion, is sometimes atypical in individuals with autism spectrum disorder; however, the brain regions associated with the illusion are still unclear. We previously reported that mice responded as if their own tails were being touched when rubber tails were grasped following synchronous stroking to rubber tails and their tails (a “rubber tail illusion”, RTI), which is a task based on the human RHI; furthermore, we reported that the RTI response was diminished in Ca 2+ -dependent activator protein for secretion 2- knockout ( Caps2 -KO) mice that exhibit autistic-like phenotypes. Importance of the posterior parietal cortex in the formation of illusory perception has previously been reported in human imaging studies. However, the local neural circuits and cell properties associated with this process are not clear. Therefore, we aimed to elucidate the neural basis of the RTI response and its impairment by investigating the c-Fos expression in both wild-type (WT) and Caps2 -KO mice during the task since the c-Fos expression occurred soon after the neural activation. Immediately following the delivery of the synchronous stroking to both rubber tails and actual tails, the mice were perfused. Subsequently, whole brains were cryo-sectioned, and each section was immunostained with anti-c-Fos antibody; finally, c-Fos positive cell densities among the groups were compared. The c-Fos expression in the posterior parietal cortex was significantly lower in the Caps2 -KO mice than in the WT mice. Additionally, we compared the c-Fos expression in the WT mice between synchronous and asynchronous conditions and found that the c-Fos-positive cell densities were significantly higher in the claustrum and primary somatosensory cortex of the WT mice exposed to the synchronous condition than those exposed to the asynchronous condition. Hence, the results suggest that decreased c-Fos expression in the posterior parietal cortex may be related to impaired multisensory integrations in Caps2 -KO mice.
Analysis of the Main Points and Common Errors in the Editing of Electronic and Electrical Textbooks

Book Standards; Editing; Quality
Sistem Informasi Perpustakaan Berbasis Web pada SMK Harapan Kartasura

Libraries are part of learning resources that must be owned by every school and college. Finding information or knowledge through the library makes it easy for students. The function of the library in providing services to students or library users, library processing is faced with various types of administrative problems, ranging from book procurement, book lending services, book returns, the place of the book and the process of making reports. The administrative system in library management is very important for good library services. At the present time, the Harapan Kartasura Vocational School Library is borrowing and returning books and calculating fines are still done manually, recorded in a ledger, as well as when making a book loan report. Thus we need a system that can improve operations in library transactions. This study aims to design and develop a web-based library information system. The development method used is the Waterfall method which consists of needs analysis, design, implementation, testing and maintenance. The results of testing the system using the Black Box method state that this system is feasible to use and runs according to its function. The implementation of the system is expected to facilitate library services at Harapan Kartasura Vocational High School.
Image Captioning for Affine Transformed Images using Image Hashing

Image captioning is the process of generating a meaningful textual description to the image. The perfect caption for the image not only consists of objects and their attributes, it also concentrates on the actions involved by the objects. There are two main tasks in Image captioning. The first and foremost task is correctly identifying objects present in the given image. Once all the objects are identified along with their attributes, the dense model is trained in order to identify the correct verbs or the actions in which these identified objects are involved. The second part in Image captioning is generating the syntactically correct natural language sentence which connects all the identified objects along with their attributes and actions. In this paper we have generated the captioning for affine transformed images using Flickr 8K dataset.
Identity and Attribute Preserving Thumbnail Upscaling

We consider the task of upscaling a low resolution thumbnail image of a person, to a higher resolution image, which preserves the person's identity and other attributes. Since the thumbnail image is of low resolution, many higher resolution versions exist. Previous approaches produce solutions where the person's identity is not preserved, or biased solutions, such as predominantly Caucasian faces. We address the existing ambiguity by first augmenting the feature extractor to better capture facial identity, facial attributes (such as smiling or not) and race, and second, use this feature extractor to generate high-resolution images which are identity preserving as well as conditioned on race and facial attributes. Our results indicate an improvement in face similarity recognition and lookalike generation as well as in the ability to generate higher resolution images which preserve an input thumbnail identity and whose race and attributes are maintained.
The Two Paths of Machine Translation

Machine translation has made sudden and spectacular progress of late, and computers are (finally) capable of producing translations that are useful for a wide range of situations. Should translators be concerned about this new form of competition? In this article, a study of “translationese,” the language of translation, explains why fully automated machine translation remains a very distant goal. In the short term, however, advances in the field are enabling humans and machines to work together more effectively.