Reviewerly/exHarmony huggingface dataset:
Automatic Music Production Using Generative Adversarial Networks

When talking about computer-based music generation, two are the main threads of research: the construction of autonomous music-making systems, and the design of computer-based environments to assist musicians. However, even though creating accompaniments for melodies is an essential part of every producer's and songwriter's work, little effort has been done in the field of automatic music arrangement in the audio domain. In this contribution, we propose a novel framework for automatic music accompaniment in the Mel-frequency domain. Using several songs converted into Mel-spectrograms, a two-dimensional time-frequency representation of audio signals, we were able to automatically generate original arrangements for both bass and voice lines. Treating music pieces as images (Mel-spectrograms) allowed us to reformulate our problem as an unpaired image-to-image translation problem, and to tackle it with CycleGAN, a well-established framework. Moreover, the choice to deploy raw audio and Mel-spectrograms enabled us to more effectively model long-range dependencies, to better represent how humans perceive music, and to potentially draw sounds for new arrangements from the vast collection of music recordings accumulated in the last century. Our approach was tested on two different downstream tasks: given a bass line creating credible and on-time drums, and given an acapella song arranging it to a full song. In absence of an objective way of evaluating the output of music generative systems, we also defined a possible metric for the proposed task, partially based on human (and expert) judgment.
In-Bounds Training Center offers a variety of indoor sports leagues, classes, camps

Brian is at In Bounds Training Center in Glendale learning a few fun tricks they can practice at home.
A Biophysics Laboratory Course Focused on Image Processing and Python

Python is one of the most popular programming languages in industry and for many scientists it is the go-to platform for computation, analysis, and simulation. Workforce demand for Python programmers is high and rising. For physics majors the ability to program also lets students put formulae in action on digital computers; process large data sets, visualize results, enabling new insights into Physics, and perhaps sparking new interest in the subject. Similarly, image processing is a powerful tool that undergraduates may encounter throughout their curriculum regardless of major (biology, chemistry, and physics) and regularly in the workplace. At Augsburg University, we are integrating Python throughout our physics major. This poster focuses on our curricular development of a laboratory associated with our junior level biophysics course (PHY 317). We share the curriculum and lab experiences we have developed to introduce students to Python and image processing. Thresholding, edge detection, color images, and fast Fourier transforms are representative of the tools student encounter in the course.
The Evaluation of Telecommunication Signal Processing Techniques for EMG Disease Classification

Electromyography (EMG) is a biological signal widely used in medical imaging. It is used by doctors for the classification and diagnosis of myopathic and neuropathic diseases. Many different techniques have been used to ease the diagnosis of these diseases like machine learning and support vector machines (SVM). In this work, various methods used in telecommunication systems for digital modulation identification have been used to extract features from EMG signals as potential features. The results show success in classifying between different types of EMG waveforms.
Conditional Networks

In this work we tackle the problem of out-of-distribution generalization through conditional computation. Real-world applications often exhibit a larger distributional shift between training and test data than most datasets used in research. On the other hand, training data in such applications often comes with additional annotation. We propose a method for leveraging this extra information by using an auxiliary network that modulates activations of the main network. We show that this approach improves performance over a strong baseline on the Inria Aerial Image Labeling and the Tumor-Infiltrating Lymphocytes (TIL) Datasets, which by design evaluate out-of-distribution generalization in both semantic segmentation and image classification.
Remote-Sensing-Based Analysis of Spatiotemporal Variation of ET and Related Parameters in Xilingol Steppe, China

Evapotranspiration (ET), as the main ecological water consumer, is crucial to assess the ecological water budget and dry conditions in arid and semi-arid areas.The objective of this study was to characterize the spatiotemporal variations of ET and determine the major parameters affecting ET by using remote sensing data and climate data at annual and seasonal scales in Xilingol steppe, China.The results of this study showed that the annual ET gradually reduced from northeast to southwest in the Xilingol steppe, with the values fluctuating around 200 mm per year during 2000-2014.The seasonal value of the spatially averaged ET was in reducing order from summer, fall, and winter to spring, accounting for approximately 35%, 23%, 22%, and 20% of the annual ET, respectively.The largest ET appeared in summer in meadow steppe, typical steppe, and sandy vegetation steppe, while in the desert steppe, it occurred in winter, accounting for 39% of the annual ET.Precipitation and NDVI are the major parameters positively affecting ET in spring, summer, and fall.However, in winter, ET was positively correlated with temperature and negatively correlated with precipitation.The results indicated that the spatiotemporal characteristics and the affecting parameters of the actual ET vary seasonally and that the characteristics of the annual ET are mainly determined by the growing season (spring-fall).Moreover, vegetation growth is directly correlated with ET and sunshine hours rather than other parameters.Combining with the natural conditions, the conclusions can be deduced that the dry conditions in the meadow and typical steppes are probably caused by uneven precipitation distribution and high ET demands during the growing season, while the low annual precipitation combined with high winter evaporation is the main reason for water scarcity in the desert steppe.
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition

In many machine learning tasks, input features with varying degrees of predictive capability are usually acquired at some cost. For example, in human activity recognition (HAR) and mobile health (mHealth) applications, monitoring performance should be achieved with a low cost to gather different sensory features, as maintaining sensors incur monetary, computation, and energy cost. We propose an adaptive feature selection method that dynamically selects features for prediction at any given time point. We formulate this problem as an l0 minimization problem across time, and cast the combinatorial optimization problem into a stochastic optimization formulation. We then utilize a differentiable relaxation to make the problem amenable to gradient-based optimization. Our evaluations on four activity recognition datasets show that our method achieves a favorable trade-off between performance and the number of features used. Moreover, the dynamically selected features of our approach are shown to be interpretable and associated with the actual activity types.
Development of Smart Band for Elderly Health Care

Designing for a healthier life means meeting the needs of a population that is aging. The dementia and mild cognitive impairment has been increasing in above 65 year persons. Although there is still no pharmacological cure, treatments for symptoms and studies have become more widely available with the aim of maintaining and promoting cognitive function, helping the individual compensate for impairments and improving the quality of life. In the present study, the application of wearable technology for cognitive rehabilitation is promising yet nascent. Hence, this investigation proposes the development of smart watch for aged persons.The results shows that the technology can give success to promising applications, even though many obstacles rise. The unique benefit of smart watch allows a constant medical tracking 24*7. The proposed system will increase the independence of older people at home as it gives reminders and monitors the heart rate. The smart watches certainly have the ability to be close assistants in their daily lives to support the elderly.Keywords - Mild cognitive impairment and dementia; Aged, Medical tracking; Elderly homecare; Autonomy and independence of older people.
OpenNLU: Open-source Web-interface NLU Toolkit for Development of Conversational Agent

The Natural Language Understanding (NLU) module in a conversational agent interprets and understands the user’s query. As the application scenario evolves, there is a need to periodically update the knowledge database that supports the agent by adapting or re-training the NLU model. Such periodic updates could be time-consuming to the developers and system administrator since it requires manual efforts. In this paper, we present the OpenNLU toolkit, a user-friendly interface for building and updating the knowledge database, and evaluating NLU module. This paper describes in detail the architecture, important features and design of the web-based tool, as well as the backend features which are supported by popular Rasa NLU toolkit, and deep learning libraries such as Tensorflow, and PyTorch. This paper also demonstrates the training and evaluation processes on in-house datasets alongside other benchmarking datasets (ATIS and Snips) to exemplify the usage of OpenNLU toolkit as to validate proof of concepts. The open-source OpenNLU toolkit is available to the research community1.1https://github.com/oyfml/opennlu
Checking Contact Tracing App Implementations

In the wake of the COVID-19 pandemic, contact tracing apps have been developed based on digital contact tracing frameworks. These allow developers to build privacy-conscious apps that detect whether an infected individual is in close-proximity with others. Given the urgency of the problem, these apps have been developed at an accelerated rate with a brief testing period. Such quick development may have led to mistakes in the apps' implementations, resulting in problems with their functionality, privacy and security. To mitigate these concerns, we develop and apply a methodology for evaluating the functionality, privacy and security of Android apps using the Google/Apple Exposure Notification API. This is a three-pronged approach consisting of a manual analysis, general static analysis and a bespoke static analysis, using a tool we've developed, dubbed MonSTER. As a result, we have found that, although most apps met the basic standards outlined by Google/Apple, there are issues with the functionality of some of these apps that could impact user safety.
A HYBRID PARSER MODEL FOR HINDI LANGUAGE

Analyzing syntactic structure is the most complicated task for Indian Languages.In this paper, a probabilistic parser is proposed for Hindi language comprising the empirical and rationalist approaches.The task of tagging is accomplished with the help of TnT POS tagger.In this research work, along with the development and evaluation of probabilistic parser, evaluation of rule based and conditional random fields (CRF) based shallow parser is also done by using a test dataset of 100 tagged sentences of Hindi.The generation of probabilistic parser is formulated mainly by using rule based shallow parser, constructing grammar rules and assigning probabilities.The proposed probabilistic parser has shown the accuracy of 66%.
Perbandingan Performa Pre-Trained Classifier dLib dan HAAR Cascade (OpenCV) Untuk Mendeteksi Wajah

Face detection is a part of Computer Vision and subsection of the detection of objects. Computer Vision is defined as the branch of science that studies how the computer can recognize an object. Face detection is not yet implemented on the learning environment. Currently, the attendace process is still using paper and cheating can occur.  In this final project will compare the two pre-trained classifier applied to the learning environment by using datasets IBAtS. Two pre-trained classifier to compare performance is haar cascade and dlib. To find the best classifier then two classifier evaluation is done in order to better compareable. From the results of the evaluations already carried out have been inferred that haar cascade is a good classifier for applied learning environment.
Module Comparison Of Transformer-Tts For Speaker Adaptation Based On Fine-Tuning

End-to-end text-to-speech (TTS) models have achieved remarkable results in recent times. However, the model requires a large amount of text and audio data for training. A speaker adaptation method based on fine-tuning has been proposed for constructing a TTS model using small scale data. Although these methods can replicate the target speaker s voice quality, synthesized speech includes the deletion and/or repetition of speech. The goal of speaker adaptation is to change the voice quality to match the target speaker ’ s on the premise that adjusting the necessary modules will reduce the amount of data to be fine-tuned. In this paper, we clarify the role of each module in the Transformer-TTS process by not updating it. Specifically, we froze character embedding, encoder, layer predicting stop token, and loss function for estimating sentence ending. The experimental results showed the following: (1) fine-tuning the character embedding did not result in an improvement in the deletion and/or repetition of speech, (2) speech deletion increases if the encoder is not fine-tuned, (3) speech deletion was suppressed when the layer predicting stop token is not fine-tuned, and (4) there are frequent speech repetitions at sentence end when the loss function estimating sentence ending is omitted.
Best Computer Cleaning Kits

For your computer to truly last long, you have to maintain it. In other words, the longevity of your laptop may depend on your ability to maintain it. ...
New Technologies in Education of pedagogical Staff in the Fieldof Elementary Art Education

The paper focuses on a specific area of further professionaleducation, on pedagogical education of staff at elementary artschools. The authors present partial results of the evaluationof educational programs in the area of modern technologies,specifically software for comprehensive art schools agendamanagement focused on classification and work withmicrocomputer in education.
Impact of COVID-19 on Power Procurement Planning in Indian State of Andhra Pradesh

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On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations

Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent works suggest that convolutional neural networks (CNNs) fail to generalize to category-viewpoint combinations not seen during training. However, it is unclear when and how such generalization may be possible. Does the number of combinations seen during training impact generalization? What architectures better enable generalization in the multi-task setting of simultaneous category and viewpoint classification? Furthermore, what are the underlying mechanisms that drive the network’s generalization? In this paper, we answer these questions by analyzing state-of-the-art CNNs trained to classify both object category and 3D viewpoint, with quantitative control over the number of category-viewpoint combinations seen during training. We also investigate the emergence of two types of specialized neurons that can explain generalization to unseen combinations—neurons selective to category and invariant to viewpoint, and vice versa. We perform experiments on MNIST extended with position or scale, the iLab dataset with vehicles at different viewpoints, and a challenging new dataset for car model recognition and viewpoint estimation that we introduce in this paper - the Biased-Cars dataset. Our results demonstrate that as the number of combinations seen during training increase, networks generalize better to unseen category-viewpoint combinations, facilitated by an increase in the selectivity and invariance of individual neurons. We find that learning category and viewpoint in separate networks compared to a shared one leads to an increase in selectivity and invariance, as separate networks are not forced to preserve information about both category and viewpoint. This enables separate networks to significantly outperform shared ones at classifying unseen category-viewpoint combinations.
“Priestess of Prometheus”

Рецензия на книгу: Жизнь в Музее. Татьяна Шаборкина и Музей А. Н. Скрябина. М.-СПб. : Петроглиф, Центр гуманитарных инициатив, 2019.
Toward Personal Authentication Using Gesture Classification with EMG Data

We examine a possibility of personal authentication using forearm surface EMG (s-EMG) during gesture operation. The s-EMG was measured 10 times from the 5 subjects during performing 6 gestures. In our previous research, we performed the gesture identification by using Support Vector Machine (SVM). As the feature values, we used the maximum and minimum values and their times of the time domain data during gesture. As the result, the identification rate was 66.7%. In this paper, to improve the identification rate, we increased the future values which we used, and we introduced the selection algorithms of the important feature values based on the random forest. As the result, the identification rate by using SVM was improved to over 80%. Moreover, the previous research claimed that the feature values in frequency domain was not effective. We found that some feature values in the frequency domain was effective for the gesture identification.
Leadership development model in a team of high school students

Тенденции развития науки и образования
El estrés en maestros de física ante la necesidad de impartir clases virtuales: La nueva realidad académica derivada de la pandemia de la COVID-19

espanolLa presencia del coronavirus Coronavirus SARS-CoV-2, ha generado una pandemia de COVID-19 que ha alterado la normalidad alrededor del mundo. Sin una preparacion estandarizada en el sector educativo para enfrentar esta nueva situacion, los maestros de educacion basica, y en especial para este estudio de educacion secundaria de la materia de fisica, han tenido que afrontar los primeras semanas de clase con problematicas relacionadas con el cumplimiento de los programas de fisica, el mantener la atencion de los grupos y de los alumnos, el sacar provecho de los programas que la Secretaria de Educacion Publica han puesto a disposicion de los alumnos. Lo anterior plantea una situacion estresante para los maestros, mismos que han no se dimensionan tanto en sus repercusiones inmediatas como mediatas tanto en la calidad de vida de los profesores, asi como en la calidad educativa. En este trabajo se presentan los resultados de estudios realizados a profesores con el dispositivo MUSE 2®, mismo que arroja datos de la actividad cerebral a traves de registros electroencefalograficos tanto en una linea basal, asi como en situacion de ensenanza virtual. Los metadatos obtenidos permiten arrojar graficas que muestran en 3D de la actividad cerebral representada en modelos anatomicos del cerebro, mostrando las ondas cerebrales (delta, theta, alpha, beta, beta2, y gama) como significativas en patrones de estres (en especial beta. EnglishThe presence of the Coronavirus SARS-CoV-2, has generated a COVID-19 pandemic that has altered normality around the world. Without a standardized preparation in the education sector to face this new situation, teachers of basic education, and especially for this study of secondary education of the subject of physics, have had to face the first weeks of class with problems related to compliance physics programs, maintaining the attention of groups and students, taking advantage of the programs that the Ministry of Public Education have made available to students. The foregoing poses a stressful situation for teachers, who are not measured both in their immediate and mediate repercussions on the quality of life of teachers, as well as on educational quality. In this paper, the results of studies carried out on teachers with the MUSE 2® device are presented, which provides data on brain activity through electroencephalographic recordings both in a baseline, as well as in virtual teaching situations. The metadata obtained allows the generation of graphs that show in 3D the brain activity represented in anatomical models of the brain, showing the brain waves (delta, theta, alpha, beta, beta2, and gamma) as significant in stress patterns (especially beta.
Covid-19 crisis in Italy. Fundamental rights and freedoms: the price to pay for human health?

The results of the appearance of the novel coronavirus meant for many States, the application of restrictive measures of constitutionally protected rights and freedoms, with the aim -preliminarily- of protecting the health and integrity of people. Thus, a real “emergency legal framework” was created, with restrictions on interpersonal contact, and other daily situations in the life of any society.&#x0D; Italy stood out for being the first country where COVID-19 attacked with particular virulence. The first steps of the government at the end of January 2020 were aimed at preventing the entry of people from China, and soon after, put in place much more restrictive measures as the number of deceased patients increased. However, to some extent, all the imposed restrictions seemed to have been a price to be paid in order to protect human health.&#x0D; &#x0D; Los resultados de la aparición del novel coronavirus significó para muchos Estados, la aplicación de medidas restrictivas de derechos y libertades constitucionalmente tutelados, con el objeto -preliminarmente- de proteger la salud e integridad de las personas. Así, se dio paso a la creación de un verdadero “sistema jurídico de emergencia”, con restricciones al contacto interpersonal, situaciones por demás cotidianas en la vida de cualquier sociedad.&#x0D; Italia se destacó por ser el primer país donde el COVID-19 atacó con particular virulencia. Los primeros pasos del gobierno a finales de enero de 2020 tuvieron el objetivo de impedir el ingreso de personas provenientes de China, para poco después disponer medidas mucho más restrictivas a medida que el número de pacientes fallecidos aumentaba. Sin embargo, en algún punto, las restricciones impuestas se presentan como el precio que se debió pagar a cambio de la salud humana.
Generalizing Tree Models for Improving Prediction Accuracy

Can we generalize and improve the representation power of tree models? Tree models are often favored over deep neural networks due to their interpretable structures in problems where the interpretability is required, such as in the classification of feature-based data where each feature is meaningful. However, most tree models have low accuracies and easily overfit to training data. In this work, we propose Decision Transformer Network (DTN), our highly accurate and interpretable tree model based on our generalized framework of tree models, decision transformers. Decision transformers allow us to describe tree models in the context of deep learning. Our DTN is proposed based on improving the generalizable components of the decision transformer, which increases the representation power of tree models while preserving the inherent interpretability of the tree structure. Our extensive experiments on 121 feature-based datasets show that DTN outperforms the state-of-the-art tree models and even deep neural networks.